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Article

Predictors of the Attempt and Failure to Lose Weight among the South African Population

by
Mashudu Manafe
Department of Human Nutrition & Dietetics, Sefako Makgatho Health Sciences University, Pretoria 0001, South Africa
Obesities 2024, 4(3), 195-211; https://doi.org/10.3390/obesities4030018
Submission received: 30 May 2024 / Revised: 30 June 2024 / Accepted: 15 July 2024 / Published: 18 July 2024
Versions Notes

Abstract

:
Background: The increase in the prevalence of obesity in South Africa is a problem, and weight management plays an important role in the treatment of the high prevalence. For this purpose, a quantitative study was carried out using a structured questionnaire to assess the predictors of attempts and failures to lose weight. Methods: One thousand and fifty adults were enrolled in the study, and 54% (n = 562) were overweight and obese (BMI ≥ 25 kg/m2). Descriptive statistics were analysed. Inferential statistical tests (chi-square tests, logistic regression, and Pearson’s correlation coefficients) were used to determine the relationship between variables. Results: The results revealed that age, sex, marital status, educational level, employment status, income level, area of residence, ethnic groups, and self-perceived BMI (normal weight, overweight, and obese) were predictors of the attempt and failure to lose weight (p < 0.05). There was a positive correlation between self-perceived BMI and the attempt and failure to lose weight, r = 0.2015 (p < 0.0001). Conclusion: Weight management is important in the realm of public health and can be regarded as an investment in the population, as it leads people toward weight control strategies grounded in empirical evidence. In addition, the findings can inform public health policies and interventions to improve weight management strategies at the population level.

1. Introduction

Obesity, a serious problem worldwide, presents a formidable obstacle to the prevention of chronic diseases. The World Health Organisation has estimated that 2% of all global deaths caused by diabetes are related to obesity [1]. South Africa, like other nations, is not exempt from this epidemic, as obesity rates are markedly higher among adults, particularly among women [2,3,4]. Obesity was found to be higher in South Africa than in other sub-Saharan African countries according to the health and demographic surveys conducted in these countries, even higher than the global average [5]. Multiple economic, environmental, and sociocultural factors have been reported for their influence on the elevated prevalence of obesity in South Africa [6]. South Africa’s rapid urbanisation mirrors a global trend where convenient, processed foods become more accessible, displacing traditional diets. However, unlike developed nations, income inequality and unemployment in South Africa limit access to healthy options for many. Unemployment becomes a challenge in terms of affordability to purchase healthier food items, and instead, there will be an increase in the consumption of processed and industrial foods dense in energy containing large amounts of sugar, salt, and fat leading to weight gain and the risk of NCDs [7]. Compared to developed nations, the drivers of obesity may differ, where there may be overconsumption rather than unhealthy consumption. Consequently, almost half of the people affected by cardiovascular disease are burdened by obesity, increasing their susceptibility to developing type 2 diabetes [8]. Therefore, the gravity of obesity cannot be ignored when considering that, in South Africa, 87% of diabetes cases, 68% of hypertensive disease cases, 38% of ischaemic heart disease cases, and 45% of ischemic stroke cases are attributed to obesity [9]. Given that the majority of people with type 2 diabetes are obese, weight reduction plays an essential role in the prevention and treatment of type 2 diabetes mellitus [10].
The goal of obesity management compared to weight reduction among obese people should be to reduce metabolic risk rather than achieve an ideal body weight. The potential benefits of weight loss for health are fundamental to policymakers looking for solutions to obesity. As a result, weight management plays an essential role in public health as a strategy for behavioural change and as a preventive measure to reduce obesity However, despite the benefits of weight loss, people with good metabolism may not be motivated to lose weight [11,12]. In the case of obese individuals without comorbid conditions, weight reduction can significantly reduce the likelihood of developing such conditions [13]. The desire to lose, reduce, or maintain weight can be influenced by factors such as the perception of weight. For example, individuals who perceive themselves as overweight, even when they are not, can use unnecessary weight loss tactics [14]. The process of trying to lose weight should primarily involve adopting healthier habits such as portion control, exercise, and making nutritious food choices. Even if weight loss is not achieved, these habits can improve the overall health and well-being of individuals.
However, as stated by Tol et al. [15], decisions to seek weight-related care can be influenced by medical needs, financial impact, access to healthcare, demographic factors, and personal health beliefs. People who are overweight or obese and are told by healthcare professionals that they are overweight are more likely to attempt to lose weight [16]. However, in the context of South Africa, there is a misconception and an inadequate understanding of the notion of body weight [17]. A study by the authors of [10] revealed that obese individuals who experience weight loss exhibit improved biochemical profiles, despite still being classified as clinically obese. Furthermore, sustained weight reduction in obese individuals can lead to a decrease in the duration of hypertension, type 2 diabetes, and expected lifetime occurrences of coronary heart disease and stroke [8,18]. In addition, the Organisation for Economic Cooperation and Development (OECD) reports that obesity prevention is cost-effective [13,19] by implementing a strategy to treat obesity, in which people who are metabolically unhealthy are transformed into a healthy obesity state [20]. The importance of people who experience a reduction in weight cannot be underestimated. From an individual point of view, there is a decrease in expenses associated with the treatment of obesity and an improvement in overall quality of life. From a social perspective, there is a reduction in lost working days due to absenteeism and decreased productivity [10].
Stress is another factor that could play a role in the inability of individuals to lose weight. Kipp et al. [21], in a study conducted among African Americans, indicated that those experiencing stress are more likely to overeat and are less likely to engage in physical activity. In the South African population, people with overweight or obesity may not be aware of or recognise the importance of weight loss for health reasons. According to the author of [20], the perception of an individual as overweight or obese determines his or her motivation to lose or gain weight. Social norms that accept and tolerate excess weight can contribute to the emergence of obesity in society [6]. A study conducted among African Americans advocates for setting realistic weight loss goals as the acceptance of larger body weights persists and the achievement of an ideal body weight might seem unattainable [16]. Despite the gaps in knowledge about body weight and weight management behaviours in the South African literature, it is evident that obesity remains a significant challenge and a threat to health. Public health efforts should therefore shift towards promoting healthy behaviours over solely focusing on weight loss.
The current study contributes to the global fight against obesity as a public health concern. The World Health Organisation (WHO) led the charge with its WHO Global Action Plan for the Prevention and Control of Noncommunicable Diseases, a plan emphasising creating environments that promote healthy choices through policy changes, promoting physical activity, and improving access to healthy foods [22]. South Africa has adopted its own strategy for preventing and managing obesity in South Africa (2023–2028) to empower South Africans to make healthier food choices, engage in physical activities, and participate in a health system to support the prevention and management of obesity. Although strategic documents are available, South Africa faces unique challenges and needs context-specific interventions to address affordability and access to healthy food options for all income levels. This might involve supporting local farmers’ markets and promoting food security programmes in underserved communities. Educational campaigns should be culturally sensitive, promoting healthy eating patterns in the context of South African traditions and preferences. Moreover, improving access to food shops with healthy options in communities is essential to healthy food choices. Research in South Africa can help identify specific areas of intervention and inform policy changes on food labelling, marketing regulations, and feeding programmes. South Africa’s unique approach can inform other developing countries facing similar challenges. The study of obesity trends in South Africa serves as a valuable case study for the development of context-specific solutions. By adopting a local approach that considers the unique socioeconomic and cultural landscape of the country, South Africa can make a significant contribution to the global fight against obesity.
The presence of obesity and its association with health risks and comorbidities, as well as its impact on public health, resulting in increased healthcare costs, is a matter of utmost importance [23]. Consequently, maintaining a healthy body weight becomes an integral aspect of an individual’s existence. The underlying assumption in the implementation of weight management approaches is that individuals must first acknowledge their overweight or obese condition. Neglecting this recognition can lead individuals to disregard the potential chronic diseases associated with their lifestyle, potentially influenced by the belief that achieving sustainable weight reduction is complex. Consequently, this perception decreases their motivation to initiate weight loss efforts [24]. As a result, the health behaviour adopted can be more consistent with short-term goals than with future health risks. Given these circumstances, this article describes the predictors of weight loss attempts and failures among the South African population.

