Free Which eating patterns are associated with a higher BMI in overweight and obesity adults seeking for treatment Dissertation Example

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Which eating patterns are associated with a higher BMI in overweight and obesity adults seeking for treatment

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A Study to establish which Eating Patterns are associated with a Higher BMI in Overweight and Obesity Adults Seeking for Treatment
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Abstract
Being overweight or obese presents significant nutritional problems in Indonesia. As a developing country, Indonesia faces a double burden of malnutrition. At one side, the traditional problem of malnutrition has not been resolved. On the other side, over nutrition or modern “mal”-nutrition has become a serious health issue, and is related to various non-communicable diseases (NCDs) such as hypertension, stroke, diabetic mellitus, coronary heart disease and cancer. This study aims to establish the eating patterns that are associated with higher BMI in adults seeking weight loss treatment. The study design is to follow a retrospective review of patients’ charts who visited the weight management clinic at Jakarta, Indonesia, during a selected period of 2 years. Selection of participants will be by systematic sampling, and from which data obtained will be used to; examine the relationship between obesity/overweight as a result of individual patterns of eating such as missing breakfast, late night eating, snacking, and eating out; determine and examine any existing connections between BMI and eating habits of the adults; and later make recommendations and issue advice to encourage the development of better eating habits based on the results and findings.
Keywords: eating patterns, Body Mass Index (BMI), breakfast skipping, snacking, obesity, overweight

A Study to establish which Eating Patterns are associated with a Higher BMI in Overweight and Obesity Adults Seeking for Treatment
Background and Introduction
Being overweight or obese presents significant nutritional problems in Indonesia. As a developing country, Indonesia faces a double burden of malnutrition. At one side, the problem of malnutrition is yet to be resolved, on the other side, overnutrition as modern “mal”-nutrition is becoming a serious health issue, and is currently related to various non-communicable diseases (NCDs) such as hypertension, stroke, diabetic mellitus, coronary heart disease, and cancer (Smetanina& Verkauskiene, 2015). Annual health care costs for obesity-related complications has reached 16% of national healthcare spending, an aspect which clearly shows the increasing occurrences of obesity. Indeed, the issue has become a heavy burden for Indonesia as a country that is still struggling to manage primary health care needs.
Given these challenges, based on the results of Indonesian Basic Health Research, the incidence of overweight and obesity are even more increasing, from 13.3% and 15.4% in 2013 to 13.6% and 21.8% in 2018. Among 34 provinces in Indonesia, DKI Jakarta as the capital is the province with the highest level of overweight and obesity. Data released by the Indonesian Central Statistics Agency (BPS) shows that regions with obesity rates above the national average are precisely regions that have a high human development index. Human development index is related to how people can access the result of the development in obtaining income, health, and education. This is in line with the findings of Unicef and WHO, which show that Asia is home to half of the world’s overweight children in 2016.
A new study by the London School of Economics (LSE) suggests that the globalized lifestyles are contributing to the rise of obesity in many of the developing countries (Costa-i-Font et al., 2013). The advanced economies have led to longer working hours, thereby limiting the time available for meals. For these reasons, people are inclined to miss breakfast and rarely consume food prepared at home. Instead, people have shifted to snacking and consuming processed food from outside the home, which tend to contain higher calories, fat, sugar and salt as compared to the meals prepared at home (Dinda, 2016). Coupled with the ease of access in getting these foods, either as a result of the ease of transportation or ordering fast food online, there exists an increasing likelihood of increased obesity and malnutrition prevalence (Imamura et al., 2015).

Currently, the Indonesian health ministry is making various efforts in attempts to combat obesity, and among which include the healthy community movement (GerMas). The program involves multiple inputs from various sectors such as the government, health professional organizations and the community. The emergence of these programs, which majorly function as public awareness initiatives, seems to have caused a general improvement in health just as much as the general increase in weight loss efforts, especially among the overweight and obese people. The Indonesians citizens spend around 2 to 4 billion dollars to treat obesity and fat-related illnesses such as diabetes, cancer, etc.

