CRD42016039991.

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Recent technological advances have transformed the research on physical activity initially based on questionnaire data to the most recent objective data from accelerometers. The shift to availability of raw accelerations has increased measurement accuracy, transparency, and the potential for data harmonization. However, it has also shifted the need for considerable processing expertise to the researcher. Many users do not have this expertise. The R package GGIR has been made available to all as a tool to convermulti-day high resolution raw accelerometer data from wearable movement sensors into meaningful evidence-based outcomes and insightful reports for the study of human daily physical activity and sleep. This paper aims to provide a one-stop overview of GGIR package, the papers underpinning the theory of GGIR, and how research contributes to the continued growth of the GGIR package. The package includes a range of literature-supported methods to clean the data and provide day-by-day, as well as full recording, weekly, weekend, and weekday estimates of physical activity and sleep parameters. In addition, the package also comes with a shell function that enables the user to process a set of input files and produce csv summary reports with a single function call, ideal for users less proficient in R. GGIR has been used in over 90 peer-reviewed scientific publications to date. The evolution of GGIR over time and widespread use across a range of research areas highlights the importance of open source software development for the research community and advancing methods in physical behavior research.

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The aims of the present article are to systematically review and meta-analyze the existing evidence on: 1) differences in physical activity (PA), sedentary behavior (SB), cardiorespiratory fitness (CRF) and muscular strength (MST) between metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO); and 2) the prognosis of all-cause mortality and cardiovascular disease (CVD) mortality/morbidity in MHO individuals, compared with the best scenario possible, i.e., metabolically healthy normal-weight (MHNW), after adjusting for PA, SB, CRF or MST. Our systematic review identified 67 cross-sectional studies to address aim 1, and 11 longitudinal studies to address aim 2. The major findings and conclusions from the current meta-analysis are: 1) MHO individuals are more active, spend less time in SB, and have a higher level of CRF (yet no differences in MST) than MUO individuals, suggesting that their healthier metabolic profile could be at least partially due to these healthier lifestyle factors and attributes. 2) The meta-analysis of cohort studies which accounted for PA (N = 10 unique cohorts, 100% scored as high-quality) support the notion that MHO individuals have a 24-33% higher risk of all-cause mortality and CVD mortality/morbidity compared to MHNW individuals. This risk was borderline significant/non-significant, independent of the length of the follow-up and lower than that reported in previous meta-analyses in this topic including all type of studies, which could be indicating a modest reduction in the risk estimates as a consequence of accounting for PA. 3) Only one study has examined the role of CRF in the prognosis of MHO individuals. This study suggests that the differences in the risk of all-cause mortality and CVD mortality/morbidity between MHO and MHNW are largely explained by differences in CRF between these two phenotypes.

This study aimed to compare estimations of sedentary time (SED) and time spent in physical activity (PA) intensities in children with overweight/obesity across different age‐appropriate cut‐points based on different body‐worn attachment sites and acceleration metrics. A total of 104 overweight/obese children (10.1 ± 1.1 years old, 43 girls) concurrently wore ActiGraph GT3X+ accelerometers on their right hip and non‐dominant wrist for 7 days (24 hours). Euclidean norm −1 g (ENMO) and activity counts from both vertical axis (VACounts) and vector magnitude (VMCounts) were derived. We calculated estimates of SED and light, moderate, vigorous, and moderate‐to‐vigorous (MVPA) intensity PA using different published cut‐points for children. The prevalence of children meeting the recommended 60 min/d of MVPA was calculated. The time spent in SED and the different PA intensities largely differed across cut‐points based on different attachment sites and acceleration metrics (ie, SED = 11‐252 min/d; light PA = 10‐217 min/d; moderate PA = 1‐48 min/d; vigorous PA = 1‐35 min/d; MVPA = 4‐66 min/d). Consequently, the prevalence of children meeting the recommended 60 min/d of MVPA varied from 8% to 96% of the study sample. The present study provides a comprehensive comparison between available cut‐points for different attachment and acceleration metrics in children. Furthermore, our data clearly show that it is not possible (and probably will never be) to know the prevalence of meeting the PA guidelines based on accelerometer data since apparent differences range from almost zero to nearly everyone meeting the guidelines.

The inter-relationship between physical activity, sedentary behaviour and sleep (collectively defined as physical behaviours) is of interest to researchers from different fields. Each of these physical behaviours has been investigated in epidemiological studies, yet their codependency and interactions need to be further explored and accounted for in data analysis. Modern accelerometers capture continuous movement through the day, which presents the challenge of how to best use the richness of these data. In recent years, analytical approaches first applied in other scientific fields have been applied to physical behaviour epidemiology (eg, isotemporal substitution models, compositional data analysis, multivariate pattern analysis, functional data analysis and machine learning). A comprehensive description, discussion, and consensus on the strengths and limitations of these analytical approaches will help researchers decide which approach to use in different situations. In this context, a scientific workshop and meeting were held in Granada to discuss: (1) analytical approaches currently used in the scientific literature on physical behaviour, highlighting strengths and limitations, providing practical recommendations on their use and including a decision tree for assisting researchers’ decision-making; and (2) current gaps and future research directions around the analysis and use of accelerometer data. Advances in analytical approaches to accelerometer-determined physical behaviours in epidemiological studies are expected to influence the interpretation of current and future evidence, and ultimately impact on future physical behaviour guidelines.