INTRODUCTION
Health-related quality of life (HRQoL) refers to an individual’s perception of how their health status affects various aspects of life, including physical, emotional, and social functioning [1]. HRQoL emphasizes the subjective experience of health—how people feel and function in their daily lives, given their physical or psychological conditions [2]. This patient-centered perspective has gained increasing relevance in both research and clinical settings, as it captures aspects that traditional biomedical indicators may overlook. Assessing HRQoL helps clinicians understand not only disease severity but also its real-life consequences on a person’s sense of well-being and life satisfaction.
HRQoL is shaped by a wide range of factors beyond disease pathology. Chronic physical conditions such as cardiovascular disease, diabetes, and musculoskeletal disorders have been consistently shown to impair individuals’ perceived health status [3]. Functional limitations, pain, and treatment burden can disrupt daily routines and reduce satisfaction in work, relationships, and leisure activities [4]. In addition, demographic and social variables—including age, gender, socioeconomic status, education, and employment—interact with health conditions to influence how people interpret and respond to illness, which in turn affects how they perceive their own health and functioning in daily life [5]. Even in the absence of overt disease, lifestyle factors such as poor sleep quality, low physical activity, and inadequate nutrition can lower perceived well-being, underscoring the importance of holistic health in shaping HRQoL [6].
Mental health conditions—including depression, anxiety, and stress-related disorders—have been consistently associated to lower HRQoL, in some cases showing a greater impact than certain physical illnesses [7,8]. These conditions can affect individuals’ cognitive and emotional appraisal of their health, amplify somatic complaints, and reduce engagement in meaningful activities, thereby impairing perceived well-being [9]. Conversely, low HRQoL can also contribute to the onset or worsening of mental health symptoms, particularly when individuals experience chronic distress, functional limitations, or social isolation [10]. This bidirectional relationship suggests that poor self-perceived health is not only an outcome of psychiatric conditions but also a potential risk factor for their development or persistence. As such, understanding the interplay between mental health and HRQoL may help identify vulnerable individuals and inform integrated treatment strategies.
Self-perceived health—how individuals internally evaluate their overall well-being—is particularly meaningful in psychiatric settings [11]. This perception often differs from clinical evaluations, especially in patients whose emotional and cognitive states shape how they interpret their physical and mental health. In psychiatric outpatient mental health care, where symptom presentation can be subtle and variable, understanding how patients themselves assess their HRQoL offers valuable insight into their functioning, unmet needs, and treatment priorities [12]. It also informs risk assessment, treatment planning, and patient engagement. For clinicians, incorporating self-perceived HRQoL into routine evaluation helps bridge the gap between symptom control and lived experience, supporting more individualized, recovery-oriented care [13]. Recognizing this perspective is essential not only for optimizing treatment outcomes but also for building therapeutic alliances in the psychiatric outpatient setting.
In this study, we aimed to examine the psychological and behavioral factors associated with HRQoL as measured by the EuroQol Visual Analogue Scale (EQ-VAS) [14], in two distinct contexts: psychiatric outpatients and the general population. Given differences in symptom burden, psychosocial stressors, and clinical environments, the determinants of self-rated health may vary substantially between these groups. However, few studies have directly compared HRQoL correlates across psychiatric and community settings. By examining both groups in parallel, this study seeks to identify context-specific and transdiagnostic factors shaping subjective health perceptions. Through this research, we hope to support more personalized approaches to care, improving the understanding of subjective health perceptions across diverse settings.
METHODS
Study design and participants
This was a cross-sectional study aimed at examining the HRQoL and the contributing factors in psychiatric outpatients and the general population. Participants were collected from two groups. One hundred fifty psychiatric outpatients were recruited from a psychiatric outpatient department of university hospital. Participants with missing data on one or more key psychological or behavioral variables required for correlation or regression analyses were excluded using listwise deletion, resulting in a final sample of 128 psychiatric outpatients. Because the primary aim of this study was to identify transdiagnostic psychosocial correlates of HRQoL in real-world outpatient settings, psychiatric diagnoses were analyzed as a combined group rather than stratified by diagnostic category. The outpatient sample included individuals with diverse psychiatric conditions, many of whom shared overlapping symptom profiles such as depressive mood, anxiety, and sleep disturbances. Prior research suggests that HRQoL is strongly shaped by these cross-cutting psychosocial features, which commonly operate across diagnostic boundaries [15,16]. Accordingly, treating the outpatient sample as a unified cohort allowed us to examine psychosocial determinants that are broadly relevant across heterogeneous clinical presentations. Therefore, analyzing the psychiatric group as a transdiagnostic cohort was deemed appropriate for capturing shared patterns relevant to subjective health. General population; a sample of 500 adults were recruited through an online research company (Macromill Embrain; www.embrain.com), stratified by age, sex, and region to provide a sample reflective of demographic structure of South Korea. Each participant received an email with a URL link to the instruction and the survey questionnaire. All participants provided written informed consent before the survey. The survey was self-administered, anonymous, and took about 20 to 30 minutes to complete. This study was approved by the Institutional Review Board (IRB) of Soonchunhyang University Bucheon Hospital, South Korea (IRB No. 2020-03-044).
