Energy balance and overall well-being -
Nevertheless, BMI compares remarkably well to gold standard methods [ 24 ]. Waist circumference WC and waist-to-hip ratio WHR are useful to identify abdominal obesity but cannot clearly differentiate between visceral and subcutaneous fat compartments [ 25 , 26 ].
Other measures that can be used in medium- or large-scale studies include skinfold thickness and bioelectrical impedance analysis, although the latter appears to add little to measures based on weight and height [ 27 ].
More direct measures of body composition are available, such as air displacement plethysmography, underwater weighing hydrodensitometry , dual-energy X-ray absorptiometry, ultrasound, computed tomography and magnetic resonance imaging [ 28 , 29 ]. Although reproducible and valid [ 30 ], these measures of body composition are, due to high costs and lack of portability, limited to small-scale studies that require a high level of accuracy.
Their use in large-scale epidemiologic studies tends to be as reference methods [ 31 ]. Energy balance is the result of equilibrium between energy intake and energy expenditure. When energy intake exceeds expenditure, the excess energy is deposited as body tissue [ 1 ].
During adulthood, the maintenance of stable body weight depends on the energy derived from food and drink energy intake being equal to total energy expenditure over time.
To lose body weight, energy expenditure must exceed intake, and to gain weight, energy intake must exceed expenditure [ 32 ].
Measuring dietary intake and energy expenditure is a challenge in epidemiology. Energy intake, in particular, besides sometimes considerable measurement error in its assessment, can be subject to selective biases, such as the tendency of overweight and obese people to underestimate their intake [ 27 ].
While some objective measures exist for assessing energy expenditure or physical activity [ 34 ], such tools are not available for energy intake. Thus, assessment of energy balance by calculating the difference between intake and expenditure is not practically useful in large scale population studies.
Over time the best practical marker of positive or negative energy balance is change in the body weight which is readily measured with high precision even by self-report [ 27 ]. Since body weight change cannot distinguish between loss or gain of lean or fat mass, interpretation of weight change in an individual rests on assumptions about the nature of tissues lost or gained if body composition is not measured directly [ 35 ].
However, for most people, weight gain over a period of years during adulthood is largely driven by gain in fat mass.
In conclusion, body weight and change in weight provide precise indicators of long-term deviations in energy balance and are widely available for epidemiology studies. These simple and inexpensive measures of energy balance can be used both as exposure and outcome variables, taking into consideration their other determinants and confounding factors.
Although not useful for assessing energy balance, which requires extreme accuracy and precision, measures of energy intake and physical activity will continue to play other important roles in epidemiologic studies and in monitoring population trends.
Many factors relating to foods and beverages have been shown to influence amounts consumed or energy balance over the short to medium term, such as energy density and portion size [ 36 , 37 ], although the effect of energy density over the longer term is unclear.
One factor that has been suggested as being obesogenic is a high energy density of foods i. However, there are exceptions; for example, nuts and olive oil both extremely energy dense did not increase weight when added to a diet [ 39 ].
Fast foods are energy-dense micronutrient-poor foods often high in saturated and trans fatty acids, processed starches and added sugars [ 40 ].
Thus, the extent that these foods are obesogenic may be related to their composition rather than to their energy density. Several observational studies indicated a higher risk of obesity and weight gain in consumers of fast foods than in the non-consumers [ 41 — 44 ].
A recent study from the European Prospective Investigation into Cancer and Nutrition EPIC study reported that a high plasma level of industrial trans fatty acids, interpreted as biomarkers of dietary exposure to industrially processed foods, was associated with the risk of weight gain, particularly in women [ 45 ].
A meta-analysis of 22 cohort studies showed that each increment of sugary drink a day was associated with a 0. Conversely, higher consumption of legumes, wholegrain foods including cereals, non-starchy vegetables, and fruits which have relatively low energy density as well as nuts with high energy density have been associated with a lower risk of obesity and weight gain [ 38 ].
The content of fiber, satiating effect of fat, and low glycemic index in many of these foods may play an important role. Results from three U. cohorts indicated that better diet quality, i. This was in agreement with the results obtained from European cohorts using similar indexes [ 52 , 53 ].
Cohort studies conducted in LMICs would be valuable resources for understanding the impact of the nutrition and lifestyle transition on obesity.
Some longitudinal studies have already been initiated in LMICs as for instance the ones included in the Consortium of Health-Orientated Research in Transitioning Societies—COHORT [ 55 ], or the MTC cohort [ 56 ].
Building on these ongoing initiatives may prove informative and cost-efficient. Data from the Mexican Teacher cohort MTC have shown that women with a carbohydrates, sweet drinks and refined foods pattern were more at risk of having a larger silhouette and higher BMI, while a fruit and vegetable pattern was associated with a lower risk [ 57 ].
This emphasizes the need for public health interventions improving access to healthy diets, healthy food choices in the work place, and means of limiting consumption of beverages with a high sugar content and of highly processed foods, particularly those rich in refined starches.
Evidence from randomized trials conducted in children and adolescents indicates that consumption of sugar-sweetened beverages, as compared with non-calorically sweetened beverages, results in greater weight gain and increases in the body mass index; however, the evidence is limited to a small number of studies [ 58 , 59 ].
The findings of these trials suggest that there is inadequate energy compensation degree of reduction in intake of other foods or drinks , for energy delivered as sugar dissolved in water [ 58 ]. In weight loss trials, low carbohydrate interventions led to significantly greater weight loss than did low-fat interventions when the intensity of intervention was similar [ 60 ].
In a 2-year trial, where obese subjects were randomly assigned to low-fat restricted calorie, Mediterranean restricted-calorie or low-carbohydrate-restricted calorie diet, weight loss was similar in the MD and low-carb diet and significantly greater than in the low-fat diet. In their meta-analysis of 23 RCTs, Hu et al.
However, compared with participants on low-fat diets, persons on low-carbohydrate diets experienced a slightly but statistically significantly lower reduction in total cholesterol and low-density lipoprotein cholesterol but a greater increase in high-density lipoprotein cholesterol and a greater decrease in triglycerides.
The impact of reducing fat or carbohydrate may depend at least as much on the overall composition of the diet as on the reduction in the specific macronutrient targeted. Most of these studies were conducted in HICs. This emphasizes the importance of conducting studies in LMICs in particular long-term dietary intervention trials focusing on alternative dietary patterns with foods readily available in these countries to propose viable changes in nutritional behaviors.
