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Search strategy, data extraction, risk of bias, data synthesis and analysis, medications, youth-directed psychosocial treatments, parent support, school interventions, cognitive training, neurofeedback, nutrition and supplements, complementary, alternative, or integrative medicine, combined medication and behavioral treatments, moderation of treatment response, long-term outcomes, clinical implications, strengths and limitations, future research needs, acknowledgments, treatments for adhd in children and adolescents: a systematic review.

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Bradley S. Peterson , Joey Trampush , Margaret Maglione , Maria Bolshakova , Mary Rozelle , Jeremy Miles , Sheila Pakdaman , Morah Brown , Sachi Yagyu , Aneesa Motala , Susanne Hempel; Treatments for ADHD in Children and Adolescents: A Systematic Review. Pediatrics April 2024; 153 (4): e2024065787. 10.1542/peds.2024-065787

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Effective treatment of attention-deficit/hyperactivity disorder (ADHD) is essential to improving youth outcomes.

This systematic review provides an overview of the available treatment options.

We identified controlled treatment evaluations in 12 databases published from 1980 to June 2023; treatments were not restricted by intervention content.

Studies in children and adolescents with clinically diagnosed ADHD, reporting patient health and psychosocial outcomes, were eligible. Publications were screened by trained reviewers, supported by machine learning.

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

In total, 312 studies reported in 540 publications were included. We grouped evidence for medication, psychosocial interventions, parent support, nutrition and supplements, neurofeedback, neurostimulation, physical exercise, complementary medicine, school interventions, and provider approaches. Several treatments improved ADHD symptoms. Medications had the strongest evidence base for improving outcomes, including disruptive behaviors and broadband measures, but were associated with adverse events.

We found limited evidence of studies comparing alternative treatments directly and indirect analyses identified few systematic differences across stimulants and nonstimulants. Identified combination of medication with youth-directed psychosocial interventions did not systematically produce better results than monotherapy, though few combinations have been evaluated.

A growing number of treatments are available that improve ADHD symptoms and other outcomes, in particular for school-aged youth. Medication therapies remain important treatment options but are associated with adverse events.

Attention-deficit/hyperactivity disorder (ADHD) is a common mental health problem in youth, with a prevalence of ∼5.3%. 1 , 2   Youth with ADHD are prone to future risk-taking problems, including substance abuse, motor vehicle accidents, unprotected sex, criminal behavior, and suicide attempts. 3   Although stimulant medications are currently the mainstay of treatment of school-age youth with ADHD, other treatments have been developed for ADHD, including cognitive training, neurofeedback, neuromodulation, and dietary and nutritional interventions. 4   – 7  

This systematic review summarizes evidence for treatments of ADHD in children and adolescents. The evidence review extends back to 1980, when contemporary diagnostic criteria for ADHD and long-acting stimulants were first introduced. Furthermore, we did not restrict to a set of prespecified known interventions for ADHD, and instead explored the range of available treatment options for children and adolescents, including novel treatments. Medication evaluations had to adhere to a randomized controlled trial (RCT) design, all other treatments could be evaluated in RCTs or nonrandomized controlled studies that are more common in the psychological literature, as long as the study reported on a concurrent comparator. Outcomes were selected with input from experts and stakeholders and were not restricted to ADHD symptoms. To our knowledge, no previous review for ADHD treatments has been as comprehensive in the range of interventions, clinical and psychosocial outcomes, participant ages, and publication years.

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 (AAP), 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. 8  

Population: Children or adolescents with a clinical diagnosis of ADHD, age <18 years

Interventions: Any ADHD treatment, alone or in combination, and ≥4 weeks’ treatment

Comparators: No treatment, waitlist, placebo, passive comparators, or active comparators

Outcomes: Patient health and psychosocial outcomes

Setting: Any

Study designs: RCTs for medication; RCTs, controlled clinical trials without random assignment, or cohort studies comparing 1 or more treatment groups for nondrug treatments. Studies either had to be large or demonstrate that they could detect effects as a standalone study (operationalized as ≥100 participants or a power calculation)

Other limiters: English-language (to ensure transparency for a US guideline), published from 1980

We searched the databases 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 search underwent peer review; the full strategy is in the Online Appendix. All citations were reviewed by trained literature reviewers supported by machine learning to ensure no studies were inadvertently missed. Two independent reviewers assessed full-text studies for eligibility. Publications reporting on the same participants were consolidated into 1 record so that no study entered the analyses more than once. The TEP reviewed studies to ensure all were captured.

The data abstraction form included extensive guidance to aid reproducibility and standardization in recording study details, outcomes, 9   – 12   study quality, and applicability. One reviewer abstracted data, and a methodologist checked its accuracy and completeness. Data are publicly available in the Systematic Review Data Repository.

We assessed 6 domains 13   : Selection, performance, attrition, detection, reporting, and study-specific biases ( Supplemental Figs 6 and 7 ).

We organized analyses by treatment and comparison type. We grouped treatments according to intervention content and target (eg, youth or parents). The intervention taxonomy differentiated medication, psychosocial interventions, parent support, nutrition and supplements, neurofeedback, neurostimulation, physical exercise, complementary medicine, school interventions, and provider approaches. We differentiated effects versus passive control groups (eg, placebo) and comparative effects (ie, comparing to an alternative treatment). The following outcomes were selected as key outcomes: (1) ADHD symptoms (eg, ADHD Rating Scale 14 , 15   ), (2) disruptive behavior (eg, conduct problems), (3) broadband measures (eg, Clinical Global Impression 16   ), (4) functional impairment (eg, Weiss Functional Impairment Rating Scale 17 , 18   ), (5) academic performance (eg, grade point average), (6) appetite suppression, and (7) number of participants reporting adverse events.

Studies reported on a large range of outcome measures as documented in the evidence table in the Online Appendix. To facilitate comparisons across studies, we converted outcomes to scale-independent standardized mean differences (SMDs) for continuous symptom outcome variables and relative risks (RRs) for categorical reports, presenting summary estimates and 95% confidence intervals (CIs) for all analyses. We used random-effects models performed in R with Metafor_v4.2-0 for statistical pooling, correcting for small numbers of studies when necessary, to synthesize available evidence. 19   We conducted sensitivity analyses for all analyses that included studies without random assignment. We also compared treatment effectiveness indirectly across studies in meta-regressions that added potential, prespecified effect modifiers to the meta-analytic model. In particular, we assessed whether ADHD presentation or cooccurring disorders modified intervention effects. We tested for heterogeneity using graphical displays, documented I 2 statistics (values >50% are highlighted in the text), and explored sources of heterogeneity in subgroup and sensitivity analyses. 20  

We assessed publication bias with Begg and Egger tests 21 , 22   and used the trim-and-fill methods for alternative estimates where necessary. 23   Applicability of findings to real-world clinical practices in typical US settings was assessed qualitatively using AHRQ’s Methods Guide. An overall strength of evidence (SoE) assessment communicating our confidence in each finding was determined initially by 1 researcher with experience in use of specified standardized criteria 24   ( Supplemental Information ), then discussed with the study team. We downgraded SoE for study limitations, imprecision, inconsistency, and reporting bias, and we differentiated high, moderate, low, and insufficient SoE.

We screened 23 139 citations and retrieved 7534 publications as full text against the eligibility criteria. In total, 312 treatment studies, reported in 540 publications (see list of included studies in the Online Appendix), met eligibility criteria ( Fig 1 ).

Literature flow diagram.

Although studies from 1980 were eligible, the earliest study meeting all eligibility criteria was from 1995. All included studies are documented in the evidence table in the Supplemental Information . The following highlights key findings. Results for intervention groups and individual studies, subgroup and sensitivity analyses, characteristics of participants and interventions contributing to the analyses, and considerations that determined the SoE for results are documented in the Online Appendix.

As a class, traditional stimulants (methylphenidate, amphetamines) significantly improved ADHD symptom severity (SMD, −0.88; CI, −1.13 to −0.63; studies = 12; n = 1620) and broadband measures (RR, 0.38; CI, 0.30–0.48; studies = 12; n = 1582) (both high SoE), but not functional impairment (SMD, 1.00; CI, −0.25 to 2.26; studies = 4; n = 540) ( Fig 2 , Supplemental Fig 8 , Supplemental Table 1 ). Methylphenidate formulations significantly improved ADHD symptoms (SMD, −0.68; CI, −0.91 to −0.46; studies = 7; n = 863) ( Fig 2 , Supplemental Table 1 ) and broadband measures (SMD, 0.66; CI, 0.04–1.28; studies = 2; n = 302). Only 1 study assessed academic performance, reporting large improvements compared with a control group (SMD, −1.37; CI, −1.72 to −1.03; n = 156) ( Supplemental Fig 9 ). 25   Methylphenidate statistically significantly suppressed appetite (RR, 2.80; CI, 1.47–5.32; studies = 8; n = 1110) ( Fig 3 ), and more patients reported adverse events (RR, 1.32; CI, 1.25–1.40; studies = 6; n = 945). Amphetamine formulations significantly improved ADHD symptoms (SMD, −1.16; CI, −1.64 to −0.67; studies = 5; n = 757) ( Fig 2 , Supplemental Table 1 ) but not broadband measures (SMD, 0.68; CI, −0.72 to 2.08; studies = 3; n = 561) ( Supplemental Fig 9 ). Amphetamines significantly suppressed appetite (RR, 7.08; CI, 2.72–18.42; studies = 8; n = 1229) ( Fig 3 ), and more patients reported adverse events (RR, 1.41; CI, 1.25–1.58; studies = 8; n = 1151). Modafinil (US Food and Drug Administration [FDA]-approved to treat narcolepsy and sleep apnea but not ADHD) in each individual study significantly improved ADHD symptoms, but aggregated estimates were nonsignificant (SMD, −0.76; CI, −1.75 to 0.23; studies = 4; n = 667) ( Fig 2 , Supplemental Table 1 ) because of high heterogeneity (I 2 = 91%). It did not improve broadband measures (RR, 0.49; CI, −0.12 to 2.07; studies = 3; n = 539) ( Supplemental Fig 9 ), and it significantly suppressed appetite (RR, 4.44; CI, 2.27–8.69; studies = 5; n = 780) ( Fig 3 ).

Medication effects on ADHD symptom severity. S-AMPH-LDX, lisdexamfetamine; S-AMPH-MAS, mixed amphetamines salts; S-MPH-DEX, dexmethylphenidate; S-MPH-ER, extended-release methylphenidate; S-MPH-IR, immediate release methylphenidate; S-MPH-OROS, osmotic-release oral system methylphenidate; S-MPH-TP, dermal patch methylphenidate; NS-NRI-ATX, atomoxetine; NS-NRI-VLX, viloxazine; NS-ALA-CLON, clonidine; NS-ALA-GXR, guanfacine extended-release.

Medication effects on appetite suppression. Abbreviations as in legend for Fig 2 .

As a class, nonstimulants significantly improved ADHD symptoms (SMD, −0.52; CI, −0.59 to −0.46; studies = 37; n = 6065; high SoE) ( Fig 2 , Supplemental Table 1 ), broadband measures (RR, 0.66; CI, 0.58–0.76; studies = 12; n = 2312) ( Supplemental Fig 8 ), and disruptive behaviors (SMD, 0.66; CI, 0.22–1.10; studies = 4; n = 523), but not functional impairment (SMD, 0.20; CI, −0.05 to 0.44; studies = 6; n = 1163). Norepinephrine reuptake inhibitors (NRI) improved ADHD symptoms (SMD, −0.55; CI, −0.62 to −0.47; studies=28; n = 4493) ( Fig 2 , Supplemental Table 1 ) but suppressed appetite (RR, 3.23; CI, 2.40–4.34; studies = 27; n = 4176) ( Fig 3 ), and more patients reported adverse events (RR, 1.31; CI, 1.18–1.46; studies = 15; n = 2600). Alpha-agonists (guanfacine and clonidine) improved ADHD symptoms (SMD, −0.52; CI, −0.67 to −0.37; studies = 11; n = 1885) ( Fig 2 , Supplemental Table 1 ), without (guanfacine) significantly suppressing appetite (RR, 1.49; CI, 0.94–2.37; studies = 4; n = 919) ( Fig 3 ), but more patients reported adverse events (RR, 1.21; CI, 1.11–1.31; studies = 14, n = 2544).

One study compared amphetamine versus methylphenidate, head-to-head, finding more improvement in ADHD symptoms (SMD, −0.46; CI, −0.73 to −0.19; n = 222) and broadband measures (SMD, 0.29; CI, 0.02–0.56; n = 211), but not functional impairment (SMD, 0.16; CI, −0.11 to 0.43; n = 211), 26   with lisdexamfetamine (an amphetamine) than osmotic-release oral system methylphenidate. No difference was found in appetite suppression (RR, 1.01; CI, 0.72–1.42; studies = 2, n = 414) ( Fig 3 ) or adverse events (RR, 1.11; CI, 0.93–1.33; study = 1, n = 222). Indirect comparisons yielded significantly larger effects for amphetamine than methylphenidate in improving ADHD symptoms ( P = .02) but not broadband measures ( P = .97) or functional impairment ( P = .68). Stimulants did not differ in appetite suppression ( P = .08) or adverse events ( P = .35).

