Introduction

Background

As life expectancy increases with advancements in medical technology, the population of older adults in Korea continues to grow. Adults aged 65 years and older constitute approximately 20.3% of the total population in 2025, and this proportion is expected to sharply increase to approximately 40.1% by 2050 [1]. Korea is experiencing the fastest rate of population aging worldwide [2]. Moreover, it has a high proportion of vulnerable older adults. According to a report by the Organization for Economic Co-operation and Development (OECD), Korea has been reported as the country with the highest poverty rate among older adults since 2009 [3]. The 2023 National Basic Livelihood Security Survey in Korea also reports that the proportion of households with members aged 65 years and above among all recipient households is approximately 40.3%, which continues to increase, resulting in significant social burdens [4].

Vulnerable older adults with chronic diseases often experience overall functional decline due to aging and illness, leading to improper disease management, such as lifestyle improvements, including diet and physical activity, medication adherence, and regular checkups [5-7]. Moreover, psychosocial problems, such as social isolation, decreased self-esteem, uncertainty, and depression resulting from the loss of social roles and economic difficulties, serve as negative factors in managing diseases and maintaining functionality for older adults with chronic diseases [8,9]. These problems progress the diseases, ultimately leading to a decline in the health-related quality of life (HRQoL) of older adults with chronic diseases [8,10]. This has severe consequences for vulnerable older adults who lack access to healthcare services compared with the access available to non-vulnerable older adults, resulting in health disparities [10,11]. Accordingly, community-based medical welfare policies are required to actively manage health and improve the HRQoL of vulnerable older adults with chronic diseases. In line with this, Korea is preparing to implement the “Act on Integrated Support for Community Care Including Medical Care and Nursing Services” in 2026, which aims to enhance the delivery of comprehensive, community-based services for vulnerable populations, including older adults living with chronic conditions [12].

Recently, healthcare access has shifted from traditional methods involving medical professionals or mass media to online sources through digital media, including various information and communication technologies (ICTs) [13]. Since the COVID-19 pandemic, individual use of digital technology in daily life has become a necessity rather than a choice [11,14]; however, the digital literacy of older adults remains lower than that of other generations [15,16]. Vulnerable older adults, in particular, have a lesser ability to use digital information [17].

Digital literacy of older adults affects their psychological well-being, quality of life (QoL), and life satisfaction [18]. Internet use has been reported to contribute to life satisfaction and health among older adults [19]. Several countries are applying health-promoting activities, such as disease management, diet, online exercise classes, smartphone applications, and robots based on ICTs, to improve the HRQoL [20]. The Korean government attempted to expand the infrastructure for digital healthcare services targeting older adults to keep health disparities from growing during the COVID-19 pandemic [21].

To promote health equity by improving the HRQoL of community-dwelling vulnerable older adults with chronic diseases and reducing social costs of their treatment and disease management, it is necessary to implement continuous and integrated community-based interventions and policies. To this end, it is necessary to comprehensively analyze factors influencing the HRQoL, including digital literacy. However, most studies on community-dwelling older adults with chronic diseases have focused on the QoL of individuals with specific chronic diseases [11,22,23] or the relationship between sociodemographic factors or a few physical and psychological variables and QoL or their fragmentary influence [5,8,9,24-27]. In addition, although several studies have examined health-related outcomes using digital interventions [28,29], few have investigated the structural pathways through which digital literacy influences HRQoL, especially among socioeconomically vulnerable populations.

Wilson and Cleary’s [30] model presented the HRQoL by integrating the biomedical paradigm (focusing on the causes of disease) with the social science paradigm (focusing on the functioning and overall well-being of patients). The model has five health concepts—biological and physiological variables, symptom status, functional status, general health perceptions, and overall QoL [30].

This model has been used as a theoretical framework in previous studies to assess the overall HRQoL of individuals with osteoarthritis [31], older women following bilateral total knee replacement [32], older adults in long-term care facilities [33], and community-dwelling older adults with chronic diseases [8,34]. Therefore, it serves as a valuable framework for explaining the HRQoL of vulnerable older adults with chronic conditions from an integrated perspective. However, few studies have applied this model to examine the multidimensional factors influencing HRQoL in this population using path analysis. Moreover, no studies have incorporated digital literacy into the model, despite the increasing reliance on digital platforms for healthcare access and information. Thus, Wilson and Cleary’s model [30] remains a useful and comprehensive theoretical framework for understanding the HRQoL of vulnerable older adults with chronic diseases in today’s digital healthcare environment.

