Identifying critical periods of greater weight gain could provide useful information to combat the obesity epidemic. We tested whether body weight (BW), body fat percentage (BF%) and blood pressure (BP) changed during the holiday season (thanksgiving to new year’s day) and the impact of regular exercise on these parameters.
A total of 48 males and 100 females (age 18–65 years) with a mean body mass index of 25.1±0.5 kg/m2 were evaluated in mid-November (visit 1) and early January (visit 2; across 57±0.5 days). Anthropometric data, BF%, BP and self-reported exercise were recorded.
Participants showed significant increases in BW (0.78±0.1 kg, P<0.001, 95% confidence interval (CI): 0.57–0.99), BF% (0.5±0.2%, P=0.007, 95% CI: 0.12–0.77), systolic blood pressure (SBP; 2.3±1.2 mm Hg, P=0.048, 95% CI: 0.01–4.63) and diastolic blood pressure (1.8±0.8 mm Hg, P=0.028, 95% CI: 0.20–3.49). Obese participants (35.2±0.8 kg/m2) showed a greater increase in BF% compared with normal weight participants (21.7±0.2 kg/m2, P<0.05, 95% CI: 0.53–2.37) and a trend vs overweight participants (26.8±0.3 kg/m2, P=0.07, 95% CI: −0.18–1.65). Exercise (4.8±0.6 h per week) did not protect against holiday weight gain and was not a significant predictor for changes in BW or BF%. Data are reported as means±s.e.
Our participants gained an average of 0.78 kg, which indicates the majority of average annual weight gain (1 kg/y) reported by others may occur during the holiday season. Obese participants are most at risk as they showed the greatest increases in BF%. Initial BW, not exercise, significantly predicted BF% and BW gain.
The average US adult body weight (BW) gain per year is ∼0.5–1.0 kg and appears to affect all populations.1, 2, 3, 4, 5, 6 If current weight gain trends continue, by the year 2030, it is estimated 51–80% of the population will be overweight or obese, defined as a body mass index (BMI) 25 kg/m2.7, 8 Despite widespread awareness that being overweight or obese is a health hazard, and amidst increased access to recommendations for healthy weight maintenance,9, 10, 11 an effective long-term strategy to prevent excessive weight gain has been elusive. This lack of long-term success is due in part to the difficulty many people have in maintaining healthy dietary and physical activity (PA) patterns in an environment that discourages PA and encourages excessive energy consumption.1, 12, 13
Identifying critical patterns or periods of greater weight gain is essential for effective weight maintenance and weight loss strategies. Recent studies have shown that the majority of annual weight gain occurs during short periods of time (such as weekends or holidays) rather than consistently over 12 months.14, 15 In the United States, the time between thanksgiving and new year’s day is thought to be a period where significant BW gain occurs, and has been termed ‘holiday weight gain’.14, 15 A study conducted by Yanovski et al.,15 reported weight gain in 195 adults from mid-November to early January to be 0.37±1.5 kg, which was the only time period where significant BW gain occurred throughout the year. Moreover, this weight gained during the holiday season was not reversed during the remainder of the year.15
Highly palatable foods, which usually contain more fat (and/or sugar), and therefore more energy, are readily available during the holiday season and may promote BW gain, specifically body fat, if a positive energy balance is achieved and maintained. Ma et al.16 followed 593 participants, aged 20–70 years, quarterly for a 1-year period and found daily caloric intake was higher by 86 kcal per day during the fall compared with spring. Percentage of calories from carbohydrate, fat and saturated fat showed seasonal variation, with a peak in the spring for carbohydrate and in fall for total fat and saturated fat intake. Interestingly, BW varied by about 0.5 kg throughout the year, with highest weight gain in the winter, which was also the season that reported the lowest PA.16 In another study, it was determined that high daily energy expenditure was not protective against holiday weight gain.17 However, more research is needed to determine whether people who exercise regularly are less susceptible to holiday weight gain.
More research is needed to explore relevant lifestyle factors associated with this seasonal weight gain that have not previously been identified, one of which is regular PA. To our knowledge, none of the studies reporting on holiday weight changes have examined the effects of regular PA on changes in BW or body fat percentage (BF%). The purpose of this study was to determine whether BW, BF%, blood pressure (BP) and heart rate (HR) changed during the holiday season, and whether these changes differed based on BMI status. Furthermore, we aimed to identify differences between exercisers and non-exercisers in BW and BF% change, in addition to identifying other parameters that might impact or predict BW and BF% changes. We hypothesized that BW and BF% would significantly increase over the holiday season and that participants who did not exercise would gain more weight and BF% than participants who regularly exercised.
