Background Mobility 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. Methods We 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. Results Area 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. Conclusions Machine 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.

中文翻译：

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预测老年人未来的行动限制：健康 ABC 研究数据的机器学习分析

背景 老年人的行动受限很常见，并且与健康状况不佳和丧失独立性有关。由于耗时的临床评估和随时间推移动态结果的统计模型的局限性，识别高危个体仍然具有挑战性。因此，我们的目标是开发机器学习模型，使用重复测量数据来预测老年人未来的移动受限。方法 我们使用来自健康、衰老和身体成分研究的 9 年随访的年度评估来模拟移动受限，定义为步行四分之一英里或爬 10 步有任何困难的自我报告。我们考虑了 46 个预测因素，包括人口统计、生活方式、慢性病和身体机能。使用拆分样本方法，我们使用 (a) 所有 46 个预测变量、(b) 变量选择算法和 (c) 前 5 个最重要的预测变量开发了混合模型（广义线性和二元混合模型森林）。所有模型都包含年龄。使用 2 个内部验证数据集中的接收器操作曲线下面积评估性能。结果 模型的受试者工作曲线下面积在 0.80 到 0.84 之间。行动受限的最重要预测因素是从椅子上站起来的难易程度、步态速度、自我报告的健康状况、体重指数和抑郁症。结论 使用重复测量的机器学习模型在识别存在行动不便风险的老年人方面具有良好的性能。