2. Materials and Methods

2.1. Sample Size

The sample size for the unknown population was calculated using the formula, n = Z2pq/e2, where Z = the confidence level (1.96) for the 95% level of confidence; p = the estimated proportion of the population; q = 1 − p (reduced to 0.3) to obtain a larger sample size; and e = the error margin (confidence interval) used elsewhere in Manafe et al. [17]. The size of the sample was calculated to ensure that the minimum number of respondents must be representative of the population under study. The sample size was calculated at the 95% confidence level, 0.5 standard deviations, and a 2% margin of error. Therefore, data were collected among 1050 participants.

2.2. Participant Recruitment and Characteristics

This study was approved by the Medunsa Research Ethics Committee (MREC/H/269/2012: PG). Participants were recruited in public places, including shopping malls, towns, and rural, urban, and mining areas. This study was carried out in the provinces of Gauteng, Northwest, and Mpumalanga, three of the nine provinces of South Africa. In the provinces, there are various ethnic groups represented. The ethnic groups represented in the provinces are the following: North Sotho, South Sotho, Tswana, Venda, Zulu, Ndebele, Xhosa, Tsonga, and Swati. The areas of the different provinces have both formal and informal residential areas. Permission to recruit participants was obtained from the local authorities of each province. A non-probability sampling method, convenience sampling, was used to sample participants. The inclusion and exclusion criteria are as follows: Potential participants included individuals of both sexes who were 18 to 35 years, old, 36 to 55 years old, and older than 55 years. Individuals with disabilities and mobility difficulties were excluded from the study. People who met the eligibility criteria received verbal information about the study and the opportunity to ask questions. Those who chose to participate provided written consent, and an identification number was assigned to the questionnaire to maintain confidentiality. Participants self-reported their perceived BMI. Weight and height were measured, and the BMI was calculated from them and recorded in the questionnaire.

2.3. Development and Validation of Questionnaire

The questionnaire consists of two sections. The first section dealt with the following demographic characteristics: age, sex, marital status, employment status, income level, education level, and region of residence. The second section focuses on questions about the self-perception of body weight, including questions about weight loss attempts and failure to lose weight. The questionnaire was written in English, designed and adapted according to the literature reviewed for the study [25]. To ensure internal coherence and reliability, the Cronbach alpha coefficient was calculated from eight elements of the data collection tool. The Cronbach alpha coefficient was 0.77, indicating a high level of reliability of the data collection tool [26]. A pilot study was conducted among individuals with similar characteristics to the participants before the actual collection of data was carried out to validate the questionnaire. Minor changes were made to the formulation of the questionnaire before the start of the study.

2.4. Data Collection

Following the informed consent process, the three trained field workers carried out data collection using a structured questionnaire. Field workers came from different ethnic groups and were able to translate information from English to local languages and vice versa during data collection for those who did not understand English. The weight and height of the respondents were measured, and the body mass index (BMI) was calculated in kg/m2. Height was measured standing, without shoes, using a stadiometer and to the nearest 0.1 cm. Weight was measured using a digital scale to the nearest 0.1 kg. The BMI cut-off points to determine body weight were classified according to the WHO references as follows: BMI less than or equal to 18.5 kg/m2, underweight; 18.5 to 24.9 kg/m2, normal BMI; and greater than or equal to 25 kg/m2, overweight and obese [25]. The responses were recorded in the questionnaire by field workers.

2.5. Data Analysis

Data were entered into an MS Excel spreadsheet and organised for statistical analysis. Continuous variables were presented as mean ± standard derivation (SD). Pearson’s chi-square test was used to test for differences in participant characteristics for categorical variables. Logistic regression analysis was used to find the relationship between sociodemographic characteristics, calculated BMI, and self-perceived BMI. The results of the logistic regression analysis were presented with an odds ratio (OR) and 95% confidence intervals (CI). The Pearson correlation coefficient was used in the study to test for a significant relationship between self-perceived BMI and attempted and failed weight loss. All statistical analyses were performed with STATA version 17 (StataCorp, LLC, College Station, TX, USA). The value of p ≤ 0.05 was considered statistically significant.

3. Results

3.1. Sociodemographic Characteristics of Respondents and Attempt to Lose Weight

The sample consisted of 1050 respondents. Table 1 presents the sociodemographic characteristics of the respondents. The 18 to 35 years (67%) group is the age group most represented. The mean age and standard deviation were 32.5 ± 11.5. Most of the respondents (56%) were women and (70%) were single. Most of the respondents had an income just above three thousand rands (R3000.00). Most of the respondents (52%) resided in a township (residence in an urban area). The Tswana ethnic group was well represented (30%) since Setswana is the language spoken in most of the areas where the study was conducted. There was a high proportion (54%) of overweight and obesity among respondents with a BMI of 25 kg/m2. The mean BMI of the respondents was 26.4 ± 6.2. From the results of the chi-square test, age, sex, marital status, education level, area of residence, and self-perceived BMI had a significant effect on the attempt to lose weight among the respondents (p < 0.001).

3.2. Sociodemographic Characteristics of Respondents and Failure to Lose Weight

The results of the chi-square test in Table 2 show that the area of residence and the self-perception of body weight have a significant effect on the failure to lose weight (p < 0.001).

3.3. Self-Reported Weight Management Behaviour by Gender

As presented in Table 3 below, most women (71%) tried to lose weight using diet pills (93%). Most respondents (84%) indicated that the methods used for weight loss were not useful. The attempt to lose weight, the method used, and whether the method helped had a significant effect (p < 0.001) on the weight management behaviour of the respondents.

3.4. Logistic Regression for Sociodemographic Characteristics and Attempt to Lose Weight

The results of the logistic regression for sociodemographic characteristics and the attempt to lose weight are shown in Table 4. The values of the odds ratio of the variable age indicate that the age group of 18–35 years were the most likely to attempt to lose weight.
The findings show that being a woman and single is an important predictor of weight loss attempts. The value of the odds ratio for the variable education further shows that those with tertiary education were most likely (2.009) to attempt to lose weight.
Participants in all income categories were likely to try to lose weight (p < 0.001). The odds ratio (1.875) of those residing in a township indicates that they were most likely to try to lose weight. The findings show that both ethnic groups were likely to attempt to lose weight.
Respondents who perceived themselves as underweight had an odds ratio of 1.284 and a 95% CI of −0.545–1.045, indicating a lower chance of trying to lose weight. Respondents who perceived themselves as obese (2.042) were the most likely to attempt to lose weight (Table 4).

