Mobile health technologies supporting colonoscopy preparation: A systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) technologies are innovative solutions for delivering instructions to patients preparing for colonoscopy. OBJECTIVE: To systematically review the literature evaluating the effectiveness of mHealth technologies supporting colonoscopy preparation on patient and clinical outcomes. METHODS: MEDLINE, EMBASE, CINAHL and CENTRAL were searched for randomized controlled trials (RCTs) that evaluated the effectiveness of mHealth technologies for colonoscopy preparation on patient and clinical outcomes. Two reviewers independently assessed study eligibility, extracted data, and appraised methodological quality using the Cochrane Risk-of-Bias tool. Data were pooled using random effects models and when heterogeneity, assessed using I2, was statistically significant, a qualitative synthesis of the data was performed. Publication bias was assessed using a funnel plot. RESULTS: Ten RCTs (3,383 participants) met inclusion criteria. MHealth interventions included smartphone apps, SMS text messages, videos, camera apps, and a social media app. Outcomes were bowel cleanliness quality, user satisfaction, colonoscopy quality indicators (cecal intubation time, withdrawal time, adenoma detection rate), adherence to diet, and cancellation/no-show rates. MHealth interventions were associated with better bowel cleanliness scores on the Boston Bowel Preparation Scale [standardized mean difference (SMD) 0.57, 95%CI 0.37-0.77, I2 = 60%, p = 0.08] and the Ottawa Bowel Preparation Scale [SMD -0.39, 95%CI -0.59-0.19, I2 = 45%, p = 0.16], but they were not associated with rates of willingness to repeat the colonoscopy using the same regimen [odds ratio (OR) 1.88, 95%CI 0.85-4.15, I2 = 48%, p = 0.12] or cancellations/no-shows [OR 0.96, 95%CI 0.68-1.35, I2 = 0%]. Most studies showed that adequate bowel preparation, user satisfaction and adherence to diet were better in the intervention groups compared to the control groups, while inconsistent findings were observed for the colonoscopy quality indicators. All trials were at high risk of bias for lack of participant blinding. Visual inspection of a funnel plot revealed publication bias. CONCLUSIONS: MHealth technologies show promise as a way to improve bowel cleanliness, but trials to date were of low methodological quality. High-quality research is required to understand the effectiveness of mHealth technologies on colonoscopy outcomes.
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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 | 0.026 | 0.081 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.035 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".