research on time management for students

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

• Review Article
• Published: 27 October 2020
• Volume 33 , pages 1319–1351, ( 2021 )

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• Christopher A. Wolters   ORCID: orcid.org/0000-0002-8406-038X 1 &
• Anna C. Brady 1  

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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.

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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.

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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

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The Impact of Time Management on Students' Academic Achievement

S N A M Razali 1 , M S Rusiman 1 , W S Gan 1 and N Arbin 2

Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series , Volume 995 , International Seminar on Mathematics and Physics in Sciences and Technology 2017 (ISMAP 2017) 28–29 October 2017, Hotel Katerina, Malaysia Citation S N A M Razali et al 2018 J. Phys.: Conf. Ser. 995 012042 DOI 10.1088/1742-6596/995/1/012042

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1 Department of Mathematics and Statistic, Faculty of Applied Science and Technology University Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia.

2 Department of Mathematic, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, 35900 Perak, Malaysia.

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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. In addition, a university environment's flexibility and freedom can derail students who have not mastered time management skills. Therefore, the aim of this study is to determine the relationship between the time management and academic achievement of the students. The factor analysis result showed three main factors associated with time management which can be classified as time planning, time attitudes and time wasting. The result also indicated that gender and races of students show no significant differences in time management behaviours. While year of study and faculty of students reveal the significant differences in the time management behaviours. Meanwhile, all the time management behaviours are significantly positively related to academic achievement of students although the relationship is weak. Time planning is the most significant correlated predictor.

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University students’ free time management and quality of life: the mediating role of leisure satisfaction

• Esranur Terzi   ORCID: orcid.org/0000-0002-1112-9307 1 ,
• Utku Isik   ORCID: orcid.org/0000-0003-1877-3960 2 ,
• Berat Can Inan   ORCID: orcid.org/0000-0001-8841-9073 1 ,
• Can Akyildiz   ORCID: orcid.org/0000-0002-8887-1665 1 &
• Umit Dogan Ustun   ORCID: orcid.org/0000-0002-1610-2840 3  

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The impact of free time management and leisure satisfaction on quality of life is distinct, however, the role of satisfaction in enhancing quality of life through free time management remains uncertain. Hence, the objective of this research is to explore how leisure satisfaction acts as a mediator between free time management and the levels of quality of life among university students. Additionally, this study aims to analyse these concepts in relation to gender, age and the number of days of activity participation. Within this particular framework, a total of 213 university students willingly participated in the survey, which included the administration of the “Free Time Management Scale,” “Leisure Satisfaction Scale,” and “Quality of Life Scale.” The analyses employed the Independent T-Test, Pearson Correlation, and Linear Regression methods. The mediating effect was analysed using Structural Equation Modelling. The study found significant relationships between gender, free time management, and life quality. There was a significant relationship between free time management, leisure satisfaction, and quality of life ( p  < 0.05). Leisure satisfaction partially mediated the quality of life-free time management relationship. As age and physical activity grow, males have a higher standard of living, and time allocation and quality of life improve. Furthermore, it was found that students who effectively managed their time experienced an enhanced quality of life, as evidenced by their increased satisfaction with leisure activities. Notably, the level of satisfaction with well-managed time was identified as a crucial factor in this association.

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Introduction

Engaging in leisure activities, which are now recognized as essential for both mental and physical well-being, is highly crucial for individuals to maintain a healthy lifestyle. The repetitive nature of one’s lifestyle and mundane daily routines can lead to many mental and physical issues. Consequently, scholars [ 1 , 2 , 3 ] have directed their attention on examining the free time activities, life satisfaction, and physical activities undertaken during leisure by individuals from diverse viewpoints. Leisure holds significance for numerous individuals, serving as a source of enjoyment and a means of evading external pressures imposed upon them [ 4 ]. In order to gain a deeper comprehension of how free time affects individuals, researchers frequently analyze the level of contentment individuals experience during their leisure activities. This analysis encompasses various elements such overall life satisfaction, quality of life, effective management of free time, limitations on leisure activities, satisfaction with one’s community, and the ability to effectively manage stress [ 4 ].

Free time management examines how individuals organize their free time and the effects of this process. This concept encompasses the effective and efficient use of time, achieving goals, and maintaining balance. Effective free time management can help individuals reduce stress, maintain balance, and enhance their quality of life [ 5 ]. Leisure satisfaction investigates how individuals assess their leisure and how these experiences affect their quality of life. It represents the level of enjoyment individuals derive from engaging in activities they enjoy. Sufficient leisure satisfaction can alleviate stress, enhance psychological well-being, and increase overall life satisfaction [ 1 ]. Quality of life refers to individuals’ overall satisfaction and well-being in life. This concept is shaped by a combination of physical, psychological, social, and environmental factors. A high quality of life enables individuals to feel generally happier, healthier, and more fulfilled [ 5 ].

Mediator role of leisure satisfaction in the effect of free time management on quality of life

Free time management examines an individual’s allocation of free time and assesses if they fulfil their spiritual and physical requirements during this period. Free time activities are crucial for the working class and students, who have less free time compared to other segments of society, since they help alleviate tension and fatigue resulting from their job life [ 1 ]. The rationale for prioritizing this domain lies in the fact that effectively utilizing one’s free time can enhance personal happiness and foster greater success in social interactions. Furthermore, numerous scholars have highlighted that quality of life is a multifaceted notion that necessitates both objective and subjective measurements [ 6 ]. Quantifiable measures of quality of life encompass factors such as the state of one’s living surroundings, physical well-being, degree of income, and socioeconomic standing [ 6 ]. On the other hand, subjective of quality of life encompass factors such as overall living conditions, life satisfaction, happiness, and personal contentment [ 1 ]. Effective management of free time enhances the quality of life by positively impacting participation, satisfaction, attitudes, health, and environment [ 5 ]. Previous studies have discovered a direct correlation between effectively managed free time and engagement in physical activities, as well as an improved quality of life in terms of health [ 7 ]. Effectively managing free time is a fundamental factor that enhances one’s quality of life. Thus, the quality of life is inherently connected to the conceptual aspects of effectively managing one’s free time and experiencing enjoyment in leisure activities. Free time management enables individuals to utilize their free time in purposeful activities, foster resilient communities, pursue favourable psychological well-being, acquire novel proficiencies, and eventually enhance their quality of life [ 6 ].

While there is evidence suggesting a positive connection between leisure satisfaction and free time management [ 5 ], researchers have not yet agreed on the precise nature of the relationship between different aspects of free time management and quality of life. When examining free time, researchers distinguish between two types of leisure variables: person-centered and place-centered [ 8 ]. Person-centered leisure variables include leisure participation, satisfaction, and attitude, while place-centered leisure variables encompass leisure resources and environment. Lloyd and Auld [ 8 ] argue that both leisure variables, person-centered and place-centered, need to be measured when assessing leisure activities, while findings by Leung and Lee [ 9 ] indicate that the interaction between person-centered and place-centered leisure activities creates and sustains life quality. Passmore and French [ 10 ] have identified three types of leisure activities in which adolescents participate: achievement-oriented leisure activities, social leisure activities, and time-passing leisure activities. Lloyd and Auld [ 8 ] have categorized leisure activities into six groups based on their frequencies: mass media, social activities, outdoor activities, sports activities, cultural activities, and hobbies. Scott and Willits [ 11 ] reported four types of leisure activities classified as socializing, creative or artistic, intellectual, and physical activities. Although there is no consensus in the literature regarding the classification of leisure activities, researchers generally agree on the contribution of leisure to life quality and suggest that the relationship between leisure and life quality is complex [ 12 , 13 ]. Various explanations underlie the relationship between leisure and life quality in the literature. According to activity theory, higher participation frequencies and more meaningful activities are associated with higher levels of life quality [ 13 ]. Previous studies have shown a positive relationship between participation in physical leisure activities and life quality [ 9 ], as well as health-related quality of life [ 7 ]. Additionally, Robinson and Martin [ 14 ] have shown that most happy individuals are more active in social activities. An alternative theoretical framework is the needs theory, which posits that meeting needs has beneficial effects on life quality [ 13 ]. The aforementioned studies specifically reported that the higher individuals perceive their needs for satisfaction and participation in recreation, the higher their quality of life. Hence, an additional objective of this essay is to elucidate the impact of effective free time management on one’s quality of life.

To explain this effect, we can examine theories such as the “Boundary Theory” and the “Psychological Separation Theory.” The Boundary Theory examines the balance between work and free time, focusing on how individuals organize their lives. According to this theory, a harmonious balance between work and leisure can enhance individuals’ quality of life [ 15 ]. Leisure activities can alleviate stress stemming from work life, thereby increasing overall life satisfaction. For instance, engaging in free time activities allows individuals to distance themselves from work-related stressors, facilitating mental and emotional relaxation and rejuvenation [ 15 ]. On the other hand, the Psychological Separation Theory emphasizes the importance of delineating clear boundaries between work and free time. According to this theory, establishing distinct boundaries between work and leisure prevents individuals from carrying work-related stress into their free time, thus enhancing quality of life. Particularly for university students, maintaining a clear separation between study periods and free time activities can improve academic performance and enhance overall life satisfaction [ 16 , 17 ]. By integrating these theories, we can better understand the relationship between free time management and quality of life. Leisure activities not only facilitate coping with work-related stress but also provide opportunities for psychological relaxation, ultimately enhancing individuals’ quality of life [ 18 ].

Given the aforementioned linkages, we formulated a hypothesis for a model that could elucidate the connections between quality of life and the management of free time:

Hypothesis 1: The free time management has a positive impact on the quality of life of university students.

Leisure satisfaction is the favourable thoughts or sensations that individuals have when they engage in leisure activities that align with their preferences, successes, and expectations. Essentially, it refers to the level of contentment that an individual experience from their leisure activities [ 2 ]. This felt fulfilment arises from fulfilling the demands that the individual perceives as deficient or believes are not being fulfilled [ 3 ].

Leisure satisfaction is frequently regarded as a higher priority compared to other factors such as economic and social status, security, and religion [ 19 ]. According to Agate et al. [ 20 ], leisure satisfaction was found to be the most accurate predictor among criteria such as family income, age, married status, and leisure involvement in determining family life satisfaction. Prior research has also emphasized the favourable association between satisfaction with leisure activities and the quality of life [ 21 ]. For instance, Chun et al. [ 22 ] discovered that a significant degree of contentment with leisure activities can mitigate stress, but a smaller degree may be linked to an unhealthy way of life. While there is a positive correlation between leisure satisfaction and quality of life, the exact nature of the relationship between different components (happiness or peacefulness) of leisure satisfaction and quality of life is still a topic of debate among academics [ 23 ]. Hence, the primary objective of this article is to elucidate the impact of leisure satisfaction on the quality of life.

We can examine this effect through theories such as the “Leisure Satisfaction Theory” and the “Social Resources Theory”. The Leisure Satisfaction Theory explores how individuals utilize their leisure and how these experiences affect their quality of life. Leisure satisfaction refers to the level of enjoyment an individual derives from engaging in activities they enjoy. Research indicates that adequate leisure satisfaction enhances quality of life. A satisfying leisure experience can reduce stress, enhance psychological well-being, and elevate overall life satisfaction [ 24 ]. This theory can be utilized to explain the quality of life among university students by focusing on how students utilize their leisure and how these experiences contribute to their overall life satisfaction. On the other hand, the Social Resources Theory examines how individuals can enhance their quality of life through social relationships and resources. For university students, social support, friendships, and family bonds are crucial. Social resources play a critical role in coping with stress and improving quality of life [ 25 ]. This theory can also be applied to explain the quality of life among university students. Social support networks, leisure activities, and friendships can positively influence students’ quality of life. By integrating these theories, we can better understand the impact of leisure satisfaction on quality of life among university students. Leisure activities not only enhance individuals’ personal satisfaction but also promote social interactions, thereby increasing access to social resources. This interaction plays a significant role in improving overall quality of life [ 26 ]. This integration allows us to better comprehend the multifaceted effects of leisure activities on quality of life and provides a more comprehensive interpretation of research findings.

Given the aforementioned linkages, we formulated a hypothesis for a model that could elucidate the connections between quality of life and satisfaction derived from leisure activities:

Hypothesis 2: The leisure satisfaction has a positive impact on the quality of life of university students.

Mediator The purpose of these studies [ 1 , 2 , 3 ] is to investigate the subjective enjoyment that individuals derive from their lives, specifically focusing on how to optimize their overall well-being. Quality of life refers to the fulfilment of one’s aspirations, taking advantage of possibilities for personal growth, engaging in diverse activities, possessing adequate resources in terms of quality, and perceiving these resources as satisfactory [ 27 ].

It may be inferred that free time management in leisure activities might lead to increased enjoyment and improved quality of life for individuals [ 28 ]. Chick et al. [ 4 ] verified that inadequate time management during free periods had a detrimental impact on the level of satisfaction derived from leisure activities. Within this framework, individuals who effectively allocate their time and structure their lives in alignment with their personal requirements, resulting in the experience of happy emotions rather than negative emotions, may exhibit elevated levels of subjective well-being and life satisfaction [ 29 ]. Therefore, individuals who have a high quality of life are more likely to meet their needs effortlessly, have control over their surroundings, exercise their autonomy in decision-making, have opportunities for personal growth, and lead a purposeful existence [ 30 ]. In general, the researchers discovered a positive correlation between leisure satisfaction and quality of life [ 31 ]. In their study, Spiers & Walker [ 23 ] found a strong correlation between leisure satisfaction and eight aspects of quality of life. These aspects include happiness, well-being, living standards, health, achievement, personal relationships, community involvement, and spirituality. Research has additionally demonstrated that effective control of free time plays a crucial role in managing the quality of life [ 28 ]. Generally, there is a favourable correlation between free time management and satisfaction with leisure activities, both of which contribute to quality of life. While prior research has examined the correlation between the management of free time and satisfaction with leisure activities, as well as the quality of life, there has been a lack of attention given to the impact of leisure satisfaction on the connection between free time management and quality of life.

Links have been found between leisure and life satisfaction, subjective well-being, and quality of life [ 32 , 33 ]. Leisure or its absence is associated with “lifestyle diseases,” particularly obesity, stress, and depression [ 34 ]. Other studies indicate that leisure reduces stress [ 35 ], enhances mood [ 36 ], and contributes to overall health and well-being [ 37 ]. Leisure participation and leisure satisfaction are associated with life satisfaction [ 38 ]. For example, Spiers and Walker [ 23 ] argue that “leisure satisfaction is likely the best predictor of happiness and quality of life.” In summary, leisure seems to contribute multifacetedly to perceived quality of life and individual life satisfaction [ 39 ]. On the other hand, leisure constraints, as defined by Jackson [ 40 ] as things or conditions that impede people from participating in leisure activities, spending more time doing so, benefiting from leisure services, or achieving a desired level of satisfaction are generally acknowledged to have negative effects on aspects of life quality including leisure participation, leisure satisfaction, emotional well-being, and health [ 23 ]. Ngai [ 41 ] found leisure satisfaction to be significantly associated with measures of quality of life in Macao, China. Hawkins et al. [ 42 ] found that although the impact of leisure satisfaction was substantially greater than other variables in both cases, leisure constraints in samples from Australia and the US were associated with life satisfaction, leisure satisfaction, and leisure activity participation. Mannell & Dupuis [ 43 ] found evidence of a positive relationship between physical leisure activity and life satisfaction.

The “Leisure Satisfaction Theory” provides a suitable framework to elucidate the relationship between university students’ quality of life and their free time management. This theory examines how individuals assess their free time and how these experiences affect their overall quality of life. Leisure satisfaction refers to the degree to which individuals enjoy engaging in activities they prefer. Research indicates that sufficient leisure satisfaction enhances quality of life [ 44 ]. A high level of leisure satisfaction can alleviate stress, enhance psychological well-being, and elevate overall life satisfaction [ 45 ]. This theory offers a pertinent framework to explain university students’ quality of life because it focuses on how students evaluate their free time and how these contribute to their overall life satisfaction. Furthermore, the “Social Psychology of Time” theory can also be instrumental in explaining this relationship. This theory explores the social and psychological dimensions of time and emphasizes the impact of time use on individuals’ quality of life. The time management of young adults, such as university students, can affect their quality of life based on their social interactions, personal development, and relaxation needs [ 46 ]. By integrating these theories, we can better understand the effects of university students’ free time management on their quality of life. Research conducted within this integrated theoretical framework can provide detailed insights into the effects of university students’ free time management on their quality of life. This, in turn, can enhance our understanding of this relationship and facilitate the development of effective interventions aimed at improving university students’ quality of life. The strong direct effects of leisure satisfaction on life satisfaction and indirect effects on self-rated health suggest that other leisure-related variables such as leisure motivations, attitudes toward leisure, and social support networks related to leisure activity could be significantly associated with life satisfaction and self-rated health. To discover the key elements influencing quality of life, it is crucial to analyze the role of leisure pleasure in the relationship between free time management and quality of life, given that these two factors have distinct impacts on quality of life.

Given the aforementioned associations, we formulated a hypothesis for a model that could elucidate the role of leisure pleasure in mediating the connection between quality of life and free time management:

Hypothesis 3: The relationship between the management of university students’ free time and their quality of life is mediated by their satisfaction with leisure activities.

