Psychometric performance of the EQ-HWB-25 and EQ-HWB-9 in Japan: evidence from a large web-based survey of multimorbidity and depression

Psychometric performance of the EQ-HWB-25 and EQ-HWB-9 in Japan: evidence from a large web-based survey of multimorbidity and depression | 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 Psychometric performance of the EQ-HWB-25 and EQ-HWB-9 in Japan: evidence from a large web-based survey of multimorbidity and depression Shinichi Noto, Shinya Saito, Takeru Shiroiwa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9025329/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background The EQ-HWB was developed to capture broader aspects of wellbeing beyond traditional health-related quality of life. However, empirical evidence from non-European populations remains limited. This study evaluated the psychometric performance of the EQ-HWB-9 utility index and the EQ-HWB-25 profile measure in a large sample of the Japanese general population, with a focus on multimorbidity and depression. Methods We analysed data from a nationwide web-based cross-sectional survey (n = 5,177). The EQ-HWB-9 utility and EQ-HWB-25 composite scores were examined across levels of multimorbidity and depression. Known-group validity was assessed via group comparisons. Multivariable ordinary least squares regression with HC3 robust standard errors was used to estimate adjusted associations between the selected conditions and the EQ-HWB-9 utility. Results The EQ-HWB-9 utility scores decreased monotonically with increasing number of comorbid conditions. The respondents reporting depression had substantially lower EQ-HWB-9 utility scores (β = -0.124 (95% CI − 0.148–−0.101)) and markedly lower standardized EQ-HWB-25 composite scores. In multivariable models adjusted for age, sex, and multimorbidity, depression, arthritis, and lower back pain were independently associated with lower EQ-HWB-9 utility scores. The EQ-HWB-25 provides complementary descriptive information, highlighting multidimensional wellbeing deficits associated with depression. Conclusions These findings support the construct validity and known-group validity of the EQ-HWB in Japan. The EQ-HWB-9 captured both the cumulative multimorbidity burden and condition-specific impacts on wellbeing, particularly for depression, whereas the EQ-HWB-25 offered broader profile-level insights. The EQ-HWB framework may be especially valuable for wellbeing assessment and economic evaluation in populations with a high prevalence of multimorbidity and depression. EQ-HWB wellbeing multimorbidity depression psychometric validation utility score Figures Figure 1 Figure 2 Figure 3 Introduction Health-related quality of life (HRQoL) instruments have traditionally focused on physical functioning and symptoms, often showing limited sensitivity to broader psychosocial aspects of wellbeing, particularly in samples from the general population [1]. HRQoL measures are conceptually distinct from overall quality of life and may omit domains that matter for subjective wellbeing, such as emotional and social functioning [2]. This limitation is increasingly recognized in ageing societies, where multimorbidity and mental health conditions are common and where conventional HRQoL measures frequently exhibit ceiling effects. The EQ Health and Wellbeing (EQ-HWB) framework was developed to address these limitations by capturing a wider conceptualization of wellbeing beyond traditional HRQoL. The EQ-HWB consists of a 25-item descriptive system (EQ-HWB-25) and a reduced 9-item version (EQ-HWB-9) designed for preference-based valuation, enabling both multidimensional wellbeing profiling and economic evaluation [3-5]. Initial development and valuation studies, conducted primarily in European settings, have demonstrated promising psychometric properties and produced utility value sets for the EQ-HWB-9 [6]. However, empirical evidence from non-European populations remains scarce. Mental health represents a particularly important domain for wellbeing measurement. Depression is a leading contributor to the global disability burden and frequently cooccurs with chronic physical conditions, substantially increasing overall wellbeing loss in individuals with multimorbidity [7,8]. However, many widely used preference-based instruments place limited emphasis on mental and social functioning. Evaluating whether the EQ-HWB adequately captures depression-related wellbeing deficits, alongside physical multimorbidity, is therefore critical for assessing its suitability for population health monitoring and economic evaluation. Japan provides a valuable context in which to examine these issues, given its rapidly aging population and growing burden of chronic disease and mental health conditions [9]. To date, however, no large-scale studies have evaluated the performance of the EQ-HWB in a Japanese general population sample. In other countries [10-16], various psychometric validations of the EQ-HWB, including its validity and reliability, are progressing, making validation in Japan an urgent task. The aim of this study was to assess the psychometric performance of the EQ-HWB-25 and EQ-HWB-9 in Japan via data from a nationwide web-based survey. Specifically, we examined (i) whether EQ-HWB-9 utility scores demonstrate a dose‒response relationship with multimorbidity, (ii) whether EQ-HWB outcomes discriminate between respondents with and without depression, and (iii) whether selected physical and mental health conditions show independent associations with EQ-HWB-9 utility scores after multivariable adjustment. By providing evidence from a non-European population, this study seeks to inform the ongoing refinement and implementation of the EQ-HWB for wellbeing assessment and economic evaluation in populations with complex health needs. Methods This study used data from a nationwide web-based cross-sectional survey conducted in Japan to evaluate the psychometric performance of the EQ-HWB-25 descriptive system and the EQ-HWB-9 utility index in a general population sample. The analyses focused on the relationships among wellbeing outcomes, multimorbidity, and selected physical and mental health conditions, with particular attention given to depression. We examined known-group validity via group comparisons and estimated condition-specific associations with EQ-HWB-9 utility scores via multivariable regression models. Study design and participants A nationwide web-based cross-sectional survey was conducted in Japan. This survey selected 5,000 respondents aged 20 years and older through random sampling from 10 regions across Japan (Hokkaido, Tohoku, Kanto, Keihin, Hokuriku, Tokai, Keihanshin, Chugoku, Shikoku, and Kyushu). Participants were recruited from an online survey panel and provided informed consent prior to participation. INTAGE Healthcare, a specialized online survey company, managed sample recruitment (via Japanese online panels), survey administration, and data collection. The eligibility criteria were age 20 years or older and completion of the EQ-HWB questionnaire. This research protocol was approved by the Ethics Committee of Niigata University of Health and Welfare (Approval No. 18922-221101). Measures EQ-HWB Well-being was assessed via the EQ-HWB-25 descriptive system and the EQ-HWB-9. The EQ-HWB-25 consists of 25 items covering physical, emotional, social, and functional aspects of wellbeing. The EQ-HWB-9 uses a value set developed by Mukuria et al. [4] via composite time trade-off and discrete choice experiments, converting values into utility scores and yielding scores on the perfect health-death scale. Health conditions and multimorbidity The participants self-reported their health status and symptoms on the basis of physician diagnosis. Multimorbidity was operationalized as the total number of reported diseases and categorized as 0, 1, 2, or ≥3. To examine disease-specific associations while maintaining model parsimony and estimation stability, representative diseases covering major disease domains represented in the survey were prespecified. The selection criteria were based on clinical relevance, sufficient prevalence, and minimal redundancy and multicollinearity in cases of multimorbidity. The main models included depression (mental health), diabetes and hypertension (cardiometabolic), eye disease (sensory organ), and musculoskeletal symptoms (composite indicators of shoulder stiffness or low back pain). Musculoskeletal symptoms were combined to reduce overlap. Alternative specifications separating musculoskeletal symptoms and additional common diseases (dyslipidemia, allergic rhinitis, and dental disease) were examined via sensitivity analyses. Owing to low case numbers, rare diseases such as dementia and Parkinson's disease were considered exploratory. Statistical analysis Descriptive statistics were used to summarize participant characteristics and EQ-HWB responses. Known-group validity was assessed by comparing the EQ-HWB results across levels of multimorbidity and mental health status. Associations between the selected conditions and the EQ-HWB-9 utility scores were estimated via ordinary least squares regression. The models were adjusted for age, sex, and multimorbidity category. The residual-versus-fit plots visually indicated heteroscedasticity, indicating the reporting of heteroscedasticity-robust (HC3) standard errors. To avoid overfitting, the main model focused on a simplified set of clinically representative conditions. Sensitivity analyses included additional common conditions and alternative musculoskeletal specifications to assess the robustness of the results. All analyses were performed via Stata 19.0 (StataCorp LLC, College Station, TX, USA). Statistical significance was assessed at the 5% level. The EQ-HWB-25 composite score was standardized (z scored) to descriptively compare well-being profiles across mental health states. Results Sample characteristics A total of 5,177 respondents were included in the analysis. Age groups were broadly distributed, with 37.5% aged 50–69 years and 18.4% aged ≥70 years. Sex was approximately balanced (49.4% male). Most participants had completed university or higher education (42.3%), and 57.7% were married. The detailed sociodemographic characteristics are presented in Table 1. Distribution of EQ-HWB responses Across the EQ-HWB dimensions, most respondents reported no or only slight difficulty in core functional domains, including personal care (90.8% no difficulty) and mobility inside and outside the home (88.9% no difficulty). In contrast, emotional and wellbeing-related items showed greater dispersion, with notable proportions reporting feeling exhausted, anxious, frustrated, or depressed at least sometimes. For example, 42.1% reported feeling depressed at least occasionally, and 53.8% reported experiencing exhaustion at least occasionally. Pain and discomfort were also common, with 65.4% reporting pain at least occasionally and 52.2% reporting