Impact of long COVID on health-related quality of life among Japanese adults: findings of CARE Japan Study

Impact of long COVID on health-related quality of life among Japanese adults: findings of CARE Japan Study | 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 Impact of long COVID on health-related quality of life among Japanese adults: findings of CARE Japan Study Sabera Sultana, Yusuke Asai, Haruhiko Ishioka, Shinichiro Ikeda, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8250532/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 Purpose We comprehensively assessed implications of long COVID on health-related quality of life (HRQoL) among Japanese adults and identified its associated factors. Methods This study used prospective cohort data from the CARE Japan Study between January 2022 and January 2023. The study outcome was HRQoL, measured using the 12-item Short Form questionnaire. Self-reported long COVID was the primary independent variable. We fit adjusted beta regression models to calculate beta regression coefficients with the 95% confidence intervals (CI) and average marginal effect (AME) to explore the determinants of HRQoL. We also performed latent class analysis to identify unobserved patterns of long COVID symptoms. Results The final sample size was 1,285 participants. Compared with participants without long COVID, HRQoL was significantly lower among patients with long COVID (β: −0.25; 95% CI: −0.36 to − 0.14; AME: −0.036). The effect of long COVID on HRQoL was most pronounced among respondents with pre-existing lung diseases (β: −0.72; 95% CI: −1.29 to − 0.16; AME: −0.114). In latent class analysis, we identified three subgroups of patients with long COVID: classes 1, 2, and 3. Compared with participants without long COVID, those belonging to class 1 (β: −0.47; 95% CI: −0.57 to − 0.36; AME: −0.065), class 2 (β: −0.48; 95% CI: −0.60 to − 0.35; AME: −0.066), and class 3 (β: −0.93; 95% CI: −1.06 to − 0.79; AME: −0.148) had poorer HRQoL. Conclusions Patients with long COVID had reduced HRQoL. Female sex, age ≤ 39 years, body mass index ≤ 18.5 kg/m 2 , and pre-existing psychological disorders were associated with lower HRQoL. Long COVID health-related quality of life SARS-CoV-2 infection latent class analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Plain English Summary As of October 2025, more than 5 years after the start of the COVID-19 pandemic, approximately 779 million COVID-19 cases have been reported. Survivors of COVID -19 infection often report complex, multisystemic, persistent, or relapsing symptoms for weeks, months, or even years after the initial infection. Long COVID refers to when COVID-19 symptoms are persistent or relapse, within up to 12 weeks after an initial acute infection. Owing to its high prevalence, heterogeneous nature, lack of clear diagnostic markers, and prolonged and obscure recovery process, long COVID poses challenges to individuals, public health care systems, and the workforce. Increasing evidence suggests that long COVID impairs the functional and cognitive capacity of affected patients, resulting in poor health-related quality of life (QOL). Longitudinal studies on health-related QOL among COVID-19 survivors with a long follow-up period are scarce in Japan. In our study, we assessed the impact of COVID-19 infection on health-related QOL among Japanese adults, with 1-year follow-up. We found that patients with long COVID had lower health-related QOL than survivors of COVID-19 without long COVID. The presence of even minimal long COVID symptoms lowered patients’ health-related QOL. For patients with COVID-19 who had comorbid lung diseases, the risk of reduced QOL remained high, even after recovery. Our research findings will enhance understanding of the implications of long COVID on individuals’ lives and underscore the need for a coordinated approach to the long-term treatment, care, and prevention of long COVID through policies, interventions, and integrated health care systems. Introduction Many survivors of COVID − 19 experience complex, multisystemic, persistent, or relapsing symptoms for weeks, months, or even years after the initial infection. Long COVID refers to when these symptoms persist or relapse even after 12 weeks of the initial acute infection [ 1 ]. A meta-analysis including 144 studies reported that, as of 2025, the global pooled prevalence of long COVID was 36% (95% confidence interval [CI]: 33%–40%) [ 2 ]. Common symptoms of long COVID include extreme tiredness, anxiety, memory problems, dizziness, taste or smell issues, insomnia, dyspnea, cough, headache, palpitation, and digestive problems [ 2 – 4 ]. Increasing evidence worldwide suggests that long COVID adversely affects physical, emotional, and social aspects of patients’ lives [ 5 – 7 ]. OpenPROMPT was a cohort study conducted in 2024 in the United Kingdom that measured the impact of long COVID on health-related quality of life (HRQoL). Among 6070 participants included in that study, a consistent impact on HRQoL was found, with greater likelihood of a reported decrease in QoL (odds ratio: 4.7, 95% CI: 3.72–5.93) among participants with long COVID compared with those who did not have long COVID [ 6 ]. Several studies have been conducted to measure the effect of long COVID on QoL among Japanese adults. In 2021, Tsuzuki and colleagues conducted a cross-sectional study in 526 patients to investigate long COVID and HRQoL [ 8 ], and Honda et al. conducted a similar cross-sectional study in 2022 [ 9 ]. Both studies found lower HRQoL among patients with long COVID; however, these studies used an older definition of long COVID (symptoms persisting for 4 weeks or more). Yagi and colleagues conducted a longitudinal study on long COVID and HRQoL in 986 patients with COVID-19, with 1 year of follow-up data. However, the data were collected between January 2020 and February 2021, when the Wuhan strain of SARS-CoV-2 was predominant in Japan [ 10 ]. Maeda and colleagues performed another longitudinal study on long COVID and HRQoL starting from April 2023, when the Omicron variant of SARS-CoV-2 was predominant in Japan. However, their sample size was only 379 and the follow-up period was only 6 months [ 11 ]. Thus, the existing studies had small sample sizes with a short duration of follow-up. Furthermore, which factors affect which aspects of life and their effects on long COVID symptoms in clusters according to HRQoL have not been studied comprehensively among patients with long COVID. Owing to this knowledge gap, we aimed to comprehensively assess the impact of COVID-19 infection on HRQoL among Japanese adults from 4 months after the acute phase of infection, using monthly follow-up data up to 1 year, and to identify psychosocial factors associated with HRQoL after COVID-19 infection. Methods Data source We used prospective cohort data from the CARE Japan Study ( https://japan.helpstopcovid19.com/ ), which has been conducted by IQVIA Japan at a national center hospital in Tokyo since January 2021. In the present study, we used a non-probability convenience sampling method. Patients who visited the hospital with flu-like symptoms, including fever, cough, sore throat, chills, body aches, and runny nose were provided with an explanation about the purpose of this study and were invited to participate. Upon collecting patients’ consent online, baseline data were collected. Patients were then e-mailed questionnaires every month for follow-up. The CARE Japan Study collects data on participants’ demographics at baseline. Information on diagnostic test results for COVID-19, vaccination status, symptoms related to COVID-19, and responses to the 12-item Short Form (SF-12) questionnaire was collected at baseline and every 4–6 weeks during the 1-year follow-up period. In this study, we used IQVIA data collected between January 2022 and January 2023. Study design and participant selection criteria This was a longitudinal study conducted among Japanese male and female adults. After the initial registration of participants in the CARE Japan Study, those who did not have COVID-19 infection and those who had follow-up data for fewer than 90 days after the initial COVID-19 infection were excluded from the study. If any participant had a second COVID-19 infection at any time within the 1-year follow-up, that participant was excluded from the study to avoid biases in the outcome. Figure 1 shows the participant selection process in this study. Dependent variable The outcome of this study was HRQoL, which was a patient-reported outcome measure. The SF-12 questionnaire measures patients’ perceived and reported outcomes, reflecting several domains of functioning, including physical functioning (limitations in physical activities due to health problems), role limitations (impact of physical functioning limitations and emotional problems on activities of daily living), bodily pain, vitality (energy levels, fatigue, and perception of general health), social functioning (impact of physical or emotional problems on social activities), and mental health (psychological well-being, including