Materials and methods
This single-blinded observational study involved two test visits. The first test visit occurred in mid-November whereas the second test visit occurred after the holiday season in early January. The average interval between assessments was 57±0.5 days. Data collected at each visit included height, BW, BF%, BP, HR and self-reported daily PA (including mode, duration, frequency and intensity of PA).
Participants were recruited for the study by fliers, word of mouth and electronic announcement through Texas Tech University. A total of 148 adults (48 males and 100 females) between the ages of 18–65 years met all inclusion criteria and completed both visits. An additional 64 participants completed baseline testing, but did not complete visit 2. Therefore, those 64 participants were excluded from any analyses. The study was approved by the Texas Tech University Institutional Review Board. Before testing, each participant gave written informed consent. All materials used during recruitment and data collection were entitled ‘short-term changes in health parameters’ to prevent any alterations in eating or activity patterns because of their involvement in the study. Therefore, the participants were blinded as to the actual purpose of the study. Pregnant or lactating females, persons with any clinically significant illness, including diabetes, persons planning to lose weight or alter their current exercise regime, persons who did not celebrate the holidays and those taking medications known to affect BW were excluded.
For visit 1, participants arrived at the Human Nutrition Lab after a 4-h fast and 12 h without moderate or vigorous exercise. Participants wore a hospital gown over undergarments, and were barefoot when anthropometric measurements were made. Height was measured using a portable PEAIM-101 stadiometer (Perspective Enterprises, Portage, MI, USA) and BW was measured to the nearest 0.1 kg using the Series 400KL standardized scale (Continental Scale Corporation, Bridgeview, IL, USA). BMI was calculated from measured height and weight using BMI=kg/m2. BF% was measured using a bioelectrical impedance analysis (BF-522, Tanita, Arlington Heights, IL, USA). BP and HR were measured after the participant had been seated for 5 min with an automated BP cuff (Prosphyg 760, American Diagnostic Corporation, Hauppauge, NY, USA). Lastly, participants were asked to report their current PA patterns. Research personnel asked each participant about the mode, duration, frequency and intensity of any PA performed on a regular basis in the past month. Following the holiday season, participants returned to the Human Nutrition Lab for visit 2. The same measurements for visit 1 were repeated for visit 2. Those who met the PA guidelines recommended by the American Heart Association 18 over the holiday season (that is, if participants reported 150 min per week of moderate intensity were categorized as ‘exercisers’). If participants reported PA of <150 min per week, they were categorized as ‘non-exercisers’. For the analysis, exercisers (n=78) and non-exercisers (n=71) were determined from reported PA data at visit 2.
Statistical analyses were performed using SAS version 9.2 statistical package (SAS Institute Inc., Cary, NC, USA) and R statistical software stats package (version 2.15.1, R Core Team R Foundation for Statistical Computing, Vienna, Austria). Shapiro–Wilk test was used to determine normality of all data parameters. Student’s paired t-tests were used to determine significant differences between visit 1 and visit 2 values for height, BW, BMI, BF%, BP and resting HR. A one-way analysis of variance (ANOVA) was also done between BMI groups, and exercisers and non-exercisers, to look for differences in change in BW, BF% and BP. To look at the variables associated with changes in BW and BF%, both Pearson’s correlation coefficients and multiple regression were used. Two-factor and three-factor ANOVA were used to identify significant factors in differences in the mean change in BW and BF%. Finally, participants were divided into tertiles based on baseline BW to identify differences between groups based on PA using n-factor ANOVA. Statistical significance was set P<0.05. Data are presented as means±s.e. unless specified otherwise.
Baseline characteristics of all study participants and changes in all outcome variables between visit 1 and visit 2 are listed in Table 1. The participants were 80% Caucasian (n=119), 12% Hispanic (n=18), 4% Asian (n=6) and 3% African American (n=5). A high w-score from the Shapiro–Wilk test for all parameters indicated nonsignificant deviation from normal distribution.19 BW increased an average of 0.78±0.1 kg and BF% increased 0.5±0.2% in our study participants (P<0.05). For males, there were significant increases in BW, BMI and BF% (P<0.05), whereas females showed significant increases in BW, BMI, diastolic blood pressure and HR (P<0.05). There were no statistically significant differences between males and females for the changes in any outcome variables. A one-way ANOVA showed no significant differences between non-exercisers and exercisers for changes in BW (0.86±0.2 vs 0.70±0.1 kg), BF% (0.7±0.3% vs 0.3±0.3%) or BMI (0.3±0.1 vs 0.2±0.1 kg/m2), respectively, (Figure 1). However, there was a trend (P=0.07) for a difference between non-exercisers and exercisers for the change in SBP. Non-exercisers showed an increase of 4.8±1.6 mm Hg, whereas exercisers showed a meager 0.3±1.6 mm Hg increase.
