Article

The Effect of Holiday Weight Gain on Body Weight.

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Abstract

The topic of holiday weight gain has been a frequent subject of the lay media, however, scientific interest has only been recent. Multiple studies in Western societies have reported average weight gains among adults during the period between mid-November and mid-January that were about 0.5kg. The range in individual weight changes was large, however, and the already overweight and obese gain more weight than those who are healthy weight. When the average gain across the year was also measured, the holiday weight was the major contributor to annual excess weight gain. Efforts patterned to increase awareness to energy balance and body weight have been shown to be successful at reducing such gain. An exception to holiday weight gain being a major contributor to annual excess gain has been children, in whom summer weight gains have been observed to be the major contributor to average excess weight gain.

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... To identify records relevant to our research aim, a comprehensive search strategy was developed using the PEO (Population; Exposure; Outcome) format. The search terms and subject headings were derived from narrative reviews [12,16] and landmark studies published on the topic [11,[17][18][19]. The three search concepts included (i) healthy populations (ii) feasting, celebrations and holidays and (iii) weightrelated outcomes. ...
... Four narrative reviews have previously identified holidays as periods of weight gain [12][13][14]16]. Using systematic scoping methodology, we have been able to confirm this finding and broaden the evidence-base to other weight-related measures including BMI, percentage body fat, waist circumference and waist-to-hip ratio. ...
... Furthermore, although previous narrative reviews broadly examined holiday periods (including school and summer holidays) that often include festive periods, we focused only on the festive periods themselves. Thus, contrary to conclusions made by previous reviews [12,16], weight gain over festive periods was observed in children Table 2 Case studies from Australia, Fiji and Mexico describing festive periods and mechanisms via which they may contribute to unhealthy eating Extended celebration period: ...
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Purpose of review: Whilst evidence indicates that weight gain occurs over holidays, the contribution of specific festive periods and celebrations to eating behaviour and weight gain is unclear. We aimed to synthesise literature on how festive periods and celebrations contribute to population weight gain and weight-related outcomes. Recent findings: Thirty-nine studies examining (i) body weight changes, (ii) determinants of eating behaviour or (iii) weight-gain prevention interventions during festive periods were systematically reviewed. Of the 23 observational studies examining changes in body weight during festive periods, 70% found significant increases (mean 0.7 kg). Only four studies investigated exposure to food cues and overeating during these periods, with heterogeneous results. All six intervention studies found that weight gain can be mitigated by self-weighing/self-monitoring and intermittent fasting. Interventions targeting festive periods could have a significant impact on population weight gain. The scalability and sustainability of such interventions require further investigation, as do the broader socioecological factors driving unhealthy eating during festive periods.
... The costs of treating obesity are placing intense pressure on health services 4 . Furthermore, the prevalence of obesity has doubled in over 70 countries since 1980 5 and an annual increase in weight among populations is reported 6 . Studies demonstrate that this weight increase is largely attributable to discrete episodes, such as holiday periods 7 , rather than a sustained excess of daily energy intake. ...
... Another review published in 2014 by Schoeller and colleagues 6 , analysed data from eight studies tracking body weight over the year 64, 66, 70, 72-74, 79, 80 . Methods used in the selection of studies were not specified. ...
... Furthermore, the Christmas holiday period saw weight increase in participants across all three counties. Narrative and systematic reviews also corroborate this finding that weight increases during holiday occasions on average by between 0.4kg to 0.9kg 6,7 ...
Thesis
Weight is increasing in the population and holidays, such as Christmas, have been identified as high-risk periods. This thesis presents the development of a behavioural intervention to prevent weight gain over the Christmas period, its evaluation in a Randomised Controlled Trial (The Winter Weight Watch study), and an exploration of participant experiences of the intervention. The possible mechanisms of action of the intervention are also explored. The intervention consisted of encouragement to regularly self-weigh and record weight, physical activity calorie equivalent (PACE) information about commonly consumed festive foods and drinks and weight management tips. The hypothesised main mechanism of action was that each component would promote restraint of energy intake, preventing weight gain over Christmas. The RCT showed the intervention to be effective in preventing weight gain. At follow up the difference in weight between intervention and control groups (adjusting for baseline weight) was -0.49kg. Conscious energy restraint scores increased in the intervention group. The qualitative study showed that participants found the concept of weight gain prevention at Christmas acceptable. Self-weighing and PACE information were key drivers in encouraging restraint of energy intake. PACE information mainly prompted participants to restrain energy intake rather than increase physical activity In conclusion, the developed intervention prevented weight gain during the Christmas period and was acceptable to participants. PACE information and self-weighing were found to be key drivers of self-regulatory behaviours. These findings hold promise for preventing weight gain during high risk periods.
... A somewhat similar observation can be made by looking at holiday weight gain, a phenomenon that occurs on a seasonal basis in high-income countries [46,47]. These overeating-promoting periods have been reported to give rise to a body weight gain of approximately 0.5 kg on average [48,49] (Fig 3), and just like the traditional rituals, holiday weight gain varies between individuals. Interestingly, it seems to affect primarily the part of the population that is already overweight [49], suggesting that a defective defense against overfeeding is involved in the etiology of obesity. ...
... These overeating-promoting periods have been reported to give rise to a body weight gain of approximately 0.5 kg on average [48,49] (Fig 3), and just like the traditional rituals, holiday weight gain varies between individuals. Interestingly, it seems to affect primarily the part of the population that is already overweight [49], suggesting that a defective defense against overfeeding is involved in the etiology of obesity. ...
... Another study, however, showed that not all of the gained weight is lost following overfeeding [54]. This observation is supported by studies of holiday weight gain [48,49] (Fig 3) and might be explained by homeostatic inaccuracies, which lead to an insufficient lowering of food intake in most people following overfeeding [58,59]. Another explanation for the variation in response to overfeeding is the heterogeneous protocols used in the studies, including differences in the duration of overfeeding [60,61], total caloric surplus [58], and diet composition [62]. ...
Article
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Human biology has evolved to keep body fat within a range that supports survival. During the last 25 years, obesity biologists have uncovered key aspects of physiology that prevent fat mass from becoming too low. In contrast, the mechanisms that counteract excessive adipose expansion are largely unknown. Evidence dating back to the 1950s suggests the existence of a blood-borne molecule that defends against weight gain. In this article, we discuss the research supporting an “unidentified factor of overfeeding” and models that explain its role in body weight control. If it exists, revealing the identity of this factor could end a long-lasting enigma of energy balance regulation and facilitate a much-needed breakthrough in the pharmacological treatment of obesity.
... As the prevalence of obesity continues to rise in the United States [1], considerable efforts are made to understand the environmental factors that impact weight gain. Multiple longitudinal studies indicate that small seasonal fluctuations in body weight, especially between the holiday months of mid-November and mid-January (~8 weeks), contribute to more than half of the weight gained annually [2][3][4][5][6][7][8][9]. Most importantly, this weight is not subsequently lost [4] and can lead to a substantial increase of 15-30 pounds over multiple decades. ...
... Most importantly, this weight is not subsequently lost [4] and can lead to a substantial increase of 15-30 pounds over multiple decades. In particular, obese adults are vulnerable to gaining more weight during this critical time [4,5]. Self-reported data imply that this weight increase could be due to excess energy intake and/or lower physical activity [4]. ...
... Statistical analyses were performed using SPSS (version 24; SPSS Inc., Chicago, IL, USA). Sample size was based on the prior studies [5,6] to detect an increase of 0.8 ± 1.4 kg in body weight with power of 80% alpha of 5%. For changes in body weight, body composition, and other anthropometric measures, we calculated the difference between V1 and V3 for the pre-holiday period, and between V3 and V6 for the holiday period. ...
Article
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The winter holiday season in the United States, which spans mid-November to mid-January, contributes to over half of annual body weight gain. Although self-reported data have linked this weight change to both increased energy intake and reduced physical activity, objective techniques have never been used; and thus, the actual cause of holiday weight gain is controversial. Here, we aimed to determine changes in components of energy balance leading to the holiday weight gain. Body weight change was compared between the pre-holiday (mid-September to mid-November) and the holiday period (mid-November to early January). Total energy expenditure (TEE) was measured using doubly labeled water during holiday time (early to mid-December). Subjective (ratings) and physiological (appetite-regulating hormones) measures of appetite, eating-away-from-home frequency, and incentive salience of food pictures were also evaluated. In 23 obese adults (87% female), body weight change during the holidays (0.41 ± 0.42 kg) was significantly higher (P = 0.02) than the body weight change during the pre-holiday period (−0.86 ± 0.42 kg). TEE was unchanged during the two periods, suggesting no role of energy expenditure on weight gain. However, participants reported lower satisfaction after a meal pre-load which was significantly correlated with increased body weight during the holiday period. An increase in number of episodes of eating at sit-down restaurants was also reported during that period. Overall, these changing behaviors were supported by a non-significant increase in energy intake (+80 kcal/day, P = 0.07) observed during the study holiday period. We conclude that a decrease in energy expenditure does not result in the weight increase, but that increase in food intake is the more likely cause. Our data imply that compromised internal satiety mechanisms in presence of external food cues and diet-related behavioral variables during the holidays may influence weight gain.
... During this time, multiple holiday-related factors are thought to influence weight-related behaviors including increased stress, greater frequency of social gatherings with high-calorie foods and drinks, and competing demands limiting opportunities to engage in physical activity. 1 This period of time represents a sociocultural shift in the environment that can make it challenging to maintain a healthy lifestyle and prevent weight gain using individual behavior change strategies, with studies consistently documenting an average weight gain of 1 kg (or approximately 2 pounds) during this time. [1][2][3] Certain groups may be especially at risk for larger holiday weight gains, including those who have a body mass index (BMI) greater than 25. 3 Likewise, those who are maintaining a significant weight loss utilize more effort and resources to manage holiday weight gain compared to individuals without a history of obesity. ...
... 1 This period of time represents a sociocultural shift in the environment that can make it challenging to maintain a healthy lifestyle and prevent weight gain using individual behavior change strategies, with studies consistently documenting an average weight gain of 1 kg (or approximately 2 pounds) during this time. [1][2][3] Certain groups may be especially at risk for larger holiday weight gains, including those who have a body mass index (BMI) greater than 25. 3 Likewise, those who are maintaining a significant weight loss utilize more effort and resources to manage holiday weight gain compared to individuals without a history of obesity. 4 The subtle weight gain observed during this time is not without health implications, as even the modest weight gains are not typically reversed following the holiday season and likely contribute to an accumulation of weight throughout the year. ...
... Consistent with previous research, participants gained an average of 1-2 pounds (0.66 kg on average) from before to after the holiday season. 1,4,8 This amount of weight gain has been observed across a wide range of samples 1,8 including previous research within the NWCR. 4 The findings indicate that a greater number of strategies were associated with better weight control. This finding is consistent with behavioral weight management protocols which rely heavily on self-monitoring but also teach individuals a variety of cognitive behavioral tools to help individuals achieve their weight loss goals. ...
Article
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Background Holidays are challenging for weight control and are consistently associated with weight gain. Managing holiday weight gain may be especially challenging for individuals with higher body weight or a history of overweight/obesity. The current study evaluated how individuals with a history of successful weight loss plan for the holiday season and how use of weight control strategies was associated with weight change. Methods A subgroup of participants in the National Weight Control Registry (NWCR) were asked to complete a survey before (November) and after the holidays (January). At pre‐holiday, participants reported height, weight, weight goals for the holiday season (lose, maintain, minimize gains, or gain), and selected the top three weight control strategies they planned to use (from a list of 18). Post‐holiday, participants reported weight and how often (frequently, infrequently, not at all) they used each of the 18 strategies throughout the holidays. Results Individuals who completed both surveys were included in the analysis (n=683; 69% female, 93% white, 54.6 years (SD: 13.2), 26.9 kg/m2 (SD: 5.5)). Pre‐holiday, 64% of participants were currently trying to lose weight. Only 35% of the sample wanted to continue losing weight during the holiday season. The most common strategies individuals planned to use during the holiday season were evidence‐based (maintaining exercise, monitoring portions, track foods, self‐weighing). Participants gained 0.66 kilograms (SD: 1.85) from pre‐ to post‐holiday and reported using an average of 12/18 strategies. Greater number of strategies was associated with less weight gain (F (1, 670)=4.28, p=.04). Daily self‐weighing (p=.03) and prioritizing food choices (p=.02) were individually associated with less weight gain. Discussion Participants in the NWCR entered the holiday season with a variety of goals for their weight and used many different strategies to control their weight. Having a wider range of strategies may be helpful to navigate the challenges to weight control during the holidays. This article is protected by copyright. All rights reserved.
... Supporting these speculations, early investigations of a cross-sectional dataset showed that 22% of adults self-reported gaining 5-10 lbs of body weight during self-quarantine [1]. The current situation of extended lockdown is similar to but longer than the winter holiday period (6-8 weeks), which contributes to a modest average weight gain of 0.5 kg [2]. This seemingly small change in body weight during the holidays contributes to more than half of annual weight gain in adults. ...
... This seemingly small change in body weight during the holidays contributes to more than half of annual weight gain in adults. More importantly, this weight is not subsequently lost [2][3][4][5][6] and can lead to a substantial increase over multiple decades. If weight gain during the pandemic follows the same trajectory as weight increases reported during the holiday period, pandemic-related weight gain will be an additional major contributor to the annual weight gain in the year 2020 and possibly even in 2021. ...
