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在美国参与“我们所有人”研究计划的成年人中使用可穿戴健康技术进行身体活动分析:多年观察性研究。

Analysis of Physical Activity Using Wearable Health Technology in US Adults Enrolled in the All of Us Research Program: Multiyear Observational Study.

作者信息

Singh Rujul, Tetrick Macy K, Fisher James L, Washington Peter, Yu Jane, Paskett Electra D, Penedo Frank J, Clinton Steven K, Benzo Roberto M

机构信息

Division of Cancer Prevention and Control, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, United States.

Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, Columbus, OH, United States.

出版信息

J Med Internet Res. 2024 Dec 10;26:e65095. doi: 10.2196/65095.

Abstract

BACKGROUND

To date, no studies have examined adherence to the 2018 Physical Activity Guidelines for Americans (PAGA) in real-world longitudinal settings using objectively measured activity monitoring data. This study addresses this gap by using commercial activity monitoring (Fitbit) data from the All of Us dataset.

OBJECTIVE

The primary objectives were to describe the prevalence of adherence to the 2018 PAGA and identify associated sociodemographic determinants. Additionally, we compared 3 distinct methods of processing physical activity (PA) data to estimate adherence to the 2008 PAGA.

METHODS

We used the National Institutes of Health's All of Us dataset, which contains minute-level Fitbit data for 13,947 US adults over a 7-year time span (2015-2022), to estimate adherence to PAGA. A published step-based method was used to estimate metabolic equivalents and assess adherence to the 2018 PAGA (ie, ≥150 minutes of moderate- to vigorous-intensity PA per week). We compared the step-based method, the heart rate-based method, and the proprietary Fitbit-developed algorithm to estimate adherence to the 2008 PAGA.

RESULTS

The average overall adherence to the 2018 PAGA was 21.6% (3006/13,947; SE 0.4%). Factors associated with lower adherence in multivariate logistic regression analysis included female sex (relative to male sex; adjusted odds ratio [AOR] 0.66, 95% CI 0.60-0.72; P<.001); BMI of 25.0-29.9 kg/m (AOR 0.53, 95% CI 0.46-0.60; P<.001), 30-34.9 kg/m (AOR 0.30, 95% CI 0.25-0.36; P<.001), or ≥35 kg/m (AOR 0.13, 95% CI 0.10-0.16; P<.001; relative to a BMI of 18.5-24.9 kg/m); being aged 30-39 years (AOR 0.66, 95% CI 0.56-0.77; P<.001), 40-49 years (AOR 0.79, 95% CI 0.68-0.93; P=.005), or ≥70 years (AOR 0.74, 95% CI 0.62-0.87; P<.001; relative to being 18-29 years); and non-Hispanic Black race or ethnicity (AOR 0.63, 95% CI 0.50-0.79; P<.001; relative to non-Hispanic White race or ethnicity). The Fitbit algorithm estimated that a larger percentage of the sample (10,307/13,947, 73.9%; 95% CI 71.2-76.6) adhered to the 2008 PAGA compared to the heart rate method estimate (4740/13,947, 34%; 95% CI 32.8-35.2) and the step-based method (1401/13,947, 10%; 95% CI 9.4-10.6).

CONCLUSIONS

Our results show significant sociodemographic differences in PAGA adherence and notably different estimates of adherence depending on the algorithm used. These findings warrant the need to account for these disparities when implementing PA interventions and the need to establish an accurate and reliable method of using commercial accelerometers to examine PA, particularly as health care systems begin integrating wearable device data into patient health records.

摘要

背景

迄今为止,尚无研究使用客观测量的活动监测数据,在现实世界的纵向环境中考察对《2018年美国人身体活动指南》(PAGA)的依从性。本研究通过使用“我们所有人”数据集中的商业活动监测(Fitbit)数据填补了这一空白。

目的

主要目的是描述对《2018年身体活动指南》的依从率,并确定相关的社会人口学决定因素。此外,我们比较了3种处理身体活动(PA)数据的不同方法,以估计对《2008年身体活动指南》的依从性。

方法

我们使用了美国国立卫生研究院的“我们所有人”数据集,该数据集包含13947名美国成年人在7年时间跨度(2015 - 2022年)内的分钟级Fitbit数据,以估计对《身体活动指南》的依从性。采用一种已发表的基于步数的方法来估计代谢当量,并评估对《2018年身体活动指南》的依从性(即每周至少150分钟的中等至剧烈强度PA)。我们比较了基于步数的方法、基于心率的方法和Fitbit开发的专有算法,以估计对《2008年身体活动指南》的依从性。

结果

对《2018年身体活动指南》的总体平均依从率为21.6%(3006/13947;标准误0.4%)。多因素逻辑回归分析中,与较低依从性相关的因素包括女性(相对于男性;调整后的优势比[AOR]0.66,95%置信区间0.60 - 0.72;P<0.001);体重指数为25.0 - 29.9kg/m²(AOR 0.53,95%置信区间0.46 - 0.60;P<0.001)、30 - 34.9kg/m²(AOR 0.30,95%置信区间0.25 - 0.36;P<0.001)或≥35kg/m²(AOR 0.13,95%置信区间0.10 - 0.16;P<0.001;相对于体重指数为18.5 - 24.9kg/m²);年龄在30 - 39岁(AOR 0.66,95%置信区间0.56 - 0.77;P<0.001)、40 - 44.79,95%置信区间0.68 - 0.93;P = 0.005)或≥70岁(AOR 0.74,95%置信区间0.62 - 0.87;P<0.001;相对于18 - 29岁);以及非西班牙裔黑人种族或族裔(AOR 0.63,95%置信区间0.50 - 0.79;P<0.001;相对于非西班牙裔白人种族或族裔)。与基于心率的方法估计值(4740/13947,34%;95%置信区间32.8 - 35.2)和基于步数的方法(1401/13947,10%;95%置信区间9.4 - 10.6)相比,Fitbit算法估计样本中更大比例(10307/13947,7

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34cb/11668988/0a1eadf39a5c/jmir_v26i1e65095_fig1.jpg

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