Author(s):
- Frans Folkvord
- Amy van Breugel
- Sanneke de Haan
- Marcella de Wolf
- Marjolein de Boer
- Mariek Vanden Abeele
Abstract:
Background: The last few decades people have increasingly started to use technological tools for health and activity monitoring, such as tracking apps and wearables. The main assumption is that these tools are effective in reinforcing self-empowerment because they support better-informed lifestyle decision-making. However, experimental research assessing the effectiveness of the technological tools on such psychological outcomes is limited.
Methods and Design: Three studies will be conducted. First, we will perform a systematic review to examine the experimental evidence on the effects of self-tracking apps on psychological outcome measurements. Second, we will conduct a longitudinal field experiment with a between subject design. Participants (N = 150) begin a 50-day exercise program, either with or without the aid of the self-tracking app Strava. Among those who use Strava, we vary between those who use all features and those who use a limited set of features. Participants complete questionnaires at baseline, at 10, 25, and 50 days, and provide details on what information has been tracked via the platform. Third, a subset of participants is interviewed to acquire additional qualitative data. The study will provide a rich set of data, enabling triangulation, and contextualization of the findings.
Discussion: People increasingly engage in self-tracking whereby they use technological tools for health and activity monitoring, although the effects are still unknown. Considering the mixed results of the existing evidence, it is difficult to draw firm conclusions, showing more research is needed to develop a comprehensive understanding.
Documentation:
https://doi.org/10.3389/fdgth.2021.708159
References:
Carroll JK, Moorhead A, Bond R, LeBlanc WG, Petrella RJ, Fiscella K. Who uses mobile phone health apps and does use matter? A secondary data analytics approach. J Med Internet Res. (2017) 19:e125. doi: 10.2196/jmir.5604
PubMed Abstract | CrossRef Full Text | Google Scholar
2. Henriksen A, Sand AS, Deraas T, Grimsgaard S, Hartvigsen G, Hopstock L. Succeeding with prolonged usage of consumer-based activity trackers in clinical studies: a mixed methods approach. BMC Public Health. (2020) 20:1300. doi: 10.1186/s12889-020-09406-w
PubMed Abstract | CrossRef Full Text | Google Scholar
3. Kay M, Santos J, Takane M. mHealth: New horizons for Health Through Mobile Technologies. Geneva: World Health Organization (2011).
4. Stragier J, Abeele MV, Mechant P, De Marez L. Understanding persistence in the use of online fitness communities: comparing novice and experienced users. Comput Hum Behav. (2016) 64:34–42. doi: 10.1016/j.chb.2016.06.013
CrossRef Full Text | Google Scholar
5. Sharon T. Self-tracking for health and the quantified self: Re-articulating autonomy, solidarity, and authenticity in an age of personalized healthcare. Philos. Technol. (2017) 30:93–121. doi: 10.1007/s13347-016-0215-5
CrossRef Full Text | Google Scholar
6. Duus R, Cooray M, Page NC. Exploring human-tech hybridity at the intersection of extended cognition and distributed agency: a focus on self-tracking devices. Front Psychol. (2018) 9:1432. doi: 10.3389/fpsyg.2018.01432
PubMed Abstract | CrossRef Full Text | Google Scholar
7. Lupton D. Self-tracking, health and medicine. Health Sociol. Rev. (2017) 26:1. doi: 10.1080/14461242.2016.1228149
CrossRef Full Text | Google Scholar
8. Swan M. The quantified self: Fundamental disruption in big data science and biological discovery. Big Data. (2013) 1:85–99. doi: 10.1089/big.2012.0002
PubMed Abstract | CrossRef Full Text | Google Scholar
9. Maturo A, Setiffi F. The gamification of risk: how health apps foster self-confidence and why this is not enough. Health Risk Soc. (2016) 17:477–94. doi: 10.1080/13698575.2015.1136599
CrossRef Full Text | Google Scholar
10. Deci EL, Ryan RM. Self-determination theory. In: Van Lange PAM, Kruglanski AW, Higgins ET, editors. Handbook of Theories of Social Psychology, Vols. 1 and 2. Thousand Oaks, CA: Sage Publications Ltd. (2011). p. 416–36. doi: 10.4135/9781446249215.n21
CrossRef Full Text | Google Scholar
11. Stragier J, Vanden Abeele M, De Marez L. Recreational athletes’ running motivations as predictors of their use of online fitness community features. Behav Inform Technol. (2018) 37:815–27. doi: 10.1080/0144929X.2018.1484516
CrossRef Full Text | Google Scholar
12. Anastasiadou D, Folkvord F, Serrano-Troncoso E, Lupiañez-Villanueva F. Mobile health adoption in mental health: user experience of a mobile health app for patients with an eating disorder. JMIR mHealth uHealth. (2019). 7:e12920. doi: 10.2196/12920
PubMed Abstract | CrossRef Full Text | Google Scholar
13. Marzano L, Bardill A, Fields B, Herd K, Veale D, Grey N, et al. The application of mHealth to mental health: opportunities and challenges. Lancet Psychiatry. (2015) 2:942–8. doi: 10.1016/S2215-0366(15)00268-0
PubMed Abstract | CrossRef Full Text | Google Scholar
14. Endeavour Partners. Inside Wearbles: How the Science of Human Behavior Change Offers to Secret to Long-Term Engagement. Wandina WA: Endeavour Partners (2014). p. 17.
15. Fuchs T. The psychopathology of hyperreflexivity. J Specul Philos. (2010) 24:239–55. doi: 10.1353/jsp.2010.0010
CrossRef Full Text | Google Scholar
16. Leder D. The Absent Body. Chicago, IL: University of Chicago Press (1990).
17. Merleau-Ponty M. Phenomenology of Perception. Transl by C. Smith. London: Routledge (1945/2002). doi: 10.4324/9780203994610
CrossRef Full Text | Google Scholar
18. Faul F, Erdfelder E, Lang AG, Buchner A. G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. (2007) 39:175–91. doi: 10.3758/BF03193146
PubMed Abstract | CrossRef Full Text | Google Scholar
19. Smith JA, Flowers P, Larkin M. Interpretative Phenomenological Analysis. Theory, Method and Research. London: Sage (2009).
20. Seo MW, Kim Y, Jung HC, Kim JH, Lee JM. Does online social connectivity promote physical activity in a wearable tracker-based intervention? A pilot randomized controlled study. Sustainability. (2020) 12:8803. doi: 10.3390/su12218803
CrossRef Full Text | Google Scholar
21. Gittus M, Fuller-Tyszkiewicz M, Brown HE, Richardson B, Fassnacht DB, Lennard GR, et al. Are Fitbits implicated in body image concerns and disordered eating in women? Health Psychol. (2020) 39:900–4. doi: 10.1037/hea0000881
PubMed Abstract | CrossRef Full Text | Google Scholar
22. Stiglbauer B, Weber S, Batinic B. Does your health really benefit from using a self tracking device? Evidence from a longitudinal randomized control trial. Comput Human Behav. (2019) 94:131–9. doi: 10.1016/j.chb.2019.01.018
CrossRef Full Text | Google Scholar
23. Maher C, Ferguson M, Vandelanotte C, Plotnikoff R, De Bourdeaudhuij I, Thomas S, et al. A web-based, social networking physical activity intervention for insufficiently active adults delivered via Facebook app: randomized controlled trial. J Med Internet Res. (2015) 17:e174. doi: 10.2196/jmir.4086