Using a Mobile Health App (ColonClean) to Enhance the Effectiveness of Bowel Preparation: Development and Usability Study
Bibliographic record
Abstract
Background: Colonoscopy is the standard diagnostic method for colorectal cancer. Patients usually receive written and verbal instructions for bowel preparation (BP) before the procedure. Failure to understand the importance of BP can lead to inadequate BP in 25%-30% of patients. The quality of BP impacts the success of colonoscopy in diagnostic yield and adenoma detection. We developed the "ColonClean" mobile health (mHealth) app for Android devices. It incorporates visual representations of dietary guidelines, steps for using bowel cleansing agents, and observations of the last bowel movement. We used the Technology Acceptance Model to investigate whether the use of the ColonClean mHealth app can improve users' attitudes and behaviors toward BP. Objective: This study aims to validate the effectiveness of the ColonClean app in enhancing user behavior and improving BP, providing safe and cost-effective outpatient colonoscopy guidance. Methods: This study uses a structured questionnaire to assess perceived usefulness, perceived ease of use, and users' attitudes and behaviors toward BP regarding the ColonClean mHealth app. A total of 40 outpatients who were physically and mentally healthy and proficient in Chinese were randomly chosen for this study. The data were analyzed using SPSS 25.0, and we used Pearson product-moment correlation and simple regression analysis to predict the perception of ColonClean. Results: The results showed that 75% (30/40) of participants achieved an "excellent" or "good" level of BP according to the Aronchick Bowel Preparation Scale. Perceived usefulness and perceived ease of use of the ColonClean mHealth app were positively correlated with users' attitudes and behaviors (P<.05). Conclusions: The ColonClean mHealth app serves as an educational reference and enhances the effectiveness of BP. Users expressed their willingness to use the app again in the future and recommend it to family and friends, highlighting its effectiveness as an educational guide for BP.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".