Brown Chelsea S, Dziewietin Luna, Partridge Virginia, Myers Jennifer Rae
Health Sciences Integrated Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.
Musical Health Technologies, Los Angeles, CA, United States.
JMIR Res Protoc. 2025 Aug 7;14:e73711. doi: 10.2196/73711.
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.
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-residing older adults to understand their goals and challenges for music-based digital health intervention. Aim 2 is to develop knowledge embedding-based machine learning (ML) models that use music metadata and survey response 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.
Participants (N=1200) will be aged 55 years or older and residing in the United States. In phase 1, participants (n=1000) will receive 5 randomized songs and complete a survey to understand the sentiment, cultural relevance, and perceived benefit for each song. Brief, researcher-created Likert surveys will be used. In phase 2, survey data will be used to develop ML algorithms in collaboration with the University of Massachusetts Amherst Center for Data Science and Artificial Intelligence. These ML models will be integrated into the digital music intervention and tested with a separate sample of 200 participants. Similar to phase 1, participants will be provided with sets of songs generated by the recommendation system based on the target goal (ie, to reduce depression). The recommendation accuracy of the ML algorithm will be assessed using multiple performance metrics, including root-mean-square error and normalized discounted cumulative gain as well as the mean acceptability score with a goal of 85% user acceptability.
Participant recruitment is complete for phases 1 and 2 as of June 2025. Data analysis for the results of aims 1, 2, and 3 are underway and results are expected to be published in the fall of 2025.
This protocol seeks to use ML to improve the equitability and accessibility of a digital lifestyle intervention for AD.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/73711.
鉴于阿尔茨海默病（AD）的高患病率和高昂成本，开发公平的干预措施以应对与AD发病率相关的生活方式因素（如抑郁症）至关重要。虽然生活方式干预有望减少认知衰退，但需要具有文化敏感性的干预措施来确保可接受性和参与度。鉴于农村老年成年人患AD的风险增加以及医疗保健障碍，使干预措施符合农村文化和独特需求对于提高可及性和依从性很重要。
本方案旨在开发一种智能推荐系统，能够识别最佳治疗音乐成分，以吸引不同的有AD风险的农村老年成年人并引起他们的共鸣。目标1是为有AD风险的农村老年成年人开发具有文化包容性的用户角色，以了解他们基于音乐的数字健康干预的目标和挑战。目标2是开发基于知识嵌入的机器学习（ML）模型，该模型使用音乐元数据和调查响应数据来识别最佳治疗音乐成分，以增强有AD风险的农村老年成年人对抑郁症的参与度和情感共鸣。目标3是用一个单独的样本评估个性化治疗音乐疗程和基于ML的音乐推荐的可接受性。
参与者（N = 一千二百）年龄在55岁及以上，居住在美国。在第1阶段，参与者（n = 一千）将收到5首随机歌曲并完成一项调查，以了解每首歌曲的情感、文化相关性和感知益处。将使用研究人员创建的简短李克特量表调查。在第2阶段，调查数据将与马萨诸塞大学阿默斯特分校数据科学与人工智能中心合作用于开发ML算法。这些ML模型将集成到数字音乐干预中，并在200名参与者的单独样本中进行测试。与第1阶段类似，将根据目标（即减轻抑郁症）向参与者提供由推荐系统生成的歌曲集。将使用多个性能指标评估ML算法的推荐准确性，包括均方根误差和归一化折损累计增益以及平均可接受性得分，目标是实现85%的用户可接受性。
截至2025年6月，第1阶段和第2阶段的参与者招募工作已完成。目标1、2和3结果的数据分析正在进行中，预计结果将于2025年秋季发表。
本方案旨在利用ML来提高针对AD的数字生活方式干预的公平性和可及性。
国际注册报告识别号（IRRID）：DERR1 - 10.2196/73711。
