Smartphone Use and Its Relation to Cognitive Impairment and Depressive Symptoms among Elderly People
Bibliographic record
Abstract
The accelerating aging process and growing digitalization of society have drawn more focus to the effects of smartphone use on cognitive performance and depression in older adults. Aim: To assess the relationship between smartphone use and cognitive impairment and depressive symptoms among elderly people. Design: A descriptive research design was used. Setting: the elderly club of Al-Taqwa Association, the elderly club of the Family Care Association, and the elderly club in Kafr Al-Maisleh. Sampling: A systemic random sample consisting of 270 elderly persons was included in the study (155 users and 115 nonusers of smartphone). Instruments: Three instruments were used: the Characteristics of Elderly Structured Interview Questionnaire, the Montreal Cognitive Assessment, and the Geriatric Depression Scale. Results: Mean score of total MoCA was higher in mobile users elderly than non-users (25.1 ± 3.6, 23.5 ± 4.7) respectively and the mean score of total depression was lower in mobile users elderly than non-users (4.3 ± 2.1, 5.5 ± 3.01) respectively with statistically significant difference between users and non-users. Conclusion: Usage of the smartphone was more associated with better cognitive functions and lower depression scores. Depression symptoms were associated with the elderly over 70 years old, those with a low educational level, widowed, the elderly who live alone, and those who have a low rate of social interaction. Recommendations: Providing elderly people with information on smartphone features to promote active smartphone use. Using and maximizing mobile phone features in nursing interventions can benefit senior citizens' health.
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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 | 0.002 |
| 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".