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Cited by 486Physical Activity and HealthContext-Aware Activity Recognition SystemsObesity, Physical Activity, Diet

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Objective vs. Self-Reported Physical Activity and Sedentary Time: Effects of Measurement Method on Relationships with Risk Biomarkers

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Celis-Morales, Carlos A., Perez-Bravo, Francisco, Ibañez, Luis, Salas, Carlos, Bailey, Mark E. S., Gill, Jason M. R.·2012·PLoS ONE
DOI 10.1371/journal.pone.0036345PMC PMC3348936
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Abstract

PURPOSE: Imprecise measurement of physical activity variables might attenuate estimates of the beneficial effects of activity on health-related outcomes. We aimed to compare the cardiometabolic risk factor dose-response relationships for physical activity and sedentary behaviour between accelerometer- and questionnaire-based activity measures. METHODS: Physical activity and sedentary behaviour were assessed in 317 adults by 7-day accelerometry and International Physical Activity Questionnaire (IPAQ). Fasting blood was taken to determine insulin, glucose, triglyceride and total, LDL and HDL cholesterol concentrations and homeostasis model-estimated insulin resistance (HOMA(IR)). Waist circumference, BMI, body fat percentage and blood pressure were also measured. RESULTS: For both accelerometer-derived sedentary time (<100 counts.min(-1)) and IPAQ-reported sitting time significant positive (negative for HDL cholesterol) relationships were observed with all measured risk factors--i.e. increased sedentary behaviour was associated with increased risk (all p ≤ 0.01). However, for HOMA(IR) and insulin the regression coefficients were >50% lower for the IPAQ-reported compared to the accelerometer-derived measure (p<0.0001 for both interactions). The relationships for moderate-to-vigorous physical activity (MVPA) and risk factors were less strong than those observed for sedentary behaviours, but significant negative relationships were observed for both accelerometer and IPAQ MVPA measures with glucose, and insulin and HOMA(IR) values (all p<0.05). For accelerometer-derived MVPA only, additional negative relationships were seen with triglyceride, total cholesterol and LDL cholesterol concentrations, BMI, waist circumference and percentage body fat, and a positive relationship was evident with HDL cholesterol (p = 0.0002). Regression coefficients for HOMA(IR), insulin and triglyceride were 43-50% lower for the IPAQ-reported compared to the accelerometer-derived MVPA measure (all p≤0.01). CONCLUSION: Using the IPAQ to determine sitting time and MVPA reveals some, but not all, relationships between these activity measures and metabolic and vascular disease risk factors. Using this self-report method to quantify activity can therefore underestimate the strength of some relationships with risk factors.

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Figure 1 Relationships between accelerometer-derived sedentary time and IPAQ-reported sitting time (top panel) and between accelerometer-derived and IPAQ-reported MVPA (bottom panel). Solid line on each plot represents the linear regression line; dotted line represents the line of equality, y = x. Pearson (r) and concordance (Pc) correlation coefficients shown.
Figure 1 Relationships between accelerometer-derived sedentary time and IPAQ-reported sitting time (top panel) and between accelerometer-derived and IPAQ-reported MVPA (bottom panel). Solid line on each plot represents the linear regression line; dotted line represents the line of equality, y = x. Pearson (r) and concordance (Pc) correlation coefficients shown.CC BY · PubMed Central
Figure 2 Relationships between sedentary/sitting time and triglyceride concentration (top panel) and MVPA and triglyceride concentration (bottom panel) for accelerometer-derived and IPAQ-reported activity measures. Solid line on each plot represents the linear regression line for accelerometer-derived activity measure; dotted line is the regression line for IPAQ-reported activity measure. Unadjusted data values presented. See Tables 4 and 5 for β coefficients and p-values for regression lines, adjusted for age, sex, ethnicity, environment and socio-economic status and smoking.
Figure 2 Relationships between sedentary/sitting time and triglyceride concentration (top panel) and MVPA and triglyceride concentration (bottom panel) for accelerometer-derived and IPAQ-reported activity measures. Solid line on each plot represents the linear regression line for accelerometer-derived activity measure; dotted line is the regression line for IPAQ-reported activity measure. Unadjusted data values presented. See Tables 4 and 5 for β coefficients and p-values for regression lines, adjusted for age, sex, ethnicity, environment and socio-economic status and smoking.CC BY · PubMed Central
Figure 3 Triglyceride concentrations by quartile of sitting/sedentary time (top panel) or MVPA (bottom panel) for the accelerometer-derived and IPAQ-reported activity measures. Trends for triglyceride concentration by sitting/sedentary time quartile significant for both accelerometer (p = 0.0001) and IPAQ (p = 0.014) measures. Trends for triglyceride concentration by MVPA quartile significant for the accelerometer (p = 0.022), but not the IPAQ (p = 0.139), activity measure.
Figure 3 Triglyceride concentrations by quartile of sitting/sedentary time (top panel) or MVPA (bottom panel) for the accelerometer-derived and IPAQ-reported activity measures. Trends for triglyceride concentration by sitting/sedentary time quartile significant for both accelerometer (p = 0.0001) and IPAQ (p = 0.014) measures. Trends for triglyceride concentration by MVPA quartile significant for the accelerometer (p = 0.022), but not the IPAQ (p = 0.139), activity measure.CC BY · PubMed Central

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Metadata

Authors
Celis-Morales, C. A., Perez-Bravo, F., Ibañez, L., Salas, C., Bailey, M. E. S., & Gill, J. M. R.
Affiliations
  • University of Glasgow (GB)
  • University of Concepción (CL)
Corresponding author
Gill, Jason M. R.
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2012-05-09
Volume
7
Issue
5
Pages
e36345
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0036345
PMC
PMC3348936
OpenAlex
W2156132533
Cited by
486
References
33
Citations by year
2026: 12 · 2025: 12 · 2024: 31 · 2023: 25 · 2022: 28 · 2021: 41 · 2020: 53 · 2019: 40 · 2018: 41 · 2017: 49 · 2016: 47 · 2015: 48 · 2014: 33 · 2013: 24
FWCI
22.51
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Universidad de Chile (submittedVersion, cc-by-nc-nd)
  • Newcastle University ePrints (Newcastle Univesity) (cc-by)
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
Keywords
physical activity measurement, sedentary behavior, accelerometer, International Physical Activity Questionnaire, self-reported activity, moderate-to-vigorous physical activity, cardiometabolic risk factors, insulin resistance, HOMA-IR, insulin, triglycerides, HDL cholesterol, LDL cholesterol, waist circumference, body fat percentage, measurement bias
MeSH terms
Adolescent, Adult, Aged, Blood Glucose, Blood Pressure, Fasting, Female, Humans, Insulin, Insulin Resistance, Cholesterol, HDL, Cholesterol, LDL, Middle Aged, Motor Activity, Risk Factors, Triglycerides, Vascular Diseases, Biomarkers, Metabolic Syndrome, Self Report

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