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Sleeping metabolic rate in relation to body mass index and body composition


OBJECTIVES: To determine whether patterns of sleeping metabolic rate (SMR) are altered in obesity. Specifically to determine the relationship between changes in SMR and body weight, body mass index (BMI, kg/m2), and fat-free mass (FFM); and to compare resting metabolic rate (RMR) with SMR during different periods of sleep.

SUBJECTS: Eighteen healthy, pre-menopausal, obese (BMI >30, n=9) and non-obese (BMI <30, n=9), female subjects (six Caucasians and 12 African-Americans), with an average age of 36 y (range 22–45).

MEASUREMENTS: Total energy expenditure (TEE or 24 h EE), metabolic rate (MR), SMR (minimum, average and maximum) and resting metabolic rate (RMR) or resting energy expenditure (REE) measured by human respiratory chamber, and external mechanical work measured by a force platform within the respiratory chamber. Physical activity index (PAL) was derived as TEE/REE. Body composition was determined by dual-energy X-ray absorptiometry (DXA).

RESULTS: SMR decreased continuously during sleep and reached its lowest point just before the subject was awakened in the morning by the research staff. Although averages for RMR and SMR were similar, RMR was lower than SMR at the beginning of the sleeping period and higher than SMR in the morning hours. The rate of decrease in SMR was faster with increasing body weight (−0.829, P<0.0001), BMI (correlation factor −0.896, P<0.0001) and FFM (−0.798, P=0.001). The relationship between the slope of SMR decrease and BMI (y=−5×10−6x2+0.0002x−0.0028) is highly significant, with a P-value of <0.0001 and r2 value of 0.9622.

CONCLUSIONS: The rate of decline in metabolic rate during sleep is directly related to body weight, BMI and FFM. Average SMR tends to be lower than RMR in obese subjects and higher than RMR in non-obese subjects.


Human 24 h energy expenditure (EE) has three major components: resting energy expenditure (REE); the thermic effect of food; and the energy cost of physical activity. REE, which accounts for as much as 60–75% of total energy expenditure (TEE), has been studied extensively. However, sleeping metabolic rate (SMR), which represents a large portion of 24 h EE, is often calculated according to the FAO/WHO/UNU1 recommendations. These estimates of daily energy requirements are based on factorial calculations and assume that the energy cost of sleep is equal to REE. This assumption might be an oversimplification of the relationship between REE and SMR since SMR is not constant and decreases during sleep, as has already been reported by some researchers.2,3,4,5,6

Sleep is a complicated process and has been studied in different aspects by different investigators. Fontvieille et al7 and other investigators2,5,8,9 have reported differences in metabolic rate between the different sleep stages. Montgomery et al10 have demonstrated that the total sleep time affects SMR. However, we are aware of no studies to date of how SMR changes with body weight, body mass index (BMI) or body composition. The present study was designed to determine whether EE patterns during sleep are related to obesity. Obese and non-obese women were compared for SMR during different sleeping periods, the decrease in SMR in relation to body weight, fat-free mass (FFM) and BMI, and the ratio of SMR to RMR at different time periods during sleep.



Subjects were 18 healthy, pre-menopausal women, six Caucasian and 12 African-American. African-American was defined as having four African-American grandparents. All volunteers had normal dietary habits and were weight stable for at least 6 months before the study. They were all non-smokers and did not use any contraceptive medications. The mean age of the subjects was 35±8 y, while mean body weight was 79.2±19.6 kg (range 54–121 kg) and BMI 29.5±6.6 (range 19.6–41.9; Table 1). Mean FFM was 48.5±7.6 (range 39.3–62.3) and fat mass averaged 37.9±11.5%, with a range of 13.7–52.5%. For analyses based on BMI, nine subjects were classified as obese (BMI>30.0) and nine non-obese (BMI ≤30.0)