3.5. Logistic Regression for Sociodemographic Characteristics and Failure to Lose Weight

The results of the logistic regression for sociodemographic characteristics and failure to lose weight are presented in Table 5 below. Respondents in the 18–35 age group, women and singles, have an increased probability of not losing weight. Those without formal education were less likely (−0.111–3.111) to fail to lose weight, and those with higher education levels, secondary (9.932), completed matric (11.767), and tertiary education (11.685), were more likely to fail to lose weight. All income categories were significant predictors (p < 0.001) of failure to lose weight, with those in the higher income bracket (R12801.00–R25600.00) most likely to fail to lose weight. Those who resided in townships and rural areas were most likely to fail to lose weight (p < 0.001). All ethnic groups except southern Sotho were most likely to fail to lose weight. Those who perceived themselves as underweight were less likely (−2.287–5.287) to fail to lose weight. The value for the odds ratio of those who perceived themselves as obese is 42.521 (2.662–4.837), which shows a higher probability of failure to lose weight (Table 5).

3.6. Pearson’s Correlation Coefficient between Self-Perceived BMI, Attempt to Lose Weight, and Failure to Lose Weight

The results of the Pearson correlation as shown in Table 6 below indicate a positive correlation between self-perceived weight and the weight loss attempt, with a Pearson correlation coefficient of r = 0.2015 (p < 0.0001). There was also a positive correlation between the failure to lose weight and self-perceived BMI.

4. Discussion

This study aimed to determine the predictors of weight loss attempts and failures among the South African population. The study results showed that, according to the calculation of the BMI, most of the people in this study are overweight and obese (BMI > 25 kg/m2), and that less than 50% of the population falls within the normal weight range (BMI = 18.5–24.9 kg/m2). Furthermore, most of the respondents perceived themselves as overweight. There was a higher proportion of women in the study, and most were single. Adeloye et al. [26], in a study conducted in Nigeria, reported a higher prevalence of obesity among women. Inversely, a study conducted in China by Zheng et al. [27] reported a higher prevalence of overweight among men compared to their female counterparts. Another study that noted a higher prevalence of obesity among men was conducted in the United States (USA) by Ogden et al. [28]. Currently, there is a different trend from the one observed in most studies, showing a high prevalence of overweight among men. The trends that are emerging among men are noteworthy and indicate a shift in the prevalence of overweight and obesity. As reported in the studies, the increase in overweight and obesity is an indication of sedentary and unhealthy lifestyles. In countries such as China and the USA, there is a growing trend towards eating at fast food restaurants which increases body weight and waist circumference [29]. In African countries, a sedentary lifestyle is the most common cause of weight gain. These findings are of great importance for the development and implementation of effective public health intervention programmes for men and women. The OR of the age group of 18–35 years was higher than that of the other age groups in the study, although all ORs in the age groups were greater than 1. Therefore, the findings suggest that people in the 18–35-year age group were more likely to attempt to lose weight. A study conducted in Brazil reported an attempt to lose weight among the ages of 19–59 years [30]. Lee and Hong [31], in a study conducted among Koreans, noticed that those who participated in weight loss efforts were individuals over 51 years of age. As the study findings have shown, individuals in all age groups attempted to lose weight, including those older than 55 years. However, the reasons for participating in weight loss among the different age groups may differ. In young people, peer pressure and social media can influence the desire to achieve ideal weight by attempting to lose weight [32]. On the other hand, in older adults, participation in weight loss efforts can be attributed to improvements in health. Education strategies for different age groups and adapted to individual needs are necessary to make informed weight management decisions.
Sex emerged as a significant predictor of the attempt to lose weight in the study. Although the point estimates for the OR for both men and women were greater than 1, the OR for women was higher, indicating the likelihood that women would try to lose weight. The study revealed that most women tried to lose weight. An interesting aspect that emerged from the analysis is that the men also tried to lose weight. Similar conclusions were drawn by Santos et al. [33] and Rompkovski et al. [30] that a higher number of women than men consistently try to lose weight. Interestingly, the findings of the current study show an emerging trend for men to attempt to lose weight. Similar trends have been reported by Zhong et al. [34] in their systematic review in which a weight loss attempt was observed among men. Men who participate in weight loss strategies may be able to explain their fears about the consequences of excess weight and obesity [35]. Strategies to increase awareness of the dangers of excessive weight gain are essential not only for overweight people but also for those who are overweight and at normal weight. It is important to understand that if you maintain a healthy weight, you will reduce the risk of chronic diseases such as diabetes, heart disease, sleep apnoea, and other chronic diseases [36]. Furthermore, the results of the study showed that the women did not lose weight when they tried. The reason can be attributed to the fact that women are more likely to be victims of weight stigma and lack the knowledge that weight loss is effective [37]. Regarding public health, individuals and communities need to be educated through community-based strategies to raise awareness of obesity. Most of the women who responded reported using diet pills and <40% of the men reported going to the gym as a weight loss strategy. The results of men going to the gym are noteworthy; however, most of the respondents are unemployed and may not have the means to go to the gym. This study also did not determine the frequency of gym trips and respondents may have offered socially desirable responses. On the other hand, the concern about the use of diet pills is that they are not suitable for long-term weight management, are impractical, are not sustainable, and can be harmful to the health of the individual [38]. Systematic reviews have stated that the effectiveness of weight loss pills cannot be guaranteed [39,40]. A study conducted in South Africa by Senekal et al. also reported the use of weight loss medications among students [19]. The study findings are consistent with those of Machado et al. [38] in a study conducted in Brazil, where there was a reported increase in the likelihood of using weight loss substances in people under the age of 60 years. Most of the respondents in the current study were unemployed and had lower incomes, making the use of diet pills unsustainable. However, no results have been reported on the effects of diet pills. Public health interventions and initiatives should be designed to educate people about various weight management strategies and raise awareness of the use of diet pills as a weight management measure. This means that individuals should seek a comprehensive lifestyle change rather than a quick and temporary solution. Public programmes aimed at preventing weight gain and promoting weight loss can benefit both those with obesity-related metabolic complications and those without.
Respondents to the study also reported eating less food as a strategy for weight control. However, it should be noted that the actual amount of food consumed by this population is not measured and that the concept of “less eating” can be interpreted differently. Furthermore, the authors of [41] similarly concluded from their data that participants who attempted to lose weight reported consuming less food as a strategy of choice. However, it should be noted that people can eat less food with high energy content and gain weight [41]. Most people live in urban areas, where the food environment encourages the consumption of high-energy, dense, high-fat, high-sugar foods, and fast food [42]. The consumption of high-fat, energy-dense, and high-fat foods contributes to the growing problem of overweight and obesity. Thapa et al. [43], in a study conducted in Myanmar, argued that people can report eating less food while consuming higher amounts of carbohydrates, leading to weight gain and increased BMI. Food choices can also be influenced by socioeconomic conditions, as people may lack the financial means to adopt a healthier lifestyle [44]. An important point to note is that by reporting their eating habits as a weight control strategy, participants can provide desirable social responses to these effects. Female participants reported that eating less was a strategy, overwhelmingly. These results differ slightly from those of Tsai et al. [36] who reported exercising and eating less fat as weight strategies used by male participants in their study. Weight management is a public health issue and can be seen as an investment in the well-being of the population by guiding people toward evidence-based weight control approaches. Nutrition education for the public is part of an effective strategy to equip people and families to make healthier food choices to combat obesity.
The values of the odds ratio of the variable marital status indicate that respondents who have ever been married and single respondents are more likely to try to lose weight, although the probability of trying to lose weight in single respondents is higher. The study findings are interesting because other studies show that it is mostly unmarried women who generally engage in weight loss practices compared to married women [45]. Tzotzas et al. [46], in their study, share this view, suggesting that unmarried persons can make more efforts to improve their attractiveness. The dominance of single participants may explain why many of them tried to lose weight. Joh et al. [47] argued that women are susceptible to obesity but may not be proactive in weight management because they are the main caregivers and cooks in the family setting. Interestingly, the results of the study show that even married people tried to lose weight. It is not possible to exclude the desire to improve the current state of health of all respondents, although, in the current study, it was not measured whether they had obesity-related diseases or not. Interventions aimed at reducing obesity, including strategies to reduce weight gain, should be implemented for married people as well.
The probability ratio for respondents without formal education shows that they are less likely to try to lose weight, and those with higher education are more likely to try to lose weight. The results for people with higher education are consistent with the findings of the study by Lee and Hong [31], which showed that upper- and middle-school graduates are more likely to exercise weight control than other graduates. The most interesting observation that emerged from the current data was that respondents without formal education were less likely to try to lose weight. Education is a potential determinant of an individual’s socioeconomic status and, in turn, influences the choice of diet and habits. Additionally, people with higher levels of education are more likely to access materials and media related to health and nutrition messages, even if they are not necessarily translated into a healthier lifestyle. Younger people may lack the knowledge of weight management and control and choose not to do anything if they are overweight or obese. Health education initiatives aimed at both individuals without formal education and those with higher education are encouraged to adopt a healthier lifestyle.
The results of this study show that employment status categories appear to be important predictors of weight loss attempts. Respondents in all employment categories are likely to try to lose weight. Similarly, the results show that respondents in different income categories are likely to try to lose weight. However, studies such as those conducted by Santos et al. [33] and Lee and Hong [31], contrary to current research, reported that people with higher socioeconomic status and higher incomes can control weight. Thapa et al.’s [43] view of people with high socioeconomic status is that they have purchasing power and are, therefore, able to access energy-dense foods and expensive food items that lead to weight gain. However, the current cohort of respondents with a lower socioeconomic status may purchase high-energy-density foods because they would be cheaper and more affordable. The study showed an interesting trend, with even low-income respondents trying to lose weight. It can also be attributed to them having been diagnosed with obesity-related diseases and being encouraged to lose weight. To implement effective and relevant strategies, the trend needs to be looked at in more detail.
In terms of the area of residence, all categories were significant predictors of weight loss attempts, although those who live in a higher township had an odds ratio which was higher than other categories. Thus, those who resided in the township were more likely to attempt to lose weight. Townships do offer opportunities for engaging in physical activity, such as walking; however, the overreliance on cars makes it difficult. Inversely, a study conducted by Mench et al. [48] observed a trend of greater weight loss occurring among those in rural areas. The findings of Mench et al. are contrary to the expectation that people in urban areas are those who lose weight because they have a higher consumption of fast food and caloric soft drinks [43]. Interestingly, the study findings show that all respondents in each residential category tried to lose weight. This result is not expected and warrants further investigation. Access to healthy food options may differ greatly among residential areas; however, weight loss attempts may not necessarily be based on eating healthy food options. Most communities due to urbanisation often rely on convenience stores with less healthy options. Therefore, individuals from different areas of residence can face both social and economic barriers to weight loss. Knowledge about healthy eating and weight management can influence behaviour. Health-related messages must be tailored to each area of residence according to their needs.
For the various ethnic groups, they were both likely to try to lose weight, as the study shows, and the Swati ethnic group was the most likely to try to lose weight. In South Africa, most studies were conducted mainly among black people and white people [49,50]. These conclusions merit further discussion and research because of the fewer studies conducted among the different ethnic groups. Future studies are needed to provide information that can explain the results of the study. There is a lack of studies investigating the association of ethnic groups, as proposed in the study. The gap is worth highlighting because the current study is the first to report on these new discoveries. With ethnicity identified as a unique predictor in the South African context, ongoing inequalities can affect access to healthy foods, education about healthy lifestyles, and resources for weight management programmes. Cultural beliefs and practices and related eating habits can influence the perception of weight management. The results will help plan and implement a suitable strategy to combat obesity among individuals, communities, and different ethnic groups. Understanding these factors can help design targeted interventions that address the specific challenges faced by different groups. For instance, promoting community gardens in rural areas or culturally appropriate healthy eating workshops would be an effective intervention.
The study findings showed a positive correlation between self-perceived BMI and the attempt to lose weight. The authors of [51] are of the opinion that weight perceptions strongly influence the attempt to lose weight. Furthermore, the findings of the study show that, with the exception of people who perceive themselves to be underweight, people who perceive themselves to be normal, overweight, and obese are likely to try to lose weight [37], also noted by Haynes et al. [52]. People who perceived themselves as overweight in this study tried to lose weight. Contrary to the study findings of the authors of [53], people of normal weight reported less desire to lose weight. Consistent with some of the findings is a study conducted in England by Piernas et al. [54], where the most common weight control attempts were observed among overweight and obese individuals. Lee and Hong [31] reported that women who considered themselves obese were more likely to try to lose weight. The possible reasons for participating in weight loss are that people are already diagnosed with cardiovascular disease and are trying to improve their health status [55]. Sirirassamee et al. [56], on the other hand, argued that women who mistakenly believe they are overweight may not try to lose weight. The authors of [34] agree with the study findings showing that those who perceive themselves as overweight are less likely to attempt weight loss. To address weight management, interventions should give priority to educating people about the understanding of body weight perception. Public health interventions indicate that people who are overweight cannot take weight control measures because they believe weight loss cannot be achieved. The study also showed that people who perceived themselves as normal, overweight, and obese did not lose weight. In support of the findings, Evans et al. [57] noted in studies carried out in Europe that people who were obese and tried to lose weight were unsuccessful. Obesogenic food environments, including high-calorie foods and sedentary lifestyles, can inhibit weight loss [58]. The affordability of healthy foods and proximity to grocery stores can significantly affect dietary choices. Healthy foods tend to be more expensive than unhealthy foods, and people in low-income neighbourhoods may not have access to supermarkets that sell fresh produce. Therefore, strategies to address body weight must include changes in lifestyle, cost-effective means, and a consideration of the community.

