INTRODUCTION
Obesity is a major public health concern that affects millions of people globally. In 2022, approximately 2.5 billion adults worldwide, aged 18 and older, were classified as overweight, with over 890 million of them being obese. This marks a significant increase from 1990, when only 25% of adults were overweight, to 43% in 2022. The prevalence of obesity varies regionally, from 31% in the WHO South-East Asia and African regions to 67% in the Americas [1]. Overweight and obesity result from an imbalance between energy intake and expenditure, influenced by various factors such as obesogenic environments, psychosocial influences, and genetic predispositions. The lack of effective healthcare systems to detect and address early weight gain further exacerbates the obesity epidemic. It was reported that a higher body mass index (BMI) was linked to approximately 5 million deaths from noncommunicable diseases [1].
This alarming trend has significant public health implications, as obesity is a well-established risk factor for numerous chronic conditions, including cardiovascular disease (CVD), diabetes, musculoskeletal disorders, and certain cancers [2]. The National Health and Morbidity Survey (NHMS) 2023 revealed that 54.4% of Malaysian adults are either overweight or obese [3]. Furthermore, 45.5% of Malaysian adults experienced weight gain during the pandemic, with sedentary behaviour identified as a key risk factor [4]. The rising prevalence of overweight and obesity among Malaysian adults highlights the urgent need to address this escalating public health issue.
The economic burden of overweight and obesity is substantial, as these conditions are strongly associated with other illnesses such as diabetes, CVDs, and cancer [1]. This leads to increased healthcare expenditures for medical services, medications, and treatments within both government and private healthcare systems. Malaysia has the highest total cost of obesity-related healthcare expenses (19.36%) among ASEAN countries, whereas the cost of other countries is typically below 10% [5].
Despite numerous public health initiatives aimed at curbing obesity, the prevalence of overweight and obesity continues to rise among Malaysian adults, posing significant health risks and economic burdens. However, there is limited understanding of the specific factors that contribute to this growing epidemic in Malaysia. Existing studies often focus on general populations or specific demographic groups, leaving gaps in knowledge regarding the unique sociodemographic and lifestyle factors influencing obesity among Malaysian adults. This lack of targeted data hampers the development of effective interventions to address overweight and obesity in this population. Consequently, there is an urgent need to determine the key factors associated with overweight and obesity among Malaysian adults to inform public health strategies and reduce the burden of obesity-related diseases. Therefore, our study aims to determine the association between sociodemographic factors, lifestyle habits such as physical activity, sleep quality with BMI among Malaysian adults.
METHODS
Study Design and Ethics Approval
A cross-sectional study was carried out to determine the prevalence and factors associated with overweight and obesity among Malaysian adults. This study was approved by the Research Ethics Committee of the University (Approval ID: MHMH170423).
A convenient sampling procedure was used to recruit participants. Malaysian adults aged 18 to 60 years old, able to speak and read Malay or English, and with good skills in Information Technology (IT) were invited to participate in this study via online platforms including emails, Facebook, Instagram, and WhatsApp. Meanwhile, those who were handicapped, blind, deaf, mute, diagnosed with mental illness, or pregnant at the time of the survey were excluded from this study.
The sample size was determined using Daniel’s method [6], which estimated a required sample of 308, based on Malaysia’s reported obesity prevalence of 50.1% from the NHMS 2019 at a 95% confidence level, with an additional 5% for potential dropouts, missing data, or incomplete survey responses.
Data Collection
An online questionnaire was distributed to Malaysian adults using social media platforms, peer-to-peer networks, and recommendations. The questions were structured into three sections. Part A: anthropometric and sociodemographic data; Part B: Pittsburgh Sleep Quality Index (PSQI) [7], and Part C: physical activity, including the frequency and duration
Part A: sociodemographic and anthropometric information
Sociodemographic information including gender, age, ethnicity, location, highest education level, employment status, smoking status, and alcohol drinking status was collected. The location of the participants was categorized into five different regions: Southern (Johor and Malacca), Central (Selangor, Putrajaya, Kuala Lumpur, and Negeri Sembilan), Northern (Perak, Penang, and Kedah), Eastern (Kelantan, Terengganu, and Pahang), and East Malaysia (Sabah and Sarawak).