There is no doubt that there exist many pieces of research conducted on obesity-related comorbidity such as diabetes, hypertension, and stroke, but little research is available to help in explaining the ever growing trends and rates of obesity among adults in Indonesia. Given that much of the explanations to the prevalence of obesity is associated with eating behaviors such as skipping breakfast, eating late at night, snacking and eating away from home snacking. According to the Indonesian experiment on the eating habits of adolescents and obesity occurrences. By means of chi- square study it exposed that in conjunction with family income and eating habits, teenagers from different families and backgrounds with incomes over Rp 2mil were thrice expected to be overweight due to the high diet calories, i.e., 95 % intake. As well as adolescents who have focused on sitting, playing games, play station and such lazy activities are more likely to face obesity. These groups have replaced their physical activities that greatly promotes energy body imbalance. Although the study is not only justified with the limited availability of research available on the Indonesian rates of obesity but also with the limited availability of research that solely focuses on obesity and overweight within the adult population that seek for treatment.

Aims and Objectives
The aim of the study is to establish the association between eating patterns and Body Mass Index (BMI) in overweight and obese adults who seek treatment in weight management clinics.
In an attempt to meet the aim, the specific objectives of the study include:
To determine the relative prevalence of obesity/overweight as a result of individual patterns of eating such as missing breakfast, late night eating, snacking, and eating out.
To determine and examine any existing connections between BMI and the individual eating habits.
To gain insight into the effects of age on eating habits and BMI
To make recommendations and issue advice to encourage the development of better eating habits based on the results and findings.
Literature review
Selection and search strategy
The literature review was carried out between October and November 2018 and included the articles published in Indonesian and English and dated within the last fifteen years only. The criterion for selecting which article to review and include in the work was guided by the research aims and objectives in consideration with the outcome measures that most reflected on the research goals; including study design, methods of analysis, and methodology. Generally, the studies under consideration were those searched and selected from PubMed databases, Google, and Google scholar engines. However, the studies used were randomly selected regardless of their technique with a method defining a control group, say for instance age of the participants. The search strategies of the literature used in the study were also aligned to the research objectives. Among the keywords used included breakfast skipping, late-night eating, snacking habits, eating away from home, and high BMI.

The table below shows the keywords used to obtain literature resources.
Key terms used Combination used
Fast food
Obesity
Overweight
Breakfast skipping
Snacking habits
Late night eating
Eating away from home
High BMI C23.888.144.699, (MESH)
C23.888.144.999

or
and
Table 1: Eating habits keywords
In selecting the studies, abstract of any study identified is critically examined autonomously. The abstracts were dissected with the aim of establishing whether or not the studies were relevant and aligned with the objectives of the review.
Literature Review
Indeed, there exist massive lots of literature available that tend to associate and link eating patterns with BMI in overweight and obese individuals. For instance, studies by Engler (2008); Kuroda et al. (2013); Smetanina et al. (2015); and Rodríguez et al. (2009), all agree in one unison that habits related to skipping meals and restricting intake of calories are attached to the creation of cravings. As a result, the occurrence of high blood sugars takes place and leads to the creation of high triglyceride amounts, which are transformed into fats for storage.

However, in a more critical manner, it is clear that many studies support the notion that breakfast-skipping, comes with increased chances for obesity, the search also gave other literature which held that consumption of breakfast also increased BMI, and there henceforth obesity and overweight. Among the studies that associated taking breakfast with obesity include Maki et al. (2016); and Vik et al. (2016). In these studies, the consumption of breakfast is associated with gaining weight.