Figure 2 shows the change in BW, BF%, SBP and diastolic blood pressure according to BMI category (<25 kg/m2 (normal weight) BMI 25–29.9 kg/m2 (overweight) and BMI 30 kg/m2 (obese)). There were five participants whose BMI was <18.5 kg/m2 (mean BMI of 17.9±0.2 kg/m2), which were included in the BMI <25 kg/m2 category. Obese participants had significantly greater increases in BF% compared with normal weight participants (1.6±0.5% vs 0.2±0.2%, respectively, P<0.05), and there was a trend (P=0.07) for greater BF% gain in obese vs overweight participants (1.6±0.5% vs 0.5±0.4%, respectively). Only obese participants showed a significant increase in SBP (6.6±2.3 mm Hg, P<0.01) and there was also a trend (P=0.06) for a greater increase in SBP in obese vs normal weight participants (6.6±2.3 vs 0.7±1.6 mm Hg, respectively). No other differences between the BMI categories were observed.
In order to explore the factors that impact changes in BW and BF%, we performed additional analyses. First, we ran a correlation analysis to determine which variables were associated with the changes in BF% and BW (Table 2). Change in BW was significantly correlated with baseline BW (r=0.203, P=0.013), but not with baseline BF%, change in BF% or age (ns). Similarly, change in BF% was also positively correlated with baseline BW (r=0.333, P<0.001) but not with baseline BF% (ns). There was a trend for an association between change in BF% and age (r=0.143, P=0.08). We also performed a two-way analysis of variance, with gender and exercise as main factors and baseline BW as a covariate to see whether these variables affected changes in BW or BF%. For BF% change, baseline BW was identified as a significant covariate (P=0.046) with no significant main effect of exercise or gender and no interaction effects. However, baseline BW was not a significant covariate for the change in BW (P=0.14). Similar to the change in BF% model, neither gender nor exercise were significant factors.
Finally, a stepwise multiple linear regression model (both ways, using Akaike Information Criterion (AIC) criterion in R statistical software, stats package (version 2.15.1, R Core Team R Foundation for Statistical Computing) was developed to identify significant predictors of change in BW and change in BF%. Age, baseline and post-holiday BW, BF% and BMI were used as independent contributing variables. Not surprisingly, baseline and post-holiday BW and BF% were the best predictors of change in BW and BF%, respectively, each contributing to ∼50% of the change (as estimated by dividing the squared individual correlation by the model R2 and multiplying by 100).20 Age and BMI were not significant predictors of changes in BW or BF%.
As initial BW has been shown to be a significant covariate in change in BF%, the participants were divided into three groups as tertiles based on baseline BW (n=49, 49 and 50 in each tertile) to identify differences as a function of exercise and gender (Table 3). Tertile 1 represents the lowest 33%, tertile 2 the intermediate and tertile 3 the highest 33% of the sample as determined by baseline BW. As a whole, ∼50% of males and females reported that their current PA matched or exceeded American Heart Association’s guidelines for weekly PA.18 This was significantly higher than the Centers for Disease Control and Prevention’s national average, which reports that only around ∼25% of the population meets recommended PA levels (χ2, P<0.001).21 Tertile 1 and tertile 2 also had a significantly higher proportion of exercisers than the national average (P<0.001). The only exception was tertile 3, which had a nonsignificant difference from the reported national average.21
A three-way, three-factor (baseline BW tertile × gender × exercise) ANOVA indicated baseline BW tertile was a significant factor in BF% change (P<0.023), but whether or not the participants exercised did not impact BF% gain, nor did their gender. Tertile 3, which had the highest baseline BW, and a sample distribution of exercisers to non-exercisers ratio that did not differ from the national average,21 had a significantly higher BF% change compared with tertile 1 (P<0.001). No significant differences were found between tertiles 1 and 2, or tertiles 2 and 3. No other outcome variables were significantly affected by baseline BW, gender or reported PA.
The primary findings from our study suggest the holiday season results in significant increases in BW and BF% (0.78±0.1 kg and 0.5±0.2%, respectively). Obese participants showed a significantly greater increase in BF% compared with normal weight participants and trended for a greater increase compared with overweight participants. Furthermore, it seems initial BW, not exercise, significantly impacted gain in BW and BF%, and exercise was not protective against BW gain during the holiday season.
