The Association Between Nutritional Status and Mental Health Status and Its Associated Factors Among Elderly Attending Community Health Screenings: A Cross Sectional Study

The Association Between Nutritional Status and Mental Health Status and Its Associated Factors Among Elderly Attending Community Health Screenings: A Cross Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Association Between Nutritional Status and Mental Health Status and Its Associated Factors Among Elderly Attending Community Health Screenings: A Cross Sectional Study Nurliana Abd Nassir, Siti Sara Yaacob, Noor Azleen Ahmad Tarmizi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6433768/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Sep, 2025 Read the published version in BMC Geriatrics → Version 1 posted 13 You are reading this latest preprint version Abstract Background Malaysia’s elderly population is projected to reach 15% by 2030. Nutritional status significantly impacts elderly health, with malnutrition linked to depression, anxiety and stress. This study assessed mental health problems and their associations with nutritional status among elderly individuals attending community health screenings. Method A cross-sectional study was conducted among elderly participants selected through convenience sampling from four community health screenings in Kuala Selangor District (June–October 2024). Sociodemographic details, nutritional status (Mini Nutritional Assessment-Short Form, MNA®-SF), and mental health problems (Depression, Anxiety, and Stress Scale-21, DASS-21) were obtained. Multiple logistic regression was used to examine the associations between nutritional status and mental health problems. Results Among 361 elderly participants, the prevalence rates of depression, anxiety, and stress were 12.7%, 9.4%, and 4.2%, respectively. At risk of malnutrition significantly increased the odds of depression (AOR = 10.94, 95% CI = 4.35–27.52), anxiety (AOR = 11.16, 95% CI = 4.10–30.41), and stress (AOR = 5.03, 95% CI = 1.55–16.34), whereas malnutrition increased the odds of depression (AOR = 45.61, 95% CI = 13.20–157.61) and anxiety (AOR = 17.80, 95% CI = 4.93–64.32). Female sex and less social engagement were associated with depression. Being a smoker or ex-smoker and having less physical activity were associated with anxiety. Finally, retirees and having sleeping issues were associated with both depression and anxiety. Conclusion Nutritional status is a key determinant of depression, anxiety, and stress in elderly individuals. Early identification, further assessment, and targeted interventions are crucial for improving mental well-being in this population Aging Mental health Nutritional status Depression Anxiety Figures Figure 1 Figure 2 INTRODUCTION The global population is aging rapidly, with the proportion of people aged 60 years and above expected to increase from 12% in 2020 to 22% by 2050, whereas Malaysia’s elderly population is projected to reach 15% by 2030 [ 1 , 2 ]. As aging increases the risk of chronic diseases, disabilities, and cognitive decline, healthcare providers must remain vigilant and well informed to address the evolving needs of this population. Beyond physical health challenges, mental health problems, particularly depression and anxiety, are also highly prevalent among elderly individuals and pose a major public health concern [ 3 ]. Depression is one of the most common psychiatric disorders in older adults and is characterized by persistent low mood, loss of interest, and functional impairment, whereas anxiety disorders, characterized by excessive worry and heightened stress responses, are also common in elderly individuals. A World Health Organization study estimated that 7% of the global elderly population experiences depression, whereas 3.8% experience anxiety, contributing to 6.6% of all disabilities measured in disability-adjusted life years (DALYs) [ 4 ]. Locally, the National Health Morbidity Survey 2018 in Malaysia reported a depression prevalence of 11% among elderly individuals, although it did not include specific data on anxiety and stress [ 5 ]. However, other studies have reported that the prevalence of depression ranges from 4–28%, indicating wide variation depending on the study population and assessment methods; however, few studies have reported an anxiety prevalence of 22.6% and a stress prevalence of 8.7% among elderly individuals. [ 6 – 8 ]. Although stress is less commonly reported due to its transient nature, it remains a significant concern among elderly individuals, with 8.7% experiencing stress during screening in a rural Malaysian study [ 7 ]. Elderly individuals are at increased risk of mental health problems due to social isolation, physical illness, and cognitive decline [ 6 , 9 ]. In addition, elderly individuals with underlying comorbidities, mobility issues, and impaired functional ability are more likely to experience depression [ 10 , 11 ]. More importantly, increasing evidence suggests that nutritional status plays a crucial role in mental health among elderly individuals [ 12 – 15 ]. The European Society of Clinical Nutrition and Metabolism (ESPEN) has defined malnutrition as “a state resulting from a lack of intake or uptake of nutrition that leads to altered body composition (decreased fat-free mass) and body cell mass, leading to diminished physical and mental function and impaired clinical outcome from disease”[ 16 ]. Malnutrition has been shown to negatively impact cognitive function, mood regulation, and overall mental well-being, and studies indicate that malnourished elderly individuals are more likely to experience depressive symptoms, anxiety, and increased psychological distress due to insufficient energy and nutrient intake, which further exacerbates their vulnerability to mental health disorders [ 13 , 17 ]. Unlike specific nutrient deficiencies, which have been linked to mental health outcomes, general malnutrition in older adults is associated with a range of adverse health effects, including increased frailty, reduced immune function, and impaired recovery from illnesses [ 18 ]. The relationship between malnutrition and mental health can be explained through physiological mechanisms such as chronic inflammation, hormonal imbalances, and metabolic disruptions that result from inadequate nutrition [ 19 ]. Another emerging concept linking nutrition to mental health is the gut‒brain axis theory, which highlights how dietary choices impact both physical and mental well-being. The gut microbiota plays a significant role in regulating mood-related neurotransmitters such as serotonin, suggesting that malnutrition may alter the gut microbiome composition and contribute to mental health disturbances [ 12 ]. Given these associations, assessing nutritional status is critical in understanding the mental health burden among elderly individuals. However, nutritional status is often overlooked in elderly care as one of the associated factors for mental health problems. Most available studies of mental health disorders in elderly individuals have focused more on sociodemographic factors, physical disability, and comorbidities, with limited attention given to nutrition as a contributing factor. The literature review has broadly examined malnutrition as a consequence of mental health disorders rather than exploring malnutrition as a risk factor. Even though depression screening is commonly performed, anxiety and stress remain primarily overlooked in this population. To address these gaps, this study assesses the nutritional status of elderly individuals attending community health screenings in Kuala Selangor District and examines its association with mental health problems, specifically depression, anxiety, and stress. Findings may enhance early detection, facilitate further diagnostic evaluation, and support targeted interventions to improve elderly well-being. MATERIALS AND METHODS Study design and population This cross-sectional study was conducted among elderly individuals attending four community health screenings in Kuala Selangor District from June 29 to October 19, 2024. These screenings, aimed at general health assessments, primarily targeted the elderly population. The inclusion criteria required participants to be aged 60 years and above with the ability to read and understand Malay or English, whereas the exclusion criteria were those with acute severe illness, dementia, or cognitive impairments affecting response reliability. Sample size calculation We used OpenEpi Version 3.01 to determine the sample size on the basis of all the study objectives. The largest sample size estimate was derived from the 27.8% prevalence of depression among elderly Malaysians reported by Abd Manaf et al. [ 7 ], with a 95% confidence level and a 5% margin of error; the calculated sample size was 301 participants. To account for potential nonresponses, this percentage was increased by 20%, resulting in a final sample size of 361 participants. Study variables The dependent variables were mental health problems, namely, depression, anxiety, and stress, which were assessed via the Depression, Anxiety, and Stress Scale-21 (DASS-21) screening tool. Nutritional status was assessed as the primary independent variable in this study via the Mini Nutritional Assessment-Short Form (MNA®-SF) screening tool, which categorizes participants into normal nutrition, at risk of malnutrition, and malnourished. Sociodemographic variables included age, which was analysed both as a continuous variable and as categorized groups (60–74, 75–84, and ≥ 85 years [ 20 ]), gender (male or female), ethnicity (Malay, Chinese, Indian, others), marital status (single, married, widowed, divorced), and living arrangements (alone, with spouse only, with spouse and children, or with extended family) were also recorded. Additionally, household income was dichotomized as < RM1500 or ≥ RM1500, on the basis of the Malaysian minimum wage guidelines [ 21 ]), whereas financial dependency was determined by whether participants were independent or relied on children’s allowances or welfare. Education level was categorized as no formal education or primary, secondary, or tertiary education, whereas employment status was recorded as either employed, unemployed or pensioner. To better understand participants' demographics in relation to lifestyle, self-reported behaviours were assessed. However, as these were not the primary focus of the study, data were obtained solely through self-reports without the use of specific screening tools. Physical activity was assessed by asking whether the participants engaged in activities such as aerobic exercise, running, brisk walking, cycling, gardening, or swimming, and their responses were categorized as frequently (5–7 times per week), sometimes (2–4 times per week), seldom (once per week), or no activity. Similarly, social engagement was evaluated by inquiring about participation in community activities, including gatherings, community programs, or religious events, with responses classified using the same frequency scale. In addition, sleep problems were assessed by asking participants whether they experienced difficulty falling asleep or maintaining sleep, with responses recorded as either yes or no. Meanwhile, functional independence was assessed on the basis of the ability to perform daily activities, including eating, dressing, bathing, toileting, transferring, and walking, without assistance. The assessment of clinical characteristics included the presence of comorbidities, particularly cardiovascular conditions such as diabetes mellitus, hypertension, and dyslipidemia. Additionally, body mass index (BMI) was calculated as weight (kg) divided by height squared (m²) and classified into six categories: underweight (< 18.5 kg/m²), normal weight (18.5–22.9 kg/m²), preobese (23.0–27.4 kg/m²), obese I (27.5–32.4 kg/m²), obese II (32.5–37.4 kg/m²), and obese III (≥ 37.5 kg/m²). Sampling Method and Data Collection The participants were invited through convenience sampling from elderly individuals attending four community health screenings in Kuala Selangor. With a population of 281,711, this district is the second-largest in Selangor and among the top three with the highest elderly population density [ 22 ]. Given this demographic advantage, Kuala Selangor was chosen as the study location. Kuala Selangor was chosen not only because of its high elderly population density but also because of its established collaboration with local leaders, nongovernmental organisations, and healthcare providers, facilitating high participation rates. However, a key limitation of this study is that the participants were self-selected and were attending voluntarily, which may introduce selection bias. Those who participate in community screenings are generally more health conscious, socially engaged, and proactive in seeking medical care. As a result, this study may have underrepresented elderly individuals who are less likely to attend such events, many of whom may have poorer health, higher rates of malnutrition, and unrecognized mental health issues. Community health screening programs are conducted annually by local leaders and healthcare clinics, which are specifically designed for the local community, with a focus on elderly participants and offer various activities, educational sessions, and free health screenings. Our research team set up our own booth for the purpose of data collection. Upon invitation to the booth, participants received an explanation of the study and provided informed consent before proceeding with the four sections of data collection. These included self-administered structured questionnaires and anthropometric measurements conducted by trained researchers. To ensure consistency and reliability, researchers have trained, including demonstrations and supervised practices, on anthropometric measurements and questionnaire structure. The first section captured demographic and clinical characteristics, whereas the second screened mental health problems via the DASS-21. In the third section, weight and height measurements were taken to calculate BMI, followed by the fourth section for nutritional status assessment via the MNA®-SF. For each section, different researchers were assigned to guide participants as needed and to explain the results of the screening. Anonymity was maintained throughout the process, and participants with abnormal DASS-21 or MNA®-SF screening results were referred to a primary care physician for further assessment to confirm a diagnosis and initiate appropriate management, subject to their consent. Study Instruments The MNA®-SF in the Malay and English languages was used to assess nutritional status. It is a widely validated screening tool for assessing nutritional status among elderly individuals, demonstrating strong validity and reliability, with reported high sensitivity (97.9%), specificity (100%), and diagnostic accuracy (98.7%) in the Malaysian population [ 23 ]. The questionnaire comprises six items assessing general nutritional status, which includes declines in food intake, involuntary weight loss, mobility, psychological and neuropsychological stress, and BMI. Each item was scored with a maximum possible total of 14 points, where higher scores indicated better nutritional status, whereas lower scores reflected poorer nutritional status. The scoring categories were as follows: 12–14 signified normal nutritional status, 8–11 indicated risk of malnutrition, and 0–7 classified participants as malnutrition [ 24 ]. For the mental health assessment, our study used the DASS-21. It is a self-report screening tool that is also available in both the Malay and English languages and allows individuals to assess their depression, anxiety, and stress levels on the basis of their recent experiences. The Malay version of the DASS-21 has demonstrated good internal consistency and construct validity in Malaysian elderly populations, making it a reliable tool for screening mental health conditions. Its validity and reliability were established through confirmatory factor analysis, with satisfactory factor loadings above 0.4 and strong Cronbach’s alpha values for the subscales: 0.84 for depression, 0.74 for anxiety, and 0.79 for stress [ 25 ]. The DASS-21 consists of 21 items divided across three subscales with seven questions each for depression, anxiety, and stress. Each item was rated on a 4-point Likert scale reflecting symptom frequency over the past week (0 “Did not apply at all” to 3 “Applied very much or most of the time”). Subscale scores were then categorized into five levels: normal, mild, moderate, severe, and extremely severe [ 26 ]. In this study, each subscale (depression, anxiety, and stress) was categorized into two groups: "normal" was classified as "absent," whereas the remaining four categories (mild, moderate, severe, and extremely severe) were grouped as "present". The cut-off scores used to define the presence of mental health problems were > 9 for depression, > 7 for anxiety, and > 14 for stress [ 7 , 27 ]. While the DASS-21 is a widely used screening instrument for detecting symptoms of depression, anxiety, and stress, it has several limitations. As a self-report measure, it does not provide a clinical diagnosis but serves as a tool to identify individuals at risk of mental health issues on the basis of symptom severity. Given its role as a screening tool, the results obtained from this study may help guide further clinical evaluation to confirm symptom severity and inform appropriate mental health interventions. Its practicality and ease of use make it a feasible option for large-scale screening in elderly populations, particularly in these community health programs, where early identification can facilitate timely support and intervention. Statistical analysis All the statistical analyses were conducted via IBM Statistical Package for the Social Sciences (SPSS) version 29.0. For descriptive statistics, categorical variables are summarized as frequencies and percentages, whereas continuous variables are presented as the means and standard deviations. To identify factors associated with mental health problems, including nutritional status, separate multivariable logistic regression models were constructed for depression, anxiety and stress using the backwards likelihood ratio method. All the models were carefully checked for multicollinearity, and potential two-way interaction terms were evaluated alongside the main effect models. Model fit was assessed via goodness-of-fit statistics to ensure that the logistic regression models adequately aligned with the observed outcomes. The final multivariable models included only significant predictors of depression, anxiety, and stress. The results are reported as adjusted odds ratios (AORs) with 95% confidence intervals, and statistical significance was set at p < 0.05. RESULTS A total of 388 elderly individuals were invited to participate. However, 27 were excluded, leaving 361 participants for the final analysis. The flow of the study is illustrated in Figure 1. Among the participants, 59.6% were female, with a mean age of 67.17 years (SD ± 5.74). The majority were aged 60–74 years (86.1%), Malay ethnicity (89.2%), and married (71.5%), while 45.2% had secondary education. Socioeconomically, 57.6% had a household income below RM 1500, and nearly half (48.5%) were unemployed, including 33.0% pensioners. Most lived with a spouse (36.6%) or extended family (20.2%). Furthermore, 78.4% had at least one comorbidity, with dyslipidaemia (58.4%), hypertension (54.6%), and diabetes (35.7%) being the most common. The mean BMI was 27.12 (SD = 5.53), with most patients being overweight (33.2%) or obese (29.1%). Nonsmoking (78.4%) and alcohol abstinence (97.2%) were prevalent, while 28.3% reported sleep difficulties. For lifestyle, 61.2% exercised at least twice a week, and 59.5% participated in social activities at the same frequency. Despite these factors, 95.8% of the participants remained independent in daily activities (Table 1). Table 1: Demographic, socioeconomic, clinical and lifestyle characteristics of the elderly respondents. (N=361) Variables Frequency, n (%) Mean (±SD) Gender Male Female 146 (40.4) 215 (59.6) Age 60 to 74 (young-old) 75 to 84 (middle-old) 85 and above (old-old) 311 (86.1) 48 (13.3) 2 (0.6) 67.17 ± 5.74 Ethnicity Malay Chinese Indian 322 (89.2) 33 (9.1) 6 (1.7) Marital status Single Married Widowed Divorced 7 (1.9) 258 (71.5) 80 (22.2) 16 (4.4) Level of Education No Formal Education Primary: Standard 1 to 6 Secondary: Form 1 to 5 Tertiary: Form 6/College/University 41 (11.4) 104 (28.8) 163 (45.2) 53 (14.7 Living arrangement Living alone Living with spouse only Living with spouse and children Living with extended family 46 (12.7) 132 (36.6) 110 (30.5) 73 9 (20.2) Job category Employed Unemployed Pensioner 67 (18.6) 175 (48.5) 119 (33.0) Income category More than RM 1500 Less than RM 1500 153 (42.2) 208 (57.6) Source of Income Independent Dependent 190 (52.6) 171 (47.4) Body Mass Index underweight: /= 37.5 18 (5.0) 62 (17.2) 120 (33.2) 105 (29.1) 41 (11.4) 15 (4.2) 27.12 ± 5.53 Comorbidities Yes No Diabetes Hypertension Dyslipidemia 283 (78.4) 78 (21.6) 129 (35.7) 197 (54.6) 211 (58.4) Smoking Status Smoker Ex smoker Non-Smoker 49 (8.0) 29 (13.6) 283 (78.4) Alcohol consumption status Ever consumed (past or present) Never consumed 10 (2.8) 351 (97.2) Sleeping problems Yes No 102 (28.3) 259 (71.7) Physical Activity in a week Frequent (5-7 times per week) Sometimes (2-4 times per week) Seldom (1 time per week) No activity 90 (30.7) 110 (30.5) 84 (23.3) 56 (15.5) Social activity in a week Frequent (5-7 times per week) Sometimes (2-4 times per week) Seldom (1 time per week) No activity 90 (24.9) 125 (34.6) 99 (27.4) 47 (13.0) Activity of Daily Living (ADL) Independent Dependent 346 (95.8) 15 (4.2) For the prevalence of malnutrition and at risk of malnutrition, of the 361 elderly participants assessed via the MNA®-SF, a substantial proportion (23.0%) were identified as being at risk of malnutrition (95% CI: 18.7%–27.7%), whereas 6.4% were classified as having malnutrition (95% CI: 4.1%–9.4%) (Table 2). Table 2: Prevalence of malnutrition and risk of malnutrition in elderly respondents from the MNA®-SF. Nutritional Status (N= 361) Prevalence (%) 95% Confidence Interval Normal nutrition 70.6 65.6% - 75.3% At risk of malnutrition 23.0 18.7% - 27.7% Malnutrition 6.4 4.1% - 9.4% MNA-SF Ò : Mini Nutritional Assessment Short Form The mental health assessment indicated that among the 361 elderly respondents, 12.7% (n = 46) experienced depression, with 28.3% classified as mild, 67.4% as moderate, 4.3% as severe, and none as extremely severe. Anxiety was present in 9.4% (n = 34) of the respondents, with 76.5% categorized as moderate, 23.5% as severe, and none as mild or extremely severe. Stress was the least common stress experienced by 4.2% (n = 15) of the participants, with 60% classified as mild and 40% as moderate, whereas no cases were recorded as severe or extremely severe. Table 3 and Figure 1 show the proportions of elderly individuals who experienced depression, anxiety and stress and their severity according to the DASS-21 score. Table 3: Descriptive analysis of the proportion of elderly respondents who experienced mental health problems (depression, anxiety and stress) according to the DASS-21 score (n = 361) Mental Health Problems (N=361) Absent Present Frequency, n (%) Depression 315 (87.3) 46 (12.7) Anxiety 327 (90.6) 34 (9.4) Stress 346 (95.8) 15 (4.2) DASS-21: Depression, Anxiety and Stress Scale 21 Multiple logistic regression revealed that nutritional status was significantly associated with depression, with those at risk of malnutrition having greater odds of depression (AOR = 10.94; 95% CI: 4.35, 27.52; p < 0.001) than individuals with a normal nutritional status. Similarly, individuals with malnutrition had even greater odds of depression (AOR = 45.61; 95% CI: 13.20, 157.61; p < 0.001). Other associated factors that were found to increase the odds of having depression are being female compared with being male (AOR = 2.79; 95% CI: 1.17, 6.63; p = 0.02), being retired compared with being employed (AOR = 8.17; 95% CI: 2.18, 30.59; p = 0.002) and having sleeping problems compared with being elderly without sleeping problems (AOR = 3.18; 95% CI: 1.43, 7.07; p = 0.005). On the other hand, frequent social engagement (5–7 times per week) had a protective effect (AOR = 0.054; 95% CI: 0.009, 0.341; p = 0.004) (Table 4). Moreover, those at risk of malnutrition had greater odds than those with normal nutritional status did (AOR = 11.16; 95% CI: 4.10, 30.41; p < 0.001), whereas those with malnutrition had even greater odds of experiencing anxiety (AOR = 17.80; 95% CI: 4.93, 64.32; p < 0.001). Other associated factors that were found to increase the odds of anxiety were retirees compared with employed individuals (AOR = 5.28; 95% CI: 1.31, 21.32; p = 0.019), smokers (AOR = 4.50; 95% CI: 1.55, 13.11; p = 0.006) and ex-smokers (AOR = 8.95; 95% CI: 2.72, 29.51; p < 0.001) had higher odds than nonsmokers did, and elderly people with sleeping problems had higher odds than did elderly people without sleeping problems (AOR = 2.99; 95% CI: 1.21, 7.39; p = 0.017). On the other hand, engaging in frequent physical activity (5–7 times per week) had a protective effect (AOR = 0.157; 95% CI: 0.041, 0.602; p = 0.007), and engaging in less physical activity (2–4 times per week) also had a protective effect against anxiety (AOR = 0.255; 95% CI: 0.079, 0.82; p = 0.022) (Table 5). The odds of stress were also greater in individuals at risk of malnutrition (AOR = 5.03; 95% CI: 1.55, 16.34; p = 0.007) and, similarly, in those with malnutrition (AOR = 4.59; 95% CI: 0.82, 25.74), although this association was not statistically significant (p = 0.083). Moreover, no other factors were found to be associated with stress (Table 6). Table 4: Multiple logistic regression for factors associated with depression in elderly respondents Variables Adj. OR (95% CI) Wald (df) a ) p– value a) Nutritional status Normal At risk of malnutrition Malnutrition 1 10.94 (4.35,27.52) 45.61 (13.20,157.61) 42.76 25.83 b) 36.46 b) <0.001* <0.001* <0.001* Gender Male Female 1 2.788 (1.173,6.627) 5.38 (1) 0.020 Job Category Employed Unemployed Pensioner 1 2.45 (0.65,9.18) 8.17 (2.18,30.59) 12.176 (2) 1.76 (1) b) 9.72 (1) b) *0.002 0.098 b) *0.002 b) Sleeping problems No Yes 1 3.18 (1.43,7.07) 8.02 (1) *0.005 Social activity in a week No activity Seldom (1 time per week) Sometimes (2-4 times per week) Frequent (5-7 times per week) 1 0.865 (0.305, 2.457) 0.352 (0.113,1.101) 0.054 (0.009,0.341) 12.52 (3) 0.074 (1) b) 3.217 (1) b) 9.630 (1) b) *0.006 0.723 0.120 *0.004 Simple logistic regression was initially conducted for all variables, and those with a p value < 0.25 were included in the multiple logistic regression model. Adj. OR: Adjusted OR, CI: Confidence interval a) Likelihood ratio, b) Wald test *p value <0.05 Table 5: Multiple logistic regression for factors associated with anxiety in elderly respondents Variables Adj. OR (95% CI) Wald (df) a) p -value a) Nutritional status Normal At risk of malnutrition Malnutrition 1 11.16 (4.10,30.41) 17.80 (4.93,64.32) 28.22 22.25 b) 19.32 b) <0.001* <0.001* <0.001* Job Category Employed Unemployed Pensioner 1 1.74 (0.42, 7.18) 5.28 (1.31,21.32) 8.232 (2) 0.583 (1) b) 5.469 (1) b) 0.016* 0.445 0.019* Smoking status Non smoker Smoker Ex smoker 1 4.502 (1.55, 13.11) 8.952 (2.72, 29.51) 16.45 (2) 6.009 (1) b) 12.97 (1) b) <0.001 0.006* <0.001 Sleeping problems No Yes 1 2.99 (1.21, 7.39) 5.670 (1) 0.017* Physical Activity in a week No activity Seldom (1 time per week) Sometimes (2-4 times per week) Frequent (5-7 times per week) 1 0.448 (0.14,1.41) 0.255 (0.079, 0.82) 0.157 (0.041,0.602) 8.729 (3) 1.883 (1) b) 5.214 (1) b) 7.280 (1) b) 0.033* 0.170 0.022 0.007* Simple logistic regression was initially conducted for all variables, and those with a p value < 0.25 were included in the multiple logistic regression model. Adj. OR: Adjusted OR, CI: Confidence interval a) Likelihood ratio, b) Wald test *p value < 0.05 Table 6: Multiple logistic regression for factors associated with stress in elderly respondents Variables Adj. OR (95% CI) Wald (df) a) p -value a) Nutritional status Normal At risk of malnutrition Malnutrition 1 5.03 (1.55,16.34) 4.59 (0.82,25.74) 7.79 7.23 b 3.01 b 0.020* 0.007* 0.083 Simple logistic regression was initially conducted for all variables, and those with a p value < 0.25 were included in the multiple logistic regression model. Adj. OR: Adjusted OR, CI: Confidence interval a) Likelihood ratio, b) Wald test *p value < 0.05 DISCUSSIONS In our study, depression (12.7%) emerged as the most common mental health condition experienced by the elderly respondents, followed by anxiety (9.4%) and stress (6.2%). Even though the proportion of these mental health conditions was relatively low, those who presented symptoms had moderate symptoms of depression and anxiety rather than only mild symptoms or transient distress. These results suggest that identification and early intervention are essential to prevent further complications. The proportion of elderly individuals with depression in this study aligned with findings from the National Health Morbidity Survey 2018, which reported an 11% depression rate among elderly Malaysians [ 5 ]. For anxiety, despite the limited number of available studies for comparison, the finding in our study was 9.4%, which was significantly lower than the 22.6% reported among elderly individuals in rural areas in Perak [ 7 ]. Similarly, research in China has shown a greater prevalence of depression in rural areas than in urban areas [ 28 ]. These rural–urban differences in the prevalence of depression and anxiety may be influenced by