Given both sets of arguments, a researcher would get confused on the pool of information with contrasting views and believes, more so upon failure to dig more into the explanations of every piece of literature involved in the study. In their views, all the authors may be right because recommendations to skip or eat breakfast for weight loss could be effective when altering self-reported habits of eating, but this might not have discernable impacts on individuals seeking to lose weight (Dhurandhar et al., 2014). Indeed, it is, therefore, shown that there is a need for studies to give in-depth coverage of their views and findings on breakfast skipping. Given the existing contrasting findings, any scientist would be able to see the importance of the empirical method of researches. Researches though have shown that breakfast taking and edible consumption has increasingly led to healthier nutrition.

Despite the contrasting studies, some articles and studies like Llauradó et al. (2016); Isa & Masuri (2011); Chaplin & Smith (2006); Kahleova et al. (2017); and van Vliet, Gustafsson & Nelson (2016). Proven of greater significance as they delved into explaining the different patterns reported by a large portion of the studies. These studies showed that the amounting differences and likely confusion created by the other studies are because of the inability of the authors to mention that with a failure to take breakfast came with an increased likelihood of snacking on high-fat foods.

Moreover, studies on feeding patterns among university and college students, just as much as on athletes by James & Miller (2016); and Saarni et al (2006) respectively further indicated that changes in weight, BMI, and prevalence of overweight and obesity only came as a result of frequent snacking, and which in most cases were resulted to by missing breakfast, and there henceforth starving to the point of snacking. Moreover, Kahleova et al. (2017) further suggest that healthy eating habits would involve taking breakfast, no snacking, eating less frequently, and consuming the largest meals in the morning.

Given the number of pieces of literature that have experimented in the aspect of skipping breakfast, it would be easy to conclude that breakfast skipping generally leads to snacking during the day. A significant percentage of the respondents acknowledged that breakfast skipping got them starving, and which brought about an increase in the rates of cravings for snacks (Saarni et al, 2006). Having established the occurrence, it is thus crucial to establish the connection between snacking, as an eating pattern, and BMI in overweight and obesity individuals.
Indeed, there also exists a lot of literature that discusses the relationship between snacking, which has mostly been shown to arise as a result of skipping breakfast, and obesity and being overweight. For instance, Llauradó et al. (2016) did a study to establish the impacts of eating and snacking occurrence on dietary quality among British adolescents. In their analysis, an increase in snacks per day led to an increase in the overall dietary quality but did not show any statistical significance. When low energy snacks were excluded, the original positive association was reversed such that an increase in the snacks lowered dietary quality.
However, much it would not be clear to establish all the aspects that had been considered to label the change as one that hampered the overall wellness of the dietary quality, it is easy to assume that it is attributed to the calorie intake that is converted to fats for storage. Also, many studies on the same have backed such matters. For instance, Njike et al (2016) as they discuss aspects of satiety, weight, and snack food, illustrate that the unevenness between energy intake and energy expenditure that creates a positive balance of energy in the primary contributing factor to the obesity development.
Like Llauradó et al. (2016), the Njike et al. (2016) also acknowledge that the effects of dietary factors may vary relative to the types of snacks, but remain a subject of controversy. Indeed, there exist little pieces of research, basing arguments on those retrieved from the search, that differentiate which types of snack are associated with a likelihood for obesity. Instead, the researches appear quite mixed with inconsistent and multiple exposures, just as much as with outcome measures, in which some pieces compare waist circumference with BMI. Moreover, it would be crucial to mention that other sources of controversy might have been resulted to by the attached variations in periods of intervention, methodology quality, and types of study population employed in the study.
The findings are also backed up by Chaplin & Smith (2011) and Lazzeri et al. (2013). Lazzeri et al. (2013) are focused on a cross-sectional study aimed at establishing the link between frequency and intake of snacks and breakfast and the consumption of vegetables and fruits. Low vegetable and fruit intake were shown to bring about irregular breakfast habits. Also, low intake of fruits was tied to irregular consumption of snacks, whereas intake of vegetables did not show any direct connections to irregular consumption of snacks. The study also showed different patterns by age and gender, which emerged only after they were used as modifying factors, and from which the female respondents with irregular patterns of breakfast consumption showed a high likelihood of low intake of vegetables and fruits.

