Wearable activity trackers (WAT) have shown high potential to improve health in the aging population. Evidence links various social factors with WAT use in older adults, but mainly within small samples and the prevalence of their WAT use during the COVID-19 is unknown. We reported WAT use prevalence before and during the first wave of COVID-19 and examined social factors associated with WAT use frequency using a nationally representative sample of 3302 U.S. older adults. We used Multinomial Logistic Regression to identify social factors associated with WAT use frequency. Only 10.3% of pre-COVID-19 and 10.9% of first-wave subsamples were frequent WAT users. Older adults aged 75 and above and those with low incomes were less likely to frequently use WATs. Our findings suggest socioeconomic and age disparities in WAT use among older Americans. Future studies should focus on enhancing low-income older adults’ WAT adoption to enable equal access to WAT-related health benefits.
AdapaA.NahF. F.-H.HallR. H.SiauK.SmithS. N. (2018). Factors influencing the adoption of smart wearable devices. International Journal of Human-Computer Interaction, 34(5), 399–409. https://doi.org/10.1080/10447318.2017.1357902
2.
AlleyS.SchoeppeS.GuertlerD.JenningsC.DuncanM. J.VandelanotteC. (2016). Interest and preferences for using advanced physical activity tracking devices: Results of a national cross-sectional survey. BMJ Open, 6(7), Article e011243. http://doi.org/10.1136/bmjopen-2016-011243
3.
BrickwoodK.-J.WatsonG.O’BrienJ.WilliamsA. D. (2019). Consumer-based wearable activity trackers increase physical activity participation: Systematic review and meta-analysis. JMIR MHealth and UHealth, 7(4), Article e11819. http://doi.org/10.2196/11819
4.
Cadmus-BertramL. A.MarcusB. H.PattersonR. E.ParkerB. A.MoreyB. L. (2015). Randomized trial of a fitbit-based physical activity intervention for women. American Journal of Preventive Medicine, 49(3), 414–418. https://doi.org/10.1016/j.amepre.2015.01.020
5.
ChandrasekaranR.KatthulaV.MoustakasE. (2020). Patterns of use and key predictors for the use of wearable health Care devices by US adults: Insights from a national survey. Journal of Medical Internet Research, 22(10), Article e22443. https://doi.org/10.2196/22443
6.
ChaudhuriS.LeT.WhiteC.ThompsonH.DemirisG. (2013). Examining health information-seeking behaviors of older adults. Computers, Informatics, Nursing: CIN, 31(11), 547–553. https://doi.org/10.1097/01.NCN.0000432131.92020.42
7.
ColbergS. R.SigalR. J.FernhallB.RegensteinerJ. G.BlissmerB. J.RubinR. R.American Diabetes Association (2010). Exercise and type 2 diabetes: The American college of sports medicine and the American diabetes association: Joint position statement. Diabetes Care, 33(12), Article e147–e167. https://doi.org/10.2337/dc10-9990
8.
DaiB.LarnyoE.TettehE. A.AboagyeA. K.MusahA.-A. I. (2020). Factors affecting caregivers’ acceptance of the use of wearable devices by patients with dementia: An extension of the unified theory of acceptance and use of technology model. American Journal of Alzheimer’s Disease and Other Dementias, 35, 1533317519883493. https://doi.org/10.1177/1533317519883493.
9.
De’R.PandeyN.PalA. (2020). Impact of digital surge during covid-19 pandemic: A viewpoint on research and practice. International Journal of Information Management, 55, 102171. https://doi.org/10.1016/j.ijinfomgt.2020.102171.
10.
Del Pozo CruzB.AhmadiM.NaismithS. L.StamatakisE. (2022). Association of daily step count and intensity with incident dementia in 78 430 adults living in the UK. JAMA Neurology, 79(10), 1059–1063. https://doi.org/10.1001/jamaneurol.2022.2672
11.
