Kim Jeong-Kyun, Mun Sujeong, Lee Siwoo
KM Data Division, Korea Institute of Oriental Medicine, 1672 Yuseong-daero, Yuseong-gu, Daejeon, Daejeon, 34054, Republic of Korea, 82 10-4654-6164, 82 42-868-9555.
JMIR Med Inform. 2025 Jul 16;13:e69328. doi: 10.2196/69328.
Wearable devices are increasingly used for monitoring health and detecting digital biomarkers related to chronic diseases such as metabolic syndrome (MetS). Although circadian rhythm disturbances are known to contribute to MetS, few studies have explored wearable-derived circadian biomarkers for MetS identification.
This study aimed to detect and analyze sleep and circadian rhythm biomarkers associated with MetS using step count and heart rate data from wearable devices and to identify the key biomarkers using explainable artificial intelligence (XAI).
Data were analyzed from 272 participants in the Korean Medicine Daejeon Citizen Cohort, collected between 2020 and 2023, including 88 participants with MetS and 184 without any MetS diagnostic criteria. Participants wore Fitbit Versa or Inspire 2 devices for at least 5 weekdays, providing minute-level heart rate, step count, and sleep data. A total of 26 indicators were derived, including sleep markers (midsleep time and total sleep time) and circadian rhythm markers (midline estimating statistic of rhythm, amplitude, interdaily stability, and relative amplitude). In addition, a novel circadian rhythm marker, continuous wavelet circadian rhythm energy (CCE), was proposed using continuous wavelet transform of heart rate signals. Statistical tests (t test and the Wilcoxon rank sum test) and machine learning models-Shapley Additive Explanations, explainable boosting machine, and tabular neural network-were applied to evaluate marker significance and importance.
Circadian rhythm markers, especially heart rate-based indicators, showed stronger associations with MetS than sleep markers. The newly proposed CCE demonstrated the highest importance for MetS identification across all XAI models, with significantly lower values observed in the MetS group (P<.001). Other heart rate-based markers, including relative amplitude and low activity period, were also identified as important contributors. Although sleep markers did not reach statistical significance, some were recognized as secondary predictors in XAI-based analyses. The CCE marker maintained a high predictive value even when adjusting for age, sex, and BMI.
This study identified CCE and relative amplitude of heart rate as key circadian rhythm biomarkers for MetS monitoring, demonstrating their high importance across multiple XAI models. In contrast, traditional sleep markers showed limited significance, suggesting that circadian rhythm analysis may offer additional insights into MetS beyond sleep-related indicators. These findings highlight the potential of wearable-based circadian biomarkers for improving MetS assessment and management.
可穿戴设备越来越多地用于监测健康状况和检测与慢性疾病（如代谢综合征，MetS）相关的数字生物标志物。尽管已知昼夜节律紊乱会导致代谢综合征，但很少有研究探索可穿戴设备衍生的昼夜节律生物标志物用于代谢综合征的识别。
本研究旨在利用可穿戴设备的步数和心率数据检测和分析与代谢综合征相关的睡眠和昼夜节律生物标志物，并使用可解释人工智能（XAI）识别关键生物标志物。
分析了2020年至2023年期间在韩国大田市民队列研究中招募的272名参与者的数据，其中包括88名患有代谢综合征的参与者和184名未符合任何代谢综合征诊断标准的参与者。参与者佩戴Fitbit Versa或Inspire 2设备至少5个工作日，提供分钟级心率、步数和睡眠数据。共得出26项指标，包括睡眠标志物（睡眠中期时间和总睡眠时间）和昼夜节律标志物（节律中线估计统计量、振幅、日间稳定性和相对振幅）。此外，利用心率信号的连续小波变换提出了一种新的昼夜节律标志物，即连续小波昼夜节律能量（CCE）。应用统计检验（t检验和Wilcoxon秩和检验）以及机器学习模型——Shapley加性解释、可解释增强机器和表格神经网络——来评估标志物的显著性和重要性。
昼夜节律标志物，尤其是基于心率的指标，与代谢综合征的关联比睡眠标志物更强。新提出的CCE在所有XAI模型中对代谢综合征识别的重要性最高，在代谢综合征组中观察到的值显著更低（P<0.001）。其他基于心率的标志物，包括相对振幅和低活动期，也被确定为重要贡献因素。尽管睡眠标志物未达到统计学显著性，但在基于XAI的分析中，一些被认为是次要预测因素。即使在调整年龄、性别和BMI后，CCE标志物仍保持较高的预测价值。
本研究确定CCE和心率相对振幅为代谢综合征监测的关键昼夜节律生物标志物，证明了它们在多个XAI模型中的高度重要性。相比之下，传统睡眠标志物的显著性有限，这表明昼夜节律分析可能为代谢综合征提供超越睡眠相关指标的额外见解。这些发现凸显了基于可穿戴设备的昼夜节律生物标志物在改善代谢综合征评估和管理方面的潜力。