Aim and objectives

The purpose of this study is to assess the HRQoL of community-dwelling vulnerable older adults with chronic diseases and to develop and validate a path model that investigates the relationships between the key factors influencing the HRQoL based on Wilson and Cleary’s model [30]. Furthermore, the aim is to provide foundational data for developing ICTs-based health intervention programs to improve the HRQoL of community-dwelling vulnerable older adults with chronic diseases. The specific research goals are as follows:

• To develop a hypothetical path model explaining the HRQoL of community-dwelling vulnerable older adults with chronic diseases.

• To propose a final path model that explains and predicts the HRQoL of community-dwelling vulnerable older adults with chronic diseases by validating the fit between the hypothetical path model and the collected data.

Methods

Study design

A path analysis of a hypothetical model for the HRQoL of community-dwelling vulnerable older adults with chronic diseases and related factors based on Wilson and Cleary’s [30] model was developed.

Participants and setting

The participants were community-dwelling vulnerable older adults aged 65 years or older with chronic diseases residing in one metropolitan city and five rural areas in Korea. The selection criteria included those who had been diagnosed with, were receiving treatment for one or more major chronic diseases (e.g., hypertension, hyperlipidemia, chronic lower back pain, diabetes mellitus, cerebrovascular disease, and osteoarthritis), and were receiving livelihood benefits from the government owing to economic difficulties. Participants with cognitive disorders were excluded.

A minimum sample size of 150 is required to apply the maximum likelihood method for path analysis; the recommended sample size is 15–20 times the number of parameters to be estimated [35]. Thus, considering an expected dropout rate of approximately 20%, the target number of participants was set at 210. Two hundred and four questionnaires were collected, and data from 195 participants were used for the final analysis, excluding nine cases with invalid responses.

Conceptual framework

This study’s conceptual framework was grounded in Wilson and Cleary’s model [30], which posits that biological/physiological variables influence symptom status, symptom status influences functional status, functional status shapes general health perceptions, and these perceptions ultimately determine overall quality of life (QoL). Individual and environmental characteristics are assumed to affect all levels except the biological/physiological level.

The components were operationalized as follows: number of diseases (biological/physiological variables) [8,31-33]; depressive symptoms (symptom status) [8,32,33]; self-efficacy for managing chronic diseases (SEMCD; functional status) [32]; self-rated health (SRH; general health perceptions) [8,31,34,36]; and HRQoL (overall QoL) [8,36]. Social support represented environmental characteristics [8,31-34,36].

Drawing on prior studies [8,31-34,36], a primary sequence was specified whereby a greater number of diseases would be associated with more depressive symptoms, higher depressive symptoms with lower SEMCD, lower SEMCD with poorer SRH, and poorer SRH with lower HRQoL. In addition, social support was hypothesized to influence depressive symptoms, SEMCD, SRH, and HRQoL. Furthermore, depressive symptoms were allowed to affect SRH and HRQoL directly, and SEMCD was allowed to affect HRQoL directly.

As previous research has linked digital literacy to self-efficacy, QoL, and social connectedness [15,37,38], associations between digital literacy, SEMCD, SRH, social support, and HRQoL were also posited. Sociodemographic characteristics (e.g., sex and age) known to influence HRQoL [39] were included as control variables (Figure 1).

Measurements

Sociodemographic questionnaire

The general and health-related characteristics included sex, age, marital status, religion, educational level, living arrangement, region of residence, employment status, number of diseases, and number of medications.

Depressive symptoms

Depressive symptoms were measured using the Short Form Geriatric Depression Scale, developed by Yesavage and Sheikh [40] and translated into Korean by Kee [41]. This scale consists of 15 dichotomous items in short form, scoring “yes” as 1 and “no” as 0, with higher scores indicating higher depressive symptom levels. Participants were categorized as having no depressive symptoms (0–4 points), mild depressive symptoms (5–9 points), or severe depressive symptoms (10 points or above). Cronbach’s α was .90 at the time of the tool’s development and .86 in this study.

