Submitted:
26 November 2024
Posted:
27 November 2024
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Abstract
Sedentary behavior (SB) is independently associated with numerous adverse health outcomes, including mental health disorders, non-communicable diseases, and increased mortality risk. This study investigated associations between sociodemographic characteristics, lifestyle factors, mental health, nutritional status, social support, and functional limitations, and SB among the elderly in Malaysia. Data from 3,977 individuals aged 60 years and above, extracted from the National Health and Morbidity Survey (NHMS) 2018, were analyzed using complex samples logistic regression. Prevalence of sedentary behaviour, defined as sitting or reclining for 8 or more hours per day, among the surveyed population was 23.2%. Older age (≥75 years) was significantly associated with higher odds of SB (AORs 1.58 to 2.76, p < 0.001 to p = 0.001). Unemployment (AOR = 1.32, p = 0.020) and indigenous Sabah and Sarawak ethnicity (AOR = 2.48, p = 0.007) were also linked to increased odds of SB. Conversely, individuals with a monthly income of RM 1000-1999 had lower odds of SB compared to those earning ≥RM 2000 (AOR = 0.64, p = 0.022), and those at risk of malnutrition were also less likely to engage in SB (AOR = 0.68, p = 0.031). No significant associations were found between SB and sex, marital status, educational level, or chronic illness. These findings suggest that public health initiatives to reduce SB among older adults should prioritize the oldest old, unemployed, and specific ethnic communities, as well as address nutritional risk to promote healthier aging among the elderly in Malaysia.
Keywords:
sedentary behaviours
; elderly
; correlates
; aging
; older adults
Introduction
In 2020, the elderly population in Malaysia aged 65 and above was estimated at 2.32 million, or 7.0% of the total population of 33.2 million. Based on the current trajectory, by 2050 the population aged 65 and above is estimated to reach an 7.14 million or 17.4% of the population, making Malaysia an aged nation[1]. Population aging has significant implications for the healthcare system, as more social and financial support will be needed to meet the increasing demand for healthcare services. This is due to the growing prevalence of non-communicable diseases (NCDs) such as cardiovascular diseases, diabetes, hypertension, dementia, and cancer among the elderly population. Therefore, supporting healthy aging is crucial, with an emphasis on identifying health-related determinants and promoting health prevention measures.
Sedentary behaviour (SB) is defined as any activities involving sitting or reclining at work, home, during travel, or with friends. This includes activities characterized by low energy expenditure (≤1.5 metabolic equivalents [METs])[2], such as sitting at a desk, socializing, traveling by car, bus, or train, reading, playing cards, or watching television, but excludes sleeping [3]. High level of SB has been defined as having at least 8 hours of sedentary time per typical day[4].
Previous studies suggest that SB has little or no effect on physical activity, i.e. increased physical activity does not necessarily reduce sedentariness. A person may be physically active while still engaging in SB simultaneously [5,[5,–8]. Furthermore, substantial evidence have accumulated that show SB’s association with various adverse health outcomes are independent of health-related physical activity[9]. These outcomes include poor mental status (depression, dementia, cognitive impairment)[10,11,12], the main non-communicable diseases (hypertension, diabetes, metabolic syndrome, cardiovascular diseases)[5,13,14], falls[15], certain cancers[9], and increased risk of mortality(9,16–18). Therefore, the two (SB and physical activity) need to be studied independently to understand their factors and their relationship with adverse health outcomes.
Despite the growing body of evidence highlighting the adverse effects of SBs on health, there is a notable gap in the literature regarding the prevalence and correlates of SBs among the Malaysian elderly population. An analysis of nationally representative, population-based data collected in 2006, estimated the prevalence of high level of SB (sitting time ≥ 6 hours/day) among Malaysian adults aged 18 years and above at 32.7%, 44.7%, and 50.3% among elderly aged 61-70, 71-80, and over 80 years, respectively. However, this study did not thoroughly investigate the factors associated with SB and failed to consider factors specific to the elderly population[19]. Consequently, there remains a significant need for more in-depth research to fully understand the factors contributing to prolonged SB among the elderly in Malaysia. Several factors are associated with SB among elderly, including sociodemographic characteristics (age, sex, marital status employment status, income, education)[20,21], lifestyles risk (tobacco and alcohol use), social activities, personal motorized transport, presence of multimorbidity[22], functional factors (activities of daily living or instrumental activities of daily living[23], verbal and vision abilities[24], quality of life[25] and environmental factors such as aspects of the indoor (type and size of house) and outdoor built environment (availability of public parks, walking pathways, low crime rate)[26]. Understanding these factors can help health authorities formulate preventive strategies and interventions to reduce SB and lower the risk of adverse health outcomes among the elderly population[27]. While factors associated with physical activity among older adults in Malaysia have been widely studied [28,29,30,31], there is sparse data on the correlates of SB among the elderly specifically [19]. Thus, in this study, we aimed to explore the associations between sociodemographic and lifestyle factors, mental health, nutritional status, social support, and functional limitations with SB among the elderly in Malaysia.
Methodology
Study design
We analysed data from National Health and Morbidity Survey (NHMS) 2018 on Elderly Health[32]. The NHMSs are nationwide population-based surveys conducted by the Ministry of Health Malaysia. NHMS 2018 was focused on elderly health and collected data for the purpose of informing the evaluation and revision of health priorities, program strategies, and activities, as well as for planning resource allocation for pre-elderly and elderly healthcare services. The survey contained of 13 scopes/areas altogether including socio-demography and living arrangement, public transport usage to access healthcare facilities, mental health status, functional status, physical activity, social support, nutritional status and dietary habits, quality of life, and health-related conditions including NCDs, urinary incontinence, oral health, visual and hearing impairment, and elder abuse. The present study was approved by the Malaysian Ministry of Health Medical Research Ethics Committee (NMRR-17-2655-39047).
Study population
The study population in the NHMS 2018 were the non-institutional, pre-elderly (age 50 to 59 years) and elderly (age 60 years and above) population in Malaysia. Non-institutional refers to not residing in institutional settings, such as hotels, hostels, hospitals, prisons, boarding houses, nursing homes, and similar institutions. However, our analysis in this study focused specifically the 3,977 elderly population aged 60 years and above (n=3,977).
