Effectiveness of mHealth Interventions in the Control of Lifestyle and Cardiovascular Risk Factors in Patients After a Coronary Event: Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background Coronary artery disease is the main cause of death and loss of disability-adjusted life years worldwide. Information and communication technology has become an important part of health care systems, including the innovative cardiac rehabilitation services through mobile phone and mobile health (mHealth) interventions. Objective In this study, we aimed to determine the effectiveness of different kinds of mHealth programs in changing lifestyle behavior, promoting adherence to treatment, and controlling modifiable cardiovascular risk factors and psychosocial outcomes in patients who have experienced a coronary event. Methods A systematic review of the literature was performed following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A thorough search of the following biomedical databases was conducted: PubMed, Embase, Web of Science, SciELO, CINAHL, Scopus, The Clinical Trial, and Cochrane. Articles that were randomized clinical trials that involved an intervention consisting of an mHealth program using a mobile app in patients after a coronary event were included. The articles analyzed some of the following variables as outcome variables: changes in lifestyle behavior, cardiovascular risk factors, and anthropometric and psychosocial variables. A meta-analysis of the variables studied was performed with the Cochrane tool. The risk of bias was assessed using the Cochrane Collaboration tool; the quality of the evidence was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation tool; and heterogeneity was measured using the I2 test. Results A total of 23 articles were included in the review, and 20 (87%) were included in the meta-analysis, with a total sample size of 4535 patients. Exercise capacity measured using the 6-minute walk test (mean difference=21.64, 95% CI 12.72-30.55; P<.001), physical activity (standardized mean difference [SMD]=0.42, 95% CI 0.04-0.81; P=.03), and adherence to treatment (risk difference=0.19, 95% CI 0.11-0.28; P<.001) were significantly superior in the mHealth group. Furthermore, both the physical and mental dimensions of quality of life were better in the mHealth group (SMD=0.26, 95% CI 0.09-0.44; P=.004 and SMD=0.27, 95% CI 0.06-0.47; P=.01, respectively). In addition, hospital readmissions for all causes and cardiovascular causes were statistically higher in the control group than in the mHealth group (SMD=–0.03, 95% CI –0.05 to –0.00; P=.04 vs SMD=–0.04, 95% CI –0.07 to –0.00; P=.05). Conclusions mHealth technology has a positive effect on patients who have experienced a coronary event in terms of their exercise capacity, physical activity, adherence to medication, and physical and mental quality of life, as well as readmissions for all causes and cardiovascular causes. Trial Registration PROSPERO (International Prospective Register of Systematic Reviews) CRD42022299931; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=299931
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.025 | 0.041 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".