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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

基本信息

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
项目类别：
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

项目摘要

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).

项目总结