Sampling Design
The sampling frame used in the NHMS 2018 was the National Household Sampling Frame issued by the Department of Statistics, Malaysia, which was updated in 2017. In the sampling frame, Malaysia is geographically divided into 83,000 Enumeration Blocks (EBs). Each EB contains, on average, between 80 to 120 LQs, and a population of between 500 to 600. The EBs are classified as either urban or rural based on population size and built-up area. Urban areas are gazetted areas that consist of adjoining built-up areas with a combined population of 10,000 or more. All other gazetted areas with populations less than 10,000 and non-gazetted areas are classified as rural areas. The NHMS 2018 employed a stratified cluster sampling design to ensure representativeness to the study population. The first stage was stratification by state/federal territory, followed by stratification by urban/rural classification. The sample size of each state was proportional to the states’ population size. The detailed sampling process has been published elsewhere[33] and will not be explained further here.
Data extraction
Dependent variable
Sedentary Behaviour
Physical activity and SB were assessed using the Global Physical Activity Questionnaire (GPAQ)[34]. In the GPAQ, the item used for measuring SB was: “On a typical day, how much time do you usually spend sitting or reclining including time spent at work, at home, in leisure and during travel but not including time spent sleeping?” Participants were asked the amount of time spent sitting or reclining at work, home, during travel, or with friends, including on activities such as sitting at a desk, socializing, traveling by car, bus, or train, reading, playing cards, or watching television, but excluding sleeping. There is currently, no universally accepted cut-off or definition of what constitutes a high level of SB. In this study, we used total of at least 8 sedentary hours on a typical day to define high level of SB. This cutoff was selected based on previous research which found that among the general population, sitting for ≥8 hours per day was linked to an increased risk of premature mortality[18,35].
Independent variables
Sociodemographic variables
Several sociodemographic characteristics of the participants selected for analysis were age (categorized as 60-64, 65-69, 70-74, 75-79, ≥80 years), gender (male or female), ethnicity (Malay, Chinese, Indian, Bumiputra Sarawak & Sabah, others), income (less than Ringgit Malaysia (RM)1000, RM1000-1999, RM2000 and above), marital status (single, married, separated/divorced, widow/widower), education level (no formal education, primary, secondary, tertiary education), and employment status (employed, unemployed). Private transport ownership was determined by the proxy question “mode of transportation used to access healthcare facilities” (for which the response options were: private transport, public transport, walking).
Lifestyle factors and comorbidity
Smoking habit was classified into three categories: current smoker, former smoker, and non-smoker. Physical activity level of respondents was assessed using the Global Physical Activity Questionnaire (GPAQ). According to GPAQ guidelines, an individual is considered 'physically active' if they meet any of the following criteria: i) engage in at least 30 minutes of moderate-intensity activity or walking per day, on at least 5 days in a typical week; ii) participate in 20 minutes of vigorous-intensity activity per day on at least 3 days in a typical week; or iii) accumulate at least 600 MET-minutes per week through any combination of walking, moderate-, or vigorous-intensity activities across 5 days [27]. Respondents were also asked whether they have been medically-diagnosed with diabetes, hypertension, hypercholesterolemia, or any form of cancer.
Quality of life
Quality of life was measured using the Summative Quality of Life (QoL) scale from the Control, Autonomy, Self-Realization, and Pleasure (CASP-19) questionnaire for assessing quality of life[36]. The CASP-19 comprises four domains: control (4 items), autonomy (5 items), pleasure (5 items), and self-realization (5 items), all scored on a 4-point Likert scale ranging from 0 to 3. The total score, ranging from 0 to 57, was obtained by summing all items, with higher scores indicating greater QoL satisfaction. Perceived poor quality of life (PPQoL) was classified for respondents scoring in the QoL score≤44[37].
Mental Health
Dementia was evaluated using the Identification and Intervention for Dementia in Elderly Africans (IDEA) Cognitive Screen, which has been validated for dementia screening in Malaysia [38]. IDEA scores of 10 or less was used to indicate possible dementia. The Geriatric Depression Scale (GDS-14), validated by Teh et al. [2004], was applied for depressive symptoms screening with a cut-off point of 6 or above to indicate clinically significant depression, and score of 8 or above for major depression [39].
Functional capacity and falls
Functional capacity refers to a person's capacity to carry out physical tasks, including personal care, mobility, and maintaining independence both at home and in social environments. In this study, elderly functional status was assessed using the Barthel Index of Activities of Daily Living which measures activities of daily living (ADL), and the Lawton and Brody instrumental activities of daily living scale (IADL). Need for assistance due to inability to independently perform one or more ADLs indicates a functional limitation and need of supportive services. A total score of 20 on the Barthel Index indicates absence of functional limitations, while scores below 20 indicate presence of functional limitations [40]. Unlike the ADL, the IADL measures activities that are not necessarily performed daily but are required for independent living. IADL score results were categorized into two groups: dependent (total score of 7 and below) and independent (total score of 8)[41]. Prevalence of falls among the elderly was determined by history of falls in the twelve months prior to the date of interview.
Visual and Hearing Disabilities
The questions used to assess vision and hearing disabilities were adapted from the Washington Group on Disability (WG) guidelines. The WG approach emphasizes functional limitations rather than impairments, making it suitable for international comparisons of prevalence rates[42]. Disability is an umbrella term that includes impairment, activity limitation, or participation restriction. Vision disability was classified based on respondents' answers, with options of 'no vision disability' or 'vision disability.' Similarly, hearing disability was determined by responses indicating either 'no hearing disability' or 'hearing disability[43].
Nutritional status
To identify individuals who are malnourished or at risk for malnutrition, the modified Mini Nutritional Assessment Short Form (MNA-SF)[44] was utilized as a screening tool. This instrument has been validated for use among older adults in Malaysia [45]. SF scores of 12 to 14 indicate normal nutritional status; scores of 8 to 11 indicate at risk of malnutrition; and scores 7 and below signify malnutrition. Additionally, calf circumference measurements were also taken as an indicator of malnutrition risk, as recommended by the WHO. Local cut-off point for calf circumference was used to determine the final MNA-SF score (Sakinah 2016). Calf circumference less than the cut-off of 30.1 cm for male or 27.3 cm for female, were scored as '0', whereas exceeding 30.1 cm for male or 27.3 cm for female,were scores as '3' [46].
