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Trever J. Ball, Elizabeth A. Joy, Lisa H. Gren, Ruthann Cunningham and Janet M. Shaw

Background:

Few have examined predictive relationships between physical activity (PA) and health using electronic health records (EHRs) of patient-reported PA.

Objective:

Assess initial predictive validity of the Physical Activity “Vital Sign” (PAVS) recorded in EHRs with BMI and disease burden.

Methods:

EHRs were from November 2011 to November 2013 (n = 34,712). Differences in not meeting Physical Activity Guidelines (PAG) were tested using chi-square analysis between being normal weight versus overweight/obese, and scoring below versus above the 50th percentile of the Charlson Comorbidity Index (CCI). Repeated measures logistic regression was used to determine odds of BMI and CCI classifications according to responses to the PAVS as not meeting PAG.

Results:

Patients who did not meet PAG according to the PAVS were more likely than normal weight patients to have a higher BMI (BMI 25.0–29.9, OR = 1.19, P = .001; BMI 30.0–34.9, OR = 1.39, P < .0001; BMI 35.0–39.9, OR = 2.42, P < .0001; BMI ≥ 40, OR = 3.7, P < .0001) and also higher disease burden (above 50th percentile for CCI, OR = 1.8, P < .0001).

Conclusions:

The strong association of the PAVS found with patient BMI and moderately-strong association with disease burden supports initial predictive validity of the PAVS recorded in EHRs. PA recorded in EHRs may be vastly useful for assessing patient disease and cost burdens attributed independently to PA behavior.

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Juliessa M. Pavon, Richard J. Sloane, Carl F. Pieper, Cathleen S. Colón-Emeric, David Gallagher, Harvey J. Cohen, Katherine S. Hall, Miriam C. Morey, Midori McCarty, Thomas L. Ortel and Susan N. Hastings

, 2016 ). Despite the need for information about patient mobility, it is not clear how consistently activity data is collected and displayed in electronic health records (EHRs). In this study, we examined (a) the availability of physical activity information in the EHR; (b) the correlation between

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Morgan N. Clennin and Russell R. Pate

trajectories in children using electronic health records . Obesity . 2015 ; 23 ( 1 ): 207 – 212 . PubMed ID: 25324223 doi:10.1002/oby.20903 32. Rossen LM . Neighbourhood economic deprivation explains racial/ethnic disparities in overweight and obesity among children and adolescents in the USA . J

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Annemarthe L. Herrema, Marjan J. Westerman, Ellen J.I. van Dongen, Urszula Kudla and Martijn Veltkamp

. Appetite, 83 , 287 – 296 . PubMed doi:10.1016/j.appet.2014.09.002 10.1016/j.appet.2014.09.002 DesRoches , C.M. , Campbell , E.G. , Rao , S.R. , Donelan , K. , Ferris , T.G. , Jha , A. , . . . Shields , A.E. ( 2008 ). Electronic health records in ambulatory care: A National Survey of