Measurements
Demographic and clinical characteristics
The survey included demographic data, such as sex, age, education, marital status, living arrangements, religion, occupation, height, weight, medical history, and monthly average income. Medical comorbidity burden was assessed using the Charlson Comorbidity Index (CCI), a widely used measure that quantifies the presence of chronic medical conditions by assigning weighted scores to diseases such as cardiovascular disorders, diabetes, chronic pulmonary disease, liver disease, and malignancy. The total score reflects overall comorbidity severity and predicts long-term mortality and functional health outcomes [17]. Body mass index (BMI) was attained from height and weight. Finally, primary psychiatric diagnosis was also measured for the psychiatric outpatients.
HRQoL and mental health assessment
The main outcome of this study was the EQ-VAS, which is part of the EQ-5D instrument and is a widely used measure of subjective health perception-HRQoL. The EQ-VAS is self-reported, whereby the participants rated how they feel about their overall health status on a continuum of 0 (the worst imaginable health state) to 100 (the best) [14]. EQ-VAS has shown its validity as a measure of subjective well-being-both in clinical and community studies.
Psychological assessment was also conducted using several scales. The Patient Health Questionnaire-9 (PHQ-9) was used to measure depressive symptoms, and the Generalized Anxiety Disorder-7 (GAD-7) was used to measure anxiety symptoms [18,19]. The PHQ-9 consists of nine items rated on a scale of 0-3 (0, never; 1, several days; 2, more than half of time; 3, nearly every day). The PHQ-9 is scored 0-27, with higher scores indicating worse symptoms (score is 10-the cutoff). The GAD-7 uses a very similar format to the PHQ-9, with scores of 0-21, and a cutoff of 10 which indicates moderate levels of anxiety, and is considered severe when scores are 15+. We also assessed psychological resilience using the Connor-Davidson Resilience Scale (CD-RISC), which consisted of 25 items rated on a 5-point Likert-type scale. Higher total scores indicate greater capacity to cope with stressful situations [20].
To assess sleep quality for participants, we used the Pittsburgh Sleep Quality Index (PSQI) as an indicator of one’s overall sleep experience over the last month; higher scores indicated a poorer sleep quality [21]. The Morningness-Eveningness Questionnaire (MEQ) was used to assess individual sleep-wake preferences, with higher scores indicating a greater tendency toward morningness and lower scores reflecting an evening-type orientation [22]. For social support, we used the Multidimensional Scale of Perceived Social Support (MSPSS) [23]. MSPSS measures perceived emotional support from family, friends, and a significant other; higher MSPSS scores indicate stronger social support.
Statistical analysis
All statistical assessments were carried out using SPSS 26.0 (IBM Corp.), with significance being interpreted as p<0.05 (two-tailed). Descriptive statistics were calculated for all variables, including means and standard deviations for continuous variables as well as percentages for categorical variables. Between-group comparisons of sociodemographic and clinical characteristics were conducted using independent samples t-tests for continuous variables and chi-square tests for categorical variables. Pearson correlation analyses were then used to examine associations between EQ-VAS and psychological/clinical variables within each group. For variables that demonstrated correlations with EQ-VAS scores, the variables were entered into stepwise multiple linear regression models to determine the major factors associated with EQ-VAS scores.
RESULTS
Table 1 presents sociodemographic and clinical characteristics of psychiatric outpatients and the general population. Compared to the general population, psychiatric outpatients were younger, had lower income and education levels, higher depressive and anxiety symptoms, poorer sleep quality, lower resilience, and lower EQ-VAS scores.