The results, as explained, therefore, suggested that adolescents with irregular consumption of snacks and breakfast exhibited a lower frequency of vegetables and fruit intake. Low consumption of vegetables was then linked to irregular consumption of breakfast, whereas age and gender became moderators to illustrate the significance of analyzing vegetable and fruit intake. The research by Chaplin & Smith (2011), on the other hand, did not involve a study that seeks to establish a pattern between eating habits and obesity and overweight. Instead, the study takes a different approach to establish an association by investigating how people define snacking, snack food perceptions, and snacking behavior. The method of data collection involved a questionnaire which was sent to 136 participants to measure their snacking beliefs and behaviors. In the study, a significant percentage of the respondents reported that they consumed at least a snack on a daily basis with an average of 4.5 episodes of eating daily. Also, the respondents grouped snack foods based on their dissimilar qualities with sample sub-groups consuming snacks within the groups. Generally, Chaplin & Smith (2011) established that snacks could only be best defined depending on meals, though more research should be done on the same. It also identified that snacking does not necessarily cause obesity because not all snack foods are attached to extra calories. Based on the pieces of literature used to illustrates in the impacts of snack foods, it would generally be argued that snacks cause a high likelihood of high BMI, and there henceforth high chances of obesity and overweight, more so in instances of unhealthy snacking habits.
When planning for any meal, be it snacks or breakfast, it is crucial to make healthy food choices intended to act as a guide, or describe the amount and type of food needed. However, much these are the recommendations in many parts of the states, many people find Food Away from Home (FAFH) as a way of life. Whether at vending machines, restaurants, corner state, or take out counters, making choices are supposed to remain of higher and possible given that once merged with physical activities, it would serve as a good way of fighting obesity and other risk diseases. D’Addezio et al. (2014); Goon et al. (2014); and Lim (2014) all did research to establish the link between the frequencies of out-of-home eating and attribution of obesity. The research by Goon et al. (2014) involved a cross-sectional study done among 426 students chosen as participants through systematic sampling. With an objective to establish and evaluate the consumption of fast foods and obesity prevalence among students, the likelihood of obesity was retrieved as significantly tied to frequency of fast food eating. Unlike in the other eating patterns, like in the case of snacking and skipping breakfast, that appeared to have many other studies contradicting one another, all the studies retrieved that focused on the impacts and connection between FAFH and obesity aligned with one another.
Such other studies are those by Pathania et al. (2009); Sedibe et al. (2018); and Seguin et al. (2016). In Pathania et al. (2009), the authors investigate the impact of the supply of fast foods on the health based on the exact geographical locations of the restaurants offering fast-food services. Based on the supply of the fast-foods, it is revealed that there exist increased risks of obesity in regions that are closer to the fast food restaurants than in regions with a greater distance away from the restaurants. In an attempt to stress their point, the authors used the findings to advocate for the implication of policies that would restrict fast-food access near schools for it would lower obesity rates among the children, but also feared that the implication would not have much of an impact within an adult population. On the same note, Sedibe et al. (2018) also look into the mater through investigating the similarities or differences in dietary habits and eating patterns between the older and younger, urban and rural South African adolescents in different environments (school, community, and home) and their linkage with obesity and overweight. After the adjustments for dietary habits, eating practices, site, and gender disparities within community, home, and school environments; consumption of the main meal with family both on some days or almost daily, just as much as irregular breakfast consumption frequency on weekdays were all attached with an increased risk of obesity and overweight. However, much the study appeared very extensive; it cannot be used singly to conclude on the matter.
The study by Seguin et al. (2016) became of the most significance as it directly linked consumption tendencies of FAFH with higher BMI and lower vegetable and fruit intake among adults, an aspect which also gives more importance given it directly aligns with the needs of this research. The research is also a cross-sectional study and starts by acknowledging that the consumption of FAFH has risen progressively since the 1970s. Seguin et al. (2016) examined the connection between FAFH and BMI and vegetable and fruit consumption to prove that high FAFH frequencies were tied to higher BMI, and that after age, race, income, smoking, physical activities, marital status, and education, there were no apparent dissimilarities; a phenomenon through which the linkage would be described as fully proven. Moreover, the findings by Seguin et al. (2016) are easily backed by that originally done by Todd, Mancino & Lin (2010) who also associated FAFH with poor dietary quality. According to Todd et al. (2010), every one meal consumes away from home every week led to about two extra pounds annually. The effects of FAFH are, however, reported as greatest depending on the number of vegetables, whole grains, and dairy, and fruits in every 1000 calories, but differ depending on the meal. Obese, or nonoverweight, the study further revealed that there exist little dissimilarities in the impacts of FAFH. Instead, an inability to compensate or cut sizes of eating portions such that one consumes less throughout the remaining meal periods of the day became the main technique through which FAFH increased the total intake of calories more among the overweight that the non-overweight (Todd et al., 2010).
There exist many pieces of literature that cover the aspects of eating patterns like snacking, skipping breakfast, and FAFH in relation to how they impact on BMI and overweight and obesity, little to no research is available to demonstrate the connection between late-night patterns and obesity. In fact, the little available studies on the same seem to contradict one another, and this calls for more research. Eng et al. (2009) performed research to establish the relation, and in which they suggested late night food intake was attached to the higher likeliness of obesity in children, but not in adults because some studies had shown a positive correlation between late night eating and obesity among adults. Ma et al. (2003) could be used as a backup of the findings by Eng et al. (2009) although the study was not solely focused on establishing the link between late night eating and obesity.

