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Understanding and Supporting Attention Deficit Hyperactivity Disorder (ADHD) in the Primary School Classroom: Perspectives of Children with ADHD and their Teachers

• Original Paper
• Open access
• Published: 01 July 2022
• Volume 53 , pages 3406–3421, ( 2023 )

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• Emily McDougal   ORCID: orcid.org/0000-0001-7684-7417 1 , 3 ,
• Claire Tai 1 ,
• Tracy M. Stewart   ORCID: orcid.org/0000-0002-8807-1174 2 ,
• Josephine N. Booth   ORCID: orcid.org/0000-0002-2867-9719 2 &
• Sinéad M. Rhodes   ORCID: orcid.org/0000-0002-8662-1742 1  

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Children with Attention Deficit Hyperactivity Disorder (ADHD) are more at risk for academic underachievement compared to their typically developing peers. Understanding their greatest strengths and challenges at school, and how these can be supported, is vital in order to develop focused classroom interventions. Ten primary school pupils with ADHD (aged 6–11 years) and their teachers (N = 6) took part in semi-structured interviews that focused on (1) ADHD knowledge, (2) the child’s strengths and challenges at school, and (3) strategies in place to support challenges. Thematic analysis was used to analyse the interview transcripts and three key themes were identified; classroom-general versus individual-specific strategies, heterogeneity of strategies, and the role of peers. Implications relating to educational practice and future research are discussed.

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Characterised by persistent inattention, hyperactivity and impulsivity (APA, 2013), ADHD is a neurodevelopmental disorder thought to affect around 5% of children (Russell et al., 2014 ) although prevalence estimates vary (Sayal et al., 2018 ). Although these core symptoms are central to the ADHD diagnosis, those with ADHD also tend to differ from typically developing children with regards to cognition and social functioning (Coghill et al., 2014 ; Rhodes et al., 2012 ), which can negatively impact a range of life outcomes such as educational attainment and employment (Classi et al., 2012 ; Kuriyan et al., 2013 ). Indeed, academic outcomes for children with ADHD are often poor, particularly when compared with their typically developing peers (Arnold et al., 2020 ) but also compared to children with other neurodevelopmental disorders, such as autism (Mayes et al., 2020 ). Furthermore, children with ADHD can be viewed negatively by their peers. For example, Law et al. ( 2007 ) asked 11–12-year-olds to read vignettes describing the behaviour of a child with ADHD symptoms, and then use an adjective checklist to endorse those adjectives that they felt best described the target child. The four most frequently ascribed adjectives were all negative (i.e. ‘careless’, ‘lonely’, ‘crazy’, and ‘stupid’). These negative perceptions can have a significant impact on the wellbeing of individuals with ADHD, including self-stigmatisation (Mueller et al., 2012 ). There is evidence that teachers with increased knowledge of ADHD report more positive attitudes towards children with ADHD compared to those with poor knowledge (Ohan et al., 2008 ) and thus research that identifies the characteristics of gaps in knowledge is likely to be important in addressing stigma.

Previous research of teachers' ADHD knowledge is mixed, with the findings of some studies indicating that teachers have good knowledge of ADHD (Mohr-Jensen et al., 2019 ; Ohan et al., 2008 ) and others suggesting that their knowledge is limited (Latouche & Gascoigne, 2019 ; Perold et al., 2010 ). Ohan et al. ( 2008 ) surveyed 140 primary school teachers in Australia who reported having experience of teaching at least one child with ADHD. Teachers completed the ADHD Knowledge Scale which consisted of 20 statements requiring a response of either true or false (e.g. “A girl/boy can be appropriately labelled as ADHD and not necessarily be over-active ”). They found that, on average, teachers answered 76.34% of items correctly, although depth of knowledge varied across the sample. Almost a third of the sample (29%) had low knowledge of ADHD (scoring less than 69%), with just under half of teachers (47%) scoring in the average range (scores of 70–80%). Only a quarter (23%) had “high knowledge” (scores above 80%) suggesting that knowledge varied considerably. Furthermore, Perold et al. ( 2010 ) asked 552 teachers in South Africa to complete the Knowledge of Attention Deficit Disorders Scale (KADDS) and found that on average, teachers answered only 42.6% questions about ADHD correctly. Responses of “don’t know” (35.4%) and incorrect responses (22%) were also recorded, indicating gaps in knowledge as well as a high proportion of misconceptions. Similar ADHD knowledge scores were reported in Latouche and Gascoigne’s ( 2019 ) study, who found that teachers enrolled into their ADHD training workshop in Australia had baseline KADDS scores of below 50% accuracy (increased to above 80% accuracy after training).

The differences in ADHD knowledge reported between Ohan et al. ( 2008 ) and the more recent studies could be due to the measures used. Importantly, when completing the KADDS, respondents can select a “don’t know” option (which receives a score of 0), whereas the ADHD Knowledge Scale requires participants to choose either true or false for each statement. The KADDS is longer, with a total of 39 items, compared to the 20-item ADHD Knowledge Scale, offering a more in-depth knowledge assessment. The heterogeneity of measures used within the described body of research is also highlighted within Mohr-Jensen et al. ( 2019 ) systematic review; the most frequently used measure (the KADDS) was only used by 4 out of the 33 reviewed studies, showing little consensus on the best way to measure ADHD knowledge. Despite these differences in measurement, the findings from most studies indicate that teacher ADHD knowledge is lacking.

Qualitative methods can provide rich data, facilitating a deeper understanding of phenomena that quantitative methods alone cannot reveal. Despite this, there are very few examples in the literature of qualitative methods being used to understand teacher knowledge of ADHD. In one example, Lawrence et al. ( 2017 ) interviewed fourteen teachers in the United States about their experiences of working with pupils with ADHD, beginning with their knowledge of ADHD. They found that teachers tended to focus on the external symptoms of ADHD, expressing knowledge of both inattentive and hyperactive symptoms. Although this provided key initial insights into the nature of teachers’ ADHD knowledge, only a small section of the interview schedule (one out of eight questions/topics) directly focused on ADHD knowledge. Furthermore, none of the questions asked directly about strengths, with answers focusing on difficulties. It is therefore difficult to determine from this study whether teachers are aware of strengths and difficulties outside of the triad of symptoms. A deeper investigation is necessary to fully understand what teachers know, and to identify areas for targeted psychoeducation.

Importantly, improved ADHD knowledge may impact positively on the implementation of appropriate support for children with ADHD in school. For example, Ohan et al. ( 2008 ) found that teachers with high or average ADHD knowledge were more likely to perceive a benefit of educational support services than those with low knowledge, and teachers with high ADHD knowledge were also more likely to endorse a need for, and seek out, those services compared to those with low knowledge. Furthermore, improving knowledge through psychoeducation may be important for improving fidelity to interventions in ADHD (Dahl et al., 2020 ; Nussey et al., 2013 ). Indeed, clinical guidelines recommend inclusion of psychoeducation in the treatment plan for children with ADHD and their families (NICE, 2018 ). Furthermore, Jones and Chronis-Tuscano ( 2008 ) found that educational ADHD training increased special education teachers’ use of behaviour management strategies in the classroom. Together, these findings suggest that understanding of ADHD may improve teachers’ selection and utilisation of appropriate strategies.

Child and teacher insight into strategy use in the classroom on a practical, day-to-day level may provide an opportunity to better understand how different strategies might benefit children, as well as the potential barriers or facilitators to implementing these in the classroom. Previous research with teachers has shown that aspects of the physical classroom can facilitate the implementation of effective strategies for autistic children, for example to support planning with the use of visual timetables (McDougal et al., 2020 ). Despite this, little research has considered the strategies that children with ADHD and their teachers are using in the classroom to support their difficulties and improve learning outcomes. Moore et al. ( 2017 ) conducted focus groups with UK-based educators (N = 39) at both primary and secondary education levels, to explore their experiences of responding to ADHD in the classroom, as well as the barriers and facilitators to supporting children. They found that educators mostly reflected on general inclusive strategies in the classroom that rarely targeted ADHD symptoms or difficulties specifically, despite the large number of strategies designed to support ADHD that are reported elsewhere in the literature (DuPaul et al., 2012 ; Richardson et al., 2015 ). Further to this, when interviewing teachers about their experiences of teaching pupils with ADHD, Lawrence et al. ( 2017 ) specifically asked about interventions or strategies used in the classroom with children with ADHD. The reported strategies were almost exclusively behaviourally based, for example, allowing children to fidget or move around the classroom, utilising rewards, using redirection techniques, or reducing distraction. This lack of focus on cognitive strategies is surprising, given the breadth of literature focusing on the cognitive difficulties in ADHD (e.g. Coghill, et al., 2014 ; Gathercole et al., 2018 ; Rhodes et al., 2012 ). Furthermore, to our knowledge research examining strategy use from the perspective of children with ADHD themselves, or strengths associated with ADHD, is yet to be conducted.

Knowledge and understanding of ADHD in children with ADHD has attracted less investigation than that of teachers. In a Canadian sample of 8- to 12-year-olds with ADHD (N = 29), Climie and Henley ( 2018 ) found that ADHD knowledge was highly varied between children; scores on the Children ADHD Knowledge and Opinions Scale ranged from 5 to 92% correct (M = 66.53%, SD = 18.96). The authors highlighted some possible knowledge gaps, such as hyperactivity not being a symptom for all people with ADHD, or the potential impact upon social relationships, however the authors did not measure participant’s ADHD symptoms, which could influence how children perceive ADHD. Indeed, Wiener et al ( 2012 ) has shown that children with ADHD may underestimate their symptoms. If this is the case, it would also be beneficial to investigate their understanding of their own strengths and difficulties, as well as of ADHD more broadly. Furthermore, if children do have a poor understanding of ADHD, they may benefit from psychoeducational interventions. Indeed, in their systematic review Dahl et al. ( 2020 ) found two studies in which the impact of psychoeducation upon children’s ADHD knowledge was examined, both of which reported an increase in knowledge as a consequence of the intervention. Understanding the strengths and difficulties of the child, from the perspective of the child and their teacher, will also allow the design of interventions that are individualised, an important feature for school-based programmes (Richardson et al., 2015 ). Given the above, understanding whether children have knowledge of their ADHD and are aware of strategies to support them would be invaluable.

Teacher and child knowledge of ADHD and strategies to support these children is important for positive developmental outcomes, however there is limited research evidence beyond quantitative data. Insights from children and teachers themselves is particularly lacking and the insights which are available do not always extend to understanding strengths which is an important consideration, particularly with regards to implications for pupil self-esteem and motivation. The current study therefore provides a vital examination of the perspectives of both strengths and weaknesses from a heterogeneous group of children with ADHD and their teachers. Our sample reflects the diversity encountered in typical mainstream classrooms in the UK and the matched pupil-teacher perspectives enriches current understandings in the literature. Specifically, we aimed to explore (1) child and teacher knowledge of ADHD, and (2) strategy use within the primary school classroom to support children with ADHD. This novel approach, from the dual perspective of children and teachers, will enable us to identify potential knowledge gaps, areas of strength, and insights on the use of strategies to support their difficulties.

Participants

Ten primary school children (3 female) aged 7 to 11 years (M = 8.7, SD = 1.34) referred to Child and Adolescent Mental Health Services (CAMHS) within the NHS for an ADHD diagnosis were recruited to the study. All participant characteristics are presented in Table 1 . All children were part of the Edinburgh Attainment and Cognition Cohort and had consented to be contacted for future research. Children who were under assessment for ADHD or who had received an ADHD diagnosis were eligible to take part. Contact was established with the parent of 13 potential participants. Two had undergone the ADHD assessment process with an outcome of no ADHD diagnosis and were therefore not eligible to take part, and one could not take part within the timeframe of the study. The study was approved by an NHS Research Ethics Committee and parents provided informed consent prior to their child taking part. Co-occurrences data for all participants was collected as part of a previous study and are reported here for added context. All of the children scored above the cut-off (T-score > 70) for ADHD on the Conners 3 rd Edition Parent diagnostic questionnaire (Conners, 2008 ). The maximum possible score for this measure is 90. At the point of interview, seven children had received a diagnosis of ADHD, two children were still under assessment, and one child had been referred for an ASD diagnosis (Table 1 ). The ADHD subtype of each participant was not recorded, however all children scored above the cut-off for both inattention (M = 87.3, SD = 5.03) and hyperactivity (M = 78.6, SD = 5.8) which is indicative of ADHD combined type. Use of stimulant medication was not recorded at the time of interview.

Following the child interview and receipt of parental consent, each child’s school was contacted to request their teacher’s participation in the study. Three teachers could not take part within the timeframe of the study, and one refused to take part. Six teachers (all female) were successfully contacted and gave informed consent to participate.

Due to the increased likelihood of co-occurring diagnoses in the target population, we also report Autism Spectrum Disorder (ASD) symptoms and Developmental Co-ordination Disorder (DCD) symptoms using the Autism Quotient 10-item questionnaire (AQ-10; Allison et al., 2012 ) and Movement ABC-2 Checklist (M-ABC2; Henderson et al., 2007 ) respectively, both completed by the child’s parent.

Scores of 6 and above on the AQ-10 indicates referral for diagnostic assessment for autism is advisable. All but one of the participants scored below the cut-off on this measure (M = 3.6, SD = 1.84).

The M-ABC2 checklist categorises children as scoring green, amber or red based on their scores. A green rating (up to the 85th percentile) indicates no movement difficulty, amber ratings (between 85 and 95th percentile) indicate risk of movement difficulty, and red ratings (95th percentile and above) indicate high likelihood of movement difficulty. Seven of the participants received a red rating, one an amber rating, and two green ratings.

Socioeconomic status (SES) is also known to impact educational outcomes, therefore the SES of each child was calculated using the Scottish Index of Multiple Deprivation (SIMD), which is an area-based measure of relative deprivation. The child’s home postcode was entered into the tool which provided a score of deprivation on a scale of 1 to 5. A score of 1 is given to the 20% most deprived data zones in Scotland, and a score of 5 indicates the area was within the 20% least deprived areas.

Semi-Structured Interview

The first author, who is a psychologist, conducted interviews with each participant individually, and then a separate interview with their teacher. This was guided by a semi-structured interview schedule (see Appendix A, Appendix B) developed in line with our research questions, existing literature, and using authors (T.S. and J.B.) expertise in educational practice. The questions were adapted to be relevant for the participant group. For example, children were asked “If a friend asked you to tell them what ADHD is, what would you tell them?” and teachers were asked, “What is your understanding of ADHD or can you describe a typical child with ADHD?”. The schedule comprised two key sections for both teachers and children. The first section focused on probing the participant’s understanding and knowledge of ADHD broadly. The second section focused on the participating child’s academic and cognitive strengths and weaknesses, and the strategies used to support them. Interviews with children took place in the child’s home and lasted between 19 and 51 min (M = 26.3, SD = 10.9). Interviews with teachers took place at their school and were between 28 and 50 min long (M = 36.5, SD = 7.61). Variation in interview length was mostly due to availability of the participant and/or age of the child (i.e. interviews with younger children tended to be shorter). All interviews were recorded on an encrypted voice recorder and transcribed by the first author prior to data analysis. Pseudonyms were randomly generated for each child to protect anonymity.

Reflexive thematic analysis was used to analyse the data (Braun & Clarke, 2019 ). This flexible approach allows the data to drive the analysis, putting the participant at the centre of the research and placing high value on the experiences and perspectives of individual participants (Braun & Clarke, 2006 ). The six phases of reflexive thematic analysis as outlined by Braun and Clarke were followed: (1) familiarisation, (2) generating codes, (3) constructing themes, (4) revising themes, (5) defining themes, (6) producing the report. Due to the exploratory nature of this study, bottom-up inductive coding was used. Two of the authors (E.M. and C.T.) worked collaboratively to construct and subsequently define the themes using the process described above. More specifically, one author (E.M.) generated codes, with support from another author (C.T.). Collated codes and data were then abstracted into potential themes, which were reviewed and refined using relevant literature, as well as within the wider context of the data. This process continued until all themes were agreed upon.

In the first part of the analysis, focus was placed on summarising the participants’ understanding of ADHD, as well as what they thought their biggest strengths and challenges were at school. Following this, an in-depth analysis of the strategies used in the classroom was conducted, taking into account the perspective of both teachers and children, aiming to generate themes from the data.

Knowledge of ADHD

Children and teachers were asked about their knowledge of ADHD. When asked if they had ever heard of ADHD, the majority of children said yes. Some of the children could not explain to the interviewer what ADHD was or responded in a way that suggested a lack of understanding ( “it helps you with skills” – Niall, 7 years; “ Well it’s when you can’t handle yourself and you’re always crazy and you can just like do things very fast”— Nathan, 8 years). Very few of the children were able to elaborate accurately on their understanding of ADHD, which exclusively focused on inattention. For example, Paige (8 years) said “ its’ kinda like this thing that makes it hard to concentrate ” and Finn (10 years) said “ they get distracted more just in different ways that other people would ”. This suggests that children with ADHD may lack or have a limited awareness or understanding of their diagnosis.

When asked about their knowledge of ADHD, teachers tended to focus on the core symptoms of ADHD. All teachers directly mentioned difficulties with attention, focus or concentration, and most directly or indirectly referred to hyperactivity (e.g. moving around, being in “ overdrive ”). Most teachers also referred to social difficulties as a feature of ADHD, including not following social rules, reacting inappropriately to other children and appearing to lack empathy, which they suggested could be linked to impulsivity. For example, “ reacting in social situations where perhaps other children might not react in a similar way” (Paige’s teacher) and “ They can react really really quickly to things and sometimes aggressively” (Eric’s teacher). Although no teachers directly mentioned cognitive difficulties, some referred to behaviours indicative of cognitive difficulties, for example, “ they can’t store a lot of information at one time” (Eric’s teacher) and, “ it’s not just the concentration it’s the amount they can take in at a time as well” (Nathan’s teacher), which may reflect processing or memory differences. Heterogeneity was mentioned, in that ADHD can mean different things for different children (e.g., “ I think ADHD differs from child to child and I think that’s really important” —Nathan’s teacher). Finally, academic difficulties as a feature of ADHD were also mentioned (e.g., “ a child… who finds some aspects of school life, some aspects of the curriculum challenging ”—Jay’s teacher).

After being asked to give a general description of ADHD, each child was asked about their own strengths at school and teachers were also asked to reflect on this topic for the child taking part.

When asked what they like most about school, children often mentioned art or P.E. as their preferred subjects. A small number of children said they enjoyed maths or reading, but this was not common and the majority described these subjects as a challenge or something they disliked. There was also clear link between the aspects of school children enjoyed, and what they perceived to be a strength for them. For example, when asked what he liked about school, Eric (10 years) said, “ Math, I’m pretty good at that”, or when later asked what they were good at, most children responded with the same answers they gave when asked what they liked about school. It is interesting to note that subjects such as art or P.E. generally have a different format to more traditionally academic subjects such as maths or literacy. Indeed, Felicity (11 years) said, “ I quite like art and drama because there’s not much reading…and not really too much writing in any of those” . Children also tended to mention the non-academic aspects of school, such as seeing their friends, or lunch and break times.

Teachers’ descriptions of the children’s strengths were much more variable compared to strengths mentioned by children. Like the children, teachers tended to consider P.E and artistic activities to be a strength for the child with ADHD. Multiple teachers referred to the child having a good imagination and creative skills. For example, “ she’s a very imaginative little girl, she has a great ability to tell stories and certainly with support write imaginative stories” (Paige’s teacher) . Teachers referred to other qualities or characteristics of the child as strengths, although these varied across teachers. These included openness, both socially but also in the context of willingness to learn or being open to new challenges, being a hard worker, or an enjoyable person to be around (e.g., “ he is the loveliest little boy, I’ve got a lot of time for [Nathan]. He makes me smile every day, you know, he just comes out with stuff he’s hilarious”— Nathan’s teacher). The most noticeable theme that emerged from this data was that when some teachers began describing one of the child’s strengths, it was suffixed with a negative. For example, Henry’s teacher said, “ He’s got a very good imagination, his writing- well not so much the writing of the stories, he finds writing quite a challenge, but his verbalising of ideas he’s very imaginative”. This may reflect that while these children have their own strengths, these can be limited by difficulties. Indeed, Paige’s teacher said, “ I think she’s a very able little girl without a doubt, but there is a definite barrier to her learning in terms of her organisation, in terms of her focus” , which reinforces this notion.

Children were asked directly about what they disliked about school, and what they found difficult. Children tended to focus more on specific subjects, with maths and aspects of literacy being the most frequently mentioned of these. Children referred to difficulties with or a dislike for reading, writing and/or spelling activities, for example, Rory (9 years) said “ Well I suppose spelling because … sometimes we have to do some boring tasks like we have to write it out three times then come up with the sentence for each one which takes forever and it’s hard for me to think of the sentences if I’m not ready” . Linking this with known cognitive difficulties in ADHD, it is interesting to note that both memory and planning are implicated in this quote from Rory about finding spelling challenging. In terms of writing, children referred to both the physical act of writing (e.g., “ probably writing cause sometimes I forget my finger spaces ”—Paige, 8 years; “ [writing the alphabet is] too hard… like the letters joined together … [and] I make mistakes” —Jay, 7 years) as well as the planning associated with writing a longer piece of work (e.g. “ when I run out of ideas for it, it’s really hard to think of some more so I don’t usually get that much writing done ”—Rory (9 years) .

Aside from academic subjects, several children referred to difficulties with focus or attention (e.g. “ when I find it hard to do something I normally kind of just zone out ”—Felicity, 11 years, “ probably concentrating sometimes ”—Rory, 9 years), but boredom was also a common and potentially related theme (e.g. “ Reading is a bit hard though … it just sometimes gets a bit boring” —Finn, 10 years, “ I absolutely hate maths … ‘cause it’s boring ”—Paige, 8 years). It could be that children with ADHD find it more difficult to concentrate during activities they find boring. Indeed, when Jay (7 years) was asked how it made him feel when he found something boring, he said “ it made me not do my work ”. Some children also alluded to the social difficulties faced at school, which included bullying and difficulties making friends (e.g. “ just making all kind of friends [is difficult] ‘cause the only friend that I’ve got is [name redacted] ”—Nathan, 8 years; “ sometimes finding a friend to play with at break time [is difficult] ” – Paige, 8 years; “ there’s a lot of people in my school that they bully me” —Eric, 10 years).

When asked what they thought were the child’s biggest challenges at school, teachers' responses were relatively variable, although some common themes were identified. As was the case for children, teachers reflected on difficulties with attention, which also included being able to sit at the table for long periods of time (e.g. “ I would say he struggles the most with sitting at his table and focusing on one piece of work ”—Henry’s teacher). Teachers did also mention difficulties with subjects such as maths and literacy, although this varied from child to child, and often they discussed these in the context of their ADHD symptom-related difficulties. For example, Eric’s teacher said, “ we’ve struggled to get a long piece of writing out of him because he just can’t really sit for very long ”. This quote also alludes to difficulties with evaluating the child’s academic abilities, due to their ADHD-related difficulties, which was supported by other teachers (e.g. “ He doesn’t particularly enjoy writing and he’s slow, very slow. And I don’t know if that’s down to attention or if that’s something he actually does find difficult to do ” —Henry’s teacher). Furthermore, some teachers reflected on the child’s confidence as opposed to a direct academic difficulty. For example, Luna’s teacher said, “ I think it’s she lacks the confidence in maths and reading like the most ” and later, elaborated with “ she’ll be like “I can’t do it” but she actually can. Sometimes she’s … even just anxious at doing a task where she thinks … she might not get it. But she does, she’s just not got that confidence”.

Teachers also commonly mentioned social difficulties, and referred to these difficulties as a barrier to collaborative learning activities (e.g. “ he doesn’t always work well with other people and other people can get frustrated” —Henry’s teacher; “ [during] collaborative group work [Paige] perhaps goes off task and does things she shouldn’t necessarily be doing and that can cause friction within the group” —Paige’s teacher). Teachers also mentioned emotion regulation, mostly in relation to the child’s social difficulties. For example, Eric’s teacher said “ I think as well he does still struggle with his emotions like getting angry very very quickly, and being very defensive when actually he’s taken the situation the wrong way” , which suggests that the child’s difficulty with regulating emotions may impact on their social relationships.

