Prevalence and Associated Factors of Problematic Use of Smartphones Among Adults in Qassim, Saudi Arabia: Cross-sectional Survey
Bibliographic record
Abstract
BACKGROUND: The Kingdom of Saudi Arabia (KSA) ranks third globally in smartphone use. Smartphones have made many aspects of life easier. However, the overuse of smartphones is associated with physical and psychosocial problems. OBJECTIVE: The aim of this paper is to estimate the prevalence and associated factors of problematic use of smartphones among adults in the Qassim region of KSA. METHODS: We enrolled 715 participants using cluster random sampling for this cross-sectional survey. We assessed the problematic use of smartphones using the short version of the Smartphone Addiction Scale. RESULTS: We estimated the prevalence of problematic smartphone use among adults at 64% (453/708). Multivariable logistic regression analysis suggested that students are 3 times more likely to demonstrate problematic use compared with unemployed individuals (P=.03); adults using more than five apps are 2 times more likely to demonstrate problematic use compared to those using a maximum of three apps (P=.007). Protective factors against problematic smartphone use include using apps for academic (odds ratio [OR] 0.66; P=.04) or religious needs (OR 0.55; P=.007) and having a monthly family income of 5001-10,000 SAR (Saudi Riyal; US $1300-$2700; OR 0.46; P=.01) or 10,001-20,000 SAR (US $2700-$5400; OR 0.51; P=.03) compared to the <1501 SAR (US $400) income group. CONCLUSIONS: We reported a very high prevalence of problematic use of smartphones in KSA. Considering its negative impact on physical and psychosocial health, public health programs should develop preventive strategies.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".