Degree of coincidence of the change in health-related QOL in the recovery-phase rehabilitation ward and the proxy’s response: Evaluation of patients with musculoskeletal disorder by EQ-5D-5L

Degree of coincidence of the change in health-related QOL in the recovery-phase rehabilitation ward and the proxy’s response: Evaluation of patients with musculoskeletal disorder by EQ-5D-5L | 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 Degree of coincidence of the change in health-related QOL in the recovery-phase rehabilitation ward and the proxy’s response: Evaluation of patients with musculoskeletal disorder by EQ-5D-5L Ryota Izumi, Shinichi Noto, Hirofumi Nagayama, Tetsuya Sano, Hirokazu Takizawa, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3889431/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Recently, a conversion table of the EuroQol 5-dimension 5-level (EQ-5D-5L) unique to Japan was created that can easily measure the health-related quality of life (QOL) of a subject. Currently, however, only a few investigations have been conducted of health-related QOL using the EQ-5D-5L in the recovery-phase rehabilitation ward. Moreover, proxy responses for patients who are unable to respond for themselves have only been reported for stroke patients. Methods In this study, we used the EQ-5D-5L to investigate changes in health-related QOL before and after rehabilitation and the degree of agreement between the patient’s responses and the occupational therapist’s proxy responses in patients with musculoskeletal disorders who were hospitalized in a recovery-phase rehabilitation ward. Results Among the 77 subjects, health-related QOL was improved after rehabilitation, and the effect size was large. Regarding the degree of agreement between the QOL values, the intraclass correlation coefficient was 0.68 for the initial evaluation and 0.72 for the re-evaluation, indicating substantial agreement. Conclusion The result of this study showed that the EQ-5D-5L was useful as an indicator of outcome of patients with musculoskeletal disorders who were hospitalized in a recovery-phase rehabilitation ward and that proxy responses by occupational therapists can measure QOL values similar to those of the individuals themselves. Health-related QOL degree of agreement recovery-phase rehabilitation ward motor disorder EQ-5D-5L Figures Figure 1 Introduction In recent years, studies on health-related quality of life (QOL) as an outcome have begun to appear in the field of rehabilitation. This is thought to be because the number of people living with chronic disabilities has increased, and the scope of medical practice has expanded from acute to chronic diseases [ 1 ]. In other words, although we cannot expect marked improvement of physical functions due to illness, it is necessary to show whether the subjects’ life has improved through various approaches, including rehabilitation. Additionally, health-related QOL is based on patient-reported outcomes (PRO), which can comprehensively measure the impact of the approaches to psychosomatic functions, activities, and participation in rehabilitation on the patient’s levels of happiness and satisfaction. However, because PRO are measured by patients themselves, it is difficult to measure in subjects who have difficulty in responding by themselves (e.g., those with dementia, aphasia, higher brain dysfunction, etc.). As a solution to this problem, although previous studies have investigated QOL evaluated by doctors, nurses, and family members acting as a proxy, the degree of agreement is reported to be low [ 2 , 3 ]. However, a previous study in Japan [ 4 ] revealed that for stroke patients in a recovery-phase rehabilitation ward, the degree of agreement between the patient’s self-response and the response by the proxy occupational therapist (OT) using the EuroQol 5-dimension 5-level (EQ-5D-5L) tool was high. Outside Japan, there are some reports on the use of this new scale, the EQ-5D-5L, but there are few reports in Japan, and the investigations of the patients with musculoskeletal disorders in recovery-phase rehabilitation wards have only been performed with the EuroQol 5-dimension 3-level (EQ-5D-3L) [ 5 ]. Therefore, we found it necessary to conduct a longitudinal study of the EQ-5D-5L, a new scale of health-related QOL, to show changes in health-related QOL and to clarify the degree of agreement between the subject’s self-responses and those of the proxy OT. The following two effects are expected from this study. First, because this is a longitudinal study, changes in health-related QOL before and after rehabilitation can be clarified, thus making possible an approach based on the impact of rehabilitation and its results. Second, if the degree of agreement between the subject’s response and the proxy’s response is high, even if the subject cannot respond by oneself about their health-related QOL, an approximate idea of the subject’s health-related QOL can be estimated, therefore making it possible to prepare a rehabilitation plan based on the health-related QOL of the subject. If the degree of agreement is low, however, this infers that a plan can be established based on the results of analysis of the degree of agreement of each item. Among the many health-related QOL scales available, the EQ-5D-5L is used for the following three reasons. First, because the questionnaire consists of 5 items with 5 choices, it can be answered in as short a time as about 5 minutes, thus reducing the burden on the subject and evaluator. Second, as there is a self-response version and a proxy response version, a third person can respond for a subject who has difficulty responding by oneself. Third, although this scale was prepared outside Japan, scoring unique to Japan has been prepared [ 6 ], thus allowing measurement of health-related QOL appropriate to Japan. Moreover, health-related QOL can be compared among countries, and the characteristics in each country can be understood. Further, the EQ-5D has been used as a measure of the cost-effectiveness of medical technology implemented in other countries, and the use of the EQ-5D in Japan has been recommended in discussions about the evaluation of