Strategy Use in the Classroom

Strategies to support learning fell into one of four categories: concrete or visual resources, information processing, seating and movement, and support from or influence of others. Examples of codes included in each of these strategy categories are presented in Table 2 .

Concrete or visual resources were the most commonly mentioned type of strategy by teachers and children, referring to the importance of having physical representations to support learning. Teachers spoke about the benefit of using visual aids (e.g. “ I think [Henry] is quite visual so making sure that there is visual prompts and clues and things like that to help him ”—Henry’s teacher), and teachers and children alluded to these resources supporting difficulties with holding information in mind. For example, when talking about the times table squares he uses, Rory said “ sometimes I forget which one I’m on…and it’s easier for me to have my finger next to it than just doing it in my head because sometimes I would need to start doing it all over again ”.

Seating and movement were also commonly mentioned, which seemed to be specific to children with ADHD in that it was linked to inattention and hyperactivity symptoms. For example, teachers referred to supporting attention or avoiding distraction by the positioning of a child’s location in the classroom (e.g. “ he’s so easily distracted, so he has an individual desk in the room and he’s away from everyone else because he wasn’t coping at a table [and] he’s been so much more settled since we got him an individual desk” —Eric’s teacher). Some teachers also mentioned the importance of allowing children to move around the room where feasible, as well as giving them errands to perform as a movement break (e.g. “ if I need something from the printer, [Nathan] is gonna go for it for me…because that’s down the stairs and then back up the stairs so if I think he’s getting a bit chatty or he’s not focused I’ll ask him to go and just give him that break as well” —Nathan’s teacher). Children also spoke about these strategies but didn’t necessarily describe why or how these strategies help them.

Information processing and cognitive strategies included methods that supported children to process learning content or instructions. For example, teachers frequently mentioned breaking down tasks or instructions into more manageable chunks (e.g. “ with my instructions to [Eric] I break them down … I’ll be like “we’re doing this and then we’re doing this” whereas the whole class wouldn’t need that ”—Eric’s teacher). Teachers and children also mentioned using memory strategies such as songs, rhymes or prompts. For example, Jay’s teacher said, “ if I was one of the other children I could see why it would be very distracting but he’s like he’s singing to himself little times table songs that we’ve been learning in class” , and Paige (8 years) referred to using mnemonics to help with words she struggles to spell, “ I keep forgetting [the word] because. But luckily we got the story big elephants can always understand little elephants [which helps because] the first letter of every word spells because” .

Both groups of participants mentioned support from and influence of others, and referred to working with peers, the teacher–child relationship, and one-to-one teaching. Peer support was a common theme across the data and is discussed in more detail in the thematic analysis findings, where teachers and children referred to the importance of the role of peers during learning activities. Understanding the child well and adapting to them was also seen as important, for example, Luna’s teacher said, “ with everything curricular [I] try and have an art element for her, just so I know it’ll engage her [because] if it’s like a boring old written worksheet she’s not gonna do it unless you’re sitting beside her and you’re basically telling her the answers” . As indicated in this quote, teachers also referred to the effectiveness of one-to-one or small group work with the child (e.g. “ when somebody sits beside her and explains it, and goes “come on [Paige] you know how to do this, let’s just work through a couple of examples”… her focus is generally better ” – Paige’s teacher), however this resource is not always available (e.g. “ I’d love for someone to be one-to-one with [Luna] but it’s just not available, she doesn’t meet that criteria apparently ” – Luna’s teacher). Children also referred to seeking direct support from their teacher (e.g. “if I can’t get an idea of what I’m doing then I ask the teacher for help” – Paige, 8 years), but were more likely to mention seeking support from their peers than the teacher.

Thematic Analysis

In addition to summarising the types of strategies that teachers and children reported using in the classroom, the data were also analysed using thematic analysis to generate themes. These are now presented. The theme names, definitions, and example quotes for each theme are presented in Table 3 .

Theme 1: Classroom-General Versus Individual-Specific Strategies

During the interviews, teachers spoke about strategies that they use as part of their teaching practice for the whole class but that are particularly helpful for the child/children with ADHD. These tended to be concrete or visual resources that are available in the classroom for anyone, for example, a visual timetable or routine checklist (e.g. “ there’s also a morning routine and listing down what’s to be done and where it’s to go … it’s very general for the class but again it’s located near her” —Paige’s teacher).

Teachers also mentioned using strategies that have been implemented specifically for that child, and these strategies tended to focus on supporting attention. For example, Nathan’s teacher spoke about the importance of using his name to attract his attention, “ maybe explaining to the class but then making sure that I’m saying “[Nathan], you’re doing this”, you know using his name quite a lot so that he knows it’s his task not just the everybody task ”, and this was a strategy that multiple teachers referred to using with the individual child and not necessarily for other children. Other strategies to support attention with a specific child also tended to be seating and movement related, such as having an individual desk or allowing them to fidget. For example, Luna’s teacher said, “ she’s a fidgeter so she’ll have stuff to fidget with … [and] even if she’s wandering around the classroom or she’s sitting on a table, I don’t let other kids do that, but as long as she’s listening, it’s fine [with me]” .

Similar to teachers, children spoke about strategies or resources that were in place for them specifically as well as about general things in the classroom that they find helpful. That said, it was less common for children to talk about why particular strategies were in place for them and how they helped them directly.

In addition to recognising strategies that teachers had put in place for them, children also referred to using their own strategies in the classroom. The most frequently mentioned strategy was fidgeting, and although some of the younger children spoke about having resources available in the classroom for fidgeting, some of the older children referred to using their own toy or an object that was readily available to them but not intended for fidgeting. For example, Finn (10 years) and Rory (9 years) both spoke about using items from their pencil case to fiddle with, and explained that this would help them to focus. (“ Sometimes I fidget with something I normally just have like a pencil holder under the table moving about … [and] it just keeps my mind clear and not from something else ”—Rory; “ Sometimes I fiddle with my fingers and that sometimes helps, but if not I get one of my coloured pencils and have a little gnaw on it because that actually takes my mind off some things and it’s easier for me to concentrate when I have something to do ”—Finn). Henry (9 years) spoke about being secretive with his fidgeting as it was not permitted in class, “ if you just bring [a fidget toy] in without permission [the teacher will] just take it off of you, so it has to be something that’s not too big. I bring in a little Lego ray which is just small enough that she won’t notice ”. Although some teachers did mention having fidget toys available, not all teachers seemed to recognise the importance of this for the child, and some children viewed fidgeting as a behaviour they should hide from the teacher.

Another strategy mentioned uniquely by children was seeing their peers as a resource for ideas or information. This is discussed in more detail in Theme 3—The role of peers , but reinforces the notion that children also develop their own strategies, independently from their teacher, rather than relying only on what is made available to them.

Theme 2: Heterogeneity of Strategies

Teachers spoke about the need for a variety of strategies in the classroom, for two reasons: (1) that different strategies work for different children (e.g. “ some [strategies] will work for the majority of the children and some just don’t seem to work for any of them ”—Jay’s teacher), and (2) what works for a child on one occasion may not work consistently for the same child (e.g. “ I think it’s a bit of a journey with him, and some things have worked and then stopped working, so I think we’re constantly adapting and changing what we’re doing ”—Eric’s teacher). One example of both of these challenges of strategy use came from Luna’s teacher, who spoke about using a reward chart with Luna and another child with ADHD, “ [Luna] and another boy in my class [with ADHD] both had [a reward chart]… but I think whereas the boy loved his and still loves his, she was getting a bit “oh I’m too cool for this” or that sort of age… so I stopped doing that for her and she’s not missing that at all” . These quotes demonstrate that strategies can work differently for different children, highlighting the need for a variety of strategies for teachers to access and trial with children.

Some children also referred to the variability of whether a strategy was helpful or not; for example, Henry (9 years) said that he finds it helpful to fidget with a toy but that sometimes it can distract him and prevent him from listening to the teacher. He said, “ Well, [the fidget toy] helps but it also gets me into trouble when the teacher spots me building it when I’m listening…but then sometimes I might not listen in maths and [use the fidget toy] which might make it worse”. This highlights that both children and teachers might benefit from support in understanding the contexts in which to use particular strategies, as well as why they are helpful from a psychological perspective.

For teachers, building a relationship with and understanding the child was also highly important in identifying strategies that would work. Luna’s teacher reflected upon the difference in Luna’s behaviour at the start of the academic year, compared to the second academic term, “ at the start of the year, we would just clash the whole time. I didn’t know her, she didn’t know me … and then when we got that bond she was absolutely fine so her behaviour has got way better ”. Eric’s teacher also reflected on how her relationship with Eric had changed, particularly after he received his diagnosis of ADHD, “ I think my approach to him has completely changed. I don’t raise my voice, I speak very calmly, I give him time to calm down before I even broach things with him. I think our relationship’s just got so much better ‘cause I kind of understand … where he’s coming from ”. She also said, “ it just takes a long time to get to know the child and get to know what works for them and trialling different things out ”, which demonstrates that building a relationship with and understanding the child can help to identify the successful strategies that work with different children.

Theme 3: The Role of Peers

Teachers and children spoke about the role of the child’s peers in their learning. Teachers talked about the benefit of partnering the child with good role models (e.g. “ I will put him with a couple of good role models and a couple of children who are patient and who will actually maybe get on with the task, and if [Jay] is not on task or not on board with what they’re doing at least he’s hearing and seeing good behaviour ”—Jay’s teacher), whereas children spoke more about their peers as a source of information, idea generation, or guidance on what to do next. For example, when asked what he does to help him with his writing, Henry (9 years) said, “ [I] listen to what my partner’s saying… my half of the table discuss what they’re going to do so I can literally hear everything they’re doing and steal some of their ideas ”. Henry wasn’t the only child to use their peers as a source of information, for example, Niall (7 years) said, “ I prefer working with the children because some things I might not know and the children might help me give ideas ”, and with a more specific example, Rory (9 years) said, “ somebody chose a very good character for their bit of writing, and I was like “I think I might choose that character”, and somebody else said “my setting was going to be the sea”, and I chose that and put that in a tiny bit of my story ”.

Some children also spoke about getting help from their peers in other ways, particularly when completing a difficult task. Paige (8 years) said, “ if the question isn’t clear I try and figure it out, and if I can’t figure it out then… don’t tell my teacher this but I sometimes get help from my classmates ”, which suggests some guilt associated with asking for help from her peers. This could be related to confidence and self-esteem, which teachers mentioned as a difficulty for some children with ADHD. In some instances, children felt it necessary to directly copy their peers’ work; for example, Nathan (8 years) spoke about needing a physical resource (i.e. “ fuzzies ”) to complete maths problems, but that when none were available he would “ just end up copying other people ”. This could also be related to a lack of confidence, as he may feel as though he may not be able to complete the task on his own. Indeed, Nathan’s teacher mentioned that when he is given the option to choose a task from different difficulty levels, Nathan would typically choose something easier, and that it was important to encourage him to choose something more difficult to build his confidence, “ I quite often say to him “come on I think you can challenge yourself” and [will] use that language”.

Peers clearly play an important role for the children with ADHD, and this is recognised both by the children themselves, and by their teachers. Teachers also mentioned that children with ADHD respond well to one-to-one learning with staff, indicating that it is important for these children to have opportunities to learn in different contexts: whole classroom learning, small group work and one-to-one.

In this study, a number of important topics surrounding ADHD in the primary school setting were explored, including ADHD knowledge, strengths and challenges, and strategy use in the classroom, each of which will now be discussed in turn before drawing together the findings and outlining the implications.

ADHD Knowledge

Knowledge of ADHD varied between children and their teachers. Whilst most of the children claimed to have heard of ADHD, very few could accurately describe the core symptoms. Previous research into this area is limited, however this finding supports Climie and Henley’s ( 2018 ) finding that children’s knowledge of ADHD can be limited. By comparison, all of the interviewed teachers had good knowledge about the core ADHD phenotype (i.e. in relation to diagnostic criteria) and some elaborated further by mentioning social difficulties or description of behaviours that could reflect cognitive difficulties. This supports and builds further upon existing research into teachers’ ADHD knowledge, demonstrating that although teachers understanding may be grounded in a focus upon inattention and hyperactivity, this is not necessarily representative of the range of their knowledge. By interviewing participants about their ADHD knowledge, as opposed to asking them to complete a questionnaire as previous studies have done (Climie & Henley, 2018 ; Latouche & Gascoigne, 2019 ; Ohan et al., 2008 ; Perold et al., 2010 ), the present study has demonstrated the specific areas of knowledge that should be targeted when designing psychoeducation interventions for children and teachers, such as broader aspects of cognitive difficulties in executive functions and memory. Improving knowledge of ADHD in this way could lead to increased positive attitudes and reduction of stigma towards individuals with ADHD (Mueller et al., 2012 ; Ohan et al., 2008 ), and in turn improving adherence to more specified interventions (Bai et al., 2015 ).

Strengths and Challenges

A range of strengths and challenges were discussed, some of which were mentioned by both children and teachers, whilst others were unique to a particular group. The main consensus in the current study was that art and P.E. tended to be the lessons in which children with ADHD thrive the most. Teachers elaborated on this notion, speaking about creative skills, such as a good imagination, and that these skills were sometimes applied in other subjects such as creative writing in literacy. Little to no research has so far focused on the strengths of children with ADHD, therefore these findings identify important areas for future investigation. For example, it is possible that these strengths could be harnessed in educational practice or intervention.

Although a strength for some, literacy was commonly mentioned as a challenge by both groups, specifically in relation to planning, spelling or the physical act of writing. Previous research has repeatedly demonstrated that literacy outcomes are poorer for children with ADHD compared to their typically developing peers (DuPaul et al., 2016; Mayes et al., 2020 ), however in these studies literacy tended to be measured using a composite achievement score, where the nuance of these difficulties can be lost. Furthermore, in line with a recent systematic review and meta-analysis (McDougal et al., 2022 ) the present study’s findings suggest that cognitive difficulties may contribute to poor literacy performance in ADHD. This issue was not unique to literacy, however, as teachers also spoke about academic challenges in the context of ADHD symptoms being a barrier to learning, such as finding it difficult to remain seated long enough to complete a piece of work. Children also raised this issue of engagement, who referred to the most challenging subjects being ‘boring’ for them. This link between attention difficulties and boredom in ADHD has been well documented (Golubchik et al., 2020 ). The findings here demonstrate the need for further research into the underlying cognitive difficulties leading to academic underachievement.

Both children and teachers also mentioned social and emotional difficulties. Research has shown that many different factors may contribute to social difficulties in ADHD (for a review see Gardner & Gerdes, 2015 ), making it a complex issue to disentangle. That said, in the current study teachers tended to attribute the children’s relationship difficulties to behaviour, such as reacting impulsively in social situations, or going off task during group work, both of which could be linked to ADHD symptoms. Despite these difficulties, peers were also considered a positive support. This finding adds to the complexity of understanding social difficulties for children with ADHD, demonstrating the necessity and value of further research into this key area.

The three key themes of classroom-general versus individual-specific strategies , heterogeneity of strategies and the role of peers were identified from the interview transcripts with children and their teachers. Within the first theme, classroom-general versus individual-specific strategies, it was clear that teachers utilise strategies that are specific to the child with ADHD, as well as strategies that are general to the classroom but that are also beneficial to the child with ADHD. Previously, Moore et al. ( 2017 ) found that teachers mostly reflected on using general inclusive strategies, rather than those targeted for ADHD specifically, however the methods differ from the current study in two key ways. Firstly, Moore et al.’s sample included secondary and primary school teachers, for whom the learning environment is very different. Secondly, focus groups were used as opposed to interviews where the voices of some participants can be lost. The merit of the current study is that children were also interviewed using the same questions as teachers; we found that children also referred to these differing types of strategies, and reported finding them useful, suggesting that the reports of teachers were accurate. Interestingly, children also mentioned their own strategies that teachers did not discuss and may not have been aware of. This finding highlights the importance of communication between the child and the teacher, particularly when the child is using a strategy considered to be forbidden or discouraged, for example copying a peer’s work or fidgeting with a toy. This communication would provide an understanding of what the child might find helpful, but more importantly identify areas of difficulty that may need more attention. Further to this, most strategies specific to the child mentioned by teachers aimed to support attention, and few strategies targeted other difficulties, particularly other aspects of cognition such as memory or executive function, which supports previous findings (Lawrence et al., 2017 ). The use of a wide range of individualised strategies would be beneficial to support children with ADHD.

Similarly, the second theme, heterogeneity of strategies , highlighted that some strategies work with some children and not others, and some strategies may not work for the same child consistently. Given the benefit of a wide range of strategy use, for both children with ADHD and their teachers, the development of an accessible tool-kit of strategies would be useful. Importantly, and as recognised in this second theme, knowing the individual child is key to identifying appropriate strategies, highlighting the essential role of the child’s teacher in supporting ADHD. Teachers mostly spoke about this in relation to the child’s interests and building rapport, however this could also be applied to the child’s cognitive profile. A tool-kit of available strategies and knowledge of which difficulties they support, as well as how to identify these difficulties, would facilitate teachers to continue their invaluable support for children and young people with ADHD. This links to the importance of psychoeducation; as previously discussed, the teachers in our study had a good knowledge of the core ADHD phenotype, but few spoke about the cognitive strengths and difficulties of ADHD. Children and their teachers could benefit from psychoeducation, that is, understanding ADHD in more depth (i.e., broader cognitive and behavioural profiles beyond diagnostic criteria), what ADHD and any co-occurrences might mean for the individual child, and why certain strategies are helpful. Improving knowledge using psychoeducation is known to improve fidelity to interventions (Dahl et al., 2020 ; Nussey et al., 2013 ), suggesting that this would facilitate children and their teachers to identify effective strategies and maintain these in the long-term.

The third theme, the role of peers , called attention to the importance of classmates for children with ADHD, and this was recognised by both children and their teachers. As peers play a role in the learning experience for children with ADHD, it is important to ensure that children have opportunities to learn in small group contexts with their peers. This finding is supported by Vygotsky’s ( 1978 ) Zone of Proximal Development; it is well established in the literature that children can benefit from completing learning activities with a partner, especially a more able peer (Vygotsky, 1978 ).

Relevance of Co-Occurrences

Co-occurring conditions are common in ADHD (Jensen & Steinhausen, 2015 ), and there are many instances within the data presented here that may reflect these co-occurrences, in particular, the overlap with DCD and ASD. For ADHD and DCD, the overlap is considered to be approximately 50% (Goulardins et al., 2015 ), whilst ADHD and autism also frequently co-occur with rates ranging from 40 to 70% (Antshel & Russo, 2019 ). It was not an aim of the current study to directly examine co-occurrences, however it is important to recognise their relevance when interpreting the findings. Indeed, in the current sample, scores for seven children (70%) indicated a high likelihood of movement difficulty. One child scored above the cut-off for autism diagnosis referral on the AQ-10, indicating heightened autism symptoms. Further to this, some of the discussions with children and teachers seemed to be related to DCD or autism, for example, the way that they can react in social situations, or difficulties with the physical act of handwriting. This finding feeds into the ongoing narrative surrounding heterogeneity within ADHD and individualisation of strategies to support learning. Recognising the potential role of co-occurrences should therefore be a vital part of any psychoeducation programme for children with ADHD and their teachers.

Limitations

Whilst a strong sample size was achieved for the current study allowing for rich data to be generated, it is important to acknowledge the issue of representativeness. The heterogeneity of ADHD is recognised throughout the current study, however the current study represents only a small cohort of children and young people with ADHD and their teachers which should be considered when interpreting the findings, particularly in relation to generalisation. Future research should investigate the issues raised using quantitative methods. Also on this point of heterogeneity, although we report some co-occurring symptoms for participants, the number of co-occurrences considered here were limited to autism and DCD. Learning disabilities and other disorders may play a role, however due to the qualitative nature of this study it was not feasible to collect data on every potential co-occurrence. Future quantitative work should aim to understand the complex interplay of diagnosed and undiagnosed co-occurrences.

Furthermore, only some of the teachers of participating children took part in the study; we were not able to recruit all 10. It may be, for example, that the six teachers who did take part were motivated to do so based on their existing knowledge or commitment to understanding ADHD, and the fact that not all child-teacher dyads are represented in the current study should be recognised. Another possibility is the impact of time pressures upon participation for teachers, particularly given the increasing number of children with complex needs within classes. Outcomes leading from the current study could support teachers in this respect.

It is also important to recognise the potential role of stimulant medication. Although it was not an aim of the current study to investigate knowledge or the role of stimulant medication in the classroom setting, it would have been beneficial to record whether the interviewed children were taking medication for their ADHD at school, particularly given the evidence to suggest that stimulant medication can improve cognitive and behavioural symptoms of ADHD (Rhodes et al., 2004 ). Examining strategy use in isolation (i.e. with children who are drug naïve or pausing medication) will be a vital aim of future intervention work.

Implications/Future Research

Taking the findings of the whole study together, one clear implication is that children and their teachers could benefit from psychoeducation, that is, understanding ADHD in more depth (i.e., broader cognitive and behavioural profiles beyond diagnostic criteria), what ADHD might mean for the individual child, and why certain strategies are helpful. Improving knowledge using psychoeducation is known to improve fidelity to interventions (Dahl et al., 2020 ; Nussey et al., 2013 ), suggesting that this would facilitate children and their teachers to identify effective strategies and maintain these in the long-term.

To improve knowledge and understanding of both strengths and difficulties in ADHD, future research should aim to develop interventions grounded in psychoeducation, in order to support children and their teachers to better understand why and in what contexts certain strategies are helpful in relation to ADHD. Furthermore, future research should focus on the development of a tool-kit of strategies to account for the heterogeneity in ADHD populations; we know from the current study’s findings that it is not appropriate to offer a one-size-fits-all approach to supporting children with ADHD given that not all strategies work all of the time, nor do they always work consistently. In terms of implications for educational practice, it is clear that understanding the individual child in the context of their ADHD and any co-occurrences is important for any teacher working with them. This will facilitate teachers to identify and apply appropriate strategies to support learning which may well result in different strategies depending on the scenario, and different strategies for different children. Furthermore, by understanding that ADHD is just one aspect of the child, strategies can be used flexibly rather than assigning strategies based on a child’s diagnosis.

This study has provided invaluable novel insight into understanding and supporting children with ADHD in the classroom. Importantly, these insights have come directly from children with ADHD and their teachers, demonstrating the importance of conducting qualitative research with these groups. The findings provide clear scope for future research, as well as guidelines for successful intervention design and educational practice, at the heart of which we must acknowledge and embrace the heterogeneity and associated strengths and challenges within ADHD.

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Wiener, J., Malone, M., Varma, A., Markel, C., Biondic, D., Tannock, R., & Humphries, T. (2012). Children’s perceptions of their ADHD symptoms: Positive illusions, attributions, and stigma. Canadian Journal of School Psychology, 27 (3), 217–242. https://doi.org/10.1177/0829573512451972

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The funding was provided by Waterloo Foundation Grant Nos. (707-3732, 707-4340, 707-4614).

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Emily McDougal, Claire Tai & Sinéad M. Rhodes

Moray House School of Education and Sport, University of Edinburgh, Edinburgh, United Kingdom

Tracy M. Stewart & Josephine N. Booth

School of Psychology, University of Surrey, Guildford, United Kingdom

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E.M. Conceptualization, Methodology, Investigation, Data Curation, Formal Analysis, Writing - original draft, C.T. Formal Analysis, Writing - review & editing, T.M.S., J.N.B. and S.M.R. Conceptualization, Methodology, Writing - review & editing.

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Interview Schedule—Teacher

Demographic/experience.

How many years have you been teaching?

Are you currently teaching pupils with ADHD and around how many?

If yes, do you feel competent/comfortable/equipped teaching pupils with ADHD?

If no, how competent/comfortable/equipped would you feel to teach pupils with ADHD?

Would you say your experience of teaching pupils with ADHD is small/moderate/significant?