feelings of anxiety and depression). The score on each domain was weighted following the methods described by Braizer (2004) and combined to produce the HRQoL score [ 12 ]. We calculated the HRQoL score at each follow-up point over 1 year of follow-up; we then took the median value of HRQoL scores. Scores on the SF-12 ranged from 0 to 1, with higher scores indicating better HRQoL. The SF-12 is a valid and reliable measurement instrument, with internal consistency estimates exceeding 0.70 [ 13 ]. Independent variables Self-reported long COVID was the primary independent variable in this study. We considered 25 pre-established long COVID symptoms with a follow-up duration of up to 1 year after the initial onset of COVID-19 infection. Long COVID was defined per the definition of the US Centers for Disease Control and Prevention: the presence of at least one infection-associated chronic condition at the entry period of the cohort (more than 90 days after the acute phase of COVID-19 infection), or at any of the follow-up time points [ 14 , 15 ]. As covariates, we included age (younger: ≤39 years, middle-aged: ≥40 and ≤ 64 years, and older: ≥65 years), sex (male or female, assigned at birth), body mass index (BMI), vaccination history, and pre-existing medical conditions. Unlike the World Health Organization guidelines, Japan adopts a lower BMI cutoff point. According to the guidelines of the Japan Society for the Study of Obesity, we categorized BMI as underweight (≤ 18.5 kg/m 2 ), normal weight (BMI > 18.5 kg/m 2 to <25 kg/m 2 ), and overweight/obese (≥ 25 kg/m 2 ) [ 16 ]. Participants were considered vaccinated if they received a COVID-19 vaccine at least 14 days before the confirmation date of COVID-19 infection. Vaccination included two categories: none or primary doses (1st and 2nd), and booster doses (primary doses plus 3rd or 4th). For each participant, we also considered pre-existing medical conditions including psychological disorders (depression or anxiety or insomnia), metabolic disorders (diabetes or hypertension), lung diseases, and seasonal allergies. Statistical analysis We used descriptive statistics including frequency and percentage to describe the characteristics of the study sample, the median to report the distribution of HRQoL scores, and interquartile range to report the distribution of participants’ follow-up. We used the log-rank test to assess whether HRQoL scores differed significantly among different sociodemographic groups. We fit adjusted beta regression models using the logit link function to calculate beta regression coefficients with 95% CIs to explore determinants of the HRQoL score. The model was adjusted for sex, age, BMI, vaccine doses, psychological disorders, metabolic disorders, lung diseases, and seasonal allergies. We selected independent variables based on previous studies and the availability of data [ 9 – 11 ]. For each independent variable in the beta regression models, we estimated the average marginal effect (AME). The AME represents the average change in the probability scale of HRQoL owing to a one-unit change in the predictor. We also conducted subgroup analysis for age, sex, and pre-existing medical conditions using adjusted beta regression models to explore the effect of long COVID on HRQoL scores. Additionally, we performed latent class analysis (LCA) to identify whether there were any unobserved distribution patterns of long COVID symptoms. LCA is an unsupervised statistical technique that can be used to identify subgroups within a population that are not directly observed but inferred from the data based on probabilistic models. Among all long COVID symptoms with a correlation > 0.75, only one was selected; thus, we included 17 long COVID symptoms in LCA. Supplementary Figure S1 shows a correlogram of the long COVID symptoms included in LCA. All statistical analyses were performed using R (v4.4.1; The R Foundation for Statistical Computing, Vienna, Austria). LCA was performed using the poLCA function in the poLCA R package v1.6.0.1 (Linzer DA, Lewis JB 2011). AMEs were estimated using the margins function in the margins R package v0.3.28 (Ben Bolker 2017). A p-value of < 0.05 was considered statistically significant. Results We included 1,285 respondents in the final analysis; among them, 349 respondents completed only one follow-up and 936 respondents completed more than one follow-up during months 4 to 12 of the follow-up period after the initial COVID-19 infection. In the study population, the mean age was 52 years and the median HRQoL score was 0.83. Younger participants and those without long COVID were less likely to complete more than one follow-up (Supplementary Table S1). Table 1 summarizes the sociodemographic characteristics and background medical conditions of the study sample, along with the median HRQoL score in each group. In total, female respondents comprised 45% of the sample, and the booster dose vaccine coverage was 85%. Over the 1-year follow-up, 70% of participants reported long COVID symptoms. The median HRQoL score among patients with long COVID was 0.79; this score was 0.92 among participants without long COVID. In total, 16%, 3.7%, and 44% of respondents had prior psychological disorders, lung diseases, and seasonal allergies, respectively. Respondents with prior medical conditions had significantly lower HRQoL scores in comparison with respondents who did not have prior medical conditions. Table 1. Basic characteristics of the participants Characteristic N=1 , 285 n (%) HRQoL score median (95% CI ) p value Sex <0.001 Male 704 (55.0) 0.86 (0.72, 0.92) Female 581 (45.0) 0.79 (0.66, 0.86) Age <0.001 ≤39 years 123 (9.6) 0.78 (0.66, 0.86) ≥40 and ≤64 years 1,041 (81.0) 0.83 (0.72, 0.92) ≥65 years 121 (9.4) 0.89 (0.75, 0.92) BMI 18.5 kg/m 2 to 2 1,098 (85.0) 0.85 (0.72, 0.92) ≤2 187 (15.0) 0.80 (0.68, 0.92) Long COVID <0.001 No 388 (30.0) 0.92 (0.80, 0.92) Yes 897 (70.0) 0.79 (0.66, 0.89) Number of symptoms (median, IQR) 2 (0,4) - <0.001 Psychological disorders <0.001 Yes 212 (16.0) 0.72 (0.65, 0.86) No 1,073 (84.0) 0.86 (0.72, 0.92) Metabolic disorders 0.140 Yes 252 (20.0) 0.86 (0.72, 0.92) No 1,033 (80.0) 0.82 (0.72, 0.92) Lung diseases 0.002 Yes 47 (3.7) 0.72 (0.64, 0.86) No 1,238 (96.3) 0.84 (0.72, 0.92) Seasonal allergies <0.001 Yes 571 (44.0) 0.80 (0.66, 0.92) No 714 (56.0) 0.86 (0.72, 0.92) Abbreviations: HRQoL, health-related quality of life; BMI, body mass index; IQR, interquartile range; CI, confidence interval. We determined the HRQoL score among participants with and without long COVID at each follow-up, as shown in Figure 2a. We found that the differences were clinically significant (minimal clinically important difference >0.05) at each follow-up time point [12]. As shown in Figure 2b, there were significant differences in perceived health quality between participants with and without long COVID in each domain of HRQoL; these differences mostly occurred in the social functioning, role limitation, and mental health domains. Among participants without long COVID, 25%–35% had suboptimal scores in the above three domains whereas this proportion was 50%–65% among patients with long COVID. In the vitality domain, more than 85% of respondents had suboptimal scores, regardless of their long COVID status. In Figure 3, beta regression analysis revealed that, compared with men, HRQoL scores were lower among women (β: −0.25; 95% CI: −0.33 to −0.16; AME: −0.037). Furthermore, HRQoL scores were lower among younger participants (β: −0.20; 95% CI: −0.39 to −0.01; AME: −0.03) as compared with older ones, as well as among participants with a low BMI (β: −0.16; 95% CI: −0.31 to −0.01; AME: −0.024), as compared with those who had normal weight. Compared with participants who did not have long COVID, HRQoL among those with long COVID (β: −0.25; 95% CI: −0.36 to −0.14; AME: −0.036) was significantly lower; every 1-point increase in long COVID symptoms associated with decrease of the HRQoL score by 0.09 (β: −0.09; 95% CI: −0.10 to −0.07) points. Psychological disorders (β: −0.26; 95% CI: −0.36 to −0.15; AME: −0.041) showed a significant inverse association with HRQoL whereas metabolic disorders, lung diseases, and seasonal allergies did not show any significant relationship with HRQoL. We also explored the effect of long COVID on HRQoL scores among different subgroups; the findings are shown in Supplementary Figure S2. In all three age groups and both sexes, patients with long COVID had poorer HRQoL scores compared with participants who did not have long COVID. The inverse association between long COVID and HRQoL scores was worst among respondents with pre-existing lung diseases (β: −0.72; 95% CI: −1.29 to −0.16; AME: −0.114) and low among those with pre-existing seasonal allergies (β: −0.27; 95% CI: −0.37 to −0.04; AME: −0.032). The number of long COVID symptoms showed strong positive associations with all subdomains of HRQoL. Using LCA, we identified three subgroups of patients with long COVID: latent classes 1, 2, and 3. Figure 4 shows the probability distribution of long COVID symptoms among the latent classes. In latent class 