Researchers and UX teams running pilot trials need a tight, reproducible approach to analyze interview and usage data. This post uses the MyIBDDiet pilot protocol (Published July 2, 2026; single-centre randomized pilot, n=40, 60 days) as a worked example for qualitative analysis of mHealth app usability. You will get a concise workflow, from ingesting semi-structured interview transcripts and MAUQ/TFA free-text to producing thematic, frequency, and cross-segment outputs that inform product and trial design. See the original protocol at PLoS ONE and a quick set of reproducible steps you can run in Evidano. This is research-focused guidance, not clinical advice.
Key Takeaways
This post explains how to run reproducible qualitative analysis of mHealth app usability for pilot trials like MyIBDDiet (n=40, 60 days) and how to scale that workflow with Evidano. Evidano is an AI-powered qualitative data analysis platform that ingests audio, questionnaires, and analytics, produces AI-assisted coding, and supports triangulated mixed-methods outputs to accelerate UX and trial decisions.
- Use a mixed-methods design: validated scales (MAUQ, TFA), semi-structured interviews, and app analytics provide complementary evidence for adoption and barriers.
- Pilot facts to note: MyIBDDiet protocol published July 2, 2026, targets n=40 with a 60-day intervention and a 30-day crossover.
- Follow the 7-step workflow in this post and use Evidano to speed transcription, AI-assisted coding, triangulation, and exportable evidence packs for manuscripts and product sprints.
Fast take: Why this study matters for qualitative teams
This pilot randomized trial protocol (Published July 2, 2026) tests MyIBDDiet, a diet guidance and tracking app co-designed with patient partners to support an anti-inflammatory Mediterranean-style diet for people with IBD, and prioritizes usability and acceptability with mixed methods. The study collects validated questionnaires (MAUQ, TFA), virtual semi-structured interviews, and app back-end analytics, recruits n=40 with a 60-day intervention and a 30-day crossover, and assesses outcomes at baseline, 30, 60, and 6 months. Read the protocol at PLoS ONE.
- Why UX and qualitative teams care: mixed-methods design (validated scales, interviews, analytics) provides triangulated signals of adoption and barriers.
- Why this post: translate the study’s qualitative plan into a reproducible analysis workflow you can scale with Evidano.
Findings snapshot (trial facts & timeline)
| Date / Metric | Value | Source / Note |
|---|---|---|
| Publication | July 2, 2026 | PLoS ONE protocol (published) |
| Trial registration | NCT06683105 (registered 8 Nov 2024) | ClinicalTrials.gov |
| Sample size (pilot) | n = 40 (20 per arm) | Pilot feasibility; no formal power calc |
| Intervention duration | 60 days (crossover at 30 days) | Primary outcomes at baseline, 30, 60, and 6 months |
| Primary qualitative instruments | Semi-structured interviews, MAUQ, Theoretical Framework of Acceptability (TFA) | Interviews analyzed until thematic saturation |
| Data collection windows | Recruitment: Mar 2026–Mar 2028; Analysis complete by Aug 2028 | Protocol timeline |
What the protocol actually does (methods in plain English)
This protocol blends validated quantitative instruments with qualitative interviews to assess the usability and acceptability of MyIBDDiet. Participants complete MAUQ and TFA questionnaires at defined timepoints and are invited to virtual semi-structured interviews informed by CFIR; interviews are transcribed verbatim, de-identified, and analyzed independently by two researchers with reconciliation by a senior reviewer. The study also collects app back-end analytics and diet recalls (ASA24) so qualitative themes can be triangulated with usage patterns and diet-change metrics.
- Semi-structured interviews target barriers and facilitators and perceptions of content such as education videos, barcode scanner, and processing tags.
- Qualitative approach: thematic analysis (Braun and Clarke) performed to saturation, with themes informing refined app versions and a future larger RCT.
- Quantitative linkage: MAUQ and TFA scores, usage analytics, and biomarkers (urine sodium/chloride, CRP, fecal calprotectin) enable mixed-methods interpretation.
So what for researchers and UX teams: implications
Design and sampling
Design and sampling guidance: a pilot of n=40 is typical for usability-focused pilots and supports variance estimation for future power calculations. Pilot teams should plan for 12–30 participants per arm to estimate variance for later trials and recruit interviews until predefined thematic saturation stop rules are met.
Triangulation is essential
Triangulation is essential: combine MAUQ and TFA numeric scales, usage analytics (frequency and feature engagement), and interview themes to separate UX issues from clinical non-adherence. For example, low barcode-scanner use plus interview complaints about loading implies UX friction, while low MAUQ usefulness scores with high engagement suggests a content mismatch rather than an access issue.
