The researcher, Jonas Mandalunes, exploited a rarely used survey item that asked students whether their parents limited screen time on school days and, crucially, whether that limit included the time they spent doing schoolwork on a screen. Students fell into three groups: an inclusive limit that treated an hour of homework like an hour of entertainment, an exempt limit that protected schoolwork and restricted only the rest, or no limit at all. Across the 28 systems, a median of 14.8 percent of students reported inclusive limits, 28.3 percent reported exempt limits, and 57.4 percent reported no limits. In other words, among families that set limits at all, roughly two-thirds carved schoolwork out of the daily budget.

The headline result is striking in its consistency. Students whose limits counted schoolwork scored 24.7 points lower in computer and information literacy than students whose limits exempted it, after adjusting for socioeconomic background, gender, home language, and immigrant background. That gap — about 0.30 within-system standard deviations, or roughly 29 percent of the width of a proficiency level — was statistically significant and negative in all 28 education systems, ranging from 48.1 points in Norway to 9.1 points in Oman. The pattern replicated almost perfectly in computational thinking, where the 19 systems that administered that assessment showed a pooled difference of 32.9 points, again negative everywhere.

Just as telling is what the study did not find. Students with no limit scored essentially the same as students with exempt limits in computer and information literacy — a pooled difference of just 0.6 points, small enough to satisfy a formal equivalence test with a margin of ten points. Having a rule, in itself, was not associated with lower competence. The entire penalty was concentrated among the minority of students, roughly one in seven, whose parents counted homework against the clock. This finding reframes the simpler comparison in the official ICILS report, which found students without limits scoring higher in most countries: the study shows mathematically that this binary gap is almost entirely a product of how many inclusive limits a system contains, since the share of inclusive limits among all limits ranged from about one-sixth in Denmark to more than half in Azerbaijan.

The study tested three competing explanations, and none fit alone. The first, an opportunity account, holds that digital skills grow through sustained, purposeful use, so a limit that swallows homework time leaves fewer chances to practice. The second, a displacement account, suggests that counting schoolwork might actually help by curbing the multitasking soup of homework and entertainment on the same device. The third, selection, argues that parents adopt inclusive limits reactively — because a child is struggling, shows weak self-control, or blends homework with play — so the rule marks pre-existing differences rather than causing them.

Several findings point toward selection. Students with inclusive limits came from lower socioeconomic backgrounds in 27 of 28 systems, had less computer experience in 27, expected a university degree less often in 24, and reported lower self-efficacy in 25. The gap shrank steadily as more characteristics were controlled: from 29.8 points without covariates to 24.7 with the basic adjustments, to 22.1 points when age and educational expectations — rough proxies for prior attainment — were added, and to 20.6 points when students were compared only with their own schoolmates. An exploratory meta-regression added a telling gradient: the gap was largest precisely where inclusive limits were rare, shrinking by 0.07 standard deviations for every 10-percentage-point increase in their prevalence. That is the pattern one would expect if an uncommon rule is adopted for specific, child-specific reasons, and harder to explain if the rule itself imposed a fixed cost.

Yet the opportunity account is not dismissed. Students with inclusive limits reported learning internet-related tasks — searching, evaluating sources, spotting scams, managing privacy — less often outside school, where parental rules apply, but not at school, where they do not. That setting-specific pattern is exactly what the opportunity account predicts. They also reported less recreational device use, which may mean fewer occasions for the informal, self-directed exploration through which some digital skills develop. But the simplest version of the story fails: these students reported just as much digital schoolwork at home as their peers, and slightly more long homework sessions. Meanwhile, students with no limits reported the most recreational use and the most multitasking, yet scored like the exempt group in literacy and lower in computational thinking — a reminder that raw hours of screen use track competence poorly.

The author also confronted the elephant in the room: unmeasured confounding. Prior achievement, self-regulation, and parental digital competence are not captured in ICILS, and any of them could shape both the rules families set and the skills students display. A sensitivity analysis following modern omitted-variable methods found that a confounder as strong as the socioeconomic index would shrink the gap only to about 21 points, and one three times as strong would leave roughly 13 points. But the analysis is candid about the limits of that reassurance: prior achievement likely predicts digital competence far better than socioeconomic status does, and a variable that strong would need only a weak link to rule type to account for the entire difference. The 25-point gap, the study stresses, should not be read as the causal effect of a rule.