DükingP.HothoA.HolmbergH.-C.FussF. K.SperlichB. (2016). Comparison of non-invasive individual monitoring of the training and health of athletes with commercially available wearable technologies. Frontiers in Physiology, 7, 71. https://doi.org/10.3389/fphys.2016.00071.
12.
EscofferyC. (2018). Gender similarities and differences for e-health behaviors among U.S. Adults. Telemedicine Journal and e-Health: The Official Journal of the American Telemedicine Association, 24(5), 335–343. https://doi.org/10.1089/tmj.2017.0136
13.
FangY.-M.ChangC.-C. (2016). Users’ psychological perception and perceived readability of wearable devices for elderly people. Behaviour and Information Technology, 35(3), 225–232. https://doi.org/10.1080/0144929X.2015.1114145
14.
FaussetC. B.MitznerT. L.PriceC. E.JonesB. D.FainW. B.RogersW. A. (2013). Older adults’ use of and attitudes toward activity monitoring technologies. Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Meeting, 57(1), 1683–1687. https://doi.org/10.1177/1541931213571374
FischerS. H.DavidD.CrottyB. H.DierksM.SafranC. (2014). Acceptance and use of health information technology by community-dwelling elders. International Journal of Medical Informatics, 83(9), 624–635. https://doi.org/10.1016/j.ijmedinf.2014.06.005
17.
FüzékiE.SchröderJ.ReerR.GronebergD. A.BanzerW. (2022). Going online? Can online exercise classes during COVID-19-related lockdowns replace in-person offers?International Journal of Environmental Research and Public Health, 19(4), 1942. https://doi.org/10.3390/ijerph19041942.
18.
HaaseK. R.CoscoT.KervinL.RiadiI.O’ConnellM. E. (2021). Older adults’ experiences with using technology for socialization during the COVID-19 pandemic: Cross-sectional survey study. JMIR Aging, 4(2), Article e28010. https://doi.org/10.2196/28010
19.
HargittaiE.DobranskyK. (2017). Old dogs, new clicks: Digital inequality in skills and uses among older adults. Canadian Journal of Communication, 42(2), 195–212. https://doi.org/10.22230/cjc.2017v42n2a3176
20.
HargittaiE.PiperA. M.MorrisM. R. (2019). From internet access to internet skills: Digital inequality among older adults. Universal Access in the Information Society, 18(4), 881–890. https://doi.org/10.1007/s10209-018-0617-5
21.
HoganH.PerezD.BellW. (2008). Who (really) are the first baby Boomers?Retrieved fromhttp://dx.doi.org/
22.
HungL.-Y.LyonsJ. G.WuC.-H. (2020). Health information technology use among older adults in the United States, 2009–2018. Current Medical Research and Opinion, 36(5), 789–797. https://doi.org/10.1080/03007995.2020.1734782
23.
IsaacowitzD. M.FreundA. M.MayrU.RothermundK.ToblerP. N. (2021). Age-related changes in the role of social motivation: Implications for healthy aging. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 76(Suppl 2), S115–S124. https://doi.org/10.1093/geronb/gbab032
KarlinB. E.DuffyM.GleavesD. H. (2008). Patterns and predictors of mental health service use and mental illness among older and younger adults in the United States. Psychological Services, 5(3), 275–294. https://doi.org/10.1037/1541-1559.5.3.275
26.
KlineC. E. (2014). The bidirectional relationship between exercise and sleep: Implications for exercise adherence and sleep improvement. American Journal of Lifestyle Medicine, 8(6), 375–379. https://doi.org/10.1177/1559827614544437
27.
LaiJ.WidmarN. O. (2021). Revisiting the digital divide in the COVID-19 era. Applied Economic Perspectives and Policy, 43(1), 458–464. https://doi.org/10.1002/aepp.13104
28.
