0366 Power Off is Better Off: The Impact of Technology Use on Sleep Among University Students
Bibliographic record
Abstract
University students are more vulnerable to sleep disturbance than other populations. Sleep problems are influenced by a number of factors, including the use of Light-emitting diode (LED)-backlit devices. LED screens are present in most modern technological devices, and emit monochromatic blue light (~460 nm) that disrupts melatonin production at night. Appropriately timed exposure to light and darkness is a key factor in sleep regulation, so night-time use of LED devices likely represents a major culprit in the sleep disturbance of university students. The present study investigated the relationship between insomnia severity and LED device use before sleep. 1,670 students at Memorial University of Newfoundland (MUN), aged 19–35, were surveyed. Participants responded to questions probing the number of devices they own, as well as their device use duration and frequency in the hours leading up to sleep and throughout the night. The Insomnia Severity Index (ISI) was used to measure insomnia symptoms. Chi-square tests of independence and odds ratios were used to examine differences in ISI scores between high- and low-level LED device users. The sample was representative of the MUN student body, with 70.6% of respondents being female, and with an average age of 22.7 years. Fifty-two percent of participants received an ISI score above the cut-off for mild insomnia, with a mean score of 8.77. Students who used their devices for one hour or more after lights out were 1.8 times more likely to experience insomnia symptoms (95% CI, 1.37 to 2.35; p = .0005). Those who endorsed having their sleep interrupted by their devices a few nights per week or more were also 1.64 times more likely to experience insomnia symptoms (95% CI, 1.16 to 2.31; p = .004). This study provides strong evidence, with a large and generalizable sample, that LED device use after lights out is associated with an increase in insomnia symptoms. Additional research is needed to strengthen these findings and to ultimately inform prevention/intervention programs specific to insomnia symptoms/disorder in this population. Support (If Any):
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".