The Pearson correlation coefficients between EQ-VAS scores and psychological variables are also shown in Table 2. In both groups, depressive symptoms, anxiety, sleep quality, and resilience showed significant correlations with EQ-VAS scores. However, the general population demonstrated a broader pattern of significant correlations whereas the psychiatric outpatient group showed a more restricted pattern centered primarily on sleep quality and emotional symptoms.
The results of the stepwise multiple regression analysis are presented in Table 3. In psychiatric outpatients, PSQI showed the largest standardized coefficient (β=-0.447, p<0.001), followed by living with family (β=0.210, p=0.046). For the general population, lower PHQ-9 (β=-0.196, p<0.001) and higher CD-RISC (β=0.248, p<0.001) were the most significantly associated factors of higher EQ-VAS scores. For the general population, lower PSQI (β=-0.130, p=0.005), lower BMI (β=-0.100, p=0.011), higher MEQ score (β=0.121, p=0.005), and lower CCI (β=-0.112, p=0.007) also contributed to higher EQ-VAS. A visual illustration of the standardized regression coefficients is presented in Figure 1. The figure shows the relative contribution of each of the significant factors associated with EQ-VAS scores in both groups, and the differing psychological and clinical profiles associated with HRQoL.
DISCUSSION
The present study aimed to investigate the clinical and psychological factors that affect HRQoL in psychiatric outpatients and the general population. Using the EQ-VAS, we explored a broad range of variables including psychiatric symptoms, sleep characteristics, health behaviors, social support, and demographic factors. Multivariate analyses were run for each group to examine how different factors were associated with HRQoL in each context.
In the psychiatric outpatient group, sleep quality was most closely associated with HRQoL. In individuals with psychiatric disorders, sleep disturbances are highly prevalent and are known to exacerbate both emotional and physical symptoms, reduce cognitive functioning, and impair daily life engagement [24]. Sleep disturbance tends to exacerbate symptoms such as irritability, concentration difficulties, and mood instability, increasing the risk of relapse across psychiatric conditions [25]. Chronic sleep deprivation is known to disrupt the regulation of mood and emotions, making it harder for patients to cope with stress and leading to heightened anxiety, irritability, and mood swings [26,27]. Additionally, inadequate sleep impairs cognitive abilities such as attention, memory, and decision-making, which further exacerbates difficulties in performing routine tasks and adhering to treatment plans [28-30]. Given that EQ-VAS captures a person’s subjective appraisal of their overall health, sleep problems are likely to influence this self-evaluation. Our finding underscores the role of sleep not merely as a symptom of psychiatric conditions, but as a central factor shaping how individuals perceive and experience their health in daily life.
From a biological standpoint, sleep disturbances affect the hypothalamic-pituitary-adrenal axis, leading to elevated cortisol levels and disrupted neuroplasticity, which have been linked to fatigue and pain sensitivity [31]. In addition, emerging evidence suggests that sleep deprivation is likely to induce immune-inflammatory responses, such as increased pro-inflammatory cytokines, which have been involved in mood dysregulation and stress-related physical reactions [32]. These neurobiological mechanisms may explain why patients with chronic sleep disturbance often report lower subjective health ratings. Our study also found sleep quality as the factor mostly associated with self-rated health among psychiatric outpatients. Accordingly, improving sleep quality in this population may serve as a practical target for enhancing subjective health well-being and promoting recovery in emotional functioning, daily life engagement, and treatment adherence.
In the psychiatric outpatient group, living with family was significantly associated with better HRQoL, suggesting that familial cohabitation may function as a meaningful source of daily structure, emotional support, and assistance with treatment routines. For individuals managing chronic mental health conditions, the presence of family in the household may help regulate lifestyle patterns, promote medication adherence, and increase access to medical care—factors that may positively influence perceived health and quality of life. Previous studies have similarly reported that family support is positively associated with treatment adherence and perceived recovery in individuals with schizophrenia, reduces relapse rates in bipolar disorder [33], and improves psychosocial functioning in patients with depression [34]. Strengthening family-based resources could therefore be a practical intervention target in mental health care. These findings underscore the importance of environmental and relational factors in shaping subjective health perceptions among psychiatric outpatients.
