Author(s):
Anne Marie Kanstrup
Pernille Bertelsen
Martin B Jensen
Abstract:
Objective: Activity trackers are designed to support individuals in monitoring and increasing their physical activity. The use of activity trackers among individuals diagnosed with depression and anxiety has not yet been examined. This pilot study investigates how this target group engages with an activity tracker during a 10-week health intervention aimed to increase their physical activity level and improve their physical and mental health.
Methods: Two groups of 11 young adults (aged 18-29 years) diagnosed with depression or anxiety participated in the digital health intervention. The study used mixed methods to investigate the research question. Quantitative health data were used to assess the intervention’s influence on the participants’ health and qualitative data provided insights into the participants’ digital health experience.
Results: The study demonstrated an ambiguous influence from the use of an activity tracker with positive physical and mental health results, but a fading and even negative digital health engagement and counterproductive competition.
Conclusions: The ambiguous results identify a need for (1) developing strategies for health professionals to provide supervised use of activity trackers and support the target groups’ abilities to convert health information about physical activity into positive health strategies, and (2) designing alternatives for health promoting IT targeted users who face challenges and need motivation beyond self-tracking and competition.
Document:
https://pubmed.ncbi.nlm.nih.gov/29942636/
References:
1. Walker S. Wearable Technology – Market Assessment. An IHS Whitepaper, London, UK: IHS Electronics & Media, 2015. [Google Scholar]
2. Chung CF, Dew K, Cole A, Zia J, Fogarty J, Kientz JA and Munson SA. Boundary negotiating artifacts in personal informatics: Patient-provider collaboration with patient-generated data. In: CSCW’16 Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, San Francisco, California, USA, 27 February–2 March 2016. New York: ACM Press, 2016, pp. 770–786. [PMC free article] [PubMed]
3. Zhu H, Colgan J, Reddy M, Choe EK. Sharing patient-generated data in clinical practices: An interview study. In: AMIA Annual Symposium Proceedings, 2016, pp. 1303–1312. [PMC free article] [PubMed]
4. Almalki M, Gray K, Sanchez FM. The use of self-quantification systems for personal health information: Big data management activities and prospects. Health Inform Sci Syst 2015; 3: 51. [PMC free article] [PubMed] [Google Scholar]
5. Swan M. Sensor Mania! The Internet of things, wearable computing, objective metrics, and the quantified self 2.0. J Sens Actuator Netw 2012; 1: 217–253. [Google Scholar]
6. O’Connor S, Hanlon P, O’Donelle CA, Garcia S, Glanville J, Mair FS. Understanding factors affecting patient and public engagement and recruitment to digital health interventions: A systematic review of qualitative studies. BMC Med Inf Decis Making 2016; 16: 120. [PMC free article] [PubMed] [Google Scholar]
7. Abril EP. Tracking myself: Assessing the contribution of mobile technologies for self-trackers of weight, diet, or exercise. J Health Commun 2016; 21: 638–646. [PubMed] [Google Scholar]
8. Petersen LS and Bertelsen P. Equality challenges in the use of eHealth – Selected results from a Danish Citizens survey. In: Medinfo2017 Proceedings of the 16th World Congress on Medical and Health Informatics, Hangzhou, China, 21–25 August 2017. Amsterdam: IOS Press.
