Exploring the Perceptions of Voice-Assisted Technology as a Tool for Speech and Voice Difficulties: Focus Group Study Among People With Parkinson Disease and Their Carers
Bibliographic record
Abstract
BACKGROUND: People with Parkinson disease (PD) often report low volume and reduced intelligibility of speech. Common household devices that use voice-assisted technology (VAT) require users to speak slowly, clearly, and loudly for the technology to function. For people with PD, this can be challenging, but this also suggests that VAT may have potential as a therapeutic tool. While VAT is an emerging health care technology, it is important to better understand the thoughts and experiences of people with PD who are already using it despite having speech and voice difficulties. OBJECTIVE: This study aimed to explore experiences of using VAT to address hypokinetic dysarthria secondary to PD, based on the perspectives of people with PD and family carers. METHODS: People with PD experiencing mild to moderate speech changes who were smart speaker users, and their carers, were invited to participate in 1 of 4 in-person focus groups. Between September and December 2024, focus groups were audiovisually recorded. A semistructured topic guide informed by published evidence was used to guide discussions. Results were transcribed and analyzed through a framework analysis approach (managed using NVivo software). RESULTS: A total of 15 participants, including 8 (53%) people with PD and 7 (47%) carers, participated in 4 in-person focus groups. Findings revealed shared experiences with VAT that were marked by its therapeutic potential and practical challenges. Five main themes were identified: (1) therapeutic potential for speech and voice, with subthemes of changes in volume, intelligibility, and clarity of speech; the role of VAT feedback; and VAT as an everyday device; (2) distrust of technology, with concerns surrounding data privacy, the listening nature of devices, and measures users take to protect themselves; (3) frustrations with devices, including devices not understanding, devices timing out, and the lack of conversation; (4) support needs, including the impact of a lack of knowledge and the need for education and guidance; and (5) design considerations for a future VAT tool in speech and language therapy (SLT). CONCLUSIONS: This study extends on previous research findings, demonstrating that VAT may be acceptable to people with PD to create changes in volume, clarity, and intelligibility. However, attention must be given to users' privacy concerns and frustrations with devices before VAT can used as a tool in SLT. Future research should design solutions to address current usability challenges with people with PD and professionals in three ways: (1) co-designing education and guidelines for people with PD, describing the use of VAT for speech and voice difficulties; (2) refining commercial VAT for use in SLT; and (3) establishing the feasibility of a therapeutic VAT intervention for people with PD with speech and voice difficulties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".