Article
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Cross-sectional analyses have shown increased obesogenic behaviors and a potential for weight gain during COVID-19 related peak-lockdown (March-May 2020), but longitudinal data are lacking. This study assessed longitudinal changes in body weight and lifestyle behaviors in the US adults during the pandemic. Methods: We used Qualtrics survey to collect self-reported data on body weight, dietary, physical activity, and psychological variables (n = 727) during the peak-lockdown (April/May) and at post-lockdown (September/October). Peak-lockdown weight data were categorized based on the magnitude of weight gained, maintained, or lost, and behavioral differences were examined between categories at two time points. Results: Body weight increased (+0.62 kg; p < 0.05) at the post-lockdown period. The body mass index also increased (26.38 ± 5.98 kg/m2 vs. 26.12 ± 5.81 kg/m2; p < 0.01) at the post-lockdown period vs. peak-lockdown period. Close to 40% of participants reported gaining either 1-4 lbs or >5 lbs of body weight during the peak-lockdown, while 18.2% lost weight. Weight-gainers engaged in riskier dietary behaviors such as frequent ultra-processed food intake (p < 0.01) and snacking (p < 0.001), were less active, and reported high stress and less craving control during peak-lockdown. Of those gaining >5 lbs, 33% continued to gain weight after the lockdown eased, while 28% maintain higher body weight. In weight-gainers, takeout meal frequency increased, and high ultra-processed food intake and stress, and low craving control continued to persist after the lockdown eased. Conclusion: We show that the COVID-19 lockdown periods disrupted weight management among many Americans and that associated health effects are likely to persist.
... Although the average weight gain of 0.5 ± 2.8 kg in this study was small (which was similar to the previous study on holiday weight gain [31]), the standard deviation of 2.8 kg was large, which means the weight gain of some individuals was relatively large, and the maximum weight gain was 20 kg. As evident from previous research, small changes in body weight and fat accumulation in relatively short periods can become permanent and lead to substantial weight gain over time [32][33][34], and changes in eating behavior are likely the main driver toward energy surplus [35]. Similarly, in this Chinese study, the increase in food intake was the most correlated with weight gain among all the factors, with the increase in staple food, snacks/beverages, and livestock and poultry intake ranking the top three. ...
... Contrary to previous studies that people with overweight and obesity were more likely to gain weight [17,35], this study yielded an unusual finding, which was that peo-ple with overweight and obesity gained significantly less weight than normal people during the COVID-19 outbreak and people with normal weight before the outbreak had higher food intake and less physical activity during the outbreak than those with overweight or obesity. We speculated that people with overweight and obesity had been accustomed to the unhealthy lifestyle of eating more and exercising less before, so the change was not obvious during the outbreak, while normal-weight people were more likely to passively change from the healthy lifestyle to unhealthy, leading to weight gain. ...
Article
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In February 2020, a novel coronavirus (SARS-COV2) broke out in Wuhan city of China. The Chinese government decisively imposed nationwide confinement. This study comprised a structured, online questionnaire, based on 40 items inquiring about socio-demographic information and anthropometric data (reporting weight and height), as well as changes in food intake, physical activity, and sleep during the COVID-19 outbreak. Questionnaires were distributed to residents of Jiangsu and other provinces from 29 March to 5 April. A total of 889 respondents were included, aged between 16 and 70 years (61% females). There was a significant increase in total food intake by 9.8% and a slight increase by 29.2% of respondents, and a significant decrease in physical activity by 31.5% and a slight decrease by 23.4% of respondents, especially in snacks and drinks, and outdoor activities. The rate of weight gain in the total population was 30.6% and the average weight gain was 0.5 ± 2.8 kg. The main factors contributing to weight gain were increased food intake and reduced physical activity. Additionally, normal-weight people were more likely to gain weight than people with overweight/obesity during the COVID-19 confinement. This study provided a good warning and educational reference value on lifestyle changes during the COVID-19 confinement.
... These initial weight trajectories during COVID-19 are associated with several socio-demographic, COVID-19-related and behavioural factors including age, gender, initial BMI, pandemic living and working conditions, diet, physical activity and alcohol intake [7,9,10,12,[18][19][20][21]. Short-term shifts in health behaviours can result in small, yet meaningful changes in bodyweight, as seen with seasonal holiday weight gain during the winter months, which accounts for a large proportion of annual weight gain [22,23]. If these weight/BMI changes seen during the COVID-19 pandemic persist or continue to shift with changes in lockdown restrictions, these changes in health risk could have long-lasting impacts on population health. ...
... We also report the percentage of the sample increasing or decreasing (i) weight or (ii) BMI between timepoints, where an increase or decrease was defined as more than a 0.5kg or 0.5kg/m 2 change, respectively, from the reference timepoint (baseline or 3-months follow-up survey). 0.5kg change was used as the cut-off in weight as it has been previously reported as the average seasonal weight gain, which has parallels to the COVID-19 pandemic [22,23]. 0.5kg/m 2 change was used as the cut-off in BMI as this has been previously used to define an unchanged BMI [37]. ...
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Background COVID-19-related restrictions impacted weight and weight-related factors during the initial months of the pandemic. However, longitudinal analyses are scarce. Methods An online, longitudinal study was conducted among self-selected UK adults (n=1,818), involving three surveys during 2020 (May-June, August-September, November-December), covering height, weight and sociodemographic, COVID-19-related and behavioural measures. Data were analysed using generalised estimating equations. Results Self-reported average weight and body mass index (BMI) significantly increased from May-June to August-September (74.95kg to 75.33kg, 26.22kg/m2 to 26.36kg/m2, both p<0.001), and then significantly decreased to November-December (to 75.06kg, 26.27kg/m2, both p<0.01), comparable to May-June levels (p=0.274/0.204). However, there was great interindividual variation, with 37.0%/26.7% reporting an increase and 34.5%/26.3% reporting a decrease in weight/BMI greater than 0.5kg/0.5kg/m2, respectively from May-June to November-December. The average weight/BMI increase was 3.64kg (95% confidence interval: 3.32,3.97)/1.64kg/m2 (1.49,1.79), and the average weight/BMI decrease was 3.59kg (3.34,3.85)/1.53kg/m2 (1.42,1.63). In fully adjusted models, increase in weight/BMI across surveys was significantly negatively associated with initial BMI, and positively associated with monthly high fat, salt and sugar (HFSS) snacks intake and alcohol consumption, and for BMI only, older age. However, associations were time-varying, such that lower initial BMI, higher HFSS snacks intake and high-risk alcohol consumption were associated with maintenance of increased weight/BMI from August-September to November-December. Conclusion The average weight/BMI of UK adults increased during the early pandemic months, before returning to baseline levels in November-December 2020. However, this masks substantial interindividual variation in weight/BMI trajectories, indicating vulnerabilities associated with changes in food and alcohol consumption throughout the pandemic.
... One recent longitudinal study using Bluetooth connected weight scales reported weight gain of 0.27 kg every 10 days during mandates (7). Two observational studies showed US adults reported weight gain during the pandemic at a rate higher than typically seen in US adults (0.3 kg per year) (6, 8,9,10). However, these studies were cross-sectional or retrospective in nature and/or did not assess possible predictors of weight gain. ...
... This article is protected by copyright. All rights reserved weight gain of 0.4 kg (10,19). If the observed rate in the current study were to continue, this would far exceed typical weight gain in a single year. ...
Article
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Objective The purpose of this study was to prospectively examine the effect of state stay-at-home mandates on weight of US adults and by BMI over 3 months during COVID-19. Methods US adults completed an online questionnaire containing demographics, weight, physical activity, sedentary time, fruit/vegetable intake, depressive symptoms, stress, and sleep at baseline (May 2020) and after 3-months (August 2020). Results Participants gained 0.6 kg (76.7 kg to 77.3 kg, p = 0.002). 26% of those with obesity gained > 2 kg as compared to 14.8% of normal weight (p < 0.001). 53.3% of individuals with obesity maintained weight within 2 kg compared to 72.5% of normal weight (p < 0.001). Greater weight gain was related to longer stay-at-home mandates (β = 0.078, p = 0.010), lower baseline minutes of physical activity per day (β = -0.107, p = 0.004), greater declines in minutes of physical activity per day (β = -0.076, p = 0.026), depressive symptoms (β = 0.098, p = 0.034), and greater increases in time preparing food (β = 0.075, p = 0.031). Conclusions US adults gained weight and stay-at-home mandates were associated with atypical weight gain and greater reported weight gain in individuals with obesity over 3-months.
... Nowadays, adults tend to gain weight at $0.4-1 kg per year [14][15][16], overweight and obese people tend to gain even more than those with a normal BMI [16]. In our study, participants in the normal weight group gained 0.5 kg per year on average, even though they had the same lifestyle counselling as the overweight/ obese participants. ...
... Nowadays, adults tend to gain weight at $0.4-1 kg per year [14][15][16], overweight and obese people tend to gain even more than those with a normal BMI [16]. In our study, participants in the normal weight group gained 0.5 kg per year on average, even though they had the same lifestyle counselling as the overweight/ obese participants. ...
Article
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Objective Overweight and obesity are increasing globally. General practitioners (GP’s) are at the first point of contact for medical support and consequently have a major role in resolving this overwhelming problem. The aim of this study was to assess the effectiveness of a brief lifestyle counselling on weight management and on the participants’ quality of life (QoL). Design A cohort study with a one-year follow-up. Setting Occupational health care, city of Pori in southwestern Finland. Participants Female municipal employees (n = 625) with a mean age of 48 (SD 9) years. Intervention A nurse and a physiotherapist gave lifestyle counselling to all the participants; however, only the overweight/obese subjects were recommended to lose at least 5% of their initial weight. Main outcome measure Success in weight management and quality of life. Results At the follow-up visit, 10.4% (95% CI: 7.5–14.0) of the overweight/obese subjects had lost at least 5% of their weight, but 10.0% (95% CI: 6.7–14.3) of the normal-weight participants had become overweight. The mean weight change was +0.1 kg (95% CI: −0.3–0.5) in the overweight/obese group and +0.5 kg (95% CI: 0.2–0.8) in the normal weight group. The change in QoL was inversely correlated with relative weight change in overweight/obese subjects, albeit the effect size was small. Conclusion Weight management counselling should also be directed to individuals with a normal weight. Even with brief lifestyle counselling it may be possible to stabilize weight gain. Successful weight loss may improve the QoL of overweight/obese individuals. • KEY POINTS • Primary health care has to deal with the increasing problem of overweight and obesity. • Brief lifestyle counselling performed by a nurse and a physiotherapist seems to be quite effective in weight stabilization, considering the effort needed. • People with normal weight tend to gain weight and weight management counselling should also be directed to them. Successful weight management may improve the quality of life of overweight/obese people.
... While the fear of excessive weight gain in the general public is palpable, this concern is anecdotal mainly due to the lack of objective scientific evidence. We know from prior literature that small changes in body weight in relatively short periods can become permanent and lead to substantial weight gain over time [1]. Thus, it is imperative to understand the challenges with shelterin-place practices as they relate to weight management. ...
... Small changes in body weight in relatively short periods may lead to substantial weight gain over time [1,15]. Notably, prior research suggests that short bouts of weight fluctuation, such as during the winter holiday period (November to January), may contribute to half of annual weight gain [16][17][18]. ...
Preprint
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Background: The COVID-19 pandemic has caused people to shelter-at-home for an extended period, resulting in a sudden rise in unstructured time. This unexpected disruption in everyday life has raised concerns about weight management, especially in high-risk populations of women and individuals with overweight and obesity. This study aimed to investigate the changes in behaviors that may impact energy intake and/or energy expenditure in U.S. adults during the home confinement. Methods: Cross-sectional data from 1,779 adults were collected using an online Qualtrics survey between April 24th and May 4th, 2020. Self-reported data on demographics, eating behaviors, physical activity, sleep, screen time, takeout food intake, and food purchasing behaviors were collected. Chi-Square analyses were conducted to evaluate differences in the percent of participants reporting increasing, decreasing, or staying the same in each health behavior since the COVID-19 outbreak in their area. Each analysis was followed by comparing whether increases or decreases were more likely for each health behavior. Similar comparisons were made between male and female participants and between body mass index (BMI) categories. Results: We observed an increase in the intake of both healthy and energy-dense unhealthy foods and snacks during the home confinement. Participants also reported increases in sedentary activities and decrease in physical activity, alcohol intake, and consumption of takeout meals during this time. In women, several behavioral changes support greater energy intake and less energy expenditure than men. No clear difference in patterns was observed across BMI status. Conclusion: Acute changes in behaviors underscore the significance of a sudden increase in unstructured time at home on potential weight gain. Our findings support the need to implement and support measures that promote strategies to maintain body weight and establish a methodology to collect body weight data at multiple time points to longitudinally assess the dynamic relationship between behaviors and body weight change.
... As evident from previous research, small changes in body weight in relatively short time periods can become permanent and lead to substantial weight gain over time. 10 Considering the surge of COVID-19 cases in the US due to the reopening of states, the stay-at-home order could be extended and may last several months. As a result, extended home confinement due to COVID-19 could result in significant body weight gain, with women with overweight/obesity being at the greatest risk for permanent change. ...
Article
Prior to the COVID-19 pandemic, African-American mothers were three times as likely to die from pregnancy-related causes compared to white mothers. The impact of the pandemic among African- Americans could further worsen the racial disparities in maternal mortality (MM) and severe maternal morbidity (SMM). This study aimed to create a theoretical framework delineating the contributors to an expected rise in maternal mortality (MM) and severe maternal morbidity (SMM) among African-Americans in the era of the COVID-19 pandemic due to preliminary studies suggesting heightened vulnerability of African-Americans to the virus as well as its adverse health effects. Rapid searches were conducted in PubMed and Google to identify published articles on the health determinants of MM and SMM that have been or likely to be disproportionately affected by the pandemic in African-Americans. We identified socioeconomic and health trends determinants that may contribute to future adverse maternal health outcomes. There is a need to intensify advocacy, implement culturally acceptable programs, and formulate policies to address social determinants of health. Keywords: • COVID-19 • Maternal mortality • Severe maternal morbidity • African-Americans Copyright © 2020 Yusuf et al. Published by Global Health and Education Projects, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in this journal, is properly cited.
