Associations between types of screen use and bedtimes among a large sample of adolescents: Age and bedtime specific variations from the COMPASS study

Associations between types of screen use and bedtimes among a large sample of adolescents: Age and bedtime specific variations from the COMPASS study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Associations between types of screen use and bedtimes among a large sample of adolescents: Age and bedtime specific variations from the COMPASS study Benjamin Tézier, Slim Haddad, Richard E. Bélanger, Claude Bacque Dion, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8704490/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted You are reading this latest preprint version Abstract Background Screen time is linked to poorer sleep among adolescents. However, most evidence focuses on total recreational screen time rather than on time spent on different screen-based activities. Also, few studies have specifically examined bedtime, an actionable determinant of adolescent sleep hygiene. To date, it is not clear how age and chronotype influence this relationship. The objective of this study was to examine the associations between time spent on different screen-based activities and adolescents’ bedtimes. We also aimed to examine how these associations vary based on age and chronotype. Methods Using a cross-sectional design, we analyzed data from 67,710 adolescents attending 154 Quebec schools in 2025 as part of the COMPASS study. Quantile regressions were conducted to assess associations between the time adolescents spend on different screen-based activities (i.e., surfing social media, playing video games, sending messages, browsing the Internet, watching/streaming television shows or movies) and their bedtime. The analyses were stratified by age and chronotype and adjusted for the adolescents’ sex and family-level material affluence. A difference-in-difference analysis was also conducted. Results Each type of recreational screen activity was positively associated with later bedtimes. Compared with adolescents who used screens ≤ 1 hour per day, those reporting more than 3 hours of screen use had bedtimes occurring approximately 45 minutes later for social media use, video gaming, and messaging, and about 30 minutes later for Internet browsing and television viewing. The difference in bedtime associated with higher versus lower screen use (≤ 1 hr vs. > 3 hrs/day) was greater among younger adolescents (11–12 years) than older ones (≥ 15 years), except for video gaming. The difference in bedtime associated with higher versus lower screen use (≤ 1 hr vs. > 3 hrs/day) was greater among late sleepers than early sleepers. Conclusions Each type of recreational screen activity among adolescents is associated with later bedtimes. Younger adolescents and late sleepers appear more susceptible to additional bedtime delays, which may ultimately influence their quantity and quality of sleep. This study offers important insights for the development of interventions and recommendations that target more vulnerable adolescents to promote healthy sleep, an essential component of adolescent health. Adolescence Screen time Screen-based activities Bedtime Figures Figure 1 Figure 2 Figure 3 Background Recreational screen time is associated with shorter sleep duration in adolescents ( 1 , 2 ), which entails consequences for health ( 3 ). Insufficient sleep has been associated with obesity ( 4 ), psychosocial difficulties, poorer academic performance, risky behaviours (e.g., alcohol and illicit substance use, physical injury) ( 5 ), and even increased suicidality among adolescents ( 6 ). Screen time reduces time available for sleep ( 2 ) and delays sleep onset due to cognitive and social stimulation ( 2 , 7 ) and disruption of circadian rhythms through blue light emission ( 3 ). Consequently, screen use has been consistently associated with poor sleep quality, shorter sleep duration, and sleep disturbances ( 8 , 9 ). Recent studies indicate that different screen-based activities – watching television, using social media, playing video games – have distinct implications for sleep ( 8 – 10 ). Some evidence suggests that screen-based activities that involve active engagement and promote physiological and psychological arousal (e.g., video games, social media) have a more deleterious impact on sleep than do passive uses (e.g., television) ( 10 – 12 ). Another study found that portable devices, easier to use in bed, drive the negative association with sleep duration ( 13 ). However, initial findings remain heterogeneous ( 2 ). Moreover, few studies have simultaneously examined or compared different types of screen-based activities ( 2 , 8 ). Some were conducted before the widespread adoption of newer platforms and devices, and thus may not fully reflect current patterns of screen use among technology-savvy adolescents ( 8 , 14 ). Given the rapid evolution of digital technologies ( 14 ) and the constant changes in adolescents’ screen-related behaviours (e.g., playing games or streaming video on phones/tablets) ( 15 ), updated evidence is needed. While some studies suggest that age influences the relationship between screen time and sleep ( 2 , 13 , 14 ), age-stratified analyses remain limited ( 14 , 16 ), and results published to date are inconsistent. Some suggest that younger adolescents experience greater sleep delays due to screen use than do older ones ( 2 , 14 ), while others report no moderating effect of age. Other studies have found stronger associations among older adolescents, who tend to use electronic devices later at night and have less parental supervision ( 13 , 14 ). These findings highlight the need to clarify how adolescent characteristics and screen types shape the relationship between screen use and sleep ( 14 , 16 , 17 ). Research on this issue has focused predominantly on total sleep duration. However, several studies underscore the value of considering bedtime as a distinct indicator rather than treating it solely as a component of total sleep duration ( 18 ). First, when studying sleep duration, focusing on both bedtime and wake-up time is important because they differentially affect sleep stages (e.g., deep sleep, rapid eye movement sleep). Another reason to focus on bedtime is that adolescents’ wake-up times are often constrained by school start times. Therefore, variations in sleep duration during adolescence are often driven by bedtimes ( 10 ). Research has shown that adolescents can increase sleep duration by advancing bedtime ( 18 ). From a health promotion perspective, bedtime offers a precise actionable target distinct from sleep duration, as it is directly influenced by modifiable behaviours and contextual factors such as screen use and evening routines. Yet few studies have focused on the association between different screen-based activities and bedtime ( 10 ), and even fewer have explored how these relationships may vary based on age. In addition, a meta-analysis ( 8 ) emphasized the need for studies that consider adolescents’ chronotypes, which refer to individual differences in preferred sleep–wake timing ( 22 ), including their usual bedtime. For example, one study ( 10 ) showed that evening-oriented “owls” and intermediate “robins” are more vulnerable to the effects of screen media use on sleep than are morning-oriented “larks”. The aim of the present study was to examine the associations between time spent on different types of screen-based activities – surfing social media, playing video games, sending messages and texts, browsing the Internet, and watching/streaming television shows or movies – and adolescents’ bedtimes. The study also examined how these relationships vary based on adolescents’ age and chronotype, that is, whether they are early, intermediate, or late sleepers. Methods Study design This study was based on a cross-sectional design. We used data collected from students attending 154 Quebec schools in 2025 as part of the COMPASS (Cannabis, Obesity, Mental health, Physical activity, Alcohol use, Smoking, and Sedentary behaviour) study, an ongoing school cohort study on adolescent health. Each year, youth in participating high schools complete a questionnaire about their lifestyle behaviours The complete COMPASS questionnaire is available online : https://uwaterloo.ca/compass-system/references/development-compass-student-questionnaire. In the province of Quebec, surveys have been administered since 2017 in partnership with school communities and regional public health departments. Data collection was conducted online using Qualtrics XM (Seattle, WA, USA). Details on the COMPASS study are available online (www.compass.uwaterloo.ca). Participants The study population included all high school students (equivalent to grades 7 to 11 in the rest of Canada and the United States) attending the 154 participating schools in Quebec. Altogether, 97,581 adolescents were eligible to participate. Parents’ active refusal rate was less than 0.4% (385), and 82,709 (84.8%) adolescents answered the online questionnaire between March and May 2025. A total of 67,710 adolescents provided responses to all of the variables of interests and were therefore included in our complete case analysis. Measures Bedtime : The students indicated the usual time at which they sleep, in 15-minute increments, by answering the question: “During the past week, what time have you usually turned out the light and fallen asleep in the evenings ?” Responses for weekdays and weekends were combined into a single weighted measure of average bedtime. Chronotype Adolescents were considered to be “early sleepers” if their average bedtime was at the 25th percentile, “intermediate sleepers” if it was at the 50th percentile, and “late sleepers” if it was at the 75th percentile. Screen-based activities : Students reported the usual time spent per day, in 15-minute increments, on five different screen-based activities: browsing/scrolling social media (e.g., Instagram, Tik Tok); playing video/computer games; surfing the Internet; texting, messaging, emailing (note: 50 texts = 30 minutes); and watching/streaming television shows or movies. Response choices were recoded into four categories: ≤ 1hr (1–60 min); 1–2hrs (61–120 min); 2–3hrs (121–180 min); > 3hrs (181 + min). These categories have been used in several studies (19, 20). Previous studies using such screen time data reported one-week test-retest intraclass correlation coefficients ranging from 0.54 to 0.86 (21). Sociodemographic variables : Students reported their sex (male, female) and age (11–12 years, 13–14 years, and 15 years and older). For family affluence, we estimated a composite score for adolescents who answered at least three of four questions regarding: 1) the average amount of money they received each week for personal spending or savings; 2) going to bed hungry because there was not enough money to buy food; 3) having the feeling that they and their family were less financially comfortable than the average student in their class; and 4) having their own bedroom. Composite scores were dichotomized (less affluent = 0 vs. more affluent = 1). This indicator was inspired by the Health Behaviour in School-Aged Children Family Affluence Scale (22). It