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: 10597202
• 负责人: ADRIENNE SARAH JUARASCIO
• 金额: $ 57.81万
• 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/10597202
• 关键词: Aftercare; Algorithms; Attention; Behavior; Binge Eating; Binge eating disorder; Bulimia; Clinical; Clinical Trials; Cognitive; Cognitive Therapy; Data; Diagnostic; Disease remission; Eating; Eating Disorders; Emotional; Food; Frequencies; Future; Goals; Health Technology; Impulsivity; Individual; Informal Social Control; Intervention; Learning Skill; Machine Learning; Methods; Modality; Monitor; National Institute of Mental Health; Outcome; Outpatients; Participant; Pathway interactions; Patients; Persons; Randomized; Research; Research Priority; Symptoms; System; Technology; Testing; Therapeutic; Time; Treatment outcome; Work; acceptability and feasibility; adaptive intervention; 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; multiphase optimization strategy; participant interview; 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).

项目摘要 暴饮暴食（即，在离散的时间内吃大量食物 控制饮食）是多种饮食失调的关键症状 饮食失调（床）。虽然认知行为疗法（CBT）可以是一种有效的治疗方法 暴饮暴食，40-50％的床患者和近70％的BN患者无法缓解。一个 越来越多的研究表明，许多患者可能无法从CBT中受益的关键原因是次优的 技能获取率（即成功地执行治疗中学习的技能的能力）和利用（即 患者练习或员工治疗技能的频率）。技能使用和获取不良可能 在某些患者子集中，例如那些体验的患者尤其高。 这项研究表明，可以改善技能使用和获取的治疗增加（部分 对于那些需要额外支持才能成功获得CBT的人可能具有很高的潜力来增强结果。 NIMH最近确定数字健康技术（DHTS）具有“促进 会议之间的技能练习/获取”并将其选择为高优先研究计划（不是 MH-18-031）。 DHT可能能够通过多种途径来改善技能的使用和获取，其中之一是 使用微型干预（即，在日常生活中向人们提供短暂的数字干预措施）。 微型干预的复杂性范围从像自动提醒那样简单的事物到练习 使用机器学习或 其他高级算法，以在需要的特定时刻提供个人量身定制的干预措施。最近的 我们团队的飞行员工作支持Jitais的实用性，以改善技能利用和获取 用作增强治疗，但没有将Jitais与更简单的自动提醒进行比较 干预措施。此外，我们的飞行员工作还发现，经常对技能使用的监测本身就是 令人惊讶的有效方法来鼓励技能练习。这些结果表明 对于所有个人而言，可能并不需要从DHT增强中获得受益。 开发最大有效DHT的能力需要使用更大的临床试验，以帮助识别 哪些数字组件（以及哪些复杂性）最有效地改善技能使用和获取为 临床结果良好。我们建议使用2 x 3的完整阶乘设计，其中有264个BN或床的人 被分配给六个治疗条件之一，即表示每个自我监控的置换 复杂性（有关关闭技能和技能监测的技能监控）和微干预的复杂性（无微小 干预措施与自动提醒消息与Jitais）作为CBT的增强。结果 组件分析设置了未来的工作，以评估仅包含有效组件的优化DHT （可以预期具有卓越的目标参与，效率，效率和传播性）。