Table 1 Subject characteristics


Subjects were admitted to a standard hospital room the evening before the 24 h EE measurement. They entered the chamber at 9:00 am the following morning and exited at 8:00 am the next day. Energy requirements for the chamber days were calculated from RMR, previously measured by hood indirect calorimetry, and by using the formula TEE=1.5×RMR. Breakfast, lunch and dinner were served at 9:30 am, 1:00 pm and 5:00 pm, respectively. Energy breakdown was 40% for breakfast, 20% for lunch and 40% for dinner with no food served between meals. The activity protocol for 23 h in the chamber included three 10 min exercise sessions on a stationary bicycle with increasing intensity: 10 min at 12:30 pm (60 W/60 rpm), 10 min at 4:00 pm (75 W/60 rpm) and 10 min at 7:00 pm (90 W/60 rpm). For the remainder of the time the subjects were asked to engage in quiet activities (reading, watching television, etc) and to go to bed at 10:30 pm. At 7:00 am the next morning, the subjects were awakened by the research staff and asked to get up (and use the bathroom if necessary). Then, subjects were asked to return to bed, remaining awake and motionless for an additional 50 min for the measurement of RMR.

The sleeping period was designated as the period from 10:30 pm to 7:00 am according to the protocol, but actual sleeping period was more precisely determined by mechanical work calculated from the force platform. Any energy expended for physical activities during the sleeping period, according to floor measurements, was excluded from SEE. The first 30 min of actual sleeping time were not included in calculation. Maximum SMR was defined as the average of MR for the first half-hour of the rest of the sleeping period; mean SMR is the average MR over the entire rest of sleeping period; minimum SMR is average of MR for the last half-hour of the rest of sleeping period before the subject was awakened (6:30–7:00 am). Subjects were under constant supervision by laboratory staff or the night nurse while in the chamber. The protocol was approved by the hospital's Institutional Review Board and all subjects signed written consent to participate.

Body composition

Body composition was determined by dual-energy X-ray absorptiometry (DXA, Lunar Model DPX, Madison, WI, USA, software version 3.8). FFM was calculated by subtracting total body fat measured by DXA from body weight in 15 women (three subjects did not complete the body composition measurements due to withdrawal from the study).

Indirect calorimetry

SMR and RMR were measured by indirect calorimetry in the human respiratory chamber at the New York Obesity Research Center at St Luke's-Roosevelt Hospital Center. The respiratory chamber is an air-tight room (22 000 l volume) equipped with a bed, chair, desk, television, VCR, radio, telephone, bicycle, sink and toilet. The temperature of the room is maintained at 23±0.2°C. A fan draws mixed-air (sample air) out of the chamber, while fresh air (reference air) is forced into the chamber by the resulting negative pressure. A flow-meter measures the flow-rate; aliquots of entering reference air as well as of exiting sample air are collected continuously. The air samples are dried and analyzed by differential oxygen (Magnos 4G) and carbon dioxide (Magnos 3G) analyzers (both Hartman-Braun, Frankfurt, Germany). Actual oxygen consumption and carbon dioxide production are calculated as the difference between the composition of the entering and exiting air. The results are corrected for barometric pressure, flow-rate and humidity. A stationary bicycle is also connected to a computer for the recording of exercise. Data are recorded every 10 s and are averaged over 2 min intervals. The overall accuracy of the system for both EE and resting quotient (RQ) measurements was evaluated by 15 pure alcohol (99.5%.) tests. The average time per test was 1381±126 min (mean±s.d.). The percentage of computed VO2 vs the VO2 required to oxidize the amount of alcohol burned was 98.48±0.82% (mean±s.d.), CV=0.0083; the percentage of computed VCO2 recovered was 98.58±0.84% (mean±s.d.), CV=0.0085. The measured RQ was 0.6675±0.0029 (mean±s.d.) vs 0.6666 calculated, CV=0.0044. From the relatively small s.d. in computed VO2 and accurate average RQ, we conclude that the error for EE measurement was within 2.0%.

