Machine Learning Models to Identify Clinically Significant Anxiety in Short‐Term Insomnia Using Accelerometers

作者：Leqin Fang, Weixiong Zeng, Shuqiong Zheng, Shixu Du, Hangyi Yang, Xue Luo, Shufei Zeng, Zhiting Huang, Weiguo Chen, Bin Zhang · 发表于：Depression and Anxiety · 年份：2025 · DOI：10.1155/da/3082856 · 被引用次数：4 · 研究领域：Sleep and related disorders、Mental Health Research Topics、Sleep and Wakefulness Research

Clinically significant anxiety (CSA) is common in individuals with short‐term insomnia. This study aims to explore the relationship between CSA and the subjective and objective parameters of sleep in patients with short‐term insomnia and construct machine learning (ML) models to determine the utility of accelerometer features in identifying significant anxiety. A total of 205 short‐term insomnia participants from China were assigned to the group with CSA ( N = 33) or the group without CSA ( N = 172). Interaction analysis based on linear regression was used to estimate the possible interactive effect of accelerometer features between CSA and sleep problems. Four feature sets and eight algorithms were used to construct ML models, with Shapley Additive exPlanations (SHAP) values used to visualize feature importance and influence processes. CSA in patients with short‐term insomnia leads to more severe subjective sleep problems, and accelerometer‐measured features warrant further attention for the identification of interactive factors. A significant interaction effect was found between anxiety symptoms and longer duration of physical activity on insomnia severity ( P interaction < 0.05). Anxiety symptoms and interdaily stability had an interactive association with sleep hygiene behaviors ( P interaction < 0.01). ML can process and analyze complex accelerometer features to identify CSA in patients with short‐term insomnia. Compared with other feature sets and algorithms, the ...