How Self-tracking and the Quantified Self Promote Health and Well-being: Systematic Review

by | Oct 20, 2020 | Research Publications

Author(s):

  • Shan Feng
  • Matti Mäntymäki
  • Amandeep Dhir
  • Hannu Salmela

Abstract:

Background: Self-tracking technologies are widely used in people’s daily lives and health care. Academic research on self-tracking and the quantified self has also accumulated rapidly in recent years. Surprisingly, there is a paucity of research that reviews, classifies, and synthesizes the state of the art with respect to self-tracking and the quantified self.

Objective: Our objective was to identify the state of the art of self-tracking and the quantified self in terms of health and well-being.

Methods: We have undertaken a systematic literature review on self-tracking and the quantified self in promoting health and well-being. After a rigorous literature search, followed by inclusions, exclusions, and the application of article quality assessment protocols, 67 empirical studies qualified for the review.

Results: Our results demonstrate that prior research has focused on 3 stakeholders with respect to self-tracking and the quantified self, namely end users, patients and people with illnesses, and health care professionals and caregivers. We used these stakeholder groups to cluster the research themes of the reviewed studies. We identified 11 research themes. There are 6 themes under the end-user cluster: user motivation and goal setting, usage and effects of self-tracking, continuance intention and long-term usage, management of personal data, rejection and discontinuance, and user characteristics. The patient and people with illnesses cluster contains three themes: usage experience of patients and people with illnesses, management of patient-generated data, and advantages and disadvantages in the clinical context. The health care professional and caregiver cluster contains two themes: collaboration among patients, health care professionals, and caregivers, and changes in the roles of patients and professionals. Moreover, we classified the future research suggestions given in the literature into 5 directions in terms of research designs and research topics. Finally, based on our reflections on the observations from the review, we suggest four future research directions: (1) users’ cognitions and emotions related to processing and interpreting the information produced by tracking devices and apps; (2) the dark side of self-tracking (eg, its adverse psychosocial consequences); (3) self-tracking as a societal phenomenon; and (4) systemic impacts of self-tracking on health care and the actors involved.

Conclusions: This systematic literature review contributes to research and practice by assisting future research activities and providing practitioners with a concise overview of the state of the art of self-tracking and the quantified self.

Documentation:

https://doi.org/10.2196/25171

References:
  1. Lupton D. The Quantified Self. Cambridge: Polity Press; 2016.
  2. Statista. Most popular Apple App Store categories in August 2020, by share of available apps. https://www.statista.com/statistics/270291/popular-categories-in-the-app-store/. 2020.   URL: https://www.statista.com/statistics/270291/popular-categories-in-the-app-store/ [accessed 2020-12-26]
  3. PocketGamer.biz. Application category distribution. POCKET GAMER.biz. 2020.   URL: https://www.pocketgamer.biz/metrics/app-store/categories/ [accessed 2020-12-26]
  4. Most popular Google Play app categories as of 3rd quarter 2020. Statista. 2020.   URL: https://www.statista.com/statistics/279286/google-play-android-app-categories/ [accessed 2020-12-26]
  5. Number of mHealth apps available at Google Play from 1st quarter 2015 to 3rd quarter 2020. Statista. 2020.   URL: https://www.statista.com/statistics/779919/health-apps-available-google-play-worldwide/ [accessed 2020-12-26]
  6. Number of mHealth apps available in the Apple App Store from 1st quarter 2015 to 3rd quarter 2020. Statista. 2020.   URL: https://www.statista.com/statistics/779910/health-apps-available-ios-worldwide/ [accessed 2020-12-26]
  7. Smartwatch shipments worldwide from 2018 to 2023. Statista. 2019.   URL: https://www.statista.com/statistics/878144/worldwide-smart-wristwear-shipments-forecast/ [accessed 2021-01-30]
  8. Maxwell H, O’Shea M, Stronach M, Pearce S. Empowerment through digital health trackers: an exploration of indigenous Australian women and physical activity in leisure settings. Ann Leis Res 2019 Oct;24(1):150-167. [CrossRef]
  9. Spotswood F, Shankar A, Piwek L. Changing emotional engagement with running through communal self‐tracking: the implications of ‘teleoaffective shaping’ for public health. Sociol Heal Illn 2020;42(4):772-788. [CrossRef]
  10. Attig C, Franke T. I track, therefore I walk – exploring the motivational costs of wearing activity trackers in actual users. Int J Hum Comput Stud 2019;127:211-224. [CrossRef]
  11. Gimpel H, Nüske N, Rückel T, Urbach N, von Entreß-Fürsteneck M. Self-tracking and gamification : analyzing the interplay of motivations, usage and motivation fulfillment. 2019 Presented at: 14th International Conference on Wirtschaftsinformatik; February 24-27, 2019; Siegen, Germany p. 1130-1140.