discomfort at least occasionally. Furthermore, responses to the reverse-coded items (“Accepted,” “Feel good,” “Do things wanted to do”) were distributed across all five levels (Table 2). EQ-HWB-9 utility scores and multimorbidity The average utility score for the EQ-HWB-9 was 0.868, as shown in Table 3. A density plot of the EQ-HWB-9 utility scores is shown in Figure S1. A total of 909 participants had a utility score of 1, representing a ceiling effect of 17.6%. The factor most significantly affecting the decrease in utility scores was pain (-0.036), followed by sad/depression (-0.021). The EQ-HWB-9 utility scores decreased monotonically with increasing levels of multimorbidity (Figure 1). Compared with respondents without comorbid conditions, those with one condition presented significantly lower utility scores, with progressively greater reductions observed among those with two or three or more conditions, demonstrating a clear dose‒response relationship between multimorbidity burden and wellbeing. Mental health status and EQ-HWB outcomes Compared with those without depression, participants reporting depression presented substantially lower EQ-HWB-9 utility scores (Figure 2A). Consistent patterns were observed for the EQ-HWB-25 standardized composite score, with markedly lower wellbeing profiles among respondents with depression (Figure 2B), indicating broad multidimensional wellbeing deficits associated with mental health status. Multivariable associations with EQ-HWB-9 utility scores The results from multivariable ordinary least squares regression with HC3 robust standard errors are shown in Table 4 and Figure 3. After adjustment for age, sex, and multimorbidity, depression demonstrated the strongest independent association with reduced EQ-HWB-9 utility scores (β = −0.124, p < 0.05). Lower back pain was also strongly associated with lower utility scores (β = −0.076, p < 0.05), as was arthritis in extended models. Multimorbidity remained independently associated with reduced utility, particularly among respondents with three or more conditions (β = −0.132, p < 0.05). In contrast, diabetes and hypertension were not significantly associated with utility after adjustment. Eye diseases showed a small positive association in the primary model, although this association was attenuated in the sensitivity analyses. In Model 2, multimorbidity remained independently associated with reduced utility, particularly among respondents with three or more conditions (β = −0.132, p < 0.05). In Model 3, the association was attenuated, although three or more conditions remained significantly negatively associated with utility (β = −0.043, p < 0.05). Age was positively associated with EQ-HWB-9 utility scores, whereas female sex was consistently associated with lower utility scores. Sensitivity analyses including additional conditions (e.g., obesity, dyslipidemia, allergic rhinitis, dental diseases, cancer, and neurological disorders) revealed similar patterns, with depression and musculoskeletal symptoms remaining the most prominent contributors to wellbeing loss (Table S1). Discussion This study provides comprehensive evidence on the performance of the EQ-HWB-25 and EQ-HWB-9 in a large sample of the Japanese general population. We observed a clear monotonic decline in EQ-HWB-9 utility scores with increasing multimorbidity, substantial reductions associated with self-reported depression, and independent condition-specific associations in multivariable models. Together, these findings support the construct and known-group validity of the EQ-HWB framework in a non-European context. First, the dose‒response relationship between multimorbidity and the EQ-HWB-9 utility score demonstrates that the instrument is sensitive to the cumulative health burden. Compared with those without comorbidities, respondents with multiple conditions, particularly those with three or more comorbidities, presented markedly lower utility scores. Although a slight plateau was observed between one and two conditions, the broader gradient remained evident. This pattern aligns with theoretical expectations and previous evidence [ 8 ] showing that multimorbidity is associated with diminished functioning and wellbeing. The observed gradient suggests that the EQ-HWB-9 captures the aggregate impact of multiple health problems on overall wellbeing. Second, depression showed the largest independent association with reduced EQ-HWB-9 utility scores, exceeding the magnitude observed for several chronic physical conditions. This finding is particularly important, as mental health conditions are often underrepresented in traditional preference-based instruments focused primarily on physical functioning. The parallel reductions observed in the EQ-HWB-25 composite scores further indicate that depression affects multiple domains of wellbeing, including emotional, social, and functional aspects. These results suggest that the EQ-HWB is sensitive to mental health-related wellbeing loss and may offer advantages in contexts where psychological distress plays a central role. Third, selected physical conditions, including musculoskeletal symptoms and cardiometabolic risk factors, demonstrated independent associations with utility, although effect sizes were generally smaller than those for depression. These findings indicate that the EQ-HWB-9 reflects both the physical and mental health dimensions of wellbeing. Eye diseases showed a small positive association with utility, which may reflect the inclusion of mild or treated conditions within this broad category. This finding should be interpreted cautiously. Importantly, the association between multimorbidity and utility remained significant even after adjustment for individual conditions, suggesting that the instrument captures both cumulative and condition-specific impacts. The attenuation of multimorbidity coefficients in the fully adjusted model likely reflects partial mediation through the specific conditions included in the analysis, given the conceptual and statistical overlap between condition counts and individual disease indicators. The mean EQ-HWB-9 utility score observed in this study was broadly comparable to estimates reported in Oceania [ 14 ] and UK samples [ 15 ], suggesting cross-cultural consistency in overall wellbeing levels. While direct comparisons should be interpreted cautiously owing to differences in sampling frames and valuation methods, the similarity in average utility scores supports the conceptual robustness of the EQ-HWB-9 descriptive system across contexts. Future studies applying country-specific value sets may further clarify potential cultural variation in preference weights. The mean EQ-HWB-9 utility score in this Japanese general population sample was 0.868, with 17.6% of respondents reporting full utility scores (1.00). This distribution suggests a modest ceiling effect at the upper bound of the scale. While a substantial proportion of participants reported no difficulty in core functional domains, emotional and psychosocial items demonstrated greater variability, contributing to dispersion in overall utility scores. Compared with traditional preference-based instruments that primarily emphasize physical functioning, the broader conceptual coverage of the EQ-HWB may help mitigate more pronounced ceiling effects in general population settings. Nevertheless, the presence of a ceiling in nearly one-fifth of the respondents highlights the inherent challenge of discriminating among individuals in relatively good health and warrants further investigation in longitudinal and clinical samples. Although this study did not include a direct comparison with other preference-based measures, prior research has noted substantial ceiling effects in instruments focused primarily on physical functioning. The distributional characteristics observed here suggest that incorporating emotional and social wellbeing domains may enhance discriminatory capacity in general population settings. From a psychometric perspective, these results extend previous European validation studies by demonstrating similar patterns in an East Asian population [ 13 , 16 ]. The consistency of associations across primary and sensitivity analyses supports the robustness of the findings. The use of heteroskedasticity-consistent (HC3) robust standard errors further strengthens the reliability of the regression estimates. Finally, we revisit the distribution of responses to the reverse-coded items (“Acceptance,” “Feeling Good,” and “Doing What I Want”), which were spread across all five response levels in this general population sample. The absence of marked floor or ceiling clustering for these positively framed items suggests that respondents were able to differentiate levels of subjective wellbeing, supporting the interpretability of these domains within the EQ-HWB descriptive system. With respect to content validity, the development of the EQ-HWB involved extensive qualitative work, including literature reviews and interviews with service users and caregivers, to ensure that the selected items reflected domains considered important to wellbeing [ 3 ]. Subsequent studies in European contexts, such as Italy, have further evaluated item relevance, clarity, and comprehensibility, with most items judged to be appropriate and understandable to participants [ 17 ]. In contrast, while the factor structure of the EQ-HWB has been re-examined in Asian settings, including confirmatory factor analysis in China [ 18 ], the formal evaluation of content validity in Asian populations remains limited. Cultural differences in the interpretation of positive affect, acceptance, and autonomy-related constructs may influence response patterns. Further qualitative and mixed-methods research in Asian contexts would therefore be valuable for confirming the cultural relevance and conceptual coverage of EQ-HWB items. Several limitations should be acknowledged. First, the cross-sectional design precludes assessment of responsiveness and minimal important differences; longitudinal studies are needed to evaluate sensitivity to change over time. Second, health conditions were self-reported and may be subject to misclassification. Although depression showed strong associations, other mental health conditions were not assessed in detail, and estimates for rare conditions such as dementia should be interpreted cautiously. Third, utility scores were derived via an existing value set, and country-specific valuation may refine estimates in future research. Finally, as the study was based on a web survey, generalisability to populations with limited internet access may be constrained. Despite these limitations, this study provides important evidence supporting the applicability of the EQ-HWB framework in Japan. The findings indicate that the EQ-HWB-9 captures both the cumulative burden of multimorbidity and condition-specific wellbeing loss, particularly in relation to mental