Shrestha Sloke, Stapp Joshua, Taylor Melissa, Leach Rebecca, Carreiro Stephanie, Indic Premananda
University of Texas at Tyler.
RAE Health.
Proc Annu Hawaii Int Conf Syst Sci. 2023;2023:3156-3163. Epub 2023 Jan 3.
Novel technologies have great potential to improve the treatment of individuals with substance use disorder (SUD) and to reduce the current high rate of relapse (i.e. return to drug use). Wearable sensor-based systems that continuously measure physiology can provide information about behavior and opportunities for real-time interventions. We have previously developed an mHealth system which includes a wearable sensor, a mobile phone app, and a cloud-based server with embedded machine learning algorithms which detect stress and craving. The system functions as a just-in-time intervention tool to help patients de-escalate and as a tool for clinicians to tailor treatment based on stress and craving patterns observed. However, in our pilot work we found that to deploy the system to diverse socioeconomic populations and to increase usability, the system must be able to work efficiently with cost-effective and popular commercial wearable devices. To make the system device agnostic, methods to transform the data from a commercially available wearable for use in algorithms developed from research grade wearable sensor are proposed. The accuracy of these transformations in detecting stress and craving in individuals with SUD is further explored.
新技术在改善物质使用障碍（SUD）患者的治疗以及降低当前较高的复发率（即重新开始吸毒）方面具有巨大潜力。基于可穿戴传感器的系统能够持续测量生理状况，可为行为提供信息并创造实时干预的机会。我们之前开发了一个移动健康系统，它包括一个可穿戴传感器、一个手机应用程序以及一个基于云的服务器，服务器中嵌入了用于检测压力和渴望的机器学习算法。该系统作为一种即时干预工具，可帮助患者缓解紧张情绪，同时也作为临床医生根据观察到的压力和渴望模式来定制治疗方案的工具。然而，在我们的试点工作中发现，为了将该系统部署到不同社会经济背景的人群中并提高其可用性，该系统必须能够与经济高效且广受欢迎的商用可穿戴设备高效协作。为了使系统与设备无关，我们提出了一些方法，用于将来自商用可穿戴设备的数据转换为可用于基于研究级可穿戴传感器开发的算法中。我们进一步探索了这些转换在检测SUD患者的压力和渴望方面的准确性。
