PDF(Pubmed)
Abstract：
BACKGROUND: 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.
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
背景：智能手机的出现引发了跨多个领域的转型，由于移动健康（mHealth）应用程序的出现，医疗保健是最值得注意的之一。随着mHealth应用程序的普及，有必要了解他们的能源消耗模式，将其作为不断发展的医疗保健技术格局的一个组成部分。
目的：本研究旨在确定导致mHealth应用程序能耗升高的关键因素，并提出优化方法。解决我们对mHealth应用程序中发挥的能量动态的理解中的一个重大空白。
方法：通过对美国Android平台上提供的10个突出的mHealth应用程序进行定量比较分析，这项研究调查了导致高能耗的因素。分析包括描述性统计，使用方差分析进行比较分析，和回归分析，以检查某些因素如何影响能源使用和消费。
结果：观察到的mHealth应用程序中的能源使用差异源于用户交互，特点,和底层技术。描述性分析揭示了应用程序能耗的可变性（150-310毫瓦小时），突出用户交互和应用程序复杂性的影响。方差分析验证了这些发现，表明参与和功能的关键作用。回归建模(能耗=β0+β1×通知频率+β2×GPS使用+β3×应用复杂度+ε),具有统计意义的P值（P值为.01的通知频率，P值为.05的GPS使用以及P值为.03的应用程序复杂性），进一步量化这些基础对能源使用的影响。
结论：观察到的饮食应用程序能量消耗的差异再次证明了需要采用多学科方法将应用程序开发人员聚集在一起。最终用户，和卫生保健专家，以促进改善节能实践，同时实现可持续实践和用户体验之间的平衡。需要进行更多的研究，以更好地了解如何扩大消费者的参与，以实现关于负责任的消费和生产的可持续发展目标12。
目的：本研究旨在确定导致mHealth应用程序能耗升高的关键因素，并提出优化方法。解决我们对mHealth应用程序中发挥的能量动态的理解中的一个重大空白。
方法：通过对美国Android平台上提供的10个突出的mHealth应用程序进行定量比较分析，这项研究调查了导致高能耗的因素。分析包括描述性统计，使用方差分析进行比较分析，和回归分析，以检查某些因素如何影响能源使用和消费。
结果：观察到的mHealth应用程序中的能源使用差异源于用户交互，特点,和底层技术。描述性分析揭示了应用程序能耗的可变性（150-310毫瓦小时），突出用户交互和应用程序复杂性的影响。方差分析验证了这些发现，表明参与和功能的关键作用。回归建模(能耗=β0+β1×通知频率+β2×GPS使用+β3×应用复杂度+ε),具有统计意义的P值（P值为.01的通知频率，P值为.05的GPS使用以及P值为.03的应用程序复杂性），进一步量化这些基础对能源使用的影响。
结论：观察到的饮食应用程序能量消耗的差异再次证明了需要采用多学科方法将应用程序开发人员聚集在一起。最终用户，和卫生保健专家，以促进改善节能实践，同时实现可持续实践和用户体验之间的平衡。需要进行更多的研究，以更好地了解如何扩大消费者的参与，以实现关于负责任的消费和生产的可持续发展目标12。
