在日常环境中通过持续监测使用被动感应检测母亲的社会孤独:纵向研究
Fatemeh Sarhaddi1, Iman Azimi2,3, Hannakaisa Niela-Vilen4
1Department of Computing, University of Turku, Turku, Finland.
JMIR formative research
|August 9, 2023
概括
可穿戴设备可以使用生理数据,如活动和心率来预测母亲的孤独感. 通过被动感应的早期检测可以改善母亲的福祉.
科学领域:
- 数字健康数字健康
- 孕产妇健康 孕产妇健康
- 机器学习在医疗保健中的应用
背景情况:
- 母亲的孤独会对母亲和孩子的健康产生负面影响.
- 可穿戴设备提供了非侵入性的方法来检测孤独感.
- 早期检测可以预防或减少不良健康结果.
研究的目的:
- 使用可穿戴设备的客观健康数据预测母亲的孤独感.
- 确定用于检测孤独的关键生理参数.
- 评估被动感应用于预测母亲孤独的可行性.
主要方法:
- 在怀孕和产后期间对31名妇女进行了长度研究.
- 通过智能手表持续收集生理数据.
- 机器学习模型 (决策树,梯度增强) 用于基于UCLA孤独调查问卷数据的孤独预测.
主要成果:
- 渐变增强和决策树模型在孤独预测方面获得了高F1分数 (0.897和0.872).
- 孤独与活动强度和分布有很强的相关性.
- 休息心率 (HR) 和心率变化 (HRV) 是重要的预测因素.
结论:
- 基于智能手表的被动传感可用于预测母亲的孤独感.
- 机器学习模型在检测孤独方面表现出高准确度.
- 活动模式和休息的生理指标提供了改善母亲福祉的干预机会.
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