RETRACTED: The Effects of Physical Exercise on Cognition: How Heart Rate Variability Can Predict Cognitive Performances
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Objectives: Investigate and identify the relationship between physical exercise and cognitive performance measured by using different cognitive tests taken from Cambridge Brain Science (CBS). Methods: Thirty subjects, divided into two groups (aerobic and effort), undergo twelve cognitive tests from CBS. A comparison between the pre- and post-exercise results in terms of cognitive performance differences is carried out. Regression analysis between Heart Rate Variability (HRV) features and CBS tests results is performed. Results: In most CBS tests, there is an improvement, or at least a confirmation, of the subject's cognitive ability, for both groups. Reasoning (80–100%), concentration (80–87%), and planning tests (93–100%) seem to undergo critical positive changes. The regression analysis, performed by using a set of different algorithms, has demonstrated that it is possible, by monitoring the HRV during the exercise, to predict to some extent the cognitive performance, i.e., the CBS tests results. The best performing regression algorithms are Simple Linear (Quade Test—aerobic group: 2.098, effort group: 3.350, both groups: 2.747) and REPTree (Quade Test—aerobic group: 2.955, effort group: 3.315, both groups: 3.121). The statistical analysis has proved that physical activity is statistically useful for the subjects in improving their cognitive performance. Conclusions: This study has numerically appraised the improvement, the conservation, or the worsening on different aspects of cognition. The found mathematical relationship between physical exercise and cognitive performance suggests that it is possible to predict the beneficial effect of various exercises on executive and attentive control.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".