Smartphone App–Delivered Mindfulness-Based Intervention for Mild Traumatic Brain Injury in Adolescents: Protocol for a Feasibility Randomized Controlled Trial (Preprint)
Bibliographic record
Abstract
BACKGROUND Concussion in children and adolescents is a significant public health concern, with 30% to 35% of patients at risk for prolonged emotional, cognitive, sleep, or physical symptoms. These symptoms negatively impact a child’s quality of life while interfering with their participation in important neurodevelopmental activities such as schoolwork, socializing, and sports. Early psychological intervention following a concussion may improve the ability to regulate emotions and adapt to postinjury symptoms, resulting in the greater acceptance of change; reduced stress; and recovery of somatic, emotional, and cognitive symptoms. OBJECTIVE The primary objective of this study is to assess the feasibility of conducting a parallel-group (1:1) randomized controlled trial (RCT) to evaluate a digital therapeutics (DTx) mindfulness-based intervention (MBI) in adolescents aged 12 to <18 years. The attention-matched comparator intervention (a math game also used in previous RCTs) will be delivered on the same DTx platform. Both groups will be provided with the standard of care guidelines. The secondary objective is to examine intervention trends for quality of life; resilience; self-efficacy; cognition such as attention, working memory, and executive functioning; symptom burden; and anxiety and depression scores at 4 weeks after concussion, which will inform a more definitive RCT. A subsample will be used to examine whether those randomized to the experimental intervention group have different brain-based imaging patterns compared with those randomized to the control group. METHODS This study is a double-blind Health Canada–regulated trial. A total of 70 participants will be enrolled within 7 days of concussion and randomly assigned to receive the 4-week DTx MBI (experimental group) or comparator intervention. Feasibility will be assessed based on the recruitment rate, treatment adherence to both interventions, and retention. All outcome measures will be evaluated before the intervention (within 7 days after injury) and at 1, 2, and 4 weeks after the injury. A subset of 60 participants will undergo magnetic resonance imaging within 72 hours and at 4 weeks after recruitment to identify the neurophysiological mechanisms underlying the potential benefits from MBI training in adolescents following a concussion. RESULTS The recruitment began in October 2022, and the data collection is expected to be completed by September 2024. Data collection and management is still in progress; therefore, data analysis is yet to be conducted. CONCLUSIONS This trial will confirm the feasibility and resolve uncertainties to inform a future definitive multicenter efficacy RCT. If proven effective, a smartphone-based MBI has the potential to be an accessible and low-risk preventive treatment for youth at risk of experiencing prolonged postconcussion symptoms and complications. CLINICALTRIAL ClinicalTrials.gov NCT05105802; https://classic.clinicaltrials.gov/ct2/show/NCT05105802 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/57226
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 | 0.024 | 0.023 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.104 | 0.017 |
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".