... The holiday season (from the last week of November to the second week of January), has been identified as a high-risk period for accelerated weight gain [3,4]. A narrative review by our research group showed that people tend to gain between 0.4 and 0.9 kg of weight during this period, even when they are enrolled in weight loss interventions [3]. ...
Preprint
BACKGROUND The holiday season is considered a high-risk period for weight gain, especially for people with overweight or obesity. However, few evaluated interventions have focused on preventing weight gain during this period and, to our knowledge, none have intended to treat obesity. OBJECTIVE To evaluate the efficacy of a web-based intervention adapted from the Diabetes Prevention Program (DPP-web) for weight loss in Mexican adults with overweight or obesity at 3 months compared to a waiting list control group. METHODS A pilot randomised controlled trial with parallel groups (DPP-web and waiting list) targeting adults with overweight or obesity. Participants were recruited using Facebook ads. A web-based app was designed for the delivery of the Cognitive Behavioural Sessions (CBS) of the DPP-web, and Facebook video calls were used for weekly online nutrition counselling sessions (NCS) by a trained dietitian. The primary outcome was the change in body weight at 3 months. The secondary outcomes were the changes in other anthropometric variables, systolic and diastolic blood pressure, depression score and health-related quality of life scores at 3 months. All measurements were conducted in a university clinic by the same technician, who was blinded to group allocation. The study was conducted between 24 September 2018 and 20 January 2019, which allowed the evaluation of the effect of the program during the holiday season. RESULTS A total of 30 participants aged 35.2 (SD 10.9) years and with a mean BMI of 33.8 (SD 5.58) kg/m2 were randomized (15 DPP-web, 15 waiting list). A retention rate of 80% was observed at 3 months (12 DPP-web, 12 waiting list). All 30 participants who started the intervention were included in the intention to treat analysis. An average weight loss of 1.57 (SD 3.54) kg was observed in the DPP-web group, compared to an average weight gain of 2.18 (SD 4.22) kg in the control group (P=.01). The estimated difference between groups at 3 months was 3.75 kg (95% CI 0.84, 6.67). CONCLUSIONS The DPP-web supported with online nutrition counselling has a significant effect on body weight, specifically preventing weight gain in adults with overweight or obesity during the holiday season. CLINICALTRIAL ClinicalTrials.gov Identifier: NCT03629301.
... 36 The findings of the current research are close to those reported in the Schoeller (2014) literature review, which described a weight gain of approximately 0.5 kg. 37 Furthermore, Moreno et al. (2013) found that all children after 1st grade in the summer demonstrated an increasing in pattern of standardized BMI (zBMI) (0.04 to 0.09) and zBMI decreasing across the school years (20.06 to 0.00; P < 0.0001). 31 Recently, Zachary et al., (2020) also quantified the impact that self-quarantine on behaviors associated with weight gain by introducing a Facebook survey. ...
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Abstract Objective: The lockdown for the COVID-19 pandemic affects lifestyle patterns globally and impacts children and adolescents. This study aims to assess the effect of the lockdown on body weight, eating habits, and physical activity of Jordanian youth (children and adolescents). Methods: A cross-sectional study was conducted on a sample of 477 Jordanian children and adolescents aged 6-17 y. The study tool was a structured validated questionnaire. It comprised 4 parts, including a general description of the study purpose, sociodemographic and anthropometric data, physical activity data, and food intake pattern. Questions were reported before and during lockdown. Changes in outcomes over the 2 study time points were evaluated. Results: After the lockdown period, the mean body weight and body mass index for age Z-scores (BAZ) showed a significant increase (P < 0.001) compared with before the lockdown period. More than 50% of the subjects reported that they spent more than 3 h in front of the screen during the lockdown. The percent of subjects who watched TV for more than 3h was increased. Moreover, physical inactivity was increased significantly during the lockdown. All food groups consumption was significantly increased during the lockdown compared to before the lockdown. Conclusions: COVID-19 lockdown period was characterized by an increase in the use of screen-based devices, lower physical activity, uncontrolled food intake, and weight gain.
... Ma et al. (2006) found that during the fall season, daily caloric intake was 86 kcal higher than the spring. Holiday season indulgence results in an average annual gain in bodyweight between mid-November to mid-January ranging from 0.4 to 0.7 kg (Schoeller, 2014). Recommendations for limiting or decreasing holiday weight gain include weighing yourself every day (Kaviani et al., 2019), decreasing consumption by reflecting on the exercise required to counteract the calorie count (Mason et al., 2018), and increasing exercise as part of New Year's resolutions (Hawkes, 2016). ...
Article
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Holiday healthfulness conversations are dominated by overindulgence of consumption and then, largely in reference to resolutions to do better, physical activity, and exercise aspirations. Consistency was found in self-reported agreement with a series of holiday healthfulness statements, across time, holidays (Thanksgiving versus Christmas), and samples of respondents. The largest proportion of respondents displaying social desirability bias (SDB) were found in response to two statements, namely “I will consume more alcohol during the holiday season than at other times of the year” at (63–66%) and “I make it a New Year’s Resolution to lose weight” (60–63%). Cheap talk was tested as a mechanism to reduce SDB in holiday healthfulness reporting, but showed only limited efficacy compared to the control group surveyed simultaneously. Nonetheless, the consistency across time in reporting and SDB are notable in both self-reporting of health-related data and in studying a unique consumption period around the holidays. Healthcare providers and researchers alike seek to improve the accuracy of self-reported data, making understanding of biases in reporting on sensitive topics, such as weight gain and eating over the holiday season, of particular interest.
... While it is a priority to mitigate the immediate impact, one area of great concern is the long-term effects of this pandemic on weight management in adults. As evident from previous research, small changes in body weight in relatively short time periods can become permanent and lead to substantial weight gain over time (1). Considering that the current situation could last a total of several months, this extended home confinement could exacerbate the problem of obesity in adults by substantially contributing to or exceeding annual weight gain. ...
Article
As the global COVID‐19 pandemic unfolds, over 90% of U.S. adult residents are confined to their homes, with restaurants, shops, schools, and workplaces shut down to prevent disease spread. While it is a priority to mitigate the immediate impact, one area of great concern is the long‐term effects of this pandemic on weight management in adults. As evident from previous research, small changes in body weight in relatively short time periods can become permanent and lead to substantial weight gain over time(1). Considering that the current situation could last a total of several months, this extended home confinement could exacerbate the problem of obesity in adults by substantially contributing to or exceeding annual weight gain.
... Nutrition knowledge, lifestyles, and holiday arrangements are also believed to be related to increasing overweight and obesity (Zhao and Yu 2019;Zeng and Yu 2019), but they are under-researched. The current literature on the linkage between holiday arranges and obesity is inconclusive (Roberts 2000;Helander, Wansink, and Chieh 2016;Hull et al. 2006;Wagner, Larson, and Wengreen 2012;Yanovski et al. 2000;Schoeller 2014). � The time of National Day holiday in China� has been fixed from 1 October to 7 October since 2000 (except in 2009, 2012, and 2017 for 8 days), it provides us a good opportunity to examine the weight change before and after the holiday. ...
Article
We use the regression discontinuity model and CHNS (China Health and Nutrition Survey) data, to study the body weight-gain effect of the week-long National Day holiday in China. We find that Chinese adults tend to significantly gain 1.56 kg of body weight during the National Day holiday, and males have a larger effect. However, the effect will be smaller and insignificant in 2 weeks. It verifies the Chinese saying: People gain 1.5 kg body weight for a good holiday (mei feng jia jie pang san jin), only in the short-run.
... Many of these celebrations are accompanied by a plethora of special treats or get-togethers that revolve around food. Evidence of indulgences are subsequently found in communal weight gain over holiday seasons, as are indications of subsequent efforts to return to the previous body weight (Helander, Wansink, & Chieh, 2016;Schoeller, 2014;Yanovski et al., 2000). This alternation of indulgence and restriction is not limited to the holidays. ...
Article
Food intake is inherently variable and often characterized by episodical restraint or overeating (uncontrolled eating). Such heightened variability in intake has been associated with higher variability in the brain response to food reward, but it is an open issue whether comparable associations with elevated variability in reward seeking exist. Here, we assessed whether restraint and uncontrolled eating as markers of trait-like variability in eating are associated with higher intra-individual variability in reward seeking as captured by a cost-benefit paradigm. To test this hypothesis, 81 healthy, overnight-fasting participants (MBMI = 23.0 kg/m² ± 3.0) completed an effort allocation task (EAT) twice. In the EAT, participants had to exert physical effort to earn monetary and food rewards and indicated levels of wanting through visual analog scales (VAS). As predicted, we found that greater trial-by-trial effort variability was associated with lower scores on cognitive restraint, rp(78) = −0.28, p = .011 (controlled for average effort). In line with previous findings, higher wanting variability was associated with higher BMI, rp(78) = 0.25, p = .026 (controlled for average effort). Collectively, our results support the idea that higher variability in reward seeking is a potential risk factor for eating beyond homeostatic need. Since associations with variability measures of reward exceeded associations with average reward seeking, our findings may indicate that variability in the representation of the reward value could be a crucial aspect driving fluctuations in food intake.
... Important lessons related to participant expectations learned in the course of this pilot study informed the larger, subsequent evaluation of iOTA. For example, the pilot study began over the winter holidays, a time which is commonly related to weight gain [53][54][55][56][57]. This may have driven enthusiasm among participants and encouraged longer participation, heightening the importance of a randomized study, where participants in both intervention and control arms begin the study at similar times of year, so such seasonal effects do not impact conclusions related to intervention engagement or efficacy. ...
Article
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Background: There is a need for workplace programs promoting healthy eating and activity that reach low-wage employees and are scalable beyond the study site. Interventions designed with dissemination in mind aim to utilize minimal resources and to fit within existing systems. Technology-based interventions have the potential to promote healthy behaviors and to be sustainable as well as scalable. We developed an interactive obesity treatment approach (iOTA), to be delivered by SMS text messaging, and therefore accessible to a broad population. The aim of this pilot study was to evaluate participant engagement with, and acceptability of, this iOTA to promote healthy eating and activity behaviors among low-wage workers with obesity. Methods: Twenty participants (self-reporting body mass index ≥ 30 kg/m2) of a single workgroup employed by a university medical practice billing office had access to the full intervention and study measures and provided feedback on the experience. Height and weight were measured by trained research staff at baseline. Each participant was offered a quarterly session with a health coach. Measured weight and a self-administered survey, including dietary and activity behaviors, were also collected at baseline, 3, 6, 12, 18, and 24 months. Participant engagement was assessed through responsiveness to iOTA SMS text messages throughout the 24-month pilot. A survey measure was used to assess satisfaction with iOTA at 3 months. Due to the small sample size and pilot nature of the current study, we conducted descriptive analyses. Engagement, weight change, and duration remaining in coaching are presented individually for each study participant. Results: The pilot was originally intended to last 3 months, but nearly all participants requested to continue; we thus continued for 24 months. Most (14/20) participants remained in coaching for 24 months. At the 3-month follow-up, eight (47%) of the remaining 17 participants had lost weight; by 24 months, five (36%) of the remaining 14 participants had lost weight (one had bariatric surgery). Participants reported very high satisfaction. Conclusions: This pilot provides important preliminary results on acceptability and participant engagement with iOTA, which has significant potential for dissemination and sustainability.
... But there are many circumstances that can interfere with our plans and lead us to eat more. As mentioned, holiday celebrations tend to result in overeating and even weight gain (43)(44)(45)(46). Even being distracted while eating can result in increased intake, especially among restrained eaters, who may not even be aware that they have eaten more than usual [e.g., (25,(47)(48)(49)(50)]. ...
... It also suggests recommendations and approaches to prevent weight gain during this crisis.It is generally well accepted that the holiday season in the Western societies that starts from mid-November to mid-January is associated with weight gain both in adults and children. [6][7][8][9][10] This weight gain seems to be more so in those who are already overweight or obese. 7,10 A narrative review of 15 studies by Díaz-Zavala and colleagues 6 found that starting from the last week of November to early January among adults a statistically significant (p ≤0.05) weight gain of 0.4 to 0.9 kg was observed. ...
Article
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Recently COVID-19 has become a pandemic affecting almost all countries around the world.There is no cure or vaccine available for this disease.The only public health measures available for prevention are isolation, quarantine, and community-wide quarantine.Community-wide quarantine in the pandemic has forced people to stay indoors which has the potential to cause weight gain (similar to holiday weight gain) due to availability of food, staying at home, emotional distress, lack of physical activity, increased cortisol levels, and altered sleep. Fourth-generation theory-based approaches for preventing weight gain promoting home-based physical activity and healthy eating through online educational programs delivered through schools, worksites and social media online forums are recommended
... 36 The findings of the current research are close to those reported in the Schoeller (2014) literature review, which described a weight gain of about 0.5 kg. 37 Furthermore, Moreno et al. (2013) found that all children after 1st grade in the summer demonstrated an increasing in pattern of standardized BMI (zBMI) (0.04 to 0.09) and zBMI decreasing across the school years (20.06 to 0.00; on behaviors associated with weight gain by introducing a Facebook survey. They found that 91% percent of the respondents stated they were spending more time at home than they were before COVID-19 the lockdown. ...
