Evidence map›Paper›PMID 41484752›Full record

ArticleBMC medical informatics and decision making2026

Predicting the risk of activities of daily living dysfunction in middle-aged and older adults with comorbid hypertension and diabetes: a national population-based survey analysis.

Fangbo Lin, Jianwen Chen, Le Xiao

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Fangbo LinRehabilitation Medicine Department, The Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University (The First Hospital of Changsha), Changsha, People's Republic of China.
Jianwen ChenSchool of Mathematics and Statistics, Hunan University of Technology and Business, Changsha, China.
Le XiaoRehabilitation Medicine Department, The Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University (The First Hospital of Changsha), Changsha, People's Republic of China. xlcssdyy@126.com.

Funding

Bureau of Science and Technology of Changsha No.kzd2401043Changsha Municipal Health Research Project KJ-202505Hunan Provincial Department of Education Scientific Research Project No.22A0427Hunan Provincial Natural Science Foundation of China 2025JJ80484Hunan Provincial Philosophy and Social Science Fund General Project No.23YBA167
6 · The paper itself

Abstract

objectiveTo develop and validate an interpretable model for predicting activities of daily living (ADL) dysfunction in middle-aged and older adults with comorbid hypertension and diabetes.

methodsThis is a cross-sectional study. Data were derived from wave 4 of the China Health and Retirement Longitudinal Study. After applying inclusion and exclusion criteria, 1,623 participants were included. Least absolute shrinkage and selection operator regression was used for feature selection, followed by multivariable logistic regression to construct a nomogram. Model performance was assessed using receiver operating characteristic curves, the area under the curve (AUC), calibration plots, and decision curve analysis.

resultsThe final nomogram incorporated seven predictors: history of falls, stroke, psychiatric disorders, number of healthy children, Center for Epidemiologic Studies Depression Scale score, number of pain sites, and level of social participation. The model achieved an AUC of 0.800 in both training (95% CI: 0.772–0.828) and testing (95% CI: 0.758–0.842) sets. Calibration analysis indicated close agreement between predicted and observed outcomes.

conclusionsWe developed and validated an interpretable model with good predictive performance. The model provides a practical basis for personalized interventions and may support clinical practice aimed at preserving functional health in aging populations with multimorbidity. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Activities of Daily LivingDiabetes MellitusHypertensionAgedChinaComorbidityCross-Sectional StudiesFemaleHumansLongitudinal StudiesMaleMiddle AgedPrediction AlgorithmsRisk AssessmentADL dysfunctionHypertension and diabetes comorbidityPopulation agingPredictive model

Identifiers

PMID41484752
PMCPMC12866553

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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.