Seniors and Self-tracking Technology

  • Clara CaldeiraEmail author
  • Yunan Chen
Part of the Human–Computer Interaction Series book series (HCIS)


Technology designed to support self-tracking has grown in numbers and popularity as smartphones have become more powerful and more ubiquitous. However, these tools are not being used by the population that self-tracks the most: older adults. This chapter discusses the use and non-use of self-tracking technologies among seniors based on a review of literature published in HCI and Health Informatics. Known barriers to seniors’ adoption of self-tracking technologies largely result from a primary focus on younger users. Seniors’ needs, interests, goals, and self-tracking practices differ from what is assumed and addressed in the tools that are currently available. To address this issue, it is necessary for future work to investigate new designs that are more compatible with seniors’ priorities and self-tracking practices without diminishing seniors’ sense of agency or emphasizing stigmatized aspects of health or aging.


  1. AARP (2015) Building a better tracker: older consumers weigh in on activity and sleep monitoring devicesGoogle Scholar
  2. Ancker JS, Witteman HO, Hafeez B et al (2015) “You get reminded you’re a sick person”: personal data tracking and patients with multiple chronic conditions. J Med Internet Res 17:e202CrossRefGoogle Scholar
  3. Araullo J, Potter LE (2016) Promoting physical activity in seniors: future opportunities with emerging technologies. In: Proceedings of the 2016 ACM SIGMIS conference on computers and people research. ACM, pp 57–64Google Scholar
  4. Arnhold M, Quade M, Kirch W (2014) Mobile applications for diabetics: a systematic review and expert-based usability evaluation considering the special requirements of diabetes patients age 50 years or older. J Med Internet Res 16:e104CrossRefGoogle Scholar
  5. Ashe MC, Winters M, Hoppmann CA et al (2015) “Not just another walking program”: everyday activity supports you (EASY) model—a randomized pilot study for a parallel randomized controlled trial. Pilot Feasibility Stud 1:4CrossRefGoogle Scholar
  6. Bagalkot N, Sokoler T (2011) MagicMirror: towards enhancing collaborative re-habilitation practices. In: Proceedings of the ACM 2011 conference on computer supported cooperative work. ACM, pp 593–596Google Scholar
  7. Barg-Walkow LH, McBride SE, Morgan MJ et al (2013) How do older adults manage osteoarthritis pain? The need for a person-centered disease model. Proc Hum Factors Ergon Soc Annu Meet 57:743–747. Scholar
  8. Barg-Walkow LH, McBride SE, Morgan Jr MJ et al (2014) Efficacy of a system for tracking and managing osteoarthritis pain for both healthcare providers and older adults. In: Proceedings of the international symposium on human factors and ergonomics in health care. Sage, New Delhi, India, pp 108–111CrossRefGoogle Scholar
  9. Binda J, Park H, Carroll JM et al (2017) Intergenerational sharing of health data among family members. In: Proceedings of the 11th EAI international conference on pervasive computing technologies for healthcare. ACM, pp 468–471Google Scholar
  10. Burton KE (2016) Evaluating activity and sleep tracking technologies for older adults. Georgia Institute of TechnologyGoogle Scholar
  11. Caldeira C, Bietz M, Chen Y (2016) Looking for the unusual: how older adults utilize self-tracking techniques for health management. In: Proceedings of the 10th EAI international conference on pervasive computing technologies for healthcare, pp 227–230Google Scholar
  12. Caldeira C, Bietz M, Vidauri M et al (2017) Senior care for aging in place: balancing assistance and independence. In: Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing. ACM, pp 1605–1617Google Scholar
  13. Casilari E, Oviedo-Jiménez MA (2015) Automatic fall detection system based on the combined use of a smartphone and a smartwatch. PLoS ONE 10:e0140929CrossRefGoogle Scholar