For anthropometric measurement, the height and weight of participants were self-reported. The BMI was calculated as weight divided by height squared (kg/m2) and classified based on the Asia-Pacific cutoff points. To categorize the BMI values, BMI values below 18.5 kg/m2 were classified as underweight, those ranging from 18.5 to 22.9 kg/m2 as normal weight, those from 23.0 to 27.4 kg/m2 as overweight, and values 27.5 kg/m2 and above as obesity [8].
Part B: PSQI
The PSQI measures the past-month sleep routines of the participants. A total of 19 self-rated questions were constructed based on the scores of seven components: sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. The participants’ overall score was used to determine how well they slept, with scores ranging from no difficulty at all (0 score) to severe difficulty (3 score). The sum of the scores for these seven components had a range of 0–21, yielding one global score. The PSQI global scores were then used to categorise the subjects; a score of ≤5 indicated good sleep quality, whereas a score of >5 indicated poor sleep quality [7].
Statistical Analysis
All data collected in this phase were analysed using IBM SPSS Statistics version 28 (IBM Corp.). The continuous variables were expressed as mean and standard deviation whereas categorical variables were expressed as frequencies in percentage (%). The differences between the means of continuous variables were determined using t-tests. The association between the categorical variables and BMI was determined using a chi-square test. All variables with p<0.25 in the chi-square test were included in the binary logistic regression to determine the factors associated with overweight and obese Malaysian adults. Statistical significance was set at p<0.05.
RESULTS
Association between Sociodemographic, and Lifestyle Factors with Body Weight among Malaysian Adults
Table 1 presents the sociodemographic characteristics of the participants and their association with lifestyle factors and body weight among Malaysian adults. The mean age of the participants was 29.4±8.39 years. A total of 64.9% of the participants were classified as either overweight or obese, with 38.0% being overweight (n=117) and 26.9% obese (n=83). A significantly higher proportion of overweight and obese participants were female (79%, p<0.001). For ethnicity, 45.5% were Chinese, 45.1% were Malay, 5.8% were Indian, and 3.6% were others (Bumiputera Sabah or Sarawak). The findings revealed that 61.1% of participants in the normal weight category were Chinese, whereas 53.5% of those classified as overweight or obese were Malay.
The mean age of overweight or obese participants (31.30±8.80 years) was significantly higher than that of normal-weight participants (25.79±6.19 years) (p<0.001). The majority of participants were single (75.6%). Lower educational attainment was significantly more prevalent among overweight or obese individuals compared to normal-weight participants. A higher proportion of overweight/obese participants were married (32.0%) and employed full-time (64.5%). Smoking status and alcohol consumption did not differ significantly between the weight groups.
Physical measurements, including weight and BMI, were significantly higher in the overweight/obese group (p<0.001), while height showed no significant difference between the groups (p=0.752). The frequency of physical activity was similar across both groups, with no significant differences observed.
However, sleep quality, measured using the PSQI, differed significantly between the groups. Normal-weight participants had better sleep quality, with 55.6% of them reporting good sleep (p<0.018). Specifically, 75% of normal-weight individuals slept for 7 to 9 hours, compared to 54.5% in the overweight/obese group (p<0.001). Fig. 1 shows the sleep quality and sleep duration between the normal BMI and overweight/obese participants.
Factors Association with Overweight and Obesity among Malaysian Adults
Table 2 presents the factors influencing the risk of overweight and obesity among Malaysian adults. A total of 308 cases were analyzed, and the logistic regression model was found to be statistically significant, χ2(9)=26.627, p<0.001. The model explained 25.8% of the variance in overweight and obesity (Nagelkerke R2 =0.258) and correctly classified 87.5% of the cases.
Full-time employment was associated with a 2.3-fold increase in the likelihood of being overweight or obese (adjusted odds ratio [aOR]=2.308, 95% confidence interval [CI]=1.035–4.080, p=0.004). Poor sleep quality also significantly increased the odds of overweight and obesity by 1.839 times (aOR=1.839, 95% CI=1.078–3.138, p=0.025). In contrast, males were significantly less likely to be overweight or obese, with a 62% lower likelihood (aOR=0.383, 95% CI=0.215–0.684, p<0.001).
