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Purpose of Review: We review the first pilot studies applying metaverse-related technologies in psychiatric patients and discuss the rationale for using this complex federation of technologies to treat mental diseases. Concerning previous virtual-reality applications in medical care, metaverse technologies provide the unique opportunity to define, control, and shape virtual scenarios shared by multi-users to exploit the “synchronized brains” potential exacerbated by social interactions. Recent Findings: The application of an avatar-based sexual therapy program conducted on a metaverse platform has been demonstrated to be more effective concerning traditional sexual coaching for treating female orgasm disorders. Again, a metaverse-based social skills training program has been tested on children with autism spectrum disorders, demonstrating a significant impact on social interaction abilities. Summary: Metaverse-related technologies could enable us to develop new reliable approaches for treating diseases where behavioral symptoms can be addressed using socio-attentive tasks and social-interaction strategies.

Developing automatic methods to measure psychological stress in everyday life has become an important research challenge. Here, we describe the design and implementation of a personalized mobile system for the detection of psychological stress episodes based on Heart-Rate Variability (HRV) indices. The system's architecture consists of three main modules: a mobile acquisition module; an analysis-decision module; and a visualization-reporting module. Once the stress level is calculated by the mobile system, the visualization-reporting module of the mobile application displays the current stress level of the user. We carried out an experience-sampling study, involving 15 participants, monitored longitudinally, for a total of 561 ECG analyzed, to select the HRV features which best correlate with self-reported stress levels. Drawing on these results, a personalized classification system is able to automatically detect stress events from those HRV features, after a training phase in which the system learns from the subjective responses given by the user. Finally, the performance of the classification task was evaluated on the empirical dataset using the leave one out cross-validation process. Preliminary findings suggest that incorporating self-reported psychological data in the system's knowledge base allows for a more accurate and personalized definition of the stress response measured by HRV indices.

Since Mark Zuckerberg's announcement about the development of new three-dimensional virtual worlds for social communication, a great debate has been raised about the promise of such a technology. The metaverse, a term formed by combining meta and universe, could open a new era in mental health, mainly in psychological disorders, where the creation of a full-body illusion via digital avatar could promote healthcare and personal well-being. Patients affected by body dysmorphism symptoms (i.e., eating disorders), social deficits (i.e. autism) could greatly benefit from this kind of technology. However, it is not clear which advantage the metaverse would have in treating psychological disorders with respect to the well-known and effective virtual reality (VR) exposure therapy. Indeed, in the last twenty years, a plethora of studies have demonstrated the effectiveness of VR technology in reducing symptoms of pain, anxiety, stress, as well as, in improving cognitive and social skills. We hypothesize that the metaverse will offer more opportunities, such as a more complex, virtual realm where sensory inputs, and recurrent feedback, mediated by a “federation” of multiple technologies - e.g., artificial intelligence, tangible interfaces, Internet of Things and blockchain, can be reinterpreted for facilitating a new kind of communication overcoming self-body representation. However, nowadays a clear starting point does not exist. For this reason, it is worth defining a theoretical framework for applying this new kind of technology in a social neuroscience context for developing accurate solutions to mental health in the future.

Mindfulness is one of the most popular psychotherapeutic techniques that help to promote good mental and physical health. Combining mindfulness with immersive virtual reality (VR) has been proven to be especially effective for a wide range of mood disorders for which traditional mindfulness has proven valuable. However, the vast majority of immersive VR-enhanced mindfulness applications have focused on clinical settings, with little evidence on healthy subjects. This narrative review evaluates the real effectiveness of state-of-the-art mindfulness interventions mediated by VR systems in influencing mood and physiological status in non-clinical populations. Only studies with an RCT study design were considered. We conclude that most studies were characterized by one single meditation experience, which seemed sufficient to induce a significant reduction in negative mood states (anxiety, anger, depression, tension) combined with increased mindfulness skills. However, physiological correlates of mindfulness practices have scarcely been investigated. The application of VR-enhanced mindfulness-based interventions in non-clinical populations is in its infancy since most studies have several limitations, such as the poor employment of the RCT study design, the lack of physiological measurements (i.e., heart rate variability), as well as the high heterogeneity in demographical data, technological devices, and VR procedures. We thus concluded that before applying mindfulness interventions mediated by VR in clinical populations, more robust and reliable methodological procedures need to be defined.