  14. Choe EK, Lee NB, Lee B et al (2014) Understanding quantified-selfers’ practices in collecting and exploring personal data. ACM, New York, NY, USA, pp 1143–1152Google Scholar
  15. Conci M, Pianesi F, Zancanaro M (2009) Useful, social and enjoyable: mobile phone adoption by older people. In: IFIP conference on human-computer interaction. Springer, pp 63–76Google Scholar
  16. Cross MJ, March LM, Lapsley HM et al (2005) Patient self-efficacy and health locus of control: relationships with health status and arthritis-related expenditure. Rheumatology 45:92–96CrossRefGoogle Scholar
  17. Davidson JL, Jensen C (2013) What health topics older adults want to track: a participatory design study. In: Proceedings of the 15th international ACM SIGACCESS conference on computers and accessibility. ACM, p 26Google Scholar
  18. Dean K, Hickey T, Holstein BE (1986) Self-care and health in old age: health behaviour implications for policy and practice. Routledge, LondonGoogle Scholar
  19. DeFriese GH, Ory MG (1998) Self care in later life: research, program, and policy issues. Springer Publishing, New YorkGoogle Scholar
  20. Dugas M, Crowley K, Gao GG et al (2018) Individual differences in regulatory mode moderate the effectiveness of a pilot mHealth trial for diabetes management among older veterans. PLoS ONE 13:e0192807. Scholar
  21. Durick J, Robertson T, Brereton M et al (2013) Dispelling ageing myths in technology design. In: Proceedings of the 25th australian computer-human interaction conference: augmentation, application, innovation, collaboration. ACM, pp 467–476Google Scholar
  22. Durrant A, Kirk D, Trujillo Pisanty D et al (2017) Transitions in digital person-hood: online activity in early retirement. In: Proceedings of the 2017 CHI conference on human factors in computing systems. ACM, pp 6398–6411Google Scholar
  23. Fan C, Forlizzi J, Dey A (2012) Considerations for technology that support physical activity by older adults. In: Proceedings of the 14th international ACM SIGACCESS conference on computers and accessibility. ACM, pp 33–40Google Scholar
  24. Fausset CB, Mitzner TL, Price CE et al (2013) Older adults’ use of and attitudes toward activity monitoring technologies. In: Proceedings of the human factors and ergonomics society annual meeting. Sage, Los Angeles, CA, pp 1683–1687CrossRefGoogle Scholar
  25. Floegel TA, Florez-Pregonero A, Hekler EB et al (2016) Validation of consumer-based hip and wrist activity monitors in older adults with varied ambulatory abilities. J Gerontol Ser Biomed Sci Med Sci 72:229–236CrossRefGoogle Scholar
  26. Fox S, Duggan M (2013) Tracking for health. Pew Research Center’s Internet & American Life ProjectGoogle Scholar
  27. French DP, Olander EK, Chisholm A et al (2014) Which behaviour change techniques are most effective at increasing older adults’ self-efficacy and physical activity behaviour? A systematic review. Ann Behav Med 48:225–234CrossRefGoogle Scholar
  28. Gatto SL, Tak SH (2008) Computer, internet, and e-mail use among older adults: benefits and barriers. Educ Gerontol 34:800–811CrossRefGoogle Scholar
  29. Gonzalez ET, Jones AM, Harley LR et al (2014) Older adults’ perceptions of a neckwear health technology. In: Proceedings of the human factors and ergonomics society annual meeting. Sage, Los Angeles, CA, pp 1815–1819CrossRefGoogle Scholar
  30. Grant MJ, Booth A (2009) A typology of reviews: an analysis of 14 review types and associated methodologies. Health Inf Libr J 26:91–108CrossRefGoogle Scholar
  31. Harvey JA, Skelton DA, Chastin SF (2016) Acceptability of novel life logging technology to determine context of sedentary behaviour in older adults. AIMS Public Health 3:158–171CrossRefGoogle Scholar
  32. Helal A, Mokhtari M, Abdulrazak B (2008) The engineering handbook of smart technology for aging, disability and independence. Wiley, HobokenGoogle Scholar
  33. Intille SS (2004) Ubiquitous computing technology for just-in-time motivation of behavior change. Medinfo 107:1434–1437Google Scholar
  34. Karkar R, Zia J, Vilardaga R et al (2015) A framework for self-experimentation in personalized health. J Am Med Inform Assoc 23:440–448CrossRefGoogle Scholar