1, all long COVID symptoms had minimal probability and no single symptom was predominant (probability >0.5); 55.2% of respondents belonged to this class. In latent class 2, headache, sore throat, and nasal congestion were predominant (probability >0.5) symptoms, and 25.7% of respondents belonged to this class. In latent class 3, comprising 19.2% of respondents, all respondents had fatigue (probability=1.0). Cough, headache, sore throat, nasal congestion, and aches and pains were other predominant (probability >0.5) long COVID symptoms in latent class 3. Median HRQoL scores were 0.80 (95% CI: 0.77 to 0.80), 0.79 (95% CI: 0.78 to 0.79), and 0.66 (95% CI: 0.65 to 0.66) in latent classes 1, 2, and 3, respectively. Adjusted beta regression analysis showed that, compared with participants without long COVID, those belonging to latent class 1 (β: −0.47; 95% CI: −0.57 to −0.36; AME: −0.065), latent class 2 (β: −0.48; 95% CI: −0.60 to −0.35; AME: −0.066), and latent class 3 (β: −0.93; 95% CI: −1.06 to −0.79; AME: −0.148) had significantly lower HRQoL. Discussion The twofold aim of this study was to assess the impact of long COVID on HRQoL among Japanese adults from 4 months after the acute phase of an initial COVID-19 infection, using monthly follow-up data up to 1 year, and to identify psychosocial factors associated with HRQoL. We found that long COVID had a consistent negative impact on HRQoL across 1 year of follow-up. Multivariable regression showed that female sex, younger age, and having long COVID, low BMI, or pre-existing psychological disorders were associated with lower HRQoL. HRQoL was decreased with an increased number of long COVID symptoms. Overall, from 4 months to 1 year after an initial COVID-19 infection, patients with long COVID had significantly worse HRQoL than those without long COVID, and HRQoL did not improve significantly over 1 year of follow-up in the cohort. Similar results have been reported in several other studies in which long COVID persisted for over 1 year and was associated with reduced HRQoL scores, compared with individuals without long COVID [17-19]. We found that female respondents were more likely to report lower HRQoL, and previous studies support this finding [2, 17, 20]. Our results showed a positive association between age and HRQoL, which might be explained by the positive association between age and the mental health domain of HRQoL (Figure 3). Similar to our findings, several other studies reported that young adults were more vulnerable to deteriorating mental health during the pandemic as compared with older individuals [7, 21, 22]. Another study in Japan reported that adjusted ORs (95%CI) for the association between COVID-19 infection and HRQoL in those aged <30 years; those aged in their 30s, 40s, 50s; and those aged ≥60 years were 0.54 (0.15–1.92), 1.70 (1.03–2.81), 1.14 (0.82–1.57), 1.05 (0.77–1.42), and 0.87 (0.46–1.64), respectively, which also supports our findings [23]. Respondents with prior psychological disorders were more likely to report poor scores in role limitation, social functioning, and mental health domains. Respondents with prior lung disease were more likely to report poor scores in role limitation, social functioning, and vitality. Therefore, long-term, multidisciplinary interventions specifically focused on mental health among women, younger and middle-aged individuals, and those with psychological disorders or lung diseases are important. The prevalence of long COVID symptoms in our study was 70%, which was higher than that in most previous studies [2, 6]; this may be because we reported the period prevalence of long COVID over 1 year of follow-up instead of the point prevalence. Owing to the relapsing nature of long COVID, period prevalence can estimate the overall burden of long COVID in a population over a certain period. LCA showed that long COVID symptoms were most concentrated among respondents belonging to latent class 3 and the least concentrated among those belonging to latent class 1. We found that one in every five patients with long COVID belonged to latent class 3 and were more likely to develop at least six symptoms (cough, headache, sore throat, nasal congestion, aches and pains, and fatigue), with median HRQoL scores as low as 0.66. Even patients with long COVID belonging to latent class 1 had significantly lower HRQoL compared with respondents who did not have long COVID. Similarly, a previous study in Japan reported that patients who had only one long COVID symptom showed significantly lower HRQoL than patients with no long COVID symptoms, and HRQoL was most severely diminished in patients with five or more long COVID symptoms [19]. Furthermore, the number of long COVID symptoms showed a strong inverse relationship with each HRQoL domain. These findings can inform primary health care physicians, nurses, and physical therapists that, even with minimal long COVID symptoms, patients’ experiences should be validated. The study results can also inform policymakers regarding the need for comprehensive primary health care and integrated long-term monitoring systems for patients with multiple long COVID symptoms. This study has several strengths. First, we used monthly follow-up data from 4 months up to 1 year after an acute phase of COVID-19 infection to measure long COVID and associated HRQoL; this study is one of the few such investigations in the context of Japan and globally. Furthermore, we used data collected between January 2022 and January 2023, when the SARS-CoV-2 Omicron variant was dominant in Japan. Thus, our study findings can reflect the effect of the Omicron variant on HRQoL among Japanese adults. This study also has several limitations. First, pre–post comparisons were not possible owing to the unavailability of HRQoL data prior to COVID-19 infection. Additionally, owing to a lack of data, we could not include the duration and intensity of long COVID symptoms and respondents’ socioeconomic status, which are important contributors to HRQoL [24, 25]. Owing to the observational nature of this study, we could not establish long COVID as solely responsible for decreased HRQoL scores. However, the study results can provide policymakers with a more comprehensive understanding of the full spectrum of long COVID and will facilitate better resource allocation for long-term monitoring, multidisciplinary treatment, and rehabilitation of patients with long COVID. Conclusions HRQoL was significantly worse among participants with long COVID than among participants without long COVID, between 4 months to 1 year after the initial acute phase of COVID-19 infection. Female sex, age ≤39 years, BMI ≤18.5 kg/m 2 , and pre-existing psychological disorders were associated with lower HRQoL. The number of long COVID symptoms was a strong determinant of HRQoL; the presence of even minimal long COVID symptoms was associated with lower HRQoL. Cough, headache, sore throat, nasal congestion, aches and pain, and fatigue were the most prevalent symptoms among patients with long COVID who had the lowest HRQoL. For patients with COVID-19 and comorbid lung diseases, the risk of reduced QoL remains high even after recovery, necessitating follow-up care that includes the post-recovery period. Our research findings will enhance understanding of the implications of long COVID on patients’ lives and underscore the need for a coordinated approach to long-term treatment, care, and prevention of long COVID through policies, interventions, and integrated health care systems. Abbreviations HRQoL health-related quality of life BMI body mass index LCA latent class analysis AME average marginal effect CI confidence interval WHO World Health Organization. Declarations Acknowledgment We thank Analisa Avila, MPH, ELS, of Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript. Ethics approval and consent to participate Ethical approval of this study was received from the Certified Review Board of Japan Institute for Health Security (approval number: NCGM-S-004318-02). Patients’ consent was collected online. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests I.S. and A.O. are staffs of IQVIA solutions Japan. Other authors have no competing interests. Funding This study was supported by the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (KAKENHI JP23K27865). Authors' contributions SS, SS and YA: conceived the study, design of the work, analysis, interpretation of the data, writing or review of the article. HI, NM, NO: conceived the study, interpretation of the data, writing or review of the article. SI, AO: acquisition of data, writing or review of the article. References Al-Aly, Z., et al., Long COVID science, research and policy. Nature Medicine, 2024. 30 (8): p. 2148-2164. 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Supplementary Files Supplementaryfilefor.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Apr, 2026 Reviews received at journal 11 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 09 Dec, 2025 Editor assigned by journal 01 Dec, 2025 Submission checks completed at journal 01 Dec, 2025 First submitted to journal 01 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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. 