Daily sleep cycles were identified based on sensor activation and used to quantify sleep time, wake-up time, sleep duration, and time spent at home. The 2018 ecobee "Donate your Data" dataset for 481 North American households was collected for use in this study. The objective of this study is to evaluate the use of smart home thermostat data to evaluate household sleep patterns and the time spent at home, and how these behaviours are influenced by weekday, seasonal and seasonal weekday variations. Advances in Smart Home technology and the Internet of Things (IoT) have the potential to overcome these challenges to behavioural monitoring. This methodology suffers from bias and population-level data collection is challenging. Research on sleep patterns has traditionally relied on self-reported data. Sleep behaviour and time spent at home are important determinants of human health. These results provide evidence of the potential of using Internet of Things data to help public health officials understand variations in sleep indicators caused by global events (eg, pandemics and climate change). This is the first study to use smart home thermostat data to monitor sleep parameters and time spent at home and their dependence on weekday, seasonal, and seasonal weekday variations at the population level. These results are in accordance with existing literature. Although no significant association is found between sleep parameters and seasonal variation, the time spent at home in the winter is significantly greater than that in summer (n=455 P<.001 OR 1.6, 95% CI 1.3-2.3). The results also indicate that households spent more time at home on Sundays than on the other weekdays (n=445 P<.001 OR 2.06, 95% CI 1.64-2.5). Consequently, the wake-up time is significantly changing between weekends and weekdays (n=450 P<.001 OR 5.6, 95% CI 4.3-6.3). There is significant sleep duration difference between Fridays and Saturdays and the rest of the week (n=450 P<.001 OR 1.8, 95% CI 1.4-2). The sleep time on Fridays and Saturdays is greater than that on Mondays, Wednesdays, and Thursdays (n=450 P<.001 odds ratio 1.8, 95% CI 1.5-3). Our results demonstrate that sleep parameters (sleep time, wake-up time, and sleep duration) were significantly influenced by the weekdays. Each household's record was divided into different subsets based on seasonal, weekday, and seasonal weekday scales. The objective of this study is to demonstrate the use of smart home thermostat data to evaluate household sleep patterns and the time spent at home and how these behaviors are influenced by different weekdays and seasonal variations.įrom the 2018 ecobee Donate your Data data set, 481 North American households were selected based on having at least 300 days of data available, equipped with ≥6 sensors, and having a maximum of 4 occupants. Advances in smart home technology and the Internet of Things have the potential to overcome these challenges in behavioral monitoring. Not only does this methodology suffer from bias but the population-level data collection is also time-consuming.
Sleep behavior and time spent at home are important determinants of human health. Higher daily ozone was associated with longer sleep duration and modest associations were observed between higher temperature and lower WASO and lower efficiency. Associations did not differ between cold (October-March) and warm (April-September) seasons. A 14 parts per billion (ppb)(interquartile range) higher daily maximum 8-hour ozone was associated with 7.51 (95% CI: 3.23, 11.79) minutes longer sleep duration on that night. A 10☏ higher daily average temperature was associated with 0.88 (95% CI: 0.06, 1.70) minutes longer WASO and 0.14% (95% CI: -0.01%, 0.30%) lower sleep efficiency on that night. The participants were 35☑2 years old and 86 were women. We used linear fixed effects models adjusting for participant, day of the week, and day of the year (for weather analysis), and additionally adjusted for temperature and relative humidity (for air pollution analysis). Daily weather parameters and air pollution levels were collected from local weather station and ground-level air quality monitors. Nightly sleep characteristics including duration, wake after sleep onset (WASO), and efficiency were assessed using wrist actigraphy. Ninety-eight participants completed daily electronic diaries and wore an actigraph for an average of 45 days, and a total 4,406 nights of data were collected. Given the lack of studies examining the associations between daily weather and air pollution with nightly objective sleep over multiple weeks, we quantified these associations in a prospective cohort of healthy participants with episodic migraine.
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