Evidence map›Paper›PMID 35123399›Full record

ArticleBMC geriatrics2022

Smart clothes-assisted home-nursing care program for family caregivers of older persons with dementia and hip fracture: a mixed-methods study.

Yi-Jun Hou, Sih-Ying Zeng, Chung-Chih Lin, Ching-Tzu Yang, Huei-Ling Huang, Min-Chi Chen, Hsiu-Hsin Tsai, Jersey Liang, Yea-Ing L Shyu

Open access · goldAbstract read
In one paragraph

Article in BMC geriatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 11% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

  1. 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

9 authors at 5 institutions in 2 countries.

Yi-Jun Hou *Department of Nursing, Chang Gung Memorial Hospital, Taoyuan, Taiwan.
Sih-Ying Zeng *Department of Nursing, Far Eastern Memorial Hospital, Banciao, Taiwan.
Chung-Chih LinDepartment of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan.
Ching-Tzu YangSchool of Nursing, Chang Gung University, 259 Wenhua 1st Road, Guishan District, Taoyuan, 33302, Taiwan.
Huei-Ling HuangDepartment of Gerontology and Health Care Management, Chang Gung University of Science and Technology, Taoyuan, Taiwan.
Min-Chi ChenDepartment of Public Health and Parasitology, Chang Gung University, Taoyuan, Taiwan.
Hsiu-Hsin TsaiSchool of Nursing, Chang Gung University, 259 Wenhua 1st Road, Guishan District, Taoyuan, 33302, Taiwan.
Jersey LiangSchool of Public Health, University of Michigan, Ann Arbor, MI, USA.
Yea-Ing L ShyuSchool of Nursing, Chang Gung University, 259 Wenhua 1st Road, Guishan District, Taoyuan, 33302, Taiwan. yeaing@mail.cgu.edu.tw.
Chang Gung University · TWChang Gung University of Science and Technology · TWChang Gung Memorial Hospital · TWFar Eastern Memorial Hospital · TWUniversity of Michigan–Ann Arbor · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe purpose of this preliminary study was to explore whether a smart clothes-assisted home-nursing care program could benefit family caregivers and their care recipients.

methodsFamily caregivers in charge of a care recipient's living situation participated in this convergent parallel, mixed methods study. We recruited older persons with dementia (n = 7) and those discharged following hip-fracture surgery (n = 6) from neurological clinics and surgical wards of a medical center, respectively, along with their family caregivers: three spouses, eight sons, one daughter, and one daughter-in-law. Care recipients were asked to wear a smart vest at least 4 days/week for 6 months, which contained a coin-size monitor hidden in an inner pocket. Sensors installed in bedrooms and living areas received signals from the smart clothing, which were transmitted to a mobile phone app of homecare nurses, who provided caregivers with transmitted information regarding activities, emergency situations and suggestions for caregiving activities. Outcomes included changes from baseline in caregivers' preparedness and depressive symptoms collected at 1- and 3-months, which were analyzed with Friedman's non-parametric test of repeated measures with post-hoc analysis. Transcripts of face-to-face semi-structured interview data about caregivers' experiences were analyzed to identify descriptive, interpretative, and pattern codes.

resultsPreparedness did not change from baseline at either 1- or 3-months for family caregivers of persons with dementia. However, depressive symptoms decreased significantly at 1-month and 3-months compared with baseline, but not between 1-months and 3-months. Analysis of the interview data revealed the smart clothes program increased family caregivers' knowledge of the care recipient's situation and condition, informed healthcare providers of the care recipient's physical health and cognitive status, helped homecare nurses provide timely interventions, balanced the care recipient's exercise and safety, motivated recipients to exercise, helped family caregivers balance work and caregiving, and provided guidance for caregiving activities.

conclusionsExperiences with the smart clothes-assisted home-nursing care program directly benefited family caregivers, which provided indirect benefits to the care recipients due to the timely interventions and caregiving guidance from homecare nurses. These benefits suggest a smart-clothes-assisted program might be beneficial for all family caregivers.

Indexed as

DementiaHome Care ServicesAgedAged, 80 and overCaregiversClothingFamilyHumansDementiaFamily caregiversHip fractureHomecare nursingSmart clothes

Identifiers

PMID35123399
PMCPMC8818174
OpenAlexW4210539354

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.