INTRODUCTION

Many countries, including Indonesia, are facing a growing aging population, which is increasing the demand for healthcare services, long-term care, and treatment of age-related diseases [1]. Although Indonesia has a predominantly young population, the number of older adults is rising rapidly. In response, the government has implemented healthcare programs, social security initiatives, and measures aimed at improving the well-being of older adults [2]. However, demographic trends within the country vary due to urbanization, economic development, and cultural influences. Regional governments are tackling the challenges of an aging population by enhancing healthcare infrastructure, community support, and social programs [3]. Residence affects older adults’ health, physical, social, economic, psychological, and spiritual well-being. Research shows quality of life (QoL) differs between rural and urban populations. QoL is a complex and multidimensional concept, resulting in varied definitions across studies [4].

The QoL of older adults in rural areas is lower than those in urban counterparts, impacted by factors like gender, education, employment, economic status, health services, and infrastructure for social activities [5]. QoL can be assessed through both objective and subjective measures. Each approach requires a multi-dimensional analysis of life domains and their interactions, as they all contribute to QoL. Krishnappa et al. [5] found that older adults in rural areas have lower QoL than those in urban areas, especially in physical activity (PA), psychology, social interactions, and environment. Older adults in rural areas experience declines in sensory, cognitive, and musculoskeletal functions which lead to balance issues, poor fitness, and reduced PA. These declines negatively affect QoL and, over time, impact mental health, vitality, and well-being [6,7]. Depression is common among older adults in rural areas, often caused by loneliness from family members moving to urban centers [7]. A meta-analysis revealed that low PA in older adults is associated with cardiovascular issues and a fear of falling (FoF), stemming from decreased confidence in their balance. Moreover, a lower socioeconomic status is correlated with reduced PA, particularly in rural areas [8].

The QoL of older adults in rural and urban areas remains a topic of debate, with numerous studies presenting varied opinions on the factors influencing QoL among this demographic. It is commonly believed that older adults in rural areas experience a greater decline in function compared to their urban counterparts. The World Health Organization (WHO) has emphasized QoL as a crucial indicator of human well-being. Similarly, the International Classification of Functioning, Disability and Health (ICF) indicates that QoL is affected by body structure and function, environmental factors, and personal factors. Therefore, this study aimed to explore the relationship between potential factors, as outlined by the ICF model, and QoL in older populations in rural and urban settings.

METHODS

This study utilized a cross-sectional survey design to assess community-dwelling older adults residing in Surakarta City, Central Java Island, Indonesia.

Participants

Participants aged 60 years or older were recruited from rural and urban areas of Surakarta City, located in Central Java Province, Indonesia. Recruitment was conducted through registration in the Chronic Disease Management Program (Prolanis) of the Ministry of Health. To screen participants, inclusion and exclusion criteria were applied, which included: (1) absence of severe pain in the lower extremities, as indicated by a Numeric Rating Scale score of less than 5 points; (2) no disabilities stemming from musculoskeletal, neurological, or cardiovascular conditions that could limit functional mobility; and (3) the ability to communicate and understand simple commands. Participants with serious medical conditions that interfered with their routine daily activities were excluded.

Instrument

Personal factors

Demographic data were collected, including age, gender, weight, height, education level (uneducated, primary school, high school, and university), marital status (single, married, widowed), presence of hypertension (HT; yes/no), hemoglobin levels (g/dL), glycemic levels (mg/dL), hyperlipidemia levels (mg/dL), visual function as assessed by the Snellen Chart test, and sleep duration (hr/day).

HT was diagnosed if the blood pressure was greater than 140/90 mmHg [9]. Participants were instructed to fast for 8 hours before the testing of glycemic, total cholesterol (TC), and hemoglobin levels. Approximately 5 mL of blood was drawn from an intravenous vessel in the forearm by a qualified medical laboratory technician, collected in a tube, and stored at 4°C to preserve its quality. The samples were then evaluated and analyzed at the integrated laboratory of Muhammadiyah University of Surakarta.

