Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Jan 30, 2025)
Date Submitted: Nov 2, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
AI-Driven Energy-Efficient mHealth Applications for Chronic Disease Management: A Review of Optimization Techniques
ABSTRACT
Background:
Mobile health (mHealth) applications offer real-time monitoring and feedback, which can help in chronic disease management. However, their continuous operation drains device battery excessively, making long-term use challenging. To mitigate this, AI-driven techniques like adaptive sampling, task scheduling, and federated learning offer promising solutions for optimizing energy consumption while maintaining functionality.
Objective:
This study provides a systematic review focusing on AI-powered approaches for energy-efficient mHealth applications. It investigates methods such as adaptive sampling, task scheduling, and predictive behavior modeling for improved energy efficiency, enabling long-term battery-powered services without affecting monitoring.
Methods:
A systematic literature review of AI-based optimization techniques in mHealth was conducted, focusing on energy-saving characteristics of adaptive sampling and task scheduling. The analysis included 30 studies from 2016 to 2024, quantified by percentage improvements in energy savings and battery life.
Results:
Task scheduling achieved up to 40% energy savings, extending battery life by several hours. Adaptive sampling reduced energy consumption by 25-30%. Federated learning showed energy savings of up to 25% by minimizing data transmission. Predictive behavior modeling further optimized energy by adjusting resource use based on user interactions.
Conclusions:
AI-driven techniques significantly reduce energy consumption in mHealth applications, particularly in chronic disease management. Task scheduling and adaptive sampling show the most promise for long-term monitoring without frequent recharging. Future research should explore advanced machine learning models and energy-harvesting technologies for more sustainable applications. Clinical Trial: None
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.