A Live Video Mind-Body Treatment to Prevent Persistent Symptoms Following Mild Traumatic Brain Injury: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Every year, approximately 42 million people sustain a mild traumatic brain injury (mTBI, also known as concussion), with particularly high rates among college-aged individuals. A substantial proportion of these people (44%-64%) develop persistent symptoms that are challenging to treat, costly, and associated with significant disability. Anxiety has emerged as a risk factor for progression from acute to persistent mTBI symptoms. OBJECTIVE: This study aims to develop, adapt, and establish the feasibility of the Toolkit for Optimal Recovery after Concussions (TOR-C), an innovative mind-body program aimed at preventing persistent symptoms among young adults with mTBI and comorbid anxiety. Here, we describe the proposed study design, methodology, measurement, and treatment manuals. METHODS: In phase 1, we will conduct individual, live video qualitative interviews (up to n=20) with college-aged individuals with mTBI and comorbid anxiety to inform adaptation of the intervention and study procedures. In phase 2, an open pilot of the live video TOR-C (n=5) with exit interviews will be conducted to explore the initial feasibility, acceptability, and credibility of the program and to refine the study procedures. Phase 3 will involve conducting a feasibility randomized controlled trial (N=50) of the TOR-C versus a health education control (Health Enhancement for Concussions; HE-C), both delivered via live video, to establish feasibility of recruitment procedures (screening, eligibility, and enrollment) and data collection; feasibility, credibility, and acceptability of the live video TOR-C and HE-C (adherence, retention, fidelity, and satisfaction) following prespecified benchmarks; and a signal of improvement in outcomes. RESULTS: Phase 1 of the study has been approved by the Massachusetts General Hospital Institutional Review Board. Study completion is anticipated by early 2025. CONCLUSIONS: We will develop and test the first mind-body intervention focused on prevention of persistent symptoms following mTBI in young adults with comorbid anxiety problems. This will allow us to establish feasibility markers in postconcussive symptoms, anxiety, disability, and fear avoidance to inform a future efficacy trial of the TOR-C versus HE-C. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/25746.
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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 | 0.035 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.076 | 0.012 |
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".