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Energy balance and eating habits

Energy balance and eating habits

Platt BS. We balanve 1, men Genetics certainly play a role in body fatness and weight and also affects food intake.

Energy balance and eating habits -

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. Obesity Silver Spring.

doi: PubMed Abstract CrossRef Full Text Google Scholar. Rundle AG, Park Y, Herbstman JB, Kinsey EW, Wang YC. COVIDrelated school closings and risk of weight gain among children. Zachary Z, Brianna F, Brianna L, Garrett P, Jade W, Alyssa D, et al. 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. Flanagan EW, Beyl RA, Fearnbach SN, Altazan AD, Martin CK, Redman LM. The impact of COVID stay-at-Home orders on health behaviors in adults.

Stevenson JL, Krishnan S, Stoner MA, Goktas Z, Cooper JA. Effects of exercise during the holiday season on changes in body weight, body composition and blood pressure. Eur J Clin Nutr. Schoeller DA. The effect of holiday weight gain on body weight.

Physiol Behav. Cooper JA, Tokar T. A prospective study on vacation weight gain in adults. Bhutani N, Finlayson G, Schoeller DA. 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.

Bhutani S, Cooper JA, Vandellen MR. Self-reported changes in energy balance behaviors during COVID related home confinement: a cross-sectional study. Am J Health Behav.

CrossRef Full Text Google Scholar. Goldman DS. Initial Observations of Psychological and Behavioral Effects of COVID in the United States, Using Google Trends Data. Ruiz-Roso MB, De Carvalho Padilha P, Matilla-Escalante DC, Brun P, Ulloa N, Acevedo-Correa D, et al. Changes of physical activity and ultra-processed food consumption in adolescents from different countries during Covid pandemic: an observational study.

Scarmozzino F, Visioli F. Covid and the subsequent lockdown modified dietary habits of almost half the population in an italian sample.

Dunton GWS, Do B, Coutney J. Early Effects of the COVID Pandemic on Physical Activity in US Adults. Cambridge: Cambridge Open Engage Neilsen G.

COVID Tracking the impact Thompson T, Rodebaugh TL, Bessaha ML, Sabbath EL. The association between social isolation and health: an analysis of parent-adolescent dyads from the family life, activity, sun, health, eating study.

Clin Soc Work J. Cellini N, Canale N, Mioni G, Costa S. Changes in sleep pattern, sense of time and digital media use during COVID lockdown in Italy. J Sleep Res. Lin LY, Wang J, Ou-Yang XY, Miao Q, Chen R, Liang FX, et al. The immediate impact of the novel coronavirus COVID outbreak on subjective sleep status.

Sleep Med. Salari N, Hosseinian-Far A, Jalali R, Vaisi-Raygani A, Rasoulpoor S, Mohammadi M, et al. Prevalence of stress, anxiety, depression among the general population during the COVID pandemic: a systematic review and meta-analysis.

Global Health. Mandelkorn U, Genzer S, Choshen-Hillel S, Reiter J, Meira ECM, Hochner H, et al. Escalation of sleep disturbances amid the COVID pandemic: a cross-sectional international study. J Clin Sleep Med. Reynolds DL, Garay JR, Deamond SL, Moran MK, Gold W, Styra R.

Understanding, compliance and psychological impact of the SARS quarantine experience. Epidemiol Infect.

Moynihan AB, Van Tilburg WA, Igou ER, Wisman A, Donnelly AE, Mulcaire JB. Eaten up by boredom: consuming food to escape awareness of the bored self. Front Psychol. Wiecha JL, Sobol AM, Peterson KE, Gortmaker SL. Household television access: associations with screen time, reading, and homework among youth.

Ambul Pediatr. Chao A, Grilo CM, White MA, Sinha R. Food cravings mediate the relationship between chronic stress and body mass index. J Health Psychol. Lv W, Finlayson G, Dando R. Sleep, food cravings and taste.

Crescioni AW, Ehrlinger J, Alquist JL, Conlon KE, Baumeister RF, Schatschneider C, et al. High trait self-control predicts positive health behaviors and success in weight loss.

Gillebaart M, Schneider IK, De Ridder DT. Effects of trait self-control on response conflict about healthy and unhealthy food.

J Pers. Burnette JL. Implicit theories of body weight: entity beliefs can weigh you down. Pers Soc Psychol Bull. Auster-Gussman LA, Rothman AJ. Understanding the prevalence and correlates of implicit theories of weight in the United States: insights from a nationally representative sample. Psychol Health.

Lyons C, Kaufman AR, Rima B. Implicit theories of the body among college women: implications for physical activity. Mason W, Suri S. Conducting behavioral research on Amazon's Mechanical Turk.