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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-8250532","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":557463995,"identity":"7bf417b4-674b-4902-a710-8d4a43332562","order_by":0,"name":"Sabera Sultana","email":"","orcid":"","institution":"Japan Institute for Health Security","correspondingAuthor":false,"prefix":"","firstName":"Sabera","middleName":"","lastName":"Sultana","suffix":""},{"id":557463998,"identity":"62f6f8f5-32b4-434e-bbec-f58041591b2b","order_by":1,"name":"Yusuke Asai","email":"","orcid":"","institution":"Japan Institute for Health 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1","display":"","copyAsset":false,"role":"figure","size":104701,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant selection flowchart\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8250532/v1/0f8724fdbf76ece9388a95e5.png"},{"id":98439278,"identity":"f3d1cf67-fc36-470d-a096-99f9bcd2f95a","added_by":"auto","created_at":"2025-12-17 17:01:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99991,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in Health-related quality of life (HRQoL) between the participants with and without long COVID\u003c/p\u003e\n\u003cp\u003e(a) Health-related quality of life (HRQoL) score at each follow-up point\u003c/p\u003e\n\u003cp\u003e(b) proportion of respondents with loss of HRQoL in each domain\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8250532/v1/b5613fc749263feca43a6985.png"},{"id":98437884,"identity":"83062433-7dad-4f30-bb1d-377ccf85c52a","added_by":"auto","created_at":"2025-12-17 16:58:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":236519,"visible":true,"origin":"","legend":"\u003cp\u003eDeterminants of health-related quality of life (HRQoL) and its subdomains\u003c/p\u003e\n\u003cp\u003eNotes: The models were adjusted for sex, age, body mass index, vaccine doses, long COVID, number of symptoms, psychological disorders, metabolic disorders, lung diseases, and seasonal allergies.\u003c/p\u003e\n\u003cp\u003eReference groups for each predictor were sex (male), age (≤39 years), body mass index (≤18.5 kg/m\u003csup\u003e2\u003c/sup\u003e), vaccine doses (≤2), long COVID (no), psychological disorders (no), metabolic disorders (no), lung diseases (no), seasonal allergies (no).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8250532/v1/0fa99ab3970d0f66132c6916.png"},{"id":98438867,"identity":"41d69392-1456-48a4-bb92-67039dd26993","added_by":"auto","created_at":"2025-12-17 17:00:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":113800,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of long COVID symptoms among latent classes of patients with long COVID\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8250532/v1/7c58bfc1d6b8e7b4894c995f.png"},{"id":98445531,"identity":"3e1a29bd-faa3-4bdf-b00a-a7d1fe88a1e4","added_by":"auto","created_at":"2025-12-17 17:19:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1146143,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8250532/v1/b9bf4aa7-ed53-46f7-ad10-e71aaa152021.pdf"},{"id":98438976,"identity":"a39c282a-9748-455e-b3de-eb66e70499c6","added_by":"auto","created_at":"2025-12-17 17:00:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":94738,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfilefor.docx","url":"https://assets-eu.researchsquare.com/files/rs-8250532/v1/1d4d544890a0a80e7f3dd923.docx"}],"financialInterests":"Competing interest reported. I.S. and A.O. are staffs of IQVIA solutions Japan. Other authors have no competing interests.","formattedTitle":"Impact of long COVID on health-related quality of life among Japanese adults: findings of CARE Japan Study","fulltext":[{"header":"Plain English Summary","content":"\u003cp\u003eAs of October 2025, more than 5 years after the start of the COVID-19 pandemic, approximately 779 million COVID-19 cases have been reported. Survivors of COVID -19 infection often report complex, multisystemic, persistent, or relapsing symptoms for weeks, months, or even years after the initial infection. Long COVID refers to when COVID-19 symptoms are persistent or relapse, within up to 12 weeks after an initial acute infection. Owing to its high prevalence, heterogeneous nature, lack of clear diagnostic markers, and prolonged and obscure recovery process, long COVID poses challenges to individuals, public health care systems, and the workforce. Increasing evidence suggests that long COVID impairs the functional and cognitive capacity of affected patients, resulting in poor health-related quality of life (QOL). Longitudinal studies on health-related QOL among COVID-19 survivors with a long follow-up period are scarce in Japan. In our study, we assessed the impact of COVID-19 infection on health-related QOL among Japanese adults, with 1-year follow-up. We found that patients with long COVID had lower health-related QOL than survivors of COVID-19 without long COVID. The presence of even minimal long COVID symptoms lowered patients’ health-related QOL. For patients with COVID-19 who had comorbid lung diseases, the risk of reduced QOL remained high, even after recovery. Our research findings will enhance understanding of the implications of long COVID on individuals’ lives and underscore the need for a coordinated approach to the long-term treatment, care, and prevention of long COVID through policies, interventions, and integrated health care systems.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eMany survivors of COVID \u0026minus;\u0026thinsp;19 experience complex, multisystemic, persistent, or relapsing symptoms for weeks, months, or even years after the initial infection. Long COVID refers to when these symptoms persist or relapse even after 12 weeks of the initial acute infection [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A meta-analysis including 144 studies reported that, as of 2025, the global pooled prevalence of long COVID was 36% (95% confidence interval [CI]: 33%\u0026ndash;40%) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Common symptoms of long COVID include extreme tiredness, anxiety, memory problems, dizziness, taste or smell issues, insomnia, dyspnea, cough, headache, palpitation, and digestive problems [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Increasing evidence worldwide suggests that long COVID adversely affects physical, emotional, and social aspects of patients\u0026rsquo; lives [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. OpenPROMPT was a cohort study conducted in 2024 in the United Kingdom that measured the impact of long COVID on health-related quality of life (HRQoL). Among 6070 participants included in that study, a consistent impact on HRQoL was found, with greater likelihood of a reported decrease in QoL (odds ratio: 4.7, 95% CI: 3.72\u0026ndash;5.93) among participants with long COVID compared with those who did not have long COVID [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral studies have been conducted to measure the effect of long COVID on QoL among Japanese adults. In 2021, Tsuzuki and colleagues conducted a cross-sectional study in 526 patients to investigate long COVID and HRQoL [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and Honda et al. conducted a similar cross-sectional study in 2022 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Both studies found lower HRQoL among patients with long COVID; however, these studies used an older definition of long COVID (symptoms persisting for 4 weeks or more). Yagi and colleagues conducted a longitudinal study on long COVID and HRQoL in 986 patients with COVID-19, with 1 year of follow-up data. However, the data were collected between January 2020 and February 2021, when the Wuhan strain of SARS-CoV-2 was predominant in Japan [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Maeda and colleagues performed another longitudinal study on long COVID and HRQoL starting from April 2023, when the Omicron variant of SARS-CoV-2 was predominant in Japan. However, their sample size was only 379 and the follow-up period was only 6 months [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Thus, the existing studies had small sample sizes with a short duration of follow-up. Furthermore, which factors affect which aspects of life and their effects on long COVID symptoms in clusters according to HRQoL have not been studied comprehensively among patients with long COVID.\u003c/p\u003e\u003cp\u003eOwing to this knowledge gap, we aimed to comprehensively assess the impact of COVID-19 infection on HRQoL among Japanese adults from 4 months after the acute phase of infection, using monthly follow-up data up to 1 year, and to identify psychosocial factors associated with HRQoL after COVID-19 infection.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData source\u003c/h2\u003e\u003cp\u003eWe used prospective cohort data from the CARE Japan Study (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://japan.helpstopcovid19.com/\u003c/span\u003e\u003cspan address=\"https://japan.helpstopcovid19.