medical technology [ 7 ]. Therefore, the investigation of the new EQ-5D-5L scale is considered essential for future of medical economics evaluations of rehabilitation. Objective The objectives of this study were to clarify the change in health-related QOL of patients with musculoskeletal disorders who were hospitalized in a recovery-phase rehabilitation ward and to investigate the degree of agreement between the subject’s own response and the proxy’s responses. Specifically, the following two points were investigated: Investigation of the change in health-related QOL before and after rehabilitation using a new scale, the EQ-5D-5L. Investigation of the degree of agreement between the subject’s response and the proxy’s response to a scale of health-related QOL, the EQ-5D-5L, and to clarify the characteristics of the proxy’s response. However, as the basis is the subject’s response, the subjects who can respond by themselves are targeted in this study. Methods Study design and subjects The study design was that of a multicenter collaborative longitudinal study that included a total of five institutions in Japan to minimize regional and hospital biases. To determine the sample size, the most stringent criteria were set according to the method of Doros and Lew [ 8 ]. The criteria indicated that the number of subjects should be set at more than 50, so to account for an expected data loss of 20%, 10 subjects were added to the 55 subjects, resulting in a minimum sample size of 65 subjects. The survey period was from March 2020 to October 2022, and the evaluation periods were within 1 week after entering the recovery-phase rehabilitation ward (initial evaluation) and 1 month later (re-evaluation). In setting the timing of re-evaluation, because the hospitalization period of the patients with musculoskeletal disorders varies [ 9 ], and to express changes in QOL values over the same hospitalization period, the timing was unified to 1 month later. The subjects were patients with musculoskeletal disorders who were hospitalized in the recovery-phase rehabilitation ward (the fee for musculoskeletal rehabilitation was calculated based on the rehabilitation classification by disease), and the exclusion criteria were patients with disturbance of consciousness, aphasia, higher brain dysfunction and cognitive impairment (based on a Mini Mental State Examination [MMSE] score of 19 points or less), and who had difficulty in responding by themselves. The recovery-phase rehabilitation ward is defined as a ward in which many specialists team up to provide intensive rehabilitation for patients recovering from the acute phase of cerebrovascular disease or femoral neck fracture who still require medical/social/psychological support with the aim of returning them to home or society in a physically and mentally recovered condition [ 10 ]. The EQ-5D-5L was used to measure health-related QOL at the same time by the subject and proxy in the order of proxy’s response and subject’s response so that the proxy would not know the subject’s response. The subject entered responses into the evaluation form by oneself, and if the subject had a visual disturbance or hearing disorder, the proxy therapist was allowed to assist the subject. The proxy was an OT in clinical practice who showed a high degree of agreement in a previous study [ 4 ]. Scale used Health-related QOL was measured using the EQ-5D-5L, which evaluates 5 subitems, “mobility,” “self-care,” “usual activities,” “pain/discomfort,” and “anxiety/depression” in 5 grades. A conversion table unique to Japan was used to converted QOL values [ 6 ]. The QOL values range from 1.00 (perfect health) to -0.025. Moreover, the MMSE and Functional Independence Measure (FIM) were used for evaluation of cognitive function and ADL, respectively, in the subjects. Statistical method and ethical procedures As statistical methods, Wilcoxon’s signed rank test was used for the comparison of each scale before and after rehabilitation, and the effect size was calculated by the standardized response mean (SRM): \(\text{S}\text{R}\text{M}=\frac{{\stackrel{-}{X}}_{Time2}-{\stackrel{-}{X}}_{Time1}}{{SD}_{Difference}}\) [ 11 ]. SRM is obtained by dividing the mean change in the initial evaluation and reevaluation values by the standard deviation (SD) of the difference score. For the effect size, 0.80 a “large effect.” Regarding the degree of agreement between the subject’s response and proxy’s response, the subject’s response was used as the external criterion, the Intraclass Correlation Coefficient (ICC) was used for the QOL value, and the weighing factor κ was used for selection of each level of the sub-items, with coefficients of 0.00 to 0.20 considered as slight agreement, 0.21 to 0.40 as fair agreement, 0.41 to 0.60 as moderate agreement, 0.61 to 0.80 as substantial agreement, and 0.81 to 1.00 as almost complete agreement [ 11 ]. For weighting, we used the second-order weighting recommended by Fayers and Machin [ 12 ]. IBM SPSS Statistics 24 was used for these analyses, and the level of significance was set as 5%. In conducting this study, ethical consideration were made in accordance with the “Ethical Guidelines for Medical Research in Humans” [ 13 ]. Moreover, written informed consent was obtained from the subjects of this study, and this study was approved by the ethical review board of our university (approval no.: 16045). Results Among the 80 candidates, 77 subjects without missing data were included. Their mean age was 77.7 years old, the mean period from the day of onset to initial evaluation was 12.2 days, and that from initial evaluation to reevaluation was 31.9 days. There were 57 females and 20 males, and the diagnosis was hip fracture in 35 subjects, osteoarthritis in 7 subjects, vertebral fracture in 10 subjects, and others in 25 subjects (Table 1). Comparison before and after rehabilitation showed significant improvement in all items. Although the effect size was inferior to that of the FIM, the QOL value was 0.90 for subject’s response and 0.95 for the proxy’s response, indicating a large effect (Table 2). For the coefficient of the degree of agreement, the ICC of the QOL value was 0.68 for the initial