Socioeconomic inequalities in health behaviours pre- and post-COVID-19 among Japanese adolescents: a three-wave repeated cross-sectional survey

Socioeconomic inequalities in health behaviours pre- and post-COVID-19 among Japanese adolescents: a three-wave repeated cross-sectional survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Socioeconomic inequalities in health behaviours pre- and post-COVID-19 among Japanese adolescents: a three-wave repeated cross-sectional survey Akira Kyan, Minoru Takakura This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5854454/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Changes in socioeconomic inequalities in health behaviours following the COVID-19 pandemic remain known. In this study, we examined changes in socioeconomic inequalities in adolescent health behaviours—including physical activity (PA), screen time (ST), sleep duration, breakfast consumption, and bowel movement frequency—before and after the pandemic. Methods: This three-wave repeated cross-sectional study utilised data from the 2019, 2021, and 2023 National Sports-Life Survey of Children and Young People in Japan, and analysed 766, 725, and 604 participants aged 12–18 years, respectively. Favourable health behaviours were defined as moderate-to-vigorous PA of ≥ 60 min/day, ST < 2 h/day, sleep duration of 8–10 h, daily breakfast consumption, and bowel movements at least every 3 days. Absolute and relative socioeconomic inequalities were assessed using the slope and relative indices of inequality across equivalent household income levels. Results: Significant quadratic trends showed narrowing inequalities in breakfast consumption by 2021 and renewed inequalities in 2023. Socioeconomic inequalities in breakfast consumption resurged by 2023, with lower prevalence in lower income groups. No inequalities and trends in inequalities were observed in sleep duration or bowel movements. PA declined for lower-income groups, while ST worsened over time. Discussion: Socioeconomic disparities in breakfast consumption resurged among Japanese adolescents post-COVID-19, with declines in the lower income groups and improvements in the higher income groups. The overall adherence to PA and ST guidelines showed worsening trends, and socioeconomic inequalities in PA showed minimal variation. Sustained public health initiatives are essential to address these disparities. Socioeconomic inequalities COVID-19 Health behaviours Physical activity Screen time Sleep duration Breakfast consumption Bowel movement frequency Background More than 770 million confirmed cases of COVID-19 and seven million deaths had been reported to the World Health Organization globally by December 2024 [1], resulting in an unprecedented global disruption. The pandemic significantly changed lifestyles and health behaviours, including those of youth. Despite the improvement in certain health behaviours owing to societal changes triggered by the pandemic, such as better diet quality [2,3], concerns have been raised regarding the deterioration of other behaviours including physical activity (PA) [4], screen time (ST) [5], and sleep patterns and quality [6,7]. Among these population-level challenges, the limited evidence about the differential impact of the pandemic on vulnerable subgroups, particularly those defined by socioeconomic status, is particularly concerning [4,6–8]. These concerns stem from the well-documented inequalities in fundamental health behaviours among adolescents, including PA [9], ST [10], sleep patterns [11], bowel movement frequency [12], and food intake [13], which have been known to vary with family or neighbourhood economic status even prior to the onset of the COVID-19 pandemic. Indeed, some evidence suggests that socioeconomic inequalities have widened following the pandemic, as demonstrated by lower levels of PA among Scottish adolescents [14] and worse sleep patterns among U.S. adolescents from low-income households [6]. Even in Japan, where COVID-19 policy restrictions were globally regarded as relatively lenient [15], infection control measures—such as restrictions on restaurant operations, cancellation of large-scale events, and limitations on inter-prefectural travel [16]—led to significant economic inequalities across occupations. For example, the service industry experienced a substantial decline in revenue, resulting in widespread unemployment and bankruptcies, whereas the information and communication sector, including online businesses, witnessed increased revenues [16]. Data from representative Japanese adults indicated that the socioeconomic inequalities in PA have become more pronounced during the COVID-19 pandemic [17]. These economic disparities, combined with behavioural restrictions such as social distancing among adolescents, may have exacerbated health inequalities. A previous study using national survey data showed widening inequalities in PA and narrowing inequalities in breakfast consumption among Japanese adolescents [18]. In response to global trends, declining number of severe cases, and lower incidence of new infections, Japan eased the COVID-19-related legal restrictions on May 8th, 2023. With the return to pre-pandemic norms, examining evolution of socioeconomic inequalities in health behaviours is crucial for evaluating the effectiveness of current policies and shaping future directions. Therefore, in this study, we aimed to elucidate trends in socioeconomic inequalities in health behaviours before and after the COVID-19 pandemic among Japanese adolescents. Methods Data source This study employed data from the 2019, 2021, and 2023 National Sports-Life Survey of Children and Young People, conducted by the Sasakawa Sports Foundation [19]. Each year, the study sample was selected using a two-stage stratified random sampling method from 225 locations, which were proportionally distributed by district/city size based on population data from the Basic Resident Register as of January 1 of the preceding year. The primary sampling unit was based on the study area established during the 2015 census. The sample size allocated to each location was in the range of 10–19 participants. Survey points were selected using the probability proportional sampling method, which calculated the sampling interval for strata where two or more survey points were assigned. The survey included a total of 3,000 individuals. The study participants were informed in advance that the data obtained through the survey would be used for academic research purposes and provided their informed consent. Data were collected through self-administered questionnaires that were completed by adolescents and their parents/guardians between June and July of each survey year. Detailed descriptions of the survey methodology are available on the Sasakawa Sports Foundation website [18,19]. This study focused on data from respondents aged 12–21 years. The numbers of respondents were as follows: 1,675 (response rate: 55.8%) in 2019, 1,663 (55.4%) in 2021, and 1,495 (49.8%) in 2023. For analysis purposes, only participants aged 12–18 years were included, excluding 18-year-olds who were not enrolled in a high school. In the Japanese educational system, most high school students are aged 15–18 years, and the majority of 17- and 18-year-olds are in the third grade, sharing similar school routines. Consequently, 18-year-olds attending high school were included in the analysis, although the age range for assessing PA, ST, and sleep recommendations was limited to 17 years. The numbers of participants meeting the inclusion criteria for age and school enrolment were 1,076 (506 girls) in 2019, 1,025 (508 girls) in 2021, and 898 (448 girls) in 2023. After excluding the participants with missing data, the final sample included 766 participants (407 girls) in 2019, 725 participants (360 girls) in 2021, and 604 participants (305 girls) in 2023. The Institutional Review Board of the University of the Ryukyus determined that the research is eligible for exemption, as the identities of research subjects could not be ascertained from the data provided. Measures Physical activity PA was assessed using the Japanese version of the PA questionnaire in the Health Behaviour in School-aged Children survey [20]. This questionnaire is widely used for monitoring moderate-to-vigorous PA in adolescents [21]. PA was defined as any activity that increased heart rate and induced breathlessness for a period, including sports, school activities, playing with friends, and walking to school. The participants were asked: “Over the past 7 days, on how many days were you physically active for a total of at least 60 min per day?” Based on the WHO guidelines on PA and sedentary behaviour [22], participants were classified as either active or inactive, depending on whether they achieved 60 min of moderate-to-vigorous PA daily across 7 days. Screen time ST was measured by assessing recreational TV/DVD viewing time and computer/game/smartphone usage separately for weekdays and weekends. The participants were asked: “For how many hours per day do you watch TV or DVDs or use computers, video games (including TV, computer, and cellular device games), or smartphones outside of school and/or work?” Response options included: “less than 30 min/day,” “30 min to 1 h/day,” “1–2 h/day,” “2–3 h/day,” “3–4 h/day,” “4–5 h/day,” “more than 5 h/day,” and “I do not know.” Responses were converted to minutes using the midpoint method, with “I do not know” categorised as missing data. The average daily ST was calculated using the following formula: ([minutes of ST on weekdays×5] + [minutes of ST on weekends×2])/7. The participants were classified using a cut-off of 2 h of