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which has been conducted by IQVIA Japan at a national center hospital in Tokyo since January 2021. In the present study, we used a non-probability convenience sampling method. Patients who visited the hospital with flu-like symptoms, including fever, cough, sore throat, chills, body aches, and runny nose were provided with an explanation about the purpose of this study and were invited to participate. Upon collecting patients\u0026rsquo; consent online, baseline data were collected. Patients were then e-mailed questionnaires every month for follow-up. The CARE Japan Study collects data on participants\u0026rsquo; demographics at baseline. Information on diagnostic test results for COVID-19, vaccination status, symptoms related to COVID-19, and responses to the 12-item Short Form (SF-12) questionnaire was collected at baseline and every 4\u0026ndash;6 weeks during the 1-year follow-up period. In this study, we used IQVIA data collected between January 2022 and January 2023.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy design and participant selection criteria\u003c/h3\u003e\n\u003cp\u003eThis was a longitudinal study conducted among Japanese male and female adults. After the initial registration of participants in the CARE Japan Study, those who did not have COVID-19 infection and those who had follow-up data for fewer than 90 days after the initial COVID-19 infection were excluded from the study. If any participant had a second COVID-19 infection at any time within the 1-year follow-up, that participant was excluded from the study to avoid biases in the outcome. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the participant selection process in this study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eDependent variable\u003c/h3\u003e\n\u003cp\u003eThe outcome of this study was HRQoL, which was a patient-reported outcome measure. The SF-12 questionnaire measures patients\u0026rsquo; perceived and reported outcomes, reflecting several domains of functioning, including physical functioning (limitations in physical activities due to health problems), role limitations (impact of physical functioning limitations and emotional problems on activities of daily living), bodily pain, vitality (energy levels, fatigue, and perception of general health), social functioning (impact of physical or emotional problems on social activities), and mental health (psychological well-being, including feelings of anxiety and depression). The score on each domain was weighted following the methods described by Braizer (2004) and combined to produce the HRQoL score [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We calculated the HRQoL score at each follow-up point over 1 year of follow-up; we then took the median value of HRQoL scores. Scores on the SF-12 ranged from 0 to 1, with higher scores indicating better HRQoL. The SF-12 is a valid and reliable measurement instrument, with internal consistency estimates exceeding 0.70 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eIndependent variables\u003c/h3\u003e\n\u003cp\u003eSelf-reported long COVID was the primary independent variable in this study. We considered 25 pre-established long COVID symptoms with a follow-up duration of up to 1 year after the initial onset of COVID-19 infection. Long COVID was defined per the definition of the US Centers for Disease Control and Prevention: the presence of at least one infection-associated chronic condition at the entry period of the cohort (more than 90 days after the acute phase of COVID-19 infection), or at any of the follow-up time points [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. As covariates, we included age (younger: \u0026le;39 years, middle-aged: \u0026ge;40 and \u0026le;\u0026thinsp;64 years, and older: \u0026ge;65 years), sex (male or female, assigned at birth), body mass index (BMI), vaccination history, and pre-existing medical conditions. Unlike the World Health Organization guidelines, Japan adopts a lower BMI cutoff point. According to the guidelines of the Japan Society for the Study of Obesity, we categorized BMI as underweight (\u0026le;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e), normal weight (BMI\u0026thinsp;\u0026gt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e to \u0026lt;25 kg/m\u003csup\u003e2\u003c/sup\u003e), and overweight/obese (\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Participants were considered vaccinated if they received a COVID-19 vaccine at least 14 days before the confirmation date of COVID-19 infection. Vaccination included two categories: none or primary doses (1st and 2nd), and booster doses (primary doses plus 3rd or 4th). For each participant, we also considered pre-existing medical conditions including psychological disorders (depression or anxiety or insomnia), metabolic disorders (diabetes or hypertension), lung diseases, and seasonal allergies.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eWe used descriptive statistics including frequency and percentage to describe the characteristics of the study sample, the median to report the distribution of HRQoL scores, and interquartile range to report the distribution of participants\u0026rsquo; follow-up. We used the log-rank test to assess whether HRQoL scores differed significantly among different sociodemographic groups. We fit adjusted beta regression models using the logit link function to calculate beta regression coefficients with 95% CIs to explore determinants of the HRQoL score. The model was adjusted for sex, age, BMI, vaccine doses, psychological disorders, metabolic disorders, lung diseases, and seasonal allergies. We selected independent variables based on previous studies and the availability of data [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For each independent variable in the beta regression models, we estimated the average marginal effect (AME). The AME represents the average change in the probability scale of HRQoL owing to a one-unit change in the predictor. We also conducted subgroup analysis for age, sex, and pre-existing medical conditions using adjusted beta regression models to explore the effect of long COVID on HRQoL scores. Additionally, we performed latent class analysis (LCA) to identify whether there were any unobserved distribution patterns of long COVID symptoms. LCA is an unsupervised statistical technique that can be used to identify subgroups within a population that are not directly observed but inferred from the data based on probabilistic models. Among all long COVID symptoms with a correlation\u0026thinsp;\u0026gt;\u0026thinsp;0.75, only one was selected; thus, we included 17 long COVID symptoms in LCA. Supplementary Figure S1 shows a correlogram of the long COVID symptoms included in LCA. All statistical analyses were performed using R (v4.4.1; The R Foundation for Statistical Computing, Vienna, Austria). LCA was performed using the poLCA function in the poLCA R package v1.6.0.1 (Linzer DA, Lewis JB 2011). AMEs were estimated using the margins function in the margins R package v0.3.28 (Ben Bolker 2017). A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eWe included 1,285 respondents in the final analysis; among them, 349 respondents completed only one follow-up and 936 respondents completed more than one follow-up during months 4 to 12 of the follow-up period after the initial COVID-19 infection. In the study population, the mean age was 52 years and the median HRQoL score was 0.83. Younger participants and those without long COVID were less likely to complete more than one follow-up (Supplementary Table S1). Table 1 summarizes the sociodemographic characteristics and background medical conditions of the study sample, along with the median HRQoL score in each group. In total, female respondents comprised 45% of the sample, and the booster dose vaccine coverage was 85%. Over the 1-year follow-up, 70% of participants reported long COVID symptoms. The median HRQoL score among patients with long COVID was 0.79; this score was 0.92 among participants without long COVID. In total, 16%, 3.7%, and 44% of respondents had prior psychological disorders, lung diseases, and seasonal allergies, respectively. Respondents with prior medical conditions had significantly lower HRQoL scores in comparison with respondents who did not have prior medical conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Basic characteristics of the participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=1\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e\u003cstrong\u003e285\u0026nbsp;\u003cbr\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHRQoL score\u003cbr\u003e\u0026nbsp;median\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(95% CI\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e704 (55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.86 (0.72, 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e581 (45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.79 (0.66, 0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026le;39 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e123 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.78 (0.66, 0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026ge;40 and \u0026le;64 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1,041 (81.