Evaluation of mHealth Interventions in Wound Care: A Systematic Review Highlighting the Involvement of Informal Caregivers
Bibliographic record
Abstract
Introduction: Contemporary wound care (WC) complexities strain healthcare systems and challenge informal caregivers (ICs), especially in home settings. Mobile health applications (mHealth apps) offer real-time solutions, and telemedicine's rise emphasizes its potential to address these challenges. The aim of this study was to systematically evaluate and synthesize existing literature on mHealth app interventions for WC, with a specific emphasis on understanding the involvement, impact, and contributions of ICs in these interventions.Material and Methods: This study followed the PRISMA guidelines for systematic reviews. Articles were sourced from three databases (PubMed, Cochrane Library, and CINAHL), focusing on WC via mHealth with IC involvement. Quality assessment tools, including the Newcastle-Ottawa Scale and the Cochrane Collaboration tool, were used to ensure high research standards.Results: Upon meticulous examination of the articles, a mere six accurately aligned with the primary objectives of the research. Modern strides in healthcare technology have undeniably augmented both patient care and education. Several studies from different nations have delved into various wound categories, including pressure ulcers, diabetic foot ulcers, surgical wounds, and burns. The participant count in these scholarly investigations fluctuated between 15 and 70. Remarkably, among these six, only a single study concentrated on ICs.Conclusion: Wound management requires an integrated technology, education, and IC training approach. Our review suggests that research on mHealth app interventions for ICs in WC needs to be more represented in global literature. Given this gap, we advocate for enhanced joint efforts to ensure that WC advances with digital healthcare without overlooking the IC population.
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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 | 0.023 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".