Burden of long COVID and associated risk factors among Japanese adults: findings of the CARE Japan Study

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We aimed to inform public health recommendations, surveillance, and health care delivery for all patients with a history of COVID-19 infection. Methods We used prospective cohort data from the CARE Japan Study (https://japan.helpstopcovid19.com/), which collects data on participants’ demographics at baseline and information on diagnostic test results for COVID-19, vaccination status, and post-COVID-19 symptoms at baseline and at monthly follow-up surveys for up to 1 year after COVID-19 infection. The primary outcome was long COVID, defined as the presence of ≥ 1 symptom for ≥ 4 weeks after acute COVID-19 infection. We used Kaplan–Meier survival analysis to determine the median survival time of long COVID and a multivariable Cox proportional hazards model to yield hazard ratios (HRs) with 95% confidence interval (CIs) so as to explore the determinants of long COVID. Results The final analysis included 1497 respondents, 1149 patients with long COVID and 348 without long COVID. The period prevalence of long COVID was 63.9%, 68.2%, and 56.8% during the 1st quartile, 2nd and 3rd quartiles, and 4th quartile, respectively. During the 2nd and 3rd quartiles, runny nose (32%) and fatigue (31%) had the highest period prevalence, followed by headache (24%) and nasal congestion (21%). Compared with female participants, their male counterparts were marginally less likely (HR: 0.87, 95% CI: 0.77–0.99) to report long COVID symptoms. Age showed an inverse association with long COVID. Participants with pre-existing psychological disorders (HR: 1.45, 95% CI: 1.24–1.69), lung diseases (HR: 1.46, 95% CI: 1.10–1.92), or seasonal allergies (HR: 1.32, 95% CI: 1.17–1.49) had considerably higher risks of developing long COVID, compared with participants who had no pre-existing medical conditions. Conclusions More than half of participants experienced long COVID symptoms, even in the 4th quartile of the study period. Fatigue, runny nose, headache, and cough were the most frequently reported long COVID symptoms. Younger age, female sex, and having pre-existing medical conditions were associated with a greater risk of developing long COVID symptoms. Clinical Trial Number: Not applicable. Long COVID Post-COVID conditions SARS-CoV-2 Japan psychological disorder vaccination. Figures Figure 1 Figure 2 Figure 3 Figure 4 Background More than 5 years after the onset of the COVID-19 ( coronavirus disease ) global pandemic, a wave of evidence suggests that some survivors of COVID-19 infection may experience persistent symptoms in single or multiple organ systems, including neurological, respiratory, or cardiovascular disorders, long after the acute infection [ 1 – 3 ]. COVID-19 infection-associated chronic conditions that persist for at least 3 months as a continuous, relapsing and remitting, or progressive disease state affecting one or more organ systems are defined as long COVID by the Centers for Disease, Control and Prevention (CDC) [ 4 ]. The global estimated pooled prevalence of long COVID between 2021 and 2024 was 36% (95% confidence interval [CI]: 33–40%) [ 5 ]. Owing to the accompanying poor health-related quality of life, a high burden of long COVID might generate an overwhelming impact on health care systems, communities, and society [ 6 , 7 ]. Clarity regarding the characteristics of long COVID is evolving and the underlying mechanisms remain unclear [ 8 ]. The cumulative total of COVID-19 cases in Japan, as reported to the World Health Organization (WHO), was 33.8 million as of March 2025 [ 9 ]. Several previous studies in Japan have reported a considerably high burden and risk of common long COVID symptoms, such as anxiety, depression, insomnia, fatigue, shortness of breath, cough, and headache, among people with a history of COVID-19 infection [ 10 – 14 ]. However, the risks and burdens of these sequelae have been assessed within a few months after the onset of COVID-19 infection, in studies with very small sample sizes, and using limited numbers of sequelae with a cross-sectional study design [ 7 , 10 – 14 ]. Owing to the relapsing nature of long COVID, cross-sectional studies might underestimate the actual burden of long COVID. Owing to this knowledge gap, we conducted the present study with the aim to comprehensively assess the burden of a wide array of long COVID symptoms and associated risk factors, including pre-existing medical conditions, in the Japanese adult population. We used a longitudinal study design with monthly follow-up data of up to 1 year after the initial onset of COVID-19 infection. The findings of this study will help to deepen our understanding of long-term monitoring, surveillance, and health care needs among patients with long COVID in Japan. Methods Data source We used prospective cohort data from the CARE Japan Study ( https://japan.helpstopcovid19.com/ ), which has been conducted by IQVIA Japan in a national center hospital in Tokyo since January 2021. The purpose of the CARE Japan Study is to collect and analyze information on patients’ experiences with new COVID-19 infections for the development of medical systems, future social infrastructure, and new drugs. In this study, we used a non-probability convenience sampling method. Patients who visited the hospital with flu-like symptoms were provided with an explanation about the purpose of this study and were invited to participate. Upon collecting patients’ consent via Internet, 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, and symptoms related to COVID-19 was collected at baseline and in the follow-up surveys. The English version of the questionnaire used in this study is available in Supplementary file. Study design and participant selection criteria This was a longitudinal study conducted among Japanese male and female adults. After initial registration of participants in the CARE Japan Study, participants who did not have COVID-19 infection and those who did not have follow-up data or had only 30 days of follow-up data after COVID-19 infection were excluded from the study. Figure 1 shows the participant selection criteria in this study. Outcome variables 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. The primary outcome was the proportion of individuals who were diagnosed with long COVID as per the previous CDC definition: the presence of ≥ 1 symptom for 4 or more weeks after the acute infection, either at entry in the cohort (2–3 months after acute infection) or at any of the follow-up visits [ 15 , 16 ]. This definition has been used extensively to define long COVID in studies worldwide. However, the CDC released a new definition of long COVID in February 2025 and extended the period of symptom persistence from 4 weeks to 12 weeks. We designed our study prior to the release of this new definition by the CDC. Therefore, to calculate estimates regarding long COVID that were comparable to those in previous studies, we used the previous CDC definition of long COVID. However, to better understand the trajectories of long COVID, we divided the follow-up period into three time segments and calculated long COVID estimates for each of these, namely, a 1st quartile (2 and 3 months after initial infection), 2nd and 3rd quartiles (4 to 9 months after initial infection), and 4th quartile (10 to 12 months after initial infection). Exposures Exposures comprised demographic factors that included age (younger: 39 years or less, middle-aged: ≥40 and ≤ 64 years, and older: ≥65 years) and sex (male or female, assigned at birth). We also included smoking status (never smoked, past smoker, smoker), body mass index (BMI), vaccination history, and pre-existing medical conditions as exposures. Unlike the WHO 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 ) [ 17 ]. 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 three categories: none or first dose only, primary doses (1st and 2nd), and booster doses (primary doses plus 3rd or 4th). In each participant, we also considered the following pre-existing medical conditions: 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 estimate the distribution of long COVID according to sociodemographic characteristics and to calculate the prevalence of each long COVID symptom. We performed Kaplan–Meier survival analysis to determine the median survival time of at least one long COVID symptom, by age group. The log-rank test was used to calculate p-values so as to assess whether the survival distributions differed significantly by age group. If any participant had a second COVID-19 infection at any time point within the 1-year follow-up period, the participant was censored at that time point to avoid bias. We fitted a multivariable Cox proportional hazards model to calculate hazard ratios (HRs) with 95% confidence intervals (CIs) to explore the determinants of long COVID. The model was adjusted for sex, age, BMI, smoking status, vaccine doses, psychological disorders, metabolic disorders, lung diseases, and seasonal allergies. We selected the covariates based on previous studies and the availability of data. We performed chi-square tests to check the strength of the association between each covariate and long COVID. We conducted subgroup analysis for