title: Determinants of physical activity in America: a first characterization of physical activity profile using the National Health and Nutrition Examination Survey (NHANES).
abstract: OBJECTIVE
  To develop and implement methodologies for characterizing accelerometry-derived patterns of physical activity (PA) in the United States in relation to demographics, anthropometrics, behaviors, and comorbidities using the National Health and Nutrition Examination Survey (NHANES) dataset.
  
  
  METHODS
  Retrospective analysis of nationally representative database.
  
  
  METHODS
  Computer-generated modeling in silico.
  
  
  METHODS
  A total of 6329 adults in the United States from the NHANES 2003-2004 database.
  
  
  METHODS
  To discover subtle multivariate signal in the dynamic and noisy accelerometry data, we developed a novel approach, termed discretized multiple adaptive regression and implemented the algorithm in SAS 9.2 (SAS Institute, Cary, NC).
  
  
  METHODS
  Demographic, anthropometric, comorbidity, and behavioral variables.
  
  
  RESULTS
  The intensity of PA decreased with both increased age and increased body mass index. Both greater education and greater income correlate with increased activity over short durations and reduced activity intensity over long durations. Numerous predictors demonstrated effects within activity ranges that may be masked by use of the standard activity intensity intervals. These include age, one of the most robust variables, where we discovered decreasing activities inside the moderate activity range. It also includes gender, where women compared with men have increased proportions of active times up to the center of light activity range, and income greater than $45,000, where a complex effect is seen with little correspondence to existing cut-points.
  
  
  CONCLUSIONS
  The results presented in this study suggest that the method of multiple regression and heat map visualization can generate insights otherwise hidden in large datasets such as NHANES. A review of the provided heat maps reveals the trends discussed previously involving demographic, anthropometric, comorbidity, and behavioral variables. It also demonstrates the power of accelerometry to expose alterations in PA. Ultimately, this study provides a US population-based norm to use in future studies of PA.
authors.affiliation: Department of Orthopaedics, PM&R Section, Stanford University, Palo Alto, CA(∗)(†)(‡)(¶).
authors.forename: Ming-Chih Jeffrey
authors.initials: MC
authors.lastname: Kao
authors.affiliation: Department of Orthopaedics, PM&R Section, Stanford University, Palo Alto, CA(∗)(†)(‡)(¶).
authors.forename: Renata
authors.initials: R
authors.lastname: Jarosz
authors.affiliation: Department of Orthopaedics, PM&R Section, Stanford University, Palo Alto, CA(∗)(†)(‡)(¶).
authors.forename: Michael
authors.initials: M
authors.lastname: Goldin
authors.affiliation: Michigan State University, East Lansing, MI(§).
authors.forename: Amy
authors.initials: A
authors.lastname: Patel
authors.affiliation: Department of Orthopaedics, PM&R Section, Stanford University, Palo Alto, CA(∗)(†)(‡)(¶). Electronic address: msmuck@stanford.edu.
authors.forename: Matthew
authors.initials: M
authors.lastname: Smuck
mesh_terms: D061725:Accelerometry / Q000379:methods*; D000328:Adult; D000368:Aged; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009043:Motor Activity; D009749:Nutrition Surveys / Q000379:methods*; D009765:Obesity / Q000453:epidemiology* / Q000517:prevention & control; D015203:Reproducibility of Results; D012189:Retrospective Studies; D014481:United States / Q000453:epidemiology; D055815:Young Adult