Force platform

Mechanical work due to the physical activities of the subjects in the chamber is measured by a force platform (9×6.5 feet) that rests on four force transducers that measure forces in the vertical direction. Except for time exercising on a stationary bicycle and using the toilet, the subject remains on the force platform the entire time while in the chamber. The four transducers measure the ground reaction forces generated by the subject for each movement. The position of subject and mechanical power spent for the movement is then calculated by using the following equation:

Position of subject

where FAz, FBz, FCz, FDz are outputs of four transducers; l is length of floor and h is width of floor; m is body mass; Δt=T is the sampling interval; Fv is the force subtracting the sum of four forces (FAz, FBz, FCz, FDz) from their averaged baseline weight; n is nth sample and N is number of samples.

The force platform was calibrated by an Omega LCCB-500 force transducer (Omega Engineering, Inc., Stamford, CT, USA). After the platform was divided into 256 sub-regions (16×16) of 0.0212 m2 each, a subject performed activities inside the room with a force transducer directly under his feet. The actual force Fv measured by the transducer under the feet and the force Fv measured by the direct sum of the four platform transducers were in good agreement. The average error (Fv−Fv/Fv) was 2%. By comparing actual positions of human volunteers on the calorimeter floor with computer-predicted positions, we determined that the platform system could acutely determine the position of the subject on the floor. Subjects were asked to stand on marks on the calorimeter floor. Most of the observed error was due to the large size of human feet in relation to the small grids marked upon the floor. The average error distance was 2.71 cm in the x direction and 2.62 cm in the y direction. The maximum error was 6.2 cm (absolute distance) from the actual position. These errors include both prediction error and calibration error caused by inaccuracy in locating the marks on which the subject was asked to stand. The size of the platform was 274×198 cm, so the relative errors for x and y were 0.99 and 1.32%, respectively.

Thus, the force platform not only detects displacements of the center of mass, but also calculates how much external mechanical work is done during the movement. As with the oxygen and the carbon dioxide data, floor data are collected continuously, analyzed every 10 s and averaged over 2 min intervals. In addition to providing a means to calculate energy expended due to physical activity, the floor data also allow an accurate determination of sleeping time.

Data analyses

Data is presented as mean±s.d. For analyses based on BMI, subjects were classified as obese (BMI >30.0) or non-obese (BMI <30.0). Sleeping period was designated as the period from 10:30 pm to 7:00 am according to the protocol, but actual sleeping period was more precisely determined by mechanical work calculated from the force platform. Any energy expended for physical activities during the sleeping period, according to floor measurements, was excluded from SEE. The first 30 min of actual sleeping time were not included in calculation. The maximum SMR was calculated from SMR for the first hour of the rest of the sleeping period; mean SMR equals SMR averaged over the entire sleeping period; minimum SMR was calculated from SMR 1h before the subject was awakened (6:00 to 7:00 am).

The slopes of SMR decrease from 18 subjects were calculated and the correlations with body weight, FFM and BMI were examined in a cross-correlation matrix of Pearson product–moment coefficients. First-order, second-order and third-order equations that describe the relationship between BMI (body weight and FFM) and the slope of SMR decrease were tried. The second-order equation (slope=−5×10−6 BMI2+0.0002×BMI−0.0028) fit the model best. The model was also examined for the six Caucasians and 12 African-Americans separately. The resulting equation for Caucasians (slope=−5×10−6 BMI2+0.0002× BMI−0.0024 with P-value of <0.0001 and r2 value of 0.9713) was similar to that for the African-Americans (slope=−5×10−6 BMI2+0.0002×BMI−0.0028 with P-value of <0.0001 and r2 value of 0.9631). Therefore, ethnicity does not appear to affect the relationship between the slope of SMR decrease and BMI.


Figure 1 shows a representative 24 h profile of metabolic rate (MR) and mechanical work done by a subject. The mechanical power has been enlarged five times to allow visualization on the scale of MR. There was a significant difference between daytime MR and SMR (P<0.0001) for this subject, with daytime MR 158% of SMR. During daytime, there was a high rate of EE due to physical activity, whereas at night MR dropped dramatically and physical activity decreased to nearly zero except when the subject reportedly woke up. During daytime, variances in physical activity accounted for about 62% of the variance in metabolic rate (Pearson correlation coefficient=0.84).