sex and pre-existing medical conditions using a univariable Cox proportional hazards model. To check the sensitivity of the association, we ran the same model using two datasets: a dataset comprising the 2nd and 3rd months of follow-up and another dataset including 1 year of follow-up. All statistical analyses were performed using R (version 4.4.1; The R Foundation for Statistical Computing, Vienna, Austria). A p-value of < 0.05 was considered statistically significant. Results In total, 1497 respondents completed at least one follow-up during the 1st quartile of the study period. This number of dropped to 1059 during the 2nd and 3rd quartiles; in the 4th quartile, the number further dropped to 572. The final analysis included 1497 respondents (between January 2022 to December 2023), with 1149 patients who had long COVID and 348 who did not. In the study population, the mean age was 52 years and 46% were female patients. Table 1 summarizes additional sociodemographic characteristics and background medical conditions of the study sample, according to the presence or absence of long COVID. Table 1 Long-COVID among Japanese adults according to sociodemographic factors. Determinants Categories Participants without long COVID, n = 348 (%) Participants with long COVID, n = 1,149 (%) Sex Women 128 (37.0) 560 (48.7) Men 218 (63.0) 591 (51.3) Age (years) ≤ 39 50 (14.5) 145 (12.6) 40–64 267 (77.2) 919 (79.8) ≥ 65 29 (8.4) 87 (7.6) BMI (kg/m2) Thin (≤ 18.5) 17 (4.9) 81 (7.0) Normal (> 18.5 & < 25) 244 (70.5) 782 (67.9) Obese (≥ 25) 85 (24.6) 288 (25.0) Smoking status Never 202 (58.4) 683 (59.3) Past 121 (35.0) 399 (34.7) Present 23 (6.6) 69 (6.0) Vaccine doses ≤ 1 14 (4.0) 54 (4.7) 2 41 (11.8) 100 (8.7) ≥ 3 291 (84.1) 997 (86.6) Psychological disorders No 317 (91.6) 953 (82.8) Yes 29 (8.4) 198 (17.2) Metabolic disorders No 287 (82.9) 946 (82.2) Yes 59 (17.1) 205 (17.8) Lung disease No 341 (98.6) 1095 (95.1) Yes 5 (1.4) 56 (4.9) Seasonal allergies No 225 (65.0) 599 (52.0) Yes 121 (35.0) 552 (48.0) BMI, body mass index. Across the entire study period, the overall period prevalence of at least one long COVID symptom was 76.7%. During the 1st quartile, the 2nd and 3rd quartiles, and the 4th quartile of the study period, this prevalence was 63.9%, 68.2%, and 56.8%, respectively. Overall, 40% of respondents reported fatigue; 35% reported cough and runny nose; ~25% reported headache, nasal congestion, and sore throat; ~16% reported body aches, pain, blurred vision, and injection site reactions; ~12% had diarrhea, dizziness, anxiety, and fever; and 9% of participants reported having shortness of breath. Figure 2 shows the period prevalence of each long COVID symptom according to the three follow-up durations. During the 1st quartile (2nd and 3rd months), fatigue (26%) and cough (26%) had the highest period prevalence, followed by runny nose (17%) and headache (17%). During the 2nd and 3rd quartiles (4th to 9th month), runny nose (32%) and fatigue (31%) had the highest period prevalence, followed by headache (24%) and nasal congestion (21%). During the 4th quartile (10th to 12th month), fatigue (29%) had the highest period prevalence, followed by runny nose (19%), cough (17%), headache (17%), and sore throat (16%). In Fig. 3 , Kaplan–Meier curves showed that median survival times for at least one long COVID symptom were 48 (95% CI: 46–59) days, 55 (95% CI: 53–58) days, and 70 (95% CI: 61–93) days for the younger, middle-aged, and older age group, respectively. The p-values showed that the survival distributions differed significantly according to age group. Additional file1 shows the distribution of long COVID symptoms by age group. Compared with older participants (age ≥ 65 years), younger ones (age ≤ 39 years) more frequently reported fatigue (younger vs. older: 43.1% vs. 25.9%), headache (younger vs. older: 37.4% vs. 12.1%), nasal congestion (younger vs. older: 28.2% vs. 12.9%), sore throat (younger vs. older: 24.1% vs. 16.4%), anxiety (younger vs. older: 16.9% vs. 3.4%), dizziness (younger vs. older: 16.4% vs. 8.6%), and depression (younger vs. older: 14.4% vs. 2.6%). By contrast, older participants more frequently reported injection site reactions (younger vs. older: 12.3% vs. 27.6%) as well as body aches and pain (younger vs. older: 11.8% vs. 17.2.6%), as compared with younger participants. The Cox proportional hazards model identified participant age and pre-existing medical conditions as strong determinants of long COVID (Fig. 4 ). In the datasets including the 2nd and 3rd months of follow-up as well as 1 year of follow-up, age showed an inverse association with long COVID. Middle-aged (HR: 0.79, 95% CI: 0.66–0.95) and older participants (HR: 0.68, 95% CI: 0.51–0.90) were less likely to experience long COVID symptoms, in comparison with younger participants. Male participants were marginally less likely (HR: 0.87, 95% CI: 0.77–0.99) to report long COVID symptoms, as compared with female participants. Participants who were overweight or obese (HR: 1.10, 95% CI: 0.95–1.26) and underweight (HR: 1.21, 95% CI: 0.96–1.53) more frequently reported long COVID symptoms, compared with participants who had a normal BMI, although the association was not statistically significant. Participants who were either present or past smokers were more likely to report long COVID symptoms, as compared with those who never smoked; however, the association was not significant. In the dataset including the 2nd and 3rd months of follow-up, participants who completed a primary COVID-19 vaccination series (1st and 2nd doses) were less likely (HR: 0.65, 95% CI: 0.46–0.93) to report long COVID symptoms, in comparison with participants who had either never been vaccinated against COVID-19 or received only the first dose. However, this association became non-significant (HR: 0.77, 95% CI: 0.55–1.07) in the dataset including 1 year of follow-up. Completion of COVID-19 booster doses (3rd and 4th doses) was not associated with any significant reduction in long COVID symptoms in either dataset. Participants with pre-existing psychological disorders including depression, anxiety, or insomnia (HR: 1.45, 95% CI: 1.24–1.69); lung diseases (HR: 1.46, 95% CI: 1.10–1.92); or seasonal allergies (HR: 1.32, 95% CI: 1.17–1.49) showed considerably higher risks of developing long COVID, compared with participants with no pre-existing medical conditions. However, having metabolic disorders including diabetes or hypertension (HR: 1.08, 95% CI: 0.92–1.27) was not associated with a higher risk of developing long COVID symptoms, compared with having no metabolic disorders. Subgroup analysis showed that, compared with men, women were more likely to develop several long COVID symptoms, including fatigue (HR: 1.54, 95% CI: 1.31–1.82), shortness of breath (HR: 1.93, 95% CI: 1.36–2.73), headache (HR: 2.54, 95% CI: 2.08–3.11), dizziness (HR: 2.29, 95% CI: 1.69–3.10), rash (HR: 2.76, 95% CI: 1.71–4.45), persistent chest pain/pressure (HR: 1.74, 95% CI: 1.11–2.72), depression (HR: 1.71, 95% CI: 1.21–2.41), and anxiety (HR: 1.63, 95% CI: 1.21–1.19). Details of subgroup analysis for long COVID symptoms by sex are provided in Additional file2. We also conducted subgroup analysis of long COVID symptoms according to pre-existing medical conditions (Additional file3). We found that participants with psychological disorders were three to five times more likely to develop long COVID symptoms of insomnia (HR: 5.45, 95% CI: 3.86–7.69), depression (HR: 5.22, 95% CI: 3.71–7.33), or anxiety (HR: 3.17, 95% CI: 2.32–4.31). However, pre-existing metabolic disorders did not show positive associations with any long COVID symptoms. Participants with lung diseases were four times more likely to report the long COVID symptom shortness of breath (HR: 4.15, 95% CI: 2.53–6.82). Participants with seasonal allergies were two times more likely to report long COVID symptoms of rash (HR: 1.88, 95% CI: 1.21–2.94), runny nose (HR: 2.12, 95% CI: 1.72–2.61), and nasal congestion (HR: 2.11, 95% CI: 1.77–2.51). Discussion In this study, we estimated the period prevalence of long COVID across 25 of the most frequently reported long COVID symptoms during 1 year of follow-up, and we explored the risk factors of long COVID among Japanese adults. The study results showed that three out of four respondents experienced at least one long COVID symptom at some point during 1 year of follow-up ≥ 30 days after the initial infection. The most frequently reported long COVID symptoms included runny nose, fatigue, cough, headache, nasal congestion, and sore throat, with period prevalence as high as 28% and as low as 12%, even in the 4th quartile (10–12 months after the initial COVID-19 infection) of the study period. Risk factor analysis identified that women, younger participants, and participants with pre-existing psychological disorders, lung diseases, or seasonal allergies were more likely to report long COVID symptoms. A previous cross-sectional study in Japan reported a point prevalence of 56% of long COVID symptoms after 1 year of COVID-19 infection [ 7 ]. Another review and meta-analysis reported a pooled global prevalence of long COVID of 41.3% (95% CI: 39.2–43.3%) at 12 months after the initial infection [ 18 ]. Another study conducted during 2022 in Thailand also reported a 77% prevalence of long COVID among Thai adults [ 19 ]. However, estimates for long COVID vary largely across studies owing to the wide range of symptoms as well as differences in the definitions of long COVID, study designs, health determinants, and participants’ demographic characteristics. In our study, fatigue and cough were among the most frequently reported long COVID symptoms, a finding that is supported by several other studies [ 19 – 23 ]. However, we found that the period prevalence for shortness of breath among Japanese adults was much lower at only 9%. Another study in Japan also reported a prevalence of shortness of breath at only 13% [ 7 ]. However, studies from other countries have found shortness of breath to be one of the most frequently reported long COVID symptoms [ 20 , 21 , 23 , 24 ]. Similarly, the period prevalence of depression, anxiety, and insomnia was much lower in our study compared with the findings of meta-analyses and review studies [ 20 , 22 ]. In risk factor analysis, we found that young and middle-aged participants were more likely to report long COVID symptoms than older participants, which is supported by some studies [ 19 , 25 ] but is in contrast to the findings of several other studies [ 26 , 27 ]. A meta-analysis reported that older age was not associated with a higher risk of long COVID [ 28 ]. This finding might be linked to the high infectivity of the Omicron variant among socially active young people; in this study, patient responses were collected during 2022 and 2023 when Omicron was the predominant COVID-19 variant in Japan. Furthermore, there is a possibility of reporting bias because older patients might be less comfortable with conducting follow-up via the Internet in comparison with younger ones. However, the prevalence of body aches and pain as well as injection site reactions was highest among patients in the older age group, which suggests the possibility of reporting bias. The effect of COVID-19 vaccination on long COVID was inconclusive in our study. However, our findings regarding a higher likelihood of developing long COVID among participants with pre-existing psychological disorders, lung diseases, or seasonal allergies are supported by several past studies [ 23 , 29 ]. The findings of the present study can inform health policymakers and health care providers—particularly primary health care physicians, nurses, and physical therapists—regarding the needs and patterns of additional health care support and services after an initial bout of treatment for COVID-19 infection. Currently, the Ministry of Health, Labour, and Welfare, the Tokyo metropolitan government, and various local authorities maintain websites that provide information on medical facilities where patients can be assessed for long COVID. It is important to develop a comprehensive medical care system for the treatment of long COVID and long-term follow-up of patients with COVID-19 who have any of the risk factors identified in this study. To our knowledge, this longitudinal study is the first to investigate long COVID in the Japanese adult population with monthly follow-up data up to 1 year after the initial COVID-19 infection. Owing to the relapsing nature of long COVID, we reported period prevalence rather than point prevalence for each long COVID symptom, which is particularly helpful in understanding the overall and symptom-specific burden of long COVID among Japanese adults. Our study also has several limitations. We conducted the survey via Internet so as to recruit a diverse range of participants. However, it is possible that some groups, such as older adults, were uncomfortable with being surveyed online. Thus, caution should be exercised in generalizing the results obtained in this study. Additionally, although participants were asked to report information each month, there were many cases in which no data were reported. Although trends similar to those of previous studies were observed, there is a possibility of overestimation or underestimation owing to censored cases. It is also known that COVID-19 symptoms vary widely depending on the mutant strain, which may have an effect on long COVID symptoms; however, information regarding predominant mutant strains was not collected in this study. Furthermore, because we used a previous CDC definition for long COVID (symptoms persisting ≥ 4 weeks), some patients who were classified as having long COVID in our study may fall within the recovery period of long COVID according to the updated CDC definition (patients with symptoms beyond the 2nd quartile). Therefore, caution should be exercised when comparing the findings of this study with those of future studies. However, our estimates of long COVID in the 2nd and 3rd quartiles as well as the 4th quartile will be compatible with the new CDC definition of long COVID. Conclusions Our study provides a comprehensive assessment of long COVID symptoms, with up to 1 year of follow-up, as well as related determinants among Japanese adults. More than half of study participants experienced at least one long COVID symptom, even in the 4th quartile of the study period. Fatigue, runny nose, headache, and cough were the most frequently reported long COVID symptoms. Younger age, female sex, and having pre-existing medical conditions were associated with a greater risk of developing long COVID. Pre-existing psychological disorders were associated with long COVID symptoms of depression, anxiety, and insomnia; pre-existing lung diseases was associated with shortness of breath; and pre-existing seasonal allergies was associated with long COVID symptoms of rash, runny nose, and nasal congestion. By adding evidence regarding the symptoms of long COVID among Japanese adults, our findings will help raise awareness regarding the need for long-term monitoring and surveillance systems for patients with COVID-19 in Japan. Abbreviations COVID-19 Coronavirus Disease CDC Centers for Disease Control and Prevention WHO World Health Organization BMI Body Mass Index HR Hazard Ratio CI Confidence Interval. Declarations Ethics approval and consent to participate The study was approved by the ethics committee of Japan Institute for Health Security (approval number: NCGM-S-004318-02), in accordance with the Declaration of Helsinki. Informed consent was obtained from all the participants. 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 Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (KAKENHI 23K27865). Authors' contributions ST, SS and YA: conceived the study, design of the work, analysis, interpretation of data, writing of the work or review. HI, NM, NO: conceived the study, interpretation of data, writing of the work or review. SI, AO: acquisition of data, writing of the work or review. Acknowledgements We thank Analisa Avila, MPH, ELS, of Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript. References Parotto M, et al. Post-acute sequelae of COVID-19: understanding and addressing the burden of multisystem manifestations. Lancet Respiratory Med. 2023;11(8):739–54. Guedj E, et al. 18F-FDG brain PET hypometabolism in patients with long COVID. Eur J Nucl Med Mol Imaging. 2021;48(9):2823–33. Lopes-Pacheco M, et al. Pathogenesis of Multiple Organ Injury in COVID-19 and Potential Therapeutic Strategies. Frontiers in Physiology; 2021. pp. 12–2021. Prevention C. f.D.C.a. Clinical overview of Long-COVID . 2025. Hou Y et al. Global Prevalence of Long COVID, its Subtypes and Risk factors: An Updated Systematic Review and Meta-Analysis. medRxiv, 2025. Malesevic S, et al. Impaired health-related quality of life in long-COVID syndrome after mild to moderate COVID-19. Sci Rep. 2023;13(1):7717. 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Rochmawati E, Iskandar AC, Kamilah F. Persistent symptoms among post-COVID-19 survivors: A systematic review and meta-analysis. J Clin Nurs. 2024;33(1):29–39. Davis HE et al. Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. EClinicalMedicine, 2021. 38. Han Q, et al. Long-Term Sequelae of COVID-19: A Systematic Review and Meta-Analysis of One-Year Follow-Up Studies on Post-COVID Symptoms. Pathogens. 2022;11(2):269. Durstenfeld MS, et al. Factors Associated With Long COVID Symptoms in an Online Cohort Study. Open Forum Infect Dis. 2023;10(2):ofad047. Grewal JS, et al. Post-COVID dyspnea: prevalence, predictors, and outcomes in a longitudinal, prospective cohort. BMC Pulm Med. 2023;23(1):84. Choudhury NA, et al. Neurologic Manifestations of Long COVID Disproportionately Affect Young and Middle-Age Adults. Ann Neurol. 2025;97(2):369–83. Mueller AL, McNamara MS, Sinclair DA. Why does COVID-19 disproportionately affect older people? Aging. 2020;12(10):9959–81. Mansell V, et al. Long COVID and older people. Lancet Healthy Longev. 2022;3(12):e849–54. Notarte KI et al. Age, Sex and Previous Comorbidities as Risk Factors Not Associated with SARS-CoV-2 Infection for Long COVID-19: A Systematic Review and Meta-Analysis. J Clin Med, 2022. 