Virtual Reality (VR) is increasingly being used in combination with psycho-physiological measures to improve assessment of distress in mental health research and therapy. However, the analysis and interpretation of multiple physiological measures is time consuming and requires specific skills, which are not available to most clinicians. To address this issue, we designed and developed a Decision Support System (DSS) for automatic classification of stress levels during exposure to VR environments. The DSS integrates different biosensor data (ECG, breathing rate, EEG) and behavioral data (body gestures correlated with stress), following a training process in which self-rated and clinical-rated stress levels are used as ground truth. Detected stress events for each VR session are reported to the therapist as an aggregated value (ranging from 0 to 1) and graphically displayed on a diagram accessible by the therapist through a web-based interface.

The incredible advancement in the ICT sector has challenged technology developers, designers, and psychologists to reflect on how to develop technologies to promote mental health. Computerized experience-sampling method appears to be a promising assessment approach to investigate the real-time fluctuation of experience in daily life in order to detect stressful events. At this purpose, we developed PsychLog (http://psychlog.com) a free open-source mobile experience sampling platform that allows psychophysiological data to be collected, aggregated, visualized and collated into reports. Results showed a good classification of relaxing and stressful events, defining the two groups with psychological analysis and verifying the discrimination with physiological measures. Within the paradigm of Positive Technology, our innovative approach offers for researchers and clinicians new effective opportunities for the assessment and treatment of the psychological stress in daily situations.

This paper reports the design and assessment of a neuro-fuzzy model to support clinicians during virtual reality therapy. The implemented model is able to automatically recognize the perceived stress levels of the patients by analyzing physiological and behavioral data during treatment. The model, consisting of a self-organizing map and a fuzzy-rule-based module, was trained unobtrusively recording electrocardiogram, breath rate and activity during stress inoculation provided by the exposure to virtual environments. Twenty nurses were exposed to sessions simulating typical stressful situations experienced at their workplace. Four levels of stress severity were evaluated for each subject by gold standard clinical scales administered by trained personnel. The model's performances were discussed and compared with the main machine learning algorithms. The neuro-fuzzy model shows better performances in terms of stress level classification with 83% of mean recognition rate. RESEARCH HIGHLIGHTS Stress levels were predicted on the basis of physiological computing using a neuro-fuzzy model during virtual reality therapy. Features were extracted from ECG and respiration obtaining high accuracy and optimization of computational costs. The neuro-fuzzy model shows better performance than the more frequently adopted classifiers. This approach may enhance the use of physiological computing for stress treatment in clinical practice

The recent convergence between technology and medicine is offering innovative methods and tools for behavioral health care. Among these, an emerging approach is the use of virtual reality (VR) within exposure-based protocols for anxiety disorders, and in particular posttraumatic stress disorder. However, no systematically tested VR protocols are available for the management of psychological stress. Objective: Our goal was to evaluate the efficacy of a new technological paradigm, Interreality, for the management and prevention of psychological stress. The main feature of Interreality is a twofold link between the virtual and the real world achieved through experiential virtual scenarios (fully controlled by the therapist, used to learn coping skills and improve self-efficacy) with real-time monitoring and support (identifying critical situations and assessing clinical change) using advanced technologies (virtual worlds, wearable biosensors, and smartphones). Methods: The study was designed as a block randomized controlled trial involving 121 participants recruited from two different worker populations-teachers and nurses-that are highly exposed to psychological stress. Participants were a sample of teachers recruited in Milan (Block 1: n=61) and a sample of nurses recruited in Messina, Italy (Block 2: n=60). Participants within each block were randomly assigned to the (1) Experimental Group (EG): n=40; B1=20, B2=20, which received a 5-week treatment based on the Interreality paradigm; (2) Control Group (CG): n=42; B1=22, B2=20, which received a 5-week traditional stress management training based on cognitive behavioral therapy (CBT); and (3) the Wait-List group (WL): n=39, B1=19, B2=20, which was reassessed and compared with the two other groups 5 weeks after the initial evaluation. Results: Although both treatments were able to significantly reduce perceived stress better than WL, only EG participants reported a significant reduction (EG=12% vs CG=0.5%) in chronic t