One study provided information on NRI versus alpha-agonists by directly comparing an alpha-agonist (guanfacine) with an NRI (atomoxetine), 27   finding significantly greater improvement in ADHD symptoms with guanfacine (SMD, −0.47; CI, −0.73 to −0.2; n = 226) but not a broadband measure (RR, 0.84; CI, 0.68–1.04; n = 226). It reported less appetite suppression for guanfacine (RR, 0.48; CI, 0.27–0.83; n = 226) but no difference in adverse events (RR, 1.14; CI, 0.97–1.34; n = 226). Indirect comparisons did not indicate significantly different effect sizes for ADHD symptoms ( P = .90), disruptive behaviors ( P = .31), broadband measures ( P = .41), functional impairment ( P = .46), or adverse events ( P = .06), but suggested NRIs more often suppressed appetite compared with guanfacine ( P = .01).

Studies directly comparing nonstimulants versus stimulants (all were the NRI atomoxetine and stimulants methylphenidate in all but 1) tended to favor stimulants but did not yield significance for ADHD symptom severity (SMD, 0.23; CI, −0.03 to 0.49; studies = 7; n = 1611) ( Fig 2 ). Atomoxetine slightly but statistically significantly produced greater improvements in disruptive behaviors (SMD, −0.08; CI, −0.14 to −0.03; studies = 4; n = 608) ( Supplemental Fig 10 ) but not broadband measures (SMD, −0.16; CI, −0.36 to 0.04; studies = 4; n = 1080) ( Supplemental Fig 9 ). They did not differ significantly in appetite suppression (RR, 0.82; CI, 0.53–1.26; studies = 8; n = 1463) ( Fig 3 ) or number with adverse events (RR, 1.11; CI, 0.90–1.37; studies = 4; n = 756). Indirect comparisons indicated significant differences favoring stimulants over nonstimulants in improving ADHD symptom severity ( P < .0001), broadband measures ( P = .0002), and functional impairment ( P = .04), but not appetite suppression ( P = .31) or number with adverse events ( P = .12).

Several studies assessed whether adding nonstimulant to stimulant medication (all were alpha-agonists added to different stimulants) improved outcomes compared with stimulant medication alone, yielding a small but significant additional improvement in ADHD symptoms (SMD, −0.36; CI, −0.52 to −0.19; studies = 5; n = 724) ( Fig 4 ).

Combination treatment. CLON, clonidine, GXR guanfacine.

We identified 32 studies evaluating psychosocial, psychological, or behavioral interventions targeting ADHD youth, either alone or combined with components for parents and teachers. Interventions were highly diverse, and most were complex with multiple components (see supplemental results in the Online Appendix). They significantly improved ADHD symptoms (SMD, −0.35; CI, −0.51 to −0.19; studies = 14; n = 1686; moderate SoE) ( Fig 4 ), even when restricting to RCTs only (SMD, −0.36; CI, −0.53 to −0.19; removing high-risk-of-bias studies left 7 with similar effects SMD, −0.38; CI, −0.69 to −0.07), with minimal heterogeneity (I 2 = 52%); but not disruptive behaviors (SMD, −0.18; CI, −0.48 to 0.12; studies = 8; n = 947) or academic performance (SMD, −0.07; CI, −0.49 to 0.62; studies = 3; n = 459) ( Supplemental Fig 11 ).

We identified 19 studies primarily targeting parents of youth aged 3 to 18 years, though only 3 included teenagers. Interventions were highly diverse (see Online Appendix), but significantly improved ADHD symptoms (SMD, −0.31; CI, −0.57 to −0.05; studies = 11; n = 1078; low SoE) ( Fig 4 ), even when restricting to RCTs only (SMD, −0.35; CI, −0.61 to −0.09; removing high-risk-of-bias studies yielded the same point estimate, but CIs were wider, and the effect was nonsignificant SMD, −0.31; CI, −0.76 to 0.14). There was some evidence of publication bias (Begg P = .16; Egger P = .02), but the trim and fill method to correct it found a similar effect (SMD, −0.43; CI, −0.63 to −0.22). Interventions improved broadband scores (SMD, 0.41; CI, 0.23–0.58; studies = 7; n = 613) and disruptive behaviors (SMD, −0.52; CI, −0.85 to −0.18; studies = 4; n = 357) but not functional impairment (SMD, 0.35; CI, −0.69 to 1.39; studies = 3; n = 252) (all low SoE) ( Supplemental Fig 12 ).

We identified 10 studies, mostly for elementary or middle schools (see Online Appendix). Interventions did not significantly improve ADHD symptoms (SMD, −0.50; CI, −1.05 to 0.06; studies = 5; n = 822; moderate SoE) ( Fig 4 ), but there was evidence of heterogeneity (I 2 = 87%). Although most studies reported improved academic performance, this was not statistically significant across studies (SMD, −0.19; CI, −0.48 to 0.09; studies = 5; n = 854) ( Supplemental Fig 13 ).

We identified 22 studies, for youth aged 6 to 17 years without intellectual disability (see Online Appendix). Cognitive training did improve ADHD symptoms (SMD, −0.37; CI, −0.65 to −0.06; studies = 12; n = 655; low SoE) ( Fig 4 ), with some heterogeneity (I 2 = 65%), but not functional impairment (SMD, 0.41; CI, −0.24 to 1.06; studies = 5; n = 387) ( Supplemental Fig 14 ) or disruptive behaviors (SMD, −0.29; CI, −0.84 to 0.27; studies [all RCTs] = 5; n = 337). It improved broadband measures (SMD, 0.50; CI, 0.12–0.88; studies = 6; n = 344; RCTs only: SMD, 0.43; CI, −0.06 to 0.93) (both low SoE). It did not increase adverse events (RR, 3.30; CI, 0.03–431.32; studies = 2; n = 402).

We identified 21 studies: Two-thirds involved θ/β EEG marker modulation, and one-third modulation of slow cortical potentials (see Online Appendix). Neurofeedback significantly improved ADHD symptoms (SMD, −0.44; CI, −0.65 to −0.22; studies = 12; n = 945; low SoE) ( Fig 4 ), with little heterogeneity (I 2 = 33%); restricting to the 10 RCTs yielded the same point estimate, also statistically significant (SMD, −0.44; CI, −0.71 to −0.16). Neurofeedback did not systematically improve disruptive behaviors (SMD, −0.33; CI, −1.33 to 0.66; studies = 4; n = 372), or functional impairment (SMD, 0.21; CI, −0.14 to 0.55; studies = 3; n = 332) ( Supplemental Fig 15 ).

We identified 39 studies with highly diverse nutrition interventions (see Online Appendix), including omega-3 (studies = 13), vitamins (studies = 3), or diets (studies = 3), and several evaluated supplements as augmentation to stimulants. Most were placebo-controlled. Across studies, interventions improved ADHD symptoms (SMD, −0.39; CI, −0.67 to −0.12; studies = 23; n = 2357) ( Fig 4 ), even when restricting to RCTs (SMD, −0.32; CI, −0.55 to −0.08), with high heterogeneity (I 2 = 89%) but no publication bias. The group of nutritional approaches also improved disruptive behaviors (SMD, −0.28; CI, −0.37 to −0.18; studies [all RCTs] = 5; n = 360) ( Supplemental Fig 16 , low SoE), without increasing the number reporting adverse events (RR, 0.77; CI, 0.47–1.27; studies = 8; n = 735). However, we did not identify any specific supplements that consistently improved outcomes, including omega-3 (eg, ADHD symptoms: SMD, −0.11; CI, −0.45, 0.24; studies = 7; n = 719; broadband measures: SMD, 0.04; CI, −0.24 to 0.32; studies = 7; n = 755, low SoE).

We identified 6 studies assessing acupuncture, homeopathy, and hippotherapy. They did not individually or as a group significantly improve ADHD symptoms (SMD, −0.15; CI, −1.84 to 1.53; studies = 3; n = 313) ( Fig 4 ) or improve other outcomes across studies (eg, broadband measures: SMD, 0.03; CI, −3.66 to 3.73; studies = 2; n = 218) ( Supplemental Fig 17 ).

Eleven identified studies evaluated a combination of medication- and youth-directed psychosocial treatments. Most allowed children to have common cooccurring conditions, but intellectual disability and severe neurodevelopmental conditions were exclusionary. Medication treatments were stimulant or atomoxetine. Psychosocial treatments included multimodal psychosocial treatment, cognitive behavioral therapy, solution-focused therapy, behavioral therapy, and a humanistic intervention. Studies mostly compared combinations of medication and psychosocial treatment to medication alone, rather than no treatment or placebo. Combined therapy did not statistically significantly improve ADHD symptoms across studies (SMD, −0.36; CI, −0.73 to 0.01; studies = 7; n = 841; low SoE; only 2 individual studies reported statistically significant effects) ( Fig 5 ) or broadband measures (SMD, 0.42; CI, −0.72 to 1.56; studies = 3; n = 171), but there was indication of heterogeneity (I 2 = 71% and 62%, respectively).

Nonmedication intervention effects on ADHD symptom severity.

We found little evidence that either ADHD presentation (inattentive, hyperactive, combined-type) or cooccurring psychiatric disorders modified treatment effects on any ADHD outcome, but few studies addressed this question systematically (see Online Appendix).

Only a very small number of studies (33 of 312) reported on outcomes at or beyond 12 months of follow-up (see Online Appendix). Many did not report on key outcomes of this review. Studies evaluating combined psychosocial and medication interventions, such as the multimodal treatment of ADHD study, 28   did not find sustained effects beyond 12 months. Analyses for medication, psychosocial, neurofeedback, parent support, school intervention, and provider-focused interventions did not find sustained effects for more than a single study reporting on the same outcome. No complementary medicine, neurostimulation, physical exercise, or cognitive training studies reported long-term outcomes.

We identified a large body of evidence contributing to knowledge of ADHD treatments. A substantial number of treatments have been evaluated in strong study designs that provide evidence statements regarding the effects of the treatments on children and adolescents with ADHD. The body of evidence shows that numerous intervention classes significantly improve ADHD symptom severity. This includes large but variable effects for amphetamines, moderate-sized effects for methylphenidate, NRIs, and alpha-agonists, and small effects for youth-directed psychosocial treatment, parent support, neurofeedback, and cognitive training. The SoE for effects on ADHD symptoms was high across FDA-approved medications (methylphenidate, amphetamines, NRIs, alpha-agonists); moderate for psychosocial interventions; and low for parent support, neurofeedback, and nutritional interventions. Augmentation of stimulant medication with non-stimulants produced small but significant additional improvement in ADHD symptoms over stimulant medication alone (low SoE).

We also summarized evidence for other outcomes beyond specific ADHD symptoms and found that broadband measures (ie, global clinical measures not restricted to assessing specific symptoms and documenting overall psychosocial adjustment), methylphenidate (low SoE), nonstimulant medications (moderate SoE), and cognitive training (low SoE) yielded significant, medium-sized effects, and parent support small effects (moderate SoE). For disruptive behaviors, nonstimulant medications (high SoE) and parent support (low SoE) produced significant improvement with medium effect. No treatment modality significantly improved functional impairment or academic performance, though the latter was rarely assessed as a treatment outcome.

The enormous variability in treatment components and delivery of youth-directed psychotherapies, parent support, neurofeedback, and nutrition and supplement therapies, and in ADHD outcomes they have targeted, complicates the synthesis and meta-analysis of their effects compared with the much more uniform interventions, delivery, and outcome assessments for medication therapies. Moreover, most psychosocial and parent support studies compared an active treatment against wait list controls or treatment as usual, which did not control well for the effects of parent or therapist attention or other nonspecific effects of therapy, and they have rarely been able to blind adequately either participants or study assessors to treatment assignment. 29 , 30   These design limitations weaken the SoE for these interventions.

The large number of studies, combined with their medium-to-large effect sizes, indicate collectively and with high SoE that FDA-approved medications improve ADHD symptom severity, broadband measures, functional impairment, and disruptive behaviors. Indirect comparison showed larger effect sizes for stimulants than for nonstimulants in improving ADHD symptoms and functional impairment. Results for amphetamines and methylphenidate varied, and we did not identify head-to-head comparisons of NRIs versus alpha-agonists that met eligibility criteria. Despite compelling evidence for their effectiveness, stimulants and nonstimulants produced more adverse events than did other interventions, with a high SoE. Stimulants and nonstimulant NRIs produced significantly more appetite suppression than placebo, with similar effect sizes for methylphenidate, amphetamine, and NRI, and much larger effects for modafinil. Nonstimulant alpha-agonists (specifically, guanfacine) did not suppress appetite. Rates of other adverse events were similar between NRIs and alpha-agonists.

