Feasibility of an Interactive Coaching App to Enhance Post-concussion Outpatient Care
Bibliographic record
Abstract
Objectives: To determine the feasibility of patients to use a web-based health app for management of post-concussion (mTBI) symptoms in an out-patient setting. Participants: Seven (7) patients who were referred to an outpatient specialist clinic (physiatry) with persisting symptoms following a concussion. Participants had to be 18 years of age or older and more than 3 months post injury. Design: This was a prospective cohort study using a web-based platform for chronic disease management to guide patients in managing symptoms based on individual clinical recommendations. Each patient received weekly Symptom Management Plans created by a health coach and a physician specialist, designed to reinforce positive progress with clinical recommendations. Main Measures: Adherence to tracking daily recommendations and symptoms (data collected through the web-interface), The Rivermead Post-Concussion Questionnaire (self report) and a Satisfaction Questionnaire (self report). Results: Adherence to assigned clinical recommendations was close to 100%. Pre-post results on the patient reported outcome measure (Rivermead Post Concussion Tool) showed improvement for most patients in their experience of symptoms. The Satisfaction Questionnaire showed high rates of satisfaction with the App and the intervention in general. Conclusions: Use of a web-based health app with a health coach is feasible in this patient population from both the patient and clinician perspective based on high adherence. There is also some evidence of improvement of symptoms with this intervention over time. Further exploration of the use of this type of intervention with post-concussion patients could potentially impact long-term outcomes.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".