9. Seifert A, Schlomann A, Rietz C, Schelling HR. The use of mobile devices for physical activity tracking in older adults’ everyday life. Digital Health 2017; 3: 1–12. [Google Scholar]
10. Showel C, Turner P. The PLU problem: Are we designing personal eHealth for people like us? Stud Health Tech Informat 2013; 183: 276–280. [PubMed] [Google Scholar]
11. World Health Organization.. Information sheet: Premature death among people with severe mental disorders, Geneva, Switzerland: WHO, 2016. [Google Scholar]
12. MacKean PR, Stewart M, Maddocks HL. Psychosocial diagnoses occurring after patients present with fatigue. Can Fam Physician 2016; 62: e465–e472. [PMC free article] [PubMed] [Google Scholar]
13. Rosenberg D, Kadokura EA, Bouldin ED, Miyawaki CE, Higano CS and Hartzler AL. Acceptability of Fitbit for physical activity tracking within clinical care among men with prostate cancer. In: AMIA Annual Symposium Proceedings, 2016, pp. 1050–1059. [PMC free article] [PubMed]
14. Riel H, Kalstrup PM, Madsen NK, Pederen ES, Rathleff CR, Pape-Haugaard LB, et al. Comparison between Mother, ActiGraph wGT3X-BT, and a hand tally for measuring steps at various walking speeds under controlled conditions. Peer J 2016; 4: e2799. [PMC free article] [PubMed] [Google Scholar]
15. Tudor-Locke C, Hatano Y, Pangrazi R, Kang M. Re-visiting “how many steps are enough?”. Med Sci Sports Exerc 2018; 40: 537–543. [PubMed] [Google Scholar]
16. Kang M, Marshall SJ, Barreira TV, Lee JO. Effects of pedometer-based physical activity interventions. Res Q Exerc Sport 2009; 80: 648–655. [PubMed] [Google Scholar]17. Kim KK, Logan HC, Young E, Sabee CM. Youth-centered design and usage results of the iN Touch mobile self-management program for overweight/obesity. Pers Ubiquit Comput 2015; 19: 59–68. [Google Scholar]
18. Fausset CB, Mitxner TL, Price CE, Jones BD, Fain WB and Rogers WA. Older adults’ use of and attitudes toward activity monitoring technology. In: Proceedings of the Human Factors and Ergonomic Society 57th Annual Meeting, 2013, pp. 1683–1687.
19. Mercer K, Li M, Giangregorio L, Burns C, Grindrod K. Behaviour change techniques present in wearable activity trackers: A critical analysis. JMIR Mhealth Uhealth 2016; 4: e40. [PMC free article] [PubMed] [Google Scholar]
20. Schwennesen N. When self-tracking enters physical rehabilitation: From ‘pushed’ self-tracking to ongoing affective encounters in arrangements of care. Digital Health 2017; 3: 1–8. [Google Scholar]
21. Jones J, Wu H, Patel J, Kasthurirathne S, Thai N and Mukherjee S. An evaluation of activity trackers for monitoring Parkinson’s disease patient outcomes. In: AMIA Annual Symposium Proceedings, 2016, p. 1451.
22. Shaw R, Fenwich E, Baker G, McAdam C, Fitzsimons C, Mutrie N. Pedometers cost buttons: The feasibility of implementing a pedometer-based walking programme within the community. BMC Public Health 2011; 11: 200. [PMC free article] [PubMed] [Google Scholar]
23. Brevata DM, Smith-Spangler C, Sundaram V, Gienger AL, Lin N, Lewis R, et al. Using pedometers to increase physical activity and improve health: A systematic review. JAMA 2007; 298: 2296–2304. [PubMed] [Google Scholar]
24. Dallinga JM, Mennes M, Alpay L, Bijwaard H, De la Faille-Deutekom MB. App use, physical activity and healthy lifestyle: A cross-sectional study. BMC Public Health 2015; 15: 833. [PMC free article] [PubMed] [Google Scholar]
25. Hermann LK, Kim J. The fitness of apps: A theory-based examination of mobile fitness app usage over 5 months. mHealth 2017; 3(2): DOI: 10.21037/mhealth.2017.01.03. [PMC free article] [PubMed] [Google Scholar]
26. Meyer J, Wasmann M, Heuten W, Ali AE and Boll SCJ. Identification and classification of usage patterns in long-term activity tracking. In: CHI 2017 Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. Denver, CO, USA, 6–11 May 2017. New York: ACM Press, 2017, pp. 667–678.
27. Patel M and O’Kane AA. Context influences on the use and non-use of digital technology while exercising at the gym. In: CHI’15 Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Republic of Korea, 18–23 April 2015. New York: ACM Press, 2015, pp. 2923–2932.
28. Rooksby J, Rost M, Morrison A and Chalmers M. Personal tracking as lived informatics. In: CHI’14 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Toronto, Ontario, Canada, 26 April–1 May 2014. New York: ACM Press, 2014, pp. 1163–1172.
29. Williams K. An anxious alliance. In: Proceedings of The Fifth Decennial Aarhus Conference on Critical Alternatives, 17–21 August 2015. Copenhagen, Denmark: AARHUS University, 2015, pp. 121–131.
30. Clawson J, Pater JA, Miller AD, Mynatt ED and Mamykina L. No longer wearing: Investigating the abandonment of personal health-tracking technologies on Craigslist. In: UbiComp’15 Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan, 7–11 September 2015. New York: ACM Press, 2015, pp. 647–658.