DISCUSSION
This study aimed to determine the prevalence and associated factors of overweight and obesity among Malaysian adults. Our findings revealed that the prevalence of overweight was 38%, while obesity affected 26.9% of the participants. These figures are notably higher compared to the NHMS 2023, which reported an overall prevalence of 54.5% (32.6% overweight and 21.8% obese). The increasing rates of overweight can be linked to rapid urbanization, increased food accessibility, and a rise in sedentary lifestyles exacerbated by the pandemic. According to a study [4], approximately 45.5% of individuals experienced weight gain during the pandemic due to reduced physical activity. Moreover, Malaysia had a greater prevalence of overweight and obesity compared to southern China [9], and other Southeast Asian nations such as Singapore [10] and Philippines [11]. This trend is alarming and underscores the urgent need for enhanced public health policies and programs focused on nutrition and lifestyle modifications to address the growing issue of overweight and obesity in Malaysia.
Our findings show that females exhibited a significantly higher risk of being overweight compared to males. Our finding is in line with other studies reporting greater obesity prevalence among females [12]. This increased risk is largely attributable to hormonal differences, particularly the effects of estrogen [13]. Estrogen, a primary sex hormone in females, plays a crucial role in regulating fat metabolism. Elevated estrogen levels, particularly during puberty and early pregnancy, promote fat storage in preparation for fertility, fetal development, and breastfeeding [13]. This hormonal influence reduces the body’s ability to burn energy efficiently after eating, leading to increased fat accumulation. Additionally, the physiological changes associated with estrogen contribute to a more substantial overall fat storage in females. In contrast, males generally have lower estrogen levels and a different fat distribution pattern, which often results in less overall fat storage. Additionally, men may be less likely to prioritize their physical appearance and health, which can contribute to their risk of overweight and obesity [14]. These factors underscore the need for targeted public health strategies that consider improving women’s health status when addressing overweight and obesity.
Our findings demonstrate a significant association between age and obesity among Malaysian adults (p<0.001), with older individuals showing a higher mean age in the overweight/obese group compared to those with normal weight. This suggests that older adults are more prone to being overweight or obese.
The aging process contributes to this increased risk in several ways. As individuals age, their metabolism tends to slow down, and physical activity levels often decrease. These changes can lead to a redistribution of fat from subcutaneous areas to abdominal and ectopic sites such as the liver and muscles. This redistribution is linked to a higher risk of chronic conditions, including CVDs, stroke, diabetes, and certain cancers [15]
The present study highlights significant ethnic differences in the prevalence of overweight and obesity among Malaysian adults (p<0.001). The highest prevalence of overweight/obesity was observed among Malay adults (53.5%), while Chinese adults exhibited a higher prevalence of normal BMI (61.1%). These findings align with previous research, which indicates that the prevalence of obesity varies significantly by ethnic group, with lower rates among Chinese populations and higher rates among Malays and Indians [16]. Cultural norms and traditional dietary practices play a crucial role in shaping eating behaviours and food choices. For example, Malay and Indian cuisines often include calorie-dense foods and high-fat ingredients, which can contribute to higher obesity rates. In contrast, Chinese dietary patterns may emphasize vegetables and lean proteins, which could be associated with lower obesity prevalence [17].
Our findings reveal a significant association between educational attainment and obesity prevalence (p<0.001). Individuals with lower levels of education exhibit a higher prevalence of overweight and obesity compared to those with higher education levels. Our results are in consistent with a study by Wang et al. [18]. Higher education is strongly linked to improved health literacy, equipping individuals with the knowledge and awareness of the risks associated with obesity. Educated individuals are more likely to adopt healthier lifestyle choices, including better dietary practices, regular physical activity, and proactive health management. This heightened awareness and access to health information contribute to a lower risk of developing obesity [18].
Our data indicate that overweight and obesity were more prevalent among married participants. This is consistent with findings from Sato [19], which suggest that marriage is associated with an increase in BMI and a higher likelihood of being overweight. These results highlight that marriage can affect men and women differently, possibly due to changes in dietary habits, reduced time for physical activity because of family responsibilities, and physiological changes in women, particularly during the post-pregnancy stage [20].
