Health Habits and Wearable Activity Tracker Devices: Analytical Cross-Sectional Study

by | Apr 12, 2022 | Research Publications

Author(s):

  • Héctor José Tricás-Vidal
  • María Orosia Lucha-López
  • César Hidalgo-García
  • María Concepción Vidal-Peracho
  • Sofía Monti-Ballano
  • José Miguel Tricás-Moreno

Abstract:

Wearable activity trackers are electronic devices that facilitate self-monitoring of information related to health. The purpose of this study was to examine the use of tracker devices to record daily activity (calories) and its associations with gender, generation, BMI, and physical activity behavior of United States of America resident adults; a cross-sectional study in 892 subjects recruited to participate in an anonymous online survey was performed. Being female increased the odds of using a tracker device by 2.3 times. Having low cardiovascular disease mortality risk related to time spent sitting increased the odds for using a tracker device by 2.7 times, and having medium risk 1.9 times, with respect to having high risk. For every 1-point increase in BMI, the odds for using a tracker device increased by 5.2%. Conclusions: Subjects who had ever used any tracker device had a higher BMI. The use of tracker devices was related to lower cardiovascular disease mortality risk related to sitting time. The amount of physical activity and the time spent walking were not associated with the usage of tracker devices. It is possible that the user of tracker devices should be supported by professionals to implement deep change in health habits.

Documentation:

