1. Program Overview 

The Massachusetts AI and Technology Center for Connected Care in Aging and 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, wearable, and contactless sensors, emerging machine learning approaches, and AI and data-driven visualization technologies that have the potential to improve the health of older adults and individuals 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.

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/). Learn more at https://www.massaitc.org/ 

3. Pilot Research Focus Areas 

Most older Americans want to age at home, yet chronic physical and cognitive conditions or environmental barriers pose challenges for them to do so. Successful aging at home will require effective ways for older adults to utilize and access health care services from their homes, but barriers currently exist including a lack of technologies that have been specifically developed for older adults, cognitively-impaired older adults, caregivers, and their clinicians. Furthermore, there is a lack of decision support tools that consider the unique needs of monitoring older adults in their homes while helping them maintain functional independence.  In addition, successful aging at home, particularly for those with cognitive limitations and those living alone, will require better monitoring and support to ensure safety.

We solicit pilot projects that focus on bridging these gaps by improving accuracy, decreasing algorithmic bias, enhancing usability, decreasing burden, and improving accessibility of AI and digital technologies. MassAITC anticipates that addressing these challenges will require cross-cutting technology solutions proposed by interdisciplinary teams. Pilot projects that involve collaborations with industry are strongly encouraged. Topics of interest include but are not limited to­­­: 

  • Development and validation of AI-enhanced data analytic solutions to distill 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 decision making about care, we need to develop data analytics tools that distill actionable knowledge from longitudinal and multi-modal data. Critically, these tools will need to provide explanations and visualizations that can be understood by all stakeholders. For example, a pilot may develop explainable AI algorithms and visualization tools that distill voluminous longitudinal health data to accurately infer medication adherence, side effects, and health status to improve clinical decisions.
  • Develop AI-enhanced devices with lower burden, less algorithmic bias, improved accuracy and enhanced usability for both patients and caregivers. AI and machine learning models may be trained on data that do not adequately represent older adults leading to poor predictive performance and adverse consequences for older adults, caregivers and clinicians who base decisions on the resulting inferences. Pilots in this area will address these issues in the context of a specific health problem, for example, a pilot may address gaps in the performance of wearable monitors for sleep, steps, activity, and gait for older adults with mobility impairments by tailoring methods and models specifically for such individuals.
  • Development and validation of data-driven visualization technologies and systems that distill large volumes of data for patients, caregivers and/or clinicians. There are significant opportunities to develop new clinician-driven, AI-enhanced interventions that leverage health monitoring data to optimize various facets of current treatments such as improving efficacy, reducing the occurrence of side effects, and assessing outcomes. For example, a pilot may develop technologies that help clinicians to understand a patient’s full medication use and side effects as well as other health determinants to reduce unsafe use of multiple medications and develop more effective medication regimens.
  • Development and validation of AI-enhanced technologies that account for social determinants of health relevant to older adults and caregivers. Loneliness, anxiety and depression are common debilitating conditions associated with aging and caregiving.  Technology solutions for interventions in the home could provide continuity of care and address issues not routinely covered in traditional clinic visits.
  • Biology of Aging. Please refer to the Addendum to RFA – Special Call in Biology of Aging (2024)

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.