GENDER SPECIFIED ASSOCIATIONS BETWEEN SMARTPHONE USE AND MULTIDIMENSIONAL COGNITION AMONG OLDER ADULTS IN CHINA
Bibliographic record
Abstract
Backgrounds: Little is known about the association of smartphone use with cognition for older adults, particularly with multi-domain cognitions, while smartphone is a most widely used and all-in-one electronic device. Methods: We carried out a face-to-face survey on 3,230 older adults aged 60+ years in Xiamen, China, 2016. The Montreal Cognitive Assessment(MoCA) was used to measure general and six specific subdomain cognitions, while smartphone usage was self-reported. Ordinal logistic regression was performed to model the joint association of the number of smartphone function use and sex on general cognitive function. Furthermore, a series of ordinal logistic regressions were used to detect the associations between the number of smartphone function use and the six subdomain cognitive functions stratified by gender. Results: 2,600 eligible participants were included with a mean age of 69.06 ± 7.06 years. Only 30% of older adults used smartphones, among who 60.72% were men. Respondents who used more smartphone functions maintain better cognitive functions for both men and women, but for women, such benefits appeared only when they used 2+ smartphone functions (OR=1.87). A multi-domain cognitive advantage also presented for smartphone users. Concretely, for men this advantage showed in five domains including memory, visuospatial ability, executive ability, attention and language. For women, it showed in visuospatial, executive ability, attention and language. Conclusions: Using smartphone showed positive associations with better general and multi-domain cognitive functions, for both older men and women. The more smartphone functions they used, the more benefits they attained, and such associations were stronger for men than women.
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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 | 0.001 | 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 | 0.000 | 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".