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Developing an Equitable Machine Learning-Based Music Intervention for Older Adults At Risk for Alzheimer Disease:

Chelsea S Brown1,2, Luna Dziewietin3, Virginia Partridge3

  • 1Health Sciences Integrated Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.

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Summary

This study develops an intelligent music recommendation system to help older adults in rural areas manage depression and reduce Alzheimer's disease (AD) risk. The system uses machine learning to personalize music, aiming for greater engagement and accessibility in digital health interventions.

Keywords:
Alzheimer diseasedigital healthlifestylemachine learningmusic

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Area of Science:

  • Gerontology
  • Digital Health
  • Artificial Intelligence

Background:

  • Alzheimer's disease (AD) poses significant public health challenges, necessitating equitable interventions targeting lifestyle factors like depression.
  • Culturally sensitive digital health interventions are crucial for engaging rural older adults, who face unique barriers and increased AD risk.
  • Tailoring interventions to rural culture and needs is essential for improving accessibility and adherence in Alzheimer's disease prevention.

Purpose of the Study:

  • To develop an intelligent recommendation system for personalized therapeutic music to engage rural older adults at risk for Alzheimer's disease (AD).
  • To create culturally inclusive user personas for rural older adults to understand their needs for music-based digital health interventions.
  • To build machine learning (ML) models identifying optimal music components for engagement and emotional resonance in this demographic, targeting depression.

Main Methods:

  • Recruited 1200 participants aged 55+ in the US for a two-phase study.
  • Phase 1 (n=1000): Randomized songs, Likert surveys on sentiment, cultural relevance, and perceived benefit.
  • Phase 2 (n=200): Developed ML algorithms using Phase 1 data, integrated them into a digital intervention, and assessed recommendation accuracy and user acceptability (target 85%).

Main Results:

  • Participant recruitment for both phases is complete as of June 2025.
  • Data analysis for all aims is currently underway.
  • Results are anticipated for publication in Fall 2025.

Conclusions:

  • The protocol utilizes machine learning (ML) to enhance the equitability and accessibility of digital lifestyle interventions for Alzheimer's disease (AD).
  • This approach aims to provide personalized music therapy, improving engagement and adherence among rural older adults at risk for AD.