Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Body composition, energy expenditure and physical activity

Effects of exercise during the holiday season on changes in body weight, body composition and blood pressure




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

Study design

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.

Table 1 Pre- to post-holiday season variables and changes in measured variables
Figure 1

A one-way ANOVA was used to test differences in the changes for each outcome variable in non-exercisers and exercisers. No significant differences were found between groups. ‘*’ denotes a trend for a difference in change between non-exercisers and exercisers (P=0.07). Values are means±s.e.

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.

Figure 2

A one-way ANOVA was used to test differences in the changes for each outcome variable by BMI category. Participants in all BMI categories had significant weight gain during the holiday season. The only between group difference was for the changes in BF%. ‘*’ denotes significant change from baseline values of at least P<0.05. ‘a’ denotes significant differences between obese and normal weight groups (P<0.05). ‘b’ denotes a trend for a difference between obese and overweight groups (P=0.07). ‘c’ denotes a trend for a difference between obese and normal weight groups (P=0.06). ‘d’ denotes a trend for a difference from baseline values (P=0.07). Values are means±s.e.

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.

Table 2 Correlation analysis between anthropometric variables

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

Table 3 Change in outcome variables in the three tertiles of visit 1 BW

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.


  1. 1

    Hill JO, Wyatt HR, Reed GW, Peters JC . Obesity and the environment: where do we go from here? Science 2003; 299: 853–855.

    CAS  Article  Google Scholar 

  2. 2

    Flegal KM, Troiano RP . Changes in the distribution of body mass index of adults and children in the US population. Int J Obes Relat Metab Disord 2000; 24: 807–818.

    CAS  Article  Google Scholar 

  3. 3

    Ogden CL, Fryar CD, Carroll MD, Flegal KM . Mean body weight, height, and body mass index, United States 1960-2002. Adv Data 2004; 347: 1–17.

    Google Scholar 

  4. 4

    Brown WJ, Williams L, Ford JH, Ball K, Dobson AJ . Identifying the energy gap: magnitude and determinants of 5-year weight gain in midage women. Obes Res 2005; 13: 1431–1441.

    Article  Google Scholar 

  5. 5

    Hill JO . Can a small-changes approach help address the obesity epidemic? A report of the Joint Task Force of the American Society for Nutrition, Institute of Food Technologists, and International Food Information Council. Am J Clin Nutr 2009; 89: 477–484.

    CAS  Article  Google Scholar 

  6. 6

    Lewis CE, Jacobs DR Jr, McCreath H, Kiefe CI, Schreiner PJ, Smith DE et al. Weight gain continues in the 1990 s: 10-year trends in weight and overweight from the CARDIA study. Coronary artery risk development in young adults. Am J Epidemiol 2000; 151: 1172–1181.

    CAS  Article  Google Scholar 

  7. 7

    Finkelstein EA, Khavjou OA, Thompson H, Trogdon JG, Pan L, Sherry B et al. Obesity and severe obesity forecasts through 2030. Am J Prev Med 2012; 42: 563–570.

    Article  Google Scholar 

  8. 8

    Wang Y, Beydoun MA, Liang L, Caballero B, Kumanyika SK . Will all Americans become overweight or obese? estimating the progression and cost of the US obesity epidemic. Obesity (Silver Spring) 2008; 16: 2323–2330.

    Article  Google Scholar 

  9. 9

    Wang SS, Brownell KD . Public policy and obesity: the need to marry science with advocacy. Psychiatr Clin North Am 2005; 28: 235–252.

    Article  Google Scholar 

  10. 10

    Nestle M . Food marketing and childhood obesity: a matter of policy. N Engl J Med 2006; 354: 2527–2529.

    CAS  Article  Google Scholar 

  11. 11

    Nestle M, Jacobson MF . Halting the obesity epidemic: a public health policy approach. Public Health Rep 2000; 115: 12–24.

    CAS  Article  Google Scholar 

  12. 12

    Hill JO, Peters JC . Environmental contributions to the obesity epidemic. Science 1998; 280: 1371–1374.

