Leveraging Adaptive Technology ("Just-in-Time") Interventions for Aging and Alzheimer's Disease and Alzheimer's Disease-related Dementias (AD/ADRD)

Audience

Researchers and anyone interested in technology and aging.

Dates

Oct. 16, 2024 | 11:00 a.m.–3:00 p.m. ET
Oct. 17, 2024 | 11:00 a.m.–3:00 p.m. ET

Location

This meeting was held virtually.

Purpose and Background

A 2017 NASEM workshop , “Preventing Cognitive Decline and Dementia”, suggested numerous ways to construct a stronger evidence base for the prevention of cognitive decline and AD/ADRD, including encouraging, but inconclusive evidence for cognitive training, blood pressure management, and physical activity interventions. This was followed by several meetings convened by the NIA , which emphasized the need for non-pharmacological approaches to address risks factors for healthy aging and AD/ADRD.

Just-In-Time Adaptive Interventions are an exciting approach that can be implemented through a combination of mobile and sensor technologies to monitor the state and context of an individual and provide the appropriate amount and type of intervention at the right time.

The workshop aimed to explore various topics related to the development and deployment of digital health-based adaptive interventions targeting positive behavior change in one or more daily behaviors to reduce risks for health aging and AD/ADRD.

Agenda

All times are in Eastern Daylight Time.

Day 1 | Wednesday, Oct. 16

11:00 a.m. Session 1 | Digital adaptive interventions: decision-focused evidence production

  • Mapping the landscape of decisions for health promotion, Eric Hekler , Ph.D., University of California, San Diego
  • Adaptive interventions and JITAIs as decision policies: What and why?, Inbal Nahum-Shani , Ph.D., University of Michigan
  • Developing good enough evidence to support decisions, Pedja Klasnja , Ph.D., University of Michigan
  • Discussion, Facilitator: Allison Moore , M.D., University of California, San Diego

12:45 p.m. Lunch

1:15 p.m. Session 2 | Research to optimize digital interventions

  • What is optimization?, Linda Collins , Ph.D., New York University
  • Experimental designs: SMART, MRT, Hybrids, Walter Dempsey , Ph.D., University of Michigan
  • System identification to optimize adaptive interventions, Daniel Rivera , Ph.D., Arizona State University
  • Discussion, Facilitator: George Demiris , Ph.D., University of Pennsylvania

3:00 p.m. Adjourn

Day 2 | Thursday, Oct. 17

11:00 a.m. Session 3 | Approaches to iterative learning

  • Community-driven process, Steven De La Torre, Ph.D., University of California, San Diego
  • Theory-driven process, Olga Perski , Ph.D., Tampere University
  • Data-driven process, Ben Marlin , Ph.D., University of Massachusetts, Amherst
  • Discussion, Facilitator: Ipsit Vahia , M.D., Harvard Medical School

12:35 p.m. Lunch

1:00 p.m. Session 4 | Approaches to learning and adaptation

  • Technology supported self-study, m.c. schraefel , ph.d. (lowercase is deliberate), University of Southampton, UK
  • Online learning: personalizing JITAIs (pJITAIs), Susan Murphy , Ph.D., Harvard University
  • Community-driven learning, Antwi Akom , Ph.D., SOUL Lab, University of California, San Francisco, San Francisco State University, & Senior Fellow Brookings Institute
  • Discussion, Facilitator: Jonathan Hakun , Ph.D., Pennsylvania State University

2:30 p.m. Overall Discussion

  • How to accelerate the development of effective and scalable interventions that leverage technology to reduce the risks for healthy Aging and AD/ADRD, Ipsit Vahia, Jonathan Hakun, Cary Reid , Susanne Jaeggi , Eric Hekler, Inbal Nahum-Shani, Pedja Klasnja

3:00 p.m. Adjourn

Guiding Questions for the Workshop

  • Review existing research methods/approaches to promote positive health behaviors via adaptation– defined as the use of dynamic information on the individual to decide if and how to intervene. Discuss when, where and for whom existing methods are suitable. Please note: “individual” refers to different “units” such as a single person, a care-dyad, a group of people, clinic/s, and community/s. Dynamic factors influencing behavior include, but are not limited to emotional, psychological, cognitive, and context.
    • Desired outcomes: Identify opportunities to apply/adapt various methods/frameworks to prevent or manage chronic conditions associated with aging and AD/ADRD.
  • Identify areas of methodological need that are not yet fully developed. This will involve a careful review of standard practice, its suitability (i.e., not default) to a given situation, and the need for more suitable alternative approaches.
    • Desired outcomes: Initial guidelines that help determine suitability of standard/ alternative approaches to prevent and/or manage chronic conditions associated with aging and AD/ADRD.
  • Discuss ways to ground methods/approaches in the priorities, principles, and values of the individual “unit” (i.e., a person, a care-dyad, a group of people, a community) being served.
    • Desired outcomes: Improved understanding on engaging individual “units” in research to develop scalable adaptive interventions.
  • Leverage insights from each session to develop corresponding curricula/guidance and resources that guide the use of “the right method” at “the right time” for the “right goal.”;
    • Desired outcomes:Enable people of interest (i.e., investigators, grant reviewers, funding entities, advisory council/boards) to coalesce around a common/consistent understanding of the considerations and processes involved in research to develop adaptive interventions.

Summary

A workshop summary is forthcoming.

Contact Information

Please contact Dinesh John at dinesh.john@nih.gov or Allie Walker at allie.walker@nih.gov for questions you may have about the workshop.