## Introduction

Healthy lifestyle habits are important for preventing disease, improving health, and maintenance of overall well-being throughout one’s life1. In general, healthy lifestyle habits include adopting a healthy diet, abstaining from smoking, maintaining a healthy weight, avoiding harmful alcohol consumption, and being physically active. Leading a healthy lifestyle also reduces one’s risk of developing major chronic diseases. While the aforementioned modifiable lifestyle factors are individually associated with a decreased risk of major chronic diseases and a longer life expectancy, several studies have found that combined healthy lifestyle habits have a greater impact on increased life expectancy than on their own2. Other studies have also found that a combination of healthy lifestyle factors is associated with improved survival3 and reduced risk of major chronic diseases, such as coronary heart disease, stroke, heart failure and diabetes4,5,6. Adherence to a combination of five healthy lifestyle factors reduced mortality risk by 61%3. In previous studies among adults in China7,8 and cancer survivors9, researchers looked into the effects of combined healthy lifestyle factors in lowering overall and cause-specific mortality. Using measures of healthy lifestyle, including exercise, diet, sleep, alcohol consumption, and smoking, it found that 7.7% of adults reported practicing five healthy behaviours10. In another study using European Social Survey data observed that around 5.8% of European adults achieving all five healthy behaviours. Finland with 9.2% and United Kingdom with 8.6% were those the highest prevalence while Hungary with 1.3% and Czech Republic with 1.9% were the lowest prevalence of five healthy behaviours11. To date, there have been no studies conducted in Malaysia focusing on the effects of combined healthy lifestyle factors. There is also a lack of information on the prevalence of healthy lifestyle habits or practices among Malaysian adults.

Numerous studies have established a link between the prevalence of adopting healthy lifestyle habits and socio-demographic factors. In Germany, it was found that healthy lifestyle habits differ according to socio-demographic characteristics such as age, sex and educational levels12. Other studies found that higher socioeconomic status was associated with combinations of healthy lifestyle habits, decreased risk of cardiovascular disease, and lower mortality rates13. Information on the influence of socio-demographic factors on the likelihood of leading a healthy lifestyle amongst the local population is important for public health policy development, as it identifies relevant target areas for appropriate individual- and population-level interventions.

In Malaysia, there are no nationally-representative studies on the prevalence of healthy lifestyle behaviours and its socio-demographic correlates in adult populations. The purpose of this study, therefore, was to assess the prevalence of healthy lifestyle behaviours based on data from the National Health and Morbidity Survey (NHMS)—a nationwide health study in Malaysia—and to identify socio-demographic factors associated with leading a healthy lifestyle in a nationally-representative sample of Malaysian adults.

## Methods

### Data and participants

This study utilized secondary data from NHMS 2019. The NHMS is a national survey coordinated by the Institute of Public Health, Ministry of Health Malaysia, and has been conducted annually since 2011. This study is a cross-sectional survey with the aims of determining Malaysians’ health status, monitoring disease trends, and assessing healthcare utilization in Malaysia. The NHMS 2019 employs a nationally representative sample of the non-institutionalised general population of Malaysians of all ages in Malaysia. This study employs two-stage stratified random sampling to ensure the resulting sample was representative of the country. The country’s population is stratified by urban and rural regions in each of the 16 states or federal territories, hence a total of 29 strata. The first stage involves systematic random sampling of Enumeration Blocks (EB) within each stratum. The second stage involves random sampling of twelve living quarters (LQ) within each selected EB. For the entire country of Malaysia, a total of 475 Enumeration Blocks (EB) were selected proportionate to the population size of each stratum. A total of 5676 LQs were selected from the selected EBs. Further details of the NHMS 2019 study have been described in the NHMS 2019 technical report14. Between July and October 2019, 14,965 participants were recruited (response rate of 87.2%). All participants signed an informed consent form.

For the present study, participants under the age of 18 years were excluded (n = 4493), as the study’s focus was on the adult population. Respondents with missing socio-demographic data and healthy lifestyle data were excluded (n = 3084). After these exclusions, 7388 respondents were included in the present analyses.

