Cited by 278Technology Use by Older AdultsInnovative Human-Technology InteractionMobile Health and mHealth Applications

Older Adults’ Experiences With Using Wearable Devices: Qualitative Systematic Review and Meta-synthesis

Open access
Kevin Moore, Emma O’Shea, Lorna Kenny, John J. Barton, Salvatore Antonio Tedesco, Marco Sica, Colum Crowe, Antti Alamäki +3 more·2021·JMIR mhealth and uhealth

Published version of a preprint

Abstract

Background: Older adults may use wearable devices for various reasons, ranging from monitoring clinically relevant health metrics or detecting falls to monitoring physical activity. Little is known about how this population engages with wearable devices, and no qualitative synthesis exists to describe their shared experiences with long-term use. Objective: This study aims to synthesize qualitative studies of user experience after a multi-day trial with a wearable device to understand user experience and the factors that contribute to the acceptance and use of wearable devices. Methods: We conducted a systematic search in CINAHL, APA PsycINFO, PubMed, and Embase (2015-2020; English) with fixed search terms relating to older adults and wearable devices. A meta-synthesis methodology was used. We extracted themes from primary studies, identified key concepts, and applied reciprocal and refutational translation techniques; findings were synthesized into third-order interpretations, and finally, a “line-of-argument” was developed. Our overall goal was theory development, higher-level abstraction, and generalizability for making this group of qualitative findings more accessible. Results: In total, we reviewed 20 papers; 2 evaluated fall detection devices, 1 tested an ankle-worn step counter, and the remaining 17 tested activity trackers. The duration of wearing ranged from 3 days to 24 months. The views of 349 participants (age: range 51-94 years) were synthesized. Four key concepts were identified and outlined: motivation for device use, user characteristics (openness to engage and functional ability), integration into daily life, and device features. Motivation for device use is intrinsic and extrinsic, encompassing many aspects of the user experience, and appears to be as, if not more, important than the actual device features. To overcome usability barriers, an older adult must be motivated by the useful purpose of the device. A device that serves its intended purpose adds value to the user’s life. The user’s needs and the support structure around the device—aspects that are often overlooked—seem to play a crucial role in long-term adoption. Our “line-of-argument” model describes how motivation, ease of use, and device purpose determine whether a device is perceived to add value to the user’s life, which subsequently predicts whether the device will be integrated into the user’s life. Conclusions: The added value of a wearable device is the resulting balance of motivators (or lack thereof), device features (and their accuracy), ease of use, device purpose, and user experience. The added value contributes to the successful integration of the device into the daily life of the user. Useful device features alone do not lead to continued use. A support structure should be placed around the user to foster motivation, encourage peer engagement, and adapt to the user’s preferences.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

OpenAlex · 10.2196/23832

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Older adults who may rely on wearables for fall detection or chronic disease monitoring are often unaware of how their data is shared with insurers or third parties, raising the possibility of discrimination in premiums or coverage based on wearable-derived metrics.

    “Similarly, older adults, who may rely on wearables for fall detection or chronic disease monitoring 63 , are often unaware of how their data is shared with insurers or third parties 64 -raising the possibility of discrimination in premiums or coverage based on wearable-derived metrics.”