The impact of age, gender, and participation in activities on the management of free time, satisfaction with leisure, and quality of life

Several factors can impact the time management, satisfaction with leisure activities, and quality of life of individuals. These variations encompass disparities in age, gender, profession, level of physical well-being, societal standing, and life responsibilities [ 47 ]. Upon reviewing the literature, it is appropriate to utilize social theories and psychological models to assess the effects of age, gender, and participation in activities on free time management, leisure satisfaction, and quality of life [ 48 , 49 , 50 , 51 ]. For instance, the impact of age and gender on free time management can be elucidated through social structure theories, while the influence of activity participation on leisure satisfaction and quality of life can be examined using psychological models. Social structure theory focuses on individuals’ roles and relationships within the social structure. Demographic factors such as age and gender influence social structure and consequently shape free time management [ 48 , 51 ]. For example, individuals belonging to different age groups may have varied social roles and responsibilities, which affect how they allocate their leisure. Gender, on the other hand, is associated with societal gender roles and expectations, which can influence leisure activities and time management [ 48 ]. In this context, research can assess the effects of age and gender on free time management to test social structure theory. Psychological models, on the other hand, focus on individuals’ internal processes and motivations to explain behavior [ 49 , 50 ]. They can be utilized to evaluate the effects of activity participation on leisure satisfaction and quality of life. For instance, individuals’ motivations and emotional experiences related to their participation in activities can affect leisure satisfaction. Engaging in specific activities can fulfill individuals’ emotional and psychological needs, thereby enhancing their quality of life [ 49 ]. In this framework, research can conduct tests to understand the effects of activity participation on leisure satisfaction and quality of life using psychological models [ 50 ]. The utilization of these theoretical models can assist in comprehensively understanding the effects of age, gender, and activity participation on free time management, leisure satisfaction, and quality of life. In the literature, Bernard and Phillipson [ 52 ] examined the relationship between age and leisure satisfaction, finding a decrease in satisfaction with increasing age. According to Dixon [ 53 ] research, women experience a lack of enjoyment when it comes to leisure activities. Ateca-Amestoy et al. [ 54 ] discovered that social factors have a more significant impact on leisure satisfaction compared to economic factors. Francken & Raaij [ 55 ] discovered a positive correlation between age and leisure satisfaction, indicating that older individuals experienced greater satisfaction in their leisure activities compared to younger individuals. Conversely, Su et al. [ 56 ] noticed a negative relationship between age and leisure satisfaction, suggesting that older people were less content with their leisure activities in comparison to younger people.

Research on gender differences in free time management [ 57 ] suggests that women encounter growing limitations in terms of structure, relationships, and personal factors. Interpersonal limitations persist in sports and leisure domains due to their predominantly male-dominated nature [ 58 ]. Furthermore, women have been reported to be significantly burdened by interpersonal limitations. For instance, Wilson & Little [ 58 ] discovered that women had greater limitations in engaging in leisure sports activities compared to men. According to a separate study conducted by Demir and Alpullu [ 59 ], it was found that the way free time is managed differs among various age groups. Moreover, prior studies have consistently revealed gender disparities indicating that females typically have a worse standard of living in comparison to males. Studies by Lassander et al. [ 60 ] indicate that men have a superior quality of life, particularly in terms of their physical and psychological well-being. In addition, research conducted by Lassander et al. [ 60 ] reveals that the impact of quality of life on individuals is universally utilizing and diminishes as they grow older.

Given the aforementioned associations, we formulated a hypothesis for a model that could elucidate the impact of gender, age, and engagement in activities on quality of life, time management, and pleasure with leisure:

Hypothesis 4: The quality of life, management of free time, and satisfaction with leisure activities among university students vary according on their gender, age, and level of engagement in activities .

The present study

The Importance of Quality of Life: The quality of life of university students is a significant indicator during young adulthood, a period characterized by intensive personal and academic development. Therefore, understanding the quality of life of university students is a critical step in assessing their overall well-being and achievements. The Importance of Free Time Management: University students are required to allocate time not only to academic studies but also to social activities and personal interests. Hence, skills in leisure time management are important for university students. Effective leisure time management can help cope with stress, maintain balance, and enhance overall quality of life. The Role of Leisure Satisfaction: Leisure satisfaction focuses on how individuals assess their leisure time and how these experiences affect their quality of life. In this context, investigating university students’ leisure satisfaction can help us understand its effects on their overall life satisfaction.

Previous research [ 57 , 58 , 59 , 60 ] has often been limited in scope, focusing on specific aspects, and has not fully addressed the relationship between leisure time management and quality of life among university students as comprehensively as our study aims to do. Some studies have only examined certain variables to explain the relationship between leisure time management and quality of life, which may not fully reflect the complexity and multifaceted nature of the relationship. The mediating role of leisure satisfaction in the relationship between leisure time management and quality of life has also been understudied. Therefore, it is important to conduct more comprehensive research to fully understand the relationship between leisure time management and quality of life among university students. This research can contribute to the development of strategies to improve the quality of life of university students.

Examining free time management, leisure satisfaction, and quality of life in a sample of university students provides insights into the mental well-being and perspectives of our future young folks. Given that the actions undertaken during university education influence individuals’ future conduct and the societal context, it is crucial to ascertain the degree to which the university students in our study may regulate their allocation of free time to engage in various activities.

Hence, the objectives of this article are two-fold: (1) to examine how leisure satisfaction influences the connection between university students’ management of free time and their quality of life, and (2) to assess whether demographic variables serve as significant predictors of free time management, leisure satisfaction, and quality of life.

Our study holds significance in giving a novel and current source to the literature by investigating the free time management, leisure satisfaction, and quality of life among university students of all genders and age groups. Furthermore, it is crucial to completely assess free time management, leisure satisfaction, and quality of life within the chosen sample, with the aim of providing guidance to educators in mitigating any mental and physical health issues.

Materials and methods

Participants.

The study was designed as a quantitative cross-sectional study, utilizing a survey method for data collecting. In the study, by revealing the relationship between university students’ free time management, leisure satisfaction and quality of life, it was determined whether demographic characteristics such as gender, age and the number of days of activity participation affect free time management and leisure satisfaction. The framework of this conceptual model is shown in Fig.  1 .

Model of the study

This study is a cross-sectional research conducted between November 2022 and December 2022, in which university students actively enrolled during this period were selected through simple random sampling. Selected students met the inclusion criteria and were administered an electronic survey followed by face-to-face interviews. Prior to participation, students were informed about the purpose, procedures, and requirements of the survey, and provided informed consent by signing a consent form after fully understanding the study. Data were collected within 2 weeks only from those who volunteered to participate. This survey encompasses both male and female university students. The sample size was determined using G*Power software. Using a priori analysis, we determined that a sample size of 174 individuals was necessary. This calculation was made with a power of 0.95 and an effect size of 0.55. The sample calculation followed the procedures recommended by Serinolli and Novaretti [ 61 ]. A total of 213 individuals, selected through random sampling based on volunteers, participated in the study. Thus, in the study, the principle of giving weight to large samples in structural equation modeling studies, and basing on a minimum of 15 cases per indicator, has been taken into account [ 62 ]. The participants had an average age of 23.61 ± 5.84. Participants who did not meet any of the criteria specified below were excluded from the study:

Age range: 18–35 years

Pursuing higher education at a university

Voluntarily participation

Data collection

The research data were gathered utilizing the “Personal Information Form,” “Free Time Management Scale,” “Leisure Satisfaction Scale,” and “Life Quality Scale”. The survey consisted of two sections; the first section pertained to explaining the scope of the research and collecting demographic information. The second section comprised 51 questions related to the main variables of the study. The researcher requested participation in the survey from 213 participants. Since there were no missing data, responses from these 213 surveys were utilized for analysis.

Data collection tools

Personal information form.

The researchers developed this form in order to gather information on several independent variables, including gender, age, height, weight, and the frequency of participation in the activity. All variables included in this form were selected based on previous research [ 57 ].

Free time management scale

The free time management scale to be used in the study was developed by Wang et al. [ 63 ]. Wang et al. [ 63 ] utilized confirmatory factor analysis (CFA) to assess the measurement model. The results of the CFA indicate that all standardized loadings exceeded 0.57. With regard to the goodness of fit of the model, the χ2 statistic was 183.41 with 83 degrees of freedom ( P  < 0.01). the goodness of fit index (GFI) was 0.94, the root mean square error of approximation (RMSEA) was 0.06, the adjusted goodness of fit index (AGFI) was 0.92, the normalized fit index (NFI) was 0.95, the comparative fit index (CFI) was 0.97, and the standardized root mean square residual (SRMR) = 0.05.

The scale was adapted into Turkish by Akgul & Karakucuk [ 64 ]. The scale consists of 15 items. Items on the scale are rated on a 5-point Likert scale as follows: 1: Strongly Disagree, 2: Disagree, 3: Neutral, 4: Agree, 5: Strongly Agree. Thus, higher scores indicated more positive free time management. Three statements in the scale questions are reverse coded. In the adaptation study of the scale, the internal consistency coefficient was calculated as .83 in the total sample. In our sample, the reliability coefficient of the scale was calculated as .86 in total.

Leisure satisfaction scale (LSS)

The LSS, developed by Beard & Ragheb [ 65 ], was adapted into Turkish by Gokce & Orhan [ 66 ]. In the research conducted by Beard & Ragheb [ 64 ], the results of CFA revealed the following statistics: model χ2 = 12.54 (df = 6, p  = 0.051); RMSEA = 0.025; CFI = 1.00; AGFI = 0.98; and SRMR = 0.018.

The LSS consists of 24 items. Items on the scale are scored as “Almost Never True (1)”, “Rarely True (2)”, “Sometimes True (3)”, “Often True (4)”, and “Almost Always True (5)”. Thus, higher scores indicated more positive leisure satisfaction. In the adaptation study of the scale, the internal consistency coefficient was calculated as .90 in the total sample. In our sample, the reliability coefficient of the scale was calculated as .93 in total.

Quality of life scale (SF-12)

The quality-of-life scale is a shortened version of the SF-36 scale, which was created by Ware et al. [ 67 ]. The 12-item version of the scale, which was translated into Turkish by Soylu & Kutuk [ 68 ], was employed in our study. Items 1, 8, 9, and 10 of the scale are coded in reverse. Items related to physical and emotional roles are answered as yes or no, while other items have Likert-type options ranging from 3 to 6. A higher score from the scale indicates better health. In the adaptation study of the scale, the internal consistency coefficient was calculated as 0.73 in the total sample. In our sample, the reliability coefficient of the scale was calculated as 0.71 in total.

Data analysis

The statistical analyses were performed using IBM SPSS Statistics 27.0 (IBM Corporation, Armonk, NY, USA) and AMOS 23.0 (IBM, New York, NY, USA) and G*Power 3.1 (Universität Düsseldorf: Psychologie-HHU). The results were assessed using a significance level of 0.05 [ 69 ]. Power analysis was employed to ascertain the magnitude of the sample size [ 70 ]. The data underwent normality tests, and pairwise comparisons of normally distributed data were conducted using the Independent T-Test [ 62 , 71 ]. The Pearson Correlation test and Linear Regression analysis were employed to investigate the correlation and impact between continuous data [ 72 ]. The main influences on quality of life students’ and the path relationships between them were explored through structural equation modeling (SEM), and the following goodness-of-fit indices were used to evaluate the model: λ 2 /df < 5, CFI > 0.90, GFI > 0.90, AGFI > 0.90, IFI > 0.90, and RMSEA < 0.05 [ 62 ].

The study utilizin a T-Test for two independent groups at a significance level of α = 0.05 to assess if there was a significant difference in the levels of free time management, leisure satisfaction, and quality of life among university students based on their gender. The results are presented in Table 1 . Test results indicate that there were no significant differences in free time management (t (211)  = 0.367; p  = 0.714) and leisure satisfaction (t (211)  = 0.193; p  = 0.847) based on gender. However, a significant difference was observed in the quality of life (t (211)  = 4.189; p  = 0.000). Males scored significantly higher than females in quality of life.

Upon analysing the effect dimensions, it was found that gender had a moderate impact on quality of life.

Table 2 shows the Pearson correlation test results applied to determine whether there is of a correlation between age and the duration of activity participation, as well as free time management, leisure satisfaction, and quality of life among university students. The test results revealed a significant positive correlation among age and both free time management ( r  = 0.261; p  = 0.000) and quality of life ( r  = 0.138; p  = 0.038). Moreover, there was a positive correlation between the number of days of activity participation and both free time management ( r  = 0.294; p  = 0.000) and quality of life ( r  = 0.189; p  = 0.006).

In Table 3 , a linear regression model was constructed to predict quality of life as a function of free time management and leisure satisfaction, and leisure satisfaction as a function of free time management. The regression model calculated for free time management and quality of life (F (1.211)  = 51.500; p  = 0.000), leisure satisfaction and quality of life (F (1.211)  = 8.899; p  = 0.003), free time management and leisure satisfaction (F (1.211)  = 16.735; p  = 0.000) was statistically significant. Free time management explains 19% of quality of life (R = 0.443; R 2  = 0.196) and 7% of leisure satisfaction (R = 0.271; R 2  = 0.073). Leisure satisfaction explains 4% of quality of life (R = 0.201; R 2  = 0.040). It was determined that a one unit increase in free time management resulted in a 4.904 unit increase in quality of life, a 0.288 unit increase in leisure satisfaction, and a one unit increase in leisure satisfaction resulted in a 2.367 unit increase in quality of life.

As shown in Table 4 , leisure satisfaction is significantly predicted by free time management (=.26; p.01). Free time management is also a significant predictor of quality of life (= 4.90; p.01). The findings also indicate that leisure satisfaction is a significant predictor of quality of life (= 2.36; p.01). Based on these findings, all the requirements for the mediation effect test are met. Path analysis was used to test the mediating effect of leisure satisfaction in the relationship between free time management and quality of life after the preconditions were met. The results are shown in Fig.  2 .

Path diagram for the research model

According to the mediating model in Fig. 2 , the predictive power of free time management on quality of life decreased from 4.91 to 4.64 in the structural equation modelling in which the mediating effect of free time in the relationship between free time management and quality of life is tested. As a result, it can be stated that leisure satisfaction has a partial mediating effect on the relationship between quality of life and free time management.

Discussion and implication

In this study, we compared university students’ free time management, leisure satisfaction, and quality of life levels across gender, age, and physical activity participation status. According to the findings, the students’ free time management and leisure satisfaction levels did not differ by gender, whereas their quality of life levels showed a significant difference in favour of males. It is thought that there is no difference between genders in free time management and leisure satisfaction because university students have similar free time levels, whereas the difference in quality of life in favour of males is thought to be due to the expectations and responsibilities defined as gender roles and imposed on women by societies, and this situation puts more pressure on female students. Similarly, in international studies conducted on students, it was observed that female students presented lower quality of life scores in physical and psychological dimensions than male students [ 28 , 29 , 30 , 61 , 73 ]. Furthermore, considering the effect of physical activity on quality of life, it can be said that this situation affects women’s quality of life [ 27 ]. However, it should be noted that the findings obtained in terms of gender may differ depending on different regions, age groups, and socioeconomic environments.

The study found a positive correlation between the students’ age, number of activity participation, and their levels of free time management and quality of life. These levels increased in tandem. The effectiveness of age and level of activity participation on free time management is believed to be attributed to the improved utilizing of time through experience. Moreover, individuals who show superior time management skills can allocate more personal time by eliminating the chaotic and frenzied aspects of their daily routine, enabling them to exert control over their level of engagement in desired activities. By engaging in more physical activities and effectively managing time as one ages, individuals can potentially enhance their quality of life, particularly in terms of physical well-being. Demir & Alpullu [ 59 ] conducted a study which revealed that age and the extent of activity participation significantly influenced the management of free time. Consistent findings were noted in other investigations [ 74 ]. It is thought that the lack of difference in leisure satisfaction between those who participate in physical activity and those who do not participate in physical activity is due to the fact that students are satisfied with the activities they prefer in their free time (even if there is no physical activity), and the difference in favour of those who participate in physical activity in free time management is because physical activity increases physical attractiveness. Similarly, students who participated in physical activity had high scores in free time management and quality of life than students who did not participate in physical activity, according to international studies. According to Mokhtari et al. [ 75 ], a sedentary lifestyle has a negative impact on the ability to use time in an international study conducted on students. Other studies on student samples found that physical activity positively influenced free time management [ 59 , 63 , 76 ]. Furthermore, it is reported in foreign sources examining the effect of physical activity on quality of life that participating in physical activity increases one’s level of well-being [ 77 ].

The study found a positive correlation between students’ ability to manage their free time, their satisfaction with leisure activities, and their quality of life. Additionally, it was observed that improvements in free time management and leisure satisfaction had a positive impact on students’ quality of life. The effectiveness of free time management on leisure satisfaction and quality of life is believed to stem from the fact that individuals who excel in managing their free time enhance their quality of life through increased engagement in various activities. Moreover, it is believed that individuals who effectively strategize and oversee their leisure activities enhance their enjoyment of free time, thereby positively impacting their quality of life (Fig. 2 ). Research conducted abroad has demonstrated that effectively managing one’s free time can enhance individuals’ quality of life [ 56 , 78 ]. Prior research [ 3 , 79 ] has demonstrated that leisure satisfaction significantly impacts individuals’ quality of life, specifically in relation to their physical health and mental well-being.

