利用健身追踪器睡眠中断预测女性日常哮喘控制情况的模型开发。
Prediction model development of women's daily asthma control using fitness tracker sleep disruption.
机构信息
The Rockefeller Heilbrunn Family Center for Research Nursing Nurse Scholar, New York, NY, USA; University at Buffalo, Buffalo, NY, USA; Castner Incorporated, Grand Island, NY 14072, USA.
University at Buffalo School of Nursing, Buffalo, NY, USA.
出版信息
Heart Lung. 2020 Sep-Oct;49(5):548-555. doi: 10.1016/j.hrtlng.2020.01.013. Epub 2020 Feb 20.
BACKGROUND
Night-time wakening with asthma symptoms is an important indicator of disease control and severity, with no gold-standard objective measurement.
OBJECTIVE
The study objective was to use fitness tracker sleep data to develop predictive models of daily disease control-related asthma-specific wakening and FEV in working-aged women with poorly controlled asthma.
METHODS
A repeated measures panel design included data from 43 women with poorly controlled asthma. Two components of asthma control were the primary outcomes, measured daily as (1) self-reported asthma-specific wakening and (2) self-administered spirometry to measure FEV. Data were analyzed using generalized linear mixed models.
RESULTS
Our models demonstrated predictive value (AUC=0.77) for asthma-specific night-time wakening and good predictive value (AUC=0.83) for daily FEV CONCLUSIONS: Fitness tracker sleep efficiency and wake counts demonstrate clinical utility as predictive of asthma-specific night-time wakening and daily FEV Fitness tracker sleep data demonstrated predictive capability for daily asthma outcomes.
背景
夜间出现哮喘症状是疾病控制和严重程度的一个重要指标，但目前尚无金标准的客观测量方法。
目的
本研究旨在利用健身追踪器的睡眠数据，为控制不佳的哮喘成年女性开发预测日常疾病控制相关哮喘特异性觉醒和 FEV 的模型。
方法
采用重复测量面板设计，纳入 43 名控制不佳的哮喘女性的数据。哮喘控制的两个方面是主要结局，每日通过（1）自我报告的哮喘特异性觉醒和（2）自我管理的肺活量测定法测量 FEV 进行测量。使用广义线性混合模型进行数据分析。
结果
我们的模型对哮喘特异性夜间觉醒具有预测价值（AUC=0.77），对每日 FEV 具有良好的预测价值（AUC=0.83）。
结论
健身追踪器的睡眠效率和唤醒次数显示出作为哮喘特异性夜间觉醒和每日 FEV 的预测指标具有临床应用价值。健身追踪器睡眠数据显示出对日常哮喘结局的预测能力。
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