CAREForMe: Contextual Multi-Armed Bandit Recommendation Framework for Mental Health (2401.15188v1)

Published 26 Jan 2024 in cs.AI

Abstract: 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.

References (45)

Summary

The paper introduces CAREForMe, a CMAB-based framework that leverages real-time sensor data to deliver personalized, context-aware mental health interventions.
It employs a modular design integrating sensor collection, feature selection, user state detection, and adaptive recommendation engines to enhance user segmentation and cold-start handling.
Results indicate that the framework significantly improves the timeliness and accuracy of mHealth recommendations, addressing gaps in traditional intervention methods.

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

Introduction

The paper "CAREForMe: Contextual Multi-Armed Bandit Recommendation Framework for Mental Health" presents a sophisticated framework designed to enhance mHealth interventions with context-awareness, personalization, and modularity. In light of the mental health challenges exacerbated by the COVID-19 pandemic, the need for effective, accessible solutions is pressing. Traditional mHealth interventions lack critical features like context-awareness and personalization, making them less effective. CAREForMe aims to address these gaps by leveraging mobile sensing technologies and advanced learning algorithms to provide timely, context-sensitive interventions.

Framework Design

CAREForMe adopts a modular architecture grounded in the principles of contextual multi-armed bandits (CMAB), emphasizing adaptability across different platforms and contexts. The framework integrates seven distinct components: sensor collection, feature selection, user state detection, the CMAB recommendation engine, user interface, intervention inventory, and user input processing.

The framework employs various in-the-wild sensors, including mobile devices, neurophysiological devices, and environmental monitors to capture real-time data. The sensor feature selection component ensures that the most relevant data is utilized based on the user's current context, improving the accuracy and responsiveness of state detection algorithms.

Figure 1: The overview of CAREForMe's framework design that is context-aware, personalized, and modular.

The CMAB recommendation engine is key to CAREForMe's personalized approach, utilizing algorithms such as Upper Confidence Bound (UCB) for adaptive learning and K-means clustering for user segmentation. This engine processes inputs from various models and user feedback to dynamically adjust recommendations.

Framework Implementation

The practical implementation of CAREForMe is demonstrated through a Chatbot recommendation system, focusing on mindfulness exercises as interventions. The intervention inventory is accessible to mental health professionals through a simple YAML configuration, allowing customization without technical expertise.

The CMAB recommendation engine's adaptability is reflected in its ability to learn from user interactions over time, tackling the cold-start problem with initial suggestions based on aggregated user data, and refining recommendations as personal data is accumulated.

Algorithm 1 describes the recommendation process, balancing user-specific data against broader trends through clustering.

\begin{spacing}{0.85}
\DontPrintSemicolon
\KwIn{User %%%%0%%%%, Context %%%%1%%%%, Set %%%%2%%%%, \
\qquad \quad Vector %%%%3%%%%, Vector %%%%4%%%%, Bool %%%%5%%%%}
...
\caption{\sc CMAB Recommendation Process}
\label{alg:user_recommendation}
\end{spacing}

The CMAB User Interface demonstrates flexibility by integrating across multiple platforms, including Discord and Telegram, showcasing the framework's modularity and ease of customization.

CAREForMe distinguishes itself from existing frameworks such as JITAIs and HRS by integrating real-time sensor data processing with adaptive CMAB approaches. Traditional Health Recommender Systems often rely on static data and user questionnaires, lacking the dynamic capabilities of CAREForMe’s real-time, context-aware engine.

Prior digital intervention systems like the one proposed by Gonul et al. provide foundational static templates but fail to address real-time adaptive capabilities and extensive sensor integration, which are core to CAREForMe.

Discussion and Vision

The paper envisions CAREForMe as a transformative tool for mHealth interventions, aiming to standardize methodologies and support interdisciplinary collaboration. By providing a comprehensive framework for recommendation systems, it encourages the integration of diverse disciplinary insights to address complex health challenges.

Future advancements may include expanded learning capabilities to predict optimal recommendation timings and a collaborative repository to enhance the modular components. Through these developments, CAREForMe aims to evolve into a robust ecosystem facilitating holistic health improvements across mental and physical dimensions.

Conclusion

CAREForMe offers a compelling solution to the challenges faced by existing mHealth interventions, providing critical advancements in context sensitivity, personalization, and modularity. Its potential applications span beyond mental health, promising significant impacts across various health domains through enhanced adaptability and interdisciplinary participation. The framework presents a valuable opportunity for future research and development, driving innovation within the mHealth sector.