Evidence map›Paper›PMID 38436792›Full record

SynthesisGeroScience2024

Performance of digital technologies in assessing fall risks among older adults with cognitive impairment: a systematic review.

Vanessa Koh, Lai Wei Xuan, Tan Kai Zhe, Navrag Singh, David B Matchar, Angelique Chan

Open access · greenAbstract readSystematic Review
In one paragraph

Synthesis in GeroScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
6.9field-weighted citation impact, top 3% 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

5 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. 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

6 authors at 3 institutions in 3 countries.

Vanessa KohProgramme in Health Services and Systems Research (HSSR), Duke-NUS Medical School, Singapore, Singapore. vanessa.kjw@u.duke.nus.edu.ORCID 0000-0002-4175-7224
Lai Wei XuanProgramme in Health Services and Systems Research (HSSR), Duke-NUS Medical School, Singapore, Singapore.ORCID 0000-0002-5802-1846
Tan Kai ZheFuture Health Technologies Programme, Singapore-ETH Centre, Singapore, Singapore.ORCID 0000-0001-8828-0278
Navrag SinghFuture Health Technologies Programme, Singapore-ETH Centre, Singapore, Singapore.ORCID 0000-0001-8074-041X
David B MatcharProgramme in Health Services and Systems Research (HSSR), Duke-NUS Medical School, Singapore, Singapore.ORCID 0000-0003-3020-2108
Angelique ChanProgramme in Health Services and Systems Research (HSSR), Duke-NUS Medical School, Singapore, Singapore.ORCID 0000-0001-8979-5142
Duke-NUS Medical School · SGDuke Medical Center · USETH Zurich · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Older adults with cognitive impairment (CI) are twice as likely to fall compared to the general older adult population. Traditional fall risk assessments may not be suitable for older adults with CI due to their reliance on attention and recall. Hence, there is an interest in using objective technology-based fall risk assessment tools to assess falls within this population. This systematic review aims to evaluate the features and performance of technology-based fall risk assessment tools for older adults with CI. A systematic search was conducted across several databases such as PubMed and IEEE Xplore, resulting in the inclusion of 22 studies. Most studies focused on participants with dementia. The technologies included sensors, mobile applications, motion capture, and virtual reality. Fall risk assessments were conducted in the community, laboratory, and institutional settings; with studies incorporating continuous monitoring of older adults in everyday environments. Studies used a combination of technology-based inputs of gait parameters, socio-demographic indicators, and clinical assessments. However, many missed the opportunity to include cognitive performance inputs as predictors to fall risk. The findings of this review support the use of technology-based fall risk assessment tools for older adults with CI. Further advancements incorporating cognitive measures and additional longitudinal studies are needed to improve the effectiveness and clinical applications of these assessment tools. Additional work is also required to compare the performance of existing methods for fall risk assessment, technology-based fall risk assessments, and the combination of these approaches.

Indexed as

Accidental FallsCognitive DysfunctionDigital TechnologyAgedGeriatric AssessmentHumansRisk AssessmentCognitive impairmentDigital technologiesFall predictionFalls risk assessmentOlder adults

Identifiers

PMID38436792
PMCPMC11009180
OpenAlexW4392371628

What Socratic holds

Textmetadata
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 Socratic graph.