College Students’ Time Management: a Self-Regulated Learning Perspective

  • Review Article
  • Published: 27 October 2020
  • Volume 33 , pages 1319–1351, ( 2021 )
  • Christopher A. Wolters   ORCID: orcid.org/0000-0002-8406-038X 1 &
  • Anna C. Brady 1  

20k Accesses

43 Citations

10 Altmetric

Explore all metrics

Despite its recognized importance for academic success, much of the research investigating time management has proceeded without regard to a comprehensive theoretical model for understanding its connections to students’ engagement, learning, or achievement. Our central argument is that self-regulated learning provides the rich conceptual framework necessary for understanding college students’ time management and for guiding research examining its relationship to their academic success. We advance this larger purpose through four major sections. We begin by describing work supporting the significance of time management within post-secondary contexts. Next, we review the limited empirical findings linking time management and the motivational and strategic processes viewed as central to self-regulated learning. We then evaluate conceptual ties between time management and processes critical to the forethought, performance, and post-performance phases of self-regulated learning. Finally, we discuss commonalities in the antecedents and contextual determinants of self-regulated learning and time management. Throughout these sections, we identify avenues of research that would contribute to a greater understanding of time management and its fit within the framework of self-regulated learning. Together, these efforts demonstrate that time management is a significant self-regulatory process through which students actively manage when and for how long they engage in the activities deemed necessary for reaching their academic goals.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Adams, G. A., & Jex, S. M. (1999). Relationships between time management, control, work family conflict, and strain. Journal of Occupational Health Psychology, 4 (1), 72–77.

Google Scholar  

Aeon, B., & Aguinis, H. (2017). It’s about time: new perspectives and insights on time management. Academy of Management Perspectives, 31 , 309–330.

Ames, C. (1992). Classrooms: goals, structures, and student motivation. Journal of Educational Psychology, 84 , 261–271.

Asikainen, H., & Gijbels, D. (2017). Do students develop towards more deep approaches to learning during studies? A systematic review on the development of students’ deep and surface approaches to learning in higher education. Educational Psychology Review, 29 , 205–234.

Balduf, M. (2009). Underachievement among college students. Journal of Advanced Academics, 20 , 274–294.

Banahan, L., & Mullendore, R. (2014). Navigating the first college year. A guide for parents and families . Columbia: University of South Carolina, National Resource Center for The First-Year Experience and Students in Transition.

Bandura, A. (1986). The explanatory and predictive scope of self-efficacy theory. Journal of Social and Clinical Psychology, 4 , 359–373.

Bartels, J. M., Magun-Jackson, S., & Ryan, J. J. (2010). Dispositional approach-avoidance achievement motivation and cognitive self-regulated learning: the mediation of achievement goals. Individual Differences Research, 8 , 97–110.

Basila, C. (2014). Good time management and motivation level predict student academic success in college on-line courses. International Journal of Cyber Behavior, Psychology and Learning, 4 , 45–52.

Bembenutty, H. (2009). Academic delay of gratification, self-regulation of learning, gender differences, and expectancy-value. Personality and Individual Differences, 46 , 347–352.

Beuhler, R., Griffin, D., & Peetz, J. (2010). The planning fallacy: cognitive, motivational, and social origins. Advances in Experimental Social Psychology, 43 , 1–62.

Bidjerano, T., & Dai, D. Y. (2007). The relationship between the big-five model of personality and self-regulated learning strategies. Learning and Individual Differences, 17 , 69–81.

Boekaerts, M. (1996). Self-regulated learning at the junction of cognition and motivation. European Psychologist, 1 , 100–112.

Boekaerts, M., & Corno, L. (2005). Self-regulation in the classroom: a perspective on assessment and intervention. Applied Psychology: An International Review, 54 , 199–231.

Bond, M. J., & Feather, N. T. (1988). Some correlates of structure and purpose in the use of time. Journal of Personality and Social Psychology, 55 , 321–329.

Britton, B. K., & Glynn, S. M. (1989). Mental management and creativity: a cognitive model of time management for intellectual productivity. In J. Glover, R. Ronning, & C. Reynolds (Eds.), Handbook of creativity (pp. 429–440). New York: Plenum.

Britton, B. K., & Tesser, A. (1991). Effects of time-management practices on college grades. Journal of Educational Psychology, 83 , 405–410.

Buehler, R., & Griffin, D. (2015). When plans lead to optimistic forecasts. In M. D. Mumford & M. Frese (Eds.), The psychology in planning in organizations: research and applications (pp. 31–57). New York: Routledge.

Burlison, J. D., Murphy, C. S., & Dwyer, W. O. (2009). Evaluation of the Motivated Strategies for Learning Questionnaire for predicting academic performance in college students of varying scholastic aptitude. College Student Journal, 43 , 1313–1323.

Burnette, J., O’Boyle, E., VanEpps, E., Pollack, J., & Finkel, E. (2013). Mind-sets matter: a meta-analytic review of implicit theories and self-regulation. Psychological Bulletin, 139 (3), 655–701.

Burt, C. D., Weststrate, A., Brown, C., & Champion, F. (2010). Development of the time management environment (TiME) scale. Journal of Managerial Psychology, 25 , 649–668.

Butler, D., & Winne, P. (1995). Feedback and self-regulated learning: a theoretical synthesis. Review of Educational Research, 65 , 245–281.

Cano, F. (2006). An in-depth analysis of the Learning and Study Strategies Inventory (LASSI). Educational and Psychological Measurement, 66 , 1023–1038.

Capdeferro, N., Romero, M., & Barberà, E. (2014). Polychronicity: review of the literature and a new configuration for the study of this hidden dimension of online learning. Distance Education, 35 , 294–310.

Cassidy, S. (2011). Self-regulated learning in higher education: identifying key component processes. Studies in Higher Education, 36 , 989–1000.

Chang, A., & Nguyen, L. T. (2011). The mediating effects of time structure on the relationships between time management behaviour, job satisfaction, and psychological well-being. Australian Journal of Psychology, 63 , 187–197.

Choi, B. (2016). How people learn in an asynchronous online learning environment: the relationships between graduate students’ learning strategies and learning satisfaction. Canadian Journal of Learning and Technology, 42 , 1–15.

Chuderski, A. (2016). Time pressure prevents relational learning. Learning and Individual Differences, 49 , 361–365.

Claessens, B. J. C., van Eerde, W., Rutte, C. G., & Roe, R. A. (2007). A review of the time management literature. Personnel Review, 36 , 255–276.

Conti, R. (2001). Time flies: investigating the connection between intrinsic motivation and the experience of time. Journal of Personality, 69 (1), 1–26.

Corno, L., Cronbach, L. J., Kupermintz, H., Lohman, D. F., Mandinach, E. B., Porteus, A. W., & Talbert, J. E. (2002). Remaking the concept of aptitude: extending the legacy of Richard E Snow . Mahwah: Lawrence Erlbaum Associates Publishers.

Crede, M., & Kuncel, N. R. (2008). Study habits, skills, and attitudes: the third pillar supporting collegiate academic performance. Perspectives on Psychological Science, 3 (6), 425–453.

Crede, M., & Phillips, L. A. (2011). A meta-analytic review of the Motivated Strategies for Learning Questionnaire. Learning and Individual Differences, 21 , 337–346.

Crede, M., Roch, S. G., & Kieszczynka, U. M. (2010). Class attendance in college: a meta-analytic review of the relationship of class attendance with grades and student characteristics. Review of Educational Research, 80 , 272–295.

Csikszentmihalyi, M. (1996). Creativity: flow and the psychology of discovery and invention . New York: HarperCollins Publishers.

Dembo, M. H., & Eaton, M. J. (2000). Self-regulation of academic learning in middle-level schools. The Elementary School Journal, 100 , 473–490.

Dent, A., & Koenka, A. (2016). The relation between self-regulated learning and academic achievement across childhood and adolescence: a meta-analysis. Educational Psychology Review, 28 , 425–474.

Díaz-Morales, J. F., Ferrari, J. R., & Cohen, J. R. (2008). Indecision and avoidant procrastination: the role of morningness—eveningness and time perspective in chronic delay lifestyles. The Journal of General Psychology, 135 (3), 228–240.

Donker, A., de Boer, H., Kostons, D., Dignath van Ewijk, C., & van der Werf, M. (2014). Effectiveness of learning strategy instruction on academic performance: a meta-analysis. Educational Psychology Review, 11 , 1–26.

Douglas, H. E., Bore, M., & Munro, D. (2016). Coping with university education: the relationships of time management behaviour and work engagement with the five factor model aspects. Learning and Individual Differences, 45 , 268–274.

Doumen, S., Broeckmans, J., & Masui, C. (2014). The role of self-study time in freshmen’s achievement. Educational Psychology, 34 , 385–402.

Duncan, T. G., & McKeachie, W. J. (2005). The making of the motivated strategies for learning questionnaire. Educational Psychologist, 40 , 117–128.

Duncheon, J. C., & Tierney, W. G. (2013). Changing conceptions of time: implications for educational research and practice. Review of Educational Research, 83 , 236–272.

Dunlosky, J., & Ariel, R. (2011). Self-regulated learning and the allocation of study time. In B. Ross (Ed.), Psychology of learning and motivation (Vol. 54, pp. 103–140). San Diego: Academic Press.

Dunning, D., Heath, C., & Suls, J. (2004). Flawed self-assessments. Psychological Science in the Public Interest, 5 (3), 69–106.

Efklides, A. (2011). Interactions of metacognition with motivation and affect in self-regulated learning: the MASRL model. Educational Psychologist, 46 , 6–25.

Eilam, B., & Aharon, I. (2003). Students’ planning in the process of self-regulated learning. Contemporary Educational Psychology, 28 , 304–334.

Ferrari, J. R., & Díaz-Morales, J. F. (2007). Procrastination: different time orientations reflect different motives. Journal of Research in Personality, 41 , 707–714.

Flake, J. K., Barron, K. E., Hulleman, C., McCoach, B. D., & Welsh, M. E. (2015). Measuring cost: the forgotten component of expectancy-value theory. Contemporary Educational Psychology, 41 , 232–244.

Flanigan, A. E., & Kiewra, K. A. (2018). What college instructors can do about student cyber-slacking. Educational Psychology Review, 30 , 585–597.

Francis-Smythe, J. A., & Robertson, I. T. (1999a). On the relationship between time management and time estimation. British Journal of Psychology, 90 , 333–347.

Francis-Smythe, J. A., & Robertson, I. T. (1999b). Time-related individual differences. Time & Society, 8 , 273–292.

Fromme, K., Corbin, W., & Kruse, M. (2008). Behavioral risks during the transition from high school to college. Developmental Psychology, 44 (5), 1497–1504.

Gable, P. A., & Poole, B. D. (2012). Time flies when you’re having approach-motivated fun: effects of motivational intensity on time perception. Psychological Science, 23 (8), 879–886.

Gevers, J. M., Rutte, C. G., & Van Eerde, W. (2006). Meeting deadlines in work groups: implicit and explicit mechanisms. Applied Psychology, 55 , 52–72.

Green, P., & Skinner, D. (2005). Does time management training work? An evaluation. International Journal of Training and Development, 9 , 124–139.

Gulec, M., Selvi, Y., Boysan, M., Aydin, A., Oral, E., & Aydin, E. F. (2013). Chronotype effects on general well-being and psychopathology levels in healthy young adults. Biological Rhythm Research, 44 , 457–468.

Guzman, G., Goldberg, T., & Swanson, H. (2018). A meta-analysis of self-monitoring on reading performance of K-12 students. School Psychology Quarterly, 33 (1), 160–168.

Hadwin, A., & Oshige, M. (2011). Self-regulation, coregulation, and socially shared regulation: exploring perspectives of social in self-regulated learning theory. Teachers College Record, 113 , 240–264.

Hafner, A., Stock, A., Pinneker, L., & Strohle, S. (2014). Stress prevention through a time management training intervention: an experimental study. Educational Psychology, 34 , 403–416.

Hahn, C., Cowell, J. M., Wiprzycka, U. J., Goldstein, D., Ralph, M., Hasher, L., & Zelazo, P. D. (2012). Circadian rhythms in executive function during the transition to adolescence: the effect of synchrony between chronotype and time of day. Developmental Science, 15 (3), 408–416.

Harkin, B., Webb, T. L., Chang, B. P. I., Prestwich, A., Conner, M., Kellar, I., et al. (2016). Does monitoring goal progress promote goal attainment? A meta-analysis of the experimental evidence. Psychological Bulletin, 142 (2), 198–229.

Hattie, J., Biggs, J., & Purdie, N. (1996). Effects of learning skills interventions on student learning: a meta-analysis. Review of Educational Research, 66 , 99–136.

Haynes, N., Comer, J., & Hamilton-Lee, M. (1988). Gender and achievement status differences on learning factors among Black high school students. Journal of Educational Research, 81 , 233–237.

Hensley, L. C., Wolters, C. A., Won, S., & Brady, A. C. (2018). Academic probation, time management, and time use in a college success course. Journal of College Reading and Learning, 48 , 105–123.

Hicks, T., & Heastie, S. (2008). High school to college transition: a profile of the stressors, physical and psychological health issues that affect the first-year on-campus college students. Journal of Cultural Diversity, 15 (3), 143–147.

Hilbrecht, M., Zuzanek, J., & Mannell, R. C. (2008). Time use, time pressure and gendered behavior in early and late adolescence. Sex Roles, 58 , 342–357.

Hofer, B. K., Yu, S. L., & Pintrich, P. R. (1998). Teaching college students to be self-regulated learners. In D. H. Schunk & B. J. Zimmerman (Eds.), Self-regulated learning: from teaching to self-reflective practice (pp. 57–85). New York: Guilford Press.

Horstmanshof, L., & Zimitat, C. (2007). Future time orientation predicts academic engagement among first-year university students. British Journal of Educational Psychology, 77 (Pt 3), 703–718.

Huie, F. C., Winsler, A., & Kitsantas, A. (2014). Employment and first-year college achievement: the role of self-regulation and motivation. Journal of Education and Work, 27 , 110–135.

Husman, J., & Lens, W. (1999). The role of the future in student motivation. Educational Psychologist, 34 , 113–125.

Kanfer, R., Frese, M., & Johnson, R. (2017). Motivation related to work: a century of progress. Journal of Applied Psychology, 102 (3), 338–355.

Kantrowitz, T. M., Grelle, D. M., Beaty, J. C., & Wolf, M. B. (2012). Time is money: polychronicity as a predictor of performance across job levels. Human Performance, 25 , 114–137.

Kauderer, S., & Randler, C. (2013). Differences in time use among chronotypes in adolescents. Biological Rhythm Research, 44 , 601–608.

Kaufman-Scarborough, C., & Lindquist, J. D. (1999). Time management and polychronicity: comparisons, contrasts, and insights for the workplace. Journal of Managerial Psychology, 14 , 288–312.

Keating, D. P. (2012). Cognitive and brain development in adolescence. Enfance, 64 , 267–279.

Kesici, Ş., Baloğlu, M., & Deniz, M. E. (2011). Self-regulated learning strategies in relation with statistics anxiety. Learning and Individual Differences, 21 , 472–477.

Kim, K. R., & Seo, E. H. (2015). The relationship between procrastination and academic performance: a meta-analysis. Personality and Individual Differences, 82 , 26–33.

Kitsantas, A., Winsler, A., & Huie, F. (2008). Self-regulation and ability predictors of academic success during college: a predictive validity study. Journal of Advanced Academics, 20 , 42–68.

Koch, C. J., & Kleinmann, M. (2002). A stitch in time saves nine: behavioural decision-making explanations for time management problem. European Journal of Work and Organizational Psychology, 11 , 199–217.

Komarraju, M., Karau, S. J., & Schmeck, R. R. (2009). Role of the Big Five personality traits in predicting college students’ academic motivation and achievement. Learning and Individual Differences, 19 , 47–52.

Konig, C. J., & Waller, M. J. (2010). Time for reflection: a critical examination of polychronicity. Human Performance, 23 , 173–190.

Kooij, D. T. A. M., Kanfer, R., Betts, M., & Rudolph, C. W. (2018). Future time perspective: a systematic review and meta-analysis. Journal of Applied Psychology, 103 (8), 867–893.

Krumrei-Mancuso, E. J., Newton, F. B., Kim, E., & Wilcox, D. (2013). Psychosocial factors predicting first-year college student success. Journal of College Student Development, 54 , 247–266.

Landrum, R. E., Turrisi, R., & Brandel, J. M. (2006). College students’ study time: course level, time of semester, and grade earned. Psychological Reports, 98 (3), 675–682.

Liborius, P., Bellhauser, H., & Schmitz, B. (2017). What makes a good study day? An intraindividual study on university students’ time investment by means of time-series analyses. Learning and Instruction.

Linnenbrink-Garcia, L., & Patall, E. (2016). Motivation. In L. Corno & E. Anderman (Eds.), Handbook of educational psychology (3rd ed., pp. 91–1030). New York: Routledge.

Liu, O. L., Rijmen, F., MacCann, C., & Roberts, R. (2009). The assessment of time management in middle-school students. Personality and Individual Differences, 47 , 174–179.

Locke, E., & Latham, G. (2019). The development of goal setting theory: a half-century retrospective. Motivation Science, 5 , 93–105.

Loureiro, F., & Garcia-Marques, T. (2015). Morning or evening person? Which type are you? Self-assessment of chronotype. Personality and Individual Differences, 86 , 168–171.

Lynch, D. J. (2010). Application of online discussion and cooperative learning strategies to online and blended college courses. College Student Journal, 44 , 920–927.

Macan, T. H. (1994). Time management: test of a process model. Journal of Applied Psychology, 79 , 381–391.

Macan, T. H., Shahani, C., Dipboye, R. L., & Phillips, A. P. (1990). College students’ time management: correlations with academic performance and stress. Journal of Educational Psychology, 82 , 760–768.

MacCann, C., & Roberts, R. D. (2010). Do time management, grit, and self-control relate to academic achievement independently of conscientiousness? In R. E. Hicks (Ed.), Personality and individual differences: current directions (pp. 79–90). Bowen Hills: Australian Academic Press.

MacCann, C., Fogarty, G. J., & Roberts, R. D. (2012). Strategies for success in education: time management is more important for part-time than full-time community college students. Learning and Individual Differences, 22 , 618–623.

McCrae, R., & Lockenhoff, C. (2010). Self-regulation and the five-factor model of personality traits. In R. Hoyle (Ed.), Handbook of personality and self-regulation (pp. 143–168). New York: Wiley.

McInerney, D., & King, R. (2018). Culture and self-regulation in educational contexts. In D. Schunk & J. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 485–502). New York: Routledge.

Meeuwisse, M., Born, M. P., & Severiens, S. E. (2013). Academic performance differences among ethnic groups: do the daily use and management of time offer explanations? Social Psychology of Education, 16 , 599–615.

Melancon, J. G. (2002). Reliability, structure, and correlates of Learning and Study Strategies Inventory scores. Educational and Psychological Measurement, 62 , 1020–1027.

Miele, D. B., & Scholer, A. A. (2018). The role of metamotivational monitoring in motivation regulation. Educational Psychologist, 53 , 1–21.

Miller, R. B., & Brickman, S. J. (2004). A model of future-oriented motivation and self-regulation. Educational Psychology Review, 16 , 9–33.

Muis, K. R. (2007). The role of epistemic beliefs in self-regulated learning. Educational Psychologist, 42 , 173–190.

Nelson, T., & Narens, L. (1990). Metamemory: a theoretical framework and some new findings. In G. H. Bower (Ed.), The psychology of learning and motivation (pp. 125–173). New York: Academic Press.

Nonis, S., Teng, J., & Ford, C. (2005). A cross-cultural investigation of time management practices and job outcomes. International Journal of Intercultural Relations, 29 , 409–428.

Nonis, S. A., Philhours, M. J., & Hudson, G. I. (2006). Where does the time go? A diary approach to business and marketing students’ time use. Journal of Marketing Education, 28 , 121–134.

