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nature publishing group
Influence of Weekend Lifestyle Patterns
on Body Weight
Susan B. Racette1, Edward P. Weiss1, Kenneth B. Schechtman2, Karen Steger-May2, Dennis T. Villareal1,
Kathleen A. Obert1, John O. Holloszy1 and the Washington University School of Medicine CALERIE Team1
Objective: To determine whether alterations in diet and/or activity patterns during weekends contribute to weight gain
or hinder weight loss.
Methods and Procedures: Randomized, controlled trial comparing 1 year of caloric restriction (CR) with 1 year
of daily exercise (EX). Subjects included 48 healthy adults (30F, 18M) aged 50–60 years with BMI 23.5–29.9 kg/m2.
Body weight was measured on 7 consecutive mornings for a total of 165 weeks at baseline and 437 weeks during
the 1-year interventions. Daily weight changes were calculated for weekends (Friday to Monday) and weekdays
(Monday to Friday). Daily energy intake was estimated using food diaries; daily physical activity was measured using
accelerometers. Both measures were validated against doubly labeled water (DLW).
Results: At baseline, participants consistently gained weight on weekend days (+0.06 ± 0.03 kg/day, (mean ± s.e.),
P = 0.02), but not on weekdays (−0.02 ± 0.02 kg/day, P = 0.18). This was attributable to higher dietary intake on
Saturdays and lower physical activity on Sundays relative to weekdays (both P < 0.05). During the interventions,
both CR and EX participants were in negative energy balance on weekdays (P < 0.005). On weekends, however,
CR participants stopped losing weight, and EX participants gained weight (+0.08 ± 0.03 kg/day, P < 0.0001) due
to higher dietary intakes on weekends. This helps to explain the slower-than-expected rate of weight loss during
Discussion: Alterations in lifestyle behaviors on weekends contribute to weight gain or cessation of weight loss on
weekends. These results provide one explanation for the relatively slow rates of weight loss observed in many studies,
and the difficulty with maintaining significant weight loss.
Obesity (2008) 16, 1826–1830. doi:10.1038/oby.2008.320
Daily fluctuations in body weight are common due to changes
in hydration status, dietary intake, and daily physical activ-
ity patterns. For many adults, diet and activity patterns dif-
fer substantially on weekends as compared to weekdays, with
potential consequences on body weight that could promote
the development or maintenance of overweight and obesity
if the pattern is repeated throughout the year. Dietary pat-
terns and body weight have been shown to vary during the
Thanksgiving weekend (1,2), the holiday period between
Thanksgiving and early January (3), and between different
seasons of the year (4), but little is known regarding the influ-
ence of weekends on short- and long-term body weight. The
best evidence that weekend eating patterns influence weight
control is based upon prospective data from 1,429 participants
in the National Weight Control Registry who had successfully
maintained a weight loss of at least 13.6 kg for an average of
7.9 ± 9.3 years (5). Participants who reported greater dieting
consistency (i.e., maintaining the same diet regimen on week-
ends as on weekdays) were more likely to maintain their weight
within 2.3 kg during the subsequent year, whereas participants
with lower dieting consistency scores were more likely to regain
weight during the subsequent year. Therefore, in addition to
contributing to the development of obesity, high dietary intake
on weekends relative to weekdays may hinder weight loss and
contribute to weight regain following weight loss.
Although the National Weight Control Registry study (5) was
prospective and involved a large sample size, the body weights
were self-reported on only two occasions (at the beginning
and end of the year). Numerous studies have involved mea-
surements of body weight longitudinally under carefully con-
trolled conditions, but we are not aware of any studies in which
weights were measured daily in the free-living environment
over a long period of time, leaving the question of a potential
1Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, USA; 2Division of Biostatistics, Washington University School of Medicine,
St. Louis, Missouri, USA. Correspondence: Susan B. Racette (email@example.com)
Received 17 May 2007; accepted 16 October 2007; published online 12 June 2008. doi:10.1038/oby.2008.320
Obesity | VOLUME 16 NUMBER 8 | AUGUST 2008 1827
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weekend effect unanswered. Furthermore, if there is indeed a
propensity for weight gain during the weekends, it is important
to determine whether dietary intake is primarily responsible,
or if physical activity patterns also play a critical role.
The purpose of this analysis was first to assess daily body
weight changes during weekends and weekdays, both before
and during weight loss interventions, and second to determine
whether changes in dietary intake and/or physical activity
on weekends were responsible for any observed weight changes.
