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揭露影响孤独的细微差别:使用数字行为标记来了解大学生社会和情感孤独感
Malik Muhammad Qirtas1, Evi Zafeiridi1, Eleanor Bantry White2
1School of Computer Science and Information Technology, University College Cork, T12 K8AF Cork, Ireland.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
智能手机和可穿戴数据的被动感知可以准确地区分大学生的社会和情感孤独. 这项技术识别了关键的行为差异,为针对性干预铺平了道路,以打击学生的孤独感.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 数字健康数字健康
背景情况:
- 孤独是一个普遍的全球问题,特别影响大学生的心理健康和学业成绩.
- 慢性孤独表现为社会孤独 (缺乏社交网络) 或情感孤独 (缺乏深层联系).
- 区分孤独类型对于开发有效的个性化干预措施至关重要.
研究的目的:
- 用被动感应数据识别学生中与社会和情感孤独相关的行为模式.
- 评估这些模式的预测准确性,以分类孤独类型.
- 在孤独预测中确定机器学习模型中最重要的数字标记.
主要方法:
- 利用被动传感技术从智能手机和可穿戴设备收集行为数据.
- 使用的统计分析,机器学习 (XGBoost) 和基于SHAP的特征的重要性.
- 分析了手机使用情况和基于位置的功能,以区分孤独类型.
主要成果:
- 确定了社会和情感孤独学生之间的显著行为差异,特别是手机使用和位置模式.
- 机器学习模型在分类孤独类型方面表现出高准确度.
- XGBoost模型在预测孤独感方面取得了78.48%的准确性.
结论:
- 被动感应数据揭示了大学生中社会和情感孤独的独特行为标记.
- 机器学习模型可以准确地分类这些孤独子类型.
- 电话使用和位置数据是区分社会和情感孤独的关键数字标记,为有针对性的干预提供信息.
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