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 52

  1. [1]Levine DM Lipsitz SR Linder JA Trends in seniors' use of digital health technology in the United States, 2011-2014 J Am Med Assoc 2016 8 02 316 5 538 40 10.1001/jama.2016.9124 27483069 27483069 · DOI
  2. [3]Malwade S Abdul SS Uddin M Nursetyo AA Fernandez-Luque L Zhu X Cilliers L Wong C Bamidis P Li Y Mobile and wearable technologies in healthcare for the ageing population Comput Methods Programs Biomed 2018 7 161 233 7 10.1016/j.cmpb.2018.04.026 29852964 29852964 · DOI
  3. [6]Sun N Rau PP The acceptance of personal health devices among patients with chronic conditions Int J Med Inform 2015 4 84 4 288 97 10.1016/j.ijmedinf.2015.01.002 25655783 25655783 · DOI
  4. [7]Nehra AK Gettman MT Rivera ME Agarwal DK O’Neil DA Jenkins SM Tollefson MK Viers BR A survey of perceptions and acceptance of wearable technology for health monitoring in a urological patient population Urol Pract 2017 11 4 6 508 14 10.1016/j.urpr.2016.10.002 37300143 · DOI
  5. [8]Fisher JM Hammerla NY Ploetz T Andras P Rochester L Walker RW Unsupervised home monitoring of Parkinson's disease motor symptoms using body-worn accelerometers Parkinsonism Relat Disord 2016 12 33 44 50 10.1016/j.parkreldis.2016.09.009 27637282 27637282 · DOI
  6. [9]Ehn M Eriksson LC Åkerberg N Johansson A Activity monitors as support for older persons' physical activity in daily life: qualitative study of the users' experiences JMIR Mhealth Uhealth 2018 2 01 6 2 e34 10.2196/mhealth.8345 29391342 29391342 PMC5814603 · DOI
  7. [10]Steele R Lo A Secombe C Wong YK Elderly persons' perception and acceptance of using wireless sensor networks to assist healthcare Int J Med Inform 2009 12 78 12 788 801 10.1016/j.ijmedinf.2009.08.001 19717335 19717335 · DOI · Paper page
  8. [11]Ozanne A Johansson D Graneheim UH Malmgren K Bergquist F Alt Murphy M Wearables in epilepsy and Parkinson's disease - a focus group study Acta Neurol Scand 2018 2 137 2 188 94 10.1111/ane.12798 28714112 28714112 · DOI · Paper page
  9. [12]Steinert A Haesner M Steinhagen-Thiessen E Activity-tracking devices for older adults: comparison and preferences Univ Access Inf Soc 2017 4 8 17 2 411 9 10.1007/s10209-017-0539-7 · DOI
  10. [13]Abouzahra M Ghasemaghaei M The antecedents and results of seniors’ use of activity tracking wearable devices Health Policy Technol 2020 6 9 2 213 7 10.1016/j.hlpt.2019.11.002 · DOI
  11. [14]Demiris G Chaudhuri S Thompson HJ Older adults' experience with a novel fall detection device Telemed J E Health 2016 9 22 9 726 32 10.1089/tmj.2015.0218 26959299 26959299 PMC5031094 · DOI
  12. [15]Farina N Sherlock G Thomas S Lowry RG Banerjee S Acceptability and feasibility of wearing activity monitors in community-dwelling older adults with dementia Int J Geriatr Psychiatry 2019 4 34 4 617 24 10.1002/gps.5064 30701592 30701592 · DOI · Paper page
  13. [16]Atkins S Lewin S Smith H Engel M Fretheim A Volmink J Conducting a meta-ethnography of qualitative literature: lessons learnt BMC Med Res Methodol 2008 4 16 8 21 10.1186/1471-2288-8-21 18416812 18416812 PMC2374791 · DOI · Paper page
  14. [17]Noblit GW Hare RD Meta-Ethnography: Synthesizing Qualitative Studies 1988 Thousand Oaks, California, United States Sage Publications Inc
  15. [18]Kekade S Hseieh C Islam MM Atique S Khalfan AM Li Y Abdul SS The usefulness and actual use of wearable devices among the elderly population Comput Methods Programs Biomed 2018 1 153 137 59 10.1016/j.cmpb.2017.10.008 29157447 29157447 · DOI
  16. [20]Baig MM Afifi S GholamHosseini H Mirza F A systematic review of wearable sensors and iot-based monitoring applications for older adults - a focus on ageing population and independent living J Med Syst 2019 6 15 43 8 233 10.1007/s10916-019-1365-7 31203472 31203472 · DOI
  17. [22]Son H Park WS Kim H Mobility monitoring using smart technologies for Parkinson’s disease in free-living environment Collegian 2018 10 25 5 549 60 10.1016/j.colegn.2017.11.005 · DOI
  18. [25]Allet L Knols RH Shirato K de Bruin ED Wearable systems for monitoring mobility-related activities in chronic disease: a systematic review Sensors (Basel) 2010 10 10 9026 52 10.3390/s101009026 22163393 22163393 PMC3230979 · DOI