Figure 1

24-h EE and mechanical work from one subject.

Figure 2 illustrates a typical pattern of 24 h MR of two subjects with different BMI (BMI =20 and BMI =30). It clearly shows that SMR continuously decreases during sleep in both subjects, and that the decrease rate (−0.00063 kcal/min) in SMR for the obese subject (BMI =30) is higher than the decrease rate (−0.00022 kcal/min) for the lean subject (BMI =20). At a low BMI, SMR is almost constant during sound sleep, whereas at higher BMI SMR might decrease by as much as 21% of its initial value over the course of night, depending on how many hours the subject slept.

Figure 2

24-h EE for two subjects with different BMI.

For the 18 subjects studied, BMI was the best predictor for the slope of SMR decrease (correlation factor −0.896, P<0.0001), although the slope of SMR was also highly correlated with body weight (−0.829, P<0.0001) and with FFM (−0.798, P=0.001; Table 2). Figure 3 illustrates the highly significant relationship between the slope of SMR decrease and BMI (P-value of <0.0001 and r2 value of 0.9622).

Table 2 The correlation of decrease in sleeping metabolic rate (SMR) with body weight, body composition and BMI
Figure 3

Decrease rate of SMR in relation to BMI.

The variability in the decrease of SMR during sleep results in differences in SMR at various sleep stages and in different ratios between SMR and RMR when calculated for different time periods over the course of a night. Mean, minimal and maximal SMR and SMR/RMR and mean SMR/FFM for the two groups are presented in Table 3. Analysis of variance (ANOVA) showed significantly different values (P<0.05) for mean, minimal and maximal SMR in non-obese and obese subjects, as well as in the ratio of mean and maximal SMR and RMR. Neither the ratio of minimal SMR/RMR nor mean SMR adjusted for FFM (kcal/kg/min) was different between the two groups.

Table 3 Comparison of SMR and RMR, and ratio of SMR/RMR at different sleep periods

Since there were significantly different (P=0.01) mean SMR/RMR ratios in obese and non-obese women, the relationship of mean SMR/RMR to BMI were compared next. Although regression analysis showed no statistically significant correlation (P=0.1234), there is a trend to a decreasing ratio of mean SMR/RMR as BMI increases. This results in a tendency for mean SMR>RMR for non-obese women and mean SMR<RMR in obese women (Figure 4).

Figure 4

Relation of the ratio of (mean SMR)/RMR and BMI.


The primary finding of this study is that SMR decreases during sleep as a function of BMI and the decrease rate in SMR is larger as BMI increases (correlation factor=−0.896). Thus, the following equation can be used to describe the change of SMR:


where a is SMR slope (kcal/min) and t is the duration of sleep (min). As shown in Figure 3, a is always negative and decreases as BMI increases.

Our data show that SMR rate continuously decreases during the sleeping period and reaches the lowest point just before waking in the morning. The absence in this study of the increase in MR prior to waking reported by Fontvieille et al7 is presumably due to the fact that our subjects were awakened by our staff, rather than being allowed to wake up naturally.

The increased slope in SMR decrease that we observed in obese vs non-obese subjects was due to both a higher MR at the beginning of sleep and a lower MR at the end of sleep. A higher value at the beginning of the night could theoretically be caused by increased or prolonged thermic effect of food and/or exercise. The standard chamber protocol observed by all subjects makes it is unlikely, however, that either of these affected values in this study. As total 24 h energy intake was provided in proportion to RMR, obese subjects could not have over-eaten compared with non-obese. Dinner was the same fraction of the total energy intake and was served at 5 pm, 5.5 h before the beginning of sleep, making it unlikely that the influence of diet-induced thermogenesis was significant in any of the subjects.

