Introduction

Population aging is one of the most significant demographic transformations of the 21st century. Advances in healthcare, sanitation, and socioeconomic development have increased life expectancy worldwide, resulting in a growing proportion of older adults [1]. While this reflects public health progress, it also poses challenges, as older adults are more susceptible to chronic diseases, functional decline, and reduced quality of life [2]. Promoting sustainable health behaviors—including regular physical activity, balanced nutrition, sufficient sleep, stress management, adherence to medications, preventive screenings, and avoidance of harmful behaviors—is essential for maintaining health and well-being in later life [3-8].

Health behaviors in older adults are influenced by multiple interrelated determinants. Socio-demographic factors such as age, sex, education, income, marital status, and residential location affect access to health information, resources, and social support [9,10]. Health literacy, defined as the ability to access, understand, evaluate, and apply health information, is a critical cognitive determinant, influencing self-management, treatment adherence, and participation in preventive behaviors [11-16]. Health perception, or an individual’s subjective evaluation of their health, also plays a key role by shaping motivation to adopt preventive or corrective behaviors [16-19].

In Southeast Asia, inadequate health literacy remains a public health concern, with socioeconomic and urban–rural disparities affecting older adults’ engagement in health-promoting behaviors [20]. In Thailand, while studies have demonstrated associations between health literacy, health behaviors, and quality of life among the elderly [19,21], evidence at the provincial or peri-urban level is limited. Pathum Thani Province, a peri-urban area adjacent to Bangkok, represents a transitional context with mixed urban and semi-rural communities, variable socioeconomic conditions, and unequal healthcare access. Data from this setting are needed to understand local determinants of elderly health behaviors and to inform targeted interventions [2,5,8,12,15].

This study aims to examine how socio-demographic factors, health literacy, and health perception influence health-related lifestyle behaviors among older adults, using a peri-urban population in Thailand as a case study. This study applies the Health Belief Model as a guiding framework to explain how individual beliefs influence engagement in health-related lifestyle behaviors. Within this framework, health literacy is conceptualized as a modifying factor that enhances individuals’ ability to understand health information and influences their beliefs. Health perception of illness reflects core HBM constructs, including perceived susceptibility and perceived severity. These perceptions, in turn, shape the likelihood of engaging in health-related behaviors, consistent with the model’s proposition that health actions are influenced by perceived threats and benefits. Therefore, this study hypothesizes that health literacy and illness perception are associated with health-related behaviors among older adults. Using a cross-sectional survey design, the findings are expected to provide empirical evidence to inform targeted health promotion strategies, enhance elderly self-management, and offer insights applicable to aging populations in other international contexts, supporting the development of sustainable health behaviors globally.

Methods

Study design

This study employed a cross-sectional survey design to assess the impact of health literacy on health-related lifestyle behaviors among elderly individuals in Pathum Thani, Thailand.

Participants and setting

The study population consisted of elderly individuals aged 60 years and above residing in Pathum Thani, Thailand. A multistage sampling approach was employed to enhance representativeness. In the first stage, community centers, primary healthcare facilities, and elderly care programs across selected districts of Pathum Thani were identified in collaboration with local health authorities. In the second stage, eligible elderly individuals were recruited using systematic sampling from attendance lists and service records at each selected site. The number of participants recruited from each site was proportionally allocated based on the estimated number of elderly service users to ensure adequate representation. The inclusion criteria were elderly individuals aged 60 years and above who were able to understand and communicate in Thai and who voluntarily agreed to participate in the study. The exclusion criteria included individuals who withdrew from the study, those with diagnosed cognitive impairments affecting their ability to provide reliable responses, and those with severe physical or medical conditions that prevented participation in the survey process.

Sample size

The sample size was determined using Yamane’s formula for finite populations. Based on the total population of older adults aged 60 years and above in Pathum Thani Province (N=56,763), the required sample size was calculated at a 95% confidence level and a 5% margin of error. A population proportion (p) of 0.50 was assumed to ensure maximum variability and obtain the most conservative estimate [22]. The calculation yielded a minimum required sample size of 398 participants. To account for potential non-response and incomplete data, additional participants were recruited. After data collection and data screening, a total of 440 respondents were included in the final statistical analysis.

Research instruments

The study employed four primary research instruments: (1) a socio-demographic questionnaire, (2) the Elder Health Literacy Questionnaire (EHLQ), (3) the Health Perception of Illness Questionnaire, and (4) the Health-Related Behavior Questionnaire.

