Methods for estimating physical activity and energy expenditure using raw accelerometry data or novel analytical approaches: a repository, framework, and reporting guidelines
Methods for estimating physical activity and energy expenditure using raw accelerometry data or novel analytical approaches: a repository, framework, and reporting guidelines
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DOI：
10.1088/1361-6579/ac89c9
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发表时间：
2022-09-30
影响因子：
3.2
通讯作者：
Pfeiffer, Karin A.
中科院分区：
文献类型：
--
作者：
Clevenger, Kimberly A.;Montoye, Alexander H. K.;Pfeiffer, Karin A.
使用原始加速度或机器学习等新的分析方法（“新方法”）分析加速度计数据的方法的激增超过了它们在实践中的实现。这可能是由于缺乏可访问性，因为作者没有提供他们开发的模型，或者因为这些模型作为补充材料时很难找到。此外，当提供对模型的访问时，作者可能不包括关于如何使用模型的示例数据或说明。这进一步阻碍了其他研究人员的使用，特别是那些不是统计学或编写计算机代码专家的人。目的：我们创建了一个分析加速度计数据的新方法库，用于估计能量消耗和/或体力活动强度，以及指导未来工作的框架和报告指南。方法：从最近的范围界定审查中确定了方法。编译或创建可用的代码、模型、示例数据和指令。主要结果：存储库中托管了63种方法，包括学龄前儿童（n = 6）、儿童/青少年（n = 20）和成人（n = 42），使用髋关节（n = 45）、腕关节（n = 25）、大腿（n = 4）、胸部（n = 4）、踝关节（n = 6）、其他（n = 4）或监护仪佩戴位置组合（n = 9）。15个模型在R中实现，而48个模型以切割点、方程或决策树的形式提供。重要性：所开发的工具应有助于使用和开发分析加速度计数据的新方法，从而提高各项研究的数据协调性和一致性。未来的进展可能涉及包括作者没有链接到原始发表文章或识别活动类型的模型。
The proliferation of approaches for analyzing accelerometer data using raw acceleration or novel analytic approaches like machine learning ('novel methods') outpaces their implementation in practice. This may be due to lack of accessibility, either because authors do not provide their developed models or because these models are difficult to find when included as supplementary material. Additionally, when access to a model is provided, authors may not include example data or instructions on how to use the model. This further hinders use by other researchers, particularly those who are not experts in statistics or writing computer code. Objective: We created a repository of novel methods of analyzing accelerometer data for the estimation of energy expenditure and/or physical activity intensity and a framework and reporting guidelines to guide future work. Approach: Methods were identified from a recent scoping review. Available code, models, sample data, and instructions were compiled or created. Main Results: Sixty-three methods are hosted in the repository, in preschoolers (n = 6), children/adolescents (n = 20), and adults (n = 42), using hip (n = 45), wrist (n = 25), thigh (n = 4), chest (n = 4), ankle (n = 6), other (n = 4), or a combination of monitor wear locations (n = 9). Fifteen models are implemented in R, while 48 are provided as cut-points, equations, or decision trees. Significance: The developed tools should facilitate the use and development of novel methods for analyzing accelerometer data, thus improving data harmonization and consistency across studies. Future advances may involve including models that authors did not link to the original published article or those which identify activity type.