Bidirectional Association Between Daily Physical Activity and Postconcussion Symptoms Among Youth
Bibliographic record
Abstract
This study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelineWe prospectively enrolled youth aged 11 to 17 years with a physician-confirmed concussion within 72 hours of injury from the emergency department and concussion clinics at Nationwide Children's Hospital between [date] and [date]. 2 We measured daily physical activity as reflected in daily step count using an ActiGraph and daily PCS using the Postconcussion Symptom Scale from day 1 to day 7 postinjury.3 We grouped daily step count and PCS into 3 waves: days 1 to 3 (wave 1), days 4 to 5 (wave 2), and days 6 to 7 (wave 3) postinjury.Using both a traditional cross-lagged panel model (CLPM) and a random-intercept cross-lagged panel model (RI-CLPM), we examined the bidirectional associations between daily step counts and PCS in this 3-wave, longitudinal design.4 RI-CLPM disentangles the within-person process from stable between-person differences, whereas CLPM does not differentiate the covariance between these 2 levels.4 Models were estimated using the package lavaan in R statistical software version 3.6.2(R Project for Statistical Computing), with 2-sided testing and a significance level of α = .05.Statistical analysis was performed from June to July 2020. ResultsThis study's participants included 83 youth with concussion (54 boys [65%]; mean [SD] age, 14.2 [1.9] years; 59 White participants [72%]; 70 sports-related concussions [84%]).The mean (SD) daily step counts were 9167 (3635) at wave 1, 10 143 (4018) at wave 2, and 10 786 (4038) steps at wave 3, whereas the mean (SD) daily PCS scores were 27.7 (19.6), 21.0 (18.4), and 15.9 (16.4), respectively.Zero-order correlation coefficients between daily step count and PCS were statistically significant (Table).In the CLPM, daily step counts and PCS scores showed significant positive autoregressive associations across all waves (Figure A), reflecting stability across time in both activity and symptoms.In contrast, in the RI-CLPM, which accounted for between-person differences, the only significant autoregressive association was the path for PCS scores from wave 1 to wave 2 (β = 0.652; SE = 0.196; P = .002)(Figure B).In the CLPM, only 1 cross-lagged path was significant, with higher PCS scores at wave 1 being associated with lower daily step counts at wave 2 (β = -0.181;SE = 0.101;
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".