We hypothesized that weight gain would occur during the
weekends at baseline, but that this effect would be blunted dur-
ing the intervention.
Methods and Procedures
Healthy adults between 50 and 60 years of age with a BMI between 23.5
and 29.9 kg/m2 were recruited from the St. Louis metropolitan area to
participate in the National Institutes of Health (NIH)-funded CALERIE
study (Comprehensive Assessment of Long-term Effects of Reducing
Intake of Energy), as described previously (6). Participants were non-
smokers and did not exercise regularly; all women were postmeno-
pausal. The study was approved by the Washington University School of
Medicine Human Studies Committee and the General Clinical Research
Center Scientific Advisory Committee. Written, informed consent was
obtained from each participant before enrollment.
Eligible participants were randomly assigned to 1 year of a calorie-
restricted (CR) diet, a comparable energy deficit induced by daily
exercise (EX), or a healthy lifestyle (HL) control group in a 2:2:1 ran-
domization scheme, as described in detail previously (6). Briefly, the
goal of the CR and EX interventions was to induce a comparable energy
deficit of 16% during the first 3 months, and 20% during the subsequent
9 months, so that the physiological effects of weight loss induced by
CR vs. exercise could be compared. The CR group was instructed to
modify their daily energy intake without changing their physical activ-
ity patterns, whereas the EX group was instructed to increase their daily
physical activity energy expenditure without changing their dietary
intake. The diet and exercise prescriptions were individualized based
upon baseline total energy expenditure (TEE) as determined using
doubly labeled water (DLW) (7). HL participants did not receive a diet
or exercise prescription and served as a control group.
Body weight was measured at the participant’s home each morning for
2–4 weeks at baseline and for 2 weeks at each assessment time point
throughout the year-long intervention (i.e., months 1, 3, 6, 9, and 12).
Measurements were taken in kilograms on the LifeSource UC-321
Precision Personal Health Scale (A&D Medical, Milpitas, CA), which
has a 31-weight memory function. Participants weighed themselves
unclothed in the fasted state after voiding, and recorded their weight on
log sheets which they turned in with the scale at the end of each record-
ing period. Weight changes were calculated for weekends (Friday to
Monday), weekdays (Monday to Friday), and for each day individually.
In order to assess the validity of the home weights, participants were
weighed on a calibrated clinic scale on several days for which home
weights were recorded. The clinic weights were measured in duplicate
by research staff in the morning, with the participant fasted and wear-
ing only a hospital gown.
anthropometrics and body composition
Height was measured to the nearest 0.1 cm. BMI was calculated
as weight/height2 (kg/m2). Body composition was assessed using
dual- energy X-ray absorptiometry (Delphi W, software version 11.2;
Hologic, Waltham, MA) on three occasions at baseline and twice at
each subsequent time point (i.e., months 1, 3, 6, 9, and 12). The results
were expressed as an average at each time point.
Seven-day food diaries were used to estimate self-reported energy
intake at baseline and at months 1, 3, 6, 9, and 12 during the inter-
vention. Participants received detailed instructions on how to weigh,
measure, and record all food and beverages consumed. Research dieti-
tians reviewed the diaries with participants and then analyzed them
using Nutrition Data System for Research (software versions 4.05,
4.06 and 5.0; Nutrition Coordinating Center, University of Minnesota,
Physical activity energy expenditure
Daily physical activity was estimated using RT3 triaxial accelerometers
(Stayhealthy, Monrovia, CA), which participants were instructed to
wear on the hip during all waking hours for 2 to 4 weeks at baseline
and for 2 weeks at months 1, 3, 6, 9, and 12. The accelerometers were
set to mode 4 with a vector magnitude of 1 min to capture minute-
by-minute activity counts, which were used to generate physical activ-
ity energy expenditure (i.e., activity above resting values, expressed in
kcal/d) using the manufacturer’s proprietary formula and participant-
specific values for sex, age, height, and weight. Accelerometer data were
used only for days on which weight was recorded, and for which at least
720 min (i.e., 12 h) of accelerometer activity data were recorded. Data
were excluded for days on which physical activity energy expenditure
was <100 kcal or >4,000 kcal, due to the implausibility of these values.
