穿戴式裝置活動訊號在死亡風險分層的角色

Abstract

穿戴式裝置可追蹤真實生活中的活動狀態，並可能提供傳統臨床評估較難捕捉的行為資訊。然而，穿戴式裝置衍生之活動模式在死亡風險分層中的角色仍未被充分了解。本研究旨在探討穿戴式裝置衍生之活動訊號於死亡風險分層與行為解釋中的應用價值。 本分析使用美國國家健康與營養調查（National Health and Nutrition Examination Survey, NHANES）2011–2014 年資料，納入 4,531 名年齡 40 歲以上、具臨床資料、穿戴式裝置資料與死亡追蹤資料之成人。研究自七天逐小時之活動及睡眠相關訊號中萃取關於活動強度、變異性、破碎化及節律相關的行為特徵。風險分層模型分別以臨床變數、穿戴式裝置衍生特徵及兩者結合作為三種輸入設定，並以 XGBoost 作為主要模型。模型解釋透過 SHAP 值、部分依賴圖（partial dependence plots）及群體層級行為模式分析，評估特徵重要性與風險相關模式。 結果顯示，整合穿戴式裝置衍生特徵與臨床變數可提供死亡風險分層的補充資訊，並能在預測風險較高之群體中辨識出較高比例的死亡個案。模型解釋辨識出數個重要的穿戴式裝置衍生行為訊號，包括峰值活動量（PAM_max）與活動破碎化程度（PAM_IV）。進一步的群體分析顯示，同時具有較低峰值活動能力與較高活動破碎化特徵的個體，其觀察到的死亡率最高。 本研究指出，穿戴式裝置衍生之活動訊號可捕捉與死亡風險分層相關的行為面向。除傳統模型預測表現外，穿戴式裝置衍生之行為模式亦可提供具解釋性的行為資訊，並可能有助於族群層級健康風險評估方法之發展。

Wearable devices enable continuous assessment of real-world activity behavior and may provide behavioral information not captured by traditional clinical evaluation. However, the role of wearable-derived activity patterns in mortality risk stratification remains incompletely understood. This study investigated the utility of wearable-derived activity signals for mortality risk stratification and behavioral interpretation. We analyzed data from 4,531 adults aged ≥40 years from the 2011–2014 U.S. National Health and Nutrition Examination Survey (NHANES) with clinical data, linked mortality records, and accelerometer data. Wearable-derived features were extracted from seven-day hourly activity and sleep-related signals to characterize activity intensity, variability, fragmentation, and rhythm-related patterns. Risk stratification models were developed using clinical variables, wearable-derived features, and their combination, with XGBoost as the primary model. Model interpretation was assessed using SHAP values, partial dependence plots, and cohort-level behavioral pattern analyses. The integration of wearable-derived features with clinical variables provided complementary information for mortality risk stratification, particularly through AUROC-based discrimination and enrichment of observed mortality events in top-ranked risk groups. Interpretability analyses identified several wearable-derived features, including peak activity level (PAM_max) and activity fragmentation (PAM_IV), as important behavioral signals. Cohort-level analysis further showed that individuals characterized by both lower peak activity capacity and higher activity fragmentation exhibited the highest observed mortality rate. These findings suggest that wearable-derived activity signals capture a complementary behavioral dimension relevant to mortality risk stratification. Beyond conventional model performance metrics, wearable-derived behavioral patterns may provide interpretable information for population-level health risk assessment.