Psychoeducation

What is your understanding of ADHD/Can you describe a typical child with ADHD?

Probe behaviour knowledge

Probe cognition knowledge

Probe impacts of behaviour/cognition difficulties

Probe knowledge that children with ADHD differ from each other

Probe knowledge that children with ADHD have co-occurring difficulties as the norm

(If they do have some knowledge) Where did you learn about ADHD?

e.g. specific training, professional experience, personal experience, personal interest/research

Cognitive skills and strategies

Can you tell me about the pupil’s strengths?

Can you tell me about the pupil’s biggest challenges/what they need most support with?

When you are supporting the pupil with their learning, are there any specific things you do to help them? (i.e. strategies)

Probe internal

Probe external

Probe whether they think those not mentioned might be useful/feasible/challenges

Probe if different for different subjects/times of the day

In your experience, which of these you have mentioned are the most useful for the pupil?

Probe for examples of how they apply it to their learning

Probe whether these strategies are pupil specific or broadly relevant

Probe if specific to particular subjects/times of the day

In your experience, which of these you have mentioned are the least useful for the pupil?

What would you like to be able to support the pupil with that you don’t already do?

Probe why they can’t access this currently e.g. lack of training, resources, knowledge, time

Is there anything you would like to understand better about ADHD?

Probe behaviour

Probe cognition

Interview Schedule—Child

Script: We’re going to have a chat about a few different things today, mostly about your time at school. This will include things like how you get on, how you think, things you’re good at and things you find more difficult. I’ve got some questions here to ask you but try to imagine that I’m just a friend that you’re talking to about these things. There are no right or wrong answers, I’m just interested in what you’ve got to say. Do you have any questions?

Script: First we’re going to talk about ADHD (Attention Deficit Hyperactivity Disorder).

Have you ever heard of/has anyone ever told you what ADHD is?

(If yes) If a friend asked you to tell them what ADHD is, what would you tell them?

Is there anything you would like to know more about ADHD?

Cognition/strategy use

Script: Now we’re going to talk about something a bit different. Everyone has things they are good at, and things they find more difficult. For example, I’m quite good at listening to what people have to say, but I’m not so good at remembering people’s names. I’d like you to think about when you’re in school, and things you’re good at and things you are not so good at. It doesn’t just have to be lessons, it can be anything.

Do you like school?

Probe why/why not?

Probe favourite lessons

What sort of things do you find you do well at in school?

Is there anything you think that you find more difficult in school?

Probe: If I asked your teacher/parent what you find difficult, what would they say?

Probe: Is there anything at school you need extra help with?

Probe: Is there anything you do to help yourself with that?

Script: Some people do things to try to help themselves do things well. For example, when someone tells me a number to remember, I repeat it in my head over and over again.

Can you try to describe to me what you do to help you do these things?

Solving a maths problem

Planning your writing

Doing spellings

Trying to remember something

Concentrating/ignoring distractions

Listening to the teacher

Remaining seated in class when doing work

Working with other children in the class

Probe: Do you use anything in lessons to help you with your work?

Probe: What kind of things do you think could help you with your work?

Probe: Is there anything you do at home, such as when you’re doing your homework, to help you finish what you are doing to do it well?

Probe: Does someone help you with your homework at home? If yes, what do they do that helps? If no, what do you think someone could do to help?

Script: In this last part we’re going to talk about your time at school.

How many teachers are in your class?

Is there anyone who helps you with your work?

Do you work mostly on your own or in groups?

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McDougal, E., Tai, C., Stewart, T.M. et al. Understanding and Supporting Attention Deficit Hyperactivity Disorder (ADHD) in the Primary School Classroom: Perspectives of Children with ADHD and their Teachers. J Autism Dev Disord 53 , 3406–3421 (2023). https://doi.org/10.1007/s10803-022-05639-3

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DOI : https://doi.org/10.1007/s10803-022-05639-3

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Selection Criteria

Search strategy, data extraction, risk of bias and applicability, data synthesis and analysis, parent ratings, teacher ratings, youth self-reports, combined rating scales, additional clinician tools, neuropsychological tests, biospecimen, neuroimaging, variation in diagnostic accuracy with clinical setting or patient subgroup, measures for diagnostic performance, available tools, importance of the comparator sample, clinical implications, future research, conclusions, acknowledgments, tools for the diagnosis of adhd in children and adolescents: a systematic review.

FUNDING: The work is based on research conducted by the Southern California Evidence-based Practice Center under contract to the Agency for Healthcare Research and Quality (AHRQ), Rockville, MD (Contract 75Q80120D00009). The Patient-Centered Outcomes Research Institute (PCORI) funded the research (PCORI Publication No. 2023-SR-03). The findings and conclusions in this manuscript are those of the authors, who are responsible for its contents; the findings and conclusions do not necessarily represent the views of AHRQ or PCORI, its Board of Governors, or Methodology Committee. Therefore, no statement in this report should be construed as an official position of PCORI, AHRQ or of the US Department of Health and Human Services.

CONFLICT OF INTEREST DISCLOSURES: The authors have indicated they have no conflicts of interest to disclose.

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Bradley S. Peterson , Joey Trampush , Morah Brown , Margaret Maglione , Maria Bolshakova , Mary Rozelle , Jeremy Miles , Sheila Pakdaman , Sachi Yagyu , Aneesa Motala , Susanne Hempel; Tools for the Diagnosis of ADHD in Children and Adolescents: A Systematic Review. Pediatrics 2024; e2024065854. 10.1542/peds.2024-065854

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Correct diagnosis is essential for the appropriate clinical management of attention-deficit/hyperactivity disorder (ADHD) in children and adolescents.

This systematic review provides an overview of the available diagnostic tools.

We identified diagnostic accuracy studies in 12 databases published from 1980 through June 2023.

Any ADHD tool evaluation for the diagnosis of ADHD, requiring a reference standard of a clinical diagnosis by a mental health specialist.

Data were abstracted and critically appraised by 1 reviewer and checked by a methodologist. Strength of evidence and applicability assessments followed Evidence-based Practice Center standards.

In total, 231 studies met eligibility criteria. Studies evaluated parental ratings, teacher ratings, youth self-reports, clinician tools, neuropsychological tests, biospecimen, EEG, and neuroimaging. Multiple tools showed promising diagnostic performance, but estimates varied considerably across studies, with a generally low strength of evidence. Performance depended on whether ADHD youth were being differentiated from neurotypically developing children or from clinically referred children.

Studies used different components of available tools and did not report sufficient data for meta-analytic models.

A valid and reliable diagnosis of ADHD requires the judgment of a clinician who is experienced in the evaluation of youth with and without ADHD, along with the aid of standardized rating scales and input from multiple informants across multiple settings, including parents, teachers, and youth themselves.

Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental conditions in youth. Its prevalence has remained constant at ∼5.3% worldwide over the years, and diagnostic criteria have remained constant when based on rigorous diagnostic procedures. 1 Clinical diagnoses, however, have increased steadily over time, 2 and currently, ∼10% of US children receive an ADHD diagnosis. 3 Higher rates of clinical compared with research-based diagnoses are because of an increasing clinician recognition of youth who have ADHD symptoms that are functionally impairing but do not fully meet formal diagnostic criteria. 4 The higher diagnostic rates over time in clinical samples also results from youth receiving a diagnosis incorrectly. Some youth, for example, are misdiagnosed as having ADHD when they have symptoms of other disorders that overlap with ADHD symptoms, such as difficulty concentrating, which occurs in many other conditions. 5 Moreover, ADHD is more than twice as likely to be diagnosed in boys than in girls, 3 in lower-income families, 6 and in white compared with nonwhite youth 7 ; differences that derive at least in part from diagnostic and cultural biases. 8 , – 11  

Improving clinical diagnostic accuracy is essential to ensure that youth who truly have ADHD benefit from receiving treatment without delay. Similarly, youth who do not have ADHD should not be diagnosed since an incorrect diagnosis risks exposing them to unbeneficial treatments. 12 , 13 Clinician judgement alone, however, especially by nonspecialist clinicians, is poor in diagnosing ADHD 14 compared with expert, research-grade diagnoses made by mental health clinicians. 15 Accurately diagnosing ADHD is difficult because diagnoses are often made using subjective clinical impressions, and putative diagnostic tools have a confusing, diverse, and poorly described evidence base that is not widely accessible. The availability of valid diagnostic tools would especially help to reduce misdiagnoses from cultural biases and symptom overlap with ADHD. 12 , 16 , – 19  

This review summarizes evidence for the performance of tools for children and adolescents with ADHD. We did not restrict to a set of known diagnostic tools but instead explored the range of available diagnostic tools, including machine-learning assisted and virtual reality-based tools. The review aimed to assess how diagnostic performance varies by clinical setting and patient characteristics.

The review aims were developed in consultation with the Agency for Healthcare Research and Quality (AHRQ), the Patient-Centered Outcomes Research Institute, the topic nominator American Academy of Pediatrics, key informants, a technical expert panel (TEP), and public input. The TEP reviewed the protocol and advised on key outcomes. Subgroup analyses and key outcomes were prespecified. The review is registered in PROSPERO (CRD42022312656) and the protocol is available on the AHRQ Web site as part of a larger evidence report on ADHD. The systematic review followed Methods of the (AHRQ) Evidence-based Practice Center Program. 20  

Population: age <18 years.

Interventions: any ADHD tool for the diagnosis of ADHD.

Comparators: diagnosis by a mental health specialist, such as a psychologist, psychiatrist, or other provider, who often used published scales or semistructured diagnostic interviews to ensure a reliable DSM-based diagnosis of ADHD.

Key outcomes: diagnostic accuracy (eg, sensitivity, specificity, area under the curve).

Setting: any.

Study design: diagnostic accuracy studies.

Other: English language, published from 1980 to June 2023.

We searched PubMed, Embase, PsycINFO, ERIC, and ClinicalTrials.gov. We identified reviews for reference-mining through PubMed, Cochrane Database of Systematic Reviews, Campbell Collaboration, What Works in Education, PROSPERO, ECRI Guidelines Trust, G-I-N, and ClinicalKey. The peer reviewed strategy is in the Supplemental Appendix . All citations were screened by trained literature reviewers supported by machine learning ( Fig 1 ). Two independent reviewers assessed full text studies for eligibility. The TEP reviewed studies to ensure all were captured. Publications reporting on the same participants were consolidated into 1 record.

Literature flow diagram.

The data abstraction form included extensive guidance to aid reproducibility and standardization in recording study details, results, risk of bias, and applicability. One reviewer abstracted data and a methodologist checked accuracy and completeness. Data are publicly available in the Systematic Review Data Repository.

We assessed characteristics pertaining to patient selection, index test, reference standard, flow and timing that may have introduced bias, and evaluated applicability of study results, such as whether the test, its conduct, or interpretation differed from how the test is used in clinical practice. 21 , 22  

We differentiated parent, teacher, and youth self-report ratings; tools for clinicians; neuropsychological tests; biospecimens; EEG; and neuroimaging. We organized analyses according to prespecified outcome measures. A narrative overview summarized the range of diagnostic performance for key outcomes. Because lack of reported detail in many individual studies hindered use of meta-analytic models, we created summary figures to document the diagnostic performance reported in each study. We used meta-regressions across studies to assess the effects of age, comorbidities, racial and ethnic composition, and diagnostic setting (differentiating primary care, specialty care, school settings, mixed settings, and not reported) on diagnostic performance. One researcher with experience in use of specified standardized criteria 23 initially assessed the overall strength of evidence (SoE) (see Supplemental Appendix ) for each study, then discussed it with the study team to communicate our confidence in each finding.

We screened 23 139 citations and 7534 publications retrieved as full text against the eligibility criteria. In total, 231 studies reported in 290 publications met the eligibility criteria (see Fig 1 ).

Methodological quality of the studies varied. Selection bias was likely in two-thirds of studies; several were determined to be problematic in terms of reported study flow and timing of assessments (eg, not stating whether diagnosis was known before the results of the index test); and several lacked details on diagnosticians or diagnostic procedures ( Supplemental Fig 1 ). Applicability concerns limited the generalizability of findings ( Supplemental Fig 2 ), usually because youth with comorbidities were excluded. Many different tools were assessed within the broader categories (eg, within neuropsychological tests), and even when reporting on the same diagnostic tool, studies often used different components of the tool (eg, different subscales of rating scales), or they combined components in a variety of ways (eg, across different neuropsychological test parameters).

The evidence table ( Supplemental Table 10 , Supplemental Appendix ) shows each study’s finding. The following highlights key findings across studies.

Fifty-nine studies used parent ratings to diagnose ADHD ( Fig 2 ). The most frequently evaluated tool was the CBCL (Child Behavior Checklist), alone or in combination with other tools, often using different score cutoffs for diagnosis, and evaluating different subscales (most frequently the attention deficit/hyperactivity problems subscale). Sensitivities ranged from 38% (corresponding specificity = 96%) to 100% (specificity = 4% to 92%). 24 , 25  

Diagnostic performance parent and teacher ratings. For a complete list of scales see Supplemental Appendix .

Area under the curve (AUC) for receiver operator characteristic curves ranged widely from 0.55 to 0.95 but 3 CBCL studies reported AUCs of 0.83 to 0.84. 26 , – 28 Few studies reported measurement of reliability. SoE was downgraded for study limitation (lack of detailed reporting), imprecision (large performance variability), and inconsistent findings ( Supplemental Table 1 ).

Twenty-three studies used teacher ratings to diagnose ADHD ( Fig 2 ). No 2 studies reported on rater agreement, internal consistency, or test-retest reliability for the same teacher rating scale. The highest sensitivity was 97% (specificity = 26%). 25 The Teacher Report Form, alone or in combination with Conners teacher rating scales, yielded sensitivities of 72% to 79% 29 and specificities of 64% to 76%. 30 , 32 reported AUCs ranged from 0.65 to 0.84. 32 SoE was downgraded to low for imprecision (large performance variability) and inconsistency (results for specific tools not replicated), see Supplemental Table 2 .

Six studies used youth self-reports to diagnose ADHD. No 2 studies used the same instrument. Sensitivities ranged from 53% (specificity = 98%) to 86% (specificity = 70%). 35 AUCs ranged from 0.56 to 0.85. 36 We downgraded SoE for domain inconsistency (only 1 study reported on a given tool and outcome), see Supplemental Table 3 .

Thirteen studies assessed diagnostic performance of ratings combined across informants, often using machine learning for variable selection. Only 1 study compared performance of combined data to performance from single informants, finding negligible improvement (AUC youth = 0.71; parent = 0.85; combined = 0.86). 37 Other studies reported on limited outcome measures and used ad hoc methods to combine information from multiple informants. The best AUC was reported by a machine learning supported study combining parent and teacher ratings (AUC = 0.98). 38  

Twenty-four studies assessed additional tools, such as interview guides, that can be used by clinicians to aid diagnosis of ADHD. Sensitivities varied, ranging from 67% (specificity = 65%) to 98% (specificity = 100%); specificities ranged from 36% (sensitivity = 89%) to 100% (sensitivity = 98%). 39 Some of the tools measured activity levels objectively using an actometer or commercially available activity tracker, either alone or as part of a diagnostic test battery. Reported performance was variable (sensitivity range 25% to 100%, 40 specificity range 66% to 100%, 40 AUCs range 0.75–0.9996 41 ). SoE was downgraded for imprecision (large performance variability) and inconsistency (outcomes and results not replicated), see Supplemental Table 4 .

Seventy-four studies used measures from various neuropsychological tests, including continuous performance tests (CPTs). Four of these included 3- and 4-year-old children. 42 , – 44 A large majority used a CPT, which assessed omission errors (reflecting inattention), commission errors (impulsivity), and reaction time SD (response time variability). Studies varied in use of traditional visual CPTs, such as the Test of Variables of Attention, more novel, multifaceted “hybrid” CPT paradigms, and virtual reality CPTs built upon environments designed to emulate real-world classroom distractibility. Studies used idiosyncratic combinations of individual cognitive measures to achieve the best performance, though many reported on CPT attention and impulsivity measures.

Sensitivity for all neuropsychological tests ranged from 22% (specificity = 96%) to 100% (specificity = 100%) 45 ( Fig 3 ), though the latter study reported performance for unique composite measures without replication. Specificities ranged from 22% (sensitivity = 91%) 46 to 100% (sensitivity = 100% to 75%). 45 , 47 AUCs ranged from 0.59 to 0.93. 48 Sensitivity for all CPT studies ranged from 22% ( specificity = 96) to 100% (specificity = 75%). 49 Specificities for CPTs ranged from 22% (sensitivity = 91%) to 100% (sensitivity = 89%) 47 ( Fig 3 ). AUCs ranged from 0.59 to 0.93. 50 , 51 SoE was deemed low for imprecise studies (large performance variability), see Supplemental Table 5.

Diagnostic performance neuropsychological tests, CPTs, activity monitors, biospecimen, EEG.

Seven studies assessed blood or urine biomarkers to diagnose ADHD. These measured erythropoietin or erythropoietin receptor, membrane potential ratio, micro RNA levels, or urine metabolites. Sensitivities ranged from 56% (specificity = 95%) to 100% (specificity = 100% for erythropoietin and erythropoietin receptors levels). 52 Specificities ranged from 25% (sensitivity = 79%) to 100% (sensitivity = 100%). 52 AUCs ranged from 0.68 to 1.00. 52 Little information was provided on reliability of markers or their combinations. SoE was downgraded for inconsistent and imprecise studies ( Supplemental Table 6 ).

Forty-five studies used EEG markers to diagnose ADHD. EEG signals were obtained in a variety of patient states, even during neuropsychological test performance. Two-thirds used machine learning algorithms to select classification parameters. Several combined EEG with demographic variables or rating scales. Sensitivity ranged widely from 46% to 100% (corresponding specificities 74 and 71%). 53 , 54 One study that combined EEG with demographics data supported by machine learning reported perfect sensitivity and specificity. 54 Specificity was also variable and ranged from 38% (sensitivity = 95%) to 100% (specificities = 71% or 100%). 53 , – 56 Reported AUCs ranged from 0.63 to 1.0. 57 , 58 SoE was downgraded for study imprecision (large performance variability) and limitations (diagnostic approaches poorly described), see Supplemental Table 7 .

Nineteen studies used neuroimaging for diagnosis. One public data set (ADHD-200) produced several analyses. All but 2 used MRI: some functional MRI (fMRI), some structural, and some in combination, with or without magnetic resonance spectroscopy (2 used near-infrared spectroscopy). Most employed machine learning to detect markers that optimized diagnostic classifications. Some combined imaging measures with demographic or other clinical data in the prediction model. Sensitivities ranged from 42% (specificity = 95%) to 99% (specificity = 100%) using resting state fMRI and a complex machine learning algorithm 56 to differentiate ADHD from neurotypical youth. Specificities ranged from 55% (sensitivity = 95%) to 100% 56 using resting state fMRI data. AUCs ranged from 0.58 to over 0.99, 57 SoE was downgraded for imprecision (large performance variability) and study limitations (diagnostic models are often not well described, and the number and type of predictor variables entering the model were unclear). Studies generally did not validate diagnostic algorithms or assess performance measures in an independent sample ( Supplemental Table 8 ).

Regression analyses indicated that setting was associated with both sensitivity ( P = .03) and accuracy ( P = .006) but not specificity ( P = .68) or AUC ( P = .28), with sensitivities lowest in primary care ( Fig 4 ). Sensitivity, specificity, and accuracy were also lower when differentiating youth with ADHD from a clinical sample than from typically developing youth (sensitivity P = .04, specificity P < .001, AUC P < .001) ( Fig 4 ), suggesting that clinical population is a source of heterogeneity in diagnostic performance. Findings should be interpreted with caution, however, as they were not obtained in meta-analytic models and, consequently, do not take into account study size or quality.

Diagnostic performance by setting and population.

Supplemental Figs 3–5 in the Supplemental Appendix document effects by age and gender. We did not detect statistically significant associations of age with sensitivity ( P = .54) or specificity ( P = .37), or associations of the proportion of girls with sensitivity ( P = .63), specificity ( P = .80), accuracy ( P = .34), or AUC ( P = .90).

We identified a large number of publications reporting on ADHD diagnostic tools. To our knowledge, no prior review of ADHD diagnostic tools has been as comprehensive in the range of tools, outcomes, participant ages, and publication years. Despite the large number of studies, we deemed the strength of evidence for the reported performance measures across all categories of diagnostic tools to be low because of large performance variability across studies and various limitations within and across studies.

We required that studies report diagnoses when using the tool compared with diagnoses made by expert mental health clinicians. Studies most commonly reported sensitivity (true-positive rate) and specificity (true-negative rate) when a study-specific diagnostic threshold was applied to measures from the tool being assessed. Sensitivity and specificity depend critically on that study-specific threshold, and their values are inherently a trade-off, such that varying the threshold to increase either sensitivity or specificity reduces the other. Interpreting diagnostic performance in terms of sensitivity and specificity, and comparing those performance measures across studies, is therefore challenging. Consequently, researchers more recently often report performance for sensitivity and specificity in terms of receiver operating characteristics (ROC) curves, a plot of sensitivity versus specificity across the entire range of possible diagnostic thresholds. The area under this ROC curve (AUC) provides an overall, single index of performance that ranges from 0.5 (indicating that the tool provides no information above chance for classification) to 1.0 (indicating a perfect test that can correctly classify all participants as having ADHD and all non-ADHD participants as not having it). AUC values of 90 to 100 are commonly classified as excellent performance; 80 to 90 as good; 70 to 80 as fair; 60 to 70 as poor; and 50 to 60 failed performance.

Most research is available on parental ratings. Overall, AUCs for parent rating scales ranged widely from “poor” 58 to “excellent.” 59 Analyses restricted to the CBCL, the most commonly evaluated scale, yielded more consistent “good” AUCs for differentiating youth with ADHD from others in clinical samples, but the number of studies contributing data were small. Internal consistency for rating scale items was generally high across most rating scales. Test-retest reliability was good, though only 2 studies reported it. One study reported moderate rater agreement between mothers and fathers for inattention, hyperactivity, and impulsivity symptoms. Few studies included youth under 7 years of age.

AUCs for teacher rating scales ranged from “failed” 33 to “good.” 34 Internal consistency for scale items was generally high. Teacher ratings demonstrated very low rater agreement with corresponding parent scales, suggesting either a problem with the instruments or a large variability in symptom presentation with environmental context (home or school).

Though data were limited, self-reports from youth seemed to perform less well than corresponding parent and teacher reports, with AUCs ranging from “failed” for CBCL or ASEBA when distinguishing ADHD from other patients 33 to “good” for the SWAN in distinguishing ADHD from neurotypical controls. 36 , 37  

Studies evaluating neuropsychological tests yielded AUCs ranging from “poor” 60 , 61 to “excellent.” 50 Many used idiosyncratic combinations of cognitive measures, which complicates interpretation of the results across studies. Nevertheless, extracting specific, comparable measures of inattention and impulsivity from CPTs yielded diagnostic performance ranging from “poor” to “excellent” in differentiating ADHD youth from neurotypical controls and “fair” in differentiating ADHD youth from other patients. 42 , 60 , 62 No studies provided an independent replication of diagnosis using the same measure.

Blood biomarkers yielded AUCs ranging from “poor” (serum miRNAs) 63 to “excellent” (erythropoietin and erythropoietin receptors levels) 52 in differentiating ADHD from neurotypical youth. None have been independently replicated, and test-retest reliability was not reported. Most EEG studies used machine learning for diagnostic classification. AUCs ranged from “poor” 64 to “excellent” when differentiating ADHD youth from neurotypical controls. 65 Diagnostic performance was not prospectively replicated in any independent samples.

Most neuroimaging studies relied on machine learning to develop diagnostic algorithms. AUCs ranged from “poor” 66 to “excellent” for distinguishing ADHD youth from neurotypically developing controls. 57 Most studies used pre-existing data sets or repositories to retrospectively discriminate youths with ADHD from neurotypical controls, not from other clinical populations and not prospectively, and none assessed test-retest reliability or the independent reproducibility of findings. Reporting of final mathematical models or algorithms for diagnosis was limited. Activity monitors have the advantage of providing inexpensive, objective, easily obtained, and quantified measures that can potentially be widely disseminated and scaled.