31. Storni C. Multiple forms of appropriation in self-monitoring technology: Reflections on the role of evaluation in future self-care. Intl J Human-Comput Interact 2010; 26: 537–556. [Google Scholar]
32. Åstrand P. Ergometri konditionsprov. Monark Exercise AB. 1964.
33. Topp CW, Østergaard SD, Søndergaard S, Bech P. The WHO-5 well-being index: A systematic review of the literature. Psychother Psychosom 2015; 84: 167–176. [PubMed] [Google Scholar]
34. Ajzen I. Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. J Appl Soc Psychol 2002; 32: 665–683. [Google Scholar]
35. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol 2006; 3: 77–101. [Google Scholar]
36. Atkinson NL, Billing AS, Desmond SM, Gold RS, Tournas-Hardt A. Assessment of the nutrition and physical activity education needs of low-income, rural mothers: Can technology play a role? J Community Health 2007; 32: 245–267. [PubMed] [Google Scholar]
37. Jimison H, Gorman P, Woods S, Nygren P, Walker M, Norris S, et al. Barriers and drivers of health information technology use for the elderly, chronically ill, and undeserved. Evid Rep Technol Assess 2008; 175: Report No.: 09-E004. [PMC free article] [PubMed] [Google Scholar]
38. Kanstrup AM and Bertelsen P. Bringing new voices to design of exercise technology: Participatory design with vulnerable young adults. In: PDC’16 Proceedings of the 14th Participatory Design Conference Volume 1, Aarhus, Denmark, 15–19 August 2016. New York: ACM Press 2016, pp. 121–130.
39. Morgan H. ‘Pushed’ self-tracking using digital technologies for chronic health condition management: A critical interpretive synthesis. Digital Health 2016; 2: 1–41. [Google Scholar]
40. Harrison D, Marshall P, Bianchi-Berthouze N and Bird J. Activity tracking: Barriers, workarounds and customisation. In: UbiComp’15. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan, 7–11 September 2015. New York: ACM Press, 2015, pp. 617–621.
41. Gouveia R, Karapanos E and Hassenzahl M. How do we engage with activity trackers? A longitudinal study of habito. In: UbiComp’15. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan, 7–11 September 2015. New York: ACM Press, 2015, pp. 1305–1316.
42. Johansen SK and Kanstrup AM. Expanding the locus of control: Design of a mobile quantified self-tracking application for whiplash patients. In: NordiCHI’16 Proceedings of the 9th Nordic Conference on Human-Computer Interaction, Gothenburg, Sweden, 23–27 October 2016. New York: ACM Press, 2016, article no. 59.
43. Kanstrup AM, Rotger-Griful S, Laplante-Lévesque A and Nielsen AC. Designing connections for hearing rehabilitation: Exploring future client journeys with elderly hearing aid users, relatives and healthcare providers. In: DIS’17 Proceedings of the 2017 Conference on Designing Interactive Systems, Edinburgh, UK, 10–14 June 2017. New York: ACM Press, 2017, pp. 1153–1163.
44. Moen A, Brennan PF. Health@Home. The work of health information management in the household (HIMH): Implications for consumer health informatics (CHI) innovations. J Am Med Inform Assoc 2005; 12: 648–656. [PMC free article] [PubMed] [Google Scholar]
45. O’Kane A, Park SY, Mentis H, Blandford A, Chen Y. Turning to Peers. Integrating understanding of self, the condition, and others’ experiences in making sense of complex chronic conditions. CSCW 2016; 25: 477–501. [Google Scholar]
46. Parsons HM. What happened at Hawthorne? Science 1974; 183: 911–932. [PubMed] [Google Scholar]
47. Sundhedsstyrelsen.. Struktur på sundheden – inspiration til sundhedsindsatser til borgere med psykiske lidelser, Copenhagen, Denmark: Sundhedsstyrelsen, 2014. [Google Scholar]
48. Parker AG, Grinter RE. Collectivistic health promotion tools: Accounting for the relationship between culture, food and nutrition. Int J Hum Comput Stud 2014; 72: 185–206. [Google Scholar]
49. Kanstrup AM, Bertelsen P, Nunez HC, Svarre T, Stage J. MOVE: a mobile app designed for social health relations in residential areas. Stud Health Technol Informat 2018; 247: 496–500. [PubMed] [Google Scholar]