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

使用移动医疗技术动态、实时预测酒精滥用情况

• 批准号: 9275293
• 负责人: John J. Curtin
• 金额: $ 38.18万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-10 至 2020-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9275293
• 关键词: Abstinence; Address; Affect; Alcohol consumption; Behavior; Biometry; Caring; Cellular Phone; Characteristics; Communities; Comorbidity; Competence; Data; Decision Modeling; Dependence; Distal; Drug Use Disorder; Drug usage; Foundations; Frequencies; Galvanic Skin Response; Goals; Health; Health Services; Health Services Accessibility; Heart Rate; Intake; Intervention; Location; Machine Learning; Measures; Medical center; Methods; Modeling; Monitor; Motivation; Participant; Patient Self-Report; Patients; Pattern; Pharmacological Treatment; Physiology; Positioning Attribute; Predictive Analytics; Probability; Process; Provider; Psychopathology; Recovery; Relapse; Reporting; Research; Risk; Risk Factors; Sampling; Services; Severities; Signal Transduction; Sleep; Support System; Technology; Text; Theoretical model; Time; Training; Treatment Efficacy; United States Department of Veterans Affairs; Validation; Voice; Withdrawal; addiction; alcohol abuse therapy; alcohol and other drug; alcohol involvement; alcohol services; alcohol use disorder; base; clinical application; coping; cost; craving; disorder later incidence prevention; experience; innovation; learning strategy; mHealth; model development; personalized care; predictive modeling; prevent; problem drinker; programs; psychosocial; public health relevance; real time model; relapse patients; relapse risk; relative cost; response; sensor; skills; standard of care; stressor

 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.

 描述（由申请人提供）：针对酒精使用障碍的现有心理社会和药物治疗对于建立戒酒是有效的。然而，绝大多数患者在一年内复发，并且通常在治疗后的最初几个月内复发。患者常常无法检测到复发风险的动态变化。此外，大多数患者无法充分维持在治疗和/或持续护理期间发展的技能的使用。完善的理论模型表明，酒精和其他药物滥用风险是远端、相对静态的患者特征和近端、诱发风险因素的时时刻刻动态变化的动态、非线性函数。然而，直到最近才能够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误风险预测问题。该项目的总体目标是开发、验证、初步优化并提供动态、实时的失误风险预测模型，用于预测戒酒者的饮酒情况。为了实现这些目标，我们建议在酒精使用障碍标准护理治疗期间或完成后对 200 名患者进行为期三个月的跟踪。我们将测量研究摄入量方面已建立的远端、静态复发风险指标。更重要的是，我们将利用创新的移动医疗技术，使用智能手机和可穿戴生物识别传感器，在三个月内每天对动态风险指标（包括患者生理、主观体验和行为）进行密集采样。这些静态和动态风险指标获得的数据将为实现以下具体目标奠定基础： 1. 评估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的负担（可行性、成本和患者可接受性）。 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误风险预测信号。 3. 使用创新的马尔可夫决策过程模型来优化有关是否、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) 计划中集成并提供风险预测和决策模型。这些项目的目标是让 A-CHESS 为患者持续提供复发预防和康复支持、信息和风险监控。与传统的持续护理相比，A-CHESS 将提供个性化护理，并在最需要的时刻提供和实施。综合实时风险预测有望通过适应性使用这些持续护理服务来鼓励持续康复。