Healthy lifestyle behaviors and persistent socioeconomic inequalities in health-related quality of life: Evidence from a population-based study in Turkey

Healthy lifestyle behaviors and persistent socioeconomic inequalities in health-related quality of life: Evidence from a population-based study in Turkey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Healthy lifestyle behaviors and persistent socioeconomic inequalities in health-related quality of life: Evidence from a population-based study in Turkey Hazal Swearinger This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8336519/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Purpose Health-related quality of life (HRQoL) is a key indicator of population well-being; however, limited evidence documents how lifestyle behaviors relate to HRQoL and its socioeconomic gradient in Turkey. This study assessed overall HRQoL levels, examined the associations between healthy lifestyle behaviors and HRQoL, and evaluated whether lifestyle factors mitigate income-related disparities among adults aged 18–64. Methods Data were obtained from the nationally representative 2014–2022 Turkey Health Survey (N = 61,362). HRQoL was measured using the EQ-5D-3L with UK TTO tariffs. A Healthy Lifestyle Score (0–5) included smoking status, alcohol consumption, physical activity, diet, and BMI. Survey-weighted OLS regressions estimated associations between lifestyle behaviors and HRQoL, and an interaction term tested whether these associations differed by income level. Sensitivity analyses assessed the contribution of individual lifestyle components. Results Mean HRQoL was moderate (EQ-5D = 0.687) and substantially lower among adults with lower income. Pain/discomfort and anxiety/depression were the most impaired domains. Healthier lifestyle behaviors were positively associated with HRQoL, with non-smoking, a healthy diet, and maintaining a BMI within the recommended range showing the strongest associations. These benefits were more pronounced among lower-income adults; however, income-based inequalities in HRQoL persisted across all levels of lifestyle behaviors. Conclusion Healthy lifestyle behaviors play an important role in shaping HRQoL and yield enhanced benefits for socioeconomically disadvantaged adults. Nonetheless, lifestyle improvements alone are insufficient to eliminate income-related inequalities. Addressing both behavioral and structural determinants is essential for improving HRQoL and reducing socioeconomic disparities. Health-related quality of life EQ-5D socioeconomic inequalities lifestyle behaviors social work population health Figures Figure 1 Figure 2 Plain English summary Health-related quality of life describes how people feel and function in their everyday lives, including their physical, mental, and emotional well-being. Understanding what shapes these outcomes is important for improving population health and reducing inequalities. In Turkey, however, little is known about how healthy lifestyle habits relate to health-related quality of life or whether these habits help narrow the gap between adults with higher and lower incomes. This study helps fill this gap by using data from a large, nationally representative sample of adults aged 18 to 64. It examined how lifestyle factors are linked to people’s overall well-being, and whether these links differ across income groups. The results showed that individuals with healthier lifestyles reported better health-related quality of life, particularly fewer problems related to pain and emotional distress. These positive effects were stronger among adults with lower incomes, suggesting that healthy lifestyle habits may offer extra benefits for groups facing more disadvantages. However, income-based differences in well-being persisted even among those with healthy lifestyles. Overall, the study suggests that encouraging healthier lifestyle habits can improve well-being, but reducing socioeconomic inequalities in health will also require broader social and economic policies that address the underlying conditions shaping people’s lives. Introduction Health-Related Quality of Life (HRQoL) reflects physical, psychological, and social well-being and is a key indicator of population health [ 1 ]. Among standardized measures, the EQ-5D, developed by the EuroQol Group, is the most widely used instrument for assessing HRQoL [ 2 ]. It evaluates five dimensions including mobility, self-care, usual activities, pain or discomfort, and anxiety/depression, and produces an index score based on population-derived utility values [ 3 ]. Extensive research using the EQ-5D consistently demonstrates marked socioeconomic disparities in HRQoL. Socioeconomically disadvantaged groups report lower EQ-5D scores across diverse settings, from high-income countries such as Sweden [ 4 ], Germany [ 5 ], and Australia [ 6 ] to middle- and lower-income countries including Iran [ 7 ], Chile [ 8 ], Bangladesh [ 9 ], the Philippines [ 10 ], Greece [ 11 ], and China [ 12 , 13 ]. These inequalities persist irrespective of how socioeconomic position is defined, whether by education, income, occupation, wealth, or savings [ 14 ]. Identifying the factors that contribute to, or may help reduce, these income-related differences is therefore a key research priority. International evidence points to several determinants of lower HRQoL beyond socioeconomic status (SES). Socioeconomic characteristics, including older age [ 4 , 7 , 11 , 12 , 13 ] and being female [ 4 , 5 , 7 , 12 , 13 15 ] are consistently linked to poorer HRQoL. Lifestyle behaviors represent another critical pathway, as physical inactivity, smoking, unhealthy body weight, excessive alcohol use, and inadequate diet are all associated with reduced HRQoL [ 6 , 7 , 12 , 13 ]. These behaviors also contribute to SES-based differences, as healthier choices tend to provide greater benefits for individuals with lower SES, both because they face higher baseline health risks [ 14 ] and because lifestyle improvements yield more substantial gains in functioning and chronic disease outcomes [ 12 ]. Building on this broader evidence, healthier lifestyles are increasingly viewed as a way to improve population well-being and reduce socioeconomic disparities in HRQoL, highlighting the need to examine whether lifestyle improvements can help narrow these gaps. Yet, despite the importance of this issue, population-based HRQoL research in Turkey remains limited. Most EQ-5D applications focused on clinical populations [ 16 – 19 ], and only a few used nationally representative data [ 20 , 21 ]. Moreover, no study to date has examined whether lifestyle behaviors influence or moderate socioeconomic inequalities in HRQoL. Addressing this gap, the present study provides the first population-based analysis in Turkey to assess how lifestyle behaviors relate to HRQoL and whether they influence or moderate income-related inequalities. Using nationally representative data from the Turkey Health Survey (2014–2022) and EQ-5D-3L index scores based on the United Kingdom (UK) time trade-off (TTO) tariff, the study examines income-based disparities in HRQoL and evaluates how these disparities vary across lifestyle profiles. Specifically, it investigates whether healthier behaviors are associated with higher HRQoL and narrower income gaps. Turkey provides a particularly relevant context for this research, as the country faces pronounced socioeconomic disparities alongside some of the highest rates of smoking and physical inactivity among countries of the Organisation for Economic Co-operation and Development (OECD), while maintaining relatively low alcohol consumption due to cultural and religious norms [ 22 ]. Recent public health policies have emphasized individual responsibility through smoking bans, obesity prevention, and physical activity promotion, yet structural barriers to equitable access to healthcare, healthy foods, and safe environments persist [ 22 ]. This combination of behavioral risks and structural constraints makes Turkey a compelling setting to examine whether lifestyle improvements can meaningfully reduce, or potentially reinforce, SES-based inequalities within a rapidly evolving health system. Methods Study Data and Sample Characteristics This secondary cross-sectional analysis used nationally representative data from the Turkey Health Survey, conducted by the Turkish Statistical Institute, pooling data from the 2014, 2016, 2019, and 2022 waves. The Turkey Health Survey has been administered biennially through in-person interviews since 2008, in collaboration with the Statistical Office of the European Union. The survey employs a multi-stage, stratified, and clustered sampling design based on the national address database, ensuring representativeness of the Turkish population. The original sample included 102,641 respondents. After listwise deletion of cases with missing data on the EQ-5D index, healthy lifestyle score, and socioeconomic status variables, the sample was reduced to 76,195. Subsequently, individuals outside the age range of 18–64 were excluded, resulting in a final analytic sample of 61,362 participants. ---Insert Table 1 --- The characteristics of the analytic sample are presented in Table 1 . Among the participants, 34,005 were classified as low income and 27,357 as high income. Compared with the high-income group, low-income participants were slightly older, more likely to be women, married, and had lower levels of education (p < 0.001). They also reported a higher mean number of chronic conditions (0.54 vs. 0.40). The mean healthy lifestyle score was modestly higher among high-income individuals (2.73 vs. 2.64). Lifestyle patterns also differed between income groups: high-income participants were more likely to engage in weekly exercise and maintain healthier diets, whereas low-income participants had slightly higher rates of non-smoking and avoidance of excessive drinking. The prevalence of a healthy BMI did not differ significantly by income level. Measurements Health-related quality of life (HRQoL) was assessed using the EQ-5D-3L instrument, which evaluates five domains: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. The mobility domain includes walking 500 meters and climbing one flight of stairs; self-care includes toileting, dressing, bathing, and feeding; usual activities include meal preparation and light or heavy housework; pain/discomfort reflects both the presence of pain and its interference with daily activities; and anxiety/depression captures feelings of depression, lack of pleasure, fatigue, and sleep problems. All domains were harmonized across survey waves and coded on a three-level scale (1 = no problems, 2 = some problems, 3 = severe problems/unable). Because no validated Turkish tariff is currently available, utility index values were calculated using the UK TTO tariff, consistent with previous studies conducted in Turkey [ 17 , 18 , 23 , 24 ]. Disutility weights were applied to each domain and summed, with an additional penalty for reporting at least one severe problem. The final scores ranged from − 0.594 (worse than death) to 1 (full health), with higher values indicating better HRQoL. Healthy lifestyle score (HLS) was constructed as a composite index