Perhaps contrary to common belief, we found no evidence that youth-directed psychosocial and medication interventions are systematically better in improving ADHD outcomes when delivered as combination treatments 31   – 33   ; both were effective as monotherapies, but the combination did not signal additional statistically significant benefits (low SoE). However, it should be noted that few psychosocial and medication intervention combinations have been studied to date. We also found that treatment outcomes did not vary with ADHD presentation or the presence of cooccurring psychiatric disorders, but indirect analyses are limited in detecting these effect modifiers, and more research is needed. Furthermore, although children of all ages were eligible for inclusion in the review, we note that very few studies assessed treatments (especially medications) in children <6 years of age; evidence is primarily available for school-age children and adolescents. Finally, despite the research volume, we still know little about long-term effects of ADHD treatments. The limited available body of evidence suggests that most interventions, including combined medication and psychological treatment, yield few significant long-term improvements for most ADHD outcomes.

This review provides compelling evidence that numerous, diverse treatments are available and helpful for the treatment of ADHD. These include stimulant and nonstimulant medications, youth-targeted psychosocial treatments, parent support, neurofeedback, and cognitive training, though nonmedication interventions appear to have considerably weaker effects than medications on ADHD symptoms. Nonetheless, the body of evidence provides youth with ADHD, their parents, and health care providers with options.

The paucity of head-to-head studies comparing treatments precludes research-based recommendations regarding which is likely to be most helpful and which should be tried first, and decisions need to be based on clinical considerations and patient preferences. Stimulant and nonstimulant NRI medications, separately and in head-to-head comparisons, have shown similar effectiveness and rates of side effects, including appetite suppression, across identified studies. The moderate effect sizes for nonstimulant alpha-agonists, their low rate of appetite suppression, and their evidence for effectiveness in augmenting the effects of stimulant medications in reducing ADHD symptom severity provides additional treatment options. Furthermore, we found low SoE that neurofeedback and cognitive training improve ADHD symptoms. We also found that nutritional supplements and dietary interventions improve ADHD symptoms and disruptive behaviors. The SoE for nutritional interventions, however, is still low, and despite the research volume, we did not identify systematic benefits for specific supplements.

Clinical guidelines currently advise starting treatment of youth >6 years of age with FDA-approved medications, 33   which the findings of this review support. Furthermore, FDA-approved medications have been shown to significantly improve broadband measures, and nonstimulant medications have been shown to improve disruptive behaviors, suggesting their clinical benefits extend beyond improving only ADHD symptoms. Clinical guidelines for preschool children advise parent training and/or classroom behavioral interventions as the first line of treatment, if available. These recommendations remain supported by the present review, given the paucity of studies in preschool children in general, and because many existing studies, in particular medication and youth-directed psychosocial interventions, do not include young children. 31   – 33  

This review incorporated publications dating from 1980, assessing diverse intervention targets (youth, parent, school) and ADHD outcomes across numerous functional domains. Limitations in its scope derive from eligibility criteria. Requiring treatment of 4 weeks ensured that interventions were intended as patient treatment rather than proof of concept experiments, but it also excluded some early studies contributing to the field and other brief but intense psychosocial interventions. Requiring studies to be sufficiently large to detect effects excluded smaller studies that contribute to the evidence base. We explicitly did not restrict to RCTs (ie, a traditional medical study design), but instead identified all studies with concurrent comparators so as not to bias against psychosocial research; nonetheless, the large majority of identified studies were RCTs. Our review aimed to provide an overview of the diverse treatment options and we abstracted findings regardless of the suitability of the study results for meta-analysis. Although many ADHD treatments are very different in nature and the clinical decision for 1 treatment approach over another is likely not made primarily on effect size estimates, future research could use the identified study pool and systematically analyze comparative effectiveness of functionally interchangeable treatments in a network meta-analysis, building on previous work on medication options. 34  

Future studies of psychosocial, parent, school-based, neurofeedback, and nutritional treatments should employ more uniform interventions and study designs that provide a higher SoE for effectiveness, including active attention comparators and effective blinding of outcome assessments. Higher-quality studies are needed for exercise and neuromodulation interventions. More trials are needed that compare alternative interventions head-to-head or compare combination treatments with monotherapy. Clinical trials should assess patient-centered outcomes other than ADHD symptoms, including functional impairment and academic performance. Much more research is needed to assess long-term treatment effectiveness, compliance, and safety, including in preschool youth. Studies should assess patient characteristics as modifiers of treatment effects, to identify which treatments are most effective for which patients. To aid discovery and confirmation of these modifiers, studies should make publicly available all individual-level demographic, clinical, treatment, and outcome data.

We thank the following individuals providing expertise and helpful comments that contributed to the systematic review: Esther Lee, Becky Nguyen, Cynthia Ramirez, Erin Tokutomi, Ben Coughli, Jennifer Rivera, Coleman Schaefer, Cindy Pham, Jerusalem Belay, Anne Onyekwuluje, Mario Gastelum, Karin Celosse, Samantha Fleck, Janice Kang, and Sreya Molakalaplli for help with data acquisition. We thank Kymika Okechukwu, Lauren Pilcher, Joanna King, and Robyn Wheatley from the American Academy of Pediatrics; Jennie Dalton and Paula Eguino Medina from the Patient-Centered Outcomes Research Institute; 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; Drs Bolshakova and Pakdaman, and Ms Rozelle, Ms Maglione, and Ms Brown 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 study is registered at PROSPERO, #CRD42022312656. Data are available in SRDRPlus.

COMPANION PAPER: A companion to this article can be found online at www.pediatrics.org/cgi/doi/10.1542/peds.2024-065854 .

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 No. 75Q80120D00009). The Patient-Centered Outcomes Research Institute funded the research (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 the AHRQ or the Patient-Centered Outcomes Research Institute, its board of governors or methodology committee. Therefore, no statement in this report should be construed as an official position of the Patient-Centered Outcomes Research Institute, the AHRQ, or the US Department of Health and Human Services.

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

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Childhood ADHD – Luke’s story

Posted on Thursday, 05 April 2018, in Child & Teen ADHD

In the final part of her ADHD series, Dr Sabina Dosani, Child and Adolescent Psychiatrist and Clinical Partner London, introduces Luke, a patient she was able to help with his ADHD.

ADHD is one of the most common diagnoses for children in the UK and it is thought that 1 in 10 children will display some signs. For some children, their ADHD is severe and can have a huge impact on their ability to engage in school and to build and sustain relationships. Left untreated, evidence shows that those with ADHD are more likely to get into car accidents, engage in criminal activity and may struggle to keep a job or maintain relationships.

Luke, aged six, gets into trouble a lot at school. His mother gets called by his teacher three or four times a week for incidents of fighting, kicking and running in corridors. He is unable to finish his work and becomes quickly distracted. At home, he seems unable to sit still for any length of time, has had several falls when climbing trees and needs endless prompts to tidy his toys.

At school, he annoys his classmates by his constant interruptions, however if he has one-to-one attention from a student teacher who happens to be in his class on a placement he is able to settle and finish the work set. His father was said to have been a ‘lively’ child, then a ‘bright underachiever’ who occasionally fell foul of the law.

The school thought a visit to the GP might be a good idea. At the GP surgery, Luke ran and jumped about making animal noises. He swung on the back legs of a chair and took the batteries out of an ophthalmoscope. He was referred to a me for an assessment.

After a careful assessment, which included collecting information from school, questionnaires and observations of Luke, a diagnosis of ADHD was made. Following a discussion of the treatment options, the family decided they did not want any medication.

The first-line treatment for school‑age children and young people with severe ADHD and severe impairment is drug treatment. If the family doesn’t want to try a pharmaceutical, a psychological intervention alone is offered but drug treatment has more benefits and is superior to other treatments for children with severe ADHD.

 Luke's mother was asked to list the behaviours that most concern her. She was encouraged to accept others like making noises or climbing as part of Luke’s development as long as it is safe.

Now, when Luke fights, kicks others or takes risks like running into the road he is given “time-out” which isolates him for a short time and allows him and his parents or teacher to calm down. To reduce aggression and impulsivity, Luke is taught to respond verbally rather than physically and channel energy into activities such as sports or energetic percussion playing.

Over time, Luke’s parents have become skilled at picking their battles. Home is more harmonious. They fenced their garden, fitted a childproof gate and cut some branches off a tree preventing him climbing it. His parents are concerned about Luke’s use of bad language. They have been supported to allow verbal responses as a short-term interim. Whilst these might be unacceptable in other children they are preferable to physical aggression.

At school, Luke is less aggressive, has a statement of special educational need and now works well with a classroom assistant. He has been moved to the front of the class, where the teacher can keep a close eye on him, and given one task at a time. He is given special tasks, like taking the register to the school office, so he can leave class without being expected to sit still for long periods.

Through parental training, Luke’s parents have been able to help Luke work with his challenges to better manage them. As Luke grows and develops and as he faces new challenges in life, Luke may need to revisit the efficacy of ADHD medication. His parents now feel a lot more confident in being able to help Luke and he is a happier child and more settled.

Dr Sabina Dosani Consultant Child & Adolescent Psychiatrist

Dr Sabina Dosani is a highly experienced Consultant Psychiatrist currently working for the Anna Freud Centre looking after Children and Adolescents. She has a Bachelor of Medicine and Bachelor of Surgery as well as being a member of the Royal College of Psychiatrists . Dr Dosani also has a certificate in Systemic Practice (Family Therapy).

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A CASE STUDY OF A CHILD WITH ATTENTION DEFICIT/HYPERACIVITY DISORDER (ADHD) AND MATHEMATICS LEARNING DIFFICULTY (MLD)

This is a case study of a male child, EE, aged 8+ years, who was described as rather disruptive in class during lesson. For past years, his parents, preschool and primary school teachers noted his challenging behavior and also complained that the child showed a strong dislike for mathematics and Chinese language – both are examinable academic subjects. As a result of the disturbing condition, EE was referred to an educational therapist at a private intervention center for a diagnostic assessment. The child was identified with Attention Deficit-Hyperactivity Disorder (ADHD)-Combined subtype. This aim of this paper is to discuss about the effects of ADHD on mathematics learning and how to avoid misdiagnosis or over-diagnosis of a behavioral-cum-learning disorder.

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Chia, K.H. (2008). Educating the whole child in a child with special needs: What we know and understand and what we can do. ASCD Review, 14, 25-31.

Chia, K.H. (2012). Psychogogy. Singapore: Pearson Education.

Code, W., Merchant, S., Maciejewski, W., Thomas, M., & Lo, J. (2016). The Mathematics Attitudes and Perceptions Survey: An instrument to assess expert-like views and dispositions among undergraduate mathematics students. International Journal of Mathematical Education in Science and Technology (21 pages). Retrieved on 14 February, 2020, from: http://dx.doi.org/10.1080/0020739X.2015.1133854.

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A CASE STUDY

Observations of a student with ADHD over a 3-week time span. 

Student X is a 14 year-old male in a 9 th  Grade English class. He is average height and build. He has no physical disabilities, but suffers from a mental disorder – ADHD. He often makes careless mistakes in schoolwork. He does not pay attention to detail. He has trouble staying focused while reading long texts. He also has difficulty staying still during a lecture. He fidgets and shakes his legs uncontrollably when seemingly annoyed or anxious. He has trouble turning in homework on time and meeting deadlines in general. He frequently does not respond when spoken to directly and appears to be distracted even though he is performing no obvious task. He lets his mind wander and appears to daydream often. When he does respond and participate, he is usually off topic. Overall, he appears uninterested and aloof. One might say that the behavior is defiant – a consciously overt reluctance to participate in school. However, this student has been diagnosed by a physician as being ADHD. He has an involuntary learning disability which requires support, therapy, social skills training and/or medication.  

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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

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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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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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Key searches, sweeping review reveals latest evidence on the diagnosis, treatment, and monitoring of adhd.

A comprehensive literature review from USC researchers summarizes the most robust findings on addressing ADHD in children and adolescents, which will inform updated clinical practice guidelines from the American Academy of Pediatrics.

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Hundreds of studies are published each year on attention deficit hyperactivity disorder (ADHD), but more work is needed to ensure those findings improve lives.

With input from expert stakeholders across the field, researchers at the Southern California Evidence Review Center , part of the Keck School of Medicine of USC , have synthesized the latest insights so that they can ultimately inform clinical practice. Broadly, they found that both medications and psychosocial treatments work for treating ADHD and that children with the condition can and do get better.

“We have more research than ever on ADHD, but we need to summarize it in a reliable and valid way,” said Susanne Hempel, PhD , a professor of clinical population and public health sciences at the Keck School of Medicine and director of the Southern California Evidence Review Center, who oversaw the work.

The team, which included researchers from the Southern California Evidence-based Practice Center, the Keck School of Medicine’s division of child psychiatry and the Children’s Hospital Los Angeles Behavioral Health Institute, reviewed more than 23,000 publications on ADHD. Their work was commissioned by the Agency for Healthcare Research and Quality and funded by the Patient-Centered Outcomes Research Institute.

