Dynamic, real-time prediction of alcohol use lapse using mHealth technologies

 描述（由申请人提供）：现有的酒精使用障碍的心理社会和药物治疗在建立戒酒方面是有效的。然而，绝大多数患者在一年内复发，通常在治疗后的头几个月内复发。患者往往无法发现其复发风险的动态变化。此外，大多数患者未能充分持续使用在治疗期间和/或通过持续护理培养的技能。成熟的理论模型表明，酒精和其他药物使用失效风险是一个动态的，非线性函数的远端，相对静态的，病人的特点和经常时刻的动态变化，近端，沉淀的风险因素。然而，直到最近才能对动态风险指标进行全面、精确的实时评估。此外，预测分析的创新方法尚未应用于失效风险预测问题。该项目的主要目标是开发，验证，初步优化和提供一个动态的，实时的风险预测模型，用于预测戒酒者的酒精使用。为了实现这些目标，我们建议在酒精使用障碍标准治疗期间或完成后对200名患者进行为期三个月的随访。我们将在研究摄入时测量完善的远端静态复发风险指标。更重要的是，我们将使用创新的mHealth技术，使用智能手机和可穿戴生物识别传感器，每天对动态风险指标进行密集采样，包括患者生理、主观体验和行为，为期三个月。这些静态和动态风险指标所获得的数据将为实现以下具体目标提供基础：1.评估负担（可行性，成本和患者可接受性），通过智能手机和可穿戴传感器收集创新的，密集采样的风险指标。2.使用机器学习方法开发、训练和验证基于静态和动态风险指标的实时定量失效风险预测信号。3.使用创新的马尔可夫决策过程模型来优化是否、何时以及如何提供额外治疗或支持的决策。4.在成瘾综合健康增强支持系统（A-CHESS）计划中集成和提供风险预测和决策模型。这些项目旨在定位A-CHESS，使复发预防和恢复支持，信息和风险监测持续提供给患者。与传统的持续护理相比，A-CHESS将提供个性化的护理，并在最需要的时候提供和实施。综合的实时风险预测具有很大的希望，通过适应性地使用这些持续护理服务来鼓励持续康复。

 DESCRIPTION (provided by applicant): Available psychosocial and pharmacological treatments for alcohol use disorder are effective at establishing abstinence. However, the vast majority of patients relapse within a year and often within the first few months following treatment. Patients often fail to detect dynamic changes in their relapse risk. Furthermore, the majority of patients fail to adequately sustain use of skills developed during treatment and/or through continuing care. Well-established theoretical models indicate that alcohol and other drug use lapse risk is a dynamic, non-linear function of both distal, relatively static, patient characteristics and often moment-by- moment dynamic changes in proximal, precipitating risk factors. However, comprehensive, precise assessment of dynamic risk indicators in real-time has not been possible until very recently. Furthermore, innovative methods from predictive analytics have not been applied to the lapse risk prediction problem. The broad goals of this project are to develop, validate, preliminarily optimize, and deliver a dynamic, real-time lapse risk prediction model for forecasting alcohol use among abstinent alcoholics. To pursue these goals, we propose to follow 200 patients for three months during or following completion of standard of care treatment for alcohol use disorder. We will measure well-established distal, static relapse risk indicators on study intake. More importantly, we will use innovative mHealth technology to densely sample dynamic risk indicators including patient physiology, subjective experience, and behavior daily for three months using smartphones and wearable biometric sensors. Data obtained for these static and dynamic risk indicators will provide the foundation to accomplish the following Specific Aims: 1. Assess burden (feasibility, cost, and patient acceptability) to collect innovative, densely sampled risk indicators via smartphone and wearable sensors. 2. Use machine learning methods to develop, train, and validate a real-time quantitative lapse risk prediction signal based on static and dynamic risk indicators. 3. Use innovative Markov decision process models to optimize decisions about if, when, and how to provide additional treatment or support. 4. Integrate and deliver risk prediction and decision model within the Comprehensive Health Enhancement Support System for Addiction (A-CHESS) program. These project aims position A-CHESS to make relapse prevention and recovery support, information, and risk monitoring available to patients continuously. Compared to conventional continuing care, A-CHESS will provide personalized care and be available and implemented during moments of greatest need. Integrated real-time risk prediction holds substantial promise to encourage sustained recovery through adaptive use of these continuing care services.