Óskarsdóttir María, Islind Anna Sigridur, August Elias, Arnardóttir Erna Sif, Patou François, Maier Anja M
Department of Computer Science, Reykjavík University, Reykjavík, Iceland.
Reykjavík University Sleep Institute, School of Technology, Reykjavík University, Reykjavík, Iceland.
JMIR Form Res. 2022 Feb 22;6(2):e31807. doi: 10.2196/31807.
The gold standard measurement for recording sleep is polysomnography performed in a hospital environment for 1 night. This requires individuals to sleep with a device and several sensors attached to their face, scalp, and body, which is both cumbersome and expensive. Self-trackers, such as wearable sensors (eg, smartwatch) and nearable sensors (eg, sleep mattress), can measure a broad range of physiological parameters related to free-living sleep conditions; however, the optimal duration of such a self-tracker measurement is not known. For such free-living sleep studies with actigraphy, 3 to 14 days of data collection are typically used.
The primary goal of this study is to investigate if 3 to 14 days of sleep data collection is sufficient while using self-trackers. The secondary goal is to investigate whether there is a relationship among sleep quality, physical activity, and heart rate. Specifically, we study whether individuals who exhibit similar activity can be clustered together and to what extent the sleep patterns of individuals in relation to seasonality vary.
Data on sleep, physical activity, and heart rate were collected over 6 months from 54 individuals aged 52 to 86 years. The Withings Aura sleep mattress (nearable; Withings Inc) and Withings Steel HR smartwatch (wearable; Withings Inc) were used. At the individual level, we investigated the consistency of various physical activities and sleep metrics over different time spans to illustrate how sensor data from self-trackers can be used to illuminate trends. We used exploratory data analysis and unsupervised machine learning at both the cohort and individual levels.
Significant variability in standard metrics of sleep quality was found between different periods throughout the study. We showed specifically that to obtain more robust individual assessments of sleep and physical activity patterns through self-trackers, an evaluation period of >3 to 14 days is necessary. In addition, we found seasonal patterns in sleep data related to the changing of the clock for daylight saving time.
We demonstrate that >2 months' worth of self-tracking data are needed to provide a representative summary of daily activity and sleep patterns. By doing so, we challenge the current standard of 3 to 14 days for sleep quality assessment and call for the rethinking of standards when collecting data for research purposes. Seasonal patterns and daylight saving time clock change are also important aspects that need to be taken into consideration when choosing a period for collecting data and designing studies on sleep. Furthermore, we suggest using self-trackers (wearable and nearable ones) to support longer-term evaluations of sleep and physical activity for research purposes and, possibly, clinical purposes in the future.
记录睡眠的金标准测量方法是在医院环境中进行一晚的多导睡眠图检查。这要求个体睡觉时佩戴一个设备以及多个连接在其面部、头皮和身体上的传感器，既麻烦又昂贵。自我追踪器，如可穿戴传感器（如智能手表）和近距传感器（如睡眠床垫），可以测量与自由生活睡眠状况相关的广泛生理参数；然而，这种自我追踪器测量的最佳时长尚不清楚。对于此类使用活动记录仪的自由生活睡眠研究，通常采用3至14天的数据收集。
本研究的主要目标是调查使用自我追踪器时3至14天的睡眠数据收集是否足够。次要目标是调查睡眠质量、身体活动和心率之间是否存在关联。具体而言，我们研究表现出相似活动的个体是否可以聚类在一起，以及个体的睡眠模式随季节变化的程度。
从54名年龄在52至86岁的个体中收集了6个月的睡眠、身体活动和心率数据。使用了Withings Aura睡眠床垫（近距传感器；Withings公司）和Withings Steel HR智能手表（可穿戴设备；Withings公司）。在个体层面，我们调查了不同时间跨度内各种身体活动和睡眠指标的一致性，以说明如何使用自我追踪器的传感器数据来阐明趋势。我们在队列和个体层面都使用了探索性数据分析和无监督机器学习。
在整个研究的不同时期，睡眠质量的标准指标存在显著差异。我们具体表明，为了通过自我追踪器获得更可靠的个体睡眠和身体活动模式评估，评估期需要超过3至14天。此外，我们发现睡眠数据中与夏令时时间变化相关的季节性模式。
我们证明需要超过2个月的自我追踪数据才能提供日常活动和睡眠模式的代表性总结。通过这样做，我们对目前3至14天的睡眠质量评估标准提出了挑战，并呼吁在为研究目的收集数据时重新思考标准。季节性模式和夏令时时间变化也是在选择数据收集时间段和设计睡眠研究时需要考虑的重要方面。此外，我们建议使用自我追踪器（可穿戴和近距的）来支持未来用于研究目的以及可能的临床目的的睡眠和身体活动的长期评估。