The present study also revealed that employed individuals are at a higher risk of being overweight or obese (aOR=2.308, 95% CI=1.035–4.080, p=0.004). This can be attributed to the demands of modern work life, where long working hours, shift work, and multiple job commitments leave little time for healthy family meals, leading to a reliance on convenience and high-calorie fast foods, thereby increasing the risk of obesity for both employees and their families [21]. High-demand work environments can significantly impact food choices and eating habits. The types of foods consumed during breaks and the nutritional quality of meals available at work are often determined by the accessibility of healthy options and the quality of workplace eating facilities [21]. In addition to these factors, job-related stress is a strong contributor to obesity. A Canadian study similarly found that individuals working under stressful conditions were more likely to gain weight [22]. Biologically, stress triggers the release of cortisol, a hormone that increases cravings for unhealthy, high-glycaemic foods, leading to overeating and disrupted sleep patterns, both of which are linked to weight gain [23,24].
The present study found that poor sleep quality was significantly associated with a 1.839-fold increase in the odds of being overweight or obese (aOR=1.839, 95% CI=1.078–3.138, p=0.025). This finding aligns with previous research, which has similarly linked poor sleep quality to an elevated risk of obesity among adults [25]. Poor sleep quality contributes to obesity through several mechanisms. Disruptions in sleep can alter the regulation of key hormones such as leptin and ghrelin, which control appetite and hunger, leading to increased food intake [26]. Additionally, insufficient sleep induces insulin resistance, impairing glucose metabolism and promoting fat storage [27]. Sleep deprivation also affects brain regions associated with reward and impulse control, causing individuals to consume more calories, particularly from high-fat and sugary foods [27]. Fatigue from poor sleep reduces physical activity, thereby decreasing energy expenditure. Furthermore, elevated cortisol levels, which are linked to sleep deprivation, promote abdominal fat accumulation, as previously noted in relation to work stress. Disrupted circadian rhythms also negatively impact metabolic processes, further increasing the risk of obesity [27].
Our data also showed that poor sleep duration, defined as less than 7 or more than 9 hours, was more prevalent among the overweight/obese group (45.5%) compared to the normal-weight group (25%). Healthy adults generally require 7 to 9 hours of sleep per night. Studies have demonstrated that shorter sleep durations are linked to higher BMI, increased weight, and larger neck circumference [28]. This suggests that both insufficient and excessive sleep play roles in the prevalence of obesity. Research by Muniandy and Ying [29] also supports the association between short sleep duration and obesity, noting its connection to decreased insulin sensitivity, metabolic dysfunction, and weight gain, which may increase the risk of diabetes and cardiovascular issues [29]. Given these findings, sleep quality should be considered an essential factor in obesity interventions, as it is often overlooked in traditional weight management programs [28].
Several limitations were identified in this study. The cross-sectional design of this study only captures data at a single point in time, making it unable to infer causal relationships between variables. The findings were based on a sample survey, and like any survey-based research, the results are potentially subject to sampling and non-sampling errors. Additionally, the reliance on self-reported data may introduce some degree of inaccuracy and bias, as participants might underreport or overreport certain lifestyle behaviours such as physical activity levels. Another limitation is the use of convenience sampling, which may limit the generalizability of the results. The results should be validated through further studies with larger and more diverse samples to confirm these associations at a national level.
In conclusion, the high prevalence of overweight and obesity (64.9%) observed in this study is a critical public health concern. Our findings indicate that individuals who are female, employed full-time, and experiencing poor sleep quality are at greater risk of being overweight or obese. These insights underscore the need for targeted public health interventions, particularly workplace health programs and comprehensive health education campaigns that address these specific risk factors. Policymakers should prioritize initiatives that promote healthier lifestyle choices in the workplace and raise awareness about the importance of sleep quality in maintaining a healthy weight. Future research should aim to conduct longitudinal studies to explore the causal relationships between gender, employment status, and sleep quality with obesity. In addition, investigating the effectiveness of interventions designed to improve sleep quality, may provide valuable strategies for obesity prevention and management. Addressing these risk factors holistically could lead to more effective solutions for reducing obesity and improving population health outcomes.
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