APP ENGAGEMENT IN TECHNOLOGY-SUPPORTED SPEECH-LANGUAGE THERAPY FOR PRIMARY PROGRESSIVE APHASIA
Bibliographic record
Abstract
Abstract As the global population ages, dementia prevalence is increasing. Primary Progressive Aphasia (PPA) is a clinical dementia syndrome characterized by progressive language decline. Access to care for individuals living with PPA is limited by a shortage of qualified clinicians and evidence-based interventions. While technology-supported interventions have the potential to overcome this barrier, there has been no systematic exploration of factors affecting web application use in this population. This study aimed to evaluate feasibility and acceptability and identify clinical and demographic factors affecting app usage in individuals with PPA. Web application data were analyzed from participants (N=95) of Communication Bridge-2, an NIH stage 2 randomized controlled trial of speech-language therapy for PPA (NCT03371706). Participants were encouraged to complete app-based practice exercises five days per week over the 12-month intervention. Data-driven user-type clustering determined engagement groups based on number of logins. Multinomial logistic regression identified demographic and clinical factors that differed between groups. On average, participants completed 13.7 (SD = 5.95) home practice exercises 3.99 days per week (SD = 1.19) and logged into the app 5.88 times per week (SD = 1.29). Older age and increased education significantly predicted increased app engagement (p <.05), even when accounting for work status; other clinical and demographic factors were nonsignificant. All participants logged into and completed practices exercises on the app at least weekly, demonstrating high feasibility and acceptability. These findings suggest that technology-supported interventions may be a valuable tool to improve access to evidence-based care to individuals with PPA and related conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".