被动数据不能改善预测或检测超出重度抑郁症时间模式的酒精消费:一项90天的交叉验证研究
Anna M Langener1, Dawson Haddox2, Daniel M Mackin3
1Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States.
Addictive behaviors
|January 29, 2026
概括
使用智能手机数据的深度学习模型在严重抑郁症 (MDD) 患者中显示出适度检测酒精使用的能力. 然而,预测信号主要来自时间模式,限制了模型.
科学领域:
- 数字化表型化是指数字化表型化.
- 机器学习在心理健康中的应用
- 同时出现的疾病.
背景情况:
- 大型抑郁症 (MDD) 经常与酒精使用障碍同时发生,增加功能障碍.
- 实时监测酒精使用对于抑郁症治疗期间的干预至关重要.
- 通过可穿戴设备的被动数据收集提供了一个低负荷的监控方法.
研究的目的:
- 研究深度学习模型在检测和预测MDD患者酒精使用的有效性.
- 为此目的评估被动收集的智能手机和智能手表数据的实用性.
主要方法:
- 训练深度学习模型通过被动收集的数据 (加速计,心率,呼吸率,屏幕使用,GPS) 从300个MDD患者超过90天.
- 使用每周自我报告的酒精使用 (时间线追溯) 作为结果.
- 在培训组中不包括的参与者的数据上验证的模型,以评估个体间和个体内变异性.
主要成果:
- 模型在预测当天和第二天的酒精使用方面取得了中等的表现 (平均AUC = 0.67).
- 对于个人内变异性预测 (AUCs = 0.69同一天,0.68第二天) 也观察到类似的表现.
- 模型性能与仅使用周日的基线可比,这表明时间模式主导了预测信号.
结论:
- 被动收集的传感器数据显示,超出时间模式预测MDD中酒精使用的附加值有限.
- 利用已识别的时间线索进行干预可能已经有效.
- 需要进一步的研究来增强数字表型化对共发生条件的预测能力.
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