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Exercise and dietary patterns

The influence of 15-week exercise training on dietary patterns among young adults

International Journal of Obesity (2019) | Download Citation



Little is currently known about how exercise may influence dietary patterns and/or food preferences. The present study aimed to examine the effect of a 15-week exercise training program on overall dietary patterns among young adults.


This study consisted of 2680 young adults drawn from the Training Intervention and Genetics of Exercise Response (TIGER) study. Subjects underwent 15 weeks of aerobic exercise training, and exercise duration, intensity, and dose were recorded for each session using computerized heart rate monitors. In total, 4355 dietary observations with 102 food items were collected using a self-administered food frequency questionnaire before and after exercise training (n = 2476 at baseline; n = 1859 at 15 weeks). Dietary patterns were identified using a Bayesian sparse latent factor model. Changes in dietary pattern preferences were evaluated based on the pre/post-training differences in dietary pattern scores, accounting for the effects of gender, race/ethnicity, and BMI.


Within each of the seven dietary patterns identified, most dietary pattern scores were decreased following exercise training, consistent with increased voluntary regulation of food intake. A longer duration of exercise was associated with decreased preferences for the western (β: −0.0793; 95% credible interval: −0.1568, −0.0017) and snacking (β: −0.1280; 95% credible interval: −0.1877, −0.0637) patterns, while a higher intensity of exercise was linked to an increased preference for the prudent pattern (β: 0.0623; 95% credible interval: 0.0159, 0.1111). Consequently, a higher dose of exercise was related to a decreased preference for the snacking pattern (β: −0.0023; 95% credible interval: −0.0042, −0.0004) and an increased preference for the prudent pattern (β: 0.0029; 95% credible interval: 0.0009, 0.0048).


The 15-week exercise training appeared to motivate young adults to pursue healthier dietary preferences and to regulate their food intake.

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  1. 1.

    Gordon-Larsen P, Nelson MC, Popkin BM. Longitudinal physical activity and sedentary behavior trends: adolescence to adulthood. Am J Prev Med. 2004;27:277–83.

  2. 2.

    Wengreen HJ, Moncur C. Change in diet, physical activity, and body weight among young-adults during the transition from high school to college. Nutr J. 2009;8:32.

  3. 3.

    Anderson DA, Shapiro JR, Lundgren JD. The freshman year of college as a critical period for weight gain: an initial evaluation. Eat Behav. 2003;4:363–7.

  4. 4.

    Kolodinsky J, Harvey-Berino JR, Berlin L, Johnson RK, Reynolds TW. Knowledge of current dietary guidelines and food choice by college students: better eaters have higher knowledge of dietary guidance. J Am Diet Assoc. 2007;107:1409–13.

  5. 5.

    Franko DL, Cousineau TM, Trant M, Green TC, Rancourt D, Thompson D, et al. Motivation, self-efficacy, physical activity and nutrition in college students: Randomized controlled trial of an Internet-based education program. Prev Med. 2008;47:369–77.

  6. 6.

    Deliens T, Clarys P, De Bourdeaudhuij I, Deforche B. Determinants of eating behaviour in university students: a qualitative study using focus group discussions. BMC Public Health. 2014;14:53.

  7. 7.

    Kattelmann KK, White AA, Greene GW, Byrd-Bredbenner C, Hoerr SL, Horacek TM, et al. Development of young adults eating and active for health (YEAH) internet-based intervention via a community-based participatory research model. J Nutr Educ Behav. 2014;46:S10–S25.

  8. 8.

    Plotnikoff RC, Costigan SA, Williams RL, Hutchesson MJ, Kennedy SG, Robards SL, et al. Effectiveness of interventions targeting physical activity, nutrition and healthy weight for university and college students: a systematic review and meta-analysis. Int J Behav Nutr Phys Act. 2015;12:45.

  9. 9.

    Gropper SS, Simmons KP, Connell LJ, Ulrich PV. Changes in body weight, composition, and shape: a 4-year study of college students. Appl Physiol Nutr Metab. 2012;37:1118–23.

  10. 10.

    Fedewa MV, Das BM, Evans EM, Dishman RK. Change in Weight and Adiposity in College Students. Am J Prev Med. 2014;47:641–52.

  11. 11.

    McTigue KM, Garrett JM, Popkin BM. The natural history of the development of obesity in a cohort of young US adults between 1981 and 1998. Ann Intern Med. 2002;136:857–64.

  12. 12.

    Martins C, Morgan L, Truby H. A review of the effects of exercise on appetite regulation: an obesity perspective. Int J Obes. 2008;32:1337–47.

  13. 13.

    Blundell JE, Gibbons C, Caudwell P, Finlayson G, Hopkins M. Appetite control and energy balance: impact of exercise. Obes Rev. 2015;16:67–76.

  14. 14.

    King NA, Burley VJ, Blundell JE, et al. Exercise-induced suppression of appetite: effects on food intake and implications for energy balance. Eur J Clin Nutr. 1994;48:715–24.

  15. 15.

    Elder SJ, Roberts SB. The effects of exercise on food intake and body fatness: a summary of published studies. Nutr Rev. 2007;65:1–19.