  12. Pingo Z, Narayan B. “My smartwatch told me to see a sleep doctor”: a study of activity tracker use. Online Inf Rev 2019;44(2):503-519. [CrossRef]
  13. Gordon ML, Althoff T, Leskovec J. Goal-setting and achievement in activity tracking apps: a case study of MyFitnessPal. In: Proceedings of the International World Wide Web Conference. 2019 Presented at: WWW ’19: The World Wide Web Conference; May 13-17, 2019; San Francisco, CA, USA p. 571-582   URL: http://europepmc.org/abstract/MED/32368761 [CrossRef]
  14. Rockmann R, Gewald H. Individual fitness app use: the role of goal orientations and motivational affordances. 2019 Presented at: 25th AMCIS – The Americas Conference on Information Systems; August 15-17, 2019; Cancún, Mexico.
  15. Kitchenham B, Pretorius R, Budgen D, Brereton OP, Turner M, Niazi M, et al. Systematic literature reviews in software engineering – a tertiary study. Inf Softw Technol 2010;52(8):792-805. [CrossRef]
  16. Petersen K, Vakkalanka S, Kuzniarz L. Guidelines for conducting systematic mapping studies in software engineering: an update. Inf Softw Technol 2015;64:1-18. [CrossRef]
  17. Almalki M, Gray K, Martin-Sanchez F. Activity theory as a theoretical framework for health self-quantification: a systematic review of empirical studies. J Med Internet Res 2016 May;18(5):e131 [FREE Full text] [CrossRef] [Medline]
  18. Paton C, Hansen M, Fernandez-Luque L, Lau AYS. Self-tracking, social media and personal health records for patient empowered self-care. Yearb Med Inform 2012;21(01):16-24. [CrossRef]
  19. West P, Van Kleek M, Giordano R, Weal M, Shadbolt N. Information quality challenges of patient-generated data in clinical practice. Front Public Health 2017;5:284 [FREE Full text] [CrossRef] [Medline]
  20. Moya JD, Pallud J, Scornavacca E. Self-tracking technologies adoption and utilization: a literature analysis. 2019 Presented at: 25th AMCIS – The Americas Conference on Information Systems; August 15-17, 2019; Cancún, Mexico.
  21. Moya JD, Pallud J. Quantified self : a literature review based on the funnel paradigm. 2017 Presented at: European Conference on Information Systems; June 5-10, 2017; Guimarães, Portugal p. 1678-1694.
  22. Jiang J, Cameron A. IT-enabled self-monitoring for chronic disease self-management: an interdisciplinary review. MISQ 2020 Jan;44(1):451-508. [CrossRef]
  23. Behne A, Arlinghaus T, Kotte N, Teuteberg F. Towards functionalities of self-tracking wearables, their effects on humans and their application areas: Where can we improve? 2020 Presented at: Americas Conference on Information Systems; August 10-14, 2020; Virtual.
  24. 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 Aug;19(8):e277 [FREE Full text] [CrossRef] [Medline]
  25. Riphagen M, van Hout M, Krijnen D, Gootjes G. Learning tomorrow: visualising student and staff physical activity and reflect on it. In: EDULEARN13. 2013 Presented at: 5th International Conference on Education and New Learning Technologies; Jul 1-3, 2013; Barcelona, Spain p. 3209-3214.