recreational ST, based on the WHO guidelines [22]. Although to the best of our knowledge, there are no standardised ST questionnaires, this method is aligned with those reported in previous studies, which used time spent on TV, smartphones, tablets, and PCs as ST indicators [23]. Sleep duration Sleep duration was calculated based on self-reported bedtimes and wake-up times for weekdays and weekends. The participants provided typical sleep and wake times for each. The average daily sleep duration was calculated as: ([sleep duration on weekdays×5] + [sleep duration on weekends×2])/7. The participants were classified based on whether their sleep duration fell within the National Sleep Foundation’s recommended range of 8–10 h per night [24]. Breakfast consumption Breakfast frequency was determined by asking: “How often do you have breakfast per week?” Responses included: “almost every day,” “4–5 days,” “2–3 days,” and “very few.” Participants were dichotomised into “almost every day” or “others,” following the recommendations by the Japan’s Ministry of Agriculture, Forestry, and Fisheries [25]. Bowel movement frequency Bowel movement frequency was assessed using the question: “How often do you have a bowel movement?” Response options included: “almost every day,” “once every 2 days,” “once every 3 days,” “less than once every 3 days,” and “irregularly.” The participants were categorised into “almost every day to once every 3 days” or “less than once every 3 days and irregular,” based on the definition of constipation [12]. Household income Household income was reported by parents/guardians of the participants using 11 options ranging from “no income” to “10 million yen or more.” Responses of “I do not know” were treated as missing data. The midpoint of each range was used to calculate equivalent household income, which was adjusted by dividing the total household income by the square root of the number of household members [26]. The income levels were categorized into three groups (I, II, and others) based on one-half of the median equivalent household income in each survey year, with Level I representing the most economically disadvantaged group [27]. One million yen corresponded to approximately 14,000 US dollars at the survey time. Covariates Covariates included place of residence, family structure, sex, age, sports participation, self-rated health, and preference for PA, which were considered as potential confounders [9,28]. Place of residence was categorised by the population size, with a threshold of 100,000 residents. Family structure was coded as “lived with both parents” or “other.” Sports participation was assessed by asking if the participants engaged in extracurricular exercise activities in school, local sports clubs, or private settings. Self-rated health was dichotomised as “good” or “poor,” and preference for PA was categorised as “liked” or “disliked.” Statistical analyses The prevalence of health behaviour was calculated for each income level and survey year. Temporal trends in the prevalence across income levels were analysed using the Cochran–Armitage test for trend. Socioeconomic inequalities in health behaviours between low- and high-income groups were evaluated using both absolute and relative measures, with 95% confidence intervals (CIs) estimated for each income level in each survey year. For absolute measures, the slope index of inequality (SII) [29–31] was calculated using generalised linear models with a binomial distribution and an identity link function. The coefficient obtained from these models represented the estimate of absolute inequality. When convergence of the binomial model was not achieved, a generalised linear model with a normal distribution and an identity link function was applied [30]. For relative measures, the relative index of inequality (RII) [29–31] was calculated using generalised linear models with a binomial distribution and a log link function. The exponentiated coefficient from these models provided an estimate of relative inequality. Both SII and RII were calculated using ridit scores for income levels as the independent variables. These indices represent summary measures of inequality, quantifying the inequality in health behaviour between the theoretical lowest and highest income groups while accounting for the cumulative income distribution [31]. SII reflects the difference in the probability of health behaviour occurrence between the two extremes of the socioeconomic spectrum. RII, on the other hand, quantifies the ratio of these probabilities. To assess whether socioeconomic inequalities had recovered by 2023, a statistical model was constructed under the assumption that time trends followed a quadratic pattern. A linear trend was first examined to identify consistent increases or decreases in socioeconomic inequalities over time. Quadratic trends were then assessed to capture any directional changes, such as levelling off or reversing. The model was specified as follows: Y = β 0 X intercept + β 1 X ridit_score + β 2 X survey year + β 3 X survey year 2 + β 4 X (ridit_score × survey year 2 ) + β n X covariates … + ϵ , where Y represents the outcome variable and ϵ denotes the error term. Survey year was treated as a continuous variable, coded as 1 for 2019, 2 for 2021, and 3 for 2023 [27]. The Wald test assessed the significance of the interaction terms. Relevant covariates were included in all models to control for potential confounders. The interpretation of trends followed guidelines from the Centres for Disease Control and Prevention [32] as follows. A significant linear trend indicated consistent increases or decreases over time. A significant quadratic trend alone suggested no overall linear change, but directional shifts in specific segments, such as levelling off or reversing. A combination of significant linear and quadratic trends indicated an overall linear pattern with directional changes in certain periods, such as levelling off or reversing. Results Table 1 shows the distribution of the participants by sociodemographic characteristics across survey years. Significant associations were observed between survey year and compliance with PA and ST guidelines ( p < 0.001). PA prevalence was significantly lower in 2021, whereas ST decreased in 2021 and 2023. Missing data proportions for each variable are detailed in the supplementary Table (see Additional file 1) and remained consistent across surveys. Household income measure had the highest proportion of missing data (22.2–25.8%), as "don't know" responses were treated as missing. Among the participants who met the age inclusion criteria, no significant differences were observed in demographic factors such as age, sex, or residence area between individuals included in the analysis and those excluded from it. For the variables of interest, differences in income and PA were only present in 2019. Individuals with lower income and non-compliance with PA recommendations were more likely to be excluded. <> Table 2 summarises the prevalence of each health behaviour by income levels and survey years. Declining trends in PA were observed among participants in poverty levels I and II. Although income-related inequalities in PA widened in 2021, they were no longer significant by 2023. For ST, declining trends were observed in poverty level II and others. By 2023, the gap between poverty level II and others was highly significant. Breakfast consumption showed no consistent temporal trend by income level. However, in 2023, the prevalence was significantly lower in poverty level II and higher among others. The proportion of poverty level II was lower than that of poverty level I in 2023. These differences by socioeconomic levels were not observed for sleep duration and bowel frequency. <> Table 3 shows SII and RII for each health behaviour across survey years. A significant linear and quadratic trend was observed only for breakfast consumption in both SII and RII. The linear trends for breakfast consumption were negative (SII: coefficient = −0.870, p = 0.001; RII: coefficient = −6.234, p = 0.001), whereas the quadratic trends were positive (SII: coefficient = 0.216, p = 0.002; RII: coefficient = 1.519, p = 0.002). Between 2019 and 2021, the SII for breakfast consumption decreased from 21.63% (95% CI: 11.3–32.0) to −0.63% (95% CI: −11.5–10.3), and became non-significant. However, the SII became significant again in 2023, with an observed increase to 16.16% (95% CI: 8.6–31.9). Similarly, the RII values became insignificant between 2019 and 2021, decreasing from 5.09 (95% CI: 2.39–10.84) to −0.63 (95% CI: −10.4–10.2), but regained significance in 2023 at 3.7 (95% CI: 1.7–8.0). This trend remained consistent after adjusting for covariates. For PA, the linear and quadratic trends were not significant, but between 2019 and 2021, the SII increased from 1.23% (95% CI −9.61–12.07) to 12.31% (95% CI 4.4–20.2) and RII increased from 1.08 (95% CI 0.6–2.1) to 3.15 (95% CI 1.3–7.6). Although both the SII and RII for 2023 were relatively high (SII: 9.79%, 95% CI: 0.5–19.1; RII: 2.18, 95% CI: 0.5–4.8), they did not reach statistical significance. The SII and RII for ST were significant in 2019 at 15.33% (95% CI: 2.64–28.03) and 1.96 (95% CI: 1.1–3.5), respectively, in the crude model. However, these indices became non-significant in 2021, with an observed decline to 0.51% (95% CI: −11.6–12.6) for SII and 1.03 (95% CI: 0.6–1.9) for RII. In 2023, both indices regained statistical significance (SII: 15.07%, 95% CI: 1.7–28.4; RII: 1.96, 95% CI: 1.1–3.5). Nevertheless, in the adjusted model, neither SII nor RII for ST reached statistical significance. No significant income-related inequalities