A new study shows how motion sensors in smart home thermostats can objectively track sleep patterns and disturbances.


Summary: A study presented at SLEEP 2024 showcases a framework for an objective, non-invasive sleep monitoring system using smart thermostats equipped with motion sensors. The study identified three distinct sleep quality clusters, demonstrating the ability of smart thermostats to differentiate sleep patterns and disturbances. Researchers analyzed eight terabytes of data from 178,706 households, highlighting the potential of smart devices and IoT data in sleep research. The study emphasizes the potential for smart devices to collect meaningful, long-term health data for public health surveillance.

Key Takeaways:

  • The study demonstrated that smart thermostats could identify distinct sleep quality clusters, showcasing their capability to monitor sleep patterns and disturbances non-invasively.
  • Researchers analyzed eight terabytes of data from 178,706 households, using sensor activations and machine learning models to discern sleep quality indicators.
  • The study highlights the potential of smart devices to collect long-term behavioral health data in the home, offering new avenues for near-real-time public health surveillance.

A new study offers a framework for an objective, non-invasive and zero-effort sleep monitoring system utilizing smart thermostats equipped with motion sensors.

Results, presented at the SLEEP 2024 annual meeting, show that smart thermostats identified three distinct sleep quality clusters, with clear variations in sleep duration, disturbances, and efficiency. Comparative analysis underscored the heterogeneity in sleep quality, highlighting the potential of smart devices and NextGen IoT data sources in identifying sleep patterns and contributing to sleep research without invasive monitoring.

Surprising Capabilities of Smart Thermostats

“Even though these smart thermostats were not originally intended for health monitoring, their capability to accurately differentiate between complex sleep patterns and disturbances were the most surprising part of this study,” says Jasleen Kaur, PhD, a postdoctoral researcher at the UbiLab, University of Waterloo in Ontario, Canada, in a release.

The researchers analyzed eight terabytes of data collected from smart thermostats in 178,706 households. Sensor activations were translated into signals that modeled sleep features, and machine learning models were used to discern sleep quality indicators.

Potential for Public Health Surveillance

The American Academy of Sleep Medicine recognizes that consumer sleep technology may be utilized to enhance the patient-clinician interaction when presented in the context of an appropriate clinical evaluation. However, these tools are not substitutes for medical evaluation.

According to Kaur, the study highlights the potential for smart devices to collect meaningful, long-term behavioral health data in the home for near-real time public health surveillance.

“Quality sleep is critical to people’s health and well-being,” says Kaur in a release. “However, collecting reliable data is difficult as it often relies on recall bias and subjective interpretation; this offers potential for integrating environmental and behavioral health data to improve sleep health.”

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