  26. Frigo A. Life-Stowing from a Digital Media Perspective: Past, Present and Future [dissertation]. Huddinge: Södertörn University; 2017.
  27. Ivanov A, Sharman R, Rao HR. Exploring factors impacting sharing health-tracking records. Heal Policy Technol 2015;4(3):263-276. [CrossRef]
  28. Maltseva K, Lutz C. A quantum of self: a study of self-quantification and self-disclosure. Comput Human Behav 2018:102-114. [CrossRef]
  29. World Health Organization. Constitution of the World Health Organization. 2006.   URL: https://www.who.int/governance/eb/who_constitution_en.pdf [accessed 2020-12-26]
  30. Pfeiffer J, Von Entress-fuersteneck M, Urbach N, Buchwald A. Quantify-me: consumer acceptance of wearable self-tracking devices. 2016 Presented at: 24th European Conference on Information Systems; June 12-15, 2016; Istanbul,Turkey p. 99.
  31. Swan M. Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking. Int J Environ Res Public Health 2009;6(2):492-525. [CrossRef]
  32. Kourogi M, Kurata T. Personal positioning based on walking locomotion analysis with self-contained sensors and a wearable camera. 2003 Presented at: IEEE / ACM International Symposium on Mixed and Augmented Reality; October 7-10, 2003; Tokyo, Japan p. 103-112.
  33. Chen Z, Lin M, Chen F, Lane ND, Cardone G, Wang R, et al. Unobtrusive sleep monitoring using smartphones. 2013 Presented at: 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops; May 5-8; Venice, Italy p. 145-152. [CrossRef]
  34. Kelley C, Lee B, Wilcox L. Self-tracking for mental wellness: understanding expert perspectives and student experiences. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. 2017 May Presented at: CHI: Conference on Human Factors in Computing Systems; May 6-11, 2017; Denver, CO, United States p. 629-641   URL: http://europepmc.org/abstract/MED/28920106 [CrossRef]
  35. Lomborg S, Frandsen K. Self-tracking as communication. Inf Commun Soc 2016;19(7):1015-1027. [CrossRef]
  36. Sharon T, Zandbergen D. From data fetishism to quantifying selves: self-tracking practices and the other values of data. New Media Soc 2017;19(11):1695-1709. [CrossRef]
  37. 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(1):93-121. [CrossRef]
  38. Piras EM, Miele F. Clinical self-tracking and monitoring technologies: negotiations in the ICT-mediated patient–provider relationship. Heal Sociol Rev 2017;26(1):38-53. [CrossRef]
  39. Ancker JS, Witteman HO, Hafeez B, Provencher T, Van de Graaf M, Wei E. “You get reminded you’re a sick person”: personal data tracking and patients with multiple chronic conditions. J Med Internet Res 2015 Aug;17(8):e202 [FREE Full text] [CrossRef] [Medline]
  40. Vogel J, Auinger A, Riedl R, Kindermann H, Helfert M, Ocenasek H. Digitally enhanced recovery: investigating the use of digital self-tracking for monitoring leisure time physical activity of cardiovascular disease (CVD) patients undergoing cardiac rehabilitation. PLoS One 2017;12(10):e0186261 [FREE Full text] [CrossRef] [Medline]
  41. Schwennesen N. When self-tracking enters physical rehabilitation: from ‘pushed’ self-tracking to ongoing affective encounters in arrangements of care. Digit Health 2017;3:2055207617725231 [FREE Full text] [CrossRef] [Medline]
  42. Kari T, Koivunen S, Frank L, Makkonen M, Moilanen P. Critical experiences during the implementation of a self-tracking technology. 2016 Presented at: Proceedings of the 20th Pacific Asia Conference on Information Systems; June 27-July 1, 2016; Chiayi, Taiwan.
  43. Sjöklint M, Constantiou I, Trier M. The complexities of self-tracking – an inquiry into user reactions and goal attainment. 2015 Presented at: 23rd European Conference on Information Systems; May 26-29, 2015; Münster, Germany. [CrossRef]
  44. Gimpel H, Nißen M, Görlitz RA. Quantifying the quantified self: a study on the motivations of patients to track their own health. 2013 Presented at: 34th International Conference on Information Systems; December 15-18, 2013; Milano, Italy.
