Wearable tech reveals: Social constructs and physical activity shape global sleep patterns

A recent study published in Scientific Reports analyzes sleep data from wearable devices to assess sleep patterns.

Study: Social dimensions impact individual sleep quantity and quality. Image Credit: New Africa / Shutterstock.com

How sleep can be affected

Sleep is modulated by the circadian rhythm, which develops according to the levels and timing of light exposure. Various social, individual, and environmental factors can also influence sleep.

Previous research suggests that different methodologies can result in discrepancies in determining actual levels of sleep, as commercial accelerometers, self-reports, polysomnography, and actigraphy can over- or underestimate sleep. However, commercial devices recording individual activity have advanced in recent years to become more precise.

One previous study reported the low efficacy of wearable devices in monitoring sleep patterns in people with sleep disorders; however, these devices remained comparable to gold-standard methods. Another study using Fitbit data identified differences in sleep patterns by age and gender between people from East Asia and Oceania.

While the positive effects of sleep on productivity, well-being, and health are established, the impact of societal factors on sleep remains undefined.

Study findings

In the present study, researchers examined individuals' physical activity and sleep data using wearable devices of the same brand. Data were obtained from 30,082 users across 19 major cities across 11 countries between 2014 and 2019. The largest and smallest cities had 8,924 and 495 wearable users, respectively.

Sensing technologies and related algorithms were utilized to infer sleep. To this end, the global median bed and wake times were 12:01 AM and 7:42 AM, respectively.

Males represented 55% of the study cohort. Additionally, users were more likely to be older, with a median age of 42. The median daily number of steps and body mass index (BMI) were 6,951 and 25.4 kg/m², respectively. People were observed to be most active on Wednesdays.

Bed and wake times were more likely delayed in females than males for all age groups younger than 70. Furthermore, females between 30 and 40 years of age had notably shorter sleep times than males.

Sleep duration, history, efficiency, K-hour deviation, and the midpoint of sleep on free days were subsequently adjusted for sleep debt on workdays. Principal component analysis showed that these sleep metrics could be stratified into quality and quantity, which, taken together, accounted for 83% of the data variations.

Social constructs often contribute to sleep variations. These social factors included gross domestic product (GDP) and Hofstede's cultural dimensions, such as individualism, indulgence, uncertainty avoidance, and long-term orientation.

Correlations with these sleep metrics confirmed the significant impact of certain social factors on sleep. The city of residence in individual-level assessments marginally explained the two sleep dimensions, thus suggesting that the location and cultural environment determine the effects on sleep.

The researchers also studied how sleep dimensions were related to popular social constructs through linear models at the city level. To this end, certain social constructs, including individualism, uncertainty avoidance, and GDP, explained 55% and 63% of variations in sleep quality and quantity, respectively. Thus, sleep quality and quantity are associated with social environments.

The study participants were also stratified according to their physical activity levels and sleep dimensions. People who exercised had improved sleep efficiency; however, the overall sleep duration was marginally shorter than non-exercising individuals. Exercising subjects also achieved better sleep quality but reduced sleep quantity than less active individuals.

Finally, the team explored the association between increased physical activity and sleep. For individuals in the United States with at least eight hours of sleep, active sleep duration increased by an average of 1.28 minutes, whereas the overall time spent in bed reduced by 2.47 minutes for every additional 1,000 steps taken each day. 

Conclusions

The current study analyzed sleep patterns in a large sample of wearable device users in 11 countries. The highest sleep quantity was observed among Finnish individuals, with a median sleep time of eight hours. In other countries, the median sleep time was shorter by 16 to 69 minutes compared to that in Finland.

Japanese individuals had the lowest median sleep time of six hours and 51 minutes. Delayed bedtime and shorter mean sleep durations were observed in high-income countries; however, wake times were less affected. Importantly, these findings are limited to high-GDP countries and might not apply to socioeconomically deprived nations. 

Written by

Tarun Sai Lomte

Tarun is a writer based in Hyderabad, India. He has a Master’s degree in Biotechnology from the University of Hyderabad and is enthusiastic about scientific research. He enjoys reading research papers and literature reviews and is passionate about writing.