Anthropometric indices
Body weight and height measurements were utilized to compute body mass index (BMI). BMI was calculated as the ratio of weight in kilograms to the square of height in meters (kg/m²) and categorized as follows: underweight (<18.5 kg/m²), normal weight (18.5-24.9 kg/m²), overweight (25.0-29.9 kg/m²), and obese (≥30.0 kg/m²). Waist circumference (WC) measurements were used to identify abdominal obesity, which was defined as waist circumference greater than 102 cm for men and greater than 88 cm for women[47].
Social support and networking
The instrument used to measure social support in this module was the 11-item Duke Social Support Index (DSSI) [48]. The reliability and validity of the DSSI for the Malaysian population has been established in a previous study [49]. The 11-item DSSI comprises two sub-scales: the first measures the size and structure of the respondents’ social network (Social Interaction) and consists of four items, while the second is a seven-item subscale that measures perceived satisfaction with the behavioral or emotional support obtained from this network (Subjective Support) (49, 50). Social support is calculated as the sum of scores for items 1 to 11, with higher scores indicating greater social support. Established cut-off points categorize scores into low to fair (≤26), high (27-29), and very high (30-33) levels of social support, as published by Strodl et al. [51] for the Australian population, and these were used in this study to determine the prevalence of individuals with poor social support.
Statistical Analysis
Weighting factors were applied to each individual data point to adjust for non-response and unequal selection probability due to the complex sample design, to ensure generalizability to the elderly population of Malaysia. The dependent variable in the analysis was sedentary (Yes/No). The independent variables examined were sociodemographic factors (gender, age, ethnicity, marital status, education level, employment status, and monthly individual income), health risk factors (smoking status, physical activity, nutritional status), mental status (depression, probable dementia), functional limitations (ADL, IADL), history of falls, vision and hearing disabilities, mode of transportation, social support, and quality of life. Descriptive statistics (frequencies and percentages) were used to summarize these variables. Initially, Pearson’s chi-square tests were conducted to determine associations between these independent variables and high level of SB (sitting for ≥8 hours per day). This was followed by complex samples multiple logistic regression analysis to determine the association between these independent factors and the odds of engaging in sedentary activities. The final model fit was assessed using the receiver operating characteristic curve and classification table. No significant two-way interactions or multicollinearity were found between the variables. All statistical analyses were carried out at a 95% significance level using IBM SPSS Statistics for Windows, version 28.
Result
Table 1 summarizes the characteristics of the respondents across various demographic, socio-economic, health, and lifestyle variables. The age distribution of respondents revealed that the majority were aged less than 70 years old (66.5%), The gender distribution was relatively balanced, with females comprising 51.1% of the respondents. Most of the respondents were Malay (57.7%), a significant majority of respondents were married (67.9%), and a large proportion of respondents received primary education (43.6%). In terms of employment, 75.7% of respondents were employed and 58.2% earned less than RM 1000 per month.
Prevalence of prolonged SB was high, with 23.2% or i.e. almost a fourth of respondents classified as sedentary. A large majority of respondents were non-smokers (86.7%) and physically active (70.2%). Anthropometrically, 54.6% were overweight or obese (based on BMI) , and 36.4% had abdominal obesity. Chronic diseases were prevalent among respondents, with 51.1% having hypertension, 41.8% having hypercholesterolemia, 27.7% having diabetes mellitus, and 1.6% having a cancer diagnosis. Mental health indicators showed depression in 11.2%, and probable dementia in 8.5% of the respondents. Most respondents had never experienced falls (85.9%), and had no hearing nor vision disabilities (hearing disabilities 6.4% , vision disabilities 4.5%). Regarding activities of daily living (ADL), 83.0% of respondents were independent, while 17.0% required assistance. For instrumental activities of daily living (IADL), 42.9% of the respondents were unable to live independently. Nutritional status showed that 23.5% were at risk of malnutrition and 7.3% were malnourished. Most respondents did not live alone (93.7%) and owned a transportation vehicle (95.1%). Approximately 30.8% of respondents reported receiving inadequate social support, and 28.6% perceived their quality of life to be poor.
Univariate analysis with the Pearson chi-square test showed significant associations between several factors and high level of SB among the elderly in Malaysia. Age was significantly associated with SB (p<0.001), with older age groups showing higher percentages of SB. Occupational status was also a significant factor (p<0.001), with unemployed individuals being more sedentary. Income level was significantly related (p = 0.004), elderly who earned less than RM 1000 a month were more sedentary. Physical activity was strongly associated with SB (p <0.001), with inactive individuals showing higher SB. Activities of daily living (ADL) status (p<0.001) and instrumental activities of daily living (IADL) status (p=0.002) were significantly related, with those dependent on others for their daily activities being more likely to be sedentary. Additionally, elderly people who were malnourished showed a significant association with being sedentary (p=0.005). Elderly individuals who had their own transportation (p=0.012) and whose perceived quality of life (p=0.013) were good were less likely to engage in sedentary activities. Previous history of falls was significantly associated with more SB (p=0.028).
The multiple regression model of the above factors to adjust for confounding, showed several factors remained significant predictors of high level of SB (Table 3). Older age groups exhibited higher odds of SB compared to those aged 60-64, with the AORs ranging from 1.58 to 2.76 (p < 0.001 to p = 0.001). Unemployment was also significantly associated with increased SB (AOR = 1.32, p = 0.02). Additionally, individuals with an income of RM 1000-1999 had lower odds of SB compared to those with an income of ≥RM 2000 (AOR = 0.64, p = 0.022). Bumiputra Sabah and Sarawak ethnicity showed a higher likelihood of SB compared to Malays (AOR = 2.48, p = 0.007). Those at risk of malnutrition had lower odds of being sedentary (AOR = 0.68, p = 0.031). Other factors, such as sex, marital status, education level, smoking status, physical activity, BMI status, abdominal obesity, chronic illness, depression, probable dementia, history of falls, disabilities, ADL and IADL status, living alone, transportation mode, social support, and perceived quality of life, were not significantly associated with SB.
