The effects of seasons and weather on sleep patterns measured through longitudinal multimodal sensing

Do seasonal variations in day length and temperature influence sleep in a world of climate control and electricity? Our study combined a year of wearable sleep data with meteorological data. We found that seasons, day length, and temperature influence when and how long individuals sleep.

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Key takeaways:

  • Sleep duration, bedtime, and wake time have been well studied in the areas of demographic, physiological, and sociological domains, but little work has examined the effects of environment after controlling for these other domains. 

  • Using an extensive combination of wearable, location, and environmental data, we demonstrate that seasonal changes to the environment modestly impact sleep, despite the comforts and environmental control available in many industrialized countries.

  • Surprisingly, when considering demographic, physiological, sociological and environmental effects in the same model, environmental effects of day length and season demonstrate the largest impact on sleep duration.

  • The environmental effects of season, day length, and temperature principle component also affect bed and wake times, but  to a lesser extent than chronotype (preference for going to bed  earlier or later). 

  • Weather effects such as wind and humidity cloud cover principle components did not significantly impact the model.


Adequate sleep is vital to many aspects of  health, and yet 1 out of 3 Americans are sleep deprived1. The biological mechanisms that govern sleep wake cycles utilize environmental signals including changes in light and temperature to adjust sleep duration based on season 2,3 . However, the differences between day and night are suppressed in industrialized nations. For instance, residents of the United States, are regularly exposed to light pollution 4,5, and light at night from common electric devices such as cell phones or e-readers can interfere with sleep 6. Constant temperatures and light across the day may reduce the effectiveness of light and temperature cues used by the body to adjust sleep duration, and warrants further study. 

What makes environmental effects on sleep especially difficult to study are the many non-environmental factors that determine sleep.  Sleep is influnced by age, sex, ethnicity, health, sociological factors such as income, personality, chronotype (morning or evening types), social status, etc1,7,8.  The many contributors to sleep make it difficult to assess the independent contribution of environmental effects.

Another difficulty in examining environmental factors is that they change slowly over time. For instance, solar day length changes gradually over the year, with one day differing from the next by a minute or two. Previous studies that examine seasonal effects generally do not have sufficient data to capture these slow changes over time. Studies of seasons tend to use large subjective datasets based on self report (e.g.9), or measure sleep only in the seasonal extremes for a brief duration (1 week in summer, 1 week in winter, e.g. 10).  In order to accurately measure gradual changes and gather sufficient data to examine environmental effects, we needed to collect day level data across a year in a large sample of individuals from a variety of locales. 

To examine environmental effects, we utilized commercial wearables. Wearables are relatively inexpensive, easy to wear longitudinally, and adequately capable of measuring sleep constructs such as bedtime, waketime, and sleep duration. This data was combined with location and meteorological data in order to assess the environmental effects of weather, season, day length, temperature, and precipitation on sleep. Given the interdependence of weather features (e.g. increased day length is associated with increased temperatures), we reduced 14 daily weather features (including sunrise and sunset time, temperature, cloud cover, wind speed, humidity, visibility, and atmospheric pressure) into principal components.


We performed analyses for bedtime, wake estime, and sleep duration, as these constructs may be subject to different contributors (e.g. wake up time during the work week may not vary seasonally, while bedtime might). The full models contained age, gender, affect, personality, organization, supervisor status, location, sleep quality, chronotype, season, day length, and 3 principal components generated from 14 weather features. In the full models, day length and season had the largest impact on sleep duration. Season, day length, and temperature affected bed and wake times, but were less important than chronotype. This shows that seasonal changes influence sleep even in the presence of prevalent light and noise pollution and access to electric lights, though it may be possible these seasonal variations would be more pronounced in the absence of electricity 11,12

Future directions:

It may be that sleep duration recommendations should be adjusted based on location and/or season; instead of recommending 8 hours per night all year long for adults, it may make sense to recommend 8 hours during winter and 7.5 hours during summer.  In addition, long term and automated controlled environments (e.g. submarines, spacecraft, smart homes, smart offices) should determine if adjusting temperatures and lights more subtly in order to better align with seasonal variations, instead of striving to keep constant levels of light and temperature, can improve sleep for individuals living and working in these environments. 


This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA Contract No. 2017-17042800007. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.


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Stephen Mattingly

Postdoctoral Researcher, University of Notre Dame