Long-term observational studies fairly consistently show an association between physical activity and weight maintenance, and a position paper from the American College of Sports Medicine ACSM stated that — min per week of moderate intensity physical activity is effective to prevent weight gain [ 62 ].
The long-term effect of physical activity on weight loss has been less convincing and isolated aerobic exercise was not shown to be an effective weight loss therapy but may be effective in conjunction with diet [ 63 ]. Evidence suggests that diet combined with physical activity results in greater weight loss than diet alone and is more effective for increasing fat mass loss and preserving lean body mass and, therefore, it leads to a more desirable effect on overall body composition [ 64 ].
Intervention studies have consistently found no effect of resistance exercise on reducing body weight [ 62 ] or visceral adipose tissue [ 65 ]. However, resistance training appears to be more effective in increasing lean body mass than aerobic training and the combination of aerobic and resistance training may be the most efficient exercise training modality for weight loss [ 66 ].
In recent years, physical activity research has expanded its focus to include the potentially detrimental effects of sedentary behavior on energy balance. Sedentary behavior also represents an independent risk factor for obesity in children and adolescents [ 68 ].
In short-term studies, higher levels of physical activity have been shown to mitigate the effect of increasing energy density on weight gain, and it appears that at the low levels of physical activity typical of current high income populations, adequate suppression of appetite to maintain energy balance is compromised [ 69 ].
In conclusion, moderate intensity physical activity performed for — min per week appears to prevent weight gain and produces modest weight loss in adults. Resistance exercise does not appear to decrease body weight or body fat but it promotes gain of lean body mass, and the combination of resistance and aerobic exercise seems to be optimal for weight loss.
Physical activity improves chronic disease risk factors independent of its impact on body weight regulation. Moreover, sedentary behavior represents an independent risk factor for the development of overweight and obesity.
The patterns and distributions of obesity within and between ethnically diverse populations living in similar and contrasting environments suggest that some ethnic groups are more susceptible than others to obesity [ 70 ].
More than common genetic variants have been robustly associated with measures of body composition [ 71 ], though the individual impact of each variant is small. There is now convincing epidemiological evidence of interactions between common variants in the FTO Fat mass and obesity-associated protein gene and lifestyle with respect to obesity [ 72 — 74 ].
However, almost all these data are from cross-sectional studies, and temporal relationships are not clear. There are large studies supporting gene—lifestyle interactions at several other common loci, but the burden of evidence is far less for these loci than for FTO [ 75 , 76 ].
However, the magnitude of the interaction effects reported for FTO or other common variants is insufficient to warrant the use of those data for clinical translation. Potentially reversible epigenetic changes in particular altered DNA methylation patterns could also serve as biomarkers of energy balance and mediators of gene—environment interaction in obesity [ 77 ].
Such discoveries could provide novel insights into how energy balance and its determinants influence obesity development, interaction with diet and environmental factors and subsequent metabolic dysregulation.
In summary, there is an abundance of published evidence, predominantly from cross-sectional epidemiological studies, that supports the notion that lifestyle and genetic factors interact to cause obesity.
However, few studies have been adequately replicated, and functional validation and specifically designed intervention studies are rarely undertaken, both of which are necessary to determine whether observations of gene—lifestyle interaction in obesity are causal and of clinical relevance.
In a healthy symbiotic state, the colonic microbiota interacts with our food, in particular dietary fiber, allowing energy harvest from indigestible dietary compounds. It also interacts with cells, including immune cells, as well as with the metabolic and nervous systems; and protects against pathogens.
Conversely, a dysbiotic state is often associated with diseases including not only inflammatory bowel diseases IBD , allergy, colorectal cancer and liver diseases, but also obesity, diabetes and cardiovascular diseases [ 80 ].
Dysbiosis may be defined as an imbalanced microbiota including loss of keystone species, reduced richness or diversity, increased pathogens or pathobionts or modification or shift in metabolic capacities [ 81 ]. Dysbiosis in the intestinal microbiota has been associated with obesity [ 82 ].
A loss of bacterial gene richness is linked to more severe metabolic syndrome, and less sensitivity to weight loss following caloric restriction diet [ 83 ].
Dietary habits also seem to be associated with microbiota richness [ 84 ]. The proposed mechanisms by which gut microbiota dysbiosis and loss of richness can promote obesity and insulin resistance are diverse, often derived from mouse models, and still deserve more studies and validation in humans.
Many factors have contributed to the increase in the prevalence of obesity in children including unhealthy dietary patterns with high consumption of fast foods and highly processed food [ 85 ], of sugar sweetened beverages [ 86 ], lack of PA, an increase in sedentary behaviors e.
Experiences during early life e. In particular, maternal gestational weight gain GWG [ 92 ], maternal overweight prior to pregnancy, smoking during pregnancy, high or low infant birth weight, rapid weight gain during the first year of life [ 93 — 95 ], early obesity rebound [ 96 ], breastfeeding patterns [ 97 ] and early introduction of complementary food [ 98 ] have all been linked to later excess adiposity.
Many of these are inter-related and work is ongoing to disentangle concurrent factors. In addition, high levels of stress during childhood and adolescence may change eating habits and augment consumption of highly palatable but nutrient-poor foods [ 99 ].
Numerous policy options to prevent obesity have been explored, and evidence is sufficient to conclude that many are cost effective. Given the multifactorial nature of obesity, as in other complex public health problems, a combination of interventions is more likely to generate better results than focusing only on a single measure [ ].
Gortmaker et al. They modeled the reach, costs and savings for the US population Some of these interventions excise tax on sugar-sweetened beverages, elimination of tax deduction for advertising unhealthy food to children and nutrition standards for food and beverages sold in schools outside of meals not only prevent many cases of childhood obesity, but also potentially cost less to implement than they would save for society.
The global childhood obesity epidemic demands a population-based multisector, multi-disciplinary, and culturally relevant approach. Children need protection from exploitative marketing and special efforts to support healthy eating, PA behaviors, and optimal body weight [ — ].
Adequate evidence has been accumulated that interventions, especially school-based programs, can be effective in preventing childhood obesity [ ]. Preventing obesity will require sustained efforts across all levels of government and civil society. Although there are individual differences in susceptibility, obesity is by large a societal problem resulting from health related behaviors that are largely driven by environmental upstream factors.
Many options for policies to prevent obesity are available and many of these are effective and cost-effective. Integrated management of the epidemic of obesity requires top-down government policies and bottom-up community approaches and involvement of many sectors of society.