11(24). Paul T, et al. Risk of long covid in patients with pre-existing chronic respiratory diseases: a systematic review and meta-analysis. BMJ Open Respiratory Res. 2025;12(1):e002528. Additional Declarations Competing interest reported. I.S. and A.O. are staffs of IQVIA solutions Japan. Other authors have no competing interests. Supplementary Files Additionalfile1.pdf File name: Additional file1 File format: .pdf Title: Long COVID symptoms among Japanese adults by age group. Description: This table shows percentage distribution of each long COVID symptom of interest by age groups. Additionalfile2.pdf File name: Additional file2 File format: .pdf Title: Long COVID symptoms among Japanese adults by gender. Description: This table shows percentage distribution along with hazard ratios of each long COVID symptom of interest by gender. Additionalfile3.pdf File name: Additional file3 File format: .pdf Title: Long COVID symptoms among Japanese adults by pre-existing medical conditions. Description: This table shows percentage distribution along with hazard ratios of each long COVID symptom of interest by pre-existing medical conditions. SupplementaryFile.docx File name: Supplementary file File format: .pdf The English version of the questionnaire used in this study 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-6910225","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486992958,"identity":"d16a008f-fba7-4362-986a-33db890a063f","order_by":0,"name":"Sabera Sultana","email":"","orcid":"","institution":"Japan Institute for Health Security","correspondingAuthor":false,"prefix":"","firstName":"Sabera","middleName":"","lastName":"Sultana","suffix":""},{"id":486992959,"identity":"1740e69b-3c58-4622-9926-3690232d03f4","order_by":1,"name":"Yusuke Asai","email":"","orcid":"","institution":"Japan Institute for Health Security","correspondingAuthor":false,"prefix":"","firstName":"Yusuke","middleName":"","lastName":"Asai","suffix":""},{"id":486992960,"identity":"fa1896f5-67d5-4140-bdeb-07b2cad19e97","order_by":2,"name":"Haruhiko Ishioka","email":"","orcid":"","institution":"Jichi Medical University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Haruhiko","middleName":"","lastName":"Ishioka","suffix":""},{"id":486992961,"identity":"3214c706-b1ed-4dce-9a8b-f808acb1db0c","order_by":3,"name":"Shinichiro Ikeda","email":"","orcid":"","institution":"IQVIA Solutions Japan G.K","correspondingAuthor":false,"prefix":"","firstName":"Shinichiro","middleName":"","lastName":"Ikeda","suffix":""},{"id":486992962,"identity":"50bf8f77-dc17-4a64-b19e-e71dbfc2500b","order_by":4,"name":"Anna Ohtera","email":"","orcid":"","institution":"IQVIA Solutions Japan G.K","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Ohtera","suffix":""},{"id":486992963,"identity":"bea8c1c5-3ff1-46ae-8efb-cc763b8eb882","order_by":5,"name":"Nobuaki Matsunaga","email":"","orcid":"","institution":"Japan Institute for Health Security","correspondingAuthor":false,"prefix":"","firstName":"Nobuaki","middleName":"","lastName":"Matsunaga","suffix":""},{"id":486992964,"identity":"263c9cc2-3ff5-475d-9545-79e3624ef1ab","order_by":6,"name":"Shinya Tsuzuki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie2PsQrCMBCGfxHsctI1IthXiHRxEPsqkUJdHAR3KQi6CM4F38IXKAQ65QEKLnVxcii4djAV0S3WTTAfXJKDfNx/gMXys6QMcGIhn60wfqaXQul3ii4m0G4UKXCVfyM1gpcUZ7moMNkDl8I4hc25TzkDPwkhkw3CJMaMmxXiIZVa6WulGyPkKSJmVFzFZa14iV6fqiYK5sN1HQw5tNLB5LOSR8vWQel4qg62YaK3/rCLs5fH8pqtBt5WhTeqxoHr7KLCpLwDgoS+2TRuU9TEeAxM6zPQj6ypYrFYLP/BHejlRRpQT4pIAAAAAElFTkSuQmCC","orcid":"","institution":"Japan Institute for Health Security","correspondingAuthor":true,"prefix":"","firstName":"Shinya","middleName":"","lastName":"Tsuzuki","suffix":""},{"id":486992965,"identity":"ff5d58a6-f201-4314-9150-e4cd4c4f76e1","order_by":7,"name":"Norio Ohmagari","email":"","orcid":"","institution":"Japan Institute for Health Security","correspondingAuthor":false,"prefix":"","firstName":"Norio","middleName":"","lastName":"Ohmagari","suffix":""}],"badges":[],"createdAt":"2025-06-17 04:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6910225/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6910225/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87318888,"identity":"b2cf19ca-78f5-4411-8cd1-61187b8b8b7a","added_by":"auto","created_at":"2025-07-22 16:18:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69828,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eParticipant selection criteria\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6910225/v1/90d560cdabb34b29cb0a9e8d.png"},{"id":87318892,"identity":"defc5dd8-9351-40a1-b611-6b02011fa4e4","added_by":"auto","created_at":"2025-07-22 16:18:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17669,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLong COVID symptoms among Japanese adults according to follow-up period\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6910225/v1/238075a8ba9d59e5bdaa631c.png"},{"id":87320399,"identity":"1769ba2a-43f2-4c93-81e6-67908aadf7fa","added_by":"auto","created_at":"2025-07-22 16:26:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":112746,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival analysis of long COVID among Japanese adults; A) Kaplan–Meier survival curve; B) Risk table; C) Distribution of censored respondents.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6910225/v1/2b174709cf19959a2a661ff3.png"},{"id":87320397,"identity":"eb86628c-5fdc-4fb4-a988-ce592196a21a","added_by":"auto","created_at":"2025-07-22 16:26:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":18194,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelates of long COVID among Japanese adults; A) 2\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003end\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e-3\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003erd\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e months of follow up data; B) Up to one year of follow up data.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNotes: Multivariable Cox proportional hazards model. The model was adjusted for sex, age, BMI, smoking status, vaccine doses, psychological disorders, metabolic disorders, lung diseases, and seasonal allergies.\u003c/p\u003e\n\u003cp\u003eHR, hazard ratio; CI, confidence interval; BMI, body mass index.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6910225/v1/c569080d2103bfd5b8812fc6.png"},{"id":101202402,"identity":"5cf12e44-1b04-49f0-b57a-c9f871f7421c","added_by":"auto","created_at":"2026-01-27 09:30:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1030562,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6910225/v1/b3bd4ff1-7d3c-40aa-8b6c-ee080e87697d.pdf"},{"id":87322091,"identity":"1e07c6be-0c46-4f73-a2ac-92e124d7e767","added_by":"auto","created_at":"2025-07-22 16:42:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":98372,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFile name: Additional file1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFile format: .pdf\u003c/p\u003e\n\u003cp\u003eTitle: Long COVID symptoms among Japanese adults by age group.\u003c/p\u003e\n\u003cp\u003eDescription: This table shows percentage distribution of each long COVID symptom of interest by age groups.\u003c/p\u003e","description":"","filename":"Additionalfile1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6910225/v1/e2679f79bcef719e05b3249e.pdf"},{"id":87318894,"identity":"2f708c1b-775c-4c83-91f4-3f0ab124ffa8","added_by":"auto","created_at":"2025-07-22 16:18:44","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":98783,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFile name: Additional file2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFile format: .pdf\u003c/p\u003e\n\u003cp\u003eTitle: Long COVID symptoms among Japanese adults by gender.\u003c/p\u003e\n\u003cp\u003eDescription: This table shows percentage distribution along with hazard ratios of each long COVID symptom of interest by gender.\u003c/p\u003e","description":"","filename":"Additionalfile2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6910225/v1/68963e66786de7027acc78d6.pdf"},{"id":87320998,"identity":"d07040ff-9841-47b4-be2c-76def02153df","added_by":"auto","created_at":"2025-07-22 16:34:44","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":120206,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFile name: Additional file3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFile format: .pdf\u003c/p\u003e\n\u003cp\u003eTitle: Long COVID symptoms among Japanese adults by pre-existing medical conditions.\u003c/p\u003e\n\u003cp\u003eDescription: This table shows percentage distribution along with hazard ratios of each long COVID symptom of interest by pre-existing medical conditions.