research on study habits

The Science of Habit and Its Implications for Student Learning and Well-being

• Review Article
• Published: 17 March 2020
• Volume 32 , pages 603–625, ( 2020 )

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• Logan Fiorella 1  

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Habits are critical for supporting (or hindering) long-term goal attainment, including outcomes related to student learning and well-being. Building good habits can make beneficial behaviors (studying, exercise, sleep, etc.) the default choice, bypassing the need for conscious deliberation or willpower and protecting against temptations. Yet educational research and practice tends to overlook the role of habits in student self-regulation, focusing instead on the role of motivation and metacognition in actively driving behavior. Habit theory may help explain ostensible failures of motivation or self-control in terms of contextual factors that perpetuate poor habits. Further, habit-based interventions may support durable changes in students’ recurring behaviors by disrupting cues that activate bad habits and creating supportive and stable contexts for beneficial ones. In turn, the unique features of educational settings provide a new area in which to test and adapt existing habit models.

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Ironically, one of the few articles on habits in Educational Psychology Review is an interview with the most productive educational psychologists, who cite consistent work habits as important for maintaining research productivity and work-life balance (Flanigan et al. 2018 ; see also Kiewra and Creswell 2000 ; Patterson-Hazley and Kiewra 2013 ). Accounts of writers, artists, musicians, and scientists concur that habits and ritual set the foundation for creativity and productivity (Currey 2013 , 2019 ).

The amount of repetition ultimately required to form a habit likely depends on the complexity of the habit (Mullan and Novoradovskaya 2018 ) and the suitability of the performance context (Wood 2019 ).

The term “study habits” is often defined broadly to include frequency of using various techniques, without specifying the nature or stability of specific context cues or the automaticity of the behavior. For example, Crede and Kuncel ( 2008 ) define study habits as “sound study routines, including but not restricted to, frequency of studying sessions, review of material, self-testing, rehearsal of learned material, and studying in a conductive environment” (p. 429).

Adriaanse, M. A., Kroese, F. M., Gillebaart, M., & De Ridder, D. T. D. (2014). Effortless inhibition: habit mediates the relation between self-control and healthy snack consumption. Frontiers in Psychology, 5 , 444.

Google Scholar  

Alapin, I., Fitchen, C. S., Libman, E., Creti, L., Bailes, S., & Wright, J. (2000). How is good and poor sleep in older adults and college students related to daytime sleepiness, fatigue, and ability to concentrate? Journal of Psychosomatic Research, 49 (5), 381–390.

Anselme, P. (2013). Dopamine, motivation, and the evolutionary significance of gambling-like behavior. Behavioral Brain Research, 256 , 1–4.

Avni-Babad. (2011). Routine and feelings of safety, confidence, and well-being. The British Psychological Society, 102 , 223–244.

Babcock, P., & Marks, M. (2011). The falling cost of college: evidence from half a century of time use data. Review of Economics and Statistics, 93 (2), 468–478.

Bachman, R. (2017). How close do you need to be to your gym? The wall street journal. Retrieved from https://www.wsj.com/articles/how-close-do-you-need-to-be-to-your-gym-1490111186 .

Baumeister, R. F., Vohs, K. D., & Tice, D. M. (2007). The strength model of self-control. Current Directions in Psychological Science, 16 , 351–355.

Bayer, J. B., & Larose, R. (2018). Technology habits: progress, problems, and prospects. In B. Verplanken (Ed.), The psychology of habit (pp. 111–130). Cham: Springer.

Bem, D. J. (1972). Self-perception theory. Advances in Experimental Social Psychology, 6 , 1–62.

Berry, T., Cook, L., Hills, N., & Stevens, K. (2010). An exploratory analysis of textbook usage and study habits: misperceptions and barriers to success. College Teaching, 59 (1), 31–39.

Bjork, R. A., Dunlosky, J., & Kornell, N. (2011). Self-regulated learning: beliefs, techniques, and illusions. Annual Review of Psychology, 64 , 417–444.

Blair, C., & Raver, C. C. (2015). School readiness and self-regulation: a developmental psychobiological approach. Annual Reviews of Psychology, 66 , 711–731.

Blasiman, R. N., Dunlosky, J., & Rawson, K. A. (2017). The what, how much, and when of study strategies: comparing intended versus actual study behaviour. Memory, 25 (6), 784–792.

Booker, L., & Mullan, B. (2012). Using the temporal self-regulation theory to examine the influence of environmental cues on maintaining a healthy lifestyle. British Journal of Health Psychology, 18 (4), 745–762.

Breslow, L., Pritchard, D. E., DeBoer, J., Stump, G. S., Ho, A. D., & Seaton, D. T. (2013). Studying learning in the worldwide classroom: research into edX’s first MOOC. Research & Practice in Assessment, 8 , 13–25.

Bui, D. C., Myerson, J., & Hale, S. (2013). Note-taking with computers: exploring alternative strategies for improved recall. Journal of Educational Psychology, 105 (2), 299–309.

Carden, L., & Wood, W. (2018). Habit formation and change. Current Opinion in Behavioral Sciences, 20 , 117–122.

Carden, L., Wood, W., Neal, D. T., & Pascoe, A. (2017). Incentives activate a control mind-set: good for deliberate behaviors, bad for habit performance. Journal of the Association for Consumer Research, 2 (3), 279–290.

Crede, M., & Kuncel, N. R. (2008). Study habits, skills, and attitudes. Perspectives on Psychological Science, 3 (6), 425–453.

Currey, M. (2013). Daily rituals: how artists work . New York: Knopf.

Currey, M. (2019). Daily rituals: women at work . New York: Knopf.

Danner, U. N., Aarts, H., & de Vries, N. K. (2008). Habit vs. intention in the prediction of future behavior: the role of frequency, context stability and mental accessibility of past behavior. British Journal of Social Psychology, 47 (2), 245–265.

Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic rewards and intrinsic motivation in education: reconsidered once again. Review of Educational Research, 71 , 1–27.

Dewald, J. F., Meijer, A. M., Oort, F. J., Kerkhof, G. A., & Bogels, S. M. (2010). The influence of sleep quality, sleep duration and sleepiness on school performance in children and adolescents: a meta-analytic review. Sleep Medicine Reviews, 14 , 179–189.

Dickinson, A. (1985). Actions and habits: the development of behavioural automaticity. Philosophical Transactions of the Royal Society of London. B, Biological Sciences, 308 (1135), 67–78.

Diekelmann, S., Wilhelm, I., & Born, J. (2009). The whats and whens of sleep-dependent memory consolidation. Sleep Medicine Reviews, 13 (5), 309–321.

Dillard, A. (1989). The writing life . New York City: Harper Collins Publishers, Inc.

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

Duckworth, A. L., & Seligman, M. E. P. (2005). Self-discipline outdoes IQ in predicting academic performance of adolescents. Psychological Science, 16 (12), 939–944.

Duckworth, A. L., Gendler, T. L., & Gross, J. J. (2014). Self-control in school-aged children. Educational Psychologist, 49 , 199–217.

Duckworth, A. L., Gendler, T. L., & Gross, J. J. (2016a). Situational strategies for self-control. Perspectives on Psychological Science, 11 (1), 35–55.

Duckworth, A. L., White, R. E., Matteucci, A. J., Shearer, A., & Gross, J. J. (2016b). A stitch in time: strategic self-control in high school and college students. Journal of Educational Psychology, 108 (3), 329–341.

Duckworth, A. L., Taxer, J. L., Eskreis-Winkler, L., Galla, B. M., & Gross, J. J. (2019). Self-control and academic achievement. Annual Review of Psychology, 70 , 373–399.

Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14 (1), 4–58.

Englert, C., Zavery, A., & Bertrams, A. (2017). Too exhausted to perform at the highest level? On the importance of self-control strength in educational settings. Frontiers in Psychology, 8 , 1290.

Evans, J., & Stanovich, K. E. (2013). Dual process theories of higher cognition: advancing the debate. Perspectives on Psychological Science, 8 (3), 223–241.

Eyal, N. (2014). Hooked: how to build habit-forming products . Penguin.

Fiorella, L., & Mayer, R. E. (2015). Learning as a generative activity: eight learning strategies that promote understanding . New York: Cambridge University Press.

Fiorella, L., & Mayer, R. E. (2016). Eight ways to promote generative learning. Educational Psychology Review, 28 (4), 717–741.

Fiorella, L., & Mayer, R. E. (2017). Spontaneous spatial strategy use in learning from scientific text. Contemporary Educational Psychology, 49 , 66–79.

Fiorella, L., & Mayer, R. E. (2018). What works and doesn’t work with instructional video. Computers in Human Behavior, 89 , 465–470.

Flanigan, A. E., Kiewra, K., & Luo, L. (2018). Conversations with four highly productive German educational psychologists: Frank Fischer, Hans Gruber, Heinz Mandl, and Alexander Renkl. Educational Psychology Review, 30 , 303–330.

Fries, S., Dietz, F., & Schmid, S. (2008). Motivational interference in learning: the impact of leisure alternatives on subsequent self-regulation. Contemporary Educational Psychology, 33 , 119–133.

Galla, B. M., & Duckworth, A. L. (2015). More than resisting temptation: beneficial habits mediate the relationship between self-control and positive life outcomes. Journal of Personality and Social Psychology, 109 (3), 508–525.

Galla, B. M., Shulman, E. P., Plummer, B. D., Gardner, M., Hutt, S. J., Goyer, J. P., ... & Duckworth, A. L. (2019). Why high school grades are better predictors of on-time college graduation than are admissions test scores: the roles of self-regulation and cognitive ability. American Educational Research Journal, 56 (6), 2077–2115.

Galvon, A. (2020). The need for sleep in the adolescent brain. Trends in Cognitive Sciences, 24 (1), 79–89.

Gardner, B., & Lally, P. (2013). Does intrinsic motivation strengthen physical activity habit? Modeling relationships between self-determinism, past behavior, and habit strength. Journal of Behavioral Medicine, 36 , 488–499.

Gillan, C. M., Otto, A. R., Phelps, E. A., & Daw, N. D. (2015). Model-based learning protects against forming habits. Cognitive, Affective, & Behavioral Neuroscience, 15 (3), 523–536.

Gillebaart, M., & Adriaanse, M. A. (2017). Self-control predicts exercise behavior by force of habit, a conceptual replication of Adriaanse et al. (2014). Frontiers in Psychology, 8 , 190.

Grave, B. S. (2011). The effect of student time allocation on academic achievement. Education Economics, 19 (3), 291–319.

Greene, J. A., & Azevedo, R. (2010). The measurement of learners’ self-regulated cognitive and metacognitive processes while using computer-based learning environments. Educational Psychologist, 45 , 203–209.

Gruber, R. (2017). School-based sleep education programs: a knowledge-to-action perspective regarding barriers, proposed solutions, and future directions. Sleep Medicine Reviews, 36 , 13–28.

Grund, A., Grunschel, C., Bruhn, D., & Fries, S. (2015a). Torn between want and should: an experience-sampling study on motivational conflict, well-being, self-control, and mindfulness. Motivation and Emotion, 39 , 506–520.

Grund, A., Schmid, S., & Fries, S. (2015b). Studying against your will: motivational interference in action. Contemporary Educational Psychology, 41 , 209–217.

Hagger, M. S., Wood, C., Stiff, C., & Chatzisarantis, N. L. D. (2010). Ego depletion and the strength model of self-control: a meta-analysis. Psychological Bulletin, 136 (4), 495–525.

Hallal, P. C., Anderson, L. B., Bull, F. C., Guthold, R., Haskell, W., Ekelund, U., & Lancet Physical Activity Series Working Group. (2012). Global physical activity levels: surveillance progress, gaps and prospects. The Lancet, 380 (9838), 247–257.

Harastzi, R. A., Ella, K., Gyongyosi, N., Roenneberg, T., & Kaldi, K. (2014). Social jetlag negatively correlates with academic performance in undergraduates. Chronobiology International, 31 (5), 603–612.

Harrison, Y., & Horne, J. A. (2000). The impact of sleep deprivation on decision making: a review. Journal of Experimental Psychology: Applied, 6 (3), 236–249.

Heatherton, T. F., & Nichols, P. A. (1994). Personal accounts of successful versus failed attempts at life change. Personality and Social Psychology Bulletin, 20 (6), 664–675.

Henderikx, M. A., Kreijns, K., & Kalz, M. (2017). Refining success and dropout in massive online courses based on the intention-behavior gap. Distance Education, 38 (3), 353–368.

Hirshkowitz, M., Whiton, K., Albert, S. M., Alessi, C., Burni, O., DonCarlos, L., et al. (2015). National Sleep Foundation’s sleep time duration recommendations: methodology and results summary. Sleep Health, 1 (1), 40–43.

Hofmann, W., Baumeister, R. F., Forster, G., & Vohs, K. D. (2012). Everyday temptations: an experience sampling study of desire, conflict, and self-control. Journal of Personality and Social Psychology, 102 (6), 1318–1335.

Holland, R. W., Aarts, H., & Langendam, D. (2006). Breaking and creating habits on the working floor: a field experiment on the power of implementation intentions. Journal of Experimental Social Psychology, 42 (6), 776–783.

Howard-Jones, P. A., & Jay, T. (2016). Reward, learning and games. Current Opinion in Behavioral Sciences, 10 , 65–72.

James, W. (1890). Principles of psychology . New York: Holt.

James, W. (1899). Talks to teachers . New York: Holt.

Ji, M. F., & Wood, W. (2007). Purchase and consumption habits: not necessarily what you intend. Journal of Consumer Psychology, 17 (4), 261–276.

Judah, G., Gardner, B., & Aunger, R. (2013). Forming a flossing habit: an exploratory study of the psychological determinants of habit formation. British Journal of Health Psychology, 18 (2), 338–353.

Kane, M. J., Smeekens, B. A., von Basian, C. C., Lurquin, J. H., Carruth, N. P., & Miyake, A. (2017). A combined experimental and individual-differences investigation into mind wandering during a video lecture. Journal of Experimental Psychology: General, 146 (11), 1649–1674.

Kaushal, N., & Rhodes, R. E. (2015). Exercise habit formation in new gym members: A longitudinal study. Journal of Behavioral Medicine, 38 , 652–663.

Kaushal, N., Rhodes, R. E., Spence, J. C., & Meldrum, J. T. (2017). Increasing physical activity through principles of habit formation in new gym members: a randomized controlled trial. Annals of Behavioral Medicine, 51 (4), 578–586.

Kiewra, K. A., & Creswell, J. W. (2000). Conversations with three highly productive educational psychologists: Richard Anderson, Richard Mayer, and Michael Pressley. Educational Psychology Review, 12 (1), 135–161.

Killingsworth, M. A., & Gilbert, D. T. (2010). A wandering mind is an unhappy mind. Science, 330 (6006), 932–932.

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

Kocken, P. L., Eeuwijk, J., Van Kesteren, N. M., Dusseldorp, E., Buijs, G., Bassa-Dafesh, Z., & Snel, J. (2012). Promoting the purchase of low-calorie foods from school vending machines: a cluster-randomized control study. Journal of School Health, 82 (3), 115–122.

Lally, P., & Gardner, B. (2013). Promoting habit formation. Health Psychology Review, 7 (1), S137–S158.

Lally, P., Van Jaarsveld, C. H. M., Potts, H. W. W., & Wardle, J. (2010). How are habits formed: modeling habit formation in the real world. European Journal of Social Psychology, 40 , 998–1009.

Lepper, M. R., & Greene, D. (Eds.). (1978). The hidden costs of reward . New York: Lawrence Erlbaum Associates, Inc..

Lim, J., & Dinges, D. F. (2010). A meta-analysis of the impact of short-term sleep deprivation on cognitive variables. Psychological Bulletin, 136 (3), 375–389.

Loh, K. K., Tan, B. Z. H., & Lim, S. W. H. (2016). Media multitasking predicts video-recorded lecture learning performance through mind wandering tendencies. Computers in Human Behavior, 63 , 943–947.

Manalo, E., Uesaka, Y., & Chinn, C. A. (Eds.). (2018). Promoting spontaneous use of learning and reasoning strategies . New York: Routledge.

Marteau, T. M., Hollands, G. J., & Fletcher, P. C. (2012). Changing human behavior to prevent disease: the importance of targeting automatic processes. Science, 337 (6101), 1492–1495.

Mazar, A., & Wood, W. (2018). Defining habit in psychology. In Verplanken (Ed.), Psychology of habit (pp. 13–30). Cham: Springer.

Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2009). Evaluation of evidence-based practices in online learning: a meta-analysis and review of online-learning studies . Washington, D.C.: U.S. Department of Education.

Mega, C., Ronconi, L., & De Beni, R. (2014). What makes a good student? How emotions, self- regulated learning, and motivation contribute to academic achievement. Journal of Educational Psychology, 106 (1), 121–131.

Milkman, K. L., Minson, J. A., & Volpp, K. G. (2014). Holding the hunger games hostage at the gym: an evaluation of temptation bundling. Management Science, 60 (2), 283–299.