https://doi.org/10.3390/s22082960

References:
  1. Düking, P.; Tafler, M.; Wallmann-Sperlich, B.; Sperlich, B.; Kleih, S. Behavior change techniques in wrist-worn wearables to promote physical activity: Content analysis. JMIR mHealth uHealth 2020, 8, e20820. [Google Scholar] [CrossRef] [PubMed]
  2. Llamas, R.; Shirer, M.; Ubrani, J. Earwear and Wristbands Drive First Quarter Growth in the Worldwide Wearables Market, Says IDC. Available online: https://www.idc.com/getdoc.jsp?containerId=prUS46432620 (accessed on 3 December 2021).
  3. Emily, A. Vogels About One-In-Five Americans Use a Smart Watch or Fitness Tracker. Available online: https://www.pewresearch.org/fact-tank/2020/01/09/about-one-in-five-americans-use-a-smart-watch-or-fitness-tracker/ (accessed on 3 December 2021).
  4. McCarthy, J. One in Five U.S. Adults Use Health Apps, Wearable Trackers. Available online: https://news.gallup.com/poll/269096/one-five-adults-health-apps-wearable-trackers.aspx (accessed on 3 December 2021).
  5. Metlaine, A.; Sauvet, F.; Chennaoui, M.; Leger, D.; Elbaz, M. Sleep and COVID-19. A Case Report of a Mild COVID-19 Patient Monitored by Consumer-Targeted Sleep Wearables. Sensors 2021, 21, 7944. [Google Scholar] [CrossRef] [PubMed]
  6. Santamaria-Granados, L.; Mendoza-Moreno, J.F.; Chantre-Astaiza, A.; Munoz-Organero, M.; Ramirez-Gonzalez, G. Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data. Sensors 2021, 21, 7854. [Google Scholar] [CrossRef] [PubMed]
  7. Abdelhamid, M. Fitness tracker information and privacy management: Empirical study. J. Med. Internet Res. 2021, 23, e23059. [Google Scholar] [CrossRef]
  8. Asimakopoulos, S.; Asimakopoulos, G.; Spillers, F. Motivation and User Engagement in Fitness Tracking: Heuristics for Mobile Healthcare Wearables. Informatics 2017, 4, 5. [Google Scholar] [CrossRef]
  9. Naglis, M.; Bhatiasevi, V. Why do people use fitness tracking devices in Thailand? An integrated model approach. Technol. Soc. 2019, 58, 101146. [Google Scholar] [CrossRef]
  10. James, T.L.; Wallace, L.; Deane, J.K. Using organismic integration theory to explore the associations between users’ exercise motivations and fitness technology feature set use. MIS Q. Manag. Inf. Syst. 2019, 43, 287–312. [Google Scholar] [CrossRef]
  11. Wang, C.; Lizardo, O.; Hachen, D.S. Using Fitbit data to examine factors that affect daily activity levels of college students. PLoS ONE 2021, 16, e0244747. [Google Scholar] [CrossRef]
  12. Western, M.J.; Thompson, D.; Peacock, O.J.; Stathi, A. The impact of multidimensional physical activity feedback on healthcare practitioners and patients. BJGP Open 2019, 3, bjgpopen18X101628. [Google Scholar] [CrossRef]
  13. Dallinga, J.M.; Zwolsman, S.E.; Dekkers, V.T.; De La Faille-Deutekom, M.B. Actiever en gezonder door leefstijl-apps? Een systematische review. Ned. Tijdschr. Geneeskd. 2016, 160, D329. [Google Scholar]
  14. Simpson, C.C.; Mazzeo, S.E. Calorie counting and fitness tracking technology: Associations with eating disorder symptomatology. Eat. Behav. 2017, 26, 89–92. [Google Scholar] [CrossRef]
  15. Yu-Huei, C.; Ja-Shen, C.; Ming-Chao, W. Why Do Older Adults Use Wearable Devices: A Case Study Adopting the Senior Technology Acceptance Model (STAM). In Proceedings of the 2019 Portland International Conference on Management of Engineering and Technology (PICMET), Portland, OR, USA, 25–29 August 2019; pp. 1–8. [Google Scholar]
  16. Abouzahra, M.; Ghasemaghaei, M. The antecedents and results of seniors’ use of activity tracking wearable devices. Health Policy Technol. 2020, 9, 213–217. [Google Scholar] [CrossRef]
  17. Kekade, S.; Hseieh, C.H.; Islam, M.M.; Atique, S.; Khalfan, A.M.; Li, Y.C.; Abdul, S.S. The usefulness and actual use of wearable devices among the elderly population. Comput. Methods Programs Biomed. 2018, 153, 137–159. [Google Scholar] [CrossRef]
  18. Farivar, S.; Abouzahra, M.; Ghasemaghaei, M. Wearable device adoption among older adults: A mixed-methods study. Int. J. Inf. Manag. 2020, 55, 102209. [Google Scholar] [CrossRef]
  19. Goodyear, V.A.; Armour, K.M.; Wood, H. Young people learning about health: The role of apps and wearable devices. Learn. Media Technol. 2019, 44, 193–210. [Google Scholar] [CrossRef]
  20. Schaefer, S.E.; Ching, C.C.; Breen, H.; German, J.B. Wearing, Thinking, and Moving: Testing the Feasibility of Fitness Tracking with Urban Youth. Am. J. Health Educ. 2016, 47, 8–16. [Google Scholar] [CrossRef]
