In the increasingly complex “stress society,” the impact of daily stress on health is significant. In the recent COVID-19 pandemic, communication between people has been restricted, intensifying this trend. In such circumstances, the importance of understanding the relationship between stress and health has increased not only in medical fields but also in various areas such as clinical psychology and occupational health.
The physiological responses to stress have been examined from various perspectives. Even when stress responses occur, the human body has functions to recover, known as “allostasis” (dynamic homeostasis). Therefore, in understanding the relationship between stress and bodily functions, the patterns of change due to stress, including the recovery process, are key, not just the presence or degree of the relationship.
Based on this understanding, this study conducts the following investigations:
① Investigating the characteristics of multi-axial physiological stress responses and recovery in stress-related disorders, in conjunction with psychological evaluations.
② Extracting clinically meaningful stress response-recovery patterns from these stress responses using various data analysis methods.
③ Constructing psychological and pathological models of stress responses from these patterns, applying the model to new subjects for validation, and considering clinical applications such as predicting disease risks.
Such stress response models are expected to have applications beyond stress research, including in medical, psychological, and industrial fields, and are anticipated to provide foundational insights.
Previous studies on stress profiles in stress-related disorders have revealed several characteristic patterns of stress responses in patient groups compared to healthy groups. This serves as a warning that some distortion may be occurring in the human body in the stress society. We are exploring clues from the perspective of psychosomatic medicine on how humans can regain their original mental and physical health.
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Pattern analysis of physiological stress responses using machine learning and construction of clinical stress response models
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