Associations between social media use, physical activity, and sleep quality in adolescent girls across three years of the COVID-19 pandemic

Associations between social media use, physical activity, and sleep quality in adolescent girls across three years of the COVID-19 pandemic | 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 Associations between social media use, physical activity, and sleep quality in adolescent girls across three years of the COVID-19 pandemic Liu Junnan, Marie H Murphy, Ian M Lahart, Angela Carlin, Alison M Gallagher, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9259267/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Social media use, sleep, and physical activity (PA) are behaviors that influence the physical and mental health of adolescent girls. Examining the associations between these behaviors during the COVID-19 pandemic is particularly important, as public health restrictions led to disruptions in normal daily life. This study aimed to examine associations between social media use, sleep quality, and PA among 887 adolescent girls (12–14 years) in Ireland and Northern Ireland (NI) across pre-pandemic (2019), lockdown (2020), and post-lockdown (2021) periods. This study was a secondary data analysis of data collected as part of the Walking In ScHools (WISH) Study. Social media use was self-reported, sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI), and PA was assessed using accelerometers. The relationship between these variables was investigated using linear regression, binary logistic regression, and mediation analysis. Increased social media use was associated with poorer sleep quality pre-pandemic (P<.001) and post-lockdown (P<.001), but not during lockdown (P=.050). Pre-pandemic, social media use was associated with lower total (P=.027) and light physical activity (LPA) (P=.030), while during lockdown, it was linked to increased moderate physical activity (MPA) (P=.025), but no associations were found post-lockdown. Sleep quality did not mediate these relationships. The relationship between social media use, sleep quality, and PA among adolescent girls varied across pre-pandemic, lockdown and post-pandemic periods as daily routines and behavioral patterns may have changed. These findings highlight the context-dependent nature of adolescent health behaviors and the importance of considering environmental and temporal factors to better support adolescent well-being. Adolescent girls Social media Sleep quality Physical activity COVID-19 pandemic Introduction Social media refers to a series of platforms that allow users to post content and interact with other users [ 1 ], and examples include Instagram, Facebook, X, TikTok, and YouTube. As smartphone ownership has increased, so too has the popularity of social media, which has gradually become part of daily life, particularly among adolescents [ 2 ]. The COVID-19 pandemic intensified this trend as public health restrictions in many countries increased the time spent at home, leading to a normalization of online communication [ 3 ]. According to a meta-analysis, during the pandemic, about one-third of adolescents spent more than five hours per day on social media, with some even spending up to ten hours [ 4 ]. Given that adolescence is a critical period for growth, development, and habit formation [ 5 ], extensive social media use during these developmental years may impact physical and mental health. Two key behaviors in adolescence are sleep quality and PA, which are essential for mental well-being and cognitive functioning [ 6 ]. Current PA guidelines published by the World Health Organization (WHO) recommend that adolescents should engage in at least 60 minutes of moderate to vigorous physical activity (MVPA) each day, on average [ 7 ]. However, global data show that 81% of adolescent girls fail to meet the guidelines, compared to 78% of adolescent boys [ 8 ]. A similar gender disparity is observed for sleep quality where studies report that 53% of adolescent girls have poor sleep quality compared to 44% of adolescent boys [ 9 ]. Previous research has shown that during adolescence, increased time spent on social media is closely related to poor sleep quality with increased cognitive arousal, delayed sleep latency, and sleep maintenance [ 10 ]. Social media use has also been linked to lower PA engagement, a recent cross-sectional study found that girls with high levels of social media addiction were not only less physically active but they also reported greater perceived barriers to PA participation and negative cognitive-behavioral impacts, including reduced self-management strategies, increased difficulty engaging in and maintaining activity, and lower expectations of positive outcomes [ 11 ]. Despite the recognized negative associations between social media and PA, evidence suggests that social media can also be leveraged to promote PA [ 12 ]. In addition to the influence of social media on sleep quality and PA, research has also identified a strong correlation between sleep and PA [ 13 ]. Considering the mutual influence between sleep quality and PA, the impact of excessive social media use could be further amplified, exacerbating the overall health impact on adolescents. Despite growing interest in these relationships, several knowledge gaps remain. Firstly, few studies have examined social media use, sleep quality and PA among adolescent girls who are more prone not to meet PA guidelines and have poor sleep quality [ 8 , 9 ]. Secondly, although many studies have explored the relationship between pairs of social media use, sleep quality, and PA engagement time [ 10 – 13 ], few studies have considered all of these behaviors collectively and analyzed whether one behavior acts as a mediator between the other two in order to better understand the interrelationships between these three behaviors. Furthermore, while some studies have shown a linear relationship between sleep, social media use and PA [ 10 , 11 ], research has also reported more complex relationships [ 14 ], which warrants further investigation. Given these gaps in the existing evidence, the aim of this study was to explore the associations between social media use, PA, and sleep quality among adolescent girls. This study hypothesized that increased social media use would be associated with reduced sleep quality and PA and that sleep quality would mediate the relationship between social media use and PA. Results A total of 887 adolescent girls aged 12–14 years were included in this study. The median age in 2019, 2020, and 2021 was 12 (IQR = 1), 13 (IQR = 1), and 13 (IQR = 1) years, respectively as outlined in Table 1 . Table 1 Demographic characteristics of participant cohorts in 2019, 2020, and 2021. 2019 2020 2021 N Mean (SD) Median (IQR) N Mean (SD) Median (IQR) N Mean (SD) Median (IQR) Age (years) 234 12.0 (1.0) 150 13.0 (1.0) 503 13.0 (1.0) Height (cm) 234 158.5 (6.5) 150 157.4 (6.1) 503 158.1 (7.0) 158.4 (9.0) Weight (kg) 234 54.2 (10.6) 150 51.4 (12.4) 503 51.4 (14.5) BMI (kg/m 2 ) 234 19.1 (4.4) 150 20.5 (4.2) 503 20.5 (5.0) Waist Circumference (cm) 234 64.3 (13.2) 150 70.8 (10.4) 503 69.0 (12.4) Hip Circumference (cm) 234 83.0 (10.5) 150 85.9 (9.7) 503 87.5 (11.3) Waist: hip ratio 234 0.8 (0.1) 150 0.8 (0.1) 503 0.8 (0.1) LPA (mins/day) 234 192.3 (43.4) 150 225.3 (57.6) 503 188.4 (58.5) MPA (mins/day) 234 18.6 (14.7) 150 22.1 (14.1) 503 27.0 (13.5) VPA (mins/day) 234 4.3 (9.0) 150 6.4 (8.2) 503 8.5 (9.6) Total PA (min/day) 234 194.1 (67.8) 150 259.0 (81.8) 503 227.8 (68.7) MVPA (mins/day) 234 23.4 (23.4) 150 30.7 (20.5) 503 36.0 (21.6) PSQI score 234 3.0 (3.0) 150 5.0 (3.0) 503 5.0 (4.0) Abbreviations: SD=standard deviation; IQR=interquartile range; BMI=body mass index; LPA=light intensity physical activity; MPA=moderate intensity physical activity; VPA=vigorous intensity physical activity; \MVPA=moderate to vigorous intensity physical activity; PSQI=Pittsburgh sleep quality index. In terms of PA, approximately 91% of girls in 2019 did not meet the PA guidelines (< 60 minutes of MVPA per day), increasing to 95% in 2020 and decreasing to 87% in 2021. The median MVPA time was 23.4 (IQR = 23.4), 30.7 (IQR = 20.5), 36.0 (IQR = 21.6) minutes per day in 2019, 2020, and 2021, respectively. With regards to sleep quality, the proportion of girls with good sleep quality (score ≤ 5) was 65% in 2019, 64% in 2020, and 57% in 2021. For social media use, 66% of girls indicated that they spent > 2 hours on social media per day in 2019 and this increased to 68% in 2020 and 75% in 2021. The relationship between social media use, sleep quality and PA in 2019 (Pre COVID-19 pandemic, before any known restrictions or cases in NI/Ireland) As shown in Table 2 , in 2019, as time spent on social media increased, sleep quality (P<.001), total PA time (P=.027) and LPA time (P=.030) decreased. However, there was no significant relationship between daily social media use and MPA, VPA or MVPA time. Accordingly, mediation analyzes were conducted between social media use, sleep quality, total PA, and LPA. As shown in Table 3 , sleep quality did not serve as a mediator for either LPA time (P=.337) or total PA time (P=.364). Table 2 Linear regression analysis of the relationship between social media use, sleep quality, and PA in 2019 (Pre COVID-19 pandemic, before any known restrictions or cases in NI/Ireland). B β Adj. R 2 Sig. 95% CI Lower Bound Upper Bound PSQI score .476 .226 .047 < .001 .210 .742 Total PA (mins/day) -5.054 2.284 .017 .027 -9.535 − .573 LPA (mins/day) -4.171 − .142 .016 .030 − .7937 − .406 MPA (mins/day) − .418 .470 − .001 .375 − .1.345 .509 VPA (mins/day) − .466 − .460 .000 .313 − .1.373 .442 MVPA (mins/day) − .883 − .805 .001 .274 − .2.470 .703 Notes: B=regression coefficient; β = standardized regression coefficient; 95% CI = 95% confidence interval. Table 3 Mediation analysis of the relationships among social media use time, sleep quality, and PA time in 2019 (Pre COVID-19 pandemic, before any known restrictions or cases in NI/Ireland). Outcome B β Adj. R 2 Sig. 95% CI Lower Bound Upper Bound Social media use LPA (mins/day) -3.745 − .127 .016 .058 -7.611 .121 PSQI score LPA (mins/day) − .896 − .064 .016 .337 -2.730 .938 Social media use Total PA (mins/day) -4.575 − .131 .016 .051 -9.177 .027 PSQI score Total PA (mins/day) -1.007 − .061 .016 .364 -3.190 1.176 Notes: B=regression coefficient; β = standardized regression coefficient; 95% CI = 95% confidence interval. Abbreviation: PSQI=Pittsburgh sleep quality index. The relationship between social media use, sleep quality and PA in 2020 (Public health restrictions in place to prevent spread of COVID-19) As shown in Table 4 , in 2020, as time spent on social media increased, only MPA time (P=.025) increased. There was no significant relationship between daily social media use and sleep quality, total PA, LPA, VPA, or MVPA. Therefore, these results did not meet the prerequisites for conducting further mediation analyzes. Table 4 Linear regression analysis of the relationship between social media use, sleep quality, and PA in 2020 (Public health restrictions in place to prevent spread of COVID-19). B β Adj. R 2 Sig. 95% CI Lower Bound Upper Bound PSQI score .301 .160 .019 .050 − .001 .602 Total PA (mins/day) 2.215 .052 − .004 .526 -4.668 9.099 LPA (mins/day) .763 3.375 − .006 .821 -5.906 7.432 MPA (mins/day) 1.484 .183 .027 .025 .186 2.783 VPA (mins/day) − .032 − .007 − .007 .935 − .816 .751 MVPA (mins/day) 1.452 .910 .010 .113 − .346 3.250 Notes: B=regression coefficient; β = standardized regression coefficient; 95% CI = 95% confidence interval. Abbreviations: PSQI=Pittsburgh sleep quality index; PA=physical activity; LPA=light intensity of physical activity; MPA=moderate intensity of physical activity; VPA=vigorous intensity of physical activity; MVPA=moderate to vigorous intensity of physical activity. The relationship between social media use, sleep quality and PA in 2021 (Restrictions were easing, but some remained) As shown in Table 5 , in 2021, as time spent on social media increased, only sleep quality (P<.001) decreased. There was no significant relationship between daily social media use and any of the PA variables. Therefore, these results did not meet the prerequisites for conducting further mediation analyzes. Table 5 Linear regression analysis of the relationship between social media use, sleep quality, and PA in 2021 (Restrictions were easing, but some remained). B β Adj. R 2 Sig. 95% CI Lower Bound Upper Bound PSQI Score .428 .209 .042 < .001 .253 .604 Total PA (mins/day) -1.932 − .056 .001 .211 -4.960 1.096 LPA (mins/day) -1.333 − .045 .000 .312 -3.922 1.256 MPA (mins/day) − .270 − .038 − .001 .396 − .895 .355 VPA (mins/day) − .329 .277 .001 .236 − .873 .215 MVPA (mins/day) − .600 − .051 .001 .257 -1.638 .439 Notes: B=regression coefficient; β = standardized regression coefficient; 95% CI = 95% confidence interval. Abbreviations: PSQI=Pittsburgh sleep quality index; PA=physical activity; LPA=light intensity of physical activity; MPA=moderate intensity of physical activity; VPA=vigorous intensity of physical activity; MVPA=moderate to vigorous intensity of physical activity. In summary, this study found that associations between social media use, sleep quality, and PA among adolescent girls varied across the three individual sampling years (2019 pre-pandemic, 2020 lockdown, and 2021 post-lockdown). Higher social media use was associated with poorer sleep quality in 2019 (pre-pandemic) and in 2021 (limited public health restrictions), whereas no such relationship was observed in 2020 (full public health restrictions). Social media use was associated with reduced total PA and LPA time in 2019. In 2020, increased social media use was positively associated with MPA time but in 2021, no relationship was observed between social media use and any PA intensities. Sleep quality did not mediate the relationship between social media use and PA time in any of the three years. Discussion The negative association between social media use and sleep quality observed in 2019 and 2021 aligns with some previous studies [ 10 , 15 ], with the exception of 2020 during which public health restrictions were in place related to the COVID-19 pandemic. Some studies have explained the negative impact of social media use time on sleep quality from a physiological perspective. For example, social media use can decrease individuals’ secretion of melatonin, causing difficulties in falling asleep [ 16 ]. Additionally, specific content on social media platforms may increase individuals’ psychophysiological arousal, such as sympathetic nervous system activities, thereby affecting their sleep quality [ 17 ]. In addition, intrinsically photosensitive retinal ganglion cells in the human eye are susceptible to blue light reflected by smartphone screens, potentially disrupting the human body’s natural sleep-wake rhythm and further damaging sleep quality [ 18 ]. In addition to these physiological impacts, research has also explained this phenomenon based on psychology. Psychologically, a “fear of missing out” associated with habitual notification checking can contribute to poor sleep hygiene [ 10 ]. Such behaviors may induce compulsive social media use and affect sleep quality. A meta-analysis further confirms these links, reporting that increased social media use was related to a shorter sleep duration, later bedtime and lower sleep quality with the authors calling for education initiatives promote healthy sleep in adolescence and address the dysfunctional and excessive use of social media [ 15 ]. The absence of an association between social media and sleep quality in 2020 aligns with a previous study by Hamilton et al. [ 19 ] which in part attributed this to substantial changes in lifestyle and daily routines during the COVID-19 lockdown. During the lockdown period(s), adolescents had greater freedom and flexibility in accessing social media, resulting in more frequent and dispersed use throughout the day, and making social media a more integral part of their daily routines than previously [ 3 ]. This change to normal routines may have meant that while adolescents were using social media more during the day, they reduced pre-bedtime use and in some way reduced the negative impact on sleep latency [ 20 ]. Moreover, the cancellation of in-person classes and commuting during lockdown may have allowed adolescent girls to wake up later and obtain longer sleep duration [ 21 ], which may mitigate the impact of poor sleep quality, resulting from staying up late on social media. Taken together, these context-specific factors suggest that the detrimental effects of social media on sleep quality may be attenuated when adolescents have more control over their time and schedules but are likely to re-emerge under normal school routines as observed in 2019 and 2021. In 2019, before the COVID-19 pandemic, higher social media use was linked to lower total PA and LPA, consistent with previous evidence that increased screen time displaces active behaviors [ 11 ], However, unlike those studies, this paper found that social media use was only associated with a decrease in LPA time, but not with other PA intensities. A possible explanation is that most of the girls included in this study had relatively low levels of MPA and VPA time already (only approximately 11% meeting PA guidelines), which may have limited the statistical power to detect such effects. In October 2020, a 8-week lockdown was imposed in NI and there was a two week extended mid-term break. During this period (when schools were open), social media use was positively associated with MPA. One possible explanation is that adolescent girls were frequently exposed to information about COVID-19 and health improvement while using social media during lockdown [ 22 ], which may have increased their awareness of the importance of health. During this time social media platforms promoted PA at home [ 23 ], such as dance workouts, fitness challenges, and exercise videos, which may have motivated engagement in similar activities [ 24 ]. Such activities are more likely to be recorded as MPA rather than LPA which may explain this association [ 25 ]. Moreover, research indicated that half of adolescents’ MVPA took place at home, suggesting that the home lockdown may have inadvertently increased their MPA time [ 26 ]. In addition, the extended time spent at home could mitigate fatigue, thereby reducing the likelihood of discontinuing MPA due to tiredness [ 27 ]. In 2021, when restrictions had eased but some remained (for example, mask-wearing and year group bubbles), no significant association was found between social media use and PA, which may be the result of multiple interacting factors. Although most restrictions gradually ended, the ongoing presence of COVID-19 continued to affect individuals’ daily lives [ 28 ]. The pandemic made social media a mainstream source for obtaining information [ 3 ], and it gradually became a normalized and integrated part of adolescents’ lives in the post-pandemic era. This was reflected in more flexible and fragmented use [ 3 ], which may have reduced its earlier influence on other activities including PA. In addition, as the perceived risk of the pandemic decreased, the amount of health and exercise-related content on social media also declined [ 29 ]. For some adolescents, PA may have become a habit during the pandemic, reducing the reliance on social media as a motivating factor and the influence of social media on PA may have diminished [ 30 ]. Sleep quality did not mediate the relationship between social media use and PA in any year. While the 2019 data permitted mediation testing (related to LPA and total PA), the lockdown-related disruptions of 2020 and 2021 prevented the establishment of the basic associations required for mediation analysis. Given the strong bidirectional links between social media use, sleep quality and PA reported elsewhere [ 10 , 11 , 13 ], the absence of mediation in this study should be interpreted with caution and warrants further investigation in more stable conditions. The key strengths of this study are the large sample size with sampling across 3 distinct years (pre-pandemic, lockdown, and post-lockdown). PA data were collected using devices (accelerometers), thus improving data validity [ 31 ]. A novel aspect of this study is that such data was collected across the first three years of the COVID-19 pandemic, enabling year-by-year comparisons during a unique period in time. However, it is important to recognize the limitations of this research. The majority of participants had low PA, and while this represents a “real-world context,” it does make it challenging to conduct nonlinear relationship analysis. Another limitation is that this study did not account for potential confounding variables and broader contextual factors. Future research should include factors identified in prior work, such as adolescents’ body image satisfaction, anxiety, and depression levels [ 32 ], as well as contextual details such as online learning or school lockdown specifics. Sleep was measured using the validated PSQI questionnaire a subjective measure. Future research could complement this with more objective measures of sleep quality. Finally, social media use was self-reported and may be subject to participant recall bias which should be considered when interpreting the results of this research. Conclusion This study examined the associations between social media use, sleep quality, and PA among adolescent girls across pre-pandemic, pandemic, and post-lockdown periods. The findings show that these relationships appear to be context-dependent and were potentially influenced by the COVID-19 pandemic and associated lockdowns. In both 2019 and 2021, increased social media use was associated with poorer sleep quality, consistent with previous research. However, this relationship was absent in 2020 (during the pandemic), likely reflecting lifestyle changes during lockdown, including altered school routines and more flexible daily schedules. For PA, social media use was negatively associated with LPA and total PA in 2019, positively associated with MPA in 2020, and unrelated to PA in 2021. These shifts suggest that external factors, such as public health restrictions and media messaging, may influence behavior patterns among adolescents. Sleep quality did not mediate the relationship between social media use and PA in any sampling year. While this does not rule out the potential mediating role of sleep, it highlights the complexity of these interrelated behaviors, particularly in the context of a global pandemic. Overall, these findings underscore the importance of considering contextual and temporal factors when examining adolescent health behaviors. Methods Study design This study was a secondary analysis of baseline data collected as part of the WISH Study [ 33 , 34 ]. Baseline data was collected in the