Examining the relationship between adolescent health behaviors, brain health, and academic achievement using fNIRS
Bibliographic record
Abstract
Abstract Background Several adolescent health behaviors have been hypothesized to improve academic performance via their beneficial impact on cognitive control and functional aspects of the prefrontal cortex (PFC). Specifically, exercise, restorative sleep, and proper diet are thought to improve PFC function, while substance abuse is thought to reduce it. Few studies have examined the relationships among all of these in the same sample, while quantifying downstream impacts on academic performance. Objective The primary objective of this study is to examine the association between lifestyle behaviors and academic performance in a sample of adolescents, and to examine the extent to which activity within the PFC and behavioural indices of inhibition may mediate this relationship. Methods Sixty-seven adolescents underwent two study sessions five days apart. Sleep and physical activity were measured using wrist-mounted accelerometry; eating habits, substance use and academic achievement were measured by self-report. Prefrontal function was quantified by performance on the Multi-Source Interference Task (MSIT), and task-related brain activity via functional near-infrared spectroscopy (fNIRS). Results Higher levels of accelerometer-assessed physical activity predicted higher MSIT accuracy scores ( ϐ = .321, ρ = 0.019) as well as greater task-related increases in activation within the right dlPFC ( ϐ =.008, SE = .004, ρ =.0322). Frequency of fast-food consumption and substance use were both negatively associated with MSIT accuracy scores ( ϐ = −.307, ρ = .023) and Math grades ( β = −3.702, SE = 1.563, ρ = .022) respectively. However, these effects were not mediated by indicators of PFC function. Conclusion Physical activity and eating behaviors predicted better interference task performance in adolescents, with the former mediated by greater task-related increases in right dlPFC activation. Substance use predicted worse Math grades, however, no other reliable effects of health behaviors on academic outcomes were evident.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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".