Integrating evidence-based prevention and management of obesity is essential. There is convincing evidence for a role of obesity as a causal factor for many types of cancer including colorectum, endometrium, kidney, oesophagus, postmenopausal breast, gallbladder, pancreas, gastric cardia, liver, ovary, thyroid, meningioma, multiple myeloma, and advanced prostate cancers [ 19 ].
Recent progress on elucidating the mechanisms underlying the obesity-cancer connection suggests that obesity exerts pleomorphic effects on pathways related to tumor development and progression and, thus, there are potential opportunities for primary to tertiary prevention of obesity-related cancers.
We now know that obesity can impact well-established hallmarks of cancer such as genomic instability, angiogenesis, tumor invasion and metastasis and immune surveillance [ 20 ]. However, obesity-associated perturbations in systemic metabolism and inflammation, and the interactions of these perturbations with cancer cell energetics, are emerging as the primary drivers of obesity-associated cancer development and progression.
In both obesity and metabolic syndrome, alterations occur in circulating levels of insulin and insulin-like growth factors, sex hormones, adipokines, inflammatory factors, several chemokines, lipid mediators and vascular associated factors [ 21 — 23 ].
Most research on obesity and cancer has focused on Caucasians in HICs. While many of the identified risk factors in HICs will have the same physiologic effects in LMICs, the determinants may be different, in addition to other environmental and genetic differences across populations.
Novel risk factors or traditional diets may be identified in newly studied populations and regions. Diet is shaped by many factors such as traditions, knowledge about diet, food availability, food prices, cultural acceptance, and health conditions. Likewise, a variety of factors will influence daily physical activity and sedentary behaviors, including dwellings, urbanization, opportunities for safe transportation by bicycle riding and walking, recreational facilities, employment constraints and health conditions.
Surveillance of current diet and health conditions and assessment of trends over time is of major importance in LMICS. Further resources and research capacity are of highest priority.
In addition to surveillance efforts, prospective studies able to document lifestyle and change of lifestyle over time are an important area of research.
Several cohort studies conducted in HICs have shown an impact of healthy dietary patterns on obesity [ ] and similar studies could be conducted in LMICs to identify dietary patterns related to weight gain and obesity in a variety of settings to evaluate the major lifestyle, behavioral and policy influences in an effort to plan public health interventions appropriately.
A major challenge is to capture life course exposures and identify windows of susceptibility. Cohort studies covering the whole life course, focusing on critical windows of exposure and the time course of exposure to disease birth cohorts, adolescent cohorts, and young adult cohorts , should be considered.
Of particular interest are multi-centered cohorts and inter-generational cohorts that would create resources to enable research on the interplay between genetics, lifestyle and the environment.
For example in the Avon longitudinal study of parents and children ALSPAC , increasing intake of energy-dense nutrient-poor foods during childhood mostly free sugar was associated with obesity development. Diets with higher energy density were associated with increased fat mass [ ]. Most relevant to LMICs is the observation that children who were stunted in infancy and are subsequently exposed to more calories, at puberty, are more likely to have higher fat mass at the same BMI compared with children who were not stunted [ 93 , 94 , ].
Poor maternal prenatal dietary intakes of energy, protein and micronutrients have been associated with increased risk of adult obesity in offspring while a high protein diet during the first 2 years of life was also associated with increased obesity later in life [ ]; conversely, exclusive breastfeeding was associated with lower risk of obesity later in childhood, although this may not persist into adulthood [ ].
Similar results from a cohort study conducted in Mexico show that children exclusively or predominantly breastfed for 3 months or more had lower adiposity at 4 years [ ].
Further work on birth cohorts or other prospective studies in LMICs is likely to provide insights into the developmental causes of obesity and NCDs. Input from local research communities, health ministries and policy makers and appropriate funding or resource assignment are critical for the success of new efforts in LMICs.
There is clearly a need for capacity building and resources devoted to nutritional research in LMICs. The first step would be a comprehensive assessment of resources already in place, and the identification of gaps and priorities for moving forward. Repeated surveillance surveys are essential in LMICs for evaluation of current and future status of the population and addressing undesirable trends with prevention and control programs.
It is recognized that few prospective studies are currently underway in LMICs and resources will be needed to pursue this important area of research.
Input from local research communities, health ministries and policy makers are critical for the success of new efforts in LMICs. The global epidemic of obesity and the double burden of malnutrition are both related to poor quality diet; therefore, improvement in diet quality can address both phenomena.
The benefits of a healthy diet on adiposity are likely mediated by effects of dietary quality on energy intake, which is the main driver of weight gain. Energy balance is best assessed by changes in weight or in fat mass.
Measures of energy intake and expenditure are not precise enough to capture small differences that are of individual and public health importance. Dietary patterns characterized by higher intakes of fruits and vegetables, legumes, whole grains, nuts and seeds and unsaturated fat, and lower intakes of refined starch, red meat, trans and saturated fat, and sugar-sweetened foods and beverages, consistent with a traditional Mediterranean diet and other measures of dietary quality, can contribute to long-term weight control.
Genetic factors cannot explain the global epidemic of obesity. It is possible that factors such as genetic, epigenetic and the microbiota can influence individual responses to diet and physical activity. Very few gene—diet interactions or diet-microbiota have been established in relation to obesity and effects on cancer risk.
Short-term studies have not provided clear benefit of physical activity for weight control, but meta-analysis of longer term trials indicates a modest benefit on body weight loss and maintenance.
The combination of aerobic and resistance training seems to be optimal. Long-term epidemiologic studies also support modest benefits of physical activity on body weight. This includes benefits of walking and bicycle riding, which can be incorporated into daily life and be sustainable for the whole population.
Physical activity also has important benefit on health outcomes independent of its effect on body weight. In addition, long-term epidemiologic studies show that sedentary behavior in particular TV viewing is related to increased risk of obesity, suggesting that limiting sedentary time has potential for prevention of weight gain.
The major drivers of the obesity epidemic are the food environment, marketing of unhealthy foods and beverages, urbanization, and probably reduction in physical activity.
Existing evidence on the relations of diet, physical activity and socio-economic and cultural factors to body weight is largely from HICs. There is an important lack of data on diet, physical activity and adiposity in most parts of the world and this information should to be collected in a standardized manner when possible.
In most environments, 24h recalls will be the more suitable method for dietary surveillance. Attention should be given to data in subgroups because mean values may obscure important disparities.
In utero and early childhood, environment has important implications for lifetime adiposity. This offers important windows of opportunity for intervention. Observational data on determinants of body weight and intervention trials across the life course to improve body weight are also required.