Studies of combined approaches, such as integrating diagnostic tools with clinician impressions, were limited. One study reported increased sensitivity and specificity when an initial clinician diagnosis combined EEG indicators (the reference standard was a consensus diagnosis from a panel of ADHD experts). 67 These findings were not independently replicated, however, and no test-retest reliability was reported.

Many studies aimed to distinguish ADHD youth from neurotypical controls, which is a distinction of limited clinical relevance. In clinically referred youth, most parents, teachers, and clinicians are reasonably confident that something is wrong, even if they are unsure whether the cause of their concern is ADHD. To be informed by a tool that the child is not typically developing is not particularly helpful. Moreover, we cannot know whether diagnostic performance for tools that discriminate ADHD youth only from neurotypical controls is determined by the presence of ADHD or by the presence of any other characteristics that accompany clinical “caseness,” such as the presence of comorbid illnesses or symptoms shared or easily confused with those of other conditions, or the effects of chronic stress or current or past treatment. The clinically more relevant and difficult question is, therefore, how well the tool distinguishes youth with ADHD from those who have other emotional and behavioral problems. Consistent with these conceptual considerations that argue for assessing diagnostic performance in differentiating youth with ADHD from those with other clinical conditions, we found significant evidence that, across all studies, sensitivity, specificity, and AUC were all lower when differentiating youth with ADHD from a clinical sample than when differentiating them from neurotypical youth. These findings also suggest that the comparison population was a significant source of heterogeneity in diagnostic performance.

Despite the large number of studies on diagnostic tools, a valid and reliable diagnosis of ADHD ultimately still requires the judgement of a clinician who is experienced in the evaluation of youth with and without ADHD, along with the aid of standardized rating scales and input from multiple informants across multiple settings, including parents, teachers, and youth themselves. Diagnostic tools perform best when the clinical question is whether a youth has ADHD or is healthy and typically developing, rather than when the clinical question is whether a youth has ADHD or another mental health or behavioral problem. Diagnostic tools yield more false-positive and false-negative diagnoses of ADHD when differentiating youth with ADHD from youth with another mental health problem than when differentiating them from neurotypically developing youth.

Scores for rating scales tended to correlate poorly across raters, and ADHD symptoms in the same child varied across settings, indicating that no single informant in a single setting is a gold-standard for diagnosis. Therefore, diagnosis using rating scales will likely benefit from a more complete representation of symptom expression across multiple informants (parents, school personnel, clinicians, and youth) across more than 1 setting (home, school, and clinic) to inform clinical judgement when making a diagnosis, thus, consistent with current guidelines. 68 , – 70 Unfortunately, methods for combining scores across raters and settings that improve diagnosis compared with scores from single raters have not been developed or prospectively replicated.

Despite the widespread use of neuropsychological testing to “diagnose” youth with ADHD, often at considerable expense, indirect comparisons of AUCs suggest that performance of neuropsychological test measures in diagnosing ADHD is comparable to the diagnostic performance of ADHD rating scales from a single informant. Moreover, the diagnostic accuracy of parent rating scales is typically better than neuropsychological test measures in head-to-head comparisons. 44 , 71 Furthermore, the overall SoE for estimates of diagnostic performance with neuropsychological testing is low. Use of neuropsychological test measures of executive functioning, such as the CPT, may help inform a clinical diagnosis, but they are not definitive either in ruling in or ruling out a diagnosis of ADHD. The sole use of CPTs and other neuropsychological tests to diagnose ADHD, therefore, cannot be recommended. We note that this conclusion regarding diagnostic value is not relevant to any other clinical utility that testing may have.

No independent replication studies have been conducted to validate EEG, neuroimaging, or biospecimen to diagnose ADHD, and no clinical effectiveness studies have been conducted using these tools to diagnose ADHD in the real world. Thus, these tools do not seem remotely close to being ready for clinical application to aid diagnosis, despite US Food and Drug Administration approval of 1 EEG measure as a purported diagnostic aid. 67 , 72  

All studies of diagnostic tools should report data in more detail (ie, clearly report false-positive and -negative rates, the diagnostic thresholds used, and any data manipulation undertaken to achieve the result) to support meta-analytic methods. Studies should include ROC analyses to support comparisons of test performance across studies that are independent of the diagnostic threshold applied to measures from the tool. They should also include assessment of test-retest reliability to help discern whether variability in measures and test performance is a function of setting or of measurement variability over time. Future studies should address the influence of co-occurring disorders on diagnostic performance and how well the tools distinguish youth with ADHD from youth with other emotional and behavioral problems, not simply from healthy controls. More studies should compare the diagnostic accuracy of different test modalities, head-to-head. Independent, prospective replication of performance measures of diagnostic tools in real-world settings is essential before US Food and Drug Administration approval and before recommendations for widespread clinical use.

Research is needed to identify consensus algorithms that combine rating scale data from multiple informants to improve the clinical diagnosis of ADHD, which at present is often unguided, ad hoc, and suboptimal. Diagnostic studies using EEG, neuroimaging, and neuropsychological tests should report precise operational definitions and measurements of the variable(s) used for diagnosis, any diagnostic algorithm employed, the selected statistical cut-offs, and the number of false-positives and false-negatives the diagnostic tool yields to support future efforts at synthetic analyses.

Objective, quantitative neuropsychological test measures of executive functioning correlate only weakly with the clinical symptoms that define ADHD. 73 Thus, many youth with ADHD have normal executive functioning profiles on neuropsychological testing, and many who have impaired executive functioning on testing do not have ADHD. 74 Future research is needed to understand how test measures of executive functioning and the real-world functional problems that define ADHD map on to one another and how that mapping can be improved.

One of the most important potential uses of systematic reviews and meta-analyses in improving the clinical diagnosis of ADHD and treatment planning would be identification of effect modifiers for the performance of diagnostic tools: determining, for example, whether tools perform better in patients who are younger or older, in ethnic minorities, or those experiencing material hardship, or who have a comorbid illness or specific ADHD presentation. Future studies of ADHD should more systematically address the modifier effects of these patient characteristics. They should make available in public repositories the raw, individual-level data and the algorithms or computer code that will aid future efforts at replication, synthesis, and new discovery for diagnostic tools across data sets and studies.

Finally, no studies meeting our inclusion criteria assessed the consequences of being misdiagnosed or labeled as either having or not having ADHD, the diagnosis of ADHD specifically in preschool-aged children, or the potential adverse consequences of youth being incorrectly diagnosed with or without ADHD. This work is urgently needed.

We thank Cynthia Ramirez, Erin Tokutomi, Jennifer Rivera, Coleman Schaefer, Jerusalem Belay, Anne Onyekwuluje, and Mario Gastelum for help with data acquisition. We thank Kymika Okechukwu, Lauren Pilcher, Joanna King, and Robyn Wheatley from the American Academy of Pediatrics (AAP), Jennie Dalton and Paula Eguino Medina from PCORI, Christine Chang and Kim Wittenberg from AHRQ, and Mary Butler from the Minnesota Evidence-based Practice Center. We thank Glendy Burnett, Eugenia Chan, MD, MPH, Matthew J. Gormley, PhD, Laurence Greenhill, MD, Joseph Hagan, Jr, MD, Cecil Reynolds, PhD, Le'Ann Solmonson, PhD, LPC-S, CSC, and Peter Ziemkowski, MD, FAAFP who served as key informants. We thank Angelika Claussen, PhD, Alysa Doyle, PhD, Tiffany Farchione, MD, Matthew J. Gormley, PhD, Laurence Greenhill, MD, Jeffrey M. Halperin, PhD, Marisa Perez-Martin, MS, LMFT, Russell Schachar, MD, Le'Ann Solmonson, PhD, LPC-S, CSC, and James Swanson, PhD who served as a technical expert panel. Finally, we thank Joel Nigg, PhD, and Peter S. Jensen, MD for their peer review of the data.

Drs Peterson and Hempel conceptualized and designed the study, collected data, conducted the analyses, drafted the initial manuscript, and critically reviewed and revised the manuscript; Dr Trampush conducted the critical appraisal; Ms Brown, Ms Maglione, Drs Bolshakova and Padkaman, and Ms Rozelle screened citations and abstracted the data; Dr Miles conducted the analyses; Ms Yagyu designed and executed the search strategy; Ms Motala served as data manager; and all authors provided critical input for the manuscript, approved the final manuscript as submitted, and agree to be accountable for all aspects of the work.

This trial has been registered at PROSPERO (identifier CRD42022312656).

COMPANION PAPER: A companion to this article can be found online at https://www.pediatrics.org/cgi/doi/10.1542/peds.2024-065787 .

Data sharing statement: Data are available in SRDRPlus.

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• CAREER COLUMN
• 13 May 2020

The ADHD paper that triggered a backlash, and what it taught me

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Anita Thapar is professor of child and adolescent psychiatry at Cardiff University, UK.

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In September 2010, I and two colleagues held a press conference on a paper we were about to have published in The Lancet . The paper was a genome-wide analysis that showed a higher burden of rare chromosomal deletions or duplications in people with attention-deficit hyperactivity disorder (ADHD) than in those unaffected by the condition (N. M. Williams et al. Lancet 376 , 1401–1408; 2010).

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Research on ADHD

Current research, research agenda.

Attention-deficit/hyperactivity disorder (ADHD) is a serious public health problem affecting a large number of children and adults. CDC conducts research to expand on what is known about ADHD. The information learned will improve knowledge about the factors that increase the risk for ADHD, as well as the causes, and best treatments, and will aid the development of resources to help people living with ADHD . Learn more about CDC’s research on ADHD on this overview page.

ADHD can cause problems in how well children do in school, in their ability to make and keep friends, and in how they function in society. Although there are treatments to improve ADHD symptoms, more information is needed about managing ADHD so that children can learn and grow into adulthood without being impaired by their symptoms.

The criteria used to diagnose ADHD have changed over time. Researchers who study ADHD have used different definitions to diagnose ADHD. This has led to different estimates for the number, characteristics, and outcomes of children with the disorder. Although the exact causes of ADHD are not known, research shows that genes play a role, but other factors may contribute or make symptoms worse. There are many unanswered questions about ADHD, and there is more we need to learn about how ADHD affects people throughout their life.

The treatment costs of ADHD and the personal and societal costs can be significant. Researchers estimate that in the United States, $31.6 billion is the combined annual cost for

• Health care for persons with ADHD specifically related to the diagnosis;
• Health care for family members of persons with ADHD specifically related to their family member’s diagnosis; and
• Work absences among adults with ADHD and adult family members of persons with ADHD. 1

Improving the health of individuals with ADHD could result in substantial financial savings to families and society, potentially reducing this financial burden.

National Surveys

CDC uses data from national surveys to understand the number of children with ADHD, other concerns and conditions they might experience, and the kind of treatment they might receive. Surveys that have data on children and on ADHD include

• National Survey of Children’s Health since 2016 ,
• National Survey of Children’s Health 2003-2012 ,
• National Survey of the Diagnosis and Treatment of ADHD and Tourette Syndrome (NS-DATA) ,
• National Health Interview Survey ,
• National Survey on Children with Special Health Care Needs

Read about key findings from the national surveys .

Learn more about the data from the national surveys .

Policy Research

In order to fully appreciate how children with ADHD are treated, one must understand the policies that affect how treatments are authorized and reimbursed by health plans. One policy that may affect medication treatment is for health plans or state programs to require pre-authorization before specific medications can be prescribed. Prescription prior-authorization policy means that the health plan or state program is required to review a physician’s prescription request before coverage for the medication is granted.

Over the past decades, the number of children being prescribed ADHD medications has increased substantially. In response to this trend, many state Medicaid programs have implemented prior-authorization policies for pediatric use of ADHD medications. These policies vary from state to state, and no comprehensive information on these policies was previously available.

To learn more about prior-authorization policies related to pediatric use of ADHD medications, CDC collaborated with Temple University to conduct a cross-sectional mapping study . Information was gathered on state Medicaid prior-authorization policies (as of April 2023) for prescribing ADHD medication to children. The study team collected

New Research: Medicaid policies to manage the use of ADHD medications: Information by state

• Prior-authorization forms,
• Memoranda from state Medicaid directors to prescribers,
• Drug utilization review board meeting notes, and
• State prescription drug lists.

The study team developed a coding scheme to capture and catalogue the key features of the prior-authorization policies. You can access a fact sheet with a summary of the results of this mapping study and also a database of state policies.

Healthcare Claims Data

CDC uses healthcare insurance claims data to understand treatment patterns for children in clinical care for ADHD, such as claims for psychological services and ADHD medication in patients covered by employer-sponsored insurance or by Medicaid.

Read more about the data from healthcare claims datasets.

Community-based Research

CDC’s National Center on Birth Defects and Developmental Disabilities (NCBDDD) supported large community-based, epidemiologic studies of ADHD in the United States. These studies

• Enhance what is known about ADHD and the co-occurring conditions in children and
• Increase the opportunity to make the most informed decisions and recommendations about potential public health prevention and intervention strategies for children with ADHD.

Project to Learn About ADHD in Youth (PLAY)

The Project to Learn About ADHD in Youth (PLAY) was a population-based research project with the University of South Carolina and the University of Oklahoma Health Sciences Center. It was conducted to shed more light on how many school-age children have ADHD, how the condition develops over time, what other conditions and risks children may experience, and about treatments they may receive. Data were collected to learn more about ADHD in diverse population groups, the quality and patterns of treatment, and the factors that affect short- and long-term outcomes for children.

Project to Learn About Youth – Mental Health

The Project to Learn About Youth – Mental Health (PLAY-MH) expanded the focus to study a range of mental, behavioral, or emotional disorders including ADHD and tic disorders (such as Tourette syndrome)  in four communities. The project provides information that can be used for public health prevention and intervention strategies to support children’s health and development.

Study questions include

• What percentage of children in the community has one or more mental, behavioral, or emotional disorders ?
• How frequently do these disorders appear together?
• What types of treatment are children receiving?

  Top of Page

Understanding Risk

It is not known what causes ADHD. ADHD is often seen in families, and genes appear to play a role, but other factors may contribute or make symptoms worse. For example, some environmental exposures have been linked to increased ADHD symptoms , but the evidence has been inconsistent. Knowing more about those factors would help with planning how to decrease the risk for ADHD. NCBDDD funded a comprehensive literature review of studies that investigate a large range of factors that might increase the risk for ADHD. The results will increase the ability of public health professionals to make the most informed decisions and recommendations about potential public health prevention strategies.

Public health issues in ADHD can be divided into three areas:

• Understanding how many children have ADHD and whether they are properly diagnosed.
• Understanding and addressing the impact of ADHD in the population.
• Understanding which treatments are effective and which are best for children of different ages and in different communities.

Key public health questions yet to be answered include

• What are the causes, and the factors that increase the risk or severity of ADHD?
• How many children have ADHD? Is the rate increasing?
• How many children have ADHD and other conditions at the same time?
• What social and economic impacts does ADHD have on families, schools, the workforce, and the judicial and health systems?
• Are ADHD and other co-occurring conditions  being appropriately diagnosed and treated?
• Are people with ADHD able to access appropriate and timely treatment?
• How effective are the treatments and what are their long-term effects?

Previous Workshop Summaries

• Epidemiologic Issues in ADHD Workshop (April 14, 1999)
• Public Health Issues in ADHD: Individual, System, and Cost Burden of the Disorder (May, 17, 1999)
• ADHD Long-term Outcomes: Comorbidity, Secondary Conditions, and Health Risk Behaviors (June 9, 1999)
• Public Health Issues in the Treatment of ADHD Workshop (June 15, 1999)

Birnbaum HG, Kessler RC, Lowe SW, Secnik K, Greenberg PE, Leong SA, et al. Costs of attention deficit-hyperactivity disorder (ADHD) in the US: excess costs of persons with ADHD and their family members in 2000. Current medical research and opinion 2005;21(2):195-206 .

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Exit Notification / Disclaimer Policy

• The Centers for Disease Control and Prevention (CDC) cannot attest to the accuracy of a non-federal website.
• Linking to a non-federal website does not constitute an endorsement by CDC or any of its employees of the sponsors or the information and products presented on the website.
• You will be subject to the destination website's privacy policy when you follow the link.
• CDC is not responsible for Section 508 compliance (accessibility) on other federal or private website.

TOP TEN RESEARCH PRIORITIES FOR ATTENTION DEFICIT/HYPERACTIVITY DISORDER TREATMENT

Affiliations.

• 1 Swedish Agency for Health Technology Assessment and Assessment of Social Services (SBU)[email protected].
• 2 Swedish Agency for Health Technology Assessment and Assessment of Social Services (SBU)Faculty of Odontology,Malmö University.
• 3 Swedish Agency for Health Technology Assessment and Assessment of Social Services (SBU).
• 4 Swedish Agency for Health Technology Assessment and Assessment of Social Services (SBU)Faculty of Odontology,Malmö University,Department of Dental Medicine,Karolinska Institutet.
• PMID: 27516379
• DOI: 10.1017/S0266462316000179

Objectives: The aim of this project was to identify the ten most important research questions for attention deficit/hyperactivity disorder (ADHD) treatment as identified by people with ADHD together with personnel involved in the treatment of ADHD in school, health, and correction services.

Methods: A working group consisting of consumers and personnel was established. The method for prioritization was primarily based on James Lind Alliance's guidebook, consisting of an interim priority setting exercise and a workshop.

Results: The top ten list includes the risk of drug dependency later in life when treated with methylphenidate as a child, teacher support, multimodal therapy, comparisons between atomoxetine and methylphenidate, methylphenidate treatment in substance abusers, parental support programmes, supported conversation, computer-aided working memory training, psychoeducative treatment, and melatonin.

Conclusions: We have shown that consumers and personnel can reach consensus on research priorities for treatments for ADHD. We encourage researchers and funders to consider the list for future studies.

Keywords: Attention deficit disorder with hyperactivity; Mental disorders; Patient participation.

• Attention Deficit Disorder with Hyperactivity / drug therapy*
• Central Nervous System Stimulants / therapeutic use
• Patient Participation
• Randomized Controlled Trials as Topic
• Central Nervous System Stimulants

Center for ADHD Research Projects

Research projects.

The Center for ADHD strives to find new and innovative approaches to treat children with attention deficit hyperactivity disorder.

Current Projects

Current projects and proposed research include:

• Studies Seeking Participants
• ADHD Study For Parents of Children 4 to 11 Years Old
• Study for Children 10 to 12 With Excessive Daydreaming, Mental Confusion, Fogginess, Spaciness and / or Slowed Behavior / Thinking
• ADHD Study for Children 8 to 12 Years Old
• Carboxylesterase 1 Genetic Variation and Methylphenidate in ADHD (CES1) Study Summary
• Down Syndrome and ADHD Study for Children and Teens 6 to 17 Years Old
• A Study for Black and Latino Caretakers of Children Newly Diagnosed with ADHD
• ADHD Medication Effects on Adolescents with ADHD
• Ongoing Studies
• Carboxylesterase 1 Genetic Variation and Methylphenidate in ADHD (CES1) Study Summary 18+
• Evaluating Assessment and Medication Treatment of ADHD in Children with Down Syndrome (TEAM-DS)
• Longitudinal Examination of Sluggish Cognitive Tempo and Internalizing Psychopathology in Adolescence (ALERT study)
• Longitudinal Evaluation of Sluggish Cognitive Tempo: Identifying Mechanisms of Educational Impairment (CASS-2 study)
• Mindful Awareness Practices (MAPs) in Adolescents with ADHD and Sluggish Cognitive Tempo (SCT)
• A Family Navigator Intervention to Improve ADHD- Related Treatment Adherence (I2-ART) for Minority Children
• Parsing Neurobiological Bases of Heterogeneity in ADHD
• Nationwide dissemination of a web-based quality improvement intervention to improve the quality of ADHD care among community-based pediatricians
• Improving ADHD Behavioral Care Quality in Community-Based Pediatric Settings
• Improving Medication Continuity Among Adolescents with ADHD
• Predictors of Stimulant Medication Continuity in Children with ADHD
• Teaching Academic Success Skills to Middle School Students with Autism Spectrum Disorders (ASD) with Executive Functioning Deficits – School Setting

Completed Projects

• Evaluating Assessment and Medication Treatment of ADHD in Children with Down Syndrome
• ADHD Study for Teens With Sleep Problems
• Improving ADHD Driving Study
• The Effects of ADHD Medication (TEAM) Study
• A Multi-Method Feasibility Study Investigating Reaction Time Variability in Autism Spectrum Disorder
• Children’s Attention Problems Study
• Evaluation of the Computerized Progressive Attention Training (CPAT) program for children with ADHD  
• Multisite Study of School-Based Treatment Approaches for ADHD
• Evaluation of an Intervention for Improving Community-Based Pediatric ADHD Care
• Multimodal Treatment Study Follow-Up
• Developing New Technologies to Improve ADHD Medication Continuity
• Shared Decision Making to Improve Care for Children with ADHD
• Medication Continuity in Children Treated for ADHD
• Improving Self-regulation & School Readiness in Preschoolers
• Interventions for Children with ADHD and Reading Difficulties
• Sleep in Teens with ADHD
• Omega-3 fatty acid supplements on ADHD brain function
• ADHD iPAD App study
• Medication Response in Children with Predominantly Inattentive Type ADHD
• Cognitive Training Program for Children with ADHD
• ADHD Collaborative
• Disseminating a Model Intervention to Promote Improved ADHD Care in the Community
• Examining the Effects of Cell Phone Use in an ADHD Population: A Pilot Study
• Organizational Skills Intervention
• Response Variability in Children with ADHD
• A novel intervention to improve the driving performance of ADHD teens
• Impact of COVID-19 in Adolescents with and without ADHD
• Treating Sleep in Adolescents with ADHD and Co-occurring Sleep Problems
• SCT Interview Study
• Phenotypic Correlates Distinguishing Sluggish Cognitive Tempo from ADHD
• Teaching Academic Success Skills to Middle School Students with Autism Spectrum Disorders (ASD) with Executive Functioning Deficits
• Longitudinal Impact of Sleep in Teens (LIST) With and Without ADHD Study
• Concentration at School Study (CASS)

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March 26, 2024

Altered brain connections in youth with ADHD

At a glance.

• Youth with ADHD have elevated brain activity connecting the frontal cortex with the information processing centers deep in the brain.
• Understanding the brain regions involved in ADHD symptoms could help point toward directions for new approaches to treatment.

People living with attention-deficit/hyperactivity disorder, or ADHD, can struggle with focus and self-control. The condition’s symptoms may interfere with daily functioning in both children and adults. ADHD can make it hard for kids to succeed in school, and for adults to thrive in the workforce and in personal relationships.

ADHD is a brain condition that requires a professional diagnosis to help guide treatment. Drugs that increase the levels of certain chemicals in the brain help some people with ADHD. But they don’t work for everyone, and can have unacceptable side effects.

To design better treatments for ADHD, scientists need to understand more about how the brain works in people with the condition. Researchers have wondered if differences in the neural connections between the brain’s frontal cortex, which sits in the front of the brain, and regions deep within the brain, called subcortical regions, may underlie some symptoms of ADHD. The frontal cortex plays a role in attention and control of unwanted behaviors. The subcortical regions are involved in learning, movement, reward, and emotion.

Previous studies used a type of brain imaging called functional magnetic resonance imaging (fMRI) to look for such connections in children with symptoms of ADHD. fMRI can measure changes in brain activity in real time. But these studies have been small and returned conflicting results.

An NIH research team re-analyzed fMRI images collected in six previous studies. Altogether, those studies had obtained fMRI images from more than 1,696 youths with ADHD, aged 6 to 18, as well as almost 7,000 without the condition. In addition to using a large number of images, the researchers strictly defined the brain areas being measured. This allowed for more accurate comparisons between individual fMRI scans. Results were published March 13, 2024, in the American Journal of Psychiatry .

The team found that the brains of youth with ADHD had more activity between several subcortical regions and the frontal cortex than those in youth without the condition. The brains of youth with ADHD also showed greater connection between the frontal cortex and part of the brain called the amygdala. The amygdala helps process emotions and had been suspected to play a role in ADHD.

