Kristie Kusibab1, John A Gallis2,3, Joseph R Egger3, Maren K Olsen2,4, Sandy Askew3,5, Dori M Steinberg3,5,6, Gary Bennett3,5,7. 1. PharPoint Research, Inc., Durham, North Carolina, USA. 2. Department of Biostatistics & Bioinformatics, Duke University, Durham, North Carolina, USA. 3. Duke Global Health Institute, Duke University, Durham, North Carolina, USA. 4. Center for Health Services Research in Primary Care, Durham VA Medical Center, Durham, North Carolina, USA. 5. Duke Global Digital Health Science Center, Duke University, Durham, North Carolina, USA. 6. Duke School of Nursing, Duke University, Durham, North Carolina, USA. 7. Department of Psychology and Neuroscience, Duke University, Durham, North Carolina, USA.
Abstract
OBJECTIVE: This study aimed to compare weight collected at clinics and recorded in the electronic health record (EHR) with primary study-collected trial weights to assess the validity of using EHR data in future pragmatic weight loss or weight gain prevention trials. METHODS: For both the Track and Shape obesity intervention randomized trials, clinic EHR weight data were compared with primary trial weight data over the same time period. In analyzing the EHR weights, intervention effects were estimated on the primary outcome of weight (in kilograms) with EHR data, using linear mixed effects models. RESULTS: EHR weight measurements were higher on average and more variable than trial weight measurements. The mean difference and 95% CI were similar at all time points between the estimates using EHR and study-collected weights. CONCLUSIONS: The results of this study can be used to help guide the planning of future pragmatic weight-related trials. This study provides evidence that body weight measurements abstracted from the EHR can provide valid, efficient, and cost-effective data to estimate treatment effects from randomized clinical weight loss and weight management trials. However, care should be taken to properly understand the data-generating process and any mechanisms that may affect the validity of these estimates.
OBJECTIVE: This study aimed to compare weight collected at clinics and recorded in the electronic health record (EHR) with primary study-collected trial weights to assess the validity of using EHR data in future pragmatic weight loss or weight gain prevention trials. METHODS: For both the Track and Shape obesity intervention randomized trials, clinic EHR weight data were compared with primary trial weight data over the same time period. In analyzing the EHR weights, intervention effects were estimated on the primary outcome of weight (in kilograms) with EHR data, using linear mixed effects models. RESULTS: EHR weight measurements were higher on average and more variable than trial weight measurements. The mean difference and 95% CI were similar at all time points between the estimates using EHR and study-collected weights. CONCLUSIONS: The results of this study can be used to help guide the planning of future pragmatic weight-related trials. This study provides evidence that body weight measurements abstracted from the EHR can provide valid, efficient, and cost-effective data to estimate treatment effects from randomized clinical weight loss and weight management trials. However, care should be taken to properly understand the data-generating process and any mechanisms that may affect the validity of these estimates.
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