### Assessment of lifestyle factors and socio-demographic variables

Body weight, height and body mass index (BMI) were determined. Body weight was measured in kilograms using a digital weighing machine (TANITA HD-319) and the height in centimetres was measured using SECA Stadiometer 213. The BMI was computed by dividing weight in kilograms by the squared height in metres. BMI was then classified according to the WHO BMI guidelines: (< 25 kg/m2 as underweight to normal weight, ≥ 25 kg/m2 as overweight or obese)15.

Four questions were used to assess fruit and vegetable consumption: (1) “In a typical week, how many days did you consume fruits?” (2) “Usually on the day you eat fruits, how many servings of fruits did you eat in a day?” (3) “In a typical week, how many days did you eat vegetables?” (4) “Usually on the day you eat vegetable, how many servings of vegetables did you eat in a day?” Food photographs were used to assist respondents in recalling the serving size of fruits and vegetables consumed. The photographs depicted a single serving of commonly consumed fruits and vegetables, including one slice of watermelon, one medium-sized orange, one cup of chopped raw leafy green vegetables and a half cup of other vegetables, cooked or chopped raw such as carrot. The total number of servings of fruits and vegetables consumed per day was calculated using responses to these four questions. Data from the first two questions were used to estimate respondents’ fruits intake. Another two questions were used to estimate respondents’ vegetable intake. The number of days in a week was multiplied by number of servings to estimate the number of servings per week that respondents consumed. Responses for the past 7-day questions were then divided by 7 to determine daily intake. The number of servings/day for fruits and vegetable intake were summed to estimate the servings of fruits and vegetables intake. The range of score was 0 to 12 servings/day. Based on the Malaysian Dietary Guidelines, consumption of ≥ 5 servings of fruits and vegetables per day was defined as adequate while consumption of < 5 servings of fruits and vegetables per day was defined as inadequate16.

The validated short version of the International Physical Activity Questionnaire (IPAQ) was used to assess levels of physical activity17. The IPAQ short form was developed to estimate overall physical activity levels through the assessment of three specific types of physical activity (low, moderate and vigorous-intensity activities) across a broad range of domains (home, at work, sitting, moderate leisure, vigorous leisure, transportation and other activities). Physical activity was quantified in terms of metabolic equivalents (MET minutes, or METs)18. The minutes and days of a week spent on low, moderate, and vigorous intensity activities were multiplied by 3.3, 4.0 and 8.0 respectively, to compute MET scores for each activity. The total physical activity score was calculated as the sum of all MET scores from three sub-components. MET score ranges from 0 to 25,704. Participants in this survey were classified as “active” if they had a total physical activity score of at least 600 METs per week according to the WHO’s recommendation19. Those who did not meet the weekly requirement of 600 METs were classified as “inactive”.

The Malay version of the Alcohol Use Disorder Identification Test (AUDIT-M) questionnaire was used to assess alcohol consumption20. The AUDIT is a validated 10-item questionnaire that includes alcohol consumption, drinking behaviours, and alcohol-related problems. Scores were classified as low risk (score ranging from 1 to 7) or risky (score ranging from 8 to 40). Non-drinker was defined as having had no alcohol consumption for the past 12 months, which includes never drinkers.

The smoking status was divided into two categories: smoker (those who smoked tobacco products on a daily or occasional basis at the time of the survey) and non-smoker (those are not currently smoked any tobacco products).

