Handheld or head-mounted? An experimental comparison of the potential of augmented reality for animal phobia treatment using smartphone and HoloLens 2

Exposure therapy is an effective treatment for specific phobia that could be further enhanced through Augmented Reality, a novel technology that can facilitate implementation of gradual exposure and promote treatment acceptability. Effective exposure interventions require stimuli evoking high levels of anxiety. Therefore, it is important to ascertain whether animals can induce anxiety in distinct Augmented Reality modalities, such as Head-Mounted Displays and smartphones, which can differ in user experience and technological embodiment. This study compared the anxiety inducing potential and experienced realism of a spider within the HoloLens 2 Augmented Reality headset and an Augmented Reality smartphone application. Sixty-five participants were exposed to a virtual spider in a 5-step Behavioral Approach Task through both the HoloLens 2 head-mounted display and the PHOBOS Augmented Reality smartphone application. Participants reported Subjective Units of Distress at each step and physiological arousal was measured using heart rate and Skin Conductance. Results show that both technological modalities induced self-reported anxiety for spiders in a Behavioral Approach Task task in a non-clinical sample. The Hololens 2 modality was also related to an skin conductance (SC) increase. Perceived realism did not differ between modalities but was associated with increased anxiety in the HoloLens 2 modality. Findings demonstrate that both implemented modalities have potential for enabling Augmented Reality Exposure Therapy, although the role of experienced realism merits additional investigation. Future research should assess the effectiveness of Augmented Reality Exposure Therapy in clinical samples and assess whether new extended reality modalities, such as passthrough virtual reality, could accommodate observed limitations and improve Augmented Reality Exposure Therapy experiences and outcomes

E-mental health implementation in inpatient care: Exploring its potential and future challenges

BackgroundThere is a great evidence base today for the effectiveness of e-mental health, or the use of technology in mental healthcare. However, large-scale implementation in mental healthcare organisations is lacking, especially in inpatient specialized mental healthcare settings.AimThe current study aimed to gain insights into the factors that promote or hinder the implementation of e-mental health applications on organisational, professional and patient levels in Belgium.MethodsFour Belgian psychiatric hospitals and psychiatric departments of general hospitals invited their professionals and patients to use Moodbuster, which is a modular web-based platform with a connected smartphone application for monitoring. The platform was used in addition to treatment as usual for three to four months. The professionals and patients completed pre- and post-implementation questionnaires on their reasons to participate or to decline participation and experiences with the Moodbuster platform.ResultsMain reasons for the organisations to participate in the implementation study were a general interest in e-mental health and seeing it is a helpful add-on to regular treatment. The actual use of Moodbuster by professionals and patients proved to be challenging with only 10 professionals and 24 patients participating. Implementation was hindered by technical difficulties and inpatient care specific factors such as lack of structural facilities to use e-mental health and patient-specific factors. Professionals saw value in using e-mental health applications for bridging the transition from inpatient to outpatient care. Twenty-two professionals and 31 patients completed the questionnaire on reasons not to participate. For the patients, lack of motivation because of too severe depressive symptoms was the most important reason not to participate. For professionals, it was lack of time and high workload.ConclusionsThe current implementation study reveals several important barriers to overcome in order to successfully implement e-mental health in inpatient psychiatric care

Development and evaluation of assistive technology to improve chronic care

Today we notice a rising life expectancy in the Western countries. Together with an aging population we see an increasing prevalence of age-related diseases such as dementia. This results already in a lack of places in living and care centres. We also notice that the Western population has an unhealthy eating and exercising habit. This can be seen in a significant increasing prevalence of lifestyle diseases such as obesity and cardiovascular diseases. This means that not only the older persons but also the active population is confronted with health problems. Besides the growing number of persons reliant on care, we see that there are fewer (young) professional caregivers available. This meansnbsp;the demographic evolution in the Western countries has made that the current healthcare systems are under enormous pressure. In addition to the demographic evolution, we also experienced anbsp;(r)evolution. In our daily lives, we are becoming more familiar with (mobile) technology and we are approaching a turning point in the healthcare sector where technology will increasingly find its way into healthcare applications. Therefore, this doctoral research is lookingnbsp;opportunities to use technology in the healthcare in order to support in chronic care. Because today there is already a need for technology to support healthcare tasks, we are looking for technology that is immediately usable in real-life conditions. In this work, we focus on three different target groups which are from old to young: demented elderly persons, adults in the prevention and rehabilitation ofnbsp;diseases, and children with epilepsy. For each of these target groups a specific application will be developed. Each ofnbsp;applications also fits into a broader context of current challenges in the healthcare. The challenges being focused upon in this worknbsp;1) an aging population, 2) a rising prevalence of lifestyle diseases, and 3) a shortage of healthcare professionals. Although not explicitly investigated in this work these systems willnbsp;contribute to an increasing quality of life of the target group, offer support to caregivers and have a positive economic impact on the cost of the healthcare. Fornbsp;three target groups the following specific applications will be discussed. First, for demented elderly persons we investigate the ability to automatically detect pain or discomfort based on their facial expressions. Secondly, for adults we will try to predict their physical activities based on knee angle measurements. Thirdly, for children with epilepsy we search for the ability to recognizenbsp;abnormal movement. For each of these applications, a specific system is developed and tested in real-life conditions. During the development of these systems five items get special attention in this work. Firstly, we consider the specific application in itself. From the application point of view, the functional requirements are determined. Based on the functional requirements the measurement system is defined. This is a combination of hardware and software which needs to work in a real environment and with a minimal impact on the user. Next, we look at the raw data, collected by the measurement system, and how it can be processed into usable data. Then we have to make these systems work in real-life conditions. Therefore thenbsp;processing should be performed quickly and accurately in order to meet thenbsp;constraints. Hence, we look for opportunities to execute the (sometimes computationally intensive)nbsp;in real-time. Finally, the added value of these systems is the clinical information and results that can be extracted in a simple and useful way fromnbsp;measurements. Therefore, we also evaluate the usability of these applications in a real-life environment. The ultimate goal of this doctoral research is to describe a methodology that can be used in the development of technological systems in the healthcare sector. Hopefully this thesis can also contribute to a faster integration of technology in the healthcare sector, and improvement of the quality of life of the patient, and support the caregiver (from professional caregiver to informal caregiver) in performing care.status: publishe