Olaussen, B. S., & Bråten, I. (1998). Identifying latent variables measured by the Learning and Study Strategies Inventory (LASSI) in Norwegian college students. The Journal of Experimental Education, 67 , 82–96.

Olejnik, S., & Nist, S. L. (1992). Identifying latent variables measured by the Learning and Study Strategies Inventory (LASSI). The Journal of Experimental Education, 60 , 151–159.

Panadero, E. (2017). A review of self-regulated learning: six models and four directions for research. Frontiers in Psychology, 8 , 1–28.

Panadero, E., Brown, G., & Strijbos, J. (2016). The future of student self-assessment: a review of known and unknowns and potential directions. Educational Psychology Review, 28 (803), 830.

Panek, E. (2014). Left to their own devices: college students’ “guilty pleasure” media use and time management. Communication Research, 41 , 561–577.

Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: a program of qualitative and quantitative research. Educational Psychologist, 37 , 91–105.

Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). San Diego: Academic.

Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16 , 385–407.

Pintrich, P. R., & Zusho, A. (2007). Student motivation and self-regulated learning in the college classroom. In R. P. Perry & J. C. Smart (Eds.), The scholarship of teaching and learning in higher education: an evidence based perspective (pp. 731–810). New York: Springer.

Pintrich, P. R., Smith, D. A., Garcia, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire. Educational and Psychological Measurement, 53 , 801–813.

Pintrich, P. R., Wolters, C. A., & Baxter, G. P. (2000). Assessing metacognition and self-regulated learning. In G. Schraw & J. Impara (Eds.), Issues in the measurement of metacognition (pp. 43–97). Lincoln: Buros Institute of Mental Measurement.

Plant, E. A., Ericsson, K. A., Hill, L., & Asberg, K. (2005). Why study time does not predict grade point average across college students: implications of deliberate practice for academic performance. Contemporary Educational Psychology, 30 , 96–116.

Prevatt, F., Petscher, Y., Proctor, B. E., Hurst, A., & Adams, K. (2006). The revised Learning and Study Strategies Inventory: an evaluation of competing models. Educational and Psychological Measurement, 66 (3), 448–458.

Pychyl, T. A., Morin, R. W., & Salmon, B. R. (2000). Procrastination and the planning fallacy: an examination of the study habits of university students. Journal of Social Behavior and Personality, 15 , 135–150.

Ranellucci, J., Hall, N. C., & Goetz, T. (2015). Achievement goals, emotions, learning, and performance: a process model. Motivation Science, 1 , 98–120.

Rhodes, M., & Tauber, S. (2011). The influence of delaying judgments of learning on metacognitive accuracy: a meta-analytic review. Psychological Bulletin, 137 (1), 131–148.

Richards, J. H. (1987). Time management—a review. Work & Stress, 1 , 73–78.

Rickert, N. P., Meras, I. L., & Witkow, M. R. (2014). Theories of intelligence and students’ daily self-handicapping behaviors. Learning and Individual Differences, 36 , 1–8.

Roskes, M., Elliot, A. J., Nijstad, B. A., & De Dreu, C. K. (2013). Time pressure undermines performance more under avoidance than approach motivation. Personality and Social Psychology Bulletin, 39 , 803–813.

Rytkonen, H., Parpala, A., Lindblom-Ylanne, S., Virtanen, V., & Postareff, L. (2012). Factors affecting bioscience students’ academic achievement. Instructional Science, 40 , 241–256.

Sanders, L., Reedy, D., & Frizell, M. (Eds.). (2018). Learning centers in the 21 st century: a modern guide for learning assistance professionals in higher education . Bentonville: AR. Iona Press.

Sansgiry, S. S., Bhosle, M., & Sail, K. (2006). Factors that affect academic performance among pharmacy students. American Journal of Pharmaceutical Education, 70 , 1–9.

Schell, K. L., & Conte, J. M. (2008). Associations among polychronicity, goal orientation, and error orientation. Personality and Individual Differences, 44 , 288–298.

Schraw, G. (2006). Knowledge: structures and processes. In P. Alexander & P. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 245–263). Mahwah: Erlbaum Associates.

Schunk, D. H., & Zimmerman, B. J. (1997). Social origins of self-regulatory competence. Educational Psychologist, 32 , 195–208.

Schunk, D. H., & Zimmerman, B. J. (Eds.). (1998). Self-regulated learning: from teaching to self-reflective practice . New York: Guilford Press.

Sen, S., & Yilmaz, A. (2016). Devising a structural equation model of relationships between preservice teachers’ time and study environment management, effort regulation, self-efficacy, control of learning beliefs, and metacognitive self-regulation. Science Education International, 27 , 301–316.

Shaunessy-Dedrick, E., Suldo, S. M., Roth, R. A., & Fefer, S. A. (2015). Students’ perceptions of factors that contribute to risk and success in accelerated high school courses. The High School Journal, 98 , 109–137.

Shell, D. F., & Husman, J. (2001). The multivariate dimensionality of personal control and future time perspective beliefs in achievement and self-regulation. Contemporary Educational Psychology, 26 (4), 481–506.

Simons, J., Vansteenkiste, M., Lens, W., & Lacante, M. (2004). Placing motivation and future time perspective theory in a temporal perspective. Educational Psychology Review, 16 , 121–139.

Sinatra, G., Kienhues, D., & Hofer, B. (2014). Addressing challenges to public understanding of science: epistemic cognition, motivated reasoning, and conceptual change. Educational Psychologist, 49 , 123–138.

Sirois, F. M. (2014). Procrastination and stress: exploring the role of self-compassion. Self and Identity, 13 , 128–145.

Sitzmann, T., & Ely, K. (2011). A meta-analysis of self-regulated learning in work-related training and educational attainment: what we know and where we need to go. Psychological Bulletin, 137 (3), 421–442.

Sitzmann, T., Ely, K., Brown, K., & Bauer, K. (2010). Self-assessment of knowledge: a cognitive learning or affective measure? Academy of Management Learning & Education, 9 , 169–191.

Snow, R. (1989). Cognitive-conative aptitude interactions in learning. In R. Kanfer, P. Ackerman, & R. Cudeck (Eds.), Abilities, motivation, and methodology: the Minnesota symposium on learning and individual differences (pp. 435–474). Hillsdale: Erlbaum Associates.

Snow, R. E., Corno, L., & Jackson III, D. (1996). Individual differences in affective and conative functions. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of educational psychology (pp. 243–310). New York: Macmillan Library Reference.

Soderstrom, N. C., & Bjork, R. A. (2014). Testing facilitates the regulation of subsequent study time. Journal of Memory and Language, 73 , 99–115.

Steel, P. (2007). The nature of procrastination: a meta-analytic and theoretical review of quintessential self-regulatory failure. Psychological Bulletin, 133 (1), 65–94.

Stevens, T., & Tallent-Runnels, M. K. (2004). The learning and study strategies inventory-high school version: issues of factorial invariance across gender and ethnicity. Educational and Psychological Measurement, 64 , 332–346.

Strunk, K. K., Cho, Y., Steele, M. R., & Bridges, S. L. (2013). Development and validation of a 2× 2 model of time-related academic behavior: procrastination and timely engagement. Learning and Individual Differences, 25 , 35–44.

Tanner, J. R., Stewart, G., Maples, G. M., & Totaro, M. W. (2009). How business students spend their time—do they really know? Research in Higher Education Journal, 3 , 1–9.

Terenzini, P., Rendon, L., Upcraft, M. L., Millar, S., Allison, K., Gregg, P., & Jalomo, R. (1994). The transition to college: diverse students, diverse stories. Research in Higher Education, 35 , 57–73.

Trueman, M., & Hartley, J. (1996). A comparison between the time-management skills and academic performance of mature and traditional-entry university students. Higher Education, 32 , 199–215.

Truschel, J., & Reedy, D. L. (2009). National survey—what is a learning center in the 21st century? Learning Assistance Review, 14 , 9–22.

Tsai, H. C., & Liu, S. H. (2015). Relationships between time-management skills, Facebook interpersonal skills and academic achievement among junior high school students. Social Psychology of Education, 18 , 503–516.

U.S. Department of Education, Institute of Education Sciences, What Works Clearinghouse (2016). Studies of interventions for students in developmental education intervention report: first year experience courses for students in developmental education . Retrieved from http://whatworks.ed.gov . Accessed 20 Jan 2020.

Urdan, T., & Schoenfelder, E. (2006). Classroom effects on student motivation: goal structures, social relationships, and competence beliefs. Journal of School Psychology, 44 , 331–349.

Usher, E. L., & Pajares, F. (2008). Sources of self-efficacy in school: critical review of the literature and future directions. Review of Educational Research, 78 , 751–796.

van Den Hurk, M. (2006). The relation between self-regulated strategies and individual study time, prepared participation and achievement in a problem-based curriculum. Active Learning in Higher Education, 7 , 155–169.

van der Meer, J., Jansen, E., & Torenbeek, M. (2010). ‘It’s almost a mindset that teachers need to change’: first-year students’ need to be inducted into time management. Studies in Higher Education, 35 , 777–791.

van Eerde, W. (2015). Time management and procrastination. In M. D. Mumford & M. Frese (Eds.), The psychology of planning in organizations: research and applications (pp. 312–333). New York: Routledge.

Wagner, P., Schober, B., & Spiel, C. (2008). Time students spend working at home for school. Learning and Instruction, 18 , 309–320.

Weiner, B. (2005). Motivation from an attribution perspective and the social psychology of perceived competence. In A. Elliot & C. Dweck (Eds.), Handbook of competence and motivation (pp. 73–84). New York: Guildford.

Weinstein, C. E., Palmer, D. R., & Acee, T. W. (2016). LASSI user’s manual (3th ed.). Clearwater: H & H Pub.

Winne, P. H. (1995). Inherent details in self-regulated learning. Educational Psychologist, 30 , 173–187.

Winne, P. H., & Baker, R. (2013). The potentials of educational data mining for researching metacognition, motivation, and self-regulated learning. Journal of Educational Data Mining, 5 , 1–8.

Winne, P., & Hadwin, A. (1998). Studying as self-regulated learning. In D. Hacker, J. Dunlosky, & A. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). Mahwah: Erlbaum.

Winne, P. H., & Hadwin, A. F. (2008). The weave of motivation and self-regulated learning. Motivation and self-regulated learning: theory, research, and applications (pp. 297–314). New York: Taylor & Francis.

Winne, P., & Perry, N. (2000). Measuring self-regulated learning. In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 531–566). San Diego: Academic Press.

Witkow, M. R. (2009). Academic achievement and adolescents’ daily time use in the social and academic domains. Journal of Research on Adolescence, 19 , 151–172.

Wolters, C. A. (2003). Regulation of motivation: evaluating an underemphasized aspect of self-regulated learning. Educational Psychologist, 38 , 189–205.

Wolters, C. A. (2011). Regulation of motivation: contextual and social aspects. Teachers College Record, 113 , 265–283.

Wolters, C. A., & Gonzalez, A. L. (2008). Classroom climate and motivation: a step toward integration. Advances in Motivation and Achievement: Social Psychological Influences, 15 , 493–519.

Wolters, C. A., & Hoops, L. D. (2015). Self-regulated learning interventions for motivationally disengaged college students. In T. L. Cleary (Ed.), Self-regulated learning interventions with at-risk youth: Enhancing adaptability, performance, and well-being (pp. 67–88). Washington: American Psychological Association.

Wolters, C., & Taylor, D. (2012). A self-regulated learning perspective on student engagement. In S. L. Christenson, A. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 635–651). New York: Springer.

Wolters, C. A., & Won, S. (2018). Validity and the use of self-report questionnaires to assess self-regulated learning. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (pp. 307–322). New York: Routledge.

Wolters, C. A., Won, S., & Hussain, M. (2017). Examining the relations of time management and procrastination within a model of self-regulated learning. Metacognition and Learning, 12 , 381–399.

Won, S., & Yu, S. L. (2018). Relations of perceived parental autonomy support and control with adolescents’ academic time management and procrastination. Learning and Individual Differences, 61 , 205–215.

Woolfolk, A. E., & Woolfolk, R. L. (1986). Time management: an experimental investigation. Journal of School Psychology, 24 , 267–275.

Xie, K., Heddy, B. C., & Greene, B. A. (2019). Affordances of using mobile technology to support experience-sampling method in examining college students’ engagement. Computers & Education, 128 , 183–198.

Xu, J. (2008). Models of secondary school students’ interest in homework: a multilevel analysis. American Educational Research Journal, 45 , 1180–1205.

Xu, J. (2010). Predicting homework time management at the secondary school level: a multilevel analysis. Learning and Individual Differences, 20 , 34–39.

Xu, J., Du, J., & Fan, X. (2013). “Finding our time”: predicting students’ time management in online collaborative groupwork. Computers & Education, 69 , 139–147.

Xu, J., Yuan, R., Xu, B., & Xu, M. (2014). Modeling students’ time management in math homework. Learning and Individual Differences, 34 , 33–42.

Yamada, M., Goda, Y., Matsuda, T., Saito, Y., Kato, H., & Miyagawa, H. (2016). How does self-regulated learning relate to active procrastination and other learning behaviors? Journal of Computing in Higher Education, 28 , 326–343.

Young, D. G., & Hopp, J. M. (2014). 2012-2013 national survey of first-year seminars: exploring high-impact practices in the first college year (Research report No. 4) . Columbia: University of South Carolina.

Zimbardo, P., & Boyd, J. (1999). Putting time in perspective: a valid, reliable individual-differences metric. Journal of Personality and Social Psychology, 77 , 1271–1288.

Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81 , 329–339.

Zimmerman, B. J. (2000). Attaining self-regulation: a social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation: theory, research, and applications (pp. 13–29). San Diego: Academic Press.

Zimmerman, B. J., & Risemberg, R. (1997). Becoming a self-regulated writer: a social cognitive perspective. Contemporary Educational Psychology, 22 , 73–101.

Zimmerman, B. J., & Schunk, D. (2012). Motivation: an essential dimension of self-regulated learning. In D. Schunk & B. Zimmerman (Eds.), Motivation and self-regulated learning: theory, research and applications (pp. 1–30). New York: Routledge.

Zimmerman, B. J., Greenberg, D., & Weinstein, C. E. (1994). Self-regulating academic study time: a strategy approach. In D. H. Schunk & B. J. Zimmerman (Eds.), Self-regulation of learning and performance: issues and educational applications (pp. 181–199). Hillsdale: Erlbaum.

Zusho, A. (2017). Toward an integrated model of student learning in the college classroom. Educational Psychology Review, 29 , 301–324.

Zusho, A., & Pintrich, P. (2003). Skill and will: the role of motivation and cognition in the learning of college chemistry. International Journal of Science Education, 25 , 1081–1094.

Download references

Author information

Authors and affiliations.

Dennis Learning Center, The Ohio State University, 250B Younkin Success Building, 1640 Neil Ave., Columbus, OH, 43201, USA

Christopher A. Wolters & Anna C. Brady

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Christopher A. Wolters .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Wolters, C.A., Brady, A.C. College Students’ Time Management: a Self-Regulated Learning Perspective. Educ Psychol Rev 33 , 1319–1351 (2021). https://doi.org/10.1007/s10648-020-09519-z

Download citation

Published : 27 October 2020

Issue Date : December 2021

DOI : https://doi.org/10.1007/s10648-020-09519-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Self-regulated learning
  • Time management
  • Postsecondary students
  • Find a journal
  • Publish with us
  • Track your research

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Does time management work? A meta-analysis

Roles Conceptualization, Data curation, Formal analysis, Methodology, Software, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Concordia University, Sir George Williams Campus, Montreal, Quebec, Canada

ORCID logo

Roles Methodology, Validation

Affiliation FSA Ulaval, Laval University, Quebec City, Quebec, Canada

Roles Validation, Writing – review & editing

  • Brad Aeon, 
  • Aïda Faber, 
  • Alexandra Panaccio

PLOS

  • Published: January 11, 2021
  • https://doi.org/10.1371/journal.pone.0245066
  • Reader Comments

Fig 1

Does time management work? We conducted a meta-analysis to assess the impact of time management on performance and well-being. Results show that time management is moderately related to job performance, academic achievement, and wellbeing. Time management also shows a moderate, negative relationship with distress. Interestingly, individual differences and contextual factors have a much weaker association with time management, with the notable exception of conscientiousness. The extremely weak correlation with gender was unexpected: women seem to manage time better than men, but the difference is very slight. Further, we found that the link between time management and job performance seems to increase over the years: time management is more likely to get people a positive performance review at work today than in the early 1990s. The link between time management and gender, too, seems to intensify: women’s time management scores have been on the rise for the past few decades. We also note that time management seems to enhance wellbeing—in particular, life satisfaction—to a greater extent than it does performance. This challenges the common perception that time management first and foremost enhances work performance, and that wellbeing is simply a byproduct.

Citation: Aeon B, Faber A, Panaccio A (2021) Does time management work? A meta-analysis. PLoS ONE 16(1): e0245066. https://doi.org/10.1371/journal.pone.0245066

Editor: Juan-Carlos Pérez-González, Universidad Nacional de Educacion a Distancia (UNED), SPAIN

Received: October 27, 2020; Accepted: December 21, 2020; Published: January 11, 2021

Copyright: © 2021 Aeon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist

Introduction

Stand-up comedian George Carlin once quipped that in the future a “time machine will be built, but no one will have time to use it” [ 1 ]. Portentously, booksellers now carry one-minute bedtime stories for time-starved parents [ 2 ] and people increasingly speed-watch videos and speed-listen to audio books [ 3 – 5 ]. These behaviors are symptomatic of an increasingly harried society suffering from chronic time poverty [ 6 ]. Work is intensifying—in 1965 about 50% of workers took breaks; in 2003, less than 2% [ 7 ]. Leisure, too, is intensifying: people strive to consume music, social media, vacations, and other leisure activities ever more efficiently [ 8 – 11 ].

In this frantic context, time management is often touted as a panacea for time pressure. Media outlets routinely extol the virtues of time management. Employers, educators, parents, and politicians exhort employees, students, children, and citizens to embrace more efficient ways to use time [ 12 – 16 ]. In light of this, it is not surprising that from 1960 to 2008 the frequency of books mentioning time management shot up by more than 2,700% [ 17 ].

Time management is defined as “a form of decision making used by individuals to structure, protect, and adapt their time to changing conditions” [ 18 ]. This means time management, as it is generally portrayed in the literature, comprises three components: structuring, protecting, and adapting time. Well-established time management measures reflect these concepts. Structuring time, for instance, is captured in such items as “Do you have a daily routine which you follow?” and “Do your main activities during the day fit together in a structured way?” [ 19 ]. Protecting time is reflected in items such as “Do you often find yourself doing things which interfere with your schoolwork simply because you hate to say ‘No’ to people?” [ 20 ]. And adapting time to changing conditions is seen in such items as “Uses waiting time” and “Evaluates daily schedule” [ 21 ].

Research has, furthermore, addressed several important aspects of time management, such as its relationship with work-life balance [ 22 ], whether gender differences in time management ability develop in early childhood [ 23 ], and whether organizations that encourage employees to manage their time experience less stress and turnover [ 24 ]. Despite the phenomenal popularity of this topic, however, academic research has yet to address some fundamental questions [ 25 – 27 ].

A critical gap in time management research is the question of whether time management works [ 28 , 29 ]. For instance, studies on the relationship between time management and job performance reveal mixed findings [ 30 , 31 ]. Furthermore, scholars’ attempts to synthesize the literature have so far been qualitative, precluding a quantitative overall assessment [ 18 , 32 , 33 ]. To tackle this gap in our understanding of time management, we conducted a meta-analysis. In addressing the question of whether time management works, we first clarify the criteria for effectiveness. In line with previous reviews, we find that virtually all studies focus on two broad outcomes: performance and wellbeing [ 32 ].