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.83 (0.72, 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026ge;65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e121 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.89 (0.75, 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026gt;18.5 kg/m\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eto \u0026lt;25 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e892 (69.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.85 (0.72, 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026ge;25 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e304 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.86 (0.69, 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026le;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e89 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.72 (0.66, 0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eVaccine\u0026nbsp;doses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026gt;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1,098 (85.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.85 (0.72, 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026le;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e187 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.80 (0.68, 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eLong COVID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e388 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.92 (0.80, 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e897 (70.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.79 (0.66, 0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eNumber of symptoms (median, IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e2 (0,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003ePsychological disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e212 (16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.72 (0.65, 0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1,073 (84.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.86 (0.72, 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eMetabolic disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e252 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.86 (0.72, 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1,033 (80.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.82 (0.72, 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eLung diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e47 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.72 (0.64, 0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1,238 (96.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.84 (0.72, 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eSeasonal allergies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e571 (44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.80 (0.66, 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e714 (56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.86 (0.72, 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: HRQoL, health-related quality of life; BMI, body mass index; IQR, interquartile range; CI, confidence interval.\u003c/p\u003e\n\u003cp\u003eWe determined the HRQoL score among participants with and without long COVID at each follow-up, as shown in Figure 2a. We found that the differences were clinically significant (minimal clinically important difference \u0026gt;0.05) at each follow-up time point [12]. As shown in Figure 2b, there were significant differences in perceived health quality between participants with and without long COVID in each domain of HRQoL; these differences mostly occurred in the social functioning, role limitation, and mental health domains. Among participants without long COVID, 25%\u0026ndash;35% had suboptimal scores in the above three domains whereas this proportion was 50%\u0026ndash;65% among patients with long COVID. In the vitality domain, more than 85% of respondents had suboptimal scores, regardless of their long COVID status.\u003c/p\u003e\n\u003cp\u003eIn Figure 3, beta regression analysis revealed that, compared with men, HRQoL scores were lower among women (\u0026beta;: \u0026minus;0.25; 95% CI: \u0026minus;0.33 to \u0026minus;0.16; AME: \u0026minus;0.037). Furthermore, HRQoL scores were lower among younger participants (\u0026beta;: \u0026minus;0.20; 95% CI: \u0026minus;0.39 to \u0026minus;0.01; AME: \u0026minus;0.03) as compared with older ones, as well as among participants with a low BMI (\u0026beta;: \u0026minus;0.16; 95% CI: \u0026minus;0.31 to \u0026minus;0.01; AME: \u0026minus;0.024), as compared with those who had normal weight. Compared with participants who did not have long COVID, HRQoL among those with long COVID (\u0026beta;: \u0026minus;0.25; 95% CI: \u0026minus;0.36 to \u0026minus;0.14; AME: \u0026minus;0.036) was significantly lower; every 1-point increase in long COVID symptoms associated with decrease of the HRQoL score by 0.09 (\u0026beta;: \u0026minus;0.09; 95% CI: \u0026minus;0.10 to \u0026minus;0.07) points. Psychological disorders (\u0026beta;: \u0026minus;0.26; 95% CI: \u0026minus;0.36 to \u0026minus;0.15; AME: \u0026minus;0.041) showed a significant inverse association with HRQoL whereas metabolic disorders, lung diseases, and seasonal allergies did not show any significant relationship with HRQoL.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe also explored the effect of long COVID on HRQoL scores among different subgroups; the findings are shown in Supplementary Figure S2. In all three age groups and both sexes, patients with long COVID had poorer HRQoL scores compared with participants who did not have long COVID. The inverse association between long COVID and HRQoL scores was worst among respondents with pre-existing lung diseases (\u0026beta;: \u0026minus;0.72; 95% CI: \u0026minus;1.29 to \u0026minus;0.16; AME: \u0026minus;0.114) and low among those with pre-existing seasonal allergies (\u0026beta;: \u0026minus;0.27; 95% CI: \u0026minus;0.37 to \u0026minus;0.04; AME: \u0026minus;0.032).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe number of long COVID symptoms showed strong positive associations with all subdomains of HRQoL. Using LCA, we identified three subgroups of patients with long COVID: latent classes 1, 2, and 3. Figure 4 shows the probability distribution of long COVID symptoms among the latent classes. In latent class 1, all long COVID symptoms had minimal probability and no single symptom was predominant (probability \u0026gt;0.5); 55.2% of respondents belonged to this class. In latent class 2, headache, sore throat, and nasal congestion were predominant (probability \u0026gt;0.5) symptoms, and 25.7% of respondents belonged to this class. In latent class 3, comprising 19.2% of respondents, all respondents had fatigue (probability=1.0). Cough, headache, sore throat, nasal congestion, and aches and pains were other predominant (probability \u0026gt;0.5) long COVID symptoms in latent class 3. Median HRQoL scores were 0.80 (95% CI: 0.77 to 0.80), 0.79 (95% CI: 0.78 to 0.79), and 0.66 (95% CI: 0.65 to 0.66) in latent classes 1, 2, and 3, respectively. Adjusted beta regression analysis showed that, compared with participants without long COVID, those belonging to latent class 1 (\u0026beta;: \u0026minus;0.47; 95% CI: \u0026minus;0.57 to \u0026minus;0.36; AME: \u0026minus;0.065), latent class 2 (\u0026beta;: \u0026minus;0.48; 95% CI: \u0026minus;0.60 to \u0026minus;0.35; AME: \u0026minus;0.066), and latent class 3 (\u0026beta;: \u0026minus;0.93; 95% CI: \u0026minus;1.06 to \u0026minus;0.79; AME: \u0026minus;0.148) had significantly lower HRQoL.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe twofold aim of this study was to assess the impact of long COVID on HRQoL among Japanese adults from 4 months after the acute phase of an initial COVID-19 infection, using monthly follow-up data up to 1 year, and to identify psychosocial factors associated with HRQoL. We found that long COVID had a consistent negative impact on HRQoL across 1 year of follow-up. Multivariable regression showed that female sex, younger age, and having long COVID, low BMI, or pre-existing psychological disorders were associated with lower HRQoL. HRQoL was decreased with an increased number of long COVID symptoms.