socioeconomic disparities and healthcare access. Given that Kuala Selangor is a mixed urban‒rural district, the lower prevalence observed in this study than in rural areas may reflect better healthcare access, social engagement, and economic opportunities. Additionally, sociocultural factors and community screenings may have contributed to lower depression and anxiety rates by offering stabilizing benefits [ 29 ]. Internationally, the prevalence of stress varies widely, with higher rates reported in some regions, such as 40.2% in Iran, where financial insecurity and socioeconomic instability are major contributing factors [ 30 ]. This variation may be attributed to the transient nature of stress, unlike depression and anxiety, which tend to persist over time [ 31 ]. Given this temporal sensitivity, the lower occurrence (4.2%) in our study with variations in stress prevalence across populations may reflect differences in the timing of assessment rather than stable psychological conditions. With respect to nutritional status, our screening revealed that nearly 30% of the elderly individuals were at risk of either malnutrition or malnutrition, which warrants further investigations. These findings are consistent with the findings of the National Health Morbidity Survey 2018, which reported malnutrition and risk rates of 7.3% and 23.5%, respectively, among elderly Malaysians [ 32 ]. Moreover, these conditions are linked to various factors, including age-related physiological changes, restricted access to nutritious food, and the presence of comorbidities [ 33 ]. In addition, the prevalence of malnutrition and those at risk among elderly individuals varies across different regions. Our findings are consistent with findings in Singapore, which reported risk rates of malnutrition and malnutrition risk rates of 2.8% and 27.6%, respectively [ 34 ]. In contrast, studies in developing countries such as Sri Lanka and India have reported significantly higher malnutrition rates, whereas developed countries such as Spain present a lower prevalence of malnutrition [ 35 , 36 ]. These findings suggest that regions experiencing greater socioeconomic hardships are well documented contributors to poor nutrition [ 37 ]. Furthermore, the relatively lower malnutrition rates observed in our study suggest that our participants may have better access to a stable nutritional environment than those in regions with greater socioeconomic challenges [ 37 ]. This study's novelty lies in identifying a significant link between nutritional status and mental health problems in elderly individuals, as participants at risk of malnutrition and malnutrition were found to have significantly increased odds of developing depression, anxiety, and stress compared with other independent variables in this study. This association could be explained by the role of essential nutrients, such as serotonin and dopamine, which are crucial for emotional stability and cognitive function, in supporting neurotransmitter production [ 12 ]. This is evidenced by a study showing that inadequate dietary iron intake was associated with a greater risk of depression among elderly individuals [ 38 ]. These findings highlight the importance of early intervention in preventing mental health problems. Global studies across diverse regions, including both developed and developing countries such as Bangladesh, Iran, and Norway, similarly reported elevated depression rates among elderly people with malnutrition [ 13 , 39 , 40 ]. However, limited studies are available investigating the effects of malnutrition on anxiety and stress among elderly individuals. Our study revealed that other factors significantly influence the mental health status of the elderly population. The female sex is significantly associated with depression, and increased vulnerability to depression has been attributed to a combination of genetic and biological predispositions, increased healthcare-seeking behaviour, and heightened sensitivity to stressful life events [ 41 , 42 ]. In contrast, social engagement has a protective effect against depression, with frequent participation in social activities linked to lower depression rates. This finding is supported by a literature review suggesting that social interactions foster mental well-being by reducing loneliness and providing emotional support [ 43 ]. Both current smokers and former smokers were found to have higher odds than nonsmokers of having anxiety. This aligns with findings from a longitudinal Irish study on older adults, which reported increased anxiety disorders in smokers due to the impact of nicotine on brain chemistry [ 44 ]. In contrast, regular physical activity was identified as a protective factor against anxiety, which is consistent with findings from a large-scale study among community-dwelling elderly individuals in Malaysia [ 9 ]. This effect may be attributed to exercise’s ability to boost the production of endorphins, which are neurotransmitters that enhance mood and well-being, thereby reducing anxiety and promoting self-esteem [ 45 ]. For both depression and anxiety, retirees in our study, who made up approximately one-third of the sample population, experienced higher odds of depression and anxiety. This finding aligns with analyses of data from six waves of the United States Health and Retirement Study, suggesting that the loss of work roles due to retirement can adversely affect mental health [ 46 ]. Sleep problems also emerged as a significant factor for both depression and anxiety, with participants experiencing sleep problems having higher odds. Rapid eye movement (REM) sleep disturbances contribute to depression by altering the balance of monoamine neurotransmitters (serotonin, norepinephrine, and dopamine), which regulate mood and sleep, leading to emotional instability and increased susceptibility to depressive symptoms [ 47 ]. The strength of this study is that we are the only study to examine the associations between nutritional status and all three mental health problems, namely, depression, anxiety, and stress, among elderly individuals. Among the limitations of our study is that the tools we used are screening tools rather than diagnostic tools for identifying mental health problems and malnutrition. In addition, its cross-sectional design limits causal inference, and the focus on Kuala Selangor may affect generalizability. Additionally, convenience sampling from health screenings could also introduce some selection bias. The findings of this study have important implications for clinical practice and future research. In geriatric care, integrating mental health assessments can aid in the early identification of elderly individuals at risk of depression, anxiety, and stress, particularly retirees, those with limited social and physical engagement, and individuals with sleep disturbances. Nutritional screening for this high-risk group is essential to enable early intervention through individualized dietary counselling. A multidisciplinary approach involving dietitians and healthcare professionals is crucial for comprehensive management, preventing malnutrition-related complications, and enhancing overall well-being. For future research, longitudinal studies are needed to explore the causal relationship between malnutrition and mental health disorders in elderly individuals. Investigating sociodemographic and lifestyle factors can help refine targeted interventions and improve early detection strategies. Future studies should also assess dietary intake to identify specific nutrient deficiencies and optimize geriatric nutrition guidelines. Additionally, research on the effectiveness of dietary modifications, community-based programs, and personalized interventions can help mitigate nutritional risk and mental health decline. Expanding research to diverse elderly populations, especially those not receiving community screening, will provide a more comprehensive understanding and improve targeted healthcare strategies for vulnerable individuals. CONCLUSION Although the prevalence of mental health conditions was relatively low in this study, those who exhibited symptoms had moderate symptoms of depression and anxiety rather than only mild symptoms or transient distress, indicating the urgent need for early intervention. Additionally, a significant portion of the elderly have some degree of nutritional issues, implying the importance of assessment and tailored management approaches. Being at risk of malnutrition and malnourished were found to be strongly linked to depression, anxiety, and stress in the elderly population. Conversely, social engagement and physical activity have a protective effect against mental health problems and should be encouraged in elderly care. Early identification and targeted nutritional interventions are essential to mitigate mental health risks and improve overall well-being in ageing individuals. Abbreviations AORs adjusted odds ratios BMI Body mass index CI Confidence interval DALYs Disability-Adjusted Life Years DASS-21 Depression, Anxiety and Stress Scale 21 MNA®-SF Mini Nutritional Assessment Short Form SD standard deviation SPSS Statistical Package for the Social Sciences Declarations ETHICS APPROVAL AND CONSENT TO PARTICIPATE The study received ethical approval from the Medical Research and Ethics Committee of the Ministry of Health, Malaysia (NMRR ID-24-00920-SIY (IIR)) and the Universiti Teknologi MARA (UiTM) Research Ethics Committee (REC/03/2024 (PG/FB/10)).All participants were thoroughly informed about the study’s purpose, procedures, potential risks, and benefits, and they were assured of their right to withdraw from the study at any point without any impact on their medical care. The confidentiality and anonymity of the participant data were rigorously maintained, with the data securely stored and accessible only to the research team. This study was therefore performed in accordance with the ethical standards of the 1964 Declaration of Helsinki. CLINICAL TRIAL NUMBER Not applicable. CONSENT FOR PUBLICATION Not applicable. COMPETING INTERESTS The authors declare that they have no competing interests. AUTHOR DETAILS 1 Department of Primary Care Medicine, Faculty of Medicine, Universiti Teknologi MARA, 47000 Sungai Buloh, Selangor, Malaysia. 2 Department of Public Health Medicine (PHM), Faculty of Medicine, Hospital Al-Sultan Abdullah, Universiti Teknologi MARA, 42300 Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia. 3 Department of Internal Medicine (Geriatric), Faculty of Medicine, Hospital Al-Sultan Abdullah, Universiti Teknologi MARA, 42300 Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia. FUNDING This research did not receive any grants from any funding agencies. Author Contribution NAN was involved in the conception and design, planning, coordination, acquisition of the data, data analysis, interpretation of the data, and drafting and revision of the manuscript; SSY and NAAT contributed to the study design, planning, coordination, acquisition of the data and drafting of the manuscript; KNK participated in the conception and design, planning, coordination and acquisition of the data; and NAS contributed to the design, planning, coordination, data analysis, interpretation of the data, and drafting and revision of the manuscript. The final manuscript was read and approved by all the authors. Acknowledgement We would like to thank Hospital Al-Sultan Abdullah, UiTM Puncak Alam and Tanjung Karang Public Health Clinic as the event organizers of the community health screening that was conducted during the data collection period. Data Availability Data is provided within the manuscript or supplementary information files. References Malaysia DS. Current Population Estimates, Malaysia [Internet]., Putrajaya DOSM. 2022 [[cited 2023 April 1]]; Available from: https://www.dosm.gov.my/v1/index.php?r=column/cthemeByCat&cat=155&bul_id=dTZXanV6UUdyUEQ0SHNWOVhpSXNMUT09&menu_id=L0phe U43NWJwRWVSZklWdzQ4TlhUUT09 UN. Population Division. World population ageing. 2019: highlights. 2019 [17th June 2023]; Available from: https://www.un.org/en/development/desa/population/publications/pdf/ageing/WorldPopulation Ageing2019-Highlights.pdf Ministry of Health Malaysia. National Strategic Plan for Mental Health 2020–2025. Non Communcable Disease Section, Disease Control Division. 2020 [17th June 2023]; Available from: https://www.moh.gov.my/moh/resources/ Penerbitan/Rujukan/NCD/National%20Strategic%20Plan/ The_National_Strategic_Plan_For_Mental_Health_2020-2025.pdf World Health Organization. Mental Health in Older Adult. 2017 [April 1, 2023]; Available from: https://www.who.int/en/news-room/fact-sheets/detail/mental-health-of-older-adults Institute for Public Health (IPH) NIoH. National Health and Morbidity Survey (NHMS) 2018: Elderly Health. Vol. II: Elderly Health Findings. 2018: Ministry of Health Malaysia; 2019. 182 p. Hamzah NAR. Contributions of sociodemographic and psychosocial characteristics, functional status and physical activity level on prevalence of depressive symptoms among rural elderly in Johor state. Nutrition Society of Malaysia; 2018. Abdul Manaf MR, Mustafa M, Abdul Rahman MR, Yusof KH, Abd Aziz NA. Factors Influencing the Prevalence of Mental Health Problems among Malay Elderly Residing in a Rural Community: A Cross-Sectional Study. PLoS ONE. 2016;11(6):e0156937. Mesbah SF, Sulaiman N, Mohd Shariff Z, Ibrahim Z. Does food insecurity contribute towards depression? A cross-sectional study among the urban elderly in Malaysia. Int J Environ Res Public Health. 2020;17(9):3118. Vanoh D, Shahar S, Yahya HM, Hamid TA. Prevalence and Determinants of Depressive Disorders among Community-dwelling Older Adults: Findings from the Towards Useful Aging Study. Int J Gerontol. 2016;10(2):81–5. Murniati N, Al Aufa B, Kusuma D, Kamso S. A Scoping Review on Biopsychosocial Predictors of Mental Health among Older Adults. Int J Environ Res Public Health. 2022;19(17):10909. Hamzah NAR, Adznam SNA, Taib MNM, Mun CY, Ibrahim Z, Azam S. Contributions of sociodemographic and psychosocial characteristics, functional status and physical activity level on prevalence of depressive symptoms among rural elderly in Johor state. Malaysian J Nutr. 2018; 24(2). Adan RA, van der Beek EM, Buitelaar JK, et al. Nutritional psychiatry: Towards improving mental health by what you eat. Eur Neuropsychopharmacol. 2019;29(12):1321–32. Alam MR, Karmokar S, Reza S, Kabir MR, Ghosh S, Mamun MAA. Geriatric malnutrition and depression: Evidence from elderly home care population in Bangladesh. Prev Med Rep. 2021;23:101478. Boyanagari VK, Panda P, Boyanagari M, Panda S. Assessment of nutritional status, psychological depression, and functional ability of elderly population in South India. Archives Mental Health. 2018;19(2):150. Turkbeyler IH, Ozturk ZA, Sayiner ZA, Gol M, Efendioglu EM, Sahinbeyoglu S. Strong association between malnutrition, inflammation, and depression in elderly patients. A new novel geriatric complex based on malnutrition; MID complex? PROGRESS IN NUTRITION. 