Deep-Breathing Biofeedback Trainability in a Virtual-Reality Action Game: A Single-Case Design Study With Police Trainers

Abstract

It is widely recognized that police performance may be hindered by psychophysiological state changes during acute stress. To address the need for awareness and control of these physiological changes, police academies in many countries have implemented Heart-Rate Variability (HRV) biofeedback training. Despite these trainings now being widely delivered in classroom setups, they typically lack the arousing action context needed for successful transfer to the operational field, where officers must apply learned skills, particularly when stress levels rise. The study presented here aimed to address this gap by training physiological control skills in an arousing decision-making context. We developed a Virtual-Reality (VR) breathing-based biofeedback training in which police officers perform deep and slow diaphragmatic breathing in an engaging game-like action context. This VR game consisted of a selective shoot/don’t shoot game designed to assess response inhibition, an impaired capacity in high arousal situations. Biofeedback was provided based on adherence to a slow breathing pace: the slower and deeper the breathing, the less constrained peripheral vision became, facilitating accurate responses to the in-game demands. A total of nine male police trainers completed 10 sessions over a 4-week period as part of a single-case experimental ABAB study-design (i.e., alternating sessions with and without biofeedback). Results showed that eight out of nine participants showed improved breathing control in action, with a positive effect on breathing-induced low frequency HRV, while also improving their in-game behavioral performance. Critically, the breathing-based skill learning transferred to subsequent sessions in which biofeedback was not presented. Importantly, all participants remained highly engaged throughout the training. Altogether, our study showed that our VR environment can be used to train breathing regulation in an arousing and active decision-making context.

Virtual Reality for Pulmonary Rehabilitation: Comprehensive Review

“…Various studies involving the treatment of anxiety disorders decided to choose healthy participants for the needs of the experimentations. In particular, among 15 studies, healthy individuals tried several VEs to perform slow (ie, 4-10 breaths per min) [ 8 , 10 , 25 , 26 , 41 , 44 , 45 ], normal [ 7 , 8 , 31 , 32 , 48 ], and diaphragmatic breathing exercises (ie, inhale through the nose moving the air toward the lower belly and exhale through the mouth) [ 39 , 40 , 46 ]. Moreover, healthy individuals participated in studies where VEs were created to guide them on breathing patterns like respiratory sinus arrhythmia (ie, synchronization between heart rate variability [HRV] and RR) [ 6 ] and pursed-lip breathing (ie, inhale through the nose with the mouth closed and exhale through tightly pressed lips) [ 38 ].…”

“…Some studies (9/32) examined the VR experience [ 6 , 10 , 31 , 33 , 38 , 42 , 44 - 46 ] through self-reported questionnaires. The questionnaires included questions related to the participants’ positive and negative emotions, game flow, engagement, ability, capacity, pressure that the said user was experiencing, and challenges that the user perceived [ 62 , 63 ].…”

“…The questionnaires included questions related to the participants’ positive and negative emotions, game flow, engagement, ability, capacity, pressure that the said user was experiencing, and challenges that the user perceived [ 62 , 63 ]. Two studies (2/32) also evaluated the motivation of the participants, using a multidimensional self-reported 7-point scale with a Likert-type format called the Intrinsic Motivation Inventory (IMI) [ 40 , 44 ]. The IMI consists of 7 subscales that measure participants’ experiences related to a target activity, such as interest/enjoyment, perceived competence, effort, value/usefulness, pressure/tension, perceived choice, and relatedness [ 64 ].…”

“…The studies analyzed the above signals and physiological data in detail. In particular, several studies extracted ECG signals to collect HR data to calculate the average beats per minute [ 10 , 25 , 28 , 31 , 37 , 38 , 49 , 50 ] and HRV to measure the specific changes in time between heart beats [ 6 , 29 , 31 , 44 ]. Moreover, a few studies preferred the use of the PPG signal to collect HR [ 27 ], blood pressure [ 50 ], and oxygen saturation [ 28 , 49 , 50 ].…”

“…It was suggested to measure stress levels using not only traditional reports, such as self-reports and the Stroop task, but also cortisol levels [ 41 ]. Future studies should consider triangulating the physiological data with interviews and other qualitative data to provide a holistic assessment of the impact of biofeedback systems [ 29 , 44 ]. Future studies can also investigate the factors that can enhance VR pulmonary rehabilitation to support existing relaxation and destress techniques, and this should be compared to traditional practices [ 27 ].…”