Overall, results suggest that time management enhances job performance, academic achievement, and wellbeing. Interestingly, individual differences (e.g., gender, age) and contextual factors (e.g., job autonomy, workload) were much less related to time management ability, with the notable exception of personality and, in particular, conscientiousness. Furthermore, the link between time management and job performance seems to grow stronger over the years, perhaps reflecting the growing need to manage time in increasingly autonomous and flexible jobs [ 34 – 37 ].

Overall, our findings provide academics, policymakers, and the general audience with better information to assess the value of time management. This information is all the more useful amid the growing doubts about the effectiveness of time management [ 38 ]. We elaborate on the contributions and implications of our findings in the discussion section.

What does it mean to say that time management works?

In the din of current debates over productivity, reduced workweeks, and flexible hours, time management comes to the fore as a major talking point. Given its popularity, it would seem rather pointless to question its effectiveness. Indeed, time management’s effectiveness is often taken for granted, presumably because time management offers a seemingly logical solution to a lifestyle that increasingly requires coordination and prioritization skills [ 39 , 40 ].

Yet, popular media outlets increasingly voice concern and frustration over time management, reflecting at least part of the population’s growing disenchantment [ 38 ]. This questioning of time management practices is becoming more common among academics as well [ 41 ]. As some have noted, the issue is not just whether time management works. Rather, the question is whether the techniques championed by time management gurus can be actually counterproductive or even harmful [ 26 , 42 ]. Other scholars have raised concerns that time management may foster an individualistic, quantitative, profit-oriented view of time that perpetuates social inequalities [ 43 , 44 ]. For instance, time management manuals beguile readers with promises of boundless productivity that may not be accessible to women, whose disproportionate share in care work, such as tending to young children, may not fit with typically male-oriented time management advice [ 45 ]. Similarly, bestselling time management books at times offer advice that reinforce global inequities. Some manuals, for instance, recommend delegating trivial tasks to private virtual assistants, who often work out of developing countries for measly wages [ 46 ]. Furthermore, time management manuals often ascribe a financial value to time—the most famous time management adage is that time is money. But recent studies show that thinking of time as money leads to a slew of negative outcomes, including time pressure, stress, impatience, inability to enjoy the moment, unwillingness to help others, and less concern with the environment [ 47 – 51 ]. What’s more, the pressure induced by thinking of time as money may ultimately undermine psychological and physical health [ 52 ].

Concerns over ethics and safety notwithstanding, a more prosaic question researchers have grappled with is whether time management works. Countless general-audience books and training programs have claimed that time management improves people’s lives in many ways, such as boosting performance at work [ 53 – 55 ]. Initial academic forays into addressing this question challenged those claims: time management didn’t seem to improve job performance [ 29 , 30 ]. Studies used a variety of research approaches, running the gamut from lab experiments, field experiments, longitudinal studies, and cross-sectional surveys to experience sampling [ 28 , 56 – 58 ]. Such studies occasionally did find an association between time management and performance, but only in highly motivated workers [ 59 ]; instances establishing a more straightforward link with performance were comparatively rare [ 31 ]. Summarizing these insights, reviews of the literature concluded that the link between time management and job performance is unclear; the link with wellbeing, however, seemed more compelling although not conclusive [ 18 , 32 ].

It is interesting to note that scholars often assess the effectiveness time management by its ability to influence some aspect of performance, wellbeing, or both. In other words, the question of whether time management works comes down to asking whether time management influences performance and wellbeing. The link between time management and performance at work can be traced historically to scientific management [ 60 ]. Nevertheless, even though modern time management can be traced to scientific management in male-dominated work settings, a feminist reading of time management history reveals that our modern idea of time management also descends from female time management thinkers of the same era, such as Lillian Gilbreth, who wrote treatises on efficient household management [ 43 , 61 , 62 ]. As the link between work output and time efficiency became clearer, industrialists went to great lengths to encourage workers to use their time more rationally [ 63 – 65 ]. Over time, people have internalized a duty to be productive and now see time management as a personal responsibility at work [ 43 , 66 , 67 ]. The link between time management and academic performance can be traced to schools’ historical emphasis on punctuality and timeliness. In more recent decades, however, homework expectations have soared [ 68 ] and parents, especially well-educated ones, have been spending more time preparing children for increasingly competitive college admissions [ 69 , 70 ]. In this context, time management is seen as a necessary skill for students to thrive in an increasingly cut-throat academic world. Finally, the link between time management and wellbeing harks back to ancient scholars, who emphasized that organizing one’s time was necessary to a life well-lived [ 71 , 72 ]. More recently, empirical studies in the 1980s examined the effect of time management on depressive symptoms that often plague unemployed people [ 19 , 73 ]. Subsequent studies surmised that the effective use of time might prevent a host of ills, such as work-life conflict and job stress [ 22 , 74 ].

Overall, then, various studies have looked into the effectiveness of time management. Yet, individual studies remain narrow in scope and reviews of the literature offer only a qualitative—and often inconclusive—assessment. To provide a more quantifiable answer to the question of whether time management works, we performed a meta-analysis, the methods of which we outline in what follows.

Literature search and inclusion criteria

We performed a comprehensive search using the keywords “time management” across the EBSCO databases Academic Search Complete , Business Source Complete , Computers & Applied Sciences Complete , Gender Studies Database , MEDLINE , Psychology and Behavioral Sciences Collection , PsycINFO , SocINDEX , and Education Source . The search had no restrictions regarding country and year of publication and included peer-reviewed articles up to 2019. To enhance comprehensiveness, we also ran a forward search on the three main time management measures: the Time Management Behavior Scale [ 21 ], the Time Structure Questionnaire [ 19 ], and the Time Management Questionnaire [ 20 ]. (A forward search tracks all the papers that have cited a particular work. In our case the forward search located all the papers citing the three time management scales available on Web of Science .)

Time management measures typically capture three aspects of time management: structuring, protecting, and adapting time to changing conditions. Structuring refers to how people map their activities to time using a schedule, a planner, or other devices that represent time in a systematic way [ 75 – 77 ]. Protecting refers to how people set boundaries around their time to repel intruders [ 78 , 79 ]. Examples include people saying no to time-consuming requests from colleagues or friends as well as turning off one’s work phone during family dinners. Finally, adapting one’s time to changing conditions means, simply put, to be responsive and flexible with one’s time structure [ 80 , 81 ]. Furthermore, time management measures typically probe behaviors related to these three dimensions (e.g., using a schedule to structure one’s day, making use of downtime), although they sometimes also capture people’s attitudes (e.g., whether people feel in control of their time).

As shown in Fig 1 , the initial search yielded 10,933 hits, excluding duplicates.

thumbnail

  • PPT PowerPoint slide
  • PNG larger image
  • TIFF original image

https://doi.org/10.1371/journal.pone.0245066.g001

The search included no terms other than “time management” to afford the broadest possible coverage of time management correlates. Nevertheless, as shown in Table 1 , we focused exclusively on quantitative, empirical studies of time management in non-clinical samples. Successive rounds of screening, first by assessing paper titles and abstracts and then by perusing full-text articles, whittled down the number of eligible studies to 158 (see Fig 1 ).

thumbnail

https://doi.org/10.1371/journal.pone.0245066.t001

Data extraction and coding

We extracted eligible effect sizes from the final pool of studies; effect sizes were mostly based on means and correlations. In our initial data extraction, we coded time management correlates using the exact variable names found in each paper. For instance, “work-life imbalance” was initially coded in those exact terms, rather than “work-life conflict.” Virtually all time management correlates we extracted fell under the category of performance and/or wellbeing. This pattern tallies with previous reviews of the literature [ 18 , 32 ]. A sizable number of variables also fell under the category of individual differences and contextual factors, such as age, personality, and job autonomy. After careful assessment of the extracted variables, we developed a coding scheme using a nested structure shown in Table 2 .

thumbnail

https://doi.org/10.1371/journal.pone.0245066.t002

Aeon and Aguinis suggested that time management influences performance, although the strength of that relationship may depend on how performance is defined [ 18 ]. Specifically, they proposed that time management may have a stronger impact on behaviors conducive to performance (e.g., motivation, proactiveness) compared to assessments of performance (e.g., supervisor rankings). For this reason, we distinguish between results- and behavior-based performance in our coding scheme, both in professional and academic settings. Furthermore, wellbeing indicators can be positive (e.g., life satisfaction) or negative (e.g., anxiety). We expect time management to influence these variables in opposite ways; it would thus make little sense to analyze them jointly. Accordingly, we differentiate between wellbeing (positive) and distress (negative).

In our second round of coding, we used the scheme shown in Table 2 to cluster together kindred variables. For instance, we grouped “work-life imbalance,” “work-life conflict” and “work-family conflict” under an overarching “work-life conflict” category. The authors reviewed each variable code and resolved rare discrepancies to ultimately agree on all coded variables. Note that certain variables, such as self-actualization, covered only one study (i.e., one effect size). While one or two effect sizes is not enough to conduct a meta-analysis, they can nonetheless be grouped with other effect sizes belonging to the same category (e.g., self-actualization and sense of purpose belong the broader category of overall wellbeing). For this reason, we included variables with one or two effect sizes for comprehensiveness.

Meta-analytic procedures

We conducted all meta-analyses following the variables and cluster of variables outlined in Table 2 . We opted to run all analyses with a random effects model. The alternative—a fixed effects model—assumes that all studies share a common true effect size (i.e., linking time management and a given outcome) which they approximate. This assumption is unrealistic because it implies that the factors influencing the effect size are the same in all studies [ 83 ]. In other words, a fixed effects model assumes that the factors affecting time management are similar across all studies—the fallacy underlying this assumption was the main theme of Aeon and Aguinis’s review [ 18 ]. To perform our analyses, we used Comprehensive Meta-Analysis v.3 [ 84 ], a program considered highly reliable and valid in various systematic assessments [ 85 , 86 ].

introduction in research paper about time management

In many cases, studies reported how variables correlated with an overall time management score. In some cases, however, studies reported only correlations with discrete time management subscales (e.g., short-range planning, attitudes toward time, use of time management tools), leaving out the overall effect. In such cases, we averaged out the effect sizes of the subscales to compute a summary effect [ 83 ]. This was necessary not only because meta-analyses admit only one effect size per study, but also because our focus is on time management as a whole rather than on subscales. Similarly, when we analyzed the link between time management and a high-level cluster of variables (e.g., overall wellbeing rather than specific variables such as life satisfaction), there were studies with more than one relevant outcome (e.g., a study that captured both life satisfaction and job satisfaction). Again, because meta-analyses allow for only one effect size (i.e., variable) per study, we used the mean of different variables to compute an overall effect sizes in studies that featured more than one outcome [ 83 ].

Overall description of the literature

We analyzed 158 studies for a total number of 490 effect sizes. 21 studies explored performance in a professional context, 76 performance in an academic context, 30 investigated wellbeing (positive), and 58 distress. Interestingly, studies did not systematically report individual differences, as evidenced by the fact that only 21 studies reported correlations with age, and only between 10 and 15 studies measured personality (depending on the personality trait). Studies that measured contextual factors were fewer still—between 3 and 7 (depending on the contextual factor). These figures fit with Aeon and Aguinis’s observation that the time management literature often overlooks internal and external factors that can influence the way people manage time [ 18 ].

With one exception, we found no papers fitting our inclusion criteria before the mid-1980s. Publication trends also indicate an uptick in time management studies around the turn of the millennium, with an even higher number around the 2010s. This trend is consistent with the one Shipp and Cole identified, revealing a surge in time-related papers in organizational behavior around the end of the 1980s [ 87 ].

It is also interesting to note that the first modern time management books came out in the early 1970s, including the The Time Trap (1972), by Alec MacKenzie and How to Get Control of your Time and your Life (1973), by Alan Lakein. These books inspired early modern time management research [ 21 , 58 , 88 ]. It is thus very likely that the impetus for modern time management research came from popular practitioner manuals.

To assess potential bias in our sample of studies, we computed different estimates of publication bias (see Table 3 ). Overall, publication bias remains relatively low (see funnel plots in S1). Publication bias occurs when there is a bias against nonsignificant or even negative results because such results are seen as unsurprising and not counterintuitive. In this case, however, the fact that time management is generally expected to lead to positive outcomes offers an incentive to publish nonsignificant or negative results, which would be counterintuitive [ 89 ]. By the same token, the fact that some people feel that time management is ineffective [ 38 ] provides an incentive to publish papers that link time management with positive outcomes. In other words, opposite social expectations surrounding time management might reduce publication bias.

thumbnail

https://doi.org/10.1371/journal.pone.0245066.t003

Finally, we note that the link between time management and virtually all outcomes studied is highly heterogeneous (as measured, for instance, by Cochran’s Q and Higgins & Thompson’s I 2 ; see tables below). This high level of heterogeneity suggests that future research should pay more attention to moderating factors (e.g., individual differences).

Time management and performance in professional settings

Overall, time management has a moderate impact on performance at work, with correlations hovering around r = .25. We distinguish between results-based and behavior-based performance. The former measures performance as an outcome (e.g., performance appraisals by supervisors) whereas the latter measures performance as behavioral contributions (e.g., motivation, job involvement). Time management seems related to both types of performance. Although the effect size for results-based performance is lower than that of behavior-based performance, moderation analysis reveals the difference is not significant (p > .05), challenging Aeon and Aguinis’s conclusions [ 18 ].

Interestingly, the link between time management and performance displays much less heterogeneity (see Q and I 2 statistics in Table 4 ) than the link between time management and other outcomes (see tables below). The studies we summarize in Table 4 include both experimental and non-experimental designs; they also use different time management measures. As such, we can discount, to a certain extent, the effect of methodological diversity. We can perhaps explain the lower heterogeneity by the fact that when people hold a full-time job, they usually are at a relatively stable stage in life. In school, by contrast, a constellation of factors (e.g., financial stability and marital status, to name a few) conspire to affect time management outcomes. Furthermore, work contexts are a typically more closed system than life in general. For this reason, fewer factors stand to disrupt the link between time management and job performance than that between time management and, say, life satisfaction. Corroborating this, note how, in Table 6 below, the link between time management and job satisfaction ( I 2 = 58.70) is much less heterogeneous than the one between time management and life satisfaction ( I 2 = 95.45).

thumbnail

https://doi.org/10.1371/journal.pone.0245066.t004

Moreover, we note that the relationship between time management and job performance (see Fig 2 ) significantly increases over the years ( B = .0106, p < .01, Q model = 8.52(1), Q residual = 15.54(9), I 2 = 42.08, R 2 analog = .75).

thumbnail

https://doi.org/10.1371/journal.pone.0245066.g002

Time management and performance in academic settings

Overall, the effect of time management on performance seems to be slightly higher in academic settings compared to work settings, although the magnitude of the effect remains moderate (see Table 5 ). Here again, we distinguish between results- and behavior-based performance. Time management’s impact on behavior-based performance seems much higher than on results-based performance—a much wider difference than the one we observed in professional settings. This suggests than results-based performance in academic settings depends less on time management than results-based performance in professional settings. This means that time management is more likely to get people a good performance review at work than a strong GPA in school.

thumbnail

https://doi.org/10.1371/journal.pone.0245066.t005

In particular, time management seems to be much more negatively related to procrastination in school than at work. Although we cannot establish causation in all studies, we note that some of them featured experimental designs that established a causal effect of time management on reducing procrastination [ 90 ].

Interestingly, time management was linked to all types of results-based performance except for standardized tests. This is perhaps due to the fact that standardized tests tap more into fluid intelligence, a measure of intelligence independent of acquired knowledge [ 91 ]. GPA and regular exam scores, in contrast, tap more into crystallized intelligence, which depends mostly on accumulated knowledge. Time management can thus assist students in organizing their time to acquire the knowledge necessary to ace a regular exam; for standardized exams that depend less on knowledge and more on intelligence, however, time management may be less helpful. Evidence from other studies bears this out: middle school students’ IQ predicts standardized achievement tests scores better than self-control while self-control predicts report card grades better than IQ [ 92 ]. (For our purposes, we can use self-control as a very rough proxy for time management.) Relatedly, we found no significant relationship between time management and cognitive ability in our meta-analysis (see Table 8 ).

Time management and wellbeing

On the whole, time management has a slightly stronger impact on wellbeing than on performance. This is unexpected, considering how the dominant discourse points to time management as a skill for professional career development. Of course, the dominant discourse also frames time management as necessary for wellbeing and stress reduction, but to a much lesser extent. Our finding that time management has a stronger influence on wellbeing in no way negates the importance of time management as a work skill. Rather, this finding challenges the intuitive notion that time management is more effective for work than for other life domains. As further evidence, notice how in Table 6 the effect of time management on life satisfaction is 72% stronger than that on job satisfaction.

thumbnail

https://doi.org/10.1371/journal.pone.0245066.t006

Time management and distress

Time management seems to allay various forms of distress, although to a lesser extent than it enhances wellbeing. The alleviating effect on psychological distress is particularly strong ( r = -0.358; see Table 7 ).

thumbnail

https://doi.org/10.1371/journal.pone.0245066.t007

That time management has a weaker effect on distress should not be surprising. First, wellbeing and distress are not two poles on opposite ends of a spectrum. Although related, wellbeing and distress are distinct [ 93 ]. Thus, there is no reason to expect time management to have a symmetrical effect on wellbeing and distress. Second, and relatedly, the factors that influence wellbeing and distress are also distinct. Specifically, self-efficacy (i.e., seeing oneself as capable) is a distinct predictor of wellbeing while neuroticism and life events in general are distinct predictors of distress [ 94 ]. It stands to reason that time management can enhance self-efficacy. (Or, alternatively, that people high in self-efficacy would be more likely to engage in time management, although experimental evidence suggests that time management training makes people feel more in control of their time [ 89 ]; it is thus plausible that time management may have a causal effect on self-efficacy. Relatedly, note how time management ability is strongly related to internal locus of control in Table 8 ) In contrast, time management can do considerably less in the way of tackling neuroticism and dampening the emotional impact of tragic life events. In other words, the factors that affect wellbeing may be much more within the purview of time management than the factors that affect distress. For this reason, time management may be less effective in alleviating distress than in improving wellbeing.

thumbnail

https://doi.org/10.1371/journal.pone.0245066.t008

Time management and individual differences

Time management is, overall, less related to individual differences than to other variables.

Age, for instance, hardly correlates with time management (with a relatively high consistency between studies, I 2 = 55.79, see Table 8 above).

Similarly, gender only tenuously correlates with time management, although in the expected direction: women seem to have stronger time management abilities than men. The very weak association with gender ( r = -0.087) is particularly surprising given women’s well-documented superior self-regulation skills [ 95 ]. That being said, women’s time management abilities seem to grow stronger over the years ( N = 37, B = -.0049, p < .05, Q model = 3.89(1), Q residual = 218.42(35), I 2 = 83.98, R 2 analog = .03; also see Fig 3 below). More realistically, this increase may not be due to women’s time management abilities getting stronger per se but, rather, to the fact that women now have more freedom to manage their time [ 96 ].

thumbnail

https://doi.org/10.1371/journal.pone.0245066.g003

Other demographic indicators, such as education and number of children, were nonsignificant. Similarly, the relationships between time management and personal attributes and attitudes were either weak or nonsignificant, save for two notable exceptions. First, the link between time management and internal locus of control (i.e., the extent to which people perceive they’re in control of their lives) is quite substantial. This is not surprising, because time management presupposes that people believe they can change their lives. Alternatively, it may be that time management helps people strengthen their internal locus of control, as experimental evidence suggests [ 89 ]. Second, the link between time management and self-esteem is equally substantial. Here again, one can make the argument either way: people with high self-esteem might be confident enough to manage their time or, conversely, time management may boost self-esteem. The two options are not mutually exclusive: people with internal loci of control and high self-esteem levels can feel even more in control of their lives and better about themselves through time management.

We also note a very weak but statistically significant negative association between time management and multitasking. It has almost become commonsense that multitasking does not lead to performance [ 97 ]. As a result, people with stronger time management skills might deliberately steer clear of this notoriously ineffective strategy.

