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

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

This intervention is further described in Brown and Myers [27]. The Sing Fit mobile health app. Due to the need for accessible and culturally relevant lifestyle interventions aimed at reducing AD risk, this protocol seeks to improve the cultural responsiveness of an existing digital music-based intervention, Sing Fit, aimed at improving depressed mood for individuals at risk for or experiencing AD. Much like digital health tools, cultural considerations are important for music-based interventions.

Chelsea S Brown, Luna Dziewietin, Virginia Partridge, Jennifer Rae Myers

JMIR Res Protoc 2025;14:e73711

Effectiveness of an mHealth Program on Reducing Blood Pressure Among Young Adults With Prehypertension: Protocol of a Pragmatic Cluster Randomized Controlled Trial

Effectiveness of an mHealth Program on Reducing Blood Pressure Among Young Adults With Prehypertension: Protocol of a Pragmatic Cluster Randomized Controlled Trial

In the equation, n is the sample size required, Z1-α/2 is the desired confidence level (ie, for a 95% confidence level, Z1-α/2=1.96), Z1-β is the power of the test (ie, for a power of 80%, Z1-β=0.84), σ² is the population variance (ie, square of the SD), m is the number of groups being compared, ρ is the correlation coefficient between groups, and d is the minimum detectable difference. Accounting for a 20% dropout rate, the final sample size was 43 participants per group.

Melita Sheilini, H Manjunatha Hande, Nagaraja Ravishankar, Akshay M J, Jyothi Nayak, Ramesh Chandrababu

JMIR Res Protoc 2025;14:e67216