### Construction of a healthy lifestyle score

BMI, diet, physical activity, cigarette smoking, and alcohol consumption were all used to calculate a healthy lifestyle score12. The composite score’s five modifiable lifestyle factors were chosen based on national recommendations for non-communicable disease risk factors21 and epidemiological evidence in Asians22,23. For each of these components, a binary variable was created with one point assigned (0 otherwise) if the participant meets each criteria as follows: a BMI of < 25 kg/m2, performs regular physical activity, has moderate (or less) alcohol consumption, consumes at least 5 servings of fruits and vegetables, and does not smoke12. The healthy lifestyle score was calculated as the sum of the binary point system, ranging from 0 to 5 points. Participants were classified into three groups: unhealthy, moderately healthy and healthy. Individuals with healthy lifestyle scores of 0–2 were classified as ‘unhealthy’, those with a healthy lifestyle score of 3 were classified as ‘moderately healthy’, and those with healthy lifestyle scores of 4–5 were classified as the ‘healthy’ group.

### Socio-demographic variables

The independent variables, which were socio-demographic variables, were used as categorical variables in the analyses. Age was captured in the data as a continuous variable and recoded into three categories (18–30 years, 31–60 years, and 60 years and above). Highest educational levels were combined into three categories; primary, secondary and tertiary. ethnicity (Malay, Chinese, Indian and others), educational level (no formal education/ primary, secondary and tertiary), residential area (urban and rural) and marital status (single, married and divorced/widowed). Income information defined as monthly household income, was collected. The income categories were divided into three categories; bottom 40% (B40), middle 40% (M40), and top 20% (T20) of household income.

### Statistical analyses

Data were analysed using IBM SPSS version 22; p-values of < 0.05 were considered statistically significant. Weighting was applied to take into account the complex study design.

A weighting factor was applied to each respondent to adjust for non-response and for the varying probabilities of selection. The weight used for estimation was given by:

$${\text{W }} = {\text{ W1 }} \times {\text{ W2 }} \times {\text{ F }} \times {\text{ PS}}$$

W1 = the inverse of probability of selecting the EBs, W2 = the inverse of probability of selecting the LQs within the EBs, F = the inverse of an EBs, LQs and individual level non-response adjustment factor, PS = a post stratification adjustment factor calculated by strata and gender.

Survey-weighted descriptive statistics were computed to provide nationally representative estimates. Survey-weighted descriptive analyses of socio-demographic factors related with healthy lifestyle behaviours (unhealthy, moderate healthy and healthy) were done. For categorical variables, frequency and weighted percentage were displayed. Rao-Scott Chi-square test was conducted to identify bivariate associations between socio-demographic factors and healthy lifestyle groups. Multinomial logistic regression adjusted for sampling design was used to determine the association of socio-demographic factors with the moderately healthy and healthy lifestyle behaviours, using the unhealthy lifestyle group as the reference group. All predictors were included in the baseline-category logic model to estimate the adjusted odd ratio (aOR) and 95% confidence interval (CI).

### Ethics approval and consent to participate

All participants signed an informed consent form. The NHMS 2019 was approved by the Medical Research and Ethics Committee of Ministry of Health Malaysia (NMRR-18-3085-44207). When conducted the analysis for this study in 2022, informed consent of the study participants was not required because the database used in this study consists of de-identified secondary data released for research purposes. This study was carried out in accordance with the ethical standards of the Declaration of Helsinki and Ministry of Health Malaysia guidelines and regulations.

## Results

Table 1 presents the general characteristics of study participants. A total of 7388 participants were included in this study. Most of them (54.3%) were in the 31–60 years age group. More than half (50.4%) of them were female. Most participants (70.7%) had at least secondary-level education. Participants were 56.3% Malay, 18.4% Chinese, 5.7% Indians and 19.6% of other ethnic groups, which includes indigenous Sabah and Sarawak people, as well as aborigines. Furthermore, most participants were married (65.4%), urban residents (78.9%) and fell in the bottom 40% household income group (64.4%).

Participants were predominantly non-smokers (78.8%), non-drinker or normal drinker (98.2%), physically active (78.7%), consumed < 5 servings of fruits and vegetables per day (95.3%) and overweight or obese (50.5%). The prevalence of participants with 0/1, 2, 3, 4, and 5 healthy lifestyle scores were 2.1%, 18.1%, 49.2%, 29.0%, and 1.6%, respectively. Based on the classification used in the present study, 20.2% were in the unhealthy lifestyle group, 49.2% were in the moderately healthy lifestyle group, and another 30.6% were in the healthy lifestyle group (Table 2).