based on five lifestyle factors: smoking status, alcohol consumption, physical activity, diet, and body mass index (BMI) [ 12 ]. Each factor was coded as a binary variable, with participants receiving a value of 1 if they met the healthy criterion and 0 otherwise. Non-smokers were coded as 1, while current smokers were coded as 0. For alcohol consumption, participants were coded as 1 if they did not binge drink and 0 if they consumed 60 grams or more of pure alcohol on at least one occasion in the past 30 days [ 25 ]. Physical activity was coded as 1 for participants reporting any weekly physical activity and 0 otherwise [ 26 ]. Diet was coded as 1 if both fruit and vegetables were consumed on most days of the week (4–6 days per week), and 0 otherwise [ 27 ]. BMI was coded as 1 for values within the healthy range of 18.5–24.9 kg/m² and 0 otherwise [ 28 ]. The overall HLS ranged from 0 to 5, with higher values indicating healthier lifestyles. Socioeconomic status was measured using household income, harmonized across survey waves. Respondents were classified as low- and high-income based on the yearly distribution within each survey wave. Covariates included age, gender, education, marital status, and number of chronic conditions. Age was measured as a continuous variable in years. Gender was coded as binary (female = 1, male = 0). Education was categorized into four levels: no formal education or non-literate (0), primary (1), secondary including middle school, high school, or vocational tracks (2), and tertiary including college, university, or postgraduate level (3). Marital status was coded as married (1) or not married (single, widowed, or divorced = 0). The number of chronic conditions was measured as a continuous count of eight self-reported, physician-diagnosed illnesses: asthma, bronchitis, infarct, heart disease, hypertension, stroke, arthrosis, and diabetes. Statistical Analysis Descriptive analyses were conducted to summarize sample characteristics and the outcome variable overall and by SES. Two sample t-tests were used for continuous variables, and chi-square tests for categorical variables. Multivariate ordinary least squares (OLS) regression models were then estimated to assess the expected change in EQ-5D index scores associated with a one-point increase in the HLS. To examine whether the association between HLS and HRQoL differed by SES, an interaction term between HLS and SES was included in the model. Predicted EQ-5D values across the observed HLS range by SES were plotted with 95% confidence intervals. Adjusted mean EQ-5D scores for individual health behaviors were then estimated using the subpopulation option for low- and high-income groups. Finally, sensitivity analyses were conducted to assess whether the observed HLS–income interaction was driven by specific lifestyle domains by re-estimating the model with disaggregated HLS components and interacting each behavior with household income. All models accounted for the complex survey design by incorporating sampling weights (fertfaktor), clustering at the household level (PSU), and stratification by NUTS-2 pseudo-regions. Analyses were conducted in Stata 17, and figures were prepared using Microsoft Excel [ 29 , 30 ]. Findings Health Related Quality of Life by Household Income Table 2 shows HRQoL patterns by household income. Overall HRQoL was moderate, with a weighted mean EQ-5D score of 0.687. High-income adults had significantly higher scores than low-income adults (0.731 vs. 0.649, p < 0.001). Across EQ-5D dimensions, pain/discomfort and mental health symptoms were the most frequently reported problems. Among low-income participants, 47.5% reported pain on several days or more (vs. 39.9% in high income), and 60.3% reported anxiety/depressive symptoms on several days or more (vs. 53.5%) (p < 0.001). Self-care limitations were least common, especially among high-income adults (98.5% with no difficulty). Low-income respondents showed consistently higher rates of difficulty across all dimensions, with the largest gaps observed in pain/discomfort and mental health. ---Insert Table 2 --- Multivariate Predictors of HRQoL Table 3 presents the multivariate OLS regression results. Older age (β = −0.002, p < 0.001) and being female (β = −0.110, p < 0.001) were associated with lower HRQoL, while higher educational attainment showed progressively stronger positive associations (primary: β = 0.102; secondary: β = 0.129; tertiary: β = 0.144; all p < 0.001). Being married was also linked to slightly higher HRQoL (β = 0.013, p < 0.001). Chronic conditions had the strongest negative association (β = −0.126, p < 0.001). Each one-point increase in the Healthy Lifestyle Score (HLS) corresponded to a 0.041-point increase in EQ-5D scores (β = 0.041, p < 0.001). High-income individuals had significantly higher HRQoL than low-income individuals (β = 0.062, p < 0.001). However, the interaction term was negative and significant (β = −0.010, p = 0.002), indicating that the positive association between HLS and HRQoL was weaker among high-income adults. In other words, lifestyle improvements were more strongly associated with HRQoL gains in the low-income group. ---Insert Table 3 --- Figure 1 displays the predicted EQ-5D scores across the range of HLS. Higher HLS values were strongly associated with higher predicted HRQoL in both income groups. At every level of HLS, high-income individuals had better HRQoL than low-income individuals; however, the income gap gradually narrowed as HLS increased. ---Insert Fig. 1 --- Adjusted HRQoL Scores by Health Behaviors and Household Income Figure 2 presents adjusted mean EQ-5D scores by health behaviors and income groups. Across all five lifestyle domains, individuals with healthier behaviors reported higher HRQoL, regardless of income. An exception was weekly exercise, where those reporting regular exercise showed slightly lower EQ-5D scores than non-exercisers in both income groups. The magnitude of HRQoL improvement associated with healthier behaviors varied by income, with the largest gains observed for a healthy diet, followed by non-smoking. These two behaviors also showed the greatest narrowing of income-related differences in HRQoL. ---Insert Fig. 2 --- Sensitivity Analysis For sensitivity analysis, the regression model was re-estimated using the individual lifestyle components (Appendix Table S1 ). Healthy diet (β = 0.072, p < 0.001), non-smoking (β = 0.033, p < 0.001), and healthy BMI (β = 0.012, p = 0.002) were positively associated with higher HRQoL scores, whereas physical activity and alcohol use showed no meaningful effects. High-income status remained positively associated with HRQoL (β = 0.044, p = 0.002). However, the benefit of a healthy diet was significantly weaker among high-income individuals (interaction β = −0.020, p = 0.001). Interaction terms for the remaining lifestyle behaviors were not statistically significant. Discussion The primary aim of this study was to assess HRQoL among adults aged 18–64 in Turkey and to examine how differences in healthy lifestyle behaviors contribute to income-related disparities within a nationally representative population. Overall HRQoL levels were moderate and lower than those typically reported in high-income countries such as Sweden, Germany, and Australia [ 4 – 6 ], aligning more closely with findings from middle-income settings including Iran, Bangladesh, and Greece during its economic downturn [ 7 , 9 , 11 ]. This pattern is consistent with global evidence showing that national income, health system capacity, and broader welfare infrastructures play central roles in shaping population-level HRQoL [ 31 ]. The distribution of domain-specific HRQoL problems, such as the high prevalence of pain/discomfort and anxiety/depression and the relatively low impairment in self-care, also mirrors international EQ-5D population norms. Psychological distress and pain remain the most frequent sources of reduced HRQoL worldwide, a trend reflected in our data as well [ 31 , 32 ]. Similarly, sociodemographic gradients observed in this study, including lower HRQoL among women, older adults, individuals with lower education, unmarried respondents, and those with multiple chronic conditions, closely parallel cross-country research and reinforce established social determinants of health frameworks [ 31 , 33 ]. Together, these findings suggest that many of the structural drivers of global health inequality are also salient in the Turkish context. Consistent with extensive literature, healthier lifestyle behaviors were associated with higher HRQoL. Non-smoking, healthy diet, and maintaining a healthy BMI emerged as the strongest contributors, supporting evidence that modifiable cardiometabolic and behavioral risk factors are key determinants of subjective health [ 6 , 7 , 12 , 13 ]. In contrast, the absence of a clear association between weekly exercise and HRQoL diverges from findings in comparable middle-income settings [ 7 ]. Several explanations are plausible. First, reverse causality may be present, as individuals with chronic pain, fatigue, or stiffness may begin exercising as a coping strategy, which can weaken the associations observed in cross-sectional analyses [ 34 ]. Second, prior studies suggest a non-linear (U-shaped) relationship between physical activity and pain, with both inactivity and very high activity levels linked to lower HRQoL [ 35 ]. Third, physical activity measures in population surveys often rely on self-report and may lack precision, weakening detectable associations [ 36 ]. A central question guiding this study was whether adopting healthier lifestyle behaviors helps buffer or reduce the socioeconomic gradient in HRQoL. Although high-income adults maintained higher EQ-5D scores at every lifestyle level, the marginal HRQoL gains associated with healthier behaviors, particularly non-smoking and a healthy diet, were substantially larger among lower-income individuals. This pattern is consistent with perspectives in social epidemiology arguing that socioeconomic disadvantage intensifies both exposure to unhealthy environments and physiological vulnerability to behavioral risks [ 37 , 38 ]. Within this framework, the same unhealthy behavior may have more severe consequences among individuals with lower SES, reflecting cumulative stress, constrained resources, and reduced access to timely, high-quality care [ 12 ]. These mechanisms help explain why healthier lifestyle practices yielded larger marginal benefits for disadvantaged groups in this study, who also face higher burdens of pain, chronic symptoms, and psychological distress [ 13 , 32 ]. Despite these lifestyle-related benefits, income-based disparities persisted across all lifestyle levels, underscoring that behavioral modification alone cannot overcome the structural determinants of HRQoL. This finding aligns with a substantial body of research demonstrating that individual health behaviors interact with broader socioeconomic conditions, such as labor environments, healthcare accessibility, neighborhood safety, and cumulative physiological wear, but cannot fully counteract them [ 39 , 40 ]. The persistence of socioeconomic gradients across lifestyle strata indicates that HRQoL inequality is shaped by