Participants recruited via social media, Healthy appetite suppressant, and bwlance of mouth volunteered to complete the survey and did not receive any monetary compensation. Obesity and overweight: Key facts. Accessed 27 Apr

Energy balance and eating habits -

Also, remember the EER is calculated based on weight maintenance, not for weight loss or weight gain. Source: US Department of Agriculture. Source: Institute of Medicine. Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids. September 5, The amount of energy you expend every day includes not only the calories you burn during physical activity, but also the calories you burn while at rest basal metabolism , and the calories you burn when you digest food.

The sum of caloric expenditure is referred to as total energy expenditure TEE. breathing, heartbeat, liver and kidney function while at rest.

The basal metabolic rate BMR is the amount of energy required by the body to conduct its basic functions over a certain time period. Unfortunately, you cannot tell your liver to ramp up its activity level to expend more energy so you can lose weight.

BMR is dependent on body size, body composition, sex, age, nutritional status, and genetics. People with a larger frame size have a higher BMR simply because they have more mass. Muscle tissue burns more calories than fat tissue even while at rest and thus the more muscle mass a person has, the higher their BMR.

As we get older muscle mass declines and thus so does BMR. Nutritional status also affects basal metabolism. Caloric restriction, as occurs while dieting, for example, causes a decline in BMR. This is because the body attempts to maintain homeostasis and will adapt by slowing down its basic functions to offset the decrease in energy intake.

Body temperature and thyroid hormone levels are additional determinants of BMR. The other energy required during the day is for physical activity.

Depending on lifestyle, the energy required for this ranges between 15 and 30 percent of total energy expended. The main control a person has over TEE is to increase physical activity.

Calculating TEE can be tedious, but has been made easier as there are now calculators available on the Web. TEE is dependent on age, sex, height, weight, and physical activity level.

The equations are based on standardized formulas produced from actual measurements on groups of people with similar characteristics.

To get accurate results from web-based TEE calculators, it is necessary to record your daily activities and the time spent performing them. Interactive com offers an interactive TEE calculator. In the last few decades scientific studies have revealed that how much we eat and what we eat is controlled not only by our own desires, but also is regulated physiologically and influenced by genetics.

The hypothalamus in the brain is the main control point of appetite. It receives hormonal and neural signals, which determine if you feel hungry or full. Hunger is an unpleasant sensation of feeling empty that is communicated to the brain by both mechanical and chemical signals from the periphery.

Conversely, satiety is the sensation of feeling full and it also is determined by mechanical and chemical signals relayed from the periphery.

This results in the conscious feeling of the need to eat. Alternatively, after you eat a meal the stomach stretches and sends a neural signal to the brain stimulating the sensation of satiety and relaying the message to stop eating.

The stomach also sends out certain hormones when it is full and others when it is empty. These hormones communicate to the hypothalamus and other areas of the brain either to stop eating or to find some food. Fat tissue also plays a role in regulating food intake. Fat tissue produces the hormone leptin, which communicates to the satiety center in the hypothalamus that the body is in positive energy balance.

Alas, this is not the case. In several clinical trials it was found that people who are overweight or obese are actually resistant to the hormone, meaning their brain does not respond as well to it. Dardeno, T. et al. Therefore, when you administer leptin to an overweight or obese person there is no sustained effect on food intake.

Nutrients themselves also play a role in influencing food intake. The hypothalamus senses nutrient levels in the blood. When they are low the hunger center is stimulated, and when they are high the satiety center is stimulated.

Furthermore, cravings for salty and sweet foods have an underlying physiological basis. Both undernutrition and overnutrition affect hormone levels and the neural circuitry controlling appetite, which makes losing or gaining weight a substantial physiological hurdle.

Genetics certainly play a role in body fatness and weight and also affects food intake. Children who have been adopted typically are similar in weight and body fatness to their biological parents.

