MassAITC Resources
Table of Contents
Practical Challenges in Implementation of mHealth studies for Older Adults
Conducting a mobile health (mHealth) study involving older adults, particularly those with dementia and their caregivers, presents several challenges that requires a deep understanding of various complex aspects from clinical knowledge to technical expertise in sensors and AI, to the use of a variety of mobile and IoT platforms. This complexity is magnified by the need to use different tools and methodologies that can reliably capture and interpret multi-modal data obtained from sensors and assessments.
We outline some key components of running an mHealth study with older adults and tools available to reduce the burden of developing the infrastructure needed to execute such a study.
Digital Trial Platforms for conducting remote pilot studies
Digital trial platforms are essential for orchestrating remote pilot studies, enabling remote consent, integrating wearable data, and facilitating Ecological Momentary Assessments (EMAs) are essential to streamline the setup, execution, and management of remote pilot studies. Detailed exploration of software solutions for conducting remote digital trials.
Wearable Software Platforms
Effective data collection from wearables and home sensors relies on robust software infrastructure. This infrastructure must support seamless data integration, real-time processing, and user-friendly interfaces for both researchers and participants. Detailed exploration of software solutions.
Assessment Instruments for Cognitive, Functional and Behavioral status
Utilizing the right assessment tools to measure cognitive, functional and behavioral changes in dementia is vital. These tools must be sensitive enough to detect subtle changes over time, contributing to the overall understanding of the progression and management of the disease. Further information on assessments.
AI and Machine Learning Models
Developing AI models to interpret time-series and multi-modal sensor data can uncover patterns that human observers might miss. These models are crucial for predicting disease progression and potentially offering insights into personalized care strategies. Advancements in AI and machine learning.
Study Design and IRB
Designing an mHealth study involves careful planning to ensure the study’s scientific validity while protecting the rights and well-being of participants. This includes obtaining approval from Institutional Review Boards (IRB), which scrutinize the study design, the informed consent process, and the safety measures for participants. Guidance on study design and IRB approval.
Tech Transfer
Translating mHealth innovations into practice involves navigating complex regulatory, commercial, and technological landscapes. Guidance on tech transfer.