LeeS. B.OhJ. H.ParkJ. H.ChoiS. P.WeeJ. H. (2018). Differences in youngest-old, middle-old, and oldest-old patients who visit the emergency department. Clinical and Experimental Emergency Medicine, 5(4), 249–255. https://doi.org/10.15441/ceem.17.261
29.
LiJ.MaQ.ChanA. H.ManS. S. (2019). Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Applied Ergonomics, 75, 162–169. https://doi.org/10.1016/j.apergo.2018.10.006.
30.
LiJ.SzantonS. L.McPhillipsM. V.LukkahataiN.PienG. W.ChenK.GooneratneN. S. (2021). An mHealth-facilitated personalized intervention for physical activity and sleep in community-dwelling older adults. Journal of Aging and Physical Activity, 30(2), 261–270. https://doi.org/10.1123/japa.2020-0463
31.
LiL.PengW.KononovaA.BowenM.CottenS. R. (2020). Factors associated with older adults’ long-term use of wearable activity trackers. Telemedicine Journal and e-Health: The Official Journal of the American Telemedicine Association, 26(6), 769–775. https://doi.org/10.1089/tmj.2019.0052
32.
LiM.McphillipsM. V.WenzelJ.SzantonS. L.LiJ. (2022). Electronic wearable device use for physical activity in older adults: A qualitative study. Work, Aging and Retirement. https://doi.org/10.1093/workar/waac023.
33.
LinkB. G.PhelanJ. (1995). Social conditions as fundamental causes of disease. Journal of Health and Social Behavior, Spec No (1995), 80–94. Retrieved fromhttps://www.ncbi.nlm.nih.gov/pubmed/7560851
LiuJ. Y.-W.KorP. P.-K.ChanC. P.-Y.KwanR. Y.-C.Sze-KiD. (2020). The effectiveness of a wearable activity tracker (WAT)-based intervention to improve physical activity levels in sedentary older adults: A systematic review and meta-analysis. Archives of Gerontology and Geriatrics, 91, 104211. https://doi.org/10.1016/j.archger.2020.104211.
36.
MacridisS.JohnstonN.JohnsonS.VallanceJ. K. (2018). Consumer physical activity tracking device ownership and use among a population-based sample of adults. PLoS One, 13(1), Article e0189298. https://doi.org/10.1371/journal.pone.0189298
37.
MubarakF.SuomiR.KantolaS.-P. (2020). Confirming the links between socio-economic variables and digitalization worldwide: The unsettled debate on digital divide. Journal of Information, Communication and Ethics in Society, 18(3), 415–430. https://doi.org/10.1108/JICES-02-2019-0021
NoftleE. E.FleesonW. (2010). Age differences in big five behavior averages and variabilities across the adult life span: Moving beyond retrospective, global summary accounts of personality. Psychology and Aging, 25(1), 95–107. https://doi.org/10.1037/a0018199
41.
O’DonoghueJ.HerbertJ. (2012). Data management within mHealth environments: Patient sensors, mobile devices, and databases. J. Data and Information Quality, 4(1), 1–20. https://doi.org/10.1145/2378016.2378021
42.
OlanderE. K.FletcherH.WilliamsS.AtkinsonL.TurnerA.FrenchD. P. (2013). What are the most effective techniques in changing obese individuals’ physical activity self-efficacy and behaviour: A systematic review and meta-analysis. International Journal of Behavioral Nutrition and Physical Activity, 10(1), 1–15. https://doi.org/10.1186/1479-5868-10-29
43.
PaolilloE. W.LeeS. Y.VandeBunteA.DjukicN.FonsecaC.KramerJ. H.CasalettoK. B. (2022). Wearable use in an observational study among older adults: Adherence, feasibility, and effects of clinicodemographic factors. Frontiers in Digital Health, 4, 884208. https://doi.org/10.3389/fdgth.2022.884208.
44.
PreusseK. C.MitznerT. L.FaussetC. B.RogersW. A. (2017). Older adults’ acceptance of activity trackers. Journal of Applied Gerontology: The Official Journal of the Southern Gerontological Society, 36(2), 127–155. https://doi.org/10.1177/0733464815624151
45.
