Evidence map›Paper›PMID 40175885›Full record

ArticleBMC geriatrics2025

Fall sensors, home emergency system, and social service for ≥ 75-year-olds living at home - a matched control intervention study.

Jan C Zoellick, Sonia Lech, Julie L O'Sullivan, Eva Jansen, Juliana Supplieth, Ronny Kuhnert, Ulrike Grittner, Johanna Schuster

Abstract read
In one paragraph

Article in BMC geriatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

1 citing paper in PubMed.

  1. 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

8 authors.

Jan C ZoellickInstitute of Medical Sociology and Rehabilitation Science, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Charitéplatz 1, 10117, Berlin, Germany. jan.zoellick@charite.de.
Sonia LechDepartment of Psychiatry and Neurosciences, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Charitéplatz 1, 10117, Berlin, Germany.
Julie L O'SullivanInstitute of Medical Sociology and Rehabilitation Science, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Charitéplatz 1, 10117, Berlin, Germany.
Eva JansenInstitute of Medical Sociology and Rehabilitation Science, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Charitéplatz 1, 10117, Berlin, Germany.
Juliana SuppliethInstitute of Medical Sociology and Rehabilitation Science, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Charitéplatz 1, 10117, Berlin, Germany.
Ronny KuhnertInstitute of Medical Sociology and Rehabilitation Science, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Charitéplatz 1, 10117, Berlin, Germany.
Ulrike GrittnerInstitute of Biometry and Clinical Epidemiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Charitéplatz 1, 10117, Berlin, Germany.
Johanna SchusterInstitute of Medical Sociology and Rehabilitation Science, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Charitéplatz 1, 10117, Berlin, Germany.

Funding

German Federal Joint Committee (Gemeinsamer Bundesausschuss) 01NVF18035
6 · The paper itself

Abstract

backgroundMedical emergencies occur frequently at home and during leisure activities. Digital technologies hold great potential for novel approaches towards emergency detection and treatment. The purpose of this study was to evaluate an integrated home-based emergency call system.

methodsWe conducted a matched-control intervention study with 180 participants (M: 81.7 years; SD: 4.1 years; 68% female) in the intervention group (IG) and 708 matched controls (M: 81.4 years; SD: 3.9 years; 68% female). The intervention targeted ≥ 75-year-old community-dwelling adults and consisted of a base station, motion sensors for the home, a necklace with fall detection sensors, and a social service. We expected fewer emergency contacts and fewer hospitalisations in the IG than in the control group (CG). Secondary outcomes were healthcare costs and subjective assessments by participants. Negative binomial regression models and linear regression analyses were used to test the primary and secondary hypotheses.

resultsOur results revealed similar rates of emergency contacts (IRR = 0.89 [95%-CI:0.62-1.28]; p = .523) and hospitalisations (IRR = 1.23 [95%-CI:0.95-1.60]; p = .122) with similar durations (M

conclusionsThe intervention had no effect on hospitalisations and emergency contacts. However, participants made also little use of the intervention. The observed decline in subjective health and other subjective outcomes may be attributed to the high age of participants at intervention start and overall circumstances due to the COVID pandemic. The market for technologies for older adults is highly dynamic and growing quickly; thus, more suitable and effective technologies might be developed soon. These novel technologies should be evaluated accordingly before entering the market.

trial registrationDRKS00023171 ( https://drks.de/search/en/trial/DRKS00023171 ).

Indexed as

Accidental FallsEmergency Medical ServicesHome Care ServicesIndependent LivingSocial WorkAgedAged, 80 and overCase-Control StudiesFemaleHumansMaleEmergency preventionHome emergency systemInterventionOlder adults

Identifiers

PMID40175885
PMCPMC11963553

What OpenQuestion holds

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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.