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Record W4387066618 · doi:10.1177/20552076231203664

User testing to modify the MyHealthyGut digital health application for inflammatory bowel disease

2023· article· en· W4387066618 on OpenAlexaff
Madeline Erlich, Sarah Lindblad, Natasha Haskey, Darlene Higbee Clarkin, Taojie Dong, Ruth Harvie, Genelle Lunken, Jess Pirnack, Kevan Jacobson

Bibliographic record

VenueDigital Health · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsSt. Francis Xavier UniversityUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaBC Children's HospitalUniversity of TorontoRoyal Victoria Regional Health CentreSt. Michael's Hospital
Fundersnot available
KeywordsMedicineInflammatory bowel diseaseThematic analysisDiseaseFocus groupUlcerative colitisPopulationHealth carePhysical therapyFamily medicineQualitative researchInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Inflammatory bowel disease, 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 inflammatory bowel disease symptom management. Given the daily burden of this disease, self-management is paramount in coping with and/or minimizing symptoms. The MyHealthyGut application, successfully proven to be a self-management tool for celiac disease, shows promise for use in an inflammatory bowel disease patient population. Objective: To conduct user testing to gather valuable insights for the development of an IBD-focused version of the existing MyHealthyGut app. Methods: Participants included inflammatory bowel disease patients and healthcare practitioners. Participants used the application for a 2-week period, followed by participation in a focus group or individual interview to provide feedback. Qualitative questionnaires 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, food and symptom tracking, ease of use, and educational content. All (100%) participants reported that they would either use the application themselves or recommend it to patients, once their suggestions were implemented. Conclusion: Through user testing and feedback collection, priorities for app modification were identified. Areas for modification in the app functions and features, ease of use, and content were identified. Once updated to meet the needs of inflammatory bowel disease patients, the MyHealthyGut app may be a useful tool for IBD self-management.

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 imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.044
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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.

Opus teacher head0.017
GPT teacher head0.294
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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