both proximal behavioral pathways and distal structural forces. Thus, while healthy lifestyles meaningfully enhance HRQoL, particularly among adults facing economic hardship, socioeconomic disadvantage continues to exert a strong influence on subjective health outcomes. Policy and Practice Implications The findings of this study offer several considerations for policy and practice aimed at improving HRQoL and reducing socioeconomic disparities. Because unhealthy behaviors are more common among lower-income individuals and healthier behaviors yield larger HRQoL gains for these groups, expanding targeted behavior-change interventions may be particularly beneficial. Evidence also shows that addressing modifiable health behaviors can substantially reduce their contribution to socioeconomic inequalities in health, highlighting the potential of well-designed interventions to narrow SES-related gaps in well-being [ 41 ]. Strategies include expanding access to safe and affordable opportunities for physical activity, such as walkable neighborhood environments [ 42 ] and subsidizing fruits and vegetables to increase fresh produce purchasing among low-income households [ 43 ]. Strengthening access to reliable, easy-to-understand health information through interpersonal communication and tailored educational materials can also support smoking abstinence, physical activity, and chronic disease self-management [ 44 , 45 ]. Another implication of this study is the high burden of pain, discomfort, and depressive symptoms, particularly among disadvantaged groups, highlighting the need for structured pain management and integrated mental health services. Evidence shows that chronic pain self-management programs can improve functioning and emotional well-being among low-income adults [ 46 ], while collaborative care models in public primary care settings significantly enhance depression outcomes and the quality of mental health care for disadvantaged populations [ 47 ]. Research from low- and middle-income countries further suggests that digital psychological interventions can also benefit socioeconomically vulnerable groups [ 48 ]. Taken together, these findings point to the importance of expanding pain management and mental health services to improve HRQoL across the population, with particular attention to lower-income groups who face a higher risk of poor HRQoL. Finally, the persistence of income-related HRQoL gaps observed in this study demonstrates that lifestyle improvements alone are insufficient to eliminate socioeconomic inequalities. Addressing upstream determinants such as income security, working conditions, housing quality, and equitable access to healthcare is essential [ 39 ]. Without progress in these foundational conditions, adults with lower incomes will continue to face disproportionate exposure to chronic stress, environmental risks, and cumulative disease burden, factors that behavioral change alone cannot compensate for [ 49 ]. Cross-country evidence further shows that national investments in health and social protection systems are associated with higher population HRQoL and smaller socioeconomic gradients [ 31 , 39 ]. Therefore, reducing socioeconomic inequalities in HRQoL will require structural reforms that address poverty, healthcare access, gender inequality, and gaps in social protection. Limitations This study has several limitations. Its cross-sectional design prevents causal inference and raises the possibility of reverse causality, leaving it unclear whether healthier lifestyle behaviors lead to higher HRQoL or whether individuals with better HRQoL are more likely to adopt healthier habits. The study also did not examine the pathways through which socioeconomic status shapes subjective health, despite their importance in prior research [ 50 ]; future work should incorporate factors such as health literacy, access to care, environmental conditions, and psychosocial stressors. Lifestyle behaviors and body measurements were self-reported, which may introduce recall or reporting bias, underscoring the need for objective or clinically validated measures in future studies. An additional limitation is the use of UK tariffs, as no Turkish EQ-5D value set is currently available, although this approach is common in countries without national tariffs, including Greece [ 11 ], Norway [ 51 ], and Jordan [ 52 ]. Finally, reliance on the EQ-5D-3L instead of the 5L may limit measurement sensitivity, though evidence indicates that both versions yield broadly comparable utility estimates [ 31 ]; future research in Turkey should consider adopting the 5L once a national value set becomes available. Conclusion This study provides a comprehensive examination of HRQoL among adults aged 18–64 in Turkey by documenting overall HRQoL levels, assessing the contribution of key lifestyle behaviors, and analyzing how these behaviors interact with income to shape health inequalities. As the first nationally representative study in Turkey to incorporate lifestyle factors into the analysis of HRQoL disparities, it builds on earlier research by demonstrating how the HRQoL income gap shifts when behavioral determinants are considered. Although healthier lifestyles were consistently linked to higher HRQoL and produced particularly large gains for socioeconomically disadvantaged adults, income-based inequalities persisted across all lifestyle groups. This persistence illustrates the enduring influence of structural determinants that improvements in individual behavior cannot fully address. Overall, the findings highlight the need to interpret HRQoL within a framework that brings together lifestyle behaviors, socioeconomic context, and broader structural conditions, contributing to international debates on how these forces shape population well-being. Reducing socioeconomic inequalities in HRQoL will therefore require a comprehensive strategy that combines targeted lifestyle and prevention programs with structural reforms addressing poverty, healthcare access, gender inequality, and gaps in social protection. Declarations Competing Interests The author has no relevant financial or non-financial interests to disclose. Ethics Approval and Consent to Participate This study used secondary, anonymized microdata publicly provided by the Turkish Statistical Institute; therefore, ethical approval was not required. Informed consent was also not required because no identifiable personal data were used. Funding The author did not receive support from any organization for the submitted work. Author Contribution H.S. conceived and designed the study, conducted the data analysis, interpreted the results, drafted the manuscript, and approved the final version for submission. 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L., & Abu-Zaytoun, L. (2021). Variables associated with poor health-related quality of life among patients with dyslipidemia in Jordan. Quality of life research , 30 (5), 1417-1424. https://doi.org/10.1007/s11136-020-02726-9 Tables Table 1. Weighted sample characteristics and healthy lifestyle score by household income Variables Low Income High Income Total Sample p N=34,005 N=27,357 N=61,362 Age (years), mean (SE) 39.18 (0.08) 38.66 (0.08) 38.95 (0.06) <0.001 Gender Female, % (n) Male, % (n) 51.37% (19,038) 48.63% (14,967) 48.32% (14,021) 51.68% (13,336) 49.98% (33,059) 50.02% (28,303) <0.001 Marital Status Married, % (n) Single/widowed, % (n) 71.00% (25,149) 29.00% (8,856) 68.85% (19,849) 31.15% (7,508) 70.02% (44,998) 29.98% (16,364) <0.001 Education Level No formal education, % (n) Primary, % (n) Secondary, % (n) Tertiary, % (n) 13.59% (4,607) 48.43% (16,939) 28.72% (9,550) 9.26% (2,909) 3.97% (929) 29.83% (7,725) 33.82% (9,398) 32.37% (9,305) 9.21% (5,536) 39.95% (24,664) 31.05% (18,948) 19.79% (12,214) <0.001 Chronic conditions, mean (SE) 0.542 (0.006) 0.398 (0.006) 0.476 (0.004) <0.001 HLS score (0–5), mean (SE) 2.636 (0.007) 2.725 (0.008) 2.677 (0.005) <0.001 HLS Dimensions Non-smoker, % (n) No excessive drinking, % (n) Exercise weekly, % (n) Healthy diet, % (n) Healthy BMI, % (n) 64.48% (22,228) 97.07% (33,092) 5.16% (1,660) 56.67% (19,463) 40.23% (12,907) 63.30% (17,357) 95.93% (26,244) 11.31% (3,156) 61.02% (16,905) 40.96% (10,877) 63.94% (39,585) 96.55% (59,336) 7.96% (4,816) 58.65% (36,368) 40.56% (23,784) 0.018 <0.001 <0.001 <0.001 0.134 Notes: Cells show survey-weighted column percentages with unweighted counts in parentheses: % (n). Inference uses design-adjusted tests (Rao–Scott χ² for categorical; survey-weighted Wald/t for continuous). Table 2. Weighted EQ-5D prevalence by household income Variables Low Income High Income Total Sample p N=34,005 N=27,357 N=61,362 Mobility No difficulty Some difficulty A lot/Unable 77.32% (25,541) 15.97% (5,955) 6.71% (2,509) 85.87% (23,474) 10.79% (2,989) 3.34% (894) 81.22% (49,015) 13.61% (8,944) 5.17% (3,403) <0.001 Self-care No difficulty Some difficulty A lot/Unable 96.66% (32,820) 2.30% (846) 1.04% (339) 98.52% (26,974) 1.11% (286) 0.37% (97) 97.51% (59,794) 1.76% (1,132) 0.74% (436) <0.001 Usual activities (IADL) No difficulty Some difficulty A lot/Unable 78.65% (26,003) 11.68% (4,401) 9.67% (3,601) 85.28% (23,141) 8.85% (2,569) 5.87% (1,647) 81.68% (49,144) 10.39% (6,970) 7.93% (5,248) <0.001 Pain/Discomfort Not at all Several days Most days 52.47% (17,045) 33.06% (11,758) 14.48% (5,202) 60.07% (16,124) 30.83% (8,715) 9.09% (2,518) 55.93% (33,169) 32.04% (20,473) 12.02% (7,720) <0.001 Mental Health Not at all Several days More than half days 39.73% (12,884) 45.42% (15,758) 14.85% (5,363) 46.50% (12,495) 41.95% (11,600) 11.56% (3,262) 42.81% (25,379) 43.84% (27,358) 13.35% (8,625) <0.001 EQ-5D Index Score Unweighted mean (SD) Weighted mean (SE) 0.631 (0.376) 0.649 (0.003) 0.726 (0.316) 0.731 (0.003) 0.673 (0.354) 0.687 (0.002) <0.001 <0.001 Notes: Cells show survey-weighted percentages with unweighted counts in parentheses: % (n). p values are design-adjusted (Rao–Scott) tests comparing low vs high income for each dimension. Table 3. Results of ordinary least squares regression: Association of Healthy Lifestyle Score with EQ-5D index survey-weighted OLS multivariate model Variable β SE t p 95% CI Healthy lifestyle score (HLS) 0.041 0.002 17.39 <0.001 [0.036 – 0.045] Household income: high vs low 0.062 0.009 6.75 <0.001 [0.044 – 0.080] Interaction: HLS × high-income −0.010 0.003 −3.17 0.002 [−0.016 – −0.004] Age −0.002 0.000 −13.46 <0.001 [−0.002 – −0.002] Female −0.110 0.003 −37.81 <0.001 [−0.116 – −0.104] Education Primary 0.102 0.007 14.34 <0.001 [0.088 – 0.116] Secondary 0.129 0.008 17.06 <0.001 [0.114 – 0.144] Tertiary 0.144 0.008 18.22 <0.001 [0.128 – 0.159] Married 0.013 0.004 3.65 <0.001 [0.006 – 0.020] Chronic conditions −0.126 0.002 −58.31 <0.001 [−0.130 – −0.122] Constant 0.636 0.012 54.76 <0.001 [0.613 – 0.658] R² = 0.238; F(10, 35 118) = 1215.30; design df = 35 127; subpop N = 61 362. Notes. Reference groups: low income, male, no formal education, single/widowed/divorced. The interaction term is the difference in the HLS slope for high-income vs low-income households. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Mar, 2026 Reviews received at journal 19 Mar, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviews received at journal 11 Feb, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviewers agreed at journal 28 Dec, 2025 Reviewers invited by journal 17 Dec, 2025 Editor assigned by journal 12 Dec, 2025 Submission checks completed at journal 12 Dec, 2025 First submitted to journal 11 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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11:15:35","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":163011,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8336519/v1/0b5bced22649c36de9b55e16.html"},{"id":98594688,"identity":"808fe8ba-fa45-4773-841e-68b1d0a9bd86","added_by":"auto","created_at":"2025-12-19 11:15:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104701,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted EQ-5D by health lifestyle score and household income using survey-weighted OLS\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e Lines show adjusted margins from Table 3 with 95% confidence intervals. Predictions are averaged across the sample, with covariates held at their observed values. HLS ranges from 0 to 5. The interaction term (HLS × High income) is statistically significant (p = 0.005).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8336519/v1/de0ada7ac0c43f6899b25471.png"},{"id":98594689,"identity":"4e6ddd7f-dc86-43fc-8f82-e94b2e682bda","added_by":"auto","created_at":"2025-12-19 11:15:35","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":357983,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted mean EQ-5D scores by healthy lifestyle behaviors and household income\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8336519/v1/0d7fdf2577e28cc80fabe02b.jpeg"},{"id":98774946,"identity":"b64d94f0-0145-4b37-ab3f-dd4d20ea3f46","added_by":"auto","created_at":"2025-12-22 12:17:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1308476,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8336519/v1/b9f95f03-5d67-42b0-adc5-4baefae1ca46.pdf"},{"id":98594690,"identity":"7fcfa012-f6be-4375-bfcf-8bfc3c457288","added_by":"auto","created_at":"2025-12-19 11:15:35","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":16955,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-8336519/v1/2f1224ec921b0eb37b3952e2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Healthy lifestyle behaviors and persistent socioeconomic inequalities in health-related quality of life: Evidence from a population-based study in Turkey","fulltext":[{"header":"Plain English summary","content":"\u003cp\u003eHealth-related quality of life describes how people feel and function in their everyday lives, including their physical, mental, and emotional well-being. Understanding what shapes these outcomes is important for improving population health and reducing inequalities. In Turkey, however, little is known about how healthy lifestyle habits relate to health-related quality of life or whether these habits help narrow the gap between adults with higher and lower incomes. This study helps fill this gap by using data from a large, nationally representative sample of adults aged 18 to 64. It examined how lifestyle factors are linked to people\u0026rsquo;s overall well-being, and whether these links differ across income groups. The results showed that individuals with healthier lifestyles reported better health-related quality of life, particularly fewer problems related to pain and emotional distress. These positive effects were stronger among adults with lower incomes, suggesting that healthy lifestyle habits may offer extra benefits for groups facing more disadvantages. However, income-based differences in well-being persisted even among those with healthy lifestyles. Overall, the study suggests that encouraging healthier lifestyle habits can improve well-being, but reducing socioeconomic inequalities in health will also require broader social and economic policies that address the underlying conditions shaping people\u0026rsquo;s lives.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eHealth-Related Quality of Life (HRQoL) reflects physical, psychological, and social well-being and is a key indicator of population health [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among standardized measures, the EQ-5D, developed by the EuroQol Group, is the most widely used instrument for assessing HRQoL [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It evaluates five dimensions including mobility, self-care, usual activities, pain or discomfort, and anxiety/depression, and produces an index score based on population-derived utility values [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExtensive research using the EQ-5D consistently demonstrates marked socioeconomic disparities in HRQoL. Socioeconomically disadvantaged groups report lower EQ-5D scores across diverse settings, from high-income countries such as Sweden [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], Germany [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and Australia [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] to middle- and lower-income countries including Iran [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], Chile [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], Bangladesh [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], the Philippines [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], Greece [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and China [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These inequalities persist irrespective of how socioeconomic position is defined, whether by education, income, occupation, wealth, or savings [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Identifying the factors that contribute to, or may help reduce, these income-related differences is therefore a key research priority.\u003c/p\u003e \u003cp\u003eInternational evidence points to several determinants of lower HRQoL beyond socioeconomic status (SES). Socioeconomic characteristics, including older age [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and being female [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] are consistently linked to poorer HRQoL. Lifestyle behaviors represent another critical pathway, as physical inactivity, smoking, unhealthy body weight, excessive alcohol use, and inadequate diet are all associated with reduced HRQoL [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These behaviors also contribute to SES-based differences, as healthier choices tend to provide greater benefits for individuals with lower SES, both because they face higher baseline health risks [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and because lifestyle improvements yield more substantial gains in functioning and chronic disease outcomes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBuilding on this broader evidence, healthier lifestyles are increasingly viewed as a way to improve population well-being and reduce socioeconomic disparities in HRQoL, highlighting the need to examine whether lifestyle improvements can help narrow these gaps. Yet, despite the importance of this issue, population-based HRQoL research in Turkey remains limited. Most EQ-5D applications focused on clinical populations [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and only a few used nationally representative data [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Moreover, no study to date has examined whether lifestyle behaviors influence or moderate socioeconomic inequalities in HRQoL. Addressing this gap, the present study provides the first population-based analysis in Turkey to assess how lifestyle behaviors relate to HRQoL and whether they influence or moderate income-related inequalities. Using nationally representative data from the Turkey Health Survey (2014\u0026ndash;2022) and EQ-5D-3L index scores based on the United Kingdom (UK) time trade-off (TTO) tariff, the study examines income-based disparities in HRQoL and evaluates how these disparities vary across lifestyle profiles. Specifically, it investigates whether healthier behaviors are associated with higher HRQoL and narrower income gaps.\u003c/p\u003e \u003cp\u003eTurkey provides a particularly relevant context for this research, as the country faces pronounced socioeconomic disparities alongside some of the highest rates of smoking and physical inactivity among countries of the Organisation for Economic Co-operation and Development (OECD), while maintaining relatively low alcohol consumption due to cultural and religious norms [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Recent public health policies have emphasized individual responsibility through smoking bans, obesity prevention, and physical activity promotion, yet structural barriers to equitable access to healthcare, healthy foods, and safe environments persist [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This combination of behavioral risks and structural constraints makes Turkey a compelling setting to examine whether lifestyle improvements can meaningfully reduce, or potentially reinforce, SES-based inequalities within a rapidly evolving health system.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Data and Sample Characteristics\u003c/h2\u003e\n \u003cp\u003eThis secondary cross-sectional analysis used nationally representative data from the Turkey Health Survey, conducted by the Turkish Statistical Institute, pooling data from the 2014, 2016, 2019, and 2022 waves. The Turkey Health Survey has been administered biennially through in-person interviews since 2008, in collaboration with the Statistical Office of the European Union. The survey employs a multi-stage, stratified, and clustered sampling design based on the national address database, ensuring representativeness of the Turkish population. The original sample included 102,641 respondents. After listwise deletion of cases with missing data on the EQ-5D index, healthy lifestyle score, and socioeconomic status variables, the sample was reduced to 76,195. Subsequently, individuals outside the age range of 18\u0026ndash;64 were excluded, resulting in a final analytic sample of 61,362 participants.\u003c/p\u003e\n \u003cp\u003e---Insert Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e---\u003c/p\u003e\n \u003cp\u003eThe characteristics of the analytic sample are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Among the participants, 34,005 were classified as low income and 27,357 as high income. Compared with the high-income group, low-income participants were slightly older, more likely to be women, married, and had lower levels of education (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). They also reported a higher mean number of chronic conditions (0.54 vs. 0.40). The mean healthy lifestyle score was modestly higher among high-income individuals (2.73 vs. 2.64). Lifestyle patterns also differed between income groups: high-income participants were more likely to engage in weekly exercise and maintain healthier diets, whereas low-income participants had slightly higher rates of non-smoking and avoidance of excessive drinking. The prevalence of a healthy BMI did not differ significantly by income level.