5. Conclusions

Findings: This study revealed the influence of age, gender, marital status, educational level, location of residence, and self-assessed weight in trying to lose weight. Furthermore, the area of residence and the perception of body weight in itself influence those who try to lose weight. Furthermore, the study found a positive correlation between age, sex, marital status, educational level, occupation, income, area of residence, and the perception of body weight, suggesting that most participants in these categories are likely to try to lose weight. In general, the study revealed a curious trend that weight loss attempts were likely to occur in all categories of sociodemographic characteristics. Consequently, an awareness of BMI measurements and weight loss are important aspects of effective weight management programmes.
Significance: The findings are of significance, as they will have an important impact on the development of interventions to promote healthy weight management practices.
Implications: The knowledge obtained from the study can be instrumental in determining public health policies and interventions aimed at making the necessary changes to the weight management strategy to improve the results at the population level. Additionally, these discoveries provide an opportunity to apply weight control strategies that meet specific requirements for individuals and communities.
Recommendations for policy and future research: The findings of the study will help promote culturally sensitive nutrition education by developing educational programmes that consider the traditional diets, beliefs, and preferences of different ethnicities. Addressing food insecurity in underserved areas by investing in initiatives like community gardens, mobile markets, or subsidies for healthier groceries in low-income neighbourhoods will empower communities to opt for healthier food choices. Encouraging engagement in physical activity in both residential areas and among different ethnic groups is critical for policy. Longitudinal studies that follow individuals over time can provide valuable insights into the long-term effectiveness of weight management interventions. Combining quantitative and qualitative data collection methods can offer a more nuanced understanding of the factors influencing weight loss attempts and failures in communities. In addition, the exploration of the use of mobile applications or SMS reminders to promote healthy food and exercise is of the utmost importance in both environments.

