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The '39 steps': an algorithm for performing statistical analysis of data on energy intake and expenditure
John R Speakman1, Quinn Fletcher, Lobke Vaanholt
1Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, AB24 2TZ, Scotland, UK. j.speakman@abdn.ac.uk
Disease Models & Mechanisms
|March 23, 2013
Summary
This study introduces a new algorithm for analyzing energy balance data in organisms from worms to humans. It provides a clear, step-by-step guide for accurate energy intake and expenditure analysis.
Area of Science:
- Metabolic research
- Biostatistics
- Animal and human physiology
Background:
- Rising obesity and diabetes epidemics necessitate accurate energy balance analysis.
- Historically, methods for analyzing energy intake and expenditure data have been debated.
- Recent consensus has emerged on best-practice statistical approaches.
Purpose of the Study:
- To present a novel algorithm for the statistically appropriate analysis of energy intake and expenditure data.
- To provide a user-friendly, step-by-step guide applicable to diverse organisms.
- To reduce confusion and controversy in energy balance data analysis.
Main Methods:
- Development of a practical algorithm for energy balance data analysis.
- Algorithm designed for use with human and experimental animal data (mammals, invertebrates).
- Compatibility with major statistical packages (SPSS, MINITAB, R) with detailed instructions.
Main Results:
- The algorithm offers a standardized approach to energy balance data analysis.
- Provides clear interpretations of results at each analytical step.
- Facilitates the application of consensus best-practice methods.
Conclusions:
- The presented algorithm aids in the statistically sound analysis of energy balance data.
- It addresses a long-standing need for clarity and consistency in the field.
- Promotes accurate research contributing to understanding obesity and diabetes.
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