health. The EQ-HWB-25 offers complementary multidimensional information, reinforcing the potential value of the EQ-HWB system for population health assessment and economic evaluation in ageing societies with complex health needs. Conclusion In conclusion, this study provides robust evidence supporting the performance of the EQ-HWB-25 and EQ-HWB-9 in a large sample of the general Japanese population. The EQ-HWB-9 utility scores demonstrated clear dose‒response relationships with multimorbidity and substantial reductions associated with depression, while multivariable analyses confirmed independent condition-specific associations. Distributional characteristics and response patterns across emotional and psychosocial domains further support the conceptual breadth of the instrument. Although additional qualitative work is warranted to strengthen content validity in Asian contexts, these findings indicate that the EQ-HWB framework is a promising tool for comprehensive wellbeing assessment and economic evaluation in ageing societies with complex health needs. Declarations Ethics approval and consent to participate The study protocol was reviewed and approved by the institutional ethics committee of Niigata University of Health and Welfare (approval number: 18922-221101). All participants provided informed consent electronically prior to participation. The survey was conducted anonymously, and all procedures were performed in accordance with the Declaration of Helsinki. Consent for publication Not applicable, as no identifiable personal data are included in this study. Availability of data and materials The datasets analysed during the current study are not publicly available owing to ethical restrictions but are available from the corresponding author upon reasonable request. Competing interests We have no conflicts of interest to declare. Funding This work was supported by the Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (KAKENHI) [grant numbers 23K20335 and 24K02677]. Authors’ contributions SN designed the study, performed the data analysis, and drafted the manuscript. SS was responsible for data acquisition. TS supervised the study. All authors reviewed and approved the final version of the manuscript. References Karimi M, Brazier J. 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Masutti S, Falivena C, Purba FD, Jommi C, Mukuria C, Finch AP. Content validity of the EQ-HWB and EQ-HWB-S in a sample of Italian patients, informal caregivers and members of the general public. J Patient Rep Outcomes. 2024; 8:36. Zhang G, Yang Z, Luo N, Peasgood T, Busschbach J. Evaluating the Factor Structure of the Preliminary Version of EuroQol Health and Well-Being Instrument in China: A Replication of the Confirmatory Factor Analysis. Value Health Reg Issues. 2026; 51:101501. Tables Table 1 Sociodemographic characteristics of the respondents N % Age 20-29 30-39 40-49 50-59 60-69 ≥70 656 748 977 987 857 952 12.7 14.4 18.8 19.1 16.6 18.4 Sex Male Female 2559 2618 49.4 50.6 Region Hokkaido Tohoku Kanto Keihin Hokuriku Tokai Keihanshin Chugoku Shikoku Kyushu 224 355 312 1544 288 606 835 288 160 565 4.3 6.8 6.0 29.8 5.6 11.7 16.2 5.6 3.1 11.0 Education Elementary or junior hige school High school College Junior college University Graduate Others 121 1719 622 521 1999 188 7 2.4 33.3 12.0 9.9 38.7 3.6 0.1 Employment Full-time worker Part-time worker Self-employed or manager Housemaker Retired Student Others 2012 346 794 336 1066 770 114 32.2 6.7 15.3 6.5 20.6 14.9 2.2 Marital status Unmarried Married Divorced/bereaved 1705 3096 567 31.8 57.7 10.6 Household Income (JPY 10,000) 2000 199 386 569 619 591 780 636 322 46 37 4.0 7.6 11.0 11.9 11.4 15.2 12.1 6.2 0.9 0.7 Table 2 Distribution of EQ-HWB responses by level N (%) N (%) N (%) N (%) N (%) No difficulty Slight difficulty Some difficulty Much difficulty Unable See 3192 (61.7) 1264 (24.4) 721 (13.9) 0 (0.0) 0 (0.0) Hear 4432 (85.6) 486 (9.4) 231 (4.5) 22 (0.4) 6 (0.1) Getting around inside and outside* 4604 (88.9) 349 (6.7) 167 (3.2) 48 (0.9) 9 (0.2) Day-to-day activities* 4426 (85.5) 477 (9.2) 199 (3.8) 57 (1.1) 18 (0.4) Personal care 4702 (90.8) 288 (5.6) 139 (2.7) 36 (0.7) 12 (0.2) None of the time Only occasionally Sometimes Often Most of the time Sleep 2647 (51.1) 1455 (28.1) 660 (12.8) 287 (5.5) 128 (2.5) Exhausted* 2394 (46.2) 1571 (30.3) 691 (13.4) 380 (7.3) 141 (2.7) Lonely* 3478 (67.8) 918 (17.7) 475 (9.2) 195 (3.8) 111 (2.1) Unsupported 3830 (74.0) 697 (13.5) 340 (6.6) 188 (3.6) 122 (2.4) Remembering 3228 (62.4) 1236 (23.9) 454 (8.8) 173 (3.3) 86 (1.7) Concentrating/thinking clearly* 3784 (73.1) 857 (16.6) 361 (7.0) 124 (2.4) 51 (1.0) Anxious* 2980 (57.6) 1190 (23.0) 518 (10.0) 311 (6.0) 178 (3.4) Unsafe 4316 (83.4) 566 (11.0) 187 (3.6) 62 (1.2) 46 (0.9) Frustrated 2372 (45.8) 1591 (30.7) 708 (16.7) 342 (6.6) 164 (3.2) Depressed* 2997 (57.9) 1269 (24.5) 520 (10.0) 246 (4.8) 145 (2.8) Look Forward 3408 (65.8) 966 (18.7) 413 (8.0) 229 (4.4) 161 (3.1) Control* 3169 (61.2) 1169 (22.6) 470(9.1) 226 (4.4) 143 (2.8) Cope 3608 (70.0) 891 (17.2) 387 (7.5) 181 (3.5) 110 (2.1) Accepted† 766 (14.8) 1120 (21.6) 989 (19.1) 802 (15.5) 1500 (29.0) Feel good† 638 (12.3) 1196 (23.1) 1264 (24.4) 933 (18.0) 1146 (22.1) Do things wanted to do† 665 (12.9) 1399 (27.0) 1395 (27.0) 969 (18.7) 749 (14.5) Pain (frequency) 1792 (34.6) 1737 (33.6) 986 (19.1) 420 (8.1) 242 (4.7) No Mild Moderate Severe Very severe Pain (severity)* 1852 (35.8) 2528 (48.8) 671 (13.0) 101 (2.0) 25 (0.5) None of the time Only occasionally Sometimes Often Most of the time Discomfort (frequency) 2475 (47.8) 1724 (33.3) 643 (12.4) 239 (4.6) 96 (1.9) No Mild Moderate Severe Very severe Discomfort (severity) 2389 (46.2) 2163 (41.8) 514 (10.0) 82 (1.6) 29 (0.6) *Part of the EQ-HWB-9. †Reverse coded for summary score. Table 3 Comparison of the EQ-HWB-9 utility by dimension Mean Std. err. 95%CI Mobility -0.008 0.000 -0.008 -0.007 Activity -0.009 0.000 -0.009 -0.008 Exhaustion -0.016 0.000 -0.017 -0.016 Loneliness -0.015 0.000 -0.016 -0.014 Cognition -0.004 0.000 -0.004 -0.003 Anxiety -0.015 0.000 -0.015 -0.014 Sad/depress -0.021 0.001 -0.022 -0.020 Control -0.010 0.000 -0.011 -0.010 Pain -0.036 0.001 -0.037 -0.035 Overall 0.868 0.002 0.863 0.872 Table 4 Relationships between utility scores and diseases and symptoms Model 1 Model 2 Model 3 Coefficient (95%CI) Coefficient (95%CI) Coefficient (95%CI) Age 0.0017 * (0.0015 to 0.0020) 0.0026 * (0.0023 to 0.0028) 0.0032 * (0.0028 to 0.0038) Sex -0.0195 * (-0.0280 to -0.0110) -0.0232 * (-0.0316 to -0.0152) -0.0259 * (-0.0404 to -0.0115) Multimorbidity 1 2 3+ -0.0545 * (-0.0661 to -0.0429) -0.0633 * (-0.0775 to -0.0490) -0.1321 * (-0.147 to -0.1173) 0.0165 (-0.0107 to 0.0440) 0.0087 (-0.0213 to 0.0387) -0.0433 * (-0.0767 to -0.0098) Diabetes (n=224) -0.0007 (-0.0306 to 0.0166) Depression (n=248) -0.1243 * (-0.1476 to -0.1010) Eye diseases (n=280) 0.0216 (-0.0007 to 0.0439) Hypertension (n=538) 0.0071 (-0.0108 to 0.0250) Angina, Myocardial infarction (n=67) -0.0278 (-0.0683 to 0.0129) Arthritis (n=86) -0.0605 * (-0.0966 to -0.0244) Lower back pain (n=177) -0.0763 * (-0.1027 to -0.0500) cons 0.7674 * (0.7468 to 0.7880) 0.7997 * (0.7812 to 0.8182) 0.7083 * (0.6664 to 0.7502) *Bolded values indicate significant differences at p < 0.05. Model 1: Adjusted for age and sex. Model 2: Model 1 + multimorbidity category. Model 3: Model 2 + selected health conditions. Reference category: absence of the condition. Additional Declarations No competing interests reported. Supplementary Files HQLQFigureS1260306.docx Figure S1. Distribution of EQ-HWB-9 utility scores. Density histogram of EQ-HWB-9 utility scores in the study sample, illustrating the overall distribution and upper-bound clustering. SupplementaryTableS1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 05 May, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 15 Mar, 2026 Editor assigned by journal 06 Mar, 2026 Submission checks completed at journal 06 Mar, 2026 First submitted to journal 03 Mar, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9025329","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606913669,"identity":"80037373-928d-4c5f-b4e9-4c8d12d2e84b","order_by":0,"name":"Shinichi Noto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqUlEQVRIiWNgGAWjYLCCDwwMCRAWD1HqmRkYZ5CshZkHroUYYM7ef/Cx7Y7DeQYHmB9+YJC5Q1iLZc9hZuPcM4eLDQ6wGUsw8DwjrMXgRjKbdG7b4cRtBxjMgH45TKQWS7AW9m8kaGEEa+Eh1pYzh40Ne9vSE/cf5imWSCDKL8cbHz742WadOLO9feOHjz1EhBgCMANxYs8BUrSAwQ/StYyCUTAKRsHwBwAqvDnd3OTj/AAAAABJRU5ErkJggg==","orcid":"","institution":"Niigata University of Health and Welfare","correspondingAuthor":true,"prefix":"","firstName":"Shinichi","middleName":"","lastName":"Noto","suffix":""},{"id":606913670,"identity":"af751b68-3d2b-4630-a19e-5a82f1f24dca","order_by":1,"name":"Shinya Saito","email":"","orcid":"","institution":"Okayama Health Professional University","correspondingAuthor":false,"prefix":"","firstName":"Shinya","middleName":"","lastName":"Saito","suffix":""},{"id":606913671,"identity":"c60fff75-341c-4e85-a197-30ab7b38c8ac","order_by":2,"name":"Takeru Shiroiwa","email":"","orcid":"","institution":"National Institute of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Takeru","middleName":"","lastName":"Shiroiwa","suffix":""}],"badges":[],"createdAt":"2026-03-04 03:38:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9025329/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9025329/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104817727,"identity":"f6fa48cc-ab18-4648-83e0-9cc17ce631d9","added_by":"auto","created_at":"2026-03-17 13:45:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":54661,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEQ-HWB-9 utility score by multimorbidity category\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMean EQ-HWB-9 utility scores with 95% confidence intervals are shown across categories of multimorbidity (0, 1, 2, and ≥3 self-reported conditions). A generally monotonic decline is observed.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9025329/v1/bcc89a2da148b1ba269e0194.png"},{"id":104817726,"identity":"be60601f-54bf-4e6f-8dea-288ecf645937","added_by":"auto","created_at":"2026-03-17 13:45:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103506,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDepression status and EQ-HWB outcomes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Mean EQ-HWB-9 utility scores with 95% confidence intervals according to self-reported depression status.\u003c/p\u003e\n\u003cp\u003e(B) Mean EQ-HWB-25 composite scores (standardized z scores) with 95% confidence intervals according to depression status.\u003c/p\u003e\n\u003cp\u003eThe respondents reporting depression exhibited substantially lower wellbeing across both measures.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9025329/v1/8e9d5903fb7659336e6b760f.png"},{"id":104817725,"identity":"da93506d-2139-4dd8-987b-1b9c3389bfa5","added_by":"auto","created_at":"2026-03-17 13:45:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":98005,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdjusted associations between selected health conditions and EQ-HWB-9 utility scores.