text: Monday. According to a recent study by Orsama and colleagues, that’s the day when most people experience their maximum body weight. And your lowest body weight? That’s most likely to be recorded on Friday. 
   
   The weekend certainly seems to play some havoc on our energy balance. 
   
   The 7 day week cycle has an impact on many of our behaviors. Take sleep, for example. During the week, we get up regularly at the sound of our alarm, but come Saturday, the alarm clock may be turned off and you allow yourself to sleep in. Your physical activity level is also likely to oscillate based on the day; individuals who actively commute to work may become more of a couch potato on the weekend. People may also be more likely to go out and have a few drinks over the weekend. All these behaviors can have an impact on a person’s energy balance, and subtly, on their body weight. And apparently, that’s exactly what happens. 
   
   In the study published earlier this year, Orsama and colleagues explored the weekly rhythm of weight to understand the normal variation of weight which occurs during the week. The researchers analysed daily self-reported body weight in 80 adults (BMI: 20.0–33.5 kg/m2; age: 25–62 years). To avoid intra-day variation in body weight individuals were instructed to record their daily weight immediately after waking up and before breakfast. The length of each individual’s time series of recorded weight varied from 15 to 330 days. 
   
   As I’ve already summarized in the first couple of sentences, body weight tended to be highest following the weekend (Sunday/Monday) and lowest just before the weekend (Friday). 
   
   As a potential explanation of this weekly weight fluctuation, the authors propose that meal sizes and calorie intake tend to increase while physical activity tends to decrease during weekends. Indeed, many people who eat a clean diet during the week allow themselves a cheat day over the weekend. Evidence seems to favor such a flexible approach to diet management, by contrast to the all or none approach. The former approach allows you to enjoy some highly-favored but potentially calorie-dense foods in relative moderation, potentially increasing long-term adherence to a diet. By contrast, the latter approach, which outright forbids certain foods, may result in dietary boredom, and complete discontinuation of the healthy diet. 
   
   This also leads us to the other interesting finding in this study. Variation in body weight was also analysed according to whether the participants lost weight (greater than 3% reduction), gained weight (greater than 1% increase), or maintained weight (weight change between –3 and 1%). What is interesting, as illustrated in the figure below, is that the biggest fluctuation in weight occurred in those who lost weight over time. This would suggest that successful weight losers were allowing themselves some serious dietary leniency over the weekend. Maybe this is exactly what allowed them to persist on their respective diet for a longer duration. 
   
   What are the implications of this research? 
   
   First, if you like to regularly weight yourself, best to compare the same day of the week (and same time of day). 
   
   Secondly, allowing yourself a few indulgences over the weekend may in fact help you stick with eating better and managing your weight over the long term. 
   
   Peter 
   
   Reference: ||||| You've been eating well all week: oatmeal for breakfast, a salad for lunch and grilled chicken with vegetables for dinner. Then the weekend hits. Suddenly your taste buds want French fries at the bar and Mom's cheesy lasagna is calling your name during Sunday dinner. 
   
   Not to worry. A new study suggests small weight gains on weekends are normal, and as long as you can compensate during the week, indulging a bit may even help you lose weight long-term. 
   
   “There is a clear weekly rhythm to weight variation for most people,” says one of the study authors, Anna-Leena Orsama, a research scientist with VTT Technical Research Center of Finland. “On the weekends there is more variability and unpredictability in what we eat.” 
   
   The study, “Weight Rhythms: Weight Increases during Weekends and Decreases during Weekdays,” was published this week in the journal Obesity Facts. 
   
   The study 
   
   Researchers analyzed data from 80 adults, ranging in age from 25 to 62 years old, who recorded their weight each day after waking up. 
   
   The participants were grouped according to their weight changes over the study period: weight losers who lost at least 3% of their body weight; weight gainers who put on at least 1%; and weight maintainers who stayed in between those minimums. 
   
   The results 
   
   All three groups, the losers, gainers and maintainers, recorded higher weights on Sundays and Mondays than other days of the week. But those who successfully lost weight had lower weights during the week, with their lowest generally recorded on Friday. 
   
   This shows they managed to compensate for the weekend gain during the work week, researchers say. 
   
