通过心率和步骤识别中风患者的独特子组,以描述身体活动的特征
Lily J Koffman1, Ciprian M Crainiceanu1, Ryan T Roemmich2,3
1Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Baltimore MD.
Journal of the American Heart Association
|September 8, 2023
概括
结合步数和心率的可穿戴设备可以在中风幸存者中识别不同的身体活动 (PA) 亚组. 这种方法有助于对个体进行分层,揭示了一个"无条件"的群体,其流动性结果较差.
科学领域:
- 生物医学工程 生物医学工程
- 康复科学 康复科学 康复科学
- 可穿戴技术可穿戴技术
背景情况:
- 脑卒中后的低体力活动 (PA) 与不良健康结果有关.
- 单靠步数计算可能无法完全捕捉PA的复杂性或风险分层.
- 心率数据为PA评估提供了额外的生理见解.
研究的目的:
- 通过使用可穿戴设备的多源PA数据来确定中风后患者的临床相关子组.
- 为了利用可穿戴设备的步数和心率来进行PA分析.
- 为了确定这些PA子组是否在功能移动性和中风后的影响上有所不同.
主要方法:
- 70名中风后患者使用Fitbit Inspire 2连续一年.
- 通过使用定义的值,PA被分为四个状态 (高/低步和心率).
- K-means集群分析了PA状态,每日步骤,久坐时间和静止心率,以确定子组.
主要成果:
- 确定了三个不同的子组:活跃 (n=8),静止 (n=29) 和无条件 (n=33).
- 在大多数聚类变量中,子组的差异很大,除了静止心率.
- 脱条件子组在急性护理后移动性活动测量T评分上显示得分明显较低.
结论:
- 使用可穿戴设备的心率和步数来量化PA,可以在中风后的人群中确定有意义的子组.
- 这种多度指标的方法提供了更细致的了解PA和相关风险.
- 可穿戴技术提供了一个可行的方法,用于个性化风险分层和康复计划在中风恢复.
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