In addition, time management was mildly related to hours spent studying but not hours spent working. (These variables cover only student samples working part- or full-time and thus do not apply to non-student populations.) This is consistent with time-use studies revealing that teenagers and young adults spend less time working and more time studying [ 98 ]. Students who manage their time likely have well-defined intentions, and trends suggest those intentions will target education over work because, it is hoped, education offers larger payoffs over the long-term [ 99 ].

In terms of contextual factors, time management does not correlate significantly with job autonomy. This is surprising, as we expected autonomy to be a prerequisite for time management (i.e., you can’t manage time if you don’t have the freedom to). Nevertheless, qualitative studies have shown how even in environments that afford little autonomy (e.g., restaurants), workers can carve out pockets of time freedom to momentarily cut loose [ 100 ]. Thus, time management behaviors may flourish even in the most stymying settings. In addition, the fact that time management is associated with less role overload and previous attendance of time management training programs makes sense: time management can mitigate the effect of heavy workloads and time management training, presumably, improves time management skills.

Finally, time management is linked to all personality traits. Moreover, previous reviews of the literature have commented on the link between time management and conscientiousness in particular [ 32 ]. What our study reveals is the substantial magnitude of the effect ( r = 0.451). The relationship is not surprising: conscientiousness entails orderliness and organization, which overlap significantly with time management. That time management correlates so strongly with personality (and so little with other individual differences) lends credence to the dispositional view of time management [ 101 – 103 ]. However, this finding should not be taken to mean that time management is a highly inheritable, fixed ability. Having a “you either have it or you don’t” view of time management is not only counterproductive [ 104 ] but also runs counter to evidence showing that time management training does, in fact, help people manage their time better.

Does time management work? It seems so. Time management has a moderate influence on job performance, academic achievement, and wellbeing. These three outcomes play an important role in people’s lives. Doing a good job at work, getting top grades in school, and nurturing psychological wellbeing contribute to a life well lived. Widespread exhortations to get better at time management are thus not unfounded: the importance of time management is hard to overstate.

Contributions

Beyond answering the question of whether time management works, this study contributes to the literature in three major ways. First, we quantify the impact of time management on several outcomes. We thus not only address the question of whether time management works, but also, and importantly, gauge to what extent time management works. Indeed, our meta-analysis covers 53,957 participants, which allows for a much more precise, quantified assessment of time management effectiveness compared to qualitative reviews.

Second, this meta-analysis systematically assesses relationships between time management and a host of individual differences and contextual factors. This helps us draw a more accurate portrait of potential antecedents of higher (or lower) scores on time management measures.

Third, our findings challenge intuitive ideas concerning what time management is for. Specifically, we found that time management enhances wellbeing—and in particular life satisfaction—to a greater extent than it does various types of performance. This runs against the popular belief that time management primarily helps people perform better and that wellbeing is simply a byproduct of better performance. Of course, it may be that wellbeing gains, even if higher than performance gains, hinge on performance; that is to say, people may need to perform better as a prerequisite to feeling happier. But this argument doesn’t jibe with experiments showing that even in the absence of performance gains, time management interventions do increase wellbeing [ 89 ]. This argument also founders in the face of evidence linking time management with wellbeing among the unemployed [ 105 ], unemployment being an environment where performance plays a negligible role, if any. As such, this meta-analysis lends support to definitions of time management that are not work- or performance-centric.

Future research and limitations

This meta-analysis questions whether time management should be seen chiefly as a performance device. Our questioning is neither novel nor subversive: historically people have managed time for other reasons than efficiency, such as spiritual devotion and philosophical contemplation [ 72 , 106 , 107 ]. It is only with relatively recent events, such as the Industrial Revolution and waves of corporate downsizing, that time management has become synonymous with productivity [ 43 , 65 ]. We hope future research will widen its scope and look more into outcomes other than performance, such as developing a sense of meaning in life [ 108 ]. One of the earliest time management studies, for instance, explored how time management relates to having a sense of purpose [ 73 ]. However, very few studies followed suit since. Time management thus stands to become a richer, more inclusive research area by investigating a wider array of outcomes.

In addition, despite the encouraging findings of this meta-analysis we must refrain from seeing time management as a panacea. Though time management can make people’s lives better, it is not clear how easy it is for people to learn how to manage their time adequately. More importantly, being “good” at time management is often a function of income, education, and various types of privilege [ 42 , 43 , 46 , 109 ]. The hackneyed maxim that “you have as many hours in a day as Beyoncé,” for instance, blames people for their “poor” time management in pointing out that successful people have just as much time but still manage to get ahead. Yet this ill-conceived maxim glosses over the fact that Beyoncé and her ilk do, in a sense, have more hours in a day than average people who can’t afford a nanny, chauffeur, in-house chefs, and a bevy of personal assistants. Future research should thus look into ways to make time management more accessible.

Furthermore, this meta-analysis rests on the assumption that time management training programs do enhance people’s time management skills. Previous reviews have noted the opacity surrounding time management interventions—studies often don’t explain what, exactly, is taught in time management training seminars [ 18 ]. As a result, comparing the effect of different interventions might come down to comparing apples and oranges. (This might partly account for the high heterogeneity between studies.) We hope that our definition of time management will spur future research into crafting more consistent, valid, and generalizable interventions that will allow for more meaningful comparisons.

Finally, most time management studies are cross-sectional. Yet it is very likely that the effect of time management compounds over time. If time management can help students get better grades, for instance, those grades can lead to better jobs down the line [ 110 ]. Crucially, learning a skill takes time, and if time management helps people make the time to learn a skill, then time management stands to dramatically enrich people’s lives. For this reason, longitudinal studies can track different cohorts to see how time management affects people’s lives over time. We expect that developing time management skills early on in life can create a compound effect whereby people acquire a variety of other skills thanks to their ability to make time.

Overall, this study offers the most comprehensive, precise, and fine-grained assessment of time management to date. We address the longstanding debate over whether time management influences job performance in revealing a positive, albeit moderate effect. Interestingly, we found that time management impacts wellbeing—and in particular life satisfaction—to a greater extent than performance. That means time management may be primarily a wellbeing enhancer, rather than a performance booster. Furthermore, individual and external factors played a minor role in time management, although this does not necessarily mean that time management’s effectiveness is universal. Rather, we need more research that focuses on the internal and external variables that affect time management outcomes. We hope this study will tantalize future research and guide practitioners in their attempt to make better use of their time.

Supporting information

S1 checklist. prisma 2009 checklist..

https://doi.org/10.1371/journal.pone.0245066.s001

S1 File. Funnel plots.

https://doi.org/10.1371/journal.pone.0245066.s002

S2 File. Dataset.

https://doi.org/10.1371/journal.pone.0245066.s003

Acknowledgments

We would like to take this opportunity to acknowledge our colleagues for their invaluable help: Mengchan Gao, Talha Aziz, Elizabeth Eley, Robert Nason, Andrew Ryder, Tracy Hecht, and Caroline Aubé.

  • 1. Carlin G. When will Jesus bring the pork chops? New York, NY: Hyperion; 2004.
  • 2. Lewis S, O’Kun L. One-minute bedtime stories. New York, NY: Doubleday; 1982.
  • View Article
  • Google Scholar
  • PubMed/NCBI
  • 8. Boerma J, Karabarbounis L. Labor Market Trends and the Changing Value of Time [Internet]. Cambridge, MA: National Bureau of Economic Research; 2019 Sep [cited 2019 Dec 20] p. w26301. Report No.: w26301. Available from: http://www.nber.org/papers/w26301.pdf
  • 12. Clinton B. My life. New York, NY: Knopf; 2004. https://doi.org/10.1080/15216540400003425 pmid:15545218
  • 16. Pausch R, Zaslow J. The last lecture. New York, NY: Hyperion; 2008.
  • 17. Google Ngram Viewer. The rise of time management. Google Books. 2016.
  • 40. Southerton D. Re-ordering temporal rhythms: Coordinating daily practices in the UK in 1937 and 2000. In: Shove E, Trentmann F, Wilk R, editors. Time, consumption, and everyday life: Practice, materiality and culture. New York, NY: Berg; 2009. p. 49–63.
  • 41. Gregg M. Getting things done: Productivity, self-management, and the order of things. In: Hillis K, Paasonen S, Petit M, editors. Networked Affect. Cambridge, MA: MIT Press; 2015. p. 187–202.
  • 42. Reagle JM. Hacking life: Systematized living and its discontents. Cambridge, MA: The MIT Press; 2019. 204 p. (Strong ideas series).
  • 43. Gregg M. Counterproductive: Time management in the knowledge economy. Durham, NC: Duke University Press; 2018.
  • 46. Costas J, Grey C. Outsourcing your life: Exploitation and exploration in “The 4-hour workweek.” In: Holmqvist M, Spicer A, editors. Managing ‘Human Resources’ by exploiting and exploring people’s potentials (Research in the sociology of organizations, volume 37). Bingley, UK: Emerald; 2013.
  • 53. Allen D. Getting things done: The art of stress-free productivity. New York, NY: Penguin; 2001.
  • 54. Lakein A. How to get control of your time and your Life. New York, NY: Signet; 1973.
  • 55. Sutherland J. Scrum: The art of doing twice the work in half the time. New York, NY: Crown Business; 2014.
  • 60. Taylor FW. The principles of scientific management. New York, NY: Harper & Brothers; 1911.
  • 63. Landes DS. Revolution in time: Clocks and the making of the modern world. Cambridge, MA: Belknap Press of Harvard University Press; 1983. 482 p.
  • 64. Martineau J. Time, capitalism and alienation: A socio-historical inquiry into the making of modern time. Boston, MA: Brill; 2015.
  • 66. Alvesson M, Deetz SA. Critical Theory and Postmodernism Approaches to Organizational Studies. In: Clegg SR, Hardy C, Lawrence TB, Nord WR, editors. The SAGE Handbook of Organization Studies. 2nd ed. Thousand Oaks, CA: SAGE; 2006. p. 255–83.
  • 70. Ramey G, Ramey V. The Rug Rat Race [Internet]. Cambridge, MA: National Bureau of Economic Research; 2009 Aug [cited 2020 Feb 27] p. w15284. Report No.: w15284. Available from: http://www.nber.org/papers/w15284.pdf
  • 71. Aurelius M. Meditations. In: Eliot CW, editor. Harvard Classics vol 2. New York, NY: P.F. Collier & Son; 1909. p. 193–306.
  • 72. Seneca LA. On the shortness of life. In: Hardship and Happiness. Chicago, IL: University Of Chicago Press; 2014. p. 110–34.
  • 76. Doob LW. Patterning of time. New Haven, CT: Yale University Press; 1971.
  • 83. Borenstein M, editor. Introduction to meta-analysis. Chichester, U.K: John Wiley & Sons; 2009. 421 p.
  • 84. Borenstein M, Hedges L, Higgins J, Rothstein H. Comprehensive Meta-Analysis Version 3. Englewood, NJ: Biostat; 2013.
  • 96. Goodin RE, Rice JM, Parpo A, Eriksson L. Discretionary time: A new measure of freedom. Cambridge, UK: Cambridge University Press; 2008.
  • 101. Burrus A. What Does Time Management Mean to You? Exploring Measures of Time Management and Group Differences [Doctoral dissertation]. University of Missouri-St. Louis; 2019.
  • 109. Sharma S. In the meantime: Temporality and cultural politics. Durham, NC: Duke University Press; 2014.

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings
  • Advanced Search
  • Journal List
  • J Educ Health Promot

Relation between stress, time management, and academic achievement in preclinical medical education: A systematic review and meta-analysis

Soleiman ahmady.

Department Medical Education, Virtual School of Medical Education and Management, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Nasrin Khajeali

1 Deprtment of Medical Education, Fasa University of Medical Sciences, Fasa, Iran

Masomeh Kalantarion

Farshad sharifi.

2 Elderly Health Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran

Mehdi Yaseri

3 Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of medical Sciences, Tehran, Iran

Identifying the learners' problems is important. Besides, many factors are associated with academic failure, among which time management and stress are more important than any others based on evidence. By using a systematic review and meta-analysis, this study aims to synthesize the findings of studies about the correlation of time management and stress with academic failure to suggest a more in-depth insight into the effect of these two factors on academic failure. Four databases were searched from the inception of January 2018. Publication bias was evaluated visually using funnel plots and sized up by Egger's test. Ninety-four articles were found to be qualified for inclusion after full-text review and additional manual reference made. Of these, 8 were studies of educational interventions that were reviewed in this paper. Regarding the relation of stress and academic performance, the Funnel plot (results not shown) and Egger's test showed no publication bias in the studies ( P = 0.719). Based on this result, the estimated pooled correlation (reverted by hyperbolic tangent transformation) between stress and academic performance was found to be -0.32 (95% confidence interval: -0.38–-0.25). In conclusion, the review recognized a series of potentially mutable medium-to-large correlates of academic achievement, time management, and stress. It would be essential to have experimental data on how easily such self-regulatory capacities can be altered, and these interventions could help students enhance their potential, providing empirical tests for offered process models of academic achievement.

Introduction

Identifying the learners' issues early and offering advice from the start is an essential investment in the training and progress of future practitioners.[ 1 ] The National Committee on Internal Medicine (1999) has described the learner as a trainee who identifies the underlying problems that required to be addressed by a program leader or manager.[ 2 ] Some educators have expressed their concern about difficult learners in case they negatively affect educational programs and other students. Although studies may predict different elements, medical educators would like to be able to predict merely.[ 3 ]

Academic failure is a problem that has turned out to be a central concern for countries in different parts of the world. In order to find the different causes of academic failure, several research projects in this field have been performed. Typically, students experience academic issues with academic and nonacademic characteristics, and the various combinations of reasons for academic failure result in different types of student profiles, suggesting different strategies of intervention.[ 4 ]

The evidence indicates that when intervention techniques are applied for failed students, their performance improves in the subsequent academic year.[ 5 ] Ahmady et al . indicate that failed students can be assisted in becoming successful in the classroom when appropriate intervention techniques are applied. Usually, in research concerning student learning and behavioral outcomes, certain personal attributes of the students are measured, which are then related to some outcome measure. Among these, study skills, such as time management, is one of the factors affecting academic achievement and also stress.[ 6 ]

Personal characteristics are personality, motivation, self-concept, cognitive style, intelligence, and locus of control. Nevertheless, some environmental and contextual difficulties, which lead to unsuccessful learning, are not considered. The purpose of this study is to identify the factors related to the failure of college students.[ 4 ]

Many factors have been related to academic failure.[ 1 ] Ahmady et al . indicate that 21 factors related to academic failure in preclinical medical students, and study skill and stress is reported to be more important among other factors. We have found several studies[ 7 , 8 ] that suggest time management is perhaps more important than any other study strategies.[ 6 ]

West et al . (2011) show that study skills (time management) are usually powerful predictors of first-semester academic performance in medical school and other higher education disciplines.[ 7 ] Practical time management skills are essential. Students who do not plan their time effectively run out of time before running out of the content. Relatively, few studies have investigated the joint contribution of academic performance and study skills.[ 9 , 10 , 11 , 12 ]

Another reason is that medical education is inherently stressful and demanding. An ideal level of stress can increase the level of learning, while over-stress can cause health problems, leading to a decrease in students' self-esteem and failure in their academic competence. A high level of stress can affect the students' learning process in medical school negatively.[ 13 ] Sources of stress include curriculum, personal competence, tolerance, and time outside of medical school. Increased anxiety is associated with increased depression and anxiety.[ 14 , 15 ]

Knowledge about the effective size of these factors (time management and stress) can help policymakers, managers, medical teachers, and counselors track the students' academic failure. It is essential to integrate the evidence produced through all studies to obtain useful information, help medical students, and provide directions for future studies. To the best of the authors' knowledge, this is the first systematic review and meta-analysis of the findings of studies concerning time management and stress associated with academic failure. It suggests a more in-depth insight into the effect of these two factors on the students' academic failure.

Materials and Methods

This systematic review was carried out following PRISMA guidelines.[ 16 ]

Search strategy

PubMed, Web of Knowledge Educational Resources, and Information Center, and Scopus databases were searched.

Using the search No., time limitation was set for searching the resources. For comprehensiveness of the search, the following keywords were used in the abstract, title, and keyword sections: “academic performance” and “academic failure” or “academic achievement” and “drop out;” “medical student” and “struggle student;” “time management” and “stress.” Hand searching was also done in Medical Teacher and Medical Education journals. Furthermore, reference lists of many articles were reviewed to identify the relevant papers. The most celebrated authors in this area were contacted for “gray literature:” conference proceedings, unpublished studies, and internal reports. The obtained data were included in the study. The inclusion criteria for the articles were as follows: being a correlation between study skill and stress with academic performance, observational study design, preclinical medical students, without any language, or time limitation from January 1987 to January 2018.

Inclusion and exclusion criteria

The exclusion criteria for the search were being secondary research or not being a preclinical medical student. All the databases were searched by one reviewer, and Endnote X8 was applied for data management. The articles were imported into Endnote X8 to remove the duplicate data before importing the data into Excel. The imported data were the list of authors, titles, journals, and years of publication. Two team members (N Kh and SA) screened the titles and abstracts to determine the potentially relevant articles. The full-text version of the study was then reviewed if the study met the selection criteria or if there was any doubt concerning the study's eligibility. Furthermore, a third independent researcher was requested to resolve any disagreements.

Quality assessment

The study quality was rated on STROBE guidelines. Over 100 journals have endorsed STROBE guidelines ( http://www.strobe-statement.org ).[ 17 , 18 , 19 , 20 ] Studies were rated for each of the following: title and abstract, introduction, methods, results, discussion, data collection methods, and other information. This yielded a quality rating with a range from 8 to 22.

Data extraction and analysis

As several different variables were tested in each article, thus the article names were repeated. Studies were coded according to author (publication year), effective factors in academic performance, measurement method, type of R, type of analysis, location, and type of study [ Table 1 ]. Two reviewers extracted data from the included articles. They compared extractions and resolved differences through discussion or with a third nonauthors.

Data extraction of articles related to study skill (time management) and stress

SMART=Study management and academic results test

This meta-analysis was conducted via Stata 15.0 software (StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC). As the distribution of the correlation was highly skewed, the inverse hyperbolic tangent transformation (z = tangh-1(rho) =1/2 ln ((rho + 1)/(rho - 1))) was applied. All the calculations were based on the transformed values. The Cochran's Q test and The I 2 statistic were used to assess and characterize the extent of the heterogeneity, respectively. I 2 -50% was indicated as considerable heterogeneity. Given the high heterogeneity of the data, the random-effects model was used. We used hyperbolic tangent transformation (rho = tangh (z) = [e 2 z - 1]/[e 2 z + 1]) to change the pooled estimates (and its 95% confidence intervals [CI]) to the pooled correlation. All the individual studies results were reported with 95% CIs and demonstrated in a forest plot. Publication bias was evaluated visually using funnel plots and sized up by Egger's test. A P < 0.05 was statistically significant.

The study selection initial database searches retrieved 13,123 articles. After exclusion of duplicate references, conference abstracts, screening titles and abstracts, 6305 articles were selected for further review (title and abstract). A total of 100 articles were found eligible for inclusion after full-text review and additional manual reference screening. Five articles, including the studies of educational interventions, were reviewed in this paper [ Figure 1 ].

An external file that holds a picture, illustration, etc.
Object name is JEHP-10-32-g001.jpg

Study flowchart demonstrates the inclusion-exclusion process

Study characteristics

Study setting and populations.

Most of the studies were completed in Europe (50%), 2 (25%) USA, and 2 (25%) Asia.

Type of design

The majority design in the articles was prospective, followed by correlational [ Table 1 ].

Aims of studies

The purpose of the studies was to report the effect level of the study skill (time management) and stress on academic performance.

Regarding the relation of stress and academic performance, the Egger' test and Funnel plot (results not shown) indicated that there was no publication bias in the studies ( P = 0.719). The same was obtained when we evaluated the relation of the study skill (time management) and academic performance, not statistically significant ( P = 0.833).