\u003c/p\u003e\n\u003cp\u003eOverall, from 4 months to 1 year after an initial COVID-19 infection, patients with long COVID had significantly worse HRQoL than those without long COVID, and HRQoL did not improve significantly over 1 year of follow-up in the cohort. Similar results have been reported in several other studies in which long COVID persisted for over 1 year and was associated with reduced HRQoL scores, compared with individuals without long COVID [17-19]. We found that female respondents were more likely to report lower HRQoL, and previous studies support this finding [2, 17, 20]. Our results showed a positive association between age and HRQoL, which might be explained by the positive association between age and the mental health domain of HRQoL (Figure 3). Similar to our findings, several other studies reported that young adults were more vulnerable to deteriorating mental health during the pandemic as compared with older individuals [7, 21, 22]. Another study in Japan reported that adjusted ORs (95%CI) for the association between COVID-19 infection and HRQoL in those aged \u0026lt;30 years; those aged in their 30s, 40s, 50s; and those aged ≥60 years were 0.54 (0.15–1.92), 1.70 (1.03–2.81), 1.14 (0.82–1.57), 1.05 (0.77–1.42), and 0.87 (0.46–1.64), respectively, which also supports our findings [23]. Respondents with prior psychological disorders were more likely to report poor scores in role limitation, social functioning, and mental health domains. Respondents with prior lung disease were more likely to report poor scores in role limitation, social functioning, and vitality. Therefore, long-term, multidisciplinary interventions specifically focused on mental health among women, younger and middle-aged individuals, and those with psychological disorders or lung diseases are important.\u003c/p\u003e\n\u003cp\u003eThe prevalence of long COVID symptoms in our study was 70%, which was higher than that in most previous studies [2, 6]; this may be because we reported the period prevalence of long COVID over 1 year of follow-up instead of the point prevalence. Owing to the relapsing nature of long COVID, period prevalence can estimate the overall burden of long COVID in a population over a certain period. LCA showed that long COVID symptoms were most concentrated among respondents belonging to latent class 3 and the least concentrated among those belonging to latent class 1. We found that one in every five patients with long COVID belonged to latent class 3 and were more likely to develop at least six symptoms (cough, headache, sore throat, nasal congestion, aches and pains, and fatigue), with median HRQoL scores as low as 0.66. Even patients with long COVID belonging to latent class 1 had significantly lower HRQoL compared with respondents who did not have long COVID. Similarly, a previous study in Japan reported that patients who had only one long COVID symptom showed significantly lower HRQoL than patients with no long COVID symptoms, and HRQoL was most severely diminished in patients with five or more long COVID symptoms [19]. Furthermore, the number of long COVID symptoms showed a strong inverse relationship with each HRQoL domain. These findings can inform primary health care physicians, nurses, and physical therapists that, even with minimal long COVID symptoms, patients’ experiences should be validated. The study results can also inform policymakers regarding the need for comprehensive primary health care and integrated long-term monitoring systems for patients with multiple long COVID symptoms.\u003c/p\u003e\n\u003cp\u003eThis study has several strengths. First, we used monthly follow-up data from 4 months up to 1 year after an acute phase of COVID-19 infection to measure long COVID and associated HRQoL; this study is one of the few such investigations in the context of Japan and globally. Furthermore, we used data collected between January 2022 and January 2023, when the SARS-CoV-2 Omicron variant was dominant in Japan. Thus, our study findings can reflect the effect of the Omicron variant on HRQoL among Japanese adults. This study also has several limitations. First, pre–post comparisons were not possible owing to the unavailability of HRQoL data prior to COVID-19 infection. Additionally, owing to a lack of data, we could not include the duration and intensity of long COVID symptoms and respondents’ socioeconomic status, which are important contributors to HRQoL [24, 25]. Owing to the observational nature of this study, we could not establish long COVID as solely responsible for decreased HRQoL scores. However, the study results can provide policymakers with a more comprehensive understanding of the full spectrum of long COVID and will facilitate better resource allocation for long-term monitoring, multidisciplinary treatment, and rehabilitation of patients with long COVID.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eHRQoL was significantly worse among participants with long COVID than among participants without long COVID, between 4 months to 1 year after the initial acute phase of COVID-19 infection. Female sex, age ≤39 years, BMI ≤18.5 kg/m\u003csup\u003e2\u003c/sup\u003e, and pre-existing psychological disorders were associated with lower HRQoL. The number of long COVID symptoms was a strong determinant of HRQoL; the presence of even minimal long COVID symptoms was associated with lower HRQoL. Cough, headache, sore throat, nasal congestion, aches and pain, and fatigue were the most prevalent symptoms among patients with long COVID who had the lowest HRQoL. For patients with COVID-19 and comorbid lung diseases, the risk of reduced QoL remains high even after recovery, necessitating follow-up care that includes the post-recovery period. Our research findings will enhance understanding of the implications of long COVID on patients’ lives and underscore the need for a coordinated approach to long-term treatment, care, and prevention of long COVID through policies, interventions, and integrated health care systems.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHRQoL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehealth-related quality of life\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elatent class analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAME\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eaverage marginal effect\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econfidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWorld Health Organization.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Analisa Avila, MPH, ELS, of\u0026nbsp;Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval of this study was received from the Certified Review Board of Japan Institute for Health Security (approval number: NCGM-S-004318-02). Patients’ consent was collected online.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI.S. and A.O. are staffs of IQVIA solutions Japan. Other authors have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (KAKENHI JP23K27865).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSS, SS and YA: conceived the study, design of the work, analysis, interpretation of the data, writing or review of the article. HI, NM, NO: conceived the study, interpretation of the data, writing or review of the article. SI, AO: acquisition of data, writing or review of the article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAl-Aly, Z., et al., \u003cem\u003eLong COVID science, research and policy.\u003c/em\u003e Nature Medicine, 2024. \u003cstrong\u003e30\u003c/strong\u003e(8): p. 2148-2164.\u003c/li\u003e\n \u003cli\u003eHou, Y., et al., \u003cem\u003eGlobal Prevalence of Long COVID, Its Subtypes, and Risk Factors: An Updated Systematic Review and Meta-analysis.\u003c/em\u003e Open Forum Infectious Diseases, 2025. \u003cstrong\u003e12\u003c/strong\u003e(9).\u003c/li\u003e\n \u003cli\u003eBowe, B., Y. Xie, and Z. Al-Aly, \u003cem\u003ePostacute sequelae of COVID-19 at 2 years.\u003c/em\u003e Nature Medicine, 2023. \u003cstrong\u003e29\u003c/strong\u003e(9): p. 2347-2357.\u003c/li\u003e\n \u003cli\u003eDehlia, A. and M.A. Guthridge, \u003cem\u003eThe persistence of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) after SARS-CoV-2 infection: A systematic review and meta-analysis.\u003c/em\u003e J Infect, 2024. \u003cstrong\u003e89\u003c/strong\u003e(6): p. 106297.\u003c/li\u003e\n \u003cli\u003eFord ND, S.D., Edwards D, et al. , \u003cem\u003eLong COVID and Significant Activity Limitation Among Adults, by Age \u0026mdash; United States, June 1\u0026ndash;13, 2022, to June 7\u0026ndash;19, 2023.\u003c/em\u003e MMWR Morb Mortal Wkly Rep 2023. \u003cstrong\u003e72\u003c/strong\u003e: p. 866\u0026ndash;870.\u003c/li\u003e\n \u003cli\u003eCarlile, O., et al., \u003cem\u003eImpact of long COVID on health-related quality-of-life: an OpenSAFELY population cohort study using patient-reported outcome measures (OpenPROMPT).\u003c/em\u003e The Lancet Regional Health \u0026ndash; Europe, 2024. \u003cstrong\u003e40\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003evan den Hoek, R., et al., \u003cem\u003eLongitudinal assessment of health-related quality of life after SARS-CoV-2 infection and the associations with clinical and social characteristics in a general practice population.\u003c/em\u003e Health and Quality of Life Outcomes, 2024. \u003cstrong\u003e22\u003c/strong\u003e(1): p. 86.\u003c/li\u003e\n \u003cli\u003eTsuzuki, S., et al., \u003cem\u003eImpact of long-COVID on health-related quality of life in Japanese COVID-19 patients.