Milyavskaya, M., & Inzlicht, M. (2017). What’s so great about self-control? Examining the importance of effortful self-control and temptation in predicting real-life depletion and goal attainment. Social Psychological and Personality Science, 8 (6), 603–611.

Morehead, K., Dunlosky, J., & Rawson, K. A. (2019). How much mightier is the pen than the keyboard for note-taking? A replication and extension of Mueller and Oppenheimer (2014). Educational Psychology Review, 31 , 753–780.

Mueller, P. A., & Oppenheimer, D. M. (2014). The pen is mightier than the keyboard: advantages of longhand over laptop note taking. Psychological Science, 25 (6), 1159–1168.

Mullan, B., & Novoradovskaya, E. (2018). Habit mechanisms and behavioral complexity. In B. Verplanken (Ed.), The psychology of habit (pp. 71–90). Cham: Springer.

Neal, D. T., Wood, W., Wu, M., & Kurlander, D. (2011). The pull of the past: when do habits persist despite conflict with motives? Personality and Social Psychology Bulletin, 37 (11), 1428–1437.

Neal, D. T., Wood, W., Labrecque, J. S., & Lally, P. (2012). How do habits guide behavior? Perceived and actual triggers of habits in daily life. Journal of Experimental Social Psychology, 48 , 492–498.

Neal, D. T., Wood, W., & Drolet, A. (2013). How do people adhere to goals when willpower is low? The profits (and pitfalls) of strong habits. Journal of Personality and Social Psychology, 104 (6), 959–975.

Neroni, J., Meijs, C., Gijselears, H. J. M., Kirschner, P. A., & de Groot, R. H. M. (2019). Learning strategies and academic performance in distance education. Learning and Individual Differences, 73 , 1–7.

Norris, E., van Steen, T., Direito, A., & Stamatakis, E. (2019). Physically active lessons in schools and their impact on physical activity, educational, health and cognition outcomes: a systematic review and meta-analysis. British Journal of Sports Medicine . Advance online publication. https://doi.org/10.1136/bjsports-2018-100502 .

Oaten, M., & Cheng, K. (2005). Academic examination stress impairs self-control. Journal of Social and Clinical Psychology, 24 , 254–279.

Okano, K., Kaczmarzyk, J. R., Dave, N., Gabrieli, J. D. E., & Grossman, J. C. (2019). Sleep quality, duration, and consistency are associated with better academic performance in college students. NPJ Science of Learning, 4 (1), 1–5.

Orzech, K. M., Salafsky, D. B., & Hamilton, L. A. (2011). The state of sleep among college students at a large public university. Journal of American College Health, 59 (7), 612–619.

Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: the multiple processes by which past behavior predicts future behavior. Psychological Bulletin, 124 (1), 54–74.

Patterson-Hazley, M., & Kiewra, K. A. (2013). Conversations with four highly productive educational psychologists: Patricia Alexander, Richard Mayer, Dale Schunk, & Barry Zimmerman. Educational Psychology Review, 25 (1), 19–45.

Pew Research Center. (2018). How teens and parents navigate screen time and device distractions . Retrieved from https://www.pewresearch.org/internet/2018/08/22/how-teens-and-parents-navigate-screen-time-and-device-distractions/ .

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

Quinn, J. M., Pascoe, A., Wood, W., & Neal, D. T. (2010). Can’t control yourself? Monitor those bad habits. Personality and Social Psychology Bulletin, 36 (4), 499–511.

Ravizza, S. M., Uitvlugt, M. G., & Fenn, K. M. (2017). Logged in and zoned out: how laptop internet use relates to classroom learning. Psychological Science, 28 (2), 171–180.

Rebar, A. L., Gardner, B., Rhodes, R. E., & Verplanken, B. (2018). The measurement of habit. In B. Verplanken (Ed.), The psychology of habit (pp. 31–50). Cham: Springer.

Reed, J. A., & Phillips, D. A. (2005). Relationships between physical activity and the proximity of exercise facilities and home exercise equipment used by undergraduate university students. Journal of American College Health, 53 (6), 285–290.

Rey, A. E., Guignard-Perret, A., Imler-Weber, F., Garcia-Larrea, L., & Mazza, S. (2020). Improving sleep, cognitive functioning and academic performance with sleep education at school in children. Learning and Instruction, 65 , 101270.

Roberts, J. A., Yaya, L. H. P., & Manolis, C. (2014). The invisible addiction: cell-phone activities and addiction among male and female college students. Journal of Behavioral Addictions, 3 (4), 254–265.

Rompotis, C. J., Grove, J. R., & Byrne, S. M. (2014). Benefits of habit-based informational interventions: a randomized controlled trial of fruit and vegetable consumption. Australian and New Zealand Journal of Public Health, 38 (3), 247–252.

Rothman, A. J., Gollwitzer, P. M., Grant, A. M., Neal, D. T., Sheeran, P., & Wood, W. (2015). Hale and hearty policies: how psychological science can create and maintain healthy habits. Perspectives on Psychological Science, 10 (6), 701–705.

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55 (1), 68–78.

Sappington, J., Kinsey, K., & Munsayac, K. (2002). Two studies of reading compliance among college students. Teaching of Psychology, 29 (4), 272–274.

Schneider, B. (2013). Sloan study of youth and social development, 1992-1997 [United States]. ICPSR04551-v2 (pp. 10–22). Ann Arbor: Inter-university Consortium for Political and Social Research [distributor].

Schunk, D. H., & Greene, J. A. (2017). Handbook of self-regulation of learning and performance (2nd ed.). New York: Cambridge University Press.

Shankland, R., & Rosset, E. (2017). Review of brief school-based positive psychological interventions: a taster for teachers and educators. Educational Psychology Review , 363–392.

Skinner, B. F. (1938). The behavior of organisms . New York: Appleton-Century-Crofts.

Solomon, L. J., & Rothblum, E. D. (1984). Academic procrastination: frequency and cognitive- behavioral correlates. Journal of Counseling Psychology, 31 (4), 503–509.

Stojanovic, M., Grund, A., & Fries, S. (2020). App-based habit building reduces motivational impairments during studying - An event sampling study. Froniers in Psychology, 11 (167), 1–15.

Susser, J. A., & McCabe, J. (2013). From the lab to the dorm room: metacognitive awareness and use of spaced study. Instructional Science, 41 , 345–363.

Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory . New York: Springer.

Tappe, K., Tarves, E., Oltarzewski, J., & Frum, D. (2013). Habit formation among regular exercisers at fitness centers: an exploratory study. Journal of Physical Activity & Health, 10 (4), 607–613.

Taraban, R., Maki, W. S., & Rynerson, K. (1999). Measuring study time distributions: implications for designing computer-based courses. Behavior Research Methods, Instruments, & Computers, 31 (2), 263–369.

Thaler, R. H., & Sunstein, C. R. (2009). Nudge: improve decisions about health, wealth, and happiness . New York: Penguin.

Thorndike, E. L. (1906). The principles of teaching based on psychology . Syracuse: Mason- Henry Press.

Thorndike, A. N., Sonnenberg, L., Riis, J., Barraclough, S., & Levy, D. E. (2012). A 2-phase labeling and choice architecture intervention to improve health food and beverage choices. American Journal of Public Health, 102 (3), 527–533.

Tindell, D. R., & Bohlander, R. W. (2012). The use and abuse of cell phones and text messaging in the classroom: a survey of college students. College Teaching, 60 (1), 1–9.

Tokunaga, R. S. (2017). A meta-analysis of the relationships between psychosocial problems and internet habits: synthesizing internet addiction, problematic internet use, and deficient self-regulation research. Communication Monographs, 84 (4), 423–446.

Tricomi, E., Balleine, B. W., & O’Doherty, J. P. (2009). A specific role for posterior dorsolateral striatum in human habit learning. European Journal of Neuroscience, 29 (11), 2225–2232.

Verplanken, B. (2006). Beyond frequency: habit as a mental construct. British Journal of Social Psychology, 45 , 639–656.

Verplanken, B. (Ed.). (2018). The psychology of habit . Cham: Springer.

Verplanken, B., & Orbell, S. (2003). Reflections on past behavior: a self-report index of habit strength. Journal of Applied Social Psychology, 33 (6), 1313–1330.

Verplanken, B., & Roy. (2016). Empowering interventions to promote sustainable lifestyles: testing the habit discontinuity hypothesis. Journal of Environmental Psychology, 45 , 127–134.

Verplanken, B., Aarts, H., van Kippenberg, A., & Moonen, A. (1998). Habit versus planned behavior: a field experiment. British Journal of Social Psychology, 37 , 111–128.

Vettese, L. C., Toneatto, T., Stea, J. N., Nguyen, L., & Wang, J. J. (2009). Do mindfulness meditation participants do their homework? And does it make a difference? A review of the empirical evidence. Journal of Cognitive Psychotherapy, 23 (3), 198–225.

Wahlstrom, K., Dretzke, B., Gordon, M., Peterson, K., Edwards, K., & Gdula, J. (2014). Examining the impact of later school start times on the health and academic performance of high school students: a multi-site study. Center for Applied Research and Educational Improvement . St Paul: University of Minnesota.

Walker, M. P., & Stickgold, R. (2006). Sleep, memory, and plasticity. Annual Reviews of Psychology, 57 , 139–166.

Waters, L., Barsky, A., Ridd, A., & Allen, K. (2015). Contemplative education: a systematic, evidence-based review of the effect of meditation interventions in schools. Educational Psychology Review, 27 (1), 103–134.

Webb, T. L., & Sheeran, P. (2006). Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychological Bulletin, 132 (2), 249–268.

Williamson, L. Z., & Wilkowski, B. M. (2019). Nipping temptation in the bud: examining strategic self-control in daily life. Personality and Social Psychology Bulletin . Advance online publication. https://doi.org/10.1177/0146167219883606 .

Wilmer, H. H., Sherman, L. E., & Chein, J. M. (2017). Smartphones and cognition: a review of research exploring the links between mobile technology habits and cognitive functioning. Frontiers in Psychology, 8 (605).

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

Wong, M. L., Lau, E. Y. Y., Wan, J. H. Y., Cheung, S. F., Hui, C. H., & Mok, D. S. Y. (2013). The interplay between sleep and mood in predicting academic functioning, physical health and psychological health: a longitudinal study. Journal of Psychosomatic Research, 74 (4), 271–277.

Wood, W. (2017). Habit in personality and social psychology. Personality and Social Psychology, 21 (4), 389–403.

Wood, W. (2019). Good habits, bad habits . New York: Farrar, Straus, and Giroux.

Wood, W., & Neal, D. T. (2007). A new look at habits and the habit-goal interface. Psychological Review, 114 (4), 843–863.

Wood, W., & Neal, D. T. (2016). Health through habit: interventions for initiating and maintaining health behavior change. Behavioral Science & Policy, 2 (1), 71–83.

Wood, W., & Runger, D. (2016). Psychology of habit. Annual Reviews of Psychology, 67 , 289–314.

Wood, W., Quinn, J. M., & Kashy, D. A. (2002). Habits in everyday life: thought, emotion, and action. Journal of Personality and Social Psychology, 83 (6), 1281–1297.

Wood, W., Tam, L., & Witt, M. G. (2005). Changing circumstances, disrupting habits. Journal of Personality and Social Psychology, 88 (6), 918–933.

Zedelius, C. M., Gross, M. E., & Schooler, J. W. (2018). Mind wandering: more than a bad habit. In B. Verplanken (Ed.), The psychology of habit (pp. 363–378). Cham: Springer.

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. 13–39).

Zou, Q., Main, A., & Wang, Y. (2010). The relations of temperamental effortful control and anger/frustration to Chinese children’s academic achievement and social adjustment: a longitudinal study. Journal of Educational Psychology, 102 , 180–196.

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Acknowledgments

I thank Wendy Wood and one anonymous reviewer for their constuctive feedback and suggestions. I also thank Deborah Barany, Qian Zhang, and Michele Lease for their helpful comments on an earlier draft of this article. Finally, I thank the students from my First Year Odyssey Seminar at the University of Georgia, Applying the Science of Habit, for their valuable insight into the role of habits in their lives.

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Fiorella, L. The Science of Habit and Its Implications for Student Learning and Well-being. Educ Psychol Rev 32 , 603–625 (2020). https://doi.org/10.1007/s10648-020-09525-1

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The impact of study habits and personal factors on the academic achievement performances of medical students

• Mohammed A. Aljaffer 1 ,
• Ahmad H. Almadani 1 ,
• Abdullah S. AlDughaither 2 ,
• Ali A. Basfar 2 ,
• Saad M. AlGhadir 2 ,
• Yahya A. AlGhamdi 2 ,
• Bassam N. AlHubaysh 2 ,
• Osamah A. AlMayouf 2 ,
• Saleh A. AlGhamdi 3 ,
• Tauseef Ahmad 4 &
• Hamza M. Abdulghani 5  

BMC Medical Education volume  24 , Article number:  888 ( 2024 ) Cite this article

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Academic achievement is essential for all students seeking a successful career. Studying habits and routines is crucial in achieving such an ultimate goal.

This study investigates the association between study habits, personal factors, and academic achievement, aiming to identify factors that distinguish academically successful medical students.

A cross-sectional study was conducted at the College of Medicine, King Saud University, Riyadh, Saudi Arabia. The participants consisted of 1st through 5th-year medical students, with a sample size of 336. The research team collected study data using an electronic questionnaire containing three sections: socio-demographic data, personal characteristics, and study habits.

The study results indicated a statistically significant association between self-fulfillment as a motivation toward studying and academic achievement ( p  = 0.04). The results also showed a statistically significant correlation between recalling recently memorized information and academic achievement ( p  = 0.05). Furthermore, a statistically significant association between preferring the information to be presented in a graphical form rather than a written one and academic achievement was also found ( p  = 0.03). Students who were satisfied with their academic performance had 1.6 times greater chances of having a high-grade point average (OR = 1.6, p  = 0.08).

The results of this study support the available literature, indicating a correlation between study habits and high academic performance. Further multicenter studies are warranted to differentiate between high-achieving students and their peers using qualitative, semi-structured interviews. Educating the students about healthy study habits and enhancing their learning skills would also be of value.

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Introduction

Academic performance is a common indicator used to measure student achievement [ 1 , 2 ]. It is a compound process influenced by many factors, among which is study habits [ 2 , 3 ]. Study habit is defined as different individual behavior in relation to studying, and is a combination of study methods and skills [ 2 , 3 , 4 ]. Put differently, study habits involve various techniques that would increase motivation and transform the study process into an effective one, thus enhancing learning [ 5 ]. Students’ perspectives and approaches toward studying were found to be the key factors in predicting their academic success [ 6 , 7 ]. However, these learning processes vary from one student to another due to variations in the students’ cognitive processing [ 8 ].

The study habits of students are the regular practices and habits they exhibit during the learning process [ 9 , 10 ]. Over time, several study habits have been developed, such as time management, setting appropriate goals, choosing a comfortable study environment, taking notes effectively, choosing main ideas, and being organized [ 11 ]. Global research shows that study habits impact academic performance and are the most important predictor of it [ 12 ]. It is difficult for medical students to organize and learn a lot of information, and they need to employ study skills to succeed [ 1 , 2 , 5 , 13 ].

Different lifestyle and social factors could affect students’ academic performance. For instance, Jafari et al. found that native students had better study habits compared to dormitory students [ 1 ]. This discrepancy between native and dormitory students was also indicated by Jouhari et al. who illustrated that dormitory students scored lower in attitude, test strategies, choosing main ideas, and concentration [ 10 ]. Regarding sleeping habits, Curcio G et al. found that students with a regular and adequate sleeping pattern had higher Grade Point Average (GPA) scores [ 14 ]. Lifestyle factors, such as watching television and listening to music, were shown to be unremarkable in affecting students’ grades [ 15 , 16 ]. Social media applications, including WhatsApp, Facebook, and Twitter, distract students during learning [ 16 , 17 ].

Motivation was found to be a major factor in students’ academic success. Bonsaksen et al. found that students who chose “to seek meaning” when studying were associated with high GPA scores [ 18 ]. In addition, low scores on “fear of failure” and high scores on “achieving” correlated with a higher GPA [ 8 , 18 ].

Resource-wise, Alzahrani et al. found that 82.7% of students relied on textbooks assigned by the department, while 46.6% mainly relied on the department’s lecture slides [ 19 ]. The study also indicated that 78.8% perceived that the scientific contents of the lectures were adequate [ 19 ]. Another study found that most students relied on the lecture slides (> 83%) along with their notes, followed by educational videos (76.1%), and reference textbooks (46.1%) [ 20 ]. Striking evidence in that study, as well as in another study, indicated that most students tended to avoid textbooks and opted for lecture slides, especially when preparing for exams [ 20 , 21 ].

Several researchers studied the association between different factors and academic performance; however, more is needed to know about this association in the process of education among medical students [ 15 , 20 , 22 ], with some limitations to the conducted studies. Such limitations include the study sample and using self-reported questionnaires, which may generate inaccurate results. Moreover, in Saudi Arabia in particular, the literature concerning the topic remains limited. Since many students are unsatisfied with their performance and seek improvement [ 10 ], the present study was designed and conducted.

