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Individual Differences in Diurnal Preference and Time-of-Exercise Interact to Predict Exercise Frequency

  • Original Article
  • Published:
Annals of Behavioral Medicine

Abstract

Background

Diurnal preference (and chronotype more generally) has been implicated in exercise behavior, but this relation has not been examined using objective exercise measurements nor have potential psychosocial mediators been examined. Furthermore, time-of-day often moderates diurnal preference’s influence on outcomes, and it is unknown whether time-of-exercise may influence the relation between chronotype and exercise frequency.

Purpose

The current study examined whether individual differences in diurnal preference (“morningness-eveningness”) predict unique variance in exercise frequency and if commonly studied psychosocial variables mediate this relation (i.e., behavioral intentions, internal exercise control, external exercise control, and conscientiousness). Moreover, the study sought to test whether individuals’ typical time-of-exercise moderated the impact of diurnal preference on exercise frequency.

Methods

One hundred twelve healthy adults (mean age = 25.4; SD = 11.6 years) completed baseline demographics and then wore Fitbit Zips® for 4 weeks to objectively measure exercise frequency and typical time-of-exercise. At the end of the study, participants also self-reported recent exercise.

Results

Diurnal preference predicted both self-reported exercise and Fitbit-recorded exercise frequency. When evaluating mediators, only conscientiousness emerged as a partial mediator of the relation between diurnal preference and self-reported exercise. In addition, time-of-exercise moderated diurnal preference’s relation to both self-reported exercise and Fitbit-recorded exercise frequency such that diurnal preference predicted higher exercise frequency when exercise occurred at a time that was congruent with one’s diurnal preference.

Conclusion

Based on these findings, diurnal preference is valuable, above and beyond other psychological constructs, in predicting exercise frequency and represents an important variable to incorporate into interventions seeking to increase exercise.

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References

  1. U.S. Department of Health and Human Services. Physical activity guidelines for Americans. Washington, DC: U.S. Government Printing Office. 2008.

    Google Scholar 

  2. U.S. Department of Health and Human Services. Healthy people 2010: understanding and improving health. Washington, DC: U.S. Government printing office; 2000.

    Google Scholar 

  3. Fishbein M, Ajzen I. Predicting and changing behavior: The reasoned action approach. New York (NY): Psychology Press; 2010

    Google Scholar 

  4. Bandura A. Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hal; 1986.

    Google Scholar 

  5. Prochaska JO, Velicer WF. The transtheoretical model of health behavior change. Am J Health Promot. 1997; 12(1): 38–48.

    Article  CAS  PubMed  Google Scholar 

  6. Bogg T, Roberts BW. Conscientiousness and health-related behaviors: A meta-analysis of the leading behavioral contributors to mortality. Psychol Bull. 2004; 130(6); 887–919.

    Article  PubMed  Google Scholar 

  7. Hagger MS, Chatzisarantis, NLD. Youth attitudes. In: Smith AL, Biddle SJH, eds. Youth physical activity and sedentary behavior: challenges and solutions. Champaign, IL: Human Kinetics; 2008: 167–192.

    Google Scholar 

  8. Roberts BW, Kuncel NR, Shiner R, Caspi A, Goldberg LR. The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perpect Psyhcol Sci. 2007; 2(4): 313–345.

    Google Scholar 

  9. Conner M, Abraham C. Conscientiousness and the theory of planned behavior: Toward a more complete model of the antecedents of intentions and behavior. Pers Soc Psychol Bull. 2001; 27(11): 1547–1561.

    Article  Google Scholar 

  10. McEachen RRC, Sutton S, Myers LB. Mediation of personality influences on physical activity within the theory of planned behavior. J Health Psychol. 2010; 15(8): 1170–1180.

    Article  Google Scholar 

  11. Van Dongen HP, Dinges DF. Circadian rhythms in fatigue, alertness, and performance. Prin Pract Sleep Med. 2000; 20:391–9.

    Google Scholar 

  12. Roenneberg T, Wirz-Justice A, Merrow M. Life between clocks: Daily temporal patterns of human chronotypes. J Biol Rhythm. 2003; 18(1): 80–90.

    Article  Google Scholar 

  13. Smith CS, Reilly C, Midkiff K. Evaluation of three circadian rhythm questionnaires with suggestions for an improved measure of morningness. J Appl Psychol. 1989; 74(5): 728–738.

    Article  CAS  PubMed  Google Scholar 

  14. Killgore WDS. Effect of sleep deprivation and morningness–eveningness traits of risk taking. Psychol Rep. 2007; 100(2): 613–626.