Article
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Objective The lockdown for the COVID-19 pandemic affects lifestyle patterns globally and impacts children and adolescents. This study aims to assess the effect of the lockdown on body weight, eating habits, and physical activity of Jordanian youth (children and adolescents). Methods A cross-sectional study was conducted on a sample of 477 Jordanian children and adolescents aged 6-17 y. The study tool was a structured validated questionnaire. It comprised 4 parts, including a general description of the study purpose, sociodemographic and anthropometric data, physical activity data, and food intake pattern. Questions were reported before and during lockdown. Changes in outcomes over the 2 study time points were evaluated. Results After the lockdown period, the mean body weight and body mass index for age Z-scores (BAZ) showed a significant increase ( P < 0.001) compared with before the lockdown period. More than 50% of the subjects reported that they spent more than 3 h in front of the screen during the lockdown. The percent of subjects who watched TV for more than 3h was increased. Moreover, physical inactivity was increased significantly during the lockdown. All food groups consumption was significantly increased during the lockdown compared to before the lockdown. Conclusions COVID-19 lockdown period was characterized by an increase in the use of screen-based devices, lower physical activity, uncontrolled food intake, and weight gain.
... 36 The findings of the current research are close to those reported in the Schoeller (2014) literature review, which described a weight gain of approximately 0.5 kg. 37 Furthermore, Moreno et al. (2013) found that all children after 1st grade in the summer demonstrated an increasing in pattern of standardized BMI (zBMI) (0.04 to 0.09) and zBMI decreasing across the school years (20.06 to 0.00; P < 0.0001). 31 Recently, Zachary et al., (2020) also quantified the impact that self-quarantine on behaviors associated with weight gain by introducing a Facebook survey. ...
Article
https://www.cambridge.org/core/journals/disaster-medicine-and-public-health-preparedness/article/abs/impact-of-covid19-lockdown-on-body-weight-eating-habits-and-physical-activity-of-jordanian-children-and-adolescents/6AF3F7BC1DCE0133ABDFCB177F6BC63F
... The response variable's evolution in time is important, as it should be dynamic, and change based on covariates and interventions. In the example of weight, the measured weight fluctuates over time based on things such as seasonality (16), temperature (17), or diet (18). ...
Preprint
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The development of mobile health technology has the potential to contribute greatly to personalized medicine. Wearable sensors can assist with determining the proper treatment plans for individuals, provide quantitative information to physicians, or give individuals an objective measurement of their health. However, though treatments and interventions have become more targeted and specific, measuring the causal impact of these actions require more careful considerations of complex covariate structures as well as temporal and spatial properties of the data. Thus, emerging data from sensors and wearables in the near future will make use of and require complex models. Here, we describe a general statistical framework for sensor and wearable data that applies a Bayesian structural time series model to analyze and understand various behavior and health data collected in different environments. We show the wide applicability of this modelling framework, and how it corrects for covariates and biases to provide accurate assessments of intervention. Furthermore, it allows for a time dependent confidence interval of impact through its use of Bayesian estimation. We give three main examples, physical sensor data, environmental air sensors and longitudinal behavioral data to show the effect of various interventions through parameter estimation and comparison in pre- and post- intervention periods. The Bayesian structural time series model shows robust performance in a wide variety of tasks, further supporting its applicability to current and future mobile health and sensor data types.
... In this context, attention should be paid to the risk of small changes in body weight in relatively short periods becoming permanent and contributing to substantial weight gain in the long term (Schoeller, 2014). Thus, it is important for research to evaluate weight gain among populations in the period of changes in routines caused by COVID-19, identifying and discussing factors that may have contributed to this increased weight gain among the population, especially because excess weight is a risk factor for COVID-19 and is associated with greater severity and negative outcomes (Wu & McGoogan, 2020).And it was observed in this research that in two months of social distancing the prevalence of being overweight and obese among university students increased by 5% and 3%, respectively. ...
Article
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This study aimed to analyze the influence of COVID-19 social distancing on the dietary pattern of university students in the Northeast of Brazil and associated factors. This is a cross-sectional study of 955 students from four universities carried out via a web survey containing social, economic, demographic, and health information. A food frequency questionnaire was used to evaluate diet. Weight and dietary alterations were reported. Exploratory factor analysis and multivariate logistic regression were used as statistical analyses. The mean age was 26 and 53.7% of the students observed an increase in their weight. Four dietary patterns were identified: (1) a predominantly in natura pattern, (2) a pattern of processed and ultra-processed foods, (3) a protein-based pattern, and (4) an infusion-based pattern. It was observed that students having a darker skin colour (OR 1.8; CI 95% 1.3-2.6) and 19 to 29 years old and not being a health course student (OR 1.5; CI 95% 1.1-2.1) were associated with greater adhesion to the in natura pattern. Not engaging in physical activity was statistically associated with not adhering (OR 0.5; CI 95% 0.4-0.7) to that pattern. The university students who saw an alteration in their weight during the social distancing period studied presented a greater probability of consuming the processed and ultra-processed foods pattern (OR 1.8; CI 95% 1.2-2.6), while the men (OR 0.7; CI 95% 0.4-0.9) and those not engaging in physical activity (OR 0.7; CI 95% 0.5-0.9) presented less adhesion to that pattern. These findings indicate that social isolation affected the dietary intake of university students, with adhesion to mixed dietary patterns in terms of health. The adhesion to the pattern of processed and ultra-processed foods identified may affect the students’ health, especially the occurrence of excess weight and obesity.
... Previous research has demonstrated that children gained significantly more body weight and showed increased BMI during out-of-school periods 5,6,[8][9][10][11][12] . Some studies have suggested that individual weight gain during the holiday period was quite variable; however, this period is more important especially for those who have already been diagnosed with obesity 9,11 . ...
Article
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It is important to pay attention to the indirect effects of the social distancing implemented to prevent the spread of coronavirus disease 2019 (COVID-19) pandemic on children and adolescent health. The aim of the present study was to explore impacts of a reduction in physical activity caused by COVID-19 outbreak in pediatric patients diagnosed with obesity. This study conducted between pre-school closing and school closing period and 90 patients aged between 6- and 18-year-old were included. Comparing the variables between pre-school closing period and school closing period in patients suffering from obesity revealed significant differences in variables related to metabolism such as body weight z-score, body mass index z-score, liver enzymes and lipid profile. We further evaluated the metabolic factors related to obesity. When comparing patients with or without nonalcoholic fatty liver disease (NAFLD), only hemoglobin A1c (HbA1c) was the only difference between the two time points (p < 0.05). We found that reduced physical activity due to school closing during COVID-19 pandemic exacerbated obesity among children and adolescents and negatively affects the HbA1C increase in NAFLD patients compared to non-NAFLD patients.
... As a result, weight control, physical inactivity and changes in eating habits have become worrying issues. Various studies have shown that small changes in body weight in relatively short periods can become permanent and lead to substantial weight gain over time (2). ...
... The response variable's evolution in time is important, as it should be dynamic and change based on covariates and interventions. In the example of weight, the measured weight fluctuates over time based on things such as seasonality [21], temperature [22], or diet [23]. ...
Article
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The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g. wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures, as well as knowledge of the temporal and spatial properties of the data. Thus, interpreting biomedical sensor data needs to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. This framework corrects for covariates to provide accurate assessments of the significance of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool, MhealthCI, around a specific implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the software implementation of MhealthCI to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity and also compare the performance to other models. Specifically, we show how the framework is able to evaluate an exercise intervention’s effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.
... Almost all students just were studying ( at the end of the period (Figure 1). In the same study, weight changes were related to physical activity, to long-term weight gain should be considered [27]. It is among the suggestions developed due to Covid-19, mainly for strengthening, balance and control, stretching exercises with walking at home, squats, sit-ups, and yoga through the internet, mobile technologies, and television [28,29]. ...
... Many studies were conducted at the beginning of the quarantine period to evaluate the impacts of the pandemic on body weight and eating habits [27,44]. These studies, which covered a relatively short period of the pandemic, stated that small changes in body weight may become permanent, and result in significant weight gain over time [45][46][47]. In 2 studies in Turkey that were conducted at the beginning of the pandemic, it was observed that the rate of participants who stated that their body weight increased (35.0%; 38.4%) and did not change (36.0%; 48.6%) was higher than those who stated that they lost weight [38,48]. ...
Article
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Background/objectives: To determine the weight change trend among the adult Turkish population after 1 yr of the coronavirus disease 2019 (COVID-19) pandemic and factors associated with weight change. Materials/methods: This cross-sectional study was conducted between 26 February and 6 March 2021 using an online questionnaire that included questions for sociodemographic variables, eating habits, stress level, and the Three-Factor Eating Questionnaire-R18. Those who weighed themselves 1-2 weeks before the pandemic was declared in Turkey and remembered their weight were invited to participate in the study. Trends in weight and body mass index (BMI) change were calculated. The variables associated with a 1% change in BMI were assessed using hierarchical regression analysis. Results: The study was conducted with 1,630 adults (70.25% female) with a mean age of 32.09 (11.62) yrs. The trend of weight change was found to increase by an average of 1.15 ± 6.10 kg (female +0.72 ± 5.51, male +2.16 ± 7.22 kg) for the first year of the COVID-19 pandemic. The rate of participants with a normal BMI (18.50-24.99 kg/m2) decreased to 51.91% from 55.75%. Consuming an "Increased amount of food compared to before the pandemic" was found to be the independent variable that had the strongest association with a 1% increase in BMI (β = 0.23 P < 0.001). The average change in the BMI was higher in older individuals than in those who were younger. A high stress level was associated with a decrease in BMI (β = -0.04 P = 0.048). Conclusions: In this study, the factors associated with weight change after 1 yr of the pandemic in the Turkish population was reported for the first time. A high stress level and increased weight gain trend still occur in Turkey after 1 yr of the pandemic.
... Physical inactivity can contribute to weight gain, and a recent study found that individuals with obesity reported gaining an average of 1.5 kgs within one month of observing stay-at-home orders (Pellegrini et al., 2020). This is important because weight gained over a short period of time can lead to lasting changes in body weight (Schoeller, 2014). Further, decreases in physical activity may exacerbate poor psychological wellbeing (Carriedo et al., 2020;Duncan et al., 2020;Jiménez-Pavón et al., 2020;Schuch et al., 2020), and further contribute to weight gain and other downstream physical health complications (Block et al., 2009). ...
Article
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The objective of this study was to investigate changes in physical activity patterns associated with the COVID-19 pandemic in individuals with overweight and obesity who were participating in a school district worksite weight loss program. We conducted comparative design interrupted time series analyses on physical activity device (Fitbit) data from the 2018–2019 and 2019–2020 school years (N = 211). We administered a questionnaire in 2020 to supplement device data. After the stay-at-home orders in 2020, participants tended to decrease their weekly step count (B = −1315.7, SE = 627.7, p = .045), decrease their weekly “Lightly active minutes” (B = −39.1, SE = 12.6, p = .007), and increase their weekly “Very active minutes” compared to their counterparts from the year before (B = 7.6, SE = 3.2, p = .020). Decreased motivation, gym closures, and safety concerns were cited as barriers to physical activity. Having more time and health consciousness were cited as facilitators of physical activity. The COVID-19 pandemic was related to changes in physical activity in both positive and negative ways, revealing opportunities to promote healthy lifestyle behaviors in this population. More research is needed to determine optimal approaches to health promotion in the post-COVID-19 era.
... Research has shown that short-term weight gain, for instance during the holiday season, often tends to be retained and that it is a major contributor to long-term excess bodyweight. 38 Working-age adults typically gain bodyweight with every additional year of age. For instance, Peeters et al 39 reported an average annual bodyweight increase of 0.34 kg among Australian adults, and Orpana et al 40 found that Canadian men gained 0.74 kg and women 0.57 kg over 2 years. ...
Article
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Background: The current study aimed to prospectively assess bodyweight change following the implementation of lockdown measures to contain the COVID-19 pandemic in the Netherlands and to explore the potentially moderating role of gender in this association. Design: Using Dutch DNB Household Survey panel data collected between 1993 and 2020, we estimated fixed-effects regression models of bodyweight change. Models were stratified by gender and formal tests of gender differences in coefficient estimates were performed. Participants: 4365 women and 4583 men aged 18-65 were included in the study. The total number of observations was 41 330. Outcome measures: The outcome of interest was self-reported bodyweight in kilograms. Additional analyses were performed using body mass index (self-reported weight in kilograms divided by self-reported height in metres squared) as the outcome. Results: The implementation of Dutch lockdown measures in 2020 was associated with bodyweight gain of approximately 800 g in working-age women compared with the 3 prior years. Bodyweight gain in 2020 relative to the prior years was significantly stronger for women than for men (F(4, 8947)=3.9, p<0.01). No evidence of bodyweight gain in working-age men was found. Conclusion: Results indicate that bodyweight gain following COVID-19 lockdown measures in the Netherlands was more pronounced among women than among men. Although necessary to contain the COVID-19 pandemic, lockdown measures may contribute to a different public health challenge in the rising prevalence of overweight and obesity.
... These changes in short periods of time can become permanent and lead to substantial weight gain. (32) In fact, up to 40% of the patients who perceived poorer control of their chronic comorbidities attributed it to diet and exercise. Regardless, given their well-known association with deleterious health outcomes, (33)(34)(35) our study highlights the need to implement strategies to further increase home-based physical activity and to encourage adherence to a healthy diet during periods of confinement. ...