Research has demonstrated that engaging in leisure activities typically enhances individuals’ overall well-being and contentment. Lee et al. [ 80 ] discovered that engagement in recreational pursuits and the experience of ennui during free time significantly impact one’s overall state of happiness and satisfaction. Trenberth [ 81 ] proposed that providing education and counselling to individuals regarding time management and leisure planning can facilitate the development of these skills and enhance their physical and mental well-being. Several studies conducted on elderly individuals have discovered that utilizing their free time for physical activity, social engagement, and leisure pursuits contributes to a sense of group affiliation and social assistance, enhanced mental and physical well-being, and an elevated standard of living [ 77 ]. Spiers and Walker [ 23 ] discovered that contentment with free time has a substantial impact on happiness, tranquilly, and overall well-being. Regarding the enjoyment of free time, a study by Mannell et al. [ 31 ] discovered a positive correlation with overall well-being. In their study, Spiers and Walker [ 23 ] discovered that leisure satisfaction had a significant impact on nine aspects of quality of life. These aspects include happiness, peace of mind, living standards, health, achievement, personal relationships, safety, community involvement, future security, and spirituality or religion [ 82 ] investigated the associations between leisure satisfaction and quality of life among individuals who participate in badminton, and discovered significant correlations between these two variables. In contrast, Tseng et al. [ 83 ] discovered that an individual’s socio-economic status has an impact on their level of satisfaction with leisure activities and quality of life.

When examining the relationship between quality of life and free time management, it was observed that satisfaction with free time partially mediates this relationship. It can be asserted that effectively managing free time is crucial for enhancing the quality of life and ensuring the satisfaction of individuals. Put simply, it has been noted that individuals who effectively manage their free time and engage in activities that bring them satisfaction play a significant role in the connection between free time management and quality of life. This implies that leisure time management serves as a mediating variable in the relationship between leisure satisfaction and quality of life, suggesting that although leisure time management does not have a direct impact on quality of life, it functions as an important intermediary variable influencing the relationship between these two variables. Effective management of leisure time can contribute to individuals feeling more satisfied with themselves. Personal satisfaction can increase when individuals fill their leisure time with activities that are satisfying and meaningful, indirectly enhancing their quality of life. This finding underscores the significance of leisure time management as an influential factor in quality of life, even if it is not directly linked to it. Thus, efficient leisure time management can affect various other factors that contribute to quality of life. These insights can assist individuals in understanding how to manage their leisure time effectively. Engaging in fulfilling and meaningful activities during leisure time can enhance overall life satisfaction and consequently improve quality of life. Moreover, existing literature demonstrates that these two concepts exert a substantial impact on the quality of life, as evidenced by studies conducted by Chick et al. [ 79 ], Chizari et al. [ 78 ], and Zhou et al. [ 3 ].

A study has investigated the impact of leisure time management on quality of life and examined the effects of leisure activities on personal satisfaction [ 84 ]. Findings suggest that leisure activities enhance individuals’ levels of personal satisfaction and consequently improve quality of life. However, it is proposed that this effect occurs through the effective management of leisure time [ 84 ]. Another research endeavor has explored the influence of leisure activities on quality of life [ 85 ]. Results indicate that personal satisfaction increases as a result of participation in leisure activities, positively affecting quality of life, which is closely linked to effective leisure time management [ 85 ]. Another study has investigated how effectively managing leisure time affects individuals’ quality of life [ 86 ]. Findings demonstrate that consciously utilizing time enhances individuals’ quality of life and consequently elevates their levels of personal satisfaction, highlighting the indirect influence of leisure time management skills on quality of life [ 86 ]. A researcher has examined the influence of leisure activities on quality of life [ 87 ]. Results show that engagement in active and social leisure activities enhances individuals’ quality of life and increases their levels of personal satisfaction [ 87 ]. In a study, the impact of effective leisure time management on individuals’ quality of life was investigated [ 37 ]. Results indicate that effective leisure time management enhances individuals’ quality of life and improves their levels of personal satisfaction [ 37 ]. Finally, a study has explored the effect of effectively utilizing leisure time on quality of life [ 88 ]. Findings reveal that effective leisure time management enhances individuals’ quality of life and increases overall life satisfaction [ 88 ].

In contrast to the existing literature, our study revealed that the interplay between free time management, leisure satisfaction, and quality of life is influenced by gender, age, and participation in physical activity. Furthermore, it was established that the level of contentment with one’s free time served as a mediator in the connection between the overall well-being and the management of free time. Consequently, it was noted that males exhibited a superior standard of living, and the accumulation of life experience and engagement in physical activity positively influenced both the ability to manage free time and quality of life. It has been observed that students who effectively organize and manage their leisure experience an improvement in their overall well-being through an increased enjoyment derived from free time activities, as opposed to those who lack proficiency in managing their free time. The satisfaction of individuals who effectively managed their free time was found to be a crucial factor in this relationship. In this scenario, students who are unable to effectively regulate their free time may transform into individuals who struggle to prioritize their tasks, meet deadlines, experience elevated stress levels, and achieve low levels of success. Individuals who encounter physical, physiological, and psychological issues may diminish their contentment with both free time and overall life. Providing training and counselling to university students, particularly those who are ambitious about their future, on free time management and planning can potentially enhance their quality of life and satisfaction with leisure activities. For this purpose:

Universities can arrange for experts to visit and enhance students’ understanding of time management skills, encouraging them to apply these skills in their daily lives.

This study specifically focuses on university students and does not include other groups. These concepts can be collectively analyzed in various demographic groups, including the elderly, individuals with disabilities, and immigrants.

Limitations

Some limitations are included in the results of this study: To begin, the research used a quantitative approach. The research was conducted using a simple random sampling method. This indicates that generalizations may be limited, and the sample may not fully represent the population. The data used in the study were largely provided by the participants themselves, which could introduce self-reporting biases and inaccuracies. The scales used in the study are adapted versions of scales available in the literature. However, it should be noted that these adaptations may be influenced by language and cultural differences, requiring additional attention to ensure the full accuracy of measurements. The research is a cross-sectional study conducted at a specific point in time. This design may limit the ability to determine variability over time or causality in the correlation. For future research, longitudinal or experimental designs could provide more robust results. SEM demands meticulous variable selection and the acknowledgment of measurement errors. Erroneous variable choices or measurement inaccuracies could compromise the model’s fidelity. SEM offers a means to scrutinize intricate relationships. Nonetheless, the formulation and interpretation of complex models pose challenges. Although the complexity of the model employed in this study is modest, it may constitute a constraint for researchers aiming to delve into more intricate relationships. The study focused on specific demographic characteristics (gender, age, participation in physical activity). However, the neglect of other potential factors may hinder a comprehensive analysis of the results. Considering these limitations provides a more balanced perspective on interpreting the results and their generalizability. Future research should address these limitations to further advance our understanding of the topic.

Availability of data and materials

No datasets were generated or analysed during the current study.

Lackey NQ, Tysor DA, McNay GD, Joyner L, Baker KH, Hodge C. Mental health benefits of nature-based recreation: a systematic review. Ann Leis Res. 2011;24(3):379–93. https://doi.org/10.1080/11745398.2019.1655459 .

Article   Google Scholar  

Rosa CD, Collado S, Profice CC, Larson LR. Nature-based recreation associated with connectedness to nature and leisure satisfaction among students in Brazil. Leis Stud. 2019;38(5):682–91. https://doi.org/10.1080/02614367.2019.1620842 .

Zhou B, Zhang Y, Dong E, Ryan C, Li P. Leisure satisfaction and quality of life of residents in Ningbo, China. J Leis Res. 2021;52(4):469–86. https://doi.org/10.1080/00222216.2021.1931989 .

Chick G, Dong E, Yeh C, Hsieh C. Cultural consonance predicts leisure satisfaction in Taiwan. Leis Stud. 2021;40(2):183–9. https://doi.org/10.1080/02614367.2020.1808052 .

Brajsa-Zganec A, Marina Merkas M, Sverko I. Quality of life and leisure activities: how do leisure activities contribute to subjective well-being? Soc Indic Res. 2011;102(1):81–91. https://doi.org/10.1007/s11205-010-9724-2 .

Liang J, Yamashita T, Scott BJ. Leisure satisfaction and quality of life in China, Japan, and South Korea: a comparative study using Asia barometer 2006. J Happiness Stud. 2013;14:753–69. https://doi.org/10.1007/s10902-012-9353-3 .

Wendel-Vos GW, Schuit AJ, Tijhuis M, Kromhout D. Leisure time physical activity and health-related quality of life: cross-sectional and longitudinal associations. Qual Life Res. 2004;13(3):667–77. https://doi.org/10.1023/B:QURE.0000021313.51397.33 .

Article   PubMed   Google Scholar  

Lloyd KM, Auld CJ. The role of leisure in determining quality of life: issues of content and measurement. Soc Indic Res. 2002;57:43–71.

Leung L, Lee PS. Multiple determinants of life quality: the roles of internet activities, use of new media, social support, and leisure activities. Telematics Inform. 2005;22(3):161–80. https://doi.org/10.1016/j.tele.2004.04.003 .

Passmore A, French D. Development and administration of a measure to assess adolescents’ participation in leisure activities. Adolescence. 2001;36(141):67.

PubMed   Google Scholar  

Scott D, Willits FK. Adolescent and adult leisure patterns: A reassessment. J Leis Res. 1998;30(3):319–30. https://doi.org/10.1080/00222216.1998.11949835 .

Iwasaki Y. Leisure and quality of life in an international and multicultural context: what are major pathways linking leisure to quality of life? Soc Indic Res. 2007;82:233–64. https://doi.org/10.1007/s11205-006-9032-z .

Rodríguez A, Látková P, Sun YY. The relationship between leisure and life satisfaction: application of activity and need theory. Soc Indic Res. 2008;86:163–75. https://doi.org/10.1007/s11205-007-9101-y .

Robinson JP, Martin S. What do happy people do? Soc Indic Res. 2008;89:565–71. https://doi.org/10.1007/s11205-008-9296-6 .

Iskender A, Gucer E. Yaşam doyumu ile boş zaman kısıtlayıcıları arasındaki ilişkinin belirlenmesinde hiyerarşik boş zaman teorisi: Üüniversite öğrencileri üzerine bir uygulama. Ekon Bilim Derg. 2018;10(2):1–16.

Google Scholar  

Cetinkaya O, Akova O. Boş zaman kısıtları ve yaşam doyumu ilişkisi. J Recreation Tour Res. 2020;7(3):375–91. https://doi.org/10.31771/jrtr.2020.69 .

Vapur M, Yavuz E. Boş zaman katılımının yaşam kalitesine etkisi: Boş zaman tatmini ve algılanan stresin aracılık rolü. J Gastronomy Hospitality Travel. 2022;5(3):1252–68.

Aykan E, Ozcelik Z. Mesai içi ve dışı boş zaman davranışlarının yönetimi: Teorik çerçeve. MANAS Sosyal Araştırmalar Dergisi. 2020;9(1):590–604. https://doi.org/10.33206/mjss.498416 .

Toepoel V. Aging, leisure, and social connectedness: how could leisure help reduce social isolation of older people? Soc Indic Res. 2013;113(1):355–72. https://doi.org/10.1007/s11205-012-0097-6 .

Agate JR, Zabriskie RB, Agate ST, Poff R. Family leisure satisfaction and satisfaction with family life. J Leis Res. 2009;41(2):205–23. https://doi.org/10.1080/00222216.2009.11950166 .

Pagan R. How do leisure activities impact on life satisfaction? Evidence for German people with disabilities. Appl Res Qual Life. 2015;10(4):557–72. https://doi.org/10.1007/s11482-014-9333-3 .

Chun S, Lee Y, Kim B, Heo J. The contribution of leisure participation and leisure satisfaction to stress-related growth. Leis Sci. 2012;34(5):436–49. https://doi.org/10.1080/01490400.2012.714704 .

Spiers A, Walker GJ. The effects of ethnicity and leisure satisfaction on happiness, peacefulness, and quality of life. Leis Sci. 2009;31(1):84–99. https://doi.org/10.1080/01490400802558277 .

Ozavci R, Sagar ME, Unver O. Genel özyeterliğin akademik özgüvene etkisinde boş zaman tatmininin aracılık rolü. Akdeniz Spor Bilimleri Dergisi. 2023;6(1):454–69. https://doi.org/10.38021/asbid.1349486 .

Sahin HM, Bozdal O. Üniversite öğrencilerinin boş zaman etkinliklerine katılım düzeylerinin boş zaman ve yaşam tatminlerine etkisi. Türkiye Bilimsel Araştırmalar Dergisi. 2020;5(2):220–35.

Kuykendall L, Tay L, Ng V. Leisure engagement and subjective well-being: a meta-analysis. Psychol Bull. 2015;141(2):364. https://doi.org/10.1037/a0038508 .

Martins J, Sarmento H, Marques A, Nicola PJ. Physical activity recommendations for health: knowledge and perceptions among college students. Retos. 2019;36:290–6.

Chazan AC, Campos MR, Portugal FB. Quality of life of medical students at the State University of Rio de Janeiro (UERJ), measured using Whoqol-bref: a multivariate analysis. Ciênc Saúde Colet. 2015;20:547–56. https://doi.org/10.1590/1413-81232015202.05182014 .

Pereira FEL, Ribeiro RC, Oliveira LMS, Araujo JL, Tabosa MNR, Gouveia PS. Correlatos da qualidade de vida com características de saúde e demográficas de estudantes de medicina. Rev Bras Qual Vida. 2017;9:247–60. https://doi.org/10.3895/rbqv.v9n3.6747 .

Cunha DH, Moraes MA, Benjamin MR, Santos AM. Percepção da qualidade de vida e fatores associados aos escores de qualidade de vida de alunos de uma escola de medicina. J Bras Psiquiatr. 2017;66:189–96. https://doi.org/10.1590/0047-2085000000170 .

Mannell RC, Kleiber DA, Staempfli M. Psychology and social psychology and the study of leisure. Palgrave Macmillan; 2006.

Book   Google Scholar  

Godbey GC, Caldwell LL, Floyd M, Payne LL. Contributions of leisure studies and recreation and park management research to the active living agenda. Am J Prev Med. 2005;28(2):150–8. https://doi.org/10.1016/j.amepre.2004.10.027 .

Ho CH, Sasidharan V, Elmendorf W, Willits FK, Graefe A, Godbey G. Gender and ethnic variations in urban park preferences, visitation, and perceived benefits. J Leis Res. 2005;37(3):281–306. https://doi.org/10.1080/00222216.2005.11950054 .

Iwasaki Y, Zuzanek J, Mannell RC. The effects of physically active leisure on stress-health relationships. Can J Public Health. 2001;92:214–8.

Article   PubMed   PubMed Central   Google Scholar  

Iwasaki Y, MacTavish J, MacKay K. Building on strengths and resilience: leisure as a stress survival strategy. Br J Guid Couns. 2005;33(1):81–100. https://doi.org/10.1080/03069880412331335894 .

Orsega-Smith E, Mowen AJ, Payne LL, Godbey G. The interaction of stress and park use on psycho-physiological health in older adults. J Leis Res. 2004;36(2):232–56. https://doi.org/10.1080/00222216.2004.11950021 .

Mannell RC. Leisure, health and well-being. World Leis J. 2007;49(3):114–28. https://doi.org/10.1080/04419057.2007.9674499 .

Agyar E. Contribution of perceived freedom and leisure satisfaction to life satisfaction in a sample of Turkish women. Soc Indic Res. 2014;116:1–15. https://doi.org/10.1007/s11205-013-0268-0 .

Baker DA, Palmer RJ. Examining the effects of perceptions of community and recreation participation on quality of life. Soc Indic Res. 2006;75:395–418. https://doi.org/10.1007/s11205-004-5298-1 .

Jackson EL. Leisure constraints: a survey of past research. Leis Sci. 1988;10(3):203–15. https://doi.org/10.1080/01490408809512190 .

Ngai VT. Leisure satisfaction and quality of life in Macao, China. Leis Stud. 2005;24(2):195–207. https://doi.org/10.1080/02614360412331313502 .

Hawkins B, Foose AK, Binkley AL. Contribution of leisure to the life satisfaction of older adults in Australia and the United States. World Leis J. 2004;46(2):4–12. https://doi.org/10.1080/04419057.2004.9674353 .

Mannel R, Dupuis S. Life satisfaction. In: Birren J, editor. Encyclopedia of gerontology: age, aging, and the aged. Los Angeles: Academic Press; 1996. p. 59–64.

Wang E, Chen LSL, Lin JYC, Wang MCH. The relationship between leisure satisfaction and life satisfaction of adolescents concerning online games. Adolescence. 2008;43(169):177–85.

Cheng A, Leung Y, Brodaty H. A systematic review of the associations, mediators and moderators of life satisfaction, positive affect and happiness in near-centenarians and centenarians. Aging Ment Health. 2022;26(4):651–66. https://doi.org/10.1080/13607863.2021.18911977 .

Roux P, Misdrahi D, Capdevielle D, Aouizerate B, Berna F, Brunel L, Urbach M. Mediation analyses of insight, quality of life, depression, and suicidality: results from the FACE-SZ cohort. J Clin Psychiatry. 2018;79(3):11442.

Lu L, Kao S. Leisure participation and leisure satisfaction: Moderating effects of personality traits. J Sport Recreation Res. 2009;3(3):1–11. https://doi.org/10.29423/JSRR.200903_3(3).0001 .

Aguillon SM, Siegmund GF, Petipas RH, Drake AG, Cotner S, Ballen CJ. Gender differences in student participation in an active-learning classroom. CBE—Life Sci Educ. 2020;19(2):ar12. https://doi.org/10.1187/cbe.19-03-0048 .

Deci EL, Ryan RM. Facilitating optimal motivation and psychological well-being across life’s domains. Can Psychol. 2008;49(1):14. https://doi.org/10.1037/0708-5591.49.1.14 .

Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol. 2000;55(1):68. https://doi.org/10.1037/0003-066X.55.1.68 .

Bernard M, Phillipson C. Retirement and leisure. In: Nussbaum JF, Coupland J, editors. Handbook of communication and aging research. Routledge; 2004. p. 353–79.