Unlike other studies in the region, this study aims to investigate the relationship between study habits and personal factors and measure their influence on academic achievement. The results of this study could raise awareness regarding the effect of study habits and personal factors on students’ performance and would also guide them toward achieving academic success. The study also seeks to identify the factors that distinguish academically successful students from their peers.

Study design, setting, and participants

This observational cross-sectional study, which took place between June and December 2022, was conducted among students attending the College of Medicine at King Saud University (KSU), Riyadh, Saudi Arabia. Its targeted population included all male and female medical students (first to fifth years) attending KSU during the academic year 2021/2022. Whereas, students at other colleges and universities, those who failed to complete the questionnaire, interns (the students who already graduated), and those who were enrolled in the university’s preparatory year, were all excluded from the current study. The sample size was calculated based on a study conducted in 2015 by Lana Al Shawwa [ 15 ]. Using the sample size formula for a single proportion (0.79), the required sample size was 255 using a confidence interval of 95% and a margin of error of 5%. After adding a 20% margin to accommodate non-responses and incomplete responses, the calculated sample size required for this study was 306. However, our research team collected a total of 336 participants for this study to ensure complete representation.

Study instrument

The research team developed and used an electronic questionnaire. The rationale is that no standardized questionnaire measuring the study objectives was found in the literature. However, the questionnaire was tested on a pilot of 15 students to test its clarity and address any possible misconceptions and ambiguity. The study questionnaire was distributed randomly to this cohort, who were asked to fill out the questionnaire. The students reported a complete understanding of the questionnaire’s contents, so the same questionnaire was used without any modifications. The questionnaire, written in English, consisted of three parts. The first part included eleven questions about the socio-demographic status of the participants. The second part contained twenty-one questions examining personal factors such as sleep and caffeine consumption. The last part included twenty-one questions regarding students’ study habits. The questionnaire was constructed based on an ordinal Likert scale which had: strongly agree, agree, neutral, disagree, and strongly disagree as possible answers. The questionnaire was sent to participants through email and social media applications like Twitter and WhatsApp to increase the study response. An informed consent that clearly states the study’s purpose was taken from all participants at the beginning of the questionnaire. In addition, all participants were assured that the collected data would be anonymous and confidential. Each participant was represented by a code for the sole purpose of analyzing the data. Furthermore, no incentives or rewards were given to the participants for their participation.

Study variables

Socio-demographic information (such as age, gender, and academic year), and personal factors (such as motivation, sleeping status, caffeine consumption, and self-management) were the independent variables. Study habits such as attendance, individual versus group study, memorization techniques, revision, learning style, and strategies were also independent variables.

Academic achievement refers to a student’s success in gaining knowledge and understanding in various subjects, as well as the ability to apply that knowledge effectively [ 23 ]. It is a measure of the student’s progress throughout the educational journey, encompassing both academic achievements and personal growth [ 3 , 24 ]. Academic achievement is judged based on the student’s GPA or performance score. In this study, students’ GPA scores, awareness, and satisfaction regarding their academic performance were the dependent variables.

We divided the study sample into two groups based on the GPA. We considered students with high GPAs to be exposed (i.e. exposed to the study habits we are investigating), and students with low GPAs to be the control group. The purpose of this study was to determine why an exposed group of students gets high grades and what study factors they adopt. Based on this exposure (high achieving students), we concluded what methods they used to achieve higher grades. Those in the first group had a GPA greater or equal to 4.5 (out of 5), while those in the second group had a GPA less than 4.5. The students’ data were kept confidential and never used for any other purpose.

Data analysis

The data collected were analyzed by using IBM SPSS Statistical software for Windows version 24.0. Descriptive statistics such as frequency and percentage were used to describe the socio-demographic data in a tabular form. Furthermore, data for categorical variables, including different study habits, motivation factors, memorizing and revising factors, and lifestyle factors, were tabulated and analyzed using the odds ratio test. Finally, we calculated the odds ratio statistic and a p-value of 0.05 to report the statistical significance of our results.

Ethical approval and consent to Participate

Before conducting the study, the research team obtained the Ethics Committee Approval from the Institutional Review Board of the College of Medicine, KSU, Riyadh, Saudi Arabia (project No. E-22-7044). Participants’ agreement/consent to participate was guaranteed by choosing “agree” after reading the consent form at the beginning of the questionnaire. Participation was voluntary, and consent was obtained from all participants. The research team carried out all methods following relevant guidelines and regulations.

The total 336 medical students participated in the study. All participants completed the study questionnaire, and there were no missing or incomplete data, with all of them being able to participate. As shown in Table  1 9.3% of participants were between 18 and 20, 44.9% were between the ages of 21 and 22, and 35.8% were 23–28 years old. In the current study, 62.5% of the participants were males and 37.5% were females. The proportion of first-year students was 21.4%, 20.8% of second-year students, 20.8% of third-year students, 18.2% of fourth-year students, and 18.8% of fifth-year students, according to academic year levels. Regarding GPA scores, 36.9% scored 4.75-5 and 32.4% scored 4.5–4.74. 23.8% achieved 4-4.49, 6.5% achieved 3-3.99, and only 0.4% achieved 2.99 or less. Participants lived with their families in 94.6% of cases, with friends in 1.2% of cases, and alone in 4.2% of cases. For smoking habits, 86.3% did not smoke, 11% reported using vapes, 2.1% used cigarettes, and 0.6% used Shisha. 91.4% of the participants did not report any chronic illnesses; however, 8.6% did. In addition, 83% had no mental illness, 8.9% had anxiety, 6% had depression, and 2.1% reported other mental illnesses.

Table  2 shows motivational factors associated with academic performance. There was a clear difference in motivation factors between students with high and low achievement in the current study. Students with high GPAs were 1.67 times more motivated toward their careers (OR = 1.67, p  = 0.09) than those with low GPAs. Furthermore, significant differences were found between those students who had self-fulfillment or ambitions in life they had ~ 2 times higher (OR = 1.93, p  = 0.04) GPA scores than low GPA students. Exam results did not motivate exposed or high GPA students (46%) or control students with low GPA students (41%), but the current study showed test results had little impact on low achiever students (OR = 1.03, p  = 0.88). Furthermore, 72.6% of high achievers were satisfied with their academic performance, while only 41% of low achiever students were satisfied. Therefore, students who were satisfied with their academic performance had 1.6 times greater chances of a higher GPA (OR = 1.6, p  = 0.08). Students who get support and help from those around them are more likely to get high GPAs (OR = 1.1, p  = 0.73) than those who do not receive any support. When students reported feeling a sense of family responsibility, the odds (odds ratio) of their receiving higher grades were 1.15 times higher (OR = 1.15, p  = 0.6) compared to those who did not feel a sense of family responsibility. The p-value, which indicates the level of statistical significance, was 0.6.

Table  3 shows the study habits of higher achiever students and low achiever students. Most of the high-achieving students (79.0%) attended most of the lectures and had 1.6 times higher chances of getting higher grades (OR = 1.6, p  = 0.2) than those who did not attend regular lectures. The current study found that studying alone had no significant impact on academic achievement in either group. However, those students who had studied alone had lower GPAs (OR = 1.07, p  = 0.81). The current study findings reported 29.8% of students walk or stand while studying rather than sit, and they had 1.57 times higher GPA chances compared to students with lower GPAs (OR = 0.73, p  = 0.27). High achievers (54.0%) preferred studying early in the morning, and these students had higher chances of achieving good GPAs (OR = 1.3, p  = 0.28) than low achiever groups of students. The number of students with high achievement (39.5%) went through the lecture before the lesson was taught. These students had 1.08 times higher chances of achieving than low achiever groups of students. Furthermore, students who made a weekly study schedule had 1.3 times higher chances of being good academic achievers than those who did not (OR = 1.3, p  = 0.37). Additionally, high-achieving students paid closer attention to the lecturer (1.2 times higher). In addition, students with high GPAs spent more time studying when exam dates approached (OR = 1.3, p  = 0.58).

Table  4 demonstrates the relationship between memorizing and revising with high and low GPA students. It was found that high achiever students (58.9%) studied lectures daily and had 1.4 times higher chances of achieving high grades (OR = 1.4, p  = 0.16) than the other group. It was found that most of the high achievers (62.1%) skim the lecture beforehand before memorizing it, which led to 1.8 times higher chances of getting good grades in this exam (OR = 1.8, p  = 0.06). One regular activity reported by high GPA students (82.3%) was recalling what had just been memorized. For this recalling technique, we found a significant difference between low-achieving students (OR = 0.8, p  = 0.63) and high-achieving students (OR = 1.83, p  = 0.05). A high achiever student writes notes before speaking out for the memorizing method, which gives 1.2 times greater chances of getting high grades (OR = 1.2, p  = 0.55) than a student who does not write notes. A major difference in the current study was that high GPA achievers (70.2%) revise lectures more frequently than low GPA achievers (57.1%). They had 1.5 times more chances of getting high grades if they practiced and revised this method (OR = 1.5, p  = 0.13).

Table  5 illustrates the relationship between negative lifestyle factors and students’ academic performance. The current study found that students are less likely to get high exam grades when they smoke. Students who smoke cigarettes and those who vape are 1.14 and 1.07 times respectively more likely to have a decrease in GPA than those who do not smoke. Those students with chronic illnesses had 1.22 times higher chances of a downgrade in the exam (OR = 1.22, p  = 0.49). Additionally, students with high GPAs had higher mental pressures (Anxiety = 1.2, Depression = 1.18, and other mental pressures = 1.57) than those with low GPAs.

Learning is a multifaceted process that evolves throughout our lifetimes. The leading indicator that sets students apart is their academic achievement. Hence, it is crucial to investigate the factors that influence it. The present study examined the relationship between different study habits, personal characteristics, and academic achievement among medical students. In medical education, and more so in Saudi Arabia, there needs to be more understanding regarding such vital aspects.

Regarding motivational factors, the present study found some differences between high and low achievers. Students with high GPA scores were more motivated toward their future careers (OR = 1.67, p  = 0.09). The study also indicated that students who had ambitions and sought self-fulfillment were more likely to have high GPA scores, which were statistically significant (OR = 1.93, p  = 0.04). This was consistent with Bin Abdulrahman et al. [ 20 ], who indicated that the highest motivation was self-fulfillment and satisfying family dreams, followed by a high educational level, aspirations to join a high-quality residency program, and high income. Their study also found that few students were motivated by the desire to be regarded as unique students. We hypothesize that this probably goes back to human nature, where a highly rewarding incentive becomes the driving force of our work. Hence, schools should utilize this finding in exploring ways to enhance students’ motivation toward learning.

The present study did not find a significant effect of previous exam results on academic performance (OR = 1.03, p  = 0.88). However, some studies reported that more than half of the high-achieving students admitted that high scores acquired on previous assessments are an important motivational factor [ 15 , 25 , 26 ]. We hypothesize that as students score higher marks, they become pleased and feel confident with their study approach. This finding shows how positive measurable results influence the students’ mentality.

The present study also explored the social environment surrounding medical students. The results indicated that those who were supported by their friends or family were slightly more likely to score higher GPAs (OR = 1.1, p  = 0.73); however, the results did not reach a statistical significance. We hypothesize that a supportive and understanding environment would push the students to be patient and look for a brighter future. Our study results were consistent with previous published studies, which showed an association [ 3 , 27 , 28 , 29 , 30 ]. We hypothesize that students who spend most of their time with their families had less time to study, which made their study time more valuable. The findings of this study will hopefully raise awareness concerning the precious time that students have each day.

The association of different study habits among medical students with high and low GPAs was also studied in our study. It was noted that the high-achieving students try to attend their lectures compared to the lower achievers. This was in line with the previous published studies, which showed that significant differences were observed between the two groups regarding the attendance of lectures, tutorials, practical sessions, and clinical teachings [ 31 , 32 ]. The present study found that most students prefer to study alone, regardless of their level of academic achievement (82.1%). This finding is consistent with the study by Khalid A Bin Abdulrahman et al., which also showed that most students, regardless of their GPA, favored studying alone [ 20 ].

The present study findings suggest that a small number of students (29.8%) prefer to walk or stand while studying rather than sit, with most being high achievers (OR = 1.57, P  = 0.15). A study reported that 40.3% of students with high GPAs seemed to favor a certain posture or body position, such as sitting or lying on the floor [ 15 ]. These contradictory findings might indicate that which position to adopt while studying should come down to personal preference and what feels most comfortable to each student. The present study also found that high achievers are more likely to prefer studying early in the morning (OR = 1.3, P  = 0.28). The authors did not find similar studies investigating this same association in the literature. However, mornings might allow for more focused studying with fewer distractions, which has been shown to be associated with higher achievement in medical students [ 3 , 15 , 33 ].

Our study also found that 39.5% of the academically successful students reviewed pre-work or went through the material before they were taught it (OR = 1.08, p  = 0.75), and 25% were neutral. Similar findings were reported in other studies, showing that academically successful students prepared themselves by doing their pre-work, watching videos, and revising slides [ 3 , 9 , 34 ]. Our study showed that 75% of high-achieving students tend to listen attentively to the lecturer (OR = 1.2, p  = 0.48). Al Shawa et al. found no significant differences between the high achievers and low achievers when talking about attending lectures [ 15 ]. This could be due to the quality of teachers and the environment of the college or university.

Regarding the relationship between memorizing and revising with high and low GPA students, the present study found that students who study lectures daily are more likely to score higher than those who do not (OR = 1.4, p  = 0.16). This finding is consistent with other studies [ 3 , 19 , 35 ]. For skimming lectures beforehand, an appreciable agreement was noted by high GPA students (62.1%), while only (42%) of low GPA students agreed to it. Similarly, previous published studies also found that highlighting and reading the content before memorization were both common among high-achieving students [ 15 , 36 ]. Furthermore, the present study has found recalling what has just been memorized to be statistically significantly associated with high GPA students (OR = 1.83, p  = 0.05). Interestingly, we could not find any study that investigated this as an important factor, which could be justified by the high specificity of this question. Besides, when it comes to writing down/speaking out what has just been memorized, our study has found no recognizable differences between high-achieving students (75%) and low-achieving students (69%), as both categories had remarkably high percentages of reading and writing while studying.

The present study has found no statistical significance between regularly revising the lectures and high GPA ( p  > 0.05), unlike the study conducted by Deborah A. Sleight et al. [ 37 ]. The difference in findings between our study and Deborah A. Sleight et al. might be due to a limitation of our study, namely the similar backgrounds of our participants. Another explanation could be related to curricular differences between the institutions where the two studies were conducted. Moreover, a statistically significant correlation between not preferring the data being presented in a written form instead of a graphical form and high GPA scores have been found in their study ( p  < 0.05). However, a study conducted by Deborah A. Sleight et al. indicated that 66% of high achievers used notes prepared by other classmates compared to 84% of low achievers. Moreover, their study showed that only 59% of high achievers used tables and graphs prepared by others compared to 92% of low achievers. About 63% and 61% of the students in their study reported using self-made study aids for revision and memory aids, respectively [ 37 ].

The present study also examined the effects of smoking and chronic and mental illness, but found no statistical significance; the majority of both groups responded by denying these factors’ presence in their life. A similar finding by Al Shawwa et al. showed no statistical significance of smoking and caffeine consumption between low GPA and high GPA students [ 15 ]. We hypothesize that our findings occurred due to the study’s broad approach to examining such factors rather than delving deeper into them.

High-achieving students’ habits and factors contributing to their academic achievement were explored in the present study. High-achieving students were found to be more motivated and socially supported than their peers. Moreover, students who attended lectures, concentrated during lectures, studied early in the morning, prepared their weekly schedule, and studied more when exams approached were more likely to have high GPA scores. Studying techniques, including skimming before memorizing, writing what was memorized, active recall, and consistent revision, were adopted by high-achievers. To gain deeper insight into students’ strategies, it is recommended that qualitative semi-structured interviews be conducted to understand what distinguishes high-achieving students from their peers. Future studies should also explore differences between public and private university students. Additionally, further research is needed to confirm this study’s findings and provide guidance to all students. Future studies should collect a larger sample size from a variety of universities in order to increase generalizability.

Limitations and recommendations

The present study has some limitations. All the study’s findings indicated possible associations rather than causation; hence, the reader should approach the results of this study with caution. We recommend in-depth longitudinal studies to provide more insight into the different study habits and their impact on academic performance. Another limitation is that the research team created a self-reported questionnaire to address the study objectives, which carries a potential risk of bias. Hence, we recommend conducting interviews and having personal encounters with the study’s participants to reduce the risk of bias and better understand how different factors affect their academic achievement. A third limitation is that the research team only used the GPA scores as indicators of academic achievement. We recommend conducting other studies and investigating factors that cannot be solely reflected by the GPA, such as the student’s clinical performance and skills. Lastly, all participants included in the study share one background and live in the same environment. Therefore, the study’s findings do not necessarily apply to students who do not belong to such a geographic area and point in time. We recommend that future studies consider the sociodemographic and socioeconomic variations that exist among the universities in Saudi Arabia.