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eMeasurements\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eHealth-related quality of life (HRQoL)\u003c/strong\u003e was assessed using the EQ-5D-3L instrument, which evaluates five domains: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. The mobility domain includes walking 500 meters and climbing one flight of stairs; self-care includes toileting, dressing, bathing, and feeding; usual activities include meal preparation and light or heavy housework; pain/discomfort reflects both the presence of pain and its interference with daily activities; and anxiety/depression captures feelings of depression, lack of pleasure, fatigue, and sleep problems. All domains were harmonized across survey waves and coded on a three-level scale (1\u0026thinsp;=\u0026thinsp;no problems, 2\u0026thinsp;=\u0026thinsp;some problems, 3\u0026thinsp;=\u0026thinsp;severe problems/unable). Because no validated Turkish tariff is currently available, utility index values were calculated using the UK TTO tariff, consistent with previous studies conducted in Turkey [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. Disutility weights were applied to each domain and summed, with an additional penalty for reporting at least one severe problem. The final scores ranged from \u0026minus;\u0026thinsp;0.594 (worse than death) to 1 (full health), with higher values indicating better HRQoL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHealthy lifestyle score (HLS)\u003c/strong\u003e was constructed as a composite index based on five lifestyle factors: smoking status, alcohol consumption, physical activity, diet, and body mass index (BMI) [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. Each factor was coded as a binary variable, with participants receiving a value of 1 if they met the healthy criterion and 0 otherwise. Non-smokers were coded as 1, while current smokers were coded as 0. For alcohol consumption, participants were coded as 1 if they did not binge drink and 0 if they consumed 60 grams or more of pure alcohol on at least one occasion in the past 30 days [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. Physical activity was coded as 1 for participants reporting any weekly physical activity and 0 otherwise [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. Diet was coded as 1 if both fruit and vegetables were consumed on most days of the week (4\u0026ndash;6 days per week), and 0 otherwise [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. BMI was coded as 1 for values within the healthy range of 18.5\u0026ndash;24.9 kg/m\u0026sup2; and 0 otherwise [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. The overall HLS ranged from 0 to 5, with higher values indicating healthier lifestyles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocioeconomic status\u003c/strong\u003e was measured using household income, harmonized across survey waves. Respondents were classified as low- and high-income based on the yearly distribution within each survey wave.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e included age, gender, education, marital status, and number of chronic conditions. Age was measured as a continuous variable in years. Gender was coded as binary (female\u0026thinsp;=\u0026thinsp;1, male\u0026thinsp;=\u0026thinsp;0). Education was categorized into four levels: no formal education or non-literate (0), primary (1), secondary including middle school, high school, or vocational tracks (2), and tertiary including college, university, or postgraduate level (3). Marital status was coded as married (1) or not married (single, widowed, or divorced\u0026thinsp;=\u0026thinsp;0). The number of chronic conditions was measured as a continuous count of eight self-reported, physician-diagnosed illnesses: asthma, bronchitis, infarct, heart disease, hypertension, stroke, arthrosis, and diabetes.\u003c/p\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eDescriptive analyses were conducted to summarize sample characteristics and the outcome variable overall and by SES. Two sample \u003cem\u003et-tests\u003c/em\u003e were used for continuous variables, and chi-square tests for categorical variables. Multivariate ordinary least squares (OLS) regression models were then estimated to assess the expected change in EQ-5D index scores associated with a one-point increase in the HLS. To examine whether the association between HLS and HRQoL differed by SES, an interaction term between HLS and SES was included in the model. Predicted EQ-5D values across the observed HLS range by SES were plotted with 95% confidence intervals.\u003c/p\u003e\n \u003cp\u003eAdjusted mean EQ-5D scores for individual health behaviors were then estimated using the \u003cem\u003esubpopulation\u003c/em\u003e option for low- and high-income groups. Finally, sensitivity analyses were conducted to assess whether the observed HLS\u0026ndash;income interaction was driven by specific lifestyle domains by re-estimating the model with disaggregated HLS components and interacting each behavior with household income. All models accounted for the complex survey design by incorporating sampling weights (fertfaktor), clustering at the household level (PSU), and stratification by NUTS-2 pseudo-regions. Analyses were conducted in Stata 17, and figures were prepared using Microsoft Excel [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Findings","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eHealth Related Quality of Life by Household Income\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows HRQoL patterns by household income. Overall HRQoL was moderate, with a weighted mean EQ-5D score of 0.687. High-income adults had significantly higher scores than low-income adults (0.731 vs. 0.649, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Across EQ-5D dimensions, pain/discomfort and mental health symptoms were the most frequently reported problems. Among low-income participants, 47.5% reported pain on several days or more (vs. 39.9% in high income), and 60.3% reported anxiety/depressive symptoms on several days or more (vs. 53.5%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Self-care limitations were least common, especially among high-income adults (98.5% with no difficulty). Low-income respondents showed consistently higher rates of difficulty across all dimensions, with the largest gaps observed in pain/discomfort and mental health.\u003c/p\u003e\n \u003cp\u003e---Insert Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e---\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eMultivariate Predictors of HRQoL\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the multivariate OLS regression results. Older age (\u0026beta; = \u0026minus;0.002, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and being female (\u0026beta; = \u0026minus;0.110, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with lower HRQoL, while higher educational attainment showed progressively stronger positive associations (primary: \u0026beta;\u0026thinsp;=\u0026thinsp;0.102; secondary: \u0026beta;\u0026thinsp;=\u0026thinsp;0.129; tertiary: \u0026beta;\u0026thinsp;=\u0026thinsp;0.144; all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Being married was also linked to slightly higher HRQoL (\u0026beta;\u0026thinsp;=\u0026thinsp;0.013, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Chronic conditions had the strongest negative association (\u0026beta; = \u0026minus;0.126, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Each one-point increase in the Healthy Lifestyle Score (HLS) corresponded to a 0.041-point increase in EQ-5D scores (\u0026beta;\u0026thinsp;=\u0026thinsp;0.041, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). High-income individuals had significantly higher HRQoL than low-income individuals (\u0026beta;\u0026thinsp;=\u0026thinsp;0.062, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the interaction term was negative and significant (\u0026beta; = \u0026minus;0.010, p\u0026thinsp;=\u0026thinsp;0.002), indicating that the positive association between HLS and HRQoL was weaker among high-income adults. In other words, lifestyle improvements were more strongly associated with HRQoL gains in the low-income group.\u003c/p\u003e\n \u003cp\u003e---Insert Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e---\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e displays the predicted EQ-5D scores across the range of HLS. Higher HLS values were strongly associated with higher predicted HRQoL in both income groups. At every level of HLS, high-income individuals had better HRQoL than low-income individuals; however, the income gap gradually narrowed as HLS increased.\u003c/p\u003e\n \u003cp\u003e---Insert Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e---\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAdjusted HRQoL Scores by Health Behaviors and Household Income\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents adjusted mean EQ-5D scores by health behaviors and income groups. Across all five lifestyle domains, individuals with healthier behaviors reported higher HRQoL, regardless of income. An exception was weekly exercise, where those reporting regular exercise showed slightly lower EQ-5D scores than non-exercisers in both income groups. The magnitude of HRQoL improvement associated with healthier behaviors varied by income, with the largest gains observed for a healthy diet, followed by non-smoking. These two behaviors also showed the greatest narrowing of income-related differences in HRQoL.\u003c/p\u003e\n\u003cp\u003e---Insert Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e---\u003c/p\u003e\n\u003ch3\u003eSensitivity Analysis\u003c/h3\u003e\n\u003cp\u003eFor sensitivity analysis, the regression model was re-estimated using the individual lifestyle components (Appendix Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Healthy diet (\u0026beta;\u0026thinsp;=\u0026thinsp;0.072, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), non-smoking (\u0026beta;\u0026thinsp;=\u0026thinsp;0.033, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and healthy BMI (\u0026beta;\u0026thinsp;=\u0026thinsp;0.012, p\u0026thinsp;=\u0026thinsp;0.002) were positively associated with higher HRQoL scores, whereas physical activity and alcohol use showed no meaningful effects. High-income status remained positively associated with HRQoL (\u0026beta;\u0026thinsp;=\u0026thinsp;0.044, p\u0026thinsp;=\u0026thinsp;0.002). However, the benefit of a healthy diet was significantly weaker among high-income individuals (interaction \u0026beta; = \u0026minus;0.020, p\u0026thinsp;=\u0026thinsp;0.001). Interaction terms for the remaining lifestyle behaviors were not statistically significant.