Investigating the Feasibility of a Submaximal Resistance Exercise Protocol in Recovery of Patients with Persistent Concussion Symptoms via a Mobile Health (mHealth) Intervention
Bibliographic record
Abstract
This thesis investigated the effectiveness of submaximal resistance exercises delivered remotely as a treatment for enduring post-concussion symptoms. Given the need for more accessible therapeutic interventions beyond traditional clinical settings, 80 adults (68 females, 11 males, 1 non-binary) diagnosed with a concussion were provided a submaximal resistance exercise protocol delivered via a mobile app. This study employed a 4-week single-arm pre-post intervention design, examining changes in symptoms of concussion, depression, and anxiety. The results demonstrated a notable reduction in symptoms, with a more pronounced effect in participants with pre-existing mental health conditions. Additionally, the study observed a correlation between lower protocol engagement and higher initial symptom severity. These findings underscore the viability of remotely delivered submaximal resistance exercises as an accessible and effective approach to mitigate long-term disability due to persistent symptoms of a concussion. These findings have important implications for future treatment methodologies across the realm of concussion management.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".