Participants were directly interviewed about their history of falls over the past year, categorized as either fallen (once or more within the year) or non-fallen. FoF was assessed using the abbreviated version of the Falls Efficacy Scale-International Indonesian version. This scale demonstrated excellent content validity, with an item content validity index ranging from 0.85 to 1.00 and a sum score content validity index of 0.93 [10].

Impairments

The cognitive level was assessed using the Indonesian version of the Montreal Cognitive Assessment, a highly reliable and valid tool for screening cognitive impairment, with agreement shown by a kappa value of 0.82 [11].

The Indonesian version of the Geriatric Depression Short-Form Scale was used to identify depression. It had excellent internal consistency reliability (Cronbach’s alpha=0.80, p<0.05) [12].

Lower extremity muscle strength was measured by the five-time sit-to-stand test (5xSST). This test is widely used with older adults, and a lower score indicates the presence of sarcopenia. The test measures the time, in seconds, it takes for an individual to sit and stand five times from a chair of standard height. This test was conducted in a single session [13].

Activity limitations

The level of PA in older adults was measured by the Physical Activity Scale for the Elderly in the Indonesian version. It has excellent internal consistency reliability (Cronbach’s alpha=0.95) and fair to good test-retest reliability (ICC3,1=0.50 to 0.86, p<0.05) [14].

Cardiovascular endurance was evaluated using the 2-minute walking test. Participants were instructed to walk as far as possible within 2 minutes. They were allowed to stop if they experienced discomfort, although the timer continued to run. The distance covered, measured in meters, indicated the level of cardiovascular endurance, with longer distances representing higher endurance [15].

The static balance was assessed using the Modified Clinical Test of Sensory Interaction on Balance (mCTSIB). The older participants were required to maintain their posture for 30 seconds under each of four conditions: standing on a firm surface with eyes open, standing on a firm surface with eyes closed, standing on foam with eyes open, and standing on foam with eyes closed. In all conditions, participants were instructed to stand with their feet together and arms crossed. A participant was considered to have failed the test if they moved their arms, took a step, leaned back, could not maintain an upright posture, slipped, or, in the eyes-closed conditions, opened their eyes [16]. The timed up-and-go (TUG) test was utilized to evaluate dynamic balance. Participants were instructed to start by sitting in a chair, then stand and walk 3 meters, turn around, walk back, and sit down again. The time taken to complete this sequence was recorded in seconds [17].

Quality of life

The Indonesian version of the WHOQoL-BREF was utilized to assess QoL. It demonstrated excellent discriminant and construct validity, along with good internal consistency among older adults. This instrument includes 26 items across four domains: physical, social, functional, and emotional. Each domain is scored on a scale ranging from 0 to 100 [18].

Statistical Analysis

The sample size in this study was estimated using a formula based on predictive sample size. To establish “rules of thumb,” a minimum of 10 participants per predictor was required [19]. With 13 predictors, we initially required 130 participants. To account for potential dropouts, we increased this number by 10%. Consequently, the total number of participants for each area involved 143 older adults.

Data were analyzed using the SPSS version 23.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were employed to describe both continuous and categorical data, which were presented as mean and standard deviation (mean±SD) and number and percentage (n, %), respectively. All variables were categorized according to the ICF model.

To assess the normality of the data, the Shapiro-Wilk test was used, indicating that the distributions of the variables approximated a normal distribution. The t-test was employed to compare variables between groups. Spearman’s rank correlation analysis was utilized to identify variables correlated with QoL, which were then selected for model formulation. Multivariate linear regression using the enter method was applied to develop a predictive model of QoL. Additionally, tests for assumptions were conducted to ensure the suitability of the data for multiple linear regression analysis; these included tests for multivariate normality and multicollinearity in both groups. The significance level was set at p-value<0.05.