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe primary aim of this study was to assess HRQoL among adults aged 18\u0026ndash;64 in Turkey and to examine how differences in healthy lifestyle behaviors contribute to income-related disparities within a nationally representative population. Overall HRQoL levels were moderate and lower than those typically reported in high-income countries such as Sweden, Germany, and Australia [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], aligning more closely with findings from middle-income settings including Iran, Bangladesh, and Greece during its economic downturn [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This pattern is consistent with global evidence showing that national income, health system capacity, and broader welfare infrastructures play central roles in shaping population-level HRQoL [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe distribution of domain-specific HRQoL problems, such as the high prevalence of pain/discomfort and anxiety/depression and the relatively low impairment in self-care, also mirrors international EQ-5D population norms. Psychological distress and pain remain the most frequent sources of reduced HRQoL worldwide, a trend reflected in our data as well [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Similarly, sociodemographic gradients observed in this study, including lower HRQoL among women, older adults, individuals with lower education, unmarried respondents, and those with multiple chronic conditions, closely parallel cross-country research and reinforce established social determinants of health frameworks [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Together, these findings suggest that many of the structural drivers of global health inequality are also salient in the Turkish context.\u003c/p\u003e \u003cp\u003eConsistent with extensive literature, healthier lifestyle behaviors were associated with higher HRQoL. Non-smoking, healthy diet, and maintaining a healthy BMI emerged as the strongest contributors, supporting evidence that modifiable cardiometabolic and behavioral risk factors are key determinants of subjective health [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In contrast, the absence of a clear association between weekly exercise and HRQoL diverges from findings in comparable middle-income settings [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Several explanations are plausible. First, reverse causality may be present, as individuals with chronic pain, fatigue, or stiffness may begin exercising as a coping strategy, which can weaken the associations observed in cross-sectional analyses [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Second, prior studies suggest a non-linear (U-shaped) relationship between physical activity and pain, with both inactivity and very high activity levels linked to lower HRQoL [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Third, physical activity measures in population surveys often rely on self-report and may lack precision, weakening detectable associations [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA central question guiding this study was whether adopting healthier lifestyle behaviors helps buffer or reduce the socioeconomic gradient in HRQoL. Although high-income adults maintained higher EQ-5D scores at every lifestyle level, the marginal HRQoL gains associated with healthier behaviors, particularly non-smoking and a healthy diet, were substantially larger among lower-income individuals. This pattern is consistent with perspectives in social epidemiology arguing that socioeconomic disadvantage intensifies both exposure to unhealthy environments and physiological vulnerability to behavioral risks [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Within this framework, the same unhealthy behavior may have more severe consequences among individuals with lower SES, reflecting cumulative stress, constrained resources, and reduced access to timely, high-quality care [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These mechanisms help explain why healthier lifestyle practices yielded larger marginal benefits for disadvantaged groups in this study, who also face higher burdens of pain, chronic symptoms, and psychological distress [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these lifestyle-related benefits, income-based disparities persisted across all lifestyle levels, underscoring that behavioral modification alone cannot overcome the structural determinants of HRQoL. This finding aligns with a substantial body of research demonstrating that individual health behaviors interact with broader socioeconomic conditions, such as labor environments, healthcare accessibility, neighborhood safety, and cumulative physiological wear, but cannot fully counteract them [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The persistence of socioeconomic gradients across lifestyle strata indicates that HRQoL inequality is shaped by both proximal behavioral pathways and distal structural forces. Thus, while healthy lifestyles meaningfully enhance HRQoL, particularly among adults facing economic hardship, socioeconomic disadvantage continues to exert a strong influence on subjective health outcomes.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePolicy and Practice Implications\u003c/h2\u003e \u003cp\u003eThe findings of this study offer several considerations for policy and practice aimed at improving HRQoL and reducing socioeconomic disparities. Because unhealthy behaviors are more common among lower-income individuals and healthier behaviors yield larger HRQoL gains for these groups, expanding targeted behavior-change interventions may be particularly beneficial. Evidence also shows that addressing modifiable health behaviors can substantially reduce their contribution to socioeconomic inequalities in health, highlighting the potential of well-designed interventions to narrow SES-related gaps in well-being [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Strategies include expanding access to safe and affordable opportunities for physical activity, such as walkable neighborhood environments [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and subsidizing fruits and vegetables to increase fresh produce purchasing among low-income households [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Strengthening access to reliable, easy-to-understand health information through interpersonal communication and tailored educational materials can also support smoking abstinence, physical activity, and chronic disease self-management [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnother implication of this study is the high burden of pain, discomfort, and depressive symptoms, particularly among disadvantaged groups, highlighting the need for structured pain management and integrated mental health services. Evidence shows that chronic pain self-management programs can improve functioning and emotional well-being among low-income adults [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], while collaborative care models in public primary care settings significantly enhance depression outcomes and the quality of mental health care for disadvantaged populations [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Research from low- and middle-income countries further suggests that digital psychological interventions can also benefit socioeconomically vulnerable groups [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Taken together, these findings point to the importance of expanding pain management and mental health services to improve HRQoL across the population, with particular attention to lower-income groups who face a higher risk of poor HRQoL.\u003c/p\u003e \u003cp\u003eFinally, the persistence of income-related HRQoL gaps observed in this study demonstrates that lifestyle improvements alone are insufficient to eliminate socioeconomic inequalities. Addressing upstream determinants such as income security, working conditions, housing quality, and equitable access to healthcare is essential [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Without progress in these foundational conditions, adults with lower incomes will continue to face disproportionate exposure to chronic stress, environmental risks, and cumulative disease burden, factors that behavioral change alone cannot compensate for [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Cross-country evidence further shows that national investments in health and social protection systems are associated with higher population HRQoL and smaller socioeconomic gradients [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Therefore, reducing socioeconomic inequalities in HRQoL will require structural reforms that address poverty, healthcare access, gender inequality, and gaps in social protection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. Its cross-sectional design prevents causal inference and raises the possibility of reverse causality, leaving it unclear whether healthier lifestyle behaviors lead to higher HRQoL or whether individuals with better HRQoL are more likely to adopt healthier habits. The study also did not examine the pathways through which socioeconomic status shapes subjective health, despite their importance in prior research [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]; future work should incorporate factors such as health literacy, access to care, environmental conditions, and psychosocial stressors. Lifestyle behaviors and body measurements were self-reported, which may introduce recall or reporting bias, underscoring the need for objective or clinically validated measures in future studies. An additional limitation is the use of UK tariffs, as no Turkish EQ-5D value set is currently available, although this approach is common in countries without national tariffs, including Greece [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], Norway [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], and Jordan [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Finally, reliance on the EQ-5D-3L instead of the 5L may limit measurement sensitivity, though evidence indicates that both versions yield broadly comparable utility estimates [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]; future research in Turkey should consider adopting the 5L once a national value set becomes available.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides a comprehensive examination of HRQoL among adults aged 18\u0026ndash;64 in Turkey by documenting overall HRQoL levels, assessing the contribution of key lifestyle behaviors, and analyzing how these behaviors interact with income to shape health inequalities. As the first nationally representative study in Turkey to incorporate lifestyle factors into the analysis of HRQoL disparities, it builds on earlier research by demonstrating how the HRQoL income gap shifts when behavioral determinants are considered. Although healthier lifestyles were consistently linked to higher HRQoL and produced particularly large gains for socioeconomically disadvantaged adults, income-based inequalities persisted across all lifestyle groups. This persistence illustrates the enduring influence of structural determinants that improvements in individual behavior cannot fully address. Overall, the findings highlight the need to interpret HRQoL within a framework that brings together lifestyle behaviors, socioeconomic context, and broader structural conditions, contributing to international debates on how these forces shape population well-being. Reducing socioeconomic inequalities in HRQoL will therefore require a comprehensive strategy that combines targeted lifestyle and prevention programs with structural reforms addressing poverty, healthcare access, gender inequality, and gaps in social protection.