It has been reported that physical activity may affect SMR11,12,13 and RMR,14,15,16,17,18,19 but in our study, the last exercise session was at 7 pm, allowing 3.5 h to recover from physical activity before going to bed. The protocol for the chamber day prescribed the same time, rate and type of exercise for all subjects. The similarity between the physical activity index (PAL, calculated as TEE/REE due to physical activity) of obese (1.41±0.11) and non-obese subjects (1.49±0.07) provides evidence that these exercises did not increase the thermic effect of exercise to a greater extent in obese. It unlikely, therefore, that differences in either TEF or thermic effect of exercise contributed to our observations of different rates of decrease in SMR in obese vs non-obese.

Nonetheless, an increased MR at the beginning of sleep could be related to a slower rate of heat dissipation by obese as has previously been hypothesized.20,212222 Rising and colleagues found that low MR was correlated with low body temperature assessed orally23 although a subsequent study failed to find a correlation of MR with core body temperature.24 The latter study, however, did find that Pima Indians had lower core body temperatures during sleep than Caucasians. Although slopes were not compared, continuous body core temperature data presented also appears to decline more during sleep in Pimas.

Lower SMR at the end of sleep may be an additional indicator of increased susceptibility to subsequent weight gain. Lower RMR is generally considered as one of the contributors to weight gain.13,25,26,27,28,29 In comparison with Caucasians, African-Americans13,25,29 and Pima Indians20,27 have lower RMR and greater prevalence of obesity. As in previous reports, our data showed that RMR of African-Americans normalized by either body weight or FFM was more than 12% lower than RMR of Caucasians. We also found that average SMR of African-American women, normalized by either body weight or FFM, was more than 14% lower than in Caucasians (more than 10% difference for minimal SMR and 15% for maximal SMI, respectively). While the lower rates for both SMR and RMR are consistent in African-American vs Caucasian women, there were no differences in rates of decline in SMR between the two ethnic groups. The even greater difference in SMR between Caucasians and African-Americans, may, however, indicate an increased propensity for further weight gain in the African-Americans.

Our data showing that SMR/RMR is lower in obese confirms the observation by Fontvieille et al,20 that RMR/SMR was inversely correlated with indices of obesity. The relationship seen here between BMI and SMR decline, may also indicate a greater susceptibility to further weight gain in our already obese subjects. This suggestion also raises the possibility that SMR in pre-obese might be even lower, than the low RMR that has already been described.27

The present study did not examine mechanisms for differences between SMR and RMR, the energy cost of arousal. However, this cost is thought to be mediated by both central nervous system and sympathetic nervous system,20,30 which has been reported to be inversely correlated with the percentage of body fat.16,20 Additional postulated factors influencing arousal costs include rates of glucoenogenesis, substrate cycling, SNS sleep regulation mechanisms and cation transport.20 Impaired glucose tolerance may also be a significant factor.31

The average ratio of (mean SMR)/RMR for all subjects is 1.0011. Therefore, the use of mean SMR=RMR, as recommended by FAO/WHO/UNU for the calculation of daily energy requirements, is not inappropriate in a general sense. However, use of RMR to estimate SMR would underestimate SMR by 3% in non-obese subjects and overestimate SMR in obese subjects by about the same amount. However, since sleeping time accounts for only about one-third of a 24 h period (8/24 h), the overall variance introduced over 24 h is not larger than±1%, which is insignificant for most analyses.

Our findings of a strong and highly significant correlation between decline in SMR and BMI are striking. They cannot be explained by differences in diet-induced or exercise-induced thermogenesis or by differences in ethnicity. Additional longitudinal studies will reveal whether increased rate of decline in SMR is an additional indicator of increased susceptibility to future weight gain and the mechanisms for these observations.


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This research was supported by National Institute of Diabetes and Digestive and Kidney Diseases Grant P30 DK-26687 and by a Swiss National Science Foundation Post-Doctoral Fellowship to P Werner.

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Zhang, K., Sun, M., Werner, P. et al. Sleeping metabolic rate in relation to body mass index and body composition. Int J Obes 26, 376–383 (2002).

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  • metabolic rate
  • sleeping metabolic rate
  • RMR
  • energy expenditure

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