# Neural Complexity of Implicit Attitudes Predicts Exercise Behavior in Hypertensive Patients: An EEG Entropy Study

## TL;DR

This study shows that brain activity measured by EEG entropy during implicit attitude tasks better predicts exercise behavior in people with hypertension than traditional reaction time measures.

## Contribution

The study introduces EEG entropy as a novel predictor of exercise behavior, outperforming traditional behavioral metrics in hypertensive patients.

## Key findings

- EEG entropy features, especially envelope entropy, better distinguish exercisers from non-exercisers than reaction time measures.
- Affective incompatible IAT conditions showed the most consistent differences in EEG entropy between exercisers and non-exercisers.
- Frontal and central brain regions contributed most to classification accuracy using envelope entropy features.

## Abstract

What are the main findings?
EEG entropy during implicit attitude processing showed stronger discrimination of subsequent exercise behavior than traditional reaction time-based D-scores in patients with hypertension.Among different task conditions, envelope entropy features derived from affective incompatible IAT conditions demonstrated the most consistent differences between exercisers and non-exercisers.

EEG entropy during implicit attitude processing showed stronger discrimination of subsequent exercise behavior than traditional reaction time-based D-scores in patients with hypertension.

Among different task conditions, envelope entropy features derived from affective incompatible IAT conditions demonstrated the most consistent differences between exercisers and non-exercisers.

What are the implications of the main findings?
Neural complexity metrics provide a complementary and interpretable perspective for understanding implicit attitude processing underlying exercise behavior beyond behavioral reaction time measures.These findings highlight the potential value of incorporating neural complexity markers of implicit processing into future models of exercise behavior, while underscoring the need for validation in larger and more diverse samples.

Neural complexity metrics provide a complementary and interpretable perspective for understanding implicit attitude processing underlying exercise behavior beyond behavioral reaction time measures.

These findings highlight the potential value of incorporating neural complexity markers of implicit processing into future models of exercise behavior, while underscoring the need for validation in larger and more diverse samples.

Background: Exercise is a key component in managing hypertension, yet adherence remains low. Beyond deliberate decision-making, implicit attitudes also play an important role in exercise behavior as automatic and unconscious evaluative processes. Traditional studies mostly rely on reaction time measures, which are susceptible to practice effects and fail to capture dynamic neural processing. Objectives: This study aimed to examine whether the EEG entropy derived from implicit attitude processing can better predict exercise behavior than traditional reaction time measures in patients with hypertension. Methods: Fifty-seven hypertensive patients completed affective and instrumental implicit association tests (IATs) with EEG recording. Seven entropy features were extracted. Multiple machine learning algorithms were applied to compare the predictive performance of reaction time with EEG entropy features. The random forest model was used to analyze the importance ranking of features from different brain regions. Results: EEG entropy outperformed reaction times in distinguishing exercisers from non-exercisers. Affective implicit attitudes consistently demonstrated stronger accuracy than instrumental attitudes. Envelope entropy showed the most robust and significant group differences. For the random forest (RF) classifier of envelope entropy, classification accuracies were 71.9% for the affective IAT (incompatible task only), and 71.9% for the model combining affective and instrumental IAT features. Frontal and central regions contributed most to classification. Conclusions: EEG entropy, particularly envelope entropy during affective IAT-incompatible tasks, provides superior discrimination of exercise behavior than reaction time measures. This suggests that exercise behavior is closely linked to the neural complexity underlying affective conflict processing. These findings advance our understanding of the neural dynamic patterns linking implicit attitudes and exercise behavior and suggest EEG entropy as a promising tool for assessing and intervening exercise behavior.