User testing to inform modification of the MyHealthyGut digital health application in inflammatory bowel disease
Bibliographic record
Abstract
Abstract Introduction Inflammatory bowel disease (IBD), characterized by chronic intestinal inflammation, can be subcategorized into Crohn’s disease and ulcerative colitis. The treatment for these conditions is unique to each patient, and may include lifestyle changes, pharmaceutical intervention, and surgery. Lifestyle changes, such as dietary intervention, are a cornerstone of IBD symptom management. Given the daily burden of this disease, self-management is paramount in coping with and/or minimizing symptoms. The MyHealthyGut application (app), successfully proven to be a self-management tool for celiac disease, shows promise for use in an IBD patient population. Objective To undertake user testing to inform the development of an IBD-focused version of the current MyHealthyGut app. Methods This study was undertaken between October 2021 and April 2022. Participants included IBD patients and healthcare practitioners (HCPs) (physicians, registered dietitians [RD], and registered nurses [RN]), using social media postings and convenience sampling. Two RDs demonstrated how to use the current functions and features of the app with each participant. Participants used the app for a 2-week period which was followed by participation in a focus group or individual interview to provide feedback on the app. Qualitative questionnaires, tailored to each patient category, were administered verbally and feedback was recorded. Thematic analysis techniques were used for data quantification and analysis. Results 15 participants were recruited and enrolled. Of these, 14 participants took part in the focus group and/or individual interviews. The feedback suggested changes related to clinical uses (e.g. incorporating information collected by the app into electronic medical record systems), food and symptom tracking (e.g. the option to track water intake), ease of use (e.g. the option to autofill food tracker with frequently consumed meals), and app content (e.g. information about IBD treatments). All (100%) of participants reported that they would either use the app themselves or recommend the app to patients, once their suggestions were implemented. Conclusion Through user testing and feedback collection, priorities for app modification were identified. Areas of modification in the app functions and features, ease of use, and content were identified. Once updated to meet the needs of IBD patients, the MyHealthyGut app may be a useful tool for IBD self-management. Funding Source Canadian Foundation of Dietetic Research
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".