Hemodynamic And Executive Function Responses To A Single Bout Of Aerobic And Deep-leg Squat Exercise
Bibliographic record
Abstract
An acute bout of aerobic exercise ‘boosts’ postexercise executive function (EF) and is a finding - in part - attributed to an exercise-induced increase in cerebral blood flow (CBF). It is, however, less well known whether body weight exercise (i.e., deep leg squats) elicit a similar EF benefit. Indeed, deep leg squats induce intermittent changes to CBF and may result in biomolecule alterations that provide a larger magnitude postexercise EF benefit. PURPOSE: The present study sought to determine whether distinct CBF reactivity to deep leg squats and continuous aerobic exercise differentially benefit the magnitude of a postexercise EF benefit. METHODS: Participants (age range: 18 to 25 years) completed three 15-min conditions involving: (1) a non-exercise control, (2) deep leg squats with eight 1-min intervals (20 squats/min) separated by 1-min of rest, and (3) 15-min of moderate intensity aerobic exercise (60% HRR via cycle ergometer at 70 rpm). During all conditions, transcranial Doppler ultrasound (TCD) measured middle cerebral artery velocity (MCAv) to provide an estimate of exercise-based changes in CBF. EF was examined prior to and following each condition via antisaccades (i.e., saccade mirror-symmetrical to target). RESULTS: As expected, baseline to steady-state MCAv did not reliably vary for the control condition (p = 0.16), whereas deep leg squats and aerobic exercise produced peak increases in systolic MCAv of 26 cm/s and 40 cm/s, respectively (ps < 0.001). For the EF task, control condition antisaccade reaction times (RTs) did not vary from pre- to postexercise (p = 0.34), whereas deep leg squats and aerobic exercise produced a postexercise reduction in RTs (ps < 0.04). Moreover, a two-one sided statistic indicated that the magnitude of the RT reduction in deep leg squat and aerobic conditions was equivalent (p < 0.05). In other words, the magnitude of the EF benefit was equivalent across the two conditions. CONCLUSION: Deep leg squats increase CBF and provide a postexercise benefit to EF equivalent to aerobic exercise. Thus, for a single bout of exercise a hysteretic (deep leg squats) and continuous (aerobic) increase in CBF does not impact the magnitude of an EF benefit. Supported by: Natural Sciences and Engineering Research Council (NSERC) of Canada
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 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".