A159 ARE MOBILE HEALTH TECHNOLOGIES SUPPORTING COLONOSCOPY PREPARATION ASSOCIATED WITH BETTER PATIENT OUTCOMES: A SYSTEMATIC REVIEW OF RANDOMIZED CONTROLLED TRIALS
Bibliographic record
Abstract
Abstract Background Mobile health technologies are innovative solutions for delivering instructions to patients preparing for their colonoscopy appointments. Aims To systematically review the literature of the effect of smartphone-based technologies supporting colonoscopy appointment preparation on patient outcomes. Methods With the assistance of a librarian, one author searched MEDLINE, EMBASE, CINAHL and CENTRAL for randomized controlled trials (RCTs) that evaluated the effect of smartphone-based technologies for colonoscopy preparation on bowel cleanliness and user satisfaction. Two independent reviewers extracted data on patient and intervention characteristics and study outcomes, and appraised study quality using the Cochrane Risk-of-Bias tool. Summary statistics were generated using random effects models for the trials that used either the Boston Bowel Preparation Scale (BPPS) or the Ottawa Bowel Preparation Scale (OBPS). Statistical heterogeneity was assessed using I2. Results Ten RCTs met our inclusion criteria. Smartphone-based interventions included apps, SMS text messages, video clips, camera apps, and social media apps. Most studies showed smartphone-based interventions were associated with better quality bowel cleanliness scores and higher user satisfaction compared to usual care. Standardized mean differences for the BBPS and OBPS differed between the intervention and control groups [SMD 0.57, 95%CI 0.18, 0.95] and [SMD -0.39, 95%CI -0.59, -0.19], respectively. Statistically significant statistical heterogeneity was found for the meta-analyses for the trials employing the BBPS (I2=80%, p=0.03) but not for the trials using the OBPS (I2=45%, p=0.16). All RCTs were at high risk of bias from non-blinded participants, and most studies were at high or unclear risk of bias due to lack of allocation concealment. Funnel plots to evaluate publication bias were not generated as there were too few studies with sufficient data to analyze. Conclusions This systematic review found that smartphone-based technology users had better bowel cleanliness quality scores and higher satisfaction with the method of delivering instructions compared to patients given usual care. Given that all RCTs were at high risk of bias, high-quality RCTs that blind participants and conceal study group allocation are needed. Funding Agencies CIHRDepartment of Medicine, McGill University and the Research Institute of the McGill University Health Centre
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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 | 0.023 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".