SEMCD

SEMCD was measured using the SEMCD 6-item Scale developed by Lorig et al. [42] and translated into Korean by Kim et al. [43]. The six items include four on symptom management and two on health behavior. The instrument uses a 10-point Likert scale ranging from “not at all confident” (1 point) to “totally confident” (10 points), with higher scores indicating greater SEMCD. Cronbach’s α was .96 in Kim et al. [43] and .94 in this study.

Social support

Social support was measured using the Multidimensional Scale of Perceived Social Support developed by Zimet et al. [44] and translated into Korean by Shin and Lee [45]. This scale consists of 12 items, divided into three subcategories—family, friends, and a significant other—each with four items. It is rated on a Likert scale ranging from “very strongly disagree” (1 point) to “very strongly agree” (5 points), with higher scores indicating higher levels of social support. Cronbach’s α was .91 in Shin and Lee [45] and .95 in this study.

Digital literacy

Digital literacy was measured using the tool developed by An et al. [46] to assess older adults’ ability to utilize health information on the internet. The instrument consists of 18 items, divided into four domains: instrumental use, information production and sharing, network expansion, and social participation. Each item is rated on a 5-point Likert scale ranging from “strongly disagree” (1 point) to “strongly agree” (5 points), with higher scores indicating greater digital literacy. Cronbach’s α was .98 in both An et al. [46] and this study.

SRH

SRH was assessed using one item from the Korea National Health and Nutrition Examination Survey (KNHANES) [47], conducted annually by the Korea Disease Control and Prevention Agency. This item is rated on a 5-point Likert scale ranging from “very good” to “very poor.”

HRQoL

HRQoL was assessed using the Korean Euro-QoL 5-dimension (EQ-5D) [27]. The EQ-5D asks respondents to select one of five responses (no, slight, moderate, severe problems, and unable) for five items: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. The health status is calculated by applying weights to the measures for each of the five items to obtain the EQ-5D index, which is the HRQoL score. The maximum and minimum scores are 1 and −1, respectively, with scores closer to 1 indicating better HRQoL. Cronbach’s α was .88 in this study.

Data collection

Data were collected for two months from April 2022 using a structured questionnaire at three community welfare centers in the metropolitan city and at a single primary health care post in each of the five rural areas. Prior to data collection, the administrators of each institution were asked for their cooperation in conducting this study. The questionnaire took 15 to 20 minutes to complete, and a research assistant helped those who had any difficulty.

Ethical considerations

Before data collection, approval was obtained from the Institutional Review Board of the Catholic University of Pusan (IRB No. CUPIRB-2021-067). For all measurements, except the sociodemographic questionnaire, the developers and adaptors were contacted by email, and permission to use the instruments was obtained. Before conducting the survey, the study’s purpose and methods were explained to the participants, and they were ensured confidentiality. They were allowed to withdraw at any time from the study. Written informed consent was obtained from all participants. Personal information was coded to maintain anonymity. All participants who completed the survey were given a small gift voucher as compensation.

Data analysis

The data were analyzed using SPSS 28.0 and Amos 28.0 (IBM Corp., Armonk, NY, USA). The general and health-related characteristics of the participants were analyzed using descriptive statistics, and the correlations between the variables were examined using the Pearson correlation coefficient. Model fit testing was conducted using the chi-square statistic (χ²), goodness of fit index (GFI), comparative fit index (CFI), normed fit index (NFI), Tucker–Lewis index (TLI), and root mean square error of approximation (RMSEA). The model was considered an acceptable fit if the RMSEA was less than .08, other fit indexes were .90 or higher and closer to 1, and normed χ² (χ²/df) was less than 3.0 [35].

Results

Participants’ characteristics and HRQoL

Of the participants, 74.4% were female, and most were over 80 years of age (47.2%). Meanwhile, 72.3% of the participants were widowed, and 70.3% lived alone. The majority (85.6%) did not engage in economic activities. The mean number of diseases among the participants was 3.24, and the number of medications per day was 7.67 (Table 1).