   Limitations 
   
   The sample size for this study was relatively small. Only 10 people were in the gain group; only 18 were in the loss group. The data was also all self-reported, meaning the study participants could have made a mistake or purposefully altered their recorded weights. 
   
   Also important to note is that the researchers are talking about slight caloric increases. This is not a free pass to eat your weight in cookie dough. 
   
   The takeaway 
   
   Long-term habits may make more of a difference than short-term splurges, the study authors say. And a flexible eating pattern - i.e., allowing yourself that brownie or beer on Friday night - may help you sustain overall healthy eating habits over time. Weight loss success depends primarily on your ability to compensate for those splurges on other days of the week. 
   
   “The big difference between those who gain weight over time and those who lose or maintain weight is directly related to the way they eat from Monday to Friday," said Cornell University behavioral economist Brian Wansink, one of the study authors. 
   
   "Some indulging during weekends makes no harm but for successful weight loss it is important to notice these rhythms and take steps to reverse the upward trends." ||||| Abstract 
   
   Background/Aims: The week's cycle influences sleep, exercise, and eating habits. An accurate description of weekly weight rhythms has not been reported yet - especially across people who lose weight versus those who maintain or gain weight. Methods: The daily weight in 80 adults (BMI 20.0-33.5 kg/m2; age, 25-62 years) was recorded and analysed to determine if a group-level weekly weight fluctuation exists. This was a retrospective study of 4,657 measurements during 15-330 monitoring days. Semi-parametric regression was used to model the rhythm. Results: A pattern of daily weight changes was found (p < 0.05), with higher weight early in the week (Sunday and Monday) and decreasing weight during the week. Increases begin on Saturday and decreases begin on Tuesday. This compensation pattern was strongest for those who lost or maintained weight and weakest for those who slowly gained weight. Conclusion: Weight variations between weekends and weekdays should be considered as normal instead of signs of weight gain. Those who compensate the most are most likely to either lose or maintain weight over time. Long-term habits may make more of a difference than short-term splurges. People prone to weight gain could be counselled about the importance of weekday compensation. 
   
   © 2014 S. Karger GmbH, Freiburg 
   
   Introduction 
   
   Obesity is a powerful, prevalent predictor of poor health. In 2010, 475 million adults were obese (BMI ≥ 30 kg/m2) [1] and the number is estimated to grow to 1,115.8 million by 2030 [2]. Harmful health consequences are manifold: Obesity increases the risk of type II diabetes and cardiovascular diseases [3], osteoarthritis [4], gallstones [4], colon and breast cancer [5], and the incidence of mental disorders [6]. While it is driven by excess intake of energy and inactivity [7,8], a better understanding of weight gain patterns could provide useful insights into the prevention and treatment of obesity. 
   
   Human behaviour and physiology exhibit variations according to biological and environmental factors. For instance, circadian rhythms that are seen in the recurrence of our daily activity and rest are also found in the fluctuations of several physiological variables such as body temperature, blood pressure [9], and glucose metabolism [10]. Menstrual cycles are an example of biological monthly rhythms. Recent studies have highlighted the importance of certain rhythms on health. These have included how disruptions in the circadian rhythm influence the development of type II diabetes [11] along with other adverse health consequences [12]. 
   
   The 7-day cycle of a week also has a strong impact on life [13] that is more environmental and behavioural than biological. For instance, days of the week are known to determine our sleeping patterns [14], physical activity [15,16,] and eating habits [16,17]. There is early indication that weight is higher during weekends and lower during weekdays [18,19,20]. However, it is unclear if this phenomenon is visible on a population level and how it is related to successful weight management in the long term. 
   
   This research examines the variation of weight across the days of the week and also examines this pattern across three segments of people - those who maintain their weight, gain weight, and lose weight. Specifically, by combining self-monitored weight measurements from four earlier studies, we endeavoured i) to find out whether there is group level variation in weight within a week or whether the weight variation is independent from days of the week; ii) to study the form of the dependence between weight and days of the week; and iii) to initially explore how this might vary across weight losers, weight gainers, and weight maintainers. 
   
