Evidence map›Paper›PMID 37765742›Full record

ArticleSensors (Basel, Switzerland)2023

Predictability of Fall Risk Assessments in Community-Dwelling Older Adults: A Scoping Review.

N F J Waterval, C M Claassen, F C T van der Helm, E van der Kruk

Open access · goldAbstract readScoping Review
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
8.8field-weighted citation impact, top 2% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
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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

4 authors at 2 institutions in 1 country.

N F J WatervalDepartment of Rehabilitation Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands.
C M ClaassenBiomechatronics & Human-Machine Control, Department of Biomechanical Engineering, Faculty of Mechanical Engineering (3me), Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands.
F C T van der HelmBiomechatronics & Human-Machine Control, Department of Biomechanical Engineering, Faculty of Mechanical Engineering (3me), Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands.
E van der KrukBiomechatronics & Human-Machine Control, Department of Biomechanical Engineering, Faculty of Mechanical Engineering (3me), Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands.ORCID 0000-0001-6933-0313
Delft University of Technology · NLAmsterdam University Medical Centers · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fall risk increases with age, and one-third of adults over 65 years old experience a fall annually. Due to the aging population, the number of falls and related medical costs will progressively increase. Correct prediction of who will fall in the future is necessary to timely intervene in order to prevent falls. Therefore, the aim of this scoping review is to determine the predictive value of fall risk assessments in community-dwelling older adults using prospective studies. A total of 37 studies were included that evaluated clinical assessments (questionnaires, physical assessments, or a combination), sensor-based clinical assessments, or sensor- based daily life assessments using prospective study designs. The posttest probability of falling or not falling was calculated. In general, fallers were better classified than non-fallers. Questionnaires had a lower predictive capability compared to the other assessment types. Contrary to conclusions drawn in reviews that include retrospective studies, the predictive value of physical tests evaluated in prospective studies varies largely, with only smaller-sampled studies showing good predictive capabilities. Sensor-based fall risk assessments are promising and improve with task complexity, although they have only been evaluated in relatively small samples. In conclusion, fall risk prediction using sensor data seems to outperform conventional tests, but the method's validity needs to be confirmed by large prospective studies.

Indexed as

Accidental FallsIndependent LivingAgedAged, 80 and overFemaleHumansMaleProspective StudiesRisk AssessmentSurveys and Questionnairesaging populationcommunity dwelling older adultsfall risk assessmentsensor

Identifiers

PMID37765742
PMCPMC10536675
OpenAlexW4386492647

What Socratic 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 Socratic graph.