Among the participants’ characteristics, the HRQoL level showed significant differences regarding age (F=3.33, p=.038), marital status (t=5.26, p<.001), living arrangement (t=9.74, p<.001), type of residence (t=−4.59, p<.001), employment status (t=−5.46, p<.001), number of diseases (F=5.18, p<.001), number of medications (F=3.14, p=.045), and depression group (F=28.95, p<.001) (Table 1).

Descriptive statistics and normality test of the observed variables

The means of key variables were depressive symptoms 6.77±4.30, SEMCD 5.43±2.32, social support 3.19±1.03, digital literacy 1.42±0.81, SRH 2.57±0.92, and HRQoL 0.65±0.21 (Table 2). The skewness of the measured variables was −0.74 to 1.21, which is within ± 2, and the kurtosis was −1.14 to 3.23, which is within ±4, indicating the data can be assumed to follow a normal distribution.

Correlations among the observed variables

HRQoL showed a significant correlation with the number of diseases (r=−.39, p<.001), depressive symptoms (r=−.51, p<.001), SEMCD (r=.51, p<.001), digital literacy (r=.64, p<.001), social support (r=.26, p<.001), and SRH (r=.39, p<.001) (Table 3).

Verification of hypothesis model path analysis

The hypothesis path model demonstrated acceptable fit (χ²(p) = 4.61(.100), χ²/df = 2.31, GFI=.99, CFI=.99, NFI=.99, TLI=.93, RMSEA=.06). The path model and standardized path coefficients are presented in Figure 2.

Table 4, which presents the standardized estimates effect of the final model, shows that digital literacy had a significant direct effect on social support (β=.27, p<.001) and number of diseases (β=.14, p=.012), and social support (β=−.56, p=.001) had a significant direct effect on depressive symptoms. Depressive symptoms had a direct effect on SEMCD (β=−.41, p<.001). Digital literacy and social support had a significant indirect effect (β=.80, p<.001; β=.23, p<.001) and total effect (β=.19, p=.015; β=.29, p<.001) on SEMCD.

SEMCD had a direct effect on SRH (β=.25, p=.002). depressive symptoms and digital literacy had a significant direct (β=−.29, p<.001; β=.18, p=.004), indirect (β=−.11, p=.002; β=.10, p<.001), and total effect (β=−.39, p<.001; β=.29, p=.002) on SEMCD. Social support had an indirect effect (β=.23, p<.001) and a total effect (β = .28, p<.001) on SEMCD. SRH had a direct effect on HRQoL (β=.43, p<.001). SEMCD had a direct (β=.19, p=.004), indirect (β=.11, p<.001), and total effect (β=.30, p<.001) on HRQoL. Depressive symptoms, digital literacy, and social support had a significant indirect effect (β=−.25, p<.001; β=.21, p<.001; β=.25, p<.001) and total effect (β=−.38, p<.001; β=.23, p<.001; β=.35, p<.001) on HRQoL. These variables explained 48.0% of the variance in HRQoL.

Discussion

In this study, a path analysis was conducted to develop and validate a hypothetical model based on Wilson and Cleary’s [30] model and previous studies to determine factors affecting the HRQoL of community-dwelling vulnerable older adults with chronic diseases. The HRQoL score of the participants was 0.65 ± 0.21 out of 1 point, which was lower than the 0.84–0.88 reported in previous studies of community-dwelling older adults [48] and older adults with complex chronic diseases [8] using the same instrument. Compared with a previous finding that the QoL of older adults with chronic diseases receiving medical care benefits was significantly lower than that of those with health insurance [23], the lower QoL in the present study may be attributable to the participants being older adults with low economic status who were receiving livelihood benefits from the government.

Vulnerable older adults with chronic diseases have weak social support systems and lack access to healthcare services compared with the access available to non-vulnerable older adults owing to lower education and economic status [49,50]. This causes diseases to worsen and manifest as psychosocial problems, such as social isolation, lower self-esteem, and depression, leading to lower HRQoL [51,52]. Therefore, to improve the HRQoL of community-dwelling vulnerable older adults with chronic diseases, the public sector, including government organizations, should provide them with financial support, social support, and education on disease and health management in association with various related institutions.