Author Contributions

M.M. contributed to the design of the paper, analysis of the results and the writing of the manuscript. The author read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

All subjects gave their informed consent for inclusion before participating in the study. The study was carried out in accordance with the Declaration of Helsinki and the protocol was approved by the Medunsa Research Ethics Committee (MREC/H/269/2012: PG).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Original data used to support the findings of this study are available from the corresponding author upon request.

Acknowledgments

The author would like to thank Chelule and Madiba, who were the researchers’ supervisors, the field workers, and the participants for their contributions to the research.

Conflicts of Interest

The author declares no conflicts of interest.

References

  1. World Health Organization. News-Room Fact-Sheets Detail Obesity and Overweight. 2020. Available online: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight (accessed on 20 March 2024).
  2. Awosan, K.J.; Adeniyi, S.A.; Bello, H.; Bello-Ibrahim, Z. Nutritional status, weight perception and weight control practices among office employees in Sokoto, Nigeria. Pan Afr. Med. J. 2017, 27, 279. [Google Scholar] [CrossRef]
  3. Manyema, M.; Veerman, J.L.; Chola, L.; Tugendhaft, A.; Labadarios, D.; Hofman, K. Decreasing the burden of type 2 diabetes in South Africa: The impact of taxing sugar-sweetened beverages. PLoS ONE 2015, 10, e0143050. [Google Scholar] [CrossRef]
  4. Olivier, N.; Wenhold, F.A.; Becker, P. Resting energy expenditure of black overweight women in South Africa is lower than of white women. Ann. Nutr. Metab. 2016, 69, 24–30. [Google Scholar] [CrossRef] [PubMed]
  5. Owobi, O.U.; Okonji, O.C.; Nzoputam, C.I.; Ekholuenetale, M. Country-Level Variations in Overweight and Obesity among Reproductive-Aged Women in Sub-Saharan Countries. Women 2022, 2, 313–325. [Google Scholar] [CrossRef]
  6. Mchiza, Z.J.; Parker, W.-A.; Makoae, M.; Sewpaul, R.; Kupamupindi, T.; Labadarios, D. Body image and weight control in South Africans 15 years or older: SANHANES-1. BMC Public Health 2015, 15, 992. [Google Scholar] [CrossRef] [PubMed]
  7. Herber, G.C.; Ruijsbroek, A.; Koopmanschap, M.; Proper, K.; van der Lucht, F.; Boshuizen, H.; Polder, J.; Uiters, E. Single transitions and persistence of unemployment are associated with poor health outcomes. BMC Public Health 2019, 19, 740. [Google Scholar] [CrossRef] [PubMed]
  8. Brill, M.J.; van Rongen, A.; Houwink, A.P.; Burggraaf, J.; van Ramshorst, B.; Wiezer, R.J.; van Dongen, E.P.; Knibbe, C.A. Midazolam pharmacokinetics in morbidly obese patients following semi-simultaneous oral and intravenous administration: A comparison with healthy volunteers. Clin. Pharmacokinet. 2014, 53, 931–941. [Google Scholar] [CrossRef]
  9. Peer, N.; Lombard, C.; Steyn, K.; Gwebushe, N.; Levitt, N. Differing patterns of overweight and obesity among black men and women in Cape Town: The CRIBSA study. PLoS ONE 2014, 9, e107471. [Google Scholar] [CrossRef]
  10. Leitner, D.R.; Frühbeck, G.; Yumuk, V.; Schindler, K.; Micic, D.; Woodward, E.; Toplak, H. Obesity and type 2 diabetes: Two diseases with a need for combined treatment strategies-EASO can lead the way. Obes. Facts 2017, 10, 483–492. [Google Scholar] [CrossRef]
  11. Buzzacott, P.; French, S.; Wood, L.; Rosenberg, M. Attitudes towards, and methods of, maintaining or losing body weight among adults. Health 2013, 5, 388–395. [Google Scholar] [CrossRef]
  12. Olaoye, O.R.; Oyetunde, O.O. Perception of weight and weight management practices among students of a tertiary institution in southwest Nigeria. J. Appl. Pharm. Sci. 2012, 2, 81–84. [Google Scholar]
  13. Boyland, E.J.; Whalen, R. Food advertising to children and its effects on diet: Review of recent prevalence and impact data. Pediatr. Diabetes 2015, 16, 331–337. [Google Scholar] [CrossRef] [PubMed]
  14. Feng, X.; Wilson, A. Does dissatisfaction with, or accurate perception of overweight status help people reduce weight? Longitudinal study of Australian adults. BMC Public Health 2019, 19, 619. [Google Scholar] [CrossRef] [PubMed]
  15. Tol, J.; Swinkels, I.C.; De Bakker, D.H.; Veenhof, C.; Seidell, J.C. Overweight and obese adults have low intentions of seeking weight-related care: A cross-sectional survey. BMC Public Health 2014, 14, 582. [Google Scholar] [CrossRef] [PubMed]
  16. Lofton, H.; Ard, J.D.; Hunt, R.R.; Knight, M.G. Obesity among African American people in the United States: A review. Obesity 2023, 31, 306–315. [Google Scholar] [CrossRef] [PubMed]
  17. Manafe, M.; Chelule, P.K.; Madiba, S. Views of Own Body Weight and the Perceived Risks of Developing Obesity and NCDs in South African Adults. Int. J. Environ. Res. Public Health 2021, 18, 11265. [Google Scholar] [CrossRef]
  18. Pearl, R.L.; Puhl, R.M.; Dovidio, J.F. Differential effects of weight bias experiences and internalization on exercise among women with overweight and obesity. J. Health Psychol. 2015, 20, 1626–1632. [Google Scholar] [CrossRef] [PubMed]
  19. Senekal, M.; Lasker, G.L.; van Velden, L.; Laubscher, R.; Temple, N.J. Weight-loss strategies of South African female university students and comparison of weight management-related characteristics between dieters and non-dieters. BMC Public Health 2016, 16, 918. [Google Scholar] [CrossRef] [PubMed]
  20. Ler, P.; Ojalehto, E.; Zhan, Y.; Finkel, D.; Dahl Aslan, A.K.; Karlsson, I.K. Conversions between metabolically unhealthy and healthy obesity from midlife to late-life. Int. J. Obes. 2024, 48, 433–436. [Google Scholar] [CrossRef]
  21. Kipp, C.; Wilson, D.K.; Brown, A.; Quattlebaum, M.; Loncar, H.; Sweeney, A.M.; Abshire, D.A. Compounding effects of stress on diet, physical activity, and wellbeing among African American parents: A qualitative study to inform the LEADS health promotion trial. J. Behav. Med. 2024, 47, 1–15. [Google Scholar] [CrossRef]