   Material and Methods 
   
   Retrospectively Collected Weight Data 
   
   Self-recorded weight measurements were aggregated from four earlier data collection efforts [18,20,21,22] in which individuals were instructed to self-monitor and record their daily weight, immediately after waking up and before breakfast. Studies 1, 3, and 4 involved health management intervention and were approved by local ethics committees. The focus of Study 2 was usability and feasibility of a mobile phone application and, thus, no approval was needed. Table 1 describes the goals and durations of the four studies as well as the demographics and size of the study populations. 
   
   Table 1 
   
   In total, the four studies involved 178 subjects and resulted in 7,408 self-recorded weight measurements (from 154 individuals). Two inclusion criteria regarding the frequency of weight measurements and the length of the monitoring period were established for the data. Only measurements taken on a minimum of 7 sequential days were included to prevent describing a biased rhythm that could result from unreported measurements. The minimum acceptable duration of monitoring was 14 days. These restrictions resulted in the inclusion of 80 adults and 4,657 weight measurements. The length of each individual's time series varied from 15 to 330 days. 
   
   Derivation of Variables 
   
   Based on the original data set, the individuals were divided into three subgroups according to their relative weight change. Relative weight changes were calculated as a difference between the averages of self-measured weights on the first and last week of monitoring. The categories of the corresponding group variable were the following: ‘LOSS' - weight change more than -3%; ‘GAIN' - weight change more than 1%; ‘MAINTAIN' - weight change between -3 and 1%. 
   
   A weight trend component was estimated by applying a two-sided moving average filter [23] with the length of 7 days. Detrended weight measurements were normalised with respect to each individual by mean centring according to one's average weight. The response variable was derived by subtracting trend from weight time series of each individual to eliminate the effects of linear weight gain or linear weight loss. A rapidly decreasing trend may cause weight to appear higher in the beginning of each week-long period which could confound the underlying ‘real' rhythm. For the day of the week variable, days were labelled in an ordinal scale as follows: 1 = Sunday, 2 = Monday, 3 = Tuesday, 4 = Wednesday, 5 = Thursday, 6 = Friday, 7 = Saturday. 
   
   Statistical Methods 
   
   Semi-parametric regression [24] was used for analysing the weekly rhythm in the weight measurement data. The method is based on the combination of non-parametric smoothing and parametric linear mixed-effects (LME) models [25]. LME models are widely used in the analysis of longitudinal data due to their ability to account for within-subject variation. Splines are continuous piecewise-defined linear functions that are formed by dividing the range of the covariate into p subintervals and modelling each of the intervals with an appropriate slope [26]. The subintervals are tied together at their cut points to make the final curve contiguous. Splines are unrestricted to parametric assumptions and, thus, enable flexible shapes for the curves. As a semi-parametric method, the splines were formulated as LME models and further estimated in the LME framework using the restricted maximum likelihood method (REML). 
   
   Student's t-test and restricted maximum likelihood ratio test (RLRT) were used for model comparisons. The RLRT is constructed by comparing maximized likelihoods of two models. The larger the difference between the likelihoods, the stronger the evidence that the smaller model is inadequate. Significance tests were conducted with the risk level α = 0.05. Analyses were done using R version 2.15 [27]. 
   
   Application 
   
   The general formula for modelling the dependence between weight and day of the week was the following: 
   
   weight = f (day of the week) + ε(1). 
   
   First, f (day of the week) was determined as a linear function of day of the week and later extended to a linear penalised spline. The formula of the spline model was: 
   
   On the right hand side, the first two terms form the linear basis for the spline (the formula is similar to the ordinary linear regression model). b r+1 's are the slope estimates for the truncated line functions that handle the departures from the linearity to obtain the appropriate form for the curve. In our models, days of the week were used as placements for the cut points (τ's) leading to six subintervals to be modelled with a different slope. 
   
