Key result
Exposure to an interactive VR game elicited significant changes in autonomic cardiac regulation and heart rate variability, with high and low immersion technologies producing distinct responses.
Why the study?
To investigate heart rate variability responses to mental stress induced by an interactive virtual reality game under different levels of immersion and evaluate visual, mathematical, and machine learning analytical approaches.
Does high immersion virtual reality compared to low immersion alter heart rate variability responses to mental stress in healthy volunteers?
Does high immersion virtual reality compared to low immersion alter heart rate variability responses to mental stress in healthy volunteers?
An integrated approach combining virtual reality, linear and nonlinear HRV analyses, and machine learning can objectively assess and model autonomic physiological responses to mental stress.
VR immersion may alter HRV during mental stress; leaves open whether high-immersion setups warrant caution or targeted autonomic studies.
Virtual reality (VR) provides an effective experimental environment for the controlled investigation of autonomic physiological responses under different levels of virtual immersion. This study investigated heart rate variability (HRV) responses to mental stress induced by an interactive VR game. Two stereoscopic conditions were compared: low immersion using polarising glasses and high immersion using a VR headset. Additionally, the applicability of visual, mathematical, and machine learning approaches for analysing VR-related changes in the autonomic nervous system is evaluated. The experiment involved 42 healthy volunteers (37 men and 5 women; mean age 26 ± 7 years). The HRV analysis is based on RR-intervals and includes time, frequency, and nonlinear indicators. Within the nonlinear framework, visual-mathematical methods, including Poincaré diagrams, recurrence plots, and histogram analysis, were applied. For automated classification of physiological states, a Random Forest model using 17 selected HRV parameters was applied. The results show exposure to the VR game elicited significant changes in autonomic cardiac regulation and heart rate variability, with different stereoscopic technologies producing distinct physiological responses. Nonlinear methods and applied visual-mathematical analyses reveal complex dynamic characteristics of heart rate that classical HRV indicators cannot fully capture. ROC and confusion matrix analyses show that the Random Forest model has good discriminative ability in distinguishing between a resting state and different levels of virtual immersion. The results confirm the potential of the proposed integrated approach, combining virtual reality, linear and nonlinear HRV analyses, and machine learning, as a tool for the objective assessment and modelling of autonomic physiological responses during VR exposure. The study has several limitations, including the relatively small and homogeneous sample size and the absence of stress and psychological state assessment through validated questionnaires, which should be addressed in future research.
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Lebamovski et al. (2026) studied Healthy volunteers (n=42). Interactive VR game (high immersion VR headset) vs. Low immersion using polarising glasses and resting state was evaluated on Heart rate variability (HRV) responses and autonomic cardiac regulation. Exposure to an interactive VR game elicited significant changes in autonomic cardiac regulation and heart rate variability, with high and low immersion technologies producing distinct responses.
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