Discussion
Our study found that the prevalence of SB among the elderly Malaysian population, defined as time spent sitting or reclining for more than 8 hours per day, was notably high, at 23.2%. The prevalence of SB increased with age: 17.1% in those aged 60-64 years, 22.8% in those aged 65-69 years, 25.5% in those aged 70-74 years, 33.2% in those aged 75-79 years, and 38.5% in those aged 80 years and above. In comparison, data from a previous nationwide survey of Malaysian adults aged 18 years and above in 2006, reported even higher prevalence rates of 32.7%, 44.7%, and 50.3% among elderly individuals aged 61-70, 71-80, and over 80 years old, respectively, but with SB defined as sitting or reclining for more than 6 hours per day[19]. A comparative study involving nationally representative samples of adults aged 50 years and older from six countries, using data from the World Health Organization’s (WHO) longitudinal Study on Global Ageing and Adult Health (SAGE) between 2007-2010, reported widely varying SB prevalence rates (sitting or reclining ≥4 hours/day): 45% in China, 58% in Russia, 43% in Ghana, 38% in India, 37% in South Africa, and 21% in Mexico[20]. Baseline data from a multicentre clinical trial in five European countries which involved 2,157 community-dwelling healthy older adults aged 70 and older, revealed that 37.1% of the participants spent ≥ 5 hours/day with SB. The highest prevalence was reported in France (41.3%), followed by Austria (38.2%), Switzerland (37.8%), Germany (34.1%) and Portugal (33.6%)[8]. However, direct comparisons between our study with other studies may not be feasible due to large variations in study methodologies, such as study design, definition of elderly, cut-offs for defining SB, and measurement tools used (objective measurement versus self-report method) for assessing SB.
This study provides valuable insights into the factors associated with SBs among the elderly in Malaysia. Several significant associations were identified, suggesting a complex interplay of demographic, socioeconomic and health-related factors on sedentary behaviour in this population. One of the key findings of our study was the significant dose-response relationship between age and SB. The positive relationship between age and sedentary behaviour corroborates findings of previous studies (19–21,52). The adjusted odds ratios (AORs) ranged from 1.58 to 2.76, indicating that as age increases, so does the likelihood of being sedentary. This may be attributed to decreased physical capability, mobility issues, and increased health problems that limit activity levels. Data from the SAGE survey showed that older age group (≥65 years) with chronic conditions such as arthritis, chronic back pain, hearing problems, visual impairment and physical multimorbidity were more likely to engage in high levels of SB compared to those in the middle age group (50-64 years) [22]. A nationwide survey of elderly individuals over 65 years old in Korea revealed that the oldest-old (≥75 years) spent significantly more time sitting compared to the young-old (65-74 years), likely due to increased mobility impairments that often accompany the aging process[53]. The strong association underscores the need for targeted interventions aimed at reducing SB in the oldest segments of the elderly population, who may be at greater risk for associated chronic illness and health related complications
Our study revealed that indigenous Sabah and Sarawak ethnicities had significantly higher odds of SB compared to Malays. However, the 2006 national health and morbidity survey reported that Malaysian adults of Chinese descent were more likely to engage in sedentary activities compared to other ethnicities[19]. A more recent study in 2019, among a multiethnic sample of Singaporeans aged 18 and above, showed higher odds of SB among the Chinese, although no statistically significant difference was found [54]. Cultural, socioeconomic and environmental changes over the past decade may account for these inconsistencies. For instance, changes in rural-urban living conditions, types of housing (landed or high-rise building), and the availability and accessibility to recreational and sport facilities[26], as well as socio-cultural and lifestyle practices[27], may vary significantly across ethnic groups, impacting physical activity levels and sedentariness. An in-depth qualitative study of the sociocultural differences among various ethnic groups and their influence on physical behaviours, particularly among older adults is needed, as understanding these cultural nuances is crucial for developing culturally sensitive interventions aimed at reducing SB across the different ethnic groups.
Unemployment was another significant factor associated with sedentariness, with unemployed elderly individuals exhibiting higher odds of being sedentary in the present study. This aligns with findings from the SAGE study conducted in China, India, South Africa and Ghana, where multivariate analysis also showed elderly individuals who were not working or were retired had higher odds (OR range: 1.5-2.0) of spending more than 4 hours daily in sedentary behaviour compared to those who were employed[20]. Similar, a study involving 8,273 community-dwelling older adults aged ≥65 years who participated in the Korean National Health and Nutrition Examination Survey reported that unemployed Korean elderly individuals were more likely to engage in sitting or reclining for more than 7 hours per day compared to their employed counterparts[52]. These findings may reflect the lack of structured daily activities and social engagement often provided by employment. Several strategies could be developed to reduce SB, particularly targeting retired or unemployed older adults. These could include promoting an active lifestyle by engagement in social activities that are aligned with the interests and abilities of the elderly. Examples include participating in volunteer work within the community, joining hobby groups such as hiking, camping, or birdwatching, and taking music, dance, or yoga classes[55]. Additionally, amending current labor laws to extend the minimum retirement age from 60 to 65, or allowing optional retirement at 60 for those not willing to work up to the age of 65, could also help reduce SB. In contrast to our findings, a cross-sectional study among a group of 397 elderly individuals who visited a polyclinic in Singapore found that SB was significantly associated with employment, with those who were employed having twice the odds of engaging in ≥8 hours of SB daily[56]. Jobs that require prolonged sitting, such as managerial or administrative work, that involve the use of computers, making telephone calls, or paperwork are typically sedentary in nature. Similarly, occupations like driving public utility vehicles may contribute to higher levels of SB among employed persons. However, the study was limited by a small sample size and was confined to a single polyclinic.
Previous studies have consistently shown that elderly people from higher income households are more likely to be sedentary[20]. In our study, the odds of engaging in SB among elderly with moderate income (RM 1000-1999) was lower compared to those with higher income (≥RM 2000). Lower-income elderly individuals, often facing financial limitations, may be less able to afford sedentary lifestyles. In contrast, higher-income individuals may have greater access to amenities and conveniences that facilitate a more sedentary lifestyle. For example, elderly individuals with lower incomes are more likely to use public transportation, such as buses or light rail, for travel, whereas those with higher incomes are more likely to use personal vehicles. Using public transport typically involves more walking and physical exertion, as lower-income elderly individuals often reside in less connected areas where bus stops or commuter stations are some distance from their homes[57]. Additionally, elderly individuals with moderate incomes may live in less accessible areas, such as neighbourhoods with fewer elevators, more stairs, and limited access to nearby convenience stores, restaurants or supermarkets for grocery shopping and dining, which require more physical movement. In contrast, higher-income elderly individuals are more likely to live in more comfortable and accessible environments that require less physical effort.
