Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lakeshore Drive, Suite 1400, Chicago, IL 60611, United States of America.
Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 N. Lakeshore Drive, Suite 1400, Chicago, IL 60611, United States of America; Department of Kinesiology, Penn State University, 266 Recreation Building, University Park, PA 16802, United States of America.
Contemp Clin Trials. 2021 Oct;109:106534. doi: 10.1016/j.cct.2021.106534. Epub 2021 Aug 8.
Relapse to smoking is commonly triggered by stress, but behavioral interventions have shown only modest efficacy in preventing stress-related relapse. Continuous digital sensing to detect states of smoking risk and intervention receptivity may make it feasible to increase treatment efficacy by adapting intervention timing.
Aims are to investigate whether the delivery of a prompt to perform stress management behavior, as compared to no prompt, reduces the likelihood of (a) being stressed and (b) smoking in the subsequent two hours, and (c) whether current stress moderates these effects.
A micro-randomized trial will be implemented with 75 adult smokers who wear Autosense chest and wrist sensors and use the mCerebrum suite of smartphone apps to report and respond to ecological momentary assessment (EMA) questions about smoking and mood for 4 days before and 10 days after a quit attempt and to access a set of stress-management apps. Sensor data will be processed on the smartphone in real time using the cStress algorithm to classify minutes as probably stressed or probably not stressed. Stressed and non-stressed minutes will be micro-randomized to deliver either a prompt to perform a stress management exercise via one of the apps or no prompt (2.5-3 stress management prompts will be delivered daily). Sensor and self-report assessments of stress and smoking will be analyzed to optimize decision rules for a just-in-time adaptive intervention (JITAI) to prevent smoking relapse.
Sense2Stop will be the first digital trial using wearable sensors and micro-randomization to optimize a just-in-time adaptive stress management intervention for smoking relapse prevention.
吸烟复吸通常是由压力引发的，但行为干预在预防与压力相关的复吸方面仅显示出适度的效果。持续的数字感应可以检测吸烟风险状态和干预接受度，这可能使通过调整干预时机来提高治疗效果成为可能。
旨在探讨与无提示相比，提供进行压力管理行为的提示是否会降低以下情况的可能性：(a)感到压力，(b)在随后的两个小时内吸烟，以及 (c)当前压力是否会调节这些效果。
将采用微型随机试验，招募 75 名成年吸烟者，他们佩戴 Autosense 胸部和手腕传感器，并使用 mCerebrum 智能手机应用套件报告和回应关于吸烟和情绪的生态瞬间评估 (EMA) 问题，为期 4 天前和戒烟尝试后的 10 天，并访问一套压力管理应用程序。使用 cStress 算法实时处理传感器数据，将分钟分类为可能感到压力或可能没有压力。将有压力和无压力的分钟进行微型随机化，以通过其中一个应用程序提供进行压力管理练习的提示或不提供提示（每天将提供 2.5-3 个压力管理提示）。将分析传感器和自我报告的压力和吸烟评估结果，以优化即时适应干预 (JITAI) 的决策规则，以预防吸烟复发。
Sense2Stop 将是第一个使用可穿戴传感器和微型随机化来优化即时适应压力管理干预措施以预防吸烟复发的数字试验。