The individual studies transformed between stress and academic performance were shown in a forest plot [ Figure 2 ]; based on this result, pulled correlation (result from hyperbolic tangent transformation) between stress and academic performance was found to be – 0.32 (95% CI [-0.38, -0.25]).

An external file that holds a picture, illustration, etc.
Object name is JEHP-10-32-g002.jpg

Correlation between stress and academic failure

The individual studies transformed between study skill (time management) and academic performance were demonstrated in a forest plot [ Figure 3 ]; based on this result, pulled correlation (result from hyperbolic tangent transformation) between stress and academic performance was found to be 0.39 (95% CI [0.29, 0.47]).

An external file that holds a picture, illustration, etc.
Object name is JEHP-10-32-g003.jpg

Correlation between study skill (time management) and academic failure

To the authors' knowledge, this is the first systematic review and meta-analysis of the evidence concerning the effect of study skill (time management) and stress on academic performance.

Overall, with this review, we found medium to high-quality evidence from a modest number of studies, suggesting that study skills (time management) and stress significantly affect academic achievement: study skill (time management) (ES: 0.39) and stress (ES: -0.32).

However, research suggests that study skills (time management) are also significant factors affecting academic achievement in medical schools.[ 8 , 21 , 22 , 23 , 24 , 25 ]

Study skills are one of the more reliable predictors of first-semester total grades.[ 7 ] The predictive strength of first-semester final average is accounted for by scores on time management,

Teaching time management rules, such as preventing postponement, previewing data, reviewing material shortly right after presented, prioritizing items, handling study periods, reviewing repeatedly, and making time for other commitments, is an essential component.[ 26 ]

For instance, sometimes, students procrastinate studying material they have problem with or do not see the applicability of. In this instance, seminars or counseling, which concentrate on arranging these projects for one's optimum time of day such that it will be simpler to focus on the material and reduce procrastination, may be offered.[ 27 ]

Time management aims to improve the nature of activities that require a limited time. The inability to use time in the learning process is the main problem for the students. Previous studies have shown that the excessive intensity of courses affects productivity negatively. In this situation, medical students, who have to cope with an intensive training curriculum, may inevitably but efficiently make the most of their time. To succeed in the education process, medical students must set goals for their education and plan for appropriate academic progress. They, therefore, have to follow course schedules, be prepared for examinations, and use the time available for other activities.[ 28 ]

Another significant issue is that there is a substantial increase in stress levels during study times, in the 1 st year in particular.[ 29 ] Perceived stress is a key factor in discriminating among students with low versus high academic performance.[ 30 ] First-year students face different challenges that can be seen as potential stressors. They have to get familiar with a new environment, get into contact with other students, choose their lectures and seminars, participate in extracurricular activities, and manage their first tests. Another source of students' perceived stress is time-related demands, such as an increasing workload, time pressure, and regulation of their self-study.[ 31 ]

Pfeiffer notes that too much stress is negatively associated with students' readiness, focus, and performance, while positive stress helps the student achieve maximum performance.[ 32 ] It should also be recommended that this situation is the first exam in which students are exposed to a significant amount of integrated curriculum. Often, students are suggested by their seniors to pursue an education in the coming years; thus, they can lower the stress levels, control stress in a better way, and enhance their academic performance.

Managing self-efficacy, flexibility, and social support also are related to academic achievement; thus, intervening to enhance self-efficacy, resilience, and social support may lessen the perception that stress is affecting performance.

Limitations

The limitation of this review is that statistically significant time management and stress have not been reported in all studies.

Conclusions

This review of 31 years of research on the correlation of stress, time management, and academic failure has been devoted to the understanding of the effect of time management and stress on academic achievement of medical students. This systematic review and meta-analysis are the first in the field. We wish that this work provides a base for more focused research and intervention. Finally, our review and others have identified a series of potentially modifiable medium-to-large correlates of academic achievement, time management and stress in particular. It would be worthful to have experimental data on how easily such self-regulatory capacities can be altered, as well as for whom, over what period, and to what extent do such changes to be effective academic performance. These interventions could help students develop their potential and would provide empirical tests for proposed process models of academic achievement.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

Acknowledgments

The authors would like to thank all of authorities and students at Medical School in Shahid Beheshti University of Medical Sciences for their assistance.

Captcha Page

We apologize for the inconvenience...

To ensure we keep this website safe, please can you confirm you are a human by ticking the box below.

If you are unable to complete the above request please contact us using the below link, providing a screenshot of your experience.

https://ioppublishing.org/contacts/

Please solve this CAPTCHA to request unblock to the website

Cart

  • SUGGESTED TOPICS
  • The Magazine
  • Newsletters
  • Managing Yourself
  • Managing Teams
  • Work-life Balance
  • The Big Idea
  • Data & Visuals
  • Reading Lists
  • Case Selections
  • HBR Learning
  • Topic Feeds
  • Account Settings
  • Email Preferences

Time Management Is About More Than Life Hacks

  • Erich C. Dierdorff

introduction in research paper about time management

Your productivity hinges on these three skills.

There is certainly no shortage of advice — books and blogs, hacks and apps — all created to boost time management with a bevy of ready-to-apply tools. Yet, the frustrating reality for individuals trying to improve their time management is that tools alone won’t work. You have to develop your time management skills in three key areas: awareness, arrangement, and adaptation. The author offers evidence-based tactics to improve in all three areas.

Project creep, slipping deadlines, and a to-do list that seems to get longer each day — these experiences are all too common in both life and work. With the New Year’s resolution season upon us, many people are boldly trying to fulfill goals to “manage time better,” “be more productive,” and “focus on what matters.” Development goals like these are indeed important to career success. Look no further than large-scale surveys that routinely find time management skills among the most desired workforce skills, but at the same time among the rarest skills to find.

introduction in research paper about time management

  • Erich C. Dierdorff is a professor of management and entrepreneurship at the Richard H. Driehaus College of Business at DePaul University and is currently an associate editor at  Personnel Psychology.

Partner Center

  • Student Support Services
  • Subject Guides

Essential Study Skills

Introduction to time management.

  • Getting Things Done
  • Creating a Weekly Schedule
  • Creating a Semester Plan
  • Planning an Assignment
  • Creating a Task List
  • Putting it all together
  • Additional Resources
  • Coping With Stress
  • Changing Your Perception of Stress
  • Problem Solving To Manage Stress
  • Reading with Purpose
  • Taking Notes in Class
  • Deciding What To Study
  • Knowing How to Study
  • Memorizing and Understanding Concepts
  • Taking Tests & Exams
  • Creating and Preparing For a Presentation
  • Presentation Anxiety
  • Delivering Presentations
  • Exploring Career Options
  • Identifying Areas of Interest
  • Knowing Yourself
  • Exploring the Labour Market
  • Researching College Programs
  • Setting Goals
  • Tackling Problems
  • Bouncing Back
  • Sleep Matters
  • Sleep Habits
  • Sleep Strategies
  • Meeting with Your Group
  • Agreeing on Expectations
  • Dealing With Problems
  • Study in Groups

The idea of time management might be new to you. Basically, time management strategies allow you to plan out your time so that you can get things done and have a more balanced, less stressful life. In this module, we’ll explore why you need time management techniques, how to figure out how much time you actually need to accomplish your tasks.

Time Audit: How much time do you need to get everything done in a week?

Time Audit: How much time do you need to get everything done in a week? Accessible Version - Opens in a new window

introduction in research paper about time management

Your Time Management Needs

  • Your Use of Time
  • Being Realistic About Your Time

Below are two videos that describe two types of typical college students: a recent high school graduate and a mature student. In additions, you can complete the Time Audit activity to assess your personal time needs.

How to Be Realistic About Your Time

Based on the earlier activity, you now know how many hours you need in a week to meet your personal and school commitments. Watch this video or read the information below for some tips and tools to help you manage your time and bring some balance to your week.

Questions to assess your time needs

Effective time management means creating a balance that allows you to do the things you need to do without getting completely overwhelmed and stressed. This requires being realistic about how much time you need. Try asking yourself these questions:

How much time do I actually have?

There are 24 hours a day, and 168 hours in a week. It sounds like a lot until you consider that you’ll spend some of that time sleeping, eating, getting from place to place, and other things like that. You may be surprised at how much time some little tasks take!

How much time do I need for school work outside of class?

Depending on your program, you should be spending about an average of 1 hour outside of class for every hour you spend in class. For example, 18 hours of class every week means 18 hours every week working on assignments, studying for tests, doing readings, preparing for labs,etc. Added to class time, that’s 36 hours every week - the equivalent of a full time job!

How can I balance my time?

Think about when you will do your outside-of-class work. Consider the following:.

  • Spread it out. If you have six hours of class on Tuesday, you don’t necessarily need to go home and study for another six hours. You could plan that study time for a lighter class day, or on the weekend, when you have more time. When you spread out your study time over 7 days of the week, it will likely take you only 2-3 hours per day outside of class time. Working a little every day will be better for establishing a routine, and it will also improve your learning and memory.
  • Plan study time for when you are best able to do your work. For example, how effective are you at 1 o'clock in the morning? Not all hours are created equal. One hour of good quality study time is better than three hours when nothing is sinking in. It’s just as much about quality as quantity.
  • Consider how your workload might change throughout the semester. Earlier on in the semester, you likely won’t have a lot of big assignments and tests to worry about; however, as the semester goes on, your workload will increase. You’ll likely have several large assignments and tests due all around the same time. Remember that you can think beyond just one week at a time. If you have several busy weeks later in the semester, it can help to get started in an earlier week, when you have more time.
  • Your Workload Tipsheet Check out this tipsheet for an illustration of how your workload will change throughout the semester:
  • << Previous: Time Management
  • Next: Getting Things Done >>
  • Last Updated: Oct 16, 2023 1:33 PM
  • URL: https://algonquincollege.libguides.com/studyskills

This paper is in the following e-collection/theme issue:

Published on 1.1.2024 in Vol 26 (2024)

Optimizing Telehealth for Diabetes Management in the Deep South of the United States: Qualitative Study of Barriers and Facilitators on the Patient and Clinician Journey

Authors of this article:

Author Orcid Image

Original Paper

  • Alessandra N Bazzano 1 , MPH, PhD   ; 
  • Tejal Patel 1 , MS   ; 
  • Elizabeth Nauman 2 , PhD   ; 
  • Dana Cernigliaro 3 , PhD   ; 
  • Lizheng Shi 4 , PhD  

1 Department of Social, Behavioral, and Population Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States

2 Louisiana Public Health Institute, New Orleans, LA, United States

3 Public Health Innovation and Action, New York, NY, United States

4 Department of Health Policy and Management, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States

Corresponding Author:

Alessandra N Bazzano, MPH, PhD

Department of Social, Behavioral, and Population Sciences

Tulane University School of Public Health and Tropical Medicine

1440 Canal St

New Orleans, LA, 70112

United States

Phone: 1 5049882338

Email: [email protected]

Background: The Deep South of the United States, and Louisiana in particular, bears a greater burden of obesity, diabetes, and heart disease compared with other regions in the United States. Throughout the COVID-19 pandemic, there has been a substantial increase in telehealth visits for diabetes management to protect the safety of patients. Although there have been significant advancements in telehealth and chronic disease management, little is known about patient and provider perspectives on the challenges and benefits of telehealth visits among people living with diabetes and providers who care for patients with diabetes in Louisiana.

Objective: This study aimed to explore barriers, facilitators, challenges, and benefits to telehealth for patients with diabetes and health care providers as they transitioned from in-person to remote care during the early COVID-19 pandemic to understand potential optimization.

Methods: A total of 24 semistructured qualitative interviews were conducted with 18 patients living with diabetes and 6 clinicians who served patients with diabetes to explore their experiences and perceptions of telehealth services for diabetes care. Approximately half of the participants identified as Black or African American, half as White, and 75% as female. Interviews were recorded, transcribed, and coded by experienced qualitative researchers using inductive and deductive techniques. A narrative, descriptive approach to the patient and clinician journey framed the study, including the development of internal journey maps, and reflexive thematic analysis was applied to the transcripts, with special attention to barriers and facilitators.

Results: In total, 5 themes illustrated barriers and facilitators for participants: convenience, safety, and comfort are the benefits of telehealth for patients and clinicians; yet telehealth and in-person visits are valued differently; the convenience of telehealth may have a downside; technology acts as a double-edged sword; and managing expectations and efficiency of the visit experience was an important factor. Individual experiences varied in relation to several factors, including comfort level and access to technology, health system protocols for providing telemedicine, and level of diabetes control among patients.

Conclusions: Recommendations for optimization include providing support to help guide and inform patients about what to expect and how to prepare for telehealth visits as well as allowing clinicians to schedule telehealth and in-person visits during discrete blocks of time to improve efficiency. Further research should address how hybrid models of telehealth and in-person care may differentially impact health outcomes for patients with diabetes, particularly for people with multiple chronic conditions in settings where access to technology and connectivity is not optimal.

Introduction

In the United States, telehealth services have significantly advanced during the last 20 years, including in the Deep South, where high rates of cardiometabolic disorders, such as obesity [ 1 ], diabetes [ 2 ], and heart disease [ 3 ], are prevalent. The growth of new technologies and the high rates of internet and electronic device use in the United States have enabled telehealth to flourish—between 2010 and 2017, the percentage of US hospitals using technology to connect with patients increased from 35% to 76% [ 4 ]. With the COVID-19 pandemic, the role of telehealth has become even more important, providing care to those who need it without in-person visits that could put patients at increased risk of exposure. During the initial peak of the COVID-19 pandemic, telehealth visits between 2019 and 2020 increased from <1% to as high as 80% in places with high COVID-19 prevalence [ 5 ]. Although the increase in telehealth visits was substantial, disparities among patient subgroups remained prevalent, with video telehealth rates lowest among populations without a high school diploma; among adults aged ≥65 years; and among Asian, Black or African American, and Latine individuals [ 5 ].

This significant increase and expansion of telehealth services has been especially important for patients with chronic diseases, such as diabetes, which requires consistent management by health care providers to prevent serious complications and mortality [ 6 ]. Diabetes affects an estimated 34.2 million people or 10.5% of the US population. For adults, there are significant disparities, with the highest prevalence of diabetes among minoritized populations, those without high school education, and populations that live below the federal poverty level [ 7 ]. Diabetes is one of the leading causes of death in Louisiana, with approximately 14.2% of the population being diagnosed with diabetes [ 7 ]. People living with diabetes, particularly those who also have other health conditions, are at an increased risk for serious complications due to COVID-19 and therefore may benefit from remote care [ 8 ]. Telehealth visits likely reduce the risk of exposure to COVID-19 infection, as patients do not have to interact with others in waiting rooms, examination rooms, and health care facilities, while also avoiding the potential risk of exposure during transportation.

The potential benefits of telehealth services can be seen most acutely in states that bear the greatest burden of COVID-19 prevalence. Early in the pandemic, Louisiana experienced the fastest growth rate of COVID-19 infections in the world, with 2 parishes comprising the New Orleans metro area exhibiting one of the highest per capita death rates among metropolitan cities in the United States [ 9 ]. There were significant racial disparities in death rates in Louisiana at a magnitude greater than other epicenters of the pandemic in the United States [ 10 ], particularly among the Black or African American population, where the rate of death due to COVID-19 was significantly higher than that of other races [ 10 ]. In March 2020, the Louisiana Department of Health directed health care providers to postpone care for 30 days and encouraged providers to use telehealth services. At the same time, the Louisiana Department of Health eased Medicaid billing restrictions on the provision of telehealth visits, expanding the reach for patients who need care [ 11 , 12 ]. This paved the way for the expansion of and increase in telehealth services in Louisiana.

With the significant increase and continuation of telehealth visits during the COVID-19 pandemic and evidence that telehealth programs can be an effective way of managing diabetes compared with face-to-face care [ 13 ], a better understanding of both patient and provider perspectives is required to identify opportunities to understand and improve the role of telehealth in patient care for this condition. A recent qualitative study of technology use for diabetes care assessed patient perspectives, indicating a good potential for benefit, with some hesitation on the part of patients about the use of technology [ 14 ]. With the rapid transition to telehealth early in the COVID-19 pandemic, remote services have been shown to be well received and result in positive encounters despite technical challenges [ 15 , 16 ]. From the health care provider’s perspective, the advantages of telehealth included increased usage of services [ 17 ], improved provider well-being [ 17 , 18 ], accommodation of patients who face challenges for in-person care [ 18 , 19 ], and fostering a sense of family-centered care [ 18 ]. The disadvantages from the clinician perspective include issues with technology and disparities in patient access to technology [ 17 - 20 ], less personal connection [ 19 ], inability to complete in-person diagnostic tests [ 20 ], reimbursement complications and out-of-state licensure restrictions [ 17 ], and a preference for in-person visits for patient populations that would benefit more from in-person care [ 17 ]. Studies exploring patient perspectives have reported that benefits, such as reduced travel time, shorter wait time, and cost savings [ 15 , 21 ], as well as convenience and safety [ 15 , 22 ], were seen as advantages, whereas internet issues, technical barriers [ 21 , 22 ], lack of connection with their provider, and unfamiliarity with the telehealth process [ 22 ] were seen as barriers. One study of patient perspectives on cardiology telehealth found that although both in-person and telehealth visits were viewed favorably, patient satisfaction was rated slightly higher for in-person visits. They found that the domain of clinical competence had the only lower mean score for telehealth [ 21 ]. A recent study highlighted racial disparities in telehealth use in the South with the transition to telehealth during the COVID-19 pandemic, finding an increase in the proportion of female and Black and Hispanic patients. However, discrepancies were observed in the likelihood of using an audio-video telehealth service, with older, Black, urban, and Medicaid or Medicare insurance carriers less likely to use audio-video telehealth services [ 23 ].

Although there have been significant advancements in telehealth and chronic disease management, they have not yet met their potential for improving and addressing the needs of all patients equally. The demographic context of patients with chronic diseases, such as diabetes, may be misaligned with access to effective telehealth modalities, such as video visits [ 23 ]; thus, to investigate broader issues around the value of telehealth, we applied qualitative methods to an exemplar population of people living with diabetes.

This study was nested within a larger natural experiment study [ 24 - 28 ] and comprised the qualitative component of a mixed method approach that aimed to explore barriers, facilitators, challenges, and benefits to telehealth for patients with diabetes and health care providers in Louisiana as they transitioned from in-person to telehealth care following the COVID-19 pandemic. Data from this study complement the larger study’s quantitative examination of facilitators and barriers to the uptake, adoption, and implementation of telehealth services among Medicare patients with diabetes from the perspectives of health systems, health care providers, and patients. The larger study provided quantitative comparison of diabetes control and continuity of care between patients with and without telehealth use during COVID-19, providing a mixed method approach alongside the qualitative data presented here [ 28 ]. This study also aimed to provide insights into the broader context of telehealth value for patients with chronic diseases in lower socioeconomic contexts.

This study is a part of the larger Louisiana Experiment Assessing Diabetes Outcomes study, which aimed to assess the reach, effectiveness, adoption, implementation, and maintenance of chronic care management services. The details of the larger study can be found elsewhere [ 28 ]. This qualitative study included individual interviews with 18 patients with diabetes and 6 clinicians who provided diabetes care, who participated in semistructured interviews to explore their experiences, including barriers and facilitators to engaging in telehealth services for diabetes care. To guide the preparation of this study, the Standards for Reporting Qualitative Research Reporting Guidelines [ 29 ] were used at each stage of the process.