The results, just published in two companion papers in the journal Pediatrics , answer big questions about what works to effectively diagnose and treat ADHD, and point to ongoing gaps in the research, including how best to monitor the condition’s progression over time. Clinicians selected by the American Academy of Pediatrics (AAP) will now use the evidence review to create updated clinical guidelines that inform best practices in ADHD care across the nation.

“Parents, teachers and providers need evidence-based information about ADHD,” Hempel said. “We included only the most robust studies in our review, which enables us to make strong evidence statements.”

New findings on diagnosis and treatment  

Before beginning the literature review, the research team developed their questions and protocols in collaboration with ADHD experts across the field to ensure they were asking and answering questions that could directly benefit patients, families and providers. During the process, the researchers also posted their preliminary findings and welcomed feedback during a 45-day public comment period.

The team conducted an extensive search that was not restricted to diagnostic tools or treatment approaches already known to be effective. From more than 23,000 publications, the researchers selected 550 studies for the final analysis. Studies were selected if they met the team’s inclusion criteria, which prioritized rigorous study designs such as randomized controlled trials.

For diagnosis of ADHD, many tools are available, including parent and teacher rating scales, patient self-reports, neuropsychological tests, EEG approaches, imaging, biomarkers, activity monitoring and observation. For several approaches, the researchers found a substantial variation in results, with some studies indicating a given method was highly effective and others indicating that it performed poorly. “We’re getting better at diagnosing ADHD, but research is still characterized by a lot of variation,” Hempel said.

Many treatments for ADHD have been rigorously tested, building a strong evidence base for medications (including both stimulants and non-stimulants), as well as psychosocial approaches, such as behavior modification. Other non-drug treatments the team analyzed include cognitive training, neurofeedback, physical exercise, nutrition and supplements, parent support, and school interventions.

“Medications have the strongest evidence for improving not only ADHD symptoms, but also other problems that often accompany ADHD, such as oppositional and disruptive behaviors,” said Bradley Peterson, MD, director of the Institute for the Developing Mind at Children’s Hospital Los Angeles (CHLA) and the lead author of the review.

Monitoring ADHD over time

In addition to reviewing the evidence on diagnosis and treatment, the researchers explored what is known about ongoing monitoring of ADHD: How can providers assess whether a child or adolescent needs to continue treatment for the condition? Experts across the field agreed that the question is a critical one, but few studies have explored the question. The evidence review team concluded that more research is needed on monitoring ADHD over time.

The publications will now be used to support an update of the AAP’s clinical practice guidelines for ADHD , providing up-to-date advice for how best to diagnose, evaluate and treat the condition.

“The overarching takeaway: ADHD is treatable. There are lots of studies that can show us that children absolutely can get better,” Hempel said.

About this research

In addition to Drs. Peterson and Hempel, the study’s other authors are Joey Trampush, Morah Brown, Margaret Maglione, Maria Bolshakova, Mary Rozelle, Jeremy Miles, Sheila Pakdaman and Aneesa Motala among others from the Southern California Evidence Review Center, Keck School of Medicine, University of Southern California.

This work is supported by the Agency for Healthcare Research and Quality [Contract No. 75Q80120D00009] and the Patient-Centered Outcomes Research Institute [Publication No. 2023-SR-03].

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ADHD: Current Concepts and Treatments in Children and Adolescents

Renate drechsler.

1 Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry, University of Zurich, Zurich, Switzerland

Silvia Brem

2 Neuroscience Center Zurich, Swiss Federal Institute of Technology and University of Zurich, Zurich, Switzerland

Daniel Brandeis

3 Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany

4 Zurich Center for Integrative Human Physiology, University of Zurich, Zurich, Switzerland

Edna Grünblatt

Gregor berger, susanne walitza.

Attention deficit hyperactivity disorder (ADHD) is among the most frequent disorders within child and adolescent psychiatry, with a prevalence of over 5%. Nosological systems, such as the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) and the International Classification of Diseases, editions 10 and 11 (ICD-10/11) continue to define ADHD according to behavioral criteria, based on observation and on informant reports. Despite an overwhelming body of research on ADHD over the last 10 to 20 years, valid neurobiological markers or other objective criteria that may lead to unequivocal diagnostic classification are still lacking. On the contrary, the concept of ADHD seems to have become broader and more heterogeneous. Thus, the diagnosis and treatment of ADHD are still challenging for clinicians, necessitating increased reliance on their expertise and experience. The first part of this review presents an overview of the current definitions of the disorder (DSM-5, ICD-10/11). Furthermore, it discusses more controversial aspects of the construct of ADHD, including the dimensional versus categorical approach, alternative ADHD constructs, and aspects pertaining to epidemiology and prevalence. The second part focuses on comorbidities, on the difficulty of distinguishing between “primary” and “secondary” ADHD for purposes of differential diagnosis, and on clinical diagnostic procedures. In the third and most prominent part, an overview of current neurobiological concepts of ADHD is given, including neuropsychological and neurophysiological researches and summaries of current neuroimaging and genetic studies. Finally, treatment options are reviewed, including a discussion of multimodal, pharmacological, and nonpharmacological interventions and their evidence base.

Introduction

With a prevalence of over 5%, attention deficit hyperactivity disorder (ADHD) is one of the most frequent disorders within child and adolescent psychiatry. Despite an overwhelming body of research, approximately 20,000 publications have been referenced in PubMed during the past 10 years, assessment and treatment continue to present a challenge for clinicians. ADHD is characterized by the heterogeneity of presentations, which may take opposite forms, by frequent and variable comorbidities and an overlap with other disorders, and by the context-dependency of symptoms, which may or may not become apparent during clinical examination. While the neurobiological and genetic underpinnings of the disorder are beyond dispute, biomarkers or other objective criteria, which could lead to an automatic algorithm for the reliable identification of ADHD in an individual within clinical practice, are still lacking. In contrast to what one might expect after years of intense research, ADHD criteria defined by nosological systems, such as the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) and the International Classification of Diseases, editions 10 and 11 (ICD-10/11) have not become narrower and more specific. Rather, they have become broader, for example, encompassing wider age ranges, thus placing more emphasis on the specialist's expertise and experience. 1 2 3

Definitions and Phenomenology

Adhd according to the dsm-5 and icd-10/11.

ADHD is defined as a neurodevelopmental disorder. Its diagnostic classification is based on the observation of behavioral symptoms. ADHD according to the DSM-5 continues to be a diagnosis of exclusion and should not be diagnosed if the behavioral symptoms can be better explained by other mental disorders (e.g., psychotic disorder, mood or anxiety disorder, personality disorder, substance intoxication, or withdrawal). 1 However, comorbidity with other mental disorders is common.

In the DSM-5, the defining symptoms of ADHD are divided into symptoms of inattention (11 symptoms) and hyperactivity/impulsivity (9 symptoms). 1 The former differentiation between subtypes in the DSM-IV proved to be unstable and to depend on the situational context, on informants, or on maturation, and was therefore replaced by “presentations.” 4 Thus, the DSM-5 distinguishes between different presentations of ADHD: predominantly inattentive (6 or more out of 11 symptoms present), predominantly hyperactive/impulsive (6 or more out of 9 symptoms present), and combined presentation (both criteria fulfilled), as well as a partial remission category. Symptoms have to be present in two or more settings before the age of 12 years for at least 6 months and have to reduce or impair social, academic, or occupational functioning. In adolescents over 17 years and in adults, five symptoms per dimension need to be present for diagnosis. 1 In adults, the use of validated instruments like the Wender Utah rating scale is recommended. 5

In contrast, the ICD-10 classification distinguishes between hyperkinetic disorder of childhood (with at least six symptoms of inattention and six symptoms of hyperactivity/impulsivity, present before the age of 6 years) and hyperkinetic conduct disorder, a combination of ADHD symptoms and symptoms of oppositional defiant and conduct disorders (CD). 3 In the ICD-11 (online release from June 2018, printed release expected 2022), the latter category has been dropped, as has the precise age limit (“onset during the developmental period, typically early to mid-childhood”). Moreover, the ICD-11 distinguishes five ADHD subcategories, which match those of the DSM-5: ADHD combined presentation, ADHD predominantly inattentive presentation, ADHD predominantly hyperactive/impulsive presentation and two residual categories, ADHD other specified and ADHD nonspecified presentation. For diagnosis, behavioral symptoms need to be outside the limits of normal variation expected for the individual's age and level of intellectual functioning. 2

Overlapping Constructs: Sluggish Cognitive Tempo and Emotional Dysregulation

Sluggish cognitive tempo (SCT) is a clinical construct characterized by low energy, sleepiness, and absent-mindedness, and is estimated to occur in 39 to 59% of (adult) individuals with ADHD. 6 7 The question of whether SCT might constitute a feature of ADHD or a separate construct that overlaps with ADHD inattention symptoms is unresolved. 8 While current studies indicate that SCT might be distinct and independent from hyperactivity/impulsivity, as well as from inattention dimensions, it remains uncertain whether it should be considered as a separate disorder. 8 9 Twin studies have revealed a certain overlap between SCT and ADHD, especially with regard to inattention symptoms, but SCT seems to be more strongly related to nonshared environmental factors. 10

Emotion dysregulation is another associated feature that has been discussed as a possible core component of childhood ADHD, although it is not included in the DSM-5 criteria. Deficient emotion regulation is more typically part of the symptom definition of other psychopathological disorders, such as oppositional defiant disorder (ODD), CD, or disruptive mood dysregulation disorder (DSM-5; for children up to 8 years). 11 However, an estimated 50 to 75% of children with ADHD also present symptoms of emotion dysregulation, for example, anger, irritability, low tolerance for frustration, and outbursts, or sometimes express inappropriate positive emotions. The presence of these symptoms increases the risk for further comorbidities, such as ODD and also for anxiety disorders. 12 13 For adult ADHD, emotional irritability is a defining symptom according to the Wender Utah criteria, and has been confirmed as a primary ADHD symptom by several studies (e.g., Hirsch et al). 5 14 15

Whether emotion dysregulation is inherent to ADHD, applies to a subgroup with combined symptoms and a singular neurobiological pathway, or is comorbid with but independent of ADHD, is still a matter of debate (for a description of these three models; Shaw et al 13 ). Faraone et al 12 distinguished three ADHD prototypes with regard to deficient emotion regulation: ADHD prototype 1 with high-emotional impulsivity and deficient self-regulation, prototype 2 with low-emotional impulsivity and deficient self-regulation, and prototype 3 with high-emotional impulsivity and effective self-regulation. All three prototypes are characterized by an inappropriate intensity of emotional response. While prototypes 1 and 3 build up their responses very quickly, prototype 2 is slower to respond but experiences higher subjective emotional upheaval than is overtly shown in the behavior. Prototypes 1 and 2 both need more time to calm down compared with prototype 3 in which emotional self-regulation capacities are intact.

Dimensional versus Categorical Nature of ADHD

Recent research on subthreshold ADHD argues in favor of a dimensional rather than categorical understanding of the ADHD construct, as its core symptoms and comorbid features are dimensionally distributed in the population. 16 17 18 Subthreshold ADHD is common in the population, with an estimated prevalence of approximately 10%. 19 According to Biederman and colleagues, clinically referred children with subthreshold ADHD symptoms show a similar amount of functional deficits and comorbid symptoms to those with full ADHD, but tend to come from higher social-class families with fewer family conflicts, to have fewer perinatal complications, and to be older and female (for the latter two, a confound with DSM-IV criteria cannot be excluded). 20

Temperament and Personality Approaches to ADHD

Another approach which is in accordance with a dimensional concept is to analyze ADHD and categorize subtypes according to temperament/personality traits (for a review and the different concepts of temperament see Gomez and Corr 21 ). Temperament/personality traits are usually defined as neurobiologically based constitutional tendencies, which determine how the individual searches for or reacts to external stimulation and regulates emotion and activity. While temperament traits per se are not pathological, extreme variations or specific combinations of traits may lead to pathological behavior. This approach has been investigated in several studies by Martel and colleagues and Nigg, 22 23 24 who employed a temperament model comprising three empirically derived domains 25 26 : (1) negative affect, such as tendencies to react with anger, frustration, or fear; (2) positive affect or surgency which includes overall activity, expression of happiness, and interest in novelty; and (3) effortful control which is related to self-regulation and the control of action. The latter domain shows a strong overlap with the concept of executive function. 27 In a community sample, early temperamental traits, especially effortful control and activity level, were found to potentially predict later ADHD. 28 Karalunas et al 29 30 distinguished three temperament profiles in a sample of children with ADHD: one with normal emotional functioning; one with high surgency, characterized by high levels of positive approach-motivated behaviors and a high–activity level; and one with high negative (“irritable”) affect, with the latter showing the strongest, albeit only moderate stability over 2 years. Irritability was not reducible to comorbidity with ODD or CD and was interpreted as an ADHD subgroup characteristic with predictive validity for an unfavorable outcome. These ADHD temperament types were distinguished by resting-state and peripheral physiological characteristics as measured by functional magnetic resonance imaging (fMRI). 29

