College of Humanities and Sciences, Prince Sultan University, Riyadh, Saudi Arabia.
Media Psychology Department, Fielding Graduate University, Santa Barbara, CA, United States.
JMIR Hum Factors. 2024 May 10;11:e58311. doi: 10.2196/58311.
The emergence of smartphones has sparked a transformation across multiple fields, with health care being one of the most notable due to the advent of mobile health (mHealth) apps. As mHealth apps have gained popularity, there is a need to understand their energy consumption patterns as an integral part of the evolving landscape of health care technologies.
This study aims to identify the key contributors to elevated energy consumption in mHealth apps and suggest methods for their optimization, addressing a significant void in our comprehension of the energy dynamics at play within mHealth apps.
Through quantitative comparative analysis of 10 prominent mHealth apps available on Android platforms within the United States, this study examined factors contributing to high energy consumption. The analysis included descriptive statistics, comparative analysis using ANOVA, and regression analysis to examine how certain factors impact energy use and consumption.
Observed energy use variances in mHealth apps stemmed from user interactions, features, and underlying technology. Descriptive analysis revealed variability in app energy consumption (150-310 milliwatt-hours), highlighting the influence of user interaction and app complexity. ANOVA verified these findings, indicating the critical role of engagement and functionality. Regression modeling (energy consumption = β₀ + β₁ × notification frequency + β₂ × GPS use + β₃ × app complexity + ε), with statistically significant P values (notification frequency with a P value of .01, GPS use with a P value of .05, and app complexity with a P value of .03), further quantified these bases' effects on energy use.
The observed differences in the energy consumption of dietary apps reaffirm the need for a multidisciplinary approach to bring together app developers, end users, and health care 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 sustainable development goal 12 on responsible consumption and production.
智能手机的出现引发了多个领域的变革，移动医疗（mHealth）应用的出现使医疗保健领域成为变革最为显著的领域之一。随着 mHealth 应用的普及，了解其能源消耗模式成为医疗保健技术不断发展的重要组成部分。
本研究旨在确定导致 mHealth 应用能源消耗过高的关键因素，并提出优化方法，旨在填补我们对 mHealth 应用中能源动态理解的空白。
通过对美国 Android 平台上 10 个流行的 mHealth 应用进行定量比较分析，研究考察了导致高能耗的因素。分析包括描述性统计、使用 ANOVA 的比较分析以及回归分析，以考察某些因素如何影响能源使用和消耗。
mHealth 应用中观察到的能源使用差异源于用户交互、功能和底层技术。描述性分析揭示了应用能源消耗的可变性（150-310 毫瓦时），突出了用户交互和应用复杂性的影响。ANOVA 验证了这些发现，表明参与度和功能的重要性。回归建模（能源消耗=β₀+β₁×通知频率+β₂×GPS 使用+β₃×应用复杂性+ε），具有统计学意义的 P 值（通知频率的 P 值为.01，GPS 使用的 P 值为.05，应用复杂性的 P 值为.03），进一步量化了这些因素对能源使用的影响。
观察到的膳食应用能源消耗差异证实了需要采取多学科方法，将应用开发者、最终用户和医疗保健专家聚集在一起，在实现可持续实践和用户体验之间的平衡的同时，促进节能实践。需要进一步研究，以更好地了解如何扩大消费者参与度，以实现可持续发展目标 12 关于负责任的消费和生产。