autumn of 2019, 2020 and 2021 from three different cohorts of pupils (girls aged 12–14 years). The methodology has been described in detail previously [ 33 , 34 ]. In brief, post-primary schools (mixed or single-sex) in the border region of Ireland and NI were invited to participate in a cluster randomized controlled trial. Following permission from the School Principal, girls in Year 9/10 (NI) and 1st/2nd year (Ireland) were invited by teaching staff to attend a recruitment presentation from the Trial Manager. All eligible pupils who provided assent and written consent from their parent/guardian were invited to baseline data collection. This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures were approved by Ulster University Research Ethics Committee (Ref: REC/19/0020). Data collection Baseline data collection took place on three occasions: (1) September - October 2019 (Pre COVID-19 pandemic, before any known restrictions or cases in Ireland/NI); (2) October - November 2020 (Public health restrictions in place to prevent spread of COVID-19); (3) October - November 2021 (Restrictions were easing, but some remained). Participants were asked to provide their date of birth to calculate age. Height (cm) and body mass (kg) were measured to the nearest 0.1 cm and 0.1 kg respectively, using a freestanding stadiometer (Leicester Height Measure), and digital scales (Seca 877) to calculate Body Mass Index (BMI) [ 35 ]. Waist and hip circumference were measured to the nearest 0.1cm using an anatomical measuring tape and waist-to-hip ratio was calculated [ 36 ]. Measurements were undertaken by trained researchers and participants were asked to complete a series of questionnaires within the school premises on electronic devices (Apple iPad®) using Qualtrics (Provo, UT). Social media use According to the study by Woods & Scott [ 37 ], three items were included in the questionnaire to assess social media usage (1) Do you use social media? (2) How often do you use social media? and (3) How many hours do you use social media on a typical day? Participants were asked to self-report their daily time spent on social media from the following options: (1) less than one hour per day; (2) two to three hours; (3) three to four hours; (4) four to six hours; or (5) more than six hours. Sleep quality Sleep quality data was measured by the PSQI questionnaire [ 38 ]. The PSQI is composed of 19 questions that assess 7 components (subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbance, use of sleep medication, and daytime dysfunction) and was used to measure sleep quality. Each question was scored 0 (no difficulty) to 3 (severe difficulty), and a global score was then calculated (0–21). According to the PSQI classification, a sleep quality score of 0 to 5 is indicative of “good sleep quality,” while a score > 5 is defined as “poor sleep quality [ 38 ].” PA PA was measured using an accelerometer (Actigraph LLC, Florida) worn for seven consecutive days. The device was placed on an elastic waistband and pupils were asked to wear the accelerometer on their right hip, removing it only for bathing, water-based activities such as swimming and when asleep. All data were downloaded and analyzed using ActiLife software (version 6.13.4; ActiGraph LLC, Florida, USA). Pupils were included in the analysis if they had ≥ 2 valid weekdays of data (500 mins/day) [ 39 ]. All valid data was included in the analysis. Minutes of total PA (light, moderate and vigorous) per day were estimated using the Evenson cut-points [ 40 ]. A sampling epoch of 15s was employed during data collection. Periods of ≥ 60 min of zero counts were categorized as “non-wear” and removed. Accelerometer data was used to calculate (1) time spent in LPA, moderate physical activity (MPA), MVPA and vigorous physical activity (VPA) [ 39 ], (2) proportion of pupils meeting current PA recommendations of ≥ 60 mins of MVPA per day [ 7 ]. Data analysis IBM SPSS software (version 29.0) was used for data analysis (Chicago, USA). Listwise deletion was used to remove missing data for social media use, sleep quality and PA variables. Reasons for missing data included accelerometer malfunctions or damage, accelerometer non-return, and participants withdrawing from the study before data collection. The final sample size available for this study was 887 adolescent girls, with sample sizes of 234, 150, and 503 in 2019, 2020, 2021, respectively. The Kolmogorov-Smirnov (K-S) test was conducted to assess the normality of variables for the years 2019, 2020, and 2021, respectively. Descriptive statistics were used to summarize demographic characteristics, social media use time, sleep quality and various intensities of PA time for 2019, 2020, and 2021, respectively. For each year (2019, 2020 and 2021), simple linear regressions were used to evaluate the relationship between daily social media use time, sleep quality and PA time, with social media use time as the independent variable. Time (mins) spent in different PA intensities (light, moderate, vigorous) and sleep quality score were the dependent variables. By comparing the significance of the effect of time spent on social media on PA in the current model with its effect in the previous linear regression model (where social media was the only independent variable), it could be determined whether sleep quality served as a partial or full mediator. Considering the potential for non-linear relationships between these variables, binary logistic regressions were also conducted to explore whether daily social media use differentially influenced PA (meeting versus not meeting the PA guidelines), as well as good versus poor sleep quality. In both the simple linear and binary logistic regressions, daily social media use was the independent variable, whilst sleep quality and PA were the dependent variables. For the binary logistic regression, sleep quality was first converted into binary variables of “good” (0–5) and “poor” (> 5) sleep quality [ 38 ] to fit the model. Similarly, PA was classified into “not meeting” (< 60 minutes MVPA per day) and “meeting” (≥ 60 minutes MVPA per day) based on the current PA Guidelines [ 7 ]. This binary variable conversion illustrated a significant imbalance between the sample sizes in comparing subgroups, especially the sample size in meeting and not meeting PA guidelines subgroups and given the bias that this imbalance posed to the data analysis, binary logistic regressions could not be performed. Abbreviations PSQI Pittsburgh sleep quality index PA physical activity LPA light intensity of physical activity MPA moderate intensity of physical activity VPA vigorous intensity of physical activity MVPA moderate to vigorous intensity of physical activity. Declarations Acknowledgments The authors would like to thank all members of the original Wish team for their contributions to this study, as well as all volunteers who participated in data collection, without whom this research would not have been possible. Author contributions Liu Junnan designed the study, organized the data, and wrote the initial draft. S Maria O’Kane worked closely with the first author on revising the manuscript, coordinated with all authors, and contributed throughout the study. Ian M Lahart advised on the statistical aspects of the study. Marie H Murphy contributed to the overall study idea and commented on the manuscript. Angela Carlin, Alison M Gallagher, Leanne C Doherty, Gary McDermott, and Maria Faulkner provided comments and suggestions on the manuscript. All authors approved the final manuscript. Competing interests The authors declare they have no competing interests. Funding information The WISH Study was funded from INTERREG VA funding of €8.84m (including 15% contribution from the Department of Health in NI and Republic of Ireland), which had been awarded to the HSC Research & Development Division of the Public Health Agency Northern Ireland and to the Health Research Board in Ireland for the Cross-border Healthcare Intervention Trials in Ireland Network (CHITIN) project. The funders had no role in the design or execution of the study, analysis, interpretation of the data or decision to submit results. Consent to participate/Consent to publish All personally identifiable information of the participants in this study has been removed from the text and tables, and informed consent was obtained for online open-assess publication. Data availability statement The datasets generated or analyzed during this study are available from the corresponding author on reasonable request. References Rhee L, Bayer JB, Lee DS, Kuru O. Social by definition: how users define social platforms and why it matters. Telemat Inf. 2021;59:101538. 10.1016/j.tele.2020.101538 . Odgers CL, Schueller SM, Ito M. Screen time, social media use, and adolescent development. Annu Rev Dev Psychol. 2020;2:485–502. Nilsson A, Rosendahl I. Jayaram-Lindström, N. Gaming and social media use among adolescents in the midst of the COVID-19 pandemic. Nordisk Alkohol Nark. 2022;39:347–61. Marciano L, Ostroumova M, Schulz PJ, Camerini A-L. Digital Media use and adolescents’ mental health during the COVID-19 pandemic: a systematic review and meta-analysis. Front Public Health. 2022;9:793868. 10.3389/fpubh.2021.793868 . Delevich K, Wilbrecht L. Role of puberty on adult behaviors. Oxford research encyclopedia of neuroscience. Oxford University Press; 2020. Carter T, et al. The effect of physical activity on anxiety in children and young people: a systematic review and meta-analysis. J Affect Disord. 2021;285:10–21. Bull FC, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54:1451–62. Guthold R, Stevens GA, Riley LM, Bull FC. Global trends in insufficient physical activity among adolescents: a pooled analysis of 298 population-based surveys with 1·6 million participants. Lancet Child Adolesc Health. 2020;4:23–35. Galan-Lopez P, et al. Sleep quality and duration in European adolescents (the AdolesHealth study): a cross-sectional, quantitative study. Child (Basel). 2021;8:188. Alonzo R, Hussain J, Stranges S, Anderson KK. Interplay between social media use, sleep quality, and Mental Health in Youth: a systematic review. Sleep Med Rev. 2021;56:101414. Sönmez Sari E, Terzi H, Şahin D. Social Media Addiction and cognitive behavioral physical activity among adolescent girls: a cross-sectional study. Public Health Nurs. 2024;42:61–9. Morningstar B, et al. The association between social media use and physical activity among Canadian adolescents: a health behaviour in school-aged children (HBSC) study. Can J Public Health. 2023;114:642–50. Lima SB, et al. Sleep hours: risk behavior in adolescents from different countries. Cien Saúde Colet. 2020;25:957–65. Shimoga SV, Erlyana E, Rebello V. Associations of social media use with physical activity and sleep adequacy among adolescents: cross-sectional survey. J Med Internet Res. 2019;21:e14290. 10.2196/14290 . Pagano M, Bacaro V, Crocetti E. Using digital media or sleeping … that is the question. A meta-analysis on digital media use and unhealthy sleep in adolescence. Comput Hum Behav. 2023;146:107813. Bhat S, Pinto-Zipp G, Upadhyay H, Polos PG. To sleep, perchance to tweet: in-bed electronic social media use and its associations with insomnia, daytime sleepiness, mood, and sleep duration in adults. Sleep Health. 