Objective To improve classification of movement behaviours in free-living accelerometer data using machine-learning methods, and to investigate the association between machine-learned movement behaviours and risk of incident cardiovascular disease (CVD) in adults. Methods Using free-living data from 152 participants, we developed a machine-learning model to classify movement behaviours (moderate-to-vigorous physical activity behaviours (MVPA), light physical activity behaviours, sedentary behaviour, sleep) in wrist-worn accelerometer data. Participants in UK Biobank, a prospective cohort, were asked to wear an accelerometer for 7 days, and we applied our machine-learning model to classify their movement behaviours. Using compositional data analysis Cox regression, we investigated how reallocating time between movement behaviours was associated with CVD incidence. Results In leave-one-participant-out analysis, our machine-learning method classified free-living movement behaviours with mean accuracy 88% (95% CI 87% to 89%) and Cohen’s kappa 0.80 (95% CI 0.79 to 0.82). Among 87 498 UK Biobank participants, there were 4105 incident CVD events. Reallocating time from any behaviour to MVPA, or reallocating time from sedentary behaviour to any behaviour, was associated with lower CVD risk. For an average individual, reallocating 20 min/day to MVPA from all other behaviours proportionally was associated with 9% (95% CI 7% to 10%) lower risk, while reallocating 1 hour/day to sedentary behaviour from all other behaviours proportionally was associated with 5% (95% CI 3% to 7%) higher risk. Conclusion Machine-learning methods classified movement behaviours accurately in free-living accelerometer data. Reallocating time from other behaviours to MVPA, and from sedentary behaviour to other behaviours, was associated with lower risk of incident CVD, and should be promoted by interventions and guidelines.

中文翻译：

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重新分配设备测量的运动行为与心血管疾病风险之间的时间

目的 使用机器学习方法改进自由生活加速度计数据中的运动行为分类，并研究机器学习的运动行为与成人心血管疾病 (CVD) 风险之间的关系。方法 使用来自 152 名参与者的自由生活数据，我们开发了一个机器学习模型，用于对腕戴式加速度计数据中的运动行为（中度到剧烈运动行为 (MVPA)、轻度运动行为、久坐行为、睡眠）进行分类. 前瞻性队列 UK Biobank 的参与者被要求佩戴加速度计 7 天，我们应用我们的机器学习模型对他们的运动行为进行分类。使用成分数据分析 Cox 回归，我们研究了重新分配运动行为之间的时间与心血管疾病发病率的关系。结果 在留一参与者分析中，我们的机器学习方法对自由生活运动行为进行分类，平均准确度为 88%（95% CI 87% 至 89%）和 Cohen 的 kappa 0.80（95% CI 0.79 至 0.82）。在 87 498 名英国生物银行参与者中，有 4105 起 CVD 事件。将时间从任何行为重新分配到 MVPA，或将时间从久坐行为重新分配到任何行为，都与较低的 CVD 风险相关。对于普通人来说，将 20 分钟/天从所有其他行为中按比例重新分配给 MVPA 与 9% (95% CI 7% 至 10%) 的风险降低相关，而将 1 小时/天从所有其他行为按比例重新分配给久坐行为是与 5% (95% CI 3% 至 7%) 更高的风险相关。结论 机器学习方法准确地对自由生活加速度计数据中的运动行为进行分类。将时间从其他行为重新分配给 MVPA，从久坐行为重新分配给其他行为，与较低的 CVD 风险相关，应通过干预措施和指南加以促进。