If you are running a qualitative analysis of hearing program pilots, the SOUND-BITES protocol (Tang et al., PLoS One, published 14 July 2026) is a practical model: mixed-methods, n≈600 clients, 60 volunteers, and optional audio interviews mapped to the Theoretical Domains Framework. This post shows how to turn that raw field material into rigorous themes, stakeholder-ready visuals, and cross-segment comparisons using AI-enabled qualitative research. We will (1) summarize the study’s data types and timeline, (2) show key coding and validity checkpoints the authors plan, and (3) map each step to Evidano capabilities, from secure transcription and PII redaction to automated thematic, frequency and cross-segment analysis. Read on for a 7-step workflow you can run in a pilot and a short checklist to avoid common pitfalls. Want to test this on your own pilot transcripts? Start a secure trial at Evidano.
Key Takeaways
Evidano is an AI-powered qualitative data analysis platform that helps teams transform SOUND-BITES audio, surveys, and clinical outputs into reproducible themes and stakeholder-ready visuals.
The SOUND-BITES pilot embeds hearing checks into Meals on Wheels NSW, generating mixed-methods data ideal for AI-assisted synthesis and cross-segment analysis.
- Design and timeline: the protocol is published in PLoS One on 14 July 2026, with full data collection expected by 30 April 2027 and preliminary results anticipated on 01 September 2027.
- Scope and sample: the study recruits 60 volunteers and targets 600 clients or household members, combining Arclight otoscopy with the Sound Scouts tablet audiometry app.
- Analytic payoff: standardized interview guides, audio recordings, survey CSVs (HHIE‑S, SSQ‑12, EQ‑5D‑5L), and NVivo-ready transcripts make the data ideal for secure transcription, TDF-aligned coding, and cross-segment frequency analysis.
Fast take: what this study offers researchers
This section summarizes what the SOUND-BITES study offers researchers in one sentence: the program embeds hearing checks into Meals on Wheels NSW deliveries and pairs Arclight otoscopy with the Sound Scouts tablet audiometry app to collect mixed-methods data.
The protocol is published in PLoS One.
- Design: mixed-methods pilot testing acceptability and feasibility; qualitative interviews + pre/post surveys + clinical screening data.
- Why it matters: task-shifted hearing checks can surface undiagnosed loss in a trusted community service and generate rich qualitative data on barriers and uptake.
- Quick payoff for analysts: standardized interview guides, audio recordings, survey CSVs, and NVivo-ready transcripts, ideal inputs for AI-assisted synthesis.
Findings snapshot
| Date / Item | Metric | Value / Note | Source |
|---|---|---|---|
| Published | Article | 14 July 2026 | PLoS One |
| Ethics | Approval date | 11 December 2025 (Macquarie University HREC ID: 16818) | Study protocol |
| Recruitment start | Students | 01 March 2026 | Study timeline |
| Target sample | Volunteers & clients | 60 volunteers; 600 clients/household members | Study design |
| Full data collection expected | Date | 30 April 2027 | Study timeline |
| Preliminary results | Date | 01 September 2027 (anticipated) | Study timeline |
How the SOUND-BITES pilot works, essential mechanics
The SOUND-BITES pilot pairs low-cost clinical tools (Arclight otoscope and the Sound Scouts app) with Meals on Wheels NSW volunteers and Master of Clinical Audiology students to deliver in-home hearing checks and education.
The protocol describes the clinical flow, data collected, and the qualitative plan for transcripts and thematic coding.
- Clinical flow: otoscopy (Arclight) → 8–10 minute Sound Scouts screening (iPad + Sennheiser headphones) → feedback and referral materials.
- Data collected: audiometric outputs (hearing numbers, pass/refer), structured otoscopy observations, baseline + 6-month surveys (HHIE‑S, SSQ‑12, EQ‑5D‑5L), and optional recorded interviews at ~4 weeks.
- Qualitative plan: audio-recorded interviews transcribed (intelligent verbatim), accuracy-checked, participant-validated, then coded in NVivo using the Theoretical Domains Framework (TDF) with dual coders and consensus processes.
So what for researchers & UX teams: three practical implications
Researchers (implementation & mixed-methods)
Researchers should treat SOUND-BITES as a repeatable template for task-shifting pilots that mixes clinical indicators with subjective experience, ideal for studies where acceptability and help-seeking are primary endpoints.
Researchers should plan for variable home environments: noise will affect screening thresholds, capture metadata (time, background noise, headphones model) and include it in analytic models.
UX teams & mHealth designers
UX teams should prioritise co-designed training and brief student or volunteer sessions (1.5–3 hrs) to support consistent delivery and collect systematic user feedback after visits.
UX teams should use co-occurrence and quote frequency visualizations to show which messages prompt help-seeking versus resistance.
Service providers / Ops
Service providers should embed checks in existing touchpoints, such as Meals on Wheels deliveries, while maintaining simple SOPs for consent, infection control, and data handoff.