has been used in previous research (23, 24). Data analysis All analyses were conducted using Stata/SE 18.5. Descriptive statistics were calculated for all variables of interest. A quantile regression model was used to analyze the relationship between bedtime and time spent on different screen-based activities. The model was adjusted for age, sex, and family affluence. Confidence intervals were estimated using a cluster bootstrap procedure (500 replications), resampling at the school level (25) to account for students being nested within schools, as their observations may not be fully independent. To examine how age influences these associations, analyses were stratified by age group. The analyses were also stratified by chronotype (early sleeper, intermediate sleeper, late sleeper). To deepen these analyses, a cross-sectional difference-in-difference analysis was performed (26). More specifically, to better understand the moderating role of age, for each screen-based activity we estimated the difference in bedtime between heavy users (> 3 hrs) and light users (≤ 1 hr) separately for adolescents 11–12 years of age and those 15 years and older. We then calculated the difference between these two differences. Similarly, to better understand the role of chronotype, the difference in bedtime between adolescents who used screens heavily (> 3 hrs) and those who used them lightly (≤ 1 hr) for each screen-based activity was estimated for early and late sleepers. We then calculated the difference between these two differences. Results Sample characteristics Table 1 shows sample characteristics of participants. Of the 67,710 adolescents included in this study, 34,693 (51.2%) were females. Mean age was 14.4 years ( ± 1.5). Mean bedtime was 22:50 (± 1.4 hrs). When examining the proportion of adolescents spending more than 2 hours per day on any given type of screen, we found that 41.5% exceeded this threshold for browsing/scrolling social media, 23% for playing video/computer games, 18.5% for watching/streaming television shows or movies, 14.6% for surfing the Internet, and 13.8% for sending messages (texting, messaging, emailing). Spending more than 3 hours per day was most common for social media use (25.1%) and video gaming (14.4%). Table 1 Descriptive characteristics of the study sample N/mean (median) %/SD 67,710 100% Age (mean) 14.4 ( 14 ) 1.5 Age categories 11–12 years 7,087 10.5 13–14 years 29,014 42.9 15 years and older 31,609 46.7 Sex Female 34,693 51.2 Male 33,017 48.8 Family affluence Less affluent 23,996 35.4 More affluent 43,714 64.6 Bedtime (mean) 22:50 (22:45) 1.4 Screen time Browsing/scrolling social media* ≤ 1 hr/day 22,729 33.6 1–2 hrs/day 16,883 24.9 2–3 hrs/day 11,112 16.4 > 3 hrs/day 16,986 25.1 Playing video/computer games* ≤ 1 hr/day 42,704 63.1 1–2hrs/day 9,433 13.9 2–3hrs/day 5,804 8.6 > 3 hrs/day 9,733 14.4 Sending messages (texting, messaging, emailing*) ≤ 1 hr/day 49,277 72.8 1–2 hrs/day 9,084 13.4 2–3hrs/day 3,722 5.5 > 3 hrs/day 5,627 8.3 Surfing the Internet* ≤ 1 hr/day 51,683 76.3 1–2 hrs/day 6,210 9.2 2–3 hrs/day 3,285 4.9 > 3 hrs/day 6,532 9.7 Watching/streaming television shows or movies* ≤ 1 hr/day 40,677 60.1 1–2hrs/day 14,472 21.4 2–3hrs/day 6,334 9.3 > 3 hrs/day 6,227 9.2 * ≤ 1hr (1–60 min); 1–2hrs (61–120 min); 2–3hrs (121–180 min); > 3hrs (181 + min) Associations between time spent on different screen-based activities and bedtimes All screen times were associated with later bedtimes, with variations depending on the type and duration of screen-related activity (Fig. 1 ). Compared with adolescents who spent 1 hour or less per day on social media, those who spent 1–2 hours went to bed about 15 minutes later (22:20, 95% CI: 22 : 17–22 : 23; vs. 22:35, 95% CI: 22 : 33–22 : 37 ). Increasing daily time spent browsing/scrolling social media (i.e., 2–3hrs, > 3hrs) was associated with progressively later bedtimes, with an approximate 15-minute delay at each higher screen time level (22:50, 95% CI: 22 : 47–22 : 53 ; then 23:05, 95% CI: 23 : 02–23 : 08 ). Thus, adolescents who spent more than 3 hours browsing/scrolling social media went to bed approximately 45 minutes later than those who spent 1 hour or less on average per day. A comparable pattern was observed for playing video games and sending messages. Increasing daily time spent playing video games was associated with progressively later bedtimes, with an approximate 15-minute delay at each higher screen time level (22:26, 95% CI: 22 : 20–22 : 32 ; 22:41, 95% CI: 22 : 35–22 : 46 ; 22:56, 95% CI: 22:51–23:00 ; 23:11, 95% CI: 23 : 06–23 : 15 ). Thus, compared with adolescents who played video games 1 hour or less per day, those who played more than 3 hours went to bed about 45 minutes later (22:26, 95% CI: 22 : 20–22 : 32 vs. 23:11, 95% CI: 23 : 06–23 : 15 ). Adolescents who spent more than 3 hours per day sending messages went to bed approximately 45 minutes later than those who spent 1 hour or less doing so (22:33, 95% CI: 22 : 30–22 : 36 vs. 23:18, 95% CI: 23 :04 –23 : 32 ), with an approximate same 15-minute delay at each higher screen time level. For both Internet surfing and watching/streaming television shows and movies, a threshold pattern was observed, with differences in predicted bedtime apparent only at higher levels of use. Bedtimes were approximately 30 minutes later among adolescents who surfed the Internet or watched television for more than 3 hours per day compared with those who did so for 2 hours or less. The moderating role of age Younger adolescents went to bed earlier than the oldest ones, regardless of average daily screen times. However, our difference-in-differences results suggest that younger adolescents are more sensitive to heavy screen time use. For comparable differences in screen uses (≤ 1 hr vs. > 3 hrs/day), younger adolescents (11–12 years) showed a greater delay in bedtime than older adolescents (15 years and older), except for video gaming (Fig. 2 ; Table 2 ). More specifically, compared with the ≤ 1hr/day group, 11–12-year-olds exposed to more than 3 hours per day of social media, Internet, or watching/streaming television shows and movies had an additional 30-minute delay in bedtime compared to the delay observed in those aged ≥ 15 years (Fig. 2 ; Table 2 ). Thus, for social media use, 11–12-year-olds had bedtimes ranging from 21:30 to 22:30, while the bedtimes of ≥ 15-year-olds ranged from 22:45 to 23:15, and for surfing the Internet and watching/streaming television shows or movies, 11–12-year-olds had bedtimes ranging from 21:45 to 22:30, while those of ≥ 15-year-olds ranged from 23:00 to 23:15. When comparing adolescents reporting 1 hour or less per day of messaging with those reporting more than 3 hours per day, predicted bedtime shifted from 21:45 to 22:45 among 11–12-year-olds. In contrast, the corresponding shift among adolescents aged ≥ 15 years was smaller, from 23:00 to 23:15. The moderating role of chronotypes For the same contrast in screen time (≤ 1 hr vs. > 3 hrs/day), late sleepers showed a larger bedtime delay than early sleepers (Fig. 3 ; Table 2 ). The additional bedtime delay associated with social media use for more than 3 hours per day (vs. ≤ 1hr) was 23 minutes greater for late sleepers than early sleepers. Late sleepers’ bedtime shifted from 23:01 to 24:01, while early sleepers’ bedtimes shifted from 21:44 to 22:22. The additional bedtime delay associated with video/computer games for more than 3 hours per day (vs. ≤ 1hr) was 38 minutes greater for late sleepers than early sleepers. Late sleepers’ bedtime shifted from 23:11 to 24:11, while early sleepers’ bedtime shifted from 21:54 to 22:17. When comparing adolescents who sent messages, surfed the Internet, and watched television for more than 3 hours per day with those who did so for 1 hour or less, late sleepers showed an additional delay in bedtime of approximately 30 minutes compared with early sleepers. Late sleepers who sent messages (e.g., texting) had a bedtime that shifted from 23:15 to 24:15, while early sleepers’ bedtime went from 21:56 to 22:26. Bedtimes of those who surfed the Internet shifted from 23:20 to 24:05 for late sleepers and from 21:57 to 22:12 for early sleepers. Finally, bedtimes of those who watching/streaming television shows or movies shifted from 23:20 to 24:05 for late sleepers and from 21:57 to 22:12 for early sleepers. Table 2 Double difference analysis of bedtime by screen-based activity and 1) age group and 2) chronotype Difference-in-differences in bedtime by screen time and age (11–12-year-olds vs. adolescents ≥ 15 years) Difference-in-differences in bedtime by screen time and chronotype (late sleepers vs. early sleepers) among adolescents Contrast 95% C.I. Contrast 95% C.I. Browsing/scrolling social media + 30 minutes 1 [25–34 minutes] + 23 minutes 2 [10–35 minutes] Playing video/computer games – 15 minutes 1 [-8(–)-22 minutes] + 38 minutes 2 [34–42 minutes] Sending messages (texting, messaging, emailing) + 45 minutes 1 [37–53 minutes] + 30 minutes 2 [17–43 minutes] Surfing the Internet + 30 minutes 1 [18–42 minutes] + 30 minutes 2 [27–36 minutes] Watching/streaming television shows or movies + 30 minutes 1 [19–41 minutes] + 30 minutes 2 [25–35 minutes] 1 Interpretation: The additional bedtime delay associated with engaging in [activity X] for > 3 hrs/day (vs. ≤ 1 hr/day) was [Y minutes] greater for 11–12-year-olds than for ≥ 15-year-olds. 2 Interpretation: The additional bedtime delay associated with engaging in [activity X] for > 3 hrs/day (vs. ≤ 1 hr) was [Y minutes] greater for later sleepers than for early sleepers. Discussion Consistent with previous research on sleep duration ( 10 , 27 ), this large-scale study on adolescents shows that greater screen times, regardless of type of activity, are associated with later bedtimes. Young adolescents and those who typically go to bed later are particularly susceptible to further bedtime delays with screen use, a finding not well documented in previous research ( 2 , 13 , 18 ). Several mechanisms may explain these associations. Social media are particularly disruptive because of their highly engaging, interactive, and emotionally stimulating nature (e.g., notifications, waiting for replies, social comparison) ( 28 , 29 ). Moreover, for phones and tablets, ease of use in bed ( 10 ) and close proximity to the face may also affect sleep ( 30 , 31 ). As well, video games could be strongly associated with later bedtime due to their competitive and immersive nature, which demands intense attention, focus, and emotional involvement, incompatible with winding down for sleep ( 32 , 33 ). Their growing social component may also lead adolescents to prioritize a sense of belonging over sleep, through group play schedules or pressure to complete missions ( 34 , 35 ). Additionally, factors like loot-boxing ( 36 ) and raging ( 37 ) can trigger anxiety and agitation that disrupt bedtime routines. Messaging may delay bedtime when adolescents bring their devices to bed and feel the need to remain constantly available and to respond quickly, often due to fear of missing out (FOMO) ( 27 , 38 ). This pressure to stay connected with, for example, friends or romantic partners, can maintain emotional and cognitive arousal and delay sleep preparation. Adolescents who watched or streamed