Article
Background: The impact of the COVID-19 outbreak and lockdown in liver transplant (LT) patients remains unknown. The aim of this cross-sectional study was to assess the consequences of the COVID-19 pandemic on the physical and mental health of LT patients during the lockdown period. Methods: Between August and October 2020 a Web-based questionnaire was emailed to 238 LT patients undergoing regular follow-up at our unit. This pseudonymized survey explored demographic and lifestyle variables (i.e. eating and physical habits), disruptions in routine medical care, and different dimensions of mental health, COVID-19-related mood and coping (worries/anxiety, depression, insomnia, fear of Covid, resilience, etc.), and health perception using different validated instruments. Results: 48.7% (116 of 238) LT recipients accepted to participate, 104 of whom gave their consent to publish the data. The median age was 63 years. Up to 39.4% presented worrying scores indicating moderate/severe generalized anxiety disorder (GAD), whereas 25.5% exhibited moderate/severe insomnia and only 10.5% moderate/severe depression. Forty patients (38.5%) gained weight, 24% experienced a worsening in their eating habits and 63.4% referred to practice less or much less exercise during the lockdown. Only 25% perceived a worsening in the control of their chronic comorbidities. Missed medical appointments (0.9%) or worsening adherence to therapy (1.9%) were exceptional. Conclusions: COVID-19 lockdown has negatively impacted the mental and physical health of LT patients. Long-term consequences remain unclear.
... We also report the percentage of the sample increasing/decreasing (i) weight or (ii) BMI between timepoints. An increase or decrease was defined as more than a 0.5 kg or 0.5 kg/m 2 change, respectively, from the reference timepoint (baseline or 3-months follow-up survey), as these are previously used cut-offs [22,23,36]. Lastly, we reported the mean change (with 95%CI) in (i) weight and (ii) BMI in those increasing/decreasing weight/BMI between timepoints. ...
Article
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COVID-19-related restrictions impacted weight and weight-related factors during the initial months of the pandemic. However, longitudinal analyses are scarce. An online, longitudinal study was conducted among self-selected UK adults (n = 1818), involving three surveys (May–June, August–September, November–December 2020), covering anthropometric, sociodemographic, COVID-19-related and behavioural measures. Data were analysed using generalised estimating equations. Self-reported average weight/body mass index (BMI) significantly increased between the May–June period and the August–September period (74.95 to 75.33 kg/26.22 kg/m2 to 26.36kg/m2, p < 0.001, respectively), and then significantly decreased to November–December (to 75.06 kg/26.27 kg/m2, p < 0.01), comparable to May–June levels (p = 0.274/0.204). However, there was great interindividual variation, 37.0%/26.7% increased (average 3.64 kg (95% confidence interval: 3.32, 3.97)/1.64 kg/m2 (1.49, 1.79)), and 34.5%/26.3% decreased (average 3.59 kg (3.34, 3.85)/1.53 kg/m2 (1.42, 1.63)) weight/BMI between May–June and November–December. Weight/BMI increase was significantly negatively associated with initial BMI, and positively associated with monthly high fat, salt and sugar (HFSS) snacks intake and alcohol consumption, and for BMI only, older age. Associations were time-varying; lower initial BMI, higher HFSS snacks intake and high-risk alcohol consumption were associated with maintaining weight/BMI increases between August–September and November–December. The average weight/BMI of UK adults fluctuated between May–June and November–December 2020. However, the substantial interindividual variation in weight/BMI trajectories indicates long-term health impacts from the pandemic, associated with food and alcohol consumption.
... Cette prise de poids, même minime, peut persister dans le temps et conduire à un gain de poids sur le long terme 8 , accentuant ainsi l'enjeu majeur de santé publique que représente l'obésité. Il est donc primordial de surveiller ces comportements en de telles situations. ...
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Introduction-Pour freiner la pandémie de Covid-19, un confinement strict a été institué en France du 17 mars au 11 mai 2020, bouleversant la vie quotidienne de la population. Dès le début du confinement, une enquête répétée de Santé publique France (enquête CoviPrev) a permis de suivre différents comportements de santé. Cet article présente l'évolution perçue des comportements alimentaires pendant le confinement et les détermi-nants de la prise de poids. Méthodes-Deux échantillons indépendants de 2 000 personnes âgées de 18 ans et plus résidant en France métropolitaine ont été interrogés par Internet lors des troisième (V3, 5 e semaine du confinement) et sixième (V6, 8 e semaine du confinement) vagues d'enquête. En V3, les évolutions du comportement alimentaire, de l'activité sportive et du poids ont été analysées de manière descriptive et des régressions logistiques ont évalué les facteurs associés à la prise de poids. La reconduction du questionnement en V6 a permis de décrire l'évolu-tion du grignotage et du poids entre les deux vagues, en comparant les données par des tests du Chi2. Résultats-En V3, 37% des individus ont déclaré avoir « modifié leur alimentation », sans différence selon le sexe. Les femmes ont toutefois été plus nombreuses que les hommes à déclarer davantage cuisiner, grignoter, consommer des produits gras, sucrés, salés (PGSS), avoir plus ou moins d'appétit, faire attention à leur poids. Parmi les comportements évalués, la majorité ont été impactés par le confinement pour 25 à 40% des répon-dants : par exemple, 37% ont déclaré cuisiner des plats faits maison plus fréquemment que d'habitude et 27% avoir pris du poids contre 11% en avoir perdu. Des associations positives ont été observées entre la prise de poids et une situation financière perçue comme difficile, manger en plus grande quantité que d'habitude, grignoter davantage, avoir réduit sa consommation de fruits et légumes, augmenté celle de PGSS, fait moins d'activité sportive, présenter un état dépressif certain et avoir des troubles du sommeil. Enfin, 27% des répondants déclaraient grignoter davantage en V6 contre 22% en V3 et 36% avoir pris du poids contre 27% en V3. Conclusions-Cette étude montre un impact du confinement sur plusieurs comportements alimentaires, parti-culièrement chez les femmes. S'il a pu favoriser la cuisine-maison, d'autres changements étaient plutôt défavo-rables à la santé. Ces résultats suggèrent que des confinements répétés risqueraient d'aggraver les pathologies liées à l'alimentation. Dans de tels contextes, il est nécessaire de poursuivre l'évaluation des comportements alimentaires et du poids corporel et de soutenir toutes mesures de santé publique encourageant des habitudes alimentaires saines.
... This is not likely to be the case as the prevalence of obesity continues to rise, and evidence from other shorter term studies reported nett weight gain (weight gain > weight loss). For example, human studies that investigated the effects of festive holiday (such as Thanksgiving, Christmas and New Year) on body weight change show that despite the weight loss that occurred after festive holidays, the magnitude of weight loss was smaller than the gained weight, resulting in nett weight gain [43][44][45][46][47][48]. It is possible that small nett gains in body weight may cumulatively lead to overweight and obesity in the long term, and this should be confirmed with properly designed long-term studies in the future. ...
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Purpose The global prevalence of overweight remains high; effective strategies that consider patterns of body weight changes to identify periods when adults are susceptible to weight gain are warranted. This systematic review aimed to investigate body weight patterns, and how they were associated with dietary intake and/or dietary behaviours (Prospero CRD42020161977). Methods Systematic literature search was conducted in the Medline, Embase, and CINAHL databases until November 2020. Observational studies in adults (18 years and over) that reported at least two measurements of weight and dietary intake in a year were included. Risk of bias was conducted using the Evidence Analysis Library by the Academy of Nutrition and Dietetics tool. This review included 16 unique studies after title, abstract, and full-text screening, and findings were narratively synthesised. Results Of the six studies conducted in the farming populations, five were conducted in countries with two seasons (dry vs. rainy seasons) and all studies observed higher body weight during the dry season (up to 3.1 kg difference between seasons). The remaining study was conducted in a sub-tropical country and did not observe temporal weight patterns. Higher dietary intake was also reported during the dry season in the tropical countries. In non-farming populations (n = 10), temporal patterns were also seen, where higher body weight and adiposity was observed during colder seasons (autumn and winter). However, the opposite was found in a study conducted in Iran, where higher weight was seen in summer. Concurrent with higher body weight, higher energy, fat, carbohydrate and soda consumption, and lower fiber and vegetable intake were observed. Conclusion Temporal weight and dietary patterns exist, and they were country- and context-specific; these patterns were also related to factors such as activity levels, seasons and occupation. Future interventions should consider temporal patterns in the design and delivery of timely and tailored dietary interventions to promote optimal body weight. PROSPERO Registration PROSPERO Registration: CRD42020161977.
... Minor body weight changes may become permanent in short periods, causing significant weight gain over time [2]. Concerns regarding weight gain were raised during the pandemic [3]. ...
... Indeed, we and others recently showed that 19-28% of adults self-reported gaining 5-10 pounds of body weight during the self-quarantine (3)(4)(5). These self-reported weight increases are of concern because literature on holiday weight gain suggests that fluctuations in body weight in a relatively short period can become permanent and lead to a substantial weight gain over decades (6)(7)(8)(9). Thus, it is imperative to understand for whom self-reported changes in energy balance behaviors categories have a potential to contribute to weight increases during the brief period of lockdown, mainly healthy and unhealthy eating, and physical and sedentary activities. ...
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Self-reported weight gain during the COVID-19 shelter-at-home has raised concerns for weight increases as the pandemic continues. We aimed to investigate the relationship of psychological and health markers with energy balance-related behaviors during the pandemic-related extended home confinement. Ratings for stress, boredom, cravings, sleep, self-control, and beliefs about weight control were collected from 1,609 adults using a questionnaire between April 24th–May 4th, 2020, while COVID-19 associated shelter-in-place guidelines were instituted across the US. We calculated four energy balance behavior scores (physical activity risk index, unhealthy eating risk index, healthy eating risk index, sedentary behavior index), and conducted a latent profile analysis of the risk factors. We examined psychological and health correlates of these risk patterns. Boredom, cravings for sweet/savory foods, and high sleepiness ratings related to high risk of increasing unhealthy eating and sedentary behavior and decreasing physical activity and healthy eating. Having greater self-control, control over cravings, or positive mood was related to lowering all aspects of energy intake and energy expenditure risks. Although individuals in risk pattern classes showed similarity in physical activity and healthy/unhealthy eating habits, they exhibited different patterns of positive mood, craving control, food cravings, boredom, and self-control. Psychological and health variables may have a significant role to play in risk behaviors associated with weight gain during the COVID-19 related home confinement. Emerging behavioral patterns may be meaningful in developing targeted weight management interventions during the current pandemic.
... It is possible that the change in seasons from fall (eg, September and October) to winter (eg, November and December; wave 1 participants) and the intervention period overlapping with seasonal holidays (eg, Christmas 2019; wave 2 participants) may have reduced overall physical activity patterns and led to potential gains in body mass. For instance, holidays typically represent a time of increased weight gain associated with increased food consumption and reduced exercise [42]. Although no significant changes in body mass from pre-to postintervention were detected for either group, very slight increases were observed in participants in the control group (mean 70.45 kg to 70.77 kg) but not for those in the movr group (mean 71.22 to 71.15 kg). ...
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Background: Numerous mobile apps available for download are geared toward health and fitness; however, limited research has evaluated the real-world effectiveness of such apps. The movr app is a mobile health app designed to enhance physical functioning by prescribing functional movement training based on individualized movement assessments. The influence of the movr app on functional movement and physical fitness (flexibility, strength, and cardiovascular fitness) has not yet been established empirically. Objective: This study aims to examine the real-world impact of the movr app on functional movement, flexibility, strength, and cardiovascular fitness. Methods: A total of 48 healthy adults (24 women and 24 men; mean age 24, SD 5 years) completed an 8-week pilot pragmatic randomized controlled trial in which they were randomly assigned to either 8-week use of the movr app (n=24) or 8-week waitlist control (n=24). Measures of functional movement (Functional Movement Screen [FMS]), strength (push-ups, handgrip strength, and countermovement jump), flexibility (shoulder flexibility, sit and reach, active straight leg raise [ASLR], and half-kneeling dorsiflexion), and cardiovascular fitness (maximal oxygen uptake [VO2max]) were collected at baseline and the 8-week follow-up. Results: Repeated measures analyses of variance revealed significant group-by-time interactions for the 100-point FMS (P<.001), shoulder flexibility (P=.01), ASLR (P=.001), half-kneeling dorsiflexion (P<.001), and push-up tests (P=.03). Pairwise comparisons showed that FMS scores increased from pre- to postintervention for those in the movr group (P<.001) and significantly decreased for those in the control group (P=.04). For shoulder flexibility, ASLR, half-kneeling dorsiflexion, and push-up tests, improvements from pre- to postintervention were found in the movr group (all values of P<.05) but not in the control group (all values of P>.05). There were no changes in the sit and reach or handgrip strength test scores for either group (all values of P>.05). A significant main effect of time was found for the countermovement jump (P=.02), such that scores decreased from pre- to postintervention in the control group (P=.02) but not in the movr group (P=.38). Finally, a significant group-by-time interaction was found for VO2max (P=.001), revealing that scores decreased pre- to postintervention in the control group (P<.001), but not in the movr group (P=.54). Conclusions: The findings revealed that movr improved indices of functional movement (FMS), flexibility (shoulder, ASLR, and dorsiflexion), and muscular endurance (push-ups) over an 8-week period compared with the control group while maintaining handgrip strength, lower body power (countermovement jump), and cardiovascular fitness ( ). Thus, this study provides initial evidence of the effectiveness of the movr app for enhancing functional movement and physical fitness among healthy adults.
... In reality, energy intake and expenditure are highly variable from day to day and even within a day, e.g., the balance is negative overnight and positive at daytime, leading to a considerable fluctuation in energy balance. Brief periods in which energy intake far exceeds energy expenditure last from one meal to several days and regularly occur over the weekend, on holidays, at periods of celebration, or during vacations (80,81). Gradual weight gain, therefore, more likely results from repeated short periods of large positive energy balance that are inadequately compensated for. ...