2018;4:166–73. Combertaldi SL, Ort A, Cordi M, Fahr A, Rasch B. Pre-sleep social media use does not strongly disturb sleep: a sleep laboratory study in healthy young participants. Sleep Med. 2021;87:191–202. Wong NA, Bahmani H. A review of the current state of research on artificial blue light safety as it applies to digital devices. Heliyon. 2022;8:e10282. 10.1016/j.heliyon.2022.e10282 . Hamilton JL, Hutchinson E, Evankovich MR, Ladouceur CD, Silk JS. Daily and average associations of physical activity, social media use, and sleep among adolescent girls during the Covid-19 pandemic. J Sleep Res. 2022;32:e13611. Scott H, Biello SM, Woods HC. Social Media use and adolescent sleep patterns: cross-sectional findings from the UK millennium cohort study. BMJ Open. 2019;9:e031161. 10.1136/bmjopen-2019-031161 . Rocha S, Fuligni A. The impact of the COVID-19 pandemic on Adolescent sleep behavior. Curr Opin Psychol. 2023;52:101648. Zhang S, Liu M, Li Y, Chung JE. Teens’ social media engagement during the Covid-19 pandemic: a time series examination of posting and emotion on reddit. Int J Environ Res Public Health. 2021;18:10079. Cugusi L, Di Blasio A, Bergamin M. The Social Media Gym-class: another lesson learnt from Covid-19 lockdown. Sport Sci Health. 2021;17:487–8. López-Carril S, Escamilla-Fajardo P, Alguacil-Jiménez M. Physical activity using social media during the COVID-19 pandemic: the perceptions of sports science students. Phys Cult Sport Stud Res. 2021;92:19–31. MacIntosh BR, Murias JM, Keir DA, Weir JM. What is moderate to vigorous exercise intensity? Front Physiol. 2021;12:682233. Richards AB, et al. A socioecological perspective of how physical activity and sedentary behaviour at home changed during the first lockdown of Covid-19 restrictions: the homespace project. Int J Environ Res Public Health. 2022;19:5070. 10.3390/ijerph19095070 . Kass L, Desai T, Sullivan K, Muniz D, Wells A. Changes to physical activity, sitting time, eating behaviours and barriers to exercise during the first COVID-19 lockdown in an English cohort. Int J Environ Res Public Health. 2021;18:10025. Franks J, Gruss B, Mulas-Granados C, Patnam M, Weber S. Reopening strategies, mobility and COVID-19 infections in Europe: panel Data Analysis. BMJ Open. 2022;12:e055938. Fletcher R, Kalogeropoulos A, Nielsen R. Social media very widely used, but use for news and information about COVID-19 is declining. Oxford Internet Institute, University of Oxford https://ora.ox.ac.uk/objects/uuid:40fdcee2-3f44-4d11-bc98-be17bc94d5bf (2020). Litt DM, Iannotti RJ, Wang J. Motivations for adolescent physical activity. J Phys Act Health. 2011;8:220–6. Bassett DR, John D. Use of pedometers and accelerometers in clinical populations: validity and reliability issues. Phys Ther Rev. 2010;15:135–42. Scully M, Swords L, Nixon E. Social comparisons on social media: online appearance-related activity and body dissatisfaction in adolescent girls. Ir J Psychol Med. 2020;40:31–42. O’Kane SM, et al. A study protocol for a clustered randomised controlled trial to evaluate the effectiveness of a peer-led school-based walking intervention on adolescent girls’ physical activity: the walking in schools (WISH) study. BMC Public Health. 2020;20:541. Murphy MH, et al. Effectiveness of the walking in schools (WISH) study, a peer-led walking intervention for adolescent girls: results of a cluster randomised controlled trial. Int J Behav Nutr Phys Act. 2024;21:19. Vidmar SI, Cole TJ, Pan H. Standardizing anthropometric measures in children and adolescents with functions for Egen: update. Stata J. 2013;13:366–78. Taylor RW, Jones IE, Williams SM, Goulding A. Evaluation of waist circumference, waist-to-hip ratio, and the CONICITY index as screening tools for high trunk fat mass, as measured by dual-energy x-ray absorptiometry, in children aged 3–19y. Am J Clin Nutr. 2000;72:490–5. Woods HC, Scott H. #Sleepyteens: social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem. J Adolesc. 2016;51:41–9. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and Research. Psychiatry Res. 1989;28:193–213. Willis K, et al. Protocol for a cluster randomised controlled trial of a peer-led physical activity intervention for adolescent girls (PLAN-A). BMC Public Health. 2019;19:644. 10.1186/s12889-019-7012-x . Evenson KR, Catellier DJ, Gill K, Ondrak KS, McMurray RG. Calibration of two objective measures of physical activity for children. J Sports Sci. 2008;26:1557–65. Additional Declarations No competing interests reported. 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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-9259267","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631069156,"identity":"91262a1b-74a2-4568-955f-24fc27d9d3ff","order_by":0,"name":"Liu Junnan","email":"","orcid":"","institution":"Beijing Jishuitan Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liu","middleName":"","lastName":"Junnan","suffix":""},{"id":631069160,"identity":"0e82c74c-8e0c-477f-b49c-b241b82a8adc","order_by":1,"name":"Marie H Murphy","email":"","orcid":"","institution":"University of Edinburgh","correspondingAuthor":false,"prefix":"","firstName":"Marie","middleName":"H","lastName":"Murphy","suffix":""},{"id":631069162,"identity":"a81dd1cb-0a77-4165-85b0-543c45bc11ca","order_by":2,"name":"Ian M Lahart","email":"","orcid":"","institution":"University of Wolverhampton","correspondingAuthor":false,"prefix":"","firstName":"Ian","middleName":"M","lastName":"Lahart","suffix":""},{"id":631069164,"identity":"93ea7021-5e9f-41c1-a881-f113cd170c53","order_by":3,"name":"Angela Carlin","email":"","orcid":"","institution":"University of Edinburgh","correspondingAuthor":false,"prefix":"","firstName":"Angela","middleName":"","lastName":"Carlin","suffix":""},{"id":631069165,"identity":"49d86457-d5d3-45ca-9574-4158eef2903f","order_by":4,"name":"Alison M Gallagher","email":"","orcid":"","institution":"Ulster University","correspondingAuthor":false,"prefix":"","firstName":"Alison","middleName":"M","lastName":"Gallagher","suffix":""},{"id":631069166,"identity":"efd8851d-225b-407a-adb6-2485ea23cdd0","order_by":5,"name":"Leanne C Doherty","email":"","orcid":"","institution":"University of Edinburgh","correspondingAuthor":false,"prefix":"","firstName":"Leanne","middleName":"C","lastName":"Doherty","suffix":""},{"id":631069167,"identity":"90ee5fd0-b447-4c7f-8387-5068556cae03","order_by":6,"name":"Gary McDermott","email":"","orcid":"","institution":"University of Edinburgh","correspondingAuthor":false,"prefix":"","firstName":"Gary","middleName":"","lastName":"McDermott","suffix":""},{"id":631069168,"identity":"ce265e16-43ac-45ea-a138-1a659b0ee6a9","order_by":7,"name":"Maria Faulkner","email":"","orcid":"","institution":"University of Edinburgh","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Faulkner","suffix":""},{"id":631069169,"identity":"1cb16adc-b80b-4ad0-978d-8bd66a2998f1","order_by":8,"name":"S Maria O’Kane","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYHACZhAhx8COJkJQizGQYmwgSUtiA9FadBuYDxv83GGTvuEw+/MHH/cwyPM38Bgb4NNidoAtObH3TFruhsM8ho0znjEYzjjAY5yAXwuP8QHetsMgLYzNPAcYGDcwAEUIaTn4t+1/usFh9ofNfw4w2BOlJZm37UCCwWEGw2aGAwyJIC34HXaYLdlYti3ZcCbQLzN7DkgkzzjMVozf+8ebD0u+bbOT5zve/uDDjwM2tv3tzZsl8GlBjwMJIiJyFIyCUTAKRgFBAADXVUVt/sVcngAAAABJRU5ErkJggg==","orcid":"","institution":"University of Edinburgh","correspondingAuthor":true,"prefix":"","firstName":"S","middleName":"Maria","lastName":"O’Kane","suffix":""}],"badges":[],"createdAt":"2026-03-29 13:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9259267/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9259267/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108183151,"identity":"121667e0-7c2d-4bbe-abff-d10829e35f8a","added_by":"auto","created_at":"2026-04-30 08:59:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":423660,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9259267/v1/30e7c67d-d3a7-41c3-8c8b-d35f8b7e59b2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between social media use, physical activity, and sleep quality in adolescent girls across three years of the COVID-19 pandemic","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSocial media refers to a series of platforms that allow users to post content and interact with other users [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], and examples include Instagram, Facebook, X, TikTok, and YouTube. As smartphone ownership has increased, so too has the popularity of social media, which has gradually become part of daily life, particularly among adolescents [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The COVID-19 pandemic intensified this trend as public health restrictions in many countries increased the time spent at home, leading to a normalization of online communication [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to a meta-analysis, during the pandemic, about one-third of adolescents spent more than five hours per day on social media, with some even spending up to ten hours [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Given that adolescence is a critical period for growth, development, and habit formation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], extensive social media use during these developmental years may impact physical and mental health.\u003c/p\u003e \u003cp\u003eTwo key behaviors in adolescence are sleep quality and PA, which are essential for mental well-being and cognitive functioning [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Current PA guidelines published by the World Health Organization (WHO) recommend that adolescents should engage in at least 60 minutes of moderate to vigorous physical activity (MVPA) each day, on average [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, global data show that 81% of adolescent girls fail to meet the guidelines, compared to 78% of adolescent boys [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A similar gender disparity is observed for sleep quality where studies report that 53% of adolescent girls have poor sleep quality compared to 44% of adolescent boys [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious research has shown that during adolescence, increased time spent on social media is closely related to poor sleep quality with increased cognitive arousal, delayed sleep latency, and sleep maintenance [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Social media use has also been linked to lower PA engagement, a recent cross-sectional study found that girls with high levels of social media addiction were not only less physically active but they also reported greater perceived barriers to PA participation and negative cognitive-behavioral impacts, including reduced self-management strategies, increased difficulty engaging in and maintaining activity, and lower expectations of positive outcomes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the recognized negative associations between social media and PA, evidence suggests that social media can also be leveraged to promote PA [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In addition to the influence of social media on sleep quality and PA, research has also identified a strong correlation between sleep and PA [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Considering the mutual influence between sleep quality and PA, the impact of excessive social media use could be further amplified, exacerbating the overall health impact on adolescents.