television shows or movies for more than two hours had later bedtimes than those who did so for one hour or less. This is likely due to the passive nature of this activity, which demands less engagement compared with other types of screen use ( 8 , 10 , 11 ). Finally, television is sometimes viewed from a greater distance, reducing the amount of light, including blue wavelengths, reaching the retina, which may in turn lessen its impact on melatonin suppression and sleep timing ( 39 , 40 ). Similarly, Internet browsing was associated with a later bedtime among adolescents who engaged in this activity for more than two hours, compared with those who browsed for one hour or less. One potential explanation for this finding could be that this activity may be goal-directed (e.g., schoolwork, information seeking), which would imply a more determined end compared to activities on social media or video games. Our findings suggest that the association between screen time and later bedtimes is stronger among younger adolescents than older adolescents for most screen-based activity, with the exception of video gaming. Several mechanisms may explain this vulnerability ( 33 , 41 , 42 ). Younger adolescents generally go to bed earlier and therefore have less discretionary time in the evening. With less free time available, screen use is more likely to encroach directly on their bedtime. Evidence further shows that they are more likely to use screens before going to bed and to engage in nighttime use ( 41 , 43 , 44 ). Additionally, blue light may have a more negative impact on physiological mechanisms, such as melatonin secretion, in younger adolescents ( 14 , 41 ). Because their identity and emotional regulation are still developing, younger adolescents may also be more sensitive to peer evaluation, conflict, or emotionally charged content ( 45 , 46 ), making it harder to disengage from screens at bedtime. Their stronger drive for social recognition and belonging can reinforce the urge to stay connected through social media and messaging, ultimately delaying bedtime. Video gaming was the only screen activity for which older adolescents (≥ 15 years) showed greater shifts to later bedtimes than younger adolescents. This pattern may reflect differences in game types and play contexts, with older adolescents more often engaging in competitive and violent games and participating in organized sessions that extend late into the night, sometimes supported by online communication platforms (e.g., Discord). This study provides new evidence on how adolescents’ chronotypes shape the association between screen use and bedtime. Early sleepers may be more accustomed to setting routines or rules ( 47 ) and practicing good sleep hygiene, which helps them better regulate their screen use (e.g., by avoiding screens after a set time). An earlier bedtime also mechanically reduces opportunities for late-night screen exposure, limiting both blue light related circadian disruption and the cognitive arousal that follows social media use, messaging, or gaming. In contrast, late sleepers may enter a vicious cycle, as cognitive arousal and circadian rhythm disturbances are likely to be more pronounced at later hours ( 48 ). To fully understand the implications of this study’s findings, it is important to consider the consequences of accumulating a sleep debt. Small nightly delays in bedtime accumulate over time, resulting in several hours of lost sleep over weeks or months. Cumulative sleep debt is associated with academic difficulties, emotional and relational problems, higher risk of depression and anxiety, as well of cardiovascular disease, diabetes, immune dysfunction, hormonal imbalances, or even Alzheimer’s disease ( 49 – 51 ). Even after periods of recovery sleep, cognitive performance often continues to deteriorate, suggesting that the adverse effects of sleep restriction are only partially reversible ( 52 ). Moreover, going to bed late can reduce deep sleep, which occurs mainly in the early part of the night ( 53 , 54 ). Because deep sleep is essential for physical restoration, delayed bedtimes may limit these regenerative processes, potentially triggering a cascade of negative health effects ( 55 ). Practical implications Our findings support efforts aimed at reducing time spent on highly stimulating screen-based activities, such as social media, video games, or messaging, to promote healthier sleep habits amongst adolescents ( 10 ). Some disconnection strategies could be cultivated, such as setting device time limits, creating screen-free zones (e.g., bedroom), or encouraging relaxing bedtime routines (e.g., reading, relaxation) ( 56 , 57 ). Moreover, integrating digital literacy and sleep literacy into school curricula, with explicit attention to the relationship between screen time and sleep, could help reach large groups of adolescents simultaneously ( 58 , 59 ). Promoting sleep literacy can enhance knowledge, attitudes, and understanding about the importance of sleep, its recommended duration, factors that influence it, and strategies to enhance it ( 60 ). Improving adolescents’ understanding of online behaviours, screen use, and their potential effects on sleep may foster healthier digital habits and bedtime routines. Redirecting screen time toward physical activity could also be particularly beneficial, as physical activity increases energy expenditure and supports circadian and thermoregulatory processes, promoting earlier sleep onset and better sleep quality ( 61 ). Evidence shows that replacing sedentary time with physical activity improves adolescents’ overall health and sleep outcomes ( 62 , 63 ). This perspective aligns with the 24-Hour Movement Guidelines, which emphasize the interdependence of physical activity, sedentary behaviour, and sleep within a 24-hour cycle ( 64 , 65 ). 4.2 Limitations Several limitations should be considered when interpreting the findings of this study. First, the cross-sectional design of our analysis precludes any inference about the causal direction between screen time and bedtime. These associations may be bidirectional. Furthermore, we did not collect information on the timing of screen use (e.g., in the evening or before bedtime vs. during the day), which would have helped deepen our understanding of this phenomenon. The specific content adolescents engaged with was also not documented (e.g., passive doomscrolling vs. active content creation, or watching intense dramatic series vs. humor or lighthearted shows). Future research should investigate these different types of content ( 66 ), as well as the locations in which screens are used, which could be decisive (e.g., playing on the phone while lying in bed vs. using a computer while sitting at a desk). Finally, since this study was conducted in Quebec (Canada), replication in other cultural and geographical contexts would help strengthen the generalizability of the results. Conclusion These findings indicate that all types of time spent on screen-based activities among adolescents are associated with later bedtimes. Some activities, such as browsing social media, playing video/computer games, and sending messages, could be prioritized for targeted action given the strength of their associations with later bedtimes. Younger adolescents and those who already have later bedtimes are more prone to additional sleep delays. This study offers important insights for the development of interventions that target adolescents, including those more vulnerable to bedtime delays. Promoting sleep (specifically earlier bedtimes) and digital literacy among adolescents, parents, educators, and other public health stakeholders could help support healthy sleep habits and reduce harmful screen use. Strengthening this collective understanding also means ensuring that adolescents and the adults who guide them are informed about how different types of screens can disrupt bedtimes and what strategies can promote healthy sleep hygiene, ultimately contributing to better overall health and well-being among youth. Declarations This study was conducted in accordance with the Declaration of Helsinki. All procedures were approved by the University of Waterloo Ethics Committee (ORE#30118), the CIUSSS de la Capitale-Nationale–Université Laval (#MP-13-2017-1264), and the participating school boards. We used an active-Information Passive-Consent Permission Protocol where parents are fully informed about the study via letter/email but only need to contact researchers to decline their child's participation. This method maximizes participation rates and reduces bias compared to active opt-in, while allowing students to opt-out at any time. Each participant provided informed consent. Ethics approval and consent to participate Each participant provided informed consent. This study was conducted in accordance with the Declaration of Helsinki. All procedures were approved by the University of Waterloo Ethics Committee (ORE#30118), the CIUSSS de la Capitale-Nationale–Université Laval (#MP-13-2017-1264), and the participating school boards. Consent for publication Not Applicable Funding The COMPASS study has been supported by a bridge grant from the CIHR Institute of Nutrition, Metabolism and Diabetes (INMD) through the Obesity – Interventions to Prevent or Treat priority funding awards (OOP-110788; awarded to RB, SH, STL), an operating grant from the CIHR Institute of Population and Public Health (IPPH) (MOP-114875; awarded to RB, SH, STL), a CIHR project grant (PJT-148562; awarded to RB, SH, STL), a CIHR bridge grant (PJT-149092; awarded to RB, SH, STL), a CIHR project grant (PJT-159693), a research funding arrangement with Health Canada (#1617-HQ-000012; contract awarded to RB, SH, STL ) , and a CIHR-Canadian Center on Substance Abuse (CCSA) team grant (OF7 B1-PCPEGT 410-10-9633; awarded to RB, SH, STL). COMPASS-Quebec additionally benefits from funding from the Ministère de la Santé et des Services sociaux du Québec, and the Direction régionale de santé publique du CIUSSS de la Capitale-Nationale. As part of the program Concerted Actions – Research Program on Screen Use and Youth Health , the COMPASS study received funds from the Fonds de recherche du Québec – Société et culture and the Ministère de la Santé et des Services sociaux du Québec (#2024-0UER-339082, #2025-0UER-361232). BT is funded by a fellowship award from the Fonds de Recherche du Québec - Santé as part of the Postdoctoral training – Citizens of other countries program (#355168). AMTT is supported by the research scholar program of the Fonds de recherche du Québec – Santé. The funding bodies played no role in the design of the study, nor in the collection, analysis, or interpretation of the data or the writing of the manuscript. Author Contribution AMTT, BT, and SH conceptualized and conducted the analysis. AMTT and BT conducted the literature review. BT led the writing and wrote the first draft of the manuscript. STL conceptualized and leads the larger COMPASS study. AMTT, RB, and SH are the COMPASS-Québec provincial leads. CBD coordinated the data collection and revised the manuscript. All authors provided feedback on the manuscript and approved the final version. Acknowledgement The authors wish to thank the Quebec public health authorities, the participating schools, school boards, and students, and the entire COMPASS team for their contributions. Data Availability The datasets used and/or analyzed during the current study are available from the authors upon reasonable request. References Carter B, Rees P, Hale L, Bhattacharjee D, Paradkar MS. Association between portable screen-based media device access or use and sleep outcomes: a systematic review and meta-analysis. JAMA Pediatr. 