The Effects of Sleep Restriction on Weight Management and Body Composition

Jordan Feigenbaum
September 24, 2021
Reading Time: 16 minutes
Table of Contents

    Obesity is a chronic disease resulting from genetic, environmental, and psychosocial factors resulting in increased adipose (fat) tissue that increases the risk of disease such as cardiovascular disease, type 2 diabetes, and certain cancers, which are some of the leading causes of preventable, premature death. Mechanick 2017  As of 2015, over 100 million children and 600 million adults globally had excess adiposity, representing a near-doubling in prevalence over 70 countries since 1980. GBD 2015 In the US alone, the prevalence of obesity increased from 30.5% in 1999-2000 to 42.4% in 2017-2018, costing over $300 billion annually in medical costs. CDC Adult Obesity Facts 2020

    Key Points:

    1. Adiposity-related chronic diseases and decreased time spent sleeping have both increased substantially in recent years.
    2. A number of mechanisms have been identified that tie decreased sleep duration to increased obesity, such as an increase in ghrelin and a decrease in leptin, which results in an increase in appetite and food-seeking behavior. Additionally, sleep restriction may reduce the ability of the body to burn fat as fuel, while increasing its ability to burn protein (derived from lean body mass).
    3. While sleep duration appears to play a role in outcomes from calorie restriction with respect to body composition, total weight loss appears to be unaffected. This suggests that energy balance is still king when it comes to weight loss.

    Interestingly, the proportion of adults in the US who sleep less than 8 hours per night has also markedly increased in recent times, from 26% to 35% in the period from 1998 to 2005. In the National Health Interview survey of over 100,000 workers, the prevalence of sleeping <6 hours per day was 30%. Luckhaupt 2010 This lifestyle behavior appears to have negative consequences related to obesity. 

    In a study of 12 healthy, normal-weight men who underwent two nights of sleep restriction to 4 hours per night showed a subsequent increase in hunger and appetite, particular for calorie-dense foods with high carbohydrate content. Spiegel 2004 Greer 2013 Additionally, a meta-analysis of 30 studies including over 600,000 participants found that, “a reduction in 1 hour of sleep per day is associated with a 0.35 kg/m2 increase in BMI.” Cappuccio 2008 According to a more recent meta-analysis, this translates to approximately a 3.1 lb weight gain in a person who is 5 feet 10 inches tall, though the authors caution that correlation does not equal causation. Cooper 2018  In any case, it appears that the prevalence of sleep restriction and obesity are both rising, which raises the question: are they related? In this article,  we’ll take a look at some mechanistic evidence to explore this a bit further by looking at a 2010 study from the University of Chicago published in the Annals of Internal Medicine, Insufficient sleep undermines dietary efforts to reduce adiposity.

    The Study

    The purpose of this study was to assess what effect, if any, altering sleep duration had on weight loss under controlled conditions. By virtue of completing the entire study in a laboratory, the researchers were able to accurately measure, observe, and control the participants’ diet, activity, and sleep, which would not be possible if the subjects were free-living outside of the lab setting.


    Twelve subjects (5 women and 7 men), ages 35-49 (average 41 years-old) were recruited to participate in this study through local newspaper advertisements. All participants were non-smokers and had a BMI between 25-32 kg/m2, i.e., they had overweight or obesity. No waist circumference data was recorded. With respect to sleep, all participants self-reported about 6.5-8.5 hours (average 7.7 hours) of sleep per night.

    Additionally, all participants were reported to be sedentary.  Unfortunately, the authors did not define what was meant by “sedentary”, however this has previously been defined as less than 600 MET-minutes of physical activity per week by the World Health Organization (WHO). WHO Global Physical Activity Questionnaire A MET, or metabolic equivalent, is a description of the rate of energy expenditure during a particular activity relative to energy expenditure at rest. By definition, humans utilize 1 MET of energy while at rest, whereas a 4 MET activity expends 4 times that amount of energy. If an individual does a 4 MET activity (e.g. cycling at 10 mph or walking at 3.5 mph, for 30 minutes), they will have accrued 120 MET-minutes of physical activity, calculated as the product of 4 METs and 30 minutes. Thus, the WHO definition of less than 600 MET-minutes of physical activity per week means that the individual is falling below aerobic activity minimums set forth in the 2018 Physical Activity Guidelines for Adults of 150 minutes of moderate intensity (4-6 METs) aerobic activity per week. Piercy 2018

