A Standardized Buffalo Concussion Treadmill Test After Sport-Related Concussion in Youth: Do ActiGraph Algorithms Matter?
Bibliographic record
Abstract
Context: Current guidelines for recovery after sport-related concussion (SRC) recommend 24 to 48 hours of rest, followed by a gradual return to activity with heart rate (HR) maintained below the symptom threshold. In addition, monitoring physical activity (PA) after SRC using ActiGraph accelerometers can provide further objective insight into the amounts of activity associated with recovery trajectories. Cutpoint algorithms for these devices allow minute-by-minute PA to be classified into intensity domains; however, researchers have shown that different algorithms used to evaluate the same healthy participant dataset can produce various classifications. Objective: To identify the more physiologically appropriate cutpoint algorithm (Evenson or Romanzini) to analyze ActiGraph data among concussed adolescents in comparison with their HR responses on the Buffalo Concussion Treadmill Test (BCTT). Design: Prospective cohort study. Setting: University sport concussion clinic. Patients or Other Participants: Eleven high school students (5 boys, 6 girls; median [range] age = 16 years [15-17 years], height = 177.8 cm [157.5-198.1 cm], mass = 67 kg [52-98 kg], body mass index = 22 [17-31]) involved in high-risk sports who sustained a physician-diagnosed SRC. Main Outcome Measures: Evenson and Romanzini algorithm PA intensity domains via ActiGraph data and HR during the BCTT. Results: = .48). The Evenson algorithm classified most of the time as moderate-intensity PA (mean = 57.03%, range = 0.00%-94.12%), whereas the Romanzini algorithm classified virtually all PA as vigorous intensity (mean = 88.25%, range = 2.94%-97.06%]). Physical activity based on HR (stages 1-7 = 20%-39% HR reserve [HRR], stages 8-13 = 40%-59% HRR, stages ≥14 = 60%-85% HRR) indicated the BCTT primarily involved light to moderate intensity and, therefore, was better represented by the Evenson algorithm. Conclusions: The Evenson algorithm better characterized the HR response during a standardized exercise test in concussed individuals and, thus, should be used to analyze ActiGraph PA data in pediatric populations with concussion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".