2016;170(12):1202–8. Hale L, Guan S. 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Kaiser Family Foundation. 2008. https://www.kff.org/wp-content/uploads/2013/01/7674.pdf Wood B, Rea MS, Plitnick B, Figueiro MG. Light level and duration of exposure determine the impact of self-luminous tablets on melatonin suppression. Appl Ergon. 2013;44(2):237–40. Ivarsson M, Anderson M, Åkerstedt T, Lindblad F. The effect of violent and nonviolent video games on heart rate variability, sleep, and emotions in adolescents with different violent gaming habits. Psychosom Med. 2013;75(4):390–6. King DL, Gradisar M, Drummond A, Lovato N, Wessel J, Micic G, et al. The impact of prolonged violent video-gaming on adolescent sleep: an experimental study. J Sleep Res. 2013;22(2):137–43. Ceranoglu TA. Video games and sleep: an overlooked challenge. Adolesc Psychiatry. 2014;4(2):104–8. Weinstein AM. Computer and video game addiction – a comparison between game users and non-game users. Am J Drug Alcohol Abuse. 2010;36(5):268–76. Ide S, Nakanishi M, Yamasaki S, Ikeda K, Ando S, Hiraiwa-Hasegawa M, et al. Adolescent problem gaming and loot box purchasing in video games: cross-sectional observational study using population-based cohort data. JMIR Serious Games. 2021;9(1):e23886. Kahila J, Piispa-Hakala S, Kahila S, Valtonen T, Vartiainen H, Tedre M. If the game does not work, it is lagging, or you die in game, you just get furious – Children’s experiences on gamer rage. In: Bujic M, Koivisto J, Hamari J, editors. GamiFIN Conference 2021. RWTH Aachen. pp. 21–9. http://ceur-ws.org/Vol-2883/paper3.pdf Woo KS, Bong SH, Choi TY, Kim JW. Mental health, smartphone use type, and screen time among adolescents in South Korea. Psychol Res Behav Manag. 2021;14:1419–28. Komada Y, Aoki K, Gohshi S, Ichioka H, Shibata S. Effects of television luminance and wavelength at habitual bedtime on melatonin and cortisol secretion in humans: blue light and melatonin secretion. Sleep Biol Rhythms. 2015;13(4):316–22. Tsai PC, Cheng MH, Peng BH, Jou JH, Cheng YH, Ku YC, et al. Permissible viewing times of educational projector and TV. Heliyon. 2023;9(5):e15522. Oshima N, Nishida A, Shimodera S, Tochigi M, Ando S, Yamasaki S, et al. The suicidal feelings, self-injury, and mobile phone use after lights out in adolescents. J Pediatr Psychol. 2012;37(9):1023–30. Vernon L, Modecki KL, Barber BL. Mobile phones in the bedroom: trajectories of sleep habits and subsequent adolescent psychosocial development. Child Dev. 2018;89(1):66–77. Brosnan B, Meredith-Jones KA, Haszard JJ, Wickham SR, Galland BC, Russell-Camp T, et al. From dusk to dawn: examining how adolescents engage with digital media using objective measures of screen time in a repeated measures study. Int J Behav Nutr Phys Act. 2025;22(1):4. Kortesoja L, Vainikainen MP, Hotulainen R, Merikanto I. Late-night digital media use in relation to chronotype, sleep and tiredness on school days in adolescence. J Youth Adolesc. 2023;52(2):419–33. Crone EA, Konijn EA. Media use and brain development during adolescence. Nat Commun. 2018;9(1):588. Hollenstein T, Faulkner K. Adolescent digital emotion regulation. J Res Adolesc. 2024;34(4):1341–51. Kosticova M, Dankulincova Veselska Z, Sokolova L, Dobiášová E. Late bedtime from the perspective of adolescents: a qualitative study. Nat Sci Sleep. 2024;16:1973–85. Weaver E, Gradisar M, Dohnt H, Lovato N, Douglas P. The effect of presleep video-game playing on adolescent sleep. J Clin Sleep Med. 2010;06(02):184–9. Shen L, Wiley JF, Bei B. Perceived daily sleep need and sleep debt in adolescents: associations with daily affect over school and vacation periods. Sleep. 2021;44(12):zsab190. Lo JC, Ong JL, Leong RL, Gooley JJ, Chee MWL. Cognitive performance, sleepiness, and mood in partially sleep deprived adolescents: the need for sleep study. Sleep. 2016;39(3):687–98. Beebe DW. The cumulative impact of adolescent sleep loss: next steps. Sleep. 2016;39(3):497–9. Smith MG, Wusk GC, Nasrini J, Baskin P, Dinges DF, Roma PG, et al. Effects of six weeks of chronic sleep restriction with weekend recovery on cognitive performance and wellbeing in high-performing adults. Sleep. 2021;44(8):zsab051. Siclari F, Tononi G. Chapter 7 - Sleep and dreaming. In: Laureys S, Gosseries O, Tononi G, editors. The neurology of conciousness: cognitive science and neuropathology. 2nd ed. San Diego: Academic; 2016. pp. 107–28. Walker MP. Sleep-dependent memory processing. In: Squire LR, editor. Encyclopedia of Neuroscience. Oxford: Academic; 2009. pp. 1055–65. Patel AK, Reddy V, Shumway KR, Araujo JF. Physiology, sleep stages. StatPearls Publishing. 2024. https://www.ncbi.nlm.nih.gov/books/NBK526132/ Bounova A, Michalopoulou M, Agelousis N, Kourtessis T, Gourgoulis V. The parental role in adolescent screen related sedentary behavior. Int J Adolesc Med Health. 2018;30(2):20160031. Jones A, Armstrong B, Weaver RG, Parker H, Von Klinggraeff L, Beets MW. Identifying effective intervention strategies to reduce children’s screen time: a systematic review and meta-analysis. Int J Behav Nutr Phys Act. 2021;18(1):126. Tinmaz H, Lee YT, Fanea-Ivanovici M, Baber H. A systematic review on digital literacy. Smart Learn Environ. 2022;9(1):21. Quraishi T, Ulusi H, Muhid A, Hakimi M, Olusi MR. Empowering students through digital literacy: a case study of successful integration in a higher education curriculum. J Digit Learn Distance Educ. 2024;2(9):667–81. Walker M. Why we sleep: unlocking the power of sleep and dreams. New York: Simon and Schuster; 2017. Castiglione-Fontanellaz CEG, Timmers TT, Lerch S, Hamann C, Kaess M, Tarokh L. Sleep and physical activity: results from a long-term actigraphy study in adolescents. BMC Public Health. 2022;22(1):1328. Saunders TJ, Gray CE, Poitras VJ, Chaput JP, Janssen I, Katzmarzyk PT, et al. Combinations of physical activity, sedentary behaviour and sleep: relationships with health indicators in school-aged children and youth. Appl Physiol Nutr Metab. 2016;41(6 Suppl 3):S283–93. Carson V, Chaput JP, Janssen I, Tremblay MS. Health associations with meeting new 24-hour movement guidelines for Canadian children and youth. Prev Med. 2017;95:7–13. Tremblay MS, Carson V, Chaput JP, Connor Gorber S, Dinh T, Duggan M, et al. Canadian 24-Hour Movement Guidelines for Children and Youth: An Integration of Physical Activity, Sedentary Behaviour, and Sleep. Appl Physiol Nutr Metab. 2016;41(6 Suppl 3):S311–27. Chaput JP, Carson V, Gray CE, Tremblay MS. Importance of all movement behaviors in a 24 hour period for overall health. Int J Environ Res Public Health. 2014;11(12):12575–81. Valkenburg PM, van Driel II, Beyens I. The associations of active and passive social media use with well-being: a critical scoping review. New Media Soc. 2022;24(2):530–49. 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Bélanger","email":"","orcid":"","institution":"VITAM – Centre de recherche en santé durable","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"E.","lastName":"Bélanger","suffix":""},{"id":592357585,"identity":"d16069d3-4f76-4233-bd20-c1380d5387fd","order_by":3,"name":"Claude Bacque Dion","email":"","orcid":"","institution":"VITAM – Centre de recherche en santé durable","correspondingAuthor":false,"prefix":"","firstName":"Claude","middleName":"Bacque","lastName":"Dion","suffix":""},{"id":592357586,"identity":"67b07762-7db1-4748-bceb-524aba1f77c1","order_by":4,"name":"Scott Leatherdale","email":"","orcid":"","institution":"University of Waterloo","correspondingAuthor":false,"prefix":"","firstName":"Scott","middleName":"","lastName":"Leatherdale","suffix":""},{"id":592357587,"identity":"dc09c640-d0a4-4601-93fe-7f3d1ecf6cae","order_by":5,"name":"Anne-Marie Turcotte-Tremblay","email":"data:image/png;base64,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","orcid":"","institution":"VITAM – Centre de recherche en santé durable","correspondingAuthor":true,"prefix":"","firstName":"Anne-Marie","middleName":"","lastName":"Turcotte-Tremblay","suffix":""}],"badges":[],"createdAt":"2026-01-27 01:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8704490/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8704490/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103224387,"identity":"a98308ea-4bf1-4ad9-bc92-b47b031d140e","added_by":"auto","created_at":"2026-02-23 10:51:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":322185,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eAssociations between predicted bedtime and time spent on screen-based activities among adolescents\u003c/u\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8704490/v1/83c6be26ee7be35847d60bed.jpg"},{"id":103224386,"identity":"96f0a80a-822e-4d94-bda8-1946a01378ae","added_by":"auto","created_at":"2026-02-23 10:51:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":433386,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eAssociations between bedtime and different screen-based activities as a function of age\u003c/u\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8704490/v1/5e4241859bfbb7f5ad7d2680.jpg"},{"id":103224388,"identity":"8f655dab-6fae-4ab0-a3a1-2c7b9970adf7","added_by":"auto","created_at":"2026-02-23 10:51:20","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":457480,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eAssociations between bedtime and different screen-based activities as a function of chronotype\u003c/u\u003e\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8704490/v1/026fea76286d56cd0348b190.jpg"},{"id":104779106,"identity":"9a60549e-74f4-4a33-8b5b-6fd89004e419","added_by":"auto","created_at":"2026-03-17 07:34:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2197471,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8704490/v1/bbcedd8b-b82d-4be8-82b2-c1fd4dc40e1c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between types of screen use and bedtimes among a large sample of adolescents: Age and bedtime specific variations from the COMPASS study","fulltext":[{"header":"Background","content":"\u003cp\u003eRecreational screen time is associated with shorter sleep duration in adolescents (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), which entails consequences for health (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Insufficient sleep has been associated with obesity (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), psychosocial difficulties, poorer academic performance, risky behaviours (e.g., alcohol and illicit substance use, physical injury) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), and even increased suicidality among adolescents (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Screen time reduces time available for sleep (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) and delays sleep onset due to cognitive and social stimulation (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) and