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Accelerometry has become a mainstay approach for objectively monitoring children's physical activity and sedentary time in epidemiological studies. The magnitude of effort underlying successful data collection, management and treatment is prodigious and its complexity has been associated with increasingly diverse methodological choices that, while defensible relative to specific research questions, conspire to undermine the ability to compare results between studies. Although respecting widespread calls for best practices, it is also important to openly share tools and resources supporting potential improvements to research practice and study design, thus allowing others to replicate, further improve, and/or otherwise build on this foundation. The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE) is a large multinational study that included accelerometer-based measures of physical activity, sedentary time and sleep. This review summarizes the unique contributions of ISCOLE to the advancement of accelerometry in large studies of children's behavior, and in particular: (1) open-access publication of the ISCOLE accelerometry Manual of Operations; (2) 24-h waist-worn accelerometry protocol; (3) identification and extraction of nocturnal total sleep episode time (with open access to editable SAS syntax); (4) development of the first interpretive infrastructure for identifying and defining an evolved list of sleep-related variables from 24-h waist-worn accelerometry; (5) provision of a detailed model for reporting accelerometer paradata (administrative data related to accelerometry); and (6) cataloging the most detailed and defensible list of accelerometry-derived physical activity and sedentary time variables to date. The novel tools and resources associated with these innovations are shared openly in an effort to support methodological harmonization and overall advancement of accelerometry in large epidemiological studies.

Unique contributions of ISCOLE to the advancement of accelerometry in large studies