Evidence map›Paper›PMID 29141837›Full record

ArticleJMIR mHealth and uHealth2017

User Acceptance of Wrist-Worn Activity Trackers Among Community-Dwelling Older Adults: Mixed Method Study.

Arjun Puri, Ben Kim, Olivier Nguyen, Paul Stolee, James Tung, Joon Lee

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04592796 (Observational Study About Sleep Quality and Its Impact on Daily Life of Nursing-home Residents, Through the Use of Wearable Technologies), which is not on this map. Cited by 89 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
89citing papers in PubMed, 6 pooled it
69.9field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT04592796 completednot on this mapstarted 2018, after this paper: background citation

Observational Study About Sleep Quality and Its Impact on Daily Life of Nursing-home Residents, Through the Use of Wearable Technologies

TypeobservationalSponsorUniversidade da CoruñaRan2018 to 2019Enrolled21ConditionsSleep, Cognitive Impairment, Quality of Life, Age ProblemArmsXiaomi MiBand2, Socio-demographic questionnaire, EuroQol-5D-5L, Mini mental State Examination, Tinetti Scale
3 · Its place in the literature

Who cites it

89 citing papers in PubMed, 6 syntheses or guidelines pooled it, 189 citations in OpenAlex.

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29 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Arjun PuriHealth Data Science Lab, School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada.ORCID http://orcid.org/0000-0001-9308-7165
Ben KimHealth Data Science Lab, School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada.ORCID http://orcid.org/0000-0002-6793-5551
Olivier NguyenHealth Data Science Lab, School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada.ORCID http://orcid.org/0000-0001-6823-2316
Paul StoleeSchool of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada.ORCID http://orcid.org/0000-0002-5685-0843
James TungDepartment of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada.ORCID http://orcid.org/0000-0002-0771-2313
Joon LeeHealth Data Science Lab, School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada.ORCID http://orcid.org/0000-0001-8593-9321
University of Waterloo · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWearable activity trackers are newly emerging technologies with the anticipation for successfully supporting aging-in-place. Consumer-grade wearable activity trackers are increasingly ubiquitous in the market, but the attitudes toward, as well as acceptance and voluntary use of, these trackers in older population are poorly understood.

objectiveThe aim of this study was to assess acceptance and usage of wearable activity trackers in Canadian community-dwelling older adults, using the potentially influential factors as identified in literature and technology acceptance model.

methodsA mixed methods design was used. A total of 20 older adults aged 55 years and older were recruited from Southwestern Ontario. Participants used 2 different wearable activity trackers (Xiaomi Mi Band and Microsoft Band) separately for each segment in the crossover design study for 21 days (ie, 42 days total). A questionnaire was developed to capture acceptance and experience at the end of each segment, representing 2 different devices. Semistructured interviews were conducted with 4 participants, and a content analysis was performed.

resultsParticipants ranged in age from 55 years to 84 years (mean age: 64 years). The Mi Band gained higher levels of acceptance (16/20, 80%) compared with the Microsoft Band (10/20, 50%). The equipment characteristics dimension scored significantly higher for the Mi Band (P<.05). The amount a participant was willing to pay for the device was highly associated with technology acceptance (P<.05). Multivariate logistic regression with 3 covariates resulted in an area under the curve of 0.79. Content analysis resulted in the formation of the following main themes: (1) smartphones as facilitators of wearable activity trackers; (2) privacy is less of a concern for wearable activity trackers, (3) value proposition: self-awareness and motivation; (4) subjective norm, social support, and sense of independence; and (5) equipment characteristics matter: display, battery, comfort, and aesthetics.

conclusionsOlder adults were mostly accepting of wearable activity trackers, and they had a clear understanding of its value for their lives. Wearable activity trackers were uniquely considered more personal than other types of technologies, thereby the equipment characteristics including comfort, aesthetics, and price had a significant impact on the acceptance. Results indicated that privacy was less of concern for older adults, but it may have stemmed from a lack of understanding of the privacy risks and implications. These findings add to emerging research that investigates acceptance and factors that may influence acceptance of wearable activity trackers among older adults.

Indexed as

fitness trackershealthmHealtholder adults

Identifiers

PMID29141837
PMCPMC5707431
OpenAlexW2769473743

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.