  35. Karshmer JF, Karshmer AI (2004) A computer-based self-health monitoring system for the elderly living in a low income housing environment. In: K Miesenberger, J Klaus, WL Zagler, D Burger (eds) International conference on computers for handicapped persons. Springer, New York, pp 385–391Google Scholar
  36. King AC, Hekler EB, Grieco LA et al (2013) Harnessing different motivational frames via mobile phones to promote daily physical activity and reduce sedentary behavior in aging adults. PLoS ONE 8:e62613CrossRefGoogle Scholar
  37. Klasnja P, Hekler EB, Korinek EV et al (2017) Toward usable evidence: optimizing knowledge accumulation in HCI research on health behavior change. In: Proceedings of the 2017 CHI conference on human factors in computing systems. ACM, pp 3071–3082Google Scholar
  38. Klassen TD, Simpson LA, Lim SB et al (2016) “Stepping Up” activity poststroke: ankle-positioned accelerometer can accurately record steps during slow walking. Phys Ther 96:355–360CrossRefGoogle Scholar
  39. Lee ML, Dey AK (2011) Reflecting on pills and phone use: supporting awareness of functional abilities for older adults. ACM, New York, NY, USA, pp 2095–2104Google Scholar
  40. Li I, Dey AK, Forlizzi J (2011) Understanding my data, myself: supporting self-reflection with ubicomp technologies. In: Proceedings of the 13th international conference on ubiquitous computing. ACM, New York, NY, USA, pp 405–414Google Scholar
  41. Light A, Leong TW, Robertson T (2015) Ageing well with CSCW. In: ECSCW 2015: proceedings of the 14th European conference on computer supported cooperative work, 19–23 September 2015, Oslo, Norway. Springer, pp 295–304Google Scholar
  42. Lo H-C, Tsai C-L, Lin K-P et al (2014) Usability evaluation of home-use glucose meters for senior users. In: International conference on human-computer interaction. Springer, pp 424–429Google Scholar
  43. Lorenz A, Mielke D, Oppermann R et al (2007) Personalized mobile health monitoring for elderly. In: Proceedings of the 9th international conference on human computer interaction with mobile devices and services. ACM, pp 297–304Google Scholar
  44. McCann L, Maguire R, Miller M et al (2009) Patients’ perceptions and experiences of using a mobile phone-based advanced symptom management system (ASyMS\copyright) to monitor and manage chemotherapy related toxicity. Eur J Cancer Care (Engl) 18:156–164CrossRefGoogle Scholar
  45. McMahon SK, Lewis B, Oakes M et al (2016) Older adults’ experiences using a commercially available monitor to self-track their physical activity. JMIR mHealth uHealth 4:e35CrossRefGoogle Scholar
  46. McMurdo ME, Sugden J, Argo I et al (2010) Do pedometers increase physical activity in sedentary older women? A randomized controlled trial. J Am Geriatr Soc 58:2099–2106CrossRefGoogle Scholar
  47. Mercer K, Giangregorio L, Schneider E et al (2016) Acceptance of commercially available wearable activity trackers among adults aged over 50 and with chronic illness: a mixed-methods evaluation. JMIR mHealth uHealth 4:e7CrossRefGoogle Scholar
  48. Miller S, Mutlu B, Lee J (2013) Artifact usage, context, and privacy management in logging and tracking personal health information in older adults. In: Proceedings of the human factors and ergonomics society annual meeting. Sage, Los Angeles, CA, pp 1027–1031CrossRefGoogle Scholar
  49. Mitzner TL, Dijkstra K (2017) Evaluating user-centered design of e-health for older adults. In: Health care delivery and clinical science: concepts, methodologies, tools, and applications, p 338Google Scholar
  50. Mohan P, Marin D, Sultan S et al (2008) MediNet: personalizing the self-care process for patients with diabetes and cardiovascular disease using mobile telephony. In: Engineering in medicine and biology society, EMBS 2008. 30th annual international conference of the IEEE. IEEE, pp 755–758Google Scholar
  51. Orji R, Moffatt K (2018) Persuasive technology for health and wellness: state-of-the-art and emerging trends. Health Inform J 24:66–91CrossRefGoogle Scholar