  45. Petticrew M, Roberts H. Systematic Reviews in the Social Sciences: A Practical Guide. Oxford, United Kingdom: Blackwell Publishing; 2006.
  46. Higgins JP, Green S. Cochrane Handbook for Systematic Reviews of Interventions. 2nd Edition. Chichester, United Kindgom: John Wiley & Sons; 2008.
  47. Victor L. Systematic reviewing. Soc Res Updat 2008(54):1-4 [FREE Full text]
  48. Okoli C, Schabram K. A guide to conducting a systematic literature review of information systems research. Sprouts Work Pap Inf Syst 2010;10(26):1-49. [CrossRef]
  49. Idri A, Amazal FA, Abran A. Analogy-based software development effort estimation: a systematic mapping and review. Inf Softw Technol 2015;58:206-230. [CrossRef]
  50. Behera RK, Bala PK, Dhir A. The emerging role of cognitive computing in healthcare: a systematic literature review. Int J Med Inform 2019 Sep;129:154-166. [CrossRef] [Medline]
  51. Sollaci LB, Pereira MG. The introduction, methods, results, and discussion (IMRAD) structure: a fifty-year survey. J Med Libr Assoc 2004 Jul;92(3):364-367. [Medline]
  52. Zapata BC, Fernández-Alemán JL, Idri A, Toval A. Empirical studies on usability of mHealth apps: a systematic literature review. J Med Syst 2015;39(2):1-19. [CrossRef]
  53. Gui X, Chen Y, Caldeira C, Xiao D, Chen Y. When fitness meets social networks: investigating fitness tracking and social practices on WeRun. In: CHI ’17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. 2017 Presented at: CHI Conference on Human Factors in Computing Systems; May 6–11, 2017; Denver, CO, United States p. 1647-1659. [CrossRef]
  54. Kim Y, Ji S, Lee H, Kim J, Yoo S, Lee J. “My doctor is keeping an eye on me!”: exploring the clinical applicability of a mobile food logger. In: CHI ’16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 2016 Presented at: 2016 CHI Conference on Human Factors in Computing Systems; May 07-12, 2016; San Jose, CA, United States p. 5620-5631. [CrossRef]
  55. Gambier-Ross K, McLernon DJ, Morgan HM. A mixed methods exploratory study of women’s relationships with and uses of fertility tracking apps. Digit Health 2018;4:2055207618785077 [FREE Full text] [CrossRef] [Medline]
  56. Zhou Y, Kankanhalli A, Huang KW. Effects of fitness applications with SNS: how do they influence physical activity. 2016 Presented at: 37th International Conference on Information Systems; December 11-14, 2016; Dublin, Ireland.
  57. Kim J. Analysis of health consumers’ behavior using self-tracker for activity, sleep, and diet. Telemed e-Health 2014;20(6):552-558. [CrossRef]
  58. Kim J. A qualitative analysis of user experiences with a self-tracker for activity, sleep, and diet. Interact J Med Res 2014 Mar;3(1):e8 [FREE Full text] [CrossRef] [Medline]
  59. Baudier P, Ammi C, Lecouteux A. Employees’ acceptance of the healthcare Internet of Things: a source of innovation in corporate human resource policies. J Innov Econ Manag 2019(3):89-111. [CrossRef]
  60. Hamari J, Hassan L, Dias A. Gamification, quantified-self or social networking? matching users’ goals with motivational technology. User Model User-Adap Inter 2018;28(1):35-74. [CrossRef]
  61. Chuah SHW. You inspire me and make my life better: investigating a multiple sequential mediation model of smartwatch continuance intention. Telemat Inform 2019 Oct;43:101245. [CrossRef]
  62. Suh A. Sustaining the use of quantified-self technology: a theoretical extension and empirical test. Asia Pacific J Inf Syst 2018;28(2):114-132. [CrossRef]
  63. Shin D, Biocca F. Health experience model of personal informatics: the case of a quantified self. Comput Human Behav 2017;69:62-74. [CrossRef]
  64. Baumgart R. Another step towards the understanding of self-tracking: a research model and pilot test. 2017 Presented at: 23rd Americas Conference on Information Systems; August 10-12, 2017; Boston, MA, United States.