   The LME model presentation, y = Xβ + Zb [25,] for the spline curve was obtained by partitioning the spline model components in formula (2) into two LME model components, i.e. fixed effects (Xβ) and random effects (Zb). One of the LME model assumptions holds that slope estimates of the random part are distributed with zero mean [25]. This property is exploited in the estimation to control the roughness of the curve. The advantage of the LME presentation is that it is straightforward to extend the spline model further to account for the within-subject variation that is present in repeated measures data. The random effect structure was extended to individual-specific intercepts and slopes by extending the Zb component and its corresponding covariance matrix [28]. As a first step, an individual-specific intercept was added to the group-level model where component u i1 is an intercept for i-th individual: 
   
   Further, the model was enlarged with individual-specific linear lines with the day of the week as a covariate. As a final step, the model was extended with individual-specific splines, meaning that each individual had their own spline curve that describes the deviation from the group-level curve. The deviation curve was not restricted to being linear; it could be of any shape. This model was expressed as 
   
   weight = f (day of the week) + g i (day of the week)(4). 
   
   where g i 's were constructed as presented in formula (2) but with fewer cut points, i.e. four in our models. 
   
   Hypotheses 
   
   Figure 1 shows the initial illustration of the weekly profile curves derived as an averaged detrended normalized weight on each day of the week. Based on the figure, three hypothesis set-ups were formulated. These resulted in the construction of six different models. Primarily, we modelled the group-level data that included the entire sample of 80 subjects. These models were denoted as M1 ALL to M6 ALL . Additionally, each hypothesis set-up was tested in the subgroups of weight losers, weight gainers, and weight maintainers. The models were denoted as M1 LOSS to M6 LOSS , M1 GAIN to M6 GAIN , and M1 MAINTAIN to M6 MAINTAIN . 
   
   Fig. 1 
   
   Day of the Week Effect 
   
   The first hypothesis involved testing whether the day of the week had a linear effect on weight when starting from Sunday, or whether the expected weight is constant and does not depend on the day of the week. The following hypothesis was tested: 
   
   H₀: weight = β₀ (M1 ALL , M1 LOSS , M1 GAIN , M1 MAINTAIN ) 
   
   H a : weight = β₀ + β 1 × day of the week (M2 ALL , M2 LOSS , M2 GAIN , M2 MAINTAIN ). 
   
   To assess the superiority of M2, the significance of β 1 was tested using the t-statistic. 
   
   Form of the Dependence 
   
   The second question consisted of two parts, 2A and 2B. In 2A, the form of the dependence between weight and day of the week was explored. It was tested whether the linear curve was adequate to describe the weekly rhythm or, alternatively, whether there was some degree of nonlinearity. M2 models were tested against their extensions, i.e. linear penalised spline models (M3) that correct departures from the linearity. The following hypothesis was tested: 
   
   RLRT was used to assess the significance of the difference between the M2 and M3 models. The comparison involved testing if all the six truncated line coefficients b r 's in M3 differed from zero. This is equivalent to testing if σ b 2 is significantly different from zero. Thus, the hypothesis was simplified as the following: 
   
   H₀: σ b 2 = 0 versus H a : σ b 2 > 0. 
   
   The testing problem is non-standard because weight measurements are not independent and because the RLRT statistic under the null hypothesis is zero, which is on the boundary of its parameter space [29]. To overcome this problem, we used RLRsim library [30] in R that exploits parametric bootstrap with 10,000 repetitions to simulate and test the hypothesis. 
   
   Effect of Longitudinal Nature of the Data 
   
   In order to take the longitudinal nature of the data into consideration, the dependence in measurements made by the same individual was accounted for in 2B. The fitted population level curves were extended with individual-specific intercepts and slopes. The following models nested to their random effect structures were fitted: 
   
   The hypothesis concerning the models was whether the added random component improves the fit. The comparison was done by RLRT. The significance of the difference between the two models was tested using Self-Liang approximation [31] which says that asymptotical RLRT follows ½ χ2 q + ½ χ2 q+1 - distribution where q is the number of fixed effects. However, the distribution holds under the assumption that y's are independent which is not the case in the longitudinal data. Therefore, the results were confirmed by comparing the most adequate model with the population average model by using RLRsim library. The observed RLRT's were compared to the 95th percentile of ½ χ2 0 + ½ χ2 1 - distribution, which is 1.921. 
   