\u003c/p\u003e","description":"","filename":"Additionalfile3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6910225/v1/ade4063cad580a6bd558841e.pdf"},{"id":87320404,"identity":"ad31739c-61b0-4d40-a654-ca95b2e57bb7","added_by":"auto","created_at":"2025-07-22 16:26:44","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1381451,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFile name: Supplementary file\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFile format: .pdf\u003c/p\u003e\n\u003cp\u003eThe English version of the questionnaire used in this study\u003c/p\u003e","description":"","filename":"SupplementaryFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-6910225/v1/494444a1c2e9e007fcb5fded.docx"}],"financialInterests":"Competing interest reported. I.S. and A.O. are staffs of IQVIA solutions Japan. Other authors have no competing interests.","formattedTitle":"Burden of long COVID and associated risk factors among Japanese adults: findings of the CARE Japan Study","fulltext":[{"header":"Background","content":"\u003cp\u003eMore than 5 years after the onset of the COVID-19 (\u003cem\u003ecoronavirus disease\u003c/em\u003e) global pandemic, a wave of evidence suggests that some survivors of COVID-19 infection may experience persistent symptoms in single or multiple organ systems, including neurological, respiratory, or cardiovascular disorders, long after the acute infection [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. COVID-19 infection-associated chronic conditions that persist for at least 3 months as a continuous, relapsing and remitting, or progressive disease state affecting one or more organ systems are defined as long COVID by the Centers for Disease, Control and Prevention (CDC) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The global estimated pooled prevalence of long COVID between 2021 and 2024 was 36% (95% confidence interval [CI]: 33\u0026ndash;40%) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Owing to the accompanying poor health-related quality of life, a high burden of long COVID might generate an overwhelming impact on health care systems, communities, and society [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Clarity regarding the characteristics of long COVID is evolving and the underlying mechanisms remain unclear [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe cumulative total of COVID-19 cases in Japan, as reported to the World Health Organization (WHO), was 33.8\u0026nbsp;million as of March 2025 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Several previous studies in Japan have reported a considerably high burden and risk of common long COVID symptoms, such as anxiety, depression, insomnia, fatigue, shortness of breath, cough, and headache, among people with a history of COVID-19 infection [\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the risks and burdens of these sequelae have been assessed within a few months after the onset of COVID-19 infection, in studies with very small sample sizes, and using limited numbers of sequelae with a cross-sectional study design [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Owing to the relapsing nature of long COVID, cross-sectional studies might underestimate the actual burden of long COVID.\u003c/p\u003e\u003cp\u003eOwing to this knowledge gap, we conducted the present study with the aim to comprehensively assess the burden of a wide array of long COVID symptoms and associated risk factors, including pre-existing medical conditions, in the Japanese adult population. We used a longitudinal study design with monthly follow-up data of up to 1 year after the initial onset of COVID-19 infection. The findings of this study will help to deepen our understanding of long-term monitoring, surveillance, and health care needs among patients with long COVID in Japan.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eData source\u003c/p\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 in a national center hospital in Tokyo since January 2021. The purpose of the CARE Japan Study is to collect and analyze information on patients\u0026rsquo; experiences with new COVID-19 infections for the development of medical systems, future social infrastructure, and new drugs. In this study, we used a non-probability convenience sampling method. Patients who visited the hospital with flu-like symptoms were provided with an explanation about the purpose of this study and were invited to participate. Upon collecting patients\u0026rsquo; consent via Internet, 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, and symptoms related to COVID-19 was collected at baseline and in the follow-up surveys. The English version of the questionnaire used in this study is available in Supplementary file.\u003c/p\u003e\u003cp\u003eStudy design and participant selection criteria\u003c/p\u003e\u003cp\u003eThis was a longitudinal study conducted among Japanese male and female adults. After initial registration of participants in the CARE Japan Study, participants who did not have COVID-19 infection and those who did not have follow-up data or had only 30 days of follow-up data after COVID-19 infection were excluded from the study. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the participant selection criteria in this study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOutcome variables\u003c/p\u003e\u003cp\u003eWe 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. The primary outcome was the proportion of individuals who were diagnosed with long COVID as per the previous CDC definition: the presence of \u0026ge;\u0026thinsp;1 symptom for 4 or more weeks after the acute infection, either at entry in the cohort (2\u0026ndash;3 months after acute infection) or at any of the follow-up visits [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This definition has been used extensively to define long COVID in studies worldwide. However, the CDC released a new definition of long COVID in February 2025 and extended the period of symptom persistence from 4 weeks to 12 weeks. We designed our study prior to the release of this new definition by the CDC. Therefore, to calculate estimates regarding long COVID that were comparable to those in previous studies, we used the previous CDC definition of long COVID. However, to better understand the trajectories of long COVID, we divided the follow-up period into three time segments and calculated long COVID estimates for each of these, namely, a 1st quartile (2 and 3 months after initial infection), 2nd and 3rd quartiles (4 to 9 months after initial infection), and 4th quartile (10 to 12 months after initial infection).\u003c/p\u003e\u003cp\u003eExposures\u003c/p\u003e\u003cp\u003eExposures comprised demographic factors that included age (younger: 39 years or less, middle-aged: \u0026ge;40 and \u0026le;\u0026thinsp;64 years, and older: \u0026ge;65 years) and sex (male or female, assigned at birth). We also included smoking status (never smoked, past smoker, smoker), body mass index (BMI), vaccination history, and pre-existing medical conditions as exposures. Unlike the WHO 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=\"CR17\" class=\"CitationRef\"\u003e17\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 three categories: none or first dose only, primary doses (1st and 2nd), and booster doses (primary doses plus 3rd or 4th). In each participant, we also considered the following pre-existing medical conditions: 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=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eWe used descriptive statistics including frequency and percentage to estimate the distribution of long COVID according to sociodemographic characteristics and to calculate the prevalence of each long COVID symptom. We performed Kaplan\u0026ndash;Meier survival analysis to determine the median survival time of at least one long COVID symptom, by age group. The log-rank test was used to calculate p-values so as to assess whether the survival distributions differed significantly by age group. If any participant had a second COVID-19 infection at any time point within the 1-year follow-up period, the participant was censored at that time point to avoid bias. We fitted a multivariable Cox proportional hazards model to calculate hazard ratios (HRs) with 95% confidence intervals (CIs) to explore the determinants of long COVID. The model was adjusted for sex, age, BMI, smoking status, vaccine doses, psychological disorders, metabolic disorders, lung diseases, and seasonal allergies. We selected the covariates based on previous studies and the availability of data. We performed chi-square tests to check the strength of the association between each covariate and long COVID. We conducted subgroup analysis for sex and pre-existing medical conditions using a univariable Cox proportional hazards model. To check the sensitivity of the association, we ran the same model using two datasets: a dataset comprising the 2nd and 3rd months of follow-up and another dataset including 1 year of follow-up. All statistical analyses were performed using R (version 4.4.1; The R Foundation for Statistical Computing, Vienna, Austria). A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn total, 1497 respondents completed at least one follow-up during the 1st quartile of the study period. This number of dropped to 1059 during the 2nd and 3rd quartiles; in the 4th quartile, the number further dropped to 572. The final analysis included 1497 respondents (between January 2022 to December 2023), with 1149 patients who had long COVID and 348 who did not. In the study population, the mean age was 52 years and 46% were female patients. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes additional sociodemographic characteristics and background medical conditions of the study sample, according to the presence or absence of long COVID.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLong-COVID among Japanese adults according to sociodemographic factors.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeterminants\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eParticipants without long COVID, n\u0026thinsp;=\u0026thinsp;348 (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eParticipants with long COVID, n\u0026thinsp;=\u0026thinsp;1,149 (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e128 (37.