The recent convergence between technology and medicine is offering innovative methods and tools for behavioral health care. Among these, an emerging approach is the use of virtual reality (VR) within exposure-based protocols for anxiety disorders, and in particular posttraumatic stress disorder. However, no systematically tested VR protocols are available for the management of psychological stress. Objective: Our goal was to evaluate the efficacy of a new technological paradigm, Interreality, for the management and prevention of psychological stress. The main feature of Interreality is a twofold link between the virtual and the real world achieved through experiential virtual scenarios (fully controlled by the therapist, used to learn coping skills and improve self-efficacy) with real-time monitoring and support (identifying critical situations and assessing clinical change) using advanced technologies (virtual worlds, wearable biosensors, and smartphones). Methods: The study was designed as a block randomized controlled trial involving 121 participants recruited from two different worker populations-teachers and nurses-that are highly exposed to psychological stress. Participants were a sample of teachers recruited in Milan (Block 1: n=61) and a sample of nurses recruited in Messina, Italy (Block 2: n=60). Participants within each block were randomly assigned to the (1) Experimental Group (EG): n=40; B1=20, B2=20, which received a 5-week treatment based on the Interreality paradigm; (2) Control Group (CG): n=42; B1=22, B2=20, which received a 5-week traditional stress management training based on cognitive behavioral therapy (CBT); and (3) the Wait-List group (WL): n=39, B1=19, B2=20, which was reassessed and compared with the two other groups 5 weeks after the initial evaluation. Results: Although both treatments were able to significantly reduce perceived stress better than WL, only EG participants reported a significant reduction (EG=12% vs CG=0.5%) in chronic t

Virtual Reality (VR) is increasingly being used in combination with psycho-physiological measures to improve assessment of distress in mental health research and therapy. However, the analysis and interpretation of multiple physiological measures is time consuming and requires specific skills, which are not available to most clinicians. To address this issue, we designed and developed a Decision Support System (DSS) for automatic classification of stress levels during exposure to VR environments. The DSS integrates different biosensor data (ECG, breathing rate, EEG) and behavioral data (body gestures correlated with stress), following a training process in which self-rated and clinical-rated stress levels are used as ground truth. Detected stress events for each VR session are reported to the therapist as an aggregated value (ranging from 0 to 1) and graphically displayed on a diagram accessible by the therapist through a web-based interface.

The incredible advancement in the ICT sector has challenged technology developers, designers, and psychologists to reflect on how to develop technologies to promote mental health. Computerized experience-sampling method appears to be a promising assessment approach to investigate the real-time fluctuation of experience in daily life in order to detect stressful events. At this purpose, we developed PsychLog (http://psychlog.com) a free open-source mobile experience sampling platform that allows psychophysiological data to be collected, aggregated, visualized and collated into reports. Results showed a good classification of relaxing and stressful events, defining the two groups with psychological analysis and verifying the discrimination with physiological measures. Within the paradigm of Positive Technology, our innovative approach offers for researchers and clinicians new effective opportunities for the assessment and treatment of the psychological stress in daily situations.

Self tracking is a recent trend in e-health that refers to the collection, elaboration and visualization of personal health data through ubiquitous computing tools such as mobile devices and wearable sensors. Here, we describe the design of a mobile self-tracking platform that has been specifically designed for clinical and research applications in the field of mental health. The smartphone-based application allows collecting a) self-reported feelings and activities from pre-programmed questionnaires; b) electrocardiographic (ECG) data from a wireless sensor platform worn by the user; c) movement activity information obtained from a tri-axis accelerometer embedded in the wearable platform. Physiological signals are further processed by the application and stored on the smartphone's memory. The mobile data collection platform is free and released under an open source licence to allow wider adoption by the research community (download at: http://sourceforge.net/projects/psychlog/).

Self tracking is a recent trend in e-health that refers to the collection, elaboration and visualization of personal health data through ubiquitous computing tools such as mobile devices and wearable sensors. Here, we describe the design of a mobile self-tracking platform that has been specifically designed for clinical and research applications in the field of mental health. The smartphone-based application allows collecting a) self-reported feelings and activities from pre-programmed questionnaires; b) electrocardiographic (ECG) data from a wireless sensor platform worn by the user; c) movement activity information obtained from a tri-axis accelerometer embedded in the wearable platform. Physiological signals are further processed by the application and stored on the smartphone's memory. The mobile data collection platform is free and released under an open source licence to allow wider adoption by the research community (download at: http://sourceforge.net/projects/psychlog/).

Prolonged exposure to stressful environments can lead to serious health problems. Therefore, measuring stress in daily life situations through non-invasive procedures has become a significant research challenge. In this paper, we describe a system for the automatic detection of momentary stress from behavioral and physiological measures collected through wearable sensors. The system’s architecture consists of two key components: a) a mobile acquisition module; b) an analysis and decision module. The mobile acquisition module is a smartphone application coupled with a newly developed sensor platform (Personal Biomonitoring System, PBS). The PBS acquires behavioral (motion activity, posture) and physiological (hearth rate) variables, performs low-level, real-time signal preprocessing, and wirelessly communicates with the smartphone application, which in turn connects to a remote server for further signal processing and storage. The decision module is realized on a knowledge basis, using neural network and fuzzy logic algorithms able to combine as input the physiological and behavioral features extracted by the PBS and to classify the level of stress, after previous knowledge acquired during a training phase. The training is based on labeling of physiological and behavioral data through self-reports of stress collected via the smartphone application. After training, the smartphone application can be configured to poll the stress analysis report at fixed time steps or at the request of the user. Preliminary testing of the system is ongoing.

