Self-tracking is one of many behaviors involved in the long-term self-management of chronic illnesses. As consumer-grade wearable sensors have made the collection of health-related behaviors commonplace, the quality, volume, and availability of such data has dramatically improved. This exploratory longitudinal N-of-1 study quantitatively assesses four years of sleep data captured via the Oura Ring, a consumer-grade sleep tracking device, along with self-reported mood data logged using eMood Tracker for iOS. After assessing the data for stationarity and computing the appropriate lag-length selection, a vector autoregressive (VAR) model was fit along with Granger causality tests to assess causal mechanisms within this multivariate time series. Oura's nightly sleep quality score was shown to Granger-cause the presence of depressed and anxious moods using a VAR(2) model.

翻译：自我追踪是慢性疾病长期自我管理中的众多行为之一。随着消费级可穿戴传感器使健康相关行为的收集变得普遍，此类数据的质量、数量和可用性已显著提升。这项探索性纵向N-of-1研究定量评估了通过消费级睡眠追踪设备Oura Ring采集的四年睡眠数据，以及使用iOS版eMood Tracker记录的自报情绪数据。在对数据进行平稳性检验并计算适当的滞后阶数选择后，拟合了向量自回归模型并辅以格兰杰因果检验，以评估该多元时间序列中的因果机制。通过VAR(2)模型证明，Oura的夜间睡眠质量评分对抑郁和焦虑情绪的存在具有格兰杰因果关系。