evaluation and 0.72 for the reevaluation (substantial agreement) (Table 3), and the scatter plot of the QOL values for the subject’s response and proxy’s response is shown in Fig. 1 . The κ coefficient of the degree of agreement of the sub-items ranged from 0.31 to 0.56 (fair to moderate agreement) (Table 3), and the levels selected by the subject and by the proxy are shown in Table 4. Discussion Before and after rehabilitation, not only ADL but also health-related QOL improved, which is considered to be due to the comprehensive approach to rehabilitation. Moreover, the effect size shown suggested this approach to be useful as an outcome for patients with musculoskeletal disorders in a recovery-phase rehabilitation ward. In previous studies, it was reported that particularly when the family member was the proxy, the degree of agreement was lower than that of the subject [ 14 , 15 ], but in the present study, the response by the proxy OT indicated a high degree of agreement. The sub-items showed a moderate degree of agreement, except for “anxiety/depression” in the initial evaluation and “normal activities” in the reevaluation. The degree of agreement for “anxiety/depression” was lower than that of the other sub-items. However, in a study by Pickard et al [ 3 ]. using the EQ-5D in stroke patients, the initial evaluation of the degree of agreement for “anxiety/depression” showed a value of 0.18, and therefore, the present study showed higher values. The reason is thought to be that occupational therapy is a curriculum that emphasizes both physical function and mental function during training, thus making it possible for OTs to understand mental functions objectively based on this education. In addition, the reason for the improvement in the degree of agreement between QOL values in the reevaluation compared with the initial evaluation was assumed to be that the degree of agreement between the sub-items was large, the degree of deviation in the selected level of “anxiety/depression” between the subject and the proxy decreased (Table 4), and the variation was also reduced as shown in the scatter plot in Fig. 1 . The mean number of units of an OT per patient in a day in the recovery-phase rehabilitation ward is 2.97 [ 9 ], indicating that an OT is in contact with subject on a one-to-one basis for an average of 1 hour every day. The mean period from the initial evaluation to reevaluation was 31.9 days, and it can be inferred that the condition of subject could be understood during this period and that the degree of agreement has improved. However, although the degree of agreement of QOL values was high, the degree of agreement was low for some sub-items, and therefore, it is necessary to consider this when examining the proxy’s response for the sub-items. As another point to consider, it is necessary for the proxy to observe the whole life of the subject in the ward instead of just during rehabilitation. On the subject side, it is necessary to understand the characteristics of subject. That is, if the subject has depressive tendencies and low self-esteem, it is predicted that the subject will show low values for items other than mental and psychological ones, and it is therefore necessary to base the proxy’s response on these factors. Limitations of study and future vision In this study, all musculoskeletal disorders were analyzed, but as clinical symptoms are different in hip fracture, osteoarthritis, and vertebral fracture, it is necessary to verify them for each diagnosis and to define the QOL value by severity, with analyses of other diseases also necessary. Short-term analysis was performed in this study, but it will also be necessary to investigate the changes in long-term health-related QOL. For the proxy’s response by this study method, the QOL value may be different based on the viewpoint of the proxy (family member, caretaker, healthcare professional, etc.), and therefore, it is necessary to interpret the results carefully [ 16 , 17 ]. Because the proxy’s responses were made for subjects who could respond by themselves, it will be necessary to further verify whether health-related QOL can be measured by proxy for those subjects who truly cannot respond by themselves. Conclusion Economic evaluation of medical technology begins with drugs, and medical technology also includes rehabilitation. The quality-adjusted life year (QALY) is used as an indicator of cost-effectiveness for economic evaluation. QALY is calculated by multiplying the number of years lived by QOL, with the EQ-5D score used as the measure of QOL. When economic evaluation in the field of rehabilitation is begun, it is expected that it will be introduced starting with recovery-phase rehabilitation wards, in which the number of subjects is the largest. This study is positioned as a preliminary investigation and may be utilized as a part of the data needed for the introduction of medical economics evaluation of rehabilitation. Abbreviations EQ-5D-5L EuroQol 5-Dimension 5-Level EQ-5D-3L EuroQol 5-Dimension 3-Level FIM Functional Independence Measure ICC Intraclass Correlation Coefficient MMSE Mini Mental State Examination OT Occupational Therapist PRO Patient-Reported Outcomes QALY Quality-Adjusted Life Year SD Standard Deviation SRM Standardized Response Mean Declarations Ethics approval and consent to participate In conducting this study, ethical consideration were made in accordance with the Ethical Guidelines for Medical Research in Humans. Moreover, written informed consent was obtained from the subjects of this study, and this study was approved by the ethical review board of Seirei Christopher university (approval no.: 16045). Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This work was supported by JSPS KAKENHI Grant Number 19K10493. Author Contribution RI, SN, & HN conceived and designed the study. RI, TS, HT, & DT conducted data collection, entry and analysis. All authors read and approved the final manuscript. Acknowledgements We very much thank the subjects and occupational therapists in the hospitals for their cooperation in this study. Availability of data and materials The datasets used and analysed during the current study are available from the corresponding author on reasonable request. References Ishikawa H, Furukawa H. Zukai sagyouryouhou gizyutsugaido 3rd, editor (in Japanese). Bunkodo; 2011. p. 263–274. Steel JL, Geller DA, Carr BI. Proxy ratings of health-related quality of life in patients with hepatocellular carcinoma. Qual Life Res. 2005;14:1025–33. Pickard AS, Johnson JA, Feeny DH, Shuaib A, Carriere KC, Nasser AM. Agreement between patient and proxy assessments of health-related quality of life after stroke using the EQ-5D and Health Utilities Index. Stroke. 2004;35:607–12. Izumi R, Noto S, Sano T, Suzuki T. [Agreement between changes in health-related quality of life and proxy responses in a recovery unit: an evaluation using the EQ-5D-5L for stroke patients] Kaifukuki Rehabilitation byoutou ni okeru kenkoukanrenQOL no henka to dairininkaitou no icchido ni tsuite -nousotyuukanzya eno EQ-5D-5L niyoru hyouka- (in Japanese). Nihon Rinshou Sagyouryouhou Kenkyu (Japanese Society of Clinical Occupational Therapy). 2021;8:31–36. Izumi R, Noto S, Sano T, Mizushima T, Sato T, Sakimura Y. [Reliability of Japanese version utility measures in health-related quality of life-agreement between self-reported and proxy-reported-] Kenkoukanren QOL ni okeru nihongoban kouyouchi syakudo no shinraisei no kentou -Honninkaitou to dairininkaitou no icchido nitsuite- (in Japanese). Sogo Rehabilitation (General Rehabilitation). 2011;39:569–75. Ikeda S, Shiroiwa T, Igarashi A, Noto S, Fukuda T, Saito S, et al. [Developing a Japanese version of the EQ-5D-5L value set] Nihongoban EQ-5D-5L ni okeru sukoaring hou no Kaihatsu (in Japanese). Hoken Iryou Kagaku (Journal of the National Institute of Public Health). 2015;64:47–55. Ministry of Health, Labour and Welfare. [Central Social Insurance Medical Council Expert Group on. Cost-effectiveness Assessment] Chuuou syakai hoken iryou kyougikai hiyou tai kouka hyouka senmon bu kai (in Japanese). https://www.mhlw.go.jp/stf/shingi/shingi-chuo_484659old.html . Accessed 18 Dec 2023. Doros G, Lew R. Design based on intra-class correlation coefficients. Am J Biostat. 2010;1:1–8. Kaifukuki Rehabilitation Ward Association. [Survey report on the current situation and tasks. recovery units] Kaifukuki Rehabilitation byoutou no genzyou to kadai ni kansuru tyousa houkokusyo (in Japanese), 2022. Kaifukuki Rehabilitation Ward Association; 2023. pp. 28–64. Kaifukuki Rehabilitation Ward Association. About Kaifukuki rehabilitation ward. http://www.rehabili.jp/eng/eng_page.html . Accessed 18 Dec 2023. Fayers PM, Machin D. Quality of life. The assessment, analysis and reporting of patient-reported outcomes. 3rd, editor ed. John Wiley & Sons; 2016. pp. 475–509. Fayers PM, Machin D. Quality of life. The assessment, analysis and reporting of patient-reported outcomes. 3rd, editor ed. John Wiley & Sons; 2016. pp. 89–124. Ministry of Education., Culture, Sports, Science and Technology. Ministry of Health, Labor and Welfare. Ethical Guidelines for Medical and Health Research Involving Human Subjects (in Japanese). https://www.mhlw.go.jp/file/06-Seisakujouhou-10600000-Daijinkanboukouseikagakuka/0000153339.pdf . Accessed 18 Dec 2023. Whynes DK, Sprigg N, Selby J, Berge E, Bath PM. ENOS Investigators. Testing for differential item functioning within the EQ-5D. Med Decis Making. 2013;33:252–60. Williams LS, Bakas T, Brizendine E, Plue L, Tu W, Hendrie H, et al. How valid are family proxy assessments of stroke patients' health-related quality of life? Stroke. 2006;37:2081–5. Rand S, Caiels J. Using proxies to assess quality of life: a review of the issues and challenges. Discussion paper. Quality and Outcomes of person-centred care policy Research Unit (QORU), University of Kent; 2015. p. 8–18. International Society for Quality of Life Research (prepared by, Aaronson N, Elliott T, Greenhalgh J, Halyard M, Hess R, Miller D et al.). User’s Guide to Implementing Patient-Reported Outcomes Assessment in Clinical Practice, Version: 2015. p. 7–10. Tables Additional Declarations No competing interests reported. Supplementary Files 240127HealthQualLifeOutcomestable34.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-3889431","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269958905,"identity":"b56805e4-a452-43e6-af3c-c8fa1c694936","order_by":0,"name":"Ryota Izumi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIie3QPwrCMBiH4V/p4FLR8RvEXiHFQQfFqzQU2kXBE0glYJeCa8XBK/QIFUGXHqBjRXBy0M2hiH82EVLdBPNuCd8TkgAq1e9GTUDzAfZc2WXjj1FqfU3A/Y9v1I6cbX5Bx1sGYno6jWDWfBxyGWlkLp+EoGGcrsQ8YrCiBB6TEaKB5Vevd0Jc6AaDFgMulZFJAfLM5U7oBUP/IyIMkI1ME/r9x3g5MQ580QBZccqFFjJyonXZWypOcj5ibJrBdo9L0e3NgtDNZQR1+/UIQDdcqUAteduqbOREpVKp/q0bV6ZB1/hc6cAAAAAASUVORK5CYII=","orcid":"","institution":"Seirei Christopher University","correspondingAuthor":true,"prefix":"","firstName":"Ryota","middleName":"","lastName":"Izumi","suffix":""},{"id":269958906,"identity":"0ea2b75f-c011-4804-8b6e-d79974e670b2","order_by":1,"name":"Shinichi Noto","email":"","orcid":"","institution":"Niigata University of Health and Welfare","correspondingAuthor":false,"prefix":"","firstName":"Shinichi","middleName":"","lastName":"Noto","suffix":""},{"id":269958907,"identity":"9adc4940-6ab4-4438-85b9-f590ad8c4167","order_by":2,"name":"Hirofumi Nagayama","email":"","orcid":"","institution":"Kanagawa University of Human Services","correspondingAuthor":false,"prefix":"","firstName":"Hirofumi","middleName":"","lastName":"Nagayama","suffix":""},{"id":269958908,"identity":"5d639d65-5066-49f7-81ee-134e886d229f","order_by":3,"name":"Tetsuya Sano","email":"","orcid":"","institution":"Seirei Christopher University","correspondingAuthor":false,"prefix":"","firstName":"Tetsuya","middleName":"","lastName":"Sano","suffix":""},{"id":269958909,"identity":"41207df1-5ff4-44e8-afea-d7cea837a60e","order_by":4,"name":"Hirokazu Takizawa","email":"","orcid":"","institution":"Niiza Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hirokazu","middleName":"","lastName":"Takizawa","suffix":""},{"id":269958910,"identity":"5d58d3b3-1de7-4eba-861c-590ca560508e","order_by":5,"name":"Daichi Tsukakoshi","email":"","orcid":"","institution":"Shinshu University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Daichi","middleName":"","lastName":"Tsukakoshi","suffix":""}],"badges":[],"createdAt":"2024-01-23 00:59:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3889431/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3889431/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50455640,"identity":"401410ed-acd9-4da5-9087-e0730cfd62fd","added_by":"auto","created_at":"2024-01-31 18:44:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":90346,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots of quality of life (QOL) scores for the self and proxy responses\u003c/p\u003e\n\u003cp\u003eThe x-axis is the QOL value for the subject’s response, and the y-axis is the QOL value for the representative’s response.