Evidence map›Paper›PMID 41960380›Full record

ArticleFrontiers in public health2026

Fall detection and pre-impact prediction technologies in older adults: a scoping review of translational maturity and public health integration.

Li Chen, Wu Yao

Abstract readScoping Review
In one paragraph

Article in Frontiers in public health, 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

2 authors.

Li ChenDepartment of Physical Education Teaching, Shanghai Sanda University, Shanghai, China.
Wu YaoDepartment of Physical Education, Shanghai Jiao Tong University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To map the current landscape of wearable and sensor-based fall detection and pre-impact prediction technologies relevant to older adults and to evaluate their translational maturity within public health contexts. Methods: A scoping review was conducted following PRISMA-ScR guidelines. Four electronic databases (PubMed, Web of Science, Scopus, and IEEE Xplore) were systematically searched for studies published between January 2005 and September 2025. Eligible studies reported the development or validation of fall detection or pre-impact prediction systems incorporating wearable, vision-based, environmental, or multimodal sensing modalities. In total, 243 studies were included in the overall synthesis, with a predefined subgroup of 21 studies involving real-world or mixed real-world validation in older adult populations (≥65 years). Results: Across the 243 included studies, wearable inertial measurement unit (IMU)-based systems constituted the dominant technological stream, and post-fall detection remained the most frequently investigated functional objective. However, more than half of studies relied primarily on laboratory-based simulated fall protocols. Within the real-world validated older adult subgroup ( Conclusion: Although technological innovation in fall-related sensing systems has expanded rapidly, translational maturity remains uneven. Bridging the gap between algorithmic performance and scalable public health implementation will require robust real-world validation, longitudinal adherence evaluation, implementation science frameworks, and economic assessment. Advancing along a continuum from reactive detection toward predictive and personalized prevention represents a critical pathway for supporting safe and independent aging.

Indexed as

Accidental FallsPublic HealthWearable Electronic DevicesAgedDigital HealthHumansPrediction Algorithmsfall detectionolder adultspre-impact predictiontranslational maturitywearable sensors

Identifiers

PMID41960380
PMCPMC13056853

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.