Availability of data materials

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

Abbreviations

Grade Point Average

King Saud University

Institutional review board

Statistical package for the social sciences

Jafari H, Aghaei A, Khatony A. Relationship between study habits and academic achievement in students of medical sciences in Kermanshah-Iran. Adv Med Educ Pract. 2019;10:637–43.

Article   Google Scholar  

Abid N, Aslam S, Alghamdi AA, Kumar T. Relationships among students’ reading habits, study skills, and academic achievement in English at the secondary level. Front Psychol. 2023;14:1020269.

Abdulghani HM, Al-Drees AA, Khalil MS, Ahmad F, Ponnamperuma GG, Amin Z. What factors determine academic achievement in high achieving undergraduate medical students? A qualitative study. Med Teach. 2014;36(Suppl 1):S43–48.

Muntean LM, Nireștean A, Sima-Comaniciu A, Mărușteri M, Zăgan CA, Lukacs E. The relationship between personality, motivation and academic performance at Medical students from Romania. Int J Environ Res Public Health 2022, 19(15).

Reza HM, Alireza HJIJME. Investigating study Habits of Library and Information Sciences Students of Isfahan University and Isfahan University of Medical Sciences. 2014, 14:751–757.

Kurtz SM, Silverman JD. The Calgary-Cambridge Referenced Observation guides: an aid to defining the curriculum and organizing the teaching in communication training programmes. Med Educ. 1996;30(2):83–9.

Pun J, Kong B. An exploratory study of communication training for Chinese medicine practitioners in Hong Kong to integrate patients’ conventional medical history. BMC Complement Med Ther. 2023;23(1):10.

İlçin N, Tomruk M, Yeşilyaprak SS, Karadibak D, Savcı S. The relationship between learning styles and academic performance in TURKISH physiotherapy students. BMC Med Educ. 2018;18(1):291.

McKeirnan KC, Colorafi K, Kim AP, Stewart AS, Remsberg CM, Vu M, Bray BS. Study behaviors Associated with Student pharmacists’ academic success in an active Classroom Pharmacy Curriculum. Am J Pharm Educ. 2020;84(7):ajpe7695.

Jouhari Z, Haghani F, Changiz T. Assessment of medical students’ learning and study strategies in self-regulated learning. J Adv Med Educ Professionalism. 2016;4(2):72–9.

Google Scholar  

Proctor BE, Prevatt FF, Adams KSS, Reaser A, Petscher Y. Study skills profiles of normal-achieving and academically-struggling College students. J Coll Student Dev. 2006;47(1):37–51.

Kyauta AMASY, Garba HS. The role of guidance and counseling service on academic performance among students of umar suleiman college of education, Gashua, Yobe State, Nigeria. KIU J Humanit. 2017;2(2):59–66.

Eva KW, Bordage G, Campbell C, Galbraith R, Ginsburg S, Holmboe E, Regehr G. Towards a program of assessment for health professionals: from training into practice. Adv Health Sci Education: Theory Pract. 2016;21(4):897–913.

Curcio G, Ferrara M, De Gennaro L. Sleep loss, learning capacity and academic performance. Sleep Med Rev. 2006;10(5):323–37.

Al Shawwa L, Abulaban AA, Abulaban AA, Merdad A, Baghlaf S, Algethami A, Abu-Shanab J, Balkhoyor A. Factors potentially influencing academic performance among medical students. Adv Med Educ Pract. 2015;6:65–75.

Ibrahim NK, Baharoon BS, Banjar WF, Jar AA, Ashor RM, Aman AA, Al-Ahmadi JR. Mobile Phone Addiction and its relationship to Sleep Quality and Academic Achievement of Medical students at King Abdulaziz University, Jeddah, Saudi Arabia. J Res Health Sci. 2018;18(3):e00420.

Alkhalaf AM, Tekian A, Park YS. The impact of WhatsApp use on academic achievement among Saudi medical students. Med Teach. 2018;40(sup1):S10–4.

Bonsaksen T, Brown T, Lim HB, Fong K. Approaches to studying predict academic performance in undergraduate occupational therapy students: a cross-cultural study. BMC Med Educ. 2017;17(1):76.

Alzahrani HA, Alzahrani OH. Learning strategies of medical students in the surgery department, Jeddah, Saudi Arabia. Adv Med Educ Pract. 2012;3:79–87.

Bin Abdulrahman KA, Khalaf AM, Bin Abbas FB, Alanazi OT. Study habits of highly effective medical students. Adv Med Educ Pract. 2021;12:627–33.

Jameel T, Gazzaz ZJ, Baig M, Tashkandi JM, Alharenth NS, Butt NS, Shafique A, Iftikhar R. Medical students’ preferences towards learning resources and their study habits at King Abdulaziz University, Jeddah, Saudi Arabia. BMC Res Notes. 2019;12(1):30.

Abdulghani HM, Alrowais NA, Bin-Saad NS, Al-Subaie NM, Haji AM, Alhaqwi AI. Sleep disorder among medical students: relationship to their academic performance. Med Teach. 2012;34(Suppl 1):S37–41.

Hwang G-J, Wang S-Y, Lai C-L. Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics. Comput Educ. 2021;160:104031.

Gamage KAA, Dehideniya D, Ekanayake SY. The role of personal values in learning approaches and student achievements. Behav Sci (Basel Switzerland) 2021, 11(7).

Linn Z, Tashiro Y, Morio K, Hori H. Peer evaluations of group work in different years of medical school and academic achievement: how are they related? BMC Med Educ. 2022;22(1):102.

Avonts M, Michels NR, Bombeke K, Hens N, Coenen S, Vanderveken OM, De Winter BY. Does peer teaching improve academic results and competencies during medical school? A mixed methods study. BMC Med Educ. 2022;22(1):431.

Topor DR, Keane SP, Shelton TL, Calkins SD. Parent involvement and student academic performance: a multiple mediational analysis. J Prev Interv Community. 2010;38(3):183–97.

Veas A, Castejón JL, Miñano P, Gilar-Corbí R. Relationship between parent involvement and academic achievement through metacognitive strategies: a multiple multilevel mediation analysis. Br J Educ Psychol. 2019;89(2):393–411.

Núñez JC, Regueiro B, Suárez N, Piñeiro I, Rodicio ML, Valle A. Student Perception of teacher and parent involvement in Homework and Student Engagement: the mediating role of motivation. Front Psychol. 2019;10:1384.

Abdulghani AH, Ahmad T, Abdulghani HM. The impact of COVID-19 pandemic on anxiety and depression among physical therapists in Saudi Arabia: a cross-sectional study. BMC Med Educ. 2022;22(1):751.

Park KH, Park JH, Kim S, Rhee JA, Kim JH, Ahn YJ, Han JJ, Suh DJ. Students’ perception of the educational environment of medical schools in Korea: findings from a nationwide survey. Korean J Med Educ. 2015;27(2):117–30.

Ahrberg K, Dresler M, Niedermaier S, Steiger A, Genzel L. The interaction between sleep quality and academic performance. J Psychiatr Res. 2012;46(12):1618–22.

Dikker S, Haegens S, Bevilacqua D, Davidesco I, Wan L, Kaggen L, McClintock J, Chaloner K, Ding M, West T, et al. Morning brain: real-world neural evidence that high school class times matter. Soc Cognit Affect Neurosci. 2020;15(11):1193–202.

Pittenger AL, Dimitropoulos E, Foag J, Bishop D, Panizza S, Bishop JR. Closing the Classroom Theory to practice gap by simulating a Psychiatric Pharmacy Practice Experience. Am J Pharm Educ. 2019;83(10):7276.

Walck-Shannon EM, Rowell SF, Frey RF. To what extent do Study habits relate to performance? CBE Life Sci Educ. 2021;20(1):ar6.

Abdulghani HM, Alanazi K, Alotaibi R, Alsubeeh NA, Ahmad T, Haque S. Prevalence of potential dropout thoughts and their influential factors among Saudi Medical Students. 2023, 13(1):21582440221146966.

Sleight DA, Mavis BE. Study skills and academic performance among second-Year Medical students in Problem-based learning. Med Educ Online. 2006;11(1):4599.

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Six research-tested ways to study better

Psychology’s latest insights for preparing students for their next exams.

• Learning and Memory

Many students are missing a lesson in a key area that can help guarantee their success: how to study effectively. 

It’s common for students to prepare for exams by re-reading class notes and cramming textbook chapters—study techniques that hinge on the assumption that memories are like recording devices that can play back memories during an exam. “But the storage and retrieval operations of human memory differ from recording devices in almost every way possible,” says psychology professor Robert Bjork, PhD, co-director of the Learning and Forgetting Lab at University of California, Los Angeles. 

What does help our brains retain information? Study strategies that require the brain to work to remember information—rather than passively reviewing material. 

Bjork coined the term “desirable difficulty” to describe this concept, and psychologists are homing in on exactly how students can develop techniques to maximize the cognitive benefits of their study time.  

Here are six research-tested strategies from psychology educators. 

1. Remember and repeat

Study methods that involve remembering information more than once—known as repeated retrieval practice—are ideal because each time a memory is recovered, it becomes more accessible in the future, explains Jeffrey Karpicke, PhD, a psychology researcher at Purdue University in Indiana who studies human learning and memory. 

The benefits of this technique were evident when Karpicke conducted a study in which students attempted to learn a list of foreign language words. Participants learned the words in one of four ways: 

• Studying the list once.
• Studying until they had successfully recalled each word once.
• Studying until they had successfully recalled each word three times consecutively.
• Studying until they had recalled each word three times spaced throughout the 30-minute learning session. 

In the last condition, the students would move on to other words after correctly recalling a word once, then recall it again after practicing other words.

A week later, the researchers tested the students on the words and discovered that participants who had practiced with repeated spaced retrieval—the last condition—far outperformed the other groups. Students in this group remembered 80% of the words, compared to 30% for those who had recalled the information three times in a row—known as massed retrieval practice—or once. The first group, which involved no recall, remembered the words less than 1% of the time ( Journal of Experimental Psychology: Learning, Memory, and Cognition , Vol. 37, No. 5, 2011).

Many students assume that recalling something they’ve learned once is proof that they’ve memorized it. But, says Karpicke, just because you can retrieve a fact in a study session doesn’t mean you will remember it later on a test. “Just a few repeated retrievals can produce significant effects, and it’s best to do this in a spaced fashion.” 

2. Adapt your favorite strategies

Other research finds support for online flashcard programs, such as Study Stack or Chegg, to practice retrieving information—as long as students continue retesting themselves in the days leading up to the test, says John Dunlosky, PhD, who studies self-regulated learning at Kent State University in Ohio. For flashcards with single-word answers, the evidence suggests that thinking of the answer is effective, but for longer responses like definitions, students should type, write down, or say aloud the answers, Dunlosky says. If the answer is incorrect, then study the correct one and practice again later in the study session. “They should continue doing that until they are correct, and then repeat the process in a couple more days,” he says. 

Concept mapping — a diagram that depicts relationships between concepts—is another well-known learning technique that has become popular, but cognitive psychology researchers caution students to use this strategy only if they try to create a map with the book closed. Karpicke demonstrated this in a study in which students studied topics by creating concept maps or by writing notes in two different conditions: with an open textbook or with the textbook closed. With the closed textbook, they were recalling as much as they could remember. One week later, the students took an exam that tested their knowledge of the material, and students who had practiced retrieving the information with the book closed had better performances ( Journal of Educational Psychology , Vol. 106, No. 3, 2014).

“Concept maps can be useful, as long as students engage in retrieval practice while using this strategy,” Karpicke says. 

3. Quiz yourself

Students should also take advantage of quizzes—from teachers, in textbooks or apps like Quizlet—to refine their ability to retain and recall information. It works even if students answer incorrectly on these quizzes, says Oregon State University psychology professor Regan Gurung, PhD. “Even the process of trying and failing is better than not trying at all,” he says. “Just attempting to retrieve something helps you solidify it in your memory.”

Gurung investigated different approaches to using quizzes in nine introductory psychology courses throughout the country. In the study, the researchers worked with instructors who agreed to participate in different conditions. Some required students to complete chapter quizzes once while others required them to take each quiz multiple times. Also, some students were told to complete all the chapter quizzes by one deadline before the exam, while others were expected to space their quizzes by meeting deadlines throughout the course. The students who spaced their quizzes and took them multiple times fared the best on the class exams ( Applied Cognitive Psychology , Vol. 33, No. 5, 2019).

Although trying and failing on practice quizzes may be an effective study strategy, psychology professor Nate Kornell, PhD, of Williams College in Massachusetts, was skeptical that students would choose to learn this way because many people inherently do not like getting things wrong. He was eager to explore whether it was possible to create a retrieval practice strategy that increased the odds of students getting the right answer without sacrificing the quality of learning. To test this possibility, he led a study in which participants tried to remember word pairs, such as “idea: seeker.” The goal was to remember the second word after seeing only the first one. The students could choose to practice by restudying all the pairs or by self-testing with different options for hints—seeing either two or four letters of the second word in the pair, or no letters at all ( Cognitive Research: Principles and Implications , Vol. 4, 2019).

Most of the students preferred self-testing over restudying, and the results showed that even with hints, the self-testing group performed better on the final test of the words than the restudying group. “It’s a win-win situation because the technique that worked most effectively was also the one that they enjoyed the most,” says Kornell. 

Even more important, students think they are learning more effectively when they answer correctly while practicing, which means they’ll be even more motivated to try retrieval practice if hints are available, says Kornell. To apply this strategy, he suggests adding hints to self-generated flash cards or quizzes, such as the first letter of the answer or one of the words in a definition.

4. Make the most of study groups

Many students also enjoy studying with classmates. But when working in groups, it’s important for students to let everyone have an opportunity to think of the answers independently, says Henry Roediger, III, PhD, a professor in the psychology department at Washington University in St. Louis. One study highlighted the importance of this: Participants tried to learn words in a foreign language by either answering aloud or by listening to their partners give the answers ( Journal of Experimental Psychology: Applied (PDF, 426KB) , Vol. 24, No. 3, 2018). As expected, those who had answered aloud outperformed the listeners on a test two days later. The researchers also compared participants who answered aloud with partners who silently tried to recall the answers. Everyone received feedback about whether they had gotten the correct answer. Both groups had comparable performances. “Waiting for others to think of answers may slow down the process, but it produces better retention for everyone because it requires individual effort,” Roediger says. 

5. Mix it up

Researchers have also investigated the potential benefits of “interleaving,” or studying for different courses in one study session ( Journal of Experimental Psychology: Applied , Vol. 23, Nov. 4, 2017). For example, rather than dedicating two hours to studying for a psychology exam, students could use that time to study for exams in psychology, biology and statistics courses. A few days later, students could study for the same courses again during another block of time. “This strategy, versus blocking one’s study by course, naturally introduces spacing, so students practice retrieving information over time,” Bjork says. 

But the research on interleaving has had mixed results, says Aaron Richmond, PhD, a professor of educational psychology and human development at Metropolitan State University in Denver. “If the concepts from two subjects overlap too closely, then this could interfere with learning,” says Richmond. “But chemistry and introduction to psychology are so different that this doesn’t create interference.”

6. Figure out what works for you

The ability to effectively evaluate one’s approach to learning and level of attainment is known as metacognitive ability. Research has shown that “when people are new to learning about a topic, their subjective impressions of how much they know are the most inflated,” says Paul Penn, PhD, a senior lecturer in the psychology department at East London University and author of the 2019 book “The Psychology of Effective Studying.” 

“If your impression of your learning is inflated, you have little incentive to look at the way you're approaching learning,” he says.

To increase awareness about the value of sound study strategies, administrators at Samford University in Alabama invited psychology professor Stephen Chew, PhD, to talk to first-year students about this topic during an annual convocation each fall semester. Though an assessment study, he realized that the lecture prompted immediate changes in beliefs and attitudes about studying, but long-term change was lacking. “Students forgot the specifics of the lecture and fell back into old habits under the stress of the semester,” Chew says. 

To provide an accessible resource, he launched a series of five 7-minute videos on the common misconceptions about studying, how to optimize learning and more. Professors throughout the school assign the videos as required classwork, and the videos have been viewed 3 million times throughout the world by high school, college and medical students. 

While this form of campus-wide education about studying is somewhat rare, psychology researchers are optimistic that this could become more common in the coming years. “There is a lot more discussion now than even 10 years ago among teachers about the science of learning,” Karpicke says. “Most students do not know how to study effectively, and teachers are increasingly eager to change that.” 

Further reading

• Improving Self-Regulated Learning with a Retrieval Practice Intervention. Ariel, R., Karpicke, J.D., Journal of Experimental Psychology: Applied , 2018.
• Practice Tests, Spaced Practice, and Successive Relearning: Tips for Classroom Use and for Guiding Students’ Learning (PDF, 53KB) . Scholarship of Teaching and Learning in Psychology , Dunlosky, J. & Rawson, K.A., 2015.
• Performance Bias: Why Judgments of Learning Are not Affected by Learning . Kornell, N. and Hausman, H., Memory & Cognition , 2017.

