Evidence map›Paper›PMID 42151572›Full record

ArticleGeroScience2026

Identifying a fitness tool in early old-age to predict long-term risk of disability, severe disability, and mortality.

Céline Ben Hassen, Aurore Fayosse, Damien Vitiello, Pauline Maillot, Benjamin Landré, Ian Meneghel Danilevicz, Séverine Sabia, Archana Singh-Manoux

Abstract read
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In one paragraph

Article in GeroScience, 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

8 authors.

Céline Ben HassenInserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, Université Paris Cité, 10 Avenue de Verdun, 75010, Paris, France. celine.ben-hassen@inserm.fr.ORCID https://orcid.org/0000-0001-7884-2986
Aurore FayosseInserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, Université Paris Cité, 10 Avenue de Verdun, 75010, Paris, France.
Damien VitielloInstitut Des Sciences du Sport, Université Paris Cité, UFR STAPS, Santé de Paris, Paris, France.
Pauline MaillotInstitut Des Sciences du Sport, Université Paris Cité, UFR STAPS, Santé de Paris, Paris, France.
Benjamin LandréInserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, Université Paris Cité, 10 Avenue de Verdun, 75010, Paris, France.
Ian Meneghel DanileviczInserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, Université Paris Cité, 10 Avenue de Verdun, 75010, Paris, France.
Séverine Sabia *Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, Université Paris Cité, 10 Avenue de Verdun, 75010, Paris, France.
Archana Singh-Manoux *Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases, Université Paris Cité, 10 Avenue de Verdun, 75010, Paris, France.

Funding

Education, socioeconomic status and Aging: transitions from multimorbidity to functional limitations and mortalityR01AG056477 · NIA · UNIVERSITY COLLEGE LONDON · PI KIVIMAKI, MIKA J, SINGH-MANOUX, ARCHANA · 2018 to 2022
$2.8M
agence nationale de la recherche France 2030 ANR-23-PAVH-0006European Union ERC 101043884NIA NIH HHS R01AG056477NIA NIH HHS R01AG062553UK Medical Research Council R024227UK Medical Research Council S011676Wellcome Trust 221854/Z/20/Z
6 · The paper itself

Abstract

Population ageing has led to an increase in prevalence of old-age disability but whether the risk of disability can be detected early remains unclear. We used ten functioning/fitness measures in early old-age to identify their predictive ability for disability at older ages. A total of 4593 participants of the Whitehall II study, mean age 65.3 years, were followed for a median of 11.00 (IQR 7.25-12.67) years for incident disability [≥ 1 limitation in activities of daily living (ADL)], and severe disability (≥ 2 ADL). We first examined whether the C-statistic for each predictor, considered individually, improved the C-statistic of a model containing age and sex. We then used LASSO regression, using decrements of 0.0001 in a step-wise manner in the lambda regularization parameter to select the most important predictors. Improvement in C-statistic of a new set of LASSO predictors was tested to select the final set of predictors. Among the functioning/fitness measures considered individually, waist circumference had the highest C-statistic for disability [0.6557, 95% confidence interval (CI) 0.6367-0.6747] and severe disability (0.6868, 0.6582-0.7153). The best set of LASSO predictors (0.6617, 0.6431-0.6804) for incident disability included age, sex, waist circumference, walking speed, timed chair rises, and balance. For severe disability, the predictors were the same without walking speed (0.6955, 0.6678-0.7233). Our findings highlight the importance of obesity measures for risk of disability, and show that a small set of functioning/fitness measures can be useful in identifying individuals at higher risk of disability at older ages.

Indexed as

ADL disabilityAgeingDisabilityFitnessPhysical functioningPrediction

Identifiers

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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.