  19. [26]Mahoney EL Mahoney DF Acceptance of wearable technology by people with Alzheimer's disease: issues and accommodations Am J Alzheimers Dis Other Demen 2010 9 25 6 527 31 10.1177/1533317510376944 20702501 20702501 PMC10845356 · DOI
  20. [27]Zimmer L Qualitative meta-synthesis: a question of dialoguing with texts J Adv Nurs 2006 2 53 3 311 8 10.1111/j.1365-2648.2006.03721.x 16441536 16441536 · DOI
  21. [28]Bergmann JH McGregor AH Body-worn sensor design: what do patients and clinicians want? Ann Biomed Eng 2011 9 39 9 2299 312 10.1007/s10439-011-0339-9 21674260 21674260 · DOI
  22. [29]Thilo FJ Hahn S Halfens RJ Schols JM Usability of a wearable fall detection prototype from the perspective of older people - a real field testing approach J Clin Nurs 2019 1 28 1-2 310 20 10.1111/jocn.14599 29964344 29964344 · DOI
  23. [30]Floegel TA Allen KD Buman MP A pilot study examining activity monitor use in older adults with heart failure during and after hospitalization Geriatr Nurs 2019 40 2 185 9 10.1016/j.gerinurse.2018.10.001 30391041 30391041 · DOI
  24. [32]Nguyen NH Hadgraft NT Moore MM Rosenberg DE Lynch C Reeves MM Lynch BM A qualitative evaluation of breast cancer survivors' acceptance of and preferences for consumer wearable technology activity trackers Support Care Cancer 2017 11 25 11 3375 84 10.1007/s00520-017-3756-y 28540402 28540402 · DOI
  25. [33]Puri A Kim B Nguyen O Stolee P Tung J Lee J User acceptance of wrist-worn activity trackers among community-dwelling older adults: mixed method study JMIR Mhealth Uhealth 2017 11 15 5 11 e173 10.2196/mhealth.8211 29141837 29141837 PMC5707431 · DOI
  26. [34]Fausset CB Mitzner TL Price CE Jones BD Fain BW Rogers WA Older adults' use of and attitudes toward activity monitoring technologies Proc Hum Factors Ergon Soc Annu Meet 2013 9 57 1 1683 7 10.1177/1541931213571374 31263349 31263349 PMC6601605 · DOI
  27. [35]Preusse KC Mitzner TL Fausset CB Rogers WA Older adults' acceptance of activity trackers J Appl Gerontol 2017 2 36 2 127 55 10.1177/0733464815624151 26753803 26753803 PMC5149448 · DOI · Paper page
  28. [36]Batsis JA Naslund JA Gill LE Masutani RK Agarwal N Bartels SJ Use of a wearable activity device in rural older obese adults: a pilot study Gerontol Geriatr Med 2016 2 1 6 10.1177/2333721416678076 28138502 PMC5120773 28138502 · DOI
  29. [37]Kononova A Li L Kamp K Bowen M Rikard RV Cotten S Peng W The use of wearable activity trackers among older adults: focus group study of tracker perceptions, motivators, and barriers in the maintenance stage of behavior change JMIR Mhealth Uhealth 2019 4 05 7 4 e9832 10.2196/mhealth.9832 30950807 30950807 PMC6473213 · DOI
  30. [38]Hermanns M Haas BK Lisk J Engaging older adults with Parkinson's disease in physical activity using technology: a feasibility study Gerontol Geriatr Med 2019 5 2333721419842671 10.1177/2333721419842671 31069250 31069250 PMC6492351 · DOI
  31. [39]Thorpe J Forchhammer BH Maier AM Adapting mobile and wearable technology to provide support and monitoring in rehabilitation for dementia: feasibility case series JMIR Form Res 2019 10 17 3 4 e12346 10.2196/12346 31625951 31625951 PMC6913510 · DOI
  32. [40]Lee BC Ajisafe TD Van Thanh Vo T Xie J Understanding long-term adoption and usability of wearable activity trackers among active older adults Human Aspects of IT for the Aged Population. Design for the Elderly and Technology Acceptance 2019 Switzerland Springer 238 249
  33. [41]Rosales A Fernández-Ardèvol M Ferran-Ferrer N Long-term appropriation of smartwatches among a group of older people Human Aspects of IT for the Aged Population. Acceptance, Communication and Participation 2018 Switzerland Springer 135 148
  34. [42]Schlomann A A case study on older adults’ long-term use of an activity tracker Gerontechnology 2017 6 30 16 2 115 24 10.4017/gt.2017.16.2.007.00 · DOI
  35. [43]Schlomann A von Storch K Rasche P Rietz C Means of motivation or of stress? The use of fitness trackers for self-monitoring by older adults HBScience 2016 6 13 7 3 111 6 10.1007/s16024-016-0275-6 · DOI