  16. 16.

    Donnelly JE, Herrmann SD, Lambourne K, Szabo AN, Honas JJ, Washburn RA. Does increased exercise or physical activity alter ad-libitum daily energy intake or macronutrient composition in healthy adults? A systematic review. PLoS ONE. 2014;9:e83498.

  17. 17.

    Schubert MM, Desbrow B, Sabapathy S, Leveritt M. Acute exercise and subsequent energy intake. A meta-analysis. Appetite. 2013;63:92–104.

  18. 18.

    Martins C, Stensvold D, Finlayson G, Holst J, Wisloff U, Kulseng B, et al. Effect of moderate- and high-intensity acute exercise on appetite in obese individuals. Med Sci Sports Exerc. 2015;47:40–48.

  19. 19.

    Liang N-C, Bello NT, Moran TH. Wheel running reduces high-fat diet intake, preference and mu-opioid agonist stimulated intake. Behav Brain Res. 2015;284:1–10.

  20. 20.

    Moody L, Liang J, Choi PP, Moran TH, Liang N-C. Wheel running decreases palatable diet preference in Sprague–Dawley rats. Physiol Behav. 2015;150:53–63.

  21. 21.

    Chen W, Wang HJ, Shang NN, Liu J, Li J, Tang DH, et al. Moderate intensity treadmill exercise alters food preference via dopaminergic plasticity of ventral tegmental area-nucleus accumbens in obese mice. Neurosci Lett. 2017;641:56–61.

  22. 22.

    Lee JR, Muckerman JE, Wright AM, Davis DJ, Childs TE, Gillespie CE, et al. Sex determines effect of physical activity on diet preference: Association of striatal opioids and gut microbiota composition. Behav Brain Res. 2017;334:16–25.

  23. 23.

    Wang H-J, Yang H-T, Chen W. Swimming exercise reduces preference for a high-fat diet by increasing insulin sensitivity in C57BL/6 mice:. Neuroreport. 2017;28:56–61.

  24. 24.

    Yang T, Xu W-J, York H, Liang N-C. Diet choice patterns in rodents depend on novelty of the diet, exercise, species, and sex. Physiol Behav. 2017;176:149–58.

  25. 25.

    Kanarek RB, Ryu M, Przypek J. Preferences for foods with varying levels of salt and fat differ as a function of dietary restraint and exercise but not menstrual cycle. Physiol Behav. 1995;57:821–6.

  26. 26.

    Leshem M, Abutbul A, Eilon R. Exercise increases the preference for salt in humans. Appetite. 1999;32:251–60.

  27. 27.

    Horio T, Kawamura Y. Influence of physical exercise on human preferences for various taste solutions. Chem Senses. 1998;23:417–21.

  28. 28.

    Horio T. Effect of physical exercise on human preference for solutions of various sweet substances. Percept Mot Skills. 2004;99:1061–70.

  29. 29.

    Panek LM, Jones KR, Temple JL. Short term aerobic exercise alters the reinforcing value of food in inactive adults. Appetite. 2014;81:320–9.

  30. 30.

    Horsch A, Wobmann M, Kriemler S, Munsch S, Borloz S, Balz A, et al. Impact of physical activity on energy balance, food intake and choice in normal weight and obese children in the setting of acute social stress: a randomized controlled trial. BMC Pediatr. 2015;15:12.

  31. 31.

    McNeil J, Cadieux S, Finlayson G, Blundell JE, Doucet É. The effects of a single bout of aerobic or resistance exercise on food reward. Appetite. 2015;84:264–70.

  32. 32.

    Anderson JW, Konz EC, Frederich RC, Wood CL. Long-term weight-loss maintenance: a meta-analysis of US studies. Am J Clin Nutr. 2001;74:579–84.

  33. 33.

    Swift DL, Johannsen NM, Lavie CJ, Earnest CP, Church TS. The role of exercise and physical activity in weight loss and maintenance. Prog Cardiovasc Dis. 2014;56:441–7.

  34. 34.

    Sailors MH, Jackson AS, McFarlin BK, Turpin I, Ellis KJ, Foreyt JP, et al. Exposing college students to exercise: the training interventions and genetics of exercise response (TIGER) study. J Am Coll Health J ACH. 2010;59:13–20.

  35. 35.

    Miller FL, O’Connor DP, Herring MP, Sailors MH, Jackson AS, Dishman RK, et al. Exercise dose, exercise adherence, and associated health outcomes in the TIGER study. Med Sci Sports Exerc. 2014;46:69–75.

  36. 36.

    Boucher B, Cotterchio M, Kreiger N, Nadalin V, Block T, Block G. Validity and reliability of the Block98 food-frequency questionnaire in a sample of Canadian women. Public Health Nutr. 2006;9:84–93.

  37. 37.

    West M. Bayesian factor regression models in the ‘Large p, Small n’ paradigm. In: Bernardo JM, Dawid AP, Berge JO, West M, Heckerman D, Bayarri MJ, Smith AFM, editors. Bayesian statistics. Oxford: Oxford University Press; 2003, p 723–32.

  38. 38.