or temporal trends were observed for sleep duration or bowel movement frequency across the survey years, suggesting relative stability in these behaviours. <> Discussion To the best of our knowledge, this is the first study to investigate time trends in socioeconomic inequalities in fundamental health behaviours among Japanese adolescents before and after the COVID-19 pandemic. Our findings, which indicated a significant quadratic trend in breakfast consumption, suggest a resurgence in socioeconomic inequality. The findings of the linear trend analysis indicated a negative direction, suggesting a reduction in inequalities over the three waves. However, according to the year-on-year changes in prevalence for each income group (Table 2 ) as well as SII and RII values for each survey year (Table 3 ), it is certain that the inequality has resurged after the COVID-19 pandemic. In particular, the decline in daily breakfast consumption among poverty level II individuals, coupled with its improvement in higher-income groups, appears to be a key factor underlying these results. A systematic review of changes in dietary patterns among youth during the COVID-19 pandemic reported mixed results regarding breakfast consumption habits, with six studies indicating improvement and five studies reporting deterioration [2]. Our findings suggest that heterogeneity by the socioeconomic status is one of the factors amplifying the variability of the results. In Japan, lower income levels are often accompanied by longer working hours [33–35]. This is attributable to several factors, including the tendency for non-regular employees, who typically receive lower wages, to take on multiple jobs to supplement their income. Additionally, high-demand occupations, such as those in convenience stores, food service industry, and transportation, are frequently characterised by low wages despite the substantial labour they require. These occupations were economically impacted during the COVID-19 pandemic [16]. Also, class closures and staggered school attendance measures implemented as infection prevention strategies during the COVID-19 pandemic [36] likely provided adolescents with more time to have breakfast [37]. Given the resumption of economic activities and behavioural conditions to the pre-COVID-19 levels, it is plausible that breakfast consumption patterns have also reverted to previous norms. To the best of our knowledge, no other studies examined dietary intake after the COVID-19 pandemic. Although in the present study we focused solely on breakfast consumption, future research should examine whether other dietary behaviours have also changed post-pandemic [3]. Socioeconomic inequalities in PA widened between 2019 and 2021 and were sustained by 2023. However, the lack of a significant quadratic trend suggests that these inequalities have not substantially changed. During the COVID-19 pandemic, extracurricular activities, which involve approximately two-thirds of adolescents [38], were suspended, and sporting events were cancelled as part of the measures to prevent the infection spread. Given that these factors may have contributed to widening of inequalities [18], the relaxation of such behavioural restrictions could have played a role in narrowing or eliminating these inequalities. Among the guidelines for each health behaviour, the compliance with PA and ST showed an overall worsening prominence. A nationwide survey conducted by the Japan's Ministry of Education, Culture, Sports, Science and Technology revealed a reduction in exercise duration and an increase in ST among middle school students [38]. A repeated cross-sectional study that has been investigating trends in health risk behaviours among high school students in Okinawa Prefecture in Japan has also reported decreased adherence to the WHO PA guidelines and increased ST in the middle of the pandemic compared to those observed before the COVID-19 pandemic [39]. This trend aligns with previous findings from a meta-analysis that included data from Europe, North America, South America, and Asia [4,5]. It is noteworthy to highlight that in the present study, although the compliance with PA guidelines has improved for both low income groups, the ST significantly worsened. It is essential to monitor whether socioeconomic inequalities in ST will continue to widen in the future. This study has certain limitations. First, this study was cross-sectional, which precluded the examination of individual trajectories over time. Second, although the sampling method was designed to ensure the data representativeness, the proportions of eligible samples were 45.7%, 43.6%, and 40.4% of all the respondents in 2019, 2021, and 2023, respectively. However, no significant differences were observed in the demographic factors between the survey years. Additionally, the distribution of participants across districts/cities, stratified by their sizes, closely mirrored the national population structure. Third, the proportion of missing income data was relatively high. However, the missing data pattern remained consistent across the survey years, mitigating concerns about systematic bias. The higher proportion of low-income and low PA levels among those excluded from the analysis might have led to an underestimation of the actual association between income and PA in 2019. Conclusions This study highlights a resurgence of socioeconomic inequalities in breakfast consumption among the Japanese adolescents, which had temporarily narrowed during the COVID-19 pandemic. By 2023, breakfast consumption significantly declined among the lower income groups and improved among the higher income groups. Additionally, the prevalence of PA and ST showed an overall worsening during this period, with socioeconomic inequalities in PA persisting without substantial change. These findings suggest that the return to the pre-pandemic economic and behavioural conditions has contributed to the re-emergence of dietary disparities and overall deterioration in health behaviours. Sustained public health efforts are essential to address these inequalities and promote healthier lifestyles among vulnerable populations. Abbreviations CI Confidence Interval PA Physical Activity RII Relative Index of Inequality SII Slope Index of Inequality ST Screen Time Declarations Ethics approval and consent to participate The Institutional Review Board of the University of the Ryukyus determined that the research is eligible for exemption, as the identities of research subjects could not be ascertained from the data provided. Consent for publication Not applicable. Availability of data and materials The data used in this study are available from the Sasakawa Sports Foundation. Researchers can access the data by submitting a web-based application through the foundation's official website (https://www.ssf.or.jp/thinktank/sports_life/application/index.html). Competing interests The authors declare that they have no competing interests . Funding This study was supported by the Grants-in-Aid for Scientific Research (JSPS KAKENHI Grant Numbers 24K00395, 24K13509, 23H04441, and 20K10473) from the Japan Society for the Promotion of Science. Authors’ contributions All authors contributed to the concept or design of the study and acquisition, analysis, or interpretation of data for the work. A.K. drafted the manuscript. M.T. critically revised the manuscript. All authors gave final approval and agreed to be accountable for all aspects of the work, thus ensuring integrity and accuracy. Acknowledgements We would like to thank Editage (www.editage.com) for English language editing. References World Health Organization. WHO Coronavirus (COVID-19) Dashboard . WHO Coronavirus (COVID-19) Dashboard With Vaccination Data. 2020. Available from: https://covid19.who.int/ Woods N, Seabrook JA, Schaafsma H, Burke S, Tucker T, Gilliland J. Dietary changes of youth during the COVID-19 pandemic: a systematic review. J Nutr. 2024;154(4):1376–403. Na X, Zhang J, Xie C, Zeng H, Wu L, Fan D, et al. The impact of the COVID-19 pandemic on dietary behaviors among school-age children, adolescents, and young adults: a systematic review and meta-analysis. Nutr Rev. 2024; Available from: https://pubmed.ncbi.nlm.nih.gov/38820346/ Neville RD, Lakes KD, Hopkins WG, Tarantino G, Draper CE, Beck R, et al. Global changes in child and adolescent physical activity during the COVID-19 pandemic: a systematic review and meta-analysis. JAMA Pediatr. 2022;176(9):886–94. Madigan S, Eirich R, Pador P, McArthur BA, Neville RD. Assessment of changes in child and adolescent screen time during the COVID-19 pandemic: a systematic review and meta-analysis: A systematic review and meta-analysis. JAMA Pediatr. 2022;176(12):1188–98. Lucchini M, Bekelman TA, Li M, Knapp EA, Dong Y, Ballard S, et al. Impact of the COVID-19 pandemic on children’s sleep habits: an ECHO study. Pediatr Res. 2023;93(3):586–94. Wang ML, Gago CM. Shifts in child health behaviors and obesity after COVID-19. JAMA Pediatr. 2024;178(5):427–8. Rocha S, Fuligni A. The impact of the COVID-19 pandemic on adolescent sleep behavior. Curr Opin Psychol. 2023;52(101648):101648. O’Donoghue G, Kennedy A, Puggina A, Aleksovska K, Buck C, Burns C, et al. Socio-economic determinants of physical activity across the life course: a “DEterminants of DIet and Physical ACtivity” (DEDIPAC) umbrella literature review. PLoS One. 2018;13(1):e0190737–e0190737. Mielke GI, Brown WJ, Nunes BP, Silva ICM, Hallal PC. Socioeconomic correlates of sedentary behavior in adolescents: systematic review and meta-analysis. Sports Med. 2017;47(1):61–75. Etindele Sosso FA, Holmes SD, Weinstein AA. Influence of socioeconomic status on objective sleep measurement: A systematic review and meta-analysis of actigraphy studies. Sleep Health. 