2020; 22(1):30–5. Cederholm T, Barazzoni R, Austin P, et al. ESPEN guidelines on definitions and terminology of clinical nutrition. Clin Nutr. 2017;36(1):49–64. Islam MZ, Disu TR, Farjana S, Rahman MM. Malnutrition and other risk factors for geriatric depression: a community-based comparative cross-sectional study in older adults in rural Bangladesh. BMC Geriatr. 2021;21:1–11. Hamirudin AH, Charlton K, Walton K. Outcomes related to nutrition screening in community living older adults: A systematic literature review. Arch Gerontol Geriatr. 2016;62:9–25. Corcoran C, Murphy C, Culligan EP, Walton J, Sleator RD. Malnutrition in elderly individuals. Sci Prog. 2019;102(2):171–80. Safian N, Shah SA, Mansor J, et al. Factors Associated with the Need for Assistance among the Elderly in Malaysia. Int J Environ Res Public Health. 2021;18(2):730. Minimum WO. 2022, (2022). MyCensus. 2020 Portal. Open DOSM: Kawasanku. Ministry of Economy, Department of Statistics Malaysia; 2020 [19th September 2023]. Rubenstein LZ, Harker JO, Salvà A, Guigoz Y, Vellas B. Screening for undernutrition in geriatric practice: developing the short-form mini-nutritional assessment (MNA-SF). The Journals of Gerontology Series A: Biological Sciences and Medical Sciences. 2001; 56(6):M366–72. Isautier JM, Bosnić M, Yeung SS, et al. Validity of nutritional screening tools for community-dwelling older adults: a systematic review and meta-analysis. J Am Med Dir Assoc. 2019;20(10):1351. e13-51. e25. Musa R, Fadzil MA, Zain Z. Translation, validation and psychometric properties of Bahasa Malaysia version of the Depression Anxiety and Stress Scales (DASS). ASEAN J Psychiatry. 2007;8(2):82–9. Gomez F. A guide to the depression, anxiety and stress scale (DASS 21). Central and Eastern Sydney primary health networks. 2016. Thapa DK, Visentin DC, Kornhaber R, Cleary M. Prevalence and factors associated with depression, anxiety, and stress symptoms among older adults: A cross-sectional population‐based study. Nurs Health Sci. 2020;22(4):1139–52. Wang Y, Li Z, Fu C. Urban–rural differences in the association between social activities and depressive symptoms among older adults in China: a cross-sectional study. BMC Geriatr. 2021;21:1–11. Du B, Mu Y. The Relationship Between Health Changes and Community Health Screening Participation Among Older People. Front Public Health. 2022;10:870157. Raeisvandi A, Amerzadeh M, Hajiabadi F, Hosseinkhani Z. Prevalence, modifiable and risk factors for depression, anxiety and stress (DASS) among elders in the northwest of Iran. 2022. Rönnlund M, Åström E, Adolfsson R, Carelli MG. Perceived Stress in Adults Aged 65 to 90: Relations to Facets of Time Perspective and COMT Val158Met Polymorphism. Front Psychol. 2018; 9. Ahmad MH, Salleh R, Siew Man C et al. Malnutrition among the Elderly in Malaysia and Its Associated Factors: Findings from the National Health and Morbidity Survey 2018. J Nutr Metab. 2021; 2021:6639935. Dent E, Wright OR, Woo J, Hoogendijk EO. Malnutrition in older adults. Lancet. 2023;401(10380):951–66. Wei K, Nyunt MSZ, Gao Q, Wee SL, Ng T-P. Frailty and malnutrition: related and distinct syndrome prevalence and association among community-dwelling older adults: Singapore longitudinal ageing studies. J Am Med Dir Assoc. 2017;18(12):1019–28. Damayanthi HDWT, Moy FM, Abdullah KL, Dharmaratne SD. Prevalence of malnutrition and associated factors among community-dwelling older persons in Sri Lanka: a cross-sectional study. BMC Geriatr. 2018;18(1):1–10. Vaish K, Patra S, Chhabra P. Nutritional status among elderly: A community-based cross-sectional study. Indian J Public Health. 2020;64(3):266–70. Besora-Moreno M, Llauradó E, Tarro L, Solà R. Social and economic factors and malnutrition or the risk of malnutrition in elderly individuals: a systematic review and meta-analysis of observational studies. Nutrients. 2020;12(3):737. Choong HT. Nutritional status in relation to depressive symptoms among Chinese elderly in Malaysia. Faculty of Medicine and Health Sciences, Universiti Putra Malaysia; 2019. Kvamme J-M, Grønli O, Florholmen J, Jacobsen BK. Risk of malnutrition is associated with mental health symptoms in community living elderly men and women: The Tromsø Study. BMC Psychiatry. 2011;11(1):1–8. Payahoo L, Khaje-Bishak Y, Pourghassem Gargari B, Kabir-Alavi MB, Asgharijafarabadi M. Assessment of Nutritional and Depression Status in Free-Living El-derly in Tabriz, Northwest Iran. Health Promot Perspect. 2013;3(2):288–93. Acciai F, Hardy M. Depression in later life: A closer look at the gender gap. Soc Sci Res. 2017;68:163–75. Lu L, Shen H, Tan L, et al. Prevalence and factors associated with anxiety and depression among community-dwelling older adults in Hunan, China: a cross-sectional study. BMC Psychiatry. 2023;23(1):107. Reynolds CF, Jeste DV, Sachdev PS, Blazer DG. Mental health care for older adults: recent advances and new directions in clinical practice and research. World Psychiatry. 2022;21(3):336–63. Monroe DC, McDowell CP, Kenny RA, Herring MP. Dynamic associations between anxiety, depression, and tobacco use in older adults: Results from The Irish Longitudinal Study on Ageing. J Psychiatr Res. 2021;139:99–105. Hossain MN, Lee J, Choi H, Kwak YS, Kim J. The impact of exercise on depression: how moving makes your brain and body feel better. Phys Act Nutr. 2024;28(2):43–51. Segel-Karpas D, Ayalon L, Lachman ME. Retirement and depressive symptoms: A 10-year cross-lagged analysis. Psychiatry Res. 2018;269:565–70. Fang H, Tu S, Sheng J, Shao A. Depression in sleep disturbance: A review on a bidirectional relationship, mechanisms and treatment. J Cell Mol Med. 2019;23(4):2324–32. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6433768","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":458171008,"identity":"8d007e55-fc49-4471-9cd7-3746e8e6149a","order_by":0,"name":"Nurliana Abd Nassir","email":"","orcid":"","institution":"Universiti Teknologi MARA","correspondingAuthor":false,"prefix":"","firstName":"Nurliana","middleName":"Abd","lastName":"Nassir","suffix":""},{"id":458171009,"identity":"39e0be73-d765-4851-be88-aed8ae04ef39","order_by":1,"name":"Siti Sara Yaacob","email":"","orcid":"","institution":"Hospital Al-Sultan Abdullah, Universiti Teknologi MARA","correspondingAuthor":false,"prefix":"","firstName":"Siti","middleName":"Sara","lastName":"Yaacob","suffix":""},{"id":458171010,"identity":"c8de2a97-1d7d-46c9-bf30-7cde0bfc6392","order_by":2,"name":"Noor Azleen Ahmad Tarmizi","email":"","orcid":"","institution":"Hospital Al-Sultan Abdullah, Universiti Teknologi MARA","correspondingAuthor":false,"prefix":"","firstName":"Noor","middleName":"Azleen Ahmad","lastName":"Tarmizi","suffix":""},{"id":458171011,"identity":"5c0d8d50-663d-47d2-b716-c4058c1fce1e","order_by":3,"name":"Khairatul Nainey Kamaruddin","email":"","orcid":"","institution":"Universiti Teknologi MARA","correspondingAuthor":false,"prefix":"","firstName":"Khairatul","middleName":"Nainey","lastName":"Kamaruddin","suffix":""},{"id":458171012,"identity":"383146e7-121d-4228-821a-d3464e02f551","order_by":4,"name":"Nur Amirah 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09:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6433768/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6433768/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12877-025-06394-7","type":"published","date":"2025-09-29T15:57:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83283517,"identity":"26b4e911-8b3d-4ce7-8846-42bc229b546c","added_by":"auto","created_at":"2025-05-22 10:51:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80932,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage141.png","url":"https://assets-eu.researchsquare.com/files/rs-6433768/v1/33c3ef035eaff2f8652c8b2d.png"},{"id":83283522,"identity":"0f6be92e-66d1-4032-acbc-792be49c10d3","added_by":"auto","created_at":"2025-05-22 10:51:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6559,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 1: Graph showing the distribution of severity levels for depression, anxiety, and stress among elderly respondents in terms of depression, anxiety and stress scale-21 (DASS-21) scores\u003c/p\u003e","description":"","filename":"Onlinedrawingimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6433768/v1/2727a6918de2cdb802b5b6c1.png"},{"id":92883719,"identity":"83c49253-8ca9-4d88-888a-e8636c9f188a","added_by":"auto","created_at":"2025-10-06 16:08:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":845715,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6433768/v1/da236a49-df5d-4fc1-8dc7-c8d9db6cdb3c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Association Between Nutritional Status and Mental Health Status and Its Associated Factors Among Elderly Attending Community Health Screenings: A Cross Sectional Study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe global population is aging rapidly, with the proportion of people aged 60 years and above expected to increase from 12% in 2020 to 22% by 2050, whereas Malaysia\u0026rsquo;s elderly population is projected to reach 15% by 2030 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As aging increases the risk of chronic diseases, disabilities, and cognitive decline, healthcare providers must remain vigilant and well informed to address the evolving needs of this population. Beyond physical health challenges, mental health problems, particularly depression and anxiety, are also highly prevalent among elderly individuals and pose a major public health concern [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Depression is one of the most common psychiatric disorders in older adults and is characterized by persistent low mood, loss of interest, and functional impairment, whereas anxiety disorders, characterized by excessive worry and heightened stress responses, are also common in elderly individuals. A World Health Organization study estimated that 7% of the global elderly population experiences depression, whereas 3.8% experience anxiety, contributing to 6.6% of all disabilities measured in disability-adjusted life years (DALYs) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Locally, the National Health Morbidity Survey 2018 in Malaysia reported a depression prevalence of 11% among elderly individuals, although it did not include specific data on anxiety and stress [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, other studies have reported that the prevalence of depression ranges from 4\u0026ndash;28%, indicating wide variation depending on the study population and assessment methods; however, few studies have reported an anxiety prevalence of 22.6% and a stress prevalence of 8.7% among elderly individuals. [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Although stress is less commonly reported due to its transient nature, it remains a significant concern among elderly individuals, with 8.7% experiencing stress during screening in a rural Malaysian study [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eElderly individuals are at increased risk of mental health problems due to social isolation, physical illness, and cognitive decline [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In addition, elderly individuals with underlying comorbidities, mobility issues, and impaired functional ability are more likely to experience depression [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. More importantly, increasing evidence suggests that nutritional status plays a crucial role in mental health among elderly individuals [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The European Society of Clinical Nutrition and Metabolism (ESPEN) has defined malnutrition as \u0026ldquo;a state resulting from a lack of intake or uptake of nutrition that leads to altered body composition (decreased fat-free mass) and body cell mass, leading to diminished physical and mental function and impaired clinical outcome from disease\u0026rdquo;[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Malnutrition has been shown to negatively impact cognitive function, mood regulation, and overall mental well-being, and studies indicate that malnourished elderly individuals are more likely to experience depressive symptoms, anxiety, and increased psychological distress due to insufficient energy and nutrient intake, which further exacerbates their vulnerability to mental health disorders [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Unlike specific nutrient deficiencies, which have been linked to mental health outcomes, general malnutrition in older adults is associated with a range of adverse health effects, including increased frailty, reduced immune function, and impaired recovery from illnesses [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The relationship between malnutrition and mental health can be explained through physiological mechanisms such as chronic inflammation, hormonal imbalances, and metabolic disruptions that result from inadequate nutrition [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Another emerging concept linking nutrition to mental health is the gut‒brain axis theory, which highlights how dietary choices impact both physical and mental well-being. The gut microbiota plays a significant role in regulating mood-related neurotransmitters such as serotonin, suggesting that malnutrition may alter the gut microbiome composition and contribute to mental health disturbances [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Given these associations, assessing nutritional status is critical in understanding the mental health burden among elderly individuals.\u003c/p\u003e \u003cp\u003eHowever, nutritional status is often overlooked in elderly care as one of the associated factors for mental health problems. Most available studies of mental health disorders in elderly individuals have focused more on sociodemographic factors, physical disability, and comorbidities, with limited attention given to nutrition as a contributing factor. The literature review has broadly examined malnutrition as a consequence of mental health disorders rather than exploring malnutrition as a risk factor. Even though depression screening is commonly performed, anxiety and stress remain primarily overlooked in this population. To address these gaps, this study assesses the nutritional status of elderly individuals attending community health screenings in Kuala Selangor District and examines its association with mental health problems, specifically depression, anxiety, and stress. Findings may enhance early detection, facilitate further diagnostic evaluation, and support targeted interventions to improve elderly well-being.