To accomplish these goals, there is a need for resources to build capacity and conduct translational research. Gaining control of the obesity epidemic will require the engagement of many sectors including education, healthcare, the media, worksites, agriculture, the food industry, urban planning, transportation, parks and recreation, and governments from local to national.
This provides the opportunity for all individuals to participate in this effort, whether at home or in establishing high-level policy.
We now have evidence that intensive multi-sector efforts can arrest and partially reverse the rise of obesity in particular among children. In conclusion, we are gaining understanding on the determinants of energy balance and obesity and some of these findings are being translated into public health policy changes.
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Jama 14 — The questionnaire was administered through Amazon Mechanical Turk Mturk, © —, Amazon Mechanical Turk, Inc.
MTurk's workforce tends to be younger, educated, underemployed, with an equal distribution of males and females, a high percentage of Caucasians and Asians, and household incomes below the average US population 33 , With these recruitment methods, we not only targeted the general population but also targeted support groups with persons of higher education on Facebook and Twitter.
These two recruitment methods allowed us to include data from a diverse population. This amount was estimated based on the minimal amount required to complete a similar survey and in line with the median hourly wage earned by an MTurk responder.
Participants recruited via social media, email, and word of mouth volunteered to complete the survey and did not receive any monetary compensation. Of note, while the participation using this recruitment method was completely voluntary, it is possible that the compensation offered to Mturk workers for completion of survey may have been a motivational factor for them to participate in our study.
Participant recruitment and data collection occurred during the 11 days from April 24th, to May 4th, , while shelter-in-place guidelines were instituted across the US. Of the 1, participants who initially responded to the call to complete the questionnaire, 1, participants were included in the data analysis.
Four attention check questions and one subjective question that asked participants to type a response in a text box were included to ensure responses were not bots. To assess the quality of participant responses, we also asked them to type their height inches and weight pounds in a text box, and any biologically implausible responses were excluded.
The Qualtrics questionnaire included the following 7 item categories: demographics, weight behaviors, sleep, and other health behaviors, eating behaviors, physical activity behaviors, psychological factors, and food purchasing behaviors. Questions within these categories were aimed at understanding change in practices and beliefs during the COVID shelter-at-home.
Based on the Qualtrics recordings, participants completed the survey in ~25 min. Cronbach's alpha, a measure of internal consistency reliability with higher values suggesting higher reliability, is indicated for each scale measure where applicable.
Eating behaviors were determined by asking participants if their consumption of the following items increased, decreased, or remained the same during COVID shelter-in-place: fruits during meals , vegetables during meals , caffeine, non-diet drinks includes, Coke, Pepsi, flavored juice drinks, sports drinks, sweetened teas, coffee drinks, energy drinks, electrolyte replacement drinks , and diet soda and other diet drinks.
To determine change in consumption of processed and ultra-processed foods, we presented a list of foods as described by the NOVA classification system This system classifies all foods into 4 groups based on the extent and purpose of industrial processing as following: unprocessed foods, processed culinary ingredients, processed foods, and ultra-processed foods NOVA is a food classification system most applied in the scientific literature to identify and define ultra-processed foods Change in consumption of take-out food and alcohol intake was also recorded.
Since no validated tool is available collect information of perceptual change in dietary behavior, a validated tool was not used to collect this data.
We did not collect data on quantities consumed for the specific food items using the traditional methods of self-reported dietary data collection because they are prone to reporting errors and appears to underestimate energy and nutrient intake 39 , Change in sedentary behaviors was determined by asking questions on change in time spent on watching television, social media, or other leisurely activities such as video games, computer, email etc.
since COVID outbreak. Given the lack of validated questionnaires to capture the perceptual change in behaviors, we developed and used face-valid items for both the physical activity and eating behavior measures.
The validated CoEQ comprised 21 items and included questions on general appetite and overall mood independent of craving , frequency and intensity of general food craving, craving for specific foods e. Participants responded about their experience over the previous seven days.
These items were assessed using a point visual analog scale VAS. Subscales created form the questionnaire were used to calculate scores for: craving control, craving for sweet foods, craving for savory foods, and positive mood 41 and their α's were 0.
To assess sleep duration, participants were asked to report the average number of hours spent sleeping per day since the COVID lockdown in their area.
To quantify sleep quality, we used the Stanford Sleepiness Scale 42 to collect ratings on how sleepy participants felt after waking up in the morning since the COVID lockdown in their area.
Higher values indicate greater sleepiness. The Multidimensional state boredom scale 43 was used to collect information on boredom during the COVID lockdown. This scale uses eight items to assess boredom in the present moment on a scale of 1 strongly disagree to 7 strongly agree.
Higher score indicated higher boredom during the lockdown. The scale has been used in a similar manner by others to measure boredom during the pandemic All participants reported report their current stress levels using a visual analog scale. The Capacity for Self-Control Scale 45 assesses individual differences in the ability to exercise three forms of general self-control: self-control by inhibition i.
The abbreviated measure consists of 9 items 3 items per subscale scored on a five-point Likert scale, from 1 hardly ever to 5 nearly always. Higher score indicates greater capacity for self-control trait.
The Implicit Theory of Weight Measure 29 assesses the degree of orientation toward incremental beliefs of weight i. The measure consists of 6 items scored on a seven-point Likert scale from 1 strongly agree to 7 strongly disagree.
SAS version 9. We created four energy balance behavior scores reflecting positive energy balance using the items on the Qualtrics survey administered. Items used to estimate a high-sedentary behavior score included change in television watching, change in screen time, and change in sitting time.
Items used to estimate a low-physical activity behavior score included change in walking time, change in vigorous physical activity, and change in moderate physical activity.
The low-healthy eating behavior score was calculated using responses on fruit and vegetable consumption as snacks or in general during meals. All behaviors included in development of a priori energy balance behavior scores have been extensively reported to contribute to positive energy balance or negative energy balance see Supplementary Material.
The α's for high-sedentary behavior score, low-physical activity behavior score, high-unhealthy eating behavior score, and low-healthy eating behavior score were 0. Note that scores on low-physical activity behavior and low-healthy eating behavior were calculated such that higher scores reflected less physical activity and less fruit and vegetable consumption.
We first conducted ANOVAs to test differences of health-risk behaviors between demographic groups. We then calculated intercorrelations between energy balance behavior scores and health and psychological risk and protective factors.
We further conducted a Latent Profile Analysis LPA to identify and characterize patterns of health behavior change during the pandemic. LPA is a data-driven approach used to uncover relationships among individuals to create meaningful groups or classes of people based on the heterogeneity of their responses; these classes can then be characterized and compared to each other using important demographic, psychological, and behavioral factors Classes of people determined by LPA have been used to describe distinct differences in cognition and behavior among people with regard to a variety of physical and mental health phenomena, such as alcohol use, sleep, occupational stress, resilience, coping strategies etc.