disruption of circadian rhythms through blue light emission (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Consequently, screen use has been consistently associated with poor sleep quality, shorter sleep duration, and sleep disturbances (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent studies indicate that different screen-based activities \u0026ndash; watching television, using social media, playing video games \u0026ndash; have distinct implications for sleep (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Some evidence suggests that screen-based activities that involve active engagement and promote physiological and psychological arousal (e.g., video games, social media) have a more deleterious impact on sleep than do passive uses (e.g., television) (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Another study found that portable devices, easier to use in bed, drive the negative association with sleep duration (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, initial findings remain heterogeneous (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, few studies have simultaneously examined or compared different types of screen-based activities (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Some were conducted before the widespread adoption of newer platforms and devices, and thus may not fully reflect current patterns of screen use among technology-savvy adolescents (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Given the rapid evolution of digital technologies (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) and the constant changes in adolescents\u0026rsquo; screen-related behaviours (e.g., playing games or streaming video on phones/tablets) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), updated evidence is needed.\u003c/p\u003e \u003cp\u003eWhile some studies suggest that age influences the relationship between screen time and sleep (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), age-stratified analyses remain limited (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), and results published to date are inconsistent. Some suggest that younger adolescents experience greater sleep delays due to screen use than do older ones (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), while others report no moderating effect of age. Other studies have found stronger associations among older adolescents, who tend to use electronic devices later at night and have less parental supervision (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). These findings highlight the need to clarify how adolescent characteristics and screen types shape the relationship between screen use and sleep (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearch on this issue has focused predominantly on total sleep duration. However, several studies underscore the value of considering bedtime as a distinct indicator rather than treating it solely as a component of total sleep duration (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). First, when studying sleep duration, focusing on both bedtime and wake-up time is important because they differentially affect sleep stages (e.g., deep sleep, rapid eye movement sleep). Another reason to focus on bedtime is that adolescents\u0026rsquo; wake-up times are often constrained by school start times. Therefore, variations in sleep duration during adolescence are often driven by bedtimes (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Research has shown that adolescents can increase sleep duration by advancing bedtime (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). From a health promotion perspective, bedtime offers a precise actionable target distinct from sleep duration, as it is directly influenced by modifiable behaviours and contextual factors such as screen use and evening routines. Yet few studies have focused on the association between different screen-based activities and bedtime (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), and even fewer have explored how these relationships may vary based on age.\u003c/p\u003e \u003cp\u003eIn addition, a meta-analysis (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) emphasized the need for studies that consider adolescents\u0026rsquo; chronotypes, which refer to individual differences in preferred sleep\u0026ndash;wake timing (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), including their usual bedtime. For example, one study (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) showed that evening-oriented \u0026ldquo;owls\u0026rdquo; and intermediate \u0026ldquo;robins\u0026rdquo; are more vulnerable to the effects of screen media use on sleep than are morning-oriented \u0026ldquo;larks\u0026rdquo;.\u003c/p\u003e \u003cp\u003eThe aim of the present study was to examine the associations between time spent on different types of screen-based activities \u0026ndash; surfing social media, playing video games, sending messages and texts, browsing the Internet, and watching/streaming television shows or movies \u0026ndash; and adolescents\u0026rsquo; bedtimes. The study also examined how these relationships vary based on adolescents\u0026rsquo; age and chronotype, that is, whether they are early, intermediate, or late sleepers.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eStudy design\u003c/h2\u003e\n \u003cp\u003eThis study was based on a cross-sectional design. We used data collected from students attending 154 Quebec schools in 2025 as part of the COMPASS (Cannabis, Obesity, Mental health, Physical activity, Alcohol use, Smoking, and Sedentary behaviour) study, an ongoing school cohort study on adolescent health. Each year, youth in participating high schools complete a questionnaire about their lifestyle behaviours The complete COMPASS questionnaire is available online : https://uwaterloo.ca/compass-system/references/development-compass-student-questionnaire. In the province of Quebec, surveys have been administered since 2017 in partnership with school communities and regional public health departments. Data collection was conducted online using Qualtrics XM (Seattle, WA, USA). Details on the COMPASS study are available online (www.compass.uwaterloo.ca).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThe study population included all high school students (equivalent to grades 7 to 11 in the rest of Canada and the United States) attending the 154 participating schools in Quebec. Altogether, 97,581 adolescents were eligible to participate. Parents’ active refusal rate was less than 0.4% (385), and 82,709 (84.8%) adolescents answered the online questionnaire between March and May 2025. A total of 67,710 adolescents provided responses to all of the variables of interests and were therefore included in our complete case analysis.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003e\u003cem\u003eBedtime\u003c/em\u003e: The students indicated the usual time at which they sleep, in 15-minute increments, by answering the question: “During the past week, what time have you usually turned out the light and fallen asleep in the evenings ?” Responses for weekdays and weekends were combined into a single weighted measure of average bedtime.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChronotype\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdolescents were considered to be “early sleepers” if their average bedtime was at the 25th percentile, “intermediate sleepers” if it was at the 50th percentile, and “late sleepers” if it was at the 75th percentile.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eScreen-based activities\u003c/em\u003e: Students reported the usual time spent per day, in 15-minute increments, on five different screen-based activities: browsing/scrolling social media (e.g., Instagram, Tik Tok); playing video/computer games; surfing the Internet; texting, messaging, emailing (note: 50 texts = 30 minutes); and watching/streaming television shows or movies. Response choices were recoded into four categories: ≤ 1hr (1–60 min); 1–2hrs (61–120 min); 2–3hrs (121–180 min); \u0026gt; 3hrs (181 + min). These categories have been used in several studies (19, 20). Previous studies using such screen time data reported one-week test-retest intraclass correlation coefficients ranging from 0.54 to 0.86 (21).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSociodemographic variables\u003c/em\u003e: Students reported their sex (male, female) and age (11–12 years, 13–14 years, and 15 years and older). For family affluence, we estimated a composite score for adolescents who answered at least three of four questions regarding: 1) the average amount of money they received each week for personal spending or savings; 2) going to bed hungry because there was not enough money to buy food; 3) having the feeling that they and their family were less financially comfortable than the average student in their class; and 4) having their own bedroom. Composite scores were dichotomized (less affluent = 0 vs. more affluent = 1). This indicator was inspired by the Health Behaviour in School-Aged Children Family Affluence Scale (22). It has been used in previous research (23, 24).\u003c/p\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003eData analysis\u003c/h2\u003e\n \u003cp\u003eAll analyses were conducted using Stata/SE 18.5. Descriptive statistics were calculated for all variables of interest. A quantile regression model was used to analyze the relationship between bedtime and time spent on different screen-based activities. The model was adjusted for age, sex, and family affluence. Confidence intervals were estimated using a cluster bootstrap procedure (500 replications), resampling at the school level (25) to account for students being nested within schools, as their observations may not be fully independent.\u003c/p\u003e\n \u003cp\u003eTo examine how age influences these associations, analyses were stratified by age group. The analyses were also stratified by chronotype (early sleeper, intermediate sleeper, late sleeper).\u003c/p\u003e\n \u003cp\u003eTo deepen these analyses, a cross-sectional difference-in-difference analysis was performed (26). More specifically, to better understand the moderating role of age, for each screen-based activity we estimated the difference in bedtime between heavy users (\u0026gt; 3 hrs) and light users (≤ 1 hr) separately for adolescents 11–12 years of age and those 15 years and older. We then calculated the difference between these two differences. Similarly, to better understand the role of chronotype, the difference in bedtime between adolescents who used screens heavily (\u0026gt; 3 hrs) and those who used them lightly (≤ 1 hr) for each screen-based activity was estimated for early and late sleepers. We then calculated the difference between these two differences.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eSample characteristics\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows sample characteristics of participants. Of the 67,710 adolescents included in this study, 34,693 (51.2%) were females. Mean age was 14.4 years (\u003cem\u003e\u0026plusmn;\u003c/em\u003e\u0026thinsp;1.5). Mean bedtime was 22:50 (\u0026plusmn;\u0026thinsp;1.4 hrs).