Optimizing digital health technologies to improve therapeutic skill use and acquisition

优化数字健康技术以改善治疗技能的使用和获取

• 批准号: 10429134
• 负责人: ADRIENNE SARAH JUARASCIO
• 金额: $ 66.41万
• 依托单位: DREXEL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10429134
• 关键词: Aftercare; Algorithms; Attention; Behavior; Binge Eating; Binge eating disorder; Bulimia; Clinical; Clinical Trials; Cognitive; Cognitive Therapy; Data; Disease remission; Eating; Eating Disorders; Emotional; Food; Frequencies; Future; Goals; Health Technology; Impulsivity; Individual; Informal Social Control; Intervention; Interview; Machine Learning; Methods; Modality; Monitor; National Institute of Mental Health; Outcome; Outpatients; Participant; Pathway interactions; Patients; Persons; Randomized; Research; Research Priority; Symptoms; System; Testing; Therapeutic; Time; Treatment outcome; Work; acceptability and feasibility; adaptive intervention; base; cognitive benefits; comparison intervention; cost; data sharing; design; digital; digital health; digital intervention; discrete time; eating pathology; effective therapy; experience; feasibility testing; follow-up; improved; loss of control over eating; patient subsets; pilot trial; skill acquisition; skills; tool

Project Summary Binge eating (i.e., eating a large amount of food within a discrete time period accompanied by a sense of loss of control over eating) is a key symptom of several eating disorders including bulimia nervosa (BN) and binge eating disorder (BED). While cognitive behavioral therapy (CBT) can be an effective treatment approach for binge eating, 40-50% of patients with BED and nearly 70% of patients with BN fail to achieve remission. A growing body of research suggests that a key reason many patients may fail to benefit from CBT is suboptimal rates of skill acquisition (i.e., the ability to successfully perform a skill learned in treatment) and utilization (i.e., the frequency with which a patient practices or employs therapeutic skills). Poor skill use and acquisition may be particularly high among certain subsets of patients such as those who experience deficits in self-regulation. This research suggests that treatment augmentations that could improve skill use and acquisition (particularly for those who need additional support to succeed in CBT) could have high potential to enhance outcomes. The NIMH has recently identified that digital health technologies (DHTs) have high potential to “promote between-session skill practice/acquisition” and have selected this as a high priority research initiative (NOT- MH-18-031). DHTs may be able to improve skill use and acquisition via several pathways, one of which is the use of micro-interventions (i.e., short digital interventions delivered to people as they go about their daily lives). Micro-interventions can range in complexity from something as simple as an automated reminder to practice a therapeutic skill to advanced just-in-time adaptive intervention (JITAI) systems that use machine learning or other advanced algorithms to deliver personally tailored interventions in specific moments of need. Recent pilot work from our team supports the utility of JITAIs as a way to improve skill utilization and acquisition when used as a treatment augmentation but did not compare JITAIs to more simple automated reminder micro- interventions. Additionally, our pilot work also found that frequent monitoring of skill use was in and of itself a surprisingly effective method for encouraging skill practice. These results suggest that the added complexity of JITAIs may not be necessary for all individuals to experience benefit from a DHT augmentation. The ability to develop maximally effective DHTs requires the use of a larger clinical trial that can help to identify which digital components (and at which complexity) are most effective at improving skill use and acquisition as well clinical outcomes. We propose to use a 2 x 3 full factorial design in which 264 individuals with BN or BED are assigned to one of six treatment conditions, i.e., representing each permutation of self-monitoring complexity (Skills-Monitoring On vs. Skills-Monitoring Off) and micro-intervention complexity (No Micro- Interventions vs. Automated Reminder Messages vs. JITAIs) as an augmentation to CBT. Results of the component analysis set up future work to evaluate an optimized DHT containing only effective components (which can be expected to have superior target engagement, efficacy, efficiency and disseminability).

项目概要 暴饮暴食（即在不连续的时间内吃掉大量食物，并伴有失落感 控制饮食）是多种饮食失调的关键症状，包括神经性贪食症（BN）和暴饮暴食 饮食失调（BED）。虽然认知行为疗法（CBT）可能是一种有效的治疗方法 暴饮暴食导致 40-50% 的 BED 患者和近 70% 的 BN 患者未能获得缓解。一个 越来越多的研究表明，许多患者无法从 CBT 中受益的一个关键原因是效果不佳 技能获取率（即成功执行治疗中学到的技能的能力）和利用率（即， 患者练习或运用治疗技能的频率）。技能使用和获取不当可能会 在某些患者亚群中尤其高，例如那些自我调节缺陷的患者。 这项研究表明，治疗增强可以改善技能的使用和获取（特别是 对于那些需要额外支持才能在 CBT 中取得成功的人来说）可能具有提高结果的巨大潜力。 NIMH 最近发现数字医疗技术 (DHT) 具有“促进 课间技能练习/习得”，并选择将此作为一项高度优先的研究计划（不是 MH-18-031）。 DHT 可能能够通过多种途径改善技能的使用和获取，其中之一是 使用微观干预（即在人们的日常生活中向他们提供简短的数字干预）。 微观干预的复杂程度各不相同，从简单的自动提醒到练习 使用机器学习或先进的即时适应性干预（JITAI）系统的治疗技能 其他先进的算法可在特定的需要时刻提供个性化的干预措施。最近的 我们团队的试点工作支持 JITAI 的实用性，作为提高技能利用率和获取的一种方式 用作治疗增强，但没有将 JITAI 与更简单的自动提醒微机进行比较 干预措施。此外，我们的试点工作还发现，频繁监控技能使用本身就是一个问题 鼓励技能练习的令人惊讶的有效方法。这些结果表明，增加的复杂性 JITAI 可能不是所有个人都需要从 DHT 增强中获益。 开发最有效的 DHT 的能力需要使用更大规模的临床试验来帮助确定 哪些数字组件（以及复杂程度）对于改善技能的使用和获取最有效 临床效果良好。我们建议使用 2 x 3 全因子设计，其中 264 名 BN 或 BED 个体 被分配到六种治疗条件之一，即代表自我监测的每种排列 复杂性（技能监控开启与技能监控关闭）和微干预复杂性（无微干预） 干预、自动提醒消息、JITAI）作为 CBT 的增强。结果 成分分析为未来评估仅包含有效成分的优化 DHT 奠定了基础 （预计将具有卓越的目标参与度、功效、效率和传播性）。