Importance of Getting Enough Sleep and Daily Activity Data to Assess Variability: Longitudinal Observational Study

Area of Science:

• Sleep Science
• Wearable Technology
• Digital Health

Background:

• Polysomnography (PSG) is the gold standard for sleep recording but is cumbersome and expensive.
• Self-trackers (wearable and nearable sensors) offer a feasible alternative for free-living sleep studies.
• The optimal duration for self-tracker data collection in sleep studies remains unknown.

Purpose of the Study:

• To determine if 3-14 days of self-tracker data is sufficient for sleep assessment.
• To explore relationships between sleep quality, physical activity, and heart rate.
• To investigate clustering of individuals by activity and variations in sleep patterns due to seasonality.

Main Methods:

• Collected sleep, physical activity, and heart rate data over 6 months from 54 adults (aged 52-86).
• Utilized Withings Aura sleep mattress and Withings Steel HR smartwatch for data acquisition.
• Employed exploratory data analysis and unsupervised machine learning for cohort and individual-level analysis.

Main Results:

• Significant variability in sleep quality metrics was observed across different study periods.
• An evaluation period longer than 3-14 days is necessary for robust individual sleep and activity pattern assessment using self-trackers.
• Identified seasonal patterns in sleep data, influenced by daylight saving time clock changes.

Conclusions:

• More than two months of self-tracking data are required for a representative summary of daily activity and sleep patterns.
• The current 3-14 day standard for sleep quality assessment should be reconsidered for research.
• Seasonal influences and daylight saving time shifts are critical factors in designing sleep studies and collecting data.