Evidence map›Paper›PMID 33541276›Full record

ArticleBMC geriatrics2021

Developing a sensor-based mobile application for in-home frailty assessment: a qualitative study.

Marcela D Blinka, Brian Buta, Kevin D Bader, Casey Hanley, Nancy L Schoenborn, Matthew McNabney, Qian-Li Xue

Open access · goldAbstract read
In one paragraph

Article in BMC geriatrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 pooled it
1.3field-weighted citation impact, top 23% 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

12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 citations in OpenAlex.

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  5. Observational
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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

7 authors at 2 institutions in 1 country.

Marcela D BlinkaCenter on Aging and Health, Johns Hopkins University, 2024 E. Monument Street, Suite 2-700, Baltimore, MD, 21205, USA. mblinka1@jhmi.edu.ORCID 0000-0003-0369-9331
Brian ButaCenter on Aging and Health, Johns Hopkins University, 2024 E. Monument Street, Suite 2-700, Baltimore, MD, 21205, USA.
Kevin D BaderThe Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Casey HanleyThe Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Nancy L SchoenbornCenter on Aging and Health, Johns Hopkins University, 2024 E. Monument Street, Suite 2-700, Baltimore, MD, 21205, USA.
Matthew McNabneyDivision of Geriatric Medicine and Gerontology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Qian-Li XueCenter on Aging and Health, Johns Hopkins University, 2024 E. Monument Street, Suite 2-700, Baltimore, MD, 21205, USA. qxue1@jhu.edu.
Johns Hopkins University · USJohns Hopkins University Applied Physics Laboratory · US

Funding

Technological Assessment and Solutions Core - RC4P30AG021334 · NIA · JOHNS HOPKINS UNIVERSITY · PI Jeremy D Walston · 2003 to 2026
$33.2M
Transitions to Family Caregiving and Its Impact on Health IndicatorsRF1AG050609 · NIA · JOHNS HOPKINS UNIVERSITY · PI ROTH, DAVID L · 2016 to 2016
$3.3M
NIA NIH HHS P30 AG021334NIA NIH HHS RF1 AG050609The Johns Hopkins Claude D. Pepper Older Americans Independence Center P30AG021334
6 · The paper itself

Abstract

backgroundFrailty syndrome disproportionately affects older people, including 15% of non-nursing home population, and is known to be a strong predictor of poor health outcomes. There is a growing interest in incorporating frailty assessment into research and clinical practice, which may provide an opportunity to improve in home frailty assessment and improve doctor patient communication.

methodsWe conducted focus groups discussions to solicit input from older adult care recipients (non-frail, pre-frail, and frail), their informal caregivers, and medical providers about their preferences to tailor a mobile app to measure frailty in the home using sensor based technologies. Focus groups were recorded, transcribed, and analyzed thematically.

resultsWe identified three major themes: 1) perspectives of frailty; 2) perceptions of home based sensors; and 3) data management concerns. These relate to the participants' insight, attitudes and concerns about having sensor-based technology to measure frailty in the home. Our qualitative findings indicate that knowing frailty status is important and useful and would allow older adults to remain independent longer. Participants also noted concerns with data management and the hope that this technology would not replace in-person visits with their healthcare provider.

conclusionsThis study found that study participants of each frailty status expressed high interest and acceptance of sensor-based technologies. Based on the qualitative findings of this study, sensor-based technologies show promise for frailty assessment of older adults with care needs. The main concerns identified related to the volume of data collected and strategies for responsible and secure transfer, reporting, and distillation of data into useful and timely care information. Sensor-based technologies should be piloted for feasibility and utility. This will inform the larger goal of helping older adults to maintain independence while tracking potential health declines, especially among the most vulnerable, for early detection and intervention.

Indexed as

FrailtyMobile ApplicationsAgedAged, 80 and overFrail ElderlyHealth PersonnelHumansQualitative ResearchFrailtyHealth servicesWearablewearable health services.

Identifiers

PMID33541276
PMCPMC7863502
OpenAlexW2983278415

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.