Computerized experience-sampling method comprising a mobile-based system that collects psychophysiological data appears to be a very promising assessment approach to investigate the real-time fluctuation of experience in daily life in order to detect stressful events. At this purpose, we developed PsychLog (http://sourceforge.net/projects/psychlog/) a free open-source mobile experience sampling platform that allows psychophysiological data to be collected, aggregated, visualized and collated into reports. Results showed a good classification of relaxing and stressful events, defining the two groups with psychological analysis and verifying the discrimination with physiological measures. Our innovative approach offers to researchers and clinicians new effective opportunities to assess and treat psychological stress in daily-life environments.

Developments in computational techniques including clinical decision support systems, information processing, wireless communication and data mining hold new premises in Personal Health Systems. Pervasive Healthcare system architecture finds today an effective application and represents in perspective a real technological breakthrough promoting a paradigm shift from diagnosis and treatment of patients based on symptoms to diagnosis and treatment based on risk assessment. Such architectures must be able to collect and manage a large quantity of data supporting the physicians in their decision process through a continuous pervasive remote monitoring model aimed to enhance the understanding of the dynamic disease evolution and personal risk. In this work an automatic simple, compact, wireless, personalized and cost efficient pervasive architecture for the evaluation of the stress state of individual subjects suitable for prolonged stress monitoring during normal activity is described. A novel integrated processing approach based on an autoregressive model, artificial neural networks and fuzzy logic modeling allows stress conditions to be automatically identified with a mobile setting analysing features of the electrocardiographic signals and human motion. The performances of the reported architecture were assessed in terms of classification of stress conditions.

Prolonged exposure to stressful environments can lead to serious health problems. Therefore, measuring stress in daily life situations through non-invasive procedures has become a significant research challenge. In this paper, we describe a system for the automatic detection of momentary stress from behavioral and physiological measures collected through wearable sensors. The system's architecture consists of two key components: a) a mobile acquisition module; b) an analysis and decision module. The mobile acquisition module is a smartphone application coupled with a newly developed sensor platform (Personal Biomonitoring System, PBS). The PBS acquires behavioral (motion activity, posture) and physiological (hearth rate) variables, performs low-level, real-time signal preprocessing, and wirelessly communicates with the smartphone application, which in turn connects to a remote server for further signal processing and storage. The decision module is realized on a knowledge basis, using neural network and fuzzy logic algorithms able to combine as input the physiological and behavioral features extracted by the PBS and to classify the level of stress, after previous knowledge acquired during a training phase. The training is based on labeling of physiological and behavioral data through self-reports of stress collected via the smartphone application. After training, the smartphone application can be configured to poll the stress analysis report at fixed time steps or at the request of the user. Preliminary testing of the system is ongoing.

In this paper we introduce PsychLog (http://www.psychlog.com/), a mobile experience sampling platform that allows real-time collection of psychological, behavioral and contextual data for research and clinical applications. The mobile platform allows administering self-report questionnaires to collect user's quality of experience in its various cognitive, affective and motivational dimensions. The researcher can schedule the administration of the questionnaires by setting a trigger, which can be launched at specific times or randomly during a day. A wireless electrocardiogram (ECG) equipped with an accelerometer allows monitoring levels of activity and heart rate information. PsychLog is freely available for Windows mobile and its open-source code can be configured to meet specific experimental or clinical requirements. Here, we provide an overview of the system and its future developments.

Ubiquitous computing technologies offer exciting new possibilities for monitoring and analyzing user’s experience in real time. In this paper, we describe the design and development of Psychlog, a mobile phone platform designed to collect users’ psychological, physiological, and activity information for mental health research. The tool allows administering self-report questionnaires at specific times or randomly within a day. The system also permits to collect heart rate and activity information from a wireless electrocardiogram equipped with a three-axial accelerometer. By combining self-reports with heart rate and activity data, the application makes it possible to investigate the relationship between psychological, physiological, and behavioral variables, as well as to monitor their fluctuations over time. The software runs on Windows mobile operative system and is available as open source