A Comparison of In-Person and Telehealth Treatment Modalities using the SpeechVive Device
Bibliographic record
Abstract
Telehealth is increasing popular as a treatment option for people with Parkinson disease (PD). The SpeechVive device is a wearable device that uses the Lombard effect to help patients speak more loudly, slowly, and clearly. This study sought to examine the effectiveness of the device to improve communication in people with PD, delivered over a telehealth modality as compared to in-person, using implementation science design. 66 people with PD were enrolled for 12 weeks with 34 choosing the in-person group and 32 in the telehealth group. Participants were assessed pre-, mid-, and post-treatment. Participants produced continuous speech samples on and off the device at each timepoint. Sound pressure level (SPL), utterance length, pause frequency, and total pause duration were measured. Psychosocial surveys were administered to evaluate the effects of treatment on depression, self-efficacy, and participation. The in-person group increased SPL when wearing the device while the telehealth group did not. Both groups paused less often while wearing the device. Utterance length increased post-treatment for the telehealth group, but not for the in-person group. An increase in communication participation ratings in the telehealth group, but not the in-person group, was the only significant change in the psychosocial metrics. The in-person group showed similar treatment effects as previous studies. The device was not as effective in the telehealth group. One limitation was data loss due to recording issues that impacted the telehealth group more than the in-person group.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".