Behav Res Methods. Paolacci G, Chandler J. Inside the turk:understanding mechanical turk as a participant pool. Curr Direct Psychol Sci. Difallah D, Filatova E, Ipeirotis P. Demographics and dynamics of mechanical turk workers.

In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. Marina Del Rey, CA: Association for Computing Machinery Giacalone D, Frost MB, Rodriguez-Perez C. Reported changes in dietary habits during the COVID lockdown in the danish population: the Danish COVIDiet study.

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Can watching sports be bad for your health? Beyond the usual suspects for healthy resolutions. July 26, The tried-and-true advice for healthful eating also applies to keeping your energy level high: eat a balanced diet that includes a variety of unrefined carbohydrates, proteins, and fats, with an emphasis on vegetables, whole grains, and healthy oils.

Eat small, frequent meals Where energy is the issue, it's better to eat small meals and snacks every few hours than three large meals a day. Smaller is better, especially at lunch Researchers have observed that the circadian rhythms of people who eat a lot at lunch typically show a more pronounced afternoon slump.

Avoid crash diets If you need to lose weight, do so gradually, without skimping on essential nutrients or starving yourself of the calories you need for energy. Use caffeine to your advantage As a stimulant, caffeine can increase or decrease your energy level, depending on when and how much of it you consume.

Limit alcohol For people who drink alcohol, one of the best hedges against the midafternoon slump is to avoid the sedative effects of drinking alcohol at lunch. Drink water Water is the main component of blood and is essential for carrying nutrients to the cells and taking away waste products.

Do power bars or energy bars pack an extra energy punch? Share This Page Share this page to Facebook Share this page to Twitter Share this page via Email. Print This Page Click to Print. Energy balance was lowest Most Age AOR The study showed lower energy intake among the respondents than the recommended value; females had a value higher than what was recommended, males had less.

Majority had positive energy balance and this was mostly found among those with obesity. Age and occupation were factors associated with positive energy balance. Nutrition education, health education and dietary counselling are recommended strategies to control sustained weight gain.

Peer Review reports. Energy intake EI that exceeds energy expenditure is the main driver of weight gain. Energy consumed in foods are transformed to substrates that are either oxidized to produce metabolically useful energy that drives biological processes or stored [ 1 ] as fat when in excess.

World Health Organization [ 2 ] reported that the principal reason for the problem of excess weight is a sustained energy imbalance between calories consumed and calories expended and numerous genetic and environmental factors play intermediary roles in this process.

Food environment, marketing of unhealthy foods, urbanization and reduction in physical activity also play important roles [ 3 ]. Expended energy reflects fuels metabolized for growth, body maintenance, physical activity, pregnancy, lactation and many other processes and the rate of whole-body energy expenditure varies within a h period and across life span [ 1 ].

Obesity, a disease of excess body fat is the driver of non-communicable diseases such as cardiovascular diseases, musculoskeletal disorders and some cancers and has been linked to more deaths worldwide than underweight with the risk increasing as BMI increases [ 5 ]. Its prevalence has increased substantially across the globe with most evidence coming from high income countries and more research required in low- and middle- income countries [ 3 ].

In Nigeria, obesity prevalence has been reported as 8. Simmond et al. The epidemic of overweight and obesity presents a major challenge to chronic disease prevention and health across the life cycle [ 11 ].

The risk for adult obesity may still be higher among young adults in urban areas as a result of excess energy intake mediated upon by rapid urbanization, change in food environment and consumption of energy dense foods and beverages, low physical activity, improved socio-economic status and means of transportation.

Few studies have been conducted on energy intake and expenditure of young adults and to the best of our knowledge, none has been conducted in the study area.

Based on this, this study aimed to assess the energy intake, energy expenditure and energy balance of young adults 20—39 years in Nsukka urban and factors associated with their energy balance.

Data generated from this study will facilitate interventions to reduce the prevalence and complications of obesity. The study was conducted in Nsukka urban. Nsukka is located in the northern part of Enugu State, Southeast, Nigeria with a total population of , people as at national census increasing at an annual rate of 3.

Major occupation includes farming, trading and civil service. Major crops and livestock consumed are cassava, yam, maize, cocoyam, rice and sweet potato, poultry, pigs, goats and sheep.

The study employed retrospective cross-sectional cohort design in the study of energy status and factors associated with energy balance of young adults 20—39 years. The study population comprised of all free living non pregnant non lactating young adults 20—39 years in Nsukka urban.

Those who refused to be included by not signing informed consent or unable to supply data for three consecutive days were also excluded. A multi-stage probability sampling technique was used in selecting the respondents. In stage one, two 2 wards Ihe and Mkpunano out of 4 wards that make up Nsukka urban were selected using simple random sampling technique by balloting without replacement.