1. Socio-demographic questionnaire

The socio-demographic questionnaire was developed by the researchers to collect background characteristics relevant to elderly populations. The form included items on age, sex, education level, marital status, occupation (current or former), Sufficiency of Income, weight and height (used to calculate body mass index: BMI), presence of chronic diseases, and sources of health information. These variables were selected based on prior evidence identifying socio-demographic characteristics as structural determinants of health behaviors and health literacy among older adults [1,2], and were treated as independent variables in the analysis. Variables related to pregnancy history were excluded as they were not applicable to the elderly study population.

2. Elder Health Literacy Questionnaire (EHLQ)

The EHLQ used in this study was adapted from the Health Literacy Questionnaire (HLQ) developed by Osborne et al. [3]. The instrument was translated into Thai using a forward–backward translation process to ensure semantic and conceptual equivalence with the original version. The Thai version was reviewed by experts in public health and gerontology to confirm clarity, cultural appropriateness, and relevance to elderly populations.

The adapted EHLQ consists of 29 items across four domains: accessing, understanding, appraising, and applying health information. Each item is rated on a 5-point Likert scale, with total scores ranging from 29 to 145, where higher scores indicate higher health literacy. As no standardized Thai cutoff scores are available, health literacy levels were classified using an equal-interval approach [4]. The Thai version demonstrated excellent internal consistency, with an overall Cronbach’s alpha coefficient of .91 and subscale coefficients ranging from .82 to .88 [5], supporting its reliability for use in this study.

3. Health perception of Illness questionnaire

The Health Perception of Illness Questionnaire used in this study was a researcher-developed instrument. It was constructed based on theoretical concepts of perceived health status and illness representation derived from health psychology and the Self-Regulation Model (Common-Sense Model) proposed by Leventhal and colleagues [6], as well as related frameworks addressing cognitive and emotional perceptions of illness [7]. No existing standardized Thai instrument was identified that specifically measured perceived health understanding in the context of elderly health management; therefore, the questionnaire was developed to align with the study objectives and the theoretical foundations described above. The instrument was designed to assess participants’ subjective understanding of their health conditions, perceived severity of illness, and confidence in managing their health problems. It consists of 8 items rated on a 0–10 scale, with total scores ranging from 0 to 80. Content validity was evaluated by three experts, yielding I-CVI values ranging from .80 to 1.00, indicating acceptable validity [11]. The Cronbach’s alpha coefficient in this study was .84 [5], demonstrating acceptable internal consistency. [11].

4. Health-related behavior questionnaire

The Health-Related Behavior Questionnaire was developed to assess lifestyle behaviors relevant to healthy aging among older adults, based on Pender’s Health Promotion Model, Social Cognitive Theory, and the WHO healthy aging framework [8-10]. The development process included literature review, item generation, expert validation, and construct validation. The final instrument consists of 32 items across three domains: exercise (12 items), health behavior modification (12 items), and stress management (8 items). The health behavior modification domain integrates dietary practices, medication adherence, preventive health behaviors, and risk behavior avoidance. Content validity was confirmed by experts, with an S-CVI/Ave of .85 [11], indicating good validity. Construct validity was supported by exploratory factor analysis (KMO=.89; Bartlett’s test p<.001), with three factors explaining 58.72% of the variance and factor loadings exceeding .50 [22]. Reliability testing showed high internal consistency, with a Cronbach’s alpha of .90 in the main study and domain-specific values ranging from .83 to .88 [22].

Theoretical framework and variable operationalization

Guided by the Health Belief Model, this study operationalizes its key constructs by aligning study variables with theoretical components of the model. Health literacy is treated as a modifying factor that influences individuals’ capacity to access, understand, and apply health information, thereby shaping their health beliefs and decision-making processes. Health perception of illness is conceptualized as reflecting core HBM constructs, particularly perceived susceptibility and perceived severity, representing individuals’ cognitive appraisal of their health condition. Health-related lifestyle behaviors are considered the outcome variable, capturing the extent to which individuals engage in preventive and health-promoting actions. Together, these variables provide a theoretically grounded framework for examining how cognitive and informational factors influence health behaviors among older adults.

Data collection

This study was conducted between April and May 2025. Participants were recruited by registered nurses at local community centers and healthcare facilities based on predefined inclusion criteria. Prior to participation, all eligible individuals were provided with detailed information regarding the study objectives, procedures, and the voluntary nature of participation. Written informed consent was obtained from all participants before data collection.

Data analysis

Socio-demographic data were analyzed using descriptive statistics, including frequency, percentage, mean, median, and standard deviation, as appropriate to the level of measurement. Differences in Health-Related Behavior across socio-demographic subgroups were examined using independent samples t-tests and one-way analysis of variance (ANOVA), depending on the number of comparison groups. Post hoc analyses were performed when statistically significant differences were identified [23]. To address the primary objective of identifying predictors of Health-Related Behavior, multiple linear regression analysis was employed as the main analytical approach. The dependent variable was Health-Related Behavior, while the independent variables included socio-demographic factors, Elder Health Literacy, and Health Perception of Illness.

