The Association Between Smartphone Overuse and Cognitive Impairment Among Adults in the United Arab Emirates: A Cross-Sectional Study
Bibliographic record
Abstract
Background The widespread usage of smartphones has led to increasing concern regarding their potential adverse effects on cognitive health. While international studies have explored associations between excessive smartphone use and cognitive decline, there remains a gap in the literature specific to the United Arab Emirates (UAE). Aim This study aimed to investigate the association between smartphone overuse and cognitive impairment among adults in the UAE. Methods A cross-sectional study was conducted with a convenience sample of 401 adults aged ≥18 years across the UAE. Data were collected using an online self-administered questionnaire. Participants were categorized into three categories: low-risk, high-risk, and addicts based on the Smartphone Addiction Scale-Short Version (SAS-SV). The assessment of cognitive impairment was done through the Ascertain Dementia-8 Scale (AD8). Results A total of 281 participants (70%) were female, and 284 (71%) were in the age range of 18-24. Among the participants, 302 (75.3%) were classified as addicts, 75 (18.7%) were high-risk, and 24 (6%) were low-risk. According to the AD8 scale, the mean score was 1.9, and 182 (45.4%) of the participants were more likely to have cognitive impairment. A statistically significant association was observed between smartphone addiction and cognitive impairment (p = 0.002). The prevalence of cognitive impairment in addicts, high-risk, and low-risk subjects was 152 (50.3%), 22 (29.3%), and eight (33.3%), respectively. Smartphone overuse was also significantly associated with sleep disturbances (p < 0.001), headaches (p = 0.007), painful fingers (p = 0.015), and visual strain (p = 0.030). Depression was found to be significantly associated with cognitive impairment (p = 0.007). Conclusion The findings highlight a significant association between excessive smartphone use and cognitive impairment among adults in the UAE. These results emphasize the need for awareness campaigns, early screening initiatives, and further longitudinal research to explore causality and mitigate the cognitive risks associated with digital overuse.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".