Dixon MA. From their perspective: a qualitative examination of physical activity and sport programming for working mothers. Sport Manag Rev. 2009;12(1):34–48. https://doi.org/10.1016/j.smr.2008.09.002 .

Ateca-Amestoy V, Serrano-del-Rosal R, Vera-Toscano E. The leisure experience. J Socio-Econ. 2008;37(1):64–78. https://doi.org/10.1016/j.socec.2006.12.025 .

Francken D, Van Raaij W. Satisfaction with leisure time activities. J Leis Res. 1981;13(4):337–52. https://doi.org/10.1080/00222216.1981.11969498 .

Su TT, Azzani M, Adewale AP, Thangiah N, Zainol R, Majid H. Physical activity and health-related quality of life among low-income adults in metropolitan Kuala Lumpur. J Epidemiol. 2019;29(2):43–9. https://doi.org/10.2188/jea.JE20170183 .

Pizzo AD, Kunkel T, Jones GJ, Baker BJ, Funk DC. Digitalization and the strategic advantage of mature-stage firms: the diversification of professional sport into esports. J Bus Res. 2022;139:257–66. https://doi.org/10.1016/j.jbusres.2021.09.057 .

Wilson E, Little DE. A “relative escape”? The impact of constraints on women who travel solo. Tour Rev Int. 2005;9(2):155–75. https://doi.org/10.3727/154427205774791672 .

Demir G, Alpullu A. Evaluation of university students leisure time management. Eurasian Res Sport Sci. 2020;5(1):94–102. https://doi.org/10.35333/ERISS.2020.172 .

Lassander M, Hintsanen M, Suominen S, Mullola S, Vahlberg T, Volanen SM. Effects of school-based mindfulness intervention on health-related quality of life: moderating effect of gender, grade, and independent practice in cluster randomized controlled trial. Qual Life Res. 2021;30(12):3407–19. https://doi.org/10.1007/s11136-021-02868-4 .

Serinolli MI, Novaretti MC. A cross-sectional study of sociodemographic factors and their influence on quality of life in medical students at Sao Paulo, Brazil. PLoS One. 2017;12:e0180009. https://doi.org/10.1371/journal.pone.0180009 .

Tabachnick BG, Fidell LS, Ullman JB. Using multivariate statistics. Boston: Pearson; 2013.

Wang WC, Kao CH, Huan TC, Wu CC. Free time management contributes to better quality of life: a study of undergraduate students in Taiwan. J Happiness Stud. 2011;12:561–73. https://doi.org/10.1007/s10902-010-9217-7 .

Akgul BM, Karakucuk S. Leisure time management scale (validity and reliability study). Int J Hum Sci. 2015;12(2):1867–80. https://doi.org/10.14687/ijhs.v12i2.3445 .

Beard JG, Ragheb MG. Measuring leisure satisfaction. J Leis Res. 1980;12(1):20–33. https://doi.org/10.1080/00222216.1980.11969416 .

Gokce H, Orhan K. Validity and reliability study of the leisure satisfaction scale into Turkish. Hacettepe Univ J Sports Sci. 2011;22(4):139–45. https://doi.org/10.17644/sbd.171376 .

Ware JE, Kosinski M, Keller SD. SF-12: how to score the SF-12 physical and mental health summary scales. Boston: The Health Institute of New England Medical Cente; 1995.

Soylu C, Kütük B. SF-12 reliability and validity study of the Turkish form of the quality of life scale. Turk J Psychiatry. 2021:1–9. https://doi.org/10.5080/u25700 .

Kalkavan A. Introduction to data analysis in sports sciences. Istanbul: Efe Akademi; 2021.

Cakir HI. Descriptive statistics. In: Kalkavan A, editor. Introduction to data analysis in sports sciences. Istanbul: Efe Akademi; 2021.

Cakir G. Normality analyzes. In: Kalkavan A, editor. Introduction to data analysis in sports sciences. Istanbul: Efe Akademi; 2021.

Kayhan RF. Relationship and impact analysis. In: Kalkavan A, editor. Introduction to data analysis in sports sciences. Istanbul: Efe Akademi; 2021.

Popović R, Samouilidou E, Popović J, Dolga M. Assessment of the quality of life, health, and social wellness in upper elementary school students: cross-cultural and gender specificity. Brit Int Humanit Soc Sci (BioHS) J. 2020;2(1):127–42. https://doi.org/10.33258/biohs.v2i1.158 .

Işıkgoz ME, Gurbuz PG, Esentas M. An investigation of university students leisure management in terms of various variables. Int J Curr Educ Res. 2021;7(1):413–29.

Mokhtari K, Reichard CA, Gardner A. The impact of internet and television use on the reading habits and practices of college students. J Adolesc Adult Lit. 2009;52(7):609–19. https://doi.org/10.1598/JAAL.52.7.6 .

Sevil-Serrano J, Aibar A, Abós Á, Generelo E, García-González L. Improving motivation for physical activity and physical education through a school-based intervention. J Exp Educ. 2022;90(2):383–403. https://doi.org/10.1080/00220973.2020.1764466 .

Schwartz GM, Campagna J. New meaning for the emotional state of the elderly, from a leisure standpoint. Leis Stud. 2008;27(2):207–11. https://doi.org/10.1080/02614360801902232 .

Chizari H, Shooshtari S, Duncan K, Menec V. Examining the effects of participation in leisure and social activities on general health and life satisfaction of older Canadian adults with disability. Pract Clin Psychol. 2020;8(3):217–32. https://doi.org/10.32598/jpcp.8.3.10.713.1 .

Chick G, Hsu YC, Yeh CK, Hsieh CM. Leisure constraints, leisure satisfaction, life satisfaction, and self-rated health in six cities in Taiwan. Leis Sci. 2015;37(3):232–51. https://doi.org/10.1080/01490400.2014.967897 .

Lee JH, Lee JH, Park SH. Leisure activity participation as predictor of quality of life in Korean urban-dwelling elderly. Occup Ther Int. 2014;21:124–32. https://doi.org/10.1002/oti.1371 .

Trenberth L. The role, nature and purpose of leisure and its contribution to individual development and well-being. Br J Guid Couns. 2005;33(1):1–6. https://doi.org/10.1080/03069880412331335849 .

Kuo CT. A study on leisure satisfaction and quality of life-based on badminton participants. J Glob Bus Manag. 2011;7(2):1.

Tseng TA, Chang DY, Shen CC. The relationships between leisure behaviours and life satisfaction of foreign labours in Taiwan–the case of Thai labours. Adv Hospitality Leis. 2009;5:25–50. https://doi.org/10.1108/S1745-3542(2009)0000005006 .

Wiese CW, Kuykendall L, Tay L. Get active? A meta-analysis of leisure-time physical activity and subjective well-being. J Posit Psychol. 2018;13(1):57–66. https://doi.org/10.1080/17439760.2017.1374436 .

Mannell RC, Kleiber DA. Psychology of leisure. In: Routledge handbook of leisure studies. Routledge Handbook of Leisure Studies; 2020. p. 40–51.

Kelly CM, Strauss K, Arnold J, Stride C. The relationship between leisure activities and psychological resources that support a sustainable career: the role of leisure seriousness and work-leisure similarity. J Vocat Behav. 2020;117:103340. https://doi.org/10.1016/j.jvb.2019.103340 .

Kuykendall L, Tay L. Employee subjective well-being and physiological functioning: an integrative model. Health Psychology Open. 2015;2(1):205510291559. https://doi.org/10.1177/2055102915592090 .

Kim JY, Lee MK, Lee DH, Kang DW, Min JH, Lee JW, Jeon JY. Effects of a 12-week home-based exercise program on quality of life, psychological health, and the level of physical activity in colorectal cancer survivors: a randomized controlled trial. Support Care Cancer. 2019;27:2933–40. https://doi.org/10.1161/CIRCULATIONAHA.121.058162 .

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Conception and design of the study: E.T, U.I. Data collection: E.T, B.C.I, C.A. Data analysis and interpretation: E.T., U.I, B.C.I, C.A. Drafting the article and/or its critical revision: E.T., U.I, B.C.I, U.D.U. Final approval of the version to be published: E.T, U.I, U.D.U.

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Terzi, E., Isik, U., Inan, B.C. et al. University students’ free time management and quality of life: the mediating role of leisure satisfaction. BMC Psychol 12 , 239 (2024). https://doi.org/10.1186/s40359-024-01745-2

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Time management for STEMM students during the continuing pandemic

Sandra a. murray.

1 Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA

7 First author.

Jamaine Davis

2 Department of Biological Sciences, Winston-Salem State University, Winston-Salem, NC 27110, USA

Haysetta D. Shuler

3 Department of Biochemistry, Cancer Biology, Neuroscience and Pharmacology, Meharry Medical College, Nashville, TN 37208, USA

Elsie C. Spencer

4 Teachers College, Columbia University, New York, NY 10027, USA

8 Co–senior authors.

Antentor Hinton, Jr.

5 Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA

6 Hinton and Garza Lopez Family Consulting Company, Iowa City, IA 52246, USA

One of the biggest obstacles to success is a lack of practical time management skills. Here, we provide suggestions on how to optimize time management.

Introduction

Many factors contribute to the academic success of students in science, technology, engineering, math, and medicine (STEMM). One important factor is time management, and lack of time management is a major impediment to student success [ 1 , 2 ]. Multiple articles discuss effective time management. However, here we focus on the skills that students, particularly those in STEMM, need to successfully employ to manage their time. STEMM students must develop skills that enable them to solve problems, such as the coronavirus disease 2019 (COVID-19) pandemic and the response of those in STEMM research to address this pandemic and its subsequent vaccine development. For STEMM students to enter, thrive, and be innovative in their future careers, they must possess excellent time management skills. We provide methods, resources, and strategies to help students better manage their time, which requires clear goal setting and proper time allocation to complete tasks. We begin by discussing goal setting followed by time management. Offering our combined experience in mentoring and training STEMM students and our time management skills, we direct our comments to students.

Goal setting

Goals are blueprints for your future that impact success and life satisfaction. To be successful, setting long-term goals such as career goals (e.g., What do I want to do with my life?) and short-term goals (e.g., What do I want to do today?) are important steppingstones to achieving long-term aspirations ( Table 1 ). A career goal should be your own rather than a path guided by another person’s hopes for you. While this may seem obvious, students often select goals based on other people’s suggestions. For instance, your mother may want you to be a physician, or your teacher may see a PhD in your future, while your friends may want you to join their career path. They all may have your best interests in mind; however, if these are not your aspirations, you will be unhappily working toward a dream that isn’t yours until you finally admit that this is not the career you want to pursue. It is important to recognize what you want, which requires you to be honest and commit to your future. Thinking about who you are, what you like, and what makes you happy will help to clarify your goals. Also, identify careers that you will enjoy that will also provide sufficient income and opportunities to live well and contribute to society. Introspective goal choices will help focus your priorities, leading to better time management and success reaching and succeeding in your future career goals.

Goal-setting resources

Manage your time

Many students struggle to balance time between academics, work, and other obligations, which may reflect poor planning and an inability to effectively prioritize time [ 3 , 4 ]. Categorizing tasks and establishing priorities will improve your planning and project management success and contribute to effective time management.

Often, you are too busy studying and taking care of outside commitments to accomplish every task on your endless to-do list. At the end of a day, we tend to focus on uncompleted tasks rather than on completed tasks, which leads to feeling overwhelmed and anxious. If this occurs daily, it can increase stress and decrease academic and work performance [ 1 ]. Stress creates an unproductive cycle that saps your energy and time and compromises your mental acuity. You can break this cycle by learning to properly manage time instead of allowing time to manage you. One effective method for managing tasks is the Eisenhower Matrix ( Table 1 ) [ 5 ].

The Eisenhower Matrix

Successfully managing multiple tasks requires prioritization. The Eisenhower Matrix prioritizes tasks by categorizing them based on urgency and importance ( Figure 1A ). Tasks that are both urgent and important are completed first, while nonurgent and unimportant tasks are last. As a deadline approaches, a task in one matrix box may need to be moved to another. For example, washing underwear may be considered neither urgent nor important if you have a drawer filled with clean underwear. However, in time, this task becomes both urgent and important as you run out of clean underwear.

(A) To prioritize the tasks using the matrix, each task should be categorized based on its urgency and importance. Tasks that are both urgent and important will need to be done first. However, try to work on tasks before they are in the urgent and important box. The use of a calendar will help you keep track of deadlines so that tasks do not become urgent. (B) Examples of scheduling (yellow timeslots) and unscheduling (red timeslots) are demonstrated on a calendar that has been set up by graying out slots for times during the day when you will be unavailable to work on tasks because of other obligations. A line has been drawn to indicate the time when work will stop every day, and fun activities (green timeslots) have been added to increase the probability that you will complete important tasks while enjoying a well-balanced life. Use of a calendar to allocate your time can increase your productivity and the probability of completing tasks and help you to use time rather than lose time.

While some things may be dropped from the matrix, you typically need to work on tasks in all four matrix boxes. When your energy is low, work on nonurgent or unimportant things. However, when energy is high, tackle urgent and important tasks. Ideally, work on the nonurgent but important box, especially if you have many obligations to juggle. One benefit of completing an important task before it becomes urgent is that there is time to get feedback from peers and instructors before the deadline. Accomplishing a task and meeting deadlines requires that you understand what must be done and allocate sufficient time to complete it.

Time allocation and deadlines

Effective time allocation requires that you know when tasks are due. Deadlines serve as a checkpoint and an effective tool to stay on task, thus preventing you from falling behind in project planning and execution. As a deadline approaches, tasks become daunting, and anxiety may take over. With limited time, planning stops; you rush to complete the task, cut corners, and overlook details; and inevitably this leads to suboptimal performance and unsatisfactory results. As the due date approaches, an important/urgent task demands your attention regardless of your energy or stress level. Ideally, allocate time in advance to ensure that tasks are completed before becoming urgent. A calendar indicating deadlines and a to-do list are valuable tools to help allocate time and complete tasks.

Making the to-do list

The common time management to-do list [ 6 ] may seem unnecessary when important tasks are looming as you know they will be easy to remember. However, a to-do list is more than just a reminder of upcoming events; it also helps you examine and prioritize activities to be accomplished before beginning an upcoming task. For example, you may consider whether to prioritize an extremely important project due in a few weeks or a less important task due in a few days. Awareness of your tasks also encourages you to categorize them, such as those necessary for your coursework or job versus those necessary for upkeep of your house or social life. Such priorities also help you to say no [ 7 ] before placing a task on your to-do list or into a matrix box (see Table 1 for organizational apps and artificial intelligence).

To-do lists allow you to integrate your home, school, and work lives, which will help you to retain a sense of identity and balance. A to-do list should be specific, defining every task, and should be reviewed and edited every morning or evening [ 6 ]. Breaking to-do list tasks into smaller subtasks will make it easier to start and complete these projects. Mentors can be particularly helpful when learning to identify roadblocks to success [ 8 ].

The calendar as a tool

Here we describe tips on calendar use, based on Fiore’s (1980) concept of ‘time mapping’ ( Figure 1B ) [ 9 ]. We suggest using a calendar with 7 days of the week, each day divided into 60-minute time slots. Seeing the entire week and 4 weeks in the month can help you manage activities and complete assignments with less stress. A feeling that there is infinite time to complete a task causes procrastination. Therefore, draw a line on the calendar indicating the time that you will stop working every day. Sometimes, you will need to work past the marked time, but do not make that a habit. Cross out times on the calendar when you will be unavailable to work on tasks (e.g., class, gym, and mealtimes). Just seeing how much or little time is available to complete tasks will significantly improve your time management. Also, add deadlines to your calendar and allow sufficient time to complete important tasks before they become urgent.

Now that you have the calendar set, add your tasks. But first add some fun stuff! Adding nonwork personal recreation and social activities to the calendar will encourage you to complete your important tasks ( Figure 1B , green). We suggest three calendar methods that can be used alone or together to complete tasks: (i) scheduling, (ii) ‘unscheduling,’ and (iii) logging. For scheduling, identify one to three tasks you want to complete, and add them to open slots on your calendar (e.g., writing a term paper introduction; Figure 1B , yellow). Scheduling tasks increases the probability that you will work on them, keep that time slot reserved to complete the tasks, and decline other obligations. Unscheduling is the process of rewarding yourself for completing a task by adding a bright color ( Figure 1B , red) on your calendar. For example, when you write the term paper introduction, add a bright color to your calendar. Psychologists have shown that seeing a bright color that you like on your calendar is an incentive that increases the will to continue working. In the third method, logging, you keep a log of your time to analyze time usage. You might be surprised how much time you spend chatting, texting, or emailing rather than working. You also might identify times of the day when you are most productive, which can be used to match tasks to your energy levels.

Concluding remarks

Effective time management requires you to identify and analyze your goals, effectively plan and manage projects, and properly allocate time. Ineffective time management not only hampers productivity, making it difficult to meet deadlines, but also disrupts your work–life balance. Work–life balance issues can disrupt your research and cause some trainees to leave the pipeline [ 10 ]. Work–life imbalance can also damage your professional reputation and increase stress [ 11 ]. In contrast, effective time management leads to efficiency and productivity. The strategies discussed here will help you achieve better planning and organization, resulting in reduced stress [ 11 ], improved judgment, and a better professional reputation. Being organized, efficient, and meeting deadlines will establish your reputation as a competent and dependable professional and lead to exciting career opportunities and advancement.

