Evening smartphone exposure impairs sleep quality and next-day performance in elite soccer players: a randomized controlled trial
Bibliographic record
Abstract
This study aimed to examine the effects of pre-bedtime smartphone use on sleep quality and athletic performance in soccer players while also investigating potential time-of-day variations. In this randomized controlled crossover trial, 16 male elite-level players were assigned to either use a smartphone for two hours prior to bedtime or read magazines (control), separated by a one-week washout period. Participants completed morning and afternoon performance tests (cognitive and physical assessments) and sleep quality measurements. Nocturnal smartphone use significantly impaired sleep quality, increasing sleepiness after days 3 and 5 (p < 0.01; d=5.74, d=5.72, respectively), decreasing total sleep time, increasing sleep onset latency, and reducing sleep efficiency (all p < 0.01; d=1, d=4.59). Cognitive performance initially showed improved afternoon results, although following five days of smartphone use, this pattern reversed with enhanced morning performance (p < 0.01; d=0.53, d=1.48). Simple and choice reaction times deteriorated significantly in afternoon sessions compared to both baseline and control conditions (p < 0.01; d=0.96-3.47). Physical performance tests revealed decreased jumping ability and slower reactive agility times following five nights of smartphone use, particularly in afternoon sessions (p < 0.01; d=0.85-0.91). Five consecutive nights of pre-bedtime smartphone use impaired sleep quality and both cognitive and physical performance in elite soccer players, with stronger effects in afternoon sessions. These findings emphasize the importance of implementing device-free periods prior to bedtime and potentially adjusting training schedules when evening screen exposure is unavoidable. Future research should explore countermeasures for managing evening device exposure in elite athletes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".