Prolonged SB indirectly impacts the ability of older adults to perform activities of daily living, with this effect being mediated by nutritional risk[23]. Malnutrition or the risk of malnutrition increases the likelihood of developing sarcopenia[58], a condition characterized by low muscle mass, low muscle strength, and poor physical performance[59]. This may explain the detrimental effects of malnutrition on both SB and physical function. Interestingly, our study found that individuals at risk of malnutrition had lower odds of being sedentary. This counterintuitive finding suggests that those at nutritional risk may still be maintaining some level of moderate and vigorous physical activity, possibly out of necessity or in an attempt to improve their health status. It also highlights the importance of comprehensive assessments in elderly populations, where nutritional status, sedentary and physical activity should be considered together to better understand their health behaviors and outcomes[20,60].
There have been mixed findings regarding the relationship between factors such as sex, marital status, education level, lifestyle, physical function, mental health, and chronic illness with SB(20,61,62). However, in the present study, these variables were not significantly associated with SB. Differences in study design, sociodemographic characteristics of the study populations, and measurement methods may partly explain the inconsistent findings across studies. Furthermore, socio-cultural and environmental factors could mediate the relationship between these variables and SB.
One significant limitation of this study is its inherent inability to establish cause-and-effect relationships between the factors and SB due to the cross-sectional design. Additionally, reliance on self-reported data for time spent in sedentary activities may introduce recall bias, objective measurements of SB using accelerometers would have provided more accurate assessments. Future research in this area should utilise longitudinal and qualitative study designs. The former, in order to better understand the temporal relationships between these factors and SB, and the latter, to obtain deeper insight into the cultural and socioeconomic contexts that contribute to the observed ethnic differences in SB. Despite the limitations mentioned, a key strength of this study is the data was derived from a large, nationally-representative and comprehensive population-based survey which allowed us to conduct an in-depth examination of a wide range of potential predictors of SB, including sociodemographic characteristics, health risk factors, mental status, functional limitations, falls, mode of transportation, vision and hearing disabilities, social support, and quality of life.
Conclusion
These findings have important implications for public health initiatives aimed at reducing SB among the elderly in Malaysia. Targeted interventions should focus on the oldest age groups, unemployed individuals, and specific ethnic communities, particularly the indigenous populations of Sabah and Sarawak, who are at higher risk. Additionally, efforts to improve nutritional status among the elderly should be integrated with programs to encourage physical activity, particularly in those who may not yet be highly sedentary. In conclusion, this study highlights the multifaceted nature of SB among the elderly in Malaysia, pointing to specific demographic and socioeconomic factors that can inform targeted public health interventions such as community-based physical activity program, awareness campaign, hourly movement reminders, and supportive environment. Addressing these factors is crucial for reducing SB and promoting healthier aging in this population.
Author Contributions
Conceptualization, K.C.C., T.L.K. and C.Y.K.; methodology, K.C.C., T.L.K., C.Y.K., T.C.H., C.Y.L. and L.H.L.; formal analysis, K.C.C. and C.Y.K.; data curation, K.C.C. and M.A.O.; writing—original draft preparation, K.C.C., T.L.K., C.Y.K. L.H.L. and T.C.H. ; writing—review and editing, C.Y.L., S.M.G. and L.H.L; project administration, K.C.C., M.A.O., and S.M.G. All authors have read and agreed to the published version of the manuscript.
Funding
There was no source of funding for this research.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Medical Research Ethics Committee, Ministry of Health Malaysia (NMRR-17-2655-39047).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
Data are available upon reasonable request. The dataset used and analyzed in this study is available from the National Institutes of Health, Ministry of Health Malaysia upon reasonable request (https://nihdars.nih.gov.my/login) and with permission from the Director General of Health, Malaysia.
Acknowledgments
We would like to express our sincere gratitude to the Manager of the National Institutes of Health (NIH) Malaysia for granting access to data from the National Health and Morbidity Survey 2018. We are also grateful to the Director General of the Ministry of Health Malaysia for giving permission to publish this paper.
Conflicts of Interest
The authors declare no conflicts of interest
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Table 1.
Respondent Characteristics.
