A Digital Health Intervention for Concussion: Development and Clinical Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Concussion is a common condition that can lead to a constellation of symptoms that affect quality of life, social integration, and return to work. There are several evidence-based behavioral and psychological interventions that have been found to improve postconcussion symptom burden. However, these are not routinely delivered, and individuals receive limited support during their concussion recovery. OBJECTIVE: This study aimed to develop and test the feasibility of a digital health intervention using a systematic evidence-, theory-, and person-based approach. METHODS: This was a mixed methodology study involving a scoping review (n=21), behavioral analysis, and logic model to inform the intervention design and content. During development, the intervention was optimized with feedback from individuals who had experienced concussions (n=12) and health care professionals (n=11). The intervention was then offered to patients presenting to the emergency department with a concussion (n=50). Participants used the intervention freely and input symptom data as part of the program. A number of outcome measures were obtained, including participant engagement with the intervention, postconcussion symptom burden, and attitudes toward the intervention. A selection of participants (n=15) took part in in-depth qualitative interviews to understand their attitudes toward the intervention and how to improve it. RESULTS: Engagement with the intervention functionality was 90% (45/50) for the symptom diary, 62% (31/50) for sleep time setting, 56% (28/50) for the alcohol tracker, 48% (24/50) for exercise day setting, 34% (17/50) for the thought diary, and 32% (16/50) for the goal setter. Metrics indicated high levels of early engagement that trailed off throughout the course of the intervention, with an average daily completion rate of the symptom diary of 28.23% (494/1750). A quarter of the study participants (13/50, 26%) were classified as high engagers who interacted with all the functionalities within the intervention. Quantitative and qualitative feedback indicated a high level of usability and positive perception of the intervention. Daily symptom diaries (n=494) demonstrated a wide variation in individual participant symptom burden but a decline in average burden over time. For participants with Rivermead scores on completion of HeadOn, there was a strong positive correlation (r=0.86; P<.001) between their average daily HeadOn symptom diary score and their end-of-program Rivermead score. Insights from the interviews were then fed back into development to optimize the intervention and facilitate engagement. CONCLUSIONS: Using this systematic approach, we developed a digital health intervention for individuals who have experienced a concussion that is designed to facilitate positive behavior change. Symptom data input as part of the intervention provided insights into postconcussion symptom burden and recovery trajectories. TRIAL REGISTRATION: ClinicalTrials.gov NCT05069948; https://clinicaltrials.gov/ct2/show/NCT05069948.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".