\u003c/p\u003e\n \u003cp\u003eWhen examining the proportion of adolescents spending more than 2 hours per day on any given type of screen, we found that 41.5% exceeded this threshold for browsing/scrolling social media, 23% for playing video/computer games, 18.5% for watching/streaming television shows or movies, 14.6% for surfing the Internet, and 13.8% for sending messages (texting, messaging, emailing). Spending more than 3 hours per day was most common for social media use (25.1%) and video gaming (14.4%).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive characteristics of the study sample\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN/mean (median)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%/SD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e67,710\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (mean)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.4 (\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge categories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u0026ndash;12 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u0026ndash;14 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 years and older\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31,609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34,693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33,017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily affluence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess affluent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23,996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore affluent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43,714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBedtime (mean)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22:50 (22:45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eScreen time\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eBrowsing/scrolling social media*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;1 hr/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22,729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;2 hrs/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16,883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026ndash;3 hrs/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3 hrs/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16,986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlaying video/computer games*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;1 hr/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42,704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;2hrs/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026ndash;3hrs/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3 hrs/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eSending messages (texting, messaging, emailing*)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;1 hr/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49,277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;2 hrs/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026ndash;3hrs/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3 hrs/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurfing the Internet*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;1 hr/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51,683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;2 hrs/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026ndash;3 hrs/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3 hrs/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWatching/streaming television shows or movies*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;1 hr/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40,677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;2hrs/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026ndash;3hrs/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3 hrs/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e* \u0026le; 1hr (1\u0026ndash;60 min); 1\u0026ndash;2hrs (61\u0026ndash;120 min); 2\u0026ndash;3hrs (121\u0026ndash;180 min); \u0026gt; 3hrs (181\u0026thinsp;+\u0026thinsp;min)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eAssociations between time spent on different screen-based activities and bedtimes\u003c/h3\u003e\n\u003cp\u003eAll screen times were associated with later bedtimes, with variations depending on the type and duration of screen-related activity (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eCompared with adolescents who spent 1 hour or less per day on social media, those who spent 1\u0026ndash;2 hours went to bed about 15 minutes later (22:20, \u003cem\u003e95% CI: 22\u003c/em\u003e:\u003cem\u003e17\u0026ndash;22\u003c/em\u003e:\u003cem\u003e23;\u003c/em\u003e vs. 22:35, \u003cem\u003e95% CI: 22\u003c/em\u003e:\u003cem\u003e33\u0026ndash;22\u003c/em\u003e:\u003cem\u003e37\u003c/em\u003e). Increasing daily time spent browsing/scrolling social media (i.e., 2\u0026ndash;3hrs, \u0026gt; 3hrs) was associated with progressively later bedtimes, with an approximate 15-minute delay at each higher screen time level (22:50, \u003cem\u003e95% CI: 22\u003c/em\u003e:\u003cem\u003e47\u0026ndash;22\u003c/em\u003e:\u003cem\u003e53\u003c/em\u003e; then 23:05, \u003cem\u003e95% CI: 23\u003c/em\u003e:\u003cem\u003e02\u0026ndash;23\u003c/em\u003e:\u003cem\u003e08\u003c/em\u003e). Thus, adolescents who spent more than 3 hours browsing/scrolling social media went to bed approximately 45 minutes later than those who spent 1 hour or less on average per day. A comparable pattern was observed for playing video games and sending messages. Increasing daily time spent playing video games was associated with progressively later bedtimes, with an approximate 15-minute delay at each higher screen time level (22:26, \u003cem\u003e95% CI: 22\u003c/em\u003e:\u003cem\u003e20\u0026ndash;22\u003c/em\u003e:\u003cem\u003e32\u003c/em\u003e; 22:41, \u003cem\u003e95% CI: 22\u003c/em\u003e:\u003cem\u003e35\u0026ndash;22\u003c/em\u003e:\u003cem\u003e46\u003c/em\u003e; 22:56, \u003cem\u003e95% CI: 22:51\u0026ndash;23:00\u003c/em\u003e; 23:11, \u003cem\u003e95% CI: 23\u003c/em\u003e:\u003cem\u003e06\u0026ndash;23\u003c/em\u003e:\u003cem\u003e15\u003c/em\u003e). Thus, compared with adolescents who played video games 1 hour or less per day, those who played more than 3 hours went to bed about 45 minutes later (22:26, \u003cem\u003e95% CI: 22\u003c/em\u003e:\u003cem\u003e20\u0026ndash;22\u003c/em\u003e:\u003cem\u003e32\u003c/em\u003e vs. 23:11, \u003cem\u003e95% CI: 23\u003c/em\u003e:\u003cem\u003e06\u0026ndash;23\u003c/em\u003e:\u003cem\u003e15\u003c/em\u003e). Adolescents who spent more than 3 hours per day sending messages went to bed approximately 45 minutes later than those who spent 1 hour or less doing so (22:33, \u003cem\u003e95% CI: 22\u003c/em\u003e:\u003cem\u003e30\u0026ndash;22\u003c/em\u003e:\u003cem\u003e36\u003c/em\u003e vs. 23:18, \u003cem\u003e95% CI: 23\u003c/em\u003e:04\u003cem\u003e\u0026ndash;23\u003c/em\u003e:\u003cem\u003e32\u003c/em\u003e), with an approximate same 15-minute delay at each higher screen time level.\u003c/p\u003e\n\u003cp\u003eFor both Internet surfing and watching/streaming television shows and movies, a threshold pattern was observed, with differences in predicted bedtime apparent only at higher levels of use. Bedtimes were approximately 30 minutes later among adolescents who surfed the Internet or watched television for more than 3 hours per day compared with those who did so for 2 hours or less.\u003c/p\u003e\n\u003ch3\u003eThe moderating role of age\u003c/h3\u003e\n\u003cp\u003eYounger adolescents went to bed earlier than the oldest ones, regardless of average daily screen times. However, our difference-in-differences results suggest that younger adolescents are more sensitive to heavy screen time use. For comparable differences in screen uses (\u0026le;\u0026thinsp;1 hr vs. \u0026gt; 3 hrs/day), younger adolescents (11\u0026ndash;12 years) showed a greater delay in bedtime than older adolescents (15 years and older), except for video gaming (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). More specifically, compared with the \u0026le;\u0026thinsp;1hr/day group, 11\u0026ndash;12-year-olds exposed to more than 3 hours per day of social media, Internet, or watching/streaming television shows and movies had an additional 30-minute delay in bedtime compared to the delay observed in those aged\u0026thinsp;\u0026ge;\u0026thinsp;15 years (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Thus, for social media use, 11\u0026ndash;12-year-olds had bedtimes ranging from 21:30 to 22:30, while the bedtimes of \u0026ge;\u0026thinsp;15-year-olds ranged from 22:45 to 23:15, and for surfing the Internet and watching/streaming television shows or movies, 11\u0026ndash;12-year-olds had bedtimes ranging from 21:45 to 22:30, while those of \u0026ge;\u0026thinsp;15-year-olds ranged from 23:00 to 23:15.\u003c/p\u003e\n\u003cp\u003eWhen comparing adolescents reporting 1 hour or less per day of messaging with those reporting more than 3 hours per day, predicted bedtime shifted from 21:45 to 22:45 among 11\u0026ndash;12-year-olds. In contrast, the corresponding shift among adolescents aged\u0026thinsp;\u0026ge;\u0026thinsp;15 years was smaller, from 23:00 to 23:15.\u003c/p\u003e\n\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe moderating role of chronotypes\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eFor the same contrast in screen time (\u0026le;\u0026thinsp;1 hr vs. \u0026gt; 3 hrs/day), late sleepers showed a larger bedtime delay than early sleepers (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe additional bedtime delay associated with social media use for more than 3 hours per day (vs. \u0026le; 1hr) was 23 minutes greater for late sleepers than early sleepers. Late sleepers\u0026rsquo; bedtime shifted from 23:01 to 24:01, while early sleepers\u0026rsquo; bedtimes shifted from 21:44 to 22:22. The additional bedtime delay associated with video/computer games for more than 3 hours per day (vs. \u0026le; 1hr) was 38 minutes greater for late sleepers than early sleepers. Late sleepers\u0026rsquo; bedtime shifted from 23:11 to 24:11, while early sleepers\u0026rsquo; bedtime shifted from 21:54 to 22:17.\u003c/p\u003e\n\u003cp\u003eWhen comparing adolescents who sent messages, surfed the Internet, and watched television for more than 3 hours per day with those who did so for 1 hour or less, late sleepers showed an additional delay in bedtime of approximately 30 minutes compared with early sleepers. Late sleepers who sent messages (e.g., texting) had a bedtime that shifted from 23:15 to 24:15, while early sleepers\u0026rsquo; bedtime went from 21:56 to 22:26. Bedtimes of those who surfed the Internet shifted from 23:20 to 24:05 for late sleepers and from 21:57 to 22:12 for early sleepers. Finally, bedtimes of those who watching/streaming television shows or movies shifted from 23:20 to 24:05 for late sleepers and from 21:57 to 22:12 for early sleepers.