These results were seen regardless of children’s sex, age, race or ethnicity, socioeconomic status, or estimated intelligence. The differences in brain connectivity also didn’t appear to be affected by the presence or absence of other mental health problems, such as anxiety or depression. However, the differences found by the researchers were small and likely capture only part of the processes involved in ADHD.

“The findings from this study help further our understanding of the brain processes contributing to ADHD symptoms. Such understanding is a first step in thinking of new ways to help those who find the symptoms cause difficulties in day-to-day life,” says Dr. Philip Shaw, who helped lead the study. “But these brain changes are only part of the story. ADHD is a complex condition, and many other changes in brain connectivity will play a role.”

Related Links

• Children’s Sleep Linked to Brain Development
• Brain Differences in Youth Linked to Increased Waist Size
• Dopamine Affects How Brain Decides Whether a Goal is Worth the Effort
• An Expanded Map of the Human Brain
• Mapping Brain Circuits Involved in Attention
• Focusing on ADHD: Attention Deficit Hyperactivity Disorder
• Keeping Up in School? Identifying Learning Problems
• Attention-Deficit/Hyperactivity Disorder (ADHD)
• Adolescent Brain Cognitive Development (ABCD) Study

References:  Subcortico-Cortical Dysconnectivity in ADHD: A Voxel-Wise Mega-Analysis Across Multiple Cohorts . Norman LJ, Sudre G, Price J, Shaw P. Am J Psychiatry . 2024 Mar 13:appiajp20230026. doi: 10.1176/appi.ajp.20230026. Online ahead of print. PMID: 38476041.

Funding:  NIH’s National Institute of Mental Health (NIMH), National Human Genome Research Institute (NHGRI), National Institute on Drug Abuse (NIDA), National Institute on Alcohol Abuse and Alcoholism (NIAAA), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institute on Aging (NIA), and Office of the Director (OD); Child Mind Institute; New York State Office of Mental Health; Research Foundation for Mental Hygiene.

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March 28, 2024

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

fact-checked

trusted source

Parental avoidance of toxic exposures could help reduce risk of autism, ADHD in children, observational study suggests

by Steven Lee, University of Texas Health Science Center at San Antonio

Autism and attention deficit hyperactivity disorder (ADHD) may be preventable if parents avoid toxic exposures and adopt interventions such as environmental house calls, according to a study , published in Journal of Xenobiotics , led by researchers from The University of Texas Health Science Center at San Antonio (UT Health San Antonio).

Using a validated, self-administered questionnaire now used worldwide to identify individuals with chemical intolerance—the Quick Environmental Exposure and Sensitivity Inventory (QEESI)—parents and practitioners can determine the risk for each family and learn which exposures to avoid in their own homes where most people spend most of their day, the researchers said.

A population-based survey of nearly 8,000 U.S. adults, using QEESI, found that parents with chemical intolerance scores in the top tenth percentile were 5.7 times as likely to report a child with autism and 2.1 times as likely with ADHD compared with parents in the bottom tenth percentile.

The findings build on a 2015 study by UT Health San Antonio that first linked chemical intolerance in patients with the risk of their children developing autism and ADHD.

"This is the first-ever article in the medical literature showing that chemical intolerance in parents can predict the risk of autism and ADHD in their children, and suggests that reducing exposures prior to and during pregnancy could help prevention," said Claudia S. Miller, MD, MS, professor emeritus with the Department of Family and Community Medicine at UT Health San Antonio. "Up to now, most interventions have been behavioral or medical, after a child is diagnosed."

Miller is senior author of the study, titled, "Assessing Chemical Intolerance in Parents Predicts the Risk of Autism and ADHD in Their Children." Co-authors include Raymond F. Palmer, Ph.D., and Rodolfo Rincon, MD and specialist, both with the Department of Family and Community Medicine at UT Health San Antonio; and David Kattari, a statistician with the Marilyn Brachman Hoffman Foundation in Fort Worth, Texas.

The researchers note that the study is observational, and further research is needed using controlled trials to confirm causality and further explore the proposed mechanism behind chemical intolerance.

Still, they wrote, "The implications of this study, if confirmed, could be significant for preventive measures and early intervention strategies in families with parental chemical intolerance. We recommend that all prospective parents be assessed for chemical intolerance at an early age."

Mast cells and autism

Physician-researcher Miller in 1996 first proposed a two-stage disease process of initiation by exposure and then triggering of symptoms called TILT, for Toxicant-Induced Loss of Tolerance, as the mechanism behind chemical intolerance. She has served as a physician/environmental consultant on exposures.

Her published papers have explored the impact of pesticides, the Gulf War, breast and other implants, 9/11, toxic molds, combustion products from fires, and indoor air pollutants in so-called "sick" homes, schools and workplaces, including the EPA's own headquarters building in Washington, D.C.

The new study comes amid a backdrop of a 317% increase in the prevalence of autism since 2000, now occurring in one of every 36 children in the country, the researchers note, citing data originating from the Centers for Disease Control and Prevention. And the prevalence of ADHD has risen to one in eight children, also according to the CDC.

Miller and colleagues in 2021 discovered a strong association between chemical intolerance and "mast cells," considered the immune system's first responders that originate in the bone marrow and migrate to the interface between tissues and the external environment where they then reside.

When exposed to "xenobiotics," foreign substances like chemicals and viruses, they can release thousands of inflammatory molecules called mediators. This response results in allergic-like reactions, some very severe. These cells can be sensitized by a single acute exposure to xenobiotics, or by repeated lower-level exposures. Thereafter, even low levels of those and other unrelated substances can cause the mast cells to release the mediators that can lead to inflammation and illness.

In their latest study, the researchers determined that the high chemical intolerance scores among parents of children with autism, coupled with the 2021 finding of mast-cell activation as a plausible biomechanism for chemical intolerance, suggest that:

• The QEESI can identify individuals at increased risk.
• Environmental counseling, such as personalized environmental house calls to assess risks at home, may reduce personal exposures to possible triggers such as pesticides, fragrances and tobacco smoke, particularly during pregnancy and childhood.
• The global rise in autism and ADHD may be due to fossil-fuel-derived and biogenic toxicants epigenetically "turning on" or "turning off" critical mast cell genes that can be transmitted trans-generationally.

The researchers conclude that once mast cells are sensitized, diverse xenobiotics that never bothered the person previously and do not bother most people trigger multisystem symptoms that wax and wane over time. And they believe that persistent activation and triggering of mast cells may underlie the brain inflammation in autism.

"The potential role of environmental toxicants in influencing epigenetics and mast cell function is a complex and emerging area of research," they wrote. "Acknowledging the need for further evidence, we hope this study contributes to an improved understanding of the potential role of environmental factors in the global rise of autism and ADHD."

The authors created tools for patients, practitioners and researchers, described in their "TILT Tutorial on Chemical Intolerance, Autism, and ADHD" , available along with other resources at https://TILTresearch.org .

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Estelle Erasmus

How to Resist the Temptation of AI When Writing

Whether you're a student, a journalist, or a business professional, knowing how to do high-quality research and writing using trustworthy data and sources, without giving in to the temptation of AI or ChatGPT , is a skill worth developing.

As I detail in my book Writing That Gets Noticed , locating credible databases and sources and accurately vetting information can be the difference between turning a story around quickly or getting stuck with outdated information.

For example, several years ago the editor of Parents.com asked for a hot-take reaction to country singer Carrie Underwood saying that, because she was 35, she had missed her chance at having another baby. Since I had written about getting pregnant in my forties, I knew that as long as I updated my facts and figures, and included supportive and relevant peer-reviewed research, I could pull off this story. And I did.

The story ran later that day , and it led to other assignments. Here are some tips I’ve learned that you should consider mastering before you turn to automated tools like generative AI to handle your writing work for you.

Identify experts, peer-reviewed research study authors, and sources who can speak with authority—and ideally, offer easily understood sound bites or statistics on the topic of your work. Great sources include professors at major universities and media spokespeople at associations and organizations.

For example, writer and author William Dameron pinned his recent essay in HuffPost Personal around a statistic from the American Heart Association on how LGBTQ people experience higher rates of heart disease based on discrimination. Although he first found the link in a secondary source (an article in The New York Times ), he made sure that he checked the primary source: the original study that the American Heart Association gleaned the statistic from. He verified the information, as should any writer, because anytime a statistic is cited in a secondary source, errors can be introduced.

Jen Malia, author of  The Infinity Rainbow Club  series of children’s books (whom I recently interviewed on my podcast ), recently wrote a piece about dinosaur-bone hunting for Business Insider , which she covers in her book Violet and the Jurassic Land Exhibit.

After a visit to the Carnegie Museum of Natural History in Pittsburgh, Pennsylvania, Malia, whose books are set in Philadelphia, found multiple resources online and on the museum site that gave her the history of the Bone Wars , information on the exhibits she saw, and the scientific names of the dinosaurs she was inspired by. She also used the Library of Congress’ website, which offers digital collections and links to the Library of Congress Newspaper Collection.

Malia is a fan of searching for additional resources and citable documents with Google Scholar . “If I find that a secondary source mentions a newspaper article, I’m going to go to the original newspaper article, instead of just stopping there and quoting,” she says.

Aarian Marshall

Eric Ravenscraft

Adrienne So

Brenda Stolyar

Your local public library is a great source of free information, journals, and databases (even ones that generally require a subscription and include embargoed research). For example, your search should include everything from health databases ( Sage Journals , Scopus , PubMed) to databases for academic sources and journalism ( American Periodical Series Online , Statista , Academic Search Premier ) and databases for news, trends, market research, and polls (t he Harris Poll , Pew Research Center , Newsbank , ProPublica ).

Even if you find a study or paper that you can’t access in one of those databases, consider reaching out to the study’s lead author or researcher. In many cases, they’re happy to discuss their work and may even share the study with you directly and offer to talk about their research.

For journalist Paulette Perhach’s article on ADHD in The New York Times, she used Epic Research to see “dual team studies.” That's when two independent teams address the same topic or question, and ideally come to the same conclusions. She recommends locating research and experts via key associations for your topic. She also likes searching via Google Scholar but advises filtering it for studies and research in recent years to avoid using old data. She suggests keeping your links and research organized. “Always be ready to be peer-reviewed yourself,” Perhach says.

When you are looking for information for a story or project, you might be inclined to start with a regular Google search. But keep in mind that the internet is full of false information, and websites that look trustworthy can sometimes turn out to be businesses or companies with a vested interest in you taking their word as objective fact without additional scrutiny. Regardless of your writing project, unreliable or biased sources are a great way to torpedo your work—and any hope of future work.

Author Bobbi Rebell researched her book Launching Financial Grownups using the IRS’ website . “I might say that you can contribute a certain amount to a 401K, but it might be outdated because those numbers are always changing, and it’s important to be accurate,” she says. “AI and ChatGPT can be great for idea generation,” says Rebell, “but you have to be careful. If you are using an article someone was quoted in, you don’t know if they were misquoted or quoted out of context.”

If you use AI and ChatGPT for sourcing, you not only risk introducing errors, you risk introducing plagiarism—there is a reason OpenAI, the company behind ChatGPT, is being sued for downloading information from all those books.

Audrey Clare Farley, who writes historical nonfiction, has used a plethora of sites for historical research, including Women Also Know History , which allows searches by expertise or area of study, and JSTOR , a digital library database that offers a number of free downloads a month. She also uses Chronicling America , a project from the Library of Congress which gathers old newspapers to show how a historical event was reported, and Newspapers.com (which you can access via free trial but requires a subscription after seven days).

When it comes to finding experts, Farley cautions against choosing the loudest voices on social media platforms. “They might not necessarily be the most authoritative. I vet them by checking if they have a history of publication on the topic, and/or educational credentials.”

When vetting an expert, look for these red flags:

• You can’t find their work published or cited anywhere.
• They were published in an obscure journal.
• Their research is funded by a company, not a university, or they are the spokesperson for the company they are doing research for. (This makes them a public relations vehicle and not an appropriate source for journalism.)

And finally, the best endings for virtually any writing, whether it’s an essay, a research paper, an academic report, or a piece of investigative journalism, circle back to the beginning of the piece, and show your reader the transformation or the journey the piece has presented in perspective.

As always, your goal should be strong writing supported by research that makes an impact without cutting corners. Only then can you explore tools that might make the job a little easier, for instance by generating subheads or discovering a concept you might be missing—because then you'll have the experience and skills to see whether it's harming or helping your work.

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ANNUAL RESEARCH REVIEW: PERSPECTIVES ON PROGRESS IN ADHD SCIENCE - FROM CHARACTERISATION TO CAUSE

Edmund j s sonuga-barke.

1. School of Academic Psychiatry, Institute of Psychology, Psychiatry & Neuroscience, King’s College London. UK

2. Department of Child & Adolescent Psychiatry, Aarhus University, Denmark

Stephen P. Becker

3. Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, United States

4. Department of Pediatrics, University of Cincinnati College of Medicine, United States

Sven Bölte

5. Department of Women’s and Children’s Health, Karolinska Institutet, Sweden

6. Division of Child and Adolescent Psychiatry, Center for Psychiatry Research, Stockholm County Council, Sweden

Francisco Xavier Castellanos

7. Department of Child and Adolescent Psychiatry, New York University Grossman School of Medicine, USA

8. Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA

Barbara Franke

9. Departments of Human Genetics and Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands

Jeffery H. Newcorn

10. Icahn School of Medicine at Mount Sinai; New York, NY, USA

Joel T. Nigg

11. Department of Psychiatry, Oregon Health and Science University, USA

Luis Augusto Rohde

12. ADHD Outpatient Program & Developmental Psychiatry Program, Hospital de Clinica de Porto Alegre, Federal University of Rio Grande do Sul, Brazil; National Institute of Developmental Psychiatry, Brazil

Emily Simonoff

The science of attention-deficit/hyperactivity disorder (ADHD) is motivated by a translational goal – the discovery and exploitation of knowledge about the nature of ADHD to the benefit of those individuals whose lives it affects. Over the past fifty years scientific research has made enormous strides in characterising the ADHD condition and in understanding its correlates and causes. However, the translation of these scientific insights into clinical benefits has been limited. In this review, we provide a selective and focused survey of the scientific field of ADHD, providing our personal perspectives on what constitutes the scientific consensus, important new leads to be highlighted, and the key outstanding questions to be addressed going forward. We cover two broad domains – clinical characterisation and, risk factors, causal processes, and neuro-biological pathways . Part one focuses on the developmental course of ADHD, co-occurring characteristics and conditions, and the functional impact of living with ADHD – including impairment, quality of life, and stigma. In part two, we explore genetic and environmental influences and putative mediating brain processes. In the final section, we reflect on the future of the ADHD construct in the light of cross-cutting scientific themes and recent conceptual reformulations that cast ADHD traits as part of a broader spectrum of neurodivergence.

1. INTRODUCTION: THE CHANGING FACE OF ATTENTION-DEFICIT/HYPERACTIVITY DISORDER SCIENCE

Attention-deficit/hyperactivity disorder (ADHD), as currently formulated in diagnostic manuals (i.e., DSM-5 and ICD-11), represents the latest stage in a long history of attempts to characterise a cluster of overlapping early onset and persistent symptoms of hyperkinesis, inattention and impulsiveness known to harm affected individual’s lives through the functional impairment they create, both in the short and long term. These formulations describe and thus implicitly conceptualise ADHD as a singular, categorical entity with clear and definable boundaries both between disorder and non-disorder and between ADHD and other disorders, caused by dysfunction within the patient ( Sonuga-Barke, 2020 ). This way of thinking about ADHD, although subject to minor adjustments in specific aspects of diagnostic criteria, introduced following periodic review of available scientific evidence, has remained fundamentally unchanged for decades. However, during the same period enormous strides have been made in our scientific understanding of ADHD that appear to challenge core elements of this conceptual model by highlighting, for instance, its dimensionality, causal heterogeneity, and genetic and neuro-biological overlap with other conditions ( Posner et al., 2020 ); characteristics known to be shared with other psychiatric and neuro-developmental conditions. These discoveries have led some to question how well the current diagnostic framework maps onto scientific findings about the underlying causal structure of the condition (e.g., Musser & Raiker, 2019 ). In this review, our goal is to take stock of the state of ADHD science; reviewing recent developments in light of past consensus while identifying key questions that need to be addressed going forward. The paper is presented in two major sections. The first focuses on the characterisation of ADHD in terms of developmental course, correlated characteristics and traits, and overlapping conditions, and its impact on the lives of affected individuals. In the second section, we examine risk factors and causal processes and neurobiological pathways in terms of genetic architecture, environmental influences and brain structure, function, and chemistry.

The current work differs from previous reviews of ADHD science in a number of ways. In particular, in fulfilling our distinctive goal of characterising prior consensus in the field while looking forward to the future, we wanted to allow the individual voices of the authors to be heard, encouraging a degree of subjectivity in the foci adopted, the interpretations made, and questions identified. To achieve this, each author was given responsibility for leading on a particular domain in which they had specialist experience (although to be honest, given the breadth of their experience, most authors could have led on most sections): Luis Rohde - charting development; Emily Simonoff and Stephen Becker – correlated characteristics and overlapping conditions; Sven Bölte – impact of living with ADHD; Barbara Franke – genes; Joel Nigg – environments; Xavier Castellanos and Jeffrey Newcorn – brain; Edmund Sonuga-Barke – the future of the ADHD concept . Drafts of sections were circulated to be modified in the light of comments from fellow authors through a process co-ordinated and moderated by Sonuga-Barke. The final decision about the content of each section, however, always fell to the responsible authors. The paper is therefore neither a systematic review (though rigorously empirically grounded), nor a consensus statement (although there was a great deal of consensus). Through this novel approach we hope to provoke debate and stimulate thinking about both the past achievements in ADHD science and the most important next steps.

2. CHARACTERISATION

Despite a number of important changes in successive DSM and ICD manuals, the diagnostic conception and formulation of ADHD has remained essentially unchanged for many decades – it is a categorical, childhood onset neuro-developmental disorder marked by age inappropriate, extreme and impairing levels of inattention and/or hyperactivity and impulsivity. In this section, we examine how well certain aspects of this formulation stand up in the light of evidence from recent studies and then frame key questions for future enquiry.

2.1. CHARTING THE DEVELOPMENTAL COURSE

In the fields of neurodevelopmental and mental health conditions, there is an increasing focus on early intervention and prevention. Understanding the course of a condition from its earliest manifestations is a prerequisite for the success of such approaches.

2.1.1. Prior consensus

ADHD has been traditionally conceptualised as a neurodevelopmental condition with an early childhood-onset and a steady course with limited remission ( Posner et al., 2020 ). Longitudinal studies of child-to-adult ADHD developmental trajectories provide qualified support for this characterisation while offering a more nuanced and complex view with regard to some aspects ( Wootton et al., 2022 ). For most individuals diagnosed with ADHD, symptoms, and to some extent impairment, do indeed first appear in early childhood, even when their first formal diagnosis is later in life ( Kieling et al., 2010 ). In fact, ADHD can be diagnosed reliably in the preschool period where hyperactivity and impulsivity tend to be the most prominent symptoms and some neurocognitive deficits associated with the disorder are already detectable ( Shephard et al., 2022 ). Furthermore, although the overall observable symptom levels decline across the life course from childhood onwards in the general population ( Wootton et al., 2022 ), the majority of individuals diagnosed with ADHD in childhood still have symptoms and/or impairments sufficient for a diagnosis in adolescence and early adulthood ( Breda et al., 2021 ; Faraone et al., 2006 ). In fact, the Multimodal Treatment of ADHD (MTA) study, using data collected at multiple ages across development, suggested that full recovery/sustained remission during the period up to adulthood is rare, occurring in less than ten percent of ADHD individuals ( Sibley et al., 2021 ).

Longitudinal studies have established that impulsivity and inattention are more persistent across development, than hyperactivity both in the general population and in clinical samples ( Willcutt et al., 2012 ). DSM presentations (inattentive vs hyperactive/impulsive) are not stable developmentally - reflecting more transient, time-limited manifestations of a fluctuating condition than real subtypes ( Willcutt et al., 2012 ). If ADHD persists to early adulthood there appears to be a higher chance of further persistence into later life thereafter ( Karam et al., 2015 ). Above and beyond core symptoms there are changes in the pattern of co-occurring mental health conditions as individuals grow. For instance, while in childhood oppositional defiant disorder (ODD) and conduct disorder (CD) are the most prevalent conditions co-occurring with ADHD, substance use disorders, mood and anxiety disorders become more common co-occurrences in adulthood ( Franke et al., 2018 ; see section 2.2.1 ). The determinants of ADHD persistence have not been definitively established. A meta-analysis of childhood factors predicting ADHD persistence into adulthood found that symptom severity and co-occurrence with conduct disorder and major depressive disorder were statistically significant predictors ( Caye et al., 2016 ; see section 2.1.3 ). Genetic factors like ADHD family history and ADHD polygenic risk, although promising, need more investigation.

2.1.2. New leads

(i) adhd can be a late onset condition:.

The notion that ADHD is always a childhood-onset condition has been challenged by recent data from a range of countries (e.g., Caye et al., 2017 ; Sibley et al., 2018 ) suggesting that some individuals with an adolescent or early adult diagnosis have a late-onset variant of the condition; although there is a dispute on exact rates of late-onset ADHD, with estimates ranging from around 30% to 87% of those presenting with ADHD in adulthood ( Breda et al., 2021 ). In-depth analysis suggests that individuals with the putative late-onset variant might differ from their childhood-onset counterparts in important ways. They seem to have less severe symptoms and/or a lower genetic liability for ADHD and/or may have lived in more supportive families during childhood and/or have higher IQ and/or a combination of these factors ( Asherson et al., 2019 ; see section 4 ). However, this is another area that needs more research. These new data on late-onset ADHD suggest that clinicians should consider the possibility of an ADHD diagnosis in adulthood even if clinical thresholds for inattentive and/or hyperactive/impulsive symptoms and/or impairment have not been met in childhood.

(ii) Symptom and impairment fluctuations across time are very common:

The notion that childhood onset ADHD is a persistent and stable condition has been challenged recently in a detailed analysis of its developmental trajectories. For instance, MTA naturalistic follow-up data demonstrated that over 60% of children with ADHD combined type had a fluctuating symptom course with, in clinical terminology, repeated periods of remission and recurrence – even when the former was conservatively defined as lack of a) substantial ADHD symptoms, b) impairment, and c) treatment in the last six months ( Sibley et al., 2021 ). It’s been suggested that this uneven profile is the result of the interplay between time-varying environmental demands and underlying genetic vulnerability ( Sibley et al., 2021 ; see section 3.1.1 ).

(iii) The male-to-female sex ratio is increasingly more balanced with age:

In childhood, approximately 2.5 and 4 times more males than females present with ADHD in population and/or clinical studies respectively ( Faraone et al., 2021 ). There is growing evidence that this ratio changes substantially as one moves through adolescence to adulthood with an increasingly large proportion of ADHD cases being female ( Franke et al., 2018 ). This phenomenon, as it relates to clinical samples, is likely to be explained in part by referral bias (e.g., higher tendency to refer males in childhood due to hyperactivity and behaviour problems, and a greater search for treatment by females in adulthood). There are other hypotheses to be explored (see Hinshaw et al., 2022 , for a summary). First, puberty-related changes in sex hormones may play a role ( Hinshaw et al., 2022 ). Second, females might need a more severe presentation to receive an ADHD diagnosis in childhood due to diagnostic operationalization relying on samples composed mostly by boys, but, as severity is the main predictor of persistence, ADHD then tends to persist into adolescence and adulthood for them more than for males. Third, female children may be better able to mask impairment in childhood with this “advantage” wearing off in adolescence and beyond. Fourth, males with ADHD might have more negative trajectories than females, for instance, more frequently ending up in prison or even dying prematurely.

2.1.3. Outstanding questions

(i) can we predict patterns of adhd onset, persistence, and remission.