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3889431/v1/2c45badbf11e311e75151ce7.png"},{"id":50461128,"identity":"5448dc3c-04d8-40ea-95e5-717272590938","added_by":"auto","created_at":"2024-01-31 20:52:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":496318,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3889431/v1/fb8e8cb4-337b-4134-a1cd-250b903de435.pdf"},{"id":50455641,"identity":"67cea1ff-38b4-4624-b1cf-80c4c6ad8657","added_by":"auto","created_at":"2024-01-31 18:44:55","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":15846,"visible":true,"origin":"","legend":"","description":"","filename":"240127HealthQualLifeOutcomestable34.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3889431/v1/2eaee0f3a151d84256adfcbd.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Degree of coincidence of the change in health-related QOL in the recovery-phase rehabilitation ward and the proxy’s response: Evaluation of patients with musculoskeletal disorder by EQ-5D-5L","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, studies on health-related quality of life (QOL) as an outcome have begun to appear in the field of rehabilitation. This is thought to be because the number of people living with chronic disabilities has increased, and the scope of medical practice has expanded from acute to chronic diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In other words, although we cannot expect marked improvement of physical functions due to illness, it is necessary to show whether the subjects\u0026rsquo; life has improved through various approaches, including rehabilitation. Additionally, health-related QOL is based on patient-reported outcomes (PRO), which can comprehensively measure the impact of the approaches to psychosomatic functions, activities, and participation in rehabilitation on the patient\u0026rsquo;s levels of happiness and satisfaction. However, because PRO are measured by patients themselves, it is difficult to measure in subjects who have difficulty in responding by themselves (e.g., those with dementia, aphasia, higher brain dysfunction, etc.). As a solution to this problem, although previous studies have investigated QOL evaluated by doctors, nurses, and family members acting as a proxy, the degree of agreement is reported to be low [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, a previous study in Japan [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] revealed that for stroke patients in a recovery-phase rehabilitation ward, the degree of agreement between the patient\u0026rsquo;s self-response and the response by the proxy occupational therapist (OT) using the EuroQol 5-dimension 5-level (EQ-5D-5L) tool was high. Outside Japan, there are some reports on the use of this new scale, the EQ-5D-5L, but there are few reports in Japan, and the investigations of the patients with musculoskeletal disorders in recovery-phase rehabilitation wards have only been performed with the EuroQol 5-dimension 3-level (EQ-5D-3L) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, we found it necessary to conduct a longitudinal study of the EQ-5D-5L, a new scale of health-related QOL, to show changes in health-related QOL and to clarify the degree of agreement between the subject\u0026rsquo;s self-responses and those of the proxy OT.\u003c/p\u003e \u003cp\u003eThe following two effects are expected from this study. First, because this is a longitudinal study, changes in health-related QOL before and after rehabilitation can be clarified, thus making possible an approach based on the impact of rehabilitation and its results. Second, if the degree of agreement between the subject\u0026rsquo;s response and the proxy\u0026rsquo;s response is high, even if the subject cannot respond by oneself about their health-related QOL, an approximate idea of the subject\u0026rsquo;s health-related QOL can be estimated, therefore making it possible to prepare a rehabilitation plan based on the health-related QOL of the subject. If the degree of agreement is low, however, this infers that a plan can be established based on the results of analysis of the degree of agreement of each item.\u003c/p\u003e \u003cp\u003eAmong the many health-related QOL scales available, the EQ-5D-5L is used for the following three reasons. First, because the questionnaire consists of 5 items with 5 choices, it can be answered in as short a time as about 5 minutes, thus reducing the burden on the subject and evaluator. Second, as there is a self-response version and a proxy response version, a third person can respond for a subject who has difficulty responding by oneself. Third, although this scale was prepared outside Japan, scoring unique to Japan has been prepared [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], thus allowing measurement of health-related QOL appropriate to Japan. Moreover, health-related QOL can be compared among countries, and the characteristics in each country can be understood.\u003c/p\u003e \u003cp\u003eFurther, the EQ-5D has been used as a measure of the cost-effectiveness of medical technology implemented in other countries, and the use of the EQ-5D in Japan has been recommended in discussions about the evaluation of medical technology [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, the investigation of the new EQ-5D-5L scale is considered essential for future of medical economics evaluations of rehabilitation.