Author(s):
- Tom H van de Belt
- Aimee de Croon
- Faye Freriks
- Thomas Blomseth Christiansen
- Jakob Eg Larsen
- Martijn de Groot
Abstract:
Background: Individuals’ self-tracking of subjectively experienced phenomena related to health can be challenging, as current options for instrumentation often involve too much effort in the moment or rely on retrospective self-reporting, which is likely to impair accuracy and compliance.
Objective: This study aims to assess the usability and perceived usefulness of low-effort, in-the-moment self-tracking using simple instrumentation and to establish the amount of support needed when using this approach.
Methods: In this exploratory study, the One Button Tracker-a press-button device that records time stamps and durations of button presses-was used for self-tracking. A total of 13 employees of an academic medical center chose a personal research question and used the One Button Tracker to actively track specific subjectively experienced phenomena for 2 to 4 weeks. To assess usability and usefulness, we combined qualitative data from semistructured interviews with quantitative results from the System Usability Scale.
Results: In total, 29 barriers and 15 facilitators for using the One Button Tracker were found. Ease of use was the most frequently mentioned facilitator. The One Button Tracker’s usability received a median System Usability Scale score of 75.0 (IQR 42.50), which is considered as good usability. Participants experienced effects such as an increased awareness of the tracked phenomenon, a confirmation of personal knowledge, a gain of insight, and behavior change. Support and guidance during all stages of the self-tracking process were judged as valuable.
Conclusions: The low-effort, in-the-moment self-tracking of subjectively experienced phenomena has been shown to support personal knowledge gain and health behavior change for people with an interest in health promotion. After addressing barriers and formally validating the collected data, self-tracking devices may well be helpful for additional user types or health questions.
Documentation:
https://doi.org/10.2196/32704
References:
- Swan M. The quantified self: fundamental disruption in big data science and biological discovery. Big Data 2013;1(2):85-99. [CrossRef] [Medline]
- Burke LE, Swigart V, Warziski Turk M, Derro N, Ewing LJ. Experiences of self-monitoring: successes and struggles during treatment for weight loss. Qual Health Res 2009;19(6):815-828 [FREE Full text] [CrossRef] [Medline]
- Kong A, Beresford SA, Alfano CM, Foster-Schubert KE, Neuhouser ML, Johnson DB, et al. Self-monitoring and eating-related behaviors are associated with 12-month weight loss in postmenopausal overweight-to-obese women. J Acad Nutr Diet 2012;112(9):1428-1435 [FREE Full text] [CrossRef] [Medline]
- Harris T, Kerry SM, Victor CR, Ekelund U, Woodcock A, Iliffe S, et al. A primary care nurse-delivered walking intervention in older adults: PACE (pedometer accelerometer consultation evaluation)-lift cluster randomised controlled trial. PLoS Med 2015;12(2):e1001783 [FREE Full text] [CrossRef] [Medline]
- Woods SS, Evans NC, Frisbee KL. Integrating patient voices into health information for self-care and patient-clinician partnerships: Veterans Affairs design recommendations for patient-generated data applications. J Am Med Inform Assoc 2016;23(3):491-495. [CrossRef] [Medline]
- Petersen C. Patient-generated health data: a pathway to enhanced long-term cancer survivorship. J Am Med Inform Assoc 2016;23(3):456-461. [CrossRef] [Medline]
- Kooiman TJ, de Groot M, Hoogenberg K, Krijnen WP, van der Schans CP, Kooy A. Self-tracking of physical activity in people with type 2 diabetes: a randomized controlled trial. Comput Inform Nurs 2018;36(7):340-349. [CrossRef] [Medline]