Epidemiology and Prevalence

While ADHD seems to be a phenomenon that is encountered worldwide, 31 prevalence rates and reported changes in prevalence are highly variable, depending on country and regions, method, and sample. 32 A meta-analysis by Polanczyk et al 32 yielded a worldwide prevalence rate of 5.8% in children and adolescents. 33 In an update published 6 years later, the authors did not find evidence for an increase in prevalence over a time span of 30 years. Other meta-analyses reported slightly higher (e.g., 7.2%) 34 or lower prevalence rates, which seems to be attributable to the different criteria adopted for defining ADHD. Prevalence rates in children and adolescents represent averaged values across the full age range, but peak prevalence may be much higher in certain age groups, for example, 13% in 9-year-old boys. 35 Universal ADHD prevalence in adults is estimated to lie at 2.8%, with higher rates in high-income (3.6%) than in low-income (1.4%) countries. 36 True prevalence rates (also called community prevalence, e.g., Sayal et al 37 ) should be based on population-based representative health surveys, that is, the actual base rate of ADHD in the population, in contrast to the administrative base rate, which is related to clinical data collection (Taylor 38 ). Recent reports on the increase in ADHD rates usually refer to administrative rates, drawn from health insurance companies, from the number of clinical referrals for ADHD, 39 clinical case identification estimates, or from the percentage of children taking stimulant medication (prescription data). Changes in these rates may be influenced by increased awareness, destigmatization, modifications in the defining criteria of ADHD, or altered medical practice. According to a recent U.S. health survey on children and adolescents (4–17 years), in which parents had to indicate whether their child had ever been diagnosed with ADHD, the percentage of diagnoses increased from 6.1% in 1997 to 10.2% in 2016. 40 A representative Danish survey based on health registry, data collected from 1995 to 2010 reported that ADHD incidence rates increased by a factor of approximately 12 (for individuals aged 4–65 years) during this period. Moreover, the gender ratio decreased from 7.5:1 to 3:1 at early school age and from 8.1:1 to 1.6:1 in adolescents in the same time frame, 41 42 probably indicating an improved awareness of ADHD symptoms in girls. In other countries, it is assumed that girls are still underdiagnosed. 38

Population register data show that the use of stimulants for ADHD has increased considerably worldwide. 43 In most countries, an increase in stimulant medication use has been observed in children since the 1990s (e.g., United Kingdom from 0.15% in 1992 to 5.1% in 2012/2013), 44 45 but in some European countries, stimulant prescription rates for children and adolescents have remained stable or decreased over the last 5 to 10 years (e.g., Germany). 35 In the United States, the prescription of methylphenidate peaked in 2012 and has since been slightly decreasing, while the use of amphetamines continues to rise. 46

Comorbidity, Differential Diagnosis, and Clinical Assessment

Comorbidity.

ADHD is characterized by frequent comorbidity and overlap with other neurodevelopmental and mental disorders of childhood and adolescence. The most frequent comorbidities are learning disorders (reading disorders: 15–50%, 4 dyscalculia: 5–30%, 47 autism spectrum disorder, which since the DSM-5 is no longer viewed as an exclusion criterion for ADHD diagnosis: 70–85%, 48 49 tic/Tourette's disorder and obsessive compulsive disorder: 20%, and 5%, 50 developmental coordination disorder: 30–50%, 51 depression and anxiety disorders: 0–45%, 52 53 and ODD and CD: 27–55% 54 ). ADHD increases the risk of substance misuse disorders 1.5-fold (2.4-fold for smoking) and problematic media use 9.3-fold in adolescence 55 56 and increases the risk of becoming obese 1.23-fold for adolescent girls. 57 58 59 It is also associated with different forms of dysregulated eating in children and adolescents. Enuresis occurs in approximately 17% of children with ADHD, 60 and sleep disorders in 25 to 70%. 61 Frequent neurological comorbidities of ADHD include migraine (about thrice more frequent in ADHD than in typically developing [TD] children) 62 63 64 and epilepsy (2.3 to thrice more frequent in ADHD than in TD children). 65 66 The risk of coexisting ADHD being seen as a comorbid condition and not the primary diagnosis is considerably enhanced in many childhood disorders of different origins. For example, the rate of comorbid ADHD is estimated at 15 to 40% 67 68 in children with reading disorders and at 26 to 41% 69 70 in children with mild intellectual dysfunction. While comorbidity in neurodevelopmental disorders may arise from a certain genetic overlap (see details under genetic associations), ADHD symptoms are also present in several disorders with well-known and circumscribed genetic defects, normally not related to ADHD (e.g., neurofibromatosis, Turner's syndrome, and Noonan's syndrome) 71 or disorders with nongenetic causes, such as traumatic brain injuries, pre-, peri- or postnatal stroke, or syndromes due to toxic agents, such as fetal alcohol syndrome. Comorbid ADHD is estimated in 20 to 50% of children with epilepsy, 72 73 in 43% of children with fetal alcohol syndrome, 74 and in 40% of children with neurofibromatosis I. 75 ADHD is three times more frequent in preterm-born children than in children born at term and four times more frequent in extremely preterm-born children. 76

Differential Diagnosis, Primary and Secondary ADHD

A range of medical and psychiatric conditions show symptoms that are also present in primary ADHD. The most important medical conditions which are known to “mimic” ADHD and need to be excluded during the diagnostic process are epilepsy (especially absence epilepsy and rolandic epilepsy), thyroid disorders, sleep disorder, drug interaction, anemia, and leukodystrophy. 77 78 The most important psychiatric conditions to be excluded are learning disorder, anxiety disorders, and affective disorders, while an adverse home environment also needs to be excluded.

However, the picture is complex, as many differential diagnoses may also occur as comorbidities. For instance, bipolar disorder, which is frequently diagnosed in children and adolescents in the United States but not in Europe, is considered as a differential diagnosis to ADHD, but ADHD has also been found to be a comorbidity of bipolar disorder in 21 to 98% of cases. 79 Similarly, absence epilepsy is a differential diagnosis of ADHD but is also considered to be a frequent comorbidity, occurring in 30 to 60% of children with absence epilepsy. 80 The prevalence of the ADHD phenotype in benign childhood epilepsy with centrotemporal spikes (rolandic epilepsy) lies at 64 to 65%, 81 and is possibly related to the occurrence of febrile convulsions. 82 The literature often does not draw a clear distinction between an ADHD phenotype, which includes all types of etiologies and causes, and a yet to be specified developmental ADHD “genotype.” Some authors use terms, such as “idiopathic” ADHD, 83 “primary,” or “genotypic” ADHD, 84 in contrast to ADHD of circumscribed origin other than developmental, the latter being referred to as ADHD “phenotype,” or “phenocopy,” 85 or “ADHD-like.” 86 “Secondary ADHD” usually refers to newly acquired ADHD symptoms arising after a known event or incident, for example, a head trauma or stroke. After early childhood stroke, the ADHD phenotype occurs in 13 to 20% of cases, and after pediatric traumatic brain injury, ADHD symptoms are observed in 15 to 20% of children. 87 Having ADHD considerably increases the risk of suffering a traumatic brain injury, 88 89 90 and most studies on secondary ADHD after traumatic brain injury control for or compare with premorbid ADHD (e.g., Ornstein et al 91 ). Whether and to what extent “phenotypic” and “genotypic” ADHD need to be distinguished on a phenomenological level is not clear. It is possible that shared neurobiological mechanisms will prevail and that genetic vulnerability and epigenetic factors may play a role in both types. For example, James et al 86 compared neurophysiological markers in two groups of adolescents with ADHD, one born very preterm and the other born at term. While the authors found very similar ADHD-specific markers in the two groups, some additional deficits only emerged in the preterm group, indicating more severe impairment. Other examples are rare genetic diseases with known genetic defects, which are often comorbid with ADHD. One may ask whether, for example, ADHD in Turner's syndrome should be considered as a rare genetic ADHD variant and count as genotypic ADHD, or whether it results from a different genetic etiology, with the status of an ADHD phenotype.

Clinical Diagnostic Procedure

Clinical assessment in children should mainly be based on a clinical interview with parents, including an exploration of the problems, the detailed developmental history of the child including medical or psychiatric antecedents, information on family functioning, peer relationships, and school history. According to the guidelines of the National Institute for Health and Care Excellence (NICE) in the United Kingdom, this may also include information on the mental health of the parents and the family's economic situation. The child's mental state should be assessed, possibly using a standardized semistructured clinical interview containing ADHD assessments (e.g., Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime version, DSM-5) 92 93 and by observer reports. The exploration should cover behavioral difficulties and strengths in several life contexts, for example, school, peer relationships, and leisure time. The use of informant rating scales, such as Conners' Rating Scales, 3rd edition, 94 or the Strengths and Difficulties Questionnaire 95 may be useful, but diagnosis should not be solely based on rating scales (NICE, AWFM ADHD). 96 97 A further interview should be conducted with the child or adolescent to gain a picture of the patient's perspective on current problems, needs, and goals, even though self-reports are considered less reliable for diagnosis. Information should also be obtained from the school, for example, by face-to-face or telephone contact with the teacher and, if possible, by direct school-based observation. A medical examination should be performed to exclude somatic causes for the behavioral symptoms and to gain an impression of the general physical condition of the patient. Current guidelines do not recommend including objective test procedures (intelligence and neuropsychological tests), neuroimaging, or neurophysiological measures in routine ADHD assessment but do suggest their use as additional tools when questions about cognitive functions, academic problems, coexisting abnormalities in electroencephalography (EEG), or unrecognized neurological conditions arise. After completion of the information gathering, the NICE guidelines recommend a period of “watchful waiting” for up to 10 weeks before delivering a formal diagnosis of ADHD. A younger age of the diagnosed child relative to his/her classmates has to be mentioned as one of the many pitfalls in the assessment of ADHD. It has been shown that the youngest children in a class have the highest probability of being diagnosed with ADHD and of being medicated with stimulants. 98

There is consensus that the diagnosis of ADHD requires a specialist, that is, a child psychiatrist, a pediatrician, or other appropriately qualified health care professionals with training and expertise in diagnosing ADHD. 97

Current Neurobiological and Neuropsychological Concepts

Neuropsychology, neuropsychological pathways and subgroups.

ADHD is related to multiple underlying neurobiological pathways and heterogeneous neuropsychological (NP) profiles. Twenty-five years ago, ADHD was characterized as a disorder of inhibitory self-control, 54 and an early dual pathway model distinguished between an inhibitory/executive function pathway and a motivational/delay aversion pathway (also called “cool” and “hot” executive function pathways in later publications), which are related to distinct neurobiological networks. 99 100 101 Still, the two systems may also interact. 102

Since then, other pathways have been added, such as time processing, 103 but a definitive number of possible pathways is difficult to define. For example, Coghill and colleagues 104 differentiated six cognitive factors in children with ADHD (working memory, inhibition, delay aversion, decision-making, timing, and response time variability) derived from seven subtests of the Cambridge neuropsychological test automated battery. Attempts to empirically classify patients into subgroups with selective performance profiles departing from comprehensive NP data collection were inconclusive. For example, using delay aversion, working memory, and response-time tasks, Lambek and colleagues 105 expected to differentiate corresponding performance profile subgroups in children with ADHD. However, their analysis resulted in subgroups differentiated by the severity of impairments, and not by selective profiles. Other empirical studies using latent profile or cluster analysis of NP tasks in large ADHD samples have differentiated three 106 107 or four 108 NP profile groups, which all included children with ADHD, as well as TD children, differing in severity but not in the type of profile. This might indicate that the identified NP deficit profiles were not ADHD-specific, but rather reflected characteristic distributions of NP performances, which are also present in the general population, with extreme values in children with ADHD. Some other empirical studies in the search for subgroups, however, identified ADHD-specific performance profiles (“poor cognitive control,” 109 “with attentional lapses and fast processing speed” 110 ), among other profiles being shared with TD controls. Obviously, divergent results regarding subgrouping may also be related to differing compilations of tested domains, consequently leading to a limited comparability of these studies.

Which Neuropsychological Functions are Impaired in ADHD and When?

A meta-analysis conducted in 2005 identified consistent executive function deficits with moderate effect sizes in children with ADHD in terms of response inhibition, vigilance, working memory, and planning. 67 Since then, a vast number of studies on NP deficits in children with ADHD compared with TD controls have been published. A recent meta-analysis included 34 meta-analyses on neurocognitive profiles in ADHD (all ages) published until 2016, referring to 12 neurocognitive domains. 111 The authors found that 96% of all standardized mean differences were positive in favor of the control group. Unweighted effect sizes ranged from 0.35 (set shifting) to 0.66 (reaction time variability). Weighted mean effect sizes above 0.50 were found for working memory (0.54), reaction time variability (0.53), response inhibition (0.52), intelligence/achievement (0.51), and planning/organization (0.51). Effects were larger in children and adolescents than in adults. The other domains comprised vigilance, set shifting, selective attention, reaction time, fluency, decision making, and memory.