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Energy turnover, defined as the average daily total metabolic rate, can be normalized for basal metabolic rate in order to compare physical activity level between individuals, whereas normalization of energy turnover for energy intake (energy flux) allows investigation of its impact on regulation of energy partitioning independent of energy balance. Appetite sensations better correspond to energy requirements at a high compared with a low energy turnover. Adaptation of energy intake to habitual energy turnover may, however, contribute to the risk of weight gain associated with accelerated growth, pregnancy, detraining in athletes, or after weight loss in people with obesity. The dose–response relationship between energy turnover and energy intake as well as the metabolic effects of energy turnover varies with the habitual level of physical activity and the etiology of energy turnover (e.g., cold-induced thermogenesis, growth, or lactation; aerobic vs. anaerobic exercise). Whether a high energy turnover due to physical activity or exercise may compensate for adverse effects of overfeeding or an unhealthy diet needs to be further investigated using the concept of energy flux. In summary, the beneficial effects of a high energy turnover on regulation of energy and macronutrient balance facilitate the prevention and treatment of obesity and associated metabolic risk.
... [13][14][15] More worryingly, small changes in body weight in relatively short periods can become permanent and lead to substantial weight gain over time. 16 Targeting excess body weight may aid counteracting the epidemic of diabetes. Fat accumulation, predominantly in the abdominal or visceral region, can induce β-cell dysfunction 17 (also linked to excess fat in the pancreas), as well as excess liver fat and poorly regulated gluconeogenesis leading to the manifestation of hyperglycaemia in T2D. ...
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Introduction: As the vast majority of people with type 2 diabetes (T2D) are also overweight or obese, healthcare professionals (HCP) are faced with the task of addressing both weight management and glucose control. In this narrative review, we aim to identify the challenges of reaching and maintaining body weight targets in people with T2D and highlight current and future treatment interventions. Methods: A search of the PubMed database was conducted using the search terms "diabetes" and "weight loss." Results: According to emerging evidence, treating obesity may be antecedent to the development and progression of T2D. While clinical benefits typically set in upon achieving a weight loss of 3-5%, these benefits are progressive leading to further health improvements, and weight loss of >15% can have a disease-modifying effect in people with T2D, an outcome that up to recently could not be achieved with any blood glucose-lowering pharmacotherapy. However, advanced treatment options with weight-loss effects currently in development including the dual GIP/GLP-1 receptor agonists may enable simultaneous achievement of individual glycemic and weight goals. Conclusion: Despite considerable therapeutic progress, there is still a large unmet medical need in patients with T2D who miss their individualized glycemic and weight-loss targets. Nonetheless, it is to be expected that development of future therapies and their use will favourably change the scenario of weight and glucose control in T2D.
... Aman and Masood stated tahat (2020), it is stated that the risk of contracting Covid 19 is higher in individuals with weak immunity [33]. Even in short periods, small changes in body weight may become permanent and may also cause weight gain [34]. This result seems to support the results of our study. ...
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With the emergence of the Covid-19 pandemic in China, it has become an important public health problem globally, causing many organizations to be canceled, education and workplace restrictions, and curfews to be declared. It is thought that following the measures taken due to the virus, staying at home for a long time, restriction of social activities, changes in eating habits, in parallel, trigger many health problems. This study aims to determine the relationship between the anxiety of catching Covid-19 in athletes and their attitudes towards nutrition. A relational screening model was used in the research. The study group consists of 227 (83 female, 144 male) athletes studying in the field of sports sciences. To determine the demographic information of the athletes in the research, "Personal Information Form", "Scale of Athletes' Anxiety of catching New Type Coronavirus (Covid-19)" and "Attitude Scale towards Healthy Eating" were used. In the analysis of the data, distribution characteristics were determined and the suitability of parametric tests was reviewed, independent sample t-test, ANOVA test, Pearson correlation analysis, and simple linear regression analysis were used to determine the relationship between variables. It is seen that those who have sufficient knowledge about Covid-19 have higher average scores on knowledge about nutrition, positive nutrition, and nutrition than those who do not have sufficient knowledge. In comparisons according to body mass index, the average of low weight individuals in the individual anxiety sub-dimension was higher than normal weights; In malnutrition, it is seen that underweight people have more averages than high weights, and normal-weight people have more averages than high weight in the total nutritional score. It has been determined that there are negative significant relationships between the individual anxiety, socialization anxiety, and total anxiety of catching Covid-19 of the athletes and their attitudes towards nutrition detected.
... 15,16 While weight gain during the pandemic may seem trivial, research has shown that small changes in weight on vacations or during the holidays can lead to substantial and permanent weight gain over time. 17,18 It is therefore likely that weight gain during the pandemic may be lasting and may accelerate the rise of obesity in America. COVID-19 has further highlighted the critical need to treat obesity as individuals with obesity are at higher risk for COVID-19-related morbidity and mortality. ...
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American adults have gained weight during the COVID‐19 pandemic. Little is known about how patients who are medically managed for overweight and obesity, including patients who are prescribed antiobesity pharmacotherapy, have fared. To assess the COVID‐19 pandemic’s effect on weight, food choices, and health behaviors in patients receiving medical treatment for overweight or obesity. Adult patients treated at an urban academic weight management center between May 1, 2019 and May 1, 2020 were electronically surveyed between February 23 and March 23, 2021. The survey assessed changes in weight, eating, behaviors, and the use of antiobesity medications (AOMs) following issuance of social distancing/stay‐at‐home policies in March 2020. In 970 respondents, median percent weight change for those taking AOMs was ‐0.459% [interquartile range (IQR) ‐5.46%‐(+3.73%)] compared to +2.33% [IQR‐1.92%‐(+6.52%)] for those not taking AOMs (p<0.001). More participants achieved ≥5% weight loss if they were taking AOMs compared to those who were not (26.7% vs 15.8%, p=0.004), and weight gain ≥5% was also lower in those taking AOMs (19.8% vs 30.3%, p=0.004). Patients with pre‐pandemic BMI ≥30 kg/m2 taking AOMs experienced the greatest weight reduction, and there was greater weight loss associated with increased physical activity. and relevance. Medical weight management protected against weight gain during this period of the COVID‐19 pandemic. Increased physical activity, decreased alcohol intake, and use of AOMs were factors that contributed to this protective effect. This article is protected by copyright. All rights reserved.
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Objectives: To assess the potential influence of lifestyle changes during the first month of COVID-19 lockdown on body weight gains (WG) in an Algerian population cohort. Subjects and Methods: A sample survey, carried out using a self-administered questionnaire, sent on social networks to a random sample (172 participants). Anthropometric measurements were obtained as well as lifestyle factors including physical activity, diet habits, sleep, and screen time. Results: The average WG was; 1.02 ± 3.36, 1.18±2.15, and 0.95±3.79 (kg) for the total sample, men and women respectively. Δ-BMI (body mass index difference before and after one month of lockdown period) increased as following; 0.42±1.43, 0.39±0.68, and 0.43±1.66 (Kg/m²) for the total sample, men and women respectively. WG induced slightly changes from the normal BMI category to the overweight category for the total cohort (24.87±6.74 vs 25.28±7.19 kg/m²), women (25.13±7.65 vs 25.56±8.19 kg/m²) whereas no effect was reported in men (24.28±4.03 vs 24.67±4.15 kg/m²). A significant difference (p < 0.001) was revealed in men and women for sport practicing (53.33 vs 40.90 % respectively) and nighttime snacking (56.60% for men against 43.55% for women). Positive correlation between body WG and number of meals/day in men (r=0,341, p=0,012), while for women there was a positive correlation between WG change and both food intake (r=0.170, p=0.066) and nighttime snacking (r=0,228, p=0,013). Furthermore, a negative correlation between WG and sport practicing was found in women (r =-0.221, p=0.016). Conclusions: Significant WG was found during a short COVID-19 lockdown. The WG results presented herein were positively associated with certain lifestyle variables during the COVID-19 lockdown. Keywords: Algerian population, BMI, COVID-19, lifestyle changes, lockdown, weight gain.
Article
Background and objectives One of the potential negative effects of a lockdown are changes in dietary and lifestyle patterns, which can lead to weight gain. Our objective was to assess the changes on dietary habits and eating patterns in a lockdown situation and their impact on weight. We aimed to determine whether the treatment with GLP1 analogues (aGLP1) could impact on these parameters. Material and methods 100 overweight/obese patients were consecutively recruited for a review at the end of the lockdown. A structured interview was designed to see changes in dietary habits, routines and exercise. Results 52% patients gained weight during lockdown. The percentage of subjects with an active history of depression or anxiety was higher among the group of patients who gained weight. The percentage of patients who worsened their hyperphagia was higher in those who gained weight (71.2% vs 10.6%; P < .0001); similar results were observed with binge eating (92% vs 10.6%; P < .0001) and cravings, both sweet and salty (69.2% vs 21.3% and 69.2% vs 14.9%; P < .0001 and P < .0001 respectively). Of the 48 patients who did not gain weight, 30 were under aGLP1 treatment (61.7%). The worsening of abnormal eating patterns was lower among patients treated with aGLP-1. Conclusions A lockdown is a vulnerable period to gain weight, especially in those patients with a psychopathological history. aGLP1 manage to control emotional eating, making them a valuable therapeutic option.
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Food intake is inherently variable and often characterized by episodical restraint or overeating (uncontrolled eating). Such heightened variability in intake has been associated with higher variability in the brain response to food reward, but it is an open issue whether comparable associations with elevated variability in reward seeking exist. Here, we assessed whether restraint and uncontrolled eating as markers of trait-like variability in eating are associated with higher intra-individual variability in reward seeking as captured by a cost-benefit paradigm. To test this hypothesis, 81 healthy, overnight-fasting participants ( MBMI = 23.0 kg/m ² ± 2.95) completed an effort allocation task (EAT) twice. In the EAT, participants had to exert physical effort to earn monetary and food rewards and indicated levels of wanting through visual analog scales (VAS). As predicted, we found that greater trial-by-trial effort variability was associated with lower scores on cognitive restraint, r p (78) = -.28, p = .011 (controlled for average effort). In line with previous findings, higher wanting variability was associated with higher BMI, r p (78) = .25, p = .026 (controlled for average effort). Collectively, our results support the idea that higher variability in reward seeking is a potential risk factor for eating beyond homeostatic need. Since associations with variability measures of reward exceeded associations with average reward seeking, our findings may indicate that variability in the representation of the reward value could be a crucial aspect driving fluctuations in food intake.
Article
Resumen Antecedentes y objetivos Uno de los potenciales efectos negativos de un confinamiento son los cambios en los patrones dietéticos y de estilo de vida, que pueden conllevar a una ganancia de peso. El objetivo fue ver los cambios sobre los hábitos higiénico-dietéticos y de patrones de ingesta en una situación de confinamiento y el impacto sobre el peso. Asimismo, ver si el estar bajo tratamiento con análogos de GLP1 (aGLP1) modificó estos parámetros. Material y métodos Se reclutó, de forma consecutiva, a 100 pacientes con sobrepeso/obesidad que acudieron a revisión al finalizar el confinamiento. Se diseñó una entrevista estructurada para ver los cambios en los hábitos dietéticos, rutinas y ejercicio. Resultados El 52% de los pacientes ganaron peso durante el confinamiento. El porcentaje de sujetos con historia activa de depresión o ansiedad fue superior entre el grupo de pacientes que ganó peso. El porcentaje de pacientes que empeoraron su hiperfagia ansiosa fue superior en aquellos que ganaron peso (71,2% vs. 10,6%; p < 0,0001); lo mismo ocurrió con los atracones (92% vs. 10,6%; p < 0,0001) y los cravings, dulces y salados (69,2% vs. 21,3% y 69,2% vs. 14,9%; p < 0,0001 y p < 0,0001, respectivamente). De los 48 pacientes que no ganaron peso, 30 estaban en tratamiento con aGLP1 (61,7%). El empeoramiento de los patrones anómalos de ingesta fue inferior entre los pacientes en tratamiento con aGLP-1. Conclusiones Un confinamiento es un período vulnerable para ganar peso, especialmente en aquellos pacientes con antecedentes psicopatológicos. Los aGLP1 consiguen controlar la ingesta emocional convirtiéndolos en una opción terapéutica valiosa.
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Background Numerous mobile apps available for download are geared toward health and fitness; however, limited research has evaluated the real-world effectiveness of such apps. The movr app is a mobile health app designed to enhance physical functioning by prescribing functional movement training based on individualized movement assessments. The influence of the movr app on functional movement and physical fitness (flexibility, strength, and cardiovascular fitness) has not yet been established empirically. Objective This study aims to examine the real-world impact of the movr app on functional movement, flexibility, strength, and cardiovascular fitness. MethodsA total of 48 healthy adults (24 women and 24 men; mean age 24, SD 5 years) completed an 8-week pilot pragmatic randomized controlled trial in which they were randomly assigned to either 8-week use of the movr app (n=24) or 8-week waitlist control (n=24). Measures of functional movement (Functional Movement Screen [FMS]), strength (push-ups, handgrip strength, and countermovement jump), flexibility (shoulder flexibility, sit and reach, active straight leg raise [ASLR], and half-kneeling dorsiflexion), and cardiovascular fitness (maximal oxygen uptake []) were collected at baseline and the 8-week follow-up. ResultsRepeated measures analyses of variance revealed significant group-by-time interactions for the 100-point FMS (P
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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/m(2); 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.