     It is not known whether the participants were engaging in resistance training, which is also part of the current physical activity guidelines, though “demanding occupations” or “regular exercise” were part of the exclusion criteria for participation in this study. Other exclusion criteria were:

    • Self-reported sleep problems, which was defined as a Pittsburgh Sleep Quality Index score > 10. The average score of participants was 3.
    • Working nights, having variable sleep habits, or taking daytime naps regularly. The average amount of sleep was 7.7 hours per night.
    • Depressed mood, defined as a Center for Epidemiologic Studies for Depression (CES-D) score > 15. The average score was 4.
    • Excessive use of alcohol (>14 drinks/week for men; >7 for women). The definition of a “standard” drink varies by country, however this study was performed in the United States where a standard drink contains ~14 g of ethanol, the amount contained in a 12 ounce (355ml) bottle of 5% ABV beer or 1.5 ounces (44 ml) of an 80-proof spirit. Kerr 2012 
    • Use of caffeine > 300mg/day
    • Use of prescription or over-the-counter drugs that affect sleep or metabolism
    • Abnormal findings on medical history, physical exam, or lab tests done prior to the start of the study. These tests included a glucose tolerance test to assess for normal metabolic functions and a sleep study.

    In short, all subjects were overweight or with obese and insufficiently active, but otherwise appeared healthy and had no known sleep issues.


    In this randomized crossover study, subjects were randomly assigned to one of two different conditions: either 8.5 or 5.5 hours of scheduled time-in-bed. For each condition, subjects spent 14-days in the lab where their diets, activity levels, and sleep quantity were controlled and measured. After completing one of the conditions, the participants crossed over to the other condition after at least 3 months of time outside of the lab (average 7 months between conditions). A few important details are outlined below.

    In the 48 hours prior to and following each 14-day study period, both body weight and body fat levels were recorded. Body weights were measured using an electronic scale, whereas body fat was assessed using dual X-ray absorptiometry scan (DEXA).

    While the nuts and bolts of the diets consumed by the participants in this study is rather complicated, I’ve provided a brief overview of what the researchers did:

    1. A registered dietitian interviewed all participants to determine their food preferences and to exclude the possibility of an eating disorder.
    2. Prior to each 14-day study period, participants had their resting energy expenditure rate assessed using indirect calorimetry. In research settings, this entails individuals breathing into a mouthpiece (called a respirometer) connected to a computer to assess both how much oxygen is being consumed and how much carbon dioxide is being produced at rest. Each liter of oxygen consumed at rest costs about 5 Calories of energy and, when combined with additional inputs such as height, weight, gender, etc. the amount of energy used at rest can be calculated. 
    3. This indirect calorimetry data was used to determine the participants’ respiratory quotient (RQ), which is calculated as the amount of carbon dioxide eliminated divided by the amount of oxygen consumed. This helps researchers see what type or types of macronutrients are being used for fuel, as the RQs for fat, protein, and carbohydrates are 0.7, 0.8, and 1.0, respectively. In mixed diets containing all of the macronutrients, the RQ is usually about 0.8, whereas shifts to primarily carbohydrate-containing diets or fat-containing diets will shift the RQ towards 1.0 or 0.7, respectively.
    4. After calculating the subjects resting energy expenditure, the researchers and registered dietitian created individualized diets for each participant that amounted to 90% of their resting metabolic rates in order to facilitate weight loss. 
    5. The diets were about 18% protein, 48% carbohydrate, and 34% fat during each study period. 
    6. During each 14-day study period, total energy expenditure was verified using doubly labelled water (DLW). To review, total daily energy expenditure is the sum of resting metabolic rate (RMR), the thermic effect of food (TEF, how much energy it costs to digest, metabolize and store food), and physical activity (PA). The DLW technique involves giving the subjects water that contains heavy hydrogen (2H, or deuterium) and oxygen (18O) isotopes at the beginning of the 14-day period, and then measuring the differing amount of these heavy isotopes in the urine at both the beginning and end of the study period. Ultimately, the difference in turnover rates tells the researchers how much carbon dioxide is being produced by the individual. This is similar to indirect calorimetry described above, but is used to calculate total daily energy expenditure, not resting metabolic rate.
    7. Hunger was assessed each day prior to the first meal and before bed (10:30 pm) using a visual analogue scale. The scale used was 10 cm long with one end being labeled, “I am not hungry at all” and the other end being labeled, “Very hungry.”
    8. As individuals were confined to the laboratory, all meals were provided to them by the researchers. Food was weighed before and after each meal to determine actual consumption 