Path analysis showed that, in community-dwelling vulnerable older adults with chronic diseases, SRH exerted the largest direct effect on HRQoL, and depressive symptoms and SEMCD were significant predictors of SRH. In other words, higher SRH levels, led to higher HRQoL, which aligns with Yu et al. [53], who analyzed KNHANES data and reported that SRH had the greatest impact on the QoL of older Koreans. Moreover, a path analysis of HRQoL in community-dwelling older adults receiving long-term care insurance in-home services [33] similarly revealed that SRH had the greatest direct effect on HRQoL. The centrality of subjective health appraisals is further supported by a structural model of QoL in patients with thyroid cancer [36], in which illness perception emerged as the most influential determinant of QoL.

SRH is a subjective indicator of an individual’s health status, including physical, mental, social, and functional aspects [54]. For older adults, it has a greater impact on life satisfaction or QoL than do objective health indicators, such as the type or severity of diseases [55,56]. Sadeghipour Rousari et al. [57] reported that older adults who strongly perceive SRH demonstrated greater independence in daily life than did in those with lower SRH. Therefore, to improve HRQoL for community-dwelling vulnerable older adults with chronic diseases, it is necessary to improve SRH.

SEMCD had a significant effect on HRQoL, both directly and indirectly via SRH. This pattern is consistent with findings based on Wilson and Cleary’s model [30], including a study in community-dwelling older adults reporting an effect of self-care agency on HRQoL through SRH [33] and a study of older women with knee osteoarthritis after bilateral total knee replacement showing that self-efficacy exerted both direct and indirect effects on HRQoL [32].

In individuals with chronic diseases, SEMCD denotes perceived capability to undertake disease management actions [42]. Patients with higher SEMCD more proactively engage in and sustain disease management behaviors despite disease-related physical and psychological functional decline [58]. This finding supports reports of concomitant improvements in SEMCD and SRH in health management interventions for individuals with chronic diseases [59], as well as prior evidence that higher self-efficacy is associated with better SRH and higher HRQoL [60]. Previous studies on improving HRQoL for patients with chronic diseases have used SEMCD as a primary outcome to evaluate intervention effectiveness [61,62]. Accordingly, communities may consider implementing integrated health management programs to strengthen SEMCD among community-dwelling vulnerable older adults with chronic diseases, thereby positively influencing SRH and HRQoL.

Depressive symptoms were higher among individuals with more comorbidities and lower social support, and they had a significant indirect effect on HRQoL via SEMCD and SRH. This supports Wilson and Cleary’s model [30], in which symptom burden, shaped by biological and physiological variables, influences HRQoL primarily through proximal mediators such as functional status and general health perceptions. According to the present findings—and as observed in a structural model of older adults with degenerative arthritis [33]—the direct path from depressive symptoms to HRQoL was not significant once SEMCD and SRH were included.

Nevertheless, previous studies have identified depression as a key factor affecting the HRQoL of older adults with chronic diseases [8,63]. This apparent discrepancy may indicate that, in vulnerable community-dwelling older adults, the influence of depressive symptoms operates primarily through self-management capability and SRH rather than as a unique direct effect. In particular, vulnerable older adults have weaker economic and social support systems, resulting in continuous social isolation, anxiety, and depression that can lead to severe outcomes, including suicide [64]. From a practical perspective, community programs for vulnerable older adults with chronic diseases should facilitate access to local resources (e.g., community mental health and welfare centers) for routine depression screening and sustained counseling, and foster social connectedness through peer support groups. In parallel, strengthening public social support through social security policies can help mitigate social isolation, anxiety, and depression. By enhancing SEMCD and SRH, these strategies are expected to improve HRQoL.

Social support did not have a significant direct effect on HRQoL; rather, it had a direct effect on depressive symptoms and thereby an indirect effect on HRQoL through SEMCD and SRH. This differs from prior studies reporting that social support exerts direct effects not only on depressive symptoms but also on SEMCD [33] and HRQoL [31,32,34,36]. The present findings suggest that, in this population, the influence of social support on HRQoL operates primarily through proximal mediators—depressive symptoms, SEMCD, and SRH—rather than via a distinct direct path, and that discrepancies with previous reports may reflect differences in the types of social support assessed, HRQoL measurement instruments used, and sample characteristics. Accordingly, future longitudinal and intervention studies that control for support type and measurement differences are warranted to re-examine the relative contributions of direct and indirect effects.

