Developing an Equitable Machine Learning-Based Music Intervention for Older Adults At Risk for Alzheimer Disease: Protocol for Algorithm Development and Validation.

Journal: JMIR research protocols
Published Date: Aug 7, 2025

Abstract

BACKGROUND: Given the high prevalence and cost of Alzheimer disease (AD), it is crucial to develop equitable interventions to address lifestyle factors associated with AD incidence (eg, depression). While lifestyle interventions show promise for reducing cognitive decline, culturally sensitive interventions are needed to ensure acceptability and engagement. Given the increased risk for AD and health care barriers among rural-residing older adults, tailoring interventions to align with rural culture and distinct needs is important to improve accessibility and adherence.

Authors

  • Chelsea S Brown
    Health Sciences Integrated Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.
  • Luna Dziewietin
    Center for Data Science and Artificial Intelligence, University of Massachusetts Amherst, Amherst, MA, United States.
  • Virginia Partridge
    Center for Data Science and Artificial Intelligence, University of Massachusetts Amherst, Amherst, MA, United States.
  • Jennifer Rae Myers
    Musical Health Technologies, Los Angeles, CA, United States.

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