预测对辩证行为治疗技能培训应用程序的反应,用于反复的暴饮暴食:一种机器学习方法
Zoe McClure1, Christopher J Greenwood2, Matthew Fuller-Tyszkiewicz3
1School of Psychology, Deakin University, 1 Gheringhap Street, Geelong, VIC, 3220, Australia.
Behaviour research and therapy
|April 26, 2025
概括
机器学习模型很难预测用户对智能手机应用程序的反应,仅使用基线数据. 整合应用程序使用数据改善了研究中断的预测,这表明跟踪干预参与的价值.
科学领域:
- 数字健康干预措施 数字健康干预措施
- 临床心理学 临床心理学
- 机器学习应用程序 机器学习应用程序
背景情况:
- 智能手机应用程序 (APP) 为饮食失调提供可扩展的干预措施,但用户的响应能力各不相同.
- 预测个人对基于应用程序的干预措施的反应仍然是一个挑战.
- 机器学习 (ML) 显示了改善复杂临床结果预测的潜力.
研究的目的:
- 应用ML技术来预测用户对基于辩证行为治疗的智能手机应用程序的响应,用于反复的暴饮暴食.
- 评估基线人口统计,临床和心理变量的预测能力.
- 评估应用程序使用数据对预测准确性的影响.
主要方法:
- 利用了一项随机对照试验 (RCT) 的数据,该试验对576名参与者进行了反复的暴饮暴食.
- 采用10种常见的分类和回归ML方法来预测干预的采用,坚持,学和症状变化.
- 使用69个基线自我报告变量和应用程序使用指标 (例如,已完成的模块) 开发模型.
主要成果:
- 仅使用基线预测因素的ML模型在预测参与 (AUCs=0.48-0.61) 和症状变化 (R2=0.00-0.07) 中表现不佳.
- 整合应用程序使用数据显著改善了研究中断的预测 (AUC=0.69-0.76).
结论:
- 仅仅基于基线预测器的ML模型无法准确预测用户对应用程序的响应.
- 提高预测准确度可能需要考虑时间变化的预测因素和用户环境.
- 追踪干预使用模式,正如改善的脱学预测所示,对于识别有风险的个体是有价值的.
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