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePoints represent adjusted mean differences in EQ-HWB-9 utility derived from multivariable ordinary least squares regression models. The horizontal lines indicate 95% confidence intervals. The models were adjusted for age, sex, and multimorbidity category, with heteroskedasticity-consistent (HC3) robust standard errors.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9025329/v1/fc7a1072823ff0d2ebc620d1.png"},{"id":104835248,"identity":"808c0e44-7499-4fbc-9aa9-39f3f0cb426e","added_by":"auto","created_at":"2026-03-17 17:42:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":935407,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9025329/v1/0c9959f6-155e-44e7-a459-7c7b9b8b1571.pdf"},{"id":104817729,"identity":"2ef3380c-9059-4fe2-a0fe-6d20a823e2a3","added_by":"auto","created_at":"2026-03-17 13:45:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":86516,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1. Distribution of EQ-HWB-9 utility scores.\u003c/p\u003e\n\u003cp\u003eDensity histogram of EQ-HWB-9 utility scores in the study sample, illustrating the overall distribution and upper-bound clustering.\u003c/p\u003e","description":"","filename":"HQLQFigureS1260306.docx","url":"https://assets-eu.researchsquare.com/files/rs-9025329/v1/5ee4c7c7565399ec635c39c7.docx"},{"id":104817728,"identity":"5c2436ab-8647-4d4e-8328-ec0cdef9b75b","added_by":"auto","created_at":"2026-03-17 13:45:50","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":25791,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9025329/v1/eaa97cfc195050967630ca04.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Psychometric performance of the EQ-HWB-25 and EQ-HWB-9 in Japan: evidence from a large web-based survey of multimorbidity and depression","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHealth-related quality of life (HRQoL) instruments have traditionally focused on physical functioning and symptoms, often showing limited sensitivity to broader psychosocial aspects of wellbeing, particularly in samples from the general population [1]. HRQoL measures are conceptually distinct from overall quality of life and may omit domains that matter for subjective wellbeing, such as emotional and social functioning [2]. This limitation is increasingly recognized in ageing societies, where multimorbidity and mental health conditions are common and where conventional HRQoL measures frequently exhibit ceiling effects.\u003c/p\u003e\n\u003cp\u003eThe EQ Health and Wellbeing (EQ-HWB) framework was developed to address these limitations by capturing a wider conceptualization of wellbeing beyond traditional HRQoL. The EQ-HWB consists of a 25-item descriptive system (EQ-HWB-25) and a reduced 9-item version (EQ-HWB-9) designed for preference-based valuation, enabling both multidimensional wellbeing profiling and economic evaluation [3-5]. Initial development and valuation studies, conducted primarily in European settings, have demonstrated promising psychometric properties and produced utility value sets for the EQ-HWB-9 [6]. However, empirical evidence from non-European populations remains scarce.\u003c/p\u003e\n\u003cp\u003eMental health represents a particularly important domain for wellbeing measurement. Depression is a leading contributor to the global disability burden and frequently cooccurs with chronic physical conditions, substantially increasing overall wellbeing loss in individuals with multimorbidity [7,8]. However, many widely used preference-based instruments place limited emphasis on mental and social functioning. Evaluating whether the EQ-HWB adequately captures depression-related wellbeing deficits, alongside physical multimorbidity, is therefore critical for assessing its suitability for population health monitoring and economic evaluation.\u003c/p\u003e\n\u003cp\u003eJapan provides a valuable context in which to examine these issues, given its rapidly aging population and growing burden of chronic disease and mental health conditions [9]. To date, however, no large-scale studies have evaluated the performance of the EQ-HWB in a Japanese general population sample.\u0026nbsp;In other countries [10-16], various psychometric validations of the EQ-HWB, including\u0026nbsp;its\u0026nbsp;validity and reliability, are progressing, making validation in Japan an urgent task.\u003c/p\u003e\n\u003cp\u003eThe aim of this study was to assess the psychometric performance of the EQ-HWB-25 and EQ-HWB-9 in Japan via data from a nationwide web-based survey. Specifically, we examined (i) whether EQ-HWB-9 utility scores demonstrate a dose‒response relationship with multimorbidity, (ii) whether EQ-HWB outcomes discriminate between respondents with and without depression, and (iii) whether selected physical and mental health conditions show independent associations with EQ-HWB-9 utility scores after multivariable adjustment. By providing evidence from a non-European population, this study seeks to inform the ongoing refinement and implementation of the EQ-HWB for wellbeing assessment and economic evaluation in populations with complex health needs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study used data from a nationwide web-based cross-sectional survey conducted in Japan to evaluate the psychometric performance of the EQ-HWB-25 descriptive system and the EQ-HWB-9 utility index in a general population sample. The analyses focused on the relationships among wellbeing outcomes, multimorbidity, and selected physical and mental health conditions, with particular attention given to depression. We examined known-group validity via group comparisons and estimated condition-specific associations with EQ-HWB-9 utility scores via multivariable regression models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudy design and participants\u003c/p\u003e\n\u003cp\u003eA nationwide web-based cross-sectional survey was conducted in Japan. This survey selected 5,000 respondents aged 20 years and older through random sampling from 10 regions across Japan (Hokkaido, Tohoku, Kanto, Keihin, Hokuriku, Tokai, Keihanshin, Chugoku, Shikoku, and Kyushu). Participants were recruited from an online survey panel and provided informed consent prior to participation. INTAGE Healthcare, a specialized online survey company, managed sample recruitment (via Japanese online panels), survey administration, and data collection. The eligibility criteria were age 20 years or older and completion of the EQ-HWB questionnaire. This research protocol was approved by the Ethics Committee of Niigata University of Health and Welfare (Approval No. 18922-221101).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMeasures\u003c/p\u003e\n\u003cp\u003eEQ-HWB\u003c/p\u003e\n\u003cp\u003eWell-being was assessed via the EQ-HWB-25 descriptive system and the EQ-HWB-9. The EQ-HWB-25 consists of 25 items covering physical, emotional, social, and functional aspects of wellbeing. The EQ-HWB-9 uses a value set developed by Mukuria et al. [4] via composite time trade-off and discrete choice experiments, converting values into utility scores and yielding scores on the perfect health-death scale.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHealth conditions and multimorbidity\u003c/p\u003e\n\u003cp\u003eThe participants self-reported their health status and symptoms on the basis of physician diagnosis. Multimorbidity was operationalized as the total number of reported diseases and categorized as 0, 1, 2, or \u0026ge;3.\u003c/p\u003e\n\u003cp\u003eTo examine disease-specific associations while maintaining model parsimony and estimation stability, representative diseases covering major disease domains represented in the survey were prespecified. The selection criteria were based on clinical relevance, sufficient prevalence, and minimal redundancy and multicollinearity in cases of multimorbidity. The main models included depression (mental health), diabetes and hypertension (cardiometabolic), eye disease (sensory organ), and musculoskeletal symptoms (composite indicators of shoulder stiffness or low back pain). Musculoskeletal symptoms were combined to reduce overlap.\u003c/p\u003e\n\u003cp\u003eAlternative specifications separating musculoskeletal symptoms and additional common diseases (dyslipidemia, allergic rhinitis, and dental disease) were examined via sensitivity analyses. Owing to low case numbers, rare diseases such as dementia and Parkinson\u0026apos;s disease were considered exploratory.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were used to summarize participant characteristics and EQ-HWB responses. Known-group validity was assessed by comparing the EQ-HWB results across levels of multimorbidity and mental health status.\u003c/p\u003e\n\u003cp\u003eAssociations between the selected conditions and the EQ-HWB-9 utility scores were estimated via ordinary least squares regression. The models were adjusted for age, sex, and multimorbidity category. The residual-versus-fit plots visually indicated heteroscedasticity, indicating the reporting of heteroscedasticity-robust (HC3) standard errors.\u003c/p\u003e\n\u003cp\u003eTo avoid overfitting, the main model focused on a simplified set of clinically representative conditions. Sensitivity analyses included additional common conditions and alternative musculoskeletal specifications to assess the robustness of the results.\u003c/p\u003e\n\u003cp\u003eAll analyses were performed via Stata 19.0 (StataCorp LLC, College Station, TX, USA). Statistical significance was assessed at the 5% level. The EQ-HWB-25 composite score was standardized (z scored) to descriptively compare well-being profiles across mental health states.