Ethics Statement

The protocol received approval from the Clinical Research Ethics Committee of the Faculty of Medicine, Universitas Muhammadiyah Surakarta with approval number 4258/B.2/KEPK-FKUMS/IV/2022. Prior to data collection, all participants were informed about the study’s protocol and provided their signed informed consent.

RESULTS

There were significant differences between older adults in rural and urban areas regarding age, BMI, education level, hemoglobin level, TC, visual function, sleep duration, faller status, and FoF (Table 1). The performance of older adults in rural areas was lower than that in urban areas. There were no differences in gender, marital status, or glycemic levels between the two areas. The rural older adults had slightly higher numbers of individuals with HT, hyperlipidemia, high glycemic levels, low visual function, faller status, moderate FoF, and shorter sleep durations, although these differences were not statistically significant.

Furthermore, older adults in urban areas exhibited better cognitive function, lower extremity muscle strength, PA, endurance, and both static and dynamic balance. However, there were no significant differences in categorical cognitive function, depression, lower extremity muscle strength, and PA between the two areas. Endurance, static balance, and all domains of the mCTSIB were reported to be lower in older adults living in rural areas. Notably, urban older adults scored higher on the total WHOQOL-BREF and across all its domains (physical, social, functional, and emotional) compared to their rural counterparts (Table 2).

Table 3 presents the correlation analysis between QoL and covariates in rural and urban settings. This model was utilized to identify variables for the multiple linear regression analysis. The data met the assumptions of collinearity and multivariate normality required for multiple linear regression. Multiple linear regression identified the primary factors influencing QoL in both areas (Table 4). In rural areas, one factor was associated with QoL in older adults, whereas in urban areas, two factors were identified. Cardiovascular endurance emerged as the strongest factor associated with QoL in rural areas (p=0.050). In urban areas, gender was the strongest factor affecting QoL (p<0.001), followed by hemoglobin level (p=0.015), age (p<0.001), and cognitive function (p=0.016).

DISCUSSION

The level of QoL, including its domains and all variables, was lower in rural areas than in urban areas in this study. This finding is consistent with observations from other countries across Asia [20]. Metabolic diseases such as HT, diabetes, and hyperlipidemia are prevalent among older adults in both settings, with slightly higher rates observed in rural areas. These conditions rise with age and remain a global health challenge despite medical advancements [21]. Understanding these conditions is crucial for promoting well-being and preventing disease burden in the aging population. Furthermore, depression was not a major issue in this study, as only a small percentage of older adults were found to be depressed. Both areas face significant challenges with visual function, but rural older adults experience higher impairment. Visual impairment affects daily activities and increases the risk of falls [22].

Cognitive function appeared to be low in both areas studied. Mild cognitive impairment, widely researched as a precursor to dementia in older adults, affects 15% to 20% of this population [23]. Cognitive function is essential for QoL. Normal aging can alter cognitive functions, impacting memory, attention, concentration, and execution abilities. Moreover, cognitive decline can compromise the independence of older adults in personal and social activities [6]. Unfortunately, the study revealed very low endurance capacity in both groups. Improved endurance, however, helps reduce the risk of dementia and is associated with maintaining normal gait speeds and the absence of mobility difficulties [24].

Active older adults often exhibit good postural balance control and a low FoF during certain activities. However, this study found a high percentage of older participants with FoF, despite a low faller status in both groups. A study identified balance performance as the strongest factor contributing to social participation among older adults [25]. Poor balance control is also believed to play a role in the onset of non-contact lower limb injuries. Approximately 28% to 35% of people aged 65 and older fall each year, with this percentage increasing to 32–42% for those over 70 years of age. This means falls escalate with age and frailty [26]. In this study, urban older participants performed better on the total mCTSIB score, which includes conditions 1 to 4, as well as on the TUG test, compared to their rural counterparts. Notably, rural older adults exhibited a higher fall rate. The mCTSIB assesses balance under different sensory conditions, with failure indicating balance deficits. Poor TUG performance suggests mobility limitations and a higher fall risk [27,28].

de Arruda et al. [29] noted that no studies were found linking a high risk of falls to rural environments. Urban older populations are at a higher risk of falling and generally have poorer muscle strength compared to their rural counterparts. Each area possesses distinct characteristics. Rural areas are frequently associated with lifelong physical labor, which may influence lower limb functionality [29]. Additionally, rural environments often feature unsafe living conditions that adversely affect daily activities and triple the risk of falls among older adults [30].