   Results 
   
   The descriptive data of the entire sample and each of the subgroups, i.e. weight losers, weight gainers, and weight maintainers, are shown in table 2 along with the number of measurements obtained from individuals. Table 3 presents how often the minimum and maximum weight observations occurred on different days of the week. In the LOSS group, there was a clear difference between days of the week that have the highest proportions for minimum and maximum weight. The minimum weight of the week was most frequently observed on Friday and, in 60% of the cases, the week's minimum weight occurred either on Friday or Saturday. Correspondingly, the maximum weight was reached most often on Sunday or Monday, i.e. in 59% of the cases. In the GAIN and MAINTAIN groups there was more variability. Both the minimum and maximum weight of the week was most frequently measured on Sunday in both subgroups. The second highest occurrence in maximum weight was seen on Monday. The distribution on other days of the week was similar. 
   
   Table 2 
   
   Table 3 
   
   When examining the day of the week effect with a linear regression model, day of the week was a significant predictor of weight in all study groups (p < 0.001 for the entire sample, for LOSS, and for MAINTAIN; p = 0.002 for GAIN). The slopes that describe the rate of weight change were all negative: β 1 = -0.0004 for the entire sample, β 1 = -0.0006 for LOSS, β 1 = -0.0003 for GAIN, and β 1 = -0.0003 for MAINTAIN. 
   
   The linear association of day of the week on weight was not adequate to explain the weekly variation of weight but it was significantly better explained by a spline curve that allowed a more flexible shape (p < 0.001 for the entire sample, p < 0.001 for LOSS, p = 0.021 for MAINTAIN, p = 0.065 for GAIN). Therefore, the spline model was used to test the 2B hypothesis. 
   
   The weekly rhythm-fitted plots with their variability bands are shown in figure 2. The upper panel shows fitted profile plots of the weekly rhythm of weight with their variability bands. Equivalent plots are fitted in the lower panel together with scatter plots of weight measurements of each group to illustrate the explorative role of rhythm curves in the weight variation. 
   
   Fig. 2 
   
   Table 4 shows the test statistics of RLRT to assess the most adequate fit that takes into account the within-subject variation in the data. The model that included subject-specific lines (M5) was the most adequate model in the LOSS, GAIN, and MAINTAIN subgroups. In all cases, the likelihood ratio statistic for assessing M5 over M4 was greater than 1.921. Further extension of the random effect structure to the individual-specific splines made no improvement. The superiority of M5 over M3 models (that did not account for individual effects) was confirmed by simulations using RLRsim. The bootstrapped p-values were: p = 0 for LOSS, p = 0.056 for GAIN, and p = 0 for MAINTAIN. For the entire sample, the most adequate model was the M6 that included individual-specific splines. The bootstrapped p-value of comparing the superiority M6 ALL against M3 ALL was 0. The addition of individual-specific trajectories to population level models significantly decreased the unexplained variability in this weight data. However, the extensions did not change the shape of curves but they remained similar to the population level curves. The fitted values of the models are plotted as separate points in figure 2. We see hardly any difference from the population level curves that are drawn as contiguous lines. 
   
   Table 4 
   
   Discussion 
   
   The purpose of this study was to explore the weekly rhythm of weight to understand the normal variation of weight which occurs during the week. In addition, we described the rhythm profiles in different weight management groups. It was found that the day of the week has a significant effect on weight: weight was higher in the beginning of the week, i.e. on Sunday and Monday, and decreased toward the end of the week. In addition, there was a slight increase in the end of the week, i.e. during Saturdays and Sundays. This phenomenon was also observed in the subgroups of weight losers and weight maintainers, but not among weight gainers. This observation may appear contradictory but is explained by the fact that we removed any linear weight change prior to analysing the effect of day of the week - therefore, it is possible for the GAIN group to exhibit this pattern. 
   
   The detected shape of the weekly rhythm of weight is consistent with the results of other studies regarding day of the week effects on human behaviour. Meal sizes and calorie intake have been found to increase [16,17] and physical activity to decrease during weekends [15], specifically on Sundays [16]. The increased calorie intake and reduced amount of exercise are indicators that are associated with weight gain. Correspondingly, we detected weight gain during weekends. The shape of the weekly rhythm curves were consistent with the findings of Racette et al. [16] who found a decreasing trend during weekdays but increased weight during weekends. Our analysis reveals that the decreasing trend is continued until Friday after which weight starts to increase over the weekend, being highest on Sunday and on Monday. 
   
