Time Series Response Analyser (TSRA) v2.0: A web-based tool for transparent summary statistics from discrete time-series data

Narang, BJ, Atkinson, G ORCID: 0000-0002-5459-9042, Gonzales, HT and Betts, JA Time Series Response Analyser (TSRA) v2.0: A web-based tool for transparent summary statistics from discrete time-series data. International Journal of Sport Nutrition and Exercise Metabolism. ISSN 1526-484X (Accepted)

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Abstract

Discrete time-series measurements collected at predefined timepoints are widely used in sport nutrition and exercise metabolism research, including tolerance tests, tracer studies, and physiological responses to experimental interventions. These datasets are commonly summarised using derived metrics such as area under the curve. The original Time Series Response Analyser (TSRA), introduced in 2020 as a spreadsheet-based tool, aimed to standardise these calculations and reduced the risk of manual errors. However, spreadsheet implementations can be difficult to maintain, extend, and version control, and provide limited transparency regarding exactly how outputs are derived. Here, we present TSRA v2.0, a web31 based successor designed to improve accessibility, usability, and long-term maintainability while preserving the analytical purpose of the original tool. The application runs in a standard web browser and guides users through a structured workflow consisting of data setup, file interpretation screening, interactive analysis, and export of results. It retains the core summary metrics of the original version while expanding visualisation capabilities and export options. In summary, TSRA v2.0 is a free, open-source web-based application for researchers analysing discrete time-series experiments across both repeated-measures and independent-groups designs, supporting transparent and reproducible analytical workflows while enabling future development through a code-based platform.

Item Type:	Article
Uncontrolled Keywords:	1106 Human Movement and Sports Sciences; 1116 Medical Physiology; Sport Sciences; 3210 Nutrition and dietetics; 4207 Sports science and exercise
Subjects:	Q Science > QA Mathematics > QA76 Computer software R Medicine > RC Internal medicine > RC1200 Sports Medicine
Divisions:	Sport and Exercise Sciences
Publisher:	Human Kinetics
Date of acceptance:	1 April 2026
Date Deposited:	09 Apr 2026 10:16
Last Modified:	09 Apr 2026 10:16
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28326

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