  21. Chung, A.E.; Skinner, A.C.; Hasty, S.E.; Perrin, E.M. Tweeting to Health: A Novel mHealth Intervention Using Fitbits and Twitter to Foster Healthy Lifestyles. Clin. Pediatr. 2016, 56, 26–32. [Google Scholar] [CrossRef]
  22. Cheatham, S.W.; Stull, K.R.; Fantigrassi, M.; Motel, I. The efficacy of wearable activity tracking technology as part of a weight loss program: A systematic review. J. Sports Med. Phys. Fit. 2018, 58, 534–548. [Google Scholar] [CrossRef]
  23. Shin, G.; Jarrahi, M.H.; Fei, Y.; Karami, A.; Gafinowitz, N.; Byun, A.; Lu, X. Wearable activity trackers, accuracy, adoption, acceptance and health impact: A systematic literature review. J. Biomed. Inform. 2019, 93, 103153. [Google Scholar] [CrossRef]
  24. Harrison, D.; Marshall, P.; Bianchi-Berthouze, N.; Bird, J. Activity Tracking: Barriers, Workarounds and Customisation. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan, 7–11 September 2019; Association for Computing Machinery: New York, NY, USA, 2015; pp. 617–621. [Google Scholar]
  25. Kori-Lindner, C. Ethical principles for medical research involving human subjects: World medical association declaration of Helsinki. Klin. Pharmakol. Aktuell 2000, 11, 26–28. [Google Scholar]
  26. The World Factbook. Population. Available online: https://relief.unboundmedicine.com/relief/view/The-World-Factbook/563798/all/Population (accessed on 1 October 2021).
  27. Soto Alvarez, J. Importancia del tamaño de la muestra en la investigación clínica. Rev. Clin. Esp. 1995, 195, 444. [Google Scholar] [PubMed]
  28. Dimock M Defining Generations: Where Millennials End and Generation Z Begins. Available online: https://www.pewresearch.org/fact-tank/2019/01/17/where-millennials-end-and-generation-z-begins/ (accessed on 9 January 2022).
  29. Kim, Y.; Park, I.; Kang, M. Convergent validity of the International Physical Activity Questionnaire (IPAQ): Meta-analysis. Public Health Nutr. 2013, 16, 440–452. [Google Scholar] [CrossRef]
  30. Craig, C.L.; Marshall, A.L.; Sjöström, M.; Bauman, A.E.; Booth, M.L.; Ainsworth, B.E.; Pratt, M.; Ekelund, U.; Yngve, A.; Sallis, J.F.; et al. International physical activity questionnaire: 12-Country reliability and validity. Med. Sci. Sports Exerc. 2003, 35, 1381–1395. [Google Scholar] [CrossRef]
  31. Owen, N.; Healy, G.N.; Matthews, C.E.; Dunstan, D.W. Too much sitting: The population health science of sedentary behavior. Exerc. Sport Sci. Rev. 2010, 38, 105–113. [Google Scholar] [CrossRef] [PubMed]
  32. Khalaf, S. Health and Fitness Apps Finally Take Off, Fueled by Fitness Fanatics. Available online: http://tinyurl.com/q4wyl7j (accessed on 31 October 2016).
  33. Ferretti, M.T.; Santuccione-Chadha, A.; Hampel, H. Account for sex in brain research for precision medicine. Nature 2019, 569, 40. [Google Scholar] [CrossRef]
  34. Guillén-Gámez, F.D.; Mayorga-Fernández, M.J. Empirical Study Based on the Perceptions of Patients and Relatives about the Acceptance of Wearable Devices to Improve Their Health and Prevent Possible Diseases. Mob. Inf. Syst. 2019, 2019, 4731048. [Google Scholar] [CrossRef]
  35. Generational Theory and Cohort Analysis; Okros, A. (Ed.) Springer International Publishing: Cham, Switzerland, 2020; pp. 33–51. ISBN 978-3-030-25726-2. [Google Scholar]
  36. Parry, E. Generations. In Encyclopedia of Electronic HRM; Bondarouk, T., Fisher, S., Eds.; De Gruyter: Berlin, Germany, 2020; pp. 231–236. [Google Scholar]
  37. Lee, J.Y.; Wong, C.P.; Lee, S.W.H. m-Health views and perception among Malaysian: Findings from a survey among individuals living in Selangor. mHealth 2020, 6. [Google Scholar] [CrossRef]
  38. Guitar, N.A.; MacDougall, A.; Connelly, D.M.; Knight, E. Fitbit Activity Trackers Interrupt Workplace Sedentary Behavior: A New Application. Work. Health Saf. 2018, 66, 218–222. [Google Scholar] [CrossRef]
  39. Healy, G.N.; Wijndaele, K.; Dunstan, D.W.; Shaw, J.E.; Salmon, J.; Zimmet, P.Z.; Owen, N. Objectively measured sedentary time, physical activity, and metabolic risk the Australian Diabetes, Obesity and Lifestyle Study (AusDiab). Diabetes Care 2008, 31, 369–371. [Google Scholar] [CrossRef]
  40. Wilmot, E.G.; Edwardson, C.L.; Achana, F.A.; Davies, M.J.; Gorely, T.; Gray, L.J.; Khunti, K.; Yates, T.; Biddle, S.J.H. Sedentary time in adults and the association with diabetes, cardiovascular disease and death: Systematic review and meta-analysis. Diabetologia 2012, 55, 2895–2905. [Google Scholar] [CrossRef]