  36. [44]Zhou S Ogihara A Nishimura S Jin Q Analyzing the changes of health condition and social capital of elderly people using wearable devices Health Inf Sci Syst 2018 12 6 1 4 10.1007/s13755-018-0044-2 29692887 29692887 PMC5910377 · DOI
  37. [45]Long AF Godfrey M An evaluation tool to assess the quality of qualitative research studies Int J Soc Res Methodol 2004 4 7 2 181 96 10.1080/1364557032000045302 · DOI
  38. [46]Hannes K Lockwood C Pearson A A comparative analysis of three online appraisal instruments' ability to assess validity in qualitative research Qual Health Res 2010 12 20 12 1736 43 10.1177/1049732310378656 20671302 20671302 · DOI
  39. [49]Toye F Seers K Allcock N Briggs M Carr E Barker K Meta-ethnography 25 years on: challenges and insights for synthesising a large number of qualitative studies BMC Med Res Methodol 2014 6 21 14 80 10.1186/1471-2288-14-80 24951054 24951054 PMC4127190 · DOI
  40. [50]France EF Cunningham M Ring N Uny I Duncan EA Jepson RG Maxwell M Roberts RJ Turley RL Booth A Britten N Flemming K Gallagher I Garside R Hannes K Lewin S Noblit GW Pope C Thomas J Vanstone M Higginbottom GM Noyes J Improving reporting of meta-ethnography: the eMERGe reporting guidance BMC Med Res Methodol 2019 1 31 19 1 25 10.1186/s12874-018-0600-0 30709371 30709371 PMC6359764 · DOI · Paper page
  41. [51]Davis FD User acceptance of information technology: system characteristics, user perceptions and behavioral impacts Int J Man Mach Stud 1993 3 38 3 475 87 10.1006/imms.1993.1022 · DOI
  42. [52]Lunney A Cunningham NR Eastin MS Wearable fitness technology: a structural investigation into acceptance and perceived fitness outcomes Comp Hum Beh 2016 12 65 114 20 10.1016/j.chb.2016.08.007 30872122 · DOI
  43. [53]Chuah SH Rauschnabel PA Krey N Nguyen B Ramayah T Lade S Wearable technologies: the role of usefulness and visibility in smartwatch adoption Comp Hum Beh 2016 12 65 276 84 10.1016/j.chb.2016.07.047 · DOI
  44. [54]Kim KJ Shin D An acceptance model for smart watches: implications for the adoption of future wearable technology Internet Res 2015 25 4 527 41 10.1108/IntR-05-2014-0126 · DOI
  45. [55]Chang HS Lee SC Ji YG Wearable device adoption model with TAM and TTF Int J Mob Commun 2016 14 5 518 10.1504/ijmc.2016.078726 · DOI
  46. [57]Li J Ma Q Chan AH Man S Health monitoring through wearable technologies for older adults: smart wearables acceptance model Appl Ergon 2019 2 75 162 69 10.1016/j.apergo.2018.10.006 30509522 30509522 · DOI
  47. [59]Lai CK Chung JC Leung NK Wong JC Mak DP A survey of older Hong Kong people's perceptions of telecommunication technologies and telecare devices J Telemed Telecare 2010 16 8 441 6 10.1258/jtt.2010.090905 20870685 20870685 · DOI
  48. [60]Lee C Coughlin JF Perspective: older adults' adoption of technology: an integrated approach to identifying determinants and barriers J Prod Innov Manag 2014 6 03 32 5 747 59 10.1111/jpim.12176 · DOI · Paper page
  49. [61]Renaud K van Biljon J Predicting technology acceptance and adoption by the elderly: a qualitative study Proceedings of the 2008 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists on It Research in Developing Countries: Riding the Wave of Technology 2008 SAICSIT '08: 2008 Annual Conference of the South African Institute of Computer Scientists and Information Technologists October, 2008 Wilderness South Africa 210 219 10.1145/1456659.1456684 · DOI · Paper page
  50. [62]Yu-Huei C Ja-Shen C Ming-Chao W Why do older adults use wearable devices: a case study adopting the Senior Technology Acceptance Model (STAM) Proceedings of the Portland International Conference on Management of Engineering and Technology (PICMET) 2019 Portland International Conference on Management of Engineering and Technology (PICMET) Aug. 25-29, 2019 Portland, OR, USA 10.23919/picmet.2019.8893767 · DOI
  51. [63]Booth A Searching for qualitative research for inclusion in systematic reviews: a structured methodological review Syst Rev 2016 5 04 5 74 10.1186/s13643-016-0249-x 27145932 27145932 PMC4855695 · DOI
  52. [64]Walsh D Downe S Meta-synthesis method for qualitative research: a literature review J Adv Nurs 2005 4 50 2 204 11 10.1111/j.1365-2648.2005.03380.x 15788085 15788085 · DOI · Paper page

