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Related Experiment Video

Updated: Jun 8, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

How to analyze longitudinal multilevel physical activity data with many zeros?

Andy H Lee¹, Yun Zhao, Kelvin K W Yau

¹School of Public Health, Curtin University of Technology, Perth, WA, Australia. Andy.Lee@curtin.edu.au

Preventive Medicine

|October 6, 2010

Summary

A novel two-part mixed regression model effectively analyzes complex physical activity data, revealing factors influencing participation and levels in older adults. This approach addresses challenges in longitudinal studies with skewed data and many zeros.

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Area of Science:

Biostatistics
Public Health
Gerontology

Background:

Physical activity (PA) is crucial for chronic disease management and overall health.
Longitudinal studies of PA face analytical challenges due to data characteristics like excess zeros, skewness, and non-independence.
Standard regression methods are often inadequate for analyzing such complex PA data.

Purpose of the Study:

To introduce and validate a two-part multilevel modeling approach for analyzing heterogeneous and correlated physical activity data.
To identify factors associated with physical activity participation and levels over time in longitudinal studies.
To provide a practical and statistically sound method for researchers dealing with complex PA data.

Main Methods:

A two-part multilevel modeling strategy was employed.
Part one utilized a logistic mixed regression model to assess PA prevalence and associated factors.
Part two employed a gamma mixed regression model to analyze PA levels among participants, accounting for nested data structures and extra variations via random effects.

Main Results:

The study demonstrated the effectiveness of a community-based PA intervention for older adults.
The two-part model successfully identified key factors influencing PA participation and levels.
The approach proved effective in analyzing longitudinal, clustered PA data.

Conclusions:

The two-part mixed regression approach offers a valid and practical solution for analyzing skewed, correlated PA data with many zeros.
This methodology is adaptable to complex hierarchical and multilevel settings.
Statistical software like STATA with GLLAMM facilitates the implementation of this advanced analytical technique.