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe author has no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study used secondary, anonymized microdata publicly provided by the Turkish Statistical Institute; therefore, ethical approval was not required. Informed consent was also not required because no identifiable personal data were used.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe author did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eH.S. conceived and designed the study, conducted the data analysis, interpreted the results, drafted the manuscript, and approved the final version for submission.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe author expresses appreciation to the Turkish Statistical Institute for granting access to the Turkey Health Survey microdata used in this study.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData from the Turkey Health Survey are available upon request from the Turkish Statistical Institute.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKarimi, M., \u0026amp; Brazier, J. 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Closing the gap in a generation: health equity through action on the social determinants of health. \u003cem\u003eThe Lancet\u003c/em\u003e, \u003cem\u003e372\u003c/em\u003e(9650), 1661-1669. https://doi.org/10.1016/S0140-6736(08)61690-6 \u003c/li\u003e\n\u003cli\u003eStringhini, S., Sabia, S., Shipley, M., Brunner, E., Nabi, H., Kivimaki, M., \u0026amp; Singh-Manoux, A. (2010). Association of socioeconomic position with health behaviors and mortality. \u003cem\u003eJournal of the American Medical Association\u003c/em\u003e, \u003cem\u003e303\u003c/em\u003e(12), 1159-1166. https://doi.org/10.1001/jama.2010.297 \u003c/li\u003e\n\u003cli\u003ePetrovic, D., de Mestral, C., Bochud, M., Bartley, M., Kivim\u0026auml;ki, M., Vineis, P., ... \u0026amp; Stringhini, S. (2018). The contribution of health behaviors to socioeconomic inequalities in health: a systematic review. \u003cem\u003ePreventive Medicine\u003c/em\u003e, \u003cem\u003e113\u003c/em\u003e, 15-31. https://doi.org/10.1016/j.ypmed.2018.05.003\u003c/li\u003e\n\u003cli\u003eOwen, N., Cerin, E., Leslie, E., Coffee, N., Frank, L. D., Bauman, A. E., ... \u0026amp; Sallis, J. F. (2007). Neighborhood walkability and the walking behavior of Australian adults. \u003cem\u003eAmerican Journal of Preventive Medicine\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(5), 387-395. https://doi.org/10.1016/j.amepre.2007.07.025 \u003c/li\u003e\n\u003cli\u003eMoran, A., Thorndike, A., Franckle, R., et al. (2019). Financial incentives increase purchases of fruit and vegetables among lower-income households. \u003cem\u003eHealth Affairs, 38\u003c/em\u003e(9), 1557\u0026ndash;1566. https://doi.org/10.1377/hlthaff.2018.05420 \u003c/li\u003e\n\u003cli\u003eDutta-Bergman, M. J. (2004). Primary sources of health information: Comparisons in the domain of health attitudes, health cognitions, and health behaviors. \u003cem\u003eHealth Communication, 16\u003c/em\u003e(3), 273\u0026ndash;288. https://doi.org/10.1207/S15327027HC1603_1\u003c/li\u003e\n\u003cli\u003eWebb, T. L., Joseph, J., Yardley, L., \u0026amp; Michie, S. (2010). Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. \u003cem\u003eJournal of Medical Internet Research, 12\u003c/em\u003e(1), e1376. https://doi.org/10.2196/jmir.1376 \u003c/li\u003e\n\u003cli\u003eTurner, B. J., Rodriguez, N., Bobadilla, R., Hernandez, A. E., \u0026amp; Yin, Z. (2020). Chronic pain self-management program for low-income patients: themes from a qualitative inquiry. \u003cem\u003ePain Medicine\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(2), 1-8. https://doi.org/10.1093/pm/pny192 \u003c/li\u003e\n\u003cli\u003eLagomasino, I. T., Dwight-Johnson, M., Green, J. M., Tang, L., Zhang, L., Duan, N., \u0026amp; Miranda, J. (2017). Effectiveness of collaborative care for depression in public-sector primary care clinics serving Latinos. \u003cem\u003ePsychiatric Services\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e(4), 353-359. https://doi.org/10.1176/appi.ps.201600187 \u003c/li\u003e\n\u003cli\u003eFu, Z., Burger, H., Arjadi, R., \u0026amp; Bockting, C. L. (2020). Effectiveness of digital psychological interventions for mental health problems in low-income and middle-income countries: a systematic review and meta-analysis. \u003cem\u003eThe Lancet Psychiatry\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(10), 851-864. https://doi.org/10.1016/S2215-0366(20)30256-X \u003c/li\u003e\n\u003cli\u003eSolar, O., \u0026amp; Irwin, A. (2010). \u003cem\u003eA conceptual framework for action on the social determinants of health.\u003c/em\u003e World Health Organization. https://www.who.int/publications/i/item/9789241500852 \u003c/li\u003e\n\u003cli\u003eMatute, I., Burgos, S., \u0026amp; Alfaro, T. (2017). Socioeconomic status and perceived health-related quality of life in Chile. \u003cem\u003eInternational Journal of Cuban Health \u0026amp; Medicine 19\u003c/em\u003e(2-3),51-56.\u003c/li\u003e\n\u003cli\u003eRobinson, E. G., Gyllensten, H., Granas, A. G., Halvorsen, K. H., \u0026amp; Garcia, B. H. (2024). Health-related quality of life among older adults following acute hospitalization: longitudinal analysis of a randomized controlled trial. \u003cem\u003eQuality of Life Research\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(8), 2219-2233. https://doi.org/10.1007/s11136-024-03689-x \u003c/li\u003e\n\u003cli\u003eJarab, A. S., Alefishat, E. A., Al-Qerem, W., Mukattash, T. L., \u0026amp; Abu-Zaytoun, L. (2021). Variables associated with poor health-related quality of life among patients with dyslipidemia in Jordan. \u003cem\u003eQuality of life research\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(5), 1417-1424. https://doi.org/10.1007/s11136-020-02726-9\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eWeighted sample characteristics and healthy lifestyle score by household income\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 198px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eLow Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eHigh Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eTotal Sample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eN=34,005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eN=27,357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eN=61,362\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eAge (years), mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e39.18 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e38.66 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e38.95 (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Female, % (n)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Male, % (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e51.37% (19,038)\u003c/p\u003e\n \u003cp\u003e48.63% (14,967)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e48.32% (14,021)\u003c/p\u003e\n \u003cp\u003e51.68% (13,336)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e49.98% (33,059)\u003c/p\u003e\n \u003cp\u003e50.02% (28,303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Married, % (n)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Single/widowed, % (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e71.00% (25,149)\u003c/p\u003e\n \u003cp\u003e29.00% (8,856)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e68.85% (19,849)\u003c/p\u003e\n \u003cp\u003e31.15% (7,508)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e70.02% (44,998)\u003c/p\u003e\n \u003cp\u003e29.98% (16,364)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eEducation Level\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No formal education, % (n)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Primary, % (n)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Secondary, % (n)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Tertiary, % (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13.59% (4,607)\u003c/p\u003e\n \u003cp\u003e48.43% (16,939)\u003c/p\u003e\n \u003cp\u003e28.72% (9,550)\u003c/p\u003e\n \u003cp\u003e9.26% (2,909)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.97% (929)\u003c/p\u003e\n \u003cp\u003e29.83% (7,725)\u003c/p\u003e\n \u003cp\u003e33.82% (9,398)\u003c/p\u003e\n \u003cp\u003e32.37% (9,305)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9.21% (5,536)\u003c/p\u003e\n \u003cp\u003e39.95% (24,664)\u003c/p\u003e\n \u003cp\u003e31.05% (18,948)\u003c/p\u003e\n \u003cp\u003e19.79% (12,214)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eChronic conditions, mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.542 (0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.398 (0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.476 (0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eHLS score (0\u0026ndash;5), mean (SE)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e2.636 (0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e2.725 (0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e2.677 (0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eHLS Dimensions\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Non-smoker, % (n)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No excessive drinking, % (n)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Exercise weekly, % (n)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Healthy diet, % (n)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Healthy BMI, % (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e64.48% (22,228)\u003c/p\u003e\n \u003cp\u003e97.07% (33,092)\u003c/p\u003e\n \u003cp\u003e5.16% (1,660) 56.67% (19,463)\u003c/p\u003e\n \u003cp\u003e40.23% (12,907)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e63.30% (17,357)\u003c/p\u003e\n \u003cp\u003e95.93% (26,244)\u003c/p\u003e\n \u003cp\u003e11.31% (3,156) 61.02% (16,905)\u003c/p\u003e\n \u003cp\u003e40.96% (10,877)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e63.94% (39,585)\u003c/p\u003e\n \u003cp\u003e96.55% (59,336) 7.96% (4,816)\u003c/p\u003e\n \u003cp\u003e58.65% (36,368)\u003c/p\u003e\n \u003cp\u003e40.56% (23,784)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e Cells show survey-weighted column percentages with unweighted counts in parentheses: % (n). Inference uses design-adjusted tests (Rao\u0026ndash;Scott \u0026chi;\u0026sup2; for categorical; survey-weighted Wald/t for continuous).