\u003c/p\u003e \u003cp\u003eDespite growing interest in these relationships, several knowledge gaps remain. Firstly, few studies have examined social media use, sleep quality and PA among adolescent girls who are more prone not to meet PA guidelines and have poor sleep quality [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Secondly, although many studies have explored the relationship between pairs of social media use, sleep quality, and PA engagement time [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], few studies have considered all of these behaviors collectively and analyzed whether one behavior acts as a mediator between the other two in order to better understand the interrelationships between these three behaviors. Furthermore, while some studies have shown a linear relationship between sleep, social media use and PA [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], research has also reported more complex relationships [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], which warrants further investigation.\u003c/p\u003e \u003cp\u003eGiven these gaps in the existing evidence, the aim of this study was to explore the associations between social media use, PA, and sleep quality among adolescent girls. This study hypothesized that increased social media use would be associated with reduced sleep quality and PA and that sleep quality would mediate the relationship between social media use and PA.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 887 adolescent girls aged 12\u0026ndash;14 years were included in this study. The median age in 2019, 2020, and 2021 was 12 (IQR\u0026thinsp;=\u0026thinsp;1), 13 (IQR\u0026thinsp;=\u0026thinsp;1), and 13 (IQR\u0026thinsp;=\u0026thinsp;1) years, respectively as outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eDemographic characteristics of participant cohorts in 2019, 2020, and 2021.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.0 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.0 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e13.0 (1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e158.5 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e157.4 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e158.1 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e158.4 (9.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.2 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51.4 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e51.4 (14.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.1 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.5 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e20.5 (5.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist Circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.3 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e70.8 (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e69.0 (12.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip Circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.0 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e85.9 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e87.5 (11.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist: hip ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.8 (0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e192.3 (43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e225.3 (57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e188.4 (58.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.6 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.1 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e27.0 (13.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVPA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.3 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.4 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8.5 (9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal PA (min/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e194.1 (67.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e259.0 (81.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e227.8 (68.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVPA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.4 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30.7 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e36.0 (21.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.0 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.0 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.0 (4.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eAbbreviations: SD=standard deviation; IQR=interquartile range; BMI=body mass index; LPA=light intensity physical activity; MPA=moderate intensity physical activity; VPA=vigorous intensity physical activity; \\MVPA=moderate to vigorous intensity physical activity; PSQI=Pittsburgh sleep quality index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn terms of PA, approximately 91% of girls in 2019 did not meet the PA guidelines (\u0026lt;\u0026thinsp;60 minutes of MVPA per day), increasing to 95% in 2020 and decreasing to 87% in 2021. The median MVPA time was 23.4 (IQR\u0026thinsp;=\u0026thinsp;23.4), 30.7 (IQR\u0026thinsp;=\u0026thinsp;20.5), 36.0 (IQR\u0026thinsp;=\u0026thinsp;21.6) minutes per day in 2019, 2020, and 2021, respectively. With regards to sleep quality, the proportion of girls with good sleep quality (score\u0026thinsp;\u0026le;\u0026thinsp;5) was 65% in 2019, 64% in 2020, and 57% in 2021. For social media use, 66% of girls indicated that they spent\u0026thinsp;\u0026gt;\u0026thinsp;2 hours on social media per day in 2019 and this increased to 68% in 2020 and 75% in 2021.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe relationship between social media use, sleep quality and PA in 2019 (Pre COVID-19 pandemic, before any known restrictions or cases in NI/Ireland)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, in 2019, as time spent on social media increased, sleep quality (P\u0026lt;.001), total PA time (P=.027) and LPA time (P=.030) decreased. However, there was no significant relationship between daily social media use and MPA, VPA or MVPA time. Accordingly, mediation analyzes were conducted between social media use, sleep quality, total PA, and LPA. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, sleep quality did not serve as a mediator for either LPA time (P=.337) or total PA time (P=.364).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear regression analysis of the relationship between social media use, sleep quality, and PA in 2019 (Pre COVID-19 pandemic, before any known restrictions or cases in NI/Ireland).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.742\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal PA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-9.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.573\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.7937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.1.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVPA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.1.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.442\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVPA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.2.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: B=regression coefficient; β\u0026thinsp;=\u0026thinsp;standardized regression coefficient; 95% CI\u0026thinsp;=\u0026thinsp;95% confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation analysis of the relationships among social media use time, sleep quality, and PA time in 2019 (Pre COVID-19 pandemic, before any known restrictions or cases in NI/Ireland).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial media use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-7.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial media use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal PA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-9.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal PA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNotes: B=regression coefficient; β\u0026thinsp;=\u0026thinsp;standardized regression coefficient; 95% CI\u0026thinsp;=\u0026thinsp;95% confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviation: PSQI=Pittsburgh sleep quality index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe relationship between social media use, sleep quality and PA in 2020 (Public health restrictions in place to prevent spread of COVID-19)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, in 2020, as time spent on social media increased, only MPA time (P=.025) increased. There was no significant relationship between daily social media use and sleep quality, total PA, LPA, VPA, or MVPA. Therefore, these results did not meet the prerequisites for conducting further mediation analyzes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear regression analysis of the relationship between social media use, sleep quality, and PA in 2020 (Public health restrictions in place to prevent spread of COVID-19).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal PA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVPA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVPA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: B=regression coefficient; β\u0026thinsp;=\u0026thinsp;standardized regression coefficient; 95% CI\u0026thinsp;=\u0026thinsp;95% confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: PSQI=Pittsburgh sleep quality index; PA=physical activity; LPA=light intensity of physical activity; MPA=moderate intensity of physical activity; VPA=vigorous intensity of physical activity; MVPA=moderate to vigorous intensity of physical activity.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe relationship between social media use, sleep quality and PA in 2021 (Restrictions were easing, but some remained)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, in 2021, as time spent on social media increased, only sleep quality (P\u0026lt;.001) decreased. There was no significant relationship between daily social media use and any of the PA variables. Therefore, these results did not meet the prerequisites for conducting further mediation analyzes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear regression analysis of the relationship between social media use, sleep quality, and PA in 2021 (Restrictions were easing, but some remained).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSQI Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal PA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVPA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVPA (mins/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: B=regression coefficient; β\u0026thinsp;=\u0026thinsp;standardized regression coefficient; 95% CI\u0026thinsp;=\u0026thinsp;95% confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: PSQI=Pittsburgh sleep quality index; PA=physical activity; LPA=light intensity of physical activity; MPA=moderate intensity of physical activity; VPA=vigorous intensity of physical activity; MVPA=moderate to vigorous intensity of physical activity.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn summary, this study found that associations between social media use, sleep quality, and PA among adolescent girls varied across the three individual sampling years (2019 pre-pandemic, 2020 lockdown, and 2021 post-lockdown). Higher social media use was associated with poorer sleep quality in 2019 (pre-pandemic) and in 2021 (limited public health restrictions), whereas no such relationship was observed in 2020 (full public health restrictions). Social media use was associated with reduced total PA and LPA time in 2019. In 2020, increased social media use was positively associated with MPA time but in 2021, no relationship was observed between social media use and any PA intensities. Sleep quality did not mediate the relationship between social media use and PA time in any of the three years.