Articles

(1) <b>Background</b>: Psychological flexibility, the ability to adapt to changing demands while staying true to personal values, is linked to better health behaviors. However, its relationship with cardiovascular health behaviors, particularly in Filipino populations, remains underexplored. (2) <b>Aim</b>: This study aimed to examine the relationship between psychological flexibility and cardiovascular health behaviors among outpatients in Rizal, Philippines, focusing on BMI, smoking, physical activity, and dietary habits. (3) <b>Methods</b>: A cross-sectional, descriptive-correlational design was used with 100 outpatient participants from selected hospitals. Psychological flexibility was measured using the Acceptance and Action Questionnaire-II (AAQ-II), and cardiovascular health behaviors were assessed using a modified version of the American Heart Association’s Life Simple 7 tool. Descriptive statistics and Pearson’s correlation were applied. (4) <b>Results</b>: The findings showed that respondents had neutral levels of psychological flexibility. While most exhibited intermediate BMI and physical activity, dietary habits varied. A significant positive correlation was found between psychological flexibility and whole grain consumption. However, no significant associations were found with BMI, smoking, or physical activity. (5) <b>Conclusions</b>: Psychological flexibility appears to influence specific health behaviors, such as dietary choices, but does not strongly correlate with other cardiovascular health behaviors. Further research is needed to explore its broader role in cardiovascular disease prevention.

Predictive modeling, supported by machine learning technology, aims to analyze data in order to guide decision-making towards actions generating desired values in the future. It encompasses the set of techniques used to build models that estimate the value of a certain variable predicting a forthcoming event from the past or current values of relevant attributes. In predictive healthcare modeling, the built models represent the relationship among the data concerning customer, provider, production, and other aspects of the healthcare environment in order to assist the decision processes in the prevention of diseases and in the planning of preventive actions by detection of high-risk patients. Contrary to trend analysis, whose goal is to describe past events, predictive models aim to provide useful indications regarding future events and changes. Predictive healthcare modeling supports actions that try to prevent the manifestation of diseases in healthy individuals or try to diagnose as early as possible the incidence of a disease in patients at risk. A sound predictive analysis encompasses not only the model-training task, but also the aspects of data quality, preprocessing, and fusion during its entire implementation lifecycle to ensure appropriate input data preparation. The robustness of the predictive model and its results depends highly on data quality. Due to the variety of data sources in healthcare environments, it becomes essential to use preprocessing in order to remove noise and inconsistencies. The increasing number of endorsable data exchange standards makes each data exchange achievable, but it demands the implementation of a data-governance program. In addition, the influence of the hospital-database architect on the architecture of an early-diagnosis model is important to guarantee appropriate input-formatting modularity.