PuriA.KimB.NguyenO.StoleeP.TungJ.LeeJ. (2017). User acceptance of wrist-worn activity trackers among community-dwelling older adults: Mixed method study. JMIR MHealth and UHealth, 5(11), Article e173. https://doi.org/10.2196/mhealth.8211
46.
RingevalM.WagnerG.DenfordJ.ParéG.KitsiouS. (2020). Fitbit-based interventions for healthy lifestyle outcomes: Systematic review and meta-analysis. Journal of Medical Internet Research, 22(10), Article e23954. https://doi.org/10.2196/23954
47.
RosalesA.Fernández-ArdèvolM.Ferran-FerrerN. (2018). Long-term appropriation of smartwatches among a group of older people. Human Aspects of IT for the Aged Population. Acceptance, Communication and Participation, 135–148. https://doi.org/10.1007/978-3-319-92034-4_11.
48.
SchusterA. M.KadylakT.CottenS. R. (2023). Correlation between socio-demographic factors and adoption and use of wearable activity trackers in online American older adults. Educational Gerontology, 49(1), 1–11. https://doi.org/10.1080/03601277.2022.2064688
49.
SharmaA.MadaanV.PettyF. D. (2006). Exercise for mental health. Primary Care Companion to the Journal of Clinical Psychiatry, 8(2), 106. https://doi.org/10.4088/pcc.v08n0208a
SieckC. J.SheonA.AnckerJ. S.CastekJ.CallahanB.SieferA. (2021). Digital inclusion as a social determinant of health. NPJ Digital Medicine, 4(1), 52. https://doi.org/10.1038/s41746-021-00413-8
StockwellS.SchofieldP.FisherA.FirthJ.JacksonS. E.StubbsB.SmithL. (2019). Digital behavior change interventions to promote physical activity and/or reduce sedentary behavior in older adults: A systematic review and meta-analysis. Experimental Gerontology, 120(2019), 68–87. https://doi.org/10.1016/j.exger.2019.02.020.
54.
TedescoS.BartonJ.O’FlynnB. (2017). A review of activity trackers for senior citizens: Research perspectives, commercial landscape and the role of the insurance industry. Sensors, 17(6). https://doi.org/10.3390/s17061277
WangH.TaoD.YuN.QuX. (2020). Understanding consumer acceptance of healthcare wearable devices: An integrated model of UTAUT and TTF. International Journal of Medical Informatics, 139(2020), 104156. https://doi.org/10.1016/j.ijmedinf.2020.104156.
59.
WesternM. J.ArmstrongM. E. G.IslamI.MorganK.JonesU. F.KelsonM. J. (2021). The effectiveness of digital interventions for increasing physical activity in individuals of low socioeconomic status: A systematic review and meta-analysis. International Journal of Behavioral Nutrition and Physical Activity, 18(1), 148. https://doi.org/10.1186/s12966-021-01218-4
60.
XieZ.JoA.HongY.-R. (2020). Electronic wearable device and physical activity among US adults: An analysis of 2019 HINTS data. International Journal of Medical Informatics, 144(2020), 104297. https://doi.org/10.1016/j.ijmedinf.2020.104297.
61.
ZhangZ.GiordaniB.MargulisA.ChenW. (2022). Efficacy and acceptability of using wearable activity trackers in older adults living in retirement communities: A mixed method study. BMC Geriatrics, 22(1), 231. https://doi.org/10.1186/s12877-022-02931-w
62.
ZytnickD.FoltaS. C.ReidK. F.ChomitzV. R. (2023). Better understanding wearable activity monitor use and non-use among older adults: A qualitative study. Journal of Applied Gerontology: The Official Journal of the Southern Gerontological Society, 42(3), 447–455. https://doi.org/10.1177/07334648221137057
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