  65. Baumgart R. How does quantified self run? 2016 Presented at: 22nd Americas Conference on Information Systems; August 11-14, 2016; San Diego, CA, United States.
  66. Baumgart R, Holten R. How does self-tracking go? a research model and pre-test. 2018 Presented at: 51st Hawaii International Conference on System Sciences; January 3-6, 2018; Waikoloa Village, Hawaii, United States p. 3189-3196. [CrossRef]
  67. 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;94:131-139. [CrossRef]
  68. Stragier J, Vanden Abeele M, Mechant P, De Marez L. Understanding persistence in the use of online fitness communities: comparing novice and experienced users. Comput Human Behav 2016;64:34-42. [CrossRef]
  69. Wittkowski K, Klein JF, Falk T, Schepers JJL, Aspara J, Bergner KN. What gets measured gets done: Can self-tracking technologies enhance advice compliance? J Serv Res 2020;23(3):281-298. [CrossRef]
  70. Abril EP. Tracking myself: assessing the contribution of mobile technologies for self-trackers of weight, diet, or exercise. J Health Commun 2016;21(6):638-646. [CrossRef]
  71. Webster J, Watson RT. Analyzing the past to prepare for the future: writing a literature review. MIS Q 2002;26(2):xiii-xxiii [FREE Full text]
  72. Nokes KM, Reyes DM, Hickey DE. Perceptions of low income HIV+ persons about finding accurate web-based health information. Appl Nurs Res 2018;42:70-76. [CrossRef]
  73. Esmonde K. Training, tracking, and traversing: digital materiality and the production of bodies and/in space in runners’ fitness tracking practices. Leis Stud 2019;38(6):804-817. [CrossRef]
  74. Rönkkö K. An activity tracker and its accompanying app as a motivator for increased exercise and better sleeping nabits for youths in need of social care: field study. JMIR Mhealth Uhealth 2018 Dec;6(12):e193 [FREE Full text] [CrossRef] [Medline]
  75. Faust L, Hachen D, Lizardo O, Chawla NV. Quantifying subjective well-being using trends in weekend activity. 2018 Presented at: IEEE International Conference on Healthcare Informatics; June 4-7, 2018; New York, NY, United States p. 123-129. [CrossRef]
  76. Depper A, Howe PD. Are we fit yet? English adolescent girls’ experiences of health and fitness apps. Heal Sociol Rev 2017;26(1):98-112. [CrossRef]
  77. McMahon SK, Lewis B, Oakes M, Guan W, Wyman JF, Rothman AJ. Older adults’ experiences using a commercially available monitor to self-track their physical activity. JMIR Mhealth Uhealth 2016 Apr;4(2):e35 [FREE Full text] [CrossRef] [Medline]
  78. Ravichandran R, Sien SW, Patel SN, Kientz JA, Pina LR. Making sense of sleep sensors: how sleep sensing technologies support and undermine sleep health. In: CHI ’17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. 2017 Presented at: CHI Conference on Human Factors in Computing Systems; May 06-11, 2017; Denver, CO, United States p. 6864-6875. [CrossRef]
  79. Didžiokaitė G, Saukko P, Greiffenhagen C. The mundane experience of everyday calorie trackers: beyond the metaphor of quantified self. New Media Soc 2018;20(4):1470-1487. [CrossRef]
  80. Milward J, Deluca P, Drummond C, Kimergård A. Developing typologies of user engagement with the BRANCH alcohol-harm reduction smartphone app: qualitative study. JMIR Mhealth Uhealth 2018 Dec;6(12):e11692 [FREE Full text] [CrossRef] [Medline]
  81. Makkonen M, Kari T, Frank L, Moilanen P. Adoption and adoption interests of self-tracking technologies: single and multiple technology perspectives. In: Proceedings of the 29th Bled eConference “Digital Economy”. 2016 Presented at: 29th Bled eConference “Digital Economy”; June 19-22, 2016; Bled, Slovenia.
