Evidence map›Paper›PMID 31095283›Full record

ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2020

When Will My Patient Fall? Sensor-Based In-Home Walking Speed Identifies Future Falls in Older Adults.

Antoine Piau, Nora Mattek, Rachel Crissey, Zachary Beattie, Hiroko Dodge, Jeffrey Kaye

Open access · bronzeAbstract read
In one paragraph

Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
13.0field-weighted citation impact, top 1% 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

27 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.

  1. Pooled it
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  8. Yearlong Evaluation of Fall Risk Determinants Among 40 Older Adults in Two Residential Assisted Living Facilities.Medical science monitor : international medical journal of experimental and clinical research · 2025
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  19. Contactless Gait Assessment in Home-like Environments.Sensors (Basel, Switzerland) · 2021
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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

6 authors at 1 institution in 2 countries.

Antoine PiauOregon Center for Aging & Technology (ORCATECH), Oregon Health & Science University, Portland.
Nora MattekOregon Center for Aging & Technology (ORCATECH), Oregon Health & Science University, Portland.
Rachel CrisseyOregon Center for Aging & Technology (ORCATECH), Oregon Health & Science University, Portland.
Zachary BeattieOregon Center for Aging & Technology (ORCATECH), Oregon Health & Science University, Portland.
Hiroko DodgeOregon Center for Aging & Technology (ORCATECH), Oregon Health & Science University, Portland.
Jeffrey KayeOregon Center for Aging & Technology (ORCATECH), Oregon Health & Science University, Portland.
Oregon Health & Science University · US

Funding

Research Education ComponentP30AG066518 · NIA · OREGON HEALTH & SCIENCE UNIVERSITY · PI KEVIN M DUFF, Lisa C Silbert · 2020 to 2026
$28.6M
Outreach, Recruitment and Education CoreP30AG008017 · NIA · OREGON HEALTH & SCIENCE UNIVERSITY · PI DODGE, HIROKO HAYAMA · 1990 to 2019
$27.7M
Pilot CoreP30AG024978 · NIA · OREGON HEALTH & SCIENCE UNIVERSITY · PI KAYE, JEFFREY A · 2004 to 2023
$9.0M
Resource CoreU2CAG054397 · NIA · OREGON HEALTH & SCIENCE UNIVERSITY · PI KAYE, JEFFREY A · 2016 to 2019
$7.0M
Intelligent Systems for Detection of Aging ChangesR01AG024059 · NIA · OREGON HEALTH & SCIENCE UNIVERSITY · PI KAYE, JEFFREY A · 2006 to 2010
$6.7M
NIA NIH HHS P30 AG008017NIA NIH HHS P30 AG024978NIA NIH HHS P30 AG066518NIA NIH HHS R01 AG024059NIA NIH HHS U2C AG054397
6 · The paper itself

Abstract

backgroundAlthough there are known clinical measures that may be associated with risk of future falls in older adults, we are still unable to predict when the fall will happen. Our objective was to determine whether unobtrusive in-home assessment of walking speed can detect a future fall.

methodIn both ISAAC and ORCATECH Living Laboratory studies, a sensor-based monitoring system has been deployed in the homes of older adults. Longitudinal mixed-effects regression models were used to explore trajectories of sensor-based walking speed metrics in those destined to fall versus controls over time. Falls were captured during a 3-year period.

resultsWe observed no major differences between those destined to fall (n = 55) and controls (n = 70) at baseline in clinical functional tests. There was a longitudinal decline in median daily walking speed over the 3 months before a fall in those destined to fall when compared with controls, p < .01 (ie, mean walking speed declined 0.1 cm s-1 per week). We also found prefall differences in sensor-based walking speed metrics in individuals who experienced a fall: walking speed variability was lower the month and the week just before the fall compared with 3 months before the fall, both p < .01.

conclusionsWhile basic clinical tests were not able to differentiate who will prospectively fall, we found that significant variations in walking speed metrics before a fall were measurable. These results provide evidence of a potential sensor-based risk biomarker of prospective falls in community living older adults.

Indexed as

Accidental FallsWalking SpeedAgedAged, 80 and overFemaleHumansIndependent LivingMaleProspective StudiesDigital biomarkersPervasive computingTechnology

Identifiers

PMID31095283
PMCPMC7164533
OpenAlexW2953295902

What OpenQuestion 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 OpenQuestion graph.