目的对儿童肥胖患者的身体活动、心血管参数和睡眠进行家庭监测。

Q1 Computer Science Digital Biomarkers Pub Date : 2022-03-31 eCollection Date: 2022-01-01 DOI:10.1159/000522185
Janine M Knijff, Euphemia C A M Houdijk, Daniëlle C M van der Kaay, Youri van Berkel, Luc Filippini, Frederik E Stuurman, Adam F Cohen, Gertjan J A Driessen, Matthijs D Kruizinga
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引用次数: 2

摘要

儿童肥胖的临床研究和治疗具有挑战性,需要在家庭环境中获得客观的生物标志物。本研究的目的是确定由家庭监测平台收集的新型数字终点在儿童肥胖中的潜力。方法:在这项前瞻性观察研究中,纳入了28名6-16岁的肥胖儿童,并对其进行了28天的监测。患者戴着智能手表,测量身体活动(PA)、心率(HR)和睡眠。此外,还进行了每日血压(BP)测量。来自128名健康儿童的数据用于比较。通过线性混合效应模型评估患者和对照组之间的差异。结果:分析了28例患者(平均年龄11.6岁,男性46%,平均体重指数30.9)和128例对照组(平均年龄11.1岁,男性46%,平均体重指数18.0)的资料。患者是在2018年11月至2020年2月期间招募的。对于患者,测量的中位依从性范围为55%至100%,与智能手表相关的测量中位依从性最高(81-100%)。患者每日PA水平较低(vs 6081步、4597步95%可信区间[CI] 862 - 2108年)和峰值PA水平(vs 1392步、1115步95%可信区间136 - 417),一个更高的夜间人力资源(81 bpm vs 71 bpm, 95%可信区间6.3 - -12.3)和白天的人力资源(98 bpm vs 88 bpm, 95%可信区间7.6 - -12.6),较高的收缩压(115毫米汞柱和104毫米汞柱,95%可信区间8.1 - -14.5)和舒张压(76毫米汞柱和65毫米汞柱,95%可信区间8.7 - -12.7),和更短的睡眠时间(差异0.5 h, 95%置信区间0.2 - -0.7)相比,控制。结论:通过可穿戴设备对儿童肥胖进行远程监测,有可能客观地衡量家庭环境中的疾病负担。新的终点表明,患者和对照组之间的PA水平、HR、BP和睡眠时间存在显著差异。未来的研究需要确定新型数字端点检测干预措施效果的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Objective Home-Monitoring of Physical Activity, Cardiovascular Parameters, and Sleep in Pediatric Obesity.

Introduction: Clinical research and treatment of childhood obesity is challenging, and objective biomarkers obtained in a home-setting are needed. The aim of this study was to determine the potential of novel digital endpoints gathered by a home-monitoring platform in pediatric obesity.

Methods: In this prospective observational study, 28 children with obesity aged 6-16 years were included and monitored for 28 days. Patients wore a smartwatch, which measured physical activity (PA), heart rate (HR), and sleep. Furthermore, daily blood pressure (BP) measurements were performed. Data from 128 healthy children were utilized for comparison. Differences between patients and controls were assessed via linear mixed effect models.

Results: Data from 28 patients (average age 11.6 years, 46% male, average body mass index 30.9) and 128 controls (average age 11.1 years, 46% male, average body mass index 18.0) were analyzed. Patients were recruited between November 2018 and February 2020. For patients, the median compliance for the measurements ranged from 55% to 100% and the highest median compliance was observed for the smartwatch-related measurements (81-100%). Patients had a lower daily PA level (4,597 steps vs. 6,081 steps, 95% confidence interval [CI] 862-2,108) and peak PA level (1,115 steps vs. 1,392 steps, 95% CI 136-417), a higher nighttime HR (81 bpm vs. 71 bpm, 95% CI 6.3-12.3) and daytime HR (98 bpm vs. 88 bpm, 95% CI 7.6-12.6), a higher systolic BP (115 mm Hg vs. 104 mm Hg, 95% CI 8.1-14.5) and diastolic BP (76 mm Hg vs. 65 mm Hg, 95% CI 8.7-12.7), and a shorter sleep duration (difference 0.5 h, 95% CI 0.2-0.7) compared to controls.

Conclusion: Remote monitoring via wearables in pediatric obesity has the potential to objectively measure the disease burden in the home-setting. The novel endpoints demonstrate significant differences in PA level, HR, BP, and sleep duration between patients and controls. Future studies are needed to determine the capacity of the novel digital endpoints to detect effect of interventions.

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来源期刊
Digital Biomarkers
Digital Biomarkers Medicine-Medicine (miscellaneous)
CiteScore
10.60
自引率
0.00%
发文量
12
审稿时长
23 weeks
期刊最新文献
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