  82. Lyall B, Robards B. Tool, toy and tutor: subjective experiences of digital self-tracking. J Sociol 2017;54(1):108-124. [CrossRef]
  83. Rockmann R, Salou T, Gewald H. If you are happy and don’t know IT: Continuance? analyzing emotion carry-over effects in activity tracking continuance decisions. 2018 Presented at: Pacific Asia Conference on Information Systems; June 26-30, 2018; Yokohama, Japan p. 238.
  84. Hardey MM. On the body of the consumer: performance-seeking with wearables and health and fitness apps. Sociol Health Illn 2019;41(6):991-1004. [CrossRef]
  85. Meyer J, Beck E, Wasmann M, Boll S. Making sense in the long run: long-term health monitoring in real lives. 2017 Presented at: IEEE International Conference on Healthcare Informatics; August 23-26, 2017; Park City, UT, United States p. 285-294. [CrossRef]
  86. Feng Y, Li K, Agosto DE. Healthy users’ personal health information management from activity trackers: the perspective of gym-goers. Proc Assoc Info Sci Tech 2017 Oct;54(1):71-81. [CrossRef]
  87. Yli-Kauhaluoma S, Pantzar M. Seeking connectivity to everyday health and wellness experiences: specificities and consequences of connective gaps in self-tracking data. Digit Health 2018;4:2055207618779714 [FREE Full text] [CrossRef] [Medline]
  88. Rapp A, Cena F. Personal informatics for everyday life: how users without prior self-tracking experience engage with personal data. Int J Hum Comput Stud 2016;94:1-17. [CrossRef]
  89. Chen J, Bauman A, Allman-Farinelli M. A study to determine the most popular lifestyle smartphone applications and willingness of the public to share their personal data for health research. Telemed J E Health 2016 Aug;22(8):655-665. [CrossRef] [Medline]
  90. Zimmer M, Kumar P, Vitak J, Liao Y, Chamberlain Kritikos K. ‘There’s nothing really they can do with this information’: unpacking how users manage privacy boundaries for personal fitness information. Inf Commun Soc 2018:1-18. [CrossRef]
  91. Attig C, Franke T. Abandonment of personal quantification: a review and empirical study investigating reasons for wearable activity tracking attrition. Comput Human Behav 2020 Jan;102:223-237. [CrossRef]
  92. Harrison D, Bianchi-berthouze P, Bird J. Activity tracking: barriers, workarounds and customisation. In: UbiComp ’15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 2015 Presented at: 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing; September 7–11, 2015; Osaka, Japan p. 617. [CrossRef]
  93. Esmonde K. ‘There’s only so much data you can handle in your life’: accommodating and resisting self-surveillance in women’s running and fitness tracking practices. Qual Res Sport Exerc Health 2019 May;12(1):76-90. [CrossRef]
  94. McKinney P, Cox AM, Sbaffi L. Information literacy in food and activity tracking among parkrunners, people with type 2 diabetes, and people with irritable bowel syndrome: exploratory study. J Med Internet Res 2019 Aug;21(8):e13652 [FREE Full text] [CrossRef] [Medline]
  95. Régnier F, Chauvel L. Digital inequalities in the use of self-tracking diet and fitness apps: interview study on the influence of social, economic, and cultural factors. JMIR Mhealth Uhealth 2018 Apr;6(4):e101 [FREE Full text] [CrossRef] [Medline]
  96. Beukenhorst AL, Howells K, Cook L, McBeth J, O’Neill TW, Parkes MJ, et al. Engagement and participant experiences with consumer smartwatches for health research: longitudinal, observational feasibility study. JMIR Mhealth Uhealth 2020 Jan;8(1):e14368 [FREE Full text] [CrossRef] [Medline]
  97. Eikey EV, Reddy MC. “It’s definitely been a journey”: a qualitative study on how women with eating disorders use weight loss apps. In: CHI ’17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. 2017 Presented at: 2017 CHI Conference on Human Factors in Computing Systems; May 6–11, 2017; Denver, CO, United States. [CrossRef]