    In summary, each individual had their resting energy expenditure calculated prior to and after each 14-day study period. An RD worked with each individual in order to create a diet that created a calorie deficit, about 10% less than their resting energy expenditure. All food consumed during the study periods were weighed and measured to provide accurate information about calorie intake.


    During each of the 14-day study periods, overnight sleep durations were modified by moving the usual go-to-bed and get-out-of bed times proportionally closer or further away, so as not to change the subjects’ circadian rhythm. For example, consider an individual who normally slept 7.5 hours per night via a go-to-bed time of 11:00 pm and a get-out-of-bed time of 6:30 am. When assigned to the 8.5 hours scheduled time-in-bed condition, their go-to-bed time would be moved up 30 minutes and their get-out-of-bed time would be moved back 30 minutes.

    Sleep was monitored nightly using a machine that measures brain wave activity, which is commonly used in the sleep lab setting. Finally, no daytime naps were allowed.

    During each of the 14-day study periods, subjects spent their waking hours engaged in home-office type work or leisure activities. No exercise was performed.

    In order to characterize any potential metabolic changes, the researchers also assessed the following before and after each 14-day study period:

    Leptin is a hormone released by adipose tissue (body fat), whose main function is to describe the status of long-term energy stores. Production and release of leptin into circulation changes proportionally with body fat, with higher and lower levels of body fat resulting in higher and lower leptin levels, respectively. Leptin signals receptors in the hypothalamus, which subsequently influences appetite, energy intake, and energy expenditure.Morris 2009  Low levels of leptin reflect reduced body fat stores and strongly promote food-seeking behavior, reduced physical activity, and increased hunger signaling in conjunction with other hormones. This has been shown during periods of starvation and in individuals with anorexia nervosa. Herpertz 2000 In contrast, high levels of leptin reflect expanded body fat stores. Individuals with obesity have higher fasting and post-meal leptin levels compared to normal weight subjects. Calrson 2009 

    When individuals with obesity lose fat via dietary interventions, leptin levels tend to decrease by ~35% and persist after 1 year when fat loss is maintained. Sumithran 2011 However, high levels of leptin do not reduce hunger or subsequent energy intake in individuals with obesity. This finding has been described as “leptin resistance”, where the body’s response to increasing levels of leptin is less than predicted. One of the original theories of leptin resistance suggested that excess adiposity produced changes in the blood-brain barrier (BBB) that limited the transport of leptin into the brain where it would normally function to control hunger and energy intake. Multiple different takes on this theory have been promoted over the past 25 years, e.g. issues with the leptin transporter, obesity-induced changes to neural cells responding to leptin, etc. The specific mechanisms for leptin resistance remain unproven. Izquierdo 2019 

    Rare mutations affecting leptin production or its receptor lead to leptin deficiency, which causes extreme early-onset obesity due to ravenous appetite and high food intake as part of a physiologic starvation response. Ozata 1999  At this time, using the pharmaceutical versions of leptin, e.g., Metreleptin, has only been shown to be effective in leptin-deficit versions of obesity, though the majority of those with obesity have elevated levels of leptin. Dornbush 2020

    Overall, the data suggest that leptin’s strongest functions occur when levels are low, rather than when they are high. This may be a result of impaired sensitivity, i.e. leptin resistance, or that elevated leptin levels don’t really influence hunger or energy intake. There is little evidence to support a major role for leptin in hunger and energy intake in individuals with obesity, thus either scenario is possible. No clinically relevant application for leptin in obesity management has been demonstrated at this time.