In their studies, they mention that the average intervals between; largest eating episode to waking up, last eating episode and bedtime, and between bedtime and first eating did not have any significant effects that would statistically show their risks of obesity. Despite the need for ore research on this issue it is important to know that the link created late night eating and obesity is a subject under discussion, since as much as taking breakfast is the ideal weight loss strategy and late night eating leads to reduced burning off extra calories due to the body’s inactivity this is not necessary for every individual. The interaction of the time of day and body metabolism is the key to solve the obesity and weight loss. Thus dietary advice can be composed to ensure the nutritional value of every food commodity and the best time a person should feed on them.

Indeed, eating patterns are directly connected to BMI in overweight and obesity Berkey et al (2003); Farshchi et al (2005); Giovannini et al. (2008); Lemos et al (2018); Sajjad et al (2014); Spence (2017); Wijtzes et al. (2016); Song et al (2005). The existence of a large variety of studies on the same is a clear indication that a lot of research has been done on the topic. However, there emerges a greater need that further researches follow an empirical method of research to increase the efficacy of the results. It is for such reasons that the intended research is aimed to better the findings of the filed.? Also, there might be varied pieces on research, but little has been done with a focus on the trends of eating patterns, BMI, and obesity in Indonesia (Hastuti et al., 2017). Besides this establishment, the research is further justified with the finding that Indonesian adults experience a high prevalence of obesity and overweight, a fact that calls for adult and obesity research in the region.

Research Methodology
The study design involves a retrospective review of patient’s’ charts who visited the weight management clinic at Jakarta, Indonesia, over a period of 2 years. As have been established, there exists a paper medical record, and based on this attribute, no specialized skills are required for navigation through the medical records. In an attempt to also establish the frequency, prevalence, and accuracy of the study. The study design will also consider assigning, or availability of chart numbers to improve accuracy and efficacy of the research.

Among the data to be obtained from the charts also include the patient’s; age and gender; three 24-hours dietary recalls; and body weight measurements of height, weight and weight waist circumference. The 24-hour dietary recalls are to be included in the data collection procedures attributed to their structured review that seeks to obtain detailed information regarding beverages, food, and possible dietary supplements likely to have been consumed by the respondents within the past 24 hours (Ma et al., 2009). Among the information and data to be obtained from the 24-hour diet recall include snack and meal patterns, participants’ usual dietary distribution of intakes, nutrient intakes form beverages and foods, total amounts of beverages and foods consumed, and the relationship between health and diet (Salvador et al., 2015).