Nearly every neuropsychological domain has been found to be significantly impaired in ADHD compared with TD controls, though effect sizes are often small. This includes, for example, altered perception (e.g., increased odor sensitivity 112 ; altered sensory profile 113 ; impaired yellow/blue color perception, e.g., Banaschewski et al, 114 for review, see Fuermaier et al 115 ), emotional tasks (e.g., facial affect discrimination), 116 social tasks (e.g., Marton et al 117 ), communication, 118 and memory. 119 Several of the described impairments may be related to deficient top-down cognitive control and strategic deficits, 120 121 122 but there is also evidence for basic processing deficits. 123

Neuropsychological Deficits as Mediators of Gene-Behavior Relations

A vast amount of research has been devoted to the search for neuropsychological endophenotypes (or intermediate phenotypes) for ADHD, that is, neurobiologically based impairments of NP performance characteristic of the disorder that may also be found in nonaffected close relatives. ADHD neuropsychological endophenotypes are assumed to mediate genetic risk from common genetic variants. 124 So far, deficits in working memory, reaction-time variability, inhibition, time processing, response preparation, arousal regulation, and others have been identified as probable endophenotypes for ADHD. 124 125 126 127 Genetic studies indicate an association of an ADHD-specific polygenetic general risk score (i.e., the total number of genetic variants that may be associated with ADHD, mostly related to dopaminergic transmission) with working memory deficits and arousal/alertness, 124 or with a lower intelligence quotient (IQ) and working memory deficits, 128 respectively. More specifically, a link of ADHD-specific variants of DAT1 genes with inattention and hyperactivity symptoms seems to be mediated by inhibitory control deficits. 129

Individual Cognitive Profiles and the Relevance of Cognitive Testing for the Clinical Assessment

Heterogeneity is found with regard to profiles, as well as with regard to the severity of cognitive impairment in individuals with ADHD, as measured by standardized tests. ADHD does not necessarily come with impaired neuropsychological test performance: about one-third of children with ADHD will not present any clinically relevant impairment, while another one-third shows unstable or partial clinical impairment, and about another one-third performs below average in NP tests. The classic concept of NP impairment, which assumes relative stability over time, possibly does not apply to NP deficits observed in ADHD, or only to a lesser extent. For the larger part, the manifestation of performance deficits may depend on contextual factors, 130 such as reward, or specifically its timing, amount, and nature, or on energetic factors, 131 for example, the rate of stimulus presentation or the activation provided by the task.

Many studies have shown that behavioral ratings of ADHD symptoms or questionnaires on executive function deficits are not, or at best weakly, correlated with NP test performance, even when both target the same NP domain. 132 133 In consequence, questionnaires on executive functioning are not an appropriate replacement for neuropsychological testing. Likewise, ADHD symptom rating scales do not predict results of objective attention or executive function tests and vice versa. Although mild intellectual disability and low IQ are more typically associated with the disorder, ADHD can be encountered across the entire IQ spectrum, including highly gifted children. 134 Therefore, an intelligence test should be part of the diagnostic procedure, but is not mandatory according to ADHD guidelines. In some children, intellectual difficulties and not ADHD may be the underlying cause for ADHD-like behaviors, while in other children with ADHD, academic underachievement despite a high IQ may be present.

It has been argued that symptoms defining ADHD may be understood as dimensional markers of several disorders belonging to an ADHD spectrum and, in consequence, the diagnosis of these behavioral symptoms should be the starting point for a more in-depth diagnosis rather than the endpoint. 135 This should include the cognitive performance profile. The ADHD behavioral phenotype predicts neither NP impairment nor intellectual achievement in the individual case, and objective testing is the only way to obtain an accurate picture of the child's cognitive performance under standardized conditions. Its goal is not ADHD classification, but rather to obtain the best possible understanding of the relation between cognitive functioning and behavioral symptoms for a given patient, to establish an individually tailored treatment plan.

Neurophysiology

Neurophysiological methods like EEG, magnetoencephalography, and event-related potentials (ERPs) as task-locked EEG averages capture brain functions in ADHD at high (ms) temporal resolution. The approach covers both fast and slow neural processes and oscillations, and clarifies the type and timing of brain activity altered in ADHD at rest and in tasks. It reveals neural precursors, as well as correlates, and consequences of ADHD behavior. 136 Neurophysiological and particularly EEG measures also have a long and controversial history as potential biomarkers of ADHD. Current evidence clarifies how multiple pathways and deficits are involved in ADHD at the group level, but recent attempts toward individual clinical translation have also revealed considerable heterogeneity, which does not yet support a clinical application for diagnostic uses or treatment personalization, as explained below.

Resting Electroencephalography

The EEG is dominated by oscillations in frequency bands ranging from slow δ (<4 Hz) and θ (4–7 Hz) via α (8–12 Hz) to faster β (13–30 Hz) and γ (30–100 Hz) band activity. The spectral profile reflects maturation and arousal, with slow frequencies dominating during early childhood and slow-wave sleep. Source models can link scalp topography to brain sources and distributed networks.

Initial studies suggested a robust link between ADHD diagnosis and resting EEG markers of reduced attention, hypoarousal, or immaturity, such as increased θ and an increased θ/β ratio (TBR). However, more recent studies, 137 138 some with large samples, 139 140 failed to replicate a consistent TBR increase in ADHD. Instead, the results indicated heterogeneous θ and β power deviations in ADHD not explained by ADHD subtype and psychiatric comorbidity. 141 A cluster analysis of EEG in children with ADHD also revealed considerable heterogeneity regarding θ excess and β attenuation in ADHD. While several clusters with EEG patterns linked to underarousal and immaturity could be identified, only three of the five EEG clusters (60% of the cases with ADHD) had increased θ. 139 Several recent θ and TBR studies that no longer found TBR association with ADHD diagnosis still replicated the reliable age effects, 137 138 142 confirming the high quality of these studies. Increasing sleepiness in adolescents, 143 or shorter EEG recordings, may have reduced the sensitivity to time effects and state regulation deficits in ADHD, 136 144 potentially contributing to these replication failures. Also, conceptualizing TBR as a marker of inattention or maturational lag may be too simple, since θ activity can also reflect concentration, cognitive effort, and activation. 145 146

During sleep, stage profiles reveal no consistent deviations in ADHD, but the slow-wave sleep topography is altered. In particular, frontal slow waves are reduced, leading to a more posterior topography as observed also in younger children. 147 This delayed frontalization can be interpreted as a maturational delay in ADHD, in line with a cluster of resting EEG, changes in task related ERPs during response inhibition, 148 and structural magnetic resonance imaging (MRI) findings. 149

Task Related Event-Related Potentials

Task-related processing measures, particularly ERPs, have critically advanced our understanding of ADHD through their high-time resolution, which can separate intact and compromised brain functions. ERPs have revealed impairments during preparation, attention, inhibition, action control, as well as error, and reward processing, with partly distinct networks but often present during different phases of the same task. In youth and adults with ADHD, the attentional and inhibitory P3 components and the preparatory contingent negative variation (CNV) component are most consistently affected, but state regulation and error or reward processing are also compromised. 136 150 Activity during preparation, attention, or inhibition is typically weaker and more variable but not delayed. This often occurs in task phases without visible behavior and precedes the compromised performance. Familial and genetic factors also modulate these markers of attention and control. Some impairment is also observed in nonaffected siblings or in parents without ADHD, 151 152 and genetic correlates often implicate the dopamine system. 125 Some ERP changes, like the attenuated CNV during preparation, remain stable throughout maturation, and are also markers of persistent ADHD, while other markers, such as the inhibition related P3, remain attenuated despite clinical remission. 148 153

Overall, the ERP results confirm attentional, cognitive, and motivational, rather than sensory or motor impairments in ADHD, in line with current psychological and neurobiological models. However, different ERP studies hardly used the same tests and measures, so valid statements regarding classification accuracy and effect size are particularly difficult, 154 and there is an urgent need for meta-analyses regarding the different ERPs.

Clinical Translation

Despite published failures to replicate robust TBR based classification of ADHD, a TBR-based EEG test was recently approved by the U.S. Food and Drug Administration to assist ADHD diagnosis. 155 Although not promoted as a stand-alone test, children with suspected ADHD, and increased TBR were claimed to likely meet full diagnostic criteria for ADHD; while children with suspected ADHD but no TBR increase should undergo further testing, as they were likely to have other disorders better explaining ADHD symptoms (see also DSM-5 exclusionary criterion E).

This multistage diagnostic approach could possibly identify a homogeneous neurophysiological subgroup, but it omits critical elements of careful, guideline-based ADHD diagnostics. Reliability and predictive value of the TBR remain untested, and the increasing evidence for poor validity of TBR renders it unsuitable for stand-alone ADHD diagnosis. Accordingly, the use of TBR as a diagnostic aid was broadly criticized. 156 157

In sum, the recent literature suggests that neither TBR nor other single EEG or ERP markers are sufficient to diagnose ADHD and are not recommended for clinical routine use, in line with the increasing evidence for heterogeneity in ADHD.

Combining measures across time, frequency, and tasks or states into multivariate patterns may better characterize ADHD. The potential of such approaches is evident in improved classification using machine-learning algorithms based on combinations of EEG measures 142 or EEG and ERP measures. 138 158 However, claims of high-classification accuracies up to 95% (e.g., Mueller et al 158 ) require further independent replication and validation with larger samples, and plausible mapping to neural systems and mechanisms. Modern pattern classification is particularly sensitive to uncontrolled sample characteristics and needs validation through independent large samples. 159

Focusing on EEG-based prediction rather than diagnosis may hold more promise for clinical translation, and may utilize the EEG heterogeneity in clinical ADHD samples. For example, early studies on predicting stimulant response suggested that children with altered wave activity, in particular increased TBR, θ or α slowing, respond well to stimulant medication. However, in recent prospective work with a large sample, TBR was not predictive, and α slowing allowed only limited prediction in a male adolescent subgroup. 160

Predicting response to intense nonpharmacological treatment is of particular interest given the high costs and time requirements. Promising findings have been reported for one neurofeedback study, where α EEG activity and stronger CNV activity together predicted nearly 30% of the treatment response. 161 Still, the lack of independent validation currently allows no clinical application.

In conclusion, neurophysiological measures have clarified a rich set of distinct impairments but also preserved functions which can also serve as markers of persistence or risk. These markers may also contribute in the classification of psychiatric disorders based on neuromarkers (research domain criteria approach). As potential predictors of treatment outcome they may support precision medicine, and proof-of-concept studies also highlight the potential of multivariate profiling. The findings also demonstrate the challenge with this approach, including notable replication failures, and generalizability of most findings remains to be tested. Neurophysiological markers are not ready to serve as tools or aids to reliably diagnose ADHD, or to personalize ADHD treatment in individual patients.

Neuroimaging

Modern brain imaging techniques have critically contributed to elucidating the etiology of ADHD. While MRI provides detailed insights into the brain microstructure, such as for example gray matter volume, density, cortical thickness, or white matter integrity, fMRI allows insights into brain functions through activation and connectivity measures with high–spatial resolution.

Delayed Maturation and Persistent Alterations in the Brain Microstructure in ADHD

The brain undergoes pronounced developmental alterations in childhood and adolescence. Gray matter volume and cortical thickness show nonlinear inverted U -shaped trajectories of maturation with a prepubertal increase followed by a subsequent decrease until adulthood while white matter volume progressively increases throughout adolescence and early adulthood in a rather linear way. 162 163 164 165 Large variations of the maturational curves in different brain regions and subregions suggest that phylogenetically older cortical areas mature earlier than the newer cortical regions. Moreover, brain areas associated with more basic motor or sensory functions mature earlier than areas associated with more complex functions including cognitive control or attention. 163 164 Altered maturation of the cortex for ADHD has been reported for multiple areas and cortical dimensions, 166 167 mainly in the form of delayed developmental trajectories in ADHD but recently also as persistent reductions, particularly in the frontal cortex. 168 Such findings speak for delayed maturation in specific areas rather than a global developmental delay of cortical maturation in ADHD. Microstructural alterations in ADHD have been associated with a decreased intracranial volume 169 and total brain size reduction of around 3 to 5%. 100 168 170 In accordance, increasing ADHD symptoms in the general population correlated negatively with the total brain size. 171 A meta-analysis (Frodl et al) and a recent cross-sectional mega- and meta-analysis (Hoogman et al) indicate that such reductions in brain volume may be due to decreased gray matter volumes in several subcortical structures, such as the accumbens, amygdala, caudate, hippocampus, and putamen but also cortical areas (prefrontal, the parietotemporal cortex) and the cerebellum. 170 172 173 174 175 176 177 Effects sizes of subcortical alterations were highest in children with ADHD and the subcortical structures showed a delayed maturation. 169 Moreover, higher levels of hyperactivity/impulsivity in children were associated with a slower rate of cortical thinning in prefrontal and cingulate regions. 167 178 Differences in brain microstructure have also been reported in a meta-analysis for white matter integrity as measured with diffusion tensor imaging in tracts subserving the frontostriatal-cerebellar circuits. 179 To summarize, diverse neuroanatomical alterations in total brain volume and multiple cortical and subcortical dimensions characterize ADHD. These alterations are most pronounced in childhood and suggest a delayed maturation of specific cortical and subcortical areas along with some persistent reductions in frontal areas in a subgroup of ADHD patients with enduring symptoms into adulthood.

