Evidence map›Paper›PMID 33502320›Full record

ArticleJMIR aging2021

Attitudes Toward Technology and Use of Fall Alert Wearables in Caregiving: Survey Study.

Deborah Vollmer Dahlke, Shinduk Lee, Matthew Lee Smith, Tiffany Shubert, Stephen Popovich, Marcia G Ory

Open access · goldAbstract read
In one paragraph

Article in JMIR aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
4.4field-weighted citation impact, top 5% 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.

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

10 citing papers in PubMed, 17 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Deborah Vollmer Dahlke *Texas A&M Center for Population Health and Aging, Texas A&M University, College Station, TX, United States.ORCID https://orcid.org/0000-0003-1674-4998
Shinduk Lee *Texas A&M Center for Population Health and Aging, Texas A&M University, College Station, TX, United States.ORCID https://orcid.org/0000-0003-1336-819X
Matthew Lee Smith *Texas A&M Center for Population Health and Aging, Texas A&M University, College Station, TX, United States.ORCID https://orcid.org/0000-0002-8232-9285
Tiffany Shubert *Shubert Consulting, Chapel Hill, NC, United States.ORCID https://orcid.org/0000-0001-6306-0738
Stephen Popovich *Texas A&M Center for Population Health and Aging, Texas A&M University, College Station, TX, United States.ORCID https://orcid.org/0000-0002-8957-1189
Marcia G Ory *Texas A&M Center for Population Health and Aging, Texas A&M University, College Station, TX, United States.ORCID https://orcid.org/0000-0001-8036-2383
Texas A&M Health Science Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWearable technology for fall alerts among older adult care recipients is one of the more frequently studied areas of technology, given the concerning consequences of falls among this population. Falls are quite prevalent in later life. While there is a growing amount of literature on older adults' acceptance of technology, less is known about how caregivers' attitudes toward technology can impact care recipients' use of such technology.

objectiveThe objective of our study was to examine associations between caregivers' attitudes toward technology for caregiving and care recipients' use of fall alert wearables.

methodsThis study examined data collected with an online survey from 626 caregivers for adults 50 years and older. Adapted from the technology acceptance model, a structural equation model tested the following prespecified hypotheses: (1) higher perceived usefulness of technologies for caregiving would predict higher perceived value of and greater interest in technologies for caregiving; (2) higher perceived value of technologies for caregiving would predict greater interest in technologies for caregiving; and (3) greater interest in technologies for caregiving would predict greater use of fall alert wearables among care recipients. Additionally, we included demographic factors (eg, caregivers' and care recipients' ages) and caregiving context (eg, caregiver type and caregiving situation) as important predictors of care recipients' use of fall alert wearables.

resultsOf 626 total respondents, 548 (87.5%) with all valid responses were included in this study. Among care recipients, 28% used fall alert wearables. The final model had a good to fair model fit: a confirmatory factor index of 0.93, a standardized root mean square residual of 0.049, and root mean square error of approximation of 0.066. Caregivers' perceived usefulness of technology was positively associated with their attitudes toward using technology in caregiving (b=.70, P<.001) and interest in using technology for caregiving (b=.22, P=.003). Greater perceived value of using technology in caregiving predicted greater interest in using technology for caregiving (b=.65, P<.001). Greater interest in using technology for caregiving was associated with greater likelihood of care recipients using fall alert wearables (b=.27, P<.001). The caregiver type had the strongest inverse relationship with care recipients' use of fall alert wearables (unpaid vs paid caregiver) (b=-.33, P<.001).

conclusionsThis study underscores the importance of caregivers' attitudes in care recipients' technology use for falls management. Raising awareness and improving perception about technologies for caregiving may help caregivers and care recipients adopt and better utilize technologies that can promote independence and enhance safety.

Indexed as

caregiverscare recipientsfallsfalls alert technologywearables

Identifiers

PMID33502320
PMCPMC8081189
OpenAlexW3125988488

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.