Lin Fangbo, Liu Chao, Liu Hua
Rehabilitation Medicine Department, The Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University, No. 311 Yinpan Road, Hunan Province, Changsha, 410008, China, 86 15111271991.
Neurology Department, Fujian Medical University Union Hospital, Fuzhou, China.
JMIR Med Inform. 2025 Jun 19;13:e73030. 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.
全球老龄化危机引发了重大的公共卫生挑战，包括慢性病增多、经济负担加重和劳动力短缺，在中国尤其如此。日常生活活动（ADL）功能障碍影响着超过4000万中国老年人（占老年人口的16%），严重损害了他们的独立性和生活质量，同时增加了医疗成本和死亡率。ADL功能障碍包括基本日常生活活动（BADL）和工具性日常生活活动（IADL），分别评估基本的自我护理能力和复杂的环境交互能力。据预测，到2030年将有6500万例病例，因此迫切需要能够预测ADL损伤并实现早期干预的工具。
本研究旨在利用中国健康与养老追踪调查（CHARLS）具有全国代表性的数据，开发并验证一个用于预测老年人ADL功能障碍的预测列线图模型。该模型旨在将关键风险因素整合到一个易于使用的临床工具中，以便于早期识别高危人群，指导有针对性的医疗保健策略和资源分配。
对5081名年龄在60 - 80岁的CHARLS第三轮（2015 - 2016年）参与者进行回顾性分析。根据BADL和IADL评估，将参与者分为ADL功能障碍组（n = 1743）和正常组（n = 3338）。分析了涵盖人口统计学、健康状况、生物测量和生活方式的46个变量。通过多重填补法处理缺失数据后，采用最小绝对收缩和选择算子（LASSO）回归和多变量逻辑回归确定了6个预测因子。使用受试者工作特征曲线、校准图、决策曲线分析和夏普利值解释（SHAP）来评估模型性能，以实现可解释性。
最终模型纳入了6个预测因子：10项流行病学研究中心抑郁量表抑郁得分、疼痛部位数量、左手握力、2.5米步行时间、体重和胱抑素C水平。该列线图显示出强大的判别能力，训练集和测试集的曲线下面积值均为0.77（95%CI 0.76 - 0.79）。校准曲线证实预测结果与观察结果之间具有高度一致性，而决策曲线分析突出了该模型相较于“全部治疗”或“不治疗”方法在临床应用上的优越性。SHAP分析显示抑郁症状和身体虚弱标志物（如步行速度慢和握力低）是主要预测因子，这与关于ADL下降机制的现有证据一致。
本研究提出了一种经过验证的列线图，用于预测老年人群中的ADL功能障碍，该列线图结合了心理、身体和生化标志物。该工具能够进行风险分层，支持个性化干预，并通过强调疼痛管理、抑郁和运动训练等可改变因素来弥补老年护理中的差距。尽管存在区域数据偏差和回顾性设计等局限性，但该模型具有可扩展的临床价值。未来的研究应纳入社会、环境和认知因素，以提高模型的准确性和通用性。
