The influence of using smart products and having hobbies on the cognitive function among the elderly living in a nursing home
Bibliographic record
Abstract
Objective To explore the effect of using smart products (including smart phones, computers and other electronic products), as well as having hobbies on their cognitive function in pension agency elderly people, and analyze if there is certain protective effect on cognitive function by using smart products and having hobbies. So as to reduce the risk of mild cognitive impairment in the future. Methods By convenience sampling, 160 residents living in the nursing home of suzhou city (mean age 60 or higher) were selected, and demographic data were collected by using a homemade questionnaire, their cognitive function was investigated by using the Montreal Cognitive Assessment Scale. Results Single factor analysis showed that the score of the elderly who often use smart products in every cognitive field and overall cognitive function were superior to those who could not use smart products, the difference was statistically significant (t=-4.47--2.15, all P < 0.05). The scores of the elderly with hobbies were higher in the overall cognitive function and the other six areas except the orientation, than those who had no hobby, and the differences were statistically significant (t=-6.80--1.81, all P < 0.05). After adjusting for age, gender, body mass index (BMI), cultural level, often using smart products in total cognitive function in the elderly (t=4.842, P < 0.01) and executive function (t=4.008, P < 0.01), attention (t=3.045, P = 0.003), abstract (t=2.135, P = 0.034), delayed recall (t=3.759, P < 0.01), the directional (t=2.866, P = 0.005) of the five areas showed significant correlation. The total cognitive function of the elderly with hobbies (t=3.496, P = 0.001) and the visual spatial execution function (t=3.316, P = 0.001), naming (t=3.241, P = 0.001), abstract (t=2.643, P = 0.009), and delayed recall (t=2.073, P= 0.04) were all significantly correlated. Conclusions Often using smart products and having certain hobbies are protective factors of cognitive function, build corresponding intervention plans for the future, by cultivating the elderly hobby, guiding the elderly using intelligent products and other measures to achieve successful aging, slow the cognitive decline, thus reducing the risk of mild cognitive impairment. Key words: Nursing home; Aged; Smart product; Hobby; Cognitive function
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".