Development and Validation of a Predictive Model for Activities of Daily Living Dysfunction in Older Adults: Retrospective Analysis of Data From the China Health and Retirement Longitudinal Study

• Published in: JMIR medical informatics Vol. 13; p. e73030
• Main Authors: Lin, Fangbo, Liu, Chao, Liu, Hua
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 19.06.2025
• Subjects: Activities of Daily Living; Aged; Aged, 80 and over; Blood; China - epidemiology; Chronic illnesses; Diabetes; Disability; Female; Geriatric Assessment - methods; Humans; Hypertension; Longitudinal Studies; Male; Marital status; Middle Aged; Missing data; Nomograms; Nonparametric statistics; Older people; Population; Public health; Regression analysis; Retirement; Retrospective Studies; China; frailty; nomogram model; activities of daily living; elderly; risk prediction; CHARLS; China Health and Retirement Longitudinal Study
• ISSN: 2291-9694; 2291-9694
• DOI: 10.2196/73030

The global aging crisis has precipitated significant public health challenges, including rising chronic diseases, economic burdens, and labor shortages, particularly in China. Activities of daily living (ADL) dysfunction, affecting over 40 million Chinese older adults (16% of the aging population), severely compromises independence and quality of life while increasing health care costs and mortality. ADL dysfunction encompasses both basic ADL (BADL) and instrumental ADL (IADL), which assess fundamental self-care and complex environmental interactions, respectively. With projections indicating 65 million cases by 2030, there is an urgent need for tools to predict ADL impairment and enable early interventions. This study aimed to develop and validate a predictive nomogram model for ADL dysfunction in older adults using nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS). The model seeks to integrate key risk factors into an accessible clinical tool to facilitate early identification of high-risk populations, guiding targeted health care strategies and resource allocation. A retrospective analysis was conducted on 5081 CHARLS wave 3 participants (2015-2016) aged 60-80 years. Participants were categorized into ADL dysfunction (n=1743) or normal (n=3338) groups based on BADL and IADL assessments. Forty-six variables spanning demographics, health status, biomeasures, and lifestyle were analyzed. After addressing missing data via multiple imputation, Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariable logistic regression identified 6 predictors. Model performance was evaluated using receiver operating characteristic curves, calibration plots, decision curve analysis, and Shapley additive explanations (SHAP) for interpretability. The final model incorporated 6 predictors: the 10-item Center for Epidemiologic Studies Depression Scale depression score, number of painful areas, left-hand grip strength, 2.5-m walking time, weight, and cystatin C level. The nomogram demonstrated robust discriminative power, with area under the curve values of 0.77 (95% CI 0.76-0.79) in both the training and testing sets. Calibration curves confirmed strong agreement between predicted and observed outcomes, while decision curve analysis highlighted superior clinical use over "treat-all" or "treat-none" approaches. SHAP analysis revealed depressive symptoms and physical frailty markers (eg, slow walking speed and low grip strength) as dominant predictors, aligning with existing evidence on ADL decline mechanisms. This study presents a validated nomogram for predicting ADL dysfunction in older adult populations, combining psychological, physical, and biochemical markers. The tool enables risk stratification, supports personalized interventions, and addresses gaps in geriatric care by emphasizing modifiable factors like pain management, depression, and mobility training. Despite limitations such as regional data biases and the retrospective design, the model offers scalable clinical value. Future research should incorporate social, environmental, and cognitive factors to enhance precision and generalizability.