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted among elderly individuals attending four community health screenings in Kuala Selangor District from June 29 to October 19, 2024. These screenings, aimed at general health assessments, primarily targeted the elderly population. The inclusion criteria required participants to be aged 60 years and above with the ability to read and understand Malay or English, whereas the exclusion criteria were those with acute severe illness, dementia, or cognitive impairments affecting response reliability.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample size calculation\u003c/h3\u003e\n\u003cp\u003eWe used OpenEpi Version 3.01 to determine the sample size on the basis of all the study objectives. The largest sample size estimate was derived from the 27.8% prevalence of depression among elderly Malaysians reported by Abd Manaf et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], with a 95% confidence level and a 5% margin of error; the calculated sample size was 301 participants. To account for potential nonresponses, this percentage was increased by 20%, resulting in a final sample size of 361 participants.\u003c/p\u003e\n\u003ch3\u003eStudy variables\u003c/h3\u003e\n\u003cp\u003eThe dependent variables were mental health problems, namely, depression, anxiety, and stress, which were assessed via the Depression, Anxiety, and Stress Scale-21 (DASS-21) screening tool. Nutritional status was assessed as the primary independent variable in this study via the Mini Nutritional Assessment-Short Form (MNA\u0026reg;-SF) screening tool, which categorizes participants into normal nutrition, at risk of malnutrition, and malnourished.\u003c/p\u003e \u003cp\u003eSociodemographic variables included age, which was analysed both as a continuous variable and as categorized groups (60\u0026ndash;74, 75\u0026ndash;84, and \u0026ge;\u0026thinsp;85 years [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]), gender (male or female), ethnicity (Malay, Chinese, Indian, others), marital status (single, married, widowed, divorced), and living arrangements (alone, with spouse only, with spouse and children, or with extended family) were also recorded. Additionally, household income was dichotomized as \u0026lt;\u0026thinsp;RM1500 or \u0026ge;\u0026thinsp;RM1500, on the basis of the Malaysian minimum wage guidelines [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]), whereas financial dependency was determined by whether participants were independent or relied on children\u0026rsquo;s allowances or welfare. Education level was categorized as no formal education or primary, secondary, or tertiary education, whereas employment status was recorded as either employed, unemployed or pensioner.\u003c/p\u003e \u003cp\u003eTo better understand participants' demographics in relation to lifestyle, self-reported behaviours were assessed. However, as these were not the primary focus of the study, data were obtained solely through self-reports without the use of specific screening tools. Physical activity was assessed by asking whether the participants engaged in activities such as aerobic exercise, running, brisk walking, cycling, gardening, or swimming, and their responses were categorized as frequently (5\u0026ndash;7 times per week), sometimes (2\u0026ndash;4 times per week), seldom (once per week), or no activity. Similarly, social engagement was evaluated by inquiring about participation in community activities, including gatherings, community programs, or religious events, with responses classified using the same frequency scale. In addition, sleep problems were assessed by asking participants whether they experienced difficulty falling asleep or maintaining sleep, with responses recorded as either yes or no. Meanwhile, functional independence was assessed on the basis of the ability to perform daily activities, including eating, dressing, bathing, toileting, transferring, and walking, without assistance.\u003c/p\u003e \u003cp\u003eThe assessment of clinical characteristics included the presence of comorbidities, particularly cardiovascular conditions such as diabetes mellitus, hypertension, and dyslipidemia. Additionally, body mass index (BMI) was calculated as weight (kg) divided by height squared (m\u0026sup2;) and classified into six categories: underweight (\u0026lt;\u0026thinsp;18.5 kg/m\u0026sup2;), normal weight (18.5\u0026ndash;22.9 kg/m\u0026sup2;), preobese (23.0\u0026ndash;27.4 kg/m\u0026sup2;), obese I (27.5\u0026ndash;32.4 kg/m\u0026sup2;), obese II (32.5\u0026ndash;37.4 kg/m\u0026sup2;), and obese III (\u0026ge;\u0026thinsp;37.5 kg/m\u0026sup2;).\u003c/p\u003e\n\u003ch3\u003eSampling Method and Data Collection\u003c/h3\u003e\n\u003cp\u003eThe participants were invited through convenience sampling from elderly individuals attending four community health screenings in Kuala Selangor. With a population of 281,711, this district is the second-largest in Selangor and among the top three with the highest elderly population density [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Given this demographic advantage, Kuala Selangor was chosen as the study location. Kuala Selangor was chosen not only because of its high elderly population density but also because of its established collaboration with local leaders, nongovernmental organisations, and healthcare providers, facilitating high participation rates. However, a key limitation of this study is that the participants were self-selected and were attending voluntarily, which may introduce selection bias. Those who participate in community screenings are generally more health conscious, socially engaged, and proactive in seeking medical care. As a result, this study may have underrepresented elderly individuals who are less likely to attend such events, many of whom may have poorer health, higher rates of malnutrition, and unrecognized mental health issues.\u003c/p\u003e \u003cp\u003eCommunity health screening programs are conducted annually by local leaders and healthcare clinics, which are specifically designed for the local community, with a focus on elderly participants and offer various activities, educational sessions, and free health screenings. Our research team set up our own booth for the purpose of data collection. Upon invitation to the booth, participants received an explanation of the study and provided informed consent before proceeding with the four sections of data collection. These included self-administered structured questionnaires and anthropometric measurements conducted by trained researchers. To ensure consistency and reliability, researchers have trained, including demonstrations and supervised practices, on anthropometric measurements and questionnaire structure. The first section captured demographic and clinical characteristics, whereas the second screened mental health problems via the DASS-21. In the third section, weight and height measurements were taken to calculate BMI, followed by the fourth section for nutritional status assessment via the MNA\u0026reg;-SF. For each section, different researchers were assigned to guide participants as needed and to explain the results of the screening. Anonymity was maintained throughout the process, and participants with abnormal DASS-21 or MNA\u0026reg;-SF screening results were referred to a primary care physician for further assessment to confirm a diagnosis and initiate appropriate management, subject to their consent.\u003c/p\u003e\n\u003ch3\u003eStudy Instruments\u003c/h3\u003e\n\u003cp\u003eThe MNA\u0026reg;-SF in the Malay and English languages was used to assess nutritional status. It is a widely validated screening tool for assessing nutritional status among elderly individuals, demonstrating strong validity and reliability, with reported high sensitivity (97.9%), specificity (100%), and diagnostic accuracy (98.7%) in the Malaysian population [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The questionnaire comprises six items assessing general nutritional status, which includes declines in food intake, involuntary weight loss, mobility, psychological and neuropsychological stress, and BMI. Each item was scored with a maximum possible total of 14 points, where higher scores indicated better nutritional status, whereas lower scores reflected poorer nutritional status. The scoring categories were as follows: 12\u0026ndash;14 signified normal nutritional status, 8\u0026ndash;11 indicated risk of malnutrition, and 0\u0026ndash;7 classified participants as malnutrition [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor the mental health assessment, our study used the DASS-21. It is a self-report screening tool that is also available in both the Malay and English languages and allows individuals to assess their depression, anxiety, and stress levels on the basis of their recent experiences. The Malay version of the DASS-21 has demonstrated good internal consistency and construct validity in Malaysian elderly populations, making it a reliable tool for screening mental health conditions. Its validity and reliability were established through confirmatory factor analysis, with satisfactory factor loadings above 0.4 and strong Cronbach\u0026rsquo;s alpha values for the subscales: 0.84 for depression, 0.74 for anxiety, and 0.79 for stress [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe DASS-21 consists of 21 items divided across three subscales with seven questions each for depression, anxiety, and stress. Each item was rated on a 4-point Likert scale reflecting symptom frequency over the past week (0 \u0026ldquo;Did not apply at all\u0026rdquo; to 3 \u0026ldquo;Applied very much or most of the time\u0026rdquo;). Subscale scores were then categorized into five levels: normal, mild, moderate, severe, and extremely severe [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In this study, each subscale (depression, anxiety, and stress) was categorized into two groups: \"normal\" was classified as \"absent,\" whereas the remaining four categories (mild, moderate, severe, and extremely severe) were grouped as \"present\". The cut-off scores used to define the presence of mental health problems were \u0026gt;\u0026thinsp;9 for depression, \u0026gt;\u0026thinsp;7 for anxiety, and \u0026gt;\u0026thinsp;14 for stress [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile the DASS-21 is a widely used screening instrument for detecting symptoms of depression, anxiety, and stress, it has several limitations. As a self-report measure, it does not provide a clinical diagnosis but serves as a tool to identify individuals at risk of mental health issues on the basis of symptom severity. Given its role as a screening tool, the results obtained from this study may help guide further clinical evaluation to confirm symptom severity and inform appropriate mental health interventions. Its practicality and ease of use make it a feasible option for large-scale screening in elderly populations, particularly in these community health programs, where early identification can facilitate timely support and intervention.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll the statistical analyses were conducted via IBM Statistical Package for the Social Sciences (SPSS) version 29.0. For descriptive statistics, categorical variables are summarized as frequencies and percentages, whereas continuous variables are presented as the means and standard deviations. To identify factors associated with mental health problems, including nutritional status, separate multivariable logistic regression models were constructed for depression, anxiety and stress using the backwards likelihood ratio method. All the models were carefully checked for multicollinearity, and potential two-way interaction terms were evaluated alongside the main effect models. Model fit was assessed via goodness-of-fit statistics to ensure that the logistic regression models adequately aligned with the observed outcomes. The final multivariable models included only significant predictors of depression, anxiety, and stress. The results are reported as adjusted odds ratios (AORs) with 95% confidence intervals, and statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 388 elderly individuals were invited to participate. However, 27 were excluded, leaving 361 participants for the final analysis. The flow of the study is illustrated in Figure 1.\u003c/p\u003e\n\u003cp\u003eAmong the participants, 59.6% were female, with a mean age of 67.17 years (SD \u0026plusmn; 5.74). The majority were aged 60\u0026ndash;74 years (86.1%), Malay ethnicity (89.2%), and married (71.5%), while 45.2% had secondary education. Socioeconomically, 57.6% had a household income below RM 1500, and nearly half (48.5%) were unemployed, including 33.0% pensioners. Most lived with a spouse (36.6%) or extended family (20.2%). Furthermore, 78.4% had at least one comorbidity, with dyslipidaemia (58.4%), hypertension (54.6%), and diabetes (35.7%) being the most common. The mean BMI was 27.12 (SD = 5.53), with most patients being overweight (33.2%) or obese (29.1%). Nonsmoking (78.4%) and alcohol abstinence (97.2%) were prevalent, while 28.3% reported sleep difficulties. For lifestyle, 61.2% exercised at least twice a week, and 59.5% participated in social activities at the same frequency. Despite these factors, 95.8% of the participants remained independent in daily activities (Table 1).\u003c/p\u003e\n\u003cp\u003eTable 1: Demographic, socioeconomic, clinical and lifestyle characteristics of the elderly respondents. (N=361)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eFrequency, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eMean (\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e146 (40.4)\u003c/p\u003e\n \u003cp\u003e215 (59.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003cp\u003e60 to 74 (young-old)\u003c/p\u003e\n \u003cp\u003e75 to 84 (middle-old)\u003c/p\u003e\n \u003cp\u003e85 and above (old-old)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e311 (86.1)\u003c/p\u003e\n \u003cp\u003e48 (13.3)\u003c/p\u003e\n \u003cp\u003e2 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e67.17\u0026nbsp;\u0026plusmn;\u0026nbsp;5.74\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003cp\u003eMalay\u003c/p\u003e\n \u003cp\u003eChinese\u003c/p\u003e\n \u003cp\u003eIndian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e322 (89.2)\u003c/p\u003e\n \u003cp\u003e33 (9.1)\u003c/p\u003e\n \u003cp\u003e6 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eMarital status\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7 (1.9)\u003c/p\u003e\n \u003cp\u003e258 (71.5)\u003c/p\u003e\n \u003cp\u003e80 (22.2)\u003c/p\u003e\n \u003cp\u003e16 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eLevel of Education\u003c/p\u003e\n \u003cp\u003eNo Formal Education\u003c/p\u003e\n \u003cp\u003ePrimary: Standard 1 to 6\u003c/p\u003e\n \u003cp\u003eSecondary: Form 1 to 5\u003c/p\u003e\n \u003cp\u003eTertiary: Form 6/College/University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e41 (11.4)\u003c/p\u003e\n \u003cp\u003e104 (28.8)\u003c/p\u003e\n \u003cp\u003e163 (45.2)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e53 (14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eLiving arrangement\u003c/p\u003e\n \u003cp\u003eLiving alone\u003c/p\u003e\n \u003cp\u003eLiving with spouse only\u003c/p\u003e\n \u003cp\u003eLiving with spouse and children\u003c/p\u003e\n \u003cp\u003eLiving with extended family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e46 (12.7)\u003c/p\u003e\n \u003cp\u003e132 (36.6)\u003c/p\u003e\n \u003cp\u003e110 (30.5)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e73 9 (20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eJob category\u003c/p\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003cp\u003ePensioner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e67 (18.6)\u003c/p\u003e\n \u003cp\u003e175 (48.5)\u003c/p\u003e\n \u003cp\u003e119 (33.