In the second stage, one community Onuiyi from Ihe and Umuakashi from Mkpunano was selected from each ward by simple random sampling. In stage three, urban settlements Onuiyi from Onuiyi and Army Barracks from Umuakashi in the two communities were identified and included on the basis of population density and ease of access to transport.

Stage four involved systematic random selection of every 5 th living house in the area. Probability proportional to size was adopted.

In the fifth stage, one household was selected from each house by simple random sampling technique. In the sixth and final stage, only two young adults within the ages of 20—39 years were selected from each selected household by simple random sampling using balloting without replacement.

Where there was only one eligible adult, a second household was selected from the same house and if there was none, the next house was selected and stages five and six repeated.

After details of the study were explained to them, respondents were requested to sign an informed consent form indicating their willingness to participate in the study. A validated questionnaire was used to obtain data on socio-demographic, dietary habits and lifestyle characteristics of respondents.

WHO global physical activity questionnaire administered by trained interviewers was used to assess physical activity level of the respondents.

Weight was measured to the nearest 0. Participants stood erect in minimal clothing with arms hanging by the sides and no shoes on. Height in cm was taken with height meter rule with bare feet parallel to each other and heels, buttocks, shoulders and back of head touching the height meter rule.

Waist circumference WC in centimetres was measured at the end of expiration using a flexible, non-stretchable tape placed at the midpoint between the top of the iliac crest and lower margin of the last palpable rib while participants stood upright.

Hip circumference in cm was measured around the widest portion of the buttocks. Ratio of waist to hip circumference WHR was calculated. Three h dietary recall involving two weekdays and one weekend day and a total of 6 meals per day was conducted by trained interviewers to determine the energy intake of the respondents [ 13 , 14 ].

Estimated amounts were weighed using kitchen scales and the results recorded in grams. The values for the three days were summed up and divided by three to obtain the mean daily energy intake.

The mean values were used in statistical analysis. PAL was determined with WHO global physical activity questionnaire that provided detailed report of types, intensity, frequency and duration in minutes of all physical activities exercise and non-exercise performed daily for three 3 consecutive days by the respondents [ 14 , 20 ].

Physical activity level factor of 1. Relationships between the outcome and exposure variables were assessed at both the binary and multivariate logistic regression.

After examining the individual effects of the above 14 exposure variables at the binary level, they were entered simultaneously into the multivariate logistic model to evaluate the effect of each of the covariates on the outcome variable when other covariates are held constant.

Crude and adjusted odds ratios were reported for each of the covariate evaluated. Data collected were entered into Microsoft excel, validated, cleaned and sorted before being transported into IBM Statistical Product and Service Solutions version 21 computer software for descriptive and inferential statistical analysis.

Descriptive statistics frequencies and percentages was used for general characteristics, anthropometric and physical activity levels of the adults. Chi square test was used to evaluate the relationship between categorical variables anthropometric parameters and physical activity level of the respondents by age and sex as well as the relationship of these parameters with energy intake, expenditure and balance.

Means and standard deviations were used for energy intake, expenditure and balance. T-test was used to assess relationships between energy intake, expenditure and balance, and sex, waist circumference and waist hip ratio.

Whereas analysis of variance was used to compare the energy parameters among four age groups of the adults and assess the relationship of mean energy intake, expenditure and balance with anthropometric parameters and physical activity level. Binary logistic regression analysis was employed to evaluate associations between the outcome variable and the predictor variables.

Since binary logistic regression analysis does not control confounding effects, multivariate logistic regression analysis was conducted to correct for simultaneous effects of multiple factors and control the effects of confounding variables on the response variable.

The adjusted odds ratios were used to define the independent strength of the associations. Mean age years of the respondents was Table 1 presents the general characteristics of the respondents.

More than half About thirty-five percent Secondary and tertiary education were attained by Majority of the respondents were engaged in an occupation Most of the respondents More than half of the respondents did not consume alcohol Anthropometric parameters, physical activity level, energy intake, expenditure and balance of the respondents by sex and age are shown in Table 2.

Female energy intake contributed Energy balance was positive among Figure 1 shows the percentage contributions of carbohydrate, protein and fat to the energy intakes of the adults by sex and age.

Carbohydrate The 30—34 year-olds had the highest carbohydrate contribution Percentage contributions of carbohydrate, protein and fat to energy intake of the adults by sex and age. Respondents with obesity had the highest energy intake Those with abdominal obesity had higher energy intake Table 4 shows the factors associated with energy balance of the respondents.

Respondents less than 30 years had nearly 3 times higher likelihood AOR: 2. Those who were not engaged in any occupation were 2 times more likely to have positive energy balance than those who were engaged in an occupation AOR: 2. Though not significant, being a male AOR: 1.