Acknowledgments

We thank Heather Beasley for helping make the figure in BioRender. We also thank Kit Neikirk for assistance with editing and formatting of the paper. This work was supported by the UNCF/BMS EE Just Grant Faculty Fund, Burroughs Wellcome Fund CASI Award, Burroughs Welcome Fund Ad-hoc Award, NIH SRP Subaward to 5R25HL106365-12 from the NIH PRIDE Program, DK020593, Vanderbilt Diabetes and Research Training Center for DRTC Alzheimer’s Disease Pilot & Feasibility Program and A.H.J. NSF grant MCB 2011577I and NIH T32 5T32GM133353 to S.A.M.

Declaration of interests

The authors declare that they have no competing interests.

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8 Time Management Tips for Students

Don't let a hectic schedule get the better of you with these time management tips.

Lian Parsons

College can be a stressful time for many students and time management can be one of the most crucial — but tricky — skills to master.

Attending classes, studying for exams, making friends, and taking time to relax and decompress can quickly fill up your schedule. If you often find yourself wishing there were more hours in the day, this guide will offer time management tips for students so you can accomplish what you need to get done, have fun with your friends, and gain back some valuable time for yourself. 

1. Create a Calendar

Don’t be caught by surprise by an important paper due two days from now or a dinner with your family the same night you planned for a group study session. Create a calendar for yourself with all your upcoming deadlines, exams, social events, and other time commitments well in advance so you can see what’s coming up. 

Keep your calendar in a place where you can see it every day, such as in your planner or on your wall above your desk. If you prefer a digital calendar, check it first thing every day to keep those important events fresh and top-of-mind. For greater efficiency, make sure you can integrate it with your other tools, such as your email.

Digital calendar options include: 

• Google Calendar 
• Outlook Calendar
• Fantastical

2. Set Reminders

After you’ve created your calendar, give yourself periodic reminders to stay on track such as to complete a study guide in advance or schedule a meeting for a group project. Knowing deadlines is important; however, staying on top of the micro tasks involved in meeting those deadlines is just as important. You can set an alarm on your phone, write it down in a physical planner, or add an alert to your digital calendar. The reminders will help to prevent things from slipping through the cracks during particularly hectic days.

Make sure you’ve allotted enough time to study for that big test or write that final paper. Time management is all about setting yourself up for success in advance and giving yourself the tools to accomplish tasks with confidence. 

Read our blogs, Your Guide to Conquering College Coursework and Top 10 Study Tips to Study Like a Harvard Student , for more suggestions.

3. Build a Personalized Schedule

Each person’s day-to-day is different and unique to them, so make sure your schedule works for you. Once you’ve accounted for consistent commitments such as classes or your shifts at work, add in study sessions, extracurriculars, chores and errands, and social engagements.

Consider your personal rhythm. If you typically start your day energized, plan to study or accomplish chores then. If you fall into an afternoon slump, give yourself that time to take a guilt-free TV break or see friends.

Having a schedule that works for you will help maximize your time. Plus, knowing exactly when your laundry day is or when your intramural volleyball practice is every week will help you avoid trying to cram everything in one day (or running out of clean socks!)

Explore summer college courses.

4. Use Tools That Work For You

Just like your calendar and schedule, the tools you use to keep you organized should be the right fit for you. Some students prefer physical planners and paper, while some prefer going totally digital. Your calendar can help you with long-term planning, but most of these tools are best for prioritizing from day to day.

Explore what best suits your needs with some of the following suggestions:

Planners can help you keep track of long-term deadlines, such as important essay deadlines, upcoming exams, and appointments and meetings. They often provide a monthly overview each month, as well as day-to-day planning sections, so you can stay ahead. 

• Papier – Offers a 20% student discount 

If your schedule is jam-packed and you have trouble figuring out what to do and when, scheduling day by day—and sometimes even hour by hour—can help you slot in everything you need to do with less stress.

• Structured app

Note Taking

From class to study sessions to errands, keeping track of everything can feel overwhelming. Keeping everything in one place, whether on the go or at your desk, can help keep you organized.

• Bullet journals

5. Prioritize

Sometimes there really is too much to do with too little time. In these instances, take just a few minutes to evaluate your priorities. Consider which deadlines are most urgent, as well as how much energy you have. 

If you are able to complete simple tasks first, try getting them out of the way before moving on to tasks that require a lot of focus. This can help to alleviate some of the pressure by checking a couple things off your to-do list without getting bogged down too early.

If you are struggling to fit everything in your schedule, consider what you can postpone or what you can simply say no to. Your friends will likely understand if you have to meet them for coffee another time in order to get in a final library session before a challenging exam. 

6. Make Time to Have Fun — And For Yourself

Time management isn’t just about getting work done. It’s also about ensuring that you can put yourself and your mental wellbeing first. Consistently including time for yourself in your schedule helps to keep your mental health and your life in balance. It can also be helpful to have things to look forward to when going through stressful periods.  

Whether it’s going for a bike ride along the river, spending time with your friends and family, or simply sleeping in on a Sunday, knowing you have space to relax and do things you enjoy can provide better peace of mind. 

7. Find Support 

Preparation and organization can sometimes only get you so far. Luckily, you have plenty of people rooting for your success. Keep yourself and your classmates on task by finding an accountability partner or study buddies. Remind your roommates when you need extra space to work on a paper. 

Your school’s academic resource center is also there to support you and point you in the right direction if you need additional help. Getting—and staying—organized is a collaborative effort and no one can do it on their own. 

8. Be Realistic and Flexible 

Sometimes unforeseen circumstances will come up or you simply may not be able to get to everything you set out to do in a given day. Be patient with yourself when things don’t go exactly to plan. When building your calendar, schedule, and priorities list, be realistic about what you can accomplish and include buffer time if you’re unsure. This can help to reduce obstacles and potential friction.

Time management isn’t just about sticking to a rigid schedule—it’s also about giving yourself space for change.

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About the Author

Lian Parsons is a Boston-based writer and journalist. She is currently a digital content producer at Harvard’s Division of Continuing Education. Her bylines can be found at the Harvard Gazette, Boston Art Review, Radcliffe Magazine, Experience Magazine, and iPondr.

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The Balancing Act of Student Time Management

Being a good student involves hard work, dedication, and understanding the concepts you learn – but many people forget the importance of another factor: time management . When you're juggling classes, homework, extracurricular activities, and social life, it can be easy to feel overwhelmed and lose track of time.

One of the best ways to succeed in academics and personal life is to master the art of time management. The earlier you can do this, the more you will be able to carry time management skills into higher education and your career.

Many of the members of National Society of High School Scholars have cultivated time management skills to excel. Read on for some of our best tips on how and why to become great at time management.

Why Time Management Matters for Students

When you can effectively manage your time as a student, you can make the most of all elements of your life: academic success, extracurricular activities, and personal time for rest and joy. This work-life balance is known to reduce stress levels and ultimately even increase productivity, and it will be important to carry throughout your career.

When you have control over your time, you can allocate it wisely to achieve your goals, whether they are academic or personal. In developing these skills, you may also learn important lessons about organization, discipline, and self-motivation , all of which are invaluable in all aspects of life.

When Students Don’t Learn Time Management

On the other hand, poor time management can have a detrimental effect on your life. When you fail to manage your time effectively, you may constantly rush to complete assignments, cram for exams, or miss deadlines. This can lead to a decline in academic performance, increased stress levels, and overall dissatisfaction with your educational experience.

Not only that, but poor time management can also spill over into other areas of your life, affecting your relationships, mental well-being, and even physical health. Taking proactive steps to avoid these issues is important to your overall well-being.

Time Management Skill #1: Understand Your Priorities and Set Goals

Before effectively managing your time, you must have a good sense of your priorities and set clear goals. Take some time to reflect on what matters most to you academically and personally, thinking about questions like:

What subjects or activities require the most work?

What long-term goals do you want to achieve?

Which things are non-negotiable, and what can be optional?

What are things that bring you joy to balance out stress?

 With these in mind, you can allocate your time accordingly and ensure that you are making progress towards your goals. Setting SMART goals (Specific, Measurable, Achievable, Relevant, Time-bound) can be a good way to ensure your goals are clear from the beginning.

Time Management Skill #2: Use Daily and Weekly Schedules

Creating schedules is one of the simplest, most effective ways to master time management.

Start by mapping out your daily and weekly commitments, including classes, study time, extracurricular activities, and personal obligations. Use a planner or digital calendar to visualize your schedule and ensure that you have allocated sufficient time for each task.

Then, look at each day. Consider your energy levels throughout the day and assign tasks accordingly. For example, if you are more alert in the morning, schedule your most challenging subjects or important assignments during that time. Be sure to include breaks in your schedule to rejuvenate and avoid burnout.

Time Management Skill #3: Study Efficiently

Studying will likely be a big element of your schedule, which means it requires time management. One way to make the most of your time is to be sure that your study methods are effective for you. Instead of spending hours mindlessly reviewing material, adopt study techniques that maximize your learning potential.

One example is the Pomodoro Technique, where you study for 25 minutes then take a non-negotiable 5-minute break. This may sound counterintuitive, but it has been shown to improve focus and prevent mental fatigue.

You can also consider active learning, in which you actively engage with the material through activities such as summarizing, discussing, or teaching the concepts to someone else.

Experiment with different study techniques to find what works best for you, and you will see your time management improve.

Time Management Skill #4: Balancing Academics and Extracurricular Activities

One of the most difficult things for students can be finding a way to balance school and other activities to have the most well-rounded experience possible. While academics are top priority, these activities may enrich your life or even help with college applications, so it is important to find room for both.

The key to balancing academics and activities is being realistic about how much time you can dedicate to any one thing and saying no when it doesn’t allow you that boundary. Quality, not quantity, is most important for extracurriculars, so find something you can give your all to instead of struggling to do many things.

Time Management #5: Utilizing Technology and Productivity Tools for Time Management

In the digital age, you don’t need notebooks and planners to manage time (unless you prefer that!). You can utilize apps and software that help you create and organize your schedules, set reminders, and track your progress. Some popular tools include Google Calendar, Trello, Todoist, and Forest.

These tools improve your time management, provide insights into your productivity habits, and help you make necessary adjustments. Try out some tools to learn what works best for you.

Time Management Skill #6: Seek Support and Guidance

If you struggle with time management, don't hesitate to find support. Reach out to your teachers, academic advisors, or mentors who can provide insights and strategies for effective time management. They can help you identify your strengths and weaknesses, offer personalized advice, and hold you accountable for your commitments.

Another idea is to join study groups or find tutoring services if you require additional assistance in managing your workload.

Working Toward Time Management

Mastering time management is a continuous journey that requires self-awareness, discipline, and a willingness to adapt. Your time is a precious resource, and it is up to you to make the most of it. Start implementing these time management tips today, and you'll be on your way to a more productive and fulfilling academic journey!

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Time management skills are essential for all college students. In order to effectively manage courses, extracurricular activities, and social life, students should be able to create and maintain effective schedules. Often, creating a schedule and sticking with it is easier said than done. This page will give an overview of how college students can effectively manage their time, show examples of how to schedule various activities, and suggest different apps to stay organized.

Assess My Skills

Managing your time well can not only ensure success at UGA, it can also help you live a balanced and whole life as a college student. How well you manage your time over each semester will determine how well you will perform academically at UGA, so don’t hesitate to spend some time each day/week/semester working on this essential skill.

If you’re ready to reach all of your goals at the University of Georgia, think about attending college as a full time job, which is typically 40 hours/week. This means you should be devoting 40 hours each week to attending class and completing schoolwork.

When thinking about a typical 24 hour day, we recommend using the 8-8-8 rule.

• 8 hours towards academics (both inside and outside of the classroom)
• 8 hours towards living (activities for fun or to take care of yourself)
• 8 hours towards sleeping (at least five days/week)

Let’s look at an example. A standard full time workload is 40/week – so if you are enrolled in 14 credit hours this semester, then you should be dedicating  at least  26 hours to your academics outside of class (40 hours total – 14 hours in class = 26 hours outside of class). Keep in mind, this estimate isn’t an exact science. Depending on your classes and if it’s during midterms/finals, you may have to spend more time to be successful in your classes. Some of the academic tasks you’ll spend outside of the classroom can include planning, reading, working on projects / problem sets / homework, and preparing for tests and exams.

Creating a Balanced Calendar

When building your personal calendar, there are a few things that are important to consider. Ask yourself the questions below:

• Am I more likely to look at (and keep up with) a digital calendar or a paper planner?
• Do I want to get my classes and academic work completed earlier in the day or later in the day?
• Beyond classes, what other commitments do I have?

Picking the type of calendar that works best for you is the most important first step. Many students prefer using a digital calendar like Google Calendar, the iPhone Calendar app, or Microsoft Outlook. These calendars typically sync between devices and have features like notifications, recurring meetings, and are easily edited. Paper planners that can be purchased at most box stores like Wal-Mart, Target, and Amazon are also frequently used by UGA students. The DAE team has even created a  24 hour calendar template  that has become a favorite with coaching students. Whichever type of calendar you choose is completely up to you!

Once you’ve chosen your calendar, it’s time to start filling it in. We recommend completing it in the order below:

• Classes and meals : Mark when all of your classes are and include important details like the location. Be sure to hold time for three meals/day as eating enough throughout the day is important to stay focused and energized.
• Work, internships, student organization meetings, etc. : Add all of your other scheduled commitments to your calendar. This might look different week-to-week depending on how frequently you meet or if your work schedule changes.
• Study times for each class:  Use the study cycle to understand when the best times to study and complete class work are. Remember that you will need to schedule multiple study sessions for each class/week. It’s also good to note where you will be studying and if you’ll be studying with anyone else.

In addition to keeping up with your daily calendar, you can use our  master semester calendar  for a bird’s eye view of the semester. On this calendar, you can add all of your significant tests, papers, presentations, and projects for each class, as well as other large commitments of your time. Once this is complete, place it somewhere you’ll see it everyday so you can understand what the coming weeks will look like.

Using Your Calendar Effectively

You’ve gotten your calendar completed, now what? It’s important to set aside some time at the beginning of each week to plan out the week. We’ve found that many students like to do this on Sunday evenings before the school week begins. When you’re planning out the week, feel free to make adjustments based on what assignments are due and what commitments you have that week. You can break down larger tasks and projects into smaller, easier tasks. You can also set weekly goals in the form of to-do lists for each course. These strategies will help keep you on track and allow you to adapt your calendar as needed.

If you need help and or if you would like some feedback and insights from an expert,  schedule an Academic Coaching appointment .

Further Reading

Links with additional information on time management strategies.

• Mastering Time Management
• 7 Effective Time Management Tips for College Students
• Time Management Tips – Dartmouth
• Top 12 Time Management Tips

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Physical activity improves stress load, recovery, and academic performance-related parameters among university students: a longitudinal study on daily level

• Monika Teuber 1 ,
• Daniel Leyhr 1 , 2 &
• Gorden Sudeck 1 , 3  

BMC Public Health volume  24 , Article number:  598 ( 2024 ) Cite this article

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Physical activity has been proven to be beneficial for physical and psychological health as well as for academic achievement. However, especially university students are insufficiently physically active because of difficulties in time management regarding study, work, and social demands. As they are at a crucial life stage, it is of interest how physical activity affects university students' stress load and recovery as well as their academic performance.

Student´s behavior during home studying in times of COVID-19 was examined longitudinally on a daily basis during a ten-day study period ( N  = 57, aged M  = 23.5 years, SD  = 2.8, studying between the 1st to 13th semester ( M  = 5.8, SD  = 4.1)). Two-level regression models were conducted to predict daily variations in stress load, recovery and perceived academic performance depending on leisure-time physical activity and short physical activity breaks during studying periods. Parameters of the individual home studying behavior were also taken into account as covariates.

While physical activity breaks only positively affect stress load (functional stress b = 0.032, p  < 0.01) and perceived academic performance (b = 0.121, p  < 0.001), leisure-time physical activity affects parameters of stress load (functional stress: b = 0.003, p  < 0.001, dysfunctional stress: b = -0.002, p  < 0.01), recovery experience (b = -0.003, p  < 0.001) and perceived academic performance (b = 0.012, p  < 0.001). Home study behavior regarding the number of breaks and longest stretch of time also shows associations with recovery experience and perceived academic performance.

Conclusions

Study results confirm the importance of different physical activities for university students` stress load, recovery experience and perceived academic performance in home studying periods. Universities should promote physical activity to keep their students healthy and capable of performing well in academic study: On the one hand, they can offer opportunities to be physically active in leisure time. On the other hand, they can support physical activity breaks during the learning process and in the immediate location of study.

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Introduction

Physical activity (PA) takes a particularly key position in health promotion and prevention. It reduces risks for several diseases, overweight, and all-cause mortality [ 1 ] and is beneficial for physical, psychological and social health [ 2 , 3 , 4 , 5 ] as well as for academic achievement [ 6 , 7 ]. However, PA levels decrease from childhood through adolescence and into adulthood [ 8 , 9 , 10 ]. Especially university students are insufficiently physically active according to health-oriented PA guidelines [ 11 ] because of academic workloads as well as difficulties in time management regarding study, work, and social demands [ 12 ]. Due to their independence and increasing self-responsibility, university students are at a crucial life stage. In this essential and still educational stage of the students´ development, it is important to study their PA behavior. Furthermore, PA as health behavior represents one influencing factor which is considered in the analytical framework of the impact of health and health behaviors on educational outcomes which was developed by the authors Suhrcke and de Paz Nieves [ 13 , 14 ]. In light of this, the present study examines how PA affects university students' academic situations.