- Kaye J, Curren L, Anderson N, Edwards K, Fullerton SM, Kanellopoulou N, et al. From patients to partners: participant-centric initiatives in biomedical research. Nat Rev Genet 2012;13(5):371-376 [FREE Full text] [CrossRef] [Medline]
- Vayena E, Tasioulas J. The ethics of participant-led biomedical research. Nat Biotechnol 2013;31(9):786-787. [CrossRef] [Medline]
- Grant AD, Wolf GI, Nebeker C. Approaches to governance of participant-led research: a qualitative case study. BMJ Open 2019;9(4):e025633 [FREE Full text] [CrossRef] [Medline]
- Wolf GI, De Groot M. A conceptual framework for personal science. Front Comput Sci 2020;2:21. [CrossRef]
- Altschuler A, Picchi T, Nelson M, Rogers JD, Hart J, Sternfeld B. Physical activity questionnaire comprehension: lessons from cognitive interviews. Med Sci Sports Exerc 2009;41(2):336-343 [FREE Full text] [CrossRef] [Medline]
- Archer E, Pavela G, Lavie CJ. The inadmissibility of what we eat in America and NHANES dietary data in nutrition and obesity research and the scientific formulation of national dietary guidelines. Mayo Clin Proc 2015;90(7):911-926 [FREE Full text] [CrossRef] [Medline]
- Hermsen S, Frost J, Renes RJ, Kerkhof P. Using feedback through digital technology to disrupt and change habitual behavior: a critical review of current literature. Comput Hum Behav 2016;57:61-74 [FREE Full text] [CrossRef]
- Bartlett Ellis RJ, Hill JH, Kerley KD, Sinha A, Ganci A, Russell CL. The feasibility of a using a smart button mobile health system to self-track medication adherence and deliver tailored short message service text message feedback. JMIR Form Res 2019;3(2):e13558 [FREE Full text] [CrossRef] [Medline]
- Larsen J, Eskelund K, Christiansen TB. Active self-tracking of subjective experience with a one-button wearable: a case study in military PTSD. In: Proceedings of the 2nd Computing and Mental Health workshop at the ACM CHI. 2017 Presented at: CHI ’17; May 6-11, 2017; Denver URL: https://arxiv.org/pdf/1703.03437.pdf
- Arendt IT, Riisager LH, Larsen JE, Christiansen TB, Moeller SB. Distinguishing between rumination and intrusive memories in PTSD using a wearable self-tracking instrument: a proof-of-concept case study. Cogn Behav Ther 2021;14:E15. [CrossRef]
- Tong AP, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care 2007;19(6):349-357 [FREE Full text] [CrossRef] [Medline]
- Radboud University Medical Center, Nijmegen, the Netherlands. Lifeguard. URL: https://lifeguard.nl/klanten/radboudumc/ [accessed 2022-02-01]
- Li I, Dey A, Forlizzi J. A stage-based model of personal informatics systems. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2010 Presented at: CHI ’10; April 10 – 15, 2010; Atlanta p. 557-566. [CrossRef]
- Almalki M, Gray K, Martin-Sanchez FJ. Refining the concepts of self-quantification needed for health self-management. A thematic literature review. Methods Inf Med 2017;56(1):46-54. [CrossRef] [Medline]
- Brooke J. SUS: a ‘Quick and Dirty’ usability scale. In: Jordan PW, Thomas B, McClelland IL, Weerdmeester B, editors. Usability evaluation in industry. London: Taylor and Francis; 1996:189-194.
- Gagnon MP, Desmartis M, Labrecque M, Car J, Pagliari C, Pluye P, et al. Systematic review of factors influencing the adoption of information and communication technologies by healthcare professionals. J Med Syst 2012;36(1):241-277 [FREE Full text] [CrossRef] [Medline]
- Gagnon MP, Ngangue P, Payne-Gagnon J, Desmartis M. m-Health adoption by healthcare professionals: a systematic review. J Am Med Inform Assoc 2016;23(1):212-220. [CrossRef] [Medline]
- Donabedian A. The quality of care. How can it be assessed? JAMA 1988;260(12):1743-1748. [CrossRef] [Medline]
- Bangor A, Kortum P, Miller J. Determining what individual SUS scores mean: adding an adjective rating scale. J Usability Stud 2009;4(3):114-123 [FREE Full text]
- Sauro J, Lew JR. Quantifying the user experience: practical statistics for user research. Burlington: Morgan Kaufmann; 2012.
