Researchers and product teams evaluating health apps need fast, reproducible thematic insights from interviews, logs, and questionnaires. The July 2, 2026 protocol for the MyIBDDiet pilot is published in PLoS One and describes a 60-day, randomized 40-person study (30-day crossover) that foregrounds mixed-method usability (MAUQ, TFA, and semi-structured interviews). This post presents a compact, AI-enabled workflow for qualitative analysis of app usability that maps source documents to themes, frequency counts, and cross-segment comparisons, reproducibly and securely with Evidano.
Key Takeaways
Evidano is an AI-powered qualitative data analysis platform that ingests interview transcripts, questionnaires, ASA24 exports, and app analytics, then produces thematic coding, cross-tabs, and secure, reproducible reports.
Evidano can accelerate synthesis for MyIBDDiet-style mixed-methods pilots by automating transcription, AI-assisted coding, cross-segmentation, and stakeholder-ready exports.
- The MyIBDDiet pilot protocol was published 2 July 2026 in PLoS One and defines a 60-day randomized pilot with n = 40 and a 30-day crossover.
- Primary outcomes are mixed-methods usability measures (MAUQ, TFA) combined with semi-structured interviews, backend app analytics, ASA24 dietary recalls, and biomarkers.
- A two-week Evidano runbook moves raw audio, CSVs, and analytics to validated transcripts, AI-assisted coding, cross-tabs, and an exportable recommendation report.
Findings snapshot
| Metric | Value | Source / note |
|---|---|---|
| Publication date | 2 July 2026 | PLoS One |
| Trial registration | NCT06683105 (registered 8 Nov 2024) | ClinicalTrials.gov |
| Sample size | n = 40 (pilot) | 20 per arm; pilot to inform larger RCT |
| Intervention length | 60 days total; 30-day crossover | Assessments at baseline, 30, 60, 180 days |
| Primary usability measures | MAUQ; TFA; semi-structured interviews | Mixed-methods usability focus |
| Dietary instruments | ASA24; Mini-EAT; HEI; MDSS | Objective + self-report |
| App features highlighted | Barcode scanner; NOVA processing tags; CNF (>5000 foods) | Supports cultural foods via multiple food DBs |
| Qualitative processing | Interviews transcribed verbatim; NVivo; thematic analysis | Intended double-coding + resolution |
| Recruitment & timeline | Start Mar 2026; complete Mar 2028; analysis Aug 2028 | Manuscript target Dec 2028 |
Fast take, why this pilot matters
The MyIBDDiet pilot (published 2 July 2026) tests usability and acceptability of a Mediterranean-style diet app for people with IBD, using mixed quantitative scales and semi-structured interviews. Full protocol: PLoS One.
- Primary outcome: usability measured with MAUQ and TFA, plus qualitative interviews.
- Design: a 60-day single-centre randomized pilot with n = 40 (20 per arm) and a 30-day crossover.
- Why researchers care: the pilot yields interview transcripts, app analytics, dietary recalls (ASA24), and biomarkers, a rich dataset for AI-enabled qualitative synthesis.
What happened: study design in plain English
MyIBDDiet is a co-designed nutrition app that tracks foods (Canadian Nutrient File, USDA, and other regional DBs), flags ultra-processed items via NOVA, and links symptom logs to meals. The pilot randomizes 40 adults with IBD to immediate app access or usual care with a 30-day delayed crossover.
- Usability is measured quantitatively with MAUQ scores and TFA constructs, and qualitatively through semi-structured interviews until thematic saturation.
- Secondary data include ASA24 dietary recalls, spot urine sodium/chloride (processed food biomarkers), CRP and fecal calprotectin, and exploratory microbiome/metabolome profiles.
- App analytics (engagement logs, barcode scans, processing-level tags) are captured at the backend, a source for mixed-method triangulation.
So what for qualitative researchers & UX teams
Qualitative researchers and UX teams should combine validated usability scales with semi-structured interviews and backend analytics to capture behavior, perception, and objective use. The MyIBDDiet protocol provides a practical template for that mix.