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe negative association between social media use and sleep quality observed in 2019 and 2021 aligns with some previous studies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], with the exception of 2020 during which public health restrictions were in place related to the COVID-19 pandemic. Some studies have explained the negative impact of social media use time on sleep quality from a physiological perspective. For example, social media use can decrease individuals\u0026rsquo; secretion of melatonin, causing difficulties in falling asleep [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, specific content on social media platforms may increase individuals\u0026rsquo; psychophysiological arousal, such as sympathetic nervous system activities, thereby affecting their sleep quality [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In addition, intrinsically photosensitive retinal ganglion cells in the human eye are susceptible to blue light reflected by smartphone screens, potentially disrupting the human body\u0026rsquo;s natural sleep-wake rhythm and further damaging sleep quality [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In addition to these physiological impacts, research has also explained this phenomenon based on psychology. Psychologically, a \u0026ldquo;fear of missing out\u0026rdquo; associated with habitual notification checking can contribute to poor sleep hygiene [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Such behaviors may induce compulsive social media use and affect sleep quality. A meta-analysis further confirms these links, reporting that increased social media use was related to a shorter sleep duration, later bedtime and lower sleep quality with the authors calling for education initiatives promote healthy sleep in adolescence and address the dysfunctional and excessive use of social media [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe absence of an association between social media and sleep quality in 2020 aligns with a previous study by Hamilton \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] which in part attributed this to substantial changes in lifestyle and daily routines during the COVID-19 lockdown. During the lockdown period(s), adolescents had greater freedom and flexibility in accessing social media, resulting in more frequent and dispersed use throughout the day, and making social media a more integral part of their daily routines than previously [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This change to normal routines may have meant that while adolescents were using social media more during the day, they reduced pre-bedtime use and in some way reduced the negative impact on sleep latency [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Moreover, the cancellation of in-person classes and commuting during lockdown may have allowed adolescent girls to wake up later and obtain longer sleep duration [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], which may mitigate the impact of poor sleep quality, resulting from staying up late on social media. Taken together, these context-specific factors suggest that the detrimental effects of social media on sleep quality may be attenuated when adolescents have more control over their time and schedules but are likely to re-emerge under normal school routines as observed in 2019 and 2021.\u003c/p\u003e \u003cp\u003eIn 2019, before the COVID-19 pandemic, higher social media use was linked to lower total PA and LPA, consistent with previous evidence that increased screen time displaces active behaviors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], However, unlike those studies, this paper found that social media use was only associated with a decrease in LPA time, but not with other PA intensities. A possible explanation is that most of the girls included in this study had relatively low levels of MPA and VPA time already (only approximately 11% meeting PA guidelines), which may have limited the statistical power to detect such effects.\u003c/p\u003e \u003cp\u003eIn October 2020, a 8-week lockdown was imposed in NI and there was a two week extended mid-term break. During this period (when schools were open), social media use was positively associated with MPA. One possible explanation is that adolescent girls were frequently exposed to information about COVID-19 and health improvement while using social media during lockdown [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], which may have increased their awareness of the importance of health. During this time social media platforms promoted PA at home [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], such as dance workouts, fitness challenges, and exercise videos, which may have motivated engagement in similar activities [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Such activities are more likely to be recorded as MPA rather than LPA which may explain this association [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Moreover, research indicated that half of adolescents\u0026rsquo; MVPA took place at home, suggesting that the home lockdown may have inadvertently increased their MPA time [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In addition, the extended time spent at home could mitigate fatigue, thereby reducing the likelihood of discontinuing MPA due to tiredness [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn 2021, when restrictions had eased but some remained (for example, mask-wearing and year group bubbles), no significant association was found between social media use and PA, which may be the result of multiple interacting factors. Although most restrictions gradually ended, the ongoing presence of COVID-19 continued to affect individuals\u0026rsquo; daily lives [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The pandemic made social media a mainstream source for obtaining information [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and it gradually became a normalized and integrated part of adolescents\u0026rsquo; lives in the post-pandemic era. This was reflected in more flexible and fragmented use [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], which may have reduced its earlier influence on other activities including PA. In addition, as the perceived risk of the pandemic decreased, the amount of health and exercise-related content on social media also declined [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. For some adolescents, PA may have become a habit during the pandemic, reducing the reliance on social media as a motivating factor and the influence of social media on PA may have diminished [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSleep quality did not mediate the relationship between social media use and PA in any year. While the 2019 data permitted mediation testing (related to LPA and total PA), the lockdown-related disruptions of 2020 and 2021 prevented the establishment of the basic associations required for mediation analysis. Given the strong bidirectional links between social media use, sleep quality and PA reported elsewhere [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], the absence of mediation in this study should be interpreted with caution and warrants further investigation in more stable conditions.\u003c/p\u003e \u003cp\u003eThe key strengths of this study are the large sample size with sampling across 3 distinct years (pre-pandemic, lockdown, and post-lockdown). PA data were collected using devices (accelerometers), thus improving data validity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A novel aspect of this study is that such data was collected across the first three years of the COVID-19 pandemic, enabling year-by-year comparisons during a unique period in time.\u003c/p\u003e \u003cp\u003eHowever, it is important to recognize the limitations of this research. The majority of participants had low PA, and while this represents a \u0026ldquo;real-world context,\u0026rdquo; it does make it challenging to conduct nonlinear relationship analysis. Another limitation is that this study did not account for potential confounding variables and broader contextual factors. Future research should include factors identified in prior work, such as adolescents\u0026rsquo; body image satisfaction, anxiety, and depression levels [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], as well as contextual details such as online learning or school lockdown specifics. Sleep was measured using the validated PSQI questionnaire a subjective measure. Future research could complement this with more objective measures of sleep quality. Finally, social media use was self-reported and may be subject to participant recall bias which should be considered when interpreting the results of this research.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examined the associations between social media use, sleep quality, and PA among adolescent girls across pre-pandemic, pandemic, and post-lockdown periods. The findings show that these relationships appear to be context-dependent and were potentially influenced by the COVID-19 pandemic and associated lockdowns.\u003c/p\u003e \u003cp\u003eIn both 2019 and 2021, increased social media use was associated with poorer sleep quality, consistent with previous research. However, this relationship was absent in 2020 (during the pandemic), likely reflecting lifestyle changes during lockdown, including altered school routines and more flexible daily schedules. For PA, social media use was negatively associated with LPA and total PA in 2019, positively associated with MPA in 2020, and unrelated to PA in 2021. These shifts suggest that external factors, such as public health restrictions and media messaging, may influence behavior patterns among adolescents. Sleep quality did not mediate the relationship between social media use and PA in any sampling year. While this does not rule out the potential mediating role of sleep, it highlights the complexity of these interrelated behaviors, particularly in the context of a global pandemic. Overall, these findings underscore the importance of considering contextual and temporal factors when examining adolescent health behaviors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis study was a secondary analysis of baseline data collected as part of the WISH Study [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Baseline data was collected in the autumn of 2019, 2020 and 2021 from three different cohorts of pupils (girls aged 12\u0026ndash;14 years). The methodology has been described in detail previously [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In brief, post-primary schools (mixed or single-sex) in the border region of Ireland and NI were invited to participate in a cluster randomized controlled trial. Following permission from the School Principal, girls in Year 9/10 (NI) and 1st/2nd year (Ireland) were invited by teaching staff to attend a recruitment presentation from the Trial Manager. All eligible pupils who provided assent and written consent from their parent/guardian were invited to baseline data collection.