Being able to prospectively predict the onset and persistence/remission of ADHD will facilitate a more personalised approach to intervention. One investigation in three population samples from the UK, Brazil, and the US recently provided evidence that a risk calculator using clinical and demographic data collected in childhood, like the Framingham Risk Calculator for cardiovascular disorders, might perform adequately in this regard (Caye et al., 2019). It remains to be determined which predictors will be most important in these models and whether clinical and/or demographics, pre- and/or perinatal, polygenic risk and/or early life determinants and/or neuropsychological data will enhance their performance. It is also unclear whether prediction models generated in one sample will generalise to others. In this regard, the risk calculator mentioned above was assessed in a new sample (the Brazilian High-Risk cohort); it demonstrated adequate performance in its original format and showed that adding information about ADHD polygenic risk and prematurity did not improve accuracy ( Lorenz et al., under review ).

(ii) Can we prevent the onset of ADHD through early intervention?

If we can prospectively identify which young children will develop ADHD later in life, it may be possible to intervene early to reduce ADHD risk or ameliorate its impact. A recent meta-analysis provides promising evidence that targeting ADHD-related precursors, such as deficient self-regulation or other executive function deficits, can reduce ADHD symptoms ( Shephard et al., 2022 ). It remains unclear from this review, however, whether interventions can modify ADHD risk precursors (i.e., genuine prevention) or just reduce early manifestations of the condition itself (i.e., treatment).

(iii) What happens to ADHD in older adults?

Currently almost nothing is known about later life stages of ADHD developmental trajectories. Do symptoms and associated impairments progress, remain stable, or remit as people move into older age? A recent investigation using the Swedish Medical Registry suggested a strong and significant association between ADHD in adulthood and later dementia ( Du Rietz et al., 2021 ). It may be particularly important to understand the clinical implications of the relationship between these two conditions especially considering their phenotypic and neuropsychological overlap ( Mendonça et al., 2021 ).

(iv) Can ADHD have positive outcomes?

Longitudinal studies have largely focused on the negative impact of ADHD on individuals’ lives. More recently the neurodiversity perspective has focused the research community’s attention on strengths-based approaches ( Sonuga-Barke & Thapar, 2021 ). This raises the question - Can people with ADHD find a productive niche in which they can excel during their development? This could involve, for instance, exploiting a creative ability to “think out of the box” or the energy, drive and risk taking required by successful entrepreneurs? Although initial findings remain controversial, a systematic review suggests potential positive traits associated with ADHD dimensional symptoms in some individuals ( Hoogman et al., 2020 ; see sections 2.3.3 and 4 ).

2.2. CONCEPTUALISING CORRELATED CHARACTERISTICS AND CO-OCCURRING CONDITIONS

Statistical studies have established that the defining symptoms of ADHD, inattention and hyperactivity/impulsivity, cluster with one another and are distinguishable from other dimensions of psychopathology ( Willcutt et al., 2012 ). Nevertheless, there is substantial correlation between this ADHD symptom cluster and other psychological characteristics and traits, and co-occurrence with other neurodevelopmental and mental health conditions is common ( Reale et al., 2017 ). This has raised questions about the status of these co-occurring elements. When should we extend the ADHD characterisation to include these features, and when should we accept them as distinct but overlapping clinical phenomena?

2.2.1. Prior consensus

The two domains perhaps most often proposed for inclusion in a broader ADHD characterisation are emotion regulation difficulties (ERD) and sluggish cognitive tempo (SCT) ( Barkley, DuPaul & McMurray, 1990 ; Lahey et al., 1987 ). ERD such as anger susceptibility/irritability or low distress tolerance are present in an estimated 40-50% of children with ADHD, especially pronounced among the combined ADHD presentation and associated with ADHD persistence ( Faraone et al., 2019 ). The association with ADHD is not accounted for by co-occurring conditions such as ODD and anxiety ( Nigg, Karalunas, Gustafsson et al., 2020 ). SCT is a constellation of cognitive (e.g., excessive daydreaming, being ‘lost in a fog’) and motor (e.g., underactive, slow moving) elements ( Becker et al., 2016 ), with an international Work Group recently proposing to change the name of this set of symptoms to cognitive disengagement syndrome (CDS; Becker et al., in press ). These behaviours affect 25-40% of ADHD youth, especially those with combined or predominantly inattentive presentations ( Barkley, 2013 ). SCT is empirically distinct from, though strongly related to, ADHD symptoms, particularly inattention ( Becker et al., 2016 ). Research examining the convergent and discriminant validity vis-à-vis SCT from ADHD have produced psychometrically strong and clinically useful instruments ( Becker, 2021 ). Both ERD and SCT contribute to ADHD-related impairment ( Becker et al., 2016 ; Faraone et al., 2019 ). ADHD-related ERD is associated with reduced quality of life (QoL), social impairment, and worse educational/occupational outcomes in children and adults ( Faraone et al., 2019 ). SCT is associated with social withdrawal, internalizing symptoms (especially depression), and poorer functional outcomes ( Becker et al., 2016 ). SCT and ERD, though statistically and clinically distinct entities, are often correlated ( Becker et al., 2016 ). However, ERD cleaves specifically with hyperactivity/impulsivity ( Faraone et al., 2019 ) and SCT with inattention ( Becker et al., 2016 ).

A third domain, sleep problems, even if not meeting threshold for a sleep disorder, are also extremely common in people with ADHD, although less frequently proposed as part of an extended phenotype despite “restless sleep” being a symptom for the diagnosis in DSM-III. Children, adolescents, and adults with ADHD are more likely than their peers to obtain insufficient and/or poorer-quality sleep ( Cortese et al., 2009 ; Diaz-Roman et al., 2018 ). These effects are stronger for subjective measures (e.g., parent reports) than objective measures (e.g., polysomnography). Sleep problems in ADHD are associated with lower QoL, worse family functioning, and increased ODD and depression ( Lunsford-Avery et al., 2016 ). ERD are increased following sleep loss (Palmer & Alfano, 2017; Short, Booth, Omar, Ostlundh, & Arora, 2020). ERD and SCT are both correlated with poor sleep at night and daytime sleepiness ( Fredrick et al., 2022 ; Lunsford-Avery et al., 2016 ).

With regard to co-occurring conditions, ‘pure’ ADHD is the exception rather than the rule clinically. Population-based studies, accounting for potential ascertainment and referral bias in clinical samples, demonstrate substantially elevated rates of virtually all psychiatric and neurodevelopmental conditions in both children and adults with ADHD ( Green, McGinity, Meltzer, Ford & Goodman, 2005 ; Lichtenstein, Carlstrom, Rastam, Gillberg & Anckarsater, 2010 ). Co-occurring ODD, anxiety, and depression are prominent in childhood ( Wilens et al., 2002 ). Co-occurring autism also typically presents in early childhood although diagnosis can be delayed, possibly due to diagnostic overshadowing ( Kentrou, de Veld, Mataw, & Begeer, 2019 ). Inattention is more strongly linked to withdrawal and depression, and hyperactivity-impulsivity is more often linked to behavioural conditions ( Willcutt et al., 2012 ). Co-occurring conditions are in general associated with greater ADHD persistence in childhood ( Riddle et al., 2013 ) and adulthood ( Caye et al., 2016 ; see section 2.1.1 ). After controlling for sex-specific population base rates, females with ADHD are more likely to have autism, intellectual disability, ODDD/CD and schizophrenia ( Ottosen et al., 2019 ), suicidal behaviour (Hinshaw, Nguyen, O’Grady, & Rosenthal, 2021; Ottosen et al., 2019 ), personality disorder ( Ottosen et al., 2019 ), and substance abuse disorder ( Biederman, Newcorn, & Sprich, 1991 ; Ottosen et al., 2019 ) but not anxiety and depression which were similarly increased in females without ADHD as they were in those with ADHD. Overlap with physical conditions is also common but outside the scope of this review (see, e.g., Galera et al., 2022). This high degree of overlap challenges current categorical diagnoses by highlighting the fuzzy boundaries of the conditions and the possibility of the distinct existence of what might be called hybrid disorders containing elements of different clinical domains.

There are a number of possible explanations for co-occurrence. First , overlap and ambiguity is common across the symptoms that define different diagnostic categories (e.g., difficulty concentrating diagnostic for ADHD, anxiety and depression, and “interrupting others” may reflect ADHD impulsiveness or impaired social understanding autism). This creates problems especially in research that relies on questionnaires or highly structured interviews, which may incorrectly assign symptoms. However, even in these cases, co-occurrences are not merely the result of overlapping symptom content as co-occurring conditions create more impairment (Mak et al., 2021). Second , twin and family studies illustrate shared genetic variance between ADHD and co-occurring conditions, especially autism ( Miller et al., 2019 ; Polderman, Hoekstra, Posthuma, & Larsson, 2014 ), but also ODD ( Thapar, Harrington, & McGuffin, 2001 ), anxiety, depression, and substance abuse ( Chang et al., 2012 ). Molecular genetic studies also showed genetic variants shared between ADHD and other conditions including autism and depression ( Cross-Disorder Group of the Psychiatric Genomics Consortium, 2019 ; see section 3.1.1 ). Third , extreme environmental exposures including prenatal toxin exposures (e.g., alcohol) ( Wozniak, Riley, & Charness, 2019 ), prematurity ( Sciberras, Mulraney, Silva, & Coghill, 2017 ), and severe psychosocial deprivation ( Sonuga-Barke et al., 2017 ) increase shared risk for multiple neurodevelopmental conditions (e.g., autism, ADHD and intellectual disability; see section 3.2.1 ). Shared common postnatal factors, such as unsupportive parenting and peer relationship problems, may play a significant role in the development of co-occurring emotional conditions ( Gustavson et al., 2021 ; Mikami & Hinshaw, 2006 ). Fourth , there may also be shared neuropsychological mediators. Deficient executive functions, common in ADHD ( Willcutt et al., 2005 ), are also observed in autism ( Craig et al., 2016 ), depression ( Fenesy & Lee, 2019 ), and anxiety ( Nyberg et al., 2021 ), and they appear to drive its co-occurrence with autism ( Lukito et al., 2017 ) and depression ( Fenesy & Lee, 2019 ) in particular. Developmentally, cognitive inflexibility is most prominent in young autistic children with other EF difficulties increasing with age, while inhibition problems are most prominent in young children with ADHD ( Visser, Rommelse, Greven, & Buitelaar, 2016 ). Genetic factors account for some of these covariances ( Brooker et al., 2020 ), though EF can be relatively state-dependent, improving with remission of depression ( Biringer et al., 2005 ) and stimulant medication in ADHD ( Coghill et al. 2014 ).

2.2.2. New leads

(i) erd may delineate a diagnostic subtype:.

Emotional impulsivity (fast-rising reactivity in response to events) and deficient emotion self-regulation may represent a specific ADHD ERD subtype ( Martel, 2009 ). This has promise as a way of parsing ADHD heterogeneity and advancing clinical prediction and guiding intervention selection ( Nigg, Karalunas, Feczko, & Fair, 2020 ). The delineation of irritable, surgent, and mild temperament profiles further improves descriptions of ADHD heterogeneity ( Karalunas, Gustafsson, Fair, Musser, & Nigg, 2019 ).

(ii) SCT appears to mediate ADHD-related academic difficulties:

Longitudinal data show that SCT and ADHD inattention are differentially associated with lower achievement in reading and math, respectively ( Becker, Burns, Leopold, Olson, & Willcutt, 2018 ). Perhaps related to this, SCT appears to represent a promising bridge between clinical science and mind wandering studies in cognitive psychology and neuroscience ( Becker & Barkley, 2021 ).

(iii) Poor or insufficient sleep could play a causal role in ADHD:

A sleep restriction/extension study found evidence that shortened sleep duration may cause increases in ADHD inattentive, but not hyperactive-impulsive symptoms, in adolescents with ADHD ( Becker, Epstein, et al., 2019 ). These findings extend the work conducted with school-aged children, which found insufficient sleep to impact attention, and to a lesser extent, ERD ( Davidson, Rusak, Chambers, & Corkum, 2019 ).

(iv) ERD and SCT share risk factors and neural processes with sleep problems:

Poor sleep and ADHD ( Gregory, et al., 2017 ) and impulsivity and anger/frustration may have common genetic origins ( Miadich et al., 2020 ). ADHD and sleep disturbances share deficits in arousal ( Owens et al. 2013 ) and have common neural correlates, particularly in cognitive control and salience networks ( Shen et al., 2020 ). In addition to the close link between them and sleep, circadian functions may be implicated in ADHD to ( Bijlenga, Vollebregt, Kooij, & Arns, 2019 ), ERD ( Gruber & Cassoff, 2014 ) and SCT ( Fredrick et al., 2022 ).

(v) Broader ADHD characteristics can help explain co-occurrence of ADHD and other conditions:

ERD has transdiagnostic relevance across emotional and behavioural psychopathologies ( Aldao, et al., 2016 ). It is a common feature of depression and anxiety as well as ADHD ( Mayer et al., 2022 ). There are behaviour genetic correlations between ERD, ADHD and mood ( Merwood et al., 2014 ), and early irritability is associated with the ADHD polygenic risk score ( Riglin et al., 2017 ). Longitudinal analyses in a population cohort suggest that both ADHD and autism-related ERD mediate risk for later depression ( Eyre et al., 2019 ). SCT, associated with both ADHD and depression ( Becker et al., 2016 ), predicts future adult depression ( Smith, Zald, & Lahey, 2020 ), perhaps via negative peer processes ( Fredrick, Langberg, & Becker, 2022 ). Sleep problems are linked to anxiety, depression and ERD in children with ADHD, with bidirectional longitudinal associations ( Lunsford-Avery et al., 2016 ). Experimental data showing sleep restriction increases depression and SCT strengthens causal inference ( Becker, Tamm, Epstein, & Beebe, 2020 ). Sleep disturbances may be mechanistically transdiagnostic via their reciprocal relation and shared neurobiology with emotion dysregulation ( Harvey, Murray, Chandler, & Soehner, 2011 ).

(vi) Gene-environment correlations can link ADHD to other conditions developmentally:

People with ADHD experience a multitude of negative environmental exposures that appear to drive some co-occurring conditions (see section 3.2 ). In particular, maltreatment rates are elevated in ADHD, and these are linked to later aggression, anxiety, and depression ( Craig, Bondi, O’Donnell, Pepler, & Weiss, 2020 ; Peleikis, Fredriksen, & Faraone, 2022). Both passive and evocative environmental correlations are implicated (e.g., Ratanatharathorn et al., 2021 , Harold et al., 2013 ): Twin studies suggest that ADHD behaviours evoke negative responses which drive later problems ( Ohlsson Gotby, Lichtenstein, Langstrom, & Pettersson, 2018 ), while the association between maltreatment and maternal ADHD symptoms is consistent with a role for passive gene-environment correlation ( Gul & Gurkan, 2018 ).

2.2.3. Outstanding questions

(i) should erd and/or sct be considered as part of the adhd diagnostic criteria and/or as specifiers within adhd.

The evidence for ERD as a core feature of ADHD is growing and these features are informing models of dysregulation as an organizing framework that can inform nosology. With regard to the latter, ADHD has extensive heterogeneity, and features within the broader phenotype may be informative beyond the ADHD symptom dimensions and DSM-defined presentations ( Nigg, Karalunas, Feczko, et al., 2020 ; see section 4 ). As mentioned above, this has been examined in relation to ERD with promising evidence ( Karalunas et al., 2019 ; Nigg, Karalunas, Feczko, et al., 2020 ). Future studies need to extend these sorts of analyses to SCT and sleep problems.

(ii) Are ADHD presentations the same when they are accompanied by co-occurring conditions?

Current diagnostic classifications, based as they are on external manifestations rather than underpinning pathophysiology, may be limited when applied to overlapping phenotypes. Future research exploring underpinning mechanisms will be required with deeper phenotyping of symptom profiles and biomarkers to elucidate shared and specific risk factors that will contribute to more valid classification (see section 4 ).

(iii) Are broader phenotype domains useful intervention targets?

Recent findings highlight the potential value of developing ADHD-specific interventions that act by targeting SCT and ERD. For instance, interventions that aim to reduce proneness to anger, a highly relevant dimension of irritability, could reduce risk for later negative outcomes such as anxiety in individuals with ADHD ( Karalunas et al., 2019 ; see section 3.3.3 ). Further, it is important to assess if SCT and ERD predict treatment response to current evidence-based interventions for ADHD.

(iv) Does effective therapeutic control of ADHD have positive long-term effects on co-occurring conditions?

Just as it has been challenging to discern the long-term effects of interventions on ADHD symptoms, it is unclear whether good control of ADHD reduces ongoing or later-onset co-occurring conditions. Understanding whether such treatment effects are mediated by improvement in ADHD symptoms is critical to decision-making about continuing treatment when ADHD symptoms are no longer impairing.

(v) Which children with ADHD are resilient to adverse environmental exposure?

While we know that children with ADHD are more likely to have adverse experiences that can lead to the development of co-occurring problems, less is understood about the processes that confer resilience to these experiences. As strong emotion regulation is linked to greater resilience in the general population, it is plausible that this may act as a protective feature in terms of reducing the risk for emotional problems (see section 3.1.3 ).

2.3. IMPACT OF LIVING WITH ADHD

Traditionally researchers have focused their ADHD characterisation, and clinicians their therapeutic efforts, primarily on core symptoms; inattention, hyperactivity, and impulsivity. However, in recent years a more holistic and person-centred focus on the impact of living with ADHD in terms of functional impairment, Quality of Life (QoL), and stigma has started to prevail.

2.3.1. Prior consensus

Impairment is a defining feature of ADHD and can be seen across multiple domains – emerging through the complex interplay between an individual’s abilities-disabilities and the environmental context in which they live and operate. Impairment profiles vary significantly between individuals with ADHD, but core functional challenges are shared widely ( Bölte et al., 2018 ). Across cultures, ADHD negatively impacts peer and sibling relationships ( Ros & Graziano, 2018 ). ADHD is associated with risky behaviours marked by higher rates of teenage pregnancy, gambling, and accidents and premature death ( Shoham, Sonuga-Barke, Yaniv, Pollak, 2021 ). Hyperactive-impulsive symptoms correlate with risk-taking, accidents, and social exclusion by peers, while inattentive symptoms correlate with low scholastic/vocational performance and low self-confidence ( Willcutt et al., 2012 ). Impairment persists into adulthood for more than half of cases ( Song et al., 2021 ) and is aggravated by the presence of other neurodevelopmental and mental health conditions (see section 2.1 ; Jangmo et al., 2021 ). Impairment may be underestimated in females during childhood ( Mowlem, Agnew-Blais, Taylor, & Asherson, 2019 ) perhaps related to sex differences in profiles of co-occurring conditions ( Rucklidge, 2010 ; see section 2.1.2 ). Females may also make greater efforts to hide challenges and live up to social expectations ( de Schipper et al., 2015 ; see section 2.1.1 ).

QoL and, relatedly, well-being and life satisfaction, are substantially reduced in ADHD – although there is great inter-individual variation. In general, self-reported global QoL is reduced across life domains and over time compared to typically developing individuals, with both children ( Jonsson et al., 2017 ) and adults affected ( Lensing, Zeiner, Sandvik, Opjordsmoen, 2015 ). QoL is affected more where co-occurring conditions are present (Klassen, Miller, & Fine, 2004). The effects of ADHD are comparable to those seen in serious paediatric health conditions ( Coghill & Hodgkins, 2016 ). Parents’ and siblings’ QoL are also reduced ( Peasgood et al., 2021 ).

People with ADHD can experience prejudice, stereotyping, and discrimination due, often, to the diagnostic label. These forms of stigma, when internalized, can lead to a sense of alienation that reduces help seeking and lowers self-esteem ( Clement et al., 2015 ). Fuelled by misconceptions about its causes and misinformation about medication ( Hinshaw & Scheffler, 2014 ), ADHD stigmatisation is common amongst family, public, and professionals (Leibowitz, 2016). ADHD-related stigma is more pronounced than in specific learning disability but less so than for bipolar disorder ( Kaushik, Kostaki, & Kyriakopoulos, 2016 ). The use of biomedical terms by clinicians and scientists (e.g., disease, abnormality) for the experience of being different can be stigmatising, although some, such as “patient”, are still more accepted in ADHD than in other neurodevelopmental conditions (e.g., Kenny et al., 2016 ).

2.3.2. New leads

(i) wholistic and person-centred assessment is increasingly seen as vital:.

Research and practice focused solely on symptoms is limiting, as it neglects the QoL as well as the performance and capacity of patients with ADHD and families. It also tends to focus attention on the individual, ignoring environmental influences and constraints ( Pellicano & den Houting, 2022 ). While DSM-5 and ICD-11 do not operationalise QoL, they have bolstered the importance of performance and impairment by introducing a mandatory impairment criterion for all conditions and recommending standardized scales for functional assessment based on the International Classification of Functioning. Recently, core sets of items tailored for detailed functional assessment in ADHD in research and practice have been developed, covering body functions, activities, participation, and environmental factors ( Bölte et al., 2018 ).

(ii) There is no clear cut impairment threshold above which ADHD should be diagnosed:

Arildskov et al. (2021) recently reported that although correlated with symptoms, there is no obvious threshold at which an increase above a certain level of symptoms leads to a disproportionate rise in impairment. Setting the level of impairment required for a diagnosis therefore appears to some extent arbitrary and based on social norms and the level of support needs. Setting those thresholds is a clinical decision and needs to be tailored to individuals given each person’s history, unique skills, life challenges, and access to support and resources.

(iii) ADHD treatments increasingly focus on reducing impairment and improving quality of life:

Reducing impairment, improving functioning and QoL should be the primary focus of future treatment trials ( Coghill et al., 2017 ). Targeting symptom severity may be stigmatizing and/or less important for clients in terms of long-term social outcome and wellbeing. For instance, it has been demonstrated that major drivers of impairment and low QoL in ADHD are sleep problems and mental health issues ( Mulraney et al., 2017 ; Ahnemark et al., 2018 ; see sections 2.2.1 ). The European Medicines Agency guidance and national European healthcare authorities have started to recommend and reinforce such an approach (e.g., Swedish Board of Health and Welfare, 2019 ).

2.3.3. Outstanding questions

(i) is adhd associated with personal strengths and societal benefits as suggested by the neurodiversity perspective.

Neurodivergent people, like all people, can display both relative and absolute strengths ( Pellicano & den Houting, 2022 ; see section 2.1.3 & 4 ). Initial research has suggested that ADHD is perceived as being associated with specific strengths such as energy and drive, creativity, hyper-focus, and agreeableness ( Sedgewick, Merwood, & Asherson, 2019 ; Hoogman et al, 2021), but the level of generalizability and specificity to ADHD remains unclear ( Groen et al., 2020 ). A refocusing on strength-based approaches to ADHD must avoid opposition to treatment or research, in ways that do an injustice to severe impairment and overlooks instances of frank neural injury (e.g., in low birth-weight children, lead exposed children with ADHD). While more research is needed in this area to clarify potential strengths and benefits associated with ADHD, a more balanced view on neurodivergent people, embracing positive psychology, and highlighting their value is required ( Bölte, Lawson, Marschik & Girdler, 2021 ). Related to this point – involvement of people with ADHD in the ADHD research process is increasingly seen as vital – allowing them to give advice and help shape the research process and order its priorities by providing essential insights into their experience of the condition.

(ii) Should interventions focus on removing barriers to functioning rather than ‘normalising’ people with ADHD?

ADHD interventions have traditionally focused on reducing an individual’s symptoms. There has been comparably little research examining intervention approaches that focus on reducing impairment through reasonable environmental adjustments, although studies in the classroom setting so far show limited effects ( Lovett & Nelson, 2021 ). However, there is a growing consensus that attempts at reasonable accommodation across settings are not only ethically desirable, but also potentially fruitful ( Bölte et al., 2021 ). More research is needed on how to ensure that reasonable accommodation that take account of the relative needs and strengths of individuals with ADHD are balanced with the need to challenge the individual on an appropriate level to promote learning and increase their capacities to cope with environmental demands ( Richardson et al., 2015 ; see section 4 ). Importantly, the removal of barriers to functioning, and consolidating and developing facilitative environments should be carried out in conjunction with psychological and psychiatric interventions aiming to empower and increase well-being.

