A148 SMARTPHONE APPS FOR IBD DISEASE MANAGEMENT: A QUANTITATIVE EVALUATION
Bibliographic record
Abstract
Smartphone Apps for inflammatory bowel disease (IBD) are becoming increasingly popular. However, there is paucity of data on the function and quality of these applications. The lack of systemic investigation on IBD Apps prevents further comparative analysis with clinically validated scoring systems. This makes it difficult for patients to choose and for physicians to recommend the most appropriate App to help manage their disease. To identify and quantitatively evaluate smartphone Apps for patient management of IBD. Systematic search of smartphone Apps were carried out in Apple App Store for iOS operating system and Google Play Store for Android operating system using the following keywords: “IBD”, “inflammatory bowel disease”, “colitis”, “ulcerative colitis” and “crohn’s”. Title inclusion (relating to management of Inflammatory Bowel Disease and in English) and exclusion criteria (only offers educational information or Conference App) were applied to all results. Apps that passed title screening were review independently by two reviewers using the validated Mobile Application Rating Scale. An App’s quality was assessed based on engagement, functionality, aesthetics and information quality. Each item in MARS scoring tool was rated on a 5 point Likert scale. Each subcategory was averaged, then added to give an overall 5 point rating for the App. The two raters’ data were presented as aggregated means and standard deviations. Spearman’s bivariate analyses were used to assess correlation between overall rating and other App characteristics. App features were represented in a table format. Fifteen smartphone Apps for IBD management were included in the analysis. The top 3 Apps were IBD Health Storylines (4.9 ± 0.53), GutCheckTM (4.78 ± 0.18), and MyGiHealth (4.63 ± 0). The information quality score of an App was most strongly correlated with the overall score (Spearman’s rho=0.87, p=0.012), while functionality score, engagement score and aesthetics scores were less correlated with the overall score (Spearman’s rho=0.61, 0.59 and 0.53 respectively). Number of features offered by an App was strongly correlated with the overall score (Spearman’s rho=0.74, p=0.012) while cost and user ratings were poorly correlated with the overall score (Spearman’s rho=0.06, 0.04 respectively). This study provides a list of IBD disease management smartphone Apps with their associated features, ranked by order of quality by a validated scoring system. Information quality and features offered by an App appear to be most closely related to the overall quality of an App. This will allow physicians to recommend and patients to choose high quality Apps to support IBD management. Future studies will explore correlation between clinical validated scoring systems and information collected by high quality IBD management Apps identified in this study. None
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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 | 0.048 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".