    Ghrelin is a hormone produced by specialized cells in the stomach in response to fasting and weight loss. It’s main functions include appetite stimulation (increased hunger) and food-seeking behaviors. Ghrelin levels tend to be higher in individuals with obesity as compared to leaner individuals. Additionally, those with the Fat Mass and Obesity-Associated gene also have a reduced response to ghrelin after consuming a meal. Young 2020

    Epinephrine and norepinephrine are alertness-promoting and stress hormones produced by the adrenal glands. It is not yet clear how weight loss alters the levels of these hormones. With respect to sleep, it appears that levels of these hormones are reduced during sleep, though they may have some function in promoting normal sleep patterns. Mitchell 2009

    Thyroid-stimulating hormone (TSH) is a hormone produced by the pituitary gland that signals the thyroid gland to make thyroid hormone. It is the first-line screening test for abnormal thyroid function.

    Free thyroxine is one version of thyroid hormone (known as T4). The thyroid gland makes three hormones: T4, T3 and calcitonin. Typically, about 80% of the thyroid hormone produced is T4, though ~99% is bound to proteins for transportation throughout the body. Free T4 is a common lab test to evaluate thyroid function if TSH is abnormal.

    Reverse T3 is an inactive metabolite of T4. This was measured in 7 of 10 participants in the study, though it is unclear why, as reverse T3 has no clinical utility in cases where the thyroid is under-active (hypothyroid) or overactive (hyperthyroid). Schmidt 2018

    In summary, subjects spent two 14-day periods in a sleep lab where they either spent 8.5 or 5.5 hours in bed each night. During both study periods, subjects consumed a calorie-restricted diet while being mostly sedentary. Testing for resting metabolic rate, RQ, weight, body composition, and hormone levels were performed before and after each study period.


    Weight and Body Composition 

    Both groups lost approximately the same amount of weight, 3 kg (6.6 lbs). However, there was a substantial difference in the type of weight loss in both groups. For example, the 8.5 hour time-in-bed group lost ~1.5 kg fat free mass and 1.4 kg fat mass, or about a 50/50 split between LBM and fat. Conversely, the 5.5 hour time-in-bed group lost ~2.4 kg of fat free mass and only 0.6 kg of fat mass, or about a 80/20 split between LBM and fat. These differences were found to be statistically significant.


    The average resting metabolic rate for participants was 1624 Calories per day. The average total daily energy expenditure for those in the 8.5 and 5.5 hours scheduled time-in-bed groups were about the same, 2136 Calories vs. 2139 Calories, respectively. On average, the subjects consumed 1447 and 1450 Calories per day in the 8.5 and 5.5 time-in-bed conditions, respectively. 

    There were no differences in the thermic effect of food between groups. However, there were differences in ratings of hunger between the groups, with the 8.5 hour group having modestly reduced ratings of hunger (0.7cm) and the 5.5 hour group having modestly higher ratings of hunger (-0.1cm) on the 10 cm visual analogue scale.

    RQ values were significantly higher in the 5.5 hour time-in-bed condition compared to the 8.5 hour group during periods of fasting and 1-4 hours after a meal. The higher value suggests that less fat is being oxidized as fuel, as fat has an RQ value of 0.7, compared to protein and carbohydrates, which have RQ values of 0.8 and 1.0, respectively. These findings were statistically significant.

    Given that the goal of the dietary intervention was for participants to consume 90% of their tested resting metabolic rate, e.g. ~1461 Calories per day, it appears the researchers’ calculations were right on the money. Practically speaking, this means that both groups were eating about a 689 Calorie-per-day deficit, which is the difference between the average consumed calories (~1447 Calories) and the average total daily energy expenditure (2136 Calories).


    The average sleep duration during the 8.5 and 5.5 hour time-in-bed conditions were 7 hours and 25 minutes and 5 hours and 14 minutes, respectively. On average, the subjects in the 5.5 hour condition went to bed at 12:43 AM and got out of bed at 6:14 AM. In contrast, the subjects in the 8.5 hour group went to bed at 11:23 PM and got out of bed at 7:52 AM.


    Serum ghrelin, the “hunger hormone”, showed a statistically significant increase during the 5.5 hour time-in-bed group, but did not change in the 8.5 hour group. 

    Serum leptin, the hormone produced by adipose tissue, declined in both groups with no statistically significant differences between either condition.

    There were no differences in serum measurements of norepinephrine, epinephrine, growth hormone, TSH, or free T4 in either condition.

    What’s the Take-Home Message?

