比较节奏与基于机器学习的身体活动强度分类:身体活动与死亡率的关联变化
Le Wei1,2, Matthew N Ahmadi1,2, Raaj Kishore Biswas1,2
1Mackenzie Wearables Research Hub, Charles Perkins Centre, The University of Sydney, Sydney, Australia.
Scandinavian journal of medicine & science in sports
|September 10, 2024
概括
机器学习 (ML) 和基于节奏的方法准确地分类体力活动 (PA) 强度. ML方法显示,所有原因和心血管疾病死亡风险的最大降低,这表明它们在观察性研究中的有用性.
科学领域:
- 运动生理学 运动生理学
- 生物医学数据科学 生物医学数据科学
- 流行病学 流行病学
背景情况:
- 步骤节奏和机器学习 (ML) 用于健康研究中的体力活动 (PA) 强度分类.
- 准确的PA强度评估对于了解其对死亡率的影响至关重要.
研究的目的:
- 用基于节奏和ML方法比较强度特定的PA持续时间与所有原因 (ACM) 和心血管疾病 (CVD) 死亡率的关联.
- 在大型观测研究中评估不同PA强度分类方法的有效性.
主要方法:
- 利用了来自68,561名英国生物库参与者的数据,他们戴着手腕加速度计.
- 在活动类型和强度分类方面采用了两阶段的ML方法.
- 应用一级 (1LC) 和两级 (2LC) 基于节奏的方法,使用适度和强烈强度PA的已确定的步骤/分钟值.
主要成果:
- 2LC方法在ACM较低的PA持续时间显示了最强的关联.
- 在所有强度级别中,ML方法证明了最大的死亡风险降低.
- 标准化分析显示,ML方法对中度PA和心血管疾病死亡率产生了最明显的关联.
结论:
- 不同的PA强度分类方法给出了与死亡结果的不同关联.
- 在观察性研究中,ML方法似乎最有效地估计了中度和强烈的PA对健康的益处.
- 准确的PA强度评估是公共卫生建议和干预措施的关键.
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