2021;7(4):417–28. Mugie SM, Benninga MA, Di Lorenzo C. Epidemiology of constipation in children and adults: A systematic review. Best Pract Res Clin Gastroenterol. 2011;25(1):3–18. Cislak A, Safron M, Pratt M, Gaspar T, Luszczynska A. Family-related predictors of body weight and weight-related behaviours among children and adolescents: a systematic umbrella review. Child Care Health Dev. 2012;38(3):321–31. Bardid F, Tomaz SA, Johnstone A, Robertson J, Craig LCA, Reilly JJ. Results from Scotland’s 2021 report card on physical activity and Health for children and youth: grades, secular trends, and socio-economic inequalities. J Exerc Sci Fit. 2022;20:317–22. Blavatnik School of Government, University of Oxford. COVID-19 government response tracker. Available from: https://www.bsg.ox.ac.uk/research/covid-19-government-response-tracker Statistics Bureau of Japan. Summary of the Results of Monthly Survey on Service Industries. 2018. Available from: https://www.stat.go.jp/english/data/mssi/kekka/index.html Kyan A, Takakura M. Socio-economic inequalities in physical activity among Japanese adults during the COVID-19 pandemic. Public Health. 2022;207:7–13. Kyan A, Takakura M. Impact of the COVID-19 pandemic on the socioeconomic inequality of health behavior among Japanese adolescents: a 2-year repeated cross-sectional survey. J Phys Act Health. 2023;20(6):538–46. Sasakawa Sports Foundation. Publications. 2023. Available from: https://www.ssf.or.jp/en/publications/ Tanaka C, Kyan A, Takakura M, Olds T, Schranz N, Tanaka S. Validation of the physical activity questions in the World Health Organization Health Behavior in School-Aged Children survey using accelerometer data in Japanese children and adolescents. J Phys Act Health. 2021;18(2):151–6. Inchley J, Currie D, Cosma A, Samdal O, Editors. Health Behaviour in School-aged Children (HBSC) study protocol: background, methodology and mandatory items for the 2017/18 survey. St Andrews: CAHRU; 2018. World Health Organization. WHO guidelines on physical activity and sedentary behaviour. Geneva: World Health Organization; 2020. Lubans DR, Hesketh K, Cliff DP, Barnett LM, Salmon J, Dollman J, et al. A systematic review of the validity and reliability of sedentary behaviour measures used with children and adolescents. Obes Rev. 2011;12(10):781–99. Hirshkowitz M, Whiton K, Albert SM, Alessi C, Bruni O, DonCarlos L, et al. National Sleep Foundation’s sleep time duration recommendations: methodology and results summary . Sleep Health. 2015. Available from: http://dx.doi.org/10.1016/J.SLEH.2014.12.010 Ministry of Agriculture FAF. What Are the Benefits of Shokuiku (Food and Nutrition Education)?. 2018. Available from: https://www.maff.go.jp/j/syokuiku/evidence/index.html Yamada M, Sekine M, Tatsuse T. Psychological stress, family environment, and constipation in Japanese children: the Toyama Birth Cohort study. Journal of Epidemiology. 2019;29(6):220–6. Ernstsen L, Strand BH, Nilsen SM, Espnes GA, Krokstad S. Trends in absolute and relative educational inequalities in four modifiable ischaemic heart disease risk factors: repeated cross-sectional surveys from the Nord-Trøndelag Health Study (HUNT) 1984-2008. BMC Public Health. 2012;12(1):1–10. Mackenbach JP, Kunst AE. Measuring the magnitude of socio-economic inequalities in health: an overview of available measures illustrated with two examples from Europe. Soc Sci Med. 1997;44(6):757–71. Regidor E. Measures of health inequalities: part 2. Journal of Epidemiology & Community Health. 2004 ;58(11):900–3. Naimi AI, Whitcomb BW. Estimating risk ratios and risk differences using regression. Am J Epidemiol. 2020;189(6):508–10. Wachtler B, Hoebel J, Lampert T. Trends in socioeconomic inequalities in self-rated health in Germany: a time-trend analysis of repeated cross-sectional health surveys between 2003 and 2012. BMJ Open. 2019;9(9):e030216–e030216. Centers for Disease Control and Prevention. Interpretation of YRBS Trend Data . Centers for Disease Control and Prevention; 2024. Available from: https://www.cdc.gov/healthyyouth/data/yrbs/pdf/2021/2021_yrbs_trend_interpretation_508.pdf Ikeda N, Saito E, Kondo N, Inoue M, Ikeda S, Satoh T, et al. What has made the population of Japan healthy? The Lancet. 2011;378(9796):1094–105. Kagamimori S, Gaina A, Nasermoaddeli A. Socioeconomic status and health in the Japanese population. Soc Sci Med. 2009;68(12):2152–60. Kitao S, Yamada T. Inequality dynamics in Japan, 1981-2021 : Economic and social research institute. Economic and Social Research Institute Discussion Paper Series. 2024;392. Available from: https://www.esri.cao.go.jp/en/esri/archive/e_dis/2024/e_dis392-e.html The Cabinet Agency for Infectious Disease Crisis Management. Analysis of the number of school closures and school holidays . The Cabinet Agency for Infectious Disease Crisis Management. Available from: https://www.caicm.go.jp/action/survey/covid19-ai.jp/ja-jp/presentation/2021_rq3_countermeasures_simulation/articles/article268/index.html Hearst MO, Shanafelt A, Wang Q, Leduc R, Nanney MS. Barriers, benefits, and behaviors related to breakfast consumption among rural adolescents. J Sch Health. 2016;86(3):187–94. Japan sports agency. National survey of the physical strength, exercise ability and exercise habits. 2021. Available from: https://www.mext.go.jp/sports/b_menu/toukei/kodomo/zencyo/1368222.htm Takakura M, Miyagi M, Kyan A. Changes in the prevalence of health-risk behaviors among Japanese adolescents before and during the COVID-19 pandemic: 2002-2021. Sch Health Rev. 2023;19:14–25. Tables Tables 1 to 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Appendixmissing.docx Tables.docx 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5854454","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":405981708,"identity":"305ea98b-f8d3-41d8-97ac-887f5a1f17d3","order_by":0,"name":"Akira Kyan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYDACHhBRAcQHIHwDqDgbAS1nSNbC2IapBTfg7zlj+Llw3jY5vgM8BsyVbYeNDW4kMH74wcCXh0uLxNkeY+mZ224bSwK1MJ5tO2wG1MIs2cPAVozTmvM8BtK8224nbjjAY/6zse2wDVALgzTQL4kNOHTIn+cx/s0753b9BpAtUC3Mv/FpMTjbYybN23A7wQCqBeQwNry2GJ45VmbNc+y24czDbAWMDefSjSXPPGyz7DHA7Re5M8mbb/PU3JbnO968gbGhzNqw73jy4Rs/Ko7hDDEGBg5oTDADMSMwAhUOMAKdZHAsAbcW9gdInD/AAIH4oQaPllEwCkbBKBhhAAA1NliYXNB+/AAAAABJRU5ErkJggg==","orcid":"","institution":"University of the Ryukyus","correspondingAuthor":true,"prefix":"","firstName":"Akira","middleName":"","lastName":"Kyan","suffix":""},{"id":405981709,"identity":"59bc0bb0-1961-41ce-8bbb-46c5f3a8ac5c","order_by":1,"name":"Minoru Takakura","email":"","orcid":"","institution":"University of the Ryukyus","correspondingAuthor":false,"prefix":"","firstName":"Minoru","middleName":"","lastName":"Takakura","suffix":""}],"badges":[],"createdAt":"2025-01-18 10:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5854454/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5854454/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78331296,"identity":"06291e73-f7ac-4126-93fb-7f96ac827219","added_by":"auto","created_at":"2025-03-12 07:10:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":522999,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5854454/v1/5bdb9e04-9087-4d0b-9763-887f0ea7fd06.pdf"},{"id":78329755,"identity":"22befc32-ee6e-4f15-ab19-1a0abac04a10","added_by":"auto","created_at":"2025-03-12 07:02:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17340,"visible":true,"origin":"","legend":"","description":"","filename":"Appendixmissing.docx","url":"https://assets-eu.researchsquare.com/files/rs-5854454/v1/04a360d6d341416cea9505d9.docx"},{"id":78329483,"identity":"f386e0a6-cd97-4c15-ae2d-50b79039ffb3","added_by":"auto","created_at":"2025-03-12 06:54:03","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":63487,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-5854454/v1/ae0a205ebf4109f2eda20f8a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eSocioeconomic inequalities in health behaviours pre- and post-COVID-19 among Japanese adolescents: a three-wave repeated cross-sectional survey\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eMore than 770\u0026nbsp;million confirmed cases of COVID-19 and seven million deaths had been reported to the World Health Organization globally by December 2024 [1], resulting in an unprecedented global disruption. The pandemic significantly changed lifestyles and health behaviours, including those of youth. Despite the improvement in certain health behaviours owing to societal changes triggered by the pandemic, such as better diet quality [2,3], concerns have been raised regarding the deterioration of other behaviours including physical activity (PA) [4], screen time (ST) [5], and sleep patterns and quality [6,7].\u003c/p\u003e \u003cp\u003eAmong these population-level challenges, the limited evidence about the differential impact of the pandemic on vulnerable subgroups, particularly those defined by socioeconomic status, is particularly concerning [4,6\u0026ndash;8]. These concerns stem from the well-documented inequalities in fundamental health behaviours among adolescents, including PA [9], ST [10], sleep patterns [11], bowel movement frequency [12], and food intake [13], which have been known to vary with family or neighbourhood economic status even prior to the onset of the COVID-19 pandemic. Indeed, some evidence suggests that socioeconomic inequalities have widened following the pandemic, as demonstrated by lower levels of PA among Scottish adolescents [14] and worse sleep patterns among U.S. adolescents from low-income households [6].