Falls Among Residents Living in Care Homes Using Real-Time Data Collection: A Large UK Case-Control Study.

Julian Gertner, Ofir Dvir, Niv Shifrin, Paul Wolfson, Robert Moskovitch, Laurence B Lovat

Abstract read

In one paragraph

Article in Health science reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Julian GertnerDivision of Surgery and Interventional Science University College London London UK.ORCID https://orcid.org/0009-0001-2880-7928

Ofir DvirSoftware and Information Systems Engineering Ben Gurion University of the Negev Beer Sheva Israel.

Niv ShifrinSoftware and Information Systems Engineering Ben Gurion University of the Negev Beer Sheva Israel.

Paul WolfsonDivision of Surgery and Interventional Science University College London London UK.

Robert MoskovitchSoftware and Information Systems Engineering Ben Gurion University of the Negev Beer Sheva Israel.

Laurence B LovatDivision of Surgery and Interventional Science University College London London UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Aims: Falls are common among older residents living in care homes and have significant associated morbidity and mortality. These incidents negatively impact quality of life and pose a substantial financial burden on healthcare systems. Methods: This case-control study explored fall patterns and identifiable risk factors for falls using real-time data collected via the Mobile Care Monitoring (MCM) app across 1,700 care homes in England. Care home staff continuously recorded data for 1 year on a matched cohort of 6,006 residents who experienced at least one fall and 6,006 who had no documented falls, aged 75 years and older. Results: The fall incidence rate was 1,249 per 1,000 residents per year and among those who had already experienced at least one fall, 45% had three or more falls. Advancing age was significantly associated with increasing fall risk (Incidence Rate Ratio (IRR): 1.11; 95% CI: 1.04-1.17; Conclusion: The use of real-time electronic data collection through the MCM app offers a robust foundation for addressing the challenges associated with falls in this population.

Indexed as

care home residentscare homesdigital healthfall riskfallsobservational studyreal‐time data

Identifiers

PMID42011271

DOI10.1002/hsr2.72350

PMCPMC13092222

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.