Systematic review of the statistical scope used in studies based on skeletal muscle autophagy and exercise
- Published
- Accepted
- Subject Areas
- Molecular Biology, Statistics
- Keywords
- exercise, microbiology, statistics, systematic review, autophagy, lysosomes, skeletal muscle
- Copyright
- © 2014 Brownlee
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2014. Systematic review of the statistical scope used in studies based on skeletal muscle autophagy and exercise. PeerJ PrePrints 2:e556v1 https://doi.org/10.7287/peerj.preprints.556v1
Abstract
Skeletal muscle reaction to exercise is an essential are of research due to its ongoing prevalence in disease research and general health. It is well documented that under exercise conditions, biogenesis and autophagy increase. One main component of this pathway are lysosomes, the essential cellular clearance machine. Statistical analyses used to analyze these data have been sustained over the years. The objective of this systematic review is to compare and contrast the different methods used for analyzing data in molecular exercise physiology. Upon investigating the research papers the majority of the papers used either a t-test or an ANOVA as their primary statistical analyses, used 41% and 64% of the time, respectively. All other statistical tests were used a maximum of 9% of the time. Another trend that was evident was the increased utilization of post hoc tests in the more recent papers compared to earlier papers. This could provide interesting evidence into the credibility of the results reported and provide more insight into the research in molecular exercise physiology.
Author Comment
This is a submission to PeerJ PrePrints for comments. Paper is for BIOL 5081 Biostats, York University, Fall 2014.