\u003c/p\u003e \u003cp\u003eEven in Japan, where COVID-19 policy restrictions were globally regarded as relatively lenient [15], infection control measures\u0026mdash;such as restrictions on restaurant operations, cancellation of large-scale events, and limitations on inter-prefectural travel [16]\u0026mdash;led to significant economic inequalities across occupations. For example, the service industry experienced a substantial decline in revenue, resulting in widespread unemployment and bankruptcies, whereas the information and communication sector, including online businesses, witnessed increased revenues [16]. Data from representative Japanese adults indicated that the socioeconomic inequalities in PA have become more pronounced during the COVID-19 pandemic [17]. These economic disparities, combined with behavioural restrictions such as social distancing among adolescents, may have exacerbated health inequalities. A previous study using national survey data showed widening inequalities in PA and narrowing inequalities in breakfast consumption among Japanese adolescents [18].\u003c/p\u003e \u003cp\u003eIn response to global trends, declining number of severe cases, and lower incidence of new infections, Japan eased the COVID-19-related legal restrictions on May 8th, 2023. With the return to pre-pandemic norms, examining evolution of socioeconomic inequalities in health behaviours is crucial for evaluating the effectiveness of current policies and shaping future directions. Therefore, in this study, we aimed to elucidate trends in socioeconomic inequalities in health behaviours before and after the COVID-19 pandemic among Japanese adolescents.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThis study employed data from the 2019, 2021, and 2023 National Sports-Life Survey of Children and Young People, conducted by the Sasakawa Sports Foundation [19]. Each year, the study sample was selected using a two-stage stratified random sampling method from 225 locations, which were proportionally distributed by district/city size based on population data from the Basic Resident Register as of January 1 of the preceding year. The primary sampling unit was based on the study area established during the 2015 census. The sample size allocated to each location was in the range of 10\u0026ndash;19 participants. Survey points were selected using the probability proportional sampling method, which calculated the sampling interval for strata where two or more survey points were assigned. The survey included a total of 3,000 individuals. The study participants were informed in advance that the data obtained through the survey would be used for academic research purposes and provided their informed consent. Data were collected through self-administered questionnaires that were completed by adolescents and their parents/guardians between June and July of each survey year. Detailed descriptions of the survey methodology are available on the Sasakawa Sports Foundation website [18,19].\u003c/p\u003e \u003cp\u003eThis study focused on data from respondents aged 12\u0026ndash;21 years. The numbers of respondents were as follows: 1,675 (response rate: 55.8%) in 2019, 1,663 (55.4%) in 2021, and 1,495 (49.8%) in 2023. For analysis purposes, only participants aged 12\u0026ndash;18 years were included, excluding 18-year-olds who were not enrolled in a high school. In the Japanese educational system, most high school students are aged 15\u0026ndash;18 years, and the majority of 17- and 18-year-olds are in the third grade, sharing similar school routines. Consequently, 18-year-olds attending high school were included in the analysis, although the age range for assessing PA, ST, and sleep recommendations was limited to 17 years. The numbers of participants meeting the inclusion criteria for age and school enrolment were 1,076 (506 girls) in 2019, 1,025 (508 girls) in 2021, and 898 (448 girls) in 2023. After excluding the participants with missing data, the final sample included 766 participants (407 girls) in 2019, 725 participants (360 girls) in 2021, and 604 participants (305 girls) in 2023.\u003c/p\u003e \u003cp\u003eThe Institutional Review Board of the University of the Ryukyus determined that the research is eligible for exemption, as the identities of research subjects could not be ascertained from the data provided.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePhysical activity\u003c/h2\u003e \u003cp\u003ePA was assessed using the Japanese version of the PA questionnaire in the Health Behaviour in School-aged Children survey [20]. This questionnaire is widely used for monitoring moderate-to-vigorous PA in adolescents [21]. PA was defined as any activity that increased heart rate and induced breathlessness for a period, including sports, school activities, playing with friends, and walking to school. The participants were asked: \u0026ldquo;Over the past 7 days, on how many days were you physically active for a total of at least 60 min per day?\u0026rdquo; Based on the WHO guidelines on PA and sedentary behaviour [22], participants were classified as either active or inactive, depending on whether they achieved 60 min of moderate-to-vigorous PA daily across 7 days.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eScreen time\u003c/h3\u003e\n\u003cp\u003eST was measured by assessing recreational TV/DVD viewing time and computer/game/smartphone usage separately for weekdays and weekends. The participants were asked: \u0026ldquo;For how many hours per day do you watch TV or DVDs or use computers, video games (including TV, computer, and cellular device games), or smartphones outside of school and/or work?\u0026rdquo; Response options included: \u0026ldquo;less than 30 min/day,\u0026rdquo; \u0026ldquo;30 min to 1 h/day,\u0026rdquo; \u0026ldquo;1\u0026ndash;2 h/day,\u0026rdquo; \u0026ldquo;2\u0026ndash;3 h/day,\u0026rdquo; \u0026ldquo;3\u0026ndash;4 h/day,\u0026rdquo; \u0026ldquo;4\u0026ndash;5 h/day,\u0026rdquo; \u0026ldquo;more than 5 h/day,\u0026rdquo; and \u0026ldquo;I do not know.\u0026rdquo; Responses were converted to minutes using the midpoint method, with \u0026ldquo;I do not know\u0026rdquo; categorised as missing data. The average daily ST was calculated using the following formula:\u003c/p\u003e \u003cp\u003e([minutes of ST on weekdays\u0026times;5] + [minutes of ST on weekends\u0026times;2])/7.\u003c/p\u003e \u003cp\u003e The participants were classified using a cut-off of 2 h of recreational ST, based on the WHO guidelines [22]. Although to the best of our knowledge, there are no standardised ST questionnaires, this method is aligned with those reported in previous studies, which used time spent on TV, smartphones, tablets, and PCs as ST indicators [23].\u003c/p\u003e\n\u003ch3\u003eSleep duration\u003c/h3\u003e\n\u003cp\u003eSleep duration was calculated based on self-reported bedtimes and wake-up times for weekdays and weekends. The participants provided typical sleep and wake times for each. The average daily sleep duration was calculated as:\u003c/p\u003e \u003cp\u003e([sleep duration on weekdays\u0026times;5] + [sleep duration on weekends\u0026times;2])/7.\u003c/p\u003e \u003cp\u003eThe participants were classified based on whether their sleep duration fell within the National Sleep Foundation\u0026rsquo;s recommended range of 8\u0026ndash;10 h per night [24].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBreakfast consumption\u003c/h2\u003e \u003cp\u003eBreakfast frequency was determined by asking: \u0026ldquo;How often do you have breakfast per week?\u0026rdquo; Responses included: \u0026ldquo;almost every day,\u0026rdquo; \u0026ldquo;4\u0026ndash;5 days,\u0026rdquo; \u0026ldquo;2\u0026ndash;3 days,\u0026rdquo; and \u0026ldquo;very few.\u0026rdquo; Participants were dichotomised into \u0026ldquo;almost every day\u0026rdquo; or \u0026ldquo;others,\u0026rdquo; following the recommendations by the Japan\u0026rsquo;s Ministry of Agriculture, Forestry, and Fisheries [25].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBowel movement frequency\u003c/h3\u003e\n\u003cp\u003eBowel movement frequency was assessed using the question: \u0026ldquo;How often do you have a bowel movement?\u0026rdquo; Response options included: \u0026ldquo;almost every day,\u0026rdquo; \u0026ldquo;once every 2 days,\u0026rdquo; \u0026ldquo;once every 3 days,\u0026rdquo; \u0026ldquo;less than once every 3 days,\u0026rdquo; and \u0026ldquo;irregularly.\u0026rdquo; The participants were categorised into \u0026ldquo;almost every day to once every 3 days\u0026rdquo; or \u0026ldquo;less than once every 3 days and irregular,\u0026rdquo; based on the definition of constipation [12].\u003c/p\u003e\n\u003ch3\u003eHousehold income\u003c/h3\u003e\n\u003cp\u003eHousehold income was reported by parents/guardians of the participants using 11 options ranging from \u0026ldquo;no income\u0026rdquo; to \u0026ldquo;10\u0026nbsp;million yen or more.\u0026rdquo; Responses of \u0026ldquo;I do not know\u0026rdquo; were treated as missing data. The midpoint of each range was used to calculate equivalent household income, which was adjusted by dividing the total household income by the square root of the number of household members [26]. The income levels were categorized into three groups (I, II, and others) based on one-half of the median equivalent household income in each survey year, with Level I representing the most economically disadvantaged group [27]. One million yen corresponded to approximately 14,000 US dollars at the survey time.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eCovariates included place of residence, family structure, sex, age, sports participation, self-rated health, and preference for PA, which were considered as potential confounders [9,28]. Place of residence was categorised by the population size, with a threshold of 100,000 residents. Family structure was coded as \u0026ldquo;lived with both parents\u0026rdquo; or \u0026ldquo;other.