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThe objectives of this study were to clarify the change in health-related QOL of patients with musculoskeletal disorders who were hospitalized in a recovery-phase rehabilitation ward and to investigate the degree of agreement between the subject\u0026rsquo;s own response and the proxy\u0026rsquo;s responses. Specifically, the following two points were investigated:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eInvestigation of the change in health-related QOL before and after rehabilitation using a new scale, the EQ-5D-5L.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eInvestigation of the degree of agreement between the subject\u0026rsquo;s response and the proxy\u0026rsquo;s response to a scale of health-related QOL, the EQ-5D-5L, and to clarify the characteristics of the proxy\u0026rsquo;s response. However, as the basis is the subject\u0026rsquo;s response, the subjects who can respond by themselves are targeted in this study.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and subjects\u003c/h2\u003e \u003cp\u003e The study design was that of a multicenter collaborative longitudinal study that included a total of five institutions in Japan to minimize regional and hospital biases. To determine the sample size, the most stringent criteria were set according to the method of Doros and Lew [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The criteria indicated that the number of subjects should be set at more than 50, so to account for an expected data loss of 20%, 10 subjects were added to the 55 subjects, resulting in a minimum sample size of 65 subjects. The survey period was from March 2020 to October 2022, and the evaluation periods were within 1 week after entering the recovery-phase rehabilitation ward (initial evaluation) and 1 month later (re-evaluation). In setting the timing of re-evaluation, because the hospitalization period of the patients with musculoskeletal disorders varies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and to express changes in QOL values over the same hospitalization period, the timing was unified to 1 month later.\u003c/p\u003e \u003cp\u003eThe subjects were patients with musculoskeletal disorders who were hospitalized in the recovery-phase rehabilitation ward (the fee for musculoskeletal rehabilitation was calculated based on the rehabilitation classification by disease), and the exclusion criteria were patients with disturbance of consciousness, aphasia, higher brain dysfunction and cognitive impairment (based on a Mini Mental State Examination [MMSE] score of 19 points or less), and who had difficulty in responding by themselves. The recovery-phase rehabilitation ward is defined as a ward in which many specialists team up to provide intensive rehabilitation for patients recovering from the acute phase of cerebrovascular disease or femoral neck fracture who still require medical/social/psychological support with the aim of returning them to home or society in a physically and mentally recovered condition [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe EQ-5D-5L was used to measure health-related QOL at the same time by the subject and proxy in the order of proxy\u0026rsquo;s response and subject\u0026rsquo;s response so that the proxy would not know the subject\u0026rsquo;s response. The subject entered responses into the evaluation form by oneself, and if the subject had a visual disturbance or hearing disorder, the proxy therapist was allowed to assist the subject. The proxy was an OT in clinical practice who showed a high degree of agreement in a previous study [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eScale used\u003c/h2\u003e \u003cp\u003eHealth-related QOL was measured using the EQ-5D-5L, which evaluates 5 subitems, \u0026ldquo;mobility,\u0026rdquo; \u0026ldquo;self-care,\u0026rdquo; \u0026ldquo;usual activities,\u0026rdquo; \u0026ldquo;pain/discomfort,\u0026rdquo; and \u0026ldquo;anxiety/depression\u0026rdquo; in 5 grades. A conversion table unique to Japan was used to converted QOL values [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The QOL values range from 1.00 (perfect health) to -0.025.\u003c/p\u003e \u003cp\u003eMoreover, the MMSE and Functional Independence Measure (FIM) were used for evaluation of cognitive function and ADL, respectively, in the subjects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical method and ethical procedures\u003c/h2\u003e \u003cp\u003eAs statistical methods, Wilcoxon\u0026rsquo;s signed rank test was used for the comparison of each scale before and after rehabilitation, and the effect size was calculated by the standardized response mean (SRM): \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{S}\\text{R}\\text{M}=\\frac{{\\stackrel{-}{X}}_{Time2}-{\\stackrel{-}{X}}_{Time1}}{{SD}_{Difference}}\\)\u003c/span\u003e\u003c/span\u003e [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. SRM is obtained by dividing the mean change in the initial evaluation and reevaluation values by the standard deviation (SD) of the difference score. For the effect size, \u0026lt;\u0026thinsp;0.5 indicates a \u0026ldquo;small effect,\u0026rdquo; 0.50\u0026ndash;0.80 a \u0026ldquo;medium effect,\u0026rdquo; and \u0026gt;\u0026thinsp;0.80 a \u0026ldquo;large effect.\u0026rdquo; Regarding the degree of agreement between the subject\u0026rsquo;s response and proxy\u0026rsquo;s response, the subject\u0026rsquo;s response was used as the external criterion, the Intraclass Correlation Coefficient (ICC) was used for the QOL value, and the weighing factor \u003cem\u003eκ\u003c/em\u003e was used for selection of each level of the sub-items, with coefficients of 0.00 to 0.20 considered as slight agreement, 0.21 to 0.40 as fair agreement, 0.41 to 0.60 as moderate agreement, 0.61 to 0.80 as substantial agreement, and 0.81 to 1.00 as almost complete agreement [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For weighting, we used the second-order weighting recommended by Fayers and Machin [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. IBM SPSS Statistics 24 was used for these analyses, and the level of significance was set as 5%.