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University students and study habits

Affiliations.

• 1 Dublin North Mental Health Services, Millmount Avenue, Drumcondra, Dublin 9, Ireland.
• 2 College Health Service, Trinity College Dublin, The University of Dublin, Dublin 2, Ireland.
• PMID: 33966668
• DOI: 10.1017/ipm.2021.28

Objectives: The objective of this study was to understand the variables or study habits that inform study in undergraduate and postgraduate students attending Trinity College Dublin.

Methods: A descriptive, cross-sectional anonymous online survey was used to gather data to explore student study habits. Survey 1 was completed by participants in April 2019 and survey 2 was completed by participants in April 2020, during the COVID-19 restrictions.

Results: A total of 1557 participants completed survey 1 in 2019, and 1793 participants completed survey 2 in 2020. In both surveys a majority reported using caffeine, library study, sleep pattern adjustment and excercise to aid academic performance. Survey 2 participants reported COVID-19 resulted in increased difficulty studying (91%). In particular loss of structure and routine was negatively impacted by the pandemic (92%), and increased feelings of stress were reported (75%).

Conclusions: Our study suggests a potential role of the college environment as a target for the implementation of interventions to promote student learning, healthy study habits and well-being. The global pandemic has resulted in additional challenging demands for universities to serve an essential role in supporting college students study habits.

Keywords: Covid-19; mental health; study habits; university students.

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Forget What You Know About Good Study Habits

By Benedict Carey

• Sept. 6, 2010

Every September, millions of parents try a kind of psychological witchcraft, to transform their summer-glazed campers into fall students, their video-bugs into bookworms. Advice is cheap and all too familiar: Clear a quiet work space. Stick to a homework schedule. Set goals. Set boundaries. Do not bribe (except in emergencies).

And check out the classroom. Does Junior’s learning style match the new teacher’s approach? Or the school’s philosophy? Maybe the child isn’t “a good fit” for the school.

Such theories have developed in part because of sketchy education research that doesn’t offer clear guidance. Student traits and teaching styles surely interact; so do personalities and at-home rules. The trouble is, no one can predict how.

Yet there are effective approaches to learning, at least for those who are motivated. In recent years, cognitive scientists have shown that a few simple techniques can reliably improve what matters most: how much a student learns from studying.

The findings can help anyone, from a fourth grader doing long division to a retiree taking on a new language. But they directly contradict much of the common wisdom about good study habits, and they have not caught on.

For instance, instead of sticking to one study location, simply alternating the room where a person studies improves retention. So does studying distinct but related skills or concepts in one sitting, rather than focusing intensely on a single thing.

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THE IMPACT OF STUDY HABITS ON THE ACADEMIC PERFORMANCE OF STUDENTS

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Impact Factor(JCC): 1.7843-This article can be downloaded from www.impactjournals.us ABSTRACT The development of a Country relies mostly on the levels of education among the people. Without education human race would have remained but as another animal race. Education is a process towards development. The term study habit can be as the students' way of study whether systematic, efficient or inefficient. Academic achievement refers to what and how an individual has learnt qualitatively and quantitatively after a period of instruction given. A habit is something that is done on a scheduled, regular, planned basis and that is not relegated to a second place or optional place in one's life. It is simply done, no reservations, no excuses, and no expectations. Study habits keep the learner perfect in getting knowledge and developing attitude towards things necessary for achievement in different field of human endeavour. Students who develop good study habits at school increase the potential to complete their assignments successfully and to learn the material they are studying. They also reduce the possibility of not knowing what is expected and of having to spend time studying at home. Study habits are the ways that your study habits that you have formed during your school years. Study habits can be good ones, or bad ones. Good study habits include being organized, keeping good notes, reading your textbooks, listening in class, and working every day. Bad study habits include skipping class, not doing your work, etc.

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INTRODUCTION The present day educational sector is becoming increasingly dynamic. The determination of every individual is to attain success and this success affects the personal and social dimensions of life. In this regard, academic performance is one of the major factors that influence individual's success in any educational setting. It is any body's guess that good habits and skills will help us to promote efficiency in our tasks. In education, proper study habits and skills requires proficiency as well as optimum learning quality (Dehghani & Soltanalgharaei, 2014). Productive study requires conceptualization and intention. It could include some skills such as note-taking, observation, asking question, listening, thinking and presenting idea with respect to new discoveries. Thus, students are expected to be interested in learning and must be able to apply requisite skills. On the other hand, inefficient study leads to waste of time and learner's energy (Hashemian & Hashemian, 2014). Study habits and skills like other skills can be taught and learnt. The interplay among motivation, habits, attitudes and behaviors has great impact on the academic performance of students. Study habit is buying out a dedicated scheduled and uninterrupted time to apply one's self to the task of learning. Studying is the procedure of getting information from prints that is information stored in written materials (magazines, newspapers, books). It is an organized gaining of intelligence and an interpretation of information and ideologies that calls for memorizing and usage. Studying can be expressed as the utilization of one's intellectual ability to the gaining, comprehending and arrangement of information; doing it over and over again entails some method of formal learning. Habit is a thing that one does often and almost without thinking; especially something that is hard to stop. A person's habit consists of plethora of ways an individual performs specific and general activity. Habit is relative to person or people. Each human being acts in a unique way. This is so because nature made things

Epoh Sedruol

The nature of study habits of elementary pupils inferred effectiveness and efficiency in school management. Many of the learning activities in school required fully brained pupils with excellent study habit skills. Pupils with good study habit skills dominated and sought to be great leaders of tomorrow. Formation of good study habits especially at the early years had immense implication towards academe. The researcher determined the study habits of the Grade V pupils in Cabalasan Elementary School, Division of Baybay City during the school year 2014-2015 with an end view of recommending ideas for an intervention in improving pupil’s study habits. The study employed normative survey method using checklist of Ralph C. Preston and Morton Botel. Interpretation of data was based on the use of simple percentage, mean and Fischer’s t-test. Results showed that less than half of the time lead in most frequent response in Grade V pupils of Cabalasan Elementary School for the school year 2014-2015 wherein 4 out of 5 descriptive responses showed that there was no significant difference between male and female grade five pupils in study habit contexts such as previewing, reading, note taking while reading, remembering, report writing, listening & taking class notes, preparing for examination, taking examination, planning time and arranging physical setting. The study implied that aside from collaborating with all the stakeholders, continuous learning must be offered in school through provision of time in searching, collecting, organizing and interpreting ideas in a learning hub with available reference books and study equipment for a more relevant and fruitful integration of knowledge. Stressing the importance of macro skills such as listening, reading, writing and speaking in language was of upmost necessity in pupil’s study habits and learning development.

Paul Ryan Cabase

Study habits is how one studies. That is, the habits which students form during their school years. Without good study habits, a student cannot succeed. Thus, this study investigated the impact of study habits on secondary school students' academic performance in the Federal Capital Territory, Abuja. The study was guided by one null hypothesis. The study adopted a descriptive survey research design as its plan. The sample of the study constituted of 1050 senior secondary school students drawn from the Federal Capital Territory, Abuja. The instrument used for data collection was questionnaire. Chi-square was used for data analysis. The finding of the study revealed that there is significant relationship between study habits and students' academic performance. It was recommended that teachers and school guidance counselors should collaboratively guide students on how to develop good study habits; thereby enhancing their academic success.

Ifeoma Obidile

The paper examined study habits as necessary tools for examination success among secondary school students. It listed the major generally accepted patterns of study students can make use of in order to succeed in their studies and examinations with ease. The following recommendations for helping students acquire good study habits are made. These include: policy makers on educational curriculum should ensure that seminar or a course on good study habits should be included in the secondary school curriculum so that students will be helped to inculcate a good study habits to enhancing their examination performances, qualified guidance counsellors should be employed by government and proprietors of secondary schools to be helping students come out of the difficulties they are experiencing in their studies, school authorities should be releasing examination time table on time for proper revision of class work and classrooms and places of study should be devoid of noise, will be well ventilated and lighted, studying environment must be kept clean to avoid unnecessary distractions and infections, Guidance Counsellors and teachers should try to be friendly with the students so that the students will not have any barrier in approaching any of them for clarifications and help and then conclusion.

Dr Yashpal D Netragaonkar

Education is regarded as an agent of nation development so factors that promote academic performance such as effective study habits should be encouraged among students. According to him, the promotion of effective study habits among students should be of profound interest to all stakeholders in the field of education. In India ineffective study habits lead to poor career performance, inefficiency on job, lack of job satisfaction, low productivity, and gross under development and retrogression of the nation’s intellectual advancement. Good study habits make student’s days in school rich, productive and enjoyable. Keyword: Study Habits , effective ,education , academic performance

American Journal of Educational Research

The pattern of behaviour adopted by a student in his/her study is his/her study habits, which reveals his/her personality. Study habits serve as the vehicle of learning and may be seen as both means and ends to learning. This study, thus, investigated the study habits of secondary school science students in the Jalingo metropolis, Taraba State, Nigeria. The study which was guided by three research questions, employed a descriptive correlational research design. The sample of the study is made up of 199 students selected from 5 secondary schools through simple random sampling. Data for the study were generated through a questionnaire, tagged “Science Students’ Study Habit Questionnaire”. Descriptive statistics of frequency counts, mean, and product-moment coefficient of correlation were used for data analysis. The study revealed that the secondary school science students in the Jalingo metropolis have poor study habits and weak academic performance. The study also found a strong posi...

IJARW Research Publication , Rah Rayo

Study habits are at the core of a learner's academic success. It is an action like reading, taking notes, conducting study groups that students perform frequently, and regularly accomplishing the learning goals. It can be defined as effective or counterproductive based on whether it serves the students well. Thus, the study's primary purpose was to determine the relationship between study habits and the students' academic performance. The descriptive-correlation design was utilized to describe the respondents' profile regarding their study habits and academic performance. A total of one hundred twenty-six (126) Grade 11 senior high school learners participated in this study. Moreover, the main research instrument utilized in the study was the Palsane and Sharma Study Habit Inventory. Its eight sub-scales are budgeting time, physical condition, reading ability, note-taking, learning motivation, memory, taking examinations, and health. The findings showed that the respondents' study habits are at a relatively average level. The result revealed no significant relationship between study habits and academic performance. Also, the results showed that the study habits of the students are at a relatively average level. Additionally, enhancing students' study habits are relevant, especially in note-taking, reading ability, and health, thus improving their academic performance.

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Good sleep habits important for overweight adults, study suggests

by Erik Robinson, Oregon Health & Science University

New research from Oregon Health & Science University reveals negative health consequences for people who are overweight and ignore their body's signals to sleep at night, with specific differences between men and women.

The study published this week in The Journal of Clinical Endocrinology & Metabolism .

"This study builds support for the importance of good sleep habits," said lead author Brooke Shafer, Ph.D., a postdoctoral researcher in the Sleep, Chronobiology and Health Laboratory in the OHSU School of Nursing. "Sleep practices, like going to bed when you're tired or setting aside your screen at night, can help to promote good overall health."

The study recruited 30 people, split evenly between men and women. All had a body mass index above 25, which put them into an overweight or obese category.

"Obesity and cardiometabolic disease are growing public health concerns," Shafer said. "Our research shows that disruptions in the body's internal biological clock could contribute to negative health consequences for people who may already be vulnerable due to weight."

Generally healthy participants contributed a saliva sample every 30 minutes until late in the night at a sleep lab on OHSU's Marquam Hill campus to determine the time at which their body started naturally producing the hormone melatonin . Melatonin is generally understood to begin the process of falling asleep, and its onset varies with an individual's internal biological clock.

Participants then went home and logged their sleep habits over the following seven days.

Researchers assessed the time difference between melatonin onset and average sleep timing for each participant, categorizing them into two groups: those who had a narrow window, with a short time duration between melatonin onset and sleep, and those with a wide window, with a longer duration between melatonin onset and sleep. A narrow window suggests someone who is staying awake too late for their internal body clock and is generally associated with poorer health outcomes.

The new study confirmed a variety of potentially harmful health measures in the group that went to sleep closer to melatonin onset.

It also found key differences between men and women. Men in this group had higher levels of belly fat and fatty triglycerides in the blood, and higher overall metabolic syndrome risk scores than the men who slept better. Women in this group had higher overall body fat percentage, glucose and resting heart rates.

"It was really somewhat surprising to see these differences present themselves in a sex-dependent manner," said senior author Andrew McHill, Ph.D., assistant professor in the OHSU School of Nursing, the School of Medicine and the Oregon Institute of Occupational Health Sciences at OHSU. "It's not one size fits all, as we sometimes think in academic medicine."

The next phase of research will determine sex-specific differences in groups that experience more severe changes in sleep patterns, such as workers pulling overnight shifts.

"We want to figure out possible interventions that keep this vital core group of the workforce healthy," Shafer said.

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Dealing with Revenge Porn and “Sextortion”

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How does social media affect mental health?

The pros of social media, the cons of social media, what’s driving your social media use, signs that social media is impacting your mental health, how to change your social media use, step 1: reduce time online, step 2: change your focus, step 3: spend more time with offline friends, step 4: express gratitude, helping a child or teen with unhealthy social media use, social media and mental health are you addicted to social media.

While many of us enjoy staying connected on social media, excessive use can fuel feelings of addiction, anxiety, depression, isolation, and FOMO. Here’s how to modify your habits and improve your mood.

Human beings are social creatures. We need the companionship of others to thrive in life, and the strength of our connections has a huge impact on our mental health and happiness. Being socially connected to others can ease stress, anxiety, and depression, boost self-worth, provide comfort and joy, prevent loneliness, and even add years to your life. On the flip side, lacking strong social connections can pose a serious risk to your mental and emotional health.

In today’s world, many of us rely on social media platforms such as Facebook, X (formerly Twitter), Snapchat, YouTube, TikTok, and Instagram to find and connect with each other. While each has its benefits, it’s important to remember that social media can never be a replacement for real-world human connection. It requires in-person contact with others to trigger the hormones that alleviate stress and make you feel happier, healthier, and more positive. Ironically for a technology that’s designed to bring people closer together, spending too much time engaging with social media can actually make you feel more lonely and isolated—and exacerbate mental health problems such as anxiety and depression.

If you’re spending an excessive amount of time on social media and feelings of sadness, dissatisfaction, frustration, or loneliness are impacting your life, it may be time to re-examine your online habits and find a healthier balance.  

Speak to a Licensed Therapist

BetterHelp is an online therapy service that matches you to licensed, accredited therapists who can help with depression, anxiety, relationships, and more. Take the assessment and get matched with a therapist in as little as 48 hours.

While virtual interaction on social media doesn’t have the same psychological benefits as face-to-face contact, there are still many positive ways in which it can help you stay connected and support your wellbeing.

Social media enables you to:

• Communicate and stay up to date with family and friends around the world.
• Find new friends and communities; network with other people who share similar interests or ambitions.
• Join or promote worthwhile causes; raise awareness on important issues.
• Seek or offer emotional support during tough times.
• Find vital social and professional connections (such as online therapy ) if you live in a remote area, for example, or have limited independence, social anxiety, or are part of a marginalized group.
• Find an outlet for your creativity and self-expression.
• Discover (with care) sources of valuable information and learning.

Since it’s a relatively new technology, there’s little research to establish the long-term consequences, good or bad, of social media use. However, multiple studies have found a strong link between heavy social media and an increased risk for depression, anxiety, loneliness, self-harm , and even suicidal thoughts .

Social media may promote negative experiences such as:

Inadequacy about your life or appearance . Even if you know that images you’re viewing on social media are manipulated, they can still make you feel insecure about how you look or what’s going on in your own life. Similarly, we’re all aware that other people tend to share just the highlights of their lives, rarely the low points that everyone experiences. But that doesn’t lessen those feelings of envy and dissatisfaction when you’re scrolling through a friend’s airbrushed photos of their tropical beach holiday or reading about their exciting new promotion at work.

Fear of missing out (FOMO) and social media addiction . While FOMO has been around far longer than social media, sites such as Facebook and Instagram seem to exacerbate feelings that others are having more fun or living better lives than you are. The idea that you’re missing out on certain things can impact your self-esteem, trigger anxiety, and fuel even greater social media use, much like an addiction. FOMO can compel you to pick up your phone every few minutes to check for updates, or compulsively respond to each and every alert—even if that means taking risks while you’re driving, missing out on sleep at night, or prioritizing social media interaction over real world relationships. 

Isolation . A study at the University of Pennsylvania found that high usage of Facebook, Snapchat, and Instagram increases rather decreases feelings of loneliness . Conversely, the study found that reducing social media usage can actually make you feel less lonely and isolated and improve your overall wellbeing.

Depression and anxiety . Human beings need face-to-face contact to be mentally healthy. Nothing reduces stress and boosts your mood faster or more effectively than eye-to-eye contact with someone who cares about you. The more you prioritize social media interaction over in-person relationships, the more you’re at risk for developing or exacerbating mood disorders such as anxiety and depression .

