Department of Human Centered Computing, Faculty of IT, Monash University, Clayton, VIC 3168, Australia.
Sensors (Basel). 2022 May 20;22(10):3893. doi: 10.3390/s22103893.
Recent years have seen significant advances in the sensing capabilities of smartphones, enabling them to collect rich contextual information such as location, device usage, and human activity at a given point in time. Combined with widespread user adoption and the ability to gather user data remotely, smartphone-based sensing has become an appealing choice for health research. Numerous studies over the years have demonstrated the promise of using smartphone-based sensing to monitor a range of health conditions, particularly mental health conditions. However, as research is progressing to develop the predictive capabilities of smartphones, it becomes even more crucial to fully understand the capabilities and limitations of using this technology, given its potential impact on human health. To this end, this paper presents a narrative review of smartphone-sensing literature from the past 5 years, to highlight the opportunities and challenges of this approach in healthcare. It provides an overview of the type of health conditions studied, the types of data collected, tools used, and the challenges encountered in using smartphones for healthcare studies, which aims to serve as a guide for researchers wishing to embark on similar research in the future. Our findings highlight the predominance of mental health studies, discuss the opportunities of using standardized sensing approaches and machine-learning advancements, and present the trends of smartphone sensing in healthcare over the years.
近年来,智能手机的传感功能有了显著的进步,使其能够在特定时间收集丰富的上下文信息,如位置、设备使用情况和人类活动。结合广泛的用户采用和远程收集用户数据的能力,基于智能手机的传感已成为健康研究的一个有吸引力的选择。多年来的众多研究表明,使用基于智能手机的传感来监测各种健康状况,特别是心理健康状况,具有很大的潜力。然而,随着研究的进展,智能手机的预测能力也在不断提高,因此更需要充分了解这项技术的能力和局限性,因为它可能会对人类健康产生影响。为此,本文对过去 5 年的智能手机传感文献进行了叙述性综述,以突出这种方法在医疗保健中的机遇和挑战。它概述了所研究的健康状况的类型、收集的数据类型、使用的工具以及在医疗保健研究中使用智能手机所面临的挑战,旨在为未来希望开展类似研究的研究人员提供指导。我们的研究结果突出了心理健康研究的主导地位,讨论了使用标准化传感方法和机器学习进展的机会,并介绍了近年来智能手机传感在医疗保健中的趋势。