CAREForMe: Contextual Multi-Armed Bandit Recommendation Framework for Mental Health

The COVID-19 pandemic has intensified the urgency for effective and accessible mental health interventions in people's daily lives. Mobile Health (mHealth) solutions, such as AI Chatbots and Mindfulness Apps, have gained traction as they expand beyond traditional clinical settings to support daily life. However, the effectiveness of current mHealth solutions is impeded by the lack of context-awareness, personalization, and modularity to foster their reusability. This paper introduces CAREForMe, a contextual multi-armed bandit (CMAB) recommendation framework for mental health. Designed with context-awareness, personalization, and modularity at its core, CAREForMe harnesses mobile sensing and integrates online learning algorithms with user clustering capability to deliver timely, personalized recommendations. With its modular design, CAREForMe serves as both a customizable recommendation framework to guide future research, and a collaborative platform to facilitate interdisciplinary contributions in mHealth research. We showcase CAREForMe's versatility through its implementation across various platforms (e.g., Discord, Telegram) and its customization to diverse recommendation features.Comment: MOBILESoft 202

Network-assisted Smart Access Point Selection for Pervasive Real-time mHealth Applications

AbstractDue to the fast evolution of wireless access networks and high-performance mobile devices together with the spreading of wearable medical sensors, electronic healthcare (eHealth) services have recently started to receive more and more attention, especially in the mobile Health (mHealth) domain. The vast majority of mHealth services require strict medical level Quality of Service (QoS) and Quality of Experience (QoE) provision. Emergency use-cases, remote patient monitoring, tele-consultation and guided surgical intervention require real-time communication and appropriate connection quality. The increasing significance of different overlapping wireless accesses makes possible to provide the required network resources for ubiquitous and pervasive mHealth applications. Aiming to support such use-cases in a heterogeneous network environment, we propose a network-assisted intelligent access point selection scheme for ubiquitous applications of Future Internet architectures focusing on real-time mobile telemedicine services. Our solution is able to discover nearby base stations that cover the current location of the mobile device efficiently and to trigger heterogeneous handovers based on the state and quality of the current access network. The solution is empirically evaluated in Wi-Fi networks used by real-life Android mobile devices and we observed that the scheme can improve the quality of mHealth applications and enhance traffic load balancing capabilities of wireless architectures

AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health

In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, well-designed software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework’s design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice

Developing mHealth Apps with researchers: multi-stakeholder design considerations

The authors have been involved with developing a number of mHealth smartphone Apps for use in health or wellness research in collaboration with researchers, clinicians and patient groups for clinical areas including Sickle Cell Disease, Attention Deficit Hyperactivity Disorder, asthma and infertility treatment. In these types of applications, end-users self-report their symptoms and quality of life or conduct psychometric tests. Physiological data may also be captured using sensors that are internal or external to the device. Following a discussion of the multiple stakeholders that are typically involved in small scale research projects involving end-user data collection, four Apps are used as case studies to explore the issue of non-functional requirements

Development of a Sensor-Based Behavioral Monitoring Solution to Support Dementia Care

Background: Mobile and wearable technology presents exciting opportunities for monitoring behavior using widely available sensor data. This could support clinical research and practice aimed at improving quality of life among the growing number of people with dementia. However, it requires suitable tools for measuring behavior in a natural real-life setting that can be easily implemented by others. Objective: The objectives of this study were to develop and test a set of algorithms for measuring mobility and activity and to describe a technical setup for collecting the sensor data that these algorithms require using off-the-shelf devices. Methods: A mobility measurement module was developed to extract travel trajectories and home location from raw GPS (global positioning system) data and to use this information to calculate a set of spatial, temporal, and count-based mobility metrics. Activity measurement comprises activity bout extraction from recognized activity data and daily step counts. Location, activity, and step count data were collected using smartwatches and mobile phones, relying on open-source resources as far as possible for accessing data from device sensors. The behavioral monitoring solution was evaluated among 5 healthy subjects who simultaneously logged their movements for 1 week. Results: The evaluation showed that the behavioral monitoring solution successfully measures travel trajectories and mobility metrics from location data and extracts multimodal activity bouts during travel between locations. While step count could be used to indicate overall daily activity level, a concern was raised regarding device validity for step count measurement, which was substantially higher from the smartwatches than the mobile phones. Conclusions: This study contributes to clinical research and practice by providing a comprehensive behavioral monitoring solution for use in a real-life setting that can be replicated for a range of applications where knowledge about individual mobility and activity is relevant

