Pragya Sharma, Matteo Danieletto, Jessica K Whang, Kyle Landell, Drew Helmus, Bruce E Sands, Mayte Suarez-Farinas, Percio S Gulko, Robert P Hirten
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Mixed effect logistic regression models evaluated changes in physiological metrics prior to the onset of flares. The study enrolled 53 participants (88.7% female) with a mean age of 51.1 (SD 15.2) years. Each contributed a mean of 105 (SD 97) days of data. Mean steps were lower, while mean nighttime HR was higher during symptomatic periods, compared to periods of symptomatic remission. Means daily HR, daytime HR, nighttime HR, and RHR were higher during periods of inflammatory flares, compared to inflammatory remission. Circadian features of HRV differentiated inflammatory and symptomatic flares from remission. All metrics were altered up to 4 weeks prior to inflammatory and symptomatic flare development. 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引用次数: 0
摘要
类风湿性关节炎(RA)的生理参数发生改变。我们评估了从可穿戴设备收集的生理指标的变化是否能识别并先于症状性和炎症性RA耀斑的发展。RA患者回答了每日疾病活动调查,并提供了炎症活动的实验室评估。他们佩戴苹果手表(n=35)、Fitbit (n=17)或Oura Ring (n=3),收集心率(HR)、静息心率(RHR)、心率变异性(HRV)和步数。使用线性混合效应模型将HR、RHR和步骤与发作期和缓解期联系起来。Cosinor混合效应模型评估HRV的昼夜节律特征。混合效应逻辑回归模型评估了耀斑发作前生理指标的变化。该研究招募了53名参与者(88.7%为女性),平均年龄为51.1岁(SD 15.2)。每组平均提供105 (SD 97)天的数据。与症状缓解期相比,症状期平均步数较低,而夜间平均HR较高。与炎症缓解期相比,平均每日HR、白天HR、夜间HR和RHR在炎症发作期间更高。HRV的昼夜特征将炎症和症状性发作与缓解区分开来。所有指标在炎症和症状发作前4周发生改变。这表明可穿戴设备在疾病监测和耀斑预测方面的潜在用途。
Wearable Devices Detect Physiological Changes that Precede and Are Associated with Symptomatic and Inflammatory Rheumatoid Arthritis Flares.
Physiological parameters are altered in rheumatoid arthritis (RA). We evaluated whether changes in physiological metrics, collected from wearable devices, identify and precede the development of both symptomatic and inflammatory RA flares. Participants with RA answered daily disease activity surveys and provided laboratory assessments of inflammatory activity. They wore an Apple Watch (n=35), Fitbit (n=17), or Oura Ring (n=3) collecting heart rate (HR), resting heart rate (RHR), heart rate variability (HRV), and steps. Linear mixed effect models were used to associate HR, RHR and steps with flare and remission periods. Cosinor mixed effect models assessed circadian features of HRV. Mixed effect logistic regression models evaluated changes in physiological metrics prior to the onset of flares. The study enrolled 53 participants (88.7% female) with a mean age of 51.1 (SD 15.2) years. Each contributed a mean of 105 (SD 97) days of data. Mean steps were lower, while mean nighttime HR was higher during symptomatic periods, compared to periods of symptomatic remission. Means daily HR, daytime HR, nighttime HR, and RHR were higher during periods of inflammatory flares, compared to inflammatory remission. Circadian features of HRV differentiated inflammatory and symptomatic flares from remission. All metrics were altered up to 4 weeks prior to inflammatory and symptomatic flare development. This suggests the potential use of wearable devices for disease monitoring and flare prediction.