Alterations in the Brain Function of Specific Networks in ADHD

Specific functional networks, mainly those involved in inhibition, attention processes, cognitive control, reward processing, working memory, or during rest have been intensively studied in ADHD using fMRI in the past. Alterations have been reported in the corresponding brain networks and the main findings are summarized below.

Atypical Resting State Connectivity in Children with ADHD

Resting state examines spontaneous, low frequency fluctuations in the fMRI signal during rest, that is , in absence of any explicit task. 180 Resting state networks describe multiple brain regions for which the fMRI signal is correlated (functionally connected) at rest, but the same networks may coactivate also during task-based fMRI. 181 One important resting state network, the so-called default mode network (DMN), comprises brain areas that show higher activation during wakeful rest and deactivations with increasing attentional demands. 182 183 While the DMN usually shows decreasing activation with increasing attentional demands, the cognitive control network shows an opposite pattern and increases its activation. This inverse correlation of DMN and the cognitive control networks is diminished or absent in children and adults with ADHD and may explain impaired sustained attention through attentional lapses that are mediated by the DMN. 181 184 185 186 In addition, a more diffuse pattern of resting state networks connectivity and a delayed functional network development in children with ADHD have been reported. 187 Finally, atypical connectivity in cognitive and limbic cortico-striato-thalamo-cortical loops of patients with ADHD suggest that the neural substrates may either reside in impaired cognitive network and/or affective, motivational systems. 181

Altered Processing of Attention and Inhibition in Fronto-basal Ganglia Circuits in ADHD

Meta-analyses summarizing the findings of functional activation studies report most consistent alterations in brain activation patterns as hypoactivation of the frontoparietal network for executive functions and the ventral attention system for attentional processes in children with ADHD. 188 189 190 More specifically, motor or interference inhibition tasks yielded consistent decreases in a (right lateralized) fronto-basal ganglia network comprising supplementary motor area, anterior cingulate gyrus, left putamen, and right caudate in children with ADHD. 189 190 For tasks targeting attentional processes, decreased activation in a mainly right lateralized dorsolateral fronto-basal ganglia-thalamoparietal network characterized children with ADHD. Depending on the task, hyperactivation can cooccur in partly or distinct cerebellar, cortical, and subcortical regions. 188 189 190

Altered Reward Processing and Motivation

Emotion regulation and motivation is mediated by extended orbitomedial and ventromedial frontolimbic networks in the brain. 191 Abnormal sensitivity to reward seems to be an important factor in the etiology of ADHD as suggested by several models of ADHD, 192 193 194 mainly due to a hypofunctioning dopaminergic system. 195 In accordance, impairments in specific signals that indicate violations of expectations, the so called reward prediction errors (RPE), were shown in the medial prefrontal cortex of adolescents with ADHD during a learning task. 196 RPE signals are known to be encoded by the dopaminergic system of the brain, and deficient learning and decision making in ADHD may thus be a consequence of impaired RPE processing. 196 Abnormal activation has also been reported for the ventral striatum during reward anticipation and in other cortical and subcortical structures of the reward circuitry. 197

Normalization of Atypical Activation and Brain Structural Measures after Treatment

Stimulant medication and neurofeedback studies have pointed to a certain normalization of dysfunctional activation patterns in critical dorsolateral frontostriatal and orbitofrontostriatal regions along with improvements in ADHD symptoms. 198 199 200 201 Also, brain microstructure, especially the right caudate, has shown some gradual normalization with long-term stimulant treatment. 176 190

To conclude, a wide range of neuroimaging studies reveal relatively consistent functional deficits in ADHD during executive functions, including inhibitory control, working memory, reward processes, and attention regulation but also during rest. Some of these alterations are more persistent, others are specific to children and may thus represent a developmental delay. Specific treatments showed trends toward a normalization of alterations in brain microstructure and functional networks.

Genetic Associations with ADHD and ADHD Related Traits

From family studies, as well as twin studies, the heritability for ADHD has been estimated to be between 75 upto 90%. 202 Moreover, the heritability was found to be similar in males and females and for inattentive and hyperactive-impulsive components of ADHD. 202 Interestingly, a strong genetic component was also found when the extreme and subthreshold continuous ADHD trait symptoms were assessed in the Swedish twins. 19 Even over the lifespan, adult ADHD was found to demonstrate high heritability that was not affected by shared environmental effects. 203 Recently, structural and functional brain connectivity assessed in families affected by ADHD has been shown to have heritable components associated with ADHD. 204 Similarly, the heritability of ERPs elicited in a Go/No-Go-task measuring response inhibition known to be altered in ADHD, was found to be significantly heritable. 205

In several studies, ADHD-related traits have also shown significant heritability. For example, in two independent, population based studies, significant single nucleotide polymorphism heritability estimates were found for attention-deficit hyperactivity symptoms, externalizing problems, and total problems. 206 In another study, investigating the two opposite ends of ADHD symptoms, low-extreme ADHD traits were significantly associated with shared environmental factors without significant heritability. 207 While on the other hand, high-extreme ADHD traits showed significant heritability without shared environmental influences. 207 A crossdisorder study including 25 brain disorders from genome wide association studies (GWAS) of 265,218 patients and 784,643 controls, including their relationship to 17 phenotypes from 1,191,588 individuals, could demonstrate significant shared heritability. 208 In particular, ADHD shared common risk variants with bipolar disorder, major depressive disorder, schizophrenia, and with migraine. 208 Indeed, in general, population-based twin studies suggest that genetic factors are associated with related-population traits for several psychiatric disorders including ADHD. 209 This suggests that many psychiatric disorders are likely to be a continuous rather than a categorical phenotype.

Though ADHD was found to be highly heritable, the underlying genetic risk factors are still not fully revealed. The current consensus suggests, as in many other psychiatric disorders, a multifactorial polygenic nature of the common disorder. Both common genetic variants studied by hypothesis-driven candidate gene association or by the hypothesis-free GWAS could only reveal the tip of the iceberg. Through the candidate gene approach, only very few findings could show replicable significant association with ADHD, as reported by meta-analysis studies for the dopaminergic, noradrenergic, and serotonergic genes. 210 211 Several GWAS have been conducted followed by meta-analysis, which again failed reaching genome-wide significant results. 212 213 214 215 216 217 218 219 220 221 222 223 224 However, recently, the first genome-wide significance has been reached in a GWAS meta-analysis consisting of over 20,000 ADHD patients and 35,000 controls. 225 Twelve independent loci were found to significantly associate with ADHD, including genes involved in neurodevelopmental processes, such as FOX2 and DUSP6 . 225 But even in these findings the effect sizes are rather small to be used for diagnostic tools. Therefore, polygenic risk score approaches have emerged as a possible tool to predict ADHD. 202 Yet this approach needs further investigation now that genome-wide significance has been reached by Demontis et al. 225 However, at this point, it is not yet possible to exclude that rare SNPs of strong effect may also be responsible (similar to breast cancer) for a small proportion of ADHD cases due to the heterogeneity of symptomatology, illness course, as well as biological marker distribution, as outlined above.

Multimodal Treatment of ADHD

A variety of national and international guidelines on the assessment and management of ADHD have been published over the last 10 years, not only for clinicians but also for patients and caregivers. 96 97 226 227 228 All guidelines recommend a multimodal treatment approach in which psychoeducation forms a cornerstone of the treatment and should be offered to all of those receiving an ADHD diagnosis, as well as to their families and caregivers.

According to the NICE Guidelines, the first step is always a planning process for the multimodal treatment with respect to the psychological, behavioral, and occupational or educational needs of the child and his/her family. 97 This planning phase could be organized as a “round table” with the child, parents, and other caregivers. The following aspects should be taken into account: the severity of ADHD symptoms and impairment, the relative impact of other neurodevelopmental or mental health conditions and how these affect or may affect everyday life (including sleep). In addition, resilience and protective factors, as well as the goals of the child and family, should be considered in the intervention process. The participation of child and parents in the planning and treatment process is more centrally outlined in recent guidelines and is emphasized in detail for the different treatment steps (e.g., NICE and S3 Guidelines). 96 97 The participation process is not just a one-time dialogue but should rather continue throughout all steps of the treatment process. Benefits and harms of nonpharmacological and pharmacological treatments should be discussed carefully and on the basis of the latest evidence. Preferences and concerns, and the importance of adherence to treatment, should be discussed and taken into account within the treatment process. Patients and their families or caregivers should be reassured, as appropriate that they can revisit decisions about treatments.

Multimodal treatment approaches also advocate a systematic adaptive procedure that combines different treatment modules according to the needs and situation of the patient and family. This may, for instance, include a first stage in which parent counseling is initiated, a second-stage encompassing, for example, individual behavioral therapy for the child, while the parents participate in a parent training program in parallel, followed by a third stage in which stimulant medication is started, etc. 229 230 Environment-centered interventions aim at the counseling or training of parents or the instruction of teachers at school or preschool. Parent training programs may be administered individually or in groups and have shown positive effects on parenting skills, ADHD behavior, and comorbid conduct problems. 231 232 233 Family therapy for ADHD focuses on the ADHD family, with the ADHD patient being a part of the family system with dysfunctional interactional patterns. 234 School-based interventions may target (1) the conditions in the classroom, for example, by minimizing distractions; (2) the instruction of the teacher, for example, by suggesting more appropriate teaching methods or by promoting peer tutoring; or (3) the student, for example, by improving self-management and social skills, or by helping to cope with stigma. 235 236 237

Pharmacological Approaches

Starting medication.

All medication for ADHD should only be initiated by a health care professional with training and expertise in diagnosing and managing ADHD. The expert should be familiar with the pharmacokinetic profiles and bioavailability of all the short- and long-acting preparations available for ADHD. The following parameters should be considered before first medication: medical history of the child but possibly also of the parents, current medication, height and weight, baseline pulse and blood pressure, a cardiovascular assessment, and an electrocardiogram if the treatment may affect the QT interval. A cardiology expert opinion should be sought before starting medication for ADHD if there is a history of congenital heart disease, previous cardiac surgery, or a history of sudden death in a first-degree relative under the age of 40 years, or if the blood pressure is consistently above the 95th centile for age and height for children and young people.

Age-Specific Needs

Treatment recommendations are often based on the specific needs of children, youth, or adults. 97 226 According to the NICE guidelines 97 and also pharmacological recommendations (e.g., Walitza and colleagues 238 239 ), a distinction should also be made between children under 5 years of age or preschool children, and school children. For the younger children (under 5 years of age), parent or career training programs and parent group training programs are always first-line treatments. Medication for children under 5 years with ADHD should only be given following a second specialist opinion from an ADHD service with expertise in managing ADHD in young children (ideally from a tertiary service). For children over 5 years of age, education and information about the causes and impact of ADHD and advice on parenting strategies should be offered, as well as liaison with school, college, or university if consent to do so is provided. 97 Children aged 5 years and over and young people should only receive medication if the ADHD symptoms are still causing a persistent significant impairment in at least one life domain after environmental modifications have been implemented and evaluated.

Selection of Pharmacotherapy

In Europe, methylphenidate either as short- or long-acting preparation is the first-line medication for ADHD across the life span. Second-line medications are lisdexamfetamine, atomoxetine, and guanfacine. A switch to lisdexamfetamine is only recommended if children have first undergone at least a 6-week trial of methylphenidate at an adequate dose and have not derived sufficient benefit in terms of reduced ADHD symptoms and associated impairment, or if patients experience adverse side effects. 238 The Canadian Guidelines (2018) recommend an individual treatment approach, which can start with different options, and if medication is to be used, long-acting formulations of psychostimulants or atomoxetine are always the first choice. 226 Comorbid disorders may necessitate adjustments to the treatment plan or alternative treatments.

