GRANADA consensus on analytical approaches to assess associations with accelerometer-determined physical behaviours (physical activity, sedentary behaviour and sleep) in epidemiological studies

Abstract

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.

The Relationship between Students’ Physical Self-Concept and Their Physical Activity Levels and Sedentary Behavior: The Role of Students’ Motivation

“…It would be interesting for further investigations including the entire spectrum of motivational regulations (also measuring extrinsic regulations) to a better understanding in how PSC could be related to other students' motivational regulations, their PA levels and SB. Finally, we have used an own created scale for the current study to assess out-of-school PA levels instead of using other previous validated instruments [52].…”

A Wrinkle in Measuring Time Use for Cognitive Health: How should We Measure Physical Activity, Sedentary Behaviour and Sleep?

“…17 Thus, while we propose exploring the complex relationships of timeuse activity behaviours and circadian rhythms with cognitive health is an interesting idea for future research, there is not a method of simultaneously field-measuring PA, SB and sleep as markers of circadian regulation. 20,83 Wrist-worn actigraphy is the only field method which is capable of measuring all three time-use activity behaviours, while also providing indices of circadian regulation based on the rest-activity cycle. 84 The actigraphy guidelines of Migueles et al 83 highlighted that measuring circadian rhythmicity provided complementary information to time-use activity behaviour data; however, while the authors provided a detailed explanation of how time-use activity behaviour actigraphy data could be analysed, they did not suggest that these data could be used to estimate circadian rhythmicity which they viewed as a separate construct.…”

Association of daily composition of physical activity and sedentary behaviour with incidence of cardiovascular disease in older adults

“…Most prospective studies using objective measures have not accounted for this co-dependency and treated SB [19,20], LIPA [11,20,22] and MVPA [3,20] as independent behaviours. Recent epidemiological research calls to account for the relative nature of movement behaviours and to use appropriate statistical methods to deal with such data [23,24]. We aim to use an innovative approach, the compositional Cox regression [25] to better examine how different compositions of SB, LIPA, and MVPA in a waking day are associated with incident CVD in older adults.…”