The growing number of people with anxiety disorders, along with increased awareness of mental health, drives the need for new technological tools that provide remote and continuous monitoring of anxiety and panic disorders. Thus, the growing trend in the wearables market, such as smartwatches and health monitoring bracelets, together with the collection of physiological data, such as heart rate and heart rate variability, machine learning and just-in-time interventions create an opportunity to integrate these technologies into mental health solutions, allowing users to monitor their health in a more accessible and practical way.
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Home / Publications / Publication
DETECTION OF ANXIETY AND PANIC ATTACKS IN REAL TIME
Publication type: Article Summary
Original title: Anxolotl – An Anxiety Companion App
Article publication date: October 2022
Source: Instituto Superior de Engenharia de Lisboa (ISEL)
Author: Nuno Gomes
Supervisors: Matilde Pato & André Lourenço
What is the goal, target audience, and areas of digital health it addresses?
The study aims to develop a tool capable of detecting and monitoring anxiety disorders such as generalized anxiety and panic attacks in real time, using physiological data obtained by wearable devices, integrated into a mobile application (app). The target audience includes individuals suffering from anxiety disorders, mental health professionals and users of wearables and health technologies looking to proactively monitor and manage their mental health. The study covers mHealth, remote patient monitoring, biofeedback and machine learning applied to health.
What is the context?
The growing number of people with anxiety disorders, along with increased awareness of mental health, drives the need for new technological tools that provide remote and continuous monitoring of anxiety and panicdisorders. Thus, the growing trend in the wearables market, such as smartwatches and health monitoring bracelets, together with the collection of physiological data, such as heart rate and heart rate variability, machine learning and just-in-time interventions create an opportunity to integrate these technologies into mental health solutions, allowing users to monitor their health in a more accessible and practical way.
What are the current approaches?
Currently, monitoring and intervention in the field of digital mental health is increasingly focused on the use of digital technologies and wearables, using artificial intelligence (AI), machine learning and the intelligent integration of physiological and contextualdata, with the intention of offering continuous monitoring solutions, personalized interventions and early detection of mental disorders. Thus, devices such as the Zephyr Biopatch and portable sensors have been used to monitor panic attacks, detecting precursor signs up to an hour before the crisis. Mobile applications with real-time biofeedback, including with the use of the smartphone’s camera, have been shown to be effective in interventions during crises. Web platforms and hybrid apps combining online sessions and self-assessments have been shown to increase motivation and improve symptom self-management.
Despite these significant recent advances, there are still many unexplored areas with many challenges to overcome, such as personalization of interventions, integration and accessibility of mental health data, prevention of acute symptoms, inclusion of more sophisticated biosensors, among others.
What does innovation consist of? How is the impact of this study assessed?
The Anxolotl System is an innovative solution for managing anxiety disorders by integrating continuous monitoring of physiological data with machine learning for real-time personalized interventions. It consists of 3 main components: the Anxolotl App, a message broker, and a datacenter.
To obtain physiological data, the Anxolotl App connects a mobile application to wearables devices equipped with sensors, such as a photoplethysmography sensor (detects changes in blood volume) and accelerometer (detects movements), together they track activity levels. This data is captured via bluetooth and transmited to the message broker, which encrypts and anonymizes the data before storing it in the datacenter. There, pre-trained machine learning models analyze the data to provide insights into users’ anxiety levels, relaying the results back to the app in real time. This scalable infrastructure allows for continuous model improvement and increasingly personalized responses to user needs.
The app provides users with a general and quick overview of their anxiety levels and panic attack occurrences, complemented by a wellbeing section that offers targeted breathing and meditation exercises for effective anxiety management. Furthermore, the app has an events section that notifies users in case of high values, encouraging them to participate in the calming exercises.
By analyzing data in real time, Anxolotl can predict episodes of anxiety or panic, facilitating immediate interventions tailored to the user’s emotional state. This proactive approach allows the system to detect changes in physiological conditions before a crisis begins, enabling timely and effective responses.
The impact of this technology has been evaluated based on various criteria, involving both the technical accuracy of the algorithms and the practical effect on users’ mental health. The main forms of evaluation include the success of the interventions, the improvement in emotional state, user adherence and the system’s ability to predict anxiety and panic attacks. In addition, usability and the potential for scalability are also considered, highlighting the project’s innovation in creating an accessible and effective solution for mental health monitoring.
What are the main results? What is the impact of these results? What is the future of this technology?
The main results of the Anxolotl project reflect the application’s success in terms of detecting and classifying emotional states, with 92% accuracy in classifying anxiety levels and 94% accuracy in detecting panic attacks, significant adherence to suggested interventions and improvements in users’ emotional states.
The impact of these results is significant in mental health management, offering an innovative solution that combines technological precision, personalized interventions and large-scale accessibility. As such, the impact ranges from individual improvements in users’ emotional well-being to strengthening the healthcare system by providing a preventative and scalable approach to anxiety and panic disorders.
The future of the technology behind Anxolotl is full of opportunities for growth and innovation. Key trends include expansion into new disorders, the use of more advanced biometric sensors, integration with digital health platforms and the development of self-adaptive and immersive systems that will revolutionize mental health treatment.
Would you like to know all the details?
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Home / Publications / Publication
DETECTION OF ANXIETY AND PANIC ATTACKS IN REAL TIME
Publication type: Article Summary
Original title: Anxolotl – An Anxiety Companion App
Article publication date: October 2022
Source: Instituto Superior de Engenharia de Lisboa (ISEL)
Author: Nuno Gomes
Supervisors: Matilde Pato & André Lourenço
What is the goal, target audience, and areas of digital health it addresses?
The study aims to develop a tool capable of detecting and monitoring anxiety disorders such as generalized anxiety and panic attacks in real time, using physiological data obtained by wearable devices, integrated into a mobile application (app). The target audience includes individuals suffering from anxiety disorders, mental health professionals and users of wearables and health technologies looking to proactively monitor and manage their mental health. The study covers mHealth, remote patient monitoring, biofeedback and machine learning applied to health.
What is the context?
The growing number of people with anxiety disorders, along with increased awareness of mental health, drives the need for new technological tools that provide remote and continuous monitoring of anxiety and panicdisorders. Thus, the growing trend in the wearables market, such as smartwatches and health monitoring bracelets, together with the collection of physiological data, such as heart rate and heart rate variability, machine learning and just-in-time interventions create an opportunity to integrate these technologies into mental health solutions, allowing users to monitor their health in a more accessible and practical way.
What are the current approaches?
Currently, monitoring and intervention in the field of digital mental health is increasingly focused on the use of digital technologies and wearables, using artificial intelligence (AI), machine learning and the intelligent integration of physiological and contextualdata, with the intention of offering continuous monitoring solutions, personalized interventions and early detection of mental disorders. Thus, devices such as the Zephyr Biopatch and portable sensors have been used to monitor panic attacks, detecting precursor signs up to an hour before the crisis. Mobile applications with real-time biofeedback, including with the use of the smartphone’s camera, have been shown to be effective in interventions during crises. Web platforms and hybrid apps combining online sessions and self-assessments have been shown to increase motivation and improve symptom self-management.
Despite these significant recent advances, there are still many unexplored areas with many challenges to overcome, such as personalization of interventions, integration and accessibility of mental health data, prevention of acute symptoms, inclusion of more sophisticated biosensors, among others.
What does innovation consist of? How is the impact of this study assessed?
The Anxolotl System is an innovative solution for managing anxiety disorders by integrating continuous monitoring of physiological data with machine learning for real-time personalized interventions. It consists of 3 main components: the Anxolotl App, a message broker, and a datacenter.
To obtain physiological data, the Anxolotl App connects a mobile application to wearables devices equipped with sensors, such as a photoplethysmography sensor (detects changes in blood volume) and accelerometer (detects movements), together they track activity levels. This data is captured via bluetooth and transmited to the message broker, which encrypts and anonymizes the data before storing it in the datacenter. There, pre-trained machine learning models analyze the data to provide insights into users’ anxiety levels, relaying the results back to the app in real time. This scalable infrastructure allows for continuous model improvement and increasingly personalized responses to user needs.
The app provides users with a general and quick overview of their anxiety levels and panic attack occurrences, complemented by a wellbeing section that offers targeted breathing and meditation exercises for effective anxiety management. Furthermore, the app has an events section that notifies users in case of high values, encouraging them to participate in the calming exercises.
By analyzing data in real time, Anxolotl can predict episodes of anxiety or panic, facilitating immediate interventions tailored to the user’s emotional state. This proactive approach allows the system to detect changes in physiological conditions before a crisis begins, enabling timely and effective responses.
The impact of this technology has been evaluated based on various criteria, involving both the technical accuracy of the algorithms and the practical effect on users’ mental health. The main forms of evaluation include the success of the interventions, the improvement in emotional state, user adherence and the system’s ability to predict anxiety and panic attacks. In addition, usability and the potential for scalability are also considered, highlighting the project’s innovation in creating an accessible and effective solution for mental health monitoring.
What are the main results? What is the impact of these results? What is the future of this technology?
The main results of the Anxolotl project reflect the application’s success in terms of detecting and classifying emotional states, with 92% accuracy in classifying anxiety levels and 94% accuracy in detecting panic attacks, significant adherence to suggested interventions and improvements in users’ emotional states.
The impact of these results is significant in mental health management, offering an innovative solution that combines technological precision, personalized interventions and large-scale accessibility. As such, the impact ranges from individual improvements in users’ emotional well-being to strengthening the healthcare system by providing a preventative and scalable approach to anxiety and panic disorders.
The future of the technology behind Anxolotl is full of opportunities for growth and innovation. Key trends include expansion into new disorders, the use of more advanced biometric sensors, integration with digital health platforms and the development of self-adaptive and immersive systems that will revolutionize mental health treatment.
Would you like to know all the details?
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