Associations between smartphone use and mental health and well-being among young Swiss men
Bibliographic record
Abstract
BACKGROUND AND AIMS: Intense use of smartphones is associated with mental health problems and low well-being. However, little is known about the mental health and well-being of non- and low-level users. This study investigated the possibly non-linear associations between time spent using a smartphone, including non-users, and mental health and well-being among young adults. METHODS: Between 2016 and 2018, 5315 young Swiss men (M = 25.45 years old, SD = 1.25) completed a questionnaire assessing smartphone use, daily time spent using a smartphone, mental health and well-being (i.e. depression, social anxiety, attention deficit hyperactivity disorder, life satisfaction, stress) and potential confounding variables (social capital, personality, education). The associations of smartphone use and time spent using a smartphone (linear and quadratic associations) with mental health and well-being were tested using regression models. RESULTS: Non-users (4.3%) reported worse mental health and well-being than smartphone users on all outcomes. Time spent using a smartphone was linearly associated with higher rates of social anxiety, depression, attention deficit hyperactivity disorder and lower levels of life satisfaction. The association with stress was non-linear, with significant linear and quadratic coefficients of time spent using a smartphone. Associations were partially attributable to confounding variables (i.e. social capital, personality, and education). CONCLUSIONS: Non-users and intense users of smartphones have lower levels of mental health and well-being than low-level users. Although society and mental health professionals are deeply concerned about the potentially negative consequences of the ever-increasing use of smartphones, the present study suggested that not using a smartphone may also indicate problems.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".