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSample characteristics\u003c/p\u003e\n\u003cp\u003eA total of 5,177 respondents were included in the analysis. Age groups were broadly distributed, with 37.5% aged 50\u0026ndash;69 years and 18.4% aged \u0026ge;70 years. Sex was approximately balanced (49.4% male). Most participants had completed university or higher education (42.3%), and 57.7% were married. The detailed sociodemographic characteristics are presented in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDistribution of EQ-HWB responses\u003c/p\u003e\n\u003cp\u003eAcross the EQ-HWB dimensions, most respondents reported no or only slight difficulty in core functional domains, including personal care (90.8% no difficulty) and mobility inside and outside the home (88.9% no difficulty). In contrast, emotional and wellbeing-related items showed greater dispersion, with notable proportions reporting feeling exhausted, anxious, frustrated, or depressed at least sometimes. For example, 42.1% reported feeling depressed at least occasionally, and 53.8% reported experiencing exhaustion at least occasionally. Pain and discomfort were also common, with 65.4% reporting pain at least occasionally and 52.2% reporting discomfort at least occasionally. Furthermore, responses to the reverse-coded items (\u0026ldquo;Accepted,\u0026rdquo; \u0026ldquo;Feel good,\u0026rdquo; \u0026ldquo;Do things wanted to do\u0026rdquo;) were distributed across all five levels (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEQ-HWB-9 utility scores and multimorbidity\u003c/p\u003e\n\u003cp\u003eThe average utility score for the EQ-HWB-9 was 0.868, as shown in Table 3. A density plot of the EQ-HWB-9 utility scores is shown in Figure S1. A total of 909 participants had a utility score of 1, representing a ceiling effect of 17.6%. The factor most significantly affecting the decrease in utility scores was pain (-0.036), followed by sad/depression (-0.021). The EQ-HWB-9 utility scores decreased monotonically with increasing levels of multimorbidity (Figure 1). Compared with respondents without comorbid conditions, those with one condition presented significantly lower utility scores, with progressively greater reductions observed among those with two or three or more conditions, demonstrating a clear dose‒response relationship between multimorbidity burden and wellbeing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMental health status and EQ-HWB outcomes\u003c/p\u003e\n\u003cp\u003eCompared with those without depression, participants reporting depression presented substantially lower EQ-HWB-9 utility scores (Figure 2A). Consistent patterns were observed for the EQ-HWB-25 standardized composite score, with markedly lower wellbeing profiles among respondents with depression (Figure 2B), indicating broad multidimensional wellbeing deficits associated with mental health status.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMultivariable associations with EQ-HWB-9 utility scores\u003c/p\u003e\n\u003cp\u003eThe results from multivariable ordinary least squares regression with HC3 robust standard errors are shown in Table 4 and Figure 3. After adjustment for age, sex, and multimorbidity, depression demonstrated the strongest independent association with reduced EQ-HWB-9 utility scores (\u0026beta; = \u0026minus;0.124, p \u0026lt; 0.05). Lower back pain was also strongly associated with lower utility scores (\u0026beta; = \u0026minus;0.076, p \u0026lt; 0.05), as was arthritis in extended models.\u003c/p\u003e\n\u003cp\u003eMultimorbidity remained independently associated with reduced utility, particularly among respondents with three or more conditions (\u0026beta; = \u0026minus;0.132, p \u0026lt; 0.05). In contrast, diabetes and hypertension were not significantly associated with utility after adjustment. Eye diseases showed a small positive association in the primary model, although this association was attenuated in the sensitivity analyses. In Model 2, multimorbidity remained independently associated with reduced utility, particularly among respondents with three or more conditions (\u0026beta; = \u0026minus;0.132, p \u0026lt; 0.05). In Model 3, the association was attenuated, although three or more conditions remained significantly negatively associated with utility (\u0026beta; = \u0026minus;0.043, p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eAge was positively associated with EQ-HWB-9 utility scores, whereas female sex was consistently associated with lower utility scores.\u003c/p\u003e\n\u003cp\u003eSensitivity analyses including additional conditions (e.g., obesity, dyslipidemia, allergic rhinitis, dental diseases, cancer, and neurological disorders) revealed similar patterns, with depression and musculoskeletal symptoms remaining the most prominent contributors to wellbeing loss (Table S1).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides comprehensive evidence on the performance of the EQ-HWB-25 and EQ-HWB-9 in a large sample of the Japanese general population. We observed a clear monotonic decline in EQ-HWB-9 utility scores with increasing multimorbidity, substantial reductions associated with self-reported depression, and independent condition-specific associations in multivariable models. Together, these findings support the construct and known-group validity of the EQ-HWB framework in a non-European context.\u003c/p\u003e \u003cp\u003eFirst, the dose‒response relationship between multimorbidity and the EQ-HWB-9 utility score demonstrates that the instrument is sensitive to the cumulative health burden. Compared with those without comorbidities, respondents with multiple conditions, particularly those with three or more comorbidities, presented markedly lower utility scores. Although a slight plateau was observed between one and two conditions, the broader gradient remained evident. This pattern aligns with theoretical expectations and previous evidence [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] showing that multimorbidity is associated with diminished functioning and wellbeing. The observed gradient suggests that the EQ-HWB-9 captures the aggregate impact of multiple health problems on overall wellbeing.\u003c/p\u003e \u003cp\u003eSecond, depression showed the largest independent association with reduced EQ-HWB-9 utility scores, exceeding the magnitude observed for several chronic physical conditions. This finding is particularly important, as mental health conditions are often underrepresented in traditional preference-based instruments focused primarily on physical functioning. The parallel reductions observed in the EQ-HWB-25 composite scores further indicate that depression affects multiple domains of wellbeing, including emotional, social, and functional aspects. These results suggest that the EQ-HWB is sensitive to mental health-related wellbeing loss and may offer advantages in contexts where psychological distress plays a central role.\u003c/p\u003e \u003cp\u003eThird, selected physical conditions, including musculoskeletal symptoms and cardiometabolic risk factors, demonstrated independent associations with utility, although effect sizes were generally smaller than those for depression. These findings indicate that the EQ-HWB-9 reflects both the physical and mental health dimensions of wellbeing. Eye diseases showed a small positive association with utility, which may reflect the inclusion of mild or treated conditions within this broad category. This finding should be interpreted cautiously. Importantly, the association between multimorbidity and utility remained significant even after adjustment for individual conditions, suggesting that the instrument captures both cumulative and condition-specific impacts. The attenuation of multimorbidity coefficients in the fully adjusted model likely reflects partial mediation through the specific conditions included in the analysis, given the conceptual and statistical overlap between condition counts and individual disease indicators.\u003c/p\u003e \u003cp\u003eThe mean EQ-HWB-9 utility score observed in this study was broadly comparable to estimates reported in Oceania [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and UK samples [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], suggesting cross-cultural consistency in overall wellbeing levels. While direct comparisons should be interpreted cautiously owing to differences in sampling frames and valuation methods, the similarity in average utility scores\u003c/p\u003e \u003cp\u003esupports the conceptual robustness of the EQ-HWB-9 descriptive system across contexts. Future studies applying country-specific value sets may further clarify potential cultural variation in preference weights.\u003c/p\u003e \u003cp\u003eThe mean EQ-HWB-9 utility score in this Japanese general population sample was 0.868, with 17.6% of respondents reporting full utility scores (1.00). This distribution suggests a modest ceiling effect at the upper bound of the scale. While a substantial proportion of participants reported no difficulty in core functional domains, emotional and psychosocial items demonstrated greater variability, contributing to dispersion in overall utility scores. Compared with traditional preference-based instruments that primarily emphasize physical functioning, the broader conceptual coverage of the EQ-HWB may help mitigate more pronounced ceiling effects in general population settings. Nevertheless, the presence of a ceiling in nearly one-fifth of the respondents highlights the inherent challenge of discriminating among individuals in relatively good health and warrants further investigation in longitudinal and clinical samples.\u003c/p\u003e \u003cp\u003eAlthough this study did not include a direct comparison with other preference-based measures, prior research has noted substantial ceiling effects in instruments focused primarily on physical functioning. The distributional characteristics observed here suggest that incorporating emotional and social wellbeing domains may enhance discriminatory capacity in general population settings.\u003c/p\u003e \u003cp\u003eFrom a psychometric perspective, these results extend previous European validation studies by demonstrating similar patterns in an East Asian population [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The consistency of associations across primary and sensitivity analyses supports the robustness of the findings. The use of heteroskedasticity-consistent (HC3) robust standard errors further strengthens the reliability of the regression estimates.\u003c/p\u003e \u003cp\u003eFinally, we revisit the distribution of responses to the reverse-coded items (\u0026ldquo;Acceptance,\u0026rdquo; \u0026ldquo;Feeling Good,\u0026rdquo; and \u0026ldquo;Doing What I Want\u0026rdquo;), which were spread across all five response levels in this general population sample. The absence of marked floor or ceiling clustering for these positively framed items suggests that respondents were able to differentiate levels of subjective wellbeing, supporting the interpretability of these domains within the EQ-HWB descriptive system. With respect to content validity, the development of the EQ-HWB involved extensive qualitative work, including literature reviews and interviews with service users and caregivers, to ensure that the selected items reflected domains considered important to wellbeing [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Subsequent studies in European contexts, such as Italy, have further evaluated item relevance, clarity, and comprehensibility, with most items judged to be appropriate and understandable to participants [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In contrast, while the factor structure of the EQ-HWB has been re-examined in Asian settings, including confirmatory factor analysis in China [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], the formal evaluation of content validity in Asian populations remains limited. Cultural differences in the interpretation of positive affect, acceptance, and autonomy-related constructs may influence response patterns. Further qualitative and mixed-methods research in Asian contexts would therefore be valuable for confirming the cultural relevance and conceptual coverage of EQ-HWB items.