Evidence map›Paper›PMID 36656636›Full record

ArticleJMIR aging2023

Factors Influencing Continued Wearable Device Use in Older Adult Populations: Quantitative Study.

Karla Muñoz Esquivel, James Gillespie, Daniel Kelly, Joan Condell, Richard Davies, Catherine McHugh, William Duffy, Elina Nevala, Antti Alamäki, Juha Jalovaara and 4 more

Abstract read
In one paragraph

Article in JMIR aging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

14 authors.

Karla Muñoz Esquivel *Department of Computer Science, Atlantic Technological University, Letterkenny, Ireland.ORCID https://orcid.org/0000-0002-4420-5847
James Gillespie *Faculty of Computing, Engineering, and the Built Environment, Ulster University, Derry, United Kingdom.ORCID https://orcid.org/0000-0003-2721-8854
Daniel KellyFaculty of Computing, Engineering, and the Built Environment, Ulster University, Derry, United Kingdom.ORCID https://orcid.org/0000-0003-0009-7634
Joan CondellFaculty of Computing, Engineering, and the Built Environment, Ulster University, Derry, United Kingdom.ORCID https://orcid.org/0000-0002-3517-2182
Richard DaviesFaculty of Computing, Engineering, and the Built Environment, Ulster University, Derry, United Kingdom.ORCID https://orcid.org/0000-0002-2663-3979
Catherine McHughFaculty of Computing, Engineering, and the Built Environment, Ulster University, Derry, United Kingdom.ORCID https://orcid.org/0000-0003-2948-9172
William DuffyFaculty of Computing, Engineering, and the Built Environment, Ulster University, Derry, United Kingdom.ORCID https://orcid.org/0000-0003-0634-6283
Elina NevalaDepartment of Physiotherapy, Karelia University of Applied Sciences, Karelia, Finland.ORCID https://orcid.org/0000-0002-8326-8445
Antti AlamäkiDepartment of Physiotherapy, Karelia University of Applied Sciences, Karelia, Finland.ORCID https://orcid.org/0000-0001-6293-6232
Juha JalovaaraDepartment of Physiotherapy, Karelia University of Applied Sciences, Karelia, Finland.ORCID https://orcid.org/0000-0003-4241-989X
Salvatore TedescoWireless Sensors Network Group, Tyndall National Institute, University College Cork, Cork, Ireland.ORCID https://orcid.org/0000-0002-7752-2240
John BartonWireless Sensors Network Group, Tyndall National Institute, University College Cork, Cork, Ireland.ORCID https://orcid.org/0000-0002-0671-5678
Suzanne TimmonsCentre for Gerontology and Rehabilitation, School of Medicine, University College Cork, Cork, Ireland.ORCID https://orcid.org/0000-0001-7790-9552
Anna NordströmDepartment of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden.ORCID https://orcid.org/0000-0003-3534-456X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe increased use of wearable sensor technology has highlighted the potential for remote telehealth services such as rehabilitation. Telehealth services incorporating wearable sensors are most likely to appeal to the older adult population in remote and rural areas, who may struggle with long commutes to clinics. However, the usability of such systems often discourages patients from adopting these services.

objectiveThis study aimed to understand the usability factors that most influence whether an older adult will decide to continue using a wearable device.

methodsOlder adults across 4 different regions (Northern Ireland, Ireland, Sweden, and Finland) wore an activity tracker for 7 days under a free-living environment protocol. In total, 4 surveys were administered, and biometrics were measured by the researchers before the trial began. At the end of the trial period, the researchers administered 2 further surveys to gain insights into the perceived usability of the wearable device. These were the standardized System Usability Scale (SUS) and a custom usability questionnaire designed by the research team. Statistical analyses were performed to identify the key factors that affect participants' intention to continue using the wearable device in the future. Machine learning classifiers were used to provide an early prediction of the intention to continue using the wearable device.

resultsThe study was conducted with older adult volunteers (N=65; mean age 70.52, SD 5.65 years) wearing a Xiaomi Mi Band 3 activity tracker for 7 days in a free-living environment. The results from the SUS survey showed no notable difference in perceived system usability regardless of region, sex, or age, eliminating the notion that usability perception differs based on geographical location, sex, or deviation in participants' age. There was also no statistically significant difference in SUS score between participants who had previously owned a wearable device and those who wore 1 or 2 devices during the trial. The bespoke usability questionnaire determined that the 2 most important factors that influenced an intention to continue device use in an older adult cohort were device comfort (τ=0.34) and whether the device was fit for purpose (τ=0.34). A computational model providing an early identifier of intention to continue device use was developed using these 2 features. Random forest classifiers were shown to provide the highest predictive performance (80% accuracy). After including the top 8 ranked questions from the bespoke questionnaire as features of our model, the accuracy increased to 88%.

conclusionsThis study concludes that comfort and accuracy are the 2 main influencing factors in sustaining wearable device use. This study suggests that the reported factors influencing usability are transferable to other wearable sensor systems. Future work will aim to test this hypothesis using the same methodology on a cohort using other wearable technologies.

Indexed as

mobile phoneolder adultsremote sensingsensor systemsusabilitywearable device

Identifiers

PMID36656636
PMCPMC9947821

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.