Furthermore, rural older adults exhibit poorer PA levels compared to their urban counterparts. A previous study highlighted significant barriers to PA in both settings, primarily due to social and environmental factors. Seventy percent of participants cited environmental factors as barriers, with many pointing out the scarcity of free PA facilities in rural areas compared to urban ones [31]. Access to facilities is a key factor in PA behavior, with rural residents often reporting this barrier, indicating PA disparities, with rural residents facing more barriers, where limited support, also hinders PA participation, which is vital for community and social connections [32].

Multiple linear regression using the enter method (Table 4) reveals factors associated with QoL among older populations in rural and urban areas. Table 4 indicates cardiovascular endurance is the most significant factor affecting QoL in rural older populations. This type of endurance encompasses both cardiovascular and muscular stamina. Cardiovascular endurance supports daily activities and lowers heart disease risk. A study showed differences in activities patterns between rural and urban older adults [33]. Activities common in rural lifestyles, such as gardening, walking, or manual labor, enhance cardiovascular endurance. Endurance is crucial for maintaining mobility, independence, and muscle strength, which contribute to improved physical fitness, QoL, social participation, and mental health [34].

In urban areas, gender most influences older adults’ QoL, followed by hemoglobin, age, and cognition. Research shows men and women view QoL differently, without one being superior QoL. Social support networks, shaped by gender roles, tend to be stronger and more extensive among women, who often receive greater support from family and friends [35]. Gender-based economic gaps impact older adults’ finances, with women facing higher risks due to wage gaps. Healthcare access and societal norms also affect health and QoL. Urban work culture often leaves them alone or in care, increasing loneliness and lowering QoL. Gender further shapes loneliness and social isolation effects on mental well-being [36].

Hemoglobin is essential for transporting oxygen and maintaining overall health. In older adults, hemoglobin levels often decrease, leading to anemia. This condition can result from nutritional deficiencies, chronic diseases, or other medical issues. Lower hemoglobin levels are associated with symptoms such as fatigue, weakness, and diminished daily functioning, all of which adversely affect QoL [37]. Aging affects QoL through declining health, sensory impairments, and cognition. Chronic illness, mobility loss, and isolation worsen it, though some maintain high QoL through lifestyle and social engagement [38]. Urban environments provide social and cultural activities that boost cognitive health and well-being of older adults. However, limited green spaces and weak community ties can lead to isolation that affects QoL [39].

QoL varies widely among individuals due to its complex factors. Tailoring interventions to meet the specific needs of older adults based on gender can improve QoL in urban areas. Considering intersectionality is key to understanding the challenges faced by older adults. Although this study identified gender as a significant factor in QoL, it did not explore the differences in QoL profiles between men and women.

Notes

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

This study was fully funded by Hibah Integrasi Tridharma (HIT) Universitas Muhammadiyah Surakarta with number 950/A.2-III/FIK/IV/2022.

Acknowledgements

The authors would like to express gratitude to all participants and personnel from the clinical settings involved in data collection.

Author Contributions

Conceptualization: Komalasari DR. Data curation: Komalasari DR, Jalayondeja C. Formal analysis: Komalasari DR, Jalayondeja C, Jalayondeja W. Funding acquisition: Komalasari DR. Methodology: Komalasari DR, Jalayondeja C. Project administration: Komalasari DR, Romadon YA. Visualization: Komalasari DR, Jalayondeja C, Jalayondeja W, Romadon YA. Writing – original draft: Komalasari DR. Writing – review & editing: Jalayondeja C, Jalayondeja W, Romadon YA.

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Figure & Data

References

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