Cyberbullying. About 10 percent of teens report being bullied on social media and many other users are subjected to offensive comments. Social media platforms such as Twitter can be hotspots for spreading hurtful rumors, lies, and abuse that can leave lasting emotional scars.

Self-absorption.  Sharing endless selfies and all your innermost thoughts on social media can create an unhealthy self-centeredness and distance you from real-life connections.

These days, most of us access social media via our smartphones or tablets. While this makes it very convenient to keep in touch, it also means that social media is always accessible. This round-the-clock, hyper connectivity can trigger impulse control problems, the constant alerts and notifications affecting your concentration and focus, disturbing your sleep, and making you a slave to your phone .

Social media platforms are designed to snare your attention, keep you online, and have you repeatedly checking your screen for updates. It’s how the companies make money. But, much like a gambling compulsion or an addiction to nicotine, alcohol, or drugs, social media use can create psychological cravings. When you receive a like, a share, or a favorable reaction to a post, it can trigger the release of dopamine in the brain, the same “reward” chemical that follows winning on a slot machine, taking a bite of chocolate, or lighting up a cigarette, for example. The more you’re rewarded, the more time you want to spend on social media, even if it becomes detrimental to other aspects of your life.

Other causes of unhealthy social media use

A fear of missing out (FOMO) can keep you returning to social media over and over again. Even though there are very few things that can’t wait or need an immediate response, FOMO will have you believing otherwise. Perhaps you’re worried that you’ll be left out of the conversation at school or work if you miss the latest news or gossip on social media? Or maybe you feel that your relationships will suffer if you don’t immediately like, share, or respond to other people’s posts? Or you could be worried you’ll miss out on an invitation or that other people are having a better time than you.

Many of us use social media as a “security blanket”. Whenever we’re in a social situation and feel anxious, awkward, or lonely, we turn to our phones and log on to social media. Of course, interacting with social media only denies you the face-to-face interaction that can help to ease anxiety .

Your heavy social media use could be masking other underlying problems , such as stress, depression, or boredom. If you spend more time on social media when you’re feeling down, lonely, or bored, you may be using it as a way to distract yourself from unpleasant feelings or self-soothe your moods. While it can be difficult at first, allowing yourself to feel can open you up to finding healthier ways to manage your moods .

The vicious cycle of unhealthy social media use

Excessive social media use can create a negative, self-perpetuating cycle:

• When you feel lonely, depressed, anxious, or stressed, you use social media more often—as a way to relieve boredom or feel connected to others.
• Using social media more often, though, increases FOMO and feelings of inadequacy, dissatisfaction, and isolation.
• In turn, these feelings negatively affect your mood and worsen symptoms of depression, anxiety, and stress.
• These worsening symptoms cause you to use social media even more, and so the downward spiral continues.

Everyone is different and there is no specific amount of time spent on social media, or the frequency you check for updates, or the number of posts you make that indicates your use is becoming unhealthy. Rather, it has to do with the impact time spent on social media has on your mood and other aspects of your life, along with your motivations for using it.

For example, your social media use may be problematic if it causes you to neglect face-to-face relationships, distracts you from work or school, or leaves you feeling envious, angry, or depressed. Similarly, if you’re motivated to use social media just because you’re bored or lonely, or want to post something to make others jealous or upset, it may be time to reassess your social media habits.

Indicators that social media may be adversely affecting your mental health include:

Spending more time on social media than with real world friends . Using social media has become a substitute for a lot of your offline social interaction. Even if you’re out with friends, you still feel the need to constantly check social media, often driven by feelings that others may be having more fun than you.

Comparing yourself unfavorably with others on social media . You have low self-esteem or negative body image. You may even have patterns of disordered eating.

Experiencing cyberbullying . Or you worry that you have no control over the things people post about you.

Being distracted at school or work . You feel pressure to post regular content about yourself, get comments or likes on your posts, or respond quickly and enthusiastically to friends’ posts.

Having no time for self-reflection . Every spare moment is filled by engaging with social media, leaving you little or no time for reflecting on who you are, what you think, or why you act the way that you do—the things that allow you to grow as a person.

Engaging in risky behavior in order to gain likes , shares, or positive reactions on social media. You play dangerous pranks, post embarrassing material, cyberbully others, or access your phone while driving or in other unsafe situations.  

[ Read: Dealing with Revenge Porn and “Sextortion” ]

Suffering from sleep problems . Do you check social media last thing at night, first thing in the morning, or even when you wake up in the night? The light from phones and other devices can disrupt your sleep , which in turn can have a serious impact on your mental health.

Worsening symptoms of anxiety or depression . Rather than helping to alleviate negative feelings and boost your mood, you feel more anxious, depressed, or lonely after using social media.

If you feel that your social media use has become an addiction, or it’s fueling your levels of anxiety, depression, FOMO, or sense of isolation, the following steps can help you modify your habits :

A 2018 University of Pennsylvania study found that reducing social media use to 30 minutes a day resulted in a significant reduction in levels of anxiety, depression, loneliness, sleep problems, and FOMO. But you don’t need to cut back on your social media use that drastically to improve your mental health. The same study concluded that just being more mindful of your social media use can have beneficial results on your mood and focus.  

While 30 minutes a day may not be a realistic target for many of us—let alone a full “social media detox”— we can still benefit from reducing the amount of time we spend on social media. For most of us, that means reducing how much we use our smartphones. The following tips can help:

• Use an app to track how much time you spend on social media each day. Then set a goal for how much you want to reduce it by.
• Turn off your phone at certain times of the day, such as when you’re driving, in a meeting, at the gym, having dinner, spending time with offline friends, or playing with your kids. Don’t take your phone with you to the bathroom.
• Don’t bring your phone or tablet to bed . Turn devices off and leave them in another room overnight to charge.
• Disable social media notifications. It’s hard to resist the constant buzzing, beeping, and dinging of your phone alerting you to new messages. Turning off notifications can help you regain control of your time and focus.
• Limit checks. If you compulsively check your phone every few minutes, wean yourself off by limiting your checks to once every 15 minutes. Then once every 30 minutes, then once an hour. There are apps that can automatically limit when you’re able to access your phone.
• Try removing social media apps from your phone so you can only check Facebook, Twitter and the like from your tablet or computer. If this sounds like too drastic a step, try removing one social media app at a time to see how much you really miss it.

For more tips on reducing your overall phone use, read Smartphone Addiction .

Many of us access social media purely out of habit or to mindlessly kill moments of downtime. But by focusing on your motivation for logging on, you can not only reduce the time you spend on social media, you can also improve your experience and avoid many of the negative aspects.

If you’re accessing social media to find specific information, check on a friend who’s been ill, or share new photos of your kids with family, for example, your experience is likely to be very different than if you’re logging on simply because you’re bored, you want to see how many likes you got from a previous post, or to check if you’re missing out on something.

Next time you go to access social media, pause for a moment and clarify your motivation for doing so.

Are you using social media as a substitute for real life? Is there a healthier substitute for your social media use? If you’re lonely, for example, invite a friend out for coffee instead. Feeling depressed? Take a walk or go to the gym. Bored? Take up a new hobby. Social media may be quick and convenient, but there are often healthier, more effective ways to satisfy a craving.

Are you an active or a passive user on social media? Passively scrolling through posts or anonymously following the interaction of others on social media doesn’t provide any meaningful sense of connection. It may even increase feelings of isolation. Being an active participant, though, will offer you more engagement with others.

Does social media leave you feeling inadequate or disappointed about your life? You can counter symptoms of FOMO by focusing on what you have, rather than what you lack. Make a list of all the positive aspects of your life and read it back when you feel you’re missing out on something better. And remember: no one’s life is ever as perfect as it seems on social media. We all deal with heartache, self-doubt, and disappointment, even if we choose not to share it online.  

We all need the face-to-face company of others to be happy and healthy. At its best, social media is a great tool for facilitating real-life connections. But if you’ve allowed virtual connections to replace real-life friendships in your life, there are plenty of ways to build meaningful connections without relying on social media.

Set aside time each week to interact offline with friends and family. Try to make it a regular get-together where you always keep your phones off.

If you’ve neglected face-to-face friendships, reach out to an old friend (or an online friend) and arrange to meet up. If you both lead busy lives, offer to run errands or exercise together .

Join a club . Find a hobby, creative endeavor, or fitness activity you enjoy and join a group of like-minded individuals that meet on a regular basis.

Don’t let social awkwardness stand in the way . Even if you’re shy, there are proven techniques to  overcome insecurity and build friendships .

If you don’t feel that you have anyone to spend time with, reach out to acquaintances . Lots of other people feel just as uncomfortable about making new friends as you do—so be the one to break the ice. Invite a coworker out for lunch or ask a neighbor or classmate to join you for coffee.

Interact with strangers . Look up from your screen and connect with people you cross paths with on public transport, at the coffee shop, or in the grocery store. Simply smiling or saying hello will improve how you feel—and you never know where it may lead.

Feeling and expressing gratitude about the important things in your life can be a welcome relief to the resentment, animosity, and discontent sometimes generated by social media.

Take time for reflection . Try keeping a gratitude journal or using a gratitude app. Keep track of all the great memories and positives in your life—as well as those things and people you’d miss if they were suddenly absent from your life. If you’re more prone to venting or negative posts, you can even express your gratitude on social media—although you may benefit more from private reflection that isn’t subject to the scrutiny of others. 

[Read: Gratitude: The Benefits and How to Practice It]

Practice mindfulness . Experiencing FOMO and comparing yourself unfavorably to others keeps you dwelling on life’s disappointments and frustrations. Instead of being fully engaged in the present, you’re focused on the “what ifs” and the “if onlys” that prevent you from having a life that matches those you see on social media. By practicing mindfulness , you can learn to live more in the present moment, lessen the impact of FOMO, and improve your overall mental wellbeing.

Volunteer . Just as human beings are hard-wired to seek social connection, we’re also hard-wired to give to others. Helping other people or animals not only enriches your community and benefits a cause that’s important to you, but it also makes you feel happier and more grateful.

Childhood and the teenage years can be filled with developmental challenges and social pressures. For some kids, social media has a way of exacerbating those problems and fueling anxiety, bullying , depression , and issues with self-esteem.

If you’re worried about your child’s social media use, it can be tempting to simply confiscate their phone or other device. But that can create further problems, separating your child from their friends and the positive aspects of social media. Instead, there are other ways to help your child use TikTok, Facebook, Instagram, and other platforms in a more responsible way.

Monitor and limit your child’s social media use. The more you know about how your child is interacting on social media, the better you’ll be able to address any problems. Parental control apps can help limit your child’s data usage or restrict their phone use to certain times of the day. You can also adjust privacy settings on the different platforms to limit their potential exposure to bullies or predators.

Talk to your child about underlying issues. Problems with social media use can often mask deeper issues. Is your child having problems fitting in at school? Are they suffering from shyness or social anxiety? Are problems at home causing them stress?

Enforce “social media” breaks. For example, you could ban social media until your child has completed their homework in the evening, not allow phones at the dinner table or in their bedroom, and plan family activities that preclude the use of phones or other devices. To prevent sleep problems, always insist phones are turned off at least one hour before bed.

Teach your child how social media is not an accurate reflection of people’s lives. They shouldn’t compare themselves or their lives negatively to others on social media. People only post what they want others to see. Images are manipulated or carefully posed and selected. And having fewer friends on social media doesn’t make your child less popular or less worthy.

Encourage exercise and offline interests. Get your child away from social media by encouraging them to pursue physical activities and hobbies that involve real-world interaction. Exercise is great for relieving anxiety and stress , boosting self-esteem, and improving mood—and is something you can do as a family. The more engaged your child is offline, the less their mood and sense of self-worth will be dependent on how many friends, likes, or shares they have on social media. 

More Information

• Study into wellbeing and social media - Details study linking time spent on social media with decreased wellbeing. (Penn Today, University of Pennsylvania)
• Impact on the mental health of young people - Briefing paper analyzing the impact of social media. (Centre for Mental Health)
• Linking child depression - How heavy Instagram and Facebook use may be affecting kids negatively. (Child Mind Institute)
• Hunt, Melissa G., Rachel Marx, Courtney Lipson, and Jordyn Young. “No More FOMO: Limiting Social Media Decreases Loneliness and Depression.” Journal of Social and Clinical Psychology 37, no. 10 (December 2018): 751–68. Link
• Riehm, Kira E., Kenneth A. Feder, Kayla N. Tormohlen, Rosa M. Crum, Andrea S. Young, Kerry M. Green, Lauren R. Pacek, Lareina N. La Flair, and Ramin Mojtabai. “Associations Between Time Spent Using Social Media and Internalizing and Externalizing Problems Among US Youth.” JAMA Psychiatry 76, no. 12 (December 1, 2019): 1266. Link
• Anderson, Monica. (2018, September 27). A majority of teens have been the target of cyberbullying, with name-calling and rumor-spreading being the most common forms of harassment. Pew Research Center: Internet, Science & Tech. Link
• Kross, Ethan, Philippe Verduyn, Emre Demiralp, Jiyoung Park, David Seungjae Lee, Natalie Lin, Holly Shablack, John Jonides, and Oscar Ybarra. “Facebook Use Predicts Declines in Subjective Well-Being in Young Adults.” PLOS ONE 8, no. 8 (August 14, 2013): e69841. Link
• Twenge, Jean M., Thomas E. Joiner, Megan L. Rogers, and Gabrielle N. Martin. “Increases in Depressive Symptoms, Suicide-Related Outcomes, and Suicide Rates Among U.S. Adolescents After 2010 and Links to Increased New Media Screen Time.” Clinical Psychological Science 6, no. 1 (January 1, 2018): 3–17. Link
• Ilakkuvan, Vinu, Amanda Johnson, Andrea C. Villanti, W. Douglas Evans, and Monique Turner. “Patterns of Social Media Use and Their Relationship to Health Risks Among Young Adults.” Journal of Adolescent Health 64, no. 2 (February 2019): 158–64. Link
• Primack, Brian A., Ariel Shensa, Jaime E. Sidani, Erin O. Whaite, Liu Yi Lin, Daniel Rosen, Jason B. Colditz, Ana Radovic, and Elizabeth Miller. “Social Media Use and Perceived Social Isolation Among Young Adults in the U.S.” American Journal of Preventive Medicine 53, no. 1 (July 2017): 1–8. Link

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Low cortisol may play a role in fueling long COVID, study suggests

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Proteins left behind by COVID-19 long after initial infection can cause cortisol levels in the brain to plummet, inflame the nervous system and prime its immune cells to hyper-react when another stressor arises, according to new animal research by CU Boulder scientists.

The study, published in the journal Brain Behavior and Immunity , sheds new light on what might underlie the neurological symptoms of long COVID, an intractable syndrome that impacts as many as 35% of those infected with the virus.

The findings come as COVID makes a striking late summer comeback, with cases rising in 84 countries .

“Our study suggests that low cortisol could be playing a key role in driving many of these physiological changes that people are experiencing with long COVID,” said lead author Matthew Frank, a senior research associate with the Department of Psychology and Neuroscience at CU Boulder.

Previous research has shown that SARS-CoV-2 antigens, immune-stimulating proteins shed by the virus that causes COVID-19, linger in the blood of long COVID patients as much as a year after infection. They’ve also been detected in the brains of COVID patients who have died.

Role of COVID spike protein subunit

To explore just how such antigens impact the brain and nervous system, the research team injected an antigen called S1 (a subunit of the “spike” protein) into the spinal fluid of rats and compared them to a control group.

After seven days, levels of the cortisol-like hormone corticosterone plummeted by 31% in the hippocampus of rats exposed to S1. That is the region of the brain associated with memory, decision making and learning. After nine days, levels were down 37%.

“Nine days is a long time in the life span of a rat,” said Frank, noting that rats live on average for two to three years.

He noted that cortisol is a critical anti-inflammatory agent, helps convert fuel into energy and is important for regulating blood pressure and the sleep-wake cycle and keeping the immune response to infection in check. One recent study showed that people with long COVID tend to have low cortisol levels—as do people with chronic fatigue syndrome, research shows.

“Cortisol has so many beneficial properties that, if it is reduced, it can have a host of negative consequences,” said Frank.

In another experiment, the researchers exposed different groups of rats to an immune stressor (a weakened bacteria) and observed their heart rate, temperature and behavior as well as the activity of glial—or immune—cells in the brain.

They found that the group of rats that had previously been exposed to the COVID protein S1 responded far more strongly to the stressor, with more pronounced changes in eating, drinking, behavior, core body temperature and heart rate, more neuroinflammation and stronger activation of glial cells.

“We show for the first time that exposure to antigens left behind by this virus can actually change the immune response in the brain so that it overreacts to subsequent stressors or infection,” said Frank.

Continuing long COVID research

He stressed that the study was in animals and that more research is necessary to determine whether and how low cortisol might lead to long COVID symptoms in people.

He theorizes that the process might go something like this: COVID antigens lower cortisol, which serves to keep inflammatory responses to stressors in check in the brain. Once a stressor arises—whether it be a bad day at work, a mild infection or a hard workout—the brain’s inflammatory response is unleashed without those limits and serious symptoms come screaming back.

