Step cadence‐based and machine‐learning (ML) methods have been used to classify physical activity (PA) intensity in health‐related research. This study examined the association of intensity‐specific PA duration with all‐cause (ACM) and CVD mortality using the cadence‐based and ML methods in 68 561 UK Biobank participants wearing wrist‐worn accelerometers. The two‐stage‐ML method categorized activity type and then intensity. The one‐level‐cadence‐method (1LC) derived intensity‐specific duration using all detected steps (including standing utilitarian steps) and cadence thresholds of ≥100 steps/min (moderate intensity) and ≥130 steps/min (vigorous intensity). The two‐level‐cadence‐method (2LC) detected ambulatory steps (i.e., walking and running) and then applied the same cadence thresholds. The 2LC exhibited the most pronounced association at the lower end of duration spectrum. For example, the 2LC showed the smallest minimum moderate‐to‐vigorous‐PA (MVPA) duration (amount associated with 50% of optimal risk reduction) with similar corresponding ACM hazard ratio (HR) to other methods (2LC: 2.8 min/day [95% CI: 2.6, 2.8], HR: 0.83 [95% CI: 0.78, 0.88]; 1LC, 11.1[10.8, 11.4], 0.80 [0.76, 0.85]; ML, 14.9 [14.6, 15.2], 0.82 [0.76, 0.87]). The ML elicited the greatest mortality risk reduction. For example, the medians and corresponding HR in VPA‐ACM association: 2LC, 2.0 min/day [95% CI: 2.0, 2.0], HR, 0.69 [95% CI: 0.61, 0.79]; 1LC, 6.9 [6.9, 7.0], 0.68 [0.60, 0.77]; ML, 3.2 [3.2, 3.2], 0.53 [0.44, 0.64]. After standardizing durations, the ML exhibited the most pronounced associations. For example, the standardized minimum durations in MPA‐CVD mortality association were: 2LC, −0.77; 1LC, −0.85; ML, −0.94; with corresponding HR of 0.82 [0.72, 0.92], 0.79 [0.69, 0.90], and 0.77 [0.69, 0.85], respectively. The 2LC exhibited the most pronounced association with all‐cause and CVD mortality at the lower end of the duration spectrum. The ML method provided the most pronounced association with all‐cause and CVD mortality, thus might be appropriate for estimating health benefits of moderate and vigorous intensity PA in observational studies.

中文翻译：

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比较节奏与基于机器学习的身体活动强度分类：身体活动与死亡率关联的变化


基于步频和机器学习（ML）的方法已被用来对健康相关研究中的体力活动（PA）强度进行分类。本研究使用基于节奏的 ML 方法对 68 561 名佩戴腕戴式加速度计的英国生物银行参与者检查了强度特异性 PA 持续时间与全因 (ACM) 和 CVD 死亡率之间的关联。两阶段机器学习方法对活动类型进行分类，然后对强度进行分类。单级踏频方法 (1LC) 使用所有检测到的步数（包括站立实用步数）和 ≥100 步/分钟（中等强度）和 ≥130 步/分钟（剧烈强度）的步频阈值得出强度特定持续时间。两级踏频方法 (2LC) 检测步行步数（即步行和跑步），然后应用相同的踏频阈值。 2LC 在持续时间谱的下端表现出最明显的关联。例如，2LC 显示了最小的中度至剧烈 PA (MVPA) 持续时间（与最佳风险降低的 50% 相关的量），且相应的 ACM 风险比 (HR) 与其他方法相似（2LC：2.8 分钟/天） [95% CI: 2.6, 2.8]，HR：0.83 [95% CI：0.78, 0.88]；1LC，11.1[10.8，11.4]，0.80 [0.76，0.85]；ML，14.9 [14.6，15.2]，0.82 0.76，0.87]）。 ML 最大程度地降低了死亡风险。例如，VPA-ACM 关联中的中位数和相应的 HR：2LC，2.0 分钟/天 [95% CI：2.0，2.0]，HR，0.69 [95% CI：0.61，0.79]； 1LC，6.9[6.9，7.0]，0.68[0.60，0.77]；毫升，3.2 [3.2，3.2]，0.53 [0.44，0.64]。在标准化持续时间后，ML 表现出最明显的关联。例如，MPA-CVD死亡率关联的标准化最短持续时间为：2LC，-0.77； 1LC，-0.85； ML，-0.94；相应的 HR 为 0.82 [0.72, 0.92]、0.79 [0.69, 0.90] 和 0。分别为 77 [0.69，0.85]。 2LC 在持续时间谱的低端表现出与全因死亡率和 CVD 死亡率最明显的关联。 ML 方法提供了与全因死亡率和 CVD 死亡率最明显的关联，因此可能适合在观察性研究中估计中等和高强度 PA 的健康益处。