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eIncome category\u003c/p\u003e\n \u003cp\u003eMore than RM 1500\u003c/p\u003e\n \u003cp\u003eLess than RM 1500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e153 (42.2)\u003c/p\u003e\n \u003cp\u003e208 (57.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eSource of Income\u003c/p\u003e\n \u003cp\u003eIndependent\u003c/p\u003e\n \u003cp\u003eDependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e190 (52.6)\u003c/p\u003e\n \u003cp\u003e171 (47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003cp\u003eunderweight: \u0026lt; 18.5\u003c/p\u003e\n \u003cp\u003eNormal: 18.5 \u0026ndash; 22.9\u003c/p\u003e\n \u003cp\u003eOverweight: 23 \u0026ndash; 27.4\u003c/p\u003e\n \u003cp\u003eObese 1: 27.5 \u0026ndash; 32.4\u003c/p\u003e\n \u003cp\u003eObese 2: 32.5 \u0026ndash; 37.4\u003c/p\u003e\n \u003cp\u003eObese 3: \u0026gt;/= 37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18 (5.0)\u003c/p\u003e\n \u003cp\u003e62 (17.2)\u003c/p\u003e\n \u003cp\u003e120 (33.2)\u003c/p\u003e\n \u003cp\u003e105 (29.1)\u003c/p\u003e\n \u003cp\u003e41 (11.4)\u003c/p\u003e\n \u003cp\u003e15 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e27.12\u0026nbsp;\u0026plusmn;\u0026nbsp;5.53\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eComorbidities\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003cp\u003eDyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e283 (78.4)\u003c/p\u003e\n \u003cp\u003e78 (21.6)\u003c/p\u003e\n \u003cp\u003e129 (35.7)\u003c/p\u003e\n \u003cp\u003e197 (54.6)\u003c/p\u003e\n \u003cp\u003e211 (58.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eSmoking Status\u003c/p\u003e\n \u003cp\u003eSmoker\u003c/p\u003e\n \u003cp\u003eEx smoker\u003c/p\u003e\n \u003cp\u003eNon-Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e49 (8.0)\u003c/p\u003e\n \u003cp\u003e29 (13.6)\u003c/p\u003e\n \u003cp\u003e283 (78.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eAlcohol consumption status\u003c/p\u003e\n \u003cp\u003eEver consumed (past or present)\u003c/p\u003e\n \u003cp\u003eNever consumed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10 (2.8)\u003c/p\u003e\n \u003cp\u003e351 (97.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eSleeping problems\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e102 (28.3)\u003c/p\u003e\n \u003cp\u003e259 (71.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003ePhysical Activity in a week\u003c/p\u003e\n \u003cp\u003eFrequent (5-7 times per week)\u003c/p\u003e\n \u003cp\u003eSometimes (2-4 times per week)\u003c/p\u003e\n \u003cp\u003eSeldom (1 time per week)\u003c/p\u003e\n \u003cp\u003eNo activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e90 (30.7)\u003c/p\u003e\n \u003cp\u003e110 (30.5)\u003c/p\u003e\n \u003cp\u003e84 (23.3)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e56 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eSocial activity in a week\u003c/p\u003e\n \u003cp\u003eFrequent (5-7 times per week)\u003c/p\u003e\n \u003cp\u003eSometimes (2-4 times per week)\u003c/p\u003e\n \u003cp\u003eSeldom (1 time per week)\u003c/p\u003e\n \u003cp\u003eNo activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e90 (24.9)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e125 (34.6)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e99 (27.4)\u003c/p\u003e\n \u003cp\u003e47 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 232px;\"\u003e\n \u003cp\u003eActivity of Daily Living (ADL)\u003c/p\u003e\n \u003cp\u003eIndependent\u003c/p\u003e\n \u003cp\u003eDependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e346 (95.8)\u003c/p\u003e\n \u003cp\u003e15 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;For the prevalence of malnutrition and at risk of malnutrition, of the 361 elderly participants assessed via the MNA\u0026reg;-SF, a substantial proportion (23.0%) were identified as being at risk of malnutrition (95% CI: 18.7%\u0026ndash;27.7%), whereas 6.4% were classified as having malnutrition (95% CI: 4.1%\u0026ndash;9.4%) (Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2:\u0026nbsp;Prevalence of malnutrition and risk of malnutrition in elderly respondents from the MNA\u0026reg;-SF.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003eNutritional Status (N= 361)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003ePrevalence (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e95% Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003eNormal nutrition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e70.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e65.6% - 75.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003eAt risk of malnutrition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e23.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e18.7% - 27.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 210px;\"\u003e\n \u003cp\u003eMalnutrition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e4.1% - 9.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMNA-SF\u003csup\u003e\u0026Ograve;\u003c/sup\u003e: Mini Nutritional Assessment Short Form\u003c/p\u003e\n\u003cp\u003eThe mental health assessment indicated that among the 361 elderly respondents, 12.7% (n = 46) experienced depression, with 28.3% classified as mild, 67.4% as moderate, 4.3% as severe, and none as extremely severe. Anxiety was present in 9.4% (n = 34) of the respondents, with 76.5% categorized as moderate, 23.5% as severe, and none as mild or extremely severe. Stress was the least common stress experienced by 4.2% (n = 15) of the participants, with 60% classified as mild and 40% as moderate, whereas no cases were recorded as severe or extremely severe. Table 3 and Figure 1 show the proportions of elderly individuals who experienced depression, anxiety and stress and their severity according to the DASS-21 score.\u003c/p\u003e\n\u003cp\u003eTable 3: Descriptive analysis of the proportion of elderly respondents who experienced mental health problems (depression, anxiety and stress) according to the DASS-21 score (n = 361)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"588\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eMental Health\u003c/p\u003e\n \u003cp\u003eProblems (N=361)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 392px;\"\u003e\n \u003cp\u003eFrequency, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e315 (87.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e46 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e327 (90.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e34 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eStress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e346 (95.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e15 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDASS-21: Depression, Anxiety and Stress Scale 21\u003c/p\u003e\n\u003cp\u003eMultiple logistic regression revealed that nutritional status was significantly associated with depression, with those at risk of malnutrition having greater odds of depression (AOR = 10.94; 95% CI: 4.35, 27.52; p \u0026lt; 0.001) than individuals with a normal nutritional status. Similarly, individuals with malnutrition had even greater odds of depression (AOR = 45.61; 95% CI: 13.20, 157.61; p \u0026lt; 0.001). Other associated factors that were found to increase the odds of having depression are being female compared with being male (AOR = 2.79; 95% CI: 1.17, 6.63; p = 0.02), being retired compared with being employed (AOR = 8.17; 95% CI: 2.18, 30.59; p = 0.002) and having sleeping problems compared with being elderly without sleeping problems (AOR = 3.18; 95% CI: 1.43, 7.07; p = 0.005). On the other hand, frequent social engagement (5\u0026ndash;7 times per week) had a protective effect (AOR = 0.054; 95% CI: 0.009, 0.341; p = 0.004) (Table 4).\u003c/p\u003e\n\u003cp\u003eMoreover, those at risk of malnutrition had greater odds than those with normal nutritional status did (AOR = 11.16; 95% CI: 4.10, 30.41; p \u0026lt; 0.001), whereas those with malnutrition had even greater odds of experiencing anxiety (AOR = 17.80; 95% CI: 4.93, 64.32; p \u0026lt; 0.001). Other associated factors that were found to increase the odds of anxiety were retirees compared with employed individuals (AOR = 5.28; 95% CI: 1.31, 21.32; p = 0.019), smokers (AOR = 4.50; 95% CI: 1.55, 13.11; p = 0.006) and ex-smokers (AOR = 8.95; 95% CI: 2.72, 29.51; p \u0026lt; 0.001) had higher odds than nonsmokers did, and elderly people with sleeping problems had higher odds than did elderly people without sleeping problems (AOR = 2.99; 95% CI: 1.21, 7.39; p = 0.017). On the other hand, engaging in frequent physical activity (5\u0026ndash;7 times per week) had a protective effect (AOR = 0.157; 95% CI: 0.041, 0.602; p = 0.007), and engaging in less physical activity (2\u0026ndash;4 times per week) also had a protective effect against anxiety (AOR = 0.255; 95% CI: 0.079, 0.82; p = 0.022) (Table 5).\u003c/p\u003e\n\u003cp\u003eThe odds of stress were also greater in individuals at risk of malnutrition (AOR = 5.03; 95% CI: 1.55, 16.34; p = 0.007) and, similarly, in those with malnutrition (AOR = 4.59; 95% CI: 0.82, 25.74), although this association was not statistically significant (p = 0.083). Moreover, no other factors were found to be associated with stress (Table 6).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 4: Multiple logistic regression for factors associated with depression in elderly respondents\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eAdj. OR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eWald (df)\u003csup\u003ea\u003cstrong\u003e)\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ep\u0026ndash; value\u003csup\u003ea)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eNutritional status\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eNormal\u003c/li\u003e\n \u003cli\u003eAt risk of malnutrition\u003c/li\u003e\n \u003cli\u003eMalnutrition\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e10.94 (4.35,27.52)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45.61 (13.20,157.61)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e42.76\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25.83\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e36.46\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eMale\u003c/li\u003e\n \u003cli\u003eFemale\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2.788 (1.173,6.627)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5.38 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eJob Category\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eEmployed\u003c/li\u003e\n \u003cli\u003eUnemployed\u003c/li\u003e\n \u003cli\u003ePensioner\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2.45 (0.65,9.18)\u003c/p\u003e\n \u003cp\u003e8.17 (2.18,30.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e12.176 (2)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.76 (1)\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e9.72 (1)\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e*0.002\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.098\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e*0.002\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eSleeping problems\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eNo\u003c/li\u003e\n \u003cli\u003eYes\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e3.18 (1.43,7.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8.02 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e*0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eSocial activity in a week\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eNo activity\u003c/li\u003e\n \u003cli\u003eSeldom (1 time per week)\u003c/li\u003e\n \u003cli\u003eSometimes (2-4 times per week)\u003c/li\u003e\n \u003cli\u003eFrequent (5-7 times per week)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.865 (0.305, 2.457)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.352 (0.113,1.101)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.054 (0.009,0.341)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e12.52 (3)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.074 (1)\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.217 (1)\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9.630 (1)\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e*0.006\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e*0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSimple logistic regression was initially conducted for all variables, and those with a p value \u0026lt; 0.25 were included in the multiple logistic regression model.\u003c/p\u003e\n\u003cp\u003eAdj. OR: Adjusted OR, CI: Confidence interval\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003csup\u003ea)\u003c/sup\u003e Likelihood ratio, \u003csup\u003eb)\u0026nbsp;\u003c/sup\u003eWald test\u003c/p\u003e\n\u003cp\u003e*p value \u0026lt;0.05\u003c/p\u003e\n\u003cp\u003eTable 5: Multiple logistic regression for factors associated with anxiety in elderly respondents\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eAdj. OR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eWald (df)\u003csup\u003ea)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003ep -value\u003csup\u003ea)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNutritional status\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eNormal\u003c/li\u003e\n \u003cli\u003eAt risk of malnutrition\u003c/li\u003e\n \u003cli\u003eMalnutrition\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e11.16 (4.10,30.41)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17.80 (4.93,64.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e28.22\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22.25\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e19.32\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eJob Category\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eEmployed\u003c/li\u003e\n \u003cli\u003eUnemployed\u003c/li\u003e\n \u003cli\u003ePensioner\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.74 (0.42, 7.18)\u003c/p\u003e\n \u003cp\u003e5.28 (1.31,21.