ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2022
Predicting Future Mobility Limitation in Older Adults: A Machine Learning Analysis of Health ABC Study Data.
Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 20 citations in OpenAlex.
- Combined Psychoeducational and Exercise Training Intervention to Improve Mental Health and Physical Functioning in Late-Life: A Randomized Controlled Trial.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2026Trial
- Benefit and Uptake: A Mixed Methods Study Exploring Older Adults' Experiences With a Psychoeducational and Exercise Training Maintenance Intervention.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2026Article
- Article
- Primary healthcare professionals' perspectives on mobility limitation assessment and predictive model optimization for community-dwelling older adults: a qualitative research.Frontiers in public health · 2026Article
- Predicting 3-year depressive symptoms among middle-aged and older adults in rural China using random forest: insights from the China health and retirement longitudinal study.BMC psychology · 2025Article
- Depressive symptoms and chronic disease trajectories and predictors in middle-aged and older adults in China: An eight-year multi-trajectory analysis.Global health & medicine · 2025Article
- Construction of disability risk prediction model for the elderly based on machine learning.Scientific reports · 2025Article
- Incremental predictive value of intrinsic capacity and environmental characteristics in the risk prediction of incident disability.JAR life · 2025Article
- Gerontologic Biostatistics and Data Science: Aging Research in the Era of Big Data.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024Review
- Verification of a comprehensive framework for mobility using data from the Canadian Longitudinal Study on Aging: a structural equation modeling analysis.BMC geriatrics · 2023Article
- Feasibility of a Smartwatch Platform to Assess Ecological Mobility: Real-Time Online Assessment and Mobility Monitor.The journals of gerontology. Series A, Biological sciences and medical sciences · 2023Article
- CCR5-Δ32 polymorphism-a possible protective factor from gait impairment amongst post-stroke patients.European journal of neurology · 2023Article
- Disability risk prediction model based on machine learning among Chinese healthy older adults: results from the China Health and Retirement Longitudinal Study.Frontiers in public health · 2023Article
- Predicting restriction of life-space mobility: a machine learning analysis of the IMIAS study.Aging clinical and experimental research · 2022Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
backgroundMobility limitation in older adults is common and associated with poor health outcomes and loss of independence. Identification of at-risk individuals remains challenging because of time-consuming clinical assessments and limitations of statistical models for dynamic outcomes over time. Therefore, we aimed to develop machine learning models for predicting future mobility limitation in older adults using repeated measures data.
methodsWe used annual assessments over 9 years of follow-up from the Health, Aging, and Body Composition study to model mobility limitation, defined as self-report of any difficulty walking a quarter mile or climbing 10 steps. We considered 46 predictors, including demographics, lifestyle, chronic conditions, and physical function. With a split sample approach, we developed mixed models (generalized linear and Binary Mixed Model forest) using (a) all 46 predictors, (b) a variable selection algorithm, and (c) the top 5 most important predictors. Age was included in all models. Performance was evaluated using area under the receiver operating curve in 2 internal validation data sets.
resultsArea under the receiver operating curve ranged from 0.80 to 0.84 for the models. The most important predictors of mobility limitation were ease of getting up from a chair, gait speed, self-reported health status, body mass index, and depression.
conclusionsMachine learning models using repeated measures had good performance for identifying older adults at risk of developing mobility limitation. Future studies should evaluate the utility and efficiency of the prediction models as a tool in clinical settings for identifying at-risk older adults who may benefit from interventions aimed to prevent or delay mobility limitation.
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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.