Study on the latent profiles of disability among the elderly and its influencing factors
TAN Suwen
WANG Zhiyi
HU Yajing
YI Xiaocong
ZHANG Yinhua
Abstract：Objective To explore the latent profiles of elderly disability and analyze the influencing factors of differ-ent latent profile classifications.Methods Convenience sampling was used to select 244 elderly individuals from 5 elder-ly care institutions in Hunan Province as research subjects.A general information questionnaire and the World Health Or-ganization Disability Assessment Schedule 2.0(WHODAS 2.0)were utilized to survey the elderly participants,while manual muscle testing method was conducted to assess their muscle strength.Latent profiles of disability among elderly were identified using latent profile analysis.Variables that showed significance in univariate analyses were then included in multivariate analysis.Additionally,decision tree model and random forest models were constructed to explore the pre-dictors of different latent profile classes and to rank their relative importance.Results The disability score of the elderly was 96.20±25.29,which could be categorized into four latent profiles:the extremely severe disability group with low activ-ity and high cognition(31.6%),the severe disability group with low activity and high self-care ability(23.8%),the mod-erate disability group with low activity(30.7%),and the mild disability group with balanced function(13.9%).The in-fluencing factors of different latent profiles of disability in the elderly included muscle strength,marital status,cardiovas-cular diseases,and surgical history.Results from the decision tree model and random forest model indicated that muscle strength has the highest importance in influencing the classification of disability status.Conclusions The disability among the elderly exhibits group heterogeneity,with muscle strength,marital status,cardiovascular diseases,and surgi-cal history as influencing factors.Healthcare providers should implement personalized interventions for the elderly based on different latent profiles and influencing factors to improve their disability status.
Keywords：ageddisabilitylatent profile analysisrandom forest algorithmdecision tree algorithminfluencing factor
Publication Date：2025-12-25
Online Publishing Date：2026-01-09(First online date of this platform, not the publication date of the document)
Pages：7( 50-56 )