Related Experiment Video
Updated: Jun 3, 2026

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
Published on: March 7, 2019
Identifying population subgroups at risk for underestimating weight health risks and overestimating physical activity
Corneel Vandelanotte1, Mitch J Duncan, Christine Hanley
1Centre for Physical Activity Studies, Institute for Health and Social Sciences Research, CQUniversity Australia, Building 18, Bruce Highway, Rockhampton QLD 4702, Australia. c.vandelanotte@cqu.edu.au
Abstract:
People may incorrectly perceive that their body weight or Physical Activity (PA) meets health recommendations; this provides an obstacle for change. In this study self-reported BMI and PA were assessed in relation to questions regarding perception of meeting weight and PA recommendations. Signal detection analysis was used to identify population subgroups. Study outcomes showed that 34.4 per cent of the 2535 adult participants underestimated weight health risks; low income, male participants who believed they were sufficiently active underestimated weight health risks the most (52.6%). A total of 23.7 per cent of participants overestimated PA health benefits; older age, low educated participants who did not believe their body weight was a health risk overestimated PA health benefits the most (58.3%). In conclusion, as weight and PA misperceptions are high, efforts are needed to reduce these misperceptions, so that behaviour change initiatives can be more effective.
Related Concept Videos
Strategies for Assessing and Addressing Confounding
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Regression Toward the Mean
Bias in Epidemiological Studies
Drug Dosing: Obese Patients
Confounding in Epidemiological Studies

