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Related Experiment Video

Updated: Jun 8, 2026

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Published on: March 7, 2019

Adults' physical activity patterns across life domains: cluster analysis with replication.

Liza S Rovniak¹, James F Sallis², Brian E Saelens³

¹Department of Public Health Sciences, Pennsylvania State University College of Medicine.

Health Psychology : Official Journal of the Division of Health Psychology, American Psychological Association

|September 15, 2010

Summary

Adult physical activity patterns can be grouped into Low Activity, Active Leisure, and Active Job clusters. Tailored interventions may benefit specific groups based on their activity patterns and support systems.

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

Published on: July 27, 2018

Area of Science:

Public Health
Behavioral Science
Epidemiology

Background:

Understanding physical activity patterns across life domains is crucial for designing effective health interventions and policies.
Previous research has often focused on single domains, potentially overlooking the complex interplay of activity across different life areas.

Purpose of the Study:

To identify distinct physical activity patterns among adults across multiple life domains (leisure, occupation, transport, home).
To examine the objective physical activity levels and associated psychosocial and built environment characteristics of these identified patterns.

Main Methods:

Cluster analysis was performed on data from adults in two U.S. regions (Baltimore/Washington, DC and Seattle, WA).
Physical activity was assessed through self-report across four domains and objectively measured using accelerometers.
Psychosocial factors, built environment characteristics, and body mass index were also analyzed in relation to activity patterns.

Main Results:

Three consistent clusters emerged across both regions: Low Activity, Active Leisure, and Active Job.
The Active Leisure group exhibited the highest support for physical activity, greatest objectively measured activity, and lowest body mass index.
The Active Job group, characterized by lower socioeconomic status, showed intermediate objectively measured activity levels.

Conclusions:

Adults can be categorized into distinct physical activity pattern groups based on domain-specific engagement.
Significant differences in psychosocial and built environment support exist across these clusters, highlighting the potential for targeted public health interventions.