Tracking 30-Day Physical Activity Behavior with Wearable Fitness Trackers in College-Aged Adults Original Research
Main Article Content
Keywords
daily activity, step counts, caloric expenditure
Abstract
Introduction: Consumer grade, wrist-worn wearable fitness trackers (WFT) are commonly worn on a daily basis by college-aged adults. Objectively assessing college-aged adults physical activity throughout a 30-day period has not been well documented to accurately assess physical activity behavior.
Methods: Fifteen college-aged participants (male n=7 and female n=9), completed the 30-day tracking assessment while wearing a wrist-worn WFT. The WFT was worn on the non-dominant hand, per manufacture guidelines and was to only be removed to charge the battery, at night during sleeping hours.
Results: Throughout the 30 days, males accumulated more steps, expended more calories, and traveled a greater distance than their female counterparts, p < 0.005 for all. There were no main effects for step counts, daily caloric expenditure and daily total distance for days of the week (p ≥ 0.128 for all). However, there was a significant sex by day of the week interaction for daily steps (p = 0.005), but not total calories or total distance (p ≥ 0.096).
Conclusions: Males were more active and expended more calories than females throughout 30 days of monitoring. However, the day of the week did not significantly alter physical activity and caloric expenditure.
References
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27. Stock C, Wille L, Kramer A. Gender-specific health behaviors of German university students predict the interest in campus health promotion. Health Promot Int. 2001;16(2):145-154.
28. Behrens TK, Dinger MK. A preliminary investigation of college students' physical activity patterns. Am J Health Studies. 2003;18(2-3).
29. Wilson OWA, Colinear C, Guthrie D, Bopp M. Gender differences in college student physical activity, and campus recreational facility use, and comfort. J Am Coll Health. 2020:1-6.
30. Rebold MJ, Lepp A, Sanders GJ, Barkley JE. The impact of cell phone use on the intensity and liking of a bout of treadmill exercise. PLoS One. 2015;10(5):e0125029.
31. Reich RR, Cummings JR, Greenbaum PE, Moltisanti AJ, Goldman MS. The temporal "pulse" of drinking: Tracking 5 years of binge drinking in emerging adults. J Abnorm Psychol. 2015;124(3):635-647.
2. Jakicic JM, Davis KK, Rogers RJ, et al. Effect of Wearable Technology Combined With a Lifestyle Intervention on Long-term Weight Loss: The IDEA Randomized Clinical Trial. JAMA. 2016;316(11):1161-1171.
3. Loncar-Turukalo T, Zdravevski E, Machado da Silva J, Chouvarda I, Trajkovik V. Literature on Wearable Technology for Connected Health: Scoping Review of Research Trends, Advances, and Barriers. J Med Internet Res. 2019;21(9):e14017.
4. Koorts H, Salmon J, Timperio A, et al. Translatability of a Wearable Technology Intervention to Increase Adolescent Physical Activity: Mixed Methods Implementation Evaluation. J Med Internet Res. 2020;22(8):e13573.
5. Nelson MC, Story M, Larson NI, Neumark-Sztainer D, Lytle LA. Emerging adulthood and college-aged youth: an overlooked age for weight-related behavior change. Obesity (Silver Spring). 2008;16(10):2205-2211.
6. Small M, Bailey-Davis L, Morgan N, Maggs J. Changes in eating and physical activity behaviors across seven semesters of college: living on or off campus matters. Health Educ Behav. 2013;40(4):435-441.
7. Deforche B, Van Dyck D, Deliens T, De Bourdeaudhuij I. Changes in weight, physical activity, sedentary behaviour and dietary intake during the transition to higher education: a prospective study. Int J Behav Nutr Phys Act. 2015;12:16.
8. Vella-Zarb RA, Elgar FJ. The 'freshman 5': a meta-analysis of weight gain in the freshman year of college. J Am Coll Health. 2009;58(2):161-166.
9. Hudd S, Dumlao J, Erdmann-Sager D, et al. Stress at college: Effects on health habits, health status and self-esteem. College Student Journal. 2000;34(2):217-227.
10. Beiter R, Nash R, McCrady M, et al. The prevalence and correlates of depression, anxiety, and stress in a sample of college students. J Affect Disord. 2015;173:90-96.
11. Keating XD, Guan J, Pinero JC, Bridges DM. A meta-analysis of college students' physical activity behaviors. J Am Coll Health. 2005;54(2):116-125.