Categorical independent variables were transformed into dummy variables prior to inclusion in the regression model to ensure appropriate statistical estimation. Assumptions underlying multiple linear regression were assessed before model interpretation. Autocorrelation of residuals was examined using the Durbin–Watson statistic. Normality of residuals was assessed using the Shapiro–Wilk test and visual inspection of histograms and normal probability plots. Multicollinearity among independent variables was evaluated using tolerance values and variance inflation factors (VIF). Variables that met the statistical assumptions were retained in the final regression model [23]. In cases where assumptions were marginally violated, appropriate data screening and diagnostic procedures were applied to improve model fit. All statistical analyses were conducted using SPSS (Statistical Package for the Social Sciences). Statistical significance was set at p<.05.

Ethical considerations

This study was conducted in April–May 2025 after obtaining approval from the Institutional Review Board (IRB) of Kanchanaphisek College of Medical Technology and Public Health (IRB No. #KMPHT-68010002). The study was conducted in accordance with the Declaration of Helsinki.

Results

Health-related behavior by participants' characteristics

The results from Table 1 indicate that several socio-demographic factors were significantly associated with Health-Related Behavior (HLB) among the elderly. Significant differences in HLB scores were observed across marital status (p<.01), education level (p=.03), occupation (p=.02), healthcare rights (p<.01), and living arrangement (p<.01). Post-hoc analyses revealed that married participants had lower HLB scores compared to single and widowed/divorced/separated individuals (p<.01). Participants with primary education showed higher HLB scores than those with a bachelor’s degree or equivalent (p<.05). Unemployed participants had higher HLB scores compared to employed individuals (p<.05). In terms of healthcare rights, participants under social insurance had significantly lower HLB scores than those covered by government welfare and reimbursable schemes (p<.01). Additionally, living arrangement was significantly associated with HLB, with differences observed between those living with family and those living with others (p<.05). No significant differences in HLB were found for sex, religion, income level, or income sufficiency (p>.05).

Correlation between health perception of illness

Table 2 presents the descriptive statistics and correlations between study variables and Health-Related Behavior (HLB). Participants’ mean age was 68.04 years (SD=4.36). Age showed a statistically significant positive correlation with HLB (r=.69, p=.02). Weight (r=-.10, p=.04) and height (r=-0.19, p<.01) were negatively correlated with HLB, although the correlations were weak. BMI was not significantly correlated with HLB (r=.01, p=.84).

Health perception of illness was significantly negatively correlated with HLB (r=-.19, p<.01). Health literacy also demonstrated a statistically significant negative correlation with HLB (r=-.14, p<.01). Overall, age, weight, height, health perception of illness, and health literacy were significantly associated with HLB, while BMI was not significantly correlated.

Health literacy, and health-related behavior, and factors associated with health-related behavior

Tables 3-4 presents the results of the hierarchical multiple regression analysis examining predictors of Health-Related Behavior (HLB). In Step 1, demographic variables (gender, marital status, religion, education level, occupation, monthly family income, income sufficiency, healthcare rights, and living arrangement) were entered into the model. These variables collectively explained 30% of the variance in HLB (R²=.30, Adjusted R²=.27) (Table 4). Significant predictors in this step included marital status (married), higher income categories, income sufficiency, and living with family. In Step 2, health literacy was added to the model. The inclusion of health literacy significantly improved the model, explaining an additional 4% of the variance (ΔR²=.04, p<.01). Health literacy emerged as a significant positive predictor of HLB (β=.16, p<.005), indicating that higher health literacy was associated with more favorable health-related behaviors (Table 3). In Step 3, health perception of illness was entered into the model. This variable further increased the explained variance by 4% (ΔR²=.04, p<.001). Health perception of illness was also a significant predictor (β=.12, p<.001) (Table 3). The final model explained 38% of the variance in HLB (R²=.38, Adjusted R²=.35) (Table 4). Overall, the hierarchical analysis demonstrates that beyond socio-demographic factors, both health literacy and illness perception independently and incrementally contribute to explaining health-related behaviors among older adults.

Discussion

This study examined the associations between socio-demographic factors, health literacy, illness perception, and health-related behavior (HLB) among older adults in Pathum Thani, Thailand, using a hierarchical regression approach. The main findings indicate that socio-demographic factors were significantly associated with HLB in the initial model, while health literacy and illness perception contributed additional explanatory power in subsequent models. Among all predictors, health literacy and illness perception remained significant after controlling for demographic variables, highlighting their independent roles. However, due to the cross-sectional design, these findings should be interpreted as associations rather than causal relationships.

