# Immersive Phobia Therapy through Adaptive Virtual Reality and Biofeedback
## 4 Testing the PhoVR System
### The Testing Procedure
State anxiety refers to subjective feelings, as well as the state and the way the person feels; it is a state at a specific moment. Measuring trait anxiety shows how the person feels in general, revealing general aspects and traits that are relatively stable over time [47]. Initially, STAI was developed for research purposes to assess anxiety in adults from the non-clinical population, and later it was used as a tool for clinical settings (in neuropsychiatry, surgery, and medical clinics) [48]. STAI comprises 40 items, 20 for each subscale (state and trait), scored on a 4-point Likert scale: 1--not at all, 2--somewhat, 3--moderately, and 4--very much [49]. The anxiety rating is calculated considering the raw scores and the percentile corresponding to each scale, by referring to the average. Scores above the mean are considered high scores and indicate the presence of state anxiety (S-Anxiety) and trait anxiety (T-Anxiety). Scores below the mean are low scores, indicating no anxiety. An additional assessment of the virtual environments' ability to reproduce stressful conditions caused by exposure to phobic stimuli was carried out by analyzing the biophysical signal recordings from the test sessions. The evaluation was performed by comparing the values of the tonic component of the electrodermal activity signal (EDA-T) and the heart rate (HR) during the training sessions (considered free of phobic stimuli) and four virtual environments: city and bridge (acrophobia) as well as tunnel and labyrinth (clautrophobia). Within each test session, the average values of the EDA-T and HR signals were calculated using a sliding window with a width of 10 s and a displacement step of 5 s.  
Since during the sessions, it was found that the PPG signal used to determine the HR was strongly affected by the movements of the subjects, sessions were chosen based on the quality of the PPG signals. The criterion for validating a session was the presence of a PPG signal amplitude greater than a threshold value at least 90% of the time. The threshold value was determined by visually inspecting the PPG signal and the heart rate extracted from it and observing the minimum amplitude for which the HR calculation algorithm correctly identified the interval between two pulses. After analyzing the quality of the PPG signal, 66 of the 76 recorded sessions were selected. A one-way ANOVA was conducted to compare the EDA-T and HR values as dependent variables and environment type as the independent variable. * The results obtained for the EDA-T signal were MTraining = 8.97 (\(\pm\)6.23) \(\upmu\)S; MCity = 8.53 (\(\pm\)6.86) \(\upmu\)S; MBridge = 11.52 (\(\pm\)6.76) \(\upmu\)S; MTunnel = 11.70 (\(\pm\)6.77) \(\upmu\)S; and MLabyrinth = 8.10 (\(\pm\)2.89) \(\upmu\)S. Unifactorial analysis revealed a significant difference between these values F(5.1871) = 16.72, \(p\) < 0.001. Post hoc Bonferroni-corrected tests were used to compare all pairs of virtual environments. The significant mean differences (MDs) were recorded between training and bridge (MD = \(-\)2.55 \(\upmu\)S, \(p\) < 0.001), training and tunnel (MD = \(-\)2.72 \(\upmu\)S, \(p\) < 0.001), as well as between city and bridge (MD = \(-\)2.99 \(\upmu\)S, \(p\) < 0.001), city and tunnel (MD = \(-\)3.17 \(\upmu\)S, \(p\) < 0.001), bridge and labyrinth (MD = \(+\)3.42 \(\upmu\)S, \(p\) = 0.002) and tunnel and labyrinth (MD = \(+\)3.60 \(\upmu\)S, \(p\) < 0.001). * The results obtained for the HR signal were MTraining = 83.52 (\(\pm\)10.56) bpm; MCity = 85.39 (\(\pm\)9.78) bpm; MBridge = 82.73 (\(\pm\)9.77) bpm; MTunnel = 82.90 (\(\pm\)10.38) bpm; and MLabyrinth = 83.78 (\(\pm\)5.36) bpm. Unifactorial analysis revealed a significant difference between these values F(5.1871) = 4.81, \(p\) < 0.001. Post hoc Bonferroni-corrected tests were used to compare all pairs of virtual environments.