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e560 (48.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e218 (63.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e591 (51.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50 (14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e145 (12.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u0026ndash;64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e267 (77.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e919 (79.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29 (8.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87 (7.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThin (\u0026le;\u0026thinsp;18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81 (7.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal (\u0026gt;\u0026thinsp;18.5 \u0026amp; \u0026lt; 25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e244 (70.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e782 (67.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObese (\u0026ge;\u0026thinsp;25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85 (24.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e288 (25.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e202 (58.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e683 (59.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e121 (35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e399 (34.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePresent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69 (6.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVaccine doses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54 (4.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100 (8.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e291 (84.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e997 (86.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychological disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e317 (91.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e953 (82.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29 (8.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e198 (17.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetabolic disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e287 (82.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e946 (82.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59 (17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e205 (17.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLung disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e341 (98.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1095 (95.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56 (4.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSeasonal allergies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e225 (65.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e599 (52.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e121 (35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e552 (48.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBMI, body mass index.\u003c/p\u003e\u003cp\u003eAcross the entire study period, the overall period prevalence of at least one long COVID symptom was 76.7%. During the 1st quartile, the 2nd and 3rd quartiles, and the 4th quartile of the study period, this prevalence was 63.9%, 68.2%, and 56.8%, respectively. Overall, 40% of respondents reported fatigue; 35% reported cough and runny nose; ~25% reported headache, nasal congestion, and sore throat; ~16% reported body aches, pain, blurred vision, and injection site reactions; ~12% had diarrhea, dizziness, anxiety, and fever; and 9% of participants reported having shortness of breath.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the period prevalence of each long COVID symptom according to the three follow-up durations. During the 1st quartile (2nd and 3rd months), fatigue (26%) and cough (26%) had the highest period prevalence, followed by runny nose (17%) and headache (17%). During the 2nd and 3rd quartiles (4th to 9th month), runny nose (32%) and fatigue (31%) had the highest period prevalence, followed by headache (24%) and nasal congestion (21%). During the 4th quartile (10th to 12th month), fatigue (29%) had the highest period prevalence, followed by runny nose (19%), cough (17%), headache (17%), and sore throat (16%).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Kaplan\u0026ndash;Meier curves showed that median survival times for at least one long COVID symptom were 48 (95% CI: 46\u0026ndash;59) days, 55 (95% CI: 53\u0026ndash;58) days, and 70 (95% CI: 61\u0026ndash;93) days for the younger, middle-aged, and older age group, respectively. The p-values showed that the survival distributions differed significantly according to age group.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAdditional file1 shows the distribution of long COVID symptoms by age group. Compared with older participants (age\u0026thinsp;\u0026ge;\u0026thinsp;65 years), younger ones (age\u0026thinsp;\u0026le;\u0026thinsp;39 years) more frequently reported fatigue (younger vs. older: 43.1% vs. 25.9%), headache (younger vs. older: 37.4% vs. 12.1%), nasal congestion (younger vs. older: 28.2% vs. 12.9%), sore throat (younger vs. older: 24.1% vs. 16.4%), anxiety (younger vs. older: 16.9% vs. 3.4%), dizziness (younger vs. older: 16.4% vs. 8.6%), and depression (younger vs. older: 14.4% vs. 2.6%). By contrast, older participants more frequently reported injection site reactions (younger vs. older: 12.3% vs. 27.6%) as well as body aches and pain (younger vs. older: 11.8% vs. 17.2.6%), as compared with younger participants.\u003c/p\u003e\u003cp\u003eThe Cox proportional hazards model identified participant age and pre-existing medical conditions as strong determinants of long COVID (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the datasets including the 2nd and 3rd months of follow-up as well as 1 year of follow-up, age showed an inverse association with long COVID. Middle-aged (HR: 0.79, 95% CI: 0.66\u0026ndash;0.95) and older participants (HR: 0.68, 95% CI: 0.51\u0026ndash;0.90) were less likely to experience long COVID symptoms, in comparison with younger participants. Male participants were marginally less likely (HR: 0.87, 95% CI: 0.77\u0026ndash;0.99) to report long COVID symptoms, as compared with female participants. Participants who were overweight or obese (HR: 1.10, 95% CI: 0.95\u0026ndash;1.26) and underweight (HR: 1.21, 95% CI: 0.96\u0026ndash;1.53) more frequently reported long COVID symptoms, compared with participants who had a normal BMI, although the association was not statistically significant. Participants who were either present or past smokers were more likely to report long COVID symptoms, as compared with those who never smoked; however, the association was not significant. In the dataset including the 2nd and 3rd months of follow-up, participants who completed a primary COVID-19 vaccination series (1st and 2nd doses) were less likely (HR: 0.65, 95% CI: 0.46\u0026ndash;0.93) to report long COVID symptoms, in comparison with participants who had either never been vaccinated against COVID-19 or received only the first dose. However, this association became non-significant (HR: 0.77, 95% CI: 0.55\u0026ndash;1.07) in the dataset including 1 year of follow-up. Completion of COVID-19 booster doses (3rd and 4th doses) was not associated with any significant reduction in long COVID symptoms in either dataset. Participants with pre-existing psychological disorders including depression, anxiety, or insomnia (HR: 1.45, 95% CI: 1.24\u0026ndash;1.69); lung diseases (HR: 1.46, 95% CI: 1.10\u0026ndash;1.92); or seasonal allergies (HR: 1.32, 95% CI: 1.17\u0026ndash;1.49) showed considerably higher risks of developing long COVID, compared with participants with no pre-existing medical conditions. However, having metabolic disorders including diabetes or hypertension (HR: 1.08, 95% CI: 0.92\u0026ndash;1.27) was not associated with a higher risk of developing long COVID symptoms, compared with having no metabolic disorders. Subgroup analysis showed that, compared with men, women were more likely to develop several long COVID symptoms, including fatigue (HR: 1.54, 95% CI: 1.31\u0026ndash;1.82), shortness of breath (HR: 1.93, 95% CI: 1.36\u0026ndash;2.73), headache (HR: 2.54, 95% CI: 2.08\u0026ndash;3.11), dizziness (HR: 2.29, 95% CI: 1.69\u0026ndash;3.10), rash (HR: 2.76, 95% CI: 1.71\u0026ndash;4.45), persistent chest pain/pressure (HR: 1.74, 95% CI: 1.11\u0026ndash;2.72), depression (HR: 1.71, 95% CI: 1.21\u0026ndash;2.41), and anxiety (HR: 1.63, 95% CI: 1.21\u0026ndash;1.19). Details of subgroup analysis for long COVID symptoms by sex are provided in Additional file2. We also conducted subgroup analysis of long COVID symptoms according to pre-existing medical conditions (Additional file3). We found that participants with psychological disorders were three to five times more likely to develop long COVID symptoms of insomnia (HR: 5.45, 95% CI: 3.86\u0026ndash;7.69), depression (HR: 5.22, 95% CI: 3.71\u0026ndash;7.33), or anxiety (HR: 3.17, 95% CI: 2.32\u0026ndash;4.31). However, pre-existing metabolic disorders did not show positive associations with any long COVID symptoms. Participants with lung diseases were four times more likely to report the long COVID symptom shortness of breath (HR: 4.15, 95% CI: 2.53\u0026ndash;6.82). Participants with seasonal allergies were two times more likely to report long COVID symptoms of rash (HR: 1.88, 95% CI: 1.21\u0026ndash;2.94), runny nose (HR: 2.12, 95% CI: 1.72\u0026ndash;2.61), and nasal congestion (HR: 2.11, 95% CI: 1.77\u0026ndash;2.51).