Accepted for/Published in: JMIR Human Factors
Date Submitted: Mar 12, 2024
Date Accepted: Apr 7, 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Warning: This is an author submission that is not peer-reviewed or edited.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.
Evaluating the Energy Efficiency of Popular US Smartphone Healthcare Applications: A Comparative Analysis Study Towards Sustainable health and nutrition applications Practices
ABSTRACT
Background:
The emergence of smartphones has sparked a transformation across multiple fields, healthcare being one of the most notable, with the advent of mobile health applications. Among these, mobile health applications have gained popularity, highlighting the need to understand their energy consumption patterns as an integral part of the evolving landscape of healthcare technologies.
Objective:
To identify the key contributors to elevated energy consumption in mobile health applications and suggest methods for their optimization, addressing a significant void in our comprehension of the energy dynamics at play within mobile health applications.
Methods:
Through quantitative comparative analysis of ten prominent mobile health applications available on Android platforms within the United States, this study examines factors contributing to high energy consumption. The analysis included descriptive statistics, comparative analysis using analysis of variance, and regression analysis to examine how certain factors impact energy use and consumption.
Results:
Observed energy use variances in mobile health applications stem from user interactions, features, and underlying technology. Descriptive analysis revealed variability in app energy consumption (150-310 mWh), highlighting the influence of user interaction and application complexity. analysis of variance verified these findings, indicating the critical role of engagement and functionality. Regression modeling (Energy Consumption = β₀ + β₁Notification Frequency + β₂GPS Usage + β₃*Application Complexity + ε), with statistically significant P-values (Notification Frequency P=.01, GPS Usage P=.05, Application Complexity P=.03), further quantified these bases' effects on energy usage.
Conclusions:
The observed differences in energy consumption of dietary applications reaffirm the need for a multidisciplinary approach to bring together application developers, end-users, and healthcare experts to foster improved energy conservation practice while achieving a balance between sustainable practice and user-experience. More research is needed to better understand how to scale up consumer engagement to achieve the Sustainable Development Goal 12 on responsible consumption and production.
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