  22. World Health Organization. Implementation Roadmap 2023–2030 for the Global Action Plan for the Prevention and Control of NCDs 2013–2030; World Health Organization: Geneva, Switzerland, 2021. [Google Scholar]
  23. Sartorius, B.; Veerman, L.J.; Manyema, M.; Chola, L.; Hofman, K. Determinants of obesity and associated population attributability, South Africa: Empirical evidence from a national panel survey, 2008–2012. PLoS ONE 2015, 10, e0130218. [Google Scholar] [CrossRef] [PubMed]
  24. Cai, L.; Han, X.; Qi, Z.; Li, Z.; Zhang, Y.; Wang, P.; Liu, A. Prevalence of overweight and obesity and weight loss practice among Beijing adults, 2011. PLoS ONE 2014, 9, e98744. [Google Scholar] [CrossRef]
  25. Sun, M.; Fritz, J.; Häggström, C.; Bjørge, T.; Nagel, G.; Manjer, J.; Engeland, A.; Zitt, E.; van Guelpen, B.; Stattin, P.; et al. Metabolically (un)healthy obesity and risk of obesity-related cancers: A pooled study. JNCI J. Natl. Cancer Inst. 2023, 115, 456–467. [Google Scholar] [CrossRef]
  26. Adeloye, D.; Ige-Elegbede, J.O.; Ezejimofor, M.; Owolabi, E.O.; Ezeigwe, N.; Omoyele, C.; Mpazanje, R.G.; Dewan, M.T.; Agogo, E.; Gadanya, M.A.; et al. Estimating the prevalence of overweight and obesity in Nigeria in 2020: A systematic review and meta-analysis. Ann. Med. 2021, 53, 495–507. [Google Scholar] [CrossRef] [PubMed]
  27. Zheng, L.; Deng, F.; Wang, H.; Yang, B.; Qu, M.; Yang, P. Prevalence and Influencing Factors of Overweight and Obesity among Adult Residents of Western China: A Cross-Sectional Study. Int. J. Chronic Dis. 2021, 2021, 9919443. [Google Scholar] [CrossRef] [PubMed]
  28. Ogden, C.L.; Fakhouri, T.H.; Carroll, M.D.; Hales, C.M.; Fryar, C.D.; Li, X.; Freedman, D.S. Prevalence of Obesity Among Adults, by Household Income and Education—United States, 2011–2014. MMWR Morb. Mortal Wkly. Rep. 2017, 66, 1369–1373. [Google Scholar] [CrossRef]
  29. Temple, N.J. The Origins of the Obesity Epidemic in the USA-Lessons for Today. Nutrients 2022, 14, 4253. [Google Scholar] [CrossRef] [PubMed]
  30. Rompkovski, C.S. Reasons that Lead Women to Weight Loss: A Study in Southern Brazil. Rev. Psicol. E Saúde 2021, 13, 81–96. [Google Scholar] [CrossRef]
  31. Hong, L.A. Factors Related to the Weight Control Practices of Korean Adults. JOMES 2021, 2021, 365–376. [Google Scholar]
  32. Ata, R.N.; Thompson, J.K.; Small, B.J. Effects of exposure to thin-ideal media images on body dissatisfaction: Testing the inclusion of a disclaimer versus warning label. Body Image 2013, 10, 472–480. [Google Scholar] [CrossRef]
  33. Santos, I.; Sniehotta, F.F.; Marques, M.M.; Carraça, E.V.; Teixeira, P.J. Prevalence of personal weight control attempts in adults: A systematic review and meta-analysis. Obes. Rev. 2017, 18, 32–50. [Google Scholar] [CrossRef] [PubMed]
  34. Zhong, Y.; McGuire, F.H.; Duncan, A.E. Who is trying to lose weight? Trends and prevalence in past-year weight loss attempts among US adults 1999-2018 at the intersection of race/ethnicity, gender, and weight status. Eat Behav. 2022, 47, 101682. [Google Scholar] [CrossRef]
  35. Elliott, M.; Gillison, F.; Barnett, J. Exploring the influences on men’s engagement with weight loss services: A qualitative study. BMC Public Health 2020, 20, 249. [Google Scholar] [CrossRef]
  36. Tsai, S.A.; Lv, N.; Xiao, L.; Ma, J. Gender differences in weight-related attitudes and behaviors among overweight and obese adults in the United States. Am. J. Men’s Health 2016, 10, 389–398. [Google Scholar] [CrossRef]
  37. Van Dyne, W. Villodas and Cronan, Racial and Ethnic Disparities in Weight Loss Behaviors Among US College Students. Psychol. Res. Behav. Manag. 2023, 2023, 857–873. [Google Scholar] [CrossRef]
  38. Machado, E.C.; Silveira, M.F.d.; Silveira, V.M.F.d. Prevalence of weight-loss strategies and use of substances for weight-loss among adults: A population study. Cad. De Saúde Pública 2012, 28, 1439–1449. [Google Scholar] [CrossRef] [PubMed]
  39. Dombrowski, S.U.; Knittle, K.; Avenell, A.; Araújo-Soares, V.; Sniehotta, F.F. Long term maintenance of weight loss with non-surgical interventions in obese adults: Systematic review and meta-analyses of randomised controlled trials. BMJ 2014, 348, g2646. [Google Scholar] [CrossRef]
  40. Nicklas, J.M.; Huskey, K.W.; Davis, R.B.; Wee, C.C. Successful weight loss among obese US adults. Am. J. Prev. Med. 2012, 42, 481–485. [Google Scholar] [CrossRef] [PubMed]
  41. Han, L.; You, D.; Zeng, F.; Feng, X.; Astell-Burt, T.; Duan, S.; Qi, L. Trends in Self-perceived Weight Status, Weight Loss Attempts, and Weight Loss Strategies Among Adults in the United States, 1999–2016. JAMA Netw. Open 2019, 2, e1915219. [Google Scholar] [CrossRef]
  42. Nojilana, B.; Bradshaw, D.; Pillay-van Wyk, V.; Msemburi, W.; Somdyala, N.; Joubert, J.D.; Groenewald, P.; Laubscher, R.; Dorrington, R.E. Persistent burden from non-communicable diseases in South Africa needs strong action. S. Afr. Med. J. 2016, 106, 436–437. [Google Scholar] [CrossRef]
  43. Thapa, R.; Dahl, C.; Aung, W.P.; Bjertness, E. Urban–rural differences in overweight and obesity among 25–64 years old Myanmar residents: A cross-sectional, nationwide survey. BMJ Open 2021, 11, e042561. [Google Scholar] [CrossRef] [PubMed]
  44. Ronquest-Ross, L.-C.; Vink, N.; Sigge, G.O. Food consumption changes in South Africa since 1994. S. Afr. J. Sci. 2015, 111, 1–12. [Google Scholar] [CrossRef] [PubMed]
  45. Kim, J.H. Associations between weight control behavior and health behaviors in Korean young women: 2013 community health survey. Korea Acad.-Ind. Coop Soc. 2015, 2015, 4734–4744. [Google Scholar]
  46. Tzotzas, T.; Vlahavas, G.; Papadopoulou, S.K.; Kapantais, E.; Kaklamanou, D.; Hassapidou, M. Marital status and educational level associated to obesity in Greek adults: Data from the National Epidemiological Survey. BMC Public Health 2010, 10, 732. [Google Scholar] [CrossRef] [PubMed]