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, the cross-sectional design precludes assessment of responsiveness and minimal important differences; longitudinal studies are needed to evaluate sensitivity to change over time. Second, health conditions were self-reported and may be subject to misclassification. Although depression showed strong associations, other mental health conditions were not assessed in detail, and estimates for rare conditions such as dementia should be interpreted cautiously. Third, utility scores were derived via an existing value set, and country-specific valuation may refine estimates in future research. Finally, as the study was based on a web survey, generalisability to populations with limited internet access may be constrained.\u003c/p\u003e \u003cp\u003eDespite these limitations, this study provides important evidence supporting the applicability of the EQ-HWB framework in Japan. The findings indicate that the EQ-HWB-9 captures both the cumulative burden of multimorbidity and condition-specific wellbeing loss, particularly in relation to mental health. The EQ-HWB-25 offers complementary multidimensional information, reinforcing the potential value of the EQ-HWB system for population health assessment and economic evaluation in ageing societies with complex health needs.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study provides robust evidence supporting the performance of the EQ-HWB-25 and EQ-HWB-9 in a large sample of the general Japanese population. The EQ-HWB-9 utility scores demonstrated clear dose‒response relationships with multimorbidity and substantial reductions associated with depression, while multivariable analyses confirmed independent condition-specific associations. Distributional characteristics and response patterns across emotional and psychosocial domains further support the conceptual breadth of the instrument. Although additional qualitative work is warranted to strengthen content validity in Asian contexts, these findings indicate that the EQ-HWB framework is a promising tool for comprehensive wellbeing assessment and economic evaluation in ageing societies with complex health needs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the institutional ethics committee of Niigata University of Health and Welfare (approval number: 18922-221101). All participants provided informed consent electronically prior to participation. The survey was conducted anonymously, and all procedures were performed in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable, as no identifiable personal data are included in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are not publicly available owing to ethical restrictions but are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eWe have no conflicts of interest to declare.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (KAKENHI) [grant numbers 23K20335 and 24K02677].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eSN designed the study, performed the data analysis, and drafted the manuscript. SS was responsible for data acquisition. TS supervised the study. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKarimi M, Brazier J. Health, Health-Related Quality of Life, and Quality of Life: What is the Difference? Pharmacoeconomics. 2016; 34:645-9.\u003c/li\u003e\n \u003cli\u003eLongworth L, Yang Y, Young T, Mulhern B, Hern\u0026aacute;ndez Alava M, Mukuria C, Rowen D, Tosh J, Tsuchiya A, Evans P, Devianee Keetharuth A, Brazier J. Use of generic and condition-specific measures of health-related quality of life in NICE decision-making: a systematic review, statistical modelling and survey. Health Technol Assess. 2014; 18:1-224.\u003c/li\u003e\n \u003cli\u003eBrazier J, Peasgood T, Mukuria C, Marten O, Kreimeier S, Luo N, Mulhern B, Pickard AS, Augustovski F, Greiner W, Engel L, Belizan M, Yang Z, Monteiro A, Kuharic M, Gibbons L, Ludwig K, Carlton J, Connell J, Rand S, Devlin N, Jones K, Tsuchiya A, Lovett R, Naidoo B, Rowen D, Rejon-Parrilla JC. The EQ-HWB: Overview of the Development of a Measure of Health and Wellbeing and Key Results. Value Health. 2022; 25:482-491.\u003c/li\u003e\n \u003cli\u003eCarlton J, Peasgood T, Mukuria C, Connell J, Brazier J, Ludwig K, Marten O, Kreimeier S, Engel L, Beliz\u0026aacute;n M, Yang Z, Monteiro A, Kuharic M, Luo N, Mulhern B, Greiner W, Pickard S, Augustovski F. Generation, Selection, and Face Validation of Items for a New Generic Measure of Quality of Life: The EQ-HWB. Value Health. 2022; 25: 512-524.\u003c/li\u003e\n \u003cli\u003ePeasgood T, Mukuria C, Brazier J, Marten O, Kreimeier S, Luo N, Mulhern B, Greiner W, Pickard AS, Augustovski F, Engel L, Gibbons L, Yang Z, Monteiro AL, Kuharic M, Belizan M, Bj\u0026oslash;rner J. Developing a New Generic Health and Wellbeing Measure: Psychometric Survey Results for the EQ-HWB. Value Health. 2022; 25: 525-533.\u003c/li\u003e\n \u003cli\u003eMukuria C, Peasgood T, McDool E, Norman R, Rowen D, Brazier J. Valuing the EQ Health and Wellbeing Short Using Time Trade-Off and a Discrete Choice Experiment: A Feasibility Study. [FR9] Value Health. 2023; 26:1073-1084.\u003c/li\u003e\n \u003cli\u003eGBD 2019 Mental Disorders Collaborators. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry. 2022; 9:137-150.\u003c/li\u003e\n \u003cli\u003eBarnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012; 380: 37-43.\u003c/li\u003e\n \u003cli\u003eNoto S. Perspectives on Aging and Quality of Life. Healthcare. 2023; 11: 2131.\u003c/li\u003e\n \u003cli\u003eMcDool E, Mukuria C, Peasgood T. Psychometric Performance of the EQ Health and Wellbeing Short in a United Kingdom Population Sample. Value Health. 2024; 27:1215-1224.\u003c/li\u003e\n \u003cli\u003eKuharić M, Pickard AS, Mukuria C, Finch AP. The Measurement Properties of the EQ-HWB and the EQ-HWB-S in Italian Population: A Comparative Study With EQ-5D-5L. Value Health. 2024; 27:955-966.\u003c/li\u003e\n \u003cli\u003eKuharic M, Mulhern B, Sharp LK, Turpin RS, Pickard AS. Comparison of the EQ-HWB and EQ-HWB-S With Other Preference-Based Measures Among United States Informal Caregivers. Value Health. 2024; 27:967-977.\u003c/li\u003e\n \u003cli\u003ePurba FD, Putri GHF, Mudita PGPR. Psychometric Performance of EQ-HWB and EQ-HWB-9 Self-Complete and Interviewer-Administered Versions in Literate, Low-Literacy, and Patient Populations in Indonesia. Value Health Reg Issues. 2025; 53:101543.\u003c/li\u003e\n \u003cli\u003eBailey C, Trapani K, Davies JN, Van Dam N, Galante J, Peasgood T. The psychometric performance of the EQ-HWB-9 for measuring health and wellbeing in a general population sample from Australia and New Zealand. Qual Life Res. 2025; 34: 3707-3719.\u003c/li\u003e\n \u003cli\u003eKeetharuth AD, Mukuria C, Peasgood T, Wailoo A. EQ Health and Wellbeing EQ-HWB: A Psychometric Assessment Across 6 Conditions and the General Population in the United Kingdom. Value Health. 2025; 28: 1857-1867.\u003c/li\u003e\n \u003cli\u003eXu RH, Yang C, Rencz F. Exploring Recall Periods for EQ-5D-5L and EQ-HWB-9: A Hong Kong Population Study. Value Health. 2026: S1098-3015(26)00005-7.\u003c/li\u003e\n \u003cli\u003eMasutti S, Falivena C, Purba FD, Jommi C, Mukuria C, Finch AP. Content validity of the EQ-HWB and EQ-HWB-S in a sample of Italian patients, informal caregivers and members of the general public. J Patient Rep Outcomes. 2024; 8:36.\u003c/li\u003e\n \u003cli\u003eZhang G, Yang Z, Luo N, Peasgood T, Busschbach J. Evaluating the Factor Structure of the Preliminary Version of EuroQol Health and Well-Being Instrument in China: A Replication of the Confirmatory Factor Analysis. Value Health Reg Issues. 2026; 51:101501.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Sociodemographic characteristics of the respondents\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;20-29\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;30-39\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;40-49\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;50-59\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;60-69\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u0026ge;70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e656\u003c/p\u003e\n \u003cp\u003e748\u003c/p\u003e\n \u003cp\u003e977\u003c/p\u003e\n \u003cp\u003e987\u003c/p\u003e\n \u003cp\u003e857\u003c/p\u003e\n \u003cp\u003e952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12.7\u003c/p\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003cp\u003e18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Male\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2559\u003c/p\u003e\n \u003cp\u003e2618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e49.4\u003c/p\u003e\n \u003cp\u003e50.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Hokkaido\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Tohoku\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Kanto\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Keihin\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Hokuriku\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Tokai\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Keihanshin\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Chugoku\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Shikoku\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Kyushu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e224\u003c/p\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003cp\u003e1544\u003c/p\u003e\n \u003cp\u003e288\u003c/p\u003e\n \u003cp\u003e606\u003c/p\u003e\n \u003cp\u003e835\u003c/p\u003e\n \u003cp\u003e288\u003c/p\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003cp\u003e565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003cp\u003e29.8\u003c/p\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003cp\u003e16.2\u003c/p\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Elementary or junior hige school\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;High school\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;College\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Junior college\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;University\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Graduate\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003cp\u003e1719\u003c/p\u003e\n \u003cp\u003e622\u003c/p\u003e\n \u003cp\u003e521\u003c/p\u003e\n \u003cp\u003e1999\u003c/p\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003cp\u003e9.9\u003c/p\u003e\n \u003cp\u003e38.7\u003c/p\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eEmployment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Full-time worker\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Part-time