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eWeighted\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eEQ-5D prevalence by household income\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 170px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eLow Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eHigh Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eTotal Sample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eN=34,005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eN=27,357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eN=61,362\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMobility\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No difficulty\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Some difficulty\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;A lot/Unable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e77.32% (25,541)\u003c/p\u003e\n \u003cp\u003e15.97% (5,955)\u003c/p\u003e\n \u003cp\u003e6.71% (2,509)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e85.87% (23,474)\u003c/p\u003e\n \u003cp\u003e10.79% (2,989)\u003c/p\u003e\n \u003cp\u003e3.34% (894)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e81.22% (49,015)\u003c/p\u003e\n \u003cp\u003e13.61% (8,944)\u003c/p\u003e\n \u003cp\u003e5.17% (3,403)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-care\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No difficulty\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Some difficulty\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;A lot/Unable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96.66% (32,820)\u003c/p\u003e\n \u003cp\u003e2.30% (846)\u003c/p\u003e\n \u003cp\u003e1.04% (339)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e98.52% (26,974)\u003c/p\u003e\n \u003cp\u003e1.11% (286)\u003c/p\u003e\n \u003cp\u003e0.37% (97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e97.51% (59,794)\u003c/p\u003e\n \u003cp\u003e1.76% (1,132)\u003c/p\u003e\n \u003cp\u003e0.74% (436)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUsual activities (IADL)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No difficulty\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Some difficulty\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;A lot/Unable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e78.65% (26,003)\u003c/p\u003e\n \u003cp\u003e11.68% (4,401)\u003c/p\u003e\n \u003cp\u003e9.67% (3,601)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e85.28% (23,141)\u003cbr\u003e\u0026nbsp;8.85% (2,569)\u003cbr\u003e\u0026nbsp;5.87% (1,647)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e81.68% (49,144)\u003cbr\u003e\u0026nbsp;10.39% (6,970)\u003c/p\u003e\n \u003cp\u003e7.93% (5,248)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePain/Discomfort\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Not at all\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Several days\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Most days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e52.47% (17,045)\u003c/p\u003e\n \u003cp\u003e33.06% (11,758)\u003c/p\u003e\n \u003cp\u003e14.48% (5,202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e60.07% (16,124)\u003c/p\u003e\n \u003cp\u003e30.83% (8,715)\u003c/p\u003e\n \u003cp\u003e9.09% (2,518)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e55.93% (33,169)\u003c/p\u003e\n \u003cp\u003e32.04% (20,473)\u003c/p\u003e\n \u003cp\u003e12.02% (7,720)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMental Health\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Not at all\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Several days\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;More than half days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e39.73% (12,884)\u003c/p\u003e\n \u003cp\u003e45.42% (15,758)\u003cbr\u003e\u0026nbsp;14.85% (5,363)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e46.50% (12,495)\u003cbr\u003e\u0026nbsp;41.95% (11,600)\u003cbr\u003e\u0026nbsp;11.56% (3,262)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e42.81% (25,379)\u003cbr\u003e\u0026nbsp;43.84% (27,358)\u003cbr\u003e\u0026nbsp;13.35% (8,625)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEQ-5D Index Score\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Unweighted mean (SD)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Weighted mean (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.631 (0.376)\u003c/p\u003e\n \u003cp\u003e0.649 (0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.726 (0.316)\u003c/p\u003e\n \u003cp\u003e0.731 (0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.673 (0.354)\u003c/p\u003e\n \u003cp\u003e0.687 (0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e Cells show survey-weighted percentages with unweighted counts in parentheses: % (n). p values are design-adjusted (Rao\u0026ndash;Scott) tests comparing low vs high income for each dimension.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eResults of ordinary least squares regression: Association of Healthy Lifestyle Score with EQ-5D index survey-weighted OLS multivariate model\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHealthy lifestyle score (HLS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[0.036 \u0026ndash; 0.045]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHousehold income: high vs low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[0.044 \u0026ndash; 0.080]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInteraction: HLS \u0026times; high-income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[\u0026minus;0.016 \u0026ndash; \u0026minus;0.004]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;13.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[\u0026minus;0.002 \u0026ndash; \u0026minus;0.002]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;37.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[\u0026minus;0.116 \u0026ndash; \u0026minus;0.104]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026emsp;Primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[0.088 \u0026ndash; 0.116]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026emsp;Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[0.114 \u0026ndash; 0.144]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026emsp;Tertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[0.128 \u0026ndash; 0.159]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[0.006 \u0026ndash; 0.020]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eChronic conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;58.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[\u0026minus;0.130 \u0026ndash; \u0026minus;0.122]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[0.613 \u0026ndash; 0.658]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eR\u0026sup2; = 0.238; F(10, 35 118) = 1215.30; design df = 35 127; subpop N = 61 362.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes.\u003c/strong\u003e Reference groups: low income, male, no formal education, single/widowed/divorced. The interaction term is the difference in the HLS slope for high-income vs low-income households.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"quality-of-life-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"qure","sideBox":"Learn more about [Quality of Life Research](https://www.springer.com/journal/11136)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/qure/default.aspx","title":"Quality of Life Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Health-related quality of life, EQ-5D, socioeconomic inequalities, lifestyle behaviors, social work, population health","lastPublishedDoi":"10.21203/rs.3.rs-8336519/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8336519/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eHealth-related quality of life (HRQoL) is a key indicator of population well-being; however, limited evidence documents how lifestyle behaviors relate to HRQoL and its socioeconomic gradient in Turkey. This study assessed overall HRQoL levels, examined the associations between healthy lifestyle behaviors and HRQoL, and evaluated whether lifestyle factors mitigate income-related disparities among adults aged 18\u0026ndash;64.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were obtained from the nationally representative 2014\u0026ndash;2022 Turkey Health Survey (N\u0026thinsp;=\u0026thinsp;61,362). HRQoL was measured using the EQ-5D-3L with UK TTO tariffs. A Healthy Lifestyle Score (0\u0026ndash;5) included smoking status, alcohol consumption, physical activity, diet, and BMI. Survey-weighted OLS regressions estimated associations between lifestyle behaviors and HRQoL, and an interaction term tested whether these associations differed by income level. Sensitivity analyses assessed the contribution of individual lifestyle components.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMean HRQoL was moderate (EQ-5D\u0026thinsp;=\u0026thinsp;0.687) and substantially lower among adults with lower income. Pain/discomfort and anxiety/depression were the most impaired domains. Healthier lifestyle behaviors were positively associated with HRQoL, with non-smoking, a healthy diet, and maintaining a BMI within the recommended range showing the strongest associations. These benefits were more pronounced among lower-income adults; however, income-based inequalities in HRQoL persisted across all levels of lifestyle behaviors.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eHealthy lifestyle behaviors play an important role in shaping HRQoL and yield enhanced benefits for socioeconomically disadvantaged adults. Nonetheless, lifestyle improvements alone are insufficient to eliminate income-related inequalities. Addressing both behavioral and structural determinants is essential for improving HRQoL and reducing socioeconomic disparities.\u003c/p\u003e","manuscriptTitle":"Healthy lifestyle behaviors and persistent socioeconomic inequalities in health-related quality of life: Evidence from a population-based study in Turkey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-19 11:15:30","doi":"10.21203/rs.3.rs-8336519/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-22T00:39:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-19T17:14:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222283484480444324922594876603525234250","date":"2026-03-03T21:36:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175928098549028950690905696182768267558","date":"2026-02-25T16:41:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-11T19:52:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45878267661964356224240040813262796399","date":"2026-01-16T14:59:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"204911564702994364079977166408748232730","date":"2026-01-13T13:54:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144920409950459738048590407605083994010","date":"2025-12-28T10:03:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-17T14:12:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-12T05:21:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-12T05:20:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Quality of Life Research","date":"2025-12-11T11:53:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"quality-of-life-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"qure","sideBox":"Learn more about [Quality of Life Research](https://www.springer.com/journal/11136)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/qure/default.aspx","title":"Quality of Life Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5db21d8a-6835-4776-9ed8-69cc0b655159","owner":[],"postedDate":"December 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-22T18:23:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-19 11:15:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8336519","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8336519","identity":"rs-8336519","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}