  47. Joh, H.-K.; Oh, J.; Lee, H.-J.; Kawachi, I. Gender and socioeconomic status in relation to weight perception and weight control behavior in Korean adults. Obes. Facts 2013, 6, 17–27. [Google Scholar] [CrossRef] [PubMed]
  48. Mench, E.; West, D.; Krukowski, R.; Harvey, J. Weight Loss Success of Participants Residing in Rural and Urban Areas. J. Rural Health 2018, 34, 396–400. [Google Scholar] [CrossRef] [PubMed]
  49. Goedecke, J.H.; Olsson, T. Pathogenesis of type 2 diabetes risk in black Africans: A South African perspective. J. Intern. Med. 2020, 288, 284–294. [Google Scholar] [CrossRef] [PubMed]
  50. Mchiza, Z.J.-R.; Parker, W.-A.; Sewpaul, R.; Onagbiye, S.O.; Labadarios, D. Body image and the double burden of nutrition among South Africans from diverse sociodemographic backgrounds: SANHANES-1. Int. J. Environ. Res. Public Health 2020, 17, 887. [Google Scholar] [CrossRef]
  51. Park, B.; Cho, H.N.; Choi, E.; Seo, D.H.; Kim, N.S.; Park, E.; Kim, S.; Park, Y.R.; Choi, K.S.; Rhee, Y. Weight control behaviors according to body weight status and accuracy of weight perceptions among Korean women: A nationwide population-based survey. Sci. Rep. 2019, 9, 9127. [Google Scholar] [CrossRef]
  52. Haynes, A.; Kersbergen, I.; Sutin, A.; Daly, M.; Robinson, E. A systematic review of the relationship between weight status perceptions and weight loss attempts, strategies, behaviours and outcomes. Obes. Rev. 2018, 19, 347–363. [Google Scholar] [CrossRef]
  53. Molarius, A.; Lindén-Boström, M.; Karlsson, J. Desire to lose weight and need of weight loss support in the adult population—Results from a cross-sectional study in Sweden. Obes. Sci. Pract. 2020, 6, 373–381. [Google Scholar] [CrossRef] [PubMed]
  54. Piernas, C.; Aveyard, P.; Jebb, S.A. Recent trends in weight loss attempts: Repeated cross-sectional analyses from the health survey for England. Int. J. Obes. 2016, 40, 1754–1759. [Google Scholar] [CrossRef] [PubMed]
  55. Krupa-Kotara, K.; Grajek, M.; Kulesza, A.; Yanakieva, A.; Sas-Nowosielski, K. Motivations in weight reduction and undesirable eating behaviors among women with excessive body weight (Poland). J. Educ. Health Sport 2023, 13, 161–172. [Google Scholar] [CrossRef]
  56. Sirirassamee, T.; Phoolsawat, S.; Limkhunthammo, S. Relationship between body weight perception and weight-related behaviours. J. Int. Med. Res. 2018, 46, 3796–3808. [Google Scholar] [CrossRef] [PubMed]
  57. Evans, M.; de Courcy, J.; de Laguiche, E.; Faurby, M.; Haase, C.L.; Matthiessen, K.S.; Moore, A.; Pearson-Stuttard, J. Obesity-related complications, healthcare resource use and weight loss strategies in six European countries: The RESOURCE survey. Int. J. Obes. 2023, 47, 750–757. [Google Scholar] [CrossRef]
  58. Natvik, E.; Råheim, M.; Andersen, J.R.; Moltu, C. Living a successful weight loss after severe obesity. Int. J. Qual. Stud. Health Well-Being 2018, 13, 1487762. [Google Scholar] [CrossRef]
Table 1. Sociodemographic characteristics of respondents and attempt to lose weight (p < 0.05).
Table 1. Sociodemographic characteristics of respondents and attempt to lose weight (p < 0.05).
VariablesFrequency
n (%)		Tried to Lose Weight
p-Value
Age<0.001 *
18–35 years711 (67%)
36–55 years280 (27%)
Older than 55 years58 (6%)
Sex<0.001 *
Female 586 (56%)
Male463 (44%)
Marital status0.014 *
Ever married311 (30%)
Single713 (70%)
Education level<0.001 *
No formal education11 (1%)
Primary education48 (5%)
Secondary education268 (26%)
Completed matric460 (44%)
Tertiary education258 (25%)
Employment status0.794
Not employed494 (47%)
Part-time employed74 (7%)
Self-employed62 (6%)
Full-time employed411 (40%)
Income0.909
<R1000.00203 (27%)
R1001.00–R3200.00214 (28%)
R3201.00–R6400.00174 (23%)
R6401.00–R12,800.0099 (13%)
R12,801.00–R25,600.0063 (8%)
>25,601.008 (1%)
Area of residence<0.001 *
Township542 (52%)
Suburb16 (1%)
Informal settlement102 (10%)
Rural373 (36%)
Ethnic group0.617
Northern Sotho190 (18%)
Tsonga132 (13%)
Tswana313 (30%)
Venda27 (3%)
Xhosa66 (6%)
Ndebele136 (13%)
Zulu89 (8%)
Southern Sotho52 (5%)
Swati45 (4%)
Calculated BMI0.816
Underweight64 (6%)
Normal weight424 (40%)
Overweight and obese562 (54%)
Self-perceived BMI<0.001 *
Underweight5 (1%)
Normal weight161 (29%)
Overweight339 (62%)
Obese42 (8%)
* Significant. p ≤ 0.05. R: South African rands.
Table 2. The sociodemographic characteristics of the respondents and the failure to lose weight.
Table 2. The sociodemographic characteristics of the respondents and the failure to lose weight.
VariablesFrequency (n%)Failure to Lose Weight (p-Value)
Age0.809
18–35 years711 (67%)
36–55 years280 (27%)
Older than 55 years58 (6%)
Sex0.061
Female 586 (56%)
Male463 (44%)
Marital status0.730
Ever married311 (30%)
Single713 (70%)
Education level0.208
No formal education11 (1%)
Primary education48 (5%)
Secondary education268 (26%)
Completed matric460 (44%)
Tertiary education258 (25%)
Employment status0.279
Not employed494 (47%)
Part-time employed74 (7%)
Self-employed62 (6%)
Full-time employed411 (40%)
Income0.589
<R1000.00203 (27%)
R1001.00–R3200.00214 (28%)
R3201.00–R6400.00174 (23%)
R6401.00–R12,800.0099 (13%)
R12,801.00–R25,600.0063 (8%)
>25,601.008 (1%)
Area of residence0.030 *
Township542 (52%)
Suburb16 (1%)
Informal settlement102 (10%)
Rural373 (36%)
Ethnic group0.172
Northern Sotho190 (18%)
Tsonga132 (13%)
Tswana313 (30%)
Venda27 (3%)
Xhosa66 (6%)
Ndebele136 (13%)
Zulu89 (8%)
Southern Sotho52 (5%)
Swati45 (4%)
Calculated BMI0.640
Underweight64 (6%)
Normal weight424 (40%)
Overweight and obese562 (54%)
Self-perceived BMI<0.001 *
Underweight5 (1%)
Normal weight161 (29%)
Overweight339 (62%)
Obese42 (8%)
* Significant. p ≤ 0.05. R: South African rands.
Table 3. Self-reported weight management behaviour by gender.
Table 3. Self-reported weight management behaviour by gender.
VariablesGenderp-Value
FemaleMale
nn
Tried losing weight <0.001 *
No137 (60%)93 (40%)
Yes227 (71%)92 (29%)
The weight loss method used
Diet pills53 (93%)4 (7%)
Exercise at home113 (65%)60 (35%)<0.001 *
Going to the gym21 (62%)13 (38%)
Eating less31 (84%)6 (16%)
