用加速度计进行体力活动研究中的数据分析:范围审查
Ya-Ting Liang1,2, Charlotte Wang2,3, Chuhsing Kate Hsiao2,3
1Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
Journal of medical Internet research
|September 11, 2024
概括
本综述总结了可穿戴体育活动 (PA) 数据的分析方法,突出了回归模型的普及程度和机器学习的日益使用. 它强调使用纵向或功能数据来获得详细的PA洞察力和改善健康结果理解.
科学领域:
- 可穿戴技术和数字健康
- 生物统计学和数据科学
- 公共卫生和流行病学.
背景情况:
- 可穿戴设备可以实时监测身体活动 (PA) 以进行健康评估和治疗调整.
- 对PA数据的可靠统计分析至关重要,但受到各种指标,研究目标和时间变化的挑战.
- 需要对PA数据的分析工具进行全面的摘要.
研究的目的:
- 审查用于体力活动 (PA) 来自加速度计数据的分析方法.
- 识别PA指标,用于分类,关联和预测的分析工具以及现有的分析挑战.
- 为未来对PA分析的统计方法的研究提供建议.
主要方法:
- 根据既定的框架进行范围审查.
- 在2024年2月搜索了PubMed,IEEE Xplore和ACM数字图书馆.
- 包括使用基于加速度计的PA数据进行分类,关联或预测研究.
主要成果:
- 428项研究符合条件,重点是分类 (17.5%),关联 (79.9%) 和预测 (7.5%).
- 大多数研究使用3D加速 (96.7%) 和时间域指标 (100%). 回归模型 (87.1%) 是普遍存在的,机器学习的使用越来越多 (43%在分类中).
- 15.9%的研究采用了使用纵向或功能数据分析的PA轨迹.
结论:
- 摘要 PA指标提供了对活动模式的快速洞察.
- 纵向或功能数据分析提供了详细的PA配置文件,增强了对健康结果的理解.
- 选择适当的分析工具是确保PA研究可靠的科学发现的关键.
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