Investigating Psychological and Physiological Effects of Forest Walking: A Machine Learning Approach

Positive effects of forest walking on well-being have been known for a long time. Researchers aim to understand the physiological and psychological effects of forest walking utilizing statistical analysis of physiological data, such as blood pressure, heart rate, and cortisol levels, and psychological data, such as mood and stress levels, from participants before and after forest walking. Recently wearables have been used to continuously monitor and analyze the effect of the forest when compared to the city conditions. However, using statistical methods to demonstrate the effect of forest walking on individual heart rate variability features has been challenging due to various confounding factors. In this study, we used a more comprehensive approach by incorporating a set of over 80 time-domain, frequency-domain, and nonlinear heart rate variability features, and applying dimensionality reduction and using machine learning classifiers to show the difference between the effects of walking in the forest and city. The findings indicate that forest walking can be a practical way to improve both physiological and psychological well-being and that machine learning can be a useful tool for analyzing the complex relationships between different environment and their effects on physiological outcomes.