The methodological care behind these estimates deserves note. The analysis used plausible values, sampling weights, and 75 jackknife replicate weights, pooled system-level estimates through random-effects meta-analysis, and verified the code by reproducing published national averages and the international report’s own limit-versus-no-limit comparison. Results proved robust to reweighting for item nonresponse, to using the parallel non-school-day item, and to adding or excluding individual systems. Students who skipped the rule item scored lower than those who answered it, but inverse-probability reweighting left the conclusions unchanged.

The practical upshot is twofold. For researchers, the message is that surveys of parental mediation must record what a limit covers, not merely whether one exists — studies linking screen rules to achievement, sleep, or wellbeing may otherwise average over rule types whose associations differ in sign and size. For families, the findings align with guidance from the American Academy of Pediatrics and Canadian movement guidelines, which already distinguish recreational from educational screen use, though the study is explicit that it cannot show that changing a rule would change a child’s competence. What it does show is that the one-in-seven students whose parents count homework as screen time differ from their peers in ways the data capture only partly — and that teachers may want to pay attention to exactly those students.

Subject of Research: The relationship between the scope of parental screen-time limits and adolescents' assessed digital competence in ICILS 2023

Article Title: When homework counts as screen time: parental limits and adolescents’ digital competence across 28 education systems in ICILS 2023

Article References: Mandalunes, J. (2026). When homework counts as screen time: parental limits and adolescents’ digital competence across 28 education systems in ICILS 2023. Large-scale Assessments in Education, 14(1), Article 54. https://doi.org/10.1186/s40536-026-00326-6

Image Credits: AI Generated

DOI: 10.1186/s40536-026-00326-6

Keywords: screen time, parental mediation, digital competence, homework, ICILS 2023, computational thinking, computer and information literacy, adolescents, large-scale assessment, meta-analysis, educational technology, parenting

The intervention, called Happy Homework 2.0, is a digital application designed to weave movement-based activities into the routines children already follow at home. Rather than adding another demand to crowded school timetables, the programme exploits a slot that already exists in every child’s evening. The concept builds on a growing body of evidence that school-based interventions, while scalable, often lose their impact once children leave the school gates. Homework, the researchers reasoned, could act as a bridge, extending the reach of classroom health messaging into the living room, the garden and the bedroom, where most sedentary time and sleep actually occur.

To test whether the idea could work in practice, the team conducted an eight-week pilot cluster randomised controlled trial across four primary schools in the West of Scotland. In a cluster randomised design, entire classes rather than individual children were allocated to either the intervention or the control condition, a method that reduces the risk of contamination between participants who see each other every day. A total of 82 children aged between 8 and 12 years took part, representing 60 percent of those invited, and 87 percent of them were still providing data at follow-up. Those retention figures, the researchers report, indicate good feasibility for a trial of this kind, which is precisely what a pilot study is designed to establish before larger, fully powered trials are attempted.

The evaluation went beyond simple attendance numbers. Feasibility, usability and acceptability were assessed through basic app-usage analytics and questionnaires completed by three separate constituencies: the children themselves, their parents, and their teachers. Gathering perspectives from all three groups matters because a digital health intervention for children sits at the intersection of competing interests. Children must find the app engaging enough to open it voluntarily, parents must be willing to accommodate it in the evening routine, and teachers must see enough value to integrate it into their homework expectations without it becoming a burden.

The study also collected objective data on the outcomes that matter most. Children wore activPAL devices, small sensors attached to the thigh that distinguish between sitting, standing and stepping based on posture rather than relying on self-report. This technology provided multiple posture- and movement-based metrics, giving the researchers a far more reliable picture of sedentary behaviour than questionnaires alone could offer. Sleep was measured through self-report questionnaires, and children’s motivation was assessed using instruments based on basic psychological need satisfaction, a framework rooted in self-determination theory, which holds that motivation flourishes when people feel autonomous, competent and connected to others.