The weight loss method helped <0.001 *
No 80 (84%)15 (16%)
Yes135 (65%)73 (35%)
* Significant. p ≤ 0.05.
Table 4. Results of logistic regression for sociodemographic characteristics and attempt to lose weight.
Table 4. Results of logistic regression for sociodemographic characteristics and attempt to lose weight.
VariablesFrequency
n (%)		p-ValueOdds Ratio [95% Conf. Interval]
Age
18–35 years711 (67%)<0.001 *1.939 (0.609–0.715)
36–55 years280 (27%)<0.001 *1.581 (0.387–0.529)
Older than 55 years58 (6%)<0.001 *1.707 (0.379–0.690)
Sex
Female 586 (56%)<0.001 *1.865 (0.573–0.673)
Male463 (44%)<0.001 *1.644(0.424–0.570)
Marital status
Ever married311 (30%)<0.001 *1.674 (0.449–0.581)
Single713 (70%)<0.001 *1.862 (0.567–0.675)
Education level
No formal education11 (1%)0.3471.117 (−0.145–0.367)
Primary education48 (5%)0.005 *1.374 (0.106–0.529)
Secondary education268 (26%)<0.001 *1.684 (0.437–0.605)
Completed matric460 (44%)<0.001 *1.793 (0.519–0.648)
Tertiary education258 (25%)<0.001 *2.009 (0.623–0.772)
Employment status
Not employed494 (47%)<0.001 *1.831 (0.541–0.669)
Part-time employed74 (7%)<0.001 *1.716 (0.372–0.708)
Self-employed62 (6%)<0.001 *1.763 (0.400–0.735)
Full-time employed411 (40%)<0.001 *1.762 (0.503–0.629)
Income
<R1000.00203 (27%)<0.001 *1.848(0.515–0.713)
R1001.00–R3200.00214 (28%)<0.001 *1.750 (0.461–0.658)
R3201.00–R6400.00174 (23%)<0.001 *1.818(0.461–0.658)
R6401.00–R12,800.0099 (13%)<0.001 *1.701 (0.498–0.697)
R12,801.00–R25,600.0063 (8%)<0.001 *1.788 (0.405–0.656)
>25,601.008 (1%)0.030 *1.770(0.427–0.735)
Area of residence
Township542 (52%)<0.001 *1.875 (0.574–0.683)
Suburb16 (1%)0.007 *1.648 (0.168–0.831)
Informal settlement102 (10%)<0.001 *1.335 (0.138–0.440)
Rural373 (36%)<0.001 *1.772 (0.500–0.643)
Ethnic group
Northern Sotho190 (18%)<0.001 *1.829 (0.506–0.700)
Tsonga132 (13%)<0.001 *1.828 (0.473–0.733)
Tswana313 (30%)<0.001 *1.755 (0.488–0.636)
Venda27 (3%)0.008 *1.535 (0.132–0.725)
Xhosa66 (6%)<0.001 *1.603 (0.300–0.643)
Ndebele136 (13%)<0.001 *1.748 (0.437–0.679)
Zulu89 (8%)<0.001 *1.919 (0.509–0.795)
Southern Sotho52 (5%)<0.001 *1.883 (0.450–0.816)
Swati45 (4%)<0.001 *2.013 (0.479–0.920)
Self-perceived BMI
Underweight5 (1%)0.3911.284 (−0.545–1.045)
Normal weight161 (29%)<0.001 *1.542 (0.354–0.511)
Overweight339 (62%)<0.001 *1.894 (0.587–0.690)
Obese42 (8%)<0.001 *2.042 (0.571–0.856)
* Significant. p ≤ 0.05.
Table 5. Results of logistic regression for sociodemographic characteristics and failure to lose weight.
Table 5. Results of logistic regression for sociodemographic characteristics and failure to lose weight.
VariablesFrequency
n (%)		p-ValueOdds
Ratio		[95% Conf. Interval]
Age
18–35 years711 (67%)<0.001 *11.4692.062–2.816
36–55 years280 (27%)<0.001 *9.6411.876–2.655
Older than 55 years58 (6%)<0.001 *8.5231.092–3.193
Sex
Female 586 (56%)<0.001 *11.6982.129–2.789
Male463 (44%)<0.001 *8.6111.728–2.577
Marital status
Ever married 311 (30%)<0.001 *9.6131.871–2.655
Single713 (70%)<0.001 *11.3552.072–2.786
Education level
No formal education11 (1%)0.0644.481−0.111–3.111
Primary education48 (5%)0.010 *5.7540.481–3.018
Secondary education268 (26%)<0.001 *9.9321.779–2.811
Completed matric460 (44%)<0.001 *11.7672.065–2.865
Tertiary education258 (25%)<0.001 *11.6851.912–3.003
Employment status
Not employed494 (47%)<0.001 *14.3392.242–3.083
Part-time employed74 (7%)<0.001 *4.1410.372–0.708
Self-employed62 (6%)<0.001 *5.5060.400–0.735
Full-time employed411 (40%)<0.001 *10.1050.503–0.629
Income
<R1000.00203 (27%)<0.001 *7.9961.368–2.789
R1001.00–R3200.00214 (28%)<0.001 *10.5901.789–2.930
R3201.00–R6400.00174 (23%)<0.001 *8.1091.442–2.743
R6401.00–R12,800.0099 (13%)<0.001 *8.0921.276–2.905
R12,801.00–R25,600.0063 (8%)<0.001 *17.4112.014–3.699
Area of residence
Township542 (52%)<0.001 *10.7842.015–2.740
Suburb16 (1%)0.030 *5.5520.231–3.197
Informal settlement102 (10%)0.001 *3.5490.546–1.986
Rural373 (36%)<0.001 *14.4482.214–3.126
Ethnic group
Northern Sotho190 (18%)<0.001 *7.2361.396–2.561
Tsonga132 (13%)<0.001 *9.4871.332–3.167
Tswana313 (30%)<0.001 *10.9941.921–2.873
Venda27 (3%)0.005 *15.6421.154–4.345
Xhosa66 (6%)<0.001 *11.5881.371–3.528
Ndebele136 (13%<0.001 *13.7032.016–3.219
Zulu89 (8%)<0.001 *9.2271.135–3.308
Southern Sotho52 (5%)0.0664.055−0.115–2.915
Self-perceived BMI
Underweight (<18.5 kg/m2)5 (1%)0.2974.481−2.287–5.287
Normal weight (18.5–24.99 kg/m2)161 (29%)<0.001 *8.8531.779–2.582
Overweight (25–29.99 kg/m2)339 (62%)<0.001 *10.9052.020–2.758
Obesity (30 kg/m2 and above)42 (8%)<0.001 *42.5212.662–4.837
* Significant. p ≤ 0.05.
Table 6. Pearson’s correlation coefficient between self-perceived BMI and attempt to lose weight.
Table 6. Pearson’s correlation coefficient between self-perceived BMI and attempt to lose weight.
Self-Perceived BMIAttempt Weight LossFailure to Lose Weight
Self-perceived BMI1.0000
Attempt weight loss0.2015 *1.0000
<0.0001
Failure to lose weight0.1342 *−0.0500 ns1.0000
0.03730.4392
* Significant correlation at p ≤ 0.05, ns: no significant correlation at p <0.05.
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Manafe, M. Predictors of the Attempt and Failure to Lose Weight among the South African Population. Obesities 2024, 4, 195-211. https://doi.org/10.3390/obesities4030018

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Manafe M. Predictors of the Attempt and Failure to Lose Weight among the South African Population. Obesities. 2024; 4(3):195-211. https://doi.org/10.3390/obesities4030018

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Manafe, Mashudu. 2024. "Predictors of the Attempt and Failure to Lose Weight among the South African Population" Obesities 4, no. 3: 195-211. https://doi.org/10.3390/obesities4030018

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Manafe, M. (2024). Predictors of the Attempt and Failure to Lose Weight among the South African Population. Obesities, 4(3), 195-211. https://doi.org/10.3390/obesities4030018

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