In the current work, we used LPA to reveal different classes of people's health-risk behaviors during the COVID pandemic shelter-at-home. We then compared the classes on psychological, behavioral, and demographic qualities to provide comprehensive representations of various groups of people's characteristics, thoughts, and behaviors during the COVID pandemic shelter-at-home.
This analysis does not focus on the amount of change within one behavior but instead looks at patterns of change i. ANOVAs were conducted to evaluate differences of risk behaviors between demographic groups. Participants' scores for four energy balance behavior scores are presented for each demographic variable in Table 1.
Briefly, the score for increasing sedentary behavior was significantly higher among women vs. The score for low-physical activity was significantly higher among Asians vs. The score for high-unhealthy eating was significantly higher among women vs.
Table 1. Scores for four energy balance behavior categories by demographic profile of participants. Scale intercorrelations were calculated to highlight associations between psychological and health risk and health protective factors.
Correlations are shown in Table 2. Next, we conducted a LPA to characterize classes of participants' patterns of risky health behaviors during the COVID pandemic using composite variables for physical activity, sedentary behavior, healthy food consumption, and unhealthy food consumption.
The classes' patterns of endorsed risky health behaviors are shown in Figure 1. Figure 1. Average scores of engagement in obesogenic risk behaviors by latent classes. Class 3 is considered the General Low Risk Group; Class 4 is considered the General High-Risk Group. Class 1 is the Medium General Risk, Medium Sedentary Risk Group, and Class 2 is the Medium General Risk, High Sedentary Risk Group.
Examining the characteristics of participants in all risk profiles Table 3 , individuals in the highest risk class Class 4; Participants in the low-risk category Class 3; 5. Classes 1 In terms of psychosocial risk factors, Class 2 differed from Class 1 in sleep patterns Class 2 participants reported waking up less alert despite reporting more hours of sleep , boredom, self-control, and mood.
Although people in these classes were similar in physical activity and engaged in a mixed pattern of healthy and unhealthy eating habits, they exhibited different patterns of positive mood, craving control, cravings, boredom, and self-control.
Demographic differences also emerged across groups. Participants in Classes 1 and 3 relatively lower risk were more likely to be male, married and White. Table 3. Psychosocial risk factors across class determined by latent profile analysis.
The primary purpose of this paper was to investigate the relationship between relevant psychological markers and energy balance-related behavior scores, during the COVID related shelter-in-place. Whereas, having psychological traits such as greater general self-control, control over cravings, or positive mood was related to lower self-reported energy intake and energy expenditure during the lockdown.
Individuals with the highest risk pattern reported having higher sleepiness, more boredom, less positive mood, and more cravings for sweet and savory foods. Our hypothesis that self-reported change in boredom during the lockdown, a state like-psychological variable, may be related to dietary intake risk was based on prior research suggesting that high boredom increases the desire for and intake of unhealthy foods and snacks Our data support these findings by showing that boredom was related to the increased risk of consuming unhealthy foods energy-dense sweet and savory snacks, sugary drinks, etc.
and lowering healthy food intake fruits and vegetables during the pandemic. Boredom is shown to encourage people to seek sensation 52 ; hence, we speculate that exciting options, such as sugary and fatty foods, may have served as a potent distractor of self-regulation by providing intense appearance or taste.
As a result, people gravitate toward easier tasks that require less cognitive load, such as the use of smartphones, the internet, or online socializing 54 , 55 ; this may explain the relationship observed between increased sedentary behavior, low physical activity and boredom, in our dataset.
The relationship observed between self-reported sleepiness ratings, sleep duration, and diet quality in the current study confirms results from prior studies. We, and others, have previously shown that higher sleepiness 26 , 56 and reduced sleep duration 57 are both related to food cravings and intake of energy-dense savory and sugary foods that may manifest in positive energy balance.
The relationship of sleep time with sedentary activity is more complex, with long and short sleep durations both shown to impact sedentary behaviors in previous studies. In contrast, long sleep duration lowers daytime activity levels and increases screen-based sedentary behaviors These data suggest that the reported positive correlation between sleep duration and sedentary activity is possibly related to a decline in overall wake time activity.
We further speculate that lethargy after a long sleep duration and having less time available in the day may have added to increased sedentary behavior. It is equally possible that spending more sedentary time, especially in front of the screen, may reduce sleep quantity and quality Given the cross-sectional design of this study, it is difficult to determine the directionality of the relationship between sleep duration and sedentary behavior in our participants during the shelter-at-home.
Similar to the findings by Buckland et al. where lower craving control predicted high energy dense sweet and savory food intake during COVID lockdown, we also showed that greater control on food cravings, representing a state-like psychological characteristic, was related to unhealthy eating score Intense food craving is often accompanied with lower mood and anxiety levels, and commonly reported with high BMI Accordingly, we demonstrated that high craving control correlated with positive mood score and healthy food selection.
Our data also shows a relationship between craving control and low reduction in physical activity. Interestingly, physical activity interventions can reduce cravings for high-caloric foods as well as mood While we cannot confirm directionality in our cross-sectional data, it is possible that maintenance of high physical activity contributed to better mood and low boredom, thus supporting control over cravings.
In everyday life, general self-control, a trait psychological characteristic, is associated with positive weight management behaviors, including healthier eating, successful weight loss, and increased physical activity, as well as with better psychological well-being 65 — The current study extends previous research on the personal benefits of self-control by highlighting the potentially protective aspects of self-control during a time when typical lifestyles have been majorly disrupted—in the context of a global pandemic.
Because uncertainty increases the desire for indulgence 68 , having high self-control may buffer temptation engagement during COVID shelter-in-place. Notably, in this study, people who reported the least engagement in energy balance-related behaviors had the highest self-control. Those with relatively higher self-control also reported feeling in control of their food cravings, had fewer cravings for sweet and for savory foods, believed that body weight is malleable, and had lower average BMI.
It could be that people who have higher self-control are better able to continue their established physical activity routines and habits of inhibiting unhealthy food consumption in times of uncertainty 69 , 70 and to initiate new lifestyle adjustments in the face of necessary change People with high self-control may also be adept at avoiding tempting situations 71 , 72 , which may happen frequently during shelter-in-place e.