Along with the promotion of PA, the reduction of sedentary behavior has also become a crucial part of modern health promotion and prevention strategies. Spending too much time sitting increases many health risks, including the risk of obesity [ 15 ], diabetes [ 16 ] and other chronic diseases [ 15 ], damage to muscular balances, bone metabolism and musculoskeletal system [ 17 ] and even early death [ 15 ]. University students are a population that has shown the greatest increase in sedentary behavior over the last two decades [ 18 ]. In Germany, they show the highest percentage of sitting time among all working professional groups [ 19 ]. Long times sitting in classes, self-study learning, and through smartphone use, all of which are connected to the university setting and its associated behaviors, might be the cause of this [ 20 , 21 ]. This goes along with technological advances which allow students to study in the comfort of their own homes without changing locations [ 22 ].

To counter a sedentary lifestyle, PA is crucial. In addition to its physical health advantages, PA is essential for coping with the intellectual and stress-related demands of academic life. PA shows positive associations with stress load and academic performance. It is positively associated with learning and educational success [ 6 ] and even shows stress-regulatory potential [ 23 ]. In contrast, sedentary behavior is associated with lower cognitive performance [ 24 ]. Moreover, theoretical derivations show that too much sitting could have a negative impact on brain health and diminish the positive effects of PA [ 16 ]. Given the theoretical background of the stressor detachment model [ 25 ] and the cybernetic approach to stress management in the workplace [ 26 ], PA can promote recovery experience, it can enhance academic performance, and it is a way to reduce the impact of study-related stressors on strain. Load-related stress response can be bilateral: On the one hand, it can be functional if it is beneficial to help cope with the study demands. On the other hand, it can be dysfunctional if it puts a strain on personal resources and can lead to load-related states of strain [ 27 ]. Thus, both, the promotion of PA and reduction of sedentary behavior are important for stress load, recovery, and performance in student life, which can be of particular importance for students in an academic context.

A simple but (presumably) effective way to integrate PA and reduce sedentary behavior in student life are short PA breaks. Due to the exercises' simplicity and short duration, students can perform them wherever they are — together in a lecture or alone at home. Short PA breaks could prevent an accumulation of negative stressors during the day and can help with prolonged sitting as well as inactivity. Especially in the university setting, evidence of the positive effects of PA breaks exists for self-perceived physical and psychological well-being of the university students [ 28 ]. PA breaks buffer university students’ perceived stress [ 29 ] and show positive impacts on recovery need [ 30 ] and better mood ratings [ 31 , 32 ]. In addition, there is evidence for reduction in tension [ 30 ], overall muscular discomfort [ 33 ], daytime sleepiness or fatigue [ 33 , 34 ] and increase in vigor [ 34 ] and experienced energy [ 30 ]. This is in line with cognitive, affective, behavioral, and biological effects of PA, all categorized as palliative-regenerative coping strategies, which addresses the consequences of stress-generating appraisal processes aiming to alleviate these consequences (palliative) or restore the baseline of the relevant reaction parameter (regenerative) [ 35 , 36 ]. This is achieved by, for example, reducing stress-induced cortisol release or tension through physical activity (reaction reduction) [ 35 ]. Such mechanisms are also in accordance with the previously mentioned stressor detachment model [ 25 ]. Lastly, there is a health-strengthening effect that impacts the entire stress-coping-health process, relying on the compensatory effects of PA which is in accordance to the stress-buffering effect of exercise [ 37 ]. Health, in turn, effects educational outcomes [ 13 , 14 ]. Therefore, stress regulating effects are also accompanied with the before mentioned analytical framework of the impact of health and health behaviors on educational outcomes [ 13 , 14 ].

Focusing on the effects of PA, this study is guided by an inquiry into how PA affects university students' stress load and recovery as well as their perceived academic performance. For that reason, the student´s behavior during home studying in times of COVID-19 is examined, a time in which reinforced prolonged sitting, inactivity, and a negative stress load response was at a high [ 38 , 39 , 40 , 41 , 42 ]. Looking separately on the relation of PA with different parameters based on the mentioned evidence, we assume that PA has a positive impact on stress load, recovery, and perceived academic performance-related parameters. Furthermore, a side effect of the home study behavior on the mentioned parameters is assumed regarding the accumulation of negative stressors during home studying. These associations are presented in Fig.  1 and summarized in the following hypotheses:

Overview of the assumed effects and investigated hypotheses of physical activity (PA) behavior on variables of stress load and recovery and perceived academic performance-related parameters

Hypothesis 1 (path 1): Given that stress load always occurs as a duality—beneficial if it is functional for coping, or exhausting if it puts a strain on personal resources [ 27 ] – we consider two variables for stress load: functional stress and dysfunctional stress. In order to reduce the length of the daily surveys, we focused the measure of recovery only on the most obvious and accessible component of recovery experience, namely psychological detachment. PA (whether performed in leisure-time or during PA breaks) encourages functional stress and reduce dysfunctional stress (1.A) and has a positive effect on recovery experience through psychological detachment (1.B).

Hypothesis 2 (path 2): The academic performance-related parameters attention difficulties and study ability are positively influenced by PA (whether done in leisure-time or during PA breaks). We have chosen to assess attention difficulties for a cognitive parameter because poor control over the stream of occurring stimuli have been associated with impairment in executive functions or academic failure [ 43 , 44 , 45 , 46 ]. Furthermore, we have assessed the study ability to refer to the self-perceived feeling of functionality regarding the demands of students. PA reduces self-reported attention difficulties (2.A) and improves perceived study ability, indicating that a student feels capable of performing well in academic study (2.B).

Hypothesis 3: We assume that a longer time spent on studying at home (so called home studying) could result in higher accumulation of stressors throughout the day which could elicit immediate stress responses, while breaks in general could reduce the influence of work-related stressors on strain and well-being [ 47 , 48 ]. Therefore, the following covariates are considered for secondary effects:

the daily longest stretch of time without a break spent on home studying

the daily number of breaks during home studying

Study setting

The study was carried out during the COVID-19 pandemic containment phase. It took place in the middle of the lecture period between 25th of November and 4th of December 2020. Student life was characterized by home studying and digital learning. A so called “digital semester” was in effect at the University of Tübingen when the study took place. Hence, courses were mainly taught online (e.g., live or via a recorded lecture). Other events and actions at the university were not permitted. As such, the university sports department closed in-person sports activities. For leisure time in general, there were contact restrictions (social distancing), the performance of sports activities in groups was not permitted, and sports facilities were closed.

Thus, the university sports department of the University of Tübingen launched various online sports courses and the student health management introduced an opportunity for a new digital form of PA breaks. This opportunity provided PA breaks via videos with guided physical exercises and health-promoting explanations for a PA break for everyday home studying: the so called “Bewegungssnack digital” [in English “exercise snack digital” (ESD)] [ 49 ]. The ESD videos took 5–7 min and were categorized into three thematic foci: activation, relaxation, and coordination. Exercises were demonstrated by one or two student exercise leaders, accompanied by textual descriptions of the relevant execution features of each exercise.

Participants

Participants were recruited within the framework of an intervention study, which was conducted to investigate whether a digital nudging intervention has a beneficial effect on taking PA breaks during home study periods [ 49 ]. Students at the University of Tübingen which counts 27,532 enrolled students were approached for participation through a variety of digital means: via an email sent to those who registered for ESD course on the homepage of the university sports department and to all students via the university email distribution list; via advertisement on social media of the university sports department (Facebook, Instagram, YouTube, homepage). Five tablets, two smart watches, and one iPad were raffled off to participants who engaged actively during the full study period in an effort to motivate them to stick with it to the end. In any case, participants knew that the study was voluntary and that they would not suffer any personal disadvantages should they opt out. There was a written informed consent prompt together with a prompt for the approval of the data protection regulations immediately within the first questionnaire (T0) presented in a mandatory selection field. Positive ethical approval for the study was given by the first author´s institution´s ethics committee of the faculty of the University of Tübingen.

Participants ( N  = 57) who completed the daily surveys on at least half of the days of the study period, were included in the sample (male = 6, female = 47, diverse = 1, not stated = 3). As not all subjects provided data on all ten study days, the total number of observations was between 468 and 540, depending on the variable under study (see Table  1 ). The average number of observations per subject was around eight. Their age was between 18 and 32 years ( M  = 23.52, SD  = 2.81) and they were studying between the 1st to 13th semester ( M  = 5.76, SD  = 4.11) within the following major courses of study: mathematical-scientific majors (34.0%), social science majors (22.6%), philosophical majors (18.9%), medicine (13.2%), theology (5.7%), economics (3.8%), or law (1.9%). 20.4% of the students had on-site classroom teaching on university campus for at least one day a week despite the mandated digital semester, as there were exceptions for special forms of teaching.

Design and procedures

To examine these hypothesized associations, a longitudinal study design with daily surveys was chosen following the suggestion of the day-level study of Feuerhahn et al. (2014) and also of Sonnentag (2001) measuring recovery potential of (exercise) activities during leisure time [ 50 , 51 ]. Considering that there are also differences between people at the beginning of the study period, initial base-line value variables respective to the outcomes measured before the study period were considered as independent covariates. Therefore, the well-being at baseline serves as a control for stress load (2.A), the psychological detachment at baseline serves as a control for daily psychological detachment (2.B), the perception of study demands serves as a control for self-reported attention difficulties (1.A), and the perceived study ability at baseline serves as a control for daily study ability (2.B).

Subjects were asked to continue with their normal home study routine and additionally perform ESD at any time in their daily routine. Data were collected one to two days before (T0) as well as daily during the ten-day study period (Wednesday to Friday). The daily surveys (t 1 -t 10 ) were sent by email at 7 p.m. every evening. Each day, subjects were asked to answer questions about their home studying behavior, study related requirements, recovery experience from study tasks, attention, and PA, including ESD participation. The surveys were conducted online using the UNIPARK software and were recorded and analyzed anonymously.

Measures and covariates

In total, five outcome variables, two independent variables, and seven covariates were included in different analyses: three variables were used for stress load and recovery parameters, two variables for academic performance-related parameters, two variables for PA behavior, two variables for study behavior, four variables for outcome specific baseline values and one variable for age.

Outcome variables

Stress load & recovery parameters (hypothesis 1).

Stress load was included in the analysis with two variables: functional stress and dysfunctional stress. Followingly, a questionnaire containing a word list of adjectives for the recording of emotions and stress during work (called “Erfassung von Emotionen und Beanspruchung “ in German, also known as EEB [ 52 ]) was used. It is an instrument which were developed and validated in the context of occupational health promotion. The items are based on mental-workload research and the assessment of the stress potential of work organization [ 52 ]. Within the questionnaire, four mental and motivational stress items were combined to form a functional stress scale (energetic, willing to perform, attentive, focused) (α = 0.89) and four negative emotional and physical stress items were combined to form dysfunctional stress scale (nervous, physically tensioned, excited, physically unwell) (α = 0.71). Participants rated the items according to how they felt about home studying in general on the following scale (adjustment from “work” to “home studying”): hardly, somewhat, to some extent, fairly, strongly, very strongly, exceptionally.

Recovery experience was measured via psychological detachment. Therefore, the dimension “detachment” of the Recovery Experience Questionnaire (RECQ [ 53 ]) was adjusted to home studying. The introductory question was "How did you experience your free time (including short breaks between learning) during home studying today?". Students responded to four statements based on the extent to which they agreed or disagreed (not at all true, somewhat true, moderately true, mostly true, completely true). The statements covered subjects such as forgetting about studying, not thinking about studying, detachment from studying, and keeping a distance from student tasks. The four items were combined into a score for psychological detachment (α = 0.94).

Academic performance-related parameters (hypothesis 2)

Attention was assessed via the subscale “difficulty maintaining focused attention performance” of the “Attention and Performance Self-Assessment” (ASPA, AP-F2 [ 54 ]). It contains nine items with statements about disturbing situations regarding concentration (e.g. “Even a small noise from the environment could disturb me while reading.”). Participants had to answer how often such situations happened to them on a given day on the following scale: never, rarely, sometimes, often, always. The nine items were combined into the AP-F2 score (α = 0.87).

The perceived study ability was assessed using the study ability index (SAI [ 55 ]). The study ability index captures the current state of perceived functioning in studying. It is based on the Work Ability Index by Hasselhorn and Freude ([ 56 ]) and consists of an adjusted short scale of three adapted items in the context of studying. Firstly, (a) the perceived academic performance was asked after in comparison to the best study-related academic performance ever achieved (from 0 = completely unable to function to 10 = currently best functioning). Secondly, the other two items were aimed at assessing current study-related performance in relation to (b) study tasks that have to be mastered cognitively and (c) the psychological demands of studying. Both items were answered on a five-point Likert scale (1 = very poor, 2 = rather poor, 3 = moderate, 4 = rather good, 5 = very good). A sum index, the SAI, was formed which can indicate values between 2 and 20, with higher values corresponding to higher assessed functioning in studies (α = 0.86). In a previous study it already showed satisfying reliability (α = 0.72) [ 55 ].

Independent variables

Pa behavior.

Two indicators for PA behavior were included via self-reports: the time spent on ESD and the time spent on leisure-time PA (LTPA). Participants were asked the following overarching question daily: “How much time did you spend on physical activity today and in what context”. For the independent variable time spent on PA breaks, participants could answer the option “I participated in the Bewegungssnack digital” with the amount of time they spent on it (in minutes). To assess the time spent on LTPA besides PA breaks, participants could report their time for four different contexts of PA which comprised two forms: Firstly, structured supervised exercise was reported via time spent on (a) university sports courses and (b) other organized sports activities. Secondly, self-organized PA was indicated via (c) independent PA at home, such as a workout or other physically demanding activity such as cleaning or tidying up, as well as via (d) independent PA outside, like walking, cycling, jogging, a workout or something similar. Referring to the different domains of health enhancing PA [ 57 ], the reported minutes of these four types of PA were summed up to a total LTPA value. The total LTPA value was included in the analysis as a metric variable in minutes.

Covariates (hypothesis 3)

Regarding hypothesis 3 and home study behavior, the longest daily stretch of time without a break spent on home studying (in hours) and the daily number of breaks during home studying was assessed. Therein, participants had to answer the overarching question “How much time did you spend on your home studying today?” and give responses to the items: (1) longest stretch of time for home studying (without a break), and (2) number of short and long breaks you took during home studying.

In principle, efforts were made to control for potential confounders at the individual level (level 2) either by including the baseline measure (T0) of the respective variable or by including variables assessing related trait-like characteristics for respective outcomes. The reason why related trait-like characteristics were used for the outcomes was because brief assessments were used for daily surveys that were not concurrently employed in the baseline assessment. To enable the continued use of controlling for person-specific baseline characteristics in the analysis of daily associations, trait-like characteristics available from the baseline assessment were utilized as the best possible approximation.To sum up, four outcome specific baseline value variables were measured before the study period (at T0). The psychological detachment with the RECQ (α = 0.87) [ 53 ] was assessed at the beginning to monitor daily psychological detachment. Further, the SAI [ 55 ] was assessed at the beginning of the study period to monitor daily study ability. To monitor daily stress load, which in part measures mental stress aspects and negative emotional stress aspects, the well-being was assessed at the beginning using the WHO-Five Well-being Index (WHO-5 [ 58 ]). It is a one-dimensional self-report measure with five items. The index value is the sum of all items, with higher values indicating better well-being. As the well-being and stress load tolerance may linked with each other, this variable was assumed to be a good fit with the daily stress load indicating mental and emotional stress aspects. With respect to student life, daily academic performance-related attention was monitored with an instrument for the perception of study demands and resources (termed “Berliner Anforderungen Ressourcen-Inventar – Studierende” in German, the so-called BARI-S [ 59 ]). It contains eight items which capture overwork in studies, time pressure during studies, and the incompatibility of studies and private life. All together they form the BARI-S demand scale (α = 0.85) which was included in the analysis. As overwork and time pressure may result in attention difficulties (e.g. Elfering et al., 2013), this variable was assumed to have a good fit with academic performance-related attention [ 60 ]. Additionally, age in years at T0 was considered as a sociodemographic factor.

Statistical analysis

Since the study design provided ten measurement points for various people, the hierarchical structure of the nested data called for two-level analyses. Pre-analyses of Random-Intercept-Only models for each of the outcome variables (hypothesis 1 to 3) revealed an Intra-Class-Correlation ( ICC ) of at least 0.10 (range 0.26 – 0.64) and confirmed the necessity to perform multilevel analyses [ 61 ]. Specifically, the day-level variables belong to Level 1 (ESD time, LTPA time, longest stretch of time without a break spent on home studying, daily number of breaks during home studying). To analyze day-specific effects within the person, these variables were centered on the person mean (cw = centered within) [ 50 , 62 , 63 , 64 ]. This means that the analyses’ findings are based on a person’s deviations from their average values. The variables assessed at T0 belong to Level 2, which describe the person level (psychological detachment baseline, SAI baseline, well-being, study demands scale, age). These covariates on person level were centered around the grand mean [ 50 ] indicating that the analyses’ findings are based how far an individual deviates from the sample's mean values. As a result, the models’ intercept reflects the outcome value of an average student in the sample at his/her daily average behavior in PA and home study when all parameters are zero. For descriptive statistics SPSS 28.0.1.1 (IBM) and for inferential statistics R (version 4.1.2) were used. The hierarchical models were calculated using the package lme4 with the lmer-function in R in the following steps [ 65 ]. The Null Model was analyzed for all models first, with the corresponding intercept as the only predictor. Afterwards, all variables were entered. The regression coefficient estimates (”b”) were considered for statistical significance for the models and the respective BIC was provided.

In total, five regression models with ‘PA break time’ and ‘LTPA time’ as independent variables were computed due to the five measured outcomes of the present study. Three models belonged to hypothesis 1 and two models to hypothesis 2.