| Variables | Estimated population |
n | % (95% CI) | |||
|---|---|---|---|---|---|---|
| Age group | ||||||
| 60-64 | 1,233,952 | 1,442 | 38.2 (35.3, 41.2) | |||
| 65-69 | 914,327 | 1121 | 28.3 (26.4, 30.3) | |||
| 70-74 | 544,986 | 675 | 16.9 (15.3, 18.5) | |||
| 75-79 | 293,874 | 429 | 9.1 (7.8, 10.6) | |||
| 80+ | 243,202 | 310 | 7.5 (6.3, 8.9) | |||
| Sex | ||||||
| Male | 1,580,226 | 1,872 | 48.9 (47.2, 50.6) | |||
| Female | 1,650,114 | 2,105 | 51.1 949.4, 52.8) | |||
| Ethnicity | ||||||
| Malay | 1,863,766 | 2591 | 57.7 (48.7, 66.2) | |||
| Chinese | 855,542 | 710 | 26.5 (19.8, 34.5) | |||
| Indians | 209,635 | 126 | 6.5 (4.1, 10.2) | |||
| Bumiputra Sabah and Sarawak | 24,2023 | 436 | 7.5 (4.3, 12.8) | |||
| Others | 59,365 | 114 | 1.8 (1.0, 3.5) | |||
| Marital status | ||||||
| Single | 98,087 | 87 | 3.0 (2.3, 4.0) | |||
| Married | 2193,327 | 2,623 | 67.9 (65.2, 70.5) | |||
| Separated or divorcee | 57,859 | 64 | 1.8 (1.1, 2.8) | |||
| Widow or widower | 879,595 | 1,200 | 27.2 (24.5, 30.1) | |||
| Education level | ||||||
| No formal education | 469,794 | 806 | 14.5 (12.5, 16.9) | |||
| Primary | 1,408,624 | 1939 | 43.6 (39.4, 47.9) | |||
| Secondary | 1,040,544 | 967 | 32.2 (28.8, 35.8) | |||
| Tertiary | 311,378 | 265 | 9.6 (7.4, 12.5) | |||
| Occupational status | ||||||
| Unemployed | 784,812 | 1,050 | 24.3 (22.3, 26.4) | |||
| Employed | 2,445,528 | 2,927 | 75.7 (73.6, 77.7) | |||
| Income (RM) | ||||||
| <1000 | 1,851,033 | 2519 | 58.2 (54.5, 61.9) | |||
| 1000-1999 | 682,569 | 845 | 21.5 (19.1, 24.1) | |||
| ≥2000 | 645,096 | 567 | 20.3 (17.1, 23.9) | |||
| Smoking | No | 2,794,286 | 3,346 | 86.7 (84.9, 88.3) | ||
| Yes | 430,134 | 622 | 13.3 (11.7, 15.1) | |||
| Physical activity | ||||||
| Active | 2,263,127 | 2,671 | 70.2 (66.9, 73.2) | |||
| Inactive | 962,291 | 1,298 | 29.8 (26.8, 33.1) | |||
| Sedentary Behaviours | ||||||
| No | 2,464,120 | 2,999 | 76.8 (70.0, 82.4) | |||
| Yes | 745,306 | 959 | 23.2 (17.6, 30.0) | |||
| BMI status | ||||||
| Underweight | 154,999 | 221 | 5.2 (4.2, 6.5) | |||
| Normal | 1,197,044 | 1,525 | 40.2 (37.7, 42.7) | |||
| Overweight | 1,100,775 | 1,292 | 37.0 (35.0, 39.0) | |||
| Obesity | 525,242 | 610 | 17.6 (15.8, 19.6) | |||
| Abdominal obesity | ||||||
| No | 1,902,100 | 2401 | 63.6 (61.2, 66.0) | |||
| Yes | 1,087,328 | 1275 | 36.4 (34.0, 38.8) | |||
| Chronic diseases (presence) | ||||||
| Diabetes mellitus | 891,213 | 1,018 | 27.7 (25.5, 30.0) | |||
| Hypertension | 1,645,628 | 2,027 | 51.1 (48.9, 53.3) | |||
| Hypercholesterolemia | 1,347,075 | 1,576 | 41.8 (39.3, 44.4) | |||
| Cancer diagnosis | 52,497 | 51 | 1.6 (1.1, 2.4) | |||
| Depression | No | 2,736,401 | 3,287 | 88.8 (86.6, 90.6) | ||
| Yes | 346,126 | 485 | 11.2 (9.4, 13.4) | |||
| Probable Dementia | No | 2,818,640 | 3366 | 91.5 (89.8, 93.0) | ||
| Yes | 260,345 | 408 | 8.5 (7.0, 10.2) | |||
| Fall | No | 277,1494 | 3409 | 85.9 (84.2, 87.5) | ||
| Yes | 453,675 | 560 | 14.1 (12.5, 15.8) | |||
| Presence of disability | ||||||
| Vision disability | 145,726 | 214 | 4.5 (3.4, 5.9) | |||
| Hearing disability | 207,613 | 235 | 6.4 (3.4, 5.9) | |||
| ADL status | Absent | 2,674,188 | 3283 | 83.0 (80.8, 85.0) | ||
| Present | 547,881 | 683 | 17.0 (15.0, 19.2) | |||
| IADL status | Independent | 1,840,829 | 2042 | 57.1 (54.0, 60.1) | ||
| Dependent | 1,384,111 | 1925 | 42.9 (39.9, 46.0) | |||
| Nutritional status | ||||||
| Not malnourished | 2,233,784 | 2558 | 69.2 (66.1, 72.0) | |||
| At risk of malnutrition | 760,140 | 1080 | 23.5 (21.2, 26.0) | |||
| Malnourished | 236,416 | 339 | 7.3 (6.0, 8.9) | |||
| Living alone | No | 3,027,143 | 3,682 | 93.7 (92.5, 94.7) | ||
| Yes | 203,198 | 295 | 6.3 (5.3, 7.5) | |||
| Transportation | Public | 131,735 | 208 | 4.1 (2.6, 6.3) | ||
| Own | 3,069,040 | 3,727 | 95.1 (92.8, 96.6) | |||
| walking | 27,864 | 37 | 0.9 (0.4, 1.7) | |||
| Poor social support | ||||||
| No | 2,227,758 | 2698 | 69.2 (65.5, 72.8) | |||
| Yes | 989,806 | 1261 | 30.8 (27.2, 34.5) | |||
| Perceived poor quality of life | ||||||
| No | 2,171,526 | 2467 | 71.4 (67.5, 75.0) | |||
| Yes | 868,670 | 1283 | 28.6 (25.0, 32.5) | |||
Table 2.
Associations between sociodemographic and lifestyle factors, mental health, nutritional status, social support, and functional limitations with sedentary behaviors among the elderly in Malaysia.
Table 2.
Associations between sociodemographic and lifestyle factors, mental health, nutritional status, social support, and functional limitations with sedentary behaviors among the elderly in Malaysia.