The significant associations between socio-demographic factors and HLB are consistent with previous studies emphasizing the role of structural and socio-economic conditions in shaping health behaviors [18,20]. In particular, the positive association between age and HLB may reflect increased health awareness and accumulated experience in managing health among older adults [20,21,24,25]. Similarly, the observed relationships with income level and income sufficiency align with prior evidence suggesting that individuals with greater economic resources have better access to healthcare, nutritious food, and health-promoting opportunities [26–28]. The associations with marital status and living arrangements also support existing literature highlighting the importance of social support and social stability in influencing health behaviors among older populations [28]. These similarities suggest that the findings are consistent with broader evidence across different contexts.

Health literacy demonstrated a significant independent association with HLB, consistent with previous research showing that individuals with higher health literacy are more likely to engage in preventive and health-promoting behaviors [29-32]. This similarity may be explained by the role of health literacy in enabling individuals to access, understand, and apply health information effectively. In the context of this study, the use of a culturally adapted Thai version of the instrument may have enhanced its ability to capture functional health competencies relevant to older adults, thereby strengthening the observed association.

Illness perception also contributed significantly to HLB, which is in line with prior studies indicating that individuals’ perceptions of their health condition influence their behavioral responses [33-35]. A possible explanation is that individuals who perceive their health status more clearly or perceive greater health risks may be more motivated to adopt healthier behaviors. This finding reinforces the importance of cognitive and psychological factors in shaping health-related behaviors among older adults.

Interestingly, education did not show a consistent independent association with HLB in the final model, which differs from some previous studies [36]. One possible explanation is the relatively homogeneous distribution of educational levels in the sample, which may have limited variability and reduced its statistical effect. Additionally, health literacy may serve as a more direct and functional measure of individuals’ ability to manage health-related information and behaviors than formal education level alone [37,38]. This finding suggests that interventions focusing on improving health literacy may be more effective than those targeting education level per se.

Thus, this study adds to the existing body of evidence by demonstrating that, beyond socio-demographic factors, health literacy and illness perception are important correlates of health-related behavior among older adults in a Thai context. The findings highlight the need to consider both structural and cognitive factors when designing interventions to promote healthy behaviors in aging populations.

Policy and practice implications

While the findings reflect associations rather than causal relationships, they suggest several practical considerations. The observed relationships between income-related factors and HLB highlight the continued importance of financial security and social protection systems for older adults [38]. The associations involving health literacy suggest that accessible and culturally appropriate health education initiatives may be relevant in this population [36]. Furthermore, because illness perception was associated with HLB, supportive interventions that address how older adults understand and interpret their health conditions may be beneficial [30,35-37].

Taken together, the results indicate that health-related behavior among older adults is associated with both structural socio-demographic factors and individual-level cognitive and perceptual factors. Future longitudinal research is needed to clarify potential directional pathways among these variables.

Limitations

The study's cross-sectional design limits the ability to infer causality between health literacy and health-related lifestyle behaviors. As data were collected at a single point in time, it is difficult to determine whether health literacy influences lifestyle behaviors or if lifestyle behaviors impact health literacy. Furthermore, the use of self-reported data introduces the potential for bias, as participants may provide responses that reflect social desirability or may misremember certain details, leading to recall inaccuracies [22]. Additionally, the convenience sampling method, while practical, may not fully represent the diversity of the elderly population in Pathum Thani, thus limiting the generalizability of the findings to a broader population. Another limitation is the potential for unmeasured confounding variables, such as socio-economic status, mental health conditions, or access to healthcare, which could influence both health literacy and lifestyle behaviors but were not directly addressed in the study. These factors could impact on the validity of the study's conclusions and should be considered in the interpretation of the results. Future studies employing longitudinal designs, objective measurements, and more diverse sampling methods may help overcome these limitations and provide deeper insights into the relationship between health literacy and lifestyle behaviors [23].

Conclusions

This study explored factors related to health-related behaviors among older adults in Pathum Thani, Thailand. The results showed that demographic characteristics were associated with health behaviors. In addition, health literacy and illness perception contributed independently to explaining these behaviors. Older adults with higher health literacy tended to engage in healthier behaviors, and the way they perceived their illness also influenced their behavioral engagement. These findings suggest that health behaviors in older adults are influenced not only by socio-demographic factors but also by how they understand health information and perceive their health conditions. Therefore, health promotion programs should focus on improving health literacy and fostering more adaptive illness perceptions to support self-management and improve quality of life in Thailand’s aging society.

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