Table 3 shows socio-demographic factors across different categories of healthy lifestyle behaviours. Age, gender, ethnicity and marital status were significantly different by healthy lifestyle groups. More participants were female in the moderately healthy and healthy groups than in the unhealthy group. In addition, participants in the unhealthy group were more likely to be 31–60 years of age, male, Malay, and married than those in the healthy group. No other significant differences were found between categories of healthy lifestyle behaviours in terms of educational level, residential area and household income.

Table 4 shows socio-demographic factors affecting healthy lifestyle behaviours using multinomial logistic regression analysis. The unhealthy lifestyle group was considered as the reference category in the multinomial logistic regression model. In the final model, gender, ethnicity and residential area appeared to be significant predictors of leading a moderately healthy lifestyle (p < 0.05). Women were 2.77 times more likely to achieve a moderately healthy lifestyle (95%CI = 2.28, 3.36). Indian participants were more likely to have moderately healthy lifestyles compared to Malays as reference category (aOR = 1.82, 95%CI = 1.32, 2.52). Participants residing in rural areas were more likely to have moderately healthy lifestyle compared with participants residing in urban areas as reference category (aOR = 1.24, 95%CI = 1.02,1.51).

The multinomial logistic regression analysis found that statistically significant factors associated with leading a healthy lifestyle were age, ethnicity and gender. Participants aged 18–30 years old were more likely to have healthy lifestyle scores of 4 and 5 (aOR = 1.74, 95% CI = 1.12, 2.71) compared to older people ≥ 61 years old as reference category. Women were 3.26 times more likely to achieve healthy lifestyles compared to males as reference category (aOR = 3.26, 95%CI = 2.58, 4.12). Chinese participants (aOR = 2.31, 95%CI = 1.62, 3.30) and participants with other ethnicities (aOR = 1.44, 95%CI = 1.05, 1.98) were more likely to achieve healthy lifestyles compared to Malay participants.

## Discussion

In this cross-sectional study among a nationally representative sample of Malaysian adults, we discovered that 30.6% of respondents scored healthy lifestyle scores of 4–5. We also found that gender, age and ethnicity significantly influenced healthy lifestyle behaviours. To our knowledge, this is the first study in Malaysia to examine the relationship between healthy lifestyle behaviours and socio-demographic factors. Similarly, in a cross-sectional study of a national sample of German adults, Finger et al.12 found that the combination of healthy lifestyle that includes daily intake of both fruits and vegetables, adequate physical activity, abstinence from smoking, abstinence from risky drinking and normal body weight increased the overall prevalence of healthy lifestyle with at least four out of five healthy lifestyle factors from 9.3% in 1990–1992 to 14.7% in 2008–2011. Adhering to a healthier lifestyle may have the potential to reduce disease burden and prolong life. However, the percentage of people who have combined healthy lifestyle factors appears to be relatively low globally. Only 10.3% of participants in the US Nurses’ Health Study and the Health Professionals Study had at least four protective lifestyle factors3. According to data from the Singapore Chinese Health Study, only 8.5% of the elderly adhered to 4–5 protective lifestyle factors22. In our study, about 31% of respondents practiced at least four healthy lifestyle habits. It is possible that a higher prevalence of individuals with healthy lifestyle habits in Malaysia could be explained by specific cultural factors. In general, the prevalence of those with risky alcohol consumption was generally low across the population. The prevalence of alcohol consumption among Malaysian adults is relatively low, which could be attributed to social and cultural reasons. The majority of participants in this study were Muslim (68%) and alcoholic drinks are totally prohibited in Islam. However, in rural Sabah, drinking is important to maintain culture and identity, especially for indigenous communities24. Hence, health promotion programs, especially effective alcohol-harm minimization programs, will need to be strengthened to increase population health in those regions. Different strategies are used for different socio-demographic groups, in order to promote ideal lifestyle patterns.