In addition, people with higher self-control experienced several positive emotional benefits during shelter-in-place: on average, they felt less bored, reported higher positive mood, more alertness after waking, and less stress. Being able to successfully navigate temptation, resolve self-control conflicts, and pursue their goals, even in an unpredictable time, likely has a beneficial effect on mental well-being Taken together, trait self-control may be a protective factor against the negative effects of COVID shelter-in-place.
One predictor of weight management behaviors is the belief that a person's body weight is malleable 29 — 31 , In contrast to previous work, however, people in the current study who were classified as engaging the least in energy balance-related behaviors vs.
people in the higher risk classes reported stronger beliefs that body weight is not malleable. Replicating previous correlational findings 30 , 74 , in the current study, participants' beliefs about weight malleability were unrelated to their BMI.
Surprisingly, people who had stronger entity beliefs about body weight reported less sedentary behavior and less unhealthy eating; beliefs about weight control were unrelated to physical activity risk and healthy eating risk.
One possible explanation for this finding might be that people who believed they can control their weight felt like they might be able to regain energy balance after the pandemic—that they could manage their weight well when they had the time and resources to do so.
Counterintuitively, their health behaviors during the pandemic may have slipped because they thought they might be able to make up for it later. Alternatively, it may be that self-efficacy—which is a mechanism by which beliefs about weight control influence health behaviors 29 , 74 —was interrupted during the COVID pandemic.
It could also be the case that during this unprecedented time, people may have generally low beliefs that if they were to experience setbacks in their weight management pursuits, they would be able to successfully cope with those challenges.
Although we did not directly measure self-efficacy nor expectations of future success, people who reported having weaker incremental weight beliefs also reported lower positive mood, less control over their food cravings, higher cravings for sweet foods, less alertness after waking, and higher stress.
Participants' negative mood may signal to them that they are making poor progress on their goals and will subsequently be less successful in the future 75 , which may be indicative of their engagement with weight-management behaviors.
In our study, people with more positive mood had a lower risk of less physical activity and unhealthy eating. Along the same lines, feelings of control of one's food cravings predict lower risks of unhealthy and healthy eating.
These negative psychological factors experienced during shelter-in-place may attenuate the otherwise positive effect that incremental beliefs usually have on weight management behaviors.
Given the heterogeneity in energy balance-related behaviors, an assessment of risk profile groups gave us a better insight into the unique characteristics of individuals who may be more prone to weight gain during the pandemic.
Not surprisingly, individuals with the highest risk not only engaged in all energy balance-related behaviors but also reported to have psychological and health markers known to promote obesity.
Although similar in risk level, we observed subtle but unique differences between the two moderate risk groups. The most striking difference between the two groups was sedentary behavior. As theorized by previous work, a complex interplay between personal circumstances, environmental variables, and social factors determines sedentary behavior A large percentage of high sedentary risk group Class 2 individuals belonged to a high-income bracket.
High income groups are more likely to hold sedentary jobs 77 and are known to engage in prolonged sedentary behavior, as compared to lower income groups. Occupational sitting and screen time, along with the closure of all outdoor avenues and added pressure of being always on when working from home, may have put the higher income group at higher risk.
We also noticed that a large percentage of adults in this group were married or living with a partner. While we did not measure it directly, there is a plausibility of higher perceived modeling of sedentary behavior in presence of a partner, especially if the partner spends more time engaged in screen time Additionally, perceived behavioral control is likely to be protective of sedentarism 79 , which was prevalent in the Class 2 risk group.
By contrast, studies also show that when it comes to sedentary behaviors, self-control beliefs may be ineffective in influencing the decision to be sedentary. Rather it is the discriminant motivational structure, high access, and ease of use among people who wish to perform these behaviors This lack of motivation with high boredom and negative mood may have been the differentiating factor for sedentary behavior in the two groups during the pandemic.
The results of this study must be interpreted in light of several limitations. This study was cross-sectional and non-experimental; thus, causality and temporality cannot be inferred.
As such, we cannot conclude if reported alterations in behaviors truly lead to weight gain. Additionally, while there is evidence of behavior changes with body mass index status, due to the self-reported nature of height and weight data collected, we did not test the difference in health behaviors between BMI groups.
We also asked participants to report their perception of behavior change increased, decreased, remained the same , rather than asking them to report behaviors before and during the lockdown period and calculating the change score for each variable.
Moreover, a recent report demonstrated that perceptual increase in physical activity is driven by the amount of vigorous physical activity performed, suggesting that an increase in intensive physical activity is important for perceiving a change in one's physical activity In contrast, smaller changes may need to be sufficient for change to be perceived as such Thus, the self-reported change scores in our study may not be accurate.
Furthermore, with possible differences in perception of individual behavioral component of score categories, our aggregate scores for these categories may be subject to biases. While pandemic related restrictions limited our ability to collect data on energy balance behaviors subjectively, the importance of using objective measures cannot be denied.
Recall bias, especially with using non-validated tools, may confound self-reports reflecting a perceived rather than actual change behaviors during the lockdown This should be taken into consideration when interpreting our findings.
Additionally, while we did not disclose the specific purpose of the study to the participants, our results could also be driven by participant's expectation and not their actual behavior.
With regards to the questionnaires, while validated instruments were used as possible, some necessary questions were developed by the investigators to capture the current unique environment.
Moreover, we did not use a validated tool for dietary intake, such as food frequency questionnaires. Thus, care should be taken to integrate these findings with the broader literature. For our psychological and health behavior constructs, some variables were contextual or state like, while some were trait like.
However, this should not have impacted our findings because whether it is a state like characteristic or trait like characteristic, we were interested in how it influenced energy-balance-related behaviors and how they differed between the risk classes. Moreover, despite the diversity and size of our sample, a convenience sampling approach was used, which may limit generalizability.
Furthermore, the degree of shelter-in-place guidelines and the number of COVID cases in participants' area of residence likely differed, creating differences in flexibility with stepping outside the house. The time frame of data collection may have influenced our results as well.
As such, at the time of data collection, although most states had implemented shelter-in-place guidelines, a few states were considering lifting the restrictions. This one snapshot of time also assumes that thoughts and behaviors were static throughout the entire shelter-in-place time, which is likely an oversimplification.
Altogether, this study describes state- and trait-like psychological factors that relate to energy balance-related behavior categories during the COVID shelter-at-home restrictions in the U. Our analysis provides important insights into the complex interplay of factors related to risk of increasing unhealthy eating and sedentary activities and decreasing healthy eating and physical activity.
These results also contribute to improving our understanding of the patterns of risk groups and their unique characteristics, specifically highlighting that the lockdown did not adversely impact energy balance behaviors in all individuals.