(iii) Does getting an ADHD diagnosis increase or reduce stigma?

The effects of diagnostic labelling and diagnostic disclosure in mental health conditions are person-specific and have therefore commonly been studied anecdotally or using vignette-based experiments ( O’Connor, Brassil, O’Sullivan, Seery, & Nearchou, 2021 ). In ADHD, knowledge relating to stigma is predominantly based on respondents who experience stigma associated with their own or their relatives’ ADHD (Mueller, Fuermaier, Koerts, & Tucha, 2012). ADHD-related social stigma appears to vary from case-to-case, and labelling can either exacerbate, ameliorate, or not affect stigma. However, the limited available research indicates more rather than less social stigma is associated with receiving a diagnosis of ADHD, when evaluated by teachers and student peers, whereas receiving a diagnosis of autism, a common co-existing condition in ADHD, is associated with ameliorated stigma ( O’Connor et al., 2021 ).

(iv) Are there sex/gender differences in ADHD-related stigma?

Research has not yet consistently and systematically considered sex/gender effects across all aspects of ADHD ( Hartung & Lefler, 2019 ). There are cultural influences on social stigma in mental health related to sex/gender, and these influence public attitudes about mental health ( Holzinger et al. 2012 ). These and other factors may impact on sex/gender differences in social stigma associated with ADHD, but there is hardly any research conducted on this topic.

3. RISK FACTORS AND CAUSAL PROCESSES AND PATHWAYS

From a translational perspective, science that deepens our understanding of the biological mechanisms and environmental processes that lead to the onset and development of neurodevelopmental and mental health conditions holds the key to the development of more effective interventions to support individuals with such problems ( Nigg, 2022 ). ADHD science has progressed our understanding of these matters enormously over recent decades, as researchers have exploited more powerful models and methods made available by technological advances. Paradoxically, if anything, however, these advances can lead to the sense that we are farther than ever away from fully understanding ADHD. This is because they have revealed just how complex and heterogeneous are the genetic and environmental influences as well as the psycho-biological processes involved in the emergence and development of ADHD are.

ADHD is a highly heritable condition (meaning individual variation is strongly related to genetic variation), although the inheritance of the condition (across generations) is complex, and familial transmission from generation to generation may not be easily discernible. Genetic research identifying the genes involved in ADHD can provide insights into ADHD-associated biological processes. Since the genetic make-up of a person is determined during conception, studying the genetics of ADHD can open a window into the molecular and cellular mechanisms that may contribute causally to these processes. Successes in collaborative, genome-wide genetic studies have led to new insights and new hypotheses about the nature of ADHD and its relationship with other conditions and traits. Although to date findings regarding individual genes are too small for clinical use and summary algorithms are still being studied, prospects for clinical intervention are appearing.

3.1 1. Prior consensus

Estimated heritability in general population twin samples is 74%. This estimate is similar for ADHD defined as a category or dimension, and the degree of heritability does not increase as symptom severity increases – confirming that it is best viewed as the extreme end of a continuous heritable trait. Heritability is similar for ADHD in children and adults ( Faraone & Larsson, 2019 ; see section 4 ). Despite its high heritability, ADHD has a complex, polygenic genetic component, with several-to-multiple genetic variants implicated in most affected individuals. As in other diseases with a similarly complex genetic make-up, most of the genetic variants involved play a very limited role in ADHD risk individually, and different combinations of genetic risk variants are present in different individuals with ADHD ( Faraone & Larsson, 2019 ; see section 4 ). To identify the genetic variants underlying polygenic conditions such as ADHD, collaborative genome-wide association study (GWAS) meta-analyses involving very large samples of cases and controls are required. For ADHD, the first report with data on around 20,000 people with ADHD and over 35,000 without identified 12 ADHD risk variants (of the single nucleotide polymorphism [SNP] type) ( Demontis et al., 2019 ), and a more recent study with over 38,000 individuals with ADHD and nearly 187,000 unaffected individuals identified 27 genome-wide significant loci ( Demontis et al., under review ). In all cases, each statistically significant SNP carried a very small risk individually; together, all studied SNPs, when aggregated, explained 25-30% of the total ADHD heritability estimated from twin studies. Other types of genetic variants, e.g. rarer variants with potentially larger effects at the level of the individual identified through (rare) copy number variant (CNV) studies (e.g. Harich et al., 2021) and whole exome sequencing (WES) studies (e.g. Satterstrom et al., 2019 ), also seem to contribute to heritability. In addition to genetic factors, environmental factors are also involved in the aetiology of ADHD, and it is likely that an interplay (involving both interaction and correlation) exists between genetic and environmental factors (see section 3.2.1 below).

Genetic studies are helping to unravel the neurobiological pathways and (brain) substrates associated with ADHD (see section 3.3.1 ). Based mainly on animal and pharmacological studies, in early genetic studies researchers had hypothesized that genetic alterations controlling monoaminergic (especially dopaminergic) neurotransmission may underlie ADHD susceptibility ( Faraone et al., 2015 ). More recently GWAS-based hypothesis-generating, data-driven analyses of biological processes have confirmed the role of these neurotransmission-related genes ( Thapar et al., 2016 ; Cabana-Domínguez et al., 2022 ) and implicated other brain-related processes - especially related to maturation, e.g., in prenatal and early post-natal neurite outgrowth ( Poelmans et al., 2011 ; Thapar et al., 2016 ; Mooney et al., 2016 ). Combining GWAS with brain imaging data has helped identify common genetic aspects determining (adult) intracranial volume ( Klein et al., 2019 ) and the surface area of cortex ( Grasby et al., 2020 ). Bioinformatic enrichment analyses has implicated genes upregulated in foetal brain development ( Demontis et al., under review ).

Genetic contributions to ADHD overlap with contributions to other psychiatric and neurodevelopmental conditions – with overlapping SNPs most prominent for major depression and autism spectrum disorder ( Cross-Disorder Group of the Psychiatric Genomics Consortium, 2019 ) - and also rare genetic variants identified ( Satterstrom et al., 2019 ). Biological overlap due to genetics has also been seen with neuroticism scores and IQ as well as with somatic traits and diseases ( Demontis et al., 2019 ). These studies have provided insight in the causes of co-occurrences between ADHD and other health conditions (e.g., Mota et al., 2020 ; see section 2.2.1 ).

3.1.2. New leads

(i) there are different genetic contributions to childhood, persistent adult, and late-onset adhd:.

Longitudinal twin studies have suggested that the genes associated with ADHD change from childhood to adolescence and adulthood: Less than half of the genetic factors involved in childhood may still play a role in adulthood ( Chang et al., 2013 ). GWAS analysis currently suggests 80% measured genetic overlap between children and adults with ADHD ( Rovira et al., 2020 ), so future studies will need to clarify this discrepancy. The late-onset form of ADHD has not yet been studied sufficiently to assess its genetic contribution; conflicting results are reported on whether the genetic contribution to this form is lower or similar to that of childhood-onset forms ( Agnew-Blais et al., 2021 ; Riglin et al., 2022). A study on late-diagnosed ADHD suggested a lower genetic burden in adults with late-diagnosed ADHD than in those with persistent ADHD ( Rajagopal et al., 2021 ; see section 2.1.2 ).

(ii) There may be genetically-based sex differences in ADHD:

A range of different study designs have tested the hypothesis that quantitative and/or qualitative sex differences in genetic factors may contribute to the differences between ADHD prevalence in boys and girls (Martin et al., 2018). A recent analysis combining epidemiological and genetic approaches suggested that the siblings of females with ADHD are at higher familial risk of ADHD than the siblings of affected male individuals; however, no increased burden of common-variant ADHD genetic risk was seen in females, and the genetic overlap of such ADHD risk variants was close to 100% (Martin et al., 2018).

(iii) Genetic association is not the same as causation:

Molecular genetic studies are correlational. Experimental designs, e.g., using cellular and animal models, post mortem material, and/or intervention studies, are required to bolster causal inference ( Klein et al., 2017 ). Furthermore, statistical approaches, such as Mendelian Randomization, can help determine the direction of gene-related effects. Recent studies using this design found, e.g., that genetic correlations observed between ADHD and lifetime cannabis use are indicative of ADHD being causal for cannabis use (Soler et al., 2021) and that genetic liability for ADHD affects educational attainment independently of general cognitive ability ( Dardani et al., 2022 ).

3.1.3. Outstanding questions

(i) why are heritability estimates derived from behavioural and molecular genetic studies different.

While the heritability of ADHD based on twin studies is 74% ( Faraone & Larsson, 2019 ) genome-wide studies of common genetic variants so far only provide heritability estimates of 20-30% ( Demontis et al., 2019 ). Additional ADHD twin heritability may be explained by rare genetic variants, as described above. Missing heritability may also be accounted for by potentially synergistic gene-environment and gene-gene interactions, which are incorporated in twin study heritability estimates alongside genetic main effects ( Purcell, 2002 ). Understanding the extent of these contributions represents an important focus for future study (see section 3.2.1 ), and it is to be expected that the implementation of whole genome sequencing technology in genetic analyses will capture additional variance over and above current methods.

(ii) Can genetics inform diagnostic/stratification (biomarkers) in clinically relevant ways?

Genetic screening is commonly used in the clinic for some medical conditions in which a single genetic mutation is responsible for a major increase in risk. In ADHD, however, several-to-multiple genetic variants contribute to the disorder in most patients, and those seem to have only limited effect sizes. Even the rare variants linked to ADHD are not sufficiently predictive of case-status to be clinically useful, although a recent finding showed the effects of such ADHD risk variants to be comparable to those associated with ASD risk ( Satterstrom et al., 2019 ). Scores capturing polygenic risk burden derived from GWAS (called polygenic scores) are starting to be used in the clinic for other multifactorial diseases (such as breast cancer and cardiovascular disease; Khera et al., 2019). While not (yet) sufficiently diagnostic of conditions like ADHD (explaining about 3% of the variance in ADHD broadly defined; Li & He, 2021 ), these scores may have utility in the clinical toolbox, as part of risk algorithms in combination with other indicators, for parsing heterogeneity, and/or for targeting interventions (see section 2.1.2 ). When talking about genetics, the possibility of prenatal screening for the risk for conditions like ADHD based on genetics needs mention, as some companies have been known to offer such services. Beyond the fact that genetics is currently not informative for prenatal screening for ADHD, this obviously raises profound ethical and moral questions related to the selection (e.g., embryo selection), engineering out (e.g., gene editing), or other interventions upon identification of an embryo/foetus at risk of ADHD. These discussions are not unique to ADHD -- similar issues hold for other mental health conditions (ASD, bipolar disorder) and even basic traits (such as sex, height, IQ). Given the rapid developments in this field, it is vital that researchers, clinicians, people with ADHD, and ethicists/moral philosophers address these issues as a matter of urgency in order to put safeguards against such practices in place.

(iii) Do genetic effects underpin resilience as well as risk?

So far, molecular genetic studies of ADHD have exclusively focussed on identifying risk factors. However, it is becoming clearer that genetic factors may underpin resilience to risk exposure in people with psychiatric conditions (Hofgaard et al. 2022; Maul et al, 2020 ; Hess et al., 2021). Understanding the interplay between genetic resilience and risk factors is an important priority for ADHD science going forward (see section 4 ).

3.2. ENVIRONMENTS

Genotype-environment coaction is axiomatic and foundational in contemporary developmental theory (Bronfenbrenner & Ceci, 2011) but specifying particular environmental effects in causal models is difficult. Indeed, it is necessary to distinguish a risk factor (i.e., correlated but not causal) from a mechanistic factor (i.e., plays a causal role). Risk factors associated with ADHD are important to clinical prediction, but mechanistic factors are important for potential prevention or treatment because they may be modifiable prevention/intervention targets. In this section, we first note key risk factors, then add cautions regarding the state of mechanistic research (which indicates that several risk factors do not appear to be causal). In particular, the existence of passive correlation between genetic and environmental risk (rGE) makes it difficult to disentangle the causal role of genes and environments based purely on observational studies ( Sonuga-Barke & Harold, 2018 ). This means that what looks like an environmental effect could be a genetic one. However, rGE can also be compatible with causal effects, in that environment can mediate genetic effects ( genetic nurturance; Armstrong-Carter et al., 2020 ; Kong et al., 2018 ; de Zeeuw et al., 2020 ). Crucially, in the case of genetic nurturance, the environment is mediating the effect of the parental (non-transmitted) alleles—in the offspring this is a purely environmental effect. This is distinct from the environment being correlated with transmitted alleles (i.e., confounded with genotype). Genetically informative designs for ADHD that have failed to find causal links have generally failed to consider the possibility of genetic nurturance. Thus, the causal status of environmental exposures often remains unclear except when direct experimental designs can be used, as illustrated below. However, given the large literature, environmental associations with ADHD cannot be ignored in explanatory accounts. Thus, although effects of individual exposures on ADHD tend to be statistically small, even these seemingly small effects may be important from a public health perspective and, clinically and conceptually, may accumulate to accentuate or add to genetic liability leading to ADHD, either in additive or multiplicative ways.

3.2.1. Prior consensus

Interest in the role of the environment in causal accounts of ADHD has ebbed and flowed over the last 50 years. Recent developments in ADHD genetics, especially the recognition of the challenge of ‘missing heritability’ (see section 3.1 ) has underscored the need to revisit this question. While few, if any, environmental effects are expected to be specific to ADHD, in as much as they invariably are related to general nervous system development, a consensus has now emerged that some understanding of environmental context is necessary to developmental and causal accounts. Twin studies have previously established that direct effects on variation in ADHD are principally due to heritable factors (genetic variation), with the remainder explained by non-shared environment (exposures unique to each twin) or random error ( Purcell, 2002 ). However, two kinds of genotype-environment interactions are not measured in the typical twin design. Of most interest is the interaction with shared environment (those shared by two twins), because these may often be due to common exposures not only in the family but that vary meaningfully in the population (such as dietary factors, pollutant exposures, or socio-economic factors). These matter because if identified as causal, they may have potentially significant implications for public health prevention strategies.

With regard to specific risk factors, the massive literature was recently summarized in an umbrella review of meta-analyses; the authors judged the credibility of associations between ADHD and a range of different environmental exposures based on the p value, size of sample and homogeneity of findings. It identified nine associations as having high credibility (random effects p<.000001, or p< .000001, n>1000; Kim et al., 2020 ). Most of these were maternal pre- and perinatal factors including pre-pregnancy obesity and pregnancy overweight and pregnancy hypertension, gestational hypertension, pre-eclampsia, acetaminophen use and smoking. Two were child factors - childhood eczema and low serum vitamin D. However, all effects were modest in size (Odds Ratio(OR)<2.0). Several other smaller associations were also deemed reliable (p<.001) including child blood lead level, child blood magnesium level, maternal stress during pregnancy and maternal selective serotonin reuptake inhibitor (SSRI) exposure during pregnancy. A number of other associations, including preterm birth or low birthweight, low paternal education, and head trauma, had larger effect sizes (OR>2.0). However, these were judged to have limited credibility based on small sample and/or low homogeneity of findings. Other literature suggests interesting but tentative initial findings regarding the role of traffic-related air pollution such as nitrous dioxide and small particulate matter ( Donzelli et al., 2019 ), polyfluoroalkyls ( Qu et al., 2021 ), and even more speculatively, manganese (particularly from soy-based baby formula; Crinella, 2012 ).

With regard to the adequacy of that review and the issue of causality, we note the following. The authors’ requirement of study homogeneity to assign high credibility to an effect can be questioned given ADHD’s expected etiological heterogeneity (see section 4 ). Their reliance on meta-analyses also limited the comprehensiveness of their findings by ruling out compelling evidence from single large studies, causally informative studies (e.g., natural and quasi-experiments) and unusual cohorts. Considering those limitations, and taking account of more recent causally informative evidence, we can underscore the likely causal role of pre-term birth/very low birth weight with a large effect size (OR>3.0) ( Franz et al., 2018 ) as well as extremely rare but extreme institutional neglect in early life ( Kennedy et al., 2016 ). While more causally-informed evaluation is needed, evidence is accumulating related to a continuum of psychosocial adversity ( Gómez-Cano et al., 2022 ) although such effects may be causally bidirectional.

Experimental evidence, while still limited, does lend support a causal role of synthetic food additives in influencing ADHD symptoms in the population ( McCann et al., 2007 ; Nigg et al., 2012 ) although these would play only a minor role in ADHD itself. One causally informative study supported a role of common background lead exposure in ADHD ( Nigg et al., 2016 ) and a review of causally informative studies concluded that birth weight exerts a causal effect on ADHD symptoms ( Rice et al., 2018 ). That said, extreme environmental effects would provide a primary explanation of ADHD status only in a minority of cases. Other, less studied, common exposures appear to add to ADHD risk; if causal, they would have a substantial public health impact on ADHD. Important candidates here include neurotoxic pollutants notably organophosphates and organochlorine pesticides as well as the now discontinued (yet still present) polychlorinated biphenyls ( Polańska, Jurewicz, & Hanke, 2013 ).

In contrast, it is crucial to note that for many ADHD risk factors, the association is not causal but explained by genetic or familial confounds, or else is reverse causality due to evocative genotype-environment correlation. For example, a causal role has not been supported in causally informative designs for maternal smoking ( Haan et al., 2022 ; Rice et al., 2018 ) and maternal pregnancy weight gain ( Musser et al., 2017 ). Child maltreatment had only a small causal association with ADHD symptoms in a population study that controlled for familial and genetic confounding ( Dinkler et al., 2017 ) More studies of these kinds are needed to determine which risk factors mandate intervention. The moderator effect of environmental exposures on genetic liability or genetic effects remains poorly mapped, though some important examples have emerged. Perhaps the most obvious one, in which clarity has been building for decades, relates to parenting. While unlikely to be an important etiological factor in ADHD in and of itself (but see below under new directions and outstanding questions), parenting behaviours or styles likely moderate the course of development of individuals with ADHD – especially in terms of the development of complicating and co-occurring sources of impairment ( Daley et al., 2018 ).

3.2.2. New leads

(i) polygenic risk scores are being used in gene x environment statistical interaction studies:.

New insights into the statistical interplay between genotype and environment are emerging ( Nigg, 2022 ). Polygenic risk scores from GWAS are being used to study the interplay between broad genomic liability and specific exposures although, albeit with limited success in identifying statistical interactions for many risk factors ( Østergaard, et al., 2020 ). Nevertheless, statistical interactions between ADHD polygenic risk and low birth weight ( Rahman et al., 2021 ) and maternal depression ( Chen et al., 2020 ) have been observed along with replicated interaction findings for maltreatment ( He et al., 2022 ; Ratanatharathorn et al., 2021 ). It is important to note that PRS’s are not mechanistically informative, so these studies identify statistical dependencies that can be exploited for risk stratification and clinical prediction, but do not to identify mechanistic or biological gene-environment interplay. Nonetheless, this type of work may open the door to advanced clinical prediction algorithms in which behavioural, psychosocial and genomic risk can be combined to form a total risk score with either additive or interactive effects (see section 2.1.1 and section 3.1.2 ).

(ii) Deeper understanding of the developmental role of gene-environment correlations:

Understanding correlations between genotypic and environmental risk in ADHD (rGE), passive and evocative, continues to advance. More work is needed to examine genetic nurturance, which could reverse or amplify genetic liability ( Garg et al., 2018 ). Crucially, the unknown frequency, timing, and magnitude of rGE effects in ADHD (like other disorders) leaves unresolved the mechanism behind many genetic and environmental exposure findings as well as some gene x environment findings. Thus, it will also be helpful to have direct mechanistic studies. For example many risk factors appear to be consistent with an hypothesis of a common pathway to ADHD potentially involving metabolic syndrome or inflammation in pregnancy, including the clear association of ADHD with maternal obesity, hypertension and pre-eclampsia as well ( Kim et al., 2020 ). Studies that directly investigate, using experimental designs, the role of anti-inflammatory intervention could evaluate a more complete mechanistic story.

(iii) Epigenetics:

Epigenetic effects are of keen interest and fascinating potential, though with important challenges (for extended discussion, see Cecil & Nigg, in press ), because they can relate to both environmental and genetic mechanisms, both of which are potentially useful practically ( Dall’Aglio et al., 2018 ). However, it is important to recognize that epigenetic effects can also be stochastic (random) and that any findings require control for genetic effects (often overlooked in the epigenetics literature to date). It is also important to recognize the limitations of current methodologies for examining epigenetic effects on neural development in humans ( Bakulski et al., 2016 ; Cecil & Nigg, in press ). Large scale studies of DNA methylation in ADHD are now beginning to be conducted ( Mooney et al., 2020 ). The first replicated finding in this space is that infant perinatal DNA methylation is correlated with ADHD symptoms in early childhood, but those same methylation marks are not correlated cross-sectionally with ADHD symptoms ( Neumann et al., 2020 ). It remains unclear to what extent this finding reflects genetic versus environmental influence on epigenetic mediators. However, it does suggest that early-life mechanisms likely cascade through development, consistent with recent developmental formulations of ADHD ( Nigg et al., 2020 ). New work on longitudinal change in ADHD and epigenetic markers, for example in peripheral tissue DNA methylation, will be an important focus of research in the coming years despite its challenges ( Cecil & Nigg, in press ).

3.2.3. Outstanding questions

(i) which environmental effects are causal and which are explained by genetic correlations.

As mentioned above, environmental effects are confounded with each other and with genetic risk. A new generation of causally informative studies, aided by clever experimental and intervention designs as well as new methods in genetics, will be essential in distinguishing mechanistic effects of exposures from those are statistical risk factors but not causal.

(ii) Can the cumulative effect of multiple common exposures be harnessed clinically?

Given the small effect size associated with most environmental (and genetic) risk factors, algorithms and models that aggregate and weight these risks will be essential for exploiting any translational potential they will have either for clinical prediction or, when causal, for prevention (but see the section on ethical concerns, section 3.1.3 ). Rapidly expanding efforts using machine learning algorithms also auger well for progress here. Alternatively, if shared mechanisms such as the aforementioned inflammation hypothesis are able to be verified, new risk biomarkers or intervention targets might emerge.

(iii) What role does early caregiving play in amplifying or dampening ADHD overall risk?

Childhood caregiving has been much studied with evidence suggesting several aspects of caregiving can moderate the development and course of ADHD symptoms (e.g., Claussen et al., 2022 ). While more genetically informative studies will be useful in that arena, a key gap in the literature related to very early life caregiving in ADHD. Early life prospective cohorts remain crucial to progress in the field of ADHD research. Genetically informative designs ( Leppert et al., 2019 ) as well as experimental studies of parenting interventions will be crucial here.

(iv) What is the relationship between the ubiquitous use of digital devices and ADHD?

The widespread use of screens and mobile devices in the modern world has created a fragmented, rapidly changing digital world where distraction (from the task in hand) is the rule rather than the exception. Children with ADHD may be especially vulnerable to problematic use ( Werling et al., 2022 ). This remains an area in need of more examination for child development in general and for ADHD risk, in particular whether children with ADHD are more vulnerable to harmful effects.

3.3. BRAINS

Alterations in brain structure, function and chemistry can be conceptualised as mediators of the pathways between originating genetic and environmental risks, ADHD onset and progression and associated impairment. To that extent, they represent potential targets for the development of new interventions. Characterising these alterations represents a key challenge in ADHD translational science. The advent of brain imaging has facilitated a more comprehensive understanding of ADHD especially with regard to underlying neurobiology and mechanisms of treatment. However, the field has recently entered a period of flux as the recent addition of large-scale and well-powered neuroimaging analyses challenge some established findings. In parallel, the utility of ADHD neurochemistry studies has been constrained by the difficulty of translating findings from in vitro or animal studies to humans, and the failure of drugs which target neurotransmitters known to indirectly impact catecholamine neurotransmission to produce improvement in ADHD symptoms in humans.

