通过密集的纵向数据了解声症状动态和临床改善
Milena Engelke1, Jorge Piano Simões2, Laura Basso3
1Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany. milena.engelke@ukr.de.
NPJ digital medicine
|January 14, 2025
概括
耳患者的每日应用程序数据准确地跟踪治疗进展. 该方法捕捉实时症状变化,改善临床评估和了解患者康复动态.
科学领域:
- 听力学 听力学是指听力学.
- 数字健康数字健康
- 临床研究 临床研究
背景情况:
- 密集的纵向采样在自然环境中捕获实时数据.
- 传统的主观数据收集方法存在局限性.
- 数字健康工具为增强患者监测提供了潜力.
研究的目的:
- 评估使用每日自我报告的应用数据来评估 tinnitus 患者临床改善的可行性.
- 为了确定基于应用程序的数据是否可以预测治疗反应.
- 为了确定与临床改善相关的特定症状变化.
主要方法:
- 来自多中心随机临床试验的应用数据的分析.
- 时间序列的特点是提取和顺序回归与弹性网规范化.
- 使用临床全球印象改善量表 (CGI-I) 预测临床改善.
主要成果:
- 一个具有50%应用程序合规性 (N=129,8480条目) 的模型显示出很好的合适性 (麦克法登R2=0.82),表明可概括性.
- 临床改善与降低耳的想法,张力和噪音相关.
- 幸福感的增加和 tinnitus 噪音/压力变化的变化也表明了改善.
结论:
- 每日自我报告的耳声症状数据对治疗反应敏感.
- 基于应用程序的数据为在耳治疗期间的特定症状变化提供了宝贵的见解.
- 这种方法提高了对 tinnitus 管理临床改善的评估.
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