0383 Associations Between Digital Technology and Sleep Health by Country, Age, and Sex
Bibliographic record
Abstract
Abstract Introduction We investigated whether the widely-observed association between digital technology use and sleep health varied by country, age, and/or sex in a global sample of adults. Methods We used 2023 survey data from 35 countries (n=35,018, ~1000/country) to characterize the self-reported effects of digital technology on physical health, sleep quality, and tiredness (see https://sync.ithra.com/research). We examined whether responses varied by country, age, and sex. Results Participants from 35 countries (52.2% male) ranged from 18-99 years old (mean=38); 18.7% of respondents were between 18-24, and 8.0% were 65+. Unadjusted analyses showed that across all participants, 31.7% reported that digital technology reduced their physical health. Respondents in China had the lowest prevalence (11.8%) of digital media worsening physical health, while respondents in Estonia had the highest prevalence (56.4%). Younger respondents (18-24) were more likely to report that digital technology worsened physical health than older (65+) respondents (38.5% vs. 21.9%). Females were slightly more likely (33.7%) than males (30.0%) to report that digital media worsened physical health. When asked which physical conditions were experienced after using digital technology for longer than usual, 40.5% reported tiredness, and 39.0% reported decreased sleep quality. Out of all 35 countries, prevalence was lowest in Italy for both the symptoms of tiredness (22.1%) and decreased sleep quality (19.7%), while they were highest in Ghana (60.6%) for tiredness and Malaysia (57.3%) for decreased sleep quality. Among the youngest age group (18-24-year-olds), 48.4% and 47.1% reported tiredness and decreased sleep quality, respectively, compared to 22.4% and 16.1% for 65+. Females were more likely to report tiredness (42.3%) and decreased sleep quality (40.9%) as symptoms compared to males (38.8% and 37.8%, respectively). Conclusion These novel global results show that over one-third of adult respondents believe heavy use of digital technology leads to sleep-related symptoms, with larger effects for younger and female adults. Variation by country suggests that cultural factors may affect the association between digital technology use and sleep health. Support (if any) Aramco
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".