Ethical methodologies for digital identity privacy in AI-driven dance movement therapy as a preventative mental health mechanism in the extended reality

Abstract

This study applies a hybrid methodology combining a systematic review and an AI-enhanced pilot study to explore the correlation between physical activity and episodic-paroxysmal anxiety (EPA) within Extended Reality (XR) environments. The pilot study uses a multimodal biometric approach (incorporating accelerometry, heart rate variability (HRV), and skin conductance) integrated with AI-driven pattern recognition algorithms to measure the real-time physiological impact of Dance Movement Therapy (DMT). By establishing a feedback loop between physical activity and anxiety-related biomarkers, the study presents a dynamic framework for non-pharmacological mental health intervention design. The emerging methodologies for AI-driven Preventative Mechanisms, are tested with a pilot study, consisting of a cohort of 20 participants, exploring the correlation between physical activity and anxiety levels through advanced biometric measures such as accelerometers, skin conductance, and heart rate variability. The key findings reveal that Dance Movement Therapy within Extended Reality environments significantly reduces anxiety levels in individuals with episodic-paroxysmal anxiety, as evidenced by measurable improvements in biometric indicators such as heart rate variability and skin conductance.

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