Modeling Energy Expenditure Estimation in Occupational Context by Actigraphy: A Multi Regression Mixed-Effects Model

Area of Science:

• Sports Science
• Occupational Health
• Biostatistics

Background:

• Accurate energy expenditure prediction is vital for health and occupational applications.
• Existing methods for assessing metabolic rate have limitations.

Purpose of the Study:

• To propose a statistical regression model for estimating energy expenditure using actigraphy and personal characteristics.
• To cross-validate the model's results against reference standardized methods.

Main Methods:

• Hierarchical mixed-effects regression modeling using multitask protocol data.
• Data collection included actigraphy, indirect calorimetry, and personal/lifestyle information from 50 healthy adults.
• Analysis focused on movement, heart rate, and anthropometric variables.

Main Results:

• Movement, heart rate, and body composition variables significantly influenced energy expenditure estimation.
• The proposed model demonstrated good agreement with indirect calorimetry measurements.
• The model outperformed existing international guidelines for metabolic rate assessment.

Conclusions:

• The developed actigraphy-based model provides a reliable method for estimating energy expenditure.
• This approach offers a practical alternative to traditional normative guidelines.
• Further research can incorporate lifestyle variables to enhance prediction accuracy.