According to the NICE guidelines, atomoxetine and guanfacine should only be offered if patients cannot tolerate methylphenidate or lisdexamfetamine or if their symptoms have not responded to separate 6-week trials of methylphenidate and lisdexamfetamine, having considered alternative preparations and adequate doses. 97

Evidence for ADHD Medications

In the first “gold standard” study comparing the different treatment approaches for ADHD alone and in combination (National Institute of Mental Health Collaborative Multimodal Treatment Study of Children with ADHD [MTA study]), the effects of both pharmacological therapy (methylphenidate and intensive counseling) and of multimodal therapy (methylphenidate and intensive behavioral therapy) were significantly more effective after 14 months than behavioral therapy alone or than the “standard” therapy (treatment as usual in the community) of the control group. The multimodal therapy was not significantly superior to pharmacological therapy alone, but did result in significant improvements in ADHD symptoms at a lower dosage of methylphenidate. 240 241 242 Since the MTA study, numerous studies have investigated methylphenidate, amphetamine, and nonstimulants like atomoxetine or α 2 -adrenoceptor agonists, such as clonidine and guanfacine, regarding different aspects of effectiveness and tolerability.

The psychostimulants methylphenidate and amphetamine are the most effective agents for the treatment of core ADHD symptoms, with a favorable efficacy and adverse event profile. 243 244 245 Compared with methylphenidate and amphetamine, which both show immediate symptom reduction, the full effects of atomoxetine and guanfacine on reducing ADHD symptoms usually only unfold after some weeks of administration. Atomoxetine and guanfacine are not controlled substances, and are licensed in various European countries and in the United States for treatment of ADHD in children above the age of 6 years. Both have been shown to be effective in decreasing ADHD core symptoms with an effect size of around 0.7, which is somewhat lower than the effect size for methylphenidate, depending on the underlying studies (e.g., Sallee et al 246 ).

Management Strategies and Duration of Pharmacological Treatment

Following an adequate dosage of medication ( Table 1 ) and treatment response, medication for ADHD should be titrated to an optimized dosage with regard to the clinical efficacy, safety, and side effects, which should be continued for as long as it remains clinically necessary and effective. This should be reviewed at least annually, also with a planned “medication break” to decide whether there is a continuing need for care. 238 239 However, there is little available empirical evidence to guide clinicians on questions, such as the optimum duration of treatment and when it is appropriate to consider drug discontinuation. As ADHD can persist into adulthood, decisions on treatment discontinuation need to be taken on a case-by-case basis. 226

Abbreviations: ADHD, attention deficit hyperactivity disorder; max. maximum.

Adapted from (1) Walitza S, Romanos M, Greenhill LL, Banaschewski T. Attention-Deficit/Hyperactivity Disorders. In: Gerlach M, Warnke A, Greenhill LL, eds. Psychiatric Drugs in Children and Adolescents. Wien: Springer; 2014:369–381 238 and (2) Walitza S, Gerlach M, Romanos M, Renner T. Psychostimulanzien und andere Arzneistoffe, die zur Behandlung der Aufmerksamkeitsdefizit-/Hyperaktivitätsstörung (ADHS) angewendet werden. In: Gerlach M, Mehler-Wex C, Walitza S, Warnke A, Wewetzer C, eds. Neuro-/Psychopharmaka im Kindes- und Jugendalter: Grundlagen und Therapie. Berlin, Heidelberg: Springer Berlin Heidelberg; 2016:289–331. 239

Among the most frequent side effects of psychostimulant therapy ( Table 2 ) are reduced appetite and sleep disturbances. 247 Appetite reduction following treatment initiation with an ADHD drug often attenuates with time. Reduced appetite at mealtimes can be avoided by taking the medication after meals rather than before. Should a clinically significant lack of appetite persist, dosage reduction (by one-fourth or half tablet of methylphenidate), discontinuation (rarely necessary), or switching to a different formulation or medication should be considered.

Adapted from (1) Walitza S, Romanos M, Greenhill LL, Banaschewski T. Attention-Deficit/Hyperactivity Disorders. In: Gerlach M, Warnke A, Greenhill LL, eds. Psychiatric Drugs in Children and Adolescents. Wien: Springer; 2014:369–381 238 ; (2) Walitza S, Gerlach M, Romanos M, Renner T. Psychostimulanzien und andere Arzneistoffe, die zur Behandlung der Aufmerksamkeitsdefizit-/Hyperaktivitätsstörung (ADHS) angewendet werden. In: Gerlach M, Mehler-Wex C, Walitza S, Warnke A, Wewetzer C, eds. Neuro-/Psychopharmaka im Kindes- und Jugendalter: Grundlagen und Therapie. Berlin, Heidelberg: Springer Berlin Heidelberg; 2016:289–331 239 ; (3) Huang YS, Tsai MH. Long-term outcomes with medications for attention-deficit hyperactivity disorder: current status of knowledge. CNS Drugs 2011;25:539–554; (4) Storebo OJ, Pedersen N, Ramstad E et al. Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of adverse events in non-randomized studies. Cochrane Database Syst Rev 2018;5:CD012069284; and (5) Wigal T, Greenhill L, Chuang S et al. Safety and tolerability of methylphenidate in preschool children with ADHD. J Am Acad Child Adolesc Psychiatry 2006;45:1294–1303.

Nonpharmacological Treatments

Cognitive behavioral therapy.

Cognitive behavioral therapy (CBT) is a form of behavioral intervention which aims at reducing ADHD behaviors or associated problems by enhancing positive behaviors and creating situations in which desired behaviors may occur. In the case of preschool and young school children, CBT focuses on parents and educators, who are instructed and trained to act according to CBT principles, while older children and adolescents may be trained directly to use more appropriate behavioral strategies. 248 CBT and its more specific forms (e.g., social skills training, training of planning and organizational skills, and self-management techniques) have positive effects on behavior, parenting skills, child–parent relationships, and certain daily living skills, 232 249 although effects on ADHD core symptoms are inconsistent and relatively low when only blinded assessments are considered. 250 A recent meta-analysis suggested that the combined treatment of medication with CBT is more efficacious than stimulant medication alone (with an estimated standardized mean difference of 0.5). 251

Neuropsychological Treatments

In cognitive training interventions, either PC-supported or in a manualized format, cognitive exercises that tap into cognitive domains, such as working memory or inhibitory control, are performed in a repetitive manner and with increasing difficulty. The evidence base for this type of intervention is poor according to recent studies (e.g., Bikic et al 252 ) and metastudies (e.g., Cortese et al 253 ). While some “near-transfer” improvements in neuropsychological tests tapping into the trained domain are probable, the evidence for “far transfer” to academic achievements or to the ADHD symptom level is weak. Most studies, however, used the same kind of cognitive training with all participants, irrespective of their actual individual cognitive difficulties. Moreover, they did not adhere to theoretically based training principles, which recommend domain-specific training for the functional improvement of a selective neuropsychological deficit. Possibly, future approaches that combine repetitive exercise and top-down strategy application may provide larger benefits for children with ADHD.

In neurofeedback training (NF), EEG activity measured by one or more electrodes applied to the head is transformed into a visual or acoustic signal and fed back online, for example, by a stimulus moving up and down. By steering the stimulus on the screen, the participant may gain control over his/her EEG activity. Many different training protocols have been applied to ADHD. Those which have received the best evaluation are the NF training of the θ/β frequency bands ratio (the goal is generally to decrease θ and to increase β frequencies) and the training of slow cortical potentials (learning to intentionally increase and decrease cortical excitability over short periods of time). However, “normalizing” an ADHD-specific deviant EEG pattern can no longer qualify as a meaningful goal, as no characteristic ADHD pattern seems to exist (Loo et al, 254 see neurophysiology section), although gaining control over one's brain activity and over attentional states continues to be a valid treatment goal. According to parent ratings, clinical improvements after NF are stronger and longer-lasting compared with other behavioral treatment methods, but teacher ratings usually fail to yield significant effects. 255 Recent research has focused on the specificity of treatment effects, defined as the association between the learned regulation of EEG activity and the behavioral outcome. 256 To date, there is no convincing evidence that the learned control over brain activity is responsible for the observed behavioral improvements. Instead, nonspecific treatment effects, such as improved self-efficacy, positive reinforcement, and learning to sit still, seem to contribute in large part to the positive clinical outcome.

Methodologically more sophisticated NF approaches, such as tomographic NF, 257 fMRI-NF, 258 or near-infrared spectroscopy feedback (feedback of hemoglobin oxygenation) 259 are still in the experimental stage.

Noninvasive Brain Stimulation

Repetitive transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) represent other potential means to modulate cortical activity. Therefore, these approaches may also be promising in terms of improving clinical and cognitive ADHD symptoms such as inattention and impulsiveness. 258 260 261 262 Based on a meta-analysis, Westwood et al 263 suggested that left and/or right prefrontal stimulation may improve performance in attention, inhibition and/or working memory tasks. However, these approaches are not yet recommended by therapy guidelines.

Alternative Nonpharmacological Treatment Methods

Mindfulness training, physical activity, and yoga seem to have positive effects on ADHD behavior, but for the time being, the scientific evidence is weak and these treatments are seen at best as complementary to other interventions. 264 265 266 267 268 Digital home treatment programs or support apps are currently being developed for ADHD patients or their parents 269 270 ; their usefulness or clinical validity still needs to be tested. Children and adolescents with ADHD often show a great affinity with digital media, which may improve compliance, but one has to take into account that the rate of problematic internet use and gaming is enhanced in youth with ADHD (estimated at 37% in ADHD vs. 12% in TD). 271 Free fatty acid supplementation has been described to bring about small but significant reductions in ADHD symptoms even with probably blinded assessments (standardized mean difference = 0.16). 250

Long-Term Outcome

Follow-up studies have reported divergent results, with some reporting high rates of persistence until adulthood (up to 79%), 153 and others showing much higher rates of remission from childhood to adolescence (e.g., 45–55% of syndromal remissions). 272 273 274 Recent population-based studies from Brazil, the United Kingdom, and New Zealand have claimed that a large portion of de novo ADHD cases emerge at adult age, 275 276 277 but these results can probably be explained by methodological artifacts and missed subthreshold cases. 76 278 279 However, meta-analytic findings by Bonvicini et al 280 indicate that in part, different genes and polymorphisms seem to contribute to childhood ADHD and adulthood ADHD, lending some genetic plausibility to findings of a late manifestation of the disorder. According to the MTA study, the contribution of interventions administered during childhood to outcome in adulthood is negligible, but controlled intervention was limited to a relatively short period of time (14 months). 281 Neurobiologically, the course of ADHD may be explained by different models. 274 According to the first model, remission at adult age may be reduced to the normalization of brain functions through maturation. A second model explains remission through the recruitment of compensatory brain functions. The third model claims that brain function anomalies show life-long persistence, even though behavioral dysfunction may have remitted. 274 Possibly, all of these models, and probably additional ones too (see e.g., Doehnert et al 148 ), apply to different subgroups of patients or functions and may account for the divergent results in the literature.

Conflict of Interest D.B. reports having served as an unpaid scientific advisor for an EU-funded neurofeedback trial unrelated to the present work.

S.W. reports grants from Gertrud Thalmann Fonds of the UPK Basel, Collaborative Project, grants from Ebnet Foundation, grants from Mensia Technologies SA & EU H2020 SME Instrument, grants from University Medical Center Utrecht & Stanley Medical Research Institute, Collaborative Project, grants from Swiss National Foundation, Investigator Initiated Clinical Trial, other from Thieme Neuropychopharmakologie des Kindes und Jugendalters, outside the submitted work; and S.W. has received in the last 5 years royalities from Thieme Hogrefe, Kohlhammer, Springer, Beltz. S.W. has received lecture honoraria from Opopharma in the last 5 years. Her work was supported in the last 5 years by the Swiss National Science Foundation (SNF), diff. EU FP7s, HSM Hochspezialisierte Medizin of the Kanton Zurich, Switzerland, Bfarm Germany, ZInEP, Hartmann Müller Stiftung, Olga Mayenfisch, Gertrud Thalmann Fonds. Outside professional activities and interests are declared under the link of the University of Zurich www.uzh.ch/prof/ssl-dir/interessenbindungen/client/web/ .

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Pediatric Case Study: Child with ADHD

This course focuses on a case study for a 9-year-old male with ADHD experiencing occupational challenges in education, ADL, IADL, and social participation.

Course created on January 30, 2020

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CEUs/Hours Offered: AOTA/0.1 Intermediate, OT Service Delivery; CE Broker/1.0 Home Study, General (FL), Patient Related (AL), General Continuing Education (GA), Direct Client/patient Services In Occupational Therapy (SC), Related To OT (AZ), Related To OT (LA), Directly Related To OT (MS), Directly Related To OT (TN), CE Broker #20-766056; IACET/0.1; IBCCES/1.0; NBCOT PDUs/1.25 Intermediate, Pediatrics

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Nicole Quint Dr.OT, OTR/L

Nicole Quint has been an occupational therapist for over 15 years, currently serving as an Associate Professor in the Occupational Therapy Department at Nova Southeastern University, teaching in both the Masters and Doctoral programs. She provides outpatient pediatric OT services, specializing in children and adolescents with Sensory Processing Disorder and concomitant disorders. She also provides consultation services for schools, professional development, and special education services. She provides continuing education on topics related to SPD, pediatric considerations on the occupation of sleep, occupational therapy and vision, reflective therapist, executive functions, leadership in occupational therapy and social emotional learning.

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