To assess the validity of the self-reported food diary data and the
accelerometer-derived physical activity data, we compared these out-
comes with objectively determined energy intake and physical activity
based upon the DLW method (7). TEE was assessed with DLW during
2-week periods at baseline and during 2-week periods at each subse-
quent time point (i.e., months 1, 3, 6, 9, and 12), as described previ-
ously (6). Two baseline urine samples were collected, after which an oral
dose of DLW was administered (0.20 g H2
total body water), and postdose urine samples were collected at 4.5 h,
6 h, 7 days (two samples), and 14 days (two samples). Samples were
analyzed by isotope ratio mass spectrometry at Pennington Biomedical
Research Center (8). Energy intake was calculated as TEE plus changes
in body energy stores measured by dual-energy X-ray absorptiometry.
Physical activity was calculated by subtracting resting metabolic rate and
the thermic effect of food from TEE. Resting metabolic rate was mea-
sured by indirect calorimetry using a DeltaTrac II Metabolic Monitor
(SensorMedics, Yorba Linda, CA) in the morning in the fasted state
after the participant spent the evening in the General Clinical Research
Center; thermic effect of food was estimated to be 10% of TEE.
18O and 0.12 g 2H2O per kg of
Analyses were performed using SAS software, version 9.1.3 of the SAS
System for Linux (SAS Institute, Cary, NC). The primary analysis was a
comparison of average weight changes during weekends vs. weekdays
using generalized estimating equation models to account for the corre-
lation of multiple measurements within each participant. Least squares
means from the generalized estimating equation model were used to
determine whether there were significant changes in weight during week-
ends and during weekdays. Secondary analyses included comparisons of
weekend and weekday self-reported energy intake and accelerometer-
based physical activity using generalized estimating equation analysis.
Subsequent generalized estimating equation models included daily
values with specific contrasts comparing each weekend day to the aver-
age weekday value. Due to skewed data distributions and the presence of
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outliers, energy intake and physical activity data during the intervention
were analyzed using rank-transformed data. Pearson correlation coef-
ficients (r) were calculated to assess the degree of agreement between
self-reported and objectively determined energy intake, and between
accelerometer-based physical activity and physical activity determined
using DLW. Statistical tests were two-tailed, with significance accepted
at P < 0.05. Data are presented as means ± s.e., except in Figure 3a,b, in
which the median and interquartile ranges are presented.
Forty-eight adults (30 women, 18 men) began the intervention,
with 19 in the CR group, 19 in the EX group, and 10 in the HL
group. At baseline, the mean age was 57 ± 1 years, and BMI was
27.3 ± 0.3 kg/m2, with the majority of participants overweight
(39 ± 1% fat mass in females; 25 ± 1% fat mass in males). Forty-
six participants (96%) completed 1 year, with weight changes
of −8.0 ± 0.9 kg in the CR group, −6.4 ± 0.9 kg in the EX group,
and −1.3 ± 0.9 kg in the HL group, as reported previously (6).
daily weight changes
During the baseline period, daily weights were available for 45
participants, who contributed a total of 1,156 days, or 165
weeks of data. During the 1-year intervention, 47 participants
contributed a total of 3,064 daily weights, or 437 weeks (177
weeks in the CR group, 176 weeks in EX, and 84 weeks in HL).
As shown in Figure 1a, body weight at baseline increased sig-
nificantly during the weekend days (+0.06 ± 0.03 kg/day, P =
0.02, all participants combined), with a nonsignificant trend
downward on weekdays (−0.02 ± 0.02 kg/day, P = 0.18). This
difference between weekend and weekday weight changes was
significant (P = 0.04), resulting in a net increase of 0.077 kg
During the 1-year intervention, a weekend effect was still
evident (Figure 1b). As prescribed, the CR and EX partici-
pants were in negative energy balance during the weekdays
(CR: −0.07 ± 0.02 kg/day, P < 0.001; EX: −0.08 ± 0.02 kg/day,
P = 0.004). During the weekends, however, CR participants
stopped losing weight (+0.02 ± 0.02 kg/day, P = 0.38), whereas
EX participants gained weight (+0.08 ± 0.03 kg, P < 0.0001).
The HL control group did not have significant weight changes
on weekends (−0.01 ± 0.00 kg/day, P = 0.94) or weekdays
(−0.02 ± 0.03 kg/day, P = 0.23).