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we estimated the period prevalence of long COVID across 25 of the most frequently reported long COVID symptoms during 1 year of follow-up, and we explored the risk factors of long COVID among Japanese adults. The study results showed that three out of four respondents experienced at least one long COVID symptom at some point during 1 year of follow-up \u0026ge;\u0026thinsp;30 days after the initial infection. The most frequently reported long COVID symptoms included runny nose, fatigue, cough, headache, nasal congestion, and sore throat, with period prevalence as high as 28% and as low as 12%, even in the 4th quartile (10\u0026ndash;12 months after the initial COVID-19 infection) of the study period. Risk factor analysis identified that women, younger participants, and participants with pre-existing psychological disorders, lung diseases, or seasonal allergies were more likely to report long COVID symptoms.\u003c/p\u003e\u003cp\u003eA previous cross-sectional study in Japan reported a point prevalence of 56% of long COVID symptoms after 1 year of COVID-19 infection [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Another review and meta-analysis reported a pooled global prevalence of long COVID of 41.3% (95% CI: 39.2\u0026ndash;43.3%) at 12 months after the initial infection [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Another study conducted during 2022 in Thailand also reported a 77% prevalence of long COVID among Thai adults [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, estimates for long COVID vary largely across studies owing to the wide range of symptoms as well as differences in the definitions of long COVID, study designs, health determinants, and participants\u0026rsquo; demographic characteristics. In our study, fatigue and cough were among the most frequently reported long COVID symptoms, a finding that is supported by several other studies [\u003cspan additionalcitationids=\"CR20 CR21 CR22\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, we found that the period prevalence for shortness of breath among Japanese adults was much lower at only 9%. Another study in Japan also reported a prevalence of shortness of breath at only 13% [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, studies from other countries have found shortness of breath to be one of the most frequently reported long COVID symptoms [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Similarly, the period prevalence of depression, anxiety, and insomnia was much lower in our study compared with the findings of meta-analyses and review studies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In risk factor analysis, we found that young and middle-aged participants were more likely to report long COVID symptoms than older participants, which is supported by some studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] but is in contrast to the findings of several other studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A meta-analysis reported that older age was not associated with a higher risk of long COVID [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This finding might be linked to the high infectivity of the Omicron variant among socially active young people; in this study, patient responses were collected during 2022 and 2023 when Omicron was the predominant COVID-19 variant in Japan. Furthermore, there is a possibility of reporting bias because older patients might be less comfortable with conducting follow-up via the Internet in comparison with younger ones. However, the prevalence of body aches and pain as well as injection site reactions was highest among patients in the older age group, which suggests the possibility of reporting bias. The effect of COVID-19 vaccination on long COVID was inconclusive in our study. However, our findings regarding a higher likelihood of developing long COVID among participants with pre-existing psychological disorders, lung diseases, or seasonal allergies are supported by several past studies [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The findings of the present study can inform health policymakers and health care providers\u0026mdash;particularly primary health care physicians, nurses, and physical therapists\u0026mdash;regarding the needs and patterns of additional health care support and services after an initial bout of treatment for COVID-19 infection. Currently, the Ministry of Health, Labour, and Welfare, the Tokyo metropolitan government, and various local authorities maintain websites that provide information on medical facilities where patients can be assessed for long COVID. It is important to develop a comprehensive medical care system for the treatment of long COVID and long-term follow-up of patients with COVID-19 who have any of the risk factors identified in this study.\u003c/p\u003e\u003cp\u003eTo our knowledge, this longitudinal study is the first to investigate long COVID in the Japanese adult population with monthly follow-up data up to 1 year after the initial COVID-19 infection. Owing to the relapsing nature of long COVID, we reported period prevalence rather than point prevalence for each long COVID symptom, which is particularly helpful in understanding the overall and symptom-specific burden of long COVID among Japanese adults. Our study also has several limitations. We conducted the survey via Internet so as to recruit a diverse range of participants. However, it is possible that some groups, such as older adults, were uncomfortable with being surveyed online. Thus, caution should be exercised in generalizing the results obtained in this study. Additionally, although participants were asked to report information each month, there were many cases in which no data were reported. Although trends similar to those of previous studies were observed, there is a possibility of overestimation or underestimation owing to censored cases. It is also known that COVID-19 symptoms vary widely depending on the mutant strain, which may have an effect on long COVID symptoms; however, information regarding predominant mutant strains was not collected in this study. Furthermore, because we used a previous CDC definition for long COVID (symptoms persisting\u0026thinsp;\u0026ge;\u0026thinsp;4 weeks), some patients who were classified as having long COVID in our study may fall within the recovery period of long COVID according to the updated CDC definition (patients with symptoms beyond the 2nd quartile). Therefore, caution should be exercised when comparing the findings of this study with those of future studies. However, our estimates of long COVID in the 2nd and 3rd quartiles as well as the 4th quartile will be compatible with the new CDC definition of long COVID.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study provides a comprehensive assessment of long COVID symptoms, with up to 1 year of follow-up, as well as related determinants among Japanese adults. More than half of study participants experienced at least one long COVID symptom, even in the 4th quartile of the study period. Fatigue, runny nose, headache, and cough were the most frequently reported long COVID symptoms. Younger age, female sex, and having pre-existing medical conditions were associated with a greater risk of developing long COVID. Pre-existing psychological disorders were associated with long COVID symptoms of depression, anxiety, and insomnia; pre-existing lung diseases was associated with shortness of breath; and pre-existing seasonal allergies was associated with long COVID symptoms of rash, runny nose, and nasal congestion. By adding evidence regarding the symptoms of long COVID among Japanese adults, our findings will help raise awareness regarding the need for long-term monitoring and surveillance systems for patients with COVID-19 in Japan.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOVID-19\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eCoronavirus Disease\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCDC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCenters for Disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eControl and Prevention\u003c/div\u003e\u003cdiv class=\"Description\"\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\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\"\u003eHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHazard Ratio\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\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the ethics committee of Japan Institute for Health Security (approval number: NCGM-S-004318-02), in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all the participants.\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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (KAKENHI 23K27865).