How does sleep affect weight?

A new study by Withings investigates the impact a person’s sleep has on weight and weight loss. Researchers analyzed the anonymized data from a pool of 18,000 adult users worldwide of both a Withings scale and a sleep tracking device.

Discover our study

To lose weight, don't go on a sleep diet

People who sleep less are more likely to be overweight or obese

There are 10% more overweight or obese people among those clocking in under 5 hours of sleep than among those getting 8 hours or more of shut eye. On average, the sleep-deprived group is 6 pounds heavier than the well-rested.

10%more overweight or obese people are observed among users sleeping less than 5 hours

Percentage of people in excess weight

average hours of sleep per night
2.7kg are gained by sleep-deprived group

Excess weight according to sleep duration

average hours of sleep per night

Lose weight just by sleeping more?

The study also showed a link between sleep and weight loss. Overweight people who slept at least an average of 7 hours were more successful in losing weight. In fact, they achieved 25% more weight loss than their overweight peers sleeping less than five hours.

25%more weight loss is achieved by people sleeping more than 7 hours, compared to those lacking sleep

Regular sleep means more weight loss

The data revealed that people who sleep in on the weekends to compensate for their lack of sleep during the week are undermining their weight loss goals. Overweight users sleeping atleast one hour more on weekends than during the week were less successful inlosing weight. They logged a weight loss of 0.6kg over the year, compared to 1kg lost by those whomaintained regular sleep patterns.

1kgis lost by people who maintained regular sleep patterns, while 0.6kg was lost by people having irregular sleep patterns

Early bird special

Going to bed late can undermine weight loss. Overweight users that went to bed after 3am lost, on average, 45% less weight than other overweight users. Additionally, people who slept late into the morning lost less weight than early risers. Our research found that overweight people who wake up before 7am lost 3x more weight than those who wake up after 10am. For more weight loss success, consider rising with the roosters and enjoying more sunlight early in the day.

45%less weight is lost by users that went to bed after 3am

Weight variation (kg) according to bedtime

3xmore weight is lost by people who wake up before 7am

Weight variation over a year vs. wake-up time

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  • Wake-up to the music you love: stream Spotify playlists, choose from over 20,000 web radio stations or enjoy specially-engineered wake-up programs
  • Fall asleep faster with light programs that promote secretion of sleep hormones and sound programs designed to induce sleep
  • Track your sleep: monitor your bed and wake-up times, sleep duration, sleep cycle variation (deep, light and REM sleep), and middle-of-the-night awakenings
  • Optimize your wake-up by choosing to be waken-up at the best time of your sleep cycle
  • Understand what disrupts your sleep: monitor room temperature, luminosity and sound levels during the night

No Withings device yet? No problem. Download our free, highly-rated Health Mate app to immediately start tracking your weight, food intake, and activity level.

About the study

This study was conducted by Withings based on data gathered from a panel of 18,000 users of Withings connected scales and Withings sleep tracking devices (Aura, Pulse Ox, or Activité watches).

Those with a BMI (body mass index) higher than or equal to 25 were considered overweight or obese. Excess weight was considered to be any pounds in excess of those that would put a user at a BMI of 25. (Those with a BMI below 25 were considered to have zero pounds of excess weight.)

In all weight loss analyses, only overweight or obese people were included, as normal-weight individuals were considered as not needing to lose weight.

Withings guarantees the confidentiality of personal data and protects the privacy of all its users. Therefore, all data used for this study was rigorously anonymized and aggregated in order to avoid any re-identification.

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