| Variables | Sedentary Behaviour | ||||||
|---|---|---|---|---|---|---|---|
| No | Yes | p-value* | |||||
| n | % (95% CI) | n | % (95% CI) | ||||
| Age group | 60-64 | 1166 | 82.9 (76.4, 87.9) | 270 | 17.1 (12.1, 23.6) | <0.001 | |
| 65-69 | 863 | 77.2 (69.7, 83.2) | 253 | 22.8 (16.8, 30.3) | |||
| 70-74 | 508 | 74.5 (65.7, 81.6) | 166 | 25.5 (18.4, 34.3) | |||
| 75-79 | 283 | 66.9 (56.4, 75.7) | 143 | 33.2 (24.3, 43.6) | |||
| 80+ | 179 | 61.5 (50.4, 71.6) | 127 | 38.5 (28.4, 49.6) | |||
| Sex | Male | 1414 | 77.1 (70.2, 82.9) | 453 | 22.9 (17.1, 29.8) | 0.676 | |
| Female | 1585 | 76.4 (69.4, 82.3) | 506 | 23.6 (17.7, 30.6) | |||
| Ethnicity | Malay | 2029 | 78.8 (70.8, 85.2) | 545 | 21.2 (14.8, 29.2) | 0.240 | |
| Chinese | 563 | 77.9 (65.5, 86.8) | 145 | 22.1 (13.2, 34.5) | |||
| Indians | 91 | 73.7 (50.0, 88.7) | 35 | 26.3 (11.3, 50.0) | |||
| Bumiputra Sabah and Sarawak | 250 | 61.9 (50.0, 72.5) | 186 | 38.1 (27.5, 50.0) | |||
| Others | 66 | 67.4 (50.4, 80.7) | 48 | 32.6 (19.3, 49.6) | |||
| Marital status | Single | 67 | 81.0 (67.2, 89.9) | 19 | 19.0 (10.1, 32.8) | 0.229 | |
| Married | 2024 | 77.6 (70.6, 83.4) | 587 | 22.4 (16.6, 29.4) | |||
| Separated or divorcee | 48 | 81.7 (65.2, 91.4) | 16 | 18.3 (8.6, 34.8) | |||
| Widow or widower | 857 | 73.8 (66.4, 80.1) | 337 | 26.2 (19.9, 33.6) | |||
| Education level | No formal education | 538 | 68.4 (61.0, 75.0) | 264 | 31.6 (25.0, 39.0) | 0.07 | |
| Primary | 1501 | 77.8 (70.7, 83.6) | 426 | 22.2 (16.4, 29.3) | |||
| Secondary | 747 | 78.2 (69.3, 85.1) | 218 | 21.8 (14.9, 30.7) | |||
| Tertiary | 213 | 79.9 (68.5, 87.8) | 51 | 20.1 (12.2, 31.5) | |||
| Occupational status | Unemployed | 2133 | 74.8 (67.6, 80.8) | 778 | 25.2 (19.2, 32.4) | <0.001 | |
| Employed | 866 | 83.0 (76.7, 87.9) | 181 | 17.0 (12.1, 23.3) | |||
| Income (RM) | <1000 | 1803 | 73.4 (66.4, 79.4) | 701 | 26.6 (20.6, 33.6) | 0.004 | |
| 1000-1999 | 711 | 83.5 (76.4, 88.8) | 133 | 16.5 (11.2, 23.6) | |||
| ≥2000 | 454 | 79.5 (69.4, 86.8) | 113 | 20.5 (13.2, 30.6) | |||
| Smoking | No | 2526 | 76.8 (70.0, 82.5) | 807 | 23.2 (17.5, 30.0) | 0.840 | |
| Yes | 470 | 76.3 (68.1, 82.9) | 152 | 23.7 (17.1, 31.9) | |||
| Physical activity | Active | 2132 | 80.5 (73.3, 86.1) | 536 | 19.5 (13.9, 26.7) | <0.001 | |
| Inactive | 866 | 68.0 (59.4, 75.5) | 423 | 32.0 (24.5, 40.6) | |||
| BMI status | Underweight | 157 | 73.4 (61.8, 82.5) | 64 | 26.6 (17.5, 38.2) | 0.429 | |
| Normal | 1184 | 77.8 (70.9, 83.4) | 339 | 22.2 (16.6, 29.1) | |||
| Overweight | 1021 | 79.6 (71.8, 85.6) | 270 | 20.4 (14.4, 28.2) | |||
| Obesity | 471 | 78.5 (69.8, 85.2) | 136 | 21.5 (14.8, 30.2) | |||
| Abdominal obesity | No | 1877 | 78.1 (72.1, 84.1) | 521 | 21.3 (15.9, 27.9) | 0.394 | |
| Yes | 965 | 77.2 (68.8, 83.9) | 306 | 22.8 (16.1, 31.2)) | |||
| Chronic diseases (presence) | Diabetes mellitus | 762 | 76.4 (67.8, 83.3) | 251 | 23.6 (16.7, 32.2) | 0.811 | |
| Hypertension | 1521 | 76.6 (69.2, 82.6) | 501 | 23.4 (17.4, 30.8) | 0.766 | ||
| Hypercholesterolemia | 1160 | 75.5 (67.8, 81.9) | 411 | 24.5 (18.1, 32.2) | 0.165 | ||
| Cancer diagnosis | 36 | 68.3 (50.1, 82.2) | 15 | 31.7 (17.8, 49.9) | 0.185 | ||
| Depression | No | 2560 | 78.2 (71.2, 83.9) | 719 | 21.8 (16.1, 28.8) | 0.092 | |
| Yes | 335 | 72.6 (63.3, 80.2) | 148 | 27.4 (19.8, 36.7) | |||
| Probable dementia | No | 26.9 | 77.9 (70.8, 83.7) | 751 | 22.1 (16.3, 29.2) | 0.439 | |
| Yes | 286 | 75.1 (66.4, 82.1) | 114 | 24.9 (17.9, 33.6) | |||
| History of falls | No | 2595 | 77.7 (71.1, 83.2) | 800 | 22.3 (16.8, 28.9) | 0.028 | |
| Yes | 402 | 71.1 (61.4, 79.2) | 158 | 28.9 (20.8, 38.6) | |||
| Presence of disabilities | Vision diabilities | 150 | 71.6 (58.3, 82.0) | 64 | 28.4 (18.0, 41.7) | 0.310 | |
| Hearing disabilities | 158 | 76.8 (66.0, 84.9) | 71 | 23.2 (15.1, 34.0) | 0.994 | ||
| Presence of functional limitation (ADL) | No | 2570 | 79.0 (72.1, 84.5) | 705 | 21.0 (15.5, 27.9) | <0.001 | |
| Yes | 423 | 65.7 (56.5, 73.8) | 252 | 34.3 (26.2, 43.5) | |||
| Limitations in instrumental activities of daily living (IADL) | No | 1595 | 80.0 (72.9, 85.6) | 442 | 20.0 (14.4, 27.1) | 0.002 | |
| Yes | 1400 | 72.5 (65.1, 78.8) | 515 | 27.5 (21.2, 34.9) | |||
| Nutritional status | Not malnourished | 1952 | 77.6 (70.3, 83.5) | 604 | 22.4 (16.5, 29.7) | 0.005 | |
| At risk of malnutrition | 842 | 78.3 (70.1, 84.7) | 233 | 21.7 (15.3, 29.9) | |||
| Malnourished | 205 | 63.9 (55.5, 71.5) | 122 | 36.1 (28.5, 44.5) | |||
| Living alone | No | 2788 | 76.9 (70.2, 82.5) | 875 | 23.1 (17.5, 29.8) | 0.601 | |
| Yes | 211 | 75.1 (64.5, 83.3) | 84 | 24.9 (16.7, 35.5) | |||
| Transportation | Public | 116 | 64.1 (52.2, 74.5) | 92 | 35.9 (25.5, 47.8) | 0.012 | |
| Own transport | 2858 | 77.4 (70.6, 83.0) | 852 | 22.6 (17.0, 29.4) | |||
| walking | 22 | 67.5 (46.7, 83.1) | 14 | 32.5 (16.9, 53.3) | |||
| Poor social support | No | 2062 | 77.8 (71.0, 83.4) | 631 | 15.4 (11.5, 20.3) | 0.227 | |
| Yes | 927 | 74.3 (65.6, 81.4) | 326 | 25.7 (18.6, 34.4) | |||
| Perceived poor quality of life | No | 1947 | 79.6 (72.4, 85.3) | 516 | 20.4 (14.7, 27.6) | 0.013 | |
| Yes | 932 | 72.3 (64.1, 79.2) | 344 | 27.7 (20.8, 35.9) | |||
| IADL, Instrumental Activities of Daily Living. * Pearson Chi-square was performed. | |||||||
Table 3.