\u0026rdquo; Sports participation was assessed by asking if the participants engaged in extracurricular exercise activities in school, local sports clubs, or private settings. Self-rated health was dichotomised as \u0026ldquo;good\u0026rdquo; or \u0026ldquo;poor,\u0026rdquo; and preference for PA was categorised as \u0026ldquo;liked\u0026rdquo; or \u0026ldquo;disliked.\u0026rdquo;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eThe prevalence of health behaviour was calculated for each income level and survey year. Temporal trends in the prevalence across income levels were analysed using the Cochran\u0026ndash;Armitage test for trend. Socioeconomic inequalities in health behaviours between low- and high-income groups were evaluated using both absolute and relative measures, with 95% confidence intervals (CIs) estimated for each income level in each survey year. For absolute measures, the slope index of inequality (SII) [29\u0026ndash;31] was calculated using generalised linear models with a binomial distribution and an identity link function. The coefficient obtained from these models represented the estimate of absolute inequality. When convergence of the binomial model was not achieved, a generalised linear model with a normal distribution and an identity link function was applied [30]. For relative measures, the relative index of inequality (RII) [29\u0026ndash;31] was calculated using generalised linear models with a binomial distribution and a log link function. The exponentiated coefficient from these models provided an estimate of relative inequality. Both SII and RII were calculated using ridit scores for income levels as the independent variables. These indices represent summary measures of inequality, quantifying the inequality in health behaviour between the theoretical lowest and highest income groups while accounting for the cumulative income distribution [31]. SII reflects the difference in the probability of health behaviour occurrence between the two extremes of the socioeconomic spectrum. RII, on the other hand, quantifies the ratio of these probabilities. To assess whether socioeconomic inequalities had recovered by 2023, a statistical model was constructed under the assumption that time trends followed a quadratic pattern. A linear trend was first examined to identify consistent increases or decreases in socioeconomic inequalities over time. Quadratic trends were then assessed to capture any directional changes, such as levelling off or reversing. The model was specified as follows:\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;β\u003csub\u003e0\u003c/sub\u003eX\u003csub\u003eintercept\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003eridit_score\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003esurvey year\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e3\u003c/sub\u003eX\u003csub\u003esurvey year\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e4\u003c/sub\u003eX\u003csub\u003e(ridit_score \u0026times; survey year\u003c/sub\u003e \u003csup\u003e2\u003c/sup\u003e\u003csub\u003e)\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003en\u003c/sub\u003eX\u003csub\u003ecovariates\u003c/sub\u003e \u0026hellip; + ϵ ,\u003c/p\u003e \u003cp\u003ewhere Y represents the outcome variable and ϵ denotes the error term. Survey year was treated as a continuous variable, coded as 1 for 2019, 2 for 2021, and 3 for 2023 [27]. The Wald test assessed the significance of the interaction terms. Relevant covariates were included in all models to control for potential confounders. The interpretation of trends followed guidelines from the Centres for Disease Control and Prevention [32] as follows. A significant linear trend indicated consistent increases or decreases over time. A significant quadratic trend alone suggested no overall linear change, but directional shifts in specific segments, such as levelling off or reversing. A combination of significant linear and quadratic trends indicated an overall linear pattern with directional changes in certain periods, such as levelling off or reversing.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable 1 shows the distribution of the participants by sociodemographic characteristics across survey years. Significant associations were observed between survey year and compliance with PA and ST guidelines (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). PA prevalence was significantly lower in 2021, whereas ST decreased in 2021 and 2023. Missing data proportions for each variable are detailed in the supplementary Table (see Additional file 1) and remained consistent across surveys. Household income measure had the highest proportion of missing data (22.2\u0026ndash;25.8%), as \u0026quot;don\u0026apos;t know\u0026quot; responses were treated as missing. Among the participants who met the age inclusion criteria, no significant differences were observed in demographic factors such as age, sex, or residence area between individuals included in the analysis and those excluded from it. For the variables of interest, differences in income and PA were only present in 2019. Individuals with lower income and non-compliance with PA recommendations were more likely to be excluded.\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026lt;Table 1\u0026gt;\u0026gt;\u003c/p\u003e\n\u003cp\u003eTable 2 summarises the prevalence of each health behaviour by income levels and survey years. Declining trends in PA were observed among participants in poverty levels I and II. Although income-related inequalities in PA widened in 2021, they were no longer significant by 2023. For ST, declining trends were observed in poverty level II and others. By 2023, the gap between poverty level II and others was highly significant. Breakfast consumption showed no consistent temporal trend by income level. However, in 2023, the prevalence was significantly lower in poverty level II and higher among others. The proportion of poverty level II was lower than that of poverty level I in 2023. These differences by socioeconomic levels were not observed for sleep duration and bowel frequency.\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026lt;Table 2\u0026gt;\u0026gt;\u003c/p\u003e\n\u003cp\u003eTable 3 shows SII and RII for each health behaviour across survey years. A significant linear and quadratic trend was observed only for breakfast consumption in both SII and RII. The linear trends for breakfast consumption were negative (SII: coefficient =\u0026nbsp;\u0026minus;0.870, \u003cem\u003ep\u003c/em\u003e = 0.001; RII: coefficient =\u0026nbsp;\u0026minus;6.234, \u003cem\u003ep\u003c/em\u003e = 0.001), whereas the quadratic trends were positive (SII: coefficient = 0.216, \u003cem\u003ep\u003c/em\u003e = 0.002; RII: coefficient = 1.519, \u003cem\u003ep\u003c/em\u003e = 0.002). Between 2019 and 2021, the SII for breakfast consumption decreased from 21.63% (95% CI: 11.3\u0026ndash;32.0) to\u0026nbsp;\u0026minus;0.63% (95% CI:\u0026nbsp;\u0026minus;11.5\u0026ndash;10.3), and became non-significant. However, the SII became significant again in 2023, with an observed increase to 16.16% (95% CI: 8.6\u0026ndash;31.9). Similarly, the RII values became insignificant between 2019 and 2021, decreasing from 5.09 (95% CI: 2.39\u0026ndash;10.84) to\u0026nbsp;\u0026minus;0.63 (95% CI:\u0026nbsp;\u0026minus;10.4\u0026ndash;10.2), but regained significance in 2023 at 3.7 (95% CI: 1.7\u0026ndash;8.0). This trend remained consistent after adjusting for covariates. For PA, the linear and quadratic trends were not significant, but between 2019 and 2021, the SII increased from 1.23% (95% CI\u0026nbsp;\u0026minus;9.61\u0026ndash;12.07) to 12.31% (95% CI 4.4\u0026ndash;20.2) and RII increased from 1.08 (95% CI 0.6\u0026ndash;2.1) to 3.15 (95% CI 1.3\u0026ndash;7.6). Although both the SII and RII for 2023 were relatively high (SII: 9.79%, 95% CI: 0.5\u0026ndash;19.1; RII: 2.18, 95% CI: 0.5\u0026ndash;4.8), they did not reach statistical significance. The SII and RII for ST were significant in 2019 at 15.33% (95% CI: 2.64\u0026ndash;28.03) and 1.96 (95% CI: 1.1\u0026ndash;3.5), respectively, in the crude model. However, these indices became non-significant in 2021, with an observed decline to 0.51% (95% CI:\u0026nbsp;\u0026minus;11.6\u0026ndash;12.6) for SII and 1.03 (95% CI: 0.6\u0026ndash;1.9) for RII. In 2023, both indices regained statistical significance (SII: 15.07%, 95% CI: 1.7\u0026ndash;28.4; RII: 1.96, 95% CI: 1.1\u0026ndash;3.5). Nevertheless, in the adjusted model, neither SII nor RII for ST reached statistical significance. No significant income-related inequalities or temporal trends were observed for sleep duration or bowel movement frequency across the survey years, suggesting relative stability in these behaviours.\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026lt;Table 3\u0026gt;\u0026gt;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this is the first study to investigate time trends in socioeconomic inequalities in fundamental health behaviours among Japanese adolescents before and after the COVID-19 pandemic. Our findings, which indicated a significant quadratic trend in breakfast consumption, suggest a resurgence in socioeconomic inequality. The findings of the linear trend analysis indicated a negative direction, suggesting a reduction in inequalities over the three waves. However, according to the year-on-year changes in prevalence for each income group (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) as well as SII and RII values for each survey year (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), it is certain that the inequality has resurged after the COVID-19 pandemic. In particular, the decline in daily breakfast consumption among poverty level II individuals, coupled with its improvement in higher-income groups, appears to be a key factor underlying these results. A systematic review of changes in dietary patterns among youth during the COVID-19 pandemic reported mixed results regarding breakfast consumption habits, with six studies indicating improvement and five studies reporting deterioration [2]. Our findings suggest that heterogeneity by the socioeconomic status is one of the factors amplifying the variability of the results.