On 598 occasions, participants’ body weights were measured
on the clinic scale on the same day that they weighed them-
selves at home on their study scales. Intraclass correlation coef-
ficients between the home scale weights and the clinic scale
weights were 0.99 at baseline and for each time point during
At baseline, energy intake was highest on Saturdays (Figure 2a),
with the average Saturday intake (2,257 ± 111 kcal/d) signifi-
cantly greater than the average intake during the weekdays
(2,021 kcal/d; P = 0.03 Saturday vs. weekdays). The higher
energy intake on weekends was attributable to greater con-
sumption of fat (36.5% of total kcal on weekends vs. 34.7% on
weekdays, P = 0.05), and relative but not absolute reductions in
the consumption of carbohydrate (46.7% vs. 48.8%, P = 0.04)
and protein (15.7% vs. 16.5%, P = 0.02). Sodium intake did not
differ significantly between weekends (3,605 mg) and week-
days (3,345 mg, P = 0.24).
During the 1-year intervention, energy intake for all 3 study
groups was significantly higher on weekends as compared
to weekdays (Figure 3a). In the CR group, energy intake on
Saturday exceeded the weekday average (P = 0.001); EX par-
ticipants had higher intakes on both Saturday (P = 0.035)
and Sunday (P = 0.012) as compared to weekdays, whereas
the HL group ate more on Sundays relative to weekdays (P =
0.04). The correlation between self-reported energy intake and
objectively determined energy intake was 0.58 (P < 0.0001) at
baseline. During the intervention, the correlations within each
group were: CR group r = 0.67, P < 0.0001; EX group r = 0.60,
P < 0.0001; HL group r = 0.49, P = 0.009.
Of the 3,653 days of accelerometer records that coincided with
daily weight measurements, 24% were excluded for insufficient
data (i.e., <720 min), with no difference between weekends and
Fri SatSunMon TuesWedThursFri
Daily weight changes at baseline (kg)
Daily weight changes during intervention (kg)
Figure 1 (a) Daily weight changes at baseline and (b) during the 1-year
interventions. Symbols represent the mean for all participants (open
circles, n = 48), caloric restriction (CR) group (filled circles, n = 18),
exercise (EX) group (hexagons, n = 18) and healthy lifestyle (HL) control
group (filled squares, n = 10). *P = 0.02 for daily weight change on
weekends at baseline; *P < 0.005 for weight change on weekends or
weekdays during the intervention year.
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weekdays. Subjects wore the accelerometers for an average of
1,003 ± 172 min each weekend day and 1,018 ± 159 min each
weekday (P = 0.36), for an average of 20.4 days at baseline and
11.2 days at the subsequent time points, with no differences
At baseline, physical activity was lowest on Sundays and
highest on Saturdays (Figure 2b), with Sunday’s activity sig-
nificantly lower than the average weekday activity (P = 0.010
Sunday vs. weekdays), and a trend for Saturday’s activity to be
higher than the average weekday activity (P = 0.082). Due to
these opposite trends on Saturdays and Sundays, the average
weekend physical activity did not differ significantly from the
average weekday activity (P = 0.24). During the intervention
(Figure 3b), physical activity was greater on weekends than
weekdays for the CR group (P = 0.040), due to higher activ-
ity on Saturdays (P = 0.045) and Sundays (P = 0.041) relative
to weekdays. The EX group did not display different activity
patterns on weekends relative to weekdays (P = 0.56), whereas
a trend for higher activity on weekends was observed in the
HL group (P = 0.06). The correlation between accelerometer-
derived physical activity and physical activity determined
from DLW was 0.53 (P = 0.0005) at baseline. During the inter-
vention, the correlations within each group were: CR group
r = 0.49, P < 0.0001; EX group r = 0.57, P < 0.0001; HL group
r = 0.49, P = 0.005.
This is the first study to demonstrate that weight gain occurs
during weekend days relative to weekdays, and that this effect
is attributable predominantly to higher energy intake dur-
ing weekends. Before the interventions began, the consistent
and significant increase in body weight from Friday through
Monday morning was due to both higher dietary intake and
lower physical activity on weekends relative to weekdays, and
the resultant weekly weight gain was 0.077 kg. Although small
on a weekly basis, this rate of weight gain could result in an
annual increase of 4.0 kg, or almost 9 lb, if it continued in a
similar pattern throughout the year. However, the anticipa-
tion of beginning a long-term CR or exercise intervention may
have contributed to overeating during the baseline period in
the present study, as the annual rate of weight gain generally
is <1 kg/year (9–11). During the year-long interventions, the
CR group stopped losing weight on weekends and the exercise
group gained weight, which was solely attributable to higher
dietary intake on weekends.
