Labie Céline, Runge Nils, Goossens Zosia, Mairesse Olivier, Nijs Jo, Malfliet Anneleen, Van Assche Dieter, de Vlam Kurt, Menghini Luca, Verschueren Sabine, De Baets Liesbet
Musculoskeletal Rehabilitation Research Group, Department of Rehabilitation Sciences, Faculty of Movement and Rehabilitation Sciences, KU Leuven, 3001 Leuven, Belgium.
Pain in Motion Research Group (PAIN), Department of Physiotherapy, Human Physiology and Anatomy, Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel, 1050 Brussels, Belgium.
Sensors (Basel). 2025 Aug 5;25(15):4813. doi: 10.3390/s25154813.
Sleep is a vital physiological process for recovery and health. In people with knee osteoarthritis (OA), disrupted sleep is common and linked to worse clinical outcomes. Commercial sleep trackers provide an accessible option to monitor sleep in this population, but their accuracy for detecting sleep, wake, and sleep stages remains uncertain. This study compared nighttime sleep data from polysomnography (PSG) and Fitbit Sense in individuals with knee OA and insomnia. Data were collected from 53 participants (60.4% women, mean age 51 ± 8.2 years) over 62 nights using simultaneous PSG and Fitbit recording. Fitbit Sense showed high accuracy (85.76%) and sensitivity (95.95%) for detecting sleep but lower specificity (50.96%), indicating difficulty separating quiet wakefulness from sleep. Agreement with PSG was higher on nights with longer total sleep time, higher sleep efficiency, shorter sleep onset, and fewer awakenings, suggesting better performance when sleep is less fragmented. The device showed limited precision in classifying sleep stages, often misclassifying deep and REM sleep as light sleep. Despite these issues, Fitbit Sense may serve as a useful complementary tool for monitoring sleep duration, timing, and regularity in this population. However, sleep stage and fragmentation data should be interpreted cautiously in both clinical and research settings.
睡眠是恢复和保持健康的重要生理过程。在膝骨关节炎（OA）患者中，睡眠中断很常见，且与更差的临床结果相关。商业睡眠追踪器为监测这一人群的睡眠提供了一种便捷的选择，但其在检测睡眠、清醒和睡眠阶段的准确性仍不确定。本研究比较了膝OA合并失眠患者的多导睡眠图（PSG）和Fitbit Sense的夜间睡眠数据。通过同时进行PSG和Fitbit记录，在62个晚上收集了53名参与者（60.4%为女性，平均年龄51±8.2岁）的数据。Fitbit Sense在检测睡眠方面显示出较高的准确性（85.76%）和敏感性（95.95%），但特异性较低（50.96%），这表明难以将安静清醒与睡眠区分开来。在总睡眠时间较长、睡眠效率较高、入睡时间较短且觉醒次数较少的晚上，与PSG的一致性更高，这表明当睡眠碎片化程度较低时，其表现更好。该设备在睡眠阶段分类方面的精度有限，经常将深度睡眠和快速眼动睡眠误分类为浅睡眠。尽管存在这些问题，Fitbit Sense可能是监测这一人群睡眠持续时间、时间和规律性的有用补充工具。然而，在临床和研究环境中，睡眠阶段和碎片化数据都应谨慎解释。