Digital literacy did not exhibit a significant direct effect on HRQoL; rather, it contributed indirectly through a sequential mediation pathway in which higher digital literacy was associated with greater social support, reduced depressive symptoms, and subsequent increases in SEMCD and SRH, culminating in improved HRQoL. In addition, digital literacy showed a significant association with SRH, suggesting an auxiliary SRH pathway to HRQoL. These findings partly accord with prior reports indicating that, among community-dwelling older adults with low socioeconomic status, digital literacy exerts a positive indirect effect on well-being and QoL via social connectedness [65], and with studies reporting significant correlations between digital literacy and older adults’ QoL [18,37,38].

The average digital literacy score was 1.42 ± 0.81, lower than the 2.02 ± 0.74 reported in previous research [46], supporting the result that digital literacy is lower among vulnerable older adults than among the general population of older adults [17]. Given the expanding role of ICT—and their heightened salience for chronic disease management in the post-COVID-19 era [11]—community-based programs to enhance digital literacy among vulnerable older adults are warranted. Such programs should be delivered in partnership with local resources and provide sustained, practical instruction (e.g., basic smartphone use, online messaging competence, and health app utilization) to strengthen social connectedness and alleviate depressive symptoms, thereby improving SEMCD and SRH and, in turn, HRQoL.

Given the global trend of population aging [8], establishing and implementing a community-based integrated healthcare delivery system is essential to enable older adults to maintain their QoL within familiar environments [66]. In response, the Korean government has implemented policies to provide integrated care for vulnerable community-dwelling populations. Building on the present findings, interventions to improve HRQoL among community-dwelling vulnerable older adults should prioritize strengthening SRH and SEMCD. SRH may be enhanced through tailored health information and counseling, and SEMCD through self-management coaching; in parallel, while integrating depression screening and intervention, linkage to social support networks and digital literacy training can strengthen the indirect pathways influencing HRQoL

Conducted in Korea—a country with the highest old-age poverty rate among OECD members [3]—this study delineates, within the Wilson and Cleary model [30], the path-level determinants of HRQoL in vulnerable older adults and provides a theory-grounded foundation for community-integrated strategies, including programs to enhance digital literacy and ICT-enabled health promotion initiatives. These contributions constitute the significance of this study.

However, caution is required in generalizing the results because participants were selected through convenience sampling. Furthermore, as individual characteristics (e.g., age, marital status, living arrangement, region of residence, and employment status) were set as control variables to examine the direct and mediating effects of key variables on HRQoL, the scope for capturing the full direct and indirect effects of related factors was limited. Future research should address these limitations and rigorously test the proposed sequential mediation whereby digital literacy influences HRQoL through social support, depressive symptoms, SEMCD, and SRH.

Conclusions

Based on the path analysis, the HRQoL of community-dwelling vulnerable older adults was explained primarily by the largest direct effect of SRH, and SEMCD showed both a direct effect on HRQoL and an additional indirect effect via SRH, identifying SEMCD as a key mediator and a primary target for intervention. The number of diseases, depressive symptoms, social support, and digital literacy influenced HRQoL indirectly. This overall pattern is consistent with the Wilson and Cleary’s framework [30], in which symptoms, functioning, and general health perceptions contribute to QoL.

Accordingly, integrated community health management programs aimed at improving HRQoL in community-dwelling vulnerable older adults should be designed, with the enhancement of SEMCD and SRH as first-line objectives. In conjunction, screening and targeted interventions for depressive symptoms, strengthening of social support systems, and education to improve digital literacy should be combined to bolster the indirect pathways, adopting a multidimensional, patient-centered approach. Furthermore, future research should enhance explanatory power and extend the model by considering various physical functioning and sociodemographic factors set as control variables in this study.

Figure 1.

Conceptual framework of this study.

Figure 2.

Path diagram in the final model.

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