  98. Schroeder J, Karkar R, Murinova N, Fogarty J, Munson SA. Examining opportunities for goal-directed self-tracking to support chronic condition management. Proc ACM Interactive, Mobile, Wearable Ubiquitous Technol 2019;3(4):1-26. [CrossRef]
  99. Mishra SR, Predrag K, John MW, Hekler EB, Omberg L, Kellen M, et al. Supporting coping with Parkinson’s disease through self-tracking. In: CHI ’19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019 Presented at: 2019 CHI Conference on Human Factors in Computing Systems; May 4–9, 2019; Glasgow, Scotland, United Kingdom p. 1-16. [CrossRef]
  100. Trauth E, Browning ER. Technologized talk: wearable technologies, patient agency, and medical communication in healthcare settings. Int J Sociotechnology Knowl Dev 2018;10(3):1-26. [CrossRef]
  101. Chung CF, Dew K, Cole A, Zia J, Fogarty J, Kientz JA, et al. 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. 2016 Presented at: 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing; February 27–March 2, 2016; San Francisco, CA, United States p. 770-786. [CrossRef]
  102. Riggare S, Scott Duncan T, Hvitfeldt H, Hägglund M. “You have to know why you’re doing this”: a mixed methods study of the benefits and burdens of self-tracking in Parkinson’s disease. BMC Med Inform Decis Mak 2019 Aug;19(1):175 [FREE Full text] [CrossRef] [Medline]
  103. Gabriels K, Moerenhout T. Exploring entertainment medicine and professionalization of self-care: interview study among doctors on the potential effects of digital self-tracking. J Med Internet Res 2018 Jan;20(1):e10 [FREE Full text] [CrossRef] [Medline]
  104. Chung CF, Gorm N, Shklovski I, Munson SA. Finding the right fit: understanding health tracking in workplace wellness programs. In: CHI ’17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. 2017 Presented at: 2017 CHI CHI Conference on Human Factors in Computing Systems; May 6–11, 2017; Denver, CO, United States p. 4875-4885. [CrossRef]
  105. Lupton D. ‘It’s made me a lot more aware’: a new materialist analysis of health self-tracking. Media Int Aust 2019;171(1):66-79. [CrossRef]
  106. Figueiredo MC, Caldeira C, Eikey E, Mazmanian M, Chen Y. Engaging with health data: the interplay between self-tracking activities and emotions in fertility struggles. Proc ACM Hum Comput Interact 2018;2:1-20. [CrossRef]
  107. Mäntymäki M, Islam AN, Benbasat I. What drives subscribing to premium in freemium services? a consumer value‐based view of differences between upgrading to and staying with premium. Info Systems J 2020 Mar;30(2):295-333. [CrossRef]
  108. Mäntymäki M, Hyrynsalmi S, Koskenvoima A. How do small and medium-sized game companies use analytics? an attention-based view of game analytics. Inf Syst Front 2019 Apr;22(5):1163-1178. [CrossRef]
  109. Lupton D. Quantified sex: a critical analysis of sexual and reproductive self-tracking using apps. Cult Health Sex 2015;17(4):440-453. [CrossRef] [Medline]
  110. Salo J, Mäntymäki M, Islam A. The dark side of social media–and Fifty Shades of Grey introduction to the special issue: the dark side of social media. Internet Res 2018;28(5):1166-1168. [CrossRef]
  111. Tarafdar M, Gupta A, Turel O. The dark side of information technology use. Inf Syst J 2013 May;23(3):269-275. [CrossRef]
  112. D’Arcy J, Gupta A, Tarafdar M, Turel O. Reflecting on the “dark side” of information technology use. Commun Assoc Inf Syst 2014;35(1):109-118. [CrossRef]
  113. Lupton D. Quantifying the body: monitoring and measuring health in the age of mHealth technologies. Crit Public Health 2013 Dec;23(4):393-403. [CrossRef]