- Kim J. A qualitative analysis of user experiences with a self-tracker for activity, sleep, and diet. Interact J Med Res 2014;3(1):e8 [FREE Full text] [CrossRef] [Medline]
- Lentferink AJ, Oldenhuis HK, de Groot M, Polstra L, Velthuijsen H, van Gemert-Pijnen JE. Key components in eHealth interventions combining self-tracking and persuasive eCoaching to promote a healthier lifestyle: a scoping review. J Med Internet Res 2017;19(8):e277 [FREE Full text] [CrossRef] [Medline]
- Crawford K, Lingel J, Karppi T. Our metrics, ourselves: a hundred years of self-tracking from the weight scale to the wrist wearable device. Eur J Cult Stud 2015;18(4-5):479-496. [CrossRef]
- Kersten-van Dijk ET, Westerink JH, Beute F, IJsselsteijn WA. Personal informatics, self-insight, and behavior change: a critical review of current literature. Hum Comput Interact 2017;32(5-6):268-296 [FREE Full text] [CrossRef]
- 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 Hum Behav 2019;94:131-139 [FREE Full text] [CrossRef]
- Kelders SM, Kok RN, Ossebaard HC, Van Gemert-Pijnen JE. Persuasive system design does matter: a systematic review of adherence to web-based interventions. J Med Internet Res 2012;14(6):e152 [FREE Full text] [CrossRef] [Medline]
- Vorderstrasse A, Lewinski A, Melkus GD, Johnson C. Social support for diabetes self-management via eHealth interventions. Curr Diab Rep 2016;16(7):56. [CrossRef] [Medline]
- Shin G, Jarrahi MH, Fei Y, Karami A, Gafinowitz N, Byun A, et al. Wearable activity trackers, accuracy, adoption, acceptance and health impact: a systematic literature review. J Biomed Inform 2019;93:103153. [CrossRef] [Medline]
- Alnosayan N, Chatterjee S, Alluhaidan A, Lee E, Houston Feenstra FL. Design and usability of a heart failure mHealth system: a pilot study. JMIR Hum Factors 2017;4(1):e9 [FREE Full text] [CrossRef] [Medline]
- Kelley C, Lee B, Wilcox L. Self-tracking for mental wellness: understanding expert perspectives and student experiences. Proc SIGCHI Conf Hum Factor Comput Syst 2017;2017:629-641 [FREE Full text] [CrossRef] [Medline]
- O’Connor S, Hanlon P, O’Donnell C, 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 Inform Decis Mak 2016;16(1):120 [FREE Full text] [CrossRef] [Medline]
- Karapanos E, Gouveia R, Hassenzahl M, Forlizzi J. Wellbeing in the making: peoples’ experiences with wearable activity trackers. Psychol Well Being 2016;6:4 [FREE Full text] [CrossRef] [Medline]
- Pacanowski CR, Linde JA, Neumark-Sztainer D. Self-weighing: helpful or harmful for psychological well-being? A review of the literature. Curr Obes Rep 2015;4(1):65-72 [FREE Full text] [CrossRef] [Medline]
- Etkin J. The hidden cost of personal quantification. J Consum Res 2016;42(6):967-984 [FREE Full text] [CrossRef]
- Andersen TO, Langstrup H, Lomborg S. Experiences with wearable activity data during self-care by chronic heart patients: qualitative study. J Med Internet Res 2020;22(7):e15873 [FREE Full text] [CrossRef] [Medline]
- Piwek L, Ellis DA, Andrews S, Joinson A. The rise of consumer health wearables: promises and barriers. PLoS Med 2016;13(2):e1001953 [FREE Full text] [CrossRef] [Medline]