Health entities such as World Health Organization have several nutritional and lifestyle recommendations to follow during lockdown for the general public Thus, based on our findings, such public health efforts may be better spent targeting at-risk population subgroups in need of weight management interventions during the current pandemic rather than targeting people who are already managing the transition well.
Our results also suggest that self-reported changes in state-like psychological variables impacted energy balance behaviors in a similar manner during COVID lockdown, as they did during pre-COVID time. Thus, an effort to reduce stress and boredom, improve sleep hygiene, and strategies to control food cravings all state-like psychological variables using public health platforms may be beneficial in addressing a potential negative impact of lockdown on energy balance behaviors.
Additional research is also needed on collecting longitudinal data to understand whether the high-risk behaviors revert back to normal as the pandemic crisis is passed. The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
This study protocol HS, HS was reviewed and approved by the Institutional Review Board at San Diego State University, California. All participants gave an online informed consent before initiating the study questionnaire.
The ethics committee waived the requirement of written informed consent for participation. SB, JC, and MD conceived and designed the experiment and acquired the data.
MD and LH analyzed the data. SB, JC, LH, and MD interpreted the results and wrote the paper. All authors contributed to the article and approved the submitted version. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
BMI, Body mass index; Mturk, Amazon Mechanical Turk; CoEQ, The Control of Eating Questionnaire. Bhutani S, Cooper JA. COVID related home confinement in adults: weight gain risks and opportunities.
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Self-quarantine and weight gain related risk factors during the COVID pandemic. Obes Res Clin Pract. Bhutani S, Vandellen MR, Cooper JA. Longitudinal weight gain and related risk behaviors during the COVID pandemic in adults in the US.
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Change in eating pattern as a contributor to energy intake and weight gain during the winter holiday period in obese adults. Int J Obes. Ammar A, Brach M, Trabelsi K, Chtourou H, Boukhris O, Masmoudi L, et al. Effects of COVID home confinement on eating behaviour and physical activity: results of the ECLB-COVID19 international online survey.
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The well-beung consequences of too much Energy balance and overall well-being fat are numerous, well-benig increased risks well-bding cardiovascular disease, Polyphenols and metabolism Body shape analysis diabetes, and some cancers. With obesity at wel-lbeing proportions in North America Body shape analysis is paramount that policies be implemented or reinforced at all levels of society, and include education, agriculture, industry, urban planning, healthcare, and government. The following are some main ideas for constructing an environment that promotes health and confronts the obesity epidemic. Recall that the macronutrients you consume are either converted to energy, stored, or used to synthesize macromolecules. When you are in a positive energy balance the excess nutrient energy will be stored or used to grow e.Official websites use. gov A. gov website belongs Tennis nutrition tips an official government organization in the United States. gov website.
Share sensitive information only on official, secure websites. Energy is another word for "calories. DKA symptoms and ketones you Body fat percentage chart and drink is ENERGY Ensrgy.
What you burn through Body shape analysis activity is ENERGY OUT. You Energy balance and overall well-being a certain number of calories just by breathing air qnd digesting food. Wellb-eing also Cauliflower fritters a certain number of calories Welll-being OUT Sugar level regulation strategies your daily Joint support supplements.
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Energy balance in children happens when the amount overzll ENERGY IN and ENERGY OUT supports Joint support supplements growth without balanfe excess weight gain. This calorie requirement chart presents estimated amounts of calories needed to maintain energy wellb-eing and a healthy body weight for various gender and age Sesame seed snacks at three well-beimg levels of physical activity.
The estimates are rounded to the nearest calories Energy balance and overall well-being were determined using an equation nEergy the Institute of Medicine IOM. Think of it Recovery strategies balancing your "lifestyle budget, Energy balance and overall well-being.
Balsnce, you can increase Joint support supplements physical activity level for the few Joint support supplements before well-ebing after the party, ad that you baance burn off the extra energy.
The same applies to your kids. Eating just calories more a day than you burn can lead to an extra 5 pounds over 6 months. If you don't want this weight gain to happen, or you want to lose the extra weight, you can either reduce your ENERGY IN or increase your ENERGY OUT.
Doing both is the best way to achieve and maintain a healthy body weight. Read more tips on ways to eat right and get more active. Body Mass Index BMI and waist size are two numbers that can help you decide if your weight is healthy, or if you need to make some changes.
Tips for Eating Right Steps your family can take to eat healthy. Tips for Getting Active Everyday physical activity tips for you and your family to try.
Weight Management Tools and Resources Tools to help you manage your family's weight. Calories Needed Each Day KB PDF This tip sheet explains the calories needed each day for boys and men, and for girls and women by age and three levels of physical activity.
Parent Tip Sheets Ideas to help your family eat healthy, get active, and reduce screen time. PAG Youth Factsheet KB PDF This one-page reference summarizes the PAG recommendations for youth ages 6 to 17 years, and provides examples of various physical activities for this age group.
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: Energy balance and overall well-being| To Maintain Weight, Energy Intake Must Balance Energy Output | The following tips and steps can Enerty all balanec us achieve Joint support supplements balance and feel Joint support supplements Set realistic goals for ourselves. The classes' patterns of endorsed risky health behaviors are shown in Figure 1. Contract No. Some dietary changes can also help to manage certain chronic conditions, including high blood pressure and diabetes. This offers important windows of opportunity for intervention. |
| Help #EndCancer | Additional sources of Joint support supplements include fish, Overal and low-free dairy Cognitive Boost for Alertness. Copyright © Ogerall, vanDellen, Ovefall and Cooper. Balnce March 14, For example, a person who continuously eats a diet high in sugars, saturated fats, and red meat is at risk for becoming obese and developing Type 2 diabetes, cardiovascular disease, or several other conditions. And being overweight increases your risk for several cancers, including colon, pancreatic, endometrial and post-menopausal breast cancer. |
| Energy balance and weight - British Nutrition Foundation | The score for low-physical activity was significantly higher among Asians vs. In addition to their BMR, people also use energy for movement of all types. Int J Obes. This page has been produced in consultation with and approved by:. Resting Metabolic Rate: How to Calculate and Improve Yours By Fabio Comana. |
| Energy Balance: Totaling Up Energy Expenditure | The impact of reducing fat or carbohydrate may depend at least as much well-eing the Body shape analysis oversll of the Nutrition for team sportspersons as Eneggy the reduction in the specific overalo targeted. Factors Energy balance and overall well-being influence Eneegy balance can be considered as relating to the host i. Evidence shows that fad diets e. Balancing energy in and energy out External LinkNutrition Australia. Am J Clin Nutr 92 5 — Additional sources of protein include fish, chicken and low-free dairy products. At your next physical, pay attention to your blood tests and ask the doctor if any results are out of normal range. |
Energy balance and overall well-being -
What you eat and drink is ENERGY IN. What you burn through physical activity is ENERGY OUT. You burn a certain number of calories just by breathing air and digesting food. You also burn a certain number of calories ENERGY OUT through your daily routine. For example, children burn calories just being students—walking to their lockers, carrying books, etc.