Sampling and Recruitment

Purposive sampling was used to identify participants who were either patients who received a telehealth visit for diabetes care after March 1, 2020, or health system staff who had conducted or coordinated such visits. Recruitment of patients occurred through patient portal messages and secure emails, with phone call follow-up, and participants were purposively selected to represent the broader population of patients with diabetes in Louisiana who depend on Medicare and a mix of insurance providers. Chain referral sampling was also used, and interviewees from the previous year of the study assisted in the identification of potential participants. In total, 60 patients were invited and 18 of them agreed to participate, and among 10 clinicians who were invited, 6 agreed to participate, fulfilling the expectation of a sample sufficient for qualitative research using the principles of code saturation in data analysis [ 30 , 31 ]. Health care provider participants were defined as clinicians and included primary care physicians, endocrinologists, and advanced practice nurses, allowing for a variety of practice experiences. Clinicians were invited via email or phone to participate in interviews and given information about the study. The study steering committee, including patient partners and researchers, reviewed and facilitated participant recruitment strategies.

Data Collection Methods

Interview guides were used to support the semistructured interviews and were developed in partnership with study steering committee members. To strengthen the validity, the guide was pretested with patient partners who had diabetes and experienced telehealth services during the pandemic. Interviews were conducted and recorded on receiving participant consent through videoconferencing for participants who stated that the technology was available to them and via telephone for those who preferred not to use videoconferencing. No patients had to travel for the interview, and all phone or videoconference interviews were conducted in private locations. Patient and provider participants were interviewed between October 2020 and April 2021 by researchers with advanced degrees in public health and experience in qualitative research. To acknowledge their contributions, participants received gift cards. A study description explaining the purpose of the research, goals for the interview, and participants’ rights was provided before the interviews, at which only the researchers and participants were present, and informed consent was obtained.

Data Analysis

All data were securely stored on encrypted computers and accessed only by the researchers who completed the analysis. A narrative descriptive approach provided a guiding orientation to the analysis, with special attention paid to the participant journey and attendant barriers and facilitators. The study researchers have used reflexive thematic analyses [ 32 ]. A total of 2 experienced qualitative researchers collaborated in coding, and through inductive and deductive strategies, they proceeded to creation of categories and candidate themes from the interview transcripts. Open coding was used to describe candidate themes, whereas deductive coding was used to check themes against the data. Journey maps were also developed with human-centered design tools applied to interview data to explore barriers and facilitators, paint points, and high points on participants’ journeys with telehealth. During the analysis process, peer debriefing and reflexive journaling through memos were conducted. NVivo qualitative analysis software (QSR International) was used to manage data. Preliminary and final themes and analysis progress were shared with the study leaders and researchers, partner organizations, and patient stakeholders to receive feedback on the findings. The researchers discussed preliminary data during the review process and consulted with patient partners for validity.

Ethics Approval

Institutional review board approval was provided for this research by Tulane University under reference number 906810.

Participant Characteristics

A total of 18 patients with diabetes and 6 health care providers were interviewed for this study. Patient participants were predominantly female (14/18, 78%) and married (8/18, 44%). The average age of participants was 60 (SE 3.0; range: 35-78) years, and, on average, patients had been living with diabetes for 13 to 14 (SE 13.5; range 1-48) years at the time of study. Half of the patient participants described themselves as Black or African American (9/18, 50%) and half described themselves as White (9/18, 50%). Table 1 presents an overview of the participant demographic data. For participating health care providers, the average age was 50 (SE 5.1; range 38-75) years, and they had been practicing in their profession for an average of 20 (SE 5.8; range 7-46) years. The participants included primary care physicians, endocrinologists, and advanced practice nurses, allowing for a variety of practice experiences. A total of 4 provider participants identified themselves as female and 2 identified themselves as male. In addition, 2 providers identified as Black or African American, 1 identified as South Asian, 2 identified as White, whereas 1 provider participant did not disclose this information.

Most patient participants had Medicaid or Medicare health insurance coverage. Almost all participants lived within a 45-minute drive of their health care provider or hospital, except 1 rural residing participant who lived 4 hours away from their provider. The participants described managing several other health conditions in addition to diabetes, including asthma, overweight or obesity, heart conditions, thyroid disease, breast cancer, vision issues, sleep apnea, kidney disease, genetic disorders, and most commonly high blood pressure. Most participants identified as independent and self-sufficient, although many had friends and family members who checked in with them and ensured that they were managing their health and helped with transportation, technology, or advice on diet and lifestyle.

In terms of previous experience with telehealth, patient participants had an average of 2 to 3 previous telehealth visits (range 1-5 visits). Some participants reported using the phone for telephonic visits (8/18, 44%), whereas others reported using the telephone and video (10/18, 56%) for telehealth visits. Of 18 patients, 14 (78%) had returned to mainly in-person care at the time of the study while continuing to have some telehealth visits. Table 2 illustrates the participants’ telehealth usage patterns.

a Characteristics, including race, refer to categorizations used by the United States Census Bureau and other entities; these are not an indicator of biological difference but are presented to provide context about socially constructed experiences.

b N/A: not applicable.

c Not available.

Overview of the Telehealth Journey for Patients and Clinicians

Both the patient and provider participants were asked to describe their user journey during the telehealth process. For patients, the activities for scheduling and accessing telehealth visits were generally similar across the different health systems they used. Participants typically described receiving a call or notification at least 1 day before their scheduled visit and receipt of a link to log into the visit. If the patient had trouble navigating the log-in process, some participants described that a member of the health care staff would reach out to help guide them. On average, telehealth visits lasted between 10 and 20 minutes. However, the reported duration of the full range of visits was between 1 minute for a patient who did not have results to discuss to a full hour for a participant who had a more complex health issue to discuss. Patients used phones and laptops to connect to visits, and video or phone visits were often conducted at home (locations within the home included kitchens, offices, living rooms, bedrooms, and bathrooms), but some took place in a car or outside the home. Where patients took telehealth visits at home, they also reported that family members might be at home or in the same room as the patient during the telehealth visit.

From the health care provider perspective, the telehealth process varied based on the standard operating procedures of the individual health system. Health systems had different scheduling approaches for telehealth; some allocated full days or half days for scheduling strictly telehealth visits, with in-person appointments scheduled during a different block of time; others had both in-person and telehealth visits interspersed throughout the day on their schedule. The latter format was considered the least efficient by clinicians due to longer wait times when switching between in-person and telehealth visits, as well as difficulties making connections with patients. Health care providers reported variable visit durations for telehealth visits between 10 and 20 minutes on average, and these were taken from different locations. Certain facilities had physician workrooms, conference rooms, or offices that the provider could use. A couple of providers reported taking calls from home; however, challenges with this approach were mentioned, including distractions. Clinicians described using smartphones, laptops, and tablets for telehealth and also mentioned apps, such as Doximity, which conceals the phone number of the caller, as being used in telehealth care provision [ 27 ].

Facilitators and Barriers to Telehealth Care for Diabetes

The 5 themes drawn from the qualitative analysis are presented in subsequent sections. Overall, participants described positive experiences with telehealth during the pandemic, although perspectives ranged from a strong preference for telehealth to a strong preference for in-person care. The participants described valuing the convenience, safety, and comfort of telehealth visits. However, patients seemed to value telehealth visits differently than in-person visits, indicating the importance of in-person interactions with their provider and physical check-ups. Furthermore, health care providers stated that patients had different expectations for a telehealth visit and that sometimes the flexibility and convenience of the telehealth visit translated to patients not being prepared for visits, having unrealistic expectations about wait times in telehealth, and taking calls from locations that were inappropriate for health care (eg, while walking through a retail store).

The role of technology as both a barrier and a facilitator was a key theme for both patients and clinicians. Clinicians who experienced it as most beneficial were typically more comfortable navigating technology, had more experience with telehealth, or were part of a health system that provided support around technology. Patients with diabetes who were more comfortable with technology used with telehealth (eg, those who could easily navigate devices, log into portals for video telehealth visits, and receive visit summaries from the patient portal) described the most positive experience with telehealth visits.

Clinicians described valuing the convenience and safety of telehealth for patients, particularly the ability of patients to check in with them without traveling to a health care facility, the efficiency of the system for those who were tech savvy, and improved safety for patients with diabetes who were at high risk for severe COVID-19. Similar to patient participants, clinicians described challenges and delays in care because of issues with technology. For providers, telehealth was perceived to be the best option for patients with controlled diabetes. Both clinicians and patients endorsed a preference for a hybrid model consisting of both in-person and telehealth care.

Convenience, Safety, and Comfort of Telehealth for Patients and Providers

Patients described many facilitators for using telehealth, with the most common being convenience, particularly for those with mobility issues, those with busy schedules, or those who lived at greater geographical distances from their provider. Patients described valuing the convenience and comfort of not having to drive, park, check in, and wait for their appointment as well as the ease of talking to their provider over the phone:

Yes, I love [telehealth visits] I don’t have to get dressed, I don’t have to drive, I don’t have to do nothing, but answer my phone...That’s a big help, you don’t have to get dressed to go out and you don’t have to spend no money on no gas. It’s just worth [it] all around. You can definitely be safe because you are not going into no crowd. [Black or African American female patient aged 77 years, living with diabetes for 11 years]
If I’m too busy, I'd rather do telehealth than trying to finagle my schedule. “Oh, I got to go to the doctor. I got to do this. I got to do that.” Telehealth would be much easier. I probably could sit in the car, going wherever I'm going, and do my telehealth visit and still be at the place wherever I need to be. [Black or African American male patient aged 46 years, living with diabetes for 2 years]
I feel like you still get the same healthcare as if you were standing there in front your doctor, unless they have to look at something, like your wound or something ...i think it is very accessible and very easy to use. [White female patient aged 43 years, living with diabetes for 8 years]

One participant’s view was that she felt more honest and open sitting at home, speaking with her provider during a telehealth visit:

I think I'm more honest when I'm sitting in my chair at home. It's harder for them to judge me virtually, in my mind, at least. I think I'm just so much more comfortable here, that I'm more open. [White female patient aged 60 years, living with diabetes for 20 years]

Participants also discussed valuing their safety. Patients who did not have a strong preference for in-person or telehealth visits mentioned that they would do whatever was the safest and recommended during the pandemic. Although an in-person visit was discussed as important if they had a physical issue they felt their physician should assess, patients acknowledged that traveling and being in a hospital could put them more at risk for COVID-19 infection:

Either way it's fine with me, but for me, the virtual is the best for everybody's sake. I believe that's the best way to do it. I’m fine with it. A lot of other people are not. [Black or African American male patient aged 69 years, living with diabetes for 18 years]
No. I think [I don’t have a preference for] either one, I mean, I like the convenience of not having to go into the office, especially right now having COVID and then hearing that I could get it again, the safety part of it. [White female patient aged 43 years, living with diabetes for 8 years]
One thing I did like, I was at my house. I didn’t have to go through all the shenanigans at the hospital with checking in and all this kind of stuff and social distancing stuff, worrying about blood pressure. Some people social distance, some people don’t. I didn’t have to be bothered with that. [Black or African American male patient aged 46 years, living with diabetes for 2 years]
I liked it because my doctors know I’m taking my medicine and I like him because he has a pleasant spirit. I don’t like driving there and finding parking. I didn’t feel rushed. I would do a virtual visit, I’m nervous of the virus so I don’t want to go in the office. [Black or African American female patient aged 66 years, living with diabetes for 18 years]

Health care providers also mentioned safety from COVID-19 as a factor in telehealth visits. This was especially important for providers who worked in locations where it was difficult to physically maintain the distance between patients or in crowded waiting rooms. In such cases, the risk was perceived as especially high, particularly for patients with multiple conditions or those who were not vaccinated:

I don’t think it's really fair or safe, [for] the patients who aren't vaccinated to come in and sit in a small lobby early with patients who have diabetes and the like. I just don’t think it's good public health. You choose not to be vaccinated then you’re probably going to get a telehealth or televideo call...I'm excited about that. [Health care provider aged 75 years, practicing for 46 years]

Other facilitators for telehealth included patients feeling that their telehealth visits provided sufficient time with their provider and that their provider was attentive and took an appropriate amount of time with the telehealth visits:

I liked that I didn’t have to go out there, and still I felt like I was right there talking to him. He is tall and big and younger than me and just like a sweet teddy bear not really overweight just like a teddy bear. He was a doctor that I could sit and talk to him and he would listen. It took me years to find him. [White female patient aged 75 years, living with diabetes for about 10-15 years]
It works fine for me. I feel like my doctor is very attentive and I can ask any questions, so it works fine either way for me. [Black or African American female patient aged 51 years, living with diabetes for 7 years]
I didn’t feel [rushed]. Everything was answered. Everything was fine as if I was in the office because I got my instructions on what I needed to do with my blood work. It was just fine. [Black or African American female patient aged 48 years, living with diabetes for 2 years]

Participants also mentioned the comfort and ease of telehealth visits. One participant mentioned that given the ease of a telehealth visit, she was more inclined to call the physician, knowing she would not have the hassle of driving in:

Because it’s so much easier for me to be able to do one of these visits just over my phone from home, you know that sometimes, there’s something that goes wrong, you think, “Oh, I should probably go to the doctor, but I don’t want to mess with it”? I think I would be more inclined to go ahead and contact the doctor knowing that I wouldn’t have to go in. [White female patient aged 60 years, living with diabetes for 20 years]

A patient reported that their telehealth visit allowed them to be prepared and enabled a thorough assessment, especially as it was the first visit:

It allows me to go ahead and have my questions prepared to raise the questions that I need to raise. They go through reports...with the first virtual visit that I had [with my doctor], I almost died. It was 45 minutes long. [That’s] the thing my son said was good about it is that he was here, so he heard everything. [White female aged 78 years, living with diabetes for about 45 years]

Valuing Telehealth Differently From In-Person Visits

Although the patient participants described appreciating the convenience, safety, and comfort of telehealth visits, a model of care that included both in-patient and telehealth visits was considered optimal. One caveat is the need for laboratory tests, such as hemoglobin A 1c (HbA 1c ), for which many patients underwent laboratory work at outside facilities before their telehealth visits. Patients who had difficulty uploading previously obtained laboratory results and navigating technology preferred in-person care:

[The telehealth visit] was good, but I told him I couldn’t wait to see him. I would rather go into the office. It was alright, but I like when I go to see him. He’s a very patient doctor the [telehealth] conversation didn’t feel rushed, I just think in person was better. [Black or African American female patient aged 67 years, living with diabetes for 7 years]
I would use [telehealth] in the future unless I was having problems, like if I had other issues like high blood pressure or something like that. I wouldn’t use it all the time, but to be convenient like, if I just needed a prescription refill, the telehealth visit would be fine for me. [Black or African American female patient aged 48 years, living with diabetes for 2 years]

In addition to endorsing the importance of a physical check, participants wondered about the billing associated with telehealth visits. One patient described this as follows:

Well, virtual—I didn’t get any information, no blood tests and there's no A1C. It was a pointless endeavor. It was just [hospital] saying, “We can’t figure this out. We're just going to do a pretend appointment so we can bill the insurance company.” It was a phone call, so I had the phone in my hand, I’m in my room by myself. They asked me how I'm doing, I said fine and that was the end of it. Probably less than a minute. [White male aged 70 years, living with diabetes for 3 years]

For clinicians, telehealth visits were generally described as a good asset for diabetes patient care. Provider sentiment was that telehealth worked best as an additional element for patients but could never fully replace in-person care. Similar to patients, clinicians discussed the convenience of telehealth visits, especially for patients who lived in rural areas, had issues with transportation, or were older adult patients who have mobility challenges:

I think probably 80% of patients are fine. We could do this over the video or no video or phone, not in person, let's say. Unless you have open wounds or unless there's changes if we had seen you before, and you’re saying, “Well, Doc, everything's similar.”...I think probably majority is fine. Not to say we should convert all of their visits to tele-visits, but possibly two out of three, or every other, or some, whatever is convenient. [Female health provider aged 38 years, practicing for 7 years]

In certain circumstances, providers could not substitute a telehealth visit for an in-person visit, such as with a new patient, patients with multiple chronic conditions, where a patient had major changes in their health status, or where a patient had symptoms of uncontrolled diabetes. One provider noted difficulty in seeing patients who were not English speaking for telehealth visits, as it was challenging to get an interpreter for a telehealth call. A physician described the need to see new patients in person as follows:

I'm personally seeing all my new people in person. If they were seen by another endocrine provider, or if they were a new referral, I see them the first time in person. I just feel more comfortable that way, knowing that I've examined them thoroughly. We have their vitals, we've gone through the meds. After that, I usually do follow up tele-visits. [Female health provider aged 40 years, practicing for about 5 years]

Clinicians discussed telehealth as generally best for patients whose diabetes was well controlled as a check-in to ensure that patients were continuing to do well. Overall, however, providers would prefer to see patients in person if there were any issues or complications:

For the patients who are doing very well, who are largely on auto pilot, for whom there’s not very much to discuss...It's usually just, “Oh the labs look very good, just keep doing what you’re doing.” It works well, it works quite well, but for the patients who have more complexity, more comorbidities for some, there's a fair amount that needs to be done. You end the meeting feeling that there's still more that needs to be done. In some cases, you actually have to tell the patient that they do need to set up an appointment to be seen in person. It all depends on how much current comorbidity and how well controlled the patient is. [Male health care provider aged 53 years, practicing for 28 years]

Given the benefits and minor drawbacks of telehealth visits for diabetes care, provider participants described telehealth as being a permanent part of patient care. One physician noted:

I really do think there’s definitely place for telemedicine for these patients even past pandemic. Maybe they come into the clinic once in six months or even once a year depending on how well their control is for diabetes. Of course, if they truly need to be seen, they need to be seen. [Female health provider aged 38 years, practicing for 7 years]

Convenience of Telehealth May Have a Downside

A downside to the flexibility and convenience of telehealth visits may be that it also affects how well prepared patients are for a visit and lessens focus and attention on the visit content itself. As telehealth visits can be taken from a wide variety of locations, patients sometimes reported having their visits in places that were not private or where significant distractions prevented them from focusing on the visit. This was especially noted during telephonic visits.

One participant described taking a call from the car from the side of the road, whereas another reported taking a call while out shopping. Patients who were multitasking and engaging in driving or shopping could be less likely to have laboratory or blood glucose results available for the visit:

With me, it was fine the first time because after we talked on text, I didn’t have to leave work and go over there...I pulled over on the side of the road and we talked and after...I went on to my destination. [Black or African American male patient aged 69 years, living with diabetes for 18 years]
I like the fact that even if I am at work or home, or even in the car, it’s just the flexibility, like, “Hey, if I can’t make it to the—If I couldn’t get to the doctor’s office, I could do it from wherever.” [Black or African American male patient aged 47 years, living with diabetes for 1 year]

Although participants valued the comfort and convenience of telehealth visits, health care providers noted that they may not be as well prepared for such visits. One provider mentioned that some patients do not see the telehealth visit as a “real” physician’s visit, so they can be less prepared for the visit or in a location that is distracting:

I find patients are going about their day and then realizing their appointment is live and [saying] “I don’t have my data because I’m at Target.” They didn’t really think of it as a doctor’s visit and so they didn’t prepare as well as they normally would...so it’s a little bit distracting. They don’t have their pills with them or they are not at home to say, “Oh, I’m taking this much of that or this medicine or that medicine.” [Female health provider aged 38 years, practicing for 7 years]

A clinic coordinator also noted that patients sometimes did not follow through laboratory tests in the context of telehealth visits, impacting their ability to follow-up:

The doctor does the telehealth visit, he put in his plan, he wants the patient to follow up in 3 months with labs. Then I mail the patient a reminder appointment, advise them, and get the labs done before their appointment. Once a patient clicks on for the visit, if they don’t do the lab—usually I prep the schedule before an appointment and be like “You didn’t do your lab?” That kind of thing. The patient being compliant is a big factor with a telehealth visit. [Male health provider aged 53 years, practicing for 23 years]

When patients are on the go, the technology can also be less reliable, as noted by another clinician:

The negative side of [telehealth] is the internet connection...If the patient is not at home or a home setting, like if they are in a car, I find those patients won’t be able to get good reception. [Female health provider aged 38 years, practicing for 7 years]

Clinicians also noted often having to wait on the line for patients to locate blood glucose logs or medicines. This situation was contrasted with in-person visits, for which patients know they are expected to arrive with their information. Where patients needed to find laboratory results or other information during a visit or call, it could significantly delay visit length having a knock-on effect for subsequent telehealth patients:

With telehealth, usually when they come into the clinic, their vitals are done and the nurse reconciles their meds for them...Most people do have the equipment at home to check. It’s not really been an issue, but sometimes you have to wait for them to do it while you’re on the phone with them just to make sure that their blood pressure is okay. The same thing with medication, sometimes they don’t remember or they have to go check. You’re waiting a little bit for that to happen because you don’t have support of staff, and you’re doing it yourself, those things take longer sometimes. [Female health provider aged 40 years, practicing for about 5 years]

Technology as a Double-Edged Sword

Comfort level and proficiency with technology were variable among both patients and health care providers, as were the different modes and platforms used by health systems to provide telehealth visits.

