The Influence of App Health Detection Early Diagnosis on the Quality of Life of the Elderly at Risk of Infectious Diseases
Bibliographic record
Abstract
Background: Type II diabetes mellitus in the elderly requires long-term management with optimal family support. Mobile health (mHealth) technology offers a promising intervention to improve patients’ quality of life. Objective: To evaluate the effectiveness of mHealth applications in improving quality of life among elderly patients with type II diabetes mellitus through a systematic literature review. Methods: A systematic literature review was conducted using the PRISMA approach and the PICO framework. Literature searches were performed in Google Scholar, PubMed, Alberta Health Services, and Wiley Online Library using keywords related to mHealth, quality of life, and type II diabetes mellitus. Articles were screened based on predetermined inclusion and exclusion criteria. Results: From 12 analyzed studies, mHealth application use for ≥3 months with active participation showed a significant improvement in quality of life compared to control groups. Early Detection Applications also had a positive impact on quality of life among elderly individuals at risk of type II diabetes mellitus. Conclusion: Implementation of mHealth applications focusing on lifestyle modifications effectively enhances quality of life in elderly patients with type II diabetes mellitus, supporting the use of technology-based interventions for chronic disease management..
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".