\u003c/em\u003e Health Qual Life Outcomes, 2022. \u003cstrong\u003e20\u003c/strong\u003e(1): p. 125.\u003c/li\u003e\n \u003cli\u003eHonda, H., et al., \u003cem\u003eProlonged Symptoms after COVID-19 in Japan: A Nationwide Survey of the Symptoms and Their Impact on Patients\u0026amp;#x2019; Quality of Life.\u003c/em\u003e The American Journal of Medicine, 2025. \u003cstrong\u003e138\u003c/strong\u003e(1): p. 98-107.e4.\u003c/li\u003e\n \u003cli\u003eYagi, K., et al., \u003cem\u003eImpact of long COVID on the health-related quality of life of Japanese patients: A prospective nationwide cohort study.\u003c/em\u003e Respir Investig, 2025. \u003cstrong\u003e63\u003c/strong\u003e(4): p. 610-616.\u003c/li\u003e\n \u003cli\u003eMaeda, H., et al., \u003cem\u003eComparison of long-term health-related quality of life and symptoms between COVID-19 patients and test-negative controls during the Omicron-predominant period in Japan.\u003c/em\u003e Archives of Public Health, 2025. \u003cstrong\u003e83\u003c/strong\u003e(1): p. 136.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eFront Matter.\u003c/em\u003e Medical Care, 2004. \u003cstrong\u003e42\u003c/strong\u003e(9).\u003c/li\u003e\n \u003cli\u003eHuo, T., et al., \u003cem\u003eAssessing the reliability of the short form 12 (SF-12) health survey in adults with mental health conditions: a report from the wellness incentive and navigation (WIN) study.\u003c/em\u003e Health Qual Life Outcomes, 2018. \u003cstrong\u003e16\u003c/strong\u003e(1): p. 34.\u003c/li\u003e\n \u003cli\u003eSantoro, A., et al., \u003cem\u003eShort and long-term trajectories of the post COVID-19 condition: Results from the EuCARE POSTCOVID study.\u003c/em\u003e BMC Infectious Diseases, 2025. \u003cstrong\u003e25\u003c/strong\u003e(1): p. 625.\u003c/li\u003e\n \u003cli\u003eO\u0026rsquo;Mahoney, L.L., et al., \u003cem\u003eThe risk of Long Covid symptoms: a systematic review and meta-analysis of controlled studies.\u003c/em\u003e Nature Communications, 2025. \u003cstrong\u003e16\u003c/strong\u003e(1): p. 4249.\u003c/li\u003e\n \u003cli\u003eOgawa, W. and S. Miyazaki, \u003cem\u003eDiagnosis criteria for obesity and obesity disease.\u003c/em\u003e Health Evaluation and Promotion, 2015. \u003cstrong\u003e42\u003c/strong\u003e(2): p. 301-306.\u003c/li\u003e\n \u003cli\u003eHalldorsdottir, T., et al., \u003cem\u003eAdolescent well‐being amid the COVID‐19 pandemic: Are girls struggling more than boys?\u003c/em\u003e JCPP advances, 2021. \u003cstrong\u003e1\u003c/strong\u003e(2): p. e12027.\u003c/li\u003e\n \u003cli\u003eO\u0026apos;Kelly, B., et al., \u003cem\u003eAssessing the impact of COVID-19 at 1-year using the SF-12 questionnaire: Data from the Anticipate longitudinal cohort study.\u003c/em\u003e Int J Infect Dis, 2022. \u003cstrong\u003e118\u003c/strong\u003e: p. 236-243.\u003c/li\u003e\n \u003cli\u003eYagi, K., et al., \u003cem\u003eImpact of long COVID on the health-related quality of life of Japanese patients: A prospective nationwide cohort study.\u003c/em\u003e Respiratory Investigation, 2025. \u003cstrong\u003e63\u003c/strong\u003e(4): p. 610-616.\u003c/li\u003e\n \u003cli\u003eMalesevic, S., et al., \u003cem\u003eImpaired health-related quality of life in long-COVID syndrome after mild to moderate COVID-19.\u003c/em\u003e Scientific Reports, 2023. \u003cstrong\u003e13\u003c/strong\u003e(1): p. 7717.\u003c/li\u003e\n \u003cli\u003eRogers, A.A., T. Ha, and S. Ockey, \u003cem\u003eAdolescents\u0026apos; perceived socio-emotional impact of COVID-19 and implications for mental health: Results from a US-based mixed-methods study.\u003c/em\u003e Journal of Adolescent Health, 2021. \u003cstrong\u003e68\u003c/strong\u003e(1): p. 43-52.\u003c/li\u003e\n \u003cli\u003eMagson, N.R., et al., \u003cem\u003eRisk and protective factors for prospective changes in adolescent mental health during the COVID-19 pandemic.\u003c/em\u003e Journal of youth and adolescence, 2021. \u003cstrong\u003e50\u003c/strong\u003e(1): p. 44-57.\u003c/li\u003e\n \u003cli\u003eKobayashi, T., et al., \u003cem\u003eImpact of COVID-19 Infection on Health-Related Quality of Life in the Japanese Population: A Large Health-Insurance-Based Database Study.\u003c/em\u003e Int J Environ Res Public Health, 2024. \u003cstrong\u003e21\u003c/strong\u003e(2).\u003c/li\u003e\n \u003cli\u003eLiira, H., et al., \u003cem\u003eAssociations of socioeconomic status and health-related quality of life in patients with long COVID and patients with persistent physical symptoms: A comparison of two cohort studies at baseline.\u003c/em\u003e J Psychosom Res, 2025. \u003cstrong\u003e197\u003c/strong\u003e: p. 112374.\u003c/li\u003e\n \u003cli\u003eMkoma, G.F., et al., \u003cem\u003eSocioeconomic disparities in long COVID diagnosis among ethnic minorities in Denmark.\u003c/em\u003e Social Science \u0026amp; Medicine, 2025. \u003cstrong\u003e372\u003c/strong\u003e: p. 117944.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"quality-of-life-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"qure","sideBox":"Learn more about [Quality of Life Research](https://www.springer.com/journal/11136)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/qure/default.aspx","title":"Quality of Life Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Long COVID, health-related quality of life, SARS-CoV-2 infection, latent class analysis","lastPublishedDoi":"10.21203/rs.3.rs-8250532/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8250532/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eWe comprehensively assessed implications of long COVID on health-related quality of life (HRQoL) among Japanese adults and identified its associated factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study used prospective cohort data from the CARE Japan Study between January 2022 and January 2023. The study outcome was HRQoL, measured using the 12-item Short Form questionnaire. Self-reported long COVID was the primary independent variable. We fit adjusted beta regression models to calculate beta regression coefficients with the 95% confidence intervals (CI) and average marginal effect (AME) to explore the determinants of HRQoL. We also performed latent class analysis to identify unobserved patterns of long COVID symptoms.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe final sample size was 1,285 participants. Compared with participants without long COVID, HRQoL was significantly lower among patients with long COVID (β: \u0026minus;0.25; 95% CI: \u0026minus;0.36 to \u0026minus;\u0026thinsp;0.14; AME: \u0026minus;0.036). The effect of long COVID on HRQoL was most pronounced among respondents with pre-existing lung diseases (β: \u0026minus;0.72; 95% CI: \u0026minus;1.29 to \u0026minus;\u0026thinsp;0.16; AME: \u0026minus;0.114). In latent class analysis, we identified three subgroups of patients with long COVID: classes 1, 2, and 3. Compared with participants without long COVID, those belonging to class 1 (β: \u0026minus;0.47; 95% CI: \u0026minus;0.57 to \u0026minus;\u0026thinsp;0.36; AME: \u0026minus;0.065), class 2 (β: \u0026minus;0.48; 95% CI: \u0026minus;0.60 to \u0026minus;\u0026thinsp;0.35; AME: \u0026minus;0.066), and class 3 (β: \u0026minus;0.93; 95% CI: \u0026minus;1.06 to \u0026minus;\u0026thinsp;0.79; AME: \u0026minus;0.148) had poorer HRQoL.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003ePatients with long COVID had reduced HRQoL. Female sex, age\u0026thinsp;\u0026le;\u0026thinsp;39 years, body mass index\u0026thinsp;\u0026le;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e, and pre-existing psychological disorders were associated with lower HRQoL.\u003c/p\u003e","manuscriptTitle":"Impact of long COVID on health-related quality of life among Japanese adults: findings of CARE Japan Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-16 19:53:53","doi":"10.21203/rs.3.rs-8250532/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-13T07:51:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-11T07:40:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321486099697645600501194304765835725305","date":"2026-04-07T09:54:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-09T08:09:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-01T14:05:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-01T14:02:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Quality of Life Research","date":"2025-12-01T12:37:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"quality-of-life-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"qure","sideBox":"Learn more about [Quality of Life Research](https://www.springer.com/journal/11136)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/qure/default.aspx","title":"Quality of Life Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0c441f6c-d6d3-4cf0-a7ec-9672bfa9ab4f","owner":[],"postedDate":"December 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-01T16:09:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-16 19:53:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8250532","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8250532","identity":"rs-8250532","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}