Implementation and Effects of an Information Technology–Based Intervention to Support Speech and Language Therapy Among Stroke Patients With Aphasia: Protocol for a Virtual Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Mobile app-based therapies are increasingly being employed by speech-language pathologists in the rehabilitation of people with aphasia as adjuncts or substitutes for traditional in-person therapy approaches. These apps can increase the intensity of treatment and have resulted in meaningful outcomes across several domains. OBJECTIVE: VoiceAdapt is a mobile therapy app designed with user and stakeholder feedback within a user-centered design framework. VoiceAdapt uses two evidence-based lexical retrieval treatments to help people with aphasia in improving their naming abilities through interactions with the app. The purpose of the randomized controlled trial (RCT) proposed here is to examine the feasibility and clinical efficacy of training with VoiceAdapt on the language and communication outcomes of people with aphasia. METHODS: A multicenter RCT is being conducted at two locations within Canada. A total of 80 people with aphasia will be recruited to participate in a two-arm, waitlist-controlled, crossover group RCT. After baseline assessment, participants will be randomized into an intervention group or a waitlist control group. The intervention group participants will engage in 5 weeks of training with the app, followed by posttreatment and follow-up assessments after an additional 5 weeks. Those in the waitlist control group will have no training for 5 weeks; this is followed by pretreatment assessment, training for 5 weeks, and posttreatment assessment. All trial procedures are being conducted remotely given the COVID-19 pandemic. RESULTS: Recruitment of participants started in September 2020, and the study is expected to be completed by March 2022. Publication of results is expected within 6 months of study completion. CONCLUSIONS: The results of the RCT will provide information on evidence-based practice using technology-based solutions to treat aphasia. If positive results are obtained from this RCT, the VoiceAdapt app can be recommended as an efficacious means of improving lexical retrieval and communicative functioning in people with aphasia in an easily accessible and a cost-effective manner. Moreover, the implementation of this RCT through remote assessment and delivery can provide information to therapists on telerehabilitation practices and monitoring of app-based home therapy programs. TRIAL REGISTRATION: ClinicalTrials.gov NCT04108364; https://clinicaltrials.gov/ct2/show/NCT04108364. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30621.
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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 | 0.048 | 0.046 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.072 | 0.014 |
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".