Factors associated with sedentary behaviours among the elderly in Malaysia.
| Independent Variables | AOR (95% CI) | p-value | |
|---|---|---|---|
| Age group | 60-64 | reference | |
| 65-69 | 1.58 (1.22, 2.06) | <0.001 | |
| 70-74 | 1.78 (1.15, 2.75) | 0.010 | |
| 75-79 | 2.25 (1.39, 3.63) | 0.001 | |
| 80+ | 2.76 (1.49, 5.10) | 0.001 | |
| Sex | Female | reference | |
| Male | 1.01 (0.69, 1.46) | 0.97 | |
| Ethnicity | Malay | reference | |
| Chinese | 1.01 (0.45, 2.25) | 0.982 | |
| Indians | 1.84 (0.65, 5.19) | 0.244 | |
| Bumiputra Sabah and Sarawak | 2.48 (1.29, 4.76) | 0.007 | |
| Others | 1.37 (0.57, 3.31) | 0.477 | |
| Marital status | Single | reference | |
| Married | 1.40 (0.74, 2.61) | 0.293 | |
| Separated or divorcee | 1.16 (0.39, 3.45) | 0.782 | |
| Widow or widower | 1.21 (0.61, 2.39) | 0.577 | |
| Education level | No formal education | reference | |
| Primary | 0.85 (0.58, 1.22) | 0.368 | |
| Secondary | 1.11 (0.64, 1.92) | 0.717 | |
| Tertiary | 0.82 (0.39, 1.72) | 0.597 | |
| Occupational status | Unemployed | 1.32 (1.05, 1.67) | 0.02 |
| Employed | reference | ||
| Income (RM) | <1000 | 1.00 (0.68, 1.47) | 0.993 |
| 1000-1999 | 0.64 (0.44, 0.94) | 0.022 | |
| ≥2000 | reference | ||
| Smoking | Yes | 1.33 (0.94, 1.88) | 0.104 |
| No | reference | ||
| Physical activity | Inactive | 1.37 (0.93, 2.00) | 0.107 |
| Active | reference | ||
| Body mass index | Underweight | reference | |
| Normal | 0.84 (0.53, 1.33) | 0.464 | |
| Overweight | 0.77 (0.47, 1.27) | 0.302 | |
| Obesity | 0.80 (0.46, 1.40) | 0.435 | |
| Abdominal obesity | Yes | 1.18 (0.86, 1.63) | 0.306 |
| No | reference | ||
| Presence of chronic diseases | Diabetes mellitus | 0.96 (0.74, 1.26) | 0.770 |
| Hypertension | 0.87 (0.68, 1.11) | 0.261 | |
| Hypercholesterolemia | 1.25 (0.95, 1.63) | 0.108 | |
| Cancer diagnosis | 1.205 (0.53, 2.75) | 0.654 | |
| Depression | Yes | 0.91 (0.63, 1.30) | 0.594 |
| No | reference | ||
| Probable dementia | Yes | 0.66 (0.43, 1.01) | 0.055 |
| No | reference | ||
| Falls | Yes | 1.14 (0.80, 1.62) | 0.477 |
| No | reference | ||
| Presence of disability | Vision disability | 0.82 (0.41 (1.66) | 0.576 |
| Hearing disability | 0.61 (0.35, 1.08) | 0.086 | |
| Presence of functional limitation (ADL) | Yes | 1.20 (0.81, 1.78) | 0.360 |
| No | reference | ||
| Limitations in instrumental activities of daily living (IADL) | Yes | 1.12 (0.84, 1.50) | 0.427 |
| No | reference | ||
| Nutritional status | At risk of malnutrition | 0.68 (0.48, 0.96) | 0.031 |
| Malnutrition | 0.83 (0.46, 1.51) | 0.542 | |
| No malnutrition | reference | ||
| Living alone | Yes | 0.99 (0.65, 1.50) | 0.944 |
| No | reference | ||
| Transportation | Public | 0.71 (0.28, 1.83) | 0.479 |
| Own transport | 0.56 (0.23, 1.40) | 0.216 | |
| Walking | reference | ||
| Poor social support | Yes | 1.01 (0.69, 1.49) | 0.954 |
| No | reference | ||
| Perceived poor quality of life | Yes | 1.21 (0.83, 1.76) | 0.319 |
| No | reference | ||
| ADL= activities of daily living, IADL= instrumental activities of daily living, AOR=adjusted odds ratio Complex samples logistic regression analysis was employed. The model fit was assessed using the receiver operating characteristic curve (Area under the curve=0.672, p<0.001) and percent of correct classification of 78.5%. No significant two-way interactions or multicollinearity were found between the variable (p>0.05) | |||
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