    Article  PubMed  Google Scholar 

  15. Wang L, Chartrand TL. Morningness–eveningness and risk taking. J Psychol Interdicip Appl. 2015; 149(4): 394–411.

    Google Scholar 

  16. Díaz-Morales JF. Morning and evening-types: Exploring their personality styles. Per. Individ. Dif. 2007; 43(4): 769–778.

    Article  Google Scholar 

  17. Facer-Childs E, Brandstaetter R. The impact of circadian phenotype and time since awakening on diurnal performance in athletes. Curr Biol. 2015; 25(4): 518–522.

    Article  CAS  PubMed  Google Scholar 

  18. Thun E, Bjorvatn B, Flo E, Harris A, Pallesen S. Sleep, circadian rhythms, and athletic performance. Sleep Med Rev. 2015; 23: 1–9.

    Article  PubMed  Google Scholar 

  19. Kunorozva L, Roden LC, Rae DE. Perception of effort in morning-type cyclists is lower when exercising in the morning. J Sports Sci. 2014; 32(10): 917–925.

    Article  PubMed  Google Scholar 

  20. Brown FM, Neft EE, LaJambe CM. Collegiate rowing crew performance varies by morningness-eveningness. J Strength Cond Res. 2008; 22(6): 1894–1900.

    Article  PubMed  Google Scholar 

  21. Schaal S, Peter M, Randler C. Morningness-eveningness and exercise in adolescents. Int J Sport Exerc Psychol. 2010; 8(2): 147–159.

    Article  Google Scholar 

  22. Lipnevich AA, Credé M, Hahn E, Spinath FM, Roberts RD, Preckel F. How distinctive are morningness and eveningness from the Big Five-Factors of personality? A meta-analytic investigation. J Pers and Soc Psychol. In press.

  23. Bailey KJ, Jung ME. The early bird gets the worm! Congruency between intentions and behavior is highest when plans to exercise are made for the morning. J Appl Biobehave Res. 2014; 19(4): 233–247.

    Article  Google Scholar 

  24. May CP, Hasher L. Synchrony effects in inhibitory control over thought and action. J Exp Psychol Hum Percept Perform. 1998: 24(2): 363–379.

    Article  CAS  PubMed  Google Scholar 

  25. Rowe G, Hasher L, Turcotte J. Age and synchrony effects in visuospatial working memory. Q J Exp Psychol. 2009; 62(10): 1873–1880.

    Article  Google Scholar 

  26. Preckel F, Lipnevich AA, Boehme K, et al. Morningness-eveningness and educational outcomes: The lark has an advantage over the owl at high school. Br J Educ Psychol. 2013; 83(1): 114–134.

    Article  PubMed  Google Scholar 

  27. Tavernier R, Willoughby T. Are all evening-types doomed? Latent class analyses of perceived morningness–eveningness, sleep and psychosocial functioning among emerging adults. Chronobiol. Int. 2014; 31(2): 232–242.

    Article  PubMed  Google Scholar 

  28. Goldstein D, Hahn CS, Hasher L, Wiprzycka UJ, Zelazo PD. Time of day, intellectual performance, and behavioral problems in morning versus evening type adolescents: Is there a synchrony effect? Pers Individ Dif. 2007; 42(3): 431–440.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Faul F, Erdfelder E, Buchner A, Lang AG. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behav Res Methods. 2009; 41, 1149–1160.

    Article  PubMed  Google Scholar 

  30. Horne JA., Ostberg O. A self-assessment questionnaire to determine morningness–eveningness in human circadian rhythms. Int J Chronobiol. 1976; 4: 97–110.

    CAS  PubMed  Google Scholar 

  31. Torsvall L, Akerstedt T. A diurnal type scale. Construction, consistency and validation in shift work. Scand J Work Environ Health. 1980; 6: 283–90.

    Article  CAS  PubMed  Google Scholar 

  32. Natale V, Alzani A. Additional validity evidence for the composite scale of morningness. Pers Individ Dif. 2001; 30(2): 293–301.

    Article  Google Scholar 

  33. Jankowski KS. Composite scale of morningness: Psychometric properties, validity with Munich ChronoType Questionnaire and age/sex differences in Poland. Eur Psychiatry. 2015; 30: 166–171.

    Article  CAS  PubMed  Google Scholar 

  34. Ajzen, I. Theory of Planned Behavior Questionnaire. Measurement Instrument Database for the Social Science. Available at www.midss.org. 2013. Accessibility verified September, 28, 2016.