3.3.1. Prior consensus

The central role of catecholamines (e.g., dopamine (DA), norepinephrine (NE)) in ADHD pathophysiology was established by a plethora of research, including in vitro studies of receptor binding, animal models and imaging of catecholamine receptors ( Pliszka, 2005 ). D1 and alpha-2 receptors within prefrontal cortex circuits modulate attention and cognitive control, allowing differentiation of signal from noise ( Arnsten, 2006 ). Striatal D2/D3 receptors and dopamine transporters (DAT) are directly or indirectly implicated in most core and associated features of ADHD, including motivation/reward, impulsive responding, delay aversion, inhibition, attention and learning ( Dalley et al., 2008 ). A variety of other neurotransmitters are involved in modulating arousal and attention linked to ADHD via their interaction with catecholamines ( Pliszka, 2005 ), i.e., glutamate (excitatory neurotransmitter inhibited by DA), acetylcholine (ACH), nicotine, GABA and histamine-3 (Tiligada et al., 2011). Serotonin (5-HT) is implicated in mood regulation ( Sargin et al., 2019 ) and impulse control ( Dalley & Roiser, 2012 ), but also contributes to executive function ( Sargin et al., 2019 ).

Positron-emission tomography (PET) imaging has shown that striatal DA transporter (DAT) and post-synaptic DA receptor function is altered in ADHD ( Fusar-Poli et al., 2012 ), informing understanding of acute ( Wilens et al., 2008 ) and chronic ( Wang et al., 2013 ) mechanisms of stimulant medication. It has also documented the high density of limbic DAT and D2/D3 receptors in amygdala and hippocampus and their role in motivation ( Volkow et al., 2011 ) and reward ( Volkow et al., 2009 ). The widespread distribution of DAT and DA receptors across brain regions and functions, and the multiplicity of neurotransmitter systems and neural networks involved in modulation of catecholamine neurotransmission, has informed the conceptualization of ADHD as a broad and heterogeneous neuro-regulatory disorder ( Pruim et al., 2019 ; see section 4 ). NE is also central to ADHD pathophysiology and treatment. Like DA, NE has both direct and indirect effects ( Berridge, 2001 ): Further, it is an important modulator of DA neurotransmission ( Oades et al., 2005 ). NE-specific drugs (e.g., atomoxetine, alpha-2 agonists) are less effective at controlling symptoms than stimulants ( Cortese et al., 2018 ), which have direct effects at both the DA and NE transporters. However, PET radioligand methodology for NE is less well developed ( Moriguchi et al., 2017 ). Therefore, the full extent of NE regulation in ADHD and the neurobiological basis of noradrenergic treatment have been harder to study.

The past 25 years have seen an explosion in research using MRI to examine the structural and functional basis of ADHD pathophysiology. These studies have elaborated the multiplicity of neural networks involved in ADHD and illustrated that it is a much more neurobiologically complex condition than previously thought. Most published MRI-based studies have reported ADHD-related alterations in either brain structure and function with some effects holding in meta-analytic reviews – however, overall these are variable and inconsistent. Meta-analyses of structural MRI studies confirmed reduced total brain volume ( Valera et al., 2007 ) and smaller localised grey matter volumes in regions including putamen/globus pallidus, caudate and anterior cingulate cortex (ACC; Ellison-Wright et al., 2008 ; Nakao et al., 2011 ). Fronto-cortical and subcortical correlates of deficits in timing ( Hart et al., 2012 ), inhibition and attention ( Rubia et al., 2012 ) have been reported in fMRI meta-analyses. Analyses have increasingly moved to a more systems-science approach focusing on brain networks and circuits rather than specific regions. Meta-analysis identified ADHD-specific task-related hypoactivation in the frontoparietal network (FPN) and hyperactivation in the default mode network (DMN) and the visual and dorsal attention networks ( Cortese et al., 2012 ) - broadly converging with the seminal hypothesis that behavioural dysregulations reflect insufficient segregation between the default mode and executive control networks ( Sonuga-Barke & Castellanos, 2007 ).

Studies of resting-state fMRI (rfMRI) examine the functional organisation of brain networks through their temporal correlations in blood oxygen level-dependent (BOLD) signal during task-free scans. Samea et al. (2019) performed an omnibus meta-analysis and found no reliable convergence when combining rfMRI, task-based fMRI and voxel-based structural studies. In contrast, a meta-analysis by Gao et al., (2019) exclusively dedicated to rfMRI reported ADHD-related hyper-connectivity between FPN, DMN and affective networks, and hypoconnectivity between the FPN and ventral attentional (VAN) and somatosensory networks (SSN). Applying multilevel kernel density analysis, Sutcubasi et al. (2020) reported reduced functional connectivity in the DMN and the cognitive control networks and between them in child studies. In contrast, in broader whole brain analyses, Cortese et al. (2021) found no evidence of ADHD-related alterations in brain functional connectivity. Studies of structural connectivity in white matter tracts have also provided inconsistent results. A meta-analysis of diffusion studies combining whole brain voxel-and tract-based analyses reported lower fractional anisotropy (FA), a measure of white matter fibre organisation, in two corpus callosal tracts as well the inferior fronto-occipital fasciculus and left inferior longitudinal fasciculus – although the authors questioned the validity of the results as they mainly reflected studies that had not addressed head motion artifacts ( Aoki et al., 2018 ).

3.3.2. New leads

(i) large scale and reliable studies show substantially reduced adhd-related structural alterations than previously reported:.

What was considered established ADHD brain science based on the meta-analysis of small scale, selective and under-powered studies has been challenged by findings from the Enhancing Neuro-Imaging Genetics through Meta-Analyses (ENIGMA) consortium studies in which sample aggregation yields analyses of individual-level data in large data sets (~3200 to ~4200 participants). This has led to a re-calibration of the scale of ADHD-related brain alterations with reliable but smaller effects found than previously reported (Cohen’s d<0.20). In childhood, ENIGMA has detected smaller total intracranial volume, total cortical surface area, and volumes of subcortical nuclei, including amygdala and hippocampus, along with long implicated striatal regions (caudate, putamen, nucleus accumbens; Hoogman et al., 2017 ; 2019 ). No effects were seen in adult samples. Even smaller effects have been reported in the Adolescent Brain Cognitive Development study (ABCD ; Casey et al., 2018 ) which recruited ~11,700 children at ages 9-to-10 years-of-age. A FreeSurfer analysis of brain structure, with careful control of movement artifacts, found no ADHD-related effects in cortical thickness or subcortical volumes ( Bernanke, et al 2022 ). Estimated total intracranial volume and total cortical surface area were significantly smaller in children with ADHD versus healthy controls although the effect sizes were even smaller than in ENIGMA (d=−0.08 for both). Small global differences were found in caudal ACC (d=−0.12), middle temporal gyrus (d=−0.07), postcentral gyrus (d-=0.09), cuneus (d=−0.08) and pericalcarine cortex (d=−0.10). The authors noted that ADHD in the community ascertained ABCD sample is less severe than in studies that recruit clinical cases, which may account for the attenuation of effect sizes. Greater severity of motoric hyperactivity can also affect neuroimaging results, as excessive head motion during scans is the principal reason for loss of analysable data. Overall, the ENIGMA and ABCD findings confirm the presence of structural differences in children with ADHD but highlight their subtlety.

Neurobiological heterogeneity may also contribute to the small effects observed – large studies combine groups of ADHD individuals who may have different neuro-structural profiles implicating different brain circuits and regions. Initial analyses using multivariate grouping statistics has been successful in identifying such sub-groups with ADHD-related effects greater in some of these than others ( Li et al., 2021 ). In a similar vein, small ADHD-related differences were observed in analyses of three ABCD fMRI tasks, tapping working memory, inhibitory control and reward processing. During the working memory task, reduced activation in task-positive and reduced deactivation in task-negative (i.e., default mode) areas were correlated with CBCL Attention Problems scores (Owens et al., 2021). Those results were noted to be consistent with the aforementioned hypothesis implicating the interplay between the default mode and cognitive control networks ( Sonuga-Barke & Castellanos, 2007 ).

(ii) Longitudinal fMRI studies reveal new insights into ADHD-related brain function:

A pioneering longitudinal study of brain function with a substantial sample and improved MRI acquisition methods detected two types of trajectories in BOLD signal correlations ( Váša et al., 2020 ). Conservative trajectories , those that remained strong across the age range sampled, characterized especially primary cortex, while disrupted trajectories involved associative networks and subcortical-cortical circuits that either strengthened or weakened with development. This suggests that complex developmental trajectories need to be considered in prospective longitudinal studies of ADHD brain function.

(iii) Dimensional perspectives may reveal unique insights:

The ABCD rfMRI baseline data have also been examined dimensionally ( Karcher et al., 2021 ). Interestingly, both a general psychopathology factor and a neurodevelopmental factor, combining inattention, hyperactivity, impulsivity, clumsiness and repetitive behaviours were found to be associated with reduced DMN connectivity (the latter having a stronger effect), with the neurodevelopmental factor also associated with stronger correlations between DMN and the cingulo-opercular (salience) network. Nevertheless, these and other brain-behaviour relationships accounted for less than 1% of the variance.

(iv) Novel molecules are informing our understanding of heterogeneity across the broader ADHD phenotypes:

Studies of the action of novel neuro-moderators are providing new insights into the possible basis of heterogeneity in the broader ADHD phenotype. This may help with the identification of novel treatment targets for ADHD sub-groups. Recent attention has focused on orexins - arousal-promoting peptides which arise in the hypothalamus and brainstem and project to the locus coeruleus, where they increase firing of noradrenergic neurons with knock on effects on dopaminergic activity ( Sakurai, Saito, Yangisawa, 2021 ). Orexins play a key role in modulating a range of ADHD implicated processes; reward, attention, arousal, appetitive behaviour and sleep ( Katzman and Katzman, 2022 ; Villano et al., 2017 ; Subramanian & Ravichandran, 2022 ). Medication naïve children with ADHD have been found to have decreased serum orexin A levels ( Baykal et al., 2019 ). Also, suvorexant – a dual orexin/hypocretin receptor antagonist – produced a decrease in cocaine-induced premature responding in an animal model of impulsivity ( Gentile et al., 2018 ). Modafinil, a DAT blocker and partial orexin agonist, has shown positive effects in ADHD clinical trials, although there have been concerns about adverse side effects ( Wang et al., 2017 ). Recent research has also focused on the serotonergic effects of viloxazine ER, a newly approved (in the US) nonstimulant ( Yu et al., 2020 ). While the currently accepted mechanism of action is attributed solely to NET blockade – viloxazine is a moderate inhibitor of NET and elicited moderate activity at noradrenergic and dopaminergic systems – it also acts on the serotonin system. In vitro binding studies with viloxazine demonstrated antagonist activity at 5-HT2B and agonistic activity at 5-HT2C receptors. In vivo administration of viloxazine to rats yielded increased extracellular serotonin levels in the prefrontal cortex (PFC). It remains to be seen whether these new insights can lead to new treatment approaches which target selected difficulties in people with ADHD. For example, might subgroups of individuals (perhaps those with impulsive aggression) respond preferentially to ADHD medications which include prominent serotonergic effects? Might novel drugs targeting the orexin system be useful in treating individuals with ADHD and low motivation and/or under-aroused clinical presentations (e.g., sluggish cognitive tempo)?

3.3.3. Outstanding questions

(i) how will smaller investigator-initiated neuroimaging studies contribute.

Improvements in phenotypic dimensionalisation and improved reliability of brain imaging methods will provide the basis for the next generation of smaller-scale individual investigator-initiated studies. These will complement Big Data efforts, such as ENIGMA and ABCD, by examining causal interventions, such as treatment effects, while benchmarking to the datasets that will define the study of brain development and its behavioural and clinical manifestations over the next decade. They could also provide analysis of ADHD components and secondary features, in ways that the large consortia to date lack the data to do, and so set the stage of hypotheses for the next generation of large consortia efforts in a kind of feed-in, positive cycle.

(ii) Which dimensions will be the most powerful predictors of brain structure and function?

Dimensional analyses outperform categorical perspectives; but which dimensions are most relevant? In the ABCD sample, Michelini and colleagues factor analysed the CBCL and the adult version (Adult Self Report) and derived five dimensions for both sets of data ( Michelini et al., 2019 ). Although the CBCL is proprietary, it is ubiquitous, with validated versions in more than 100 languages. While the essential validation will occur in prospective longitudinal efforts such as ABCD, quantifying the advantages of this approach could be straightforward in many existing datasets and in ENIGMA. As this dimensionalisation putatively extends across the lifespan ( Michelini et al., 2019 ), it may inform the puzzling absence of significant brain findings in adult ADHD.

(iii) How can brain imaging be made more reliable?

Advancing science depends on improving instrumentation. MRI technology is both astonishingly advanced in some ways, and, at the same time, rudimentary in others. The field has been sufficiently humbled by lack of replicability to coalesce on a range of best practices ( Nichols et al., 2017 ), which include implementing proven methods to enhance reliability (e.g., ME-ICA ; Raimondo et al., 2021 ) and using movies as stimuli for functional imaging ( Vanderwal et al., 2019 ).

(iv) Can drug discoveries enhance personalised care by targeting brain-processes implicated in different sub-groups of people with ADHD?

Increased awareness of clinical and neurobiological heterogeneity in ADHD (see section 4 ) has given rise to calls for a more a personalized approach to intervention. In this context, a nuanced understanding of neurochemistry and its clinical application could be highly relevant. Unfortunately, several novel drugs recently examined for use in ADHD either failed in Phase 3 or need additional study to establish their therapeutic benefit - including: vortioxetine (SNRI; 5-HT1A/1B partial agonist); metadoxine (ion pair salt of pyridoxine; 5-HT2B agonist/GABA modulator), dasotraline (DA + NE reuptake inhibitor), fasoracetam (metabotropic glutamate agonist); molindone (D2 and 5-HT2B antagonist) ( Nageye and Cortese, 2019 ; Pozzi et al., 2020 ). Previous research using nicotinic agonists ( Potter et al., 2014 ), H3 antagonists ( Weisler et al., 2012 ) and ampakines ( Danysz, 2002 ) - all known modulators of catecholamine neurotransmission - also failed to show efficacy. While there are no doubt a multiplicity of possible reasons for these failed trials, a key question yet to be adequately considered is whether medications which indirectly impact catecholamine function might be more suitable for a subpopulation of individuals with ADHD – in which case studying them in the larger ADHD population might yield an overall inadequate response. It is hoped that future research will use a more targeted approach with refined ADHD phenotypes.

4. THE FUTURE OF THE ADHD CONSTRUCT IN THE LIGHT OF SCIENTIFIC PROGRESS – SOME CROSS CUTTING THEMES

The historical introduction of the ADHD diagnosis, like those relating to other psychiatric/neurodevelopmental conditions in DSM, provided a bridge between science and practice that, for the first time, allowed a truly evidence-based system of care ( Coghill & Sonuga-Barke, 2012 ). It did this, most obviously, by providing clinicians and researchers with a systematic conceptual framework allowing the rigorous characterisation and reliable measurement of the condition. Just as importantly, it provided a catalyst for decades of scientific progress by making explicit the transitional status of the ADHD concept as a working hypothesis open to updating in the light of new scientific discoveries during periodic review – most recently through the publication of DSM-5 ( Posner et al., 2020 ). Essential to its scientific value are the stable set of assumptions about what disorder generally, and ADHD specifically, at its core, is – something that philosophers of science call a paradigm ( Sonuga-Barke, 2020 ). The assumptions within a paradigm are essential for incremental progress during periods of normal science. At the same time, they provide a meta-theory of ADHD that constrains enquiry by defining the research questions considered relevant, the methods used to address those questions, and interpretations of findings made. Kuhn believed that these periods of normal science are punctuated by periods of radical change when it becomes clear that paradigmatic assumptions are no longer reconcilable with scientific reality. The current review and accompanying perspectives highlight the way in which research data increasingly challenges some core assumptions of the current DSM paradigm. Here, a number of the most significant of these are reviewed.

(I) ADHD - a category?

A core assumption in DSM is that ADHD is a category with nonarbitrary boundaries that distinguish cases from non-cases. The most direct test of this assumption comes from large-scale, population-based taxometric studies which statistically test for discontinuities in underlying causes around diagnostic boundaries ( Haslam et al., 2022 ). To date, evidence from these and other studies (see sections 3.1.1 ) do not support the categorical assumption – rather, ADHD is best understood as an extreme expression of a continuous trait.

(II) ADHD - a singular entity?

A second DSM assumption is that, despite its obvious surface level heterogeneity (i.e., symptom profiles, co-occurring conditions, and impairment), ADHD represents a causally distinctive condition at a deeper level, marked by a common aetiology and pathophysiology. As extensively described here, this is not the case (see sections 2.1 , 2.2 , 3.1 , 3.2 & 3.3 ). On the one hand, cases that manifest clinically in a similar way (e.g., combined type) show great heterogeneity in genetic and environmental risk, underlying pathophysiology and associated neuropsychological deficits and differences, no particular factor or combination of factors being a necessary and sufficient basis for the condition. Such heterogeneity almost certainly contributes to the very small ADHD-related effects reported in recent large-scale neuroimaging and genetic studies. At the same time, there also appears to be considerable genetic, environmental, and neuro-psychological/-biological overlap between ADHD and conditions that DSM conceptualises as distinct from it. This encourages a fundamental re-mapping of ADHD symptoms to their underlying causes as proposed, for instance, within the RDoC initiative ( Pacheco et al., 2022 ).

(III) ADHD - a neurodevelopmental condition?

DSM also assumes that ADHD is a neurodevelopmental condition unfolding across development from roots in early childhood. The evidence from longitudinal studies testing this assumption is somewhat mixed (see section 2.1.1 ). On the one hand, there appears strong continuity in childhood cases and little evidence of permanent remission. On the other hand, some individuals are first identified with ADHD in adulthood with apparently unclear evidence of the condition in childhood. Many of these late-onset cases appear distinctive in terms of genetic risk, cognitive profile, environmental exposures, and cooccurring mental health problems. This has led some to suggest that, at least from a research perspective, late- and early-onset cases should be considered different conditions – although this point of view remains controversial. However, given the heterogeneity in ADHD mentioned above, it may be more accurate to consider late onset ADHD as a non-neurodevelopmental presentation. In addressing this issue, it will be important to explore whether early- and late-onset cases require different clinical management approaches.

(iv) ADHD - a unisex condition?

ADHD was first characterised largely based on clinical observations made about male children. However, like other neurodevelopmental conditions, it is conceptualised as a disorder of equivalent meaning and significance for males and females – despite substantial sex-related differences in presentation and prevalence. Most sections of this review have described important new leads and raised key questions for future research that highlight ways in which ADHD may be different for males and females – in terms of clinical manifestation, developmental pathways, the experience of stigma, as well causal factors (see sections 2.1 , 2.2 , 2.3 and 3.1 in particular). These questions in turn beg the broader question of whether ADHD needs to be reformulated to some degree to ensure that females with ADHD receive the care that they require sufficiently early in their development.

(v) ADHD – neurodivergence not disorder?

Perhaps the most radical challenge to the ADHD paradigm comes not from the empirically-based arguments outlined above, but rather from a new socio-cultural rights-based concept that has emerged outside the clinical and scientific sphere – neurodiversity ( Pellicano et al., 2022 ). This both casts ADHD as part of a wider spectrum of naturally occurring variation and challenges the DSM assumption that ADHD is a disorder caused by dysfunction within the individual - replacing it with the assumption of ADHD as neuro-divergence ( Sonuga-Barke & Thapar, 2021 ). It emphasizes acceptance of, and accommodation to, ADHD by family, peers, schools/employers, and the wider community and looks to build the best ways to create environments that promote personal agency and build resilience to foster developmental growth. Impairment shifts from being something intrinsic to ADHD per se, to being something that is socially constructed, situated, and context-contingent. Crucially it privileges the personal perspectives of the individual with ADHD as being as important as those of clinicians or scientists and therefore promotes participatory approaches to research. The implications of the this approach for science are still being worked through, but they have the potential to reframe its purpose in fundamental ways ( Sonuga-Barke & Thapar, 2021 ). The neurodivergence perspective highlights how vital is that people with ADHD are involved in shaping research priorities in a way that ensures that their expertise by experience can inform the research questions asked, the methods employed and the interpretations of results made.

5. CONCLUDING REMARKS

As we have illustrated here, scientific progress is challenging some of the core assumptions of the current DSM model of ADHD and the way it is conceptualised with regard to related disorders. Given this, it is worth reflecting on whether the time is ripe for a paradigm shift in the field to ensure that diagnostic constructs accurately reflect the underlying multiform and dimensional structure of ADHD-related psychopathology, acknowledging also its situated nature and the role of normative environmental constraints in determining associated impairment. However, in considering this, caution is required. First, because we don’t want to risk losing the advances that the current model has afforded. Second, far more research is needed to identify the core underlying cross-cutting dimensions that could replace current categories. Third, because even after we have identified these, they need to be operationalised as clinically workable constructs in ways that avoid abruptly breaking the bridge of meaning that links research to clinical practice.

• Remarkable scientific progress has been made in understanding ADHD – with many discoveries challenging the assumptions on which current models rest.
• Recent science demonstrates that it is the extreme expression of complex and dimensional traits that are mostly persistent with diverse developmental trajectories.
• It commonly overlaps with other mental health and neuro-development conditions, phenotypically and in terms of risk processes.
• Similar to many other neuro-developmental and psychiatric conditions, its causes are complex and heterogeneous with an accumulation of multiple and interacting contributing genetic and environmental risks associated with subtle alterations in brain structure and function.
• Key foci for the future relate to clinical and causal heterogeneity and overlap, risk prediction, gene-environment interplay, the female presentation, the social environment’s role in impairment and stigma, resilience and personal growth.
• Going forward, participatory research approaches involving people with ADHD will be vital.

ACKNOWLEDGMENTS

ES-B and ES were supported by the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. SPB was supported by R01MH122415 from the U.S. National Institutes of Health (NIH) and R305A200028 from the Institute of Education Sciences (IES), U.S. Department of Education. JN was supported by MH-R3759105 from the U.S. National Institutes of Health (NIH). BF is supported by funding from the European Community’s Horizon 2020 Programme (H2020/2014 – 2020), under grant agreements n° 728018 (Eat2beNICE) and n° 847879 (PRIME), and by the National Institute Of Mental Health of the NIH under Award Number R01MH124851. LAR was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil. The views expressed are those of the authors and not necessarily those of the NIH, IES, NHS, the NIHR or the Department of Health and Social Care.

CONFLICTS OF INTEREST

SB discloses that he has in the last 3 years acted as an author, consultant or lecturer for Medice and Roche. He receives royalties for textbooks and diagnostic tools from Hogrefe, and Liber. Bolte is shareholder in SB Education/Psychological Consulting AB and NeuroSupportSolutions International AB. FXC is on the scientific advisory board of BOL Pharma, Israel and is Co-Chief Editor of Frontiers in Neuroimaging. JHN.: consultant/advisory board for Adlon Therapeutics, Arbor, Cingulate Therapeutics, Corium, Eisai, Ironshore, Lumos, Lundbeck, Medice, Myriad, NLS, OnDosis, Rhodes, Shire/Takeda, and Supernus; research support from Adlon, Otsuka, Shire, Supernus; honoraria for disease state lectures from Otsuka and Shire/Takeda, and served as a consultant for the US National Football League. ES-B speaker fee from Takeda and Medice. Consultancy Neurotech Solutions International; Research support QBTech. LAR has received grant or research support from, served as a consultant to, and served on the speakers’ bureau of Ache, Bial, Medice, Novartis/Sandoz, Pfizer/Upjohn, and Shire/Takeda in the last three years. The ADHD and Juvenile Bipolar Disorder Outpatient Programs chaired by Dr Rohde have received unrestricted educational and research support from the following pharmaceutical companies in the last three years: Novartis/Sandoz and Shire/Takeda. Dr Rohde has received authorship royalties from Oxford Press and ArtMed. ES Speaker fee for 24/7 Conference Evelina Children’s Hospital, London. SPB receives book royalties from Guilford Press. JN receives book royalties from Guilford Press. Other authors report no conflicts of interest. BF has received educational speaking fees from Medice.

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