Those might include fatigue, depression, brain fog, insomnia and memory problems.

Frank said he is doubtful that cortisol treatments alone could be an effective treatment for long COVID, as they would not get at the root cause and come with a host of side effects.

Instead, the findings suggest that identifying and minimizing different stressors might help manage symptoms.

Rooting out the source of antigens—including tissue reservoirs where bits of virus continue to hide out—might also be an approach worth exploring.

The study was funded by the nonprofit PolyBio Research Foundation.

“There are many individuals out there suffering from this debilitating syndrome. This research gets us closer to understanding what, neurobiologically, is going on and how cortisol may be playing a role,” said Frank.

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How to Form Good Habits? A Longitudinal Field Study on the Role of Self-Control in Habit Formation

Anouk van der weiden.

1 Department of Social Health and Organizational Psychology, Utrecht University, Utrecht, Netherlands

2 Department of Social Economic and Organizational Psychology, Leiden University, Leiden, Netherlands

Jeroen Benjamins

3 Department of Experimental Psychology, Utrecht University, Utrecht, Netherlands

Marleen Gillebaart

Jan fekke ybema, denise de ridder, associated data.

The datasets generated for this study are available on request to the corresponding author.

When striving for long-term goals (e.g., healthy eating, saving money, reducing energy consumption, or maintaining interpersonal relationships), people often get in conflict with their short-term goals (e.g., enjoying tempting snacks, purchasing must-haves, getting warm, or watching YouTube video’s). Previous research suggests that people who are successful in controlling their behavior in line with their long-term goals rely on effortless strategies, such as good habits. In the present study, we aimed to track how self-control capacity affects the development of good habits in real life over a period of 90 days. Results indicated that habit formation increased substantially over the course of three months, especially for participants who consistently performed the desired behavior during this time. Contrary to our expectations, however, self-control capacity did not seem to affect the habit formation process. Directions for future research on self-control and other potential moderators in the formation of good habits are discussed.

Introduction

Sometimes people find themselves mindlessly watching TV while they had the intention to be more physically active; eating sweets while they wanted to eat more healthily; or lashing out at others while they wanted to be more patient or open-minded. Sounds familiar? Although people may often be able to control themselves in order to attain long-term goals such as healthy living or maintaining satisfactory relationships, there are also many instances in which they are unable or unwilling to exert self-control (e.g., when temptations are strong or when tired; e.g., Muraven and Slessareva, 2003 ; Baumeister et al., 2007 ; Hofmann et al., 2010 ). Also, some people are less successful in controlling their behaviors than others ( Schmeichel and Zell, 2007 ). In these cases, people often revert to effortless, habitual behavior ( Ouellette and Wood, 1998 ; Webb and Sheeran, 2006 ; Neal et al., 2013 ) – often bad habits. This reliance on habits may, however, also be used to peoples’ advantage if they manage to form good habits that are in line with their long-term goals. Indeed, recent research suggests that people who are successful in controlling their behavior, more effortlessly rely on good habits ( Adriaanse et al., 2014 ; Gillebaart and de Ridder, 2015 ). But how are good habits formed?

Research on habit formation has shown that behavior is likely to become habitual when it is frequently and consistently performed in the same context (e.g., Ouellette and Wood, 1998 ). For example, when one frequently and consistently eats vegetables for lunch, at some point eating vegetables for lunch will become a habit. This is because the frequent co-occurrence of context and behavior instigates an association that may guide future behavior (e.g., Aarts and Dijksterhuis, 2000 ; Neal et al., 2012 ). Specifically, when encountering a context (e.g., having lunch) that is associated with a certain behavior (e.g., eating vegetables), this context will automatically trigger this associated behavior. Hence, once a good habit is formed, it is rather effortless to perform desired behavior. However, the process of habit formation itself may vary in the amount of effort needed – although some people manage to form certain habits as quickly as 18 days, others need as much as half a year ( Lally et al., 2010 ). This raises the question how exactly do habits form over time?

Although research on habit formation is still in its infancy, recent studies have uncovered some of the mechanisms that underlie the habit formation process. One of the main findings is that the habit formation process within individuals unfolds asymptotically ( Lally et al., 2010 ; Fournier et al., 2017 ). That is, habit strength increases steeply at first, and then levels off. In addition, studies that studied habit formation on the group level (i.e., averaging over participants) have provided insight into the processes that facilitate such increases in habit strength. Specifically, the frequency and consistency with which the desired behavior is performed, the inherently rewarding nature of the behavior, a comfortable environment (e.g., no threats or obstacles), and easy rather than difficult behaviors have been shown to facilitate the process of habit formation ( Kaushal and Rhodes, 2015 ; Fournier et al., 2017 ).

Besides these factors, there are still many others unexplored that may explain the variation in the time it takes people to form a habit. One such likely candidate is self-control capacity. That is, habit formation crucially depends on the repeated performance of behavior that is in line with one’s long-term goal. The initiation of such new behavior, as well as the inhibition of acting upon short-term temptations is likely to require effortful self-control, especially in the early stages of habit formation. Indeed a study among teenagers indicates that those who initially had higher self-control capacity reported having stronger meditation habits after three months of meditation sessions ( Galla and Duckworth, 2015 , Study 5). Other studies revealed that habit strength mediates the effect of self-control strength and behavior. Specifically, self-control was related to increased habit strength, which was in turn related to increased exercise behavior ( Gillebaart and Adriaanse, 2017 ) and decreased snack intake ( Adriaanse et al., 2014 ). However, although these studies have indicated that self-control is related to habit strength, they do not provide insight in the role of self-control capacity in the initial stages of habit formation.

The current study was a first attempt to track how self-control capacity affects the development of good habits in daily life over a relatively long period of time. We expected both repeated goal-congruent behavior performance and self-control capacity to facilitate the formation of good habits. Possibly, self-control capacity may affect habit formation via increased behavior performance (as the initiation of new behavior and inhibition of conflicting behavior requires self-control at first). To test these hypotheses, we recruited people who wanted to form a good habit in the domain of health behavior (eating fruit or vegetables, exercising, or drinking water), interpersonal relationships (making more contact with others, being more patient or open-minded, or having more attention for others), personal finance (saving money), or environmental-friendly behavior (recycling). Over the course of three months, we then measured their goal-congruent behavior performance, self-control capacity, and habit strength to examine how self-control related to behavior performance and habit strength over time.

Participants and Design

A community sample was recruited via the population register of the city of Utrecht in the Netherlands as well as social media and the alumni register of Utrecht University. Anyone between the age of 18 and 65 who possessed a smartphone was eligible (we could provide a limited number of participants with a smartphone for the duration of the study if they did not possess one, N = 5). All participants indicated they wanted to form a habit in the health, sustainability, interpersonal, or financial domain.

The within-subjects design consisted of a pre-measurement administered in groups of 2–13 participants at a university location, 1 followed by a three-month interval of daily measures administered through an in-house developed mobile application, and after 90–110 days, a post-measurement (again in group sessions at a university location). In total, 180 people participated in the pre-measurement, of whom 90 participated in the post-measurement. Participants took part in the daily measures over a range of 17–110 days ( M = 77.0, SD = 26.7). During this time period, the number of bi-weekly self-control assessments ranged from 1 to 10 ( M = 6.5, SD = 2.3), which were alternated with bi-weekly habit strength assessments, of which the number ranged from 2 to 9 ( M = 5.7, SD = 2.0). In total, 146 participants (118 women; M age = 31.9; SD age = 12.7; range 18–61 years) who completed at least one follow-up assessment of habit strength were included in the analyses. More than half of them (65.8%) were community residents (including alumni) and the remainder (34.2%) were bachelor students. Based on participants’ postal code (which is indicative of education, income, and work status; Netherlands Institute for Social Research), we inferred their socio-economic status. About 10.3% of the participants lived in underprivileged neighborhoods, 61.0% lived in middle class neighborhoods, and 26.0% came from privileged neighborhoods (postal code data was missing for 4 participants). Participants’ initial level of habit strength was moderate ( M = 3.1, SD = 1.1).

Procedure and Materials

Registration.

Those who were interested in participating received an information letter via e-mail, containing a link to register for the study with a unique participation code. In the registration form, participants were reminded of the terms and conditions (i.e., voluntary nature of participation, ability to withdraw without explanation, etc.), after which they were required to give their consent for participating in the study. Participants could then schedule an appointment for the pre-measurement.

Pre-measurement

Participants came to the university for a pre-measurement as part of a larger longitudinal prospective study on trait self-control (i.e., to see whether self-control could be trained by daily performance of a behavior that requires self-control – which indeed seemed to be the case; de Ridder et al., 2019 ). As such, the different measurements (pre-, app-, and post-) also included measures that were not of interest for the current study. 2

Goal setting

At the start of the study, participants selected a specific behavior they wanted to turn into a habit over the course of the study. Choices covered health, interpersonal, financial, and ecological behaviors (e.g., eating fruit, being patient, saving money, recycling). Depending on the type of behavior chosen, participants could then choose from three to seven contexts for behavioral practice (e.g., eating fruit when having breakfast, being patient when talking to someone, 3 saving money when in the supermarket, or recycling when tidying up). As such, participants could choose which habit they wanted to form based on 60 preset combinations of behaviors and contexts. See Figure 1 for an overview of which behaviors were selected by the participants. It was emphasized that the selected behavior needed to be personally relevant to them, had to be a behavior they did not regularly perform yet, and had to be feasible for them to perform on a daily basis. After selecting a behavior and context, participants had to specify for themselves what this behavior entailed (e.g., when they chose exercise as their goal, it was explained that a ten minute routine at home was more feasible on a daily basis than an hour at the gym). As such, participants were intrinsically motivated and there was room for forming a new habit.

Overview of the number of participants selecting each behavior. Please note that exercise (“sporten” in Dutch) and physical activity (“bewegen” in Dutch) refers to different types of behaviors. Whereas exercise is typically associated with certain rules and competitiveness, but most of all with high intensity (e.g., playing football, cross fit, running), physical activity refers to more casual and less intense behaviors (e.g., walking or biking, gardening, household chores).

App instructions

For the purpose of this study, we developed a mobile app (which ran on iOS and android) to assess self-control capacity and habit strength on a regular basis. At the end of the pre-measurement, participants were instructed to install and use this app for daily tests and questionnaires. Participants were also informed that they would receive a reminder every morning via the mobile app.

App Measurements

Habit strength.

Habit strength was assessed bi-weekly with the Self-Report Habit Index ( Verplanken and Orbell, 2003 ), which consists of 12 statements (e.g., ‘[self-chosen behavior (e.g., eating fruit)] is something I do …frequently; …automatically; …without thinking)’. For each statement, participants indicated to what extent they felt the statement applied to them on a scale from 1 (completely disagree) to 7 (completely agree). The scale proved reliable with a Cronbach’s alpha of.94. 4

Goal-congruent behavior performance

On a daily basis, participants indicated (dichotomously) whether or not they had performed the self-chosen behavior that day, and whether they performed this behavior in their self-chosen context. 5

Self-control capacity

Self-control capacity was assessed bi-weekly with the Brief Self-Control Scale ( Tangney et al., 2004 ), which consists of 13 statements (e.g., “I am good at resisting temptation” or “People would say I have iron self-discipline”). For each statement, participants indicated to what extent they felt the statement applied to them on a scale from 1 (not at all) to 5 (very much). The scale proved reliable with a Cronbach’s alpha of 0.79.

Data Analysis

Habit formation over time – individual level analysis.

First, following Lally et al. (2010) approach, we attempted to fit an asymptotic curve to individual participants’ habit strength scores over time (by means of a Least Squares Curve Fit algorithm in Matlab), to then see whether we could predict the individual (rate of) change in habit strength as a function of goal-congruent behavior performance and self-control capacity. However, the individual patterns fluctuated too much (possibly because bi-weekly measurements were too infrequent; M = 5.73, SD = 1.99, range = 2–9 observations per participant; see Figure 2 for the number of observations plotted against the number of participants 6 ), and curve fitting could only be achieved for 4.11% of our participants (see results under point 2, Supplementary Material ). As an alternative, we also tried fitting a less constrained power curve ( y = ax b ), with even less success (2.4%). We therefore decided to analyze the data on the group level instead.

Number of observations for habit strength (total N = 836) plotted against the number of participants ( N = 146).

Habit Formation Over Time – Group Level Analysis

We examined the data in SPSS 24 with the Linear Mixed Models, using Maximum Likelihood estimation. In the first analysis, we carried out a growth curve modeling for habit formation, in which a random intercept, and fixed effects of a linear and a quadratic time trend were estimated. In addition, the random slopes of the linear and quadratic trend were tested to allow for individual differences in the growth curve. In a second analysis we tested whether habit formation was influenced by self-control capacity and the performance of the behavior. In Model 1, the random intercept was included to determine the intraclass correlation (ICC) of habit strength as an indicator of the variance at person level. In Model 2, lagged habit strength (i.e., habit strength at the previous measurement) was entered to analyze habit formation. Because we controlled for lagged habit strength, the linear and quadratic trend were not included in this analysis. In Model 3, self-control capacity at the previous bi-weekly measurement of self-control and daily practice of the chosen behavior (measured by the proportion of daily app-measurements in which the chosen behavior was performed during the interval between the previous and the current habit assessment) was entered, as well as a number of control variables, i.e., the measurement number of bi-weekly habit assessment, the length of the interval since the previous habit assessments, and the number of daily behavioral assessments.

Habit Formation Over Time

We first examined whether habit strength increased over time. Figure 3 shows a significant increase of about 0.8 SD (a large effect size according to Cohen, 1992 ) in habit strength over a period of 110 days with a stronger increase in beginning of the study period, leveling off at the end. Both the linear trend ( t = 15.30, p < 0.001) and the quadratic trend ( t = −3.39, p < 0.001) were significant. Adding the random slopes for the linear (Wald Z = 5.37, p < 0.001) and quadratic (Wald Z = 2.40, p < 0.05) improved the fit of the model, showing that habit formation differed over participants.

Habit strength fitted as a function time, with 95% confidence bands.

Effects of Goal-Congruent Behavior Performance and Self-Control Capacity on Habit Formation

Table 1 shows the results of a hierarchical multilevel analysis of habit formation. As can be seen in Model 2, habit strength is rather stable and strongly predicted by lagged habit strength at the previous measurement of habit. Nevertheless, entering lagged self-control capacity and goal-congruent behavior performance in the time period during both habit strength measurements further increased the fit of the model. Self-control capacity did not contribute to higher habit strength 7 . However, participants who carried out the self-chosen behavior more consistently (higher proportion of goal-congruent behavior performance 8 ), showed stronger increases in habit strength. In line with the trend in habit formation shown before, the time of measurement (i.e., the umpteenth time) had a small negative influence on habit strength increase. This is in line with the lower increase in habit strength later on during the study period.

The multilevel regression of habit strength.

People often struggle in the pursuit of their long-term goals. As good habits may help people in this pursuit, we set out to gain more insight in how good habits are formed in daily life. We specifically focused on goal-congruent behavior performance and self-control capacity as potential facilitators of habit formation. We were able to test our hypotheses in a diverse and highly committed sample. Results showed a large increase in habit strength over the course of three months, which was strongest for participants who consistently performed the self-chosen goal-congruent behavior during this time. Contrary to our expectations and previous findings by Galla and Duckworth (2015) , however, we did not find support for self-control capacity as a predictor of the habit formation process.

One reason why self-control capacity may not have facilitated habit formation, could be that participants experienced little conflict between their long-term goal and an immediately gratifying alternative. In contrast to well-controlled lab experiments where participants are simultaneously confronted with goal-congruent stimuli (e.g., broccoli) and conflicting temptations (e.g., apple pie), such temptations may not always be present when the opportunity presents itself to perform goal-congruent behavior in real life. If so, the reason that participants did not yet regularly perform the desired behavior before participating in the study, may not have been because they were unable to control their behavior in the presence of temptations. Alternatively, in the absence of temptation, participants may have had difficulty monitoring their behavior and identifying opportunities for goal pursuit. In the current study monitoring was facilitated by specifying a specific context for goal pursuit and registering their behavior daily via the smartphone application, which may have facilitated goal-congruent behavior performance, and hence, habit formation. Indeed, monitoring has been proven to be very effective in goal progress and attainment (see Harkin et al., 2016 for meta-analyses; Michie et al., 2009 ). Future research could extend the current findings by assessing how often people run into temptations during long-term goal pursuit and whether its impact on the habit formation process is modulated by self-control capacity. Also, future research could investigate whether habit formation can be facilitated even more by frequent monitoring at regular intervals during the day.

