The analysis of time series data is common in nutrition and metabolism research for quantifying the physiological responses to various stimuli. The reduction of many data from a time series into a summary statistic(s) can help quantify and communicate the overall response in a more straightforward way and in line with a specific hypothesis. Nevertheless, many summary statistics have been selected by various researchers, and some approaches are still complex. The time-intensive nature of such calculations can be a burden for especially large datasets and may, therefore, introduce computational errors, which are difficult to recognize and correct. In this short commentary, we introduce a newly-developed tool that automates many of the processes commonly used by researchers for discrete-time series analysis, with particular emphasis on how the tool may be implemented within nutrition and exercise science research.
|Journal||International Journal of Sport Nutrition and Exercise Metabolism|
|Publication status||Accepted/In press - 3 Jul 2020|