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在校园环境中基于智能手机的抑郁症检测:通过小样本行为分析进行概念验证研究

Yichen Bai1, Yueze Liu1, Yang Zhang2

  • 1School of Information Science and Engineering, Lanzhou University, Lanzhou, China.

Frontiers in psychiatry
|August 25, 2025
PubMed
概括
此摘要是机器生成的。

智能手机的传感器可以检测到中国大学生的抑郁, 这项技术为早期心理健康干预提供了一种新的方法.

关键词:
每日移动行为分析抑郁症检测特性工程机器学习小型数据样本智能手机传感器

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科学领域:

  • 数字健康
  • 心理健康技术
  • 行为科学

背景情况:

  • 抑郁症是全世界越来越普遍的现象,尤其是在青少年中.
  • 大学学生面临着独特的心理健康挑战.
  • 早期发现抑郁症对于有效的干预至关重要.

研究的目的:

  • 探索使用智能手机传感器数据检测中国大学生抑郁症的可行性.
  • 使用智能手机传感器识别与抑郁症状相关的行为模式.
  • 根据传感器数据,评估机器学习模型在检测抑郁的准确性.

主要方法:

  • 收集了来自12名大学生加速计,陀螺仪和光传感器的数据.
  • 为校园环境开发一个定制的数据处理方案.
  • 提取了18个特征序列,使用皮尔森相关性进行特征选择,并通过交叉验证验证模型.
  • 用于模型培训和评估的常用分类算法.

主要成果:

  • 检测准确率在73.11%至88.24%之间.
  • 确定了抑郁症得分 (PHQ-9) 和饮食规律性,睡觉时间一致性和身体活动水平之间的显著负相关性.
  • 通过智能手机获得的行为数据与自我报告的抑郁症状之间存在联系.

结论:

  • 智能手机传感器在中国的高等教育机构中有望在早期发现抑郁症.
  • 智能手机捕捉到的行为模式可以作为抑郁症症状的指标.
  • 这种方法支持为学生开发新的以技术为导向的心理健康支持系统.