Mobile device use and the cognitive function and depressive symptoms of older adults living in residential care homes
Bibliographic record
Abstract
BACKGROUND: With the fast-paced aging and increasing digitalization of society, there has been a growing interest in the effect of mobile device use on cognitive function and depression in older adults. However, research examining this issue among older adults in residential care homes (RCHs) is scant. Therefore, this study aimed to examine the impact of mobile device use on the cognitive function and depressive symptoms of older adults living in RCHs. METHODS: A cross-sectional survey was conducted using a sociodemographic questionnaire, the Montreal Cognitive Assessment (MoCA) and the 15-item Geriatric Depression Scale (GDS-15). RESULTS: A total of 235 senior residents (aged 82.58 ± 5.54) in four RCHs were surveyed. Users of mobile devices had a significantly higher total MoCA score (25.02 ± 4.14) and a significantly lower GDS-15 score (3.28 ± 2.74) than non-users (MoCA: 19.34 ± 5.21, GDS-15: 4.69 ± 2.90). Multivariate linear regression indicate that mobile device use is significantly associated with total MoCA score, six of the seven sub-scores (visuospatial abilities and execution functions, attention, language, abstraction, delayed recall, and orientation)(P < 0.05). Logistic regression showed that mobile device use was significantly associated with the level of depressive symptoms (OR = 0.458, 95%CI = 0.249-0.845). CONCLUSIONS: Use of mobile devices has a significant association with the cognitive function and depressive symptoms of older adults living in RCHs, and thus should be encouraged as a measure to maintain and improve cognition and prevent depression.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".