ZotCare: A Flexible, Personalizable, and Affordable mHealth Service Provider
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Abstract (arXiv)
The proliferation of Internet-connected health devices and the widespread availability of mobile connectivity have resulted in a wealth of reliable digital health data and the potential for delivering just-in-time interventions. However, leveraging these opportunities for health research requires the development and deployment of mobile health (mHealth) applications, which present significant technical challenges for researchers. While existing mHealth solutions have made progress in addressing some of these challenges, they often fall short in terms of time-to-use, affordability, and flexibility for personalization and adaptation. ZotCare aims to address these limitations by offering ready-to-use and flexible services, providing researchers with an accessible, cost-effective, and adaptable solution for their mHealth studies. This article focuses on ZotCare's service orchestration and highlights its capabilities in creating a programmable environment for mHealth research. Additionally, we showcase several successful research use cases that have utilized ZotCare, both in the past and in ongoing projects. Furthermore, we provide resources and information for researchers who are considering ZotCare as their mHealth research solution.
Key facts. Cited by 6 papers in the map (data through 13 Oct 2026). First posted on arXiv 4 Jul 2023.
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Citing papers per year: 2023: 1; 2024: 5.
6 papers.
- 2023Conversational Health Agents: A Personalized LLM-Powered Agent Frameworkcited by 92
- 2024Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detectioncited by 16
- 2024Integrating Wearable Sensor Data and Self-reported Diaries for Personalized Affect Forecastingcited by 7
- 2024Enhancing Performance and User Engagement in Everyday Stress Monitoring: A Context-Aware Active Reinforcement Learning Approachcited by 7
- 2024Graph-Augmented LLMs for Personalized Health Insights: A Case Study in Sleep Analysiscited by 3
- 2024Loneliness Forecasting Using Multi-modal Wearable and Mobile Sensing in Everyday Settingscited by 3
- 2024Loneliness Forecasting Using Multi-modal Wearable and Mobile Sensing in Everyday Settings15 Sep 2024
- 2024Enhancing Performance and User Engagement in Everyday Stress Monitoring: A Context-Aware Active Reinforcement Learning Approach11 Jul 2024
- 2024Graph-Augmented LLMs for Personalized Health Insights: A Case Study in Sleep Analysis24 Jun 2024
- 2024Integrating Wearable Sensor Data and Self-reported Diaries for Personalized Affect Forecasting16 Mar 2024
- 2024Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection16 Feb 2024
- 2023Conversational Health Agents: A Personalized LLM-Powered Agent Framework3 Oct 2023