Some patients were only comfortable using their phones for a visit, whereas others had no trouble navigating video telehealth visits accessed through a patient portal. Those who felt most comfortable with technology were more amenable to telehealth as part of their care and seemed to have a better and more efficient experience. One patient described this as follows:

No, [no difficulties with the technology]. I was fortunate enough, we had been using that type of communication at work, so I was a little familiar with the process, how it works. No, I didn’t have any issues with it at all. [White male patient aged 57 years, living with diabetes for 1 year]

However, navigating technology for many patient participants was an issue. Depending on their technological capabilities and schedules, patients preferred either phone or video visits. Patients who were less technologically savvy described frustrations navigating the system as well as getting used to the telehealth visit experience and were more likely to value in-person care. Some participants mentioned having help from family members or having someone from the hospital call before their first visit to ensure that they could log on; however, others did not have support with the technology. One participant mentioned the following:

I just think that when it’s your first time on there, if they actually had help for someone, an elderly person to do telehealth. For the first time, I think somebody might need to coach that person or assist that person logging on. Just make it friendlier for the elderly or the up in age people. [Black or African American female patient aged 46 years, living with diabetes for 2 years]
I was a little concerned for the very first time, I was like, “Oh, I hope I don’t have a problem with the phone, what if my video doesn’t work?” I had no problems from day one, everything worked out perfectly. [Black or African American female patient aged 51 years, living with diabetes for 7 years]
The first time it was aggravating because I couldn’t make the [technology] connection, so they just got me later. Generally speaking, I prefer to go in person, but depending on which doctor and what reason [for it], telehealth is just fine. [White female patient aged 59 years, living with diabetes for about 15 years]

From the clinician perspective, the specific technology, for example, the internet connection and how comfortable the patient was in navigating the connection, was mentioned as being very challenging. This was likely to be more of an issue for health systems primarily serving low-income patients. A clinician within a federally qualified health center described it as follows:

It was challenging, even to get them registered between the back and forth, we would have my medical assistant, myself and an IT person and the nurse, so you would have 4 people trying to launch one visit. It would take 45 min to do a back and forth...just trying to troubleshoot and talk a patient through on a landline, to help launch something on an iPad. [Female health provider aged 51 years, practicing for 22 years]

One provider described how some lower-income patients had “Katrina phones,” which were provided just after Hurricane Katrina in 2005 and do not have video, voicemail, or text options, making it difficult to call back or get in touch with the patients. Other issues described were patients not answering a call from a phone number that they did not recognize or not being savvy with text or voicemail, which hampered scheduling or contacting patients:

Some patients have phones that they call Katrina phones. It was government- sponsored phones that don’t have voicemail, that have very few minutes and so…they either pick up or they don’t pick up. The other issues is when we call from the hospital, the phone [number] may be private...most of our patients…don’t answer any of those...Then some patients have a fancy phone…and they don’t really know how to use it...I think, definitely, a good portion of my population is not really tech-savvy. Again, I think training them to use this more and more it will work, it’s fine. It’s just we couldn’t make it all happen in a month. [Provider 2]
There were technical issues going on that I think made [the telehealth process], particularly at the beginning, it wasn’t always very smooth and patients weren’t looking at times and everything. The elderly were having difficulty linking in. [Male health provider aged 75 years, practicing for 46 years]

Telehealth visits were most beneficial and efficient when patients could navigate the system and upload their health information, such as blood pressure, glucose levels, and weight. When patients did not have this information available, telehealth visits were not as comprehensive:

Some patients don’t have access to take their own vital signs, like their blood pressure, pulse, they don’t [have] a pressure kit. When they log into the app, the link is going to ask them their weight, blood pressure, and if they have that available, they’re supposed to input it...some patients input their vitals some don’t. [Female health provider aged 44 years, practicing for 12 years]

However, it was noted that as patients had more telehealth visits, the process improved. One clinician stated as follows:

We still have occasional glitches where you have a patient who is scheduled and signed up for a telehealth visit, and there’s trouble with them logging in. It’s a lot less now because people are becoming more accustomed. If they’ve done it once, they’ve done it twice, now it’s a matter of habit. They’re familiar with it now. [Female health provider aged 51 years, practicing for 22 years]

Managing Expectations and Efficiency of the Visit Experience

Expectations about how efficient telehealth visits should be, including scheduling, logging in, and undertaking a visit, were frequently noted. On the one hand, patient expectations for timely and efficient care were not met, whereas on the other hand, care providers were limited by their ability to meet expectations based on how their schedules were set up and how their health system operated telehealth visits.

Patients sometimes described long wait times for health care providers to log into their telehealth visits when they had an appointment scheduled for a specific time. As telehealth calls can be taken from anywhere and had less of a wait time to schedule, there was a perception that there would not be a wait time for a visit. The participants were frustrated when these expectations were not met:

I did have a problem when it was time for my appointment because on the app, you have like a grey screen, and it stays like that for a while because you’re waiting on the doctor to come in, so I called him twice because the appointment was going over time. My appointment was for 10:00. It was 10:30 and nobody had came on the line. I thought I was doing something wrong or I thought something had happened or I missed the appointment. [Black or African American male patient aged 46 years, living with diabetes for 2 years]

Providers noted that they could not meet patients’ expectations of efficiency because of scheduling and other issues that were outside their control. This contrasted with the expectation that patients had with in-person visits. One clinician described this as follows:

I think when a patient is in your office and they’re sitting for 20 minutes, it's expected my doctor is running late...Unfortunately, that's the reality. It shouldn't be that way, but that's how it is. On the phone, when you say I'm going to call around 11, they really think it's going to be exactly 11. You may be 11:30 just like you would be in clinic or even harder because you can’t get in touch with them themselves let alone the rest of your clinic flow. [Female health provider aged 38 years, practicing for 7 years]

One patient participant described being in his car, not expecting to have to wait for the provider to log on. Participants mentioned that once they had 1 or 2 visits, the process became easier, and they knew what to expect:

I just assumed that I could [log in to telehealth] if I was out. I assumed that I can still have a telehealth visit where I was at, but I remember saying I can go sit in my car. I think it was going to be too long to sit in the car. It had to be rescheduled. [Black or African American female patient aged 48 years, living with diabetes for 2 years]

Another participant waited 15 minutes to reschedule because of technical issues from the provider. They stated the following:

For me, it wasn’t hard, but I know on the other end, I don’t know if it’s because just they were just starting using it because there were a couple of times where I’ve called...I logged in for the telehealth visit and nobody was there and I waited 15 minutes and I said, “Either they are running over or they forgot, or something happened.” I log out, then I get a message saying, “Hey sorry, we were having technical difficulties. Can we reschedule for such and such a time?” [Black or African American male patient aged 47 years, living with diabetes for 1 year]

Clinicians lamented the challenges and need to temper patient expectations of timeliness in telehealth scheduling:

If you had patients scheduled on half an hour visit, but it took you 45 minutes just to launch it, and then another 15 to 20 minutes on the visit, we would be behind. We’re trying to call the patient saying, “We’re sorry we’re late. Just give us a minute.” One patient could take upwards of of an our to do a 15 minute visit or a 20 minute visit. [Female health provider aged 44 years, practicing for 12 years]
Yes, definitely, patients have expressed their frustration with, “Well, I thought they were going to call at 11 and it’s 11:30, or 40, or 20, whatever it may be.” I think they expect a little more easier contact just because it’s by the phone, and to us, it’s harder, I think. [Female health provider aged 38 years, practicing for 7 years]

Scheduling issues and wait times were commonly discussed by health care providers as some of the biggest barriers to efficient care:

There are patients who they may get in 30 minutes before the appointment and then leave out 10 minutes before...and then we have to track them down and say, “You didn’t wait long enough...your appointment was at 2:00, you went in at 1:30 and left at 1:45.” Then they've lost the link...particularly at the beginning it wasn't always very smooth and patients weren't looking at times and everything. [Male health provider aged 75 years, practicing for 46 years]

Clinician participants described challenges with scheduling in-person versus telehealth visits, which affected patient wait times and depended on how their health system organized the clinician workflow:

When we initially started, we had them interjected into the day and that’s when we realized the inefficiencies with getting the system to launch. We quickly transitioned to one or two, and each provider had their preference. I had a telehealth day. I had one day that was designated for telehealth and that's it. Then some providers they had a certain time frame designated for telehealth with maybe their mornings, they saw patients in the office, and then in the afternoon they would have a few telehealth scheduled. [Female health provider aged 51 years, practicing for 22 years]
I try to group them all together. We’re going to do virtual in the morning and in-person evening. I don’t like to have a virtual and then in-person, because...you’re going to run late behind if you have an in-person patient and then you got a televisit. Because somewhere you might have went over with the in-person visit, and the patient is on to the televisit. Those kinds of situations make a doctor run late too. [Female health provider aged 44 years, practicing for 12 years]

Principal Findings

The study found that both patients and providers appreciated the flexibility and convenience of telehealth visits. However, telehealth visits were considered by clinicians to be best for those whose diabetes is well controlled or less complicated for patients who were more tech savvy and have internet connectivity and for those with transportation challenges. A caveat of telehealth visits was that patients seemed not to approach the visit with the same focus and preparation as an in-person visit—patients could be distracted, lack important information on their condition, or hold different expectations about the visit, such as short wait times. In addition, some patients felt that telehealth visits were not as comprehensive. A mixed model of both in-person and telehealth visits was considered optimal by most patients and providers of diabetes care.

A recent study in Missouri found that women, older patients, and those with Medicare, Medicaid, and self-pay statuses used telehealth more during the beginning of the COVID-19 pandemic, although older patients; Black patients; urban patients; and those with Medicare, Medicaid, and self-pay status were more likely to use phone only compared with audio-video telehealth services [ 23 ]. Our study sheds light on some of the nuances of the experiences of a similar population in the South, as our patient population identified as predominantly female, older, and using Medicaid or Medicare and half identified as Black.

Our results showed that overall, for both patients and health care providers, telehealth was viewed as an important part of diabetes management and care; however, a hybrid model incorporating both in-person and telehealth visits was considered optimal, as has been noted in other studies [ 15 ]. Despite issues with connectivity and scheduling complications, both patients and providers appreciated the flexibility and convenience of telehealth visits, as has been described in other studies [ 19 , 21 , 22 , 33 ]. However, from both client and clinician perspectives, telehealth visits were considered best for those whose diabetes was well controlled, for patients without complications, and for those who were tech savvy or have good internet or cellular connectivity, which echoes the findings from prior studies on telehealth [ 5 , 17 , 19 ]. The benefits of telehealth appear to be especially important for patients with diabetes who have difficulty traveling to see their physician, including older adult patients, with the caveat that they must also be comfortable navigating technology [ 33 , 34 ], and in particular, telehealth has been shown to be beneficial for patients in rural communities [ 35 ].

For patients with chronic diseases, such as diabetes, health management through telemedicine can be crucial for continuity of care and avoiding exposure to COVID-19 [ 6 , 36 ]. However, the convenience and flexibility of telehealth visits must be weighed against the perception that virtual care may be less comprehensive or effective. Health care providers in the study discussed how patients sometimes would not approach a telehealth visit with the same focus and preparation as an in-person visit. Motivation to continue engaging in diabetes care is another important consideration, as one study noted the importance of combining “eHealth” with regular face-to-face consultations to avoid reducing patient motivation for engagement [ 37 ]. There may be downsides to the flexibility and convenience of telehealth, as patients and providers described visits taking place in distracting environments, difficulty in managing expectations regarding wait times for visits, or lack of diagnostic information, such as available blood glucose results.

Comparison With Prior Studies

Studies have indicated that most patients feel that telehealth visits can be as effective as in-person visits [ 16 , 38 ]. However, one study identified that patient respondents with higher HbA 1c levels who completed a telehealth visit were more likely to report that video care did not save them time, money, or stress, although a majority of those respondents still felt that video care was effective or beneficial in some way. In the same study, respondents with higher HbA 1c levels who had not yet had a video appointment were also more likely to report difficulty connecting remotely and were more concerned about the quality of care using video [ 15 ].

Technology was discussed by participants in this study as both a barrier and a facilitator for telehealth. Both patients and providers described a learning curve for using telehealth services. Older adults who were not as comfortable with technology seemed to prefer telephonic visits compared with video visits because of the ease of cell phone use, which has been noted in other studies [ 17 , 23 , 39 , 40 ]. Our study highlights some barriers to audio-video telehealth visits for this population, such as outdated technology (Katrina phones), inconsistent internet service, and lack of comfort with technology. Furthermore, age, sex, median household income, insurance status, and marital status have been found to be associated with patient participation in telehealth [ 41 ], providing evidence of structural inequality affecting patient comfort and the ability to engage in and benefit from telehealth. One study found that communities at greater risk of needing support after a disaster were significantly more likely to experience more barriers to telehealth, including access to reliable internet, low uptake of use of web-based medical portals, not feeling comfortable with technology, and more likely to report language barriers as a concern [ 42 ]. The Gulf South and Louisiana in particular are at high risk for similar events.

For patients, having the support to navigate early visits appeared to be important for improving the experience, and with more exposure to telehealth, the process became easier. Patients in this study had a broad range of experiences with telehealth (from 1 to 5 visits), which has been noted to impact perceptions of telehealth, particularly related to comfort with technology [ 35 ]. Issues and challenges with technology and computer literacy have been found to be significant barriers to the adoption of telehealth [ 43 ]. Similar hindrances have also been noted with other forms of non–face-to-face care in this setting [ 24 , 26 ], indicating that in all such remote clinical encounters, patients will benefit from support along the journey of chronic care management. Given the potential for telemedical interventions to be clinically effective in improving diabetes control overall and significantly improving HbA 1c concentrations [ 44 ], it is vital that these strategies be optimized to address potential challenges in advance, ensuring quality of care and value in health.

Understanding the experiences of patients with diabetes and clinicians who care for populations with diabetes is important, especially given the significant risks that patients with diabetes face due to infectious diseases such as COVID-19 [ 8 ]. The ability to avoid facility waiting rooms, offices, and exposure to large numbers of people makes telehealth an important tool for people with diabetes, and this factor coupled with the dramatic increase in the use of telehealth services in recent years [ 11 ] makes it crucial to investigate lived experiences with telehealth. Some of this study’s findings may be representative of an abrupt change to telehealth; for example, the facilitator of increased safety and comfort of telehealth during the pandemic and technological challenges. However, other themes are likely to be more persistent, such as the downside of convenience and mismatched expectations of telehealth visit efficiency and wait times. Additional studies should focus on whether barriers to telehealth seen in the immediate period after the declaration of the COVID-19 pandemic are persistent for people with diabetes and their health care providers or whether these fade for patients and clinicians.

Strengths and Limitations

This study used a rigorous qualitative approach to understand the telehealth experiences of patients living with diabetes during COVID-19 as well as the experiences of clinicians who treat patients with diabetes in Louisiana. This study had some limitations. First, the participants were mostly older adults, which may not be fully representative of all populations with diabetes who use telehealth, but were representative of key populations of interest in the region with the largest burden of chronic disease. Clinicians from a range of ages and years of practice were included. Different characteristics and experiences did provide a range of perspectives on telehealth barriers and facilitators. Second, some challenges were encountered in recruitment, including pandemic shutdowns and a hurricane in the region, which may have affected which participants were ultimately able to participate in the study. Finally, participant reports of experiences may have been influenced by recall bias or social desirability bias. Interviews took place at different lengths of time from the participants’ last experiences with telehealth. Certain limitations were outside our control (pandemic and hurricane); however, efforts to ensure reliability and trustworthiness were used throughout the study. As with all studies, there is a risk that participants may selectively present positive and socially desirable responses to experiences.

Conclusions

Telehealth plays an important role in the management of diabetes and may be especially important in areas with high prevalence, such as Louisiana. Issues of internet connectivity and proficiency using technology must be addressed to ensure equitable access across patient populations. In addition, support to help guide and inform patients on what to expect and how to prepare for telehealth visits is recommended to improve the experience of both health systems and patients. Preparing patients with diabetes for what to expect during telehealth visits, including having important health information available during the visit and managing expectations over wait times, is an important strategy. Similarly, allowing clinicians to schedule telehealth and in-person visits during discrete blocks of time could encourage efficiency. Further research should address how hybrid models of telehealth and in-person care may differentially impact health outcomes for patients with diabetes, particularly for people with multiple chronic conditions and in settings where access to technology and connectivity is not optimal.

Acknowledgments

The Louisiana Experiment Assessing Diabetes study would like to acknowledge the contributions of our partners, without whom this research would not be possible. These partners include Ochsner Health System, Tulane Medical Center, University Medical Center New Orleans, Research Action for Health Network (REACHnet, a PCORnet Clinical Research Network) and their multistakeholder Diabetes Advisory Groups, Pennington Biomedical Research Center, and Blue Cross and Blue Shield of Louisiana. Patients and community partners were also actively engaged in the research and contributed to this study. Patient Engagement Statement: patients and other relevant stakeholders as research partners were engaged in the planning of the study as well as the refining and finalizing of data collection forms and protocols and the results and discussion of this study. The authors collaborated with the program-wide steering committee, including patient partners, to ensure that the final study and analytical protocol were consistent with the relevant US Centers for Disease Control and Prevention, National Institute of Diabetes and Digestive and Kidney Diseases, and Patient-Centered Outcomes Research Institute (PCORI) methodological standards. Cathy Glover and Patricia Dominick are gratefully acknowledged for their contributions and specifically for the review of this work. This work was supported by a PCORI Award (NEN 1508-32257) through the NEXT-D2 Program, jointly sponsored by the US Centers for Disease Control and Prevention, the National Institute of Diabetes and Digestive and Kidney Diseases, and PCORI. All statements in this paper, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of PCORI, its Board of Governors, or Methodology Committee.

Data Availability

All data generated or analyzed during this study are included in this published paper.

Authors' Contributions

ANB and LS contributed to conceptualization. ANB, TP, and DC contributed to data curation and formal analysis. LS, ANB, and EN contributed to funding acquisition. ANB and TP contributed to investigation. ANB contributed to methodology. ANB, TP, and DC contributed to project administration. ANB, TP, and DC contributed to writing the original draft. ANB and DC contributed to reviewing and editing the paper. All authors reviewed and approved the final version of this manuscript.

Conflicts of Interest

None declared.

