Statistical Issues


Regardless of how sedentary behavior and physical activity are assessed (eg, self-report instruments or wearable devices), appropriate statistical considerations are necessary. Historically, analyses have relied on the simple approach of fitting models with separate terms for sedentary behavior and physical activity. However, both variables are part of overall time use, meaning more time in one behavior may be compensated by less time in the other. This creates statistical dependence that undermines the validity of traditional models to examine the joint and indepen- dent effects of both variables on health. To overcome the interdependence of sedentary behavior and physical activity,


several statistical approaches may be considered. A leading example is isotem- poral substitution, which seeks to model the change in a health outcome when reallocating time from one behavior to another.107,108


For example, a model may


report the predicted change in body weight associated with replacing 30 minutes of sedentary time with moderate-to-vigorous physical activity over a 6-month span. Another approach to consider is clustering-focused analyses, which places partici- pants into categories rather than assessing sedentary behavior and physical activity on a continuous scale.109-111


This can be as simple as creating a two-by-two table


using median splits on each variable (ie, high/low sedentary behavior and high/low physical activity). This approach can work well in certain situations but may lead to imbalanced categories in others. In addition, median splits prevent compari- sons across studies, as the high and low cutoffs will differ from sample to sample. Practitioners can use data mining algorithms (eg, k-medoid clustering) to discover richer clusters in the data, which can lead to better balance across categories when that is a priority. The cluster discovery process can also be accompanied by cali- bration of predictive models (eg, random forests) to determine which cluster an individual belongs to, providing an avenue for standardized assessments across studies. While the above methods offer many advantages, they can be difficult to implement and interpret, which reinforces the need for interdisciplinary teams and especially for guidance from a biostatistician.


Practical Issues


Sedentary behavior and physical activity assessments need to capture represen- tative behavior patterns for each individual at the time of assessment. Therefore, measurements should generally be made over several days (often a week or more), and repeated measurements should be made for trials that span months or years. To avoid reactivity effects, it may also be beneficial to discard the first day or two of data collected in each assessment. Ideally, any assessment should include both weekdays and weekend days to allow for capture of normal variations in day-to- day behavior. This can also play a role in distinguishing different chronotypes of physical activity, such as “weekend warriors.”112 The chosen assessment methods for sedentary behavior and physical activ-


ity have a profound impact on the soundness of research findings. Historically, self-report instruments have been the default method for assessing both behaviors. However, wearable devices are increasingly becoming the standard method for research assessments of sedentary behavior and physical activity, and the draw- backs (eg, higher participant burden than what occurs for self-report methods) are often offset by the advantages (eg, objective and high-resolution data capture that is not possible with self-report).


CHAPTER 8: Physical Activity Assessment 137


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