Evidence map›Paper›PMID 39608020›Full record

Trial reportJournal of medical Internet research2025

Impact of the Smarter Safer Homes Solution on Quality of Life and Health Outcomes in Older People Living in Their Own Homes: Randomized Controlled Trial.

Wei Lu, David Silvera-Tawil, Hwan-Jin Yoon, Liesel Higgins, Qing Zhang, Mohanraj Karunanithi, Julia Bomke, Joshua Byrnes, Jennifer Hewitt, Vanessa Smallbon and 3 more

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Article
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

13 authors.

Wei LuAustralian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia.ORCID 0000-0002-2869-8450
David Silvera-TawilAustralian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia.ORCID 0000-0003-2653-0141
Hwan-Jin YoonAustralian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia.ORCID 0000-0003-1772-7911
Liesel HigginsAustralian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia.ORCID 0000-0001-5470-7034
Qing ZhangResearch Center for Frontier Fundamental Studies, Zhejiang Lab, Hangzhou, ZheJiang, China.ORCID 0000-0003-1405-8505
Mohanraj KarunanithiChild Health Research Centre, The University of Queensland, Brisbane, QLD, Australia.ORCID 0000-0002-9141-932X
Julia BomkeAustralian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia.ORCID 0000-0002-8548-9145
Joshua ByrnesCentre for Applied Health Economics, Griffith University, Brisbane, QLD, Australia.ORCID 0000-0001-6562-711X
Jennifer HewittSydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.ORCID 0000-0003-2736-005X
Vanessa SmallbonAustralian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia.ORCID 0000-0002-8183-0916
Jill FreyneAustralian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia.ORCID 0000-0001-7659-0535
Deepa PrabhuAustralian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia.ORCID 0000-0002-0257-2200
Marlien VarnfieldAustralian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia.ORCID 0000-0003-4848-0181

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAn increasingly aging population, accompanied by a shortage of residential aged care homes and workforce and consumer feedback, has driven a growing interest in enabling older people to age in place through home-based care. In this context, smart home technologies for remote health monitoring have gained popularity for supporting older people to live in their own homes.

objectiveThis study aims to investigate the impact of smart home monitoring on multiple outcomes, including quality of life, activities of daily living, and depressive symptoms among older people living in their own homes over a 12-month period.

methodsWe conducted an open-label, parallel-group randomized controlled trial. The control group continued to receive their existing care from aged care service providers. Meanwhile, the intervention group, in addition to receiving their usual aged care services, had their activities of daily living monitored using a smart home platform. Surveys including the Adult Social Care Outcomes Toolkit (ASCOT), EuroQol-5 Dimensions-5 Levels (EQ-5D-5L), Katz Index of Independence in Activities of Daily Living (Katz ADL), Lawton Instrumental Activities of Daily Living Scale (IADL), and Geriatric Depression Scale (GDS) were conducted at baseline and 6 and 12 months from baseline. Linear mixed-effects models were used to compare the difference between the intervention and control groups, with the ASCOT as the primary outcome measure.

resultsData from 130 participants were used in the analysis, with no significant differences in baseline characteristics between the control group (n=61) and the intervention group (n=69). In comparison to the control group, the intervention group had a higher ASCOT score at the 6-month assessment (mean difference 0.045, 95% CI 0.001 to 0.089; Cohen d=0.377). However, this difference did not persist at the 12-month assessment (mean difference 0.031, 95% CI -0.014 to 0.076; Cohen d=0.259). There were no significant differences in EQ-5D-5L, Katz ADL, IADL, and GDS observed between the intervention and control groups at the 6-month and 12-month assessments.

conclusionsThe study demonstrates that smart home monitoring can improve social care-related quality of life for older people living in their own homes. However, the improvement was not sustained over the long term. The lack of statistically significant findings and diminished long-term improvements may be attributed to the influence of the COVID-19 pandemic during the later stage of the trial. Further research with a larger sample size is needed to evaluate the effect of smart home monitoring on broader quality-of-life measures.

trial registrationAustralian New Zealand Clinical Trials Registry ACTRN12618000829213; https://tinyurl.com/2n6a75em. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/31970.

Indexed as

Home Care ServicesIndependent LivingQuality of LifeActivities of Daily LivingAgedAged, 80 and overDepressionFemaleHumansMaleaged careaging in placedigital healtheHealthhealth monitoringhome monitoringolder adultquality of liferandomized controlled trialsensorsmart home

Identifiers

PMID39608020
PMCPMC11799804

What OpenQuestion holds

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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.