  • Adult obesity maps. Centers for Disease Control and Prevention. 2022. URL: https://www.cdc.gov/obesity/data/prevalence -maps.html [accessed 2023-11-21]
  • National and state diabetes trends. Centers for Disease Control and Prevention. 2022. URL: https://www.cdc.gov/diabetes/data/statistics-report/index.html [accessed 2023-11-29]
  • Heart disease mortality by state. Centers for Disease Control and Prevention. 2022. URL: https://www.cdc.gov/nchs/press room/sosmap/heart_disease_mortality/heart_disease.htm [accessed 2023-11-29]
  • Fact sheet: telehealth. American Heart Association. 2019. URL: https://www.aha.org/system/files/2019-02/fact-sheet-tele health-2-4-19.pdf [accessed 2023-11-21]
  • Karimi M, Lee E, Couture S, Gonzales A, Grigorescu V, Smith S, et al. National survey trends in telehealth use in 2021: disparities in utilization and audio vs video services. Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. 2022. URL: https://collections.nlm.nih.gov/catalog/nlm:nlmuid-991859008750 6676-pdf [accessed 2023-11-29]
  • Diabetes management: team-based care for patients with type 2 diabetes. The Community Guide. 2016. URL: https:/​/www.​thecommunityguide.org/​findings/​diabetes-management-team-based-care-patients-type-2-diabetes [accessed 2023-11-29]
  • National diabetes statistics report website. Centers for Disease Control and Prevention. 2022. URL: https://www.cdc.gov/diabetes/data/statistics-report/index.html [accessed 2023-11-29]
  • How COVID-19 impacts people with diabetes. American Diabetes Association. 2022. URL: https://tinyurl.com/4bfnke7m [accessed 2023-11-21]
  • Adhikari S, Pantaleo NP, Feldman JM, Ogedegbe O, Thorpe L, Troxel AB. Assessment of community-level disparities in coronavirus disease 2019 (COVID-19) infections and deaths in large US metropolitan areas. JAMA Netw Open. 2020 Jul 01;3(7):e2016938 [ https://europepmc.org/abstract/MED/32721027 ] [ CrossRef ] [ Medline ]
  • Price-Haywood EG, Burton J, Fort D, Seoane L. Hospitalization and mortality among black patients and white patients with COVID-19. N Engl J Med. 2020 Jun 25;382(26):2534-2543 [ https://europepmc.org/abstract/MED/32459916 ] [ CrossRef ] [ Medline ]
  • Poeran J, Cho LD, Wilson L, Zhong H, Mazumdar M, Liu J, et al. Pre-existing disparities and potential implications for the rapid expansion of telemedicine in response to the Coronavirus Disease 2019 pandemic. Med Care. 2021 Aug 01;59(8):694-698 [ https://europepmc.org/abstract/MED/34054024 ] [ CrossRef ] [ Medline ]
  • Dave D, Friedson AI, Matsuzawa K, Sabia JJ. When do shelter-in-place orders fight COVID-19 best? Policy heterogeneity across states and adoption time. Econ Inq. 2021 Jan;59(1):29-52 [ https://europepmc.org/abstract/MED/32836519 ] [ CrossRef ] [ Medline ]
  • Santos DS, Batistelli CR, Lara MM, Ferreira ED, Moreira TR, Cotta RM. The effectiveness of the use of telehealth programs in the care of individuals with hypertension and, or diabetes mellitus: systematic review and meta-analysis. Diabetol Metab Syndr. 2022 May 28;14(1):76 [ https://dmsjournal.biomedcentral.com/articles/10.1186/s13098-022-00846-5 ] [ CrossRef ] [ Medline ]
  • Lauffenburger JC, Barlev RA, Sears ES, Keller PA, McDonnell ME, Yom-Tov E, et al. Preferences for mHealth technology and text messaging communication in patients with type 2 diabetes: qualitative interview study. J Med Internet Res. 2021 Jun 11;23(6):e25958 [ https://www.jmir.org/2021/6/e25958/ ] [ CrossRef ] [ Medline ]
  • Crossen SS, Romero CC, Loomba LA, Glaser NS. Patient perspectives on use of video telemedicine for type 1 diabetes care in the United States during the COVID-19 pandemic. Endocrines. 2021 Dec 01;2(4):449-456 [ https://europepmc.org/abstract/MED/35373189 ] [ CrossRef ] [ Medline ]
  • The rapid transition to telemedicine:insights and early trends. Press Ganey. 2020. URL: https://www.matrc.org/wp-content/uploads/2020/04/Press-Ganey-Telemedicine_-1.pdf [accessed 2023-11-29]
  • Lipschitz JM, Connolly SL, Van Boxtel R, Potter JR, Nixon N, Bidargaddi N. Provider perspectives on telemental health implementation: lessons learned during the COVID-19 pandemic and paths forward. Psychol Serv (Forthcoming). 2022 Mar 24 [ https://europepmc.org/abstract/MED/35201809 ] [ CrossRef ] [ Medline ]
  • DePuccio MJ, Gaughan AA, Shiu-Yee K, McAlearney AS. Doctoring from home: physicians' perspectives on the advantages of remote care delivery during the COVID-19 pandemic. PLoS One. 2022 Jun 2;17(6):e0269264 [ https://dx.plos.org/10.1371/journal.pone.0269264 ] [ CrossRef ] [ Medline ]
  • Rao L, Comfort AB, Dojiri SS, Goodman S, Yarger J, Shah N, et al. Telehealth for contraceptive services during the COVID-19 pandemic: provider perspectives. Womens Health Issues. 2022 Sep;32(5):477-483 [ https://linkinghub.elsevier.com/retrieve/pii/S1049-3867(22)00050-0 ] [ CrossRef ] [ Medline ]
  • Lee M, Luna P, Lynch S, Nagpal S, Dominguez YC, Ahmed Z, et al. Telehealth provider perspectives during COVID-19: insights from an academic cardiology practice. J Am Coll Cardiol. 2021 May;77(18):3212 [ CrossRef ]
  • Singh A, Mountjoy N, McElroy D, Mittal S, Al Hemyari B, Coffey N, et al. Patient perspectives with telehealth visits in cardiology during COVID-19: online patient survey study. JMIR Cardio. 2021 Jan 22;5(1):e25074 [ https://cardio.jmir.org/2021/1/e25074/ ] [ CrossRef ] [ Medline ]
  • Auchus IC, Jaradeh K, Tang A, Marzan J, Boslett B. Transitioning to telehealth during the COVID-19 pandemic: patient perspectives and attendance at an HIV clinic in San Francisco. AIDS Patient Care STDS. 2021 Jul;35(7):249-254 [ https://europepmc.org/abstract/MED/34242090 ] [ CrossRef ] [ Medline ]
  • Pierce RP, Stevermer JJ. Disparities in the use of telehealth at the onset of the COVID-19 public health emergency. J Telemed Telecare. 2023 Jan;29(1):3-9 [ https://journals.sagepub.com/doi/abs/10.1177/1357633X20963893?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub 0pubmed ] [ CrossRef ] [ Medline ]
  • Wharton MK, Shi L, Eragoda S, Monnette AM, Nauman E, Price-Haywood EG, et al. Qualitative analysis of health systems utilizing non-face-to-face chronic care management for medicare-insured patients with diabetes. J Ambul Care Manage. 2020 Oct;43(4):326-334 [ CrossRef ] [ Medline ]
  • Bazzano AN, Monnette AM, Wharton MK, Price-Haywood EG, Nauman E, Dominick P, et al. Older patients' preferences and views related to non-face-to-face diabetes chronic care management: a qualitative study from southeast Louisiana. Patient Prefer Adherence. 2019 May;13:901-911 [ https://europepmc.org/abstract/MED/31213782 ] [ CrossRef ] [ Medline ]
  • Bazzano AN, Wharton MK, Monnette A, Nauman E, Price-Haywood E, Glover C, et al. Barriers and facilitators in implementing non-face-to-face chronic care management in an elderly population with diabetes: a qualitative study of physician and health system perspectives. J Clin Med. 2018 Nov 20;7(11):451 [ https://www.mdpi.com/resolver?pii=jcm7110451 ] [ CrossRef ] [ Medline ]
  • Shao Y, Stoecker C, Hong D, Nauman E, Fonseca V, Hu G, et al. The impact of reimbursement for non-face-to-face chronic care management on comprehensive metabolic biomarkers among multimorbid patients with type 2 diabetes. Med Care. 2023 Mar 01;61(3):157-164 [ CrossRef ] [ Medline ]
  • Walker B, Stoecker C, Shao Y, Nauman E, Fort D, Shi L. Telehealth and medicare type 2 diabetes care outcomes: evidence from Louisiana. Med Care. 2023 Apr 01;61(Suppl 1):S77-S82 [ https://europepmc.org/abstract/MED/36893422 ] [ CrossRef ] [ Medline ]
  • O'Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research: a synthesis of recommendations. Acad Med. 2014 Sep;89(9):1245-1251 [ https://journals.lww.com/24979285.pmid ] [ CrossRef ] [ Medline ]
  • Hennink M, Kaiser BN. Sample sizes for saturation in qualitative research: a systematic review of empirical tests. Soc Sci Med. 2022 Jan;292:114523 [ https://linkinghub.elsevier.com/retrieve/pii/S0277-9536(21)00855-8 ] [ CrossRef ] [ Medline ]
  • Hennink MM, Kaiser BN, Marconi VC. Code saturation versus meaning saturation: how many interviews are enough? Qual Health Res. 2017 Mar 26;27(4):591-608 [ https://europepmc.org/abstract/MED/27670770 ] [ CrossRef ] [ Medline ]
  • Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006 Jan;3(2):77-101 [ https://www.tandfonline.com/doi/abs/10.1191/1478088706qp063oa ] [ CrossRef ]
  • Goldberg EM, Lin MP, Burke LG, Jiménez FN, Davoodi NM, Merchant RC. Perspectives on telehealth for older adults during the COVID-19 pandemic using the quadruple aim: interviews with 48 physicians. BMC Geriatr. 2022 Mar 08;22(1):188 [ https://bmcgeriatr.biomedcentral.com/articles/10.1186/s12877-022-02860-8 ] [ CrossRef ] [ Medline ]
  • Kichloo A, Albosta M, Dettloff K, Wani F, El-Amir Z, Singh J, et al. Telemedicine, the current COVID-19 pandemic and the future: a narrative review and perspectives moving forward in the USA. Fam Med Community Health. 2020 Aug;8(3):e000530 [ https://fmch.bmj.com/lookup/pmidlookup?view=long&pmid=32816942 ] [ CrossRef ] [ Medline ]
  • Gajarawala SN, Pelkowski JN. Telehealth benefits and barriers. J Nurse Pract. 2021 Feb;17(2):218-221 [ http://europepmc.org/abstract/MED/33106751 ] [ CrossRef ] [ Medline ]
  • Monaghesh E, Hajizadeh A. The role of telehealth during COVID-19 outbreak: a systematic review based on current evidence. BMC Public Health. 2020 Aug 01;20(1):1193 [ https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-020-09301-4 ] [ CrossRef ] [ Medline ]
  • Lie SS, Karlsen B, Oord ER, Graue M, Oftedal B. Dropout from an eHealth intervention for adults with type 2 diabetes: a qualitative study. J Med Internet Res. 2017 May 30;19(5):e187 [ http://www.jmir.org/2017/5/e187/ ] [ CrossRef ] [ Medline ]
  • Powell RE, Henstenburg JM, Cooper G, Hollander JE, Rising KL. Patient perceptions of telehealth primary care video visits. Ann Fam Med. 2017 Dec;15(3):225-229 [ http://www.annfammed.org/cgi/pmidlookup?view=long&pmid=28483887 ] [ CrossRef ] [ Medline ]
  • Yeager VA, Wharton MK, Monnette A, Price-Haywood EG, Nauman E, Angove RS, et al. Non-face-to-face chronic care management: a qualitative study assessing the implementation of a new CMS reimbursement strategy. Popul Health Manag. 2018 Dec;21(6):454-461 [ CrossRef ] [ Medline ]
  • Friedman EE, Devlin SA, Gilson SF, Ridgway JP. Age and racial disparities in telehealth use among people with HIV during the COVID-19 pandemic. AIDS Behav. 2022 Aug;26(8):2686-2691 [ CrossRef ] [ Medline ]
  • Darrat I, Tam S, Boulis M, Williams AM. Socioeconomic disparities in patient use of telehealth during the coronavirus disease 2019 surge. JAMA Otolaryngol Head Neck Surg. 2021 Mar 01;147(3):287-295 [ https://europepmc.org/abstract/MED/33443539 ] [ CrossRef ] [ Medline ]
  • Chang JE, Lai AY, Gupta A, Nguyen AM, Berry CA, Shelley DR. Rapid transition to telehealth and the digital divide: implications for primary care access and equity in a post-COVID era. Milbank Q. 2021 Jun;99(2):340-368 [ https://europepmc.org/abstract/MED/34075622 ] [ CrossRef ] [ Medline ]
  • Scott KC, Karem P, Shifflett K, Vegi L, Ravi K, Brooks M. Evaluating barriers to adopting telemedicine worldwide: a systematic review. J Telemed Telecare. 2018 Jan;24(1):4-12 [ https://doi.org/10.1177/1357633X16674087 ] [ CrossRef ] [ Medline ]
  • Eberle C, Stichling S. Clinical improvements by telemedicine interventions managing type 1 and type 2 diabetes: systematic meta-review. J Med Internet Res. 2021 Feb 19;23(2):e23244 [ https://www.jmir.org/2021/2/e23244/ ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by G Eysenbach, T Leung; submitted 24.10.22; peer-reviewed by D McElroy, N Haff; comments to author 24.12.22; revised version received 01.03.23; accepted 17.11.23; published 01.01.24

©Alessandra N Bazzano, Tejal Patel, Elizabeth Nauman, Dana Cernigliaro, Lizheng Shi. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 01.01.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

IMAGES

  1. Essay On Time Management

    introduction in research paper about time management

  2. Time Management Critique Essay

    introduction in research paper about time management

  3. 💌 Time management paper. Time Management Paper. 2022-11-14

    introduction in research paper about time management

  4. The Importance of Time Management

    introduction in research paper about time management

  5. Mastering Time Management: Tips for Productivity and Reduced Stress

    introduction in research paper about time management

  6. (PDF) A Review of Time Management Literature

    introduction in research paper about time management

VIDEO

  1. Bachelor of Project Management

  2. How To Write A Research Paper For School ?

  3. Bachelor of Project Management

  4. Step-by-step approach to starting and completing a good research paper

  5. Essay On Time Management

  6. How to write introduction of Thesis and Research papers

COMMENTS

  1. (PDF) The Impact of Time Management on the Students ...

    Time management is very important and it may actually affect individual's overall performance and achievements. However, all of these are related by how individuals manage their time to suit...

  2. Does time management work? A meta-analysis

    Introduction. Stand-up comedian ... A critical gap in time management research is the question of whether time management works [28, 29]. ... provides an incentive to publish papers that link time management with positive outcomes. In other words, opposite social expectations surrounding time management might reduce publication bias.

  3. Impact of Time Management Behaviors on Undergraduate Engineering

    Empirical evidence suggests that effective time management is associated with greater academic achievement ( McKenzie & Gow, 2004; Trueman & Hartley, 1996) as students learn coping strategies that allow them to negotiate competing demands.

  4. Time Management: A Realistic Approach

    Valerie P. Jackson, MD DOI: https://doi.org/10.1016/j.jacr.2008.11.018 Time Management: A Realistic Approach Realistic time management and organization plans can improve productivity and the quality of life. However, these skills can be difficult to develop and maintain.

  5. (PDF) Time Management

    Article Increasing Personal Efficiency: A Case Study January 1982 · Higher Education Research and Development Harry E. Stanton A case study demonstrating how a poorly functioning academic was...

  6. College Students' Time Management: a Self-Regulated Learning

    Time management has been defined as "the self-controlled attempt to use time in a subjectively efficient way to achieve outcomes" (Koch and Kleinmann 2002, p. 201) and as "achieving an effective use of time while performing certain goal-directed activities" (Claessens et al. 2007, p. 262).

  7. Does time management work? A meta-analysis

    Does time management work? We conducted a meta-analysis to assess the impact of time management on performance and well-being. Results show that time management is moderately related to job performance, academic achievement, and wellbeing. Time management also shows a moderate, negative relationship with distress. Interestingly, individual differences and contextual factors have a much weaker ...

  8. Relation between stress, time management, and academic achievement in

    Introduction. Identifying the ... However, research suggests that study skills (time management) are also significant factors affecting academic achievement in medical schools.[8,21,22,23,24,25] ... Time management aims to improve the nature of activities that require a limited time. The inability to use time in the learning process is the main ...

  9. It's About Time: New Perspectives and Insights on Time Management

    The effect of time-management training on employee attitudes and behavior: A field experiment. Journal of Psychology, 128, 393-396. Google Scholar; Peeters M. A. G., Rutte C. G. (2005). Time management behavior as a moderator for the job demand-control interaction. Journal of Occupational Health Psychology, 10, 64-75. Google Scholar; Penn W ...

  10. How Did It Get So Late So Soon? The Effects of Time Management

    Time management is regarded as an important prerequisite for effective and efficient learning in higher education. However, university students' time management frequently proves to be deficient, especially with freshman students, who can therefore benefit from appropriate time management interventions. The aim of this study was to compare the effects of an intervention focused on imparting ...

  11. The Impact of Time Management on Students' Academic Achievement

    Article PDF References Article and author information Abstract Time management is very important and it may actually affect individual's overall performance and achievements. Students nowadays always commented that they do not have enough time to complete all the tasks assigned to them.

  12. PDF The effectiveness of Time Management Strategies Instruction on ...

    Introduction Time management which involves goal setting, prioritization, planning, hesitation and ways of coping with it, studying and learning strategies, note taking, stress ... individual characteristics and others influence in time management research (Claessens et al, 2007). This is also in line with related empirical findings. For ...

  13. Time Management Strategies for Research Productivity

    March 2015. Vibhuti Patel. Researcher is prone to various distractions that can derail productivity and decrease efficiency. Effective time management allows researchers to maintain focus on their ...

  14. PDF The Effect of Time Management on Academic Performance among Students of

    performance in addition to the differences in the time management level between students according to faculty, gender and curriculum. Keywords: Time management, Cross-sectional study, CGPA, Extra-curricular activities, Jazan University students. INTRODUCTION Time management plays a vital role in improving student's academic performance.

  15. (PDF) A STUDY ON TIME MANAGEMENT: CASE OF NORTHEAST ...

    Mohamed, Hamal, and Mohamed (2018) claimed that time management is a skill that allows a learner to apportion time to various goal-oriented tasks which are necessary for academic adjustment....

  16. (PDF) The Role of Time Management and its Impact On ...

    This study aims to analyze students' perspectives at Northern Borders University (NBU) toward how time could be managed in terms of planning, organizing upon academic achievement, and what is the...

  17. PDF TIME MANAGEMENT AND ACADEMIC ACHIEVEMENT OF HIGHER SECONDARY STUDENTS

    Time management is actually self management. The skills that people need to manage others are the same skills that are required to manage themselves. The purpose of the present study was to explore the relation between time management and academic achievement of Higher Secondary students.

  18. Time Management Strategies for Research Productivity

    This article presents time management strategies addressing behaviors surrounding time assessment, planning, and monitoring. Herein, the Western Journal of Nursing Research editorial board recommends strategies to enhance time management, including setting realistic goals, prioritizing, and optimizing planning.

  19. (PDF) Time management Essentials and Importance

    This research paper is an attempt to learn the essentials of time management and also its importance. For this purpose, literature survey was done. It was found that the most important tool...

  20. Time Management Is About More Than Life Hacks

    Summary. There is certainly no shortage of advice — books and blogs, hacks and apps — all created to boost time management with a bevy of ready-to-apply tools. Yet, the frustrating reality for...

  21. A Qualitative Investigation of Time Management Interventions for

    A Qualitative Investigation of Time Management Interventions for Working Students in the Philippines to Balance Academics and Work International Journal of Research Publication and Reviews...

  22. PDF A Study of Time Management for Students Performance

    Keyword: students, time, life and career I. INTRODUCTION Time management can be defined as activities or tools which allow you to effectively manage your time. ... the term time management in the present paper. In spite of all popular attention to managing time, relatively little research has been conducted on the processes involved in

  23. Introduction to Time Management

    Introduction to Time Management The idea of time management might be new to you. Basically, time management strategies allow you to plan out your time so that you can get things done and have a more balanced, less stressful life.

  24. Journal of Medical Internet Research

    Background: The Deep South of the United States, and Louisiana in particular, bears a greater burden of obesity, diabetes, and heart disease compared with other regions in the United States. Throughout the COVID-19 pandemic, there has been a substantial increase in telehealth visits for diabetes management to protect the safety of patients.