\u003c/p\u003e \u003cp\u003e This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures were approved by Ulster University Research Ethics Committee (Ref: REC/19/0020).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eBaseline data collection took place on three occasions: (1) September - October 2019 (Pre COVID-19 pandemic, before any known restrictions or cases in Ireland/NI); (2) October - November 2020 (Public health restrictions in place to prevent spread of COVID-19); (3) October - November 2021 (Restrictions were easing, but some remained).\u003c/p\u003e \u003cp\u003eParticipants were asked to provide their date of birth to calculate age. Height (cm) and body mass (kg) were measured to the nearest 0.1 cm and 0.1 kg respectively, using a freestanding stadiometer (Leicester Height Measure), and digital scales (Seca 877) to calculate Body Mass Index (BMI) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Waist and hip circumference were measured to the nearest 0.1cm using an anatomical measuring tape and waist-to-hip ratio was calculated [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Measurements were undertaken by trained researchers and participants were asked to complete a series of questionnaires within the school premises on electronic devices (Apple iPad\u0026reg;) using Qualtrics (Provo, UT).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSocial media use\u003c/h2\u003e \u003cp\u003eAccording to the study by Woods \u0026amp; Scott [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], three items were included in the questionnaire to assess social media usage (1) Do you use social media? (2) How often do you use social media? and (3) How many hours do you use social media on a typical day? Participants were asked to self-report their daily time spent on social media from the following options: (1) less than one hour per day; (2) two to three hours; (3) three to four hours; (4) four to six hours; or (5) more than six hours.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSleep quality\u003c/h3\u003e\n\u003cp\u003eSleep quality data was measured by the PSQI questionnaire [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The PSQI is composed of 19 questions that assess 7 components (subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbance, use of sleep medication, and daytime dysfunction) and was used to measure sleep quality. Each question was scored 0 (no difficulty) to 3 (severe difficulty), and a global score was then calculated (0\u0026ndash;21). According to the PSQI classification, a sleep quality score of 0 to 5 is indicative of \u0026ldquo;good sleep quality,\u0026rdquo; while a score\u0026thinsp;\u0026gt;\u0026thinsp;5 is defined as \u0026ldquo;poor sleep quality [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u0026rdquo;\u003c/p\u003e\n\u003ch3\u003ePA\u003c/h3\u003e\n\u003cp\u003ePA was measured using an accelerometer (Actigraph LLC, Florida) worn for seven consecutive days. The device was placed on an elastic waistband and pupils were asked to wear the accelerometer on their right hip, removing it only for bathing, water-based activities such as swimming and when asleep. All data were downloaded and analyzed using ActiLife software (version 6.13.4; ActiGraph LLC, Florida, USA). Pupils were included in the analysis if they had\u0026thinsp;\u0026ge;\u0026thinsp;2 valid weekdays of data (500 mins/day) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. All valid data was included in the analysis. Minutes of total PA (light, moderate and vigorous) per day were estimated using the Evenson cut-points [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. A sampling epoch of 15s was employed during data collection. Periods of \u0026ge;\u0026thinsp;60 min of zero counts were categorized as \u0026ldquo;non-wear\u0026rdquo; and removed. Accelerometer data was used to calculate (1) time spent in LPA, moderate physical activity (MPA), MVPA and vigorous physical activity (VPA) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], (2) proportion of pupils meeting current PA recommendations of \u0026ge;\u0026thinsp;60 mins of MVPA per day [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eIBM SPSS software (version 29.0) was used for data analysis (Chicago, USA). Listwise deletion was used to remove missing data for social media use, sleep quality and PA variables. Reasons for missing data included accelerometer malfunctions or damage, accelerometer non-return, and participants withdrawing from the study before data collection. The final sample size available for this study was 887 adolescent girls, with sample sizes of 234, 150, and 503 in 2019, 2020, 2021, respectively.\u003c/p\u003e \u003cp\u003eThe Kolmogorov-Smirnov (K-S) test was conducted to assess the normality of variables for the years 2019, 2020, and 2021, respectively. Descriptive statistics were used to summarize demographic characteristics, social media use time, sleep quality and various intensities of PA time for 2019, 2020, and 2021, respectively.\u003c/p\u003e \u003cp\u003eFor each year (2019, 2020 and 2021), simple linear regressions were used to evaluate the relationship between daily social media use time, sleep quality and PA time, with social media use time as the independent variable. Time (mins) spent in different PA intensities (light, moderate, vigorous) and sleep quality score were the dependent variables. By comparing the significance of the effect of time spent on social media on PA in the current model with its effect in the previous linear regression model (where social media was the only independent variable), it could be determined whether sleep quality served as a partial or full mediator.\u003c/p\u003e \u003cp\u003e Considering the potential for non-linear relationships between these variables, binary logistic regressions were also conducted to explore whether daily social media use differentially influenced PA (meeting versus not meeting the PA guidelines), as well as good versus poor sleep quality. In both the simple linear and binary logistic regressions, daily social media use was the independent variable, whilst sleep quality and PA were the dependent variables. For the binary logistic regression, sleep quality was first converted into binary variables of \u0026ldquo;good\u0026rdquo; (0\u0026ndash;5) and \u0026ldquo;poor\u0026rdquo; (\u0026gt;\u0026thinsp;5) sleep quality [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] to fit the model. Similarly, PA was classified into \u0026ldquo;not meeting\u0026rdquo; (\u0026lt;\u0026thinsp;60 minutes MVPA per day) and \u0026ldquo;meeting\u0026rdquo; (\u0026ge;\u0026thinsp;60 minutes MVPA per day) based on the current PA Guidelines [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This binary variable conversion illustrated a significant imbalance between the sample sizes in comparing subgroups, especially the sample size in meeting and not meeting PA guidelines subgroups and given the bias that this imbalance posed to the data analysis, binary logistic regressions could not be performed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSQI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePittsburgh sleep quality index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ephysical activity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elight intensity of physical activity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emoderate intensity of physical activity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003evigorous intensity of physical activity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMVPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emoderate to vigorous intensity of physical activity.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThe authors would like to thank all members of the original Wish team for their contributions to this study, as well as all volunteers who participated in data collection, without whom this research would not have been possible.\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eLiu Junnan designed the study, organized the data, and wrote the initial draft.\u0026nbsp;S Maria O\u0026rsquo;Kane worked closely with the first author on revising the manuscript, coordinated with all authors, and contributed throughout the study. Ian M Lahart advised on the statistical aspects of the study. Marie H Murphy contributed to the overall study idea and commented on the manuscript. Angela Carlin, Alison M Gallagher, Leanne C Doherty, Gary McDermott, and Maria Faulkner provided comments and suggestions on the manuscript. All authors approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding information\u003c/h2\u003e\n\u003cp\u003eThe WISH Study was funded from INTERREG VA funding of \u0026euro;8.84m (including 15% contribution from the Department of Health in NI and Republic of Ireland), which had been awarded to the HSC Research \u0026amp; Development Division of the Public Health Agency Northern Ireland and to the Health Research Board in Ireland for the Cross-border Healthcare Intervention Trials in Ireland Network (CHITIN) project. The funders had no role in the design or execution of the study, analysis, interpretation of the data or decision to submit results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate/Consent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll personally identifiable information of the participants in this study has been removed from the text and tables, and informed consent was obtained for online open-assess publication.\u003c/p\u003e\n\n\u003ch2\u003eData availability statement\u003c/h2\u003e\n\u003cp\u003eThe datasets generated or analyzed during this study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRhee L, Bayer JB, Lee DS, Kuru O. Social by definition: how users define social platforms and why it matters. Telemat Inf. 2021;59:101538. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tele.2020.101538\u003c/span\u003e\u003cspan address=\"10.1016/j.tele.2020.101538\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOdgers CL, Schueller SM, Ito M. Screen time, social media use, and adolescent development. Annu Rev Dev Psychol. 2020;2:485\u0026ndash;502.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNilsson A, Rosendahl I. Jayaram-Lindstr\u0026ouml;m, N. Gaming and social media use among adolescents in the midst of the COVID-19 pandemic. Nordisk Alkohol Nark. 2022;39:347\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarciano L, Ostroumova M, Schulz PJ, Camerini A-L. Digital Media use and adolescents\u0026rsquo; mental health during the COVID-19 pandemic: a systematic review and meta-analysis. Front Public Health. 2022;9:793868. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpubh.2021.793868\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2021.793868\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelevich K, Wilbrecht L. Role of puberty on adult behaviors. Oxford research encyclopedia of neuroscience. Oxford University Press; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarter T, et al. The effect of physical activity on anxiety in children and young people: a systematic review and meta-analysis. J Affect Disord. 2021;285:10\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBull FC, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54:1451\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuthold R, Stevens GA, Riley LM, Bull FC. Global trends in insufficient physical activity among adolescents: a pooled analysis of 298 population-based surveys with 1\u0026middot;6 million participants. Lancet Child Adolesc Health. 