\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDouble difference analysis of bedtime by screen-based activity and 1) age group and 2) chronotype\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDifference-in-differences in bedtime by screen time and age (11\u0026ndash;12-year-olds vs. adolescents \u0026ge;\u0026nbsp;15 years)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDifference-in-differences in bedtime by screen time and chronotype (late sleepers vs. early sleepers) among adolescents\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eContrast\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% C.I.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eContrast\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% C.I.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrowsing/scrolling social media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;30 minutes\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[25\u0026ndash;34 minutes]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;23 minutes\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[10\u0026ndash;35 minutes]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlaying video/computer games\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ndash; 15 minutes\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-8(\u0026ndash;)-22 minutes]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;38 minutes\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[34\u0026ndash;42 minutes]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSending messages (texting, messaging, emailing)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;45 minutes\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[37\u0026ndash;53 minutes]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;30 minutes\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[17\u0026ndash;43 minutes]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurfing the Internet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;30 minutes\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[18\u0026ndash;42 minutes]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;30 minutes\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[27\u0026ndash;36 minutes]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWatching/streaming television shows or movies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;30 minutes\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[19\u0026ndash;41 minutes]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;30 minutes\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[25\u0026ndash;35 minutes]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e\u0026nbsp;\u003cem\u003e1\u003c/em\u003e\u0026nbsp;\u003c/sup\u003e \u003cem\u003eInterpretation: The additional bedtime delay associated with engaging in [activity X] for \u0026gt;\u0026thinsp;3 hrs/day (vs. \u0026le; 1 hr/day) was [Y minutes] greater for 11\u0026ndash;12-year-olds than for \u0026ge;\u0026thinsp;15-year-olds.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026nbsp;\u003cem\u003e2\u003c/em\u003e\u0026nbsp;\u003c/sup\u003e \u003cem\u003eInterpretation: The additional bedtime delay associated with engaging in [activity X] for \u0026gt;\u0026thinsp;3 hrs/day (vs. \u0026le; 1 hr) was [Y minutes] greater for later sleepers than for early sleepers.\u003c/em\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eConsistent with previous research on sleep duration (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), this large-scale study on adolescents shows that greater screen times, regardless of type of activity, are associated with later bedtimes. Young adolescents and those who typically go to bed later are particularly susceptible to further bedtime delays with screen use, a finding not well documented in previous research (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral mechanisms may explain these associations. Social media are particularly disruptive because of their highly engaging, interactive, and emotionally stimulating nature (e.g., notifications, waiting for replies, social comparison) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Moreover, for phones and tablets, ease of use in bed (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) and close proximity to the face may also affect sleep (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). As well, video games could be strongly associated with later bedtime due to their competitive and immersive nature, which demands intense attention, focus, and emotional involvement, incompatible with winding down for sleep (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Their growing social component may also lead adolescents to prioritize a sense of belonging over sleep, through group play schedules or pressure to complete missions (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Additionally, factors like loot-boxing (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) and raging (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) can trigger anxiety and agitation that disrupt bedtime routines. Messaging may delay bedtime when adolescents bring their devices to bed and feel the need to remain constantly available and to respond quickly, often due to fear of missing out (FOMO) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). This pressure to stay connected with, for example, friends or romantic partners, can maintain emotional and cognitive arousal and delay sleep preparation.\u003c/p\u003e \u003cp\u003eAdolescents who watched or streamed television shows or movies for more than two hours had later bedtimes than those who did so for one hour or less. This is likely due to the passive nature of this activity, which demands less engagement compared with other types of screen use (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Finally, television is sometimes viewed from a greater distance, reducing the amount of light, including blue wavelengths, reaching the retina, which may in turn lessen its impact on melatonin suppression and sleep timing (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Similarly, Internet browsing was associated with a later bedtime among adolescents who engaged in this activity for more than two hours, compared with those who browsed for one hour or less. One potential explanation for this finding could be that this activity may be goal-directed (e.g., schoolwork, information seeking), which would imply a more determined end compared to activities on social media or video games.\u003c/p\u003e \u003cp\u003eOur findings suggest that the association between screen time and later bedtimes is stronger among younger adolescents than older adolescents for most screen-based activity, with the exception of video gaming. Several mechanisms may explain this vulnerability (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Younger adolescents generally go to bed earlier and therefore have less discretionary time in the evening. With less free time available, screen use is more likely to encroach directly on their bedtime. Evidence further shows that they are more likely to use screens before going to bed and to engage in nighttime use (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Additionally, blue light may have a more negative impact on physiological mechanisms, such as melatonin secretion, in younger adolescents (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Because their identity and emotional regulation are still developing, younger adolescents may also be more sensitive to peer evaluation, conflict, or emotionally charged content (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), making it harder to disengage from screens at bedtime. Their stronger drive for social recognition and belonging can reinforce the urge to stay connected through social media and messaging, ultimately delaying bedtime.\u003c/p\u003e \u003cp\u003eVideo gaming was the only screen activity for which older adolescents (\u0026ge;\u0026thinsp;15 years) showed greater shifts to later bedtimes than younger adolescents. This pattern may reflect differences in game types and play contexts, with older adolescents more often engaging in competitive and violent games and participating in organized sessions that extend late into the night, sometimes supported by online communication platforms (e.g., Discord).\u003c/p\u003e \u003cp\u003eThis study provides new evidence on how adolescents\u0026rsquo; chronotypes shape the association between screen use and bedtime. Early sleepers may be more accustomed to setting routines or rules (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) and practicing good sleep hygiene, which helps them better regulate their screen use (e.g., by avoiding screens after a set time). An earlier bedtime also mechanically reduces opportunities for late-night screen exposure, limiting both blue light related circadian disruption and the cognitive arousal that follows social media use, messaging, or gaming. In contrast, late sleepers may enter a vicious cycle, as cognitive arousal and circadian rhythm disturbances are likely to be more pronounced at later hours (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo fully understand the implications of this study\u0026rsquo;s findings, it is important to consider the consequences of accumulating a sleep debt. Small nightly delays in bedtime accumulate over time, resulting in several hours of lost sleep over weeks or months. Cumulative sleep debt is associated with academic difficulties, emotional and relational problems, higher risk of depression and anxiety, as well of cardiovascular disease, diabetes, immune dysfunction, hormonal imbalances, or even Alzheimer\u0026rsquo;s disease (\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Even after periods of recovery sleep, cognitive performance often continues to deteriorate, suggesting that the adverse effects of sleep restriction are only partially reversible (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Moreover, going to bed late can reduce deep sleep, which occurs mainly in the early part of the night (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Because deep sleep is essential for physical restoration, delayed bedtimes may limit these regenerative processes, potentially triggering a cascade of negative health effects (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePractical implications\u003c/h2\u003e \u003cp\u003eOur findings support efforts aimed at reducing time spent on highly stimulating screen-based activities, such as social media, video games, or messaging, to promote healthier sleep habits amongst adolescents (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Some disconnection strategies could be cultivated, such as setting device time limits, creating screen-free zones (e.g., bedroom), or encouraging relaxing bedtime routines (e.g., reading, relaxation) (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, integrating digital literacy and sleep literacy into school curricula, with explicit attention to the relationship between screen time and sleep, could help reach large groups of adolescents simultaneously (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). Promoting sleep literacy can enhance knowledge, attitudes, and understanding about the importance of sleep, its recommended duration, factors that influence it, and strategies to enhance it (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). Improving adolescents\u0026rsquo; understanding of online behaviours, screen use, and their potential effects on sleep may foster healthier digital habits and bedtime routines.