The data obtained from the collection tools will be entered into SPSS computerized program to be analyzed. There exist about 25 to 50 new patients who come to the clinic each month, allowing a target population of 300 to 600 new patients a year or 600 to 1200 new patients within two years. Systematic random sampling will be used in the selection of participants. So, a sample member will be selected according to a random starting point, and a fixed, periodic interval, which is calculated by dividing the population’s size by the desired sample size. Borrowing explanations from Taylor (2003) and Zhang (2006), given the period considered for the research, amounts to two years, the maximum population of patients would be 1200. However, not all the patients would be included in the research as participants, and the research would require selection of a randomized group, say, 60 patients.
Through the systematic sampling technique, all the potential respondents will be listed, and then a starting point would be selected. After formation of the list, every 20th individual on the list (chosen through counting from a selected point) would be included as a participant because 1200/60 gives position 20. Assuming the selected starting point would be 10, the 30thpatient in the list is to be chosen as a participant followed by the 50th, 70th, and so on. When the list ends and additional respondents are needed, the count will be looped to reach the 20thcount. There will be a random selection of the patients; participants will be selected based on the inclusion and exclusion criteria tabulated below.
The individuals are between 18-70years old because around 70 years is the life expectancy age limit

The patients experience high BMI and are overweight and are obese
Inclusion criteria
Individuals under age 18 years
Patients recorded as diabetic, or exhibit genetic propensity
Patients with a habit of smoking or drinking alcohol
Individuals under medications that encourage weight gain or weight loss
Individuals who portrayed obesity and have recorded eating disorders
Patients with mental health disorders like addiction, mood disorder, anxiety, schizophrenia, personality disorder, alcoholism, substance use disorder, and mental distress

Exclusion criteria
No smokers or alcohol users are allowed in this criteria investigation since any person who has abused drugs is likely to mess the data collected. This is because both drinkings of alcohol and smoking of cigarettes interfere with the metabolic activity functions and mental thinking capacity.

Data analysis
This proposal investigating the body mass index and the eating habits will be analyzed, using an SPSS data analysis command program that aims at giving a brief description of the data to be analyzed. In this case, it is important to type in the certain variables in the questionnaires in the command program so as to complete the analysis. A suitable statistical test in this investigation would be a chi-square goodness fit test that allows the research to be investigated through observation of the proportion that differs from the hypothesized proportions. Given a probability of p<0.05 in this research it will mean that the chosen significant value, one has to reject the null hypothesis.

Ethical Considerations
The aspect of ethical considerations must be held of the high regard in any research, more so those that involve the use of human being as respondents. Ethical considerations in research define the norms and standards that guide how research is performed, more so on the notion of data collection, as they define acceptable and unacceptable behaviors while remaining true to research. The drive towards the ethical considerations are as a result of the collaborative environment, and mutual respect created not just between colleague researchers but also between researchers and participants. These aspects ensure accountability and trust, and there henceforth efficacy.
However, much in retrospective researches, the study is done on data which could be described as already available; the aspect of the patients’ consent stirs controversy given it is their data to be used, more so if the research is to be published. The main criterion which must be considered is the aspect of autonomy. This is attributed to the fact that any harm likely to result from publications or sharing the study with other outside parties would be caused by a lack of secrecy.
This brings into consideration other aspects of ethical consideration like respect for anonymity, confidentiality, and privacy; all which stresses on the importance of ensuring any private information obtained about the patients remain private, just as much as ensuring respect for dignity and fidelity. According to the copyright law of the United Kingdom obesity levels are considerably higher. The study shows that certain food choices are associated with people’s culture and social, economical lifestyles of which are easily manipulated by advertising, food packaging, and presentation. This can be easily controlled by the corporate laws that achieve and influences all food choices that aid in redirecting people to make healthier, food choices and food products. According to this study of Body Mass Index and eating habits, with regards to reducing obesity and overweight among adults and adolescents, the Indonesian government are allowed to hold health management programs with permission from the United Kingdom laws that govern them on how to conduct the data.

