title: Calibration of Self-Report Measures of Physical Activity and Sedentary Behavior.
abstract: Calibration equations offer potential to improve the accuracy and utility of self-report measures of physical activity (PA) and sedentary behavior (SB) by rescaling potentially biased estimates. The present study evaluates calibration models designed to estimate PA and SB in a representative sample of adults from the Physical Activity Measurement Study.
  
  
  
  Participants in the Physical Activity Measurement Study project completed replicate single-day trials that involved wearing a Sensewear armband (SWA) monitor for 24 h followed by a telephone administered 24-h PA recall (PAR). Comprehensive statistical model selection and validation procedures were used to develop and test separate calibration models designed to predict objectively measured SB and moderate-to-vigorous PA (MVPA) from self-reported PAR data. Equivalence testing was used to evaluate the equivalence of the model-predicted values with the objective measures in a separate holdout sample.
  
  
  
  The final prediction model for both SB and MVPA included reported time spent in SB and MVPA, as well as terms capturing sex, age, education, and body mass index. Cross-validation analyses on an independent sample exhibited high correlations with observed SB (r = 0.72) and MVPA (r = 0.75). Equivalence testing demonstrated that the model-predicted values were statistically equivalent to the corresponding objective values for both SB and MVPA.
  
  
  
  The results demonstrate that simple regression models can be used to statistically adjust for overestimation or underestimation in self-report measures among different segments of the population. The models produced group estimates from the PAR that were statistically equivalent to the observed time spent in SB and MVPA obtained from the objective SWA monitor; however, additional work is needed to correct for estimates of individual behavior.
authors.affiliation: 1Department of Kinesiology, Iowa State University, Ames, IA; 2Department of Data Science and Statistics, Mathematica Policy Research, Washington, DC; 3MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, UNITED KINGDOM; 4Division of Cancer Epidemiology and Genetics, Nutritional Epidemiology Branch, National Cancer Institute, Rockville, MD.
authors.forename: Gregory J
authors.initials: GJ
authors.lastname: Welk
authors.forename: Nicholas K
authors.initials: NK
authors.lastname: Beyler
authors.forename: Youngwon
authors.initials: Y
authors.lastname: Kim
authors.forename: Charles E
authors.initials: CE
authors.lastname: Matthews
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D015992:Body Mass Index; D004522:Educational Status; D015444:Exercise; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D011939:Mental Recall; D008875:Middle Aged; D015203:Reproducibility of Results; D057185:Sedentary Behavior; D057566:Self Report; D011795:Surveys and Questionnaires; D055815:Young Adult