A Conversational, Virtual, Avatar-Led Cognitive Behavioral Therapy App Intervention for Improving the Quality of Life and Mental Health of People With Epilepsy: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Epilepsy is a common neurological disorder affecting about 1 in 100 people in the United Kingdom. Many individuals experience a lower quality of life as a result of their epilepsy diagnosis and are more likely to develop mental health problems, such as anxiety and depression. Medical interventions for this client group tend to focus on the treatment of seizures, whereas mental health disorders often remain undiagnosed and untreated. Early identification and treatment of mental health difficulties in people with epilepsy are vital to ensure better outcomes and improvements in quality of life. OBJECTIVE: The aim of this exploratory randomized controlled trial is to evaluate whether an 8-week cognitive behavioral therapy-based intervention delivered through a mobile app-ThinkNinja for Epilepsy-is a clinically effective tool to improve quality of life, mental health, and emotional well-being in a large sample of people with epilepsy and anxiety or comorbid anxiety and depression. METHODS: The study aims to recruit 184 individuals, 18 to 65 years of age, with a self-reported diagnosis of epilepsy and anxiety or comorbid anxiety and depression. Participants will be randomly assigned to the ThinkNinja for Epilepsy app condition (arm A) or the waiting-list control group (arm B). Participants in arm A will receive access to the ThinkNinja for Epilepsy app first. After 8 weeks, participants in arm B will receive the same full access to the ThinkNinja for Epilepsy app as the participants in arm A. This design will allow an initial between-subjects analysis between the two conditions as well as a within-subject analysis including all participants. The primary outcome is participants' quality of life, measured by the 10-item patient-weighted Quality of Life in Epilepsy questionnaire. The secondary outcomes include measures of anxiety, using the 7-item Generalized Anxiety Disorder assessment; depression, using the 9-item Patient Health Questionnaire; medication adherence, using the Medication Adherence Questionnaire; and impression of change, using the Patient Global Impression of Change questionnaire. RESULTS: Recruitment for this study began in March 2022 and was completed in October 2022. We expect data collection to be finalized by May 2023 and study results to be available within 12 months of the final data collection date. Results of the study will be written up as soon as possible thereafter, with the intention of publishing the outcomes in high-quality peer-reviewed journals. CONCLUSIONS: This study aims to determine the clinical efficacy and safety of the ThinkNinja for Epilepsy intervention at improving the quality of life, mental health, and emotional well-being of people with epilepsy. The findings from our study will hopefully contribute to addressing the critical gap in universal provision and accessibility of mental health and emotional well-being support for people with epilepsy. TRIAL REGISTRATION: ISRCTN Registry 16270209 (04/03/2022); https://www.isrctn.com/ISRCTN16270209. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/40261.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.025 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.078 | 0.011 |
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".