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Abstract

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.

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References

  • AARP (2015) Building a better tracker: older consumers weigh in on activity and sleep monitoring devices

    Google Scholar 

  • 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:e202

    Article  Google Scholar 

  • 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 57–64

    Google Scholar 

  • 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:e104

    Article  Google Scholar 

  • 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 1:4

    Article  Google Scholar 

  • 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 593–596

    Google Scholar 

  • 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. https://doi.org/10.1177/1541931213571162

    Article  Google Scholar 

  • 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 108–111

    Article  Google Scholar 

  • 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 468–471

    Google Scholar 

  • Burton KE (2016) Evaluating activity and sleep tracking technologies for older adults. Georgia Institute of Technology

    Google Scholar 

  • 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 227–230

    Google Scholar 

  • 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 1605–1617

    Google Scholar 

  • Casilari E, Oviedo-Jiménez MA (2015) Automatic fall detection system based on the combined use of a smartphone and a smartwatch. PLoS ONE 10:e0140929

    Article  Google Scholar 

  • 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 1143–1152

    Google Scholar 

  • 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 63–76

    Google Scholar 

  • 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 45:92–96

    Article  Google Scholar 

  • 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 26

    Google Scholar 

  • Dean K, Hickey T, Holstein BE (1986) Self-care and health in old age: health behaviour implications for policy and practice. Routledge, London

    Google Scholar 

  • DeFriese GH, Ory MG (1998) Self care in later life: research, program, and policy issues. Springer Publishing, New York

    Google Scholar 

  • 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. https://doi.org/10.1371/journal.pone.0192807

    Article  Google Scholar 

  • 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 467–476

    Google Scholar 

  • 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 6398–6411

    Google Scholar 

  • 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 33–40

    Google Scholar 

  • 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 1683–1687

    Article  Google Scholar 

  • 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 72:229–236

    Article  Google Scholar 

  • Fox S, Duggan M (2013) Tracking for health. Pew Research Center’s Internet & American Life Project

    Google Scholar 

  • 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 48:225–234

    Article  Google Scholar 

  • Gatto SL, Tak SH (2008) Computer, internet, and e-mail use among older adults: benefits and barriers. Educ Gerontol 34:800–811

    Article  Google Scholar 

  • 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 1815–1819

    Article  Google Scholar 

  • Grant MJ, Booth A (2009) A typology of reviews: an analysis of 14 review types and associated methodologies. Health Inf Libr J 26:91–108

    Article  Google Scholar 

  • 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 3:158–171

    Article  Google Scholar 

  • Helal A, Mokhtari M, Abdulrazak B (2008) The engineering handbook of smart technology for aging, disability and independence. Wiley, Hoboken

    Google Scholar 

  • Intille SS (2004) Ubiquitous computing technology for just-in-time motivation of behavior change. Medinfo 107:1434–1437

    Google Scholar 

  • Karkar R, Zia J, Vilardaga R et al (2015) A framework for self-experimentation in personalized health. J Am Med Inform Assoc 23:440–448

    Article  Google Scholar 

  • 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 385–391

    Google Scholar 

  • 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:e62613

    Article  Google Scholar 

  • 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 3071–3082

    Google Scholar 

  • 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 96:355–360

    Article  Google Scholar 

  • 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 2095–2104

    Google Scholar 

  • 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 405–414

    Google Scholar 

  • 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 295–304

    Google Scholar 

  • 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 424–429

    Google Scholar 

  • 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 297–304

    Google Scholar 

  • 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) 18:156–164

    Article  Google Scholar 

  • 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:e35

    Article  Google Scholar 

  • 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 58:2099–2106

    Article  Google Scholar 

  • 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:e7

    Article  Google Scholar 

  • 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 1027–1031

    Article  Google Scholar 

  • 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 338

    Google Scholar 

  • 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 755–758

    Google Scholar 

  • Orji R, Moffatt K (2018) Persuasive technology for health and wellness: state-of-the-art and emerging trends. Health Inform J 24:66–91

    Article  Google Scholar 

  • Phillips LJ, Petroski GF, Conn VS et al (2016) Exploring path models of disablement in residential care and assisted living residents. J Appl Gerontol https://doi.org/10.1177/0733464816672048

    Article  Google Scholar 

  • Preusse KC, Mitzner TL, Fausset CB et al (2017) Older adults’ acceptance of activity trackers. J Appl Gerontol 36:127–155

    Article  Google Scholar 

  • 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 3217–3222

    Google Scholar 

  • 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 1411–1416

    Google Scholar 

  • 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 

  • Rettberg JW (2014) Seeing ourselves through technology: how we use selfies, blogs and wearable devices to see and shape ourselves. Palgrave Macmillan, London

    Google Scholar 

  • Sailer F, Pobiruchin M, Wiesner M et al (2015) An approach to improve medication adherence by smart watches. In: MIE, pp 956–958

    Google Scholar 

  • 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 7:111–116

    Article  Google Scholar 

  • 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 47:830–835

    Article  Google Scholar 

  • Snyder A, Colvin B, Gammack JK (2011) Pedometer use increases daily steps and functional status in older adults. J Am Med Dir Assoc 12:590–594

    Article  Google Scholar 

  • 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 17:1277

    Article  Google Scholar 

  • Thompson WG, Kuhle CL, Koepp GA et al (2014) “Go4Life” exercise counseling, accelerometer feedback, and activity levels in older people. Arch Gerontol Geriatr 58:314–319

    Article  Google Scholar 

  • 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 201–210

    Google Scholar 

  • Ward BW, Schiller JS, Goodman RA (2014) Peer reviewed: multiple chronic conditions among us adults: a 2012 update. Prev Chronic Dis 11:E62

    Google Scholar 

  • 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 659–660

    Google Scholar 

  • 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 211–221

    Google Scholar 

  • 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 1:39–44

    Google Scholar 

  • Yusif S, Soar J, Hafeez-Baig A (2016) Older people, assistive technologies, and the barriers to adoption: a systematic review. Int J Med Inf 94:112–116

    Article  Google Scholar 

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Correspondence to Clara Caldeira .

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Caldeira, C., Chen, Y. (2019). Seniors and Self-tracking Technology. In: Sayago, S. (eds) Perspectives on Human-Computer Interaction Research with Older People. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-030-06076-3_5

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