英国生物库中客观和主观测量的体力活动及其与心脏代谢风险的关联:回顾性队列研究

IF 6.2 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Charlyne Bürki, Caiwei Tian, Kenneth Westerman, Chirag Patel
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引用次数: 0

摘要

背景:体育活动(PA)行为与心脏代谢危险因素之间的关联很大程度上依赖于基于问卷的报告。越来越多的研究转向移动医疗(mHealth)设备解决方案来测量PA。虽然自我报告的活动水平和客观测量的基于加速度计的活动之间存在差异,但这些差异如何在疾病风险中表现出来尚不清楚。目的:在这里,我们试图评估自我报告和基于移动健康的PA之间的这些差异,并模拟它们与心脏代谢因素的关联。我们的研究提供了一个框架来评估由移动健康技术测量的关系质量,这可以推广到其他传感器或活动测量设备。方法:我们在2013年至2015年期间使用腕带加速度计数据和自报告问卷对16,000名英国生物银行(UKB)参与者进行了PA评估,重点关注步行,睡眠,久坐和中度至剧烈体育活动(MVPA)。我们比较了自我报告和客观测量之间的一致性。我们还比较了客观测量或自我报告的PA与未来临床生物标志物水平(如BMI、脉搏率、血糖控制和胆固醇)之间的关系。结果:参与者低估了他们每周平均2.86小时的久坐时间,主观和客观活动之间的相关系数(r)为久坐时间0.12,MVPA 0.16,步行0.18,睡眠0.13。我们发现客观测量的MVPA与心脏代谢生物标志物(如BMI和脉搏率)之间呈负相关,但主观上报告的活动与心脏代谢生物标志物之间没有关联。我们估计,在健康成人中,主观测量的MVPA和BMI之间的关联比客观测量的要大6%。我们还估计,如果依靠主观报告的观察结果而不是测量的PA,健康成年人的心跳(健康范围:60-100 bpm)会有2%-3%的差异。结论:这些发现表明,与客观测量的PA相比,基于自我报告活动的关联可能被高估和偏倚。因此,在评估自我报告的PA对关键心脏代谢因子(如BMI和脉搏率)的影响时应谨慎。我们强调,虽然在比较PA模式时存在偏差,但我们无法得出哪种方法更能反映日常活动负荷的结论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Objectively and Subjectively Measured Physical Activity and Their Associations With Cardiometabolic Risk in the UK Biobank: Retrospective Cohort Study.

Objectively and Subjectively Measured Physical Activity and Their Associations With Cardiometabolic Risk in the UK Biobank: Retrospective Cohort Study.

Objectively and Subjectively Measured Physical Activity and Their Associations With Cardiometabolic Risk in the UK Biobank: Retrospective Cohort Study.

Objectively and Subjectively Measured Physical Activity and Their Associations With Cardiometabolic Risk in the UK Biobank: Retrospective Cohort Study.

Background: The association between physical activity (PA) behavior and cardiometabolic risk factors has depended largely on questionnaire-based reporting. More studies are turning to mobile health (mHealth) device solutions to measure PA. While there are differences between self-reported activity levels and objectively measured accelerometer-based activity, how these differences manifest in disease risk is unknown.

Objective: Here, we sought to evaluate these differences between self-reported and mHealth-based PA and to model the impact on their association with cardiometabolic factors. Our study provides a framework to assess the quality of relationships measured by mHealth technologies, which is generalizable to other sensors or activity-measuring devices.

Methods: We assessed PA using both wrist-worn accelerometer data and self-reported questionnaires in 16,000 participants of the UK Biobank (UKB) between 2013 and 2015, focusing on walking, sleeping, sedentary, and moderate-to-vigorous physical activity (MVPA). We compared the concordance between self-reported and objective measures of PA. We also compared the association between objectively measured or self-reported PA and future clinical biomarker levels (eg, BMI, pulse rate, glucose control, and cholesterol).

Results: Participants underestimated their weekly sedentary duration on average of 2.86 hours, and the coefficient of correlation (r) between subjective and objective activity was 0.12 for sedentary time, 0.16 for MVPA, 0.18 for walking, and 0.13 for sleeping. We found an inverse association between objectively measured MVPA and cardiometabolic biomarkers such as BMI and pulse rate, but found no association between subjectively reported activity and cardiometabolic biomarkers. We estimated that there is a 6% larger association between subjectively measured MVPA and BMI in healthy adults (vs the objective counterpart). We also estimated a 2%-3% difference on a healthy adult heartbeat (healthy range: 60-100 bpm) if relying on subjectively reported observations instead of measured PA.

Conclusions: These findings suggest that the association based on self-reported activity is likely overestimated and biased compared with objectively measured PA. Therefore, care should be taken when assessing the effects of self-reported PA on key cardiometabolic factors, such as BMI and pulse rate. We emphasize that while the associations are biased when comparing PA modalities, we cannot conclude which method more closely reflects the daily activity load.

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来源期刊
JMIR mHealth and uHealth
JMIR mHealth and uHealth Medicine-Health Informatics
CiteScore
12.60
自引率
4.00%
发文量
159
审稿时长
10 weeks
期刊介绍: JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.
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