Literature DB >> 35523955

Comparison of weight loss data collected by research technicians versus electronic medical records: the PROPEL trial.

Peter T Katzmarzyk1, Emily F Mire2, Corby K Martin2, Robert L Newton2, John W Apolzan2, Eboni G Price-Haywood3, Kara D Denstel2, Ronald Horswell2, San T Chu2, William D Johnson2.   

Abstract

BACKGROUND/
OBJECTIVES: Pragmatic trials are increasingly used to study the implementation of weight loss interventions in real-world settings. This study compared researcher-measured body weights versus electronic medical record (EMR)-derived body weights from a pragmatic trial conducted in an underserved patient population. SUBJECTS/
METHODS: The PROPEL trial randomly allocated 18 clinics to usual care (UC) or to an intensive lifestyle intervention (ILI) designed to promote weight loss. Weight was measured by trained technicians at baseline and at 6, 12, 18, and 24 months. A total of 11 clinics (6 UC/5 ILI) with 577 enrolled patients also provided EMR data (n = 561), which included available body weights over the period of the trial.
RESULTS: The total number of assessments were 2638 and 2048 for the researcher-measured and EMR-derived body weight values, respectively. The correlation between researcher-measured and EMR-derived body weights was 0.988 (n = 1 939; p < 0.0001). The mean difference between the EMR and researcher weights (EMR-researcher) was 0.63 (2.65 SD) kg, and a Bland-Altman graph showed good agreement between the two data collection methods; the upper and lower boundaries of the 95% limits of agreement are -4.65 kg and +5.91 kg, and 71 (3.7%) of the values were outside the limits of agreement. However, at 6 months, percent weight loss in the ILI compared to the UC group was 7.3% using researcher-measured data versus 5.5% using EMR-derived data. At 24 months, the weight loss maintenance was 4.6% using the technician-measured data versus 3.5% using EMR-derived data.
CONCLUSION: At the group level, body weight data derived from researcher assessments and an EMR showed good agreement; however, the weight loss difference between ILI and UC was blunted when using EMR data. This suggests that weight loss studies that rely on EMR data may require larger sample sizes to detect significant effects. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov number NCT02561221.
© 2022. The Author(s), under exclusive licence to Springer Nature Limited.

Entities:  

Mesh:

Year:  2022        PMID: 35523955      PMCID: PMC9329211          DOI: 10.1038/s41366-022-01129-9

Source DB:  PubMed          Journal:  Int J Obes (Lond)        ISSN: 0307-0565            Impact factor:   5.551


  22 in total

1.  Promoting Successful Weight Loss in Primary Care in Louisiana (PROPEL): Rationale, design and baseline characteristics.

Authors:  Peter T Katzmarzyk; Corby K Martin; Robert L Newton; John W Apolzan; Connie L Arnold; Terry C Davis; Kara D Denstel; Emily F Mire; Tina K Thethi; Phillip J Brantley; William D Johnson; Vivian Fonseca; Jonathan Gugel; Kathleen B Kennedy; Carl J Lavie; Eboni G Price-Haywood; Daniel F Sarpong; Benjamin Springgate
Journal:  Contemp Clin Trials       Date:  2018-02-08       Impact factor: 2.226

Review 2.  Primary Care Interventions for Obesity: Review of the Evidence.

Authors:  Jena Shaw Tronieri; Thomas A Wadden; Ariana M Chao; Adam Gilden Tsai
Journal:  Curr Obes Rep       Date:  2019-06

3.  Can Electronic Health Records Validly Estimate the Effects of Health System Interventions Aimed at Controlling Body Weight?

Authors:  Kristie Kusibab; John A Gallis; Joseph R Egger; Maren K Olsen; Sandy Askew; Dori M Steinberg; Gary Bennett
Journal:  Obesity (Silver Spring)       Date:  2020-09-27       Impact factor: 5.002

4.  Validation of clinic weights from electronic health records against standardized weight measurements in weight loss trials.

Authors:  Lan Xiao; Nan Lv; Lisa G Rosas; David Au; Jun Ma
Journal:  Obesity (Silver Spring)       Date:  2017-01-06       Impact factor: 5.002

5.  Weight loss in primary care: A pooled analysis of two pragmatic cluster-randomized trials.

Authors:  Peter T Katzmarzyk; John W Apolzan; Byron Gajewski; William D Johnson; Corby K Martin; Robert L Newton; Michael G Perri; Jeffrey J VanWormer; Christie A Befort
Journal:  Obesity (Silver Spring)       Date:  2021-10-29       Impact factor: 9.298

6.  Track: A randomized controlled trial of a digital health obesity treatment intervention for medically vulnerable primary care patients.

Authors:  Perry Foley; Dori Steinberg; Erica Levine; Sandy Askew; Bryan C Batch; Elaine M Puleo; Laura P Svetkey; Hayden B Bosworth; Abigail DeVries; Heather Miranda; Gary G Bennett
Journal:  Contemp Clin Trials       Date:  2016-03-17       Impact factor: 2.226

7.  Weight Loss in Underserved Patients - A Cluster-Randomized Trial.

Authors:  Peter T Katzmarzyk; Corby K Martin; Robert L Newton; John W Apolzan; Connie L Arnold; Terry C Davis; Eboni G Price-Haywood; Kara D Denstel; Emily F Mire; Tina K Thethi; Phillip J Brantley; William D Johnson; Vivian Fonseca; Jonathan Gugel; Kathleen B Kennedy; Carl J Lavie; Daniel F Sarpong; Benjamin Springgate
Journal:  N Engl J Med       Date:  2020-09-03       Impact factor: 91.245

8.  Behavioral treatment for weight gain prevention among black women in primary care practice: a randomized clinical trial.

Authors:  Gary G Bennett; Perry Foley; Erica Levine; Jessica Whiteley; Sandy Askew; Dori M Steinberg; Bryan Batch; Mary L Greaney; Heather Miranda; Thomas H Wroth; Marni Gwyther Holder; Karen M Emmons; Elaine Puleo
Journal:  JAMA Intern Med       Date:  2013-10-28       Impact factor: 21.873

Review 9.  A pragmