Revised on 11/14/2024
1. Program Overview
The Massachusetts AI and Technology Center for Connected Care in Aging &Alzheimer’s Disease (MassAITC) is a multidisciplinary National Institute of Aging (NIA) P30 Research Collaboratory (1P30AG073107) spanning five sites – the University of Massachusetts Amherst, Brigham and Women’s Hospital, Massachusetts General Hospital, Brandeis University and Northeastern University. The Center aims to foster multidisciplinary research on the development, validation and translation of emerging AI and digital technologies to support healthy aging and the care of people living with Alzheimer’s Disease and Alzheimer’s Disease Related Dementias (AD/ADRD) more effectively in their home environments.
MassAITC is pleased to issue a call for proposals to support pilot studies on mobile and wearables, active and passive sensors, emerging machine learning approaches, and AI and data-driven technologies that have the potential to improve the health of older adults and individuals living with AD/ADRD. MassAITC has a particular focus on supporting successful aging at home through the development of technologies for at-home monitoring and support and to better connect older adults, caregivers, and clinicians. We are also seeking projects that use computational biology AI models and multiscale data to enhance our mechanistic understanding of aging and AD/ADRD, identify biomarkers, and aid in drug discovery.
2. Funding Opportunity Description
For key dates, FAQs, and to register for or view an informational webinar, please visit https://www.a2collective.ai/pilotawards
For the upcoming year, the MassAITC will fund approximately 10 pilot projects. Projects will typically be funded for a 12-month period with a maximum budget of $100,000 in direct costs, but higher levels of funding (i.e., up to $200,000 in direct costs) will also be considered with appropriate justification.
Applicants who do not have access to facilities and resources needed to carry out the proposed research can request access to the Center’s state-of-art research facilities and diverse cohorts provided by the MassAITC partner institutions (see https://massaitc.org/resources/). Note: Applicants planning on using any of the MassAITC affiliated facilities or resources should submit an inquiry in advance of their submission to be formally connected to that resource to appropriately incorporate it into your proposal.
Applicants are highly encouraged to meet with a representative of MassAITC in advance of your Round 1 submission: Consultation Request Form
– General questions about the MassAITC RFP Focus Areas can be submitted to massaitc@umass.edu
3. Pilot Research Focus Areas
Most older Americans want to age at home, yet chronic physical and cognitive conditions and environmental barriers often make this challenging. Successful aging at home requires effective ways for older adults to access and utilize health care services from their homes. Currently, barriers exist, including a lack of technologies specifically developed for older adults – especially those with cognitive impairments – their caregivers, and their clinicians. There is also a shortage of decision support and monitoring tools that address the unique needs of older adults living in their homes.
We are seeking to fund pilot projects aimed at closing these gaps by developing and validating new models and tools, or improving upon existing AI and digital technologies. MassAITC anticipates that addressing these challenges will require comprehensive technology solutions proposed by interdisciplinary teams. Collaborative pilot projects involving industry partnerships are strongly encouraged.
Topics of interest include but are not limited to:
Development and validation of AI-driven data analytic solutions that transform multi-modal sensor and cognitive performance data into interpretable and actionable information to enhance self-care, support caregivers, and/or improve clinical and caregiving decision-making. To enable effective and timely decisions, data analytics tools must extract meaningful information from complex, multi-modal, and longitudinal data. Critically, these tools should offer explanations and visualizations that are easily understandable to all stakeholders. For example, pilots may:
- Design an AI-driven tool that uses multi-sensor data to provide caregivers with clear alerts about changes in daily routines or behavior that may signal health concerns.
- Develop a visual analytics dashboard that integrates cognitive performance and physical activity data to support early detection of cognitive decline.
- Create an AI-driven tool that consolidates heart rate, sleep, and movement data from wearables to help track and manage heart health in older adults.
Develop AI-enhanced devices that minimize user burden, reduce algorithmic bias, improve accuracy and enhance usability for both patients and caregivers. Many AI and machine learning models are trained on data that do not adequately represent older adults, resulting in poor predictive performance and potentially harmful impacts on older adults, caregivers and clinicians. Pilots in this area will address these issues in the context of a specific health problem. For example, pilots may:
- Create wearable monitors optimized for accurate gait, sleep, and activity tracking in older adults with mobility impairments.
- Develop AI-powered fall detection systems that adjust to the unique movement patterns of older adults.
- Improve the accuracy of AI-driven symptom monitoring apps for older adults with chronic conditions by training models on age-specific data.
Develop and validate data-driven visualization technologies that distill large volumes of data for patients, caregivers and/or clinicians to interpret and act on. There are significant opportunities to create clinician-centered, AI-enhanced interventions that leverage health monitoring data to optimize treatment by improving efficacy, reducing side effects, and assessing health outcomes. For example, pilots may:
- Create a visual dashboard that integrates data on a patient’s vital signs and medication side effects to aid in identifying potential adverse interactions.
- Develop an AI-based tool that visualizes trends in symptom progression and treatment outcomes for chronic disease management.
- Design a caregiver-focused app that distills daily health monitoring data into easy-to-read summaries, highlighting areas that may need attention or adjustments in care.
Develop and validate AI-enhanced technologies that consider social determinants of health critical to older adults and caregivers. Loneliness, anxiety and depression are common and potentially debilitating conditions associated with aging and caregiving. Technology solutions can address these issues through at-home interventions that provide continuity of care and address concerns not routinely covered in routine clinic visits. For example, pilots may:
- Design an AI-powered companion app that detects signs of loneliness or anxiety based on interaction patterns and prompts social engagement or support.
- Develop a remote monitoring system that uses AI to assess and report on mental health indicators, such as sleep and activity patterns.
- Create a caregiver support platform that integrates stress and wellness tracking to suggest tailored resources and self-care practices.
Develop machine learning and AI models that utilize multiscale biological data to enhance our mechanistic understanding of aging and AD/ADRD, identify biomarkers, and aid in drug discovery. With an exponential increase in high-dimensional multiscale biology data there is an opportunity to develop AI algorithms that bridge biological and temporal scales to pave the way for breakthroughs in understanding, predicting, intervening, and potentially reversing aging and neurodegenerative diseases. For example, pilots may:
- Build AI models that integrates genomic, transcriptomic, and proteomic data to define diagnostic and prognostic biomarkers for AD/ADRD and/or healthy aging. Of interest would be novel RNA editing, alternative splicing, and RNA modifications associated with the aging process and aging-related diseases.
- Develop a simulation model using AI that predicts how patients with different genetic backgrounds might respond to existing and novel AD/ADRD therapies.
- Identify potential drug candidates for preventing or reversing aging-associated cellular damage and AD/ADRD symptoms.
- Development of foundational and explainable AI models to decipher the biological underpinnings of aging. Of interest would be models that also incorporate at home wearable and sensor data.
All pilot applications must describe why the problem that they are addressing is a critical barrier to achieving successful aging and/or AD/ADRD care at home, and how the proposed AI methods and digital technologies are well-suited to address the problem.
4. Pilot Grant Recipient Expectations
The pilot project PI will meet with an assigned mentor from the MassAITC regularly during the lifecycle of the pilot project. The assigned mentor will be available to help strategize, identify resources that are available, assist with navigating pitfalls, etc. Pilot investigators will be required to engage with the MassAITC by:
- Participating and offering new content in their area of expertise to enhance the training activities of the Center (e.g., tutorial on relevant topic, developing a “best practices” document).
- Attending and presenting at the in-person MassAITC Annual Meeting.
- Presenting webinars in the Center’s webinar series.