最大化参与,信任和临床益处的人工智能生成的恢复支持消息的酒精使用障碍:优化研究的协议
Kendra Wyant1, Sarah J Sant'Ana1, Claire E Punturieri1
1Department of Psychology, University of Wisconsin-Madison, Madison, WI, United States.
JMIR research protocols
|November 7, 2025
概括
这项研究优化了自动化酒精使用障碍恢复支持系统的日常信息. 结果将为未来的数字治疗提供信息,用于持续的自我监测和复发预防.
科学领域:
- 数字治疗学数字治疗学
- 机器学习在医疗保健中的应用.
- 行为科学和成研究研究.
背景情况:
- 长期监测失效风险对于酒精使用障碍 (AUD) 恢复至关重要.
- 由于复杂,动态的风险因素,AUD恢复的自我监测具有挑战性.
- 具有机器学习的自动支持系统可以增强持续的,个性化的自我监控.
研究的目的:
- 优化日常支持消息组件,以增加用户参与度.
- 提高自动化恢复监控支持系统的有效性.
- 通过个性化的数字干预来改善从酒精使用障碍中持续恢复.
主要方法:
- 美国304名中度至重度AUD的成年人将在17周内参加.
- 将使用每日调查和地理位置数据收集.
- 参与者将每天收到信息,其中包含个性化的失误预测信息,各种组件,如概率,模型特征,建议和语气.
主要成果:
- 该项目由国家酒精滥用和酒研究所 (R01AA031762) 资助.
- 机构审查委员会获得了威斯康星大学 - 麦迪逊健康科学大学 (IRB #2024-0869) 的批准.
- 参与者招生计划于2025年12月开始.
结论:
- 确定能提高参与度或临床结果的信息组件是关键.
- 建议将指导未来恢复监测系统的开发.
- 这项研究旨在改善AUD恢复的数字治疗方法.
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