Evidence map›Paper›PMID 39128300›Full record

ArticleThe journal of nutrition, health & aging2024

Serial gait speed measurements over time and dynamic survival prediction in older adults.

Subashan Perera, Xiao Zhang, Charity G Patterson, Robert M Boudreau

Abstract read
In one paragraph

Article in The journal of nutrition, health & aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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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.

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Subashan PereraDivision of Geriatric Medicine, University of Pittsburgh, PA, United States; Department of Biostatistics, University of Pittsburgh, PA, United States. Electronic address: ksp9@pitt.edu.
Xiao ZhangDepartment of Epidemiology, University of Pittsburgh, Pittsburgh, PA, United States; Vanke School of Public Health, Tsinghua University, Beijing, China; Institute for Healthy China, Tsinghua University, Beijing, China.
Charity G PattersonDepartment of Physical Therapy, University of Pittsburgh, PA, United States.
Robert M BoudreauDepartment of Epidemiology, University of Pittsburgh, Pittsburgh, PA, United States.

Funding

VARIABILITY, STABILITY & SMOOTHNESS OF WALKINGP30AG024827 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI JENNIFER S BRACH · 2004 to 2026
$28.6M
Peripheral Nerve Function Decline in an Aged CohortR01AG028050 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI STROTMEYER, ELSA S. · 2007 to 2015
$3.5M
End of Life in the Very OldR01NR012459 · NINR · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI ALBERT, STEVEN M, LUNNEY, JUNE R · 2011 to 2015
$2.7M
NIA NIH HHS P30 AG024827NIA NIH HHS R01 AG028050NINR NIH HHS R01 NR012459
6 · The paper itself

Abstract

backgroundA one-time gait speed measurement predicts mortality risk. A framework for updating a clinician's mortality risk perception with new information from each clinic visit is needed. We used joint modeling of longitudinal and survival data for dynamic prediction of mortality risk.

methodsWe fit sex-stratified joint models to 20-meter (bi)annual longitudinal gait speed measured every 6 months and 14-year survival data from the Health, Aging and Body Composition Study allowing for non-linear fluctuations of gait speed and controlling for important covariates such as age, recent hospitalization, blood pressure, obesity, and comorbidities.

resultsParticipants (N = 3048) were 74 years old with gait speed 1.18 m/s. They were 42% Black, and 52% died over 14 years. Higher gait speed of 0.1 m/s was associated with 23% (95% confidence interval or CI = 20-25%) and 25% (CI = 21-28%) reductions in mortality risk in men and women; and a 0.05 m/s annualized slowing (slope) with 31% (CI = 13-51%) increase in men (all p < 0.05), with findings persisting after covariate adjustment. Distant gait speed history over a year prior contributed little for mortality risk prediction with mean change of only 1-2% in 5-year risk.

conclusionThe two most recent gait speeds appear sufficient to consider for mortality risk in the present initial analysis. More frequent gait speeds need to be considered in mortality risk prediction before definitive conclusions supporting real-world application.

Indexed as

Walking SpeedAgedAged, 80 and overAgingFemaleGaitGeriatric AssessmentHumansLongitudinal StudiesMaleMortalityRisk AssessmentRisk FactorsGait speedJoint modelingMortalityOlder adultsSurvival

Identifiers

PMID39128300
PMCPMC12880057

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LicenceCC BY-NC-ND
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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.