Ambulatory monitoring of physical activity based on knee flexion/extension measured by inductive sensor technology

We developed a knee brace to measure the knee angle and implicitly the flexion/extension (f/e) of the knee joint during daily activities. The goal of this study is to classify and validate a limited set of physical activities on ten young healthy subjects based on knee f/e. Physical activities included in this study are walking, ascending and descending of stairs, and fast locomotion (such as jogging, running, and sprinting) at self-selected speeds. The knee brace includes 2 accelerometers for static measurements and calibration and an inductive sensor for dynamic measurements.Aswe focus on physical activities, the inductive sensorwill provide the required information on knee f/e. In this study, the subjects traversed a predefined track which consisted of indoor paths, outdoor paths, and obstacles. The activity classification algorithm based on peak detection in the knee f/e angle resulted in a detection rate of 95.9% for walking, 90.3% for ascending stairs, 78.3% for descending stairs, and 82.2% for fast locomotion.We conclude that we developed a measurement device which allows long-termand ambulatorymonitoring. Furthermore, it is possible to predict the aforementioned activities with an acceptable performance.status: publishe

Carewear: wearables in de geestelijke gezondheidszorg

Wearables laten toe om over een langere periode waardevolle data te verzamelen die preventie en behandeling binnen de geestelijke gezondheidszorg kan verrijken. In het kader van het Carewear project werd er een online platform ontwikkeld, met bijhorende algoritmes en handleiding. Dat laat toe om die gegevens van wearables specifiek inzetbaar te maken in de preventie van burn-out en de behandeling van depressie. Hiervoor wordt er ingezet op fysieke activiteit, stress en hartritmevariabiliteit. Verschillende organisaties testen Carewear momenteel bij hun cliënten om na te gaan wat de meerwaarde is in de klinische praktijk.status: publishe

CAREWEAR: INTEGRATING WEARABLE TECHNOLOGY IN MENTAL HEALTHCARE

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Making Wearable Technology Available for Mental Healthcare through an Online Platform with Stress Detection Algorithms: The Carewear Project

Over the past years, mobile health (mHealth) applications and specifically wearables have become able and available to collect data of increasing quality of relevance for mental health. Despite the large potential of wearable technology, mental healthcare professionals are currently lacking tools and knowledge to properly implement and make use of this technology in practice. The Carewear project is aimed at developing and evaluating an online platform, allowing healthcare professionals to use data from wearables in their clinical practice. Carewear implements data collection through self-tracking, which is aimed at helping people in their behavioral change process, as a component of a broader intervention or therapy guided by a mental healthcare professional. The Empatica E4 wearables are used to collect accelerometer data, electrodermal activity (EDA), and blood volume pulse (BVP) in real life. This data is uploaded to the Carewear platform where algorithms calculate moments of acute stress, average resting heart rate (HR), HR variability (HRV), step count, active periods, and total active minutes. The detected moments of acute stress can be annotated to indicate whether they are associated with a negative feeling of stress. Also, the mood of the day can be elaborated on. The online platform presents this information in a structured way to both the client and their mental healthcare professional. The goal of the current study was a first assessment of the accuracy of the algorithms in real life through comparisons with comprehensive annotated data in a small sample of five healthy participants without known stress-related complaints. Additionally, we assessed the usability of the application through user reports concerning their experiences with the wearable and online platform. While the current study shows that a substantial amount of false positives are detected in a healthy sample and that usability could be improved, the concept of a user-friendly platform to combine physiological data with self-report to inform on stress and mental health is viewed positively in our pilots

Making Wearable Technology Available for Mental Healthcare through an Online Platform with Stress Detection Algorithms: The Carewear Project

Over the past years, mobile health (mHealth) applications and specifically wearables have become able and available to collect data of increasing quality of relevance for mental health. Despite the large potential of wearable technology, mental healthcare professionals are currently lacking tools and knowledge to properly implement and make use of this technology in practice. The Carewear project is aimed at developing and evaluating an online platform, allowing healthcare professionals to use data from wearables in their clinical practice. Carewear implements data collection through self-tracking, which is aimed at helping people in their behavioral change process, as a component of a broader intervention or therapy guided by a mental healthcare professional. The Empatica E4 wearables are used to collect accelerometer data, electrodermal activity (EDA), and blood volume pulse (BVP) in real life. This data is uploaded to the Carewear platform where algorithms calculate moments of acute stress, average resting heart rate (HR), HR variability (HRV), step count, active periods, and total active minutes. The detected moments of acute stress can be annotated to indicate whether they are associated with a negative feeling of stress. Also, the mood of the day can be elaborated on. The online platform presents this information in a structured way to both the client and their mental healthcare professional. The goal of the current study was a first assessment of the accuracy of the algorithms in real life through comparisons with comprehensive annotated data in a small sample of five healthy participants without known stress-related complaints. Additionally, we assessed the usability of the application through user reports concerning their experiences with the wearable and online platform. While the current study shows that a substantial amount of false positives are detected in a healthy sample and that usability could be improved, the concept of a user-friendly platform to combine physiological data with self-report to inform on stress and mental health is viewed positively in our pilots.status: Published onlin