\u003c/p\u003e \u003cp\u003eIn conducting this study, ethical consideration were made in accordance with the \u0026ldquo;Ethical Guidelines for Medical Research in Humans\u0026rdquo; [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, written informed consent was obtained from the subjects of this study, and this study was approved by the ethical review board of our university (approval no.: 16045).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAmong the 80 candidates, 77 subjects without missing data were included. Their mean age was 77.7 years old, the mean period from the day of onset to initial evaluation was 12.2 days, and that from initial evaluation to reevaluation was 31.9 days. There were 57 females and 20 males, and the diagnosis was hip fracture in 35 subjects, osteoarthritis in 7 subjects, vertebral fracture in 10 subjects, and others in 25 subjects (Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eComparison before and after rehabilitation showed significant improvement in all items. Although the effect size was inferior to that of the FIM, the QOL value was 0.90 for subject\u0026rsquo;s response and 0.95 for the proxy\u0026rsquo;s response, indicating a large effect (Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the coefficient of the degree of agreement, the ICC of the QOL value was 0.68 for the initial evaluation and 0.72 for the reevaluation (substantial agreement) (Table\u0026nbsp;3), and the scatter plot of the QOL values for the subject\u0026rsquo;s response and proxy\u0026rsquo;s response is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The \u003cem\u003eκ\u003c/em\u003e coefficient of the degree of agreement of the sub-items ranged from 0.31 to 0.56 (fair to moderate agreement) (Table\u0026nbsp;3), and the levels selected by the subject and by the proxy are shown in Table\u0026nbsp;4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eBefore and after rehabilitation, not only ADL but also health-related QOL improved, which is considered to be due to the comprehensive approach to rehabilitation. Moreover, the effect size shown suggested this approach to be useful as an outcome for patients with musculoskeletal disorders in a recovery-phase rehabilitation ward.\u003c/p\u003e \u003cp\u003eIn previous studies, it was reported that particularly when the family member was the proxy, the degree of agreement was lower than that of the subject [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], but in the present study, the response by the proxy OT indicated a high degree of agreement. The sub-items showed a moderate degree of agreement, except for \u0026ldquo;anxiety/depression\u0026rdquo; in the initial evaluation and \u0026ldquo;normal activities\u0026rdquo; in the reevaluation. The degree of agreement for \u0026ldquo;anxiety/depression\u0026rdquo; was lower than that of the other sub-items. However, in a study by Pickard et al [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. using the EQ-5D in stroke patients, the initial evaluation of the degree of agreement for \u0026ldquo;anxiety/depression\u0026rdquo; showed a value of 0.18, and therefore, the present study showed higher values. The reason is thought to be that occupational therapy is a curriculum that emphasizes both physical function and mental function during training, thus making it possible for OTs to understand mental functions objectively based on this education. In addition, the reason for the improvement in the degree of agreement between QOL values in the reevaluation compared with the initial evaluation was assumed to be that the degree of agreement between the sub-items was large, the degree of deviation in the selected level of \u0026ldquo;anxiety/depression\u0026rdquo; between the subject and the proxy decreased (Table\u0026nbsp;4), and the variation was also reduced as shown in the scatter plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe mean number of units of an OT per patient in a day in the recovery-phase rehabilitation ward is 2.97 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], indicating that an OT is in contact with subject on a one-to-one basis for an average of 1 hour every day. The mean period from the initial evaluation to reevaluation was 31.9 days, and it can be inferred that the condition of subject could be understood during this period and that the degree of agreement has improved.\u003c/p\u003e \u003cp\u003eHowever, although the degree of agreement of QOL values was high, the degree of agreement was low for some sub-items, and therefore, it is necessary to consider this when examining the proxy\u0026rsquo;s response for the sub-items. As another point to consider, it is necessary for the proxy to observe the whole life of the subject in the ward instead of just during rehabilitation. On the subject side, it is necessary to understand the characteristics of subject. That is, if the subject has depressive tendencies and low self-esteem, it is predicted that the subject will show low values for items other than mental and psychological ones, and it is therefore necessary to base the proxy\u0026rsquo;s response on these factors.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eLimitations of study and future vision\u003c/h2\u003e \u003cp\u003eIn this study, all musculoskeletal disorders were analyzed, but as clinical symptoms are different in hip fracture, osteoarthritis, and vertebral fracture, it is necessary to verify them for each diagnosis and to define the QOL value by severity, with analyses of other diseases also necessary. Short-term analysis was performed in this study, but it will also be necessary to investigate the changes in long-term health-related QOL. For the proxy\u0026rsquo;s response by this study method, the QOL value may be different based on the viewpoint of the proxy (family member, caretaker, healthcare professional, etc.), and therefore, it is necessary to interpret the results carefully [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Because the proxy\u0026rsquo;s responses were made for subjects who could respond by themselves, it will be necessary to further verify whether health-related QOL can be measured by proxy for those subjects who truly cannot respond by themselves.