Impact of FIFA World Cup 2022 on Children’s Sleep Patterns: An International Survey
Bibliographic record
Abstract
Abstract Background Poor sleep quality in children can lead to physical and psychosocial problems. The FIFA World Cup has been shown to impact adult behaviors, but its effect on children’s sleep patterns is less understood. The study aimed to evaluate the impact of the FIFA World Cup 2022 (FWC-2022) on children’s sleep patterns. Methods A cross-sectional survey was conducted between 27 November and 25 December 2022, targeting parents in Saudi Arabia (Arabia standard time) and countries with a +6-hour time difference. Participants completed the validated Children’s Sleep Habits Questionnaire (CSHQ), alongside demographics, time spent watching matches, and parental perceptions on sleep. Results A total of 848 parents participated, with 60.6% being mothers. The study found that children averaged 9.10 hours of sleep; 64.2% of parents observed no change, while 10.4% reported substantial changes. Parents aged ≥45 and those noticing shifts in sleep habits reported higher problematic sleep scores. Larger families reported fewer sleep issues, with a negative correlation between family size and sleep problems. Children’s CSHQ scores indicate mild to moderate sleep difficulties across domains. No significant differences were observed between Saudi Arabia and countries with +6-hour time difference. However, one-third of children experienced delays in sleep onset exceeding one hour on weekdays during the World Cup. Conclusion Sociodemographic factors, family dynamics, and major events like the FWC-2022 influence parental perceptions of child sleep issues. Older parents and smaller families reported more challenges, while higher socioeconomic status was linked to fewer bedtime difficulties. Our findings may be particularly relevant for FIFA 2026, where transcontinental hosting across North America will expose children globally to matches at even more variable times. Subtle impacts of prolonged event schedules highlight the need for interventions supporting healthy routines during such events, potentially through engaging, sleep-friendly technologies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".