The Effect of Combining mHealth and Health Professional–Led Intervention for Improving Health-Related Outcomes in Chronic Diseases: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Chronic diseases such as diabetes and cardiovascular disease are global health challenges, affecting millions of people worldwide. Traditional health care often falls short in chronic disease management. This has led to the exploration of innovative solutions, such as mobile health (mHealth) technologies. mHealth, which leverages mobile and wireless technologies, has the potential to transform health care delivery by providing continuous, accessible, and personalized care. However, the effectiveness of mHealth, particularly when integrated with traditional health care interventions delivered by professionals, warrants comprehensive investigation. Understanding the combined impact of mHealth and professional-led interventions is critical to maximizing the potential of mHealth to improve patient outcomes and adherence. Objective This study aims to investigate the effectiveness of combining mHealth and health professional–led intervention for improving health-related outcomes in chronic diseases Methods This systematic review and meta-analysis focused on randomized controlled trials. We searched Web of Science, CENTRAL, MEDLINE, and CINAHL through July 17, 2023. The study targeted patients aged 18 years and older, experiencing at least 1 chronic condition. The interventions were a combination of mHealth and the use of a health care professional. The comparison groups consisted of participants receiving either general care and follow-up or those using mHealth devices without any health care professional involvement. The outcomes measured in this review included hemoglobin A1c (HbA1c), quality of life (QoL), and physical activity. Results The study included 26 research papers, encompassing 7360 individuals. Meta-analysis was conducted for HbA1c, QoL, and physical activity. For HbA1c, short-term improvement was significant (standardized mean difference [SMD] –0.43; 95% CI –0.64 to –0.21; I2=69%) and medium term (SMD –0.49; 95% CI –0.49 to –0.09; I2=21%). However, in the long term, the improvement was not significant (SMD –0.23; 95% CI –0.49 to 0.03; I2=88%). For QoL, significant improvements were observed in the short term (SMD –0.23; 95% CI –0.42 to –0.05; I2=62%), and in the medium term (SMD –0.16; 95% CI –0.24 to –0.07; I2=0%). In the long term, however, the improvement was not significant (SMD –0.12; 95% CI –0.41 to 0.16; I2=71%). For physical activity, both subjective (questionnaire) and objective (number of steps) outcomes were analyzed. In the short term, subjective outcomes showed significant improvement (SMD 0.31; 95% CI 0.12-0.50; I2=0%), while objective outcomes did not (SMD 0.11; 95% CI –0.05 to 0.27; I2=0%). Medium- and long-term subjective outcomes showed no significant improvement. Meta-analysis for objective outcomes in the medium and long term was not possible due to insufficient studies. Conclusions This study confirmed short- and medium-term benefits of mHealth combined with professional interventions for HbA1c, QoL, and short-term physical activity, supporting effective chronic disease management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.092 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".