This post explains how to extract rigorous qualitative insights from the DizzyVR feasibility study so clinical researchers and UX teams can make evidence-based decisions. The primary keyword is VR vestibular rehabilitation and this article uses the PLOS One study published 30 July 2026 as the test case. According to the PLOS One article published 30 July 2026, the authors ran a mixed-methods feasibility study with ten participants who completed eight weekly immersive VR sessions, and the paper reported detailed session logs and semi-structured interview transcripts suitable for qualitative analysis.
Key Takeaways
The PLOS One feasibility study published 30 July 2026 tested DizzyVR and found high adherence and promising exploratory improvements in gait and balance, while collecting verbatim interview transcripts and per-second kinematic logs for later qualitative and quantitative analysis: PLOS One.
- Sample and dates: The study enrolled 13 starters between October 2024 and January 2025 and reported ten completers, as reported in PLOS One on 30 July 2026.
- Adherence and usability: The authors reported a session adherence rate of 91.25% among completers and a mean System Usability Scale of 68.5 in July 2026.
- Safety and adverse events: PLOS One recorded that three participants (30%) reported mild simulator sickness across five sessions, representing 6.84% of completed sessions.
- Exploratory clinical signals: The PLOS One results showed a reduction in Timed Up and Go time from 9.53s to 7.34s (p = 0.002) in the pre–post comparison reported 30 July 2026.
What happened and how the study was measured
Answer: According to the PLOS One report published 30 July 2026, the authors ran a prospective, single-arm feasibility study that combined quantitative clinical tests with semi-structured qualitative interviews.
According to the PLOS One article published 30 July 2026, the study recruited participants at Dr. López Cano Hospital and delivered eight weekly 50-minute DizzyVR sessions, with kinematic tracking recorded once per second and semi-structured interviews transcribed verbatim.
According to the PLOS One article published 30 July 2026, feasibility was defined as protocol adherence with a success threshold of 80%, usability used the System Usability Scale, satisfaction used the USEQ score, safety used the Simulator Sickness Questionnaire and an adverse event registry, and exploratory clinical outcomes included DHI, TUG, ABC, and FGA.
Findings Snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| 30 Jul 2026 | Participants completed | 10 completers (13 starters, 15 invited in Oct 2024-Jan 2025) | Small feasibility sample, usable verbatim interviews for qualitative coding |
| 30 Jul 2026 | Session adherence | 91.25% among completers | High within-study engagement, supports deeper thematic analysis of experience |
| 30 Jul 2026 | Usability and satisfaction | SUS 68.5 mean, USEQ 24.6 mean | Acceptable usability, positive user satisfaction themes in interviews |
| 30 Jul 2026 | Adverse events | 3 participants (30%), 5 sessions affected (6.84% of sessions) | Mild simulator sickness decreased over time, document for safety coding |
| 30 Jul 2026 | Exploratory clinical change | TUG improved 9.53s → 7.34s, p = 0.002 | Quantitative signal that can be triangulated with participant narratives about mobility |
Implications for clinical researchers and UX teams
Answer: The PLOS One feasibility study shows that small-sample VR trials can generate rich mixed datasets that need integrated qualitative and quantitative analysis to be useful for design and clinical decisions.
According to the PLOS One article published 30 July 2026, the study delivered per-second kinematic logs, session success rates, standard clinical scales, and verbatim interviews, which together let teams triangulate usability problems, safety signals, and functional change.
According to the PLOS One article published 30 July 2026, the qualitative interviews revealed consistent themes: perceived safety with a therapist present, early simulator sickness that decreased over sessions, and reported improvements in daily activities, which UX teams should map to specific exercise blocks when iterating the design.
How Evidano Helps: mapping DizzyVR research needs to AI-enabled QA
Evidano definition and role
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Use case: According to the PLOS One article published 30 July 2026, DizzyVR produced 1) verbatim interview transcripts, 2) per-second head and hand kinematic logs, and 3) structured session metadata; Evidano ingests all three formats and links text themes to timestamps and quantitative events, enabling mixed-methods synthesis.
Problem: Manual transcript coding is slow and inconsistent
Solution: Evidano applies automated transcription with custom dictionaries and PII redaction, then generates thematic codes and subcodes and exports human-audited codebooks to accelerate reproducible analysis.
Learn more: See the platform overview at Evidano features.
Problem: Linking subjective reports to objective kinematics is complex
Solution: Evidano supports time-aligned analysis so teams can link interview excerpts saying, for example, "I felt dizzy during the gait task" to the exact session timestamp and kinematic markers for targeted UX fixes.
Technical: For verbatim audio to text workflows, use Evidano’s speech-to-text pipeline which preserves speaker labels and allows custom vocabulary (useful for clinical terms such as 'vestibulopathy' and 'Cawthorne-Cooksey').
Problem: Cross-segment comparisons are needed but tedious
Solution: Evidano produces cross-segment frequency and sentiment reports so clinical teams can compare experiences by diagnosis, baseline DHI, or simulator sickness history within minutes.
FAQ: VR vestibular rehabilitation qualitative analysis
How do I extract themes from interviews in a small feasibility VR study?
Answer: Start with verbatim transcription, open coding, and then group codes into themes while linking them to session metadata for context.
Supporting detail: According to the PLOS One study published 30 July 2026, the authors transcribed interviews verbatim and constructed a system of categories and codes, which is the standard approach for small mixed-methods feasibility work.
Can interview quotes be time-aligned to VR kinematics for richer interpretation?
Answer: Yes, time-align interview excerpts with session logs to triangulate subjective events with objective measurements.
Supporting detail: According to the PLOS One article published 30 July 2026, DizzyVR recorded head and hand positions once per second and session completion timestamps, which enables precise linking between narrative reports and kinematic events.
What qualitative signals should I look for to explain a quantitative TUG improvement?
Answer: Look for participant language about confidence, specific ADL changes, and perceived gait stability in interview transcripts.
Supporting detail: According to the PLOS One study published 30 July 2026, participants reported increased balance confidence and specific examples such as reduced dizziness when getting out of the bathroom, which helps interpret the TUG decrease from 9.53s to 7.34s.
How should I report safety-related qualitative data from VR sessions?
Answer: Report counts of adverse events, SSQ trends, and representative quotations that capture onset, severity, and recovery.
Supporting detail: According to PLOS One on 30 July 2026, three participants reported nausea or disorientation and SSQ scores decreased over time, so combine the quantitative session-level rates with short verbatim quotes for transparency.
Conclusion & Next Steps
Answer: The PLOS One DizzyVR feasibility study shows that immersive VR trials can produce high-value mixed datasets when they combine per-second kinematics, structured clinical scales, and verbatim interviews.
Next step for researchers: According to the PLOS One article published 30 July 2026, plan randomized trials with larger and more homogeneous samples and preserve transcripts and session logs for integrated analysis.
If you want to scale that mixed-methods workflow, Evidano ingests transcripts, logs, and survey data, then produces thematic, frequency, and cross-segment analyses to speed decision-making.
Get started: Try Evidano for free.