worker\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Self-employed or manager\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Housemaker\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Retired\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Student\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003cp\u003e346\u003c/p\u003e\n \u003cp\u003e794\u003c/p\u003e\n \u003cp\u003e336\u003c/p\u003e\n \u003cp\u003e1066\u003c/p\u003e\n \u003cp\u003e770\u003c/p\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e32.2\u003c/p\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003cp\u003e15.3\u003c/p\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003cp\u003e20.6\u003c/p\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Unmarried\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Married\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Divorced/bereaved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1705\u003c/p\u003e\n \u003cp\u003e3096\u003c/p\u003e\n \u003cp\u003e567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e31.8\u003c/p\u003e\n \u003cp\u003e57.7\u003c/p\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eHousehold Income (JPY 10,000)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;100\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;100-200\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;200-300\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;300-400\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;400-500\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;500-700\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;700-1000\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;1000-1500\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;1500-2000\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u0026gt;2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003cp\u003e386\u003c/p\u003e\n \u003cp\u003e569\u003c/p\u003e\n \u003cp\u003e619\u003c/p\u003e\n \u003cp\u003e591\u003c/p\u003e\n \u003cp\u003e780\u003c/p\u003e\n \u003cp\u003e636\u003c/p\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 \u0026nbsp;Distribution of EQ-HWB responses by\u0026nbsp;level\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"102%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eNo difficulty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eSlight difficulty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eSome difficulty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMuch difficulty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eUnable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eSee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3192 (61.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1264 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e721 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eHear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4432 (85.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e486 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e231 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e22 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e6 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eGetting around inside and outside*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4604 (88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e349 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e167 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e48 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e9 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eDay-to-day activities*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4426 (85.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e477 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e199 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e57 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e18 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003ePersonal care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4702 (90.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e288 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e139 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e36 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e12 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eNone of\u003c/p\u003e\n \u003cp\u003ethe time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eOnly occasionally\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eSometimes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eOften\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMost of\u003c/p\u003e\n \u003cp\u003ethe time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eSleep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2647 (51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1455 (28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e660 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e287 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e128 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eExhausted*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2394 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1571 (30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e691 (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e380 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e141 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eLonely*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3478 (67.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e918 (17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e475 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e195 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e111 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eUnsupported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3830 (74.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e697 (13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e340 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e188 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e122 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eRemembering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3228 (62.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1236 (23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e454 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e173 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e86 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eConcentrating/thinking clearly*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3784 (73.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e857 (16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e361 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e124 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e51 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eAnxious*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2980 (57.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1190 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e518 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e311 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e178 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eUnsafe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4316 (83.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e566 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e187 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e62 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e46 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eFrustrated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2372 (45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1591 (30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e708 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e342 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e164 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eDepressed*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2997 (57.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1269 (24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e520 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e246 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e145 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eLook Forward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3408 (65.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e966 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e413 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e229 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e161 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eControl*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3169 (61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1169 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e470(9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e226 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e143 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eCope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3608 (70.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e891 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e387 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e181 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e110 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eAccepted\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e766 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1120 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e989 (19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e802 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1500 (29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eFeel good\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e638 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1196 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1264 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e933 (18.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1146 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eDo things wanted to do\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e665 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1399 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1395 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e969 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e749 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003ePain (frequency)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1792 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1737 (33.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e986 (19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e420 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e242 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eVery severe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003ePain (severity)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1852 (35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2528 (48.