Hypothesis 1: To test hypothesis 1.A two outcome variables were chosen for two separate models: ‘functional stress’ and ‘dysfunctional stress’. Besides the PA behavior variables, the ‘number of breaks’, the ‘longest stretch of time without a break spent on home studying’, ‘age’, and the ‘well-being’ at the beginning of the study as corresponding baseline variable to the output variable were also included as independent variables in both models. The outcome variable ‘psychological detachment’ was utilized in conjunction with the aforementioned independent variables to test hypotheses 1.B, with one exception: psychological detachment at the start of the study was chosen as the corresponding baseline variable.

Hypothesis 2: To investigate hypothesis 2.A the outcome variable ‘attention difficulties’ was selected. Hypothesis 2.B was tested with the outcome variables ‘study ability’. Both models included both PA behavior variables as well as the ‘number of breaks’, the ‘longest stretch of time without a break spent on home studying’, ‘age’ and one corresponding baseline variable each: the ‘study demand scale’ at the start of the study for ‘attention difficulties’ and the ‘SAI’ at the beginning of the study for the daily ‘study ability’.

Hypothesis 3: In addition to both PA behavior variables, age and one baseline variable that matched the outcome variable, the covariates ‘daily longest stretch of time spent on home studying’ and ‘daily number of breaks during home studying’ were included in the models for all five outcome variables.

Handling missing data

The dataset had up to 18% missing values (most exhibit the variables ‘daily longest stretch of time without a break spent on home studying’ with 17.89% followed by ‘daily number of breaks during homes studying’ with 16.67%, and ‘functional / dysfunctional stress’ with 12.45%). Therefore, a sensitivity analysis was performed using the multiple imputation mice-package in the statistical program R [ 66 ], the package howManyImputation based on Von Hippel (2020, [ 67 ]), and the additional broom package [ 68 ]. The results of the models remained the same, with one exception for the Attention Difficulties Model: The daily longest stretch of time without a break spent on home studying showed a significant association (Table  1 in supplement). Due to this almost perfect consistency of results between analyses based on the dataset with missing data and those with imputed data alongside the lack of information provided by the packages for imputed datasets, we decided to stick with the main analysis including the missing data. Thus, in the following the results of the main analysis without imputations are presented.

Table 1 shows the descriptive statistics of the variables used in the analysis. An overview of the analysed models is presented in Table  2 .

Effects on stress load and recovery (hypothesis 1)

Hypothesis 1.A: The Model Functional Stress explained 13% of the variance by fixed factors (marginal R 2  = 0.13), and 52% by both fixed and random factors (conditional R 2  = 0.52). The time spent on ESD as well as the time spent on PA in leisure showed a positive significant influence on functional stress (b = 0.032, p  < 0.01). The same applied to LTPA (b = 0.003, p  < 0.001). The Model Dysfunctional Stress (marginal R 2  = 0.027, conditional R 2  = 0.647) showed only one significant result. The dysfunctional stress was only significantly negatively influenced by the time spent on LTPA (b = 0.002, p  < 0.01).

Hypothesis 1.B: With the Model Detachment, fixed factors contributed 18% of the explained variance and fixed and random factors 46% of the explained variance for psychological detachment. Only the amount of time spent on LTPA revealed a positive impact on psychological detachment (b = 0.003, p  < 0.001).

Effects on academic performance-related parameters (hypothesis 2)

Hypothesis 2.A: The Model Attention Difficulties showed 13% of the variance explained by fixed factors, and 51% explained by both fixed and random factors. It showed a significant negative association only for the time spent on LTPA (b = 0.003, p  < 0.001).

Hypothesis 2.B: The Model SAI showed 18% of the variance explained by fixed factors, and 39% explained by both fixed and random factors. There were significant positive associations for time spent on ESD (b = 0.121, p  < 0.001) and time spent on LTPA (b = 0.012, p  < 0.001). The same applied to LTPA (b = 0.012, p  < 0.001).

Effects of home study behavior (hypothesis 3)

Regarding the independent covariates for the outcome variables functional and dysfunctional stress, there were no significant results for the number of breaks during homes studying or the longest stretch of time without a break spent on home studying. Considering the outcome variable ‘psychological detachment’, there were significant results with negative impact for both study behavior variables: breaks during home studying (b = 0.058, p  < 0.01) and daily longest stretch of time without a break (b = 0.120, p  < 0.01). Evaluating the outcome variables ‘attention difficulties’, there were no significant results for the number of breaks during home studying or the longest stretch of time without a break spent on home studying. Testing the independent study behavior variables for the SAI, it increased with increasing number in daily breaks during homes studying relative to the person´s mean (b = 0.183, p  < 0.05). No significant effect was found for the longest stretch of time without a break spent on home studying ( p  = 0.07).

The baseline covariates of the models showed expected associations and thus confirmed their inclusion. The baseline variables well-being showed a significant impact on functional stress (b = 0.089, p  < 0.001), psychological detachment showed a positive effect on the daily output variables psychological detachment (b = 0.471, p  < 0.001), study demand scale showed a positive association on difficulties in attention (b = 0.240, p  < 0.01), and baseline SAI had a positive effect on the daily SAI (b = 0.335, p  < 0.001).

The present study theorized that PA breaks and LTPA positively influence the academic situation of university students. Therefore, impact on stress load (‘functional stress’ and ‘dysfunctional stress’) and ‘psychological detachment’ as well as academic performance-related parameters ‘self-reported attention difficulties’ and ‘perceived study ability’ was taken into account. The first and second hypotheses assumed that both PA breaks and LTPA are positively associated with the aforementioned parameters and were confirmed for LTPA for all parameters and for PA breaks for functional stress and perceived study ability. The third hypothesis assumed that home study behavior regarding the daily number of breaks during home studying and longest stretch of time without a break spent on home studying has side effects. Detected negative effects for both covariates on psychological detachment and positive effects for the daily number of breaks on perceived study ability were partly unexpected in their direction. These results emphasize the key position of PA in the context of modern health promotion especially for students in an academic context.

Regarding hypothesis 1 and the detected positive associations for stress load and recovery parameters with PA, the results are in accordance with the stress-regulatory potential of PA from the state of research [ 23 ]. For hypothesis 1.A, there is a positive influence of PA breaks and LTPA on functional stress and a negative influence of LTPA on dysfunctional stress. Given the bilateral role of stress load, the results indicate that PA breaks and LTPA are beneficial for coping with study demands, and may help to promote feelings of joy, pride, and learning progress [ 27 ]. This is in line with previous evidence that PA breaks in lectures can buffer university students’ perceived stress [ 29 ], lead to better mood ratings [ 29 , 31 ], and increase in motivation [ 28 , 69 ], vigor [ 34 ], energy [ 30 ], and self-perceived physical and psychological well-being [ 28 ]. Looking at dysfunctional stress, the result point that LTPA counteract load-related states of strain such as inner tension, irritability and nervous restlessness or feelings of boredom [ 27 ]. In contrast, short PA breaks during the day could not have enough impact in countering dysfunctional stress at the end of the day regarding the accumulation of negative stressors during home studying which might have occurred after the participant took PA breaks. Other studies have been able to show a reduction in tension [ 30 ] and general muscular discomfort [ 33 ] after PA breaks. However, this was measured as an immediate effect of PA breaks and not with general evening surveys. Blasche and colleagues [ 34 ] measured effects immediately and 20 min after different kind of breaks and found that PA breaks led to an additional short‐ and medium‐term increase in vigor while the relaxation break lead to an additional medium‐term decrease in fatigue compared to an unstructured open break. This is consistent with the results of the present study that an effect of PA breaks is only observed for functional stress and not for dysfunctional stress. Furthermore, there is evidence that long sitting during lectures leads to increased fatigue and lower concentration [ 31 , 70 ], which could be counteracted by PA breaks. For both types of stress loads, functional and dysfunctional stress, there is an influence of students´ well-being in this study. This shows that the stress load is affected by the way students have mentally felt over the last two weeks. The relevance of monitoring this seems important especially in the time of COVID-19 as, for example, 65.3% of the students of a cross-sectional online survey at an Australian university reported low to very low well-being during that time [ 71 ]. However, since PA and well-being can support functional stress load, they should be of the highest priority—not only as regards the pandemic, but also in general.

Looking at hypothesis 1.B; while there is a positive influence of LTPA on experienced psychological detachment, no significant influence for PA breaks was detected. The fact that only LTPA has a positive effect can be explained by the voluntary character of the activity [ 50 ]. The voluntary character ensures that stressors no longer affect the student and, thus, recovery as detachment can take place. Home studying is not present in leisure times, and thus detachment from study is easier. The PA break videos, on the other hand, were shot in a university setting, which would have made it more difficult to detach from study. In order to further understand how PA breaks affect recovery and whether there is a distinction between PA breaks and LTPA, future research should also consider other types of recovery (e.g. relaxation, mastery, and control). Additionally, different types of PA breaks, such as group PA breaks taken on-site versus video-based PA breaks, should be taken into account.

Considering the confirmed positive associations for academic performance-related parameters of hypothesis 2, the results are in accordance with the evidence of positive associations between PA and learning and educational success [ 6 ], as well as between PA breaks and better cognitive functioning [ 28 ]. Looking at the self-reported attention difficulties of hypothesis 2.A, only LTPA can counteract it. PA breaks showed no effects, contrary to the results of a study of Löffler and collegues (2011, [ 31 ]), in which acute effects of PA breaks could be found for higher attention and cognitive performance. Furthermore, the perception of study demands before the study periods has a positive impact on difficulties in attention. That means that overload in studies, time pressure during studies, and incompatibility of studies and private life leads to higher difficulties with attention in home studying. In these conditions, PA breaks might have been seen as interfering, resulting in the expected beneficial effects of exercise on attention and task-related participation behavior [ 72 , 73 ] therefore remaining undetected. With respect to the COVID-19 pandemic, accompanying education changes, and an increase in student´s worries [ 74 , 75 ], the perception of study demands could be affected. This suggests that especially in times of constraint and changes, it is important to promote PA in order to counteract attention difficulties. This also applies to post-pandemic phase.

Regarding the perceived academic performance of hypothesis 2.B, both PA breaks and LTPA have a positive effect on perceived study ability. This result confirms the positive short-term effects on cognition tasks [ 76 ]. It is also in line with the positive function of PA breaks in interrupting sedentary behavior and therefore counteracting the negative association between sitting behavior and lower cognitive performance [ 24 ]. Additionally, this result also fits with the previously mentioned positive relationship between LTPA and functional stress and between PA breaks and functional stress.

According to hypothesis 3, in relation to the mentioned stress load and recovery parameters, there are negative effects of the daily number of breaks during home studying and the longest stretch of time without a break spent on home studying on psychological detachment. As stressors result in negative activation, which impede psychological detachment from study during non-studying time [ 25 ], it was expected and confirmed that the longest stretch of time without a break spent on home studying has a negative effect on detachment. Initially unexpected, the number of breaks has a negative influence on psychological detachment, as breaks could prevent the accumulation of strain reactions. However, if the breaks had no recovery effect through successful detachment, the number might not have any influence on recovery via detachment. This is indicated by the PA breaks, which had no impact on psychological detachment. Since there are other ways to recover from stress besides psychological detachment, such as relaxation, mastery, and control [ 53 ], PA breaks must have had an additional impact in relation to the positive results for functional stress.

In relation to the mentioned academic performance-related parameters, only the number of breaks has a positive influence on the perceived study ability. This indicates that not only PA breaks but also breaks in general lead to better perceived functionality in studying. Paulus and colleagues (2021) found out that an increase in cognitive skills is not only attributed to PA breaks and standing breaks, but also to open breaks with no special instructions [ 28 ]. Either way, they found better improvement in self-perceived physical and psychological well-being of the university students with PA breaks than with open breaks. This is also reflected in the present study with the aforementioned positive effects of PA breaks on functional stress, which does not apply to the number of breaks.

Overall, it must be considered that the there is a more complex network of associations between the examined parameters. The hypothesized separate relation of PA with different parameters do not consider associations between parameters of stress load / recovery and academic performance although there might be a interdependency. Furthermore, moderation aspects were not examined. For example, PA could be a moderator which buffer negative effects of stress on the study ability [ 55 ]. Moreover, perceived study ability might moderate stress levels and academic performance. Further studies should try to approach and understand the different relationships between the parameters in its complexity.

Limitations

Certain limitations must be taken into account. Regarding the imbalanced design toward more female students in the sample (47 female versus 6 male), possible sampling bias cannot be excluded. Gender research on students' emotional states during COVID-19, when this study took place, or students´ acceptance of PA breaks is diverse and only partially supplied with inconsistent findings. For example, during the COVID-19 pandemic, some studies reported that female students were associated with lower well-being [ 71 ] or worse mental health trajectories [ 75 , 77 ]. Another study with a large sample of students from 62 countries reported that male students were more strongly affected by the pandemic because they were significantly less satisfied with their academic life [ 74 ]. However, Keating and colleges (2020) discovered that, despite the COVID-19 pandemic, females rated some aspects of PA breaks during lectures more positively than male students did. However, this was also based on a female slanted sample [ 78 ]. Further studies are needed to get more insights into gender bias.

Furthermore, the small sample size combined with up to 16% missing values comprises a significant short-coming. There were a lot of possibilities which could cause such missing data, like refused, forgotten or missed participation, technical problems, or deviation of the personal code for the questionnaire between survey times. Although the effects could be excluded by sensitive analysis due to missing data, the sample is still small. To generalize the findings, future replication studies are needed.

Additionally, PA breaks were only captured through participation in the ESD, the specially instructed PA break via video. Effects of other short PA breaks were not include in the study. However, participants were called to participate in ESD whenever possible, so the likelihood that they did take part in PA breaks in addition to the ESD could be ignored.

With respect to the baseline variables, it must be considered that two variables (stress load, attention difficulties) were adjusted not with their identical variable in T0, but with other conceptually associated variables (well-being index, BARI-S). Indeed, contrary to the assumption the well-being index does only show an association with functional stress, indicating that it does not control dysfunctional stress. Although the other three assumed associations were confirmed there might be a discrepancy between the daily measured variables and the variables measured in T0. Further studies should either proof the association between these used variables or measure the same variables in T0 for control the daily value of these variables.

Moreover, the measuring instruments comprised the self-assessed perception of the students and thus do not provide an objective information. This must be considered, especially for measuring cognitive and academic-performance-related measures. Here, existing objective tests, such as multiple choice exams after a video-taped lecture [ 72 ] might have also been used. Nevertheless, such methods were mostly used in a lab setting and do not reflect reality. Due to economic reasons and the natural learning environment, such procedures were not applied in this study. However, the circumstances of COVID-19 pandemic allowed a kind of lab setting in real life, as there were a lot of restrictions in daily life which limited the influence of other covariates. The study design provides a real natural home studying environment, producing results that are applicable to the healthy way that students learn in the real world. As this study took place under the conditions of COVID-19, new transformations in studying were also taken into account, as home studying and digital learning are increasingly part of everyday study.

However, the restrictions during the COVID-19 pandemic could result in a greater extent of leisure time per se. As the available leisure time in general was not measured on daily level, it is not possible to distinguish if the examined effects on the outcomes are purely attributable to PA. It is possible that being more physical active is the result of having a greater extent of leisure time and not that PA but the leisure time itself effected the examined outcomes. To address this issue in future studies, it is necessary to measure the proportion of PA in relation to the leisure time available.

Furthermore, due to the retrospective nature of the daily assessments of the variables, there may be overstated associations which must be taken into account. Anyway, the daily level of the study design provides advantages regarding the ability to observe changes in an individual's characteristics over the period of the study. This design made it possible to find out the necessity to analyze the hierarchical structure of the intraindividual data nested within the interindividual data. The performed multilevel analyses made it possible to reflect the outcome of an average student in the sample at his/her daily average behavior in PA and home study.

Conclusion and practical implications

The current findings confirm the importance of PA for university students` stress load, recovery experience, and academic performance-related parameters in home studying. Briefly summarized, it can be concluded that PA breaks positively affect stress load and perceived study ability. LTPA has a positive impact on stress load, recovery experience, and academic performance-related parameters regarding attention difficulties and perceived study ability. Following these results, universities should promote PA in both fashions in order to keep their students healthy and functioning: On the one hand, they should offer opportunities to be physically active in leisure time. This includes time, environment, and structural aspects. The university sport department, which offers sport courses and provides sport facilities on university campuses for students´ leisure time, is one good example. On the other hand, they should support PA breaks during the learning process and in the immediate location of study. This includes, for example, providing instructor videos for PA breaks to use while home studying, and furthermore having instructors to lead in-person PA breaks in on-site learning settings like universities´ libraries or even lectures and seminars. This not only promotes PA, but also reduces sedentary behavior and thereby reduces many other health risks. Further research should focus not only on the effect of PA behavior but also of sedentary behavior as well as the amount of leisure time per se. They should also try to implement objective measures for example on academic performance parameters and investigate different effect directions and possible moderation effects to get a deeper understanding of the complex network of associations in which PA plays a crucial role.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Attention and Performance Self-Assessment

"Berliner Anforderungen Ressourcen-Inventar – Studierende" (instrument for the perception of study demands and resources)

Centered within

Grand centered

“Erfassung von Emotionen und Beanspruchung “ (questionnaire containing a word list of adjectives for the recording of emotions and stress during work)

Exercise snack digital (special physical activity break offer)

Intra-Class-Correlation

Leisure time physical activity

• Physical activity

Recovery Experience Questionnaire

Study ability index

World Health Organization-Five Well-being index

Knight JA. Physical inactivity: associated diseases and disorders. Ann Clin Lab Sci. 2012;42(3):320–37.