12. Lund HG, Reider BD, Whiting AB, Prichard JR. Sleep patterns and predictors of disturbed sleep in a large population of college students. J Adolesc Health. 2010;46(2):124-132.
13. Fagaras SP, Radu LE, Vanvu G. The Level of Physical Activity of University Students. Soc Behav Sci. 2015;197:1454-1457.
14. Alkahtani S, Awad N. Comparing the physical activity patterns of male and female students in the preparatory year in Saudi Arabia. J Obes Weight Loss Ther. 2016;6(2).
15. Buckworth J, Nigg C. Physical activity, exercise, and sedentary behavior in college students. J Am Coll Health. 2004;53(1):28-34.
16. Barkley JE, Farnell G, Boyko B, Turner B, Wiet R. Impact of Activity Monitoring on Physical Activity, Sedentary Behavior, and Body Weight during the COVID-19 Pandemic. Int J Environ Res Public Health. 2021;18(14).
17. Lepp A, Barkley JE, Sanders GJ, Rebold M, Gates P. The relationship between cell phone use, physical and sedentary activity, and cardiorespiratory fitness in a sample of U.S. college students. Int J Behav Nutr Phys Act. 2013;10:79.
18. Vankim NA, Nelson TF. Vigorous physical activity, mental health, perceived stress, and socializing among college students. Am J Health Promot. 2013;28(1):7-15.
19. Downs A, Van Hoomissen J, Lafrenz A, Julka DL. Accelerometer-measured versus self-reported physical activity in college students: implications for research and practice. J Am Coll Health. 2014;62(3):204-212.
20. Washington WD, Banna KM, Gibson AL. Preliminary efficacy of prize-based contingency management to increase activity levels in healthy adults. J Appl Behav Anal. 2014;47(2):231-245.
21. Pesonen AK, Kuula L. The Validity of a New Consumer-Targeted Wrist Device in Sleep Measurement: An Overnight Comparison Against Polysomnography in Children and Adolescents. J Clin Sleep Med. 2018;14(4):585-591.
22. Gibbons RD, Hedeker D. Application of random-effects probit regression models. . Journal of Consulting and Clinical Psychology. 1994;62(2):285-296.
23. Hedeker D, Gibbons RD. MIXOR: a computer program for mixed-effects ordinal regression analysis. Computer methods and programs in biomedicine. 1996;49(2):157-176.
24. Samuels TY, Raedeke TD, Mahar MT, Karvinen KH, DuBose KD. A randomized controlled trial of continuous activity, short bouts, and a 10,000 step guideline in inactive adults. Prev Med. 2011;52(2):120-125.
25. Henriksen A, Grimsgaard S, Horsch A, Hartvigsen G, Hopstock L. Validity of the Polar M430 Activity Monitor in Free-Living Conditions: Validation Study. JMIR Form Res. 2019;3(3):e14438.
26. Henriksen A, Johansson J, Hartvigsen G, Grimsgaard S, Hopstock L. Measuring Physical Activity Using Triaxial Wrist Worn Polar Activity Trackers: A Systematic Review. Int J Exerc Sci. 2020;13(4):438-454.
27. Stock C, Wille L, Kramer A. Gender-specific health behaviors of German university students predict the interest in campus health promotion. Health Promot Int. 2001;16(2):145-154.
28. Behrens TK, Dinger MK. A preliminary investigation of college students' physical activity patterns. Am J Health Studies. 2003;18(2-3).
29. Wilson OWA, Colinear C, Guthrie D, Bopp M. Gender differences in college student physical activity, and campus recreational facility use, and comfort. J Am Coll Health. 2020:1-6.
30. Rebold MJ, Lepp A, Sanders GJ, Barkley JE. The impact of cell phone use on the intensity and liking of a bout of treadmill exercise. PLoS One. 2015;10(5):e0125029.
31. Reich RR, Cummings JR, Greenbaum PE, Moltisanti AJ, Goldman MS. The temporal "pulse" of drinking: Tracking 5 years of binge drinking in emerging adults. J Abnorm Psychol. 2015;124(3):635-647.
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Jul 11, 2022
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Tracking 30-Day Physical Activity Behavior with Wearable Fitness Trackers in College-Aged Adults: Original Research. (2022). Research Directs in Psychology and Behavior, 2(1). https://doi.org/10.53520/rdpb2022.10736
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