A Reference Architecture Proposal for Secure Data Management in Mobile Health

Mobile health (mHealth) is becoming a prominent component of healthcare. As the border between wearable consumer devices and medical devices begins to thin, we extend the mHealth definition including sports, lifestyle, and wellbeing apps that may connect to smart bracelets and watches as well as medical device apps running on consumer platforms and dedicated connected medical devices. This trend raises security and privacy concerns, since these technologies collect data ubiquitously and continuously, both on the individual user and on the surroundings. Security issues include lack of authentication and authorization mechanisms, as well as insecure data transmission and storage. Privacy issues include users' lack of control on data flow, poor quality consent management, and limitations on the possibility to remain anonymous. In response to these threats, we propose an advanced reference platform, securing the use of wearables and mobile apps in the mHealth domains through citizens' active protection and information

m-Path:An easy-to-use and highly tailorable platform for ecological momentary assessment and intervention in behavioral research and clinical practice

In this paper, we present m-Path (www.m-Path.io), an online platform that provides an easy-to-use and highly tailorable framework for implementing smartphone-based ecological momentary assessment (EMA) and intervention (EMI) in both research and clinical practice in the context of blended care. Because real-time monitoring and intervention in people's everyday lives have unparalleled benefits compared to traditional data collection techniques (e.g., retrospective surveys or lab-based experiments), EMA and EMI have become popular in recent years. Although a surge in the use of these methods has led to a myriad of EMA and EMI applications, many existing platforms only focus on a single aspect of daily life data collection (e.g., assessment vs. intervention, active self-report vs. passive mobile sensing, research-dedicated vs. clinically-oriented tools). With m-Path, we aim to integrate all of these facets into a single platform, as it is exactly this all-in-one approach that fosters the clinical utility of accumulated scientific knowledge. To this end, we offer a comprehensive platform to set up complex and highly adjustable EMA and EMI designs with advanced functionalities, using an intuitive point-and click web interface that is accessible for researchers and clinicians with limited programming skills. We discuss the strengths of daily life data collection and intervention in general and m-Path in particular. We describe the regular workflow to set up an EMA or EMI design within the m-Path framework, and summarize both the basic functionalities and more advanced features of our software

Technical Challenges of a Mobile Application Supporting Intersession Processes in Psychotherapy

The usage of mobile applications in healthcare is gaining popularity in recent years. The ubiquity of a sophisticated mobile appliance that is applicable to sample ecological patient data in real life by acquiring both mental state and environmental data has enabled new possibilities for researchers and healthcare providers. Collecting data using the mentioned approach is often called Ecological Momentary Assessment (EMA) and is characterized by an unidirectional data flow towards the platform provider. A more challenging approach, in turn, is called Ecological Momentary Intervention (EMI). The latter requires a bidirectional data flow in order to enable the possibility of sending feedback to the patients and controlling their experiences through interventions. Although both approaches are established parts of IT-supported treatments in the field of psychology and psychotherapy until now, the so-called intersession process has not been technically supported appropriately yet. Therefore, the Intersession-Online platform was developed in order to (a) assess intersession processes systematically, (b) monitor a patient, and (c) intervene by suppressing negative thoughts concerning the therapy. In this paper, the technical requirements, architecture, and features of the mobile application of the Intersession-Online platform are presented. In this context, the development of a patient data sampling mechanism, which consists of a sophisticated, inter-questionnaire dependent sampling schedule and synchronization strategy is particularly illustrated and discussed. Altogether, the technical challenges will show that a mobile application supporting intersession processes in psychotherapy is an endeavor which requires many considerations. However, on the other, such a mobile application may be the basis for new technical as well as psychological insights

A modular framework for home healthcare monitoring

Many patients with chronic health problems have multiple ailments but different patients may have different such ailments. Home monitoring systems for individual ailments exist but a patient may have multiple of these, all designed independently. There are no standard architectures so this leads to unmanageable diversity which causes problems for patients in having to learn to use a variety of monitors and for physicians in trying to monitor many patients. The purpose of this project was to design and prototype a next generation modular remote healthcare monitoring system capable of monitoring multiple ailments and extensible to new ailments in order to explore and evaluate the feasibility of such a one-size-fits-all system and assess a practical way to implement it. The project was designed and programmed as if it were to be deployed in a real world situation using real monitors and a smart phone based monitoring scheme and was also implemented and tested in part using a 3D virtual world, Second Life. Using this virtual world platform provided freedom in exploring some of the alternative designs. Implementing such a system using real world devices and not simply designing it conceptually gave a better view of the future of home health monitoring as well as a better framework for developing a future family of remote monitoring systems. The system was evaluated and was determined to provide a reasonable proof of concept patient monitoring architecture that could potentially influence a next generation of modular home healthcare monitoring systems