- Use paired MAUQ and TFA to quantify acceptability domains and link those numeric constructs to interview themes (for example, link burden to login friction).
- Triangulate app logs with interview claims to prioritize UX fixes, for example compare claimed daily use with actual session counts.
- Collect diet recalls and simple biomarkers to test whether behavior-change claims map to objective signals when product claims touch clinical outcomes.
Do more, faster with Evidano
Problem: fragmented inputs
MyIBDDiet yields transcripts, MAUQ and TFA responses, ASA24 recalls, backend logs, and biomarker tables, and stitching these manually is slow and error-prone.
Solution: unified ingestion and thematic analysis
Evidano ingests interview transcripts, questionnaires (CSV/XLS), ASA24 exports, and app analytics, then produces thematic coding, frequency counts, and cross-segment comparisons such as high-engagers versus low-engagers.
Evidano’s transcription and translation tools, including custom dictionaries and PII redaction, streamline interview intake; tuned AI models extract themes and representative quotes while preserving research-style rigor.
Solution: link qualitative themes to quantitative signals
Evidano cross-tabulates themes with MAUQ and TFA scores and backend metrics, for example identify themes correlated with low MAUQ usefulness scores and quantify how many low-use participants cited confusing processing tags.
Security & compliance
Evidano encrypts data end-to-end and never uses customer data to train third-party models, a fit for health research pipelines that must protect PHI.
Two-week runbook: from raw files to insight
A two-week runbook converts raw files into stakeholder-ready insight using Evidano.
- Day 1–2: Ingest files, upload interview audio, MAUQ and TFA CSVs, ASA24 exports, and app analytics CSVs. Use a custom dictionary to normalize food terms and IBD jargon.
- Day 3–5: Auto-transcribe audio with PII redaction if needed; review and approve transcripts.
- Day 6–8: Auto-code with an initial codebook (MAUQ domains and CFIR constructs); run thematic extraction and a co-occurrence network to reveal dominant theme clusters.
- Day 9–11: Cross-segment analysis, compare themes by engagement quartile, MAUQ score bands, or biomarker change (for example, sodium delta).
- Day 12–14: Export a stakeholder-ready report with top themes, representative quotes, word clouds, and a recommendation checklist for UX and clinical teams.
FAQ: qualitative analysis of app usability
How do I ensure interview coding is reliable?
Use double-coding with conflict resolution to ensure reliable interview coding, the PLoS protocol plans two coders plus a third reviewer. In Evidano, import your codebook and run AI-assisted coding, review coder agreement metrics, and resolve discrepancies in the transcript viewer.
Can I link MAUQ and TFA numeric scores to qualitative themes?
Yes, you can link MAUQ and TFA numeric scores to qualitative themes by mapping participant IDs across datasets and running cross-segmentation to surface themes over-represented in low-scoring participants, for example burden-related themes in low MAUQ usefulness scores.
Is this mixed-methods approach appropriate for clinical research?
Yes, the MyIBDDiet pilot’s mixed-methods approach is designed for clinical contexts. Maintain ethics approvals and consent when handling transcripts and biomarkers, and remember that analysis is research-focused and not diagnostic.
Wrapping up, next steps
The MyIBDDiet pilot (protocol published 2 July 2026 in PLoS One) is a concise example of rigorous mixed-methods evaluation for a diet-focused mHealth app: combine validated usability scales, semi-structured interviews, and backend analytics to capture perception and behavior.
- Start by prescribing the same data collection mix used in the pilot: validated usability scales (MAUQ and TFA), semi-structured interviews, and backend analytics.
- Use Evidano to ingest, code, cross-tab, and visualize themes securely, then export reproducible reports for clinicians and product owners.
- See the protocol in PLoS One.
Learn how to run a pilot-to-insight pipeline on Evidano and start a secure trial with your transcripts and analytics. Try Evidano for free.