PubMed   Google Scholar  

Kemel PN, Porter JE, Coombs N. Improving youth physical, mental and social health through physical activity: a systematic literature review. Health Promot J Austr. 2002;33(3):590–601.

Article   Google Scholar  

Gothe NP, Ehlers DK, Salerno EA, Fanning J, Kramer AF, McAuley E. Physical activity, sleep and quality of life in older adults: influence of physical, mental and social well-being. Behav Sleep Med. 2020;18(6):797–808.

Article   PubMed   Google Scholar  

Eime RM, Young JA, Harvey JT, Charity MJ, Payne WR. A systematic review of the psychological and social benefits of participation in sport for children and adolescents: informing development of a conceptual model of health through sport. Int J Behav Nutr Phys Act. 2013;10(1):1–21.

Google Scholar  

Iannotti RJ, Janssen I, Haug E, Kololo H, Annaheim B, Borraccino A. Interrelationships of adolescent physical activity, screen-based sedentary behaviour, and social and psychological health. Int J Public Health. 2009;54:191–8.

Dadaczynski K, Schiemann S. Welchen Einfluss haben körperliche Aktivität und Fitness im Kindes-und Jugendalter auf Bildungsoutcomes? German J Exerc Sport Res. 2015;4(45):190–9.

Kari JT, Pehkonen J, Hutri-Kähönen N, Raitakari OT, Tammelin TH. Longitudinal associations between physical activity and educational outcomes. Med Sci Sports  Exerc. 2017;49(11).

Grim M, Hortz B, Petosa R. Impact evaluation of a pilot web-based intervention to increase physical activity. Am J Health Promot. 2011;25(4):227–30.

Irwin JD. Prevalence of university students’ sufficient physical activity: A systematic review. Percept Mot Skills. 2004;98:927–43.

Kwan MY, Cairney J, Faulkner GE, Pullenayegum EE. Physical activity and other health-risk behaviors during the transition into early adulthood: a longitudinal cohort study. Am J Prev Med. 2012;42(1):14–20.

John JM, Gropper H, Thiel A. The role of critical life events in the talent development pathways of athletes and musicians: A systematic review. Psychol Sport Exerc. 2019;45.

Bopp M, Bopp C, Schuchert M. Active transportation to and on campus is associated with objectively measured fitness outcomes among college students. J Phys Act Health. 2015;12(3):418–23.

Dadaczynski K. Stand der Forschung zum Zusammenhang von Gesundheit und Bildung. Überblick und Implikationen für die schulische Gesundheitsförderung. Zeitschrift für Gesundheitspsychologie. 2012;20(3):141–53

Suhrcke M, de Paz NC. The impact of health and health behaviours on educational outcomes in high-income countries: a review of the evidence. Copenhagen: WHO Regional Offi ce for Europe; 2011.

Lynch BM, Owen N. Too much sitting and chronic disease risk: steps to move the science forward. Ann Intern Med. 2015;16(2):146–7.

Voss MW, Carr LJ, Clark R, Weng T. Revenge of the “sit” II: Does lifestyle impact neuronal and cognitive health through distinct mechanisms associated with sedentary behavior and physical activity? Ment Health Phys Act. 2014;7(1):9–24.

Huber G. Ist Sitzen eine tödliche Aktivität? B&G Bewegungstherapie und Gesundheitssport. 2014;30(01):13–6.

Peterson NE, Sirard JR, Kulbok PA, DeBoer MD, Erickson JM. Sedentary behavior and physical activity of young adult university students. Res Nurs Health. 2018;4(1):30–8.

Rupp R, Dold C, Bucksch J. Sitzzeitreduktion und Bewegungsaktivierung in der Hochschullehre – Entwicklung und Implementierung der Mehrebenen-Intervention Kopf-Stehen. Die Hochschullehre. 2019;5:525–42.

Ickes MJ, McMullen J, Pflug C, Westgate PM. Impact of a University-based Program on Obese College Students’ Physical Activity Behaviors, Attitudes, and Self-efficacy. Am J Health Educ. 2016;47(1):47–55.

Lepp A, Barkley JE, Karpinski AC. The relationship between cell phone use and academic performance in a sample of US college students. Sage Open. 2015;5(1).

Stapp AC, Prior LF. The Impact of Physically Active Brain Breaks on College Students’ Activity Levels and Perceptions. J Physic Activ Res. 2018;3(1):60–7.

Fuchs R, Klaperski S. Sportliche Aktivität und Stressregulation. In: Fuchs R, Schlicht W, editors. Sportaktivität und seelische Gesundheit Göttingen: Hogrefe; 2012. p. 100–21.

Falck RS, Davis JC, Liu-Ambrose T. What is the association between sedentary behaviour and cognitive function? A systematic review. Br J Sports Med. 2017;51(10):800–11.

Sonnentag S, Fritz C. Recovery from job stress: The stressor-detachment model as an integrative framework. J Organ Behav. 2015;36:72–103.

Edwards JR. A cybernetic theory of stress, coping, and well-being in organizations. Acad Manag Rev. 1992;17(2):238–74.

Wieland R. Status-Bericht: Psychische Gesundheit in der betrieblichen Gesundheitsförderung – eine arbeitspsychologische Perspektive. In: Nold H, Wenninger G, editors. Rückengesundheit und psychische Gesundheit. Rückengesundheit und psychische Gesundheit.: Asanger Verlag; 2013.

Paulus M, Kunkel J, Schmidt SCE, Bachert P, Wäsche H, Neumann R, et al. Standing breaks in lectures improve university students’ self-perceived physical, mental, and cognitive condition. Int J Environ Res Public Health. 2021;18.

Marschin V, Herbert C. A Short, Multimodal Activity Break Incorporated Into the Learning Context During the Covid-19 Pandemic: Effects of Physical Activity and Positive Expressive Writing on University Students’ Mental Health — Results and Recommendations From a Pilot Study. Front Psychol. 2021;12.

Gollner E, Savil M, Schnabel F, Braun C, Blasche G. Unterschiede in der Wirksamkeit von Kurzpausenaktivitäten im Vergleich von Bewegungspausen zu psychoregulativen Pausen bei kognitiver Belastung. Bewegungstherapie Gesundheitssport. 2019;35:134–43.

Löffler SN, Dominok E, von Haaren B, Schellhorn R, Gidion G. Aktivierung, Konzentration, Entspannung: Interventionsmöglichkeiten zur Förderung fitnessrelevanter Kompetenzen im Studium: KIT Scientific Publishing; 2011.

Marschin V, Herbert C. A short, multimodal activity break incorporated into the learning context during the Covid-19 pandemic: effects of physical activity and positive expressive writing on university students' mental health—results and recommendations from a pilot study. Front Psychol. 2021.

Kowalsky RJ, Farney TM, Hearon CM. Resistance Exercise Breaks Improve Ratings of Discomfort and Sleepiness in College Students. Res Q Exerc Sport. 2022;94(1):210–5.

Blasche G, Szabo B, Wagner-Menghin M, Ekmekcioglu C, Gollner E. Comparison of rest-break interventions during a mentally demanding task. Stress Health. 2018;34(5):629–38.

Article   PubMed   PubMed Central   Google Scholar  

Fuchs R, Klaperski S. Stressregulation durch Sport und Bewegung. In: Fuchs R, Gerber M, editors. Handbuch Stressregulation und Sport. Berlin: Springer; 2018. p. 205–26.

Chapter   Google Scholar  

Kaluza G, Renneberg B. Stressbewältigung. In: Bengel J, Jerusalem M, editors. HandbuchGesundheitspsychologie und medizinische Psychologie. Göttingen: Hogrefe; 2009. p. 265–72.

Klaperski S. Exercise, Stress and Health: The Stress-Buffering Effect of Exercise. In: Fuchs R, Gerber M, editors. Handbuch Stressregulation und Sport. Berlin: Springer; 2018. p. 227–50.

Lesser IA, Nienhuis CP. The impact of COVID-19 on physical activity behavior and well-being of Canadians. Int J Environ Res Public Health. 2020;17(11):3899.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Moore SA, Faulkner G, Rhodes RE, Brussoni M, Chulak-Bozzer T, Ferguson LJ, et al. Impact of the COVID-19 virus outbreak on movement and play behaviours of Canadian children and youth: a national survey. Int J Behav Nutr Phys Act. 2020;17(1):1–11.

Rodríguez-Larrad A, Mañas A, Labayen I, González-Gross M, Espin A, Aznar S, et al. Impact of COVID-19 confinement on physical activity and sedentary behaviour in Spanish university students: Role of gender. Int J Environ Res Public Health. 2021;18(2):369.

Stanton R, To QG, Khalesi S, Williams SL, Alley SJ, Thwaite TL, et al. Depression, anxiety and stress during COVID-19: associations with changes in physical activity, sleep, tobacco and alcohol use in Australian adults. Int J Environ Res Public Health. 2020;17(11):4065.

Zheng C, Huang WY, Sheridan S, Sit CHP, Chen XK, Wong SHS. COVID-19 pandemic brings a sedentary lifestyle in young adults: a cross-sectional and longitudinal study. Int J Environ Res Public Health. 2020;17(17):6035.

Commodari E. Attention Skills and Risk of Developing Learning Difficulties. Curr Psychol. 2012;31:17–34.

Commodari E, Guarnera M. Attention and reading skills. Percept Mot Skills. 2005;100:3753–86.

Raaijmakers MAJ, Smidts DP, Sergeant JA, Maassen GH, Posthumus JA, van Engeland H, et al. Executive functions in preschool children with aggressive behavior: impairments in inhibitory control. J Abnorm Child Psychol. 2008;36:1097–107.

Vellutino FR, Scanlon DM, Sipay ER, Small SG, Pratt A, Chen RS, et al. Cognitive profiles of difficulty to remediate and readily remediate poor readers: early intervention as a vehicle for distinguishing between cognitive and experiential deficits as basic of specific reading disability. J Educ Psychol. 1996;88:601–38.

Ilies R, Dimotakis N, De Pater IE. Psychological and physiological reactions to high workloads: Implications for well-being. Pers Psychol. 2010;63(2):407–36.

Rodell JB, Judge TA. Can “good” stressors spark “bad” behaviors? The mediating role of emotions in links of challenge and hindrance stressors with citizenship and counterproductive behaviors. J Appl Psychol. 2009;94(6).

Teuber M, Leyhr D, Moll J, Sudeck G. Nudging digital physical activity breaks for home studying of university students—A randomized controlled trial during the COVID-19 pandemic with daily activity measures. Front Sports Active Living. 2022;4.

Feuerhahn N, Sonnentag S, Woll A. Exercise after work, psychological mediators, and affect: A day-level study. Eur J Work Organ Psy. 2014;23(1):62–79.

Sonnentag S. Work, Recovery Activities, ans Individual Well-Being: A Diary Study. J Occup Health Psychol. 2001;6(3):196–210.

Article   CAS   PubMed   Google Scholar  

Wieland R. Gestaltung gesundheitsförderlicher Arbeitsbedingungen. In: Kleinbeck U, Schmidt K-H, editors. Arbeitspsychologie (Enzyklopädie der Psychologie, Serie Wirtschafts-, Organisations- und Arbeitspsychologie). 1. Göttingen: Hogrefe; 2010. p. 869–919.

Sonnentag S, Fritz C. The Recovery Experience Questionnaire: development and validation of a measure for assessing recuperation and unwinding from work. J Occup Health Psychol. 2007;12(3):204–21.

Bankstahl US, Görtelmeyer R. Measuring subjective complaints of attention and performance failures development and psychometric validation in tinnitus of the self-assessment scale APSA. Health and Quality of Life Outcomes. 2013;11(86).

Teuber M, Arzberger I, Sudeck G. Körperliche Aktivität, Gesundheit und Funktionsfähigkeit im Studium: Sportliche Freizeitaktivitäten und aktive Fortbewegung als Ressource im Studium? In: Göring A, Mayer J, Jetzke M, editors. Sport und Studienerfolg - Analysen zur Bedeutung sportlicher Aktivität im Setting Hochschule. Hochschulsport: Bildung und Wissenschaft, 4. Göttingen: Universitätsverlag Göttingen; 2020. p. 27–49.

Hasselhorn H-M, Freude G. Der Work-Ability-Index: ein Leitfaden In: Arbeitsmedizin BfAu, editor. Dortmund/Berlin/Dresden: Wirtschaftsverl. NW, Verlag für Neue Wissenschaft GmbH; 2007.

Rütten A, Pfeifer K. Nationale Empfehlungen für Bewegung und Bewegungsförderung. Köln: Bundeszentrale für Gesundheitliche Aufklärung (BZgA); 2017.

Brähler E, Mühlan H, Albani C, Schmidt S. Teststatistische Prüfung und Normierung der deutschen Versionen des EUROHIS-QOL Lebensqualität-Index und des WHO-5 Wohlbefindens-Index. Diagnostica. 2007;53(2):83–96.

Gusy B, Wörfel F, Lohmann K. Erschöpfung und Engagement im Studium. Zeitschrift für Gesundheitspsychologie. 2016;24(1):41–53.

Elfering A, Grebner S, de Tribolet-Hardy F. The long arm of time pressure at work: Cognitive failure and commuting near-accidents. Eur J Work Organ Psy. 2013;22(6):737–49.

Kreft IG, de Leeuw J. Introducing multilevel modeling. London: Sage; 1998.

Book   Google Scholar  

Bates D, Mächler M, Bolker BM, Walker SC. Fitting Linear Mixed-Effects Models Using lme4. J Stat Softw. 2015;67(1):1–48.

Hofmann DA, Gavin MB. Centering decisions in hierarchical linear models: Implications for research in organizations. J Manag. 1998;24(5):623–41.

Nezlek J. Diary Studies in Social and Personality Psychology: An Introduction With Some Recommendations and Suggestions. Social Psychological Bulletin. 2020;15(2).

Knapp G. Gemischte Modelle in R. Begleitskriptum zur Weiterbildung. In: Dortmund TU, editor. Braunschweig2019.

Van Buuren S, Groothuis-Oudshoorn K. mice: Multivariate imputation by chained equations in R. J Stat Softw. 2011;45:1–67.

von Hippel PT. How Many Imputations Do You Need? A Twostage Calculation Using a Quadratic Rule. Sociological Methods & Research. 2020;49(3):699–718.

Article   MathSciNet   Google Scholar  

Robinson D. broom: An R package for converting statistical analysis objects into tidy data frames. arXiv preprint arXiv:14123565. 2014.

Young-Jones A, McCain J, Hart B. Let’s Take a Break: The Impact of Physical Activity on Academic Motivation. Int J Teach Learn High Educ. 2022;33(3):110–8.

Barr-Anderson DJ, AuYoung M, Whitt-Glover MC, Glenn BA, Yancey AK. Integration of short bouts of physical activity into organizational routine: A systematic review of the literature. Am J Prev Med. 2011;40(1):76–93.

Dodd RH, Dadaczynski K, Okan O, McCaffery KJ, Pickles K. Psychological Wellbeing and Academic Experience of University Students in Australia during COVID-19. Int J Environ Res Public Health 2021;18.

Fenesi B, Lucibello K, Kim JA, Heisz JJ. Sweat so you don’t forget: exercise breaks during a university lecture increase on-task attention and learning. J Appl Res Mem Cogn. 2018;7(2):261–9.

Ruhland S, Lange KW. Effect of classroom-based physical activity interventions on attention and on-task behavior in schoolchildren: A systematic review. Sports Med Health Sci. 2021;3:125–33.

Aristovnik A, Keržič D, Ravšelj D, Tomaževič N, Umek L. Impacts of the COVID-19 Pandemic on Life of Higher Education Students: A Global Perspective. Sustainability. 2020;12(20).

Browning MHEM, Larson LR, Sharaievska I, Rigolon A, McAnirlin O, Mullenbach L, et al. Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States. PLoS ONE 2021;16(1).

Chang Y-K, Labban JD, Gapin JI, Etnier JL. The effects of acute exercise on cognitive performance: a meta-analysis. Brain Res. 2012;1453:87–101.

Elmer T, Mepham K, Stadtfeld C. Students under lockdown: Comparisons of students’ social networks and mental health before and during the COVID-19 crisis in Switzerland. PLoS ONE. 2020;15(7):e0236337.

Keating R, Ahern S, Bisgood L, Mernagh K, Nicolson GH, Barrett EM. Stand up, stand out. Feasibility of an active break targeting prolonged sitting in university students. J Am Coll Health. 2020;70(7).

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Acknowledgements

We would like to thank Juliane Moll, research associate of the Student Health Management of University of Tübingen, for the support in the coordination and realization study. We would like to express our thanks also to Ingrid Arzberger, Head of University Sports at the University of Tübingen, for providing the resources and co-applying for the funding. We acknowledge support by Open Access Publishing Fund of University of Tübingen.

Open Access funding enabled and organized by Projekt DEAL. This research regarding the conduction of the study was funded by the Techniker Krankenkasse, health insurance fund.

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Contributions

M.T. and G.S. designed the study. M.T. coordinated and carried out participant recruitment and data collection. M.T. analyzed the data and M.T. and D.L. interpreted the data. M.T. drafted the initial version of the manuscript and prepared the figure and all tables. All authors contributed to reviewing and editing the manuscript and have read and agreed to the final version of the manuscript.

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Correspondence to Monika Teuber .

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Teuber, M., Leyhr, D. & Sudeck, G. Physical activity improves stress load, recovery, and academic performance-related parameters among university students: a longitudinal study on daily level. BMC Public Health 24 , 598 (2024). https://doi.org/10.1186/s12889-024-18082-z

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Received : 30 June 2023

Accepted : 12 February 2024

Published : 24 February 2024

DOI : https://doi.org/10.1186/s12889-024-18082-z

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