  52. Phillips LJ, Petroski GF, Conn VS et al (2016) Exploring path models of disablement in residential care and assisted living residents. J Appl Gerontol Scholar
  53. Preusse KC, Mitzner TL, Fausset CB et al (2017) Older adults’ acceptance of activity trackers. J Appl Gerontol 36:127–155CrossRefGoogle Scholar
  54. Qian H, Kuber R, Sears A (2010) Maintaining levels of activity using a haptic personal training application. In: CHI’10 extended abstracts on human factors in computing systems. ACM, New York, NY, USA, pp 3217–3222Google Scholar
  55. Rasche P, Wille M, Theis S et al (2015) Activity tracker and elderly. In: 2015 IEEE international conference on computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computing (CIT/IUCC/DASC/PICOM). IEEE, pp 1411–1416Google Scholar
  56. Rasche P, Wille M, Theis S, Schäfer K, Schlick CM, Mertens A (2016) Self monitoring—an age-related comparison. In: D de Waard, KA Brookhuis, A Toffetti, A Stuiver, C Weikert, D Coelho, D Manzey, AB Ünal, S Röttger, N Merat (eds). Proceedings of the Human Factors and Ergonomics Society Europe Chapter 2015 Annual Conference (pp.7–19)Google Scholar
  57. Rettberg JW (2014) Seeing ourselves through technology: how we use selfies, blogs and wearable devices to see and shape ourselves. Palgrave Macmillan, LondonGoogle Scholar
  58. Sailer F, Pobiruchin M, Wiesner M et al (2015) An approach to improve medication adherence by smart watches. In: MIE, pp 956–958Google Scholar
  59. Schlomann A, von Storch K, Rasche P et al (2016) Means of motivation or of stress? The use of fitness trackers for self-monitoring by older adults. Motivierend oder überfordernd? Die Nutzung von Fitness Trackern zum Selbst-Monitoring älterer Menschen. HeilberufeScience 7:111–116CrossRefGoogle Scholar
  60. Simpson LA, Eng JJ, Klassen TD et al (2015) Capturing step counts at slow walking speeds in older adults: comparison of ankle and waist placement of measuring device. J Rehabil Med 47:830–835CrossRefGoogle Scholar
  61. Snyder A, Colvin B, Gammack JK (2011) Pedometer use increases daily steps and functional status in older adults. J Am Med Dir Assoc 12:590–594CrossRefGoogle Scholar
  62. Tedesco S, Barton J, O’Flynn B (2017) A review of activity trackers for senior citizens: research perspectives, commercial landscape and the role of the insurance industry. Sensors 17:1277CrossRefGoogle Scholar
  63. Thompson WG, Kuhle CL, Koepp GA et al (2014) “Go4Life” exercise counseling, accelerometer feedback, and activity levels in older people. Arch Gerontol Geriatr 58:314–319CrossRefGoogle Scholar
  64. Tsai W-C, Chang C-L, Lin H (2015) The design of pain management and creative service for older adults with chronic disease. In: International conference on human aspects of IT for the aged population. Springer, pp 201–210Google Scholar
  65. Ward BW, Schiller JS, Goodman RA (2014) Peer reviewed: multiple chronic conditions among us adults: a 2012 update. Prev Chronic Dis 11:E62Google Scholar
  66. White GE, Connelly KH, Caine KE (2012) Opportunities for ubiquitous computing in the homes of low SES older adults. In: Proceedings of the 2012 ACM conference on ubiquitous computing. ACM, pp 659–660Google Scholar
  67. Whitlock LA, McLaughlin AC, Harris M, Bradshaw J (2015) The design of mobile technology to support diabetes self-management in older adults. In: International conference on human aspects of IT for the aged population. Springer, pp 211–221Google Scholar
  68. Yamada M, Mori S, Nishiguchi S et al (2012) Pedometer-based behavioral change program can improve dependency in sedentary older adults: a randomized controlled trial. J Frailty Aging 1:39–44Google Scholar
  69. Yusif S, Soar J, Hafeez-Baig A (2016) Older people, assistive technologies, and the barriers to adoption: a systematic review. Int J Med Inf 94:112–116CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of CaliforniaIrvineUSA

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