Object-oriented data analysis is a fascinating and developing field in modern statistical science with the potential to make significant and valuable contributions to biomedical applications. This statistical framework allows for the formalization of new methods to analyze complex data objects that capture more information than traditional clinical biomarkers. The paper applies the object-oriented framework to analyzing and predicting physical activity measured by accelerometers. As opposed to traditional summary metrics, we utilize a recently proposed representation of physical activity data as a distributional object, providing a more sophisticated and complete profile of individual energetic expenditure in all ranges of monitoring intensity. For the purpose of predicting these distributional objects, we propose a novel hybrid Frechet regression model and apply it to US population accelerometer data from NHANES 2011-2014. The semi-parametric character of the new model allows us to introduce non-linear effects for essential variables, such as age, that are known from a biological point of view to have nuanced effects on physical activity. At the same time, the inclusion of a global for linear term retains the advantage of interpretability for other variables, particularly categorical covariates such as ethnicity and sex. The results obtained in our analysis are helpful from a public health perspective and may lead to new strategies for optimizing physical activity interventions in specific American subpopulations.

翻译：面向对象数据分析是现代统计科学中一个引人入胜且不断发展的领域，有望为生物医学应用做出重要且有价值的贡献。该统计框架允许形式化新方法，以分析比传统临床生物标志物包含更多信息的复杂数据对象。本文应用面向对象框架来分析和预测由加速度计测量的体力活动。与传统总结性指标不同，我们利用近期提出的将体力活动数据表示为分布对象的方法，从而在监测强度的所有范围内提供个体能量消耗更全面、更精细的轮廓。为了预测这些分布对象，我们提出了一种新颖的混合弗雷歇回归模型，并将其应用于2011-2014年NHANES美国人口加速度计数据。新模型的半参数特性使我们能够引入关键变量（如年龄）的非线性效应——从生物学角度已知该变量对体力活动具有微妙影响。同时，全局线性项的纳入保留了对其他变量（尤其是种族和性别等分类协变量）的可解释性优势。本研究结果从公共卫生角度具有参考价值，并可能为优化美国特定亚人群的体力活动干预策略提供新思路。