机器学习对体力活动相关健康结果的影响:系统性审查和元分析
Ezgi Hasret Kozan Cikirikci1, Melek Nihal Esin1
1Florence Nightingale Faculty of Nursing, Department of Public Health Nursing, Istanbul University-Cerrahpasa, Istanbul, Turkey.
International nursing review
|May 12, 2025
概括
机器学习 (ML) 干预措施有效地提高了身体活动水平和每日步数. 护士可以使用ML进行个性化,经济高效的健康促进,尽管需要更多由护士领导的研究.
科学领域:
- 医疗信息学 医疗信息学
- 数字健康数字健康
- 行为科学 行为科学
背景情况:
- 对基于机器学习 (ML) 的身体活动干预措施的证据有限.
- 合成随机对照试验 (RCT) 对护理实践至关重要.
- 机器学习的进步可以为促进健康的干预提供信息.
研究的目的:
- 分析基于ML的干预措施的RCT,以促进体育活动.
- 综合关于ML在增加身体活动方面的有效性证据.
主要方法:
- 搜索了PubMed,EBSCO,Cochrane和土耳其的RCT数据库 (2013-2024年).
- 使用Cochrane风险偏差1工具评估方法质量.
- 包括10项研究,涉及2269名参与者,遵守PRISMA指南.
主要成果:
- ML干预有效地检测身体活动水平,并增加每日步数和中度至强度的身体活动.
- ML准确地预测了对体育活动目标的坚持,并定制了建议.
- 分析结果显示,每天步数显著增加 (赫奇的g=0.402,p<0.000).
结论:
- ML通过实现自我监测,个性化建议和适应性干预来支持公共卫生.
- 护士可以利用ML进行及时,定制和具有成本效益的体育活动推广.
- 护士和开发人员之间的合作对于将ML纳入公共卫生计划至关重要,护士领导这一过程.
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