Another reason why self-control may not have affected habit formation, is because our instructions to participants may have created an association between the specific, self-chosen behavior and a specific context. Research has shown that if people form specific “if…, then…” plans (also referred to as implementation intentions), in which a specific behavior is linked to a specific context (e.g., if I open the fridge, then I will grab the cherry tomatoes), this will automatically trigger the specific behavior upon encounter of the specific context ( Gollwitzer, 1999 ; Webb and Sheeran, 2007 ). As such, habit strength – or rather, behavioral automaticity – should increase instantly and self-control is no longer required. Although we did not ask our participants to form implementation intentions, our request to select a specific context in which to perform the specific self-chosen behavior may have resulted in cue-behavior associations that facilitate effortless behavior performance. However, our data as well as the data of Lally and colleagues ( Lally et al., 2010 ; in which implementation intentions were actually formed) do not seem to support this line of reasoning. Even if cue-behavior associations were formed, they did not result in instant increases in habit strength, as habit formation unfolded gradually over the course of several months, leaving room for self-control capacity to influence the habit formation process. It would be interesting, though, to further investigate the role of self-control capacity in the presence versus absence of cue-behavior associations in an experimental field study.

Yet another reason for not finding an effect of self-control on the habit formation process may be that we focused on trait rather than state self-control. Although trait self-control did increase over time (see de Ridder et al., 2019 ; and hence, may have benefited the habit formation process), trait self-control is a relatively stable factor. Future research should assess within-individual fluctuations of state self-control in the habit formation process – preferably also fitting habit formation on the individual level. Our findings suggest that more data points are required for such analyses.

In line with previous research ( Lally et al., 2010 ; Fournier et al., 2017 ), the current (aggregated) data provided support for the asymptotic contribution of repeated goal-congruent behavior performance to the formation of habit. Unfortunately, we were unable to show this trend on the individual level, due to the bi-weekly assessment of habit strength. Hence, future studies would benefit from more frequent assessments. These studies may also want to test further moderators of habit formation, e.g., what type of contextual cues may be the best triggers for behavior, the role of motivation, and how the formation of good habits affect the bad habits they aim to substitute (see also Gardner and Lally, 2018 ).

Beside the strengths of our study (a diverse and highly committed sample), it is important to note that the self-report measurement of habit strength may have been subject to biases. Although the SRHI is commonly used and well validated ( Verplanken and Orbell, 2003 ; Gardner et al., 2011 ), it would be even more compelling if the current findings could be corroborated by more implicit measures of habit strength, such as a lexical decision task ( Meyer et al., 1972 ). In the current study, we have attempted to measure habit strength by means of a lexical decision task in the mobile app. However, the mobile app measurements were not sensitive enough to detect any effects (see point 5, Supplementary Material ). Future research may instead opt for online computer measurements.

To conclude, our study was the first to track the role of self-control capacity in the habit formation process in a longitudinal field experiment. Although we did not find evidence for self-control as a facilitator of habit formation, the current findings do offer new directions for future research on self-control and other potential moderators in the formation of good habits.

Data Availability Statement

Ethics statement.

The studies involving human participants were reviewed and approved by The Faculty Ethics Review Board – Faculty of Social and Behavioral Sciences at Utrecht University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

AW, JB, MG, and DR developed the theory and study design. AW carried out the experiment and data preparations, and took the lead in writing of the manuscript. AW and JB performed the individual-level analyses. JY performed the group-level analyses. All authors provided critical feedback and helped to shape the analyses and manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank Django den Boer and Roy van Koten for developing the Habit Tracker app, and Demi Blom for recruiting participants and keeping them involved.

1 The group administration served to allow more participants to start around the same time, i.e., to minimize seasonal influences (e.g., new year’s resolutions). We minimized the degree to which participants influenced each other by stressing the importance of independent answers and reactions, as well as the importance of being silent during the measurements. Also, one or two researchers were always present to monitor participants and answer questions.

2 In the pre-measurement we measured explicit habit strength (with the Self-Report Habit Index; Verplanken and Orbell, 2003 ) and implicit habit strength (by means of a Lexical Decision Task), implicit state self-control (an adapted version of the mouse-tracker task; Freeman and Ambady, 2010 ) and explicit self-control capacity (Brief Self-Control Scale; Tangney et al., 2004 ), general attributional style (General Attributional Style Questionnaire; Peterson et al., 1982 ), goal importance, and motivation. In the smartphone app, behavioral performance, context encounter, and attributions of failure were measured daily, while habit formation, self-control capacity, general self-efficacy (General Self-Efficacy Scale; Jerusalem and Schwarzer, 1979 ), and willpower beliefs ( Job et al., 2010 ) were measured bi-weekly. Additionally, a mouse tracker task was alternated with a lexical decision task every other day to measure implicit self-control and implicit habit formation, respectively. During the post-measurement, participants completed the same tasks and questionnaires as during the pre-measurement, except that the General Attributional Style Questionnaire was replaced by an ego-depletion task.

3 The option “when talking to someone” could be further specified into “when talking to a friend/partner/parent/child/neighbor”.

4 We have also run the analyses with the SRBAI subscale, which led to the same results (see point 1, Supplementary Material ).

5 For our main analysis, we looked at whether the behavior was performed or not, regardless of the context it was performed in. Analyzing whether the behavior was performed in context or not yielded similar results.

6 Participants for whom an asymptotic curve could be fitted did not differ from participants for whom an asymptotic curve could not be fitted in their number of data points [M = 6.33, SD = 2.25 vs. M = 6.6, SD = 1.42, respectively; F (1,116) = 0.19, p = 0.67] or behavioral consistency [M = 0.88, SD = 0.15 vs. M = 0.77, SD = 0.26, respectively; F (1,144) = 1.02, p = 0.31].

7 One might argue that the effect of self-control capacity on habit formation is via goal-congruent behavior performance. However, our data do not provide support for a relationship between lagged self-control and goal-congruent behavior performance (see de Ridder et al., 2019 ). Also, entering lagged self-control in the model first (in Model 3), and subsequently adding the other variables (in Model 4), does not reveal any relationship between self-control and habit strength (see point 4, Supplementary Material ).

8 Behavioral consistency differed between the different behaviors chosen [ F (9,136) = 3.02, p = 0.003, η 2 = 0.17], such that people were most consistent in performing prosocial behaviors, whereas people were least consistent in exercising and saving money (see point 3, Supplementary Material ). Adding the chosen behavior to the model did not improve the model fit [Δχ 2 (df = 9) = 7.10 ns], nor did it change any of the reported effects.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2020.00560/full#supplementary-material

• Aarts H., Dijksterhuis A. (2000). Habits as knowledge structures: automaticity in goal-directed behavior. J. Pers. Soc. Psychol. 78 53–63. 10.1037/0022-3514.78.1.53 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Adriaanse M. A., Kroese F. M., Gillebaart M., De Ridder D. T. D. (2014). Effortless inhibition: habit mediates the relation between self-control and unhealthy snack consumption. Front. Psychol. 5 : 444 . 10.3389/fpsyg.2014.00444 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Baumeister R. F., Vohs K. D., Tice D. M. (2007). The strength model of self-control. Curr. Direct. Psychol. Sci. 16 351–355. 10.1111/j.1467-8721.2007.00534.x [ CrossRef ] [ Google Scholar ]
• Cohen J. (1992). A power primer. Psychol. Bull. 112 155–159. [ PubMed ] [ Google Scholar ]
• de Ridder D., van der Weiden A., Gillebaart M., Benjamins J., Ybema J. F. (2019). Just do it: engaging in self-control on a daily basis improves the capacity for self-control. Motiv. Sci . 10.1037/mot0000158 [ CrossRef ] [ Google Scholar ]
• Fournier M., d’Arripe-Longueville F., Rovere C., Easthope C. S., Schwabe L., El Methni J., et al. (2017). Effects of circadian cortisol on the development of a health habit. Health Psychol. 36 1059–1064. 10.1037/hea0000510 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Freeman J. B., Ambady N. (2010). MouseTracker: software for studying real-time mental processing using a computer mouse-tracking method. Behav. Res. Methods 42 226–241. 10.3758/BRM.42.1.226 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Galla B. M., Duckworth A. L. (2015). More than resisting temptation: beneficial habits mediate the relationship between self-control and positive life outcomes. J. Pers. Soc. Psychol. 109 508–525. 10.1037/pspp0000026 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gardner B., de Bruijn G.-J., Lally P. (2011). A systematic review and meta-analysis of applications of the self-report habit index to nutrition and physical activity behaviours. Ann. Behav. Med. 42 174–187. 10.1007/s12160-011-9282-0 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gardner B., Lally P. (2018). Modelling Habit Formation and Its Determinants. In The Psychology of Habit (Cham: Springer International Publishing; ), 207–229. [ Google Scholar ]
• Gillebaart M., Adriaanse M. A. (2017). Self-control predicts exercise behavior by force of habit, a conceptual replication of Adriaanse et al. (2014). Front. Psychol. 8 : 190 . 10.3389/fpsyg.2017.00190 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gillebaart M., de Ridder D. T. D. (2015). Effortless self-control: a novel perspective on response conflict strategies in trait self-control. Soc. Pers. Psychol. Compass 9 88–99. 10.1111/spc3.12160 [ CrossRef ] [ Google Scholar ]
• Gollwitzer P. M. (1999). Implementation intentions: strong effects of simple plans. Am. Psychol. 54 493–503. 10.1037/0003-066X.54.7.493 [ CrossRef ] [ Google Scholar ]
• 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. Psychol. Bull. 142 198–229. 10.1037/bul0000025 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hofmann W., van Koningsbruggen G. M., Stroebe W., Ramanathan S., Aarts H. (2010). As pleasure unfolds. Hedonic responses to tempting food. Psychol. Sci. 21 1863–1870. 10.1177/0956797610389186 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jerusalem M., Schwarzer R. (1979). The general self-efficacy scale. [ Google Scholar ]
• Job V., Dweck C. S., Walton G. M. (2010). Ego depletion–is it all in your head? implicit theories about willpower affect self-regulation. Psychol. Sci. 21 1686–1693. 10.1177/0956797610384745 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kaushal N., Rhodes R. E. (2015). Exercise habit formation in new gym members: a longitudinal study. J. Behav. Med. 38 652–663. 10.1007/s10865-015-9640-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lally P., van Jaarsveld C. H. M., Potts H. W. W., Wardle J. (2010). How are habits formed: modelling habit formation in the real world. Eur. J. Soc. Psychol. 40 998–1009. 10.1002/ejsp.674 [ CrossRef ] [ Google Scholar ]
• Meyer D. E., Schvaneveldt R. W., Ruddy M. G. (1972). “ Activation of lexical memory ,” in Proceedings of the meeting in the Psychonomic Society , St. Louis. [ Google Scholar ]
• Michie S., Abraham C., Whittington C., McAteer J., Gupta S. (2009). Effective techniques in healthy eating and physical activity interventions: a meta-regression. Health Psychol. 28 690–701. 10.1037/a0016136 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Muraven M., Slessareva E. (2003). Mechanisms of self-control failure: motivation and limited resources. Pers. Soc. Psychol. Bull. 29 894–906. 10.1177/0146167203029007008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Neal D. T., Wood W., Drolet A. (2013). How do people adhere to goals when willpower is low? The profits (and pitfalls) of strong habits. J. Pers. Soc. Psychol. 104 959–975. 10.1037/a0032626 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Neal D. T., Wood W., Labrecque J. S., Lally P. (2012). How do habits guide behavior? Perceived and actual triggers of habits in daily life. J. Exp. Soc. Psychol. 48 492–498. 10.1016/j.jesp.2011.10.011 [ CrossRef ] [ Google Scholar ]
• Ouellette J., Wood W. (1998). Habit and intention in everyday life: the multiple processes by which past behavior predicts future behavior. Psychol. Bull. 124 54–74. 10.1037/0033-2909.124.1.54 [ CrossRef ] [ Google Scholar ]
• Peterson C., Semmel A., von Baeyer C., Abramson L. Y., Metalsky G. I., Seligman M. E. P. (1982). The attributional style questionnaire. Cogn. Ther. Res. 6 287–299. 10.1007/BF01173577 [ CrossRef ] [ Google Scholar ]
• Schmeichel B. J., Zell A. (2007). Trait self-control predicts performance on behavioral tests of self-control. J. Pers . 75 , 743–756. 10.1111/j.1467-6494.2007.00455.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tangney J. P., Baumeister R. F., Boone A. L. (2004). High self-control predicts good adjustment, less pathology, better grades, and interpersonal success. J. Pers. 72 271–324. 10.1111/j.0022-3506.2004.00263.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Verplanken B., Orbell S. (2003). Reflections on past behavior: a self-report index of habit strength1. J. Appl. Soc. Psychol. 33 1313–1330. 10.1111/j.1559-1816.2003.tb01951.x [ CrossRef ] [ Google Scholar ]
• Webb T. L., Sheeran P. (2006). Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychol. Bull. 132 249–268. 10.1037/0033-2909.132.2.249 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Webb T. L., Sheeran P. (2007). How do implementation intentions promote goal attainment? A test of component processes. J. Exp. Soc. Psychol. 43 295–302. 10.1016/J.JESP.2006.02.001 [ CrossRef ] [ Google Scholar ]

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Children's Health

Nearly 60% of baby foods in the u.s. don't meet nutritional guidelines, study says.

Ayana Archie

This March 11, 2015, photo shows the World Health Organization (WHO) headquarters building in Geneva, Switzerland. A new study says Nearly 60% of food products made for toddlers and babies did not meet nutrition standards set by the WHO. Raphael Satter/AP hide caption

Nearly 60% of food products made for toddlers and babies did not meet nutrition standards set by the World Health Organization, according to a new study.

Researchers tested 651 products in 2023, across eight food retailers in North Carolina, including Kroger, Walmart, Costco, Ahold Delhaize, Publix, Sam’s Club, Target and Aldi. Other samples were included from the websites of Safeway and H-E-B.

Only about 30% of products complied with the agency’s protein recommendations, while 56% were compliant with sugar guidelines. About 93% of the products aligned with the fat recommendations, according to the study that was published in the Nutrients journal Wednesday.

About 1 in 4 products did not meet calorie requirements and about 20% exceeded recommended sodium limits.

Shots - Health News

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“Early childhood is a crucial period of rapid growth and when taste preferences and dietary habits form, potentially paving the way for the development of chronic diseases such as obesity, diabetes and some cancers later in life,” said Dr. Elizabeth Dunford, a professor of nutrition at the University of North Carolina, which co-authored the study.

She added, “Time-poor parents are increasingly choosing convenience foods, unaware that many of these products lack key nutrients needed for their child’s development and tricked into believing they are healthier than they really are.”

About 60% of products complied with WHO’s age-label recommendations, which say age should be measured in months and years. However, many of the labels used signifiers such as “sitter”, “tots”, “crawling baby”, or “toddler”.

Products had, on average, almost five health and nutrient-related claims on a single product. However, many of the claims made are prohibited by WHO, including “no pesticides,” “organic” and “no preservatives,” the study said.

• World Health Organization

2023 Theses Doctoral

Can a Changing Food Environment Tip the Scale? A Mixed-Methods Study of Food Habitus and Obesity in a Neighborhood Undergoing Gentrification

Rhodes-Bratton, Brennan

The disproportionate concentration of unhealthy food in communities of color in the United States may contribute to health inequities and food insecurity. Gentrification has been associated with residents’ increased adverse health outcomes in its early and rapid phases. This study adds to the growing body of research by examining the relationship between gentrification, the food environment, food habits (the interplay between food chances and food choices), and health in New York City. I used a mixed methods approach to assess the food landscape in NYC between 1990 and 2014, using group-based trajectory modeling, the National Establishments Time-Series database, census data, and in-depth interviews with mothers from the Columbia Center for Children’s Environmental Health study. I found that the growth in the food environment was unevenly distributed. While healthy food chances declined across all examined neighborhoods, unhealthy food chances quickly grew, commanding dominance. It was gentrifying neighborhoods; however, that surprisingly experienced the most remarkable growth in unhealthy food chances compared to other neighborhoods. A cross-tabulation of the food chance trajectories of New York City census tracts indicated the presence of food ecologies that exhibit both healthy and unhealthy food chances. There was a strong association between the type of food ecology and gentrification status (p < 0.001). The in-depth interviews corroborated these findings and revealed that food insecurity is a by-product of gentrification in two ways. First, neighborhoods in the early stages of gentrification are inundated with unhealthy food chances, such as fast-food chains, without adequate access to quality, fresh, healthy foods. Secondly, when healthy food chances finally arrive in resource-deprived areas through gentrification, families are forced to relocate to areas without access to fresh, affordable, healthy foods due to the increased cost of living. This cycle of food insecurity is inequitable due to historical racial segregation, exploitative capitalistic markets, and racist stereotypes. Speculators invest in unhealthy food chances aligned with pre-existing stereotypes, assumptions, and beliefs that such communities do not or will not consume healthier foods. Therefore, a cycle of structural racism reinvents itself through this investment in unhealthy food chances, constructing food deserts and swamps bestowed upon communities experiencing poverty and disproportionate adverse cardiovascular health conditions. Strengthening policy focused on the relationship between gentrification mitigation and health outcomes is needed.

Geographic Areas

• New York (State)--New York
• Public health
• Obesity--Epidemiology
• Obesity in children
• Food habits--Health aspects
• Gentrification
• Environmental health

This item is currently under embargo. It will be available starting 2026-06-14.

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