Service providers should map roles clearly: who schedules, who uploads screening outputs, and who follows up on referrals.
FAQ: SOUND-BITES qualitative analysis
What does the SOUND-BITES pilot do?
The SOUND-BITES pilot embeds hearing checks into Meals on Wheels NSW deliveries, combining Arclight otoscopy with the Sound Scouts tablet audiometry app and student or volunteer delivery.
The study collects clinical screening outputs, structured observations, baseline and 6-month surveys, and optional audio-recorded interviews for qualitative analysis.
What data types does the protocol collect and analyse?
The protocol collects audiometric outputs (hearing numbers, pass/refer), otoscopy observations, survey CSVs (HHIE‑S, SSQ‑12, EQ‑5D‑5L), and optional interview audio that is transcribed for thematic coding.
These data types support cross-segment analyses such as pass/refer by help-seeking intention mapped to qualitative theme prevalence.
How are qualitative interviews processed in the study?
The study audio-records interviews, produces intelligent verbatim transcripts that are accuracy-checked and participant-validated, and then codes transcripts in NVivo using the Theoretical Domains Framework with dual coders and consensus.
The protocol specifies participant validation, dual-coder agreement processes, and NVivo-ready outputs to support reproducibility.
When is the study expected to complete data collection?
Full data collection for the study is expected by 30 April 2027, with preliminary results anticipated on 01 September 2027.
Ethics approval for the protocol was granted on 11 December 2025 (Macquarie University HREC ID: 16818) and recruitment of students began on 01 March 2026.
Do more, faster with Evidano (mapped to SOUND-BITES needs)
Problem: messy audio + delayed transcripts → Solution: secure, accurate transcription
Evidano can ingest the program’s interview audio, produce intelligent verbatim transcripts with a custom dictionary (device names, clinical terms, local service names), and apply optional PII redaction before analysis.
Evidano supports transcript review loops consistent with the protocol’s participant validation step.
Problem: small-N interviews + coding drift → Solution: reproducible thematic coding
Evidano can import NVivo-ready transcripts or raw text and run AI-assisted thematic coding aligned to the Theoretical Domains Framework, producing hierarchical codes, subcodes, co-occurrence networks, and inter-coder reliability reports.
Evidano’s dual-coder workflows and disagreement reports help reduce coding drift and improve reproducibility.
Problem: linking surveys, clinical outputs, and quotes → Solution: cross-segment analysis
Evidano allows upload of survey CSVs (HHIE‑S, SSQ‑12, EQ‑5D‑5L) and merging with transcript metadata to run cross-segment frequency analyses, for example pass/refer × help-seeking intention × qualitative theme prevalence.
Evidano can produce GLM-ready exports for simple quantitative modelling of linked outcomes.
Problem: stakeholder buy-in → Solution: visual, exportable evidence
Evidano can generate word clouds, co‑occurrence networks, and grouped quote sets and export decision-ready summaries for Meals on Wheels, funders, and ethics committees.
Evidano exports visuals and 1‑page decision memos to support operational Q&A and stakeholder reporting.
Security & compliance
Evidano stores data encrypted and does not use research data to train third-party models, and Evidano supports consent-driven workflows and transcript review loops aligned to the protocol’s participant validation step.
Research teams should follow local ethics and consent processes for any clinical recommendations.
Checklist: 7-step workflow to reproduce SOUND-BITES analysis in Evidano
This checklist describes the seven steps to go from recordings to recommendations using the protocol’s materials and Evidano capabilities.
- 1) Collect audio and screening outputs and label metadata (site, date, hearing pass/refer, volunteer ID).
- 2) Upload audio and CSV surveys to Evidano; enable a custom dictionary with local clinical terms and service names.
- 3) Run secure transcripts with PII redaction; send transcripts back to participants for the two-week validation window (per protocol).
- 4) Import transcripts and the TDF codebook into Evidano; run automated pre-coding and review dual-coder disagreements.
- 5) Generate thematic summaries, quote atlases, and co-occurrence networks; filter by segment (age group, pass/refer, geography).
- 6) Merge survey outcomes (HHIE‑S change, help-seeking) for cross-segment analysis and simple GLM-ready exports.
- 7) Produce a stakeholder brief (visuals and a 1‑page decision memo) and schedule a Q&A with operational partners.
Wrapping up & next steps
This section summarises the study and next steps: SOUND-BITES (Tang et al., PLoS One, 14 July 2026) is a timely, well-documented pilot that pairs accessible mHealth tools with a trusted community service to reach older adults and creates a strong basis for AI-enabled qualitative synthesis.
- Try a secure pilot: upload a small set (5–10) of interviews and the associated survey CSV to Evidano to validate code mapping and theme recovery before scaling.
- Security note: this research-focused analysis is non-diagnostic; follow local ethics and consent processes for any clinical recommendations.
Ready to test these steps with your own SOUND-BITES-style data? Try Evidano for free and we will help you map transcripts, codes, and survey merges into a reproducible analysis pipeline.