Efficacy of Mobile App–Based Dietary Interventions Among Cancer Survivors: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: The World Health Organization recommends that cancer survivors maintain a healthy diet and weight control to prevent cancer recurrence. Albeit a growing interest in using mobile apps for health promotion, there is a need for comprehensive evidence on the effects of mobile apps, particularly on dietary behaviors. Objective: This study aims to evaluate the efficacy, feasibility, and acceptability of mobile app-based dietary interventions among cancer survivors and explore the potential mobile app features worth incorporating. Methods: In this systematic review and meta-analysis, we searched Embase, Cochrane Library, PubMed, and Web of Science from inception to September 2023 without language restriction. We identified studies that used mobile apps for dietary interventions as a major module for cancer survivors. In addition, 2 independent reviewers screened the studies, extracted data, and assessed methodological quality using Cochrane's risk of bias tools for randomized trials (RoB 2) and nonrandomized studies (ROBINS-I). A meta-analysis was conducted on body weight, BMI, nutritional outcomes, and quality of life using random-effects models. Results: Of the 2621 records identified, 22 studies involving 1204 cancer survivors were included. Notably, existing trials involved only breast and gastrointestinal cancer survivors. Preliminary evidence suggested that mobile app-based dietary interventions demonstrated a beneficial effect on energy intake (Hedges g=1.00, 95% CI 0.96-1.03) and weight changes (Hedges g=-0.43, 95% CI -0.45 to -0.41); as well as a potential to improve protein intake and quality of life among gastrointestinal cancer survivors. The usability, quality, and satisfaction of app use as measured by standardized questionnaires, including the System Usability Scale, the Mobile Application Rating Scale, and the Questionnaire for User Interface Satisfaction, were positive. While feedback messages and dietary goal setting were considered facilitators of mobile app use, concerns regarding the time required for app use and limited food logging options were raised. Conclusions: Our review found the preliminary efficacy, feasibility, and acceptability of mobile app-based dietary interventions for cancer survivors. However, study heterogeneity should be recognized. More trials are warranted to confirm the effectiveness of these interventions and explore any differences based on cancer types, staging, treatment statuses, the mode of communication with dietitians, and the engagement of family or caregivers. Existing mobile apps could maintain important features such as feedback messages and dietary goal setting while considering the incorporation of artificial intelligence-powered food recognition in food logging and cancer-specific dietary recommendations.
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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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".