    CAS  Article  Google Scholar 

  13. 13

    Peters JC, Wyatt HR, Donahoo WT, Hill JO . From instinct to intellect: the challenge of maintaining healthy weight in the modern world. Obes Rev 2002; 3: 69–74.

    CAS  Article  Google Scholar 

  14. 14

    Phelan S, Wing RR, Raynor HA, Dibello J, Nedeau K, Peng W . Holiday weight management by successful weight losers and normal weight individuals. J Consult Clin Psychol 2008; 76: 442–448.

    Article  Google Scholar 

  15. 15

    Yanovski JA, Yanovski SZ, Sovik KN, Nguyen TT, O'Neil PM, Sebring NG . A prospective study of holiday weight gain. N Engl J Med 2000; 342: 861–867.

    CAS  Article  Google Scholar 

  16. 16

    Ma Y, Olendzki BC, Li W, Hafner AR, Chiriboga D, Hebert JR et al. Seasonal variation in food intake, physical activity, and body weight in a predominantly overweight population. Eur J Clin Nutr 2006; 60: 519–528.

    CAS  Article  Google Scholar 

  17. 17

    Cook CM, Subar AF, Troiano RP, Schoeller DA . Relation between holiday weight gain and total energy expenditure among 40- to 69-y-old men and women (OPEN study). Am J Clin Nutr 2012; 95: 726–731.

    CAS  Article  Google Scholar 

  18. 18

    Haskell WL, Lee IM, Pate RR, Powell KE, Blair SN, Franklin BA et al. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation 2007; 116: 1081–1093.

    Article  Google Scholar 

  19. 19

    Shapiro SS, Wilk MB . An analysis of variance test for normality (complete samples). Biometrika 1965; 52: 591–611.

    Article  Google Scholar 

  20. 20

    Johnson JW . A heuristic method for estimating the relative weight of predictor variables in multiple regression. Multivar Behav Res 2000; 35: 1–19.

    CAS  Article  Google Scholar 

  21. 21

    Centers for Disease Control and Prevention (CDC). Behavioral risk factor surveillance system survey data. U.S. Department of Health of Health and Human Services, Centers for Disease Control and Prevention: Atlanta, GA, 2011.

  22. 22

    Andersson I, Rossner S . The Christmas factor in obesity therapy. Int J Obes Relat Metab Disord 1992; 16: 1013–1015.

    CAS  PubMed  Google Scholar 

  23. 23

    Hull HR, Radley D, Dinger MK, Fields DA . The effect of the Thanksgiving holiday on weight gain. Nutr J 2006; 5: 29.

    Article  Google Scholar 

  24. 24

    Hull HR, Hester CN, Fields DA . The effect of the holiday season on body weight and composition in college students. Nutr Metab (Lond) 2006; 3: 44.

    Article  Google Scholar 

  25. 25

    Asikainen TM, Miilunpalo S, Kukkonen-Harjula K, Nenonen A, Pasanen M, Rinne M et al. Walking trials in postmenopausal women: effect of low doses of exercise and exercise fractionization on coronary risk factors. Scand J Med Sci Sports 2003; 13: 284–292.

    Article  Google Scholar 

  26. 26

    World Health Organization. Obesity and overweight fact sheet (cited 11 November 2012). Available from

  27. 27

    Eckerson JM, Stout JR, Housh TJ, Johnson GO . Validity of bioelectrical impedance equations for estimating percent fat in males. Med Sci Sports Exerc 1996; 28: 523–530.

    CAS  Article  Google Scholar 

  28. 28

    Wang JG, Zhang Y, Chen HE, Li Y, Cheng XG, Xu L et al. Comparison of two bioelectrical impedance analysis devices with dual energy X-ray absorptiometry and magnetic resonance imaging in the estimation of body composition. J Strength Cond Res 2013; 27: 236–243.

    CAS  Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to J A Cooper.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Stevenson, J., Krishnan, S., Stoner, M. et al. Effects of exercise during the holiday season on changes in body weight, body composition and blood pressure. Eur J Clin Nutr 67, 944–949 (2013).

Download citation


  • holiday
  • weight gain
  • body composition
  • exercise
  • physical activity

Further reading


Quick links