Our data compare favorably with previous findings on holiday weight gain. Yanovski et al.15 reported weight gain from mid-November to early January of 0.37±0.1 kg. Although their reported weight gain was less than that in our study (0.37 vs 0.78 kg), they showed holiday weight gain was not reversed during the remainder of the year. Other studies have focused on weight gain during either the thanksgiving holiday or Christmas itself. Andersson and Rössner22 measured BW over the Christmas holiday and found a mean increase of 0.5 kg. Similarly, Hull et al.23 found a significant increase in BW of 0.5 kg over thanksgiving. Taken together, these findings suggest significant weight gain does occur during this relatively short period of time. These modest increases in BW and BF% do not appear to be reversed during the remainder of the year and may lead to cumulative weight gain over time, thus contributing to overweight and eventually obesity.15
Yanovski et al.15 also found a trend toward a greater holiday weight gain as the degree of overweight increased. These findings are consistent with Hull et al.24 who found a significant 1.0 kg BW gain in the overweight/obese group compared with a nonsignificant 0.2 kg gain in the normal weight group. Although we found similar BW gain in all BMI groups, our obese participants had significantly greater increases in BF% compared with normal weight participants and a trend for greater increases compared with overweight participants. Therefore, these results suggest the likelihood of gaining more BW or BF% increases as the degree of overweight increases, furthermore increasing the risk of chronic disease in this population.
Interestingly, we found that initial BW, not initial BF%, age, gender or exercise, was significantly correlated with changes in BW and BF%. All of the participants showed significant increases in SBP and diastolic blood pressure despite only having modest weight gain. However, regular PA may have a protective effect against adverse changes in BP as we saw a trend for an increase in SBP for non-exercisers, which was not present in exercisers. This was also shown previously where low doses of PA actually decreased BP.25
To date, our study is the only study performed over the holiday season to examine the effects of regular PA on BW and BF% changes. Our exercisers reported an average of 4.8±0.6 h per week over the holiday season, which exceeds the American Heart Association’s recommendation of 150 min per week (2.5 h per week). We were somewhat disappointed that PA was not a significant covariate and was not protective against BW gain. We can only speculate that with more participants or a more robust measure of PA, we may have found significant differences between the two groups with respect to changes in BW, BF% and BP. Using data from our study, we calculated the effect size. We were powered for identifying differences between pre- to post-holiday season. However, this effect size estimated that we would need 2296 participants to identify differences between exerciser and non-exercisers. Therefore, we could not anticipate recruiting 2296 participants for this study to be adequately powered for non-exercisers vs exercisers. Although using different validated PA questionnaires may have been more sensitive to detect differences between exercisers and non-exercisers, based on the very small differences between our groups, it appears more likely that these outcome variables simply do not differ between these groups. Conversely, it is equally possible that total energy intake exceeded any potential energy expenditure benefits of obtaining daily PA. This is an important message for the general public. Simply getting daily PA does not mean an individual will not be susceptible to holiday weight gain. It is also possible that participants overestimated self-reported PA that could impact the results of our study. This is a potential problem or limitation with any self-report data. However, based on the data collected in this study, all individuals need to take caution and avoid excessive energy intake during this time of the year.
Our results also indicate that pre-BW was a significant factor over exercise for gain in BW and BF%. However, tertiles 1 and 2 of our study sample showed no significant gain in BF% and also had a greater proportion of exercisers than the national average, tertile 3, which did not differ from the national average for PA gained significantly higher BF%.21 Therefore, although based on classifying participants according to their reported PA habits did not show significance in the ANOVA, the exerciser: non-exerciser ratio in these tertiles indicates that exercise might impact BF% gain over a longer duration, as current obesity prevalence rates in the United States are projected to increase,8 and one of the two primary causes for this is reduction in PA.26
Some limitations of the current investigation include using self-reported PA data, which relies on the participant’s memory and accuracy of reporting. Both under-reporting PA and over-reporting PA could affect the results of this study. Future studies should include objective measures to quantify PA, such as accelerometers or HR monitors. Another limitation is that we used bioelectrical impedance analysis to assess BF%. Although this is not the gold standard for assessing body composition, when the appropriate prediction equation is chosen, and hydration status and exercise are controlled for it is a validated measurement too with a s.e.±3–4%.27, 28 A final limitation was we were unable to perform long-term, follow-up measurements from our participants in order to collect data on how their BW further changed throughout the year. Therefore, we can only speculate on this weight retention based off of previous work showing that participants maintained their holiday weight gain 1 year later.15
In conclusion, significant increases in BW, BF%, BP and resting HR were observed in healthy adults during the holiday season. Participants showed an average BW gain of 0.78 kg, although the change in BW ranged from –2.6 kg to 6.3 kg. Our findings suggest the likelihood of gaining more body fat increases as the degree of overweight increases, and that initial BW significantly predicted BF% and BW gain. Finally, PA was not a significant predictor for change in BW or BF% and was not protective against holiday weight gain. The holiday season may have adverse effects on the health of an individual, and future intervention studies on weight loss or weight maintenance should place a special emphasis on the holiday season.