de Almeida Mendes Márcio, da Silva Inácio C M, Ramires Virgílio V, Reichert Felipe F, Martins Rafaela C, Tomasi Elaine
Post-Graduate Program in Epidemiology, Federal University of Pelotas, Brazil.
School of Physical Education, Federal University of Pelotas, Brazil.
Gait Posture. 2018 Mar;61:98-110. doi: 10.1016/j.gaitpost.2017.12.028. Epub 2017 Dec 30.
Most of calibration studies based on accelerometry were developed using count-based analyses. In contrast, calibration studies based on raw acceleration signals are relatively recent and their evidences are incipient. The aim of the current study was to systematically review the literature in order to summarize methodological characteristics and results from raw data calibration studies. The review was conducted up to May 2017 using four databases: PubMed, Scopus, SPORTDiscus and Web of Science. Methodological quality of the included studies was evaluated using the Landis and Koch's guidelines. Initially, 1669 titles were identified and, after assessing titles, abstracts and full-articles, 20 studies were included. All studies were conducted in high-income countries, most of them with relatively small samples and specific population groups. Physical activity protocols were different among studies and the indirect calorimetry was the criterion measure mostly used. High mean values of sensitivity, specificity and accuracy from the intensity thresholds of cut-point-based studies were observed (93.7%, 91.9% and 95.8%, respectively). The most frequent statistical approach applied was machine learning-based modelling, in which the mean coefficient of determination was 0.70 to predict physical activity energy expenditure. Regarding the recognition of physical activity types, the mean values of accuracy for sedentary, household and locomotive activities were 82.9%, 55.4% and 89.7%, respectively. In conclusion, considering the construct of physical activity that each approach assesses, linear regression, machine-learning and cut-point-based approaches presented promising validity parameters.
大多数基于加速度计的校准研究是使用基于计数的分析方法开展的。相比之下，基于原始加速度信号的校准研究相对较新，其证据也刚刚出现。本研究的目的是系统回顾文献，以总结原始数据校准研究的方法学特征和结果。该综述截至2017年5月，使用了四个数据库：PubMed、Scopus、SPORTDiscus和Web of Science。采用Landis和Koch指南评估纳入研究的方法学质量。最初，识别出1669篇标题，在评估标题、摘要和全文后，纳入了20项研究。所有研究均在高收入国家进行，其中大多数样本量相对较小且针对特定人群。各研究间的身体活动方案不同，间接测热法是最常用的标准测量方法。基于切点研究的强度阈值的敏感性、特异性和准确性的平均值较高（分别为93.7%、91.9%和95.8%）。应用最频繁的统计方法是基于机器学习的建模，其中预测身体活动能量消耗的平均决定系数为0.70。关于身体活动类型的识别，久坐、家务和 locomotive 活动的准确性平均值分别为82.9%、55.4%和89.7%。总之，考虑到每种方法所评估的身体活动结构，线性回归、机器学习和基于切点的方法呈现出有前景的效度参数。