  35. Hagger MS, Chatzisarantis NLD, Biddle SJH. A meta-analytic review of the theories of reasoned action and planned behavior in physical activity: Predictive validity and contribution of additional variables. J Sport Exerc Psychol. 2002; 24: 3–32.

    Article  Google Scholar 

  36. Jekauc D, Völkle M, Wagner MO, Mess F, Reiner M, Renner B. Prediction of attendance at fitness center: A comparison between the theory of planned behavior, the social cognitive theory, and the physical activity maintenance theory. Front Psychol. 2015; 121(6): 1–10.

    Google Scholar 

  37. Sechrist KR, Walker SN, Pender NJ. Development and psychometric evaluation of the exercise benefits/barriers scale. Res Nurs Health. 1987; 10(6): 357–365.

    Article  CAS  PubMed  Google Scholar 

  38. Grubbs L, Carter J. The relationship of perceived benefits and barriers to reported exercise behaviors in college undergraduates. Fam Com Health. 2002; 25(2):76–84.

    Article  Google Scholar 

  39. Goldberg LR, Johnson JA, Eber HW, et al. The international personality item pool and the future of public-domain personality measures. J Res Pers. 2006; 40(1): 84–96.

    Article  Google Scholar 

  40. Costa PT, McCrae RR. The revised NEO personality inventory (NEO-PIR). In: Boyle GJ, Matthews G, Saklosfke DH, eds. The SAGE handbook of personality theory and assessment. London: SAGE; 2008: 179–198.

    Google Scholar 

  41. Ainsworth BE, Haskell WL, Herrmann SD, et al. Compendium of Physical Activities: The second update of activity codes and MET intensities to classify the energy cost of human physical activities. Med Sci Sports Exerc. 2011; 43(8), 1575–1581.

    Article  PubMed  Google Scholar 

  42. Mammen G, Gardiner S, Senthinathan A, McClemont L, Stone M, Faulkner G. (2012). Is this bit fit? Measuring the quality of the Fitbit step-counter. Health Fit J of Can, 5(4); 30–39.

    Google Scholar 

  43. Gusmer R, Bosch T, Watkins A, Ostrem J, Dengel D. Comparison of FitBit® Ultra to ActiGraph™ GT1M for assessment of physical activity in young adults during treadmill walking. Open Sports Med J. 2014; 8: 11–15.

    Article  Google Scholar 

  44. Takacs J, Pollock CL, Guenther JR, Bahar M, Napier C, Hunt MA. Validation of the Fitbit one activity monitor device during treadmill walking. J Sci Med Sport. 2013; 17(5):496–500.

    Article  PubMed  Google Scholar 

  45. Craig CL, Marshall AL, Sjostrom M, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003; 195: 1381–1395.

    Article  Google Scholar 

  46. IBM Corp. IBM SPSS statistics for windows, version 22.0. Armonk, NY: IBM Corp; 2013.

    Google Scholar 

  47. Hayes AF. PROCESS: A versatile computational tool for observed variable mediation, moderation, and conditional process modeling. www.afhayes.com. Published 2012.

  48. Bauer DJ, Curran PJ. Probing interactions in fixed and multilevel regression: Inferential and graphical techniques. Multivariate Beh Res. 2005; 40(3): 373–400.

    Article  Google Scholar 

  49. Wittmann M, Dinich J, Merrow M, et al. Social jetlag: Misalignment of biological and social time. Chronobiol Int. 2006; 23(1&2): 497–509.

    Article  PubMed  Google Scholar 

  50. Kitamura, S., Hida, A., Watanabe, M., et al. Evening preference is related to the incidence of depressive states independent of sleep-wake conditions. Chronobiol Int. 2010; 27(9–10): 1797–1812.

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Psychology, Iowa State University, Ames, IA, USA

    Garrett C. Hisler MS, Alison L. Phillips PhD & Zlatan Krizan PhD

Authors
  1. Garrett C. Hisler MS
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  2. Alison L. Phillips PhD
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  3. Zlatan Krizan PhD
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Correspondence to Garrett C. Hisler MS.

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Conflict of Interest

Authors’ Statement of Conflict of Interest and Adherence to Ethical Standards Authors Hisler, Phillips and Krizan declare that they have no conflict of interest. All procedures, including the informed consent process, were conducted in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000.

Ethical Adherence

This research was approved by an institutional review board and involved human participants who provided informed consent to participate in the research.

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Hisler, G.C., Phillips, A.L. & Krizan, Z. Individual Differences in Diurnal Preference and Time-of-Exercise Interact to Predict Exercise Frequency. ann. behav. med. 51, 391–401 (2017). https://doi.org/10.1007/s12160-016-9862-0

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