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Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
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在临床和非临床环境中收集的智能手机数据的开源,步数算法:算法开发和验证研究.

Marcin Straczkiewicz1, Nancy L Keating2,3, Embree Thompson4

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.

JMIR cancer
|November 15, 2023
PubMed
概括
此摘要是机器生成的。

这项研究验证了开源智能手机步数计数方法,表明它可靠地估计了各种条件和人群的步数,为商业追踪器提供了可扩展的替代方案.

关键词:
加速度计的加速度计.癌症 癌症 癌症 癌症 癌症这是一个开源的开源软件.智能手机的智能手机智能手机的智能手机.步数计是指一个步数.验证验证的时间可以穿戴的可穿戴设备.

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科学领域:

  • 生物医学工程 生物医学工程
  • 数字健康数字健康
  • 可穿戴技术可穿戴技术

背景情况:

  • 步数计数对于公共卫生和临床研究至关重要.
  • 商业活动追踪器在可复制性和可扩展性方面存在局限性.
  • 智能手机为步数计数提供了一个有希望的,可访问的替代方案.

研究的目的:

  • 评估一个开源智能手机步数计数方法.
  • 验证与跨体,视觉评估和商业可穿戴设备数据进行了验证.
  • 该研究评估了在不同测量条件下的性能.

主要方法:

  • 利用了来自智能手机和加速度计的8个独立数据集.
  • 采用以前发表的智能手机步数算法,使用原始加速度计数据.
  • 布兰德-阿尔特曼分析计算了平均偏差和一致性极限.

主要成果:

  • 交叉体验证显示平均偏差为-7.2步 (-0.5%).
  • 视觉评估验证的平均偏差为-0.4步 (0.1%).
  • 商业可穿戴设备验证 (Fitbit) 显示3.4%的差异 (-67.1步).

结论:

  • 开源智能手机方法提供可靠的步骤计数.
  • 在不同的传感器位置和测量场景中,准确性是一致的.
  • 该方法在健康成年人和癌症患者中得到了验证.