In the general population sample, depression, stress resilience, comorbidity burden, BMI, sleep quality, and morningness tendency were significantly associated with HRQoL. The results suggest that individual differences in emotional and behavioral health may play a role in shaping how people perceive their quality of life. Depressive symptoms showed a negative correlation with HRQoL reaffirming findings from previous population-based studies that mental health status plays a critical role in shaping individuals’ perceptions of their HRQoL [35,36]. Subjective experiences such as fatigue, low motivation, or poor concentration—often linked to low mood— may still restrict one’s ability to engage in health-promoting behaviors or derive satisfaction from daily functioning, thereby lowering perceived HRQoL. Resilience also demonstrated a significant positive association with HRQoL. This suggests that internal adaptive resources, such as psychological flexibility and stress tolerance, can buffer against perceived health-related impairments and contribute to a more favorable evaluation of one’s physical and mental functioning [37]. This suggests that building internal coping resources may serve not only to improve perceived well-being but also to buffer against emotional distress and promote mental health maintenance, especially in community settings.
While CCI is traditionally used to estimate disease burden in clinical populations, it showed a negative association with HRQoL in general population group. This is consistent with prior findings showing that higher CCI scores are associated with lower HRQoL, even in non-clinical populations [38,39]. Similarly, a higher BMI was related to lower HRQoL, possibly due to the physical discomfort, reduced energy levels, or body image concerns that accompany excess weight. Additionally, both better sleep quality and higher morningness tendency were associated with more favorable HRQoL ratings. These two factors may reflect that more consistent sleep-wake cycles and healthier behavioral routines can facilitate emotional regulation and promote better self-care practices. Better synchronization between internal circadian rhythms and societal demands may lead to more regular sleep patterns, higher daytime alertness, and greater consistency in engaging with work, social, and physical activities. Sleep quality emerged as a significant predictor of HRQoL in both psychiatric outpatients and the general population; however, its standardized effect size was larger in the psychiatric outpatient group. This suggests that while sleep is an important determinant of subjective health across populations, its impact may be particularly pronounced in clinical psychiatric settings, where sleep disturbances are more prevalent and closely linked to emotional and functional impairment.
Differences in significant predictors between the psychiatric outpatient and general population samples may be explained by differences in clinical context and variability of psychosocial factors. In the psychiatric sample, depressive symptoms, anxiety, resilience, and social support showed significant correlations with HRQoL; however, these variables share substantial variance with sleep quality and with each other. As a result, only sleep quality and living arrangement remained as independent predictors in multivariable models. In contrast, the general population sample showed greater heterogeneity and a wider distribution of psychological and health-related characteristics, allowing emotional symptoms, resilience, medical comorbidity, and lifestyle-related factors to emerge as independent contributors to HRQoL. This broader pattern of associations in the general population may also reflect the larger sample size, which allowed greater variability in psychosocial characteristics to be captured. These findings suggest that while multiple psychosocial factors are relevant to subjective health in psychiatric patients, their effects may be more interdependent and contextually constrained compared to community populations. In addition, the correlation patterns themselves differed between the two groups: the general population showed a broader set of significant correlations with EQ-VAS, whereas the psychiatric outpatient group demonstrated a more restricted pattern centered primarily on sleep quality and emotional symptoms, helping explain the divergence between correlation and regression results.
There are several limitations to consider. Firstly, the cross-sectional design of our study limits conclusions concerning causality; longitudinal studies are needed to clarify the temporal associations between psychological factors and HRQoL. Secondly, all variables were measured via self-report, which may lead to concerns of reporting bias. Additionally, differences in recruitment methods between psychiatric outpatients and the general population may introduce selection bias. Also, information on illness severity, remission status, and psychotropic medication use was not systematically available and could not be included in the analyses. Finally, formal psychiatric diagnoses were not assessed in the general population survey.
This study explored factors associated with HRQoL in different contexts. In the psychiatric outpatient sample, psychosocial and lifestyle factors such as living with family and sleep quality were related to HRQoL. HRQoL in the general population was associated with emotional symptoms, resilience, BMI, morningness tendency, sleep quality, and comorbidity burden highlighting the role of both psychological status and daily health behaviors. Taken together, these findings indicate that determinants of HRQoL differ meaningfully by clinical context. In the general population, subjective health appears to be shaped by a broad combination of emotional symptoms, resilience, medical comorbidity, and lifestyle-related factors, whereas in psychiatric outpatients, more immediate and closely interrelated factors—particularly sleep difficulties and living arrangements—play a central role. Overall, these results highlight the importance of context-sensitive approaches when interpreting and improving subjective health across diverse populations.
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