  41. Stamatakis, E.; Rogers, K.; Ding, D.; Berrigan, D.; Chau, J.; Hamer, M.; Bauman, A. All-cause mortality effects of replacing sedentary time with physical activity and sleeping using an isotemporal substitution model: A prospective study of 201,129 mid-aged and older adults. Int. J. Behav. Nutr. Phys. Act. 2015, 12, 121. [Google Scholar] [CrossRef]
  42. Hamilton, M.T.; Healy, G.N.; Dunstan, D.W.; Zderic, T.W.; Owen, N. Too little exercise and too much sitting: Inactivity physiology and the need for new recommendations on sedentary behavior. Curr. Cardiovasc. Risk Rep. 2008, 2, 292–298. [Google Scholar] [CrossRef]
  43. Bey, L.; Hamilton, M.T. Suppression of skeletal muscle lipoprotein lipase activity during physical inactivity: A molecular reason to maintain daily low-intensity activity. J. Physiol. 2003, 551, 673–682. [Google Scholar] [CrossRef]
  44. Hamilton, M.T.; Etienne, J.; McClure, W.C.; Pavey, B.S.; Holloway, A.K. Role of local contractile activity and muscle fiber type on LPL regulation during exercise. Am. J. Physiol. Endocrinol. Metab. 1998, 275, E1016–E1022. [Google Scholar] [CrossRef]
  45. Ng, K.; Kokko, S.; Tammelin, T.; Kallio, J.; Belton, S.; O’Brien, W.; Murphy, M.; Powell, C.; Woods, C. Clusters of adolescent physical activity tracker patterns and their associations with physical activity behaviors in Finland and Ireland: Cross-sectional study. J. Med. Internet Res. 2020, 22, e18509. [Google Scholar] [CrossRef]
  46. Patel, M.S.; Asch, D.A.; Volpp, K.G. Wearable devices as facilitators, not drivers, of health behavior change. J. Am. Med. Assoc. 2015, 313, 459–460. [Google Scholar] [CrossRef]
  47. Franssen, W.M.A.; Franssen, G.H.L.M.; Spaas, J.; Solmi, F.; Eijnde, B.O. Can consumer wearable activity tracker-based interventions improve physical activity and cardiometabolic health in patients with chronic diseases? A systematic review and meta-analysis of randomised controlled trials. Int. J. Behav. Nutr. Phys. Act. 2020, 17, 57. [Google Scholar] [CrossRef]
  48. Makroum, M.A.; Adda, M.; Bouzouane, A.; Ibrahim, H. Machine Learning and Smart Devices for Diabetes Management: Systematic Review. Sensors 2022, 22, 1843. [Google Scholar] [CrossRef]
  49. Li, C.; Chen, X.; Bi, X. Wearable activity trackers for promoting physical activity: A systematic meta-analytic review. Int. J. Med. Inform. 2021, 152, 104487. [Google Scholar] [CrossRef]
  50. Brickwood, K.J.; Watson, G.; O’brien, J.; Williams, A.D. Consumer-based wearable activity trackers increase physical activity participation: Systematic review and meta-analysis. JMIR mHealth uHealth 2019, 7, e11819. [Google Scholar] [CrossRef]
  51. Abedtash, H.; Holden, R.J. Systematic review of the effectiveness of health-related behavioral interventions using portable activity sensing devices (PASDs). J. Am. Med. Inform. Assoc. 2017, 24, 1002–1013. [Google Scholar] [CrossRef] [PubMed]
  52. Holzmann, S.L.; Holzapfel, C. A Scientific Overview of Smartphone Applications and Electronic Devices for Weight Management in Adults. J. Pers. Med. 2019, 9, 31. [Google Scholar] [CrossRef] [PubMed]
  53. Jakicic, J.M.; Davis, K.K.; Rogers, R.J.; King, W.C.; Marcus, M.D.; Helsel, D.; Rickman, A.D.; Wahed, A.S.; Belle, S.H. Effect of wearable technology combined with a lifestyle intervention on long-term weight loss: The IDEA randomized clinical trial. J. Am. Med. Assoc. 2016, 316, 1161–1171. [Google Scholar] [CrossRef] [PubMed]
  54. Pourzanjani, A.; Quisel, T.; Foschini, L. Adherent use of digital health trackers is associated with weight loss. PLoS ONE 2016, 11, e0152504. [Google Scholar] [CrossRef]
  55. Khan, M.B.; Mustafa, A.; Rehman, M.; AbuAli, N.A.; Yuan, C.; Yang, X.; Shah, F.H.; Abbasi, Q.H. Non-Contact Smart Sensing of Physical Activities during Quarantine Period Using SDR Technology. Sensors 2022, 22, 1348. [Google Scholar] [CrossRef]
  56. Dooley, E.E.; Golaszewski, N.M.; Bartholomew, J.B. Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices. JMIR mHealth uHealth 2017, 5, e34. [Google Scholar] [CrossRef]
  57. Shcherbina, A.; Mattsson, C.; Waggott, D.; Salisbury, H.; Christle, J.; Hastie, T.; Wheeler, M.; Ashley, E. Accuracy in Wrist-Worn, Sensor-Based Measurements of Heart Rate and Energy Expenditure in a Diverse Cohort. J. Pers. Med. 2017, 7, 3. [Google Scholar] [CrossRef]
  58. Osei, E.; Agyei, K.; Tlou, B.; Mashamba-Thompson, T.P. Availability and Use of Mobile Health Technology for Disease Diagnosis and Treatment Support by Health Workers in the Ashanti Region of Ghana: A Cross-Sectional Survey. Diagnostics 2021, 11, 1233. [Google Scholar] [CrossRef]