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A significant proportion of the average annual body weight (BW) gain in US adults (~0.5-1 kg/y) may result from modest episodes of positive energy balance during the winter holiday season. We tested whether holiday BW gain was reduced in participants with high baseline total energy expenditure (TEE) or whether it varied by BMI (in kg/m(2)). In a secondary analysis of previously published data, ΔBW normalized over 90 d from mid-September/mid-October 1999 to mid-January/early March 2000 was analyzed by sex, age, and BMI in 443 men and women (40-69 y of age). TEE was measured by doubly labeled water. High or low energy expenditure was assessed as residual TEE after linear adjustment for age, height, and BW. No correlations between ΔBW and TEE or TEE residuals were found. Sixty-five percent of men and 58% of women gained ≥0.5 kg BW, with ~50% of both groups gaining ≥1% of preholiday BW. Obese men (BMI ≥30) gained more BW than did obese women. A high preholiday absolute TEE or residual TEE did not protect against BW gain during the winter holiday quarter. It is not known whether higher than these typical TEE levels would protect against weight gain or if the observed gain may be attributed to increased food consumption and/or reduced physical activity during the holiday quarter.
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The classic rule stating that restricting intake by 3500 kcal/wk will lead to a 1-lb/wk rate of weight loss has come under intense scrutiny. Generally not a component of most weight loss prediction models, the "early" rapid weight loss phase may represent a period during which the energy content of weight change (ΔEC/ΔW) is low and thus does not follow the classic "rule." The current study tested this hypothesis. Dynamic ΔEC/ΔW changes were examined in 23 Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy Study overweight men and women evaluated by dual-energy x-ray absorptiometry during weight loss at treatment weeks 4 to 24. Changes from baseline in body energy content were estimated from fat and fat-free mass. Repeated-measures analysis of variance was used to determine if ΔEC/ΔW changed significantly over time. The evaluation was expanded with addition of the Kiel 13-week weight loss study of 75 obese men and women to test with adequate power if there are sex differences in ΔEC/ΔW. The analysis of variance CALERIE time effect was significant (P < .001), with post hoc tests indicating that ΔEC/ΔW (kilocalories per kilogram) increased significantly from week 4 (X ± SEM; 4, 858 ± 388) to 6 (6, 041 ± 376, P < .01) and changed insignificantly thereafter; ΔEC/ΔW was significantly larger for Kiel women (6, 804 ± 226) vs men (6, 119 ± 240, P < .05). Sex-specific dynamic relative changes in body composition and related ΔEC/ΔW occur with weight loss initiation that extend for 1 month or more. These observations provide new information for developing energy balance models and further define limitations of the 3500-kcal energy deficit → 1-lb weight loss rule.
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Obesity interventions can result in weight loss, but accurate prediction of the bodyweight time course requires properly accounting for dynamic energy imbalances. In this report, we describe a mathematical modelling approach to adult human metabolism that simulates energy expenditure adaptations during weight loss. We also present a web-based simulator for prediction of weight change dynamics. We show that the bodyweight response to a change of energy intake is slow, with half times of about 1 year. Furthermore, adults with greater adiposity have a larger expected weight loss for the same change of energy intake, and to reach their steady-state weight will take longer than it would for those with less initial body fat. Using a population-averaged model, we calculated the energy-balance dynamics corresponding to the development of the US adult obesity epidemic. A small persistent average daily energy imbalance gap between intake and expenditure of about 30 kJ per day underlies the observed average weight gain. However, energy intake must have risen to keep pace with increased expenditure associated with increased weight. The average increase of energy intake needed to sustain the increased weight (the maintenance energy gap) has amounted to about 0·9 MJ per day and quantifies the public health challenge to reverse the obesity epidemic.
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Previous studies suggest adults gain extra weight during the holiday season, however, few studies have been done with children during this time. The purpose of this study was to evaluate gains in growth among elementary children, and compare differences by gender and weight status. Childrens (n = 90) height and weight were measured before and after their holiday break. Height, weight and body mass index (BMI) and body mass index-percentiles (BMI-%) were evaluated and compared by groups using repeated measures ANCOVAs. On average, children grew 0.82 cm (0.32 in), and gained 0.56 kg (1.2 lbs) and 0.28 BMI units, however the average BMI-% slightly decreased by 0.4%. Overweight and obese children gained significantly more weight, BMI units and BMI-% units compared with normal weight children. This study supports that the holiday period may be an important time to target children, especially those who are already overweight and obese. Holiday weight gain; Childhood obesity.
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Predicting the magnitude and rate of weight gain for a given increase of energy intake requires a model of whole-body energy expenditure that includes the energy cost of tissue deposition. Here, I introduce a mathematical framework for modelling energy expenditure that elucidates conceptual problems with the classical Kielanowski method for estimating the efficiencies of body fat and protein deposition. An alternative approach uses the theoretical biochemical efficiencies for protein and fat synthesis in combination with models of energy expenditure that include body fat and protein turnover costs. I illustrate this alternative approach using a simple mathematical model applied to previously published data from growing rats and human infants and compare the simple model results with the classical Kielanowski model. While both models fit the data reasonably well (R2>0.87 in rats and R2>0.67 in infants), the Kielanowski method resulted in parameter estimates that varied widely across experiments, had poor precision, and occasionally produced efficiency estimates greater than 1. In contrast, the new method provided precise parameter values and revealed consistencies across different experiments. The proposed mathematical framework has implications for interpreting studies of animal nutrition as well as providing a roadmap for future modelling efforts.
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In 1998 the medical costs of obesity were estimated to be as high as $78.5 billion, with roughly half financed by Medicare and Medicaid. This analysis presents updated estimates of the costs of obesity for the United States across payers (Medicare, Medicaid, and private insurers), in separate categories for inpatient, non-inpatient, and prescription drug spending. We found that the increased prevalence of obesity is responsible for almost $40 billion of increased medical spending through 2006, including $7 billion in Medicare prescription drug costs. We estimate that the medical costs of obesity could have risen to $147 billion per year by 2008.
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This study examined the efficacy of augmenting standard weekly cognitive-behavioral treatment for obesity with a self-monitoring intervention during the high risk holiday season. Fifty-seven participants in a long-term cognitive-behavioral treatment program were randomly assigned to self-monitoring intervention or comparison groups. During 2 holiday weeks (Christmas-New Years), the intervention group's treatment was supplemented with additional phone calls and daily mailings, all focused on self-monitoring. As hypothesized, the intervention group self-monitored more consistently and managed their weight better than the comparison group during the holidays. However, both groups struggled with weight management throughout the holidays. These findings support the critical role of self-monitoring in weight control and demonstrate the benefits of a low-cost intervention for assisting weight controllers during the holidays.
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Epidemiological studies of diet and disease rely on the accurate determination of dietary intake and subsequent estimates of nutrient exposure. Although methodically developed and tested, the instruments most often used to collect self-reported intake data are subject to error. It had been assumed that this error was only random in nature; however, an increasing body of literature suggests that systematic error in the reporting of true dietary intake exists as well. Here, we review studies in which dietary intake by self report was determined while energy expenditure was simultaneously measured using the doubly labeled water (DLW) method. In seeking to establish the relative accuracy of each instrument to capture true habitual energy intake, we conclude that none of the self-reported intake instruments demonstrates greater accuracy against DLW. Instead, it is evident that the physical and psychological characteristics of study participants play a significant role in the underreporting bias observed in these studies. Further research is needed to identify underreporters and to determine how to account for this bias in studies of diet and health.
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Multiple-day food records or 24-hour dietary recalls (24HRs) are commonly used as "reference" instruments to calibrate food frequency questionnaires (FFQs) and to adjust findings from nutritional epidemiologic studies for measurement error. Correct adjustment requires that the errors in the adopted reference instrument be independent of those in the FFQ and of true intake. The authors report data from the Observing Protein and Energy Nutrition (OPEN) Study, conducted from September 1999 to March 2000, in which valid reference biomarkers for energy (doubly labeled water) and protein (urinary nitrogen), together with a FFQ and 24HR, were observed in 484 healthy volunteers from Montgomery County, Maryland. Accounting for the reference biomarkers, the data suggest that the FFQ leads to severe attenuation in estimated disease relative risks for absolute protein or energy intake (a true relative risk of 2 would appear as 1.1 or smaller). For protein adjusted for energy intake by using either nutrient density or nutrient residuals, the attenuation is less severe (a relative risk of 2 would appear as approximately 1.3), lending weight to the use of energy adjustment. Using the 24HR as a reference instrument can seriously underestimate true attenuation (up to 60% for energy-adjusted protein). Results suggest that the interpretation of findings from FFQ-based epidemiologic studies of diet-disease associations needs to be reevaluated.
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This report presents trends in national estimates of mean weight, height, and body mass index (BMI) from the National Health Examination and the National Health and Nutrition Examination Surveys between 1960 and 2002. The tables included in this report present data for adults by sex, race/ethnicity, and age group and for children by sex and year of age. Mean weight and BMI have increased for both sexes, all race/ethnic groups, and all ages. Among adults, mean weight increased more than 24 pounds. Although not as dramatically, mean height has also increased for most ages and for both males and females.
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To describe seasonal variation in food intake, physical activity, and body weight in a predominantly overweight population. A longitudinal observational study. Most of the study participants were recruited from a health maintenance organization (HMO) in central Massachusetts, USA. Additional individuals of Hispanic descent were recruited from outside of the HMO population to increase the ethnic diversity of this sample. Data from 593 participants, aged 20-70, were used for this investigation. Each participant was followed quarterly (five sampling points: baseline and four consecutive quarters) for 1-year period. Body weight measurements and three 24-h dietary and physical activity recalls were obtained on randomly selected days (including 2 weekdays and 1 weekend day) per quarter. Sinusoidal regression models were used to estimate peak-to-trough amplitude and phase of the peaks. Daily caloric intake was higher by 86 kcal/day during the fall compared to the spring. Percentage of calories from carbohydrate, fat and saturated fat showed slight seasonal variation, with a peak in the spring for carbohydrate and in the fall for total fat and saturated fat intake. The lowest physical activity level was observed in the winter and the highest in the spring. Body weight varied by about 1/2 kg throughout the year, with a peak in the winter (P<0.001 winter versus summer). Greater seasonal variation was observed in subjects who were male, middle aged, nonwhite, and less educated. Although there is seasonal variation in diet, physical activity and body weight, the magnitude of the change is generally small in this population. US National Heart, Lung and Blood Institute.
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With the rapid increase in obesity rates, determining critical periods for weight gain and the effects of changes in fat mass is imperative. The purpose of this study was to examine changes in body weight and composition over the holiday season (Thanksgiving through New Year's) in male and female college students. Subjects completed three visits: the first occurred within 2 weeks prior to Thanksgiving, the second occurred within 5 to 7 days following Thanksgiving, and the third occurred within 10 days following New Year's Day. A total of 82 healthy male and female college age subjects participated. Body composition by dual energy x-ray absorptiometry (DXA) was assessed at visits 1 and 3 while body weight was assessed at all three visits. Average body weight remained relatively unchanged from pre-Thanksgiving to post-New Year's (71.3 +/- 14 kg vs. 71.2 +/- 15 kg; P = 0.71) and, in fact, a subset of normal weight subjects lost a significant amount of body weight. However, percent body fat (25.9 +/- 9 %fat vs. 27.0 +/- 9 %fat; P < 0.01) and fat mass (18.3 +/- 8 kg and 19.1 +/- 8 kg; P < 0.01) significantly increased from pre-Thanksgiving to post-New Year's while fat-free mass (48.7 +/- 12 kg and 48.3 +/- 11 kg; P = 0.08) was not significantly different than the post-New Year's. A significant positive relationship (P < 0.001) between the change in BMI and percent fat, total fat mass, total fat free mass, and trunk fat mass for the pre-Thanksgiving and post-New Year's visits were found. The same significant positive relationships (P < 0.001) were also observed between the change in body weight and percent fat, total fat mass, total fat free mass, and trunk fat mass. Despite the fact that body weight remained unchanged over the course of the holiday season, a significant increase in %body fat and fat mass was observed. With recent evidence showing marked morbidity and mortality to be associated with increased body fat (particularly abdominal adiposity), results from this study suggest body weight alone may underestimate the potentially deleterious effects of the holiday season.
Conference Paper
This study examined the efficacy of augmenting standard weekly cognitive-behavioral treatment for obesity with a self-monitoring intervention during the high risk: holiday season. Fifty-seven participants in a long-term cognitive-behavioral treatment program were randomly assigned to self-monitoring intervention or comparison groups. During 2 holiday weeks (Christmas-New Years), the intervention group's treatment was supplemented with additional phone calls and daily mailings, all focused on self-monitoring. As hypothesized, the intervention group self-monitored more consistently and managed their weight better than the comparison group during the holidays. However, both groups straggled with weight management throughout the holidays. These findings support the critical role of self-monitoring in weight control and demonstrate the benefits of a low-cast intervention for assisting weight controllers during the holidays.