   Based on the subgroup analyses, our results suggest that weekly variation of weight might be associated with weight management. Figure 2 shows that the magnitude of the weekly weight variation is greater in the LOSS group than in the GAIN group. This suggests that individuals in the GAIN group have less consistent weight differences between days of the week and that their weight varies with less regularity. This is also noticeable in table 3: In the LOSS group, the minimum weight of the week is most often observed at the end of the week while the week's maximum weight is most often seen either on Sunday or Monday. In the GAIN group, maximum and minimum weights of the week are distributed more evenly to all days of the week. Similar findings can be seen in the research of Racette et al. [16]. In both of their intervention groups, significant decreases in weight (-8.0 ± 0.9 kg and -6.4 ± 0.9 kg) were detected whereas in the control group the average weight change was -1.3 ± 0.9 kg. Correspondingly, both of the intervention groups had decreasing weight during weekdays and stable or increasing weight during weekends whereas the control group did not show significant weight changes on weekends or weekdays. 
   
   Based on the subgroup analysis, it appears that successful weight control (weight maintenance or weight loss) is not distracted by slight increases in weight during weekends. Instead, their success seems to be related to their ability to compensate and turn down the upward trend. This compensation mechanism thwarts minor weight regains, which Phelan et al. [32] found difficult for many people to recover from. However, relapses are expected and likely to occur in weight management. Successful weight loss strategies have indicated that flexible dietary restraint involves less pressure on diet and a stronger understanding of how diet and energy balance relate to sustained weight loss [33]. 
   
   Flexible eating regulation patterns that allow one to enjoy all foods and compensate for more caloric meals are more likely to be sustained in a long-term rather than a dichotomous way of thinking (‘all or nothing' or ‘allowed/forbidden foods') [33]. Similarly, Dohm et al. [34] reported that when dietary lapses occur, successful weight maintainers are more likely to use direct coping. Allowing more flexibility on weekends and holidays might be more realistic from a long-term perspective because it avoids dietary boredom [35]. Indeed, according to a study by Smith et al. [36], the most common reasons for stopping a weight loss regimen were ‘boredom/loss of interest' and ‘missing certain foods'. Therefore, contrary to the conclusions of Racette and co-authors [16], slight variations - including increases in weight during the weekend - may have a positive role in supporting long-term successful weight management. 
   
   One limitation of this study pertains to the threshold value of the GAIN group (1% of weight increase). Whether 1% is an actual increase or merely a part of daily weight variation may be discussed. However, to eliminate day-to-day fluctuations in weight, the individuals were classified according to weight change that was calculated as an average of weight self-measurements over one week. Secondly, as self-monitoring relates to weight loss and successful loss maintenance [37,38,39], individuals showing early signs of weight gain despite regular self-monitoring were classified in the GAIN group of this study to emphasize their deviation from the expected pattern. Despite this low threshold, our sample size of the GAIN group was small, which limits the generalizability of the results and highlights the overall challenge of learning more about weight gainers. It is difficult to find people who are committed to consistently monitoring their weight with frequency and continuity even in the presence of unfavourable outcomes. The threshold value for the LOSS group was set to a decrease of weight of >3%. For obese people (BMI > 30 kg/m2), a moderate weight reduction of 5% is generally considered to be beneficial for health [40]. In this study, individuals were on average overweight (mean BMI 27.3 kg/m2) but not obese and the monitoring time was relatively short, i.e. 80 days on average. Therefore, we expected smaller weight loss and considered a decrease of 3% sufficient. This threshold is also supported by Stevens et al. [41]. 
   
   A challenge that we faced in the analysis was the depletion of data. The unbalance of the data and the self-selected nature of weight measurements make the data sensitive to bias. To achieve generalizable results, certain requirements were established for the measurement frequency and consistency to avoid the slightest fluctuations of weight. As a result, the number of weight measurements decreased from 7,408 to 4,657. Due to the small sizes of GAIN and LOSS groups (10 and 18 individuals, respectively), the suggested relationship between the weekly rhythm and weight management success should be considered preliminary. 
   