    Joo J, Williamson SA, Vazquez AI, Fernandez JR, Bray MS. Advanced dietary patterns analysis using sparse latent factor models in young adults. J Nutr. 2018.

  39. 39.

    Wang Q, Carvalho CM, Lucas J, West M. BFRM: software for bayesian factor regression models. Bull Int Soc Bayesian Anal 2007;14:4–5.

  40. 40.

    Carpenter B, Gelman A, Hoffman MD, Lee D, Goodrich B, Betancourt M, et al. Stan: a probabilistic programming language. J Stat Softw. 2017;76.

  41. 41.

    Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer; 2009.

  42. 42.

    R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2017.

  43. 43.

    Herring MP, Sailors MH, Bray MS. Genetic factors in exercise adoption, adherence and obesity. Obes Rev. 2014;15:29–39.

  44. 44.

    Stensel D. Exercise, appetite and appetite-regulating hormones: implications for food intake and weight control. Ann Nutr Metab. 2010;57:36–42.

  45. 45.

    Nigg CR, Burbank PM, Padula C, Dufresne R, Rossi JS, Velicer WF, et al. Stages of change across ten health risk behaviors for older adults. Gerontologist. 1999;39:473–82.

  46. 46.

    Tucker M, Reicks M. Exercise as a gateway behavior for healthful eating among older adults: an exploratory study. J Nutr Educ Behav. 2002;34:S14–S19.

  47. 47.

    Blakely F, Dunnagan T, Haynes G, Moore S, Pelican S. Moderate physical activity and its relationship to select measures of a healthy diet. J Rural Health. 2004;20:160–5.

  48. 48.

    Knäuper B, Rabiau M, Cohen O, Patriciu N. Compensatory health beliefs: scale development and psychometric properties. Psychol Health. 2004;19:607–24.

  49. 49.

    Strong KA, Parks SL, Anderson E, Winett R, Davy BM. Weight gain prevention: identifying theory-based targets for health behavior change in young adults. J Am Diet Assoc. 2008;108:1708–15.

  50. 50.

    Schweitzer AL, Ross JT, Klein CJ, Lei KY, Mackey ER. An electronic wellness program to improve diet and exercise in college students: a pilot study. JMIR Res Protoc. 2016;5.

  51. 51.

    Thayer RE, Peters DP, Takahashi PJ, Birkhead-Flight AM. Mood and behavior (smoking and sugar snacking) following moderate exercise: a partial test of self-regulation theory. Personal Individ Differ. 1993;14:97–104.

  52. 52.

    Taylor AH, Oliver AJ. Acute effects of brisk walking on urges to eat chocolate, affect, and responses to a stressor and chocolate cue. Exp Study Appetite. 2009;52:155–60.

  53. 53.

    Oh H, Taylor AH. Brisk walking reduces ad libitum snacking in regular chocolate eaters during a workplace simulation. Appetite. 2012;58:387–92.

  54. 54.

    Oh H, Taylor AH. Self-regulating smoking and snacking through physical activity. Health Psychol. 2014;33:349–59.

  55. 55.

    Roininen K, Tuorila H. Health and taste attitudes in the prediction of use frequency and choice between less healthy and more healthy snacks. Food Qual Prefer. 1999;10:357–65.

  56. 56.

    Marquis M. Exploring convenience orientation as a food motivation for college students living in residence halls. Int J Consum Stud. 2005;29:55–63.

  57. 57.

    Hess JM, Jonnalagadda SS, Slavin JL. What is a snack, why do we snack, and how can we choose better snacks? A review of the definitions of snacking, motivations to snack, contributions to dietary intake, and recommendations for improvement. Adv Nutr Int Rev J. 2016;7:466–75.

  58. 58.

    Teo W, Newton MJ, McGuigan MR. Circadian rhythms in exercise performance: implications for hormonal and muscular adaptation. J Sports Sci Med. 2011;10:600–6.

  59. 59.

    Chtourou H, Souissi N. The effect of training at a specific time of day: a review. J Strength Cond Res. 2012;26:1984–2005.

  60. 60.

    Seo DY, Lee S, Kim N, Ko KS, Rhee BD, Park BJ, et al. Morning and evening exercise. Integr Med Res. 2013;2:139–44.

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This work was supported by award number R01DK062148 from the National Institute of Diabetes and Digestive and Kidney Disease (NIDDK).


This work was supported by award number R01DK062148 from the National Institute of Diabetes and Digestive and Kidney Disease (NIDDK).

Author information


  1. Department of Nutritional Sciences, The University of Texas at Austin, Austin, TX, USA

    • Jaehyun Joo
    •  & Molly S. Bray
  2. Departments of Information, Risk, & Operations Management and Statistics & Data Science, The University of Texas at Austin, Austin, TX, USA

    • Sinead A. Williamson
  3. Department of Epidemiology & Biostatistics and Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA

    • Ana I. Vazquez
  4. Department of Nutrition Sciences, University of Alabama at Birmingham, Birmingham, AL, USA

    • Jose R. Fernandez


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The authors declare that they have no conflict of interest.

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Correspondence to Molly S. Bray.

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