\u003c/p\u003e \u003cp\u003eIn Japan, lower income levels are often accompanied by longer working hours [33\u0026ndash;35]. This is attributable to several factors, including the tendency for non-regular employees, who typically receive lower wages, to take on multiple jobs to supplement their income. Additionally, high-demand occupations, such as those in convenience stores, food service industry, and transportation, are frequently characterised by low wages despite the substantial labour they require. These occupations were economically impacted during the COVID-19 pandemic [16]. Also, class closures and staggered school attendance measures implemented as infection prevention strategies during the COVID-19 pandemic [36] likely provided adolescents with more time to have breakfast [37]. Given the resumption of economic activities and behavioural conditions to the pre-COVID-19 levels, it is plausible that breakfast consumption patterns have also reverted to previous norms. To the best of our knowledge, no other studies examined dietary intake after the COVID-19 pandemic. Although in the present study we focused solely on breakfast consumption, future research should examine whether other dietary behaviours have also changed post-pandemic [3].\u003c/p\u003e \u003cp\u003eSocioeconomic inequalities in PA widened between 2019 and 2021 and were sustained by 2023. However, the lack of a significant quadratic trend suggests that these inequalities have not substantially changed. During the COVID-19 pandemic, extracurricular activities, which involve approximately two-thirds of adolescents [38], were suspended, and sporting events were cancelled as part of the measures to prevent the infection spread. Given that these factors may have contributed to widening of inequalities [18], the relaxation of such behavioural restrictions could have played a role in narrowing or eliminating these inequalities.\u003c/p\u003e \u003cp\u003e Among the guidelines for each health behaviour, the compliance with PA and ST showed an overall worsening prominence. A nationwide survey conducted by the Japan's Ministry of Education, Culture, Sports, Science and Technology revealed a reduction in exercise duration and an increase in ST among middle school students [38]. A repeated cross-sectional study that has been investigating trends in health risk behaviours among high school students in Okinawa Prefecture in Japan has also reported decreased adherence to the WHO PA guidelines and increased ST in the middle of the pandemic compared to those observed before the COVID-19 pandemic [39]. This trend aligns with previous findings from a meta-analysis that included data from Europe, North America, South America, and Asia [4,5]. It is noteworthy to highlight that in the present study, although the compliance with PA guidelines has improved for both low income groups, the ST significantly worsened. It is essential to monitor whether socioeconomic inequalities in ST will continue to widen in the future.\u003c/p\u003e \u003cp\u003eThis study has certain limitations. First, this study was cross-sectional, which precluded the examination of individual trajectories over time. Second, although the sampling method was designed to ensure the data representativeness, the proportions of eligible samples were 45.7%, 43.6%, and 40.4% of all the respondents in 2019, 2021, and 2023, respectively. However, no significant differences were observed in the demographic factors between the survey years. Additionally, the distribution of participants across districts/cities, stratified by their sizes, closely mirrored the national population structure. Third, the proportion of missing income data was relatively high. However, the missing data pattern remained consistent across the survey years, mitigating concerns about systematic bias. The higher proportion of low-income and low PA levels among those excluded from the analysis might have led to an underestimation of the actual association between income and PA in 2019.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study highlights a resurgence of socioeconomic inequalities in breakfast consumption among the Japanese adolescents, which had temporarily narrowed during the COVID-19 pandemic. By 2023, breakfast consumption significantly declined among the lower income groups and improved among the higher income groups. Additionally, the prevalence of PA and ST showed an overall worsening during this period, with socioeconomic inequalities in PA persisting without substantial change. These findings suggest that the return to the pre-pandemic economic and behavioural conditions has contributed to the re-emergence of dietary disparities and overall deterioration in health behaviours. Sustained public health efforts are essential to address these inequalities and promote healthier lifestyles among vulnerable populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCI Confidence Interval\u003c/p\u003e\n\u003cp\u003ePA Physical Activity\u003c/p\u003e\n\u003cp\u003eRII Relative Index of Inequality\u003c/p\u003e\n\u003cp\u003eSII Slope Index of Inequality\u003c/p\u003e\n\u003cp\u003eST Screen Time\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Institutional Review Board of the University of the Ryukyus determined that the research is eligible for exemption, as the identities of research subjects could not be ascertained from the data provided.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are available from the Sasakawa Sports Foundation. Researchers can access the data by submitting a web-based application through the foundation\u0026apos;s official website (https://www.ssf.or.jp/thinktank/sports_life/application/index.html).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Grants-in-Aid for Scientific Research (JSPS KAKENHI Grant Numbers 24K00395, 24K13509, 23H04441, and 20K10473) from the Japan Society for the Promotion of Science.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the concept or design of the study and acquisition, analysis, or interpretation of data for the work. A.K. drafted the manuscript. M.T. critically revised the manuscript. All authors gave final approval and agreed to be accountable for all aspects of the work, thus ensuring integrity and accuracy.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Editage (www.editage.com) for English language editing.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. WHO Coronavirus (COVID-19) Dashboard . WHO Coronavirus (COVID-19) Dashboard With Vaccination Data. 2020. Available from: https://covid19.who.int/\u003c/li\u003e\n\u003cli\u003eWoods N, Seabrook JA, Schaafsma H, Burke S, Tucker T, Gilliland J. Dietary changes of youth during the COVID-19 pandemic: a systematic review. J Nutr. 2024;154(4):1376\u0026ndash;403.\u003c/li\u003e\n\u003cli\u003eNa X, Zhang J, Xie C, Zeng H, Wu L, Fan D, et al. The impact of the COVID-19 pandemic on dietary behaviors among school-age children, adolescents, and young adults: a systematic review and meta-analysis. Nutr Rev. 2024; Available from: https://pubmed.ncbi.nlm.nih.gov/38820346/\u003c/li\u003e\n\u003cli\u003eNeville RD, Lakes KD, Hopkins WG, Tarantino G, Draper CE, Beck R, et al. Global changes in child and adolescent physical activity during the COVID-19 pandemic: a systematic review and meta-analysis. JAMA Pediatr. 2022;176(9):886\u0026ndash;94.\u003c/li\u003e\n\u003cli\u003eMadigan S, Eirich R, Pador P, McArthur BA, Neville RD. Assessment of changes in child and adolescent screen time during the COVID-19 pandemic: a systematic review and meta-analysis: A systematic review and meta-analysis. JAMA Pediatr. 2022;176(12):1188\u0026ndash;98.\u003c/li\u003e\n\u003cli\u003eLucchini M, Bekelman TA, Li M, Knapp EA, Dong Y, Ballard S, et al. Impact of the COVID-19 pandemic on children\u0026rsquo;s sleep habits: an ECHO study. Pediatr Res. 2023;93(3):586\u0026ndash;94.\u003c/li\u003e\n\u003cli\u003eWang ML, Gago CM. Shifts in child health behaviors and obesity after COVID-19. JAMA Pediatr. 2024;178(5):427\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eRocha S, Fuligni A. The impact of the COVID-19 pandemic on adolescent sleep behavior. Curr Opin Psychol. 2023;52(101648):101648.\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Donoghue G, Kennedy A, Puggina A, Aleksovska K, Buck C, Burns C, et al. Socio-economic determinants of physical activity across the life course: a \u0026ldquo;DEterminants of DIet and Physical ACtivity\u0026rdquo; (DEDIPAC) umbrella literature review. PLoS One. 2018;13(1):e0190737\u0026ndash;e0190737.\u003c/li\u003e\n\u003cli\u003eMielke GI, Brown WJ, Nunes BP, Silva ICM, Hallal PC. Socioeconomic correlates of sedentary behavior in adolescents: systematic review and meta-analysis. Sports Med. 2017;47(1):61\u0026ndash;75.\u003c/li\u003e\n\u003cli\u003eEtindele Sosso FA, Holmes SD, Weinstein AA. Influence of socioeconomic status on objective sleep measurement: A systematic review and meta-analysis of actigraphy studies. Sleep Health. 