The state of Indonesia uses its quantitative data that’s already analysed from the owner’s clinic and as per Indonesian law, for the clinic in Indonesia where the data can only be kept for 2 years. Privacy is maintained where, there is no need for asking permission on how the data will be used or dealt with, with regards to the educational purpose, therefore, there is no need to ask the permission from the patients as long as the patient’s full names are not mentioned. Data is usually kept in the clinic filing room and the clinic data base, once data is taken offsite. The Data obtained follows the Indonesian law for this is base on the Indonesia minister of health Decisions no 269 the year 2008.

APPENDICESAppendix 1: Questionnaires of the patients’ charts who visited the weight management clinic at Jakarta, Indonesia
Introduction
This questionnaire is on assessment of a comparative study of patients who had visited the weight management clinic. There exists a paper medical record, and based on this attribute, no specialized skills are required for navigation through the medical records Kindly respond to all questions in all the sections by indicating a tick in the space provided or by explaining your opinion briefly on the space provided.
SECTION A: DEMOGRAPHIC DATA
Gender of the business person
Male ( ) female ( )
What is your marital status?
Married ( ) Single ( )
What is your education level?
Primary level and below ( ) secondary ( ) post-secondary ( )
What are your 3 24 hours dietary calls meals in a day
Food ( )
Beverage ( )
Name your snack meal patterns ( )
How often do you have your meals?
Breakfast frequency ( )
Snacking frequency ( )
What are some of the food you snack on (give examples)
Do you often eat out or eat at home? And if so why? Give reasons
Do you prefer late night eating to breakfast?
Yes, or no …….. if so why?
Do you have any Alcohol history?
Do you have any history of chronic or Acute illnesses in the pastor the present? If so further explain.

Appendix 2|: Definition of terms
Age. As illustrated by Petry (2002), age groupings are divided into the form; young adults

(18 to 35 years old), middle age adults (36 to 55 years old) and older age adults (more than 55 years old).
Body Mass Index. The BMI was calculated as weight (Kg) divided by height (m) squared. The subjects were classified according to Asian BMI as underweight (<18.5 kg/m2), normal weight (18.5 to 23 kg/m2), overweight (23 to 25 kg/m2) and obese (>=25 kg/m2) (Emmerzaal, Kiliaan & Gustafson, 2015).
Breakfast. This is the meal that should be taken in the morning. Skipping breakfast. Borrowing the explanations by Isa & Masuri (2011), the aspect of skipping breakfast as used in work implies the act of fully missing breakfast, or considering taking breakfast five times, or less, a week.
Snacking. This implies the consumption of any food or drinks not regarded by the individual as a meal at least once per day. The snacks include food eaten during movement, food is eaten between or after main meals, foods not consumed for the purpose of filling the stomach, and food that can be consumed quickly and easily (Hess, Jonnalagadda & Slavin, 2016). These food types include breakfast cereals, biscuits, chocolates, juice, cakes and pastries (Hess, Jonnalagadda & Slavin, 2016).

Late night eating pattern. Borrowing explanations from Miller, Lumeng & LeBourgeois
(2015) this is used in the research to imply the pattern demonstrated by those who have developed eating habits that go past 9 pm, and which is repeated at least five times a week.

Eating away from home. As used in the study, the term has been applied to mean the meals were taken away from home at least five times, or less, a week. The idea of frequency is borrowed from Isa & Masuri (2011) and Miller, Lumeng & LeBourgeois (2015). Eating away from home includes the times when someone is not able to cook at home, due to frequent work schedules and pressure means that he or she might decide to go to a fast food joint and order some fast food or rather buy snacks or fruits from the grocery stores.

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