Given the large increase in the prevalence of obesity through-
out the past two decades, it is important to understand the
influence that weekend lifestyle patterns have on long-term
weight control. The main finding of the present study was that
FriSat SunMon TuesWedThurs
Energy intake at baseline (kcal/day)
Fri SatSun MonTuesWedThurs
Physical activity at baseline (kcal/day)
Figure 2 (a) Daily energy intake and (b) physical activity at baseline.
Symbols represent mean values ± s.e.m. for all participants. *P <
0.05 vs. mean weekday value.
Energy intake during intervention (kcal/d)
Physical activity during intervention (kcal/d)
Figure 3 (a) Daily energy intake and (b) physical activity during the
1-year interventions. Symbols represent median values and interquartile
ranges for the caloric restriction (CR) group (filled circles), exercise (EX)
group (hexagons), and healthy lifestyle (HL) group (filled squares).
*P < 0.05 vs. median weekday value.
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weekend dietary indulgences contribute to weight gain or ces-
sation of weight loss. These data are consistent with those of
Gorin et al. (5) from the National Weight Control Registry,
which indicate that people whose diets were less consistent
between weekdays and weekends were more likely to gain
weight during the subsequent year. Our observation that the
extra weekend calories were attributable to increases in dietary
fat are consistent with data from the 1994–1996 Continuing
Survey of Food Intakes by Individuals (12).
Our accelerometer results at baseline reveal low physical
activity levels on Sundays, which support the pedometer-
based findings of Clemes et al. (13), in which there was a
significant reduction in steps on Sundays among overweight
adults. Interestingly, physical activity levels during the weight
loss interventions were consistent or even higher on week-
ends relative to weekdays. Taken together, our results support
the importance of maintaining consistent dietary and physi-
cal activity patterns throughout the week to avoid unwanted
weight gain and to facilitate consistent weight loss.
Despite the strengths of the present analysis, including the
correlations between our main outcome measures (i.e., home
weights, self-reported dietary intake, and accelerometer-based
physical activity) and more validated methodology (i.e., clinic
weights, and DLW assessments of energy intake and expen-
diture, which incorporate dual-energy X-ray absorptiometry-
determined changes in body composition), there are limitations.
First, we cannot determine with certainty what proportion of
the weekend weight increase was attributable to higher dietary
intake or lower physical activity relative to weekdays, as the
accuracy of self-reported dietary intake is variable between
individuals (14–16), and the accelerometers may not be sen-
sitive enough to detect small day-to-day differences in physi-
cal activity (17,18). However, the food diaries were kept for
1 week, and the accelerometers were worn for nearly 2 weeks at
each assessment time point, thereby minimizing intra-subject
variability. Another limitation is that participants contributed
differentially to the data, with some participants missing data
at a follow-up time point and therefore being underrepresented
relative to participants with data at all study time points. By
design, the control group was smaller than the intervention
groups, which may explain why they did not display the week-
end weight effect during the year-long study that was observed
among the whole sample at baseline. Despite these limitations,
the large number of days included in these analyses and the sta-
tistical controls used should have minimized sampling biases.
In summary, our results demonstrate the adverse effect of
weekend lifestyle behaviors on daily body weight, and indicate
that higher dietary intake on weekends is the greatest contributor
to weekend weight gain or cessation of weight loss, with physical
activity playing a smaller role. This information has important
implications from a public health perspective and in clinical trials
when the rate of weight loss may not be as great as expected.
We are grateful to the study participants for their cooperation, the staff of the
Applied Physiology Laboratory at Washington University School of Medicine
for their skilled assistance, Manjushri Bhapkar of Duke Clinical Research
Institute for analyzing the accelerometer data, and James P. DeLany at
the Pennington Biomedical Research Center for the doubly labeled water
analyses. This research was supported by the NIH Cooperative Agreement
5- U01-AG20487, Clinical Nutrition Research Unit Grant DK56341, and
General Clinical Research Center Grant RR00036. E.P.W. was supported
by Institutional National Research Service Award AG00078. The manuscript
was written by S.B.R. and edited by S.B.R., E.P.W., K.B.S., K.S.-M., D.T.V.,
and J.O.H.; data collection was performed by S.B.R., E.P.W., D.T.V., and
K.O.; data analyses and interpretation were performed by K.S.-M, K.B.S,
S.B.R., and E.P.W.; the overall study was designed by J.O.H.
The authors declared no conflict of interest.
© 2008 The Obesity Society
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