Operational cautions
Operational cautions: prespecify coding procedures including two independent coders, a codebook, and reconciliation methods and log decisions for reproducibility. Teams should plan for 10–15% attrition (the protocol anticipates this), set missing-data rules, and record partial interviews and salvageable segments for qualitative analysis.
Do more, faster with Evidano (map to this use case)
Ingest & clean, transcripts, questionnaires, analytics
Ingest study data into Evidano by importing interview audio and REDCap CSVs directly into the platform. Use built-in transcription with a custom dictionary for medical and brand terms such as MyIBDDiet, MAUQ, and TFA and apply automatic PII redaction before analysts access data.
Codebook → AI-assisted coding
AI-assisted coding accelerates the initial tagging of themes after you upload the study’s CFIR-derived codebook into Evidano. Analysts then review, accept or modify codes, and lock the final codebook, reducing manual pass time by multiples.
Triangulate themes with usage metrics
Triangulate themes with usage metrics by combining backend analytics with coded quotes to produce cross-segment analysis, for example early versus delayed or high versus low engagers. Evidano’s cross-segment and frequency analyses help surface which features drive adoption.
Visualization & evidence packs
Visualization and evidence packs enable stakeholders to inspect co-occurrence networks and hierarchical code to subcode visuals for decks and manuscripts. Clickable quotes and exportable theme tables accelerate writing mixed-methods sections for manuscripts and grant applications.
Secure & auditable
Security and auditability are core: Evidano stores data encrypted and offers proprietary LLMs tuned for qualitative research that do not use customer data to train third-party models, aligning with trial privacy needs and research ethics board expectations.
7-step workflow: from audio to thematic recommendations (for a 60-day pilot)
This 7-step workflow converts interview audio and study data into decision-focused thematic recommendations for a 60-day pilot. Follow these steps to produce reproducible outputs tied to segments and analytics.
- Step 1: Ingest study materials, upload interview audio, MAUQ and TFA CSV exports, and app analytics CSVs into Evidano.
- Step 2: Transcribe with a custom dictionary and PII redaction, and timestamp transcripts to match analytics events.
- Step 3: Import or define the codebook (CFIR plus usability subcodes) and run AI-assisted initial coding across transcripts.
- Step 4: Perform an analyst review pass, reconcile code differences, and finalize hierarchical codes and subcodes.
- Step 5: Run thematic frequency and co-occurrence analyses and filter by segment such as early versus crossover or high versus low engagement.
- Step 6: Create visual exports such as word clouds and co-occurrence networks and assemble stakeholder evidence packs with top quotes.
- Step 7: Produce decision-focused recommendations such as UX fixes, content changes, and recruitment messaging and map these into the next sprint or a trial amendment.
FAQ: qualitative analysis of mHealth app usability
How do I compare interview themes to app metrics?
Direct answer: Link transcript timestamps to analytics events and run cross-segment theme comparisons to see how themes differ by engagement level. Use Evidano to join transcripts and analytics and to produce segment-level theme reports that show frequency and co-occurrence.
How many interviews are enough?
Direct answer: Continue interviews until thematic saturation is reached and document the saturation criteria. The MyIBDDiet pilot plans interviews until thematic saturation; for targeted usability studies, 12–20 interviews often suffice but always predefine and report your stop rules.
Can validated scales (MAUQ, TFA) be processed with qualitative analysis?
Direct answer: Yes, process validated scales as quantitative stratifiers and merge free-text with thematic coding for mixed-methods interpretation. Treat numeric MAUQ and TFA scores as stratifiers and include free-text responses in crosswalk tables to show where perception diverges from behavior.
How do I preserve privacy in transcription?
Direct answer: Apply PII redaction at transcription, store de-identified transcripts, and restrict analyst access to comply with ethical protocols. Use automatic PII redaction, encrypted storage, and role-based access controls as part of your pipeline.
Wrapping up: next steps & call to action
This section summarizes next steps and provides a call to action to start turning transcripts and analytics into prioritized UX fixes and publishable mixed-methods results. If you are running a pilot like MyIBDDiet (Published July 2, 2026; n=40; 60-day crossover), follow the 7-step workflow above to get rapid, reproducible qualitative insights that directly inform product sprints and trial design.
Ready to transform transcripts and analytics into prioritized UX fixes and publishable mixed-methods results? Try Evidano for free and bring your trial evidence to decisions faster.