Smartphone Use and Mental Health among Youth: It Is Time to Develop Smartphone-Specific Screen Time Guidelines
Bibliographic record
Abstract
Smartphone use has become increasingly popular and almost all age cohorts engage in smartphone usage for a wide variety of activities. This study aims to investigate the relationship between high smartphone use and mental health among youth and in two urban centres in Canada. This study is part of the Smart Platform, a digital epidemiological and citizen science initiative. Citizen scientists provided all data via their own smartphones using a custom-built smartphone application. The baseline questionnaire included measures of smartphone screen time behaviours (internet use, gaming, and texting), demographic characteristics, and health outcomes including anxiety, suicide ideation, feelings of depression, and self-rated health. Binary regression models determined the relationship between smartphone use and mental health measures. Among the 437 participants (13–21 years old), 71.2% reported high total smartphones use during a typical week (5 weekdays and 2 weekend days). High weekday and high weekly total smartphone use were associated with an almost two times higher risk of screening positive for anxiety, while high weekend gaming and high total smartphone use were associated with an almost three times higher risk of suicide ideation. Moreover, high weekend total smartphone use was also associated with an almost three times higher risk of poor self-rated mental health. Our findings suggest that high smartphone use’s association with mental health varies by type of activity as well as type of day (weekday/weekend day). Smartphone usage among youth has become near universal and it is important to factor in variations in smartphone usage’s impact on mental health in developing smartphone-specific screen time guidelines by taking into context both type of activities, as well as type of day (weekday/weekend day).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".