IS HEALTH AT RISK? A QUANTITATIVE STUDY ASSESSING THE IMPACT OF EXCESSIVE MOBILE APPLICATION USE ON PHYSICAL AND MENTAL WELL-BEING AMONG ADULTS IN SAUDI ARABIA.
Bibliographic record
Abstract
BACKGROUND: The widespread adoption of smartphones and mobile applications has transformed communication, education, and productivity but also raised concerns about their potential impact on physical and mental well-being. Excessive daily use is linked to sleep disturbance, musculoskeletal discomfort, visual strain, anxiety, and depressive symptoms. In Saudi Arabia, where smartphone penetration is remarkably high, these challenges demand systematic attention and targeted interventions. METHODS: This study adopted a cross-sectional design and addressed the relationship between mobile application use and health outcomes among adults. Data were collected using a validated survey that included sociodemographic variables, app usage patterns, sleep quality, physical symptoms, and psychological status. The assessment incorporated the PSQI, NMP-Q, and DASS-21. Statistical analysis included descriptive measures, chi-square tests, correlations, and regression models to evaluate predictors of health outcomes. RESULTS: A total of 823 participants completed the survey. Excessive app use (>4 hours/day) was highly prevalent. Eye strain, neck and shoulder pain, and headaches were the most frequent physical symptoms, while insomnia, anxiety, and depression were common psychological complaints. Poor sleep quality was significantly associated with longer app use, shorter sleep duration, and anxiety. Nomophobia scores revealed moderate to high dependency, with participants frequently reporting discomfort and anxiety when disconnected from their phones. CONCLUSION: The findings highlight a strong relationship between mobile application overuse and negative health outcomes. Excessive use, particularly of social media, entertainment, and gaming apps, was linked with impaired sleep, physical discomfort, and psychological distress. These results call for greater attention in clinical practice, targeted public health interventions, and national policies to promote balanced and mindful technology use.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".