The likelihood of having positive energy balance decreased as body mass index increased though this did not attain significant proportions. This study which assessed the energy intake, energy expenditure and energy balance of young adults 20—39 years and examined factors associated with their energy balance was conducted in southeast Nigerian urban setting.

While Hattingh et al. The mean energy intake of males reported in this study is similar to Fyfe et al. According to Bennette et al.

This is in line with the findings of this study in which male intake contributed only This means that other nutrient requirements will also not be met because all other nutrients must be provided within the quantity of food required to fulfil the energy requirements [ 29 ].

Energy intake reported in this study may be functions of portion sizes and diet composition. Fatty foods and diets contribute more to energy intake than carbohydrate and protein.

A study also reported a higher percentage contribution from fat and less from carbohydrate and protein to energy intake [ 24 ]. According to Sudo et al. Females have been reported to consume foods more times during the day and uncontrollably too [ 31 ]; this may be responsible for the higher energy intake observed among them though relationship with male intake was not significant.

Mean energy expenditure of the males was significantly higher than that of the females. This is in line with the report of Redman et al.

This higher energy expenditure in males could be attributed to larger muscle mass. Contrary to the findings of other researchers [ 24 , 27 ] on energy balance, this study reported positive energy balance among males and females raising concerns over weight gain if sustained.

Very small differences have been shown to lead to important gains in weight over time [ 33 ]. The positive energy balance of most of the respondents in this study may have contributed to the high prevalence of overweight and obesity among them.

The higher mean energy balance among females implies possible weight gain in the face of low energy expenditure. That up to Multivariate logistic regression analysis showed that respondents who were less than 30 years were more likely to have positive energy balance than those aged 30 years and above.

This may be a consequence of consumption of energy dense foods and beverages coupled with newly gained socioeconomic independence to make food choices. Livingstone et al.

The likelihood of having positive energy balance increased by 2 among those who were not engaged in any occupation. This was attributed to low physical activity. Being engaged in an occupation increases energy expenditure though research has shown reduction in occupation related energy expenditure and reported that increases observed in fat percentage and body mass index are independent of occupation [ 35 , 36 ].

Those not engaged in any occupation do not benefit from any occupation related activity and therefore, more likely to have low physical activity level which leads to positive energy balance, sustained weight gains and consequences of obesity.

Though not significant, respondents who eat outside their homes were almost 2 times more likely to have positive energy balance. Most foods consumed outside homes are fast foods and fast food consumers have been reported to have higher mean energy, carbohydrate, protein and fat intakes than non-fast food consumers [ 37 ].

Fast foods are mainly energy dense nutrient-poor foods and beverages. It was not a surprise therefore that three or less times weekly snack consumption was associated with less likelihood of having positive energy balance than a higher consumption of above three times a week; though this is not significant.

Smoking of cigarette and other substances increased the risk of having positive energy balance by 2; this however did not reach significant proportions.

In affirmation, a strong linear relationship was observed between smoking pattern and dietary energy density in current smokers with daily and non-daily smokers having significantly higher dietary energy density than non-smokers [ 38 ].

In a study to determine the effect of smoking status on total energy expenditure, the authors [ 39 ] reported no significant differences in total energy expenditure between smokers and non-smokers implying that the issue may lie with energy intake.

That smoking significantly reduced dietary calorie intake [ 40 ] was contrary to our findings and may be attributed to type, frequency and quantity of smoke inhaled.

Interestingly, the likelihood of having positive energy balance decreased as body mass index increased showing that those with normal body mass index were more likely to have positive energy balance than those with overweight and obesity.

This may be attributed to lack of caution in consuming energy dense foods and drinks. People with normal BMI should, therefore, guard against excessive energy intake and low physical activity level as it may lead to weight gain and retention.

This study is not without limitations. Firstly, the study was limited to an urban area in southeast Nigeria which did not represent the whole of Nigeria.

Self-reported weight Enrrgy during the COVID shelter-at-home has raised concerns for weight increases as the pandemic continues. Diabetic nephropathy kidney function Consistent power output to investigate the relationship of Consistent power output and health markers with energy Eenrgy behaviors during the pandemic-related extended home Ezting. Ratings ahd stress, boredom, ans, sleep, self-control, balancw beliefs about weight control were collected from 1, adults using a questionnaire between April 24th—May 4th,while COVID 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 indexand conducted a latent profile analysis of the risk factors. We examined psychological and health correlates of these risk patterns. Having greater self-control, control over cravings, or positive mood was related to lowering all aspects of energy intake and energy expenditure risks. Energy balance and eating habits

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