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eEconomic evaluation of medical technology begins with drugs, and medical technology also includes rehabilitation. The quality-adjusted life year (QALY) is used as an indicator of cost-effectiveness for economic evaluation. QALY is calculated by multiplying the number of years lived by QOL, with the EQ-5D score used as the measure of QOL. When economic evaluation in the field of rehabilitation is begun, it is expected that it will be introduced starting with recovery-phase rehabilitation wards, in which the number of subjects is the largest. This study is positioned as a preliminary investigation and may be utilized as a part of the data needed for the introduction of medical economics evaluation of rehabilitation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eEQ-5D-5L\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;EuroQol 5-Dimension 5-Level\u003c/p\u003e\n\u003cp\u003eEQ-5D-3L\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;EuroQol 5-Dimension 3-Level\u003c/p\u003e\n\u003cp\u003eFIM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Functional Independence Measure\u003c/p\u003e\n\u003cp\u003eICC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Intraclass Correlation Coefficient\u003c/p\u003e\n\u003cp\u003eMMSE\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Mini Mental State Examination\u003c/p\u003e\n\u003cp\u003eOT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Occupational Therapist\u003c/p\u003e\n\u003cp\u003ePRO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Patient-Reported Outcomes\u003c/p\u003e\n\u003cp\u003eQALY\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Quality-Adjusted Life Year\u003c/p\u003e\n\u003cp\u003eSD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Standard Deviation\u003c/p\u003e\n\u003cp\u003eSRM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Standardized Response Mean\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eIn conducting this study, ethical consideration were made in accordance with the Ethical Guidelines for Medical Research in Humans. Moreover, written informed consent was obtained from the subjects of this study, and this study was approved by the ethical review board of Seirei Christopher university (approval no.: 16045).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by JSPS KAKENHI Grant Number 19K10493.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRI, SN, \u0026amp; HN conceived and designed the study. RI, TS, HT, \u0026amp; DT conducted data collection, entry and analysis. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe very much thank the subjects and occupational therapists in the hospitals for their cooperation in this study.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIshikawa H, Furukawa H. Zukai sagyouryouhou gizyutsugaido 3rd, editor (in Japanese). Bunkodo; 2011. p. 263\u0026ndash;274.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteel JL, Geller DA, Carr BI. Proxy ratings of health-related quality of life in patients with hepatocellular carcinoma. 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Med Decis Making. 2013;33:252\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams LS, Bakas T, Brizendine E, Plue L, Tu W, Hendrie H, et al. How valid are family proxy assessments of stroke patients' health-related quality of life? Stroke. 2006;37:2081\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRand S, Caiels J. Using proxies to assess quality of life: a review of the issues and challenges. Discussion paper. Quality and Outcomes of person-centred care policy Research Unit (QORU), University of Kent; 2015. p. 8\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Society for Quality of Life Research (prepared by, Aaronson N, Elliott T, Greenhalgh J, Halyard M, Hess R, Miller D et al.). User\u0026rsquo;s Guide to Implementing Patient-Reported Outcomes Assessment in Clinical Practice, Version: 2015. p. 7\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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Currently, however, only a few investigations have been conducted of health-related QOL using the EQ-5D-5L in the recovery-phase rehabilitation ward. Moreover, proxy responses for patients who are unable to respond for themselves have only been reported for stroke patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e In this study, we used the EQ-5D-5L to investigate changes in health-related QOL before and after rehabilitation and the degree of agreement between the patient’s responses and the occupational therapist’s proxy responses in patients with musculoskeletal disorders who were hospitalized in a recovery-phase rehabilitation ward.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e Among the 77 subjects, health-related QOL was improved after rehabilitation, and the effect size was large. Regarding the degree of agreement between the QOL values, the intraclass correlation coefficient was 0.68 for the initial evaluation and 0.72 for the re-evaluation, indicating substantial agreement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e The result of this study showed that the EQ-5D-5L was useful as an indicator of outcome of patients with musculoskeletal disorders who were hospitalized in a recovery-phase rehabilitation ward and that proxy responses by occupational therapists can measure QOL values similar to those of the individuals themselves.\u003c/p\u003e","manuscriptTitle":"Degree of coincidence of the change in health-related QOL in the recovery-phase rehabilitation ward and the proxy’s response: Evaluation of patients with musculoskeletal disorder by EQ-5D-5L","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-31 18:44:50","doi":"10.21203/rs.3.rs-3889431/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e3519723-41f7-4623-8749-98e91a05d503","owner":[],"postedDate":"January 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-31T20:44:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-31 18:44:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3889431","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3889431","identity":"rs-3889431","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}