Calibration of raw accelerometer data to measure physical activity: A systematic review

• Published in: Gait & posture Vol. 61; pp. 98 - 110
• Main Authors: de Almeida Mendes, Márcio, da Silva, Inácio C.M., Ramires, Virgílio V., Reichert, Felipe F., Martins, Rafaela C., Tomasi, Elaine
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 01.03.2018
• Subjects: Accelerometry; Accelerometry - standards; Calibration; Calibration study; Calorimetry, Indirect; Energy Metabolism - physiology; Exercise - physiology; Humans; Machine Learning; Physical activity; Raw accelerometer data; Physical activity; Raw accelerometer data; Accelerometry; Calibration study
• ISSN: 0966-6362; 1879-2219
• DOI: 10.1016/j.gaitpost.2017.12.028

•Differences in methodology influence the comparability between calibration methods/studies.•Generalization of the results of calibration studies requires higher sample heterogeneity.•The different calibration approaches presented promising validity results. 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.