   The findings of this study suggest that weight should not be expected to be constant throughout the week. Slight increases during weekends can be treated as normal weight variation instead of signs of weight gain. However, the slight weight gain over weekends needs to be compensated during weekdays to avoid an increasing long-term trend. Based on the indicators of this study, a clear weekly rhythm may be favourable, allowing for more flexible eating patterns. However, more research is needed to confirm these results across a broader range of ages and life circumstances. The results were inherited from a retrospective set-up, and none of the studies was primarily planned for the analysis of weekly variation of weight. Future studies require careful consideration to obtain a sufficient amount of data regarding weight gainers while at the same time retaining the free-living set-up to obtain generalizable results. 
   
   Conclusion 
   
   This study found that there is a clear weekly rhythm to weight variation. Weight was at the highest point after weekends, on Sunday and Monday, and decreased toward the end of the week. The beginning of the weekend stopped the decrease and weight started to increase after Friday. Additionally, there is preliminary evidence that those people who show the largest compensating change from the weekend to the weekdays are those who - over time - are most likely to either lose weight or maintain their weight. It appears that long-term habits make more of a difference than short-term splurges. 
   
   Based on the findings of this study, we can expect weight to rise during weekends and treat it as a normal variation. Our results provide scientific support to the tenet that in weight management, allowing more flexibility during weekends and holidays might be more realistic and successful in the long term than a strict regimen. 
   
   Acknowledgments 
   
   AO, EM, ME, MV, BW, and IK conceived and designed the study. AO, EM, ME, MV, and IK collected and analysed data. AO, BW, and IK wrote the manuscript. Julia Hastings-Black provided editorial assistance. 
   
   Disclosure Statement 
   
   This work was done as a part of the ITEA2 Care4me project. The project is funded by the Finnish Funding Agency for Technology and Innovation (Tekes) and the VTT Technical Research Centre of Finland (VTT). 
   
   The authors have no conflicts of interest to disclose. 
   
   References 
   
   Author Contacts 
   
   Dr. Brian Wansink Charles S. Dyson School of Applied Economics and Management, Cornell University 15 Warren Hall, Ithaca, NY 14850 (USA) foodandbrandlab@cornell.edu 
   
   Article / Publication Details 
   
   Received: May 14, 2013 
   
   Accepted: September 04, 2013 
   
   Published online: January 31, 2014 
   
   Issue release date: February 2014 Number of Print Pages: 12 
   
   Number of Figures: 0 
   
   Number of Tables: 0 ISSN: 1662-4025 (Print) 
   
   eISSN: 1662-4033 (Online) For additional information: https://www.karger.com/OFA 
   
   Open Access License / Drug Dosage / Disclaimer 
   
   Open Access License: This is an Open Access article licensed under the terms of the Creative Commons Attribution-NonCommercial 3.0 Unported license (CC BY-NC) (www.karger.com/OA-license), applicable to the online version of the article only. Distribution permitted for non-commercial purposes only. 
   
   Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug. 
   
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target: – For most of us, weekends are a time to relax and indulge—maybe through more sleep, more couch time, or more calories in the food and beverages we consume. The result is that we tend to weigh slightly more on Sunday and Monday, but then slightly less again by Friday, reports the Smithsonian. And enough of us do this that scientists say they've observed the general weight trend in a small study published in the journal Obesity Facts. The researchers analyzed the recorded daily weights of 80 adults ages 25 to 62 and found weight "increases begin on Saturday and decreases begin on Tuesday." Before we all fall into a pit of despair in the days leading up to Thanksgiving, one doctor stresses on PLOS' Obesity Panacea blog that it's not a bad thing to "cheat" diets and habits on the weekends. In fact, it "allows you to enjoy some highly-favored but potentially calorie-dense foods in relative moderation, potentially increasing long-term adherence to a diet." The study's authors agree, telling CNN that "some indulging" does no harm. (Meanwhile, if you want to lose weight, research indicates you shouldn't forgo fats.)