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比较基于节奏和基于机器学习的身体活动强度分类估计:英国生物银行。

Comparing cadence-based and machine learning based estimates for physical activity intensity classification: The UK Biobank.

作者信息

Wei Le, Ahmadi Matthew N, Hamer Mark, Blodgett Joanna M, Small Scott, Trost Stewart, Stamatakis Emmanuel

机构信息

Mackenzie Wearables Research Hub, Charles Perkins Centre, The University of Sydney, Australia; School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Australia.

Division of Surgery and Interventional Sciences, Institute of Sport Exercise and Health, Faculty of Medical Sciences, University College London, United Kingdom.

出版信息

J Sci Med Sport. 2024 Aug;27(8):551-556. doi: 10.1016/j.jsams.2024.05.002. Epub 2024 May 22.

Abstract

OBJECTIVES

Cadence thresholds have been widely used to categorize physical activity intensity in health-related research. We examined the convergent validity of two cadence-based intensity classification approaches against a machine-learning-based intensity schema in 84,315 participants (≥40 years) with wrist-worn accelerometers.

DESIGN

Validity study.

METHODS

Both cadence-based methods (one-level cadence, two-level cadence) calculated intensity-specific time based on cadence-thresholds while the two-level cadence identified stepping behaviors first. We used an overlapping plot, mean absolute error, and Spearman's correlation coefficient to examine agreements between the cadence-based and machine-learning methods. We also evaluated agreements between methods based on practically-important-difference (moderate-to-vigorous-physical activity: ±20 min/day, moderate-physical activity: ±15, vigorous-physical activity: ±2.5, light-physical activity: ±30).

RESULTS

The group-level (median) minutes of moderate-to-vigorous- and moderate-physical activity estimated by one-level cadence were within the range of practically-important-difference compared to the machine-learning method (bias of median: moderate-to-vigorous-physical activity, -3.5, interquartile range [-15.8, 12.2]; moderate-physical activity, -6.0 [-17.2, 4.1]). The group-level vigorous- and light-physical activity minutes derived by two-level cadence were within practically-important-difference range (vigorous-physical activity: -0.9 [-3.1, 0.5]; light-physical activity, -1.3 [-28.2, 28.9]). The individual-level differences between the cadence-based and machine learning methods were high across intensities (e.g., moderate-to-vigorous-physical activity: mean absolute error [one-level cadence: 24.2 min/day; two-level cadence: 26.2]), with the proportion of participants within the practically-important-difference ranging from 8.4 % to 61.6 %.

CONCLUSIONS

One-level cadence showed acceptable group-level estimates of moderate-to-vigorous and moderate-physical activity while two-level cadence showed acceptable group-level estimates of vigorous- and light-physical activity. The cadence-based methods might not be appropriate for individual-level intensity-specific time estimation.

摘要

目的

在与健康相关的研究中,步频阈值已被广泛用于对身体活动强度进行分类。我们在84315名佩戴腕部加速度计的参与者(≥40岁)中,检验了两种基于步频的强度分类方法与基于机器学习的强度模式之间的收敛效度。

设计

效度研究。

方法

两种基于步频的方法(一级步频法、二级步频法)均根据步频阈值计算特定强度时间,而二级步频法首先识别步行行为。我们使用重叠图、平均绝对误差和斯皮尔曼相关系数来检验基于步频的方法与机器学习方法之间的一致性。我们还基于实际重要差异(中等至剧烈身体活动:±20分钟/天,中等身体活动:±15,剧烈身体活动:±2.5,轻度身体活动:±30)评估了方法之间的一致性。

结果

与机器学习方法相比,一级步频法估计的中等至剧烈和中等身体活动的组水平(中位数)分钟数在实际重要差异范围内(中位数偏差:中等至剧烈身体活动,-3.5,四分位间距[-15.8, 12.2];中等身体活动,-6.0 [-17.2, 4.1])。二级步频法得出的组水平剧烈和轻度身体活动分钟数在实际重要差异范围内(剧烈身体活动:-0.9 [-3.1, 0.5];轻度身体活动,-1.3 [-28.2, 28.9])。基于步频的方法与机器学习方法在个体水平上的差异在各强度下都很高(例如,中等至剧烈身体活动:平均绝对误差[一级步频法:24.2分钟/天;二级步频法:26.2]),实际重要差异范围内的参与者比例在8.4%至61.6%之间。

结论

一级步频法对中等至剧烈和中等身体活动的组水平估计可接受,而二级步频法对剧烈和轻度身体活动的组水平估计可接受。基于步频的方法可能不适用于个体水平的特定强度时间估计。

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