2021;7(4):417\u0026ndash;28.\u003c/li\u003e\n\u003cli\u003eMugie SM, Benninga MA, Di Lorenzo C. Epidemiology of constipation in children and adults: A systematic review. Best Pract Res Clin Gastroenterol. 2011;25(1):3\u0026ndash;18.\u003c/li\u003e\n\u003cli\u003eCislak A, Safron M, Pratt M, Gaspar T, Luszczynska A. Family-related predictors of body weight and weight-related behaviours among children and adolescents: a systematic umbrella review. Child Care Health Dev. 2012;38(3):321\u0026ndash;31.\u003c/li\u003e\n\u003cli\u003eBardid F, Tomaz SA, Johnstone A, Robertson J, Craig LCA, Reilly JJ. Results from Scotland\u0026rsquo;s 2021 report card on physical activity and Health for children and youth: grades, secular trends, and socio-economic inequalities. J Exerc Sci Fit. 2022;20:317\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eBlavatnik School of Government, University of Oxford. COVID-19 government response tracker. Available from: https://www.bsg.ox.ac.uk/research/covid-19-government-response-tracker\u003c/li\u003e\n\u003cli\u003eStatistics Bureau of Japan. Summary of the Results of Monthly Survey on Service Industries. 2018. Available from: https://www.stat.go.jp/english/data/mssi/kekka/index.html\u003c/li\u003e\n\u003cli\u003eKyan A, Takakura M. Socio-economic inequalities in physical activity among Japanese adults during the COVID-19 pandemic. Public Health. 2022;207:7\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eKyan A, Takakura M. Impact of the COVID-19 pandemic on the socioeconomic inequality of health behavior among Japanese adolescents: a 2-year repeated cross-sectional survey. J Phys Act Health. 2023;20(6):538\u0026ndash;46.\u003c/li\u003e\n\u003cli\u003eSasakawa Sports Foundation. Publications. 2023. Available from: https://www.ssf.or.jp/en/publications/\u003c/li\u003e\n\u003cli\u003eTanaka C, Kyan A, Takakura M, Olds T, Schranz N, Tanaka S. Validation of the physical activity questions in the World Health Organization Health Behavior in School-Aged Children survey using accelerometer data in Japanese children and adolescents. J Phys Act Health. 2021;18(2):151\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eInchley J, Currie D, Cosma A, Samdal O, Editors. Health Behaviour in School-aged Children (HBSC) study protocol: background, methodology and mandatory items for the 2017/18 survey. St Andrews: CAHRU; 2018.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. WHO guidelines on physical activity and sedentary behaviour. Geneva: World Health Organization; 2020. \u003c/li\u003e\n\u003cli\u003eLubans DR, Hesketh K, Cliff DP, Barnett LM, Salmon J, Dollman J, et al. A systematic review of the validity and reliability of sedentary behaviour measures used with children and adolescents. Obes Rev. 2011;12(10):781\u0026ndash;99.\u003c/li\u003e\n\u003cli\u003eHirshkowitz M, Whiton K, Albert SM, Alessi C, Bruni O, DonCarlos L, et al. National Sleep Foundation\u0026rsquo;s sleep time duration recommendations: methodology and results summary . Sleep Health. 2015. Available from: http://dx.doi.org/10.1016/J.SLEH.2014.12.010\u003c/li\u003e\n\u003cli\u003eMinistry of Agriculture FAF. What Are the Benefits of Shokuiku (Food and Nutrition Education)?. 2018. Available from: https://www.maff.go.jp/j/syokuiku/evidence/index.html\u003c/li\u003e\n\u003cli\u003eYamada M, Sekine M, Tatsuse T. Psychological stress, family environment, and constipation in Japanese children: the Toyama Birth Cohort study. Journal of Epidemiology. 2019;29(6):220\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eErnstsen L, Strand BH, Nilsen SM, Espnes GA, Krokstad S. Trends in absolute and relative educational inequalities in four modifiable ischaemic heart disease risk factors: repeated cross-sectional surveys from the Nord-Tr\u0026oslash;ndelag Health Study (HUNT) 1984-2008. BMC Public Health. 2012;12(1):1\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eMackenbach JP, Kunst AE. Measuring the magnitude of socio-economic inequalities in health: an overview of available measures illustrated with two examples from Europe. Soc Sci Med. 1997;44(6):757\u0026ndash;71.\u003c/li\u003e\n\u003cli\u003eRegidor E. Measures of health inequalities: part 2. Journal of Epidemiology \u0026amp; Community Health. 2004 ;58(11):900\u0026ndash;3.\u003c/li\u003e\n\u003cli\u003eNaimi AI, Whitcomb BW. Estimating risk ratios and risk differences using regression. Am J Epidemiol. 2020;189(6):508\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eWachtler B, Hoebel J, Lampert T. Trends in socioeconomic inequalities in self-rated health in Germany: a time-trend analysis of repeated cross-sectional health surveys between 2003 and 2012. BMJ Open. 2019;9(9):e030216\u0026ndash;e030216.\u003c/li\u003e\n\u003cli\u003eCenters for Disease Control and Prevention. Interpretation of YRBS Trend Data . Centers for Disease Control and Prevention; 2024. Available from: https://www.cdc.gov/healthyyouth/data/yrbs/pdf/2021/2021_yrbs_trend_interpretation_508.pdf\u003c/li\u003e\n\u003cli\u003eIkeda N, Saito E, Kondo N, Inoue M, Ikeda S, Satoh T, et al. What has made the population of Japan healthy? The Lancet. 2011;378(9796):1094\u0026ndash;105.\u003c/li\u003e\n\u003cli\u003eKagamimori S, Gaina A, Nasermoaddeli A. Socioeconomic status and health in the Japanese population. Soc Sci Med. 2009;68(12):2152\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eKitao S, Yamada T. Inequality dynamics in Japan, 1981-2021 : Economic and social research institute. Economic and Social Research Institute Discussion Paper Series. 2024;392. Available from: https://www.esri.cao.go.jp/en/esri/archive/e_dis/2024/e_dis392-e.html\u003c/li\u003e\n\u003cli\u003eThe Cabinet Agency for Infectious Disease Crisis Management. Analysis of the number of school closures and school holidays . The Cabinet Agency for Infectious Disease Crisis Management. Available from: https://www.caicm.go.jp/action/survey/covid19-ai.jp/ja-jp/presentation/2021_rq3_countermeasures_simulation/articles/article268/index.html\u003c/li\u003e\n\u003cli\u003eHearst MO, Shanafelt A, Wang Q, Leduc R, Nanney MS. Barriers, benefits, and behaviors related to breakfast consumption among rural adolescents. J Sch Health. 2016;86(3):187\u0026ndash;94.\u003c/li\u003e\n\u003cli\u003eJapan sports agency. National survey of the physical strength, exercise ability and exercise habits. 2021. Available from: https://www.mext.go.jp/sports/b_menu/toukei/kodomo/zencyo/1368222.htm\u003c/li\u003e\n\u003cli\u003eTakakura M, Miyagi M, Kyan A. Changes in the prevalence of health-risk behaviors among Japanese adolescents before and during the COVID-19 pandemic: 2002-2021. Sch Health Rev. 2023;19:14\u0026ndash;25. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Socioeconomic inequalities, COVID-19, Health behaviours, Physical activity, Screen time, Sleep duration, Breakfast consumption, Bowel movement frequency","lastPublishedDoi":"10.21203/rs.3.rs-5854454/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5854454/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Changes in socioeconomic inequalities in health behaviours following the COVID-19 pandemic remain known. In this study, we examined changes in socioeconomic inequalities in adolescent health behaviours—including physical activity (PA), screen time (ST), sleep duration, breakfast consumption, and bowel movement frequency—before and after the pandemic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This three-wave repeated cross-sectional study utilised data from the 2019, 2021, and 2023 National Sports-Life Survey of Children and Young People in Japan, and analysed 766, 725, and 604 participants aged 12–18 years, respectively. Favourable health behaviours were defined as moderate-to-vigorous PA of ≥ 60 min/day, ST \u0026lt; 2 h/day, sleep duration of 8–10 h, daily breakfast consumption, and bowel movements at least every 3 days. Absolute and relative socioeconomic inequalities were assessed using the slope and relative indices of inequality across equivalent household income levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Significant quadratic trends showed narrowing inequalities in breakfast consumption by 2021 and renewed inequalities in 2023. Socioeconomic inequalities in breakfast consumption resurged by 2023, with lower prevalence in lower income groups. No inequalities and trends in inequalities were observed in sleep duration or bowel movements. PA declined for lower-income groups, while ST worsened over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion:\u003c/strong\u003e Socioeconomic disparities in breakfast consumption resurged among Japanese adolescents post-COVID-19, with declines in the lower income groups and improvements in the higher income groups. The overall adherence to PA and ST guidelines showed worsening trends, and socioeconomic inequalities in PA showed minimal variation. Sustained public health initiatives are essential to address these disparities.\u003c/p\u003e","manuscriptTitle":"Socioeconomic inequalities in health behaviours pre- and post-COVID-19 among Japanese adolescents: a three-wave repeated cross-sectional survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-12 06:53:54","doi":"10.21203/rs.3.rs-5854454/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":"7c4b643a-fa18-4f26-a741-f6331426c8ba","owner":[],"postedDate":"March 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-12T06:53:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-12 06:53:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5854454","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5854454","identity":"rs-5854454","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}