Cited by

Sorted by citations

Authors
Moore, K., O’Shea, E., Kenny, L., Barton, J. J., Tedesco, S. A., Sica, M., Crowe, C., Alamäki, A., Condell, J. V., Nordström, A., & Timmons, S.
Affiliations
  • University College Cork (IE)
  • Tyndall National Institute (IE)
  • Karelia University of Applied Sciences (FI)
  • University of Ulster (GB)
  • UiT The Arctic University of Norway (NO)
Corresponding author
Kevin Moore
Venue
JMIR mhealth and uhealth
Published
2021-06-03
Volume
9
Issue
6
Pages
e23832
ISSN
2291-5222 (ISSN-L)
OpenAlex
W3150750468
Cited by
278
References
10
Citations by year
2026: 88 · 2025: 79 · 2024: 52 · 2023: 40 · 2022: 16 · 2021: 3
FWCI
4.91
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR mhealth and uhealth (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Ulster University Research Portal (Ulster University) (submittedVersion, cc-by)
  • Arrow@dit (Dublin Institute of Technology) (submittedVersion, cc-by)
  • KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
  • Cork Open Research Archive (University College Cork, Ireland) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
  • Lenus, The Irish Health Repository (Dr Steevens Hospital Library) (submittedVersion)
Funders
  • Enterprise Ireland (IP 2017 0625)
  • European Regional Development Fund (2014-2020’)
  • Interreg (2014-2020)
Topics
  • Social Sciences › Demography › Technology Use by Older Adults
  • Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Keywords
wearable devices, older adults, user experience, technology acceptance, wearable activity trackers, long-term device use, usability barriers, step counter, value addition, theory development, physical activity monitoring, health monitoring, functional capacity, social support, technology adoption in aging, older adults with disabilities
MeSH terms
Fitness Trackers, Wearable Electronic Devices, Aged, Aged, 80 and over, Humans, Middle Aged, Motivation, Exercise, Qualitative Research

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review