Article
Multiple-day food records or 24-hour dietary recalls (24HRs) are commonly used as “reference” instruments to calibrate food frequency questionnaires (FFQs) and to adjust findings from nutritional epidemiologic studies for measurement error. Correct adjustment requires that the errors in the adopted reference instrument be independent of those in the FFQ and of true intake. The authors report data from the Observing Protein and Energy Nutrition (OPEN) Study, conducted from September 1999 to March 2000, in which valid reference biomarkers for energy (doubly labeled water) and protein (urinary nitrogen), together with a FFQ and 24HR, were observed in 484 healthy volunteers from Montgomery County, Maryland. Accounting for the reference biomarkers, the data suggest that the FFQ leads to severe attenuation in estimated disease relative risks for absolute protein or energy intake (a true relative risk of 2 would appear as 1.1 or smaller). For protein adjusted for energy intake by using either nutrient density or nutrient residuals, the attenuation is less severe (a relative risk of 2 would appear as approximately 1.3), lending weight to the use of energy adjustment. Using the 24HR as a reference instrument can seriously underestimate true attenuation (up to 60% for energy-adjusted protein). Results suggest that the interpretation of findings from FFQ-based epidemiologic studies of diet-disease associations needs to be reevaluated. bias (epidemiology); biological markers; diet; energy intake; epidemiologic methods; nutrition assessment; questionnaires; reference values
Article
Objective Obesity prevalence in the United States (US) appears to be leveling, but the reasons behind the plateau remain unknown. Mechanistic insights can be provided from a mathematical model. The objective of this study is to model known multiple population parameters associated with changes in body mass index (BMI) classes and to establish conditions under which obesity prevalence will plateau. Design and Methods A differential equation system was developed that predicts population-wide obesity prevalence trends. The model considers both social and non-social influences on weight gain, incorporates other known parameters affecting obesity trends, and allows for country specific population growth. Results The dynamic model predicts that: obesity prevalence is a function of birth rate and the probability of being born in an obesogenic environment; obesity prevalence will plateau independent of current prevention strategies; and the US prevalence of obesity, overweight, and extreme obesity will plateau by about 2030 at 28%, 32%, and 9%, respectively. Conclusions The US prevalence of obesity is stabilizing and will plateau, independent of current preventative strategies. This trend has important implications in accurately evaluating the impact of various anti-obesity strategies aimed at reducing obesity prevalence.
Article
Evidence suggests that children gain more weight during the summer months compared with the school year. To examine the impact of the school and summer environment on children's weight further, we conducted a 5-year longitudinal study examining changes in standardized BMI (zBMI) of students entering kindergarten. Heights and weights were obtained at the beginning and end of each school year for 3,588 ethnically diverse (Caucasian: 27.2%, Black: 29.0%, Hispanic: 26.4%, and Asian 17.4%) students aged 5-7. A significant difference in change in zBMI during the school and summer months was found (-0.52, 95% CI: -0.59 to -0.45, p < .001; Wald χ(2) = 171.89, p < .001). Overall, children decreased BMI percentile during time spent in school by 1.5 percentile points and increased by 5.2 percentile points during summer months. Differences in the velocity of weight gain were found across weight classification categories with only overweight and obese children decreasing their zBMI during the school year. Time spent in school was shown to have a beneficial impact on students' weight, especially for students who were overweight or obese. However, these results are alarming because weight gain during elementary school occurs primarily during the relatively short span of summer break.
Article
Objective: The objective of this study was to test the hypothesis that community-based environmental change intervention prevents undesirable weight gain in children. Method: The method used in this study was a two-year, non-randomized, controlled trial (2003-2005) using community-based participatory methodology in three diverse cities in Massachusetts: one intervention and two socio-demographically-matched control communities (pooled for analysis). Children (n=1028), with a mean age=7.61+1.04years participated. Interventions were made to improve energy balance by increasing physical activity options and availability of healthful foods (Year 1). To firmly secure sustainability, the study team supported policies and shifted intervention work to community members (Year 2). Results: Change in body mass index z-score (BMIz) was assessed by multiple regression, accounting for clustering within communities and adjusting for baseline covariates. Sex-specific overweight/obesity prevalence, incidence and remission were assessed. Over the two-year period, BMIz of children in the intervention community decreased by -0.06 [p=0.005, 95% confidence interval: -0.08 to -0.04] compared to controls. Prevalence of overweight/obesity decreased in males (OR=0.61, p=0.01) and females (OR=0.78, p=0.01) and remission increased in males (OR 3.18, p=0.03) and females (OR 1.93, p=0.03) in intervention compared to controls. Conclusion: Results demonstrate promise for preventing childhood obesity using a sustainable multi-level community-based model and reinforce the need for wide-reaching environmental and policy interventions.
Article
Background/objectives: 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. Subjects/methods: A total of 48 males and 100 females (age 18-65 years) with a mean body mass index of 25.1±0.5 kg/m(2) 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. Results: 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/m(2)) showed a greater increase in BF% compared with normal weight participants (21.7±0.2 kg/m(2), P<0.05, 95% CI: 0.53-2.37) and a trend vs overweight participants (26.8±0.3 kg/m(2), 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. Conclusion: 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.
Article
Children's lifestyles have changed recently in Japan. These changes are thought to be associated with their physical growth. The aim of the present paper was to describe and interpret the growth seasonality of children attending day-nurseries in Osaka Prefecture, Japan. Results were based on a 6 year follow-up study of preschool children aged 0 at baseline to 6 years old at the end of the follow up. Longitudinal growth data for 148 boys and 113 girls, born between April 1990 and March 1991, attending day-nursery were obtained monthly from April 1990 through to March 1996. Children were divided into two groups: those with a normal (<17) or high (≥ 17) body mass index (BMI) at 5 years of age. Weight and height seasonal gains were estimated for each group. Weight gain was higher during autumn among the group with a normal BMI at 5 years of age. Among high BMI group, weight gain was higher during autumn when they were 0 to 4 years of age, while it was higher during summer when they were 5 years of age. Height gain was higher in spring and summer in both BMI groups. Results were similar in boys and girls. Although the findings are consistent with previous studies for normal BMI children, the present study reports new findings for children with a high BMI at 5 years of age, for whom a large weight gain occurred during summer.
Article
To compare the validity of various physical activity measures with doubly labeled water (DLW)-measured physical activity energy expenditure (PAEE) in free-living older adults. Fifty-six adults aged ≥65 yr wore three activity monitors (New Lifestyles pedometer, ActiGraph accelerometer, and a SenseWear (SW) armband) during a 10-d free-living period and completed three different surveys (Yale Physical Activity Survey (YPAS), Community Health Activities Model Program for Seniors (CHAMPS), and a modified Physical Activity Scale for the Elderly (modPASE)). Total energy expenditure was measured using DLW, resting metabolic rate was measured with indirect calorimetry, the thermic effect of food was estimated, and from these, estimates of PAEE were calculated. The degree of linear association between the various measures and PAEE was assessed, as were differences in group PAEE, when estimable by a given measure. All three monitors were significantly correlated with PAEE (r=0.48-0.60, P<0.001). Of the questionnaires, only CHAMPS was significantly correlated with PAEE (r=0.28, P=0.04). Statistical comparison of the correlations suggested that the monitors were superior to YPAS and modPASE. Mean squared errors for all correlations were high, and the median PAEE from the different tools was significantly different from DLW for all but the YPAS and regression-estimated PAEE from the ActiGraph. Objective devices more appropriately rank PAEE than self-reported instruments in older adults, but absolute estimates of PAEE are not accurate. Given the cost differential and ease of use, pedometers seem most useful in this population when ranking by physical activity level is adequate.
Article
We investigated the mechanisms of body weight regulation in young men of normal body weight leading unrestricted lives. Changes in total and resting energy expenditure, body composition, and subsequent voluntary nutrient intakes in response to overeating by 4,230 +/- 115 (SE) kJ/day (1,011 +/- 27 kcal/day) for 21 days were measured in seven subjects consuming a typical diet. On average, 85-90% of the excess energy intake was deposited (with 87% of this amount in fat and 13% in protein on average). There was no detectable difference between individuals in susceptibility to energy deposition. The resting metabolic rate, averaged for fasting and fed states, increased during overfeeding (mean +/- SE, 628 +/- 197 kJ/day, P less than 0.01), but at least some of this amount was obligatory expenditure associated with nutrient assimilation. No significant increase in energy expenditure for physical activity or thermoregulation resulted from overfeeding. Thus energy expenditure did not substantially adapt to increased energy intake. However, significant decreases in voluntary energy intake (1,991 +/- 824 kJ/day, P less than 0.05) and fat intake (48 +/- 11 g/day, P less than 0.01) followed overeating, indicating that adaptive changes in nutrient intakes can contribute significantly to body weight regulation after overeating.
Article
From a total of 12 pairs of young male identical twins who were overfed by an estimated 84,000 kcal over a period of 100 days, several pairs (eight to 11, depending on variables) were remeasured for body weight, body composition with the underwater weighing technique, regional fat distribution from skinfolds, girths, computed tomography (CT) fat areas in the abdominal region, and fasting plasma glucose, insulin, total cholesterol, high-density lipoprotein (HDL) cholesterol, and triglycerides 4 months and 5 years after completion of the overfeeding protocol. At 4 months, the twins had lost approximately 7 of 8 kg that they had gained with overfeeding. However, 5 years later, body weight had increased by 5 kg over the preoverfeeding level. Fluctuations in fat mass were greater than those in fat-free mass. The younger twins gained approximately twice as much as the older twins in the late recovery period, a difference attributed to the late phase of growth in body mass in the former. Upper-body fat was reduced at 4 months of follow-up study, but was increased in the late recovery phase. All blood values were normalized in the postoverfeeding periods. A within-pair resemblance was generally observed for the changes noted in the recovery periods, but it was more striking when variations between preoverfeeding and 4-month or 5-year values were considered. We conclude from these observations that there were no persistent effects of exposure to the overfeeding protocol over the expected age-associated increases in body mass, body fat, upper-body fat, abdominal visceral fat (AVF), and metabolic variables predictive of risk for common diseases in individuals of normal body weight and with no family history of obesity. The intrapair resemblance suggests that the genotype contributes to the alterations observed in the recovery from overfeeding and in the age-associated changes.
Article
(1) to compare dietary intake in summer and winter time; (2) to measure the change in body mass index (BMI), blood pressure and serum cholesterol between winter and summer; and (3) to determine the relationships between seasonal differences in dietary intake and BMI, blood pressure and serum cholesterol measurements. Ninety-four male industrial employees were screened twice in one year, in their work place, at winter and summer time. Workers were recruited from two factories and response rate was 95%. Health-related variables, including dietary intake, blood pressure and serum cholesterol were evaluated at each season and were compared. Correlation coefficients between seasonal differences in dietary intake and in BMI, blood pressure and serum cholesterol were calculated. From summer to winter the mean values of BMI increase from 26.1 kg/cm2 to 26.6 (P=0.038), systolic blood pressure from 119.6 to 121.6 (P=0.025), diastolic blood pressure from 75.2 to 77.2 mmHg (P=0.001), total cholesterol from 200.8 to 208.6 mg/dL (P=0.001), LDL cholesterol from 125.2 to 134.9 (P=0.001) and HDL cholesterol from 42.7 to 44.3 (P=0.0084). Triglycerides levels decrease from 174 to 145 in the winter (P=0.03). Mean dietary intake of fat increases from 99.1 to 106.0 (P=0.0016), saturated fat from 43.6 to 46.3 (P=0.0137), polyunsaturated fat from 25.1 to 28.3 (P=0.0002), cholesterol from 462.0 to 497.9 (P=0.0313), sodium from 5778.5 to 8208.2 (P=0.0035), zinc from 11.6 to 12.3 (P=0.0001), vitamin B1 from 1.4 to 1.5 (P=0.002), vitamin D from 4.3 to 4.9 (P=0.0323) and vitamin E from 11.2 to 12.7 (P=0.0073). Significant correlation was shown between the seasonal increase in saturated fat and the increase in BMI (r=0.37), total cholesterol (r=0.21) and LDL cholesterol (r=0.29). Seasonal change in dietary cholesterol intake was significantly and positively correlated with serum total cholesterol (r=0.24) and LDL cholesterol (r=0.24). Blood pressure was not associated with nutritional intake variables. Dietary intake in summer and winter is different as well as blood pressure, BMI and serum cholesterol. The seasonal increase in fat and cholesterol intake at winter time is associated with changes in BMI and serum cholesterol.
Article
Objective: The incidence of obesity and overweight in the US has increased considerably during the past two decades and currently affects 65% of the adult population. Research has indicated that small, yet irreversible, gains during the holiday season contribute to increases in weight during adulthood. Conjugated linoleic acid (CLA), a naturally occurring dietary fatty acid, has been found to reduce weight gain and dramatically decrease fat mass in animals. Although research in humans has shown inconsistent results, most studies have been of insufficient duration or have utilized body composition methods that are less accurate than the currently accepted criterion. Design: Randomized, double-blind, placebo-controlled study of 3.2 g/day CLA for 6 months. Subjects: Forty healthy, overweight subjects (age: 18-44 years; body mass index: 25-30 kg/m(2)). Measurements: Body composition by the four-compartment model, resting metabolic rate (RMR) by indirect calorimetry, self-reported physical activity and dietary intake, and blood chemistries were determined at baseline and after 6 months. Body weight was measured monthly during the pre-holiday season (August-October), holiday season (November-December) and post-holiday season (January-March). Adverse events were assessed monthly. Results: Compared to CLA, the placebo group showed a greater rate of weight gain during the holiday season (P=0.01). Within the placebo group, holiday weight change was significantly greater compared to the pre-holiday period (August-October) (P=0.03). Six-month change in body composition was improved with CLA compared to placebo (P=0.02), and body fat was significantly reduced within the CLA group (-1.0+/-2.2 kg, P=0.05). CLA had no effect on RMR, physical activity or dietary intake. The rate of reported negative emotions decreased significantly with CLA, although there was no difference in any other category of adverse event. In comparison to the placebo, CLA did not affect insulin resistance, blood lipids and markers of liver function or markers of inflammation, with the exception of a significant decrease in a biomarker of endothelial dysfunction. Conclusion: CLA supplementation among overweight adults significantly reduced body fat over 6 months and prevented weight gain during the holiday season. Although no adverse effects were seen, additional studies should evaluate the effect of prolonged use of CLA.
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xxx–xxx Please cite this article as: Schoeller DA, The effect of holiday weight gain on body weight
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D.A. Schoeller / Physiology & Behavior xxx (2014) xxx–xxx Please cite this article as: Schoeller DA, The effect of holiday weight gain on body weight, Physiol Behav (2014), http://dx.doi.org/10.1016/ j.physbeh.2014.03.018