Accepted for/Published in: JMIR Research Protocols
Date Submitted: Mar 11, 2025
Date Accepted: Jul 17, 2025
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Developing an Equitable Machine Learning-based Music Intervention for Older Adults At-risk for Alzheimer’s: Research Protocol
ABSTRACT
Background:
Given the high prevalence and cost of Alzheimer’s Disease (AD), it is crucial to develop equitable interventions to address lifestyle factors associated with AD incidence (i.e., 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 healthcare barriers among rural older adults, tailoring interventions to align with rural culture and distinct needs is important to improve accessibility and adherence.
Objective:
This protocol aims to develop an intelligent recommendation system capable of identifying the optimal therapeutic music components to elicit engagement and resonate with diverse rural-residing older adults at-risk for AD. Aim 1 is to develop culturally inclusive user personas for rural older adults to understand their goals and challenges for music-based digital health intervention. Aim 2 is to develop Knowledge Embedding-based (KE) machine learning (ML) models that utilize music metadata and survey responses data to identify optimal therapeutic music components for enhancing engagement and emotional resonance for depression among rural-residing older adults at-risk for AD. Aim 3 is to assess acceptability for personalized therapeutic music sessions and ML-based music recommendations with a separate sample.
Methods:
Participants (N=1,200) will be 55 years or older, residing in the United States. In phase I, participants (n= 1000) will receive 5 randomized songs and complete a survey to understand sentiment, cultural relevance, and perceived benefit for each song. Brief researcher-created Likert surveys are used. In Phase II, survey data will be used to develop ML algorithms in collaboration with University of Massachusetts Amherst AI and Technology Center. These ML models will be integrated into the digital music intervention and tested with a separate sample of 200 participants. Similar to Phase I, participants will be provided with sets of songs/guided prompts generated by the recommendation system based on the target goal (i.e., Reduce Depression). The recommendation accuracy of the ML algorithm will be assessed using multiple performance metrics, including root-mean-square error (RMSE) and normalized discounted cumulative gain (NDCG), as well as, mean acceptability score with a goal of 85% user acceptability.
Results:
Phase I surveys are underway with 498 participants recruited thus far.
Conclusions:
This protocol seeks to use ML to improve the equitability and accessibility of a digital lifestyle intervention for AD. Clinical Trial: N/A
Citation
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Copyright
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