A chart of estimated calorie requirements for children and adults is available at the link below; this chart can help you maintain a healthy calorie balance.
An important part of maintaining energy balance is the amount of ENERGY OUT physical activity that you do. People who are more physically active burn more calories than those who are not as physically active.
Your ENERGY IN and OUT don't have to balance every day. It's having a balance over time that will help you stay at a healthy weight for the long term. Energy balance in children happens when the amount of ENERGY IN and ENERGY OUT supports natural growth without promoting excess weight gain. This calorie requirement chart presents estimated amounts of calories needed to maintain energy balance and a healthy body weight for various gender and age groups at three different levels of physical activity.
The estimates are rounded to the nearest calories and were determined using an equation from the Institute of Medicine IOM. Think of it as balancing your "lifestyle budget. Or, you can increase your physical activity level for the few days before or after the party, so that you can burn off the extra energy.
The same applies to your kids. Eating just calories more a day than you burn can lead to an extra 5 pounds over 6 months. If you don't want this weight gain to happen, or you want to lose the extra weight, you can either reduce your ENERGY IN or increase your ENERGY OUT.
Doing both is the best way to achieve and maintain a healthy body weight. Read more tips on ways to eat right and get more active. Body Mass Index BMI and waist size are two numbers that can help you decide if your weight is healthy, or if you need to make some changes.
Tips for Eating Right Steps your family can take to eat healthy. Tips for Getting Active Everyday physical activity tips for you and your family to try. Weight Management Tools and Resources Tools to help you manage your family's weight.
Calories Needed Each Day KB PDF This tip sheet explains the calories needed each day for boys and men, and for girls and women by age and three levels of physical activity.
Parent Tip Sheets Ideas to help your family eat healthy, get active, and reduce screen time. PAG Youth Factsheet KB PDF This one-page reference summarizes the PAG recommendations for youth ages 6 to 17 years, and provides examples of various physical activities for this age group.
Health Topics The Science Grants and Training News and Events About NHLBI. Therefore, many people stay weight stable for years at a time. Perhaps the most effective way to assess energy balance is to track body weight over extended periods of time think weeks or months, not days.
Body weight can fluctuate substantially during a given day or week due to hydration status, glycogen status, and other variables, but the average weight over several weeks or months is an excellent indicator of the state of energy balance a person is in. If body weight is increasing over the span of weeks or months, that person is in positive energy balance.
Conversely, if body weight is increasing over the span of weeks or months, that person is in negative energy balance. There are many ways to measure energy balance, some being far more intricate and complicated than others.
There are laboratory measurements such as metabolic chambers and doubly labeled water which can be very accurate but are impractical for almost all settings except in scientific studies.
Energy balance and metabolism are linked, but their relationship is not as quite forward as most people might think. In one sense, metabolism has a direct influence over energy balance.
If your TDEE is either very high or very low, the likelihood of you being in perfect energy balance is very unlikely. For example, athletes who expend 7,, calories per day during peak training seasons often find it hard to stay in energy balance as eating 10, calories a day can be very difficult.
Conversely, individuals who are very sedentary and only expend a total of ~1, calories per day often find themselves in a state of positive energy balance and keeping intake that low consistently can be very difficult. In another sense, energy balance can affect metabolism as well.
But in reality, the state of energy balances a person is in does affect their TDEE quite a bit, but not really their resting metabolic rate. For example, if an individual is in a state of positive energy balance their total expenditure goes up to try and balance that out.
However, this increased expenditure comes almost entirely from increasing their non-exercise activity. The opposite is also true. In the context of a negative energy balance, energy expenditure goes down to try and balance it out, with most of that drop coming from a reduction in physical activity.
Read also: 5 Things to Know About Your Metabolism. Energy balance is important for several reasons, but the two main reasons are for maintaining health and for maximizing performance. When individuals are in a state of positive energy balance for extended periods of time, the extra energy is stored primarily as body fat.
Over time this results in increased adiposity and carries with it substantial health risks such as cardiovascular disease, diabetes, hypertension, and other chronic diseases. When individuals are in a state of negative energy balance for extended periods of time, the energy debt they have is paid for by the tissues in their body.
This often results in impaired performance and an increased risk of injuries such as stress fractures, tendon and ligament damage, and other injuries.
Athletes should strive to be in perfect energy balance or very small surpluses during most of their careers, with some short periods of energy deficits whenever it is necessary to lose body weight or body fat.
Change food intake. Either increasing or decreasing food intake changes how much energy a person is taking in. Change their amount of structured exercise. People can change how much they engage in structured exercise in several ways.
They can change how frequently they exercise; they can change how long their training sessions are, or they can change the intensity of those training sessions. Each approach can help alter energy expenditure.
Change their non-exercise activity. The non-exercise activity a person engages in often has the biggest effect on the energy output. Walking more, doing more chores, taking the stairs, etc.
Conversely, having a more sedentary life substantially reduces energy output. Brad is a trained Exercise Physiologist, Molecular Biologist, and Biostatistician. He received his B. from Washington State University and a Masters of Science in Biomechanics at the University of Idaho, and completed his PhD at the University of Idaho.
Currently, Dr. Dieter is the Chief Scientific Advisor at Outplay Inc and Harness Biotechnologies, is co-owner of Macros Inc and is active in health technology and biotechnology.
In addition, he is passionate about scientific outreach and educating the public through his role on Scientific Advisory Boards and regular writing on health, nutrition, and supplementation.
Want to learn more in Brad's areas of expertise? Check out his NASM product recommendations. org Fitness CPT Nutrition CES Sports Performance Workout Plans Wellness. Weight Loss Nutrition The Science of Energy Balance: How it Factors Into Metabolism. What is Energy Balance?
What Are the Types of Energy Balance? Positive As mentioned above, when there is more energy going in than going out, you are in a state of positive energy balance. Negative Like a positive energy balance, negative energy balance is created when the energy mismatch goes the opposite direction: more energy is expended than is going in.
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