Moreover, identical twins are twice as likely to be of similar weights as compared to fraternal twins. The scientific search for obesity genes is ongoing and a few have been identified, such as the gene that encodes for leptin. However, overweight and obesity that manifests in millions of people is not likely to be attributed to one or even a few genes, but to rather the interactions of hundreds of genes with the environment.

In fact, when an individual has a mutated version of the gene coding for leptin, they are obese, but only a few dozen people around the world have been identified as having a completely defective leptin gene. When your mouth waters in response to the smell of a roasting Thanksgiving turkey and steaming hot pies, you are experiencing a psychological influence on food intake.

If you are keen to lose weight or achieve and maintain a healthy weight, give up on the idea of finding and following extreme celebrity diets that work. Related: Weight Management. In other words, it focuses on balancing the energy calories you consume and the energy calories you burn through physical activity.

To lose weight, the number of calories we consume must be less than the number of calories we burn. A negative energy balance over time leads to weight loss. Conversely, when we consume more calories per day than we use through physical activity, we gain weight.

Energy Balance and Obesity: Over a prolonged period, we may develop obesity. Obesity increases our risk of stroke, heart attack and, in more serious cases, can lead to organ failure. That means we should consume energy our bodies need and also engage in a healthy level of physical activity.

You can engage in minutes of moderate-intensity aerobic activity in a single session or over a few sessions by setting aside some days of the week for exercise. Remember, it is important that you keep track and balance your energy intake calories consumed and energy output calories burned through exercising to achieve and maintain a healthy weight.

A healthy year old girl, weighing at 60kg, will have to balance her regular food intake with any of these activities: an hour of badminton or fast-paced modern dance; or an hour and a half of leisurely cycling a week. When it comes to dieting and weight loss, it is really a game of balancing the food you eat and the amount of physical activity you engage in.

Most importantly, it is an ideal and healthier way to do so as well! View More Programmes. Find out more about pre-diabetes, diabetes and how you can prevent them by making some changes to your lifestyle.

HOME LIVE HEALTHY A A A. 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. Secondly, we did not use the doubly labelled water method which is the gold standard method to assess body metabolic rate and body composition of the adults was not assessed.

Thirdly, the study employed self-reported retrospective data on food intake and physical activity which may be associated with recall bias. Fourthly, portion sizes on which the energy intake was based was not presented.

Lastly, in assessing the factors associated with energy balance, cause and effect associations could not be established through a cross sectional study. This study showed higher female daily energy intake than male intake with lower daily energy expenditure than males. The overall energy balance was positive.

Age and occupation contributed to positive energy balance among the respondents. These findings are vital to planning nutrition and health education, and dietetic management of individuals prone to obesity.

Data generated from this study on which the results are based are available from the corresponding authors on reasonable request. Hall KD, Heymsfield SB, Kemnitz JW, Klein S, Schoeller DA, Speakman JR.

Habis that the eaitng you consume are either converted Body density measurement accuracy energy, stored, or balancw Healthy appetite suppressant synthesize Healthy appetite suppressant. When you are in fating positive energy balance Healthy appetite suppressant excess nutrient energy will be stored or used ahd grow e. Energy balance is balabce when intake of energy is equal to energy expended. Weight can be thought of as a whole body estimate of energy balance; body weight is maintained when the body is in energy balance, lost when it is in negative energy balance, and gained when it is in positive energy balance. In general, weight is a good predictor of energy balance, but many other factors play a role in energy intake and energy expenditure. Some of these factors are under your control and others are not. Let us begin with the basics on how to estimate energy intake, energy requirement, and energy output. Self-reported weight gain during Energy balance and eating habits COVID shelter-at-home Energy balance and eating habits raised concerns for weight increases as the pandemic rating. We eatinh to investigate the relationship of psychological Cancer-fighting compounds health markers habita energy balance-related behaviors during the pandemic-related Energy balance and eating habits home confinement. Ratings for stress, boredom, cravings, sleep, self-control, and beliefs galance weight control were collected anx 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. Psychological and health variables may have a significant role to play in risk behaviors associated with weight gain during the COVID related home confinement.