Effects of Smartphone-Based Hospital-Family Transitional Care on Symptom Burden and Quality of Life in Elderly Patients with Depression
Bibliographic record
Abstract
Objective: To explore the effects of smartphone-based hospital-family transitional care on symptom burden and quality of life in elderly patients with depression. Methods: This study retrospective analyzed the clinical data of 168 elderly patients with depression admitted to our hospital from January 2022 to January 2024. A total of 79 patients were included in the reference group (routine transitional management), and 89 subjects were included in the observation group (smartphone-based hospital-family transitional care). The symptom burden and quality of life in both groups before and after management were compared. The main statistical methods used in this study were the chi-squared test and the Mann-Whitney U test. Results: Before discharge, no significant difference existed in Geriatric Depression Scale (GDS) scores, P300 latency, P300 amplitude, Montreal Cognitive Assessment (MoCA) scores, and the scores of each domain in the World Health Organization Quality of Life (WHOQOL)-BREF between the two groups (all p > 0.05). After 5 months, the observation group demonstrated a significantly lower GDS score (p = 0.016), shorter P300 latency (p < 0.001), higher P300 amplitude (p < 0.001), higher MoCA score (p = 0.001), and significantly higher scores in physiological, psychological, and environmental domains than the reference group (p < 0.001), with no significant difference in social relation domain (p > 0.05). Conclusions: Smartphone-based hospital-family transitional care can improve the symptom burden, cognitive function, and quality of life of elderly patients with depression.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".