Development and Effectiveness of a Mobile Health Intervention in Improving Health Literacy and Self-management of Patients With Multimorbidity and Heart Failure: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Patients with multimorbidity and complex health needs are defined as a priority by the World Health Organization (WHO) and the European Union. There is a need to develop appropriate strategies with effective measures to meet the challenge of chronicity, reorienting national health systems. The increasing expansion of mobile health (mHealth) interventions in patient communication, the reduction of health inequalities, improved access to health care resources, adherence to treatment, and self-care of chronic diseases all point to an optimistic outlook. However, only few mobile apps demonstrate their effectiveness in these patients, which is diminished when they are not based on evidence, or when they are not designed by and for users with different levels of health literacy (HL). OBJECTIVE: This study aims to evaluate the efficacy of an mHealth intervention relative to routine clinical practice in improving HL and self-management in patients with multimorbidity with heart failure (HF) and complex health needs. METHODS: This is a randomized, multicenter, blinded clinical trial evaluating 2 groups, namely, a control group (standard clinical practice) and an intervention group (standard clinical practice and an ad hoc designed mHealth intervention previously developed), for 12 months. RESULTS: The contents of the mHealth intervention will address user-perceived needs based on the development of user stories regarding diet, physical exercise, cardiac rehabilitation, therapeutic adherence, warning signs and symptoms, and emotional management. These contents have been validated by expert consensus. The creation and development of the contents of the mHealth intervention (app) took 18 months and was completed during 2021. The mobile app is expected to be developed by the end of 2022, after which it will be applied to the experimental group as an adjunct to standard clinical care during 12 months. CONCLUSIONS: The trial will demonstrate whether the mobile app improves HL and self-management in patients with HF and complex health needs, improves therapeutic adherence, and reduces hospital admissions. This study can serve as a starting point for developing other mHealth tools in other pathologies and for their generalization to other contexts. TRIAL REGISTRATION: ClinicalTrials.gov NCT04725526; https://tinyurl.com/bd8va27w. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35945.
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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 | 0.035 | 0.030 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.062 | 0.011 |
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".