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e671 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e101 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e25 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eNone of\u003c/p\u003e\n \u003cp\u003ethe time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eOnly occasionally\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eSometimes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eOften\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMost of\u003c/p\u003e\n \u003cp\u003ethe time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eDiscomfort (frequency)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2475 (47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1724 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e643 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e239 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e96 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eVery severe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eDiscomfort (severity)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2389 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2163 (41.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e514 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e82 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e29 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Part of the EQ-HWB-9. \u0026dagger;Reverse coded for summary score.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3\u0026nbsp;Comparison of the EQ-HWB-9 utility by dimension\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eStd. err.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eMobility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eActivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eExhaustion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eLoneliness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eCognition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eSad/depress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003ePain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 Relationships between utility scores and diseases and symptoms\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"98%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eCoefficient (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eCoefficient (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eCoefficient (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u003cstrong\u003e0.0017\u003c/strong\u003e*\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(0.0015 to 0.0020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u003cstrong\u003e0.0026\u003c/strong\u003e*\u003c/p\u003e\n \u003cp\u003e(0.0023 to 0.0028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u0026nbsp;0.0032\u003c/strong\u003e*\u003c/p\u003e\n \u003cp\u003e(0.0028 to 0.0038)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003cstrong\u003e-0.0195\u003c/strong\u003e*\u003c/p\u003e\n \u003cp\u003e(-0.0280 to -0.0110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003cstrong\u003e-0.0232\u003c/strong\u003e*\u003c/p\u003e\n \u003cp\u003e(-0.0316 to -0.0152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003cstrong\u003e-0.0259\u003c/strong\u003e*\u003c/p\u003e\n \u003cp\u003e(-0.0404 to -0.0115)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eMultimorbidity\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;-0.0545\u003c/strong\u003e*\u003c/p\u003e\n \u003cp\u003e(-0.0661 to -0.0429)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003cstrong\u003e-0.0633\u003c/strong\u003e*\u003c/p\u003e\n \u003cp\u003e(-0.0775 to -0.0490)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;-0.1321\u003c/strong\u003e*\u003c/p\u003e\n \u003cp\u003e(-0.147 to -0.1173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;0.0165\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(-0.0107 to 0.0440)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;0.0087\u003c/p\u003e\n \u003cp\u003e(-0.0213 to 0.0387)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003cstrong\u003e-0.0433\u003c/strong\u003e*\u003c/p\u003e\n \u003cp\u003e(-0.0767 to -0.0098)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eDiabetes (n=224)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e-0.0007\u003c/p\u003e\n \u003cp\u003e(-0.0306 to 0.0166)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eDepression (n=248)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;-0.1243\u003c/strong\u003e*\u003c/p\u003e\n \u003cp\u003e(-0.1476 to -0.1010)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eEye diseases (n=280)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.0216\u003c/p\u003e\n \u003cp\u003e(-0.0007 to 0.0439)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eHypertension (n=538)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.0071\u003c/p\u003e\n \u003cp\u003e(-0.0108 to 0.0250)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eAngina, Myocardial infarction (n=67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e-0.0278\u003c/p\u003e\n \u003cp\u003e(-0.0683 to 0.0129)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eArthritis (n=86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;-0.0605\u003c/strong\u003e*\u003c/p\u003e\n \u003cp\u003e(-0.0966 to -0.0244)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eLower back pain (n=177)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;-0.0763\u003c/strong\u003e*\u003c/p\u003e\n \u003cp\u003e(-0.1027 to -0.0500)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003econs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;0.7674\u003c/strong\u003e*\u003c/p\u003e\n \u003cp\u003e(0.7468 to 0.7880)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;0.7997\u003c/strong\u003e*\u003c/p\u003e\n \u003cp\u003e(0.7812 to 0.8182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;0.7083\u003c/strong\u003e*\u003c/p\u003e\n \u003cp\u003e(0.6664 to 0.7502)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Bolded values indicate significant differences at p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eModel 1: Adjusted for age and sex.\u003c/p\u003e\n\u003cp\u003eModel 2: Model 1 + multimorbidity category.\u003c/p\u003e\n\u003cp\u003eModel 3: Model 2 + selected health conditions.\u003c/p\u003e\n\u003cp\u003eReference category: absence of the condition.\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":"health-and-quality-of-life-outcomes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hqlo","sideBox":"Learn more about [Health and Quality of Life Outcomes](http://hqlo.biomedcentral.com)","snPcode":"12955","submissionUrl":"https://submission.nature.com/new-submission/12955/3","title":"Health and Quality of Life Outcomes","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"EQ-HWB, wellbeing, multimorbidity, depression, psychometric validation, utility score","lastPublishedDoi":"10.21203/rs.3.rs-9025329/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9025329/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe EQ-HWB was developed to capture broader aspects of wellbeing beyond traditional health-related quality of life. However, empirical evidence from non-European populations remains limited. This study evaluated the psychometric performance of the EQ-HWB-9 utility index and the EQ-HWB-25 profile measure in a large sample of the Japanese general population, with a focus on multimorbidity and depression.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analysed data from a nationwide web-based cross-sectional survey (n\u0026thinsp;=\u0026thinsp;5,177). The EQ-HWB-9 utility and EQ-HWB-25 composite scores were examined across levels of multimorbidity and depression. Known-group validity was assessed via group comparisons. Multivariable ordinary least squares regression with HC3 robust standard errors was used to estimate adjusted associations between the selected conditions and the EQ-HWB-9 utility.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe EQ-HWB-9 utility scores decreased monotonically with increasing number of comorbid conditions. The respondents reporting depression had substantially lower EQ-HWB-9 utility scores (β = -0.124 (95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.148\u0026ndash;\u0026minus;0.101)) and markedly lower standardized EQ-HWB-25 composite scores. In multivariable models adjusted for age, sex, and multimorbidity, depression, arthritis, and lower back pain were independently associated with lower EQ-HWB-9 utility scores. The EQ-HWB-25 provides complementary descriptive information, highlighting multidimensional wellbeing deficits associated with depression.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese findings support the construct validity and known-group validity of the EQ-HWB in Japan. The EQ-HWB-9 captured both the cumulative multimorbidity burden and condition-specific impacts on wellbeing, particularly for depression, whereas the EQ-HWB-25 offered broader profile-level insights. The EQ-HWB framework may be especially valuable for wellbeing assessment and economic evaluation in populations with a high prevalence of multimorbidity and depression.\u003c/p\u003e","manuscriptTitle":"Psychometric performance of the EQ-HWB-25 and EQ-HWB-9 in Japan: evidence from a large web-based survey of multimorbidity and depression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-17 13:45:45","doi":"10.21203/rs.3.rs-9025329/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-05T06:34:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316917098545703904824144214297139127308","date":"2026-04-15T09:40:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104486162041079458240178437877798219659","date":"2026-04-13T09:52:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-15T22:29:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-06T17:34:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-06T12:19:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Health and Quality of Life Outcomes","date":"2026-03-04T03:28:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"health-and-quality-of-life-outcomes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hqlo","sideBox":"Learn more about [Health and Quality of Life Outcomes](http://hqlo.biomedcentral.com)","snPcode":"12955","submissionUrl":"https://submission.nature.com/new-submission/12955/3","title":"Health and Quality of Life Outcomes","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dcd27a4f-500c-472d-a94c-312f4a286aea","owner":[],"postedDate":"March 17th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-05T06:34:25+00:00","index":25,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-17T13:45:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-17 13:45:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9025329","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9025329","identity":"rs-9025329","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}