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eST, SS and YA: conceived the study, design of the work, analysis, interpretation of data, writing of the work or review. HI, NM, NO: conceived the study, interpretation of data, writing of the work or review. SI, AO: acquisition of data, writing of the work or review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Analisa Avila, MPH, ELS, of Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eParotto M, et al. Post-acute sequelae of COVID-19: understanding and addressing the burden of multisystem manifestations. Lancet Respiratory Med. 2023;11(8):739\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuedj E, et al. 18F-FDG brain PET hypometabolism in patients with long COVID. Eur J Nucl Med Mol Imaging. 2021;48(9):2823\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLopes-Pacheco M, et al. Pathogenesis of Multiple Organ Injury in COVID-19 and Potential Therapeutic Strategies. Frontiers in Physiology; 2021. pp. 12\u0026ndash;2021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrevention C. f.D.C.a. \u003cem\u003eClinical overview of Long-COVID\u003c/em\u003e. 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\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 medRxiv, 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMalesevic S, et al. Impaired health-related quality of life in long-COVID syndrome after mild to moderate COVID-19. Sci Rep. 2023;13(1):7717.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eImoto W, et al. A cross-sectional, multicenter survey of the prevalence and risk factors for Long COVID. Sci Rep. 2022;12(1):22413.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCastanares-Zapatero D, et al. Pathophysiology and mechanism of long COVID: a comprehensive review. Ann Med. 2022;54(1):1473\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. WHO COVID-19 dashboard. WHO; 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurata F, et al. Acute and delayed psychiatric sequelae among patients hospitalised with COVID-19: a cohort study using LIFE study data. Gen Psychiatr. 2022;35(3):e100802.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOmori T, et al. Specific sequelae symptoms of COVID-19 of Omicron variant in comparison with non-COVID-19 patients: a retrospective cohort study in Japan. J Thorac Dis. 2024;16(5):3170\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShirasu D, et al. Prognosis and sequelae of severe COVID-19 patients after 6 months of hospital discharge: A retrospective cohort study. Int J Crit Illn Inj Sci. 2022;12(4):211\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOtsuka Y et al. Clinical characteristics of Japanese patients who visited a COVID-19 aftercare clinic for post-acute sequelae of COVID-19/long COVID. Cureus, 2021. 13(10).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSugiyama A, et al. Long COVID occurrence in COVID-19 survivors. Sci Rep. 2022;12(1):6039.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSantoro A, et al. Short and long-term trajectories of the post COVID-19 condition: Results from the EuCARE POSTCOVID study. BMC Infect Dis. 2025;25(1):625.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO\u0026rsquo;Mahoney LL, et al. The risk of Long Covid symptoms: a systematic review and meta-analysis of controlled studies. Nat Commun. 2025;16(1):4249.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOgawa W, Miyazaki S. Diagnosis criteria for obesity and obesity disease. Health Evaluation Promotion. 2015;42(2):301\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUkwishaka J, et al. Global prevalence of coronavirus disease 2019 reinfection: a systematic review and meta-analysis. BMC Public Health. 2023;23(1):778.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJangnin R, et al. Long-COVID Prevalence and Its Association with Health Outcomes in the Post-Vaccine and Antiviral-Availability Era. J Clin Med. 2024;13(5):1208.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRochmawati E, Iskandar AC, Kamilah F. Persistent symptoms among post-COVID-19 survivors: A systematic review and meta-analysis. J Clin Nurs. 2024;33(1):29\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDavis HE et al. Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. EClinicalMedicine, 2021. 38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan Q, et al. Long-Term Sequelae of COVID-19: A Systematic Review and Meta-Analysis of One-Year Follow-Up Studies on Post-COVID Symptoms. Pathogens. 2022;11(2):269.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDurstenfeld MS, et al. Factors Associated With Long COVID Symptoms in an Online Cohort Study. Open Forum Infect Dis. 2023;10(2):ofad047.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrewal JS, et al. Post-COVID dyspnea: prevalence, predictors, and outcomes in a longitudinal, prospective cohort. BMC Pulm Med. 2023;23(1):84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChoudhury NA, et al. Neurologic Manifestations of Long COVID Disproportionately Affect Young and Middle-Age Adults. Ann Neurol. 2025;97(2):369\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMueller AL, McNamara MS, Sinclair DA. Why does COVID-19 disproportionately affect older people? Aging. 2020;12(10):9959\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMansell V, et al. Long COVID and older people. Lancet Healthy Longev. 2022;3(12):e849\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNotarte KI et al. \u003cem\u003eAge, Sex and Previous Comorbidities as Risk Factors Not Associated with SARS-CoV-2 Infection for Long COVID-19: A Systematic Review and Meta-Analysis.\u003c/em\u003e J Clin Med, 2022. 11(24).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaul T, et al. Risk of long covid in patients with pre-existing chronic respiratory diseases: a systematic review and meta-analysis. BMJ Open Respiratory Res. 2025;12(1):e002528.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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},"keywords":"Long COVID, Post-COVID conditions, SARS-CoV-2, Japan, psychological disorder, vaccination.","lastPublishedDoi":"10.21203/rs.3.rs-6910225/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6910225/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a comprehensive assessment of long COVID and its associated risk factors, including pre-existing medical conditions, in a Japanese adult population. We aimed to inform public health recommendations, surveillance, and health care delivery for all patients with a history of COVID-19 infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used prospective cohort data from the CARE Japan Study (https://japan.helpstopcovid19.com/), which collects data on participants’ demographics at baseline and information on diagnostic test results for COVID-19, vaccination status, and post-COVID-19 symptoms at baseline and at monthly follow-up surveys for up to 1 year after COVID-19 infection. The primary outcome was long COVID, defined as the presence of ≥ 1 symptom for ≥ 4 weeks after acute COVID-19 infection. We used Kaplan–Meier survival analysis to determine the median survival time of long COVID and a multivariable Cox proportional hazards model to yield hazard ratios (HRs) with 95% confidence interval (CIs) so as to explore the determinants of long COVID.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final analysis included 1497 respondents, 1149 patients with long COVID and 348 without long COVID. The period prevalence of long COVID was 63.9%, 68.2%, and 56.8% during the 1st quartile, 2nd and 3rd quartiles, and 4th quartile, respectively. During the 2nd and 3rd quartiles, runny nose (32%) and fatigue (31%) had the highest period prevalence, followed by headache (24%) and nasal congestion (21%). Compared with female participants, their male counterparts were marginally less likely (HR: 0.87, 95% CI: 0.77–0.99) to report long COVID symptoms. Age showed an inverse association with long COVID. Participants with pre-existing psychological disorders (HR: 1.45, 95% CI: 1.24–1.69), lung diseases (HR: 1.46, 95% CI: 1.10–1.92), or seasonal allergies (HR: 1.32, 95% CI: 1.17–1.49) had considerably higher risks of developing long COVID, compared with participants who had no pre-existing medical conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMore than half of participants experienced long COVID symptoms, even in the 4th quartile of the study period. Fatigue, runny nose, headache, and cough were the most frequently reported long COVID symptoms. Younger age, female sex, and having pre-existing medical conditions were associated with a greater risk of developing long COVID symptoms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number: \u003c/strong\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"Burden of long COVID and associated risk factors among Japanese adults: findings of the CARE Japan Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 16:18:39","doi":"10.21203/rs.3.rs-6910225/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":"0c441f6c-d6d3-4cf0-a7ec-9672bfa9ab4f","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T08:53:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-22 16:18:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6910225","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6910225","identity":"rs-6910225","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}