    Overall, this study suggests that sedentary adults with obesity who consume a calorie-restricted diet with reduced sleep, tend to be hungrier and lose more lean body mass than if they were able to sleep more. This is corroborated by additional evidence showing that sleep duration can have a significant effect on appetite and satiety. Hibi 2017 Greer 2013 

    We would expect that these changes in appetite and satiety would alter the amount of weight lost between groups, however the sleep-restricted individuals in this study lost the same amount of weight as those sleeping normally. What gives?!  This is most likely a function of the controlled nature of the study, e.g. individuals were fed exact quantities of food rather than eating ad libitum like they would at home. If  this had been conducted in otherwise free-living individuals, e.g. sleep-restricted vs. not, it is likely that the sleep-restricted group would actually consume more energy than those sleeping an adequate amount. Still,  sleep restriction did not reduce the total amount of body weight lost during this controlled study, but it  that while sleep plays a role in the resulting body composition from a diet, energy balance plays the major role in total body mass. 

    This has important implications for improving adherence to a health-promoting dietary pattern in individuals. In my view, the underlying cause of obesity is a mismatch between an individual’s appetite (hunger) and satiety (fullness) that results from a genetically susceptible individual interacting with an obesogenic food environment. The capacity to consciously “choose” to engage health-promoting dietary behaviors is limited and cannot be relied upon day-in and day-out to overcome this mismatch.  Rather, we’d prefer to set an individual up for success by increasing their satiety response. This can be done a number of ways:

    • Manage the food environment to encourage consumption of foods high in protein, fiber, and water and low in fat, added sugar, and sodium, e.g. hyper-palatable, energy-dense foods.
    • Change the eating environment to avoid distracted eating, e.g. reduce use of smart phones, watching TV, or computer use during a meal.
    • Increase exercise to meet or exceed the current physical activity guidelines, as exercise increases sensitivity to satiety signals.
    • Get enough sleep and manage stress to avoid increases in appetite and reductions in satiety.
    • Consider escalating care to medications (e.g. GLP-1 agonists) and/or surgery if necessary, which both primarily work by improving an individual’s satiety response.

    Another benefit of this study is that the researchers obtained a substantial amount of outcome data using mostly reliable methods such as using DEXA for body fat analysis and doubly-labeled water for total daily energy expenditure. On the other hand, it is unclear why reverse T3 and growth hormone measurements were obtained, as these two tests have little relevance or clinical utility, especially in the weight loss setting. 

    I also am surprised by the decision not to track physical activity while the subjects were restricted to the laboratory setting, as one major factor that could potentially explain the difference in body composition results between each group would be activity level. At present, the data show that both resistance training and moderate intensity aerobic activity can preserve muscle mass in individuals following a calorie-restricted diet. Cava 2017 Given the small sample size of 10 subjects,  I think obtaining this information would have helped address this question. 

    Additionally, the small sample size limits the ability to interpret the body composition differences seen between both groups, as there was a larger than expected variance in the total energy expenditure data determined by the doubly-labeled water. Larger variances require larger sample sizes to adequately “power” the study to detect true differences, if they exist. In this case, the null hypothesis would be no difference between groups when different sleep durations are combined with the same level of calorie restriction. At present, we can’t confidently reject this hypothesis due to the small sample size.

    When considering the impact of sleep on weight loss outcomes, it is prudent to first ensure that the person is consuming an energy-restricted diet, as this should produce weight loss regardless of sleep habits. If the person is losing weight, but not seeing the expected body composition improvements, sleep duration and quality should be addressed to see if it’s a contributing factor – although ideally sleep habits should be addressed up front in any situation. Finally, it is probably unhelpful to go down the rabbit hole of testing different hormones in hopes of finding a magic bullet outside of clinical suspicion for an endocrinopathy, which should be performed by a physician. 


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    Jordan Feigenbaum
    Jordan Feigenbaum
    Jordan Feigenbaum, owner of Barbell Medicine, has an academic background including a Bachelor of Science in Biology, Master of Science in Anatomy and Physiology, and Doctor of Medicine. Jordan also holds accreditations from many professional training organizations including the American College of Sports Medicine, National Strength and Conditioning Association, USA Weightlifting, CrossFit, and is a former Starting Strength coach and staff member. He’s been coaching folks from all over the world  for over a decade through Barbell Medicine. As a competitive powerlifter, Jordan has competition best lifts of a 640lb squat, 430lb bench press, 275lb overhead press, and 725lb deadlift as a 198lb raw lifter.

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