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e8.232 (2)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.583 (1)\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e5.469 (1)\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.016*\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003cp\u003e0.019*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eNon smoker\u003c/li\u003e\n \u003cli\u003eSmoker\u003c/li\u003e\n \u003cli\u003eEx smoker\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e4.502 (1.55, 13.11)\u003c/p\u003e\n \u003cp\u003e8.952 (2.72, 29.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e16.45 (2)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6.009 (1)\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e12.97 (1)\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.006*\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eSleeping problems\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eNo\u003c/li\u003e\n \u003cli\u003eYes\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2.99 (1.21, 7.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5.670 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.017*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePhysical Activity in a week\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eNo activity\u003c/li\u003e\n \u003cli\u003eSeldom (1 time per week)\u003c/li\u003e\n \u003cli\u003eSometimes (2-4 times per week)\u003c/li\u003e\n \u003cli\u003eFrequent (5-7 times per week)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e0.448 (0.14,1.41)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.255 (0.079, 0.82)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.157 (0.041,0.602)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e8.729 (3)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.883 (1)\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5.214 (1)\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.280 (1)\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.033*\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.007*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSimple logistic regression was initially conducted for all variables, and those with a p value \u0026lt; 0.25 were included in the multiple logistic regression model.\u003c/p\u003e\n\u003cp\u003eAdj. OR: Adjusted OR, CI: Confidence interval\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003csup\u003ea)\u003c/sup\u003e Likelihood ratio, \u003csup\u003eb)\u0026nbsp;\u003c/sup\u003eWald test\u003c/p\u003e\n\u003cp\u003e*p value \u0026lt; 0.05\u003c/p\u003e\n\u003cp\u003eTable 6: Multiple logistic regression for factors associated with stress in elderly respondents\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003eAdj. OR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eWald (df)\u003csup\u003ea)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003ep -value\u003csup\u003ea)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNutritional status\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eNormal\u003c/li\u003e\n \u003cli\u003eAt risk of malnutrition\u003c/li\u003e\n \u003cli\u003eMalnutrition\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 173px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e5.03 (1.55,16.34)\u003c/p\u003e\n \u003cp\u003e4.59 (0.82,25.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e7.79\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.23\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e3.01\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e0.020*\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.007*\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSimple logistic regression was initially conducted for all variables, and those with a p value \u0026lt; 0.25 were included in the multiple logistic regression model.\u003c/p\u003e\n\u003cp\u003eAdj. OR: Adjusted OR, CI: Confidence interval\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003csup\u003ea)\u003c/sup\u003e Likelihood ratio, \u003csup\u003eb)\u0026nbsp;\u003c/sup\u003eWald test\u003c/p\u003e\n\u003cp\u003e*p value \u0026lt; 0.05\u003c/p\u003e"},{"header":"DISCUSSIONS","content":"\u003cp\u003eIn our study, depression (12.7%) emerged as the most common mental health condition experienced by the elderly respondents, followed by anxiety (9.4%) and stress (6.2%). Even though the proportion of these mental health conditions was relatively low, those who presented symptoms had moderate symptoms of depression and anxiety rather than only mild symptoms or transient distress. These results suggest that identification and early intervention are essential to prevent further complications.\u003c/p\u003e \u003cp\u003eThe proportion of elderly individuals with depression in this study aligned with findings from the National Health Morbidity Survey 2018, which reported an 11% depression rate among elderly Malaysians [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. For anxiety, despite the limited number of available studies for comparison, the finding in our study was 9.4%, which was significantly lower than the 22.6% reported among elderly individuals in rural areas in Perak [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Similarly, research in China has shown a greater prevalence of depression in rural areas than in urban areas [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These rural\u0026ndash;urban differences in the prevalence of depression and anxiety may be influenced by socioeconomic disparities and healthcare access. Given that Kuala Selangor is a mixed urban‒rural district, the lower prevalence observed in this study than in rural areas may reflect better healthcare access, social engagement, and economic opportunities. Additionally, sociocultural factors and community screenings may have contributed to lower depression and anxiety rates by offering stabilizing benefits [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInternationally, the prevalence of stress varies widely, with higher rates reported in some regions, such as 40.2% in Iran, where financial insecurity and socioeconomic instability are major contributing factors [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This variation may be attributed to the transient nature of stress, unlike depression and anxiety, which tend to persist over time [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Given this temporal sensitivity, the lower occurrence (4.2%) in our study with variations in stress prevalence across populations may reflect differences in the timing of assessment rather than stable psychological conditions.\u003c/p\u003e \u003cp\u003eWith respect to nutritional status, our screening revealed that nearly 30% of the elderly individuals were at risk of either malnutrition or malnutrition, which warrants further investigations. These findings are consistent with the findings of the National Health Morbidity Survey 2018, which reported malnutrition and risk rates of 7.3% and 23.5%, respectively, among elderly Malaysians [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Moreover, these conditions are linked to various factors, including age-related physiological changes, restricted access to nutritious food, and the presence of comorbidities [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In addition, the prevalence of malnutrition and those at risk among elderly individuals varies across different regions. Our findings are consistent with findings in Singapore, which reported risk rates of malnutrition and malnutrition risk rates of 2.8% and 27.6%, respectively [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In contrast, studies in developing countries such as Sri Lanka and India have reported significantly higher malnutrition rates, whereas developed countries such as Spain present a lower prevalence of malnutrition [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These findings suggest that regions experiencing greater socioeconomic hardships are well documented contributors to poor nutrition [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Furthermore, the relatively lower malnutrition rates observed in our study suggest that our participants may have better access to a stable nutritional environment than those in regions with greater socioeconomic challenges [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study's novelty lies in identifying a significant link between nutritional status and mental health problems in elderly individuals, as participants at risk of malnutrition and malnutrition were found to have significantly increased odds of developing depression, anxiety, and stress compared with other independent variables in this study. This association could be explained by the role of essential nutrients, such as serotonin and dopamine, which are crucial for emotional stability and cognitive function, in supporting neurotransmitter production [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This is evidenced by a study showing that inadequate dietary iron intake was associated with a greater risk of depression among elderly individuals [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These findings highlight the importance of early intervention in preventing mental health problems. Global studies across diverse regions, including both developed and developing countries such as Bangladesh, Iran, and Norway, similarly reported elevated depression rates among elderly people with malnutrition [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. However, limited studies are available investigating the effects of malnutrition on anxiety and stress among elderly individuals.\u003c/p\u003e \u003cp\u003eOur study revealed that other factors significantly influence the mental health status of the elderly population. The female sex is significantly associated with depression, and increased vulnerability to depression has been attributed to a combination of genetic and biological predispositions, increased healthcare-seeking behaviour, and heightened sensitivity to stressful life events [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In contrast, social engagement has a protective effect against depression, with frequent participation in social activities linked to lower depression rates. This finding is supported by a literature review suggesting that social interactions foster mental well-being by reducing loneliness and providing emotional support [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBoth current smokers and former smokers were found to have higher odds than nonsmokers of having anxiety. This aligns with findings from a longitudinal Irish study on older adults, which reported increased anxiety disorders in smokers due to the impact of nicotine on brain chemistry [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In contrast, regular physical activity was identified as a protective factor against anxiety, which is consistent with findings from a large-scale study among community-dwelling elderly individuals in Malaysia [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This effect may be attributed to exercise\u0026rsquo;s ability to boost the production of endorphins, which are neurotransmitters that enhance mood and well-being, thereby reducing anxiety and promoting self-esteem [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor both depression and anxiety, retirees in our study, who made up approximately one-third of the sample population, experienced higher odds of depression and anxiety. This finding aligns with analyses of data from six waves of the United States Health and Retirement Study, suggesting that the loss of work roles due to retirement can adversely affect mental health [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Sleep problems also emerged as a significant factor for both depression and anxiety, with participants experiencing sleep problems having higher odds. Rapid eye movement (REM) sleep disturbances contribute to depression by altering the balance of monoamine neurotransmitters (serotonin, norepinephrine, and dopamine), which regulate mood and sleep, leading to emotional instability and increased susceptibility to depressive symptoms [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe strength of this study is that we are the only study to examine the associations between nutritional status and all three mental health problems, namely, depression, anxiety, and stress, among elderly individuals. Among the limitations of our study is that the tools we used are screening tools rather than diagnostic tools for identifying mental health problems and malnutrition. In addition, its cross-sectional design limits causal inference, and the focus on Kuala Selangor may affect generalizability. Additionally, convenience sampling from health screenings could also introduce some selection bias.\u003c/p\u003e \u003cp\u003eThe findings of this study have important implications for clinical practice and future research. In geriatric care, integrating mental health assessments can aid in the early identification of elderly individuals at risk of depression, anxiety, and stress, particularly retirees, those with limited social and physical engagement, and individuals with sleep disturbances. Nutritional screening for this high-risk group is essential to enable early intervention through individualized dietary counselling. A multidisciplinary approach involving dietitians and healthcare professionals is crucial for comprehensive management, preventing malnutrition-related complications, and enhancing overall well-being.\u003c/p\u003e \u003cp\u003eFor future research, longitudinal studies are needed to explore the causal relationship between malnutrition and mental health disorders in elderly individuals. Investigating sociodemographic and lifestyle factors can help refine targeted interventions and improve early detection strategies. Future studies should also assess dietary intake to identify specific nutrient deficiencies and optimize geriatric nutrition guidelines. Additionally, research on the effectiveness of dietary modifications, community-based programs, and personalized interventions can help mitigate nutritional risk and mental health decline. Expanding research to diverse elderly populations, especially those not receiving community screening, will provide a more comprehensive understanding and improve targeted healthcare strategies for vulnerable individuals.