\u003c/p\u003e \u003cp\u003eRedirecting screen time toward physical activity could also be particularly beneficial, as physical activity increases energy expenditure and supports circadian and thermoregulatory processes, promoting earlier sleep onset and better sleep quality (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Evidence shows that replacing sedentary time with physical activity improves adolescents\u0026rsquo; overall health and sleep outcomes (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). This perspective aligns with the 24-Hour Movement Guidelines, which emphasize the interdependence of physical activity, sedentary behaviour, and sleep within a 24-hour cycle (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e4.2 Limitations\u003c/span\u003e \u003c/p\u003e \u003cp\u003eSeveral limitations should be considered when interpreting the findings of this study. First, the cross-sectional design of our analysis precludes any inference about the causal direction between screen time and bedtime. These associations may be bidirectional. Furthermore, we did not collect information on the timing of screen use (e.g., in the evening or before bedtime vs. during the day), which would have helped deepen our understanding of this phenomenon. The specific content adolescents engaged with was also not documented (e.g., passive doomscrolling vs. active content creation, or watching intense dramatic series vs. humor or lighthearted shows). Future research should investigate these different types of content (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e), as well as the locations in which screens are used, which could be decisive (e.g., playing on the phone while lying in bed vs. using a computer while sitting at a desk). Finally, since this study was conducted in Quebec (Canada), replication in other cultural and geographical contexts would help strengthen the generalizability of the results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThese findings indicate that all types of time spent on screen-based activities among adolescents are associated with later bedtimes. Some activities, such as browsing social media, playing video/computer games, and sending messages, could be prioritized for targeted action given the strength of their associations with later bedtimes. Younger adolescents and those who already have later bedtimes are more prone to additional sleep delays. This study offers important insights for the development of interventions that target adolescents, including those more vulnerable to bedtime delays. Promoting sleep (specifically earlier bedtimes) and digital literacy among adolescents, parents, educators, and other public health stakeholders could help support healthy sleep habits and reduce harmful screen use. Strengthening this collective understanding also means ensuring that adolescents and the adults who guide them are informed about how different types of screens can disrupt bedtimes and what strategies can promote healthy sleep hygiene, ultimately contributing to better overall health and well-being among youth.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. All procedures were approved by the University of Waterloo Ethics Committee (ORE#30118), the CIUSSS de la Capitale-Nationale\u0026ndash;Universit\u0026eacute; Laval (#MP-13-2017-1264), and the participating school boards. We used an active-Information Passive-Consent Permission Protocol where parents are fully informed about the study via letter/email but only need to contact researchers to decline their child\u0026apos;s participation. This method maximizes participation rates and reduces bias compared to active opt-in, while allowing students to opt-out at any time. Each participant provided informed consent.\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e Each participant provided informed consent. This study was conducted in accordance with the Declaration of Helsinki. All procedures were approved by the University of Waterloo Ethics Committee (ORE#30118), the CIUSSS de la Capitale-Nationale\u0026ndash;Universit\u0026eacute; Laval (#MP-13-2017-1264), and the participating school boards.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot Applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe COMPASS study has been supported by a bridge grant from the CIHR Institute of Nutrition, Metabolism and Diabetes (INMD) through the \u003cem\u003eObesity \u0026ndash; Interventions to Prevent or Treat\u003c/em\u003e priority funding awards (OOP-110788; awarded to RB, SH, STL), an operating grant from the CIHR Institute of Population and Public Health (IPPH) (MOP-114875; awarded to RB, SH, STL), a CIHR project grant (PJT-148562; awarded to RB, SH, STL), a CIHR bridge grant (PJT-149092; awarded to RB, SH, STL), a CIHR project grant (PJT-159693), a research funding arrangement with Health Canada (#1617-HQ-000012; contract awarded to RB, SH, STL\u003cem\u003e)\u003c/em\u003e, and a CIHR-Canadian Center on Substance Abuse (CCSA) team grant (OF7 B1-PCPEGT 410-10-9633; awarded to RB, SH, STL). COMPASS-Quebec additionally benefits from funding from the Minist\u0026egrave;re de la Sant\u0026eacute; et des Services sociaux du Qu\u0026eacute;bec, and the Direction r\u0026eacute;gionale de sant\u0026eacute; publique du CIUSSS de la Capitale-Nationale. As part of the program \u003cem\u003eConcerted Actions \u0026ndash; Research Program on Screen Use and Youth Health\u003c/em\u003e, the COMPASS study received funds from the Fonds de recherche du Qu\u0026eacute;bec \u0026ndash; Soci\u0026eacute;t\u0026eacute; et culture and the Minist\u0026egrave;re de la Sant\u0026eacute; et des Services sociaux du Qu\u0026eacute;bec (#2024-0UER-339082, #2025-0UER-361232). BT is funded by a fellowship award from the Fonds de Recherche du Qu\u0026eacute;bec - Sant\u0026eacute; as part of the \u003cem\u003ePostdoctoral training \u0026ndash; Citizens of other countries\u003c/em\u003e program (#355168). AMTT is supported by the research scholar program of the Fonds de recherche du Qu\u0026eacute;bec \u0026ndash; Sant\u0026eacute;. The funding bodies played no role in the design of the study, nor in the collection, analysis, or interpretation of the data or the writing of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAMTT, BT, and SH conceptualized and conducted the analysis. AMTT and BT conducted the literature review. BT led the writing and wrote the first draft of the manuscript. STL conceptualized and leads the larger COMPASS study. AMTT, RB, and SH are the COMPASS-Qu\u0026eacute;bec provincial leads. CBD coordinated the data collection and revised the manuscript. All authors provided feedback on the manuscript and approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e The authors wish to thank the Quebec public health authorities, the participating schools, school boards, and students, and the entire COMPASS team for their contributions.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the authors upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCarter B, Rees P, Hale L, Bhattacharjee D, Paradkar MS. Association between portable screen-based media device access or use and sleep outcomes: a systematic review and meta-analysis. JAMA Pediatr. 2016;170(12):1202\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHale L, Guan S. 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New Media Soc. 2022;24(2):530\u0026ndash;49.\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":"Adolescence, Screen time, Screen-based activities, Bedtime","lastPublishedDoi":"10.21203/rs.3.rs-8704490/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8704490/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eScreen time is linked to poorer sleep among adolescents. However, most evidence focuses on total recreational screen time rather than on time spent on different screen-based activities. Also, few studies have specifically examined bedtime, an actionable determinant of adolescent sleep hygiene. To date, it is not clear how age and chronotype influence this relationship. The objective of this study was to examine the associations between time spent on different screen-based activities and adolescents\u0026rsquo; bedtimes. We also aimed to examine how these associations vary based on age and chronotype.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing a cross-sectional design, we analyzed data from 67,710 adolescents attending 154 Quebec schools in 2025 as part of the COMPASS study. Quantile regressions were conducted to assess associations between the time adolescents spend on different screen-based activities (i.e., surfing social media, playing video games, sending messages, browsing the Internet, watching/streaming television shows or movies) and their bedtime. The analyses were stratified by age and chronotype and adjusted for the adolescents\u0026rsquo; sex and family-level material affluence. A difference-in-difference analysis was also conducted.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEach type of recreational screen activity was positively associated with later bedtimes. Compared with adolescents who used screens\u0026thinsp;\u0026le;\u0026thinsp;1 hour per day, those reporting more than 3 hours of screen use had bedtimes occurring approximately 45 minutes later for social media use, video gaming, and messaging, and about 30 minutes later for Internet browsing and television viewing. The difference in bedtime associated with higher versus lower screen use (\u0026le;\u0026thinsp;1 hr vs. \u0026gt; 3 hrs/day) was greater among younger adolescents (11\u0026ndash;12 years) than older ones (\u0026ge;\u0026thinsp;15 years), except for video gaming. The difference in bedtime associated with higher versus lower screen use (\u0026le;\u0026thinsp;1 hr vs. \u0026gt; 3 hrs/day) was greater among late sleepers than early sleepers.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eEach type of recreational screen activity among adolescents is associated with later bedtimes. Younger adolescents and late sleepers appear more susceptible to additional bedtime delays, which may ultimately influence their quantity and quality of sleep. This study offers important insights for the development of interventions and recommendations that target more vulnerable adolescents to promote healthy sleep, an essential component of adolescent health.\u003c/p\u003e","manuscriptTitle":"Associations between types of screen use and bedtimes among a large sample of adolescents: Age and bedtime specific variations from the COMPASS study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 10:51:10","doi":"10.21203/rs.3.rs-8704490/v1","editorialEvents":[{"type":"communityComments","content":0}],"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":"48241ff4-cb4e-4c4b-ac58-9e641f9a2571","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T04:41:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 10:51:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8704490","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8704490","identity":"rs-8704490","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}