Sleep is crucial for physical and mental health, but traditional sleep quality assessment methods have limitations. This scoping review analyzes 35 articles from the past decade, evaluating 62 wearable setups with varying sensors, algorithms, and features. Our analysis indicates a trend towards combining accelerometer and photoplethysmography (PPG) data for out-of-lab sleep staging. Devices using only accelerometer data are effective for sleep/wake detection but fall short in identifying multiple sleep stages, unlike those incorporating PPG signals. To enhance the reliability of sleep staging wearables, we propose five recommendations: (1) Algorithm validation with equity, diversity, and inclusion considerations, (2) Comparative performance analysis of commercial algorithms across multiple sleep stages, (3) Exploration of feature impacts on algorithm accuracy, (4) Consistent reporting of performance metrics for objective reliability assessment, and (5) Encouragement of open-source classifier and data availability. Implementing these recommendations can improve the accuracy and reliability of sleep staging algorithms in wearables, solidifying their value in research and clinical settings.

睡眠对于身心健康至关重要，但传统的睡眠质量评估方法存在局限性。这份范围审查分析了过去十年的 35 篇文章，评估了具有不同传感器、算法和功能的 62 种可穿戴设备。我们的分析表明了将加速度计和光电体积描记法 (PPG) 数据结合起来进行实验室外睡眠分期的趋势。仅使用加速度计数据的设备对于睡眠/唤醒检测有效，但在识别多个睡眠阶段方面存在不足，这与结合 PPG 信号的设备不同。为了提高睡眠分期可穿戴设备的可靠性，我们提出了五项建议：（1）考虑公平性、多样性和包容性的算法验证，（2）跨多个睡眠阶段的商业算法的性能比较分析，（3）探索功能对睡眠分期的影响算法准确性，(4) 一致报告客观可靠性评估的性能指标，以及 (5) 鼓励开源分类器和数据可用性。实施这些建议可以提高可穿戴设备中睡眠分期算法的准确性和可靠性，从而巩固其在研究和临床环境中的价值。