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eAlthough the prevalence of mental health conditions was relatively low in this study, those who exhibited symptoms had moderate symptoms of depression and anxiety rather than only mild symptoms or transient distress, indicating the urgent need for early intervention. Additionally, a significant portion of the elderly have some degree of nutritional issues, implying the importance of assessment and tailored management approaches. Being at risk of malnutrition and malnourished were found to be strongly linked to depression, anxiety, and stress in the elderly population. Conversely, social engagement and physical activity have a protective effect against mental health problems and should be encouraged in elderly care. Early identification and targeted nutritional interventions are essential to mitigate mental health risks and improve overall well-being in ageing individuals.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAORs adjusted odds ratios\u003c/p\u003e \u003cp\u003eBMI Body mass index\u003c/p\u003e \u003cp\u003eCI Confidence interval\u003c/p\u003e \u003cp\u003eDALYs Disability-Adjusted Life Years\u003c/p\u003e \u003cp\u003eDASS-21 Depression, Anxiety and Stress Scale 21\u003c/p\u003e \u003cp\u003eMNA\u0026reg;-SF Mini Nutritional Assessment Short Form\u003c/p\u003e \u003cp\u003eSD standard deviation\u003c/p\u003e \u003cp\u003eSPSS Statistical Package for the Social Sciences\u003c/p\u003e \u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eETHICS APPROVAL AND CONSENT TO PARTICIPATE\u003c/h2\u003e \u003cp\u003e The study received ethical approval from the Medical Research and Ethics Committee of the Ministry of Health, Malaysia (NMRR ID-24-00920-SIY (IIR)) and the Universiti Teknologi MARA (UiTM) Research Ethics Committee (REC/03/2024 (PG/FB/10)).All participants were thoroughly informed about the study\u0026rsquo;s purpose, procedures, potential risks, and benefits, and they were assured of their right to withdraw from the study at any point without any impact on their medical care. The confidentiality and anonymity of the participant data were rigorously maintained, with the data securely stored and accessible only to the research team. This study was therefore performed in accordance with the ethical standards of the 1964 Declaration of Helsinki.\u003c/p\u003e \u003ch2\u003eCLINICAL TRIAL NUMBER\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003ch2\u003eCONSENT FOR PUBLICATION\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003ch2\u003eAUTHOR DETAILS\u003c/h2\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003e Department of Primary Care Medicine, Faculty of Medicine, Universiti Teknologi\u003c/p\u003e \u003cp\u003eMARA, 47000 Sungai Buloh, Selangor, Malaysia. \u003csup\u003e2\u003c/sup\u003e Department of Public Health Medicine (PHM), Faculty of Medicine, Hospital Al-Sultan Abdullah, Universiti Teknologi MARA, 42300 Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia. \u003csup\u003e3\u003c/sup\u003e Department of Internal Medicine (Geriatric), Faculty of Medicine, Hospital Al-Sultan Abdullah, Universiti Teknologi MARA, 42300 Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFUNDING\u003c/h2\u003e \u003cp\u003eThis research did not receive any grants from any funding agencies.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eNAN was involved in the conception and design, planning, coordination, acquisition of the data, data analysis, interpretation of the data, and drafting and revision of the manuscript; SSY and NAAT contributed to the study design, planning, coordination, acquisition of the data and drafting of the manuscript; KNK participated in the conception and design, planning, coordination and acquisition of the data; and NAS contributed to the design, planning, coordination, data analysis, interpretation of the data, and drafting and revision of the manuscript. The final manuscript was read and approved by all the authors.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank Hospital Al-Sultan Abdullah, UiTM Puncak Alam and Tanjung Karang Public Health Clinic as the event organizers of the community health screening that was conducted during the data collection period.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMalaysia DS. 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Int J Environ Res Public Health. 2021;18(2):730.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinimum WO. 2022, (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMyCensus. 2020 Portal. Open DOSM: Kawasanku. Ministry of Economy, Department of Statistics Malaysia; 2020 [19th September 2023].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRubenstein LZ, Harker JO, Salv\u0026agrave; A, Guigoz Y, Vellas B. Screening for undernutrition in geriatric practice: developing the short-form mini-nutritional assessment (MNA-SF). The Journals of Gerontology Series A: Biological Sciences and Medical Sciences. 2001; 56(6):M366\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsautier JM, Bosnić M, Yeung SS, et al. Validity of nutritional screening tools for community-dwelling older adults: a systematic review and meta-analysis. J Am Med Dir Assoc. 2019;20(10):1351. e13-51. e25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMusa R, Fadzil MA, Zain Z. Translation, validation and psychometric properties of Bahasa Malaysia version of the Depression Anxiety and Stress Scales (DASS). ASEAN J Psychiatry. 2007;8(2):82\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGomez F. A guide to the depression, anxiety and stress scale (DASS 21). Central and Eastern Sydney primary health networks. 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThapa DK, Visentin DC, Kornhaber R, Cleary M. Prevalence and factors associated with depression, anxiety, and stress symptoms among older adults: A cross-sectional population‐based study. Nurs Health Sci. 2020;22(4):1139\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Li Z, Fu C. Urban\u0026ndash;rural differences in the association between social activities and depressive symptoms among older adults in China: a cross-sectional study. BMC Geriatr. 2021;21:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu B, Mu Y. The Relationship Between Health Changes and Community Health Screening Participation Among Older People. Front Public Health. 2022;10:870157.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaeisvandi A, Amerzadeh M, Hajiabadi F, Hosseinkhani Z. Prevalence, modifiable and risk factors for depression, anxiety and stress (DASS) among elders in the northwest of Iran. 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR\u0026ouml;nnlund M, \u0026Aring;str\u0026ouml;m E, Adolfsson R, Carelli MG. Perceived Stress in Adults Aged 65 to 90: Relations to Facets of Time Perspective and COMT Val158Met Polymorphism. Front Psychol. 2018; 9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad MH, Salleh R, Siew Man C et al. Malnutrition among the Elderly in Malaysia and Its Associated Factors: Findings from the National Health and Morbidity Survey 2018. J Nutr Metab. 2021; 2021:6639935.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDent E, Wright OR, Woo J, Hoogendijk EO. Malnutrition in older adults. Lancet. 2023;401(10380):951\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei K, Nyunt MSZ, Gao Q, Wee SL, Ng T-P. Frailty and malnutrition: related and distinct syndrome prevalence and association among community-dwelling older adults: Singapore longitudinal ageing studies. J Am Med Dir Assoc. 2017;18(12):1019\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDamayanthi HDWT, Moy FM, Abdullah KL, Dharmaratne SD. Prevalence of malnutrition and associated factors among community-dwelling older persons in Sri Lanka: a cross-sectional study. BMC Geriatr. 2018;18(1):1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaish K, Patra S, Chhabra P. Nutritional status among elderly: A community-based cross-sectional study. Indian J Public Health. 2020;64(3):266\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBesora-Moreno M, Llaurad\u0026oacute; E, Tarro L, Sol\u0026agrave; R. Social and economic factors and malnutrition or the risk of malnutrition in elderly individuals: a systematic review and meta-analysis of observational studies. Nutrients. 2020;12(3):737.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoong HT. Nutritional status in relation to depressive symptoms among Chinese elderly in Malaysia. Faculty of Medicine and Health Sciences, Universiti Putra Malaysia; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKvamme J-M, Gr\u0026oslash;nli O, Florholmen J, Jacobsen BK. Risk of malnutrition is associated with mental health symptoms in community living elderly men and women: The Troms\u0026oslash; Study. BMC Psychiatry. 2011;11(1):1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePayahoo L, Khaje-Bishak Y, Pourghassem Gargari B, Kabir-Alavi MB, Asgharijafarabadi M. Assessment of Nutritional and Depression Status in Free-Living El-derly in Tabriz, Northwest Iran. Health Promot Perspect. 2013;3(2):288\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcciai F, Hardy M. Depression in later life: A closer look at the gender gap. Soc Sci Res. 2017;68:163\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu L, Shen H, Tan L, et al. Prevalence and factors associated with anxiety and depression among community-dwelling older adults in Hunan, China: a cross-sectional study. BMC Psychiatry. 2023;23(1):107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReynolds CF, Jeste DV, Sachdev PS, Blazer DG. Mental health care for older adults: recent advances and new directions in clinical practice and research. World Psychiatry. 2022;21(3):336\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonroe DC, McDowell CP, Kenny RA, Herring MP. Dynamic associations between anxiety, depression, and tobacco use in older adults: Results from The Irish Longitudinal Study on Ageing. J Psychiatr Res. 2021;139:99\u0026ndash;105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHossain MN, Lee J, Choi H, Kwak YS, Kim J. The impact of exercise on depression: how moving makes your brain and body feel better. Phys Act Nutr. 2024;28(2):43\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSegel-Karpas D, Ayalon L, Lachman ME. Retirement and depressive symptoms: A 10-year cross-lagged analysis. Psychiatry Res. 2018;269:565\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang H, Tu S, Sheng J, Shao A. Depression in sleep disturbance: A review on a bidirectional relationship, mechanisms and treatment. J Cell Mol Med. 2019;23(4):2324\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Aging, Mental health, Nutritional status, Depression, Anxiety","lastPublishedDoi":"10.21203/rs.3.rs-6433768/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6433768/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMalaysia\u0026rsquo;s elderly population is projected to reach 15% by 2030. Nutritional status significantly impacts elderly health, with malnutrition linked to depression, anxiety and stress. This study assessed mental health problems and their associations with nutritional status among elderly individuals attending community health screenings.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted among elderly participants selected through convenience sampling from four community health screenings in Kuala Selangor District (June\u0026ndash;October 2024). Sociodemographic details, nutritional status (Mini Nutritional Assessment-Short Form, MNA\u0026reg;-SF), and mental health problems (Depression, Anxiety, and Stress Scale-21, DASS-21) were obtained. Multiple logistic regression was used to examine the associations between nutritional status and mental health problems.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 361 elderly participants, the prevalence rates of depression, anxiety, and stress were 12.7%, 9.4%, and 4.2%, respectively. At risk of malnutrition significantly increased the odds of depression (AOR\u0026thinsp;=\u0026thinsp;10.94, 95% CI\u0026thinsp;=\u0026thinsp;4.35\u0026ndash;27.52), anxiety (AOR\u0026thinsp;=\u0026thinsp;11.16, 95% CI\u0026thinsp;=\u0026thinsp;4.10\u0026ndash;30.41), and stress (AOR\u0026thinsp;=\u0026thinsp;5.03, 95% CI\u0026thinsp;=\u0026thinsp;1.55\u0026ndash;16.34), whereas malnutrition increased the odds of depression (AOR\u0026thinsp;=\u0026thinsp;45.61, 95% CI\u0026thinsp;=\u0026thinsp;13.20\u0026ndash;157.61) and anxiety (AOR\u0026thinsp;=\u0026thinsp;17.80, 95% CI\u0026thinsp;=\u0026thinsp;4.93\u0026ndash;64.32). Female sex and less social engagement were associated with depression. Being a smoker or ex-smoker and having less physical activity were associated with anxiety. Finally, retirees and having sleeping issues were associated with both depression and anxiety.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eNutritional status is a key determinant of depression, anxiety, and stress in elderly individuals. Early identification, further assessment, and targeted interventions are crucial for improving mental well-being in this population\u003c/p\u003e","manuscriptTitle":"The Association Between Nutritional Status and Mental Health Status and Its Associated Factors Among Elderly Attending Community Health Screenings: A Cross Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-22 10:51:06","doi":"10.21203/rs.3.rs-6433768/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-13T15:07:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-29T05:46:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-27T13:00:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111006616967783879752352176756803041853","date":"2025-05-26T10:50:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245782897090833386438152867860503644484","date":"2025-05-24T03:39:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-20T04:34:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217303216833338499256785158707036972602","date":"2025-05-16T08:17:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6858572963590143220414255535315488731","date":"2025-05-16T08:03:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-16T07:37:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-29T22:51:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-17T09:55:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-17T09:52:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-04-12T09:45:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bdd55473-ecba-4d0d-b732-b344012b413e","owner":[],"postedDate":"May 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-06T16:01:26+00:00","versionOfRecord":{"articleIdentity":"rs-6433768","link":"https://doi.org/10.1186/s12877-025-06394-7","journal":{"identity":"bmc-geriatrics","isVorOnly":false,"title":"BMC Geriatrics"},"publishedOn":"2025-09-29 15:57:19","publishedOnDateReadable":"September 29th, 2025"},"versionCreatedAt":"2025-05-22 10:51:06","video":"","vorDoi":"10.1186/s12877-025-06394-7","vorDoiUrl":"https://doi.org/10.1186/s12877-025-06394-7","workflowStages":[]},"version":"v1","identity":"rs-6433768","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6433768","identity":"rs-6433768","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}