2020;4:23\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalan-Lopez P, et al. Sleep quality and duration in European adolescents (the AdolesHealth study): a cross-sectional, quantitative study. Child (Basel). 2021;8:188.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlonzo R, Hussain J, Stranges S, Anderson KK. Interplay between social media use, sleep quality, and Mental Health in Youth: a systematic review. Sleep Med Rev. 2021;56:101414.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026ouml;nmez Sari E, Terzi H, Şahin D. Social Media Addiction and cognitive behavioral physical activity among adolescent girls: a cross-sectional study. Public Health Nurs. 2024;42:61\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorningstar B, et al. The association between social media use and physical activity among Canadian adolescents: a health behaviour in school-aged children (HBSC) study. Can J Public Health. 2023;114:642\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLima SB, et al. Sleep hours: risk behavior in adolescents from different countries. Cien Sa\u0026uacute;de Colet. 2020;25:957\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimoga SV, Erlyana E, Rebello V. Associations of social media use with physical activity and sleep adequacy among adolescents: cross-sectional survey. J Med Internet Res. 2019;21:e14290. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/14290\u003c/span\u003e\u003cspan address=\"10.2196/14290\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePagano M, Bacaro V, Crocetti E. Using digital media or sleeping \u0026hellip; that is the question. A meta-analysis on digital media use and unhealthy sleep in adolescence. Comput Hum Behav. 2023;146:107813.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhat S, Pinto-Zipp G, Upadhyay H, Polos PG. To sleep, perchance to tweet: in-bed electronic social media use and its associations with insomnia, daytime sleepiness, mood, and sleep duration in adults. Sleep Health. 2018;4:166\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCombertaldi SL, Ort A, Cordi M, Fahr A, Rasch B. Pre-sleep social media use does not strongly disturb sleep: a sleep laboratory study in healthy young participants. Sleep Med. 2021;87:191\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong NA, Bahmani H. A review of the current state of research on artificial blue light safety as it applies to digital devices. Heliyon. 2022;8:e10282. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heliyon.2022.e10282\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2022.e10282\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamilton JL, Hutchinson E, Evankovich MR, Ladouceur CD, Silk JS. Daily and average associations of physical activity, social media use, and sleep among adolescent girls during the Covid-19 pandemic. J Sleep Res. 2022;32:e13611.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScott H, Biello SM, Woods HC. Social Media use and adolescent sleep patterns: cross-sectional findings from the UK millennium cohort study. BMJ Open. 2019;9:e031161. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjopen-2019-031161\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2019-031161\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRocha S, Fuligni A. The impact of the COVID-19 pandemic on Adolescent sleep behavior. Curr Opin Psychol. 2023;52:101648.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang S, Liu M, Li Y, Chung JE. Teens\u0026rsquo; social media engagement during the Covid-19 pandemic: a time series examination of posting and emotion on reddit. Int J Environ Res Public Health. 2021;18:10079.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCugusi L, Di Blasio A, Bergamin M. The Social Media Gym-class: another lesson learnt from Covid-19 lockdown. Sport Sci Health. 2021;17:487\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026oacute;pez-Carril S, Escamilla-Fajardo P, Alguacil-Jim\u0026eacute;nez M. Physical activity using social media during the COVID-19 pandemic: the perceptions of sports science students. Phys Cult Sport Stud Res. 2021;92:19\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacIntosh BR, Murias JM, Keir DA, Weir JM. What is moderate to vigorous exercise intensity? Front Physiol. 2021;12:682233.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichards AB, et al. A socioecological perspective of how physical activity and sedentary behaviour at home changed during the first lockdown of Covid-19 restrictions: the homespace project. Int J Environ Res Public Health. 2022;19:5070. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph19095070\u003c/span\u003e\u003cspan address=\"10.3390/ijerph19095070\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKass L, Desai T, Sullivan K, Muniz D, Wells A. Changes to physical activity, sitting time, eating behaviours and barriers to exercise during the first COVID-19 lockdown in an English cohort. Int J Environ Res Public Health. 2021;18:10025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranks J, Gruss B, Mulas-Granados C, Patnam M, Weber S. Reopening strategies, mobility and COVID-19 infections in Europe: panel Data Analysis. BMJ Open. 2022;12:e055938.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFletcher R, Kalogeropoulos A, Nielsen R. Social media very widely used, but use for news and information about COVID-19 is declining. Oxford Internet Institute, University of Oxford \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ora.ox.ac.uk/objects/uuid:40fdcee2-3f44-4d11-bc98-be17bc94d5bf\u003c/span\u003e\u003cspan address=\"https://ora.ox.ac.uk/objects/uuid:40fdcee2-3f44-4d11-bc98-be17bc94d5bf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLitt DM, Iannotti RJ, Wang J. Motivations for adolescent physical activity. J Phys Act Health. 2011;8:220\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBassett DR, John D. Use of pedometers and accelerometers in clinical populations: validity and reliability issues. Phys Ther Rev. 2010;15:135\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScully M, Swords L, Nixon E. Social comparisons on social media: online appearance-related activity and body dissatisfaction in adolescent girls. Ir J Psychol Med. 2020;40:31\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Kane SM, et al. A study protocol for a clustered randomised controlled trial to evaluate the effectiveness of a peer-led school-based walking intervention on adolescent girls\u0026rsquo; physical activity: the walking in schools (WISH) study. BMC Public Health. 2020;20:541.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurphy MH, et al. Effectiveness of the walking in schools (WISH) study, a peer-led walking intervention for adolescent girls: results of a cluster randomised controlled trial. Int J Behav Nutr Phys Act. 2024;21:19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVidmar SI, Cole TJ, Pan H. Standardizing anthropometric measures in children and adolescents with functions for Egen: update. Stata J. 2013;13:366\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor RW, Jones IE, Williams SM, Goulding A. Evaluation of waist circumference, waist-to-hip ratio, and the CONICITY index as screening tools for high trunk fat mass, as measured by dual-energy x-ray absorptiometry, in children aged 3\u0026ndash;19y. Am J Clin Nutr. 2000;72:490\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoods HC, Scott H. #Sleepyteens: social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem. J Adolesc. 2016;51:41\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and Research. Psychiatry Res. 1989;28:193\u0026ndash;213.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWillis K, et al. Protocol for a cluster randomised controlled trial of a peer-led physical activity intervention for adolescent girls (PLAN-A). BMC Public Health. 2019;19:644. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12889-019-7012-x\u003c/span\u003e\u003cspan address=\"10.1186/s12889-019-7012-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvenson KR, Catellier DJ, Gill K, Ondrak KS, McMurray RG. Calibration of two objective measures of physical activity for children. J Sports Sci. 2008;26:1557\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Adolescent girls, Social media, Sleep quality, Physical activity, COVID-19 pandemic","lastPublishedDoi":"10.21203/rs.3.rs-9259267/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9259267/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSocial media use, sleep, and physical activity (PA) are behaviors that influence the physical and mental health of adolescent girls. Examining the associations between these behaviors during the COVID-19 pandemic is particularly important, as public health restrictions led to disruptions in normal daily life. This study aimed to examine associations between social media use, sleep quality, and PA among 887 adolescent girls (12\u0026ndash;14 years) in Ireland and Northern Ireland (NI) across pre-pandemic (2019), lockdown (2020), and post-lockdown (2021) periods. This study was a secondary data analysis of data collected as part of the Walking In ScHools (WISH) Study. Social media use was self-reported, sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI), and PA was assessed using accelerometers. The relationship between these variables was investigated using linear regression, binary logistic regression, and mediation analysis. Increased social media use was associated with poorer sleep quality pre-pandemic (P\u0026lt;.001) and post-lockdown (P\u0026lt;.001), but not during lockdown (P=.050). Pre-pandemic, social media use was associated with lower total (P=.027) and light physical activity (LPA) (P=.030), while during lockdown, it was linked to increased moderate physical activity (MPA) (P=.025), but no associations were found post-lockdown. Sleep quality did not mediate these relationships. The relationship between social media use, sleep quality, and PA among adolescent girls varied across pre-pandemic, lockdown and post-pandemic periods as daily routines and behavioral patterns may have changed. These findings highlight the context-dependent nature of adolescent health behaviors and the importance of considering environmental and temporal factors to better support adolescent well-being.\u003c/p\u003e","manuscriptTitle":"Associations between social media use, physical activity, and sleep quality in adolescent girls across three years of the COVID-19 pandemic","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-29 16:22:22","doi":"10.21203/rs.3.rs-9259267/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-21T09:50:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-01T06:48:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T04:46:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-31T04:45:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-03-29T13:36:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bc3f0a5e-4ed1-4db5-a961-ddec023fc89b","owner":[],"postedDate":"April 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T16:22:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-29 16:22:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9259267","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9259267","identity":"rs-9259267","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}