The statistical analysis used linear mixed-effects models, a technique well suited to this kind of trial because it accounts for two sources of dependency in the data: the clustering of children within schools, and the fact that each child was measured repeatedly over time. By modelling group-by-time interactions, the researchers could ask whether children in the intervention arm changed differently from those in the control arm, rather than simply comparing average values at the end of the study. This approach is now standard in behavioural trials, but its careful application in a small pilot is notable, because it demonstrates that the study design can detect genuine signals even before scaling up.

What did those signals show? The most striking finding concerned sedentary behaviour. The activPAL data revealed a significant group-by-time difference, with sedentary time increasing more in the intervention group than in the control group, a difference of 66.2 minutes per day that reached statistical significance. Self-reported sleep also showed a significant interaction, with smaller increases in reported sleep duration among children using the app relative to controls. No other secondary outcomes, including measures of physical activity, showed significant group-by-time differences. Taken at face value, these results are not the triumphant headline one might hope for from a movement intervention: the app did not reduce sitting or boost activity in the intended direction during this pilot period.

The researchers are candid about the most likely explanation: engagement. The app-use analytics showed repeated engagement among participants, but overall use was modest, defined by declining frequency and duration of interactions over the eight weeks. In other words, children tried the app, returned to it for a while, and then gradually drifted away, a pattern familiar to anyone who has downloaded a fitness app with good intentions in January and abandoned it by March. When the dose of an intervention falls, its effects tend to attenuate with it. The team concludes that Happy Homework 2.0 was feasible and acceptable as a homework-based health intervention, but that engagement levels likely limited its effectiveness, and the unexpected shifts in sedentary time and sleep may reflect this weak implementation rather than any inherent failure of the concept.

That interpretation is where the real scientific value of the study lies. Pilot trials are not meant to deliver definitive answers; they are meant to expose the practical friction points that would otherwise sabotage a large trial. Here, the friction is clear: sustaining children’s voluntary engagement with a digital health tool over weeks, not days, remains the central challenge of mHealth interventions for young people. The authors emphasise the need to strengthen implementation strategies, sustain engagement, and enhance the involvement of parents and teachers, the two adult groups whose encouragement could counteract the natural decay of novelty. The study was retrospectively registered as NCT07631754, funded through a University of the West of Scotland Vice-Chancellor’s Studentship Scheme, and published open access in the Journal of Activity, Sedentary and Sleep Behaviors.

The broader lesson extends well beyond one Scottish app. Digital interventions promise scale and personalisation that classroom programmes cannot match, but they also compete for attention in an ecosystem saturated with far more compelling screen content. The paradox is sharp: an app designed to pull children away from screens must first win a battle for screen time. Future iterations of Happy Homework, and similar tools now in development through co-design studies with children and families, will need to treat engagement not as an afterthought but as the primary design target, using gamification, family involvement and curriculum integration to keep the dose high enough for biology to respond. The 24-hour movement behaviours of children remain a modifiable target, and the homework slot remains an underexploited opportunity. What this pilot establishes is that the pipeline from idea to evidence works; what it also establishes is that the hardest variable to move is not the child’s body, but the child’s attention.

Subject of Research: A pilot cluster randomised controlled trial testing the feasibility, acceptability and usability of a digital homework app targeting physical activity, sedentary behaviour and sleep in primary school children.

Article Title: Happy Homework 2.0: effects on 24-hour movement behaviours in children—findings from a pilot cluster randomised controlled trial

Article References: Forrest, A., Buchan, D. S., Sculthorpe, N., & Robinson, S. (2026). Happy Homework 2.0: effects on 24-hour movement behaviours in children—findings from a pilot cluster randomised controlled trial. Journal of Activity, Sedentary and Sleep Behaviors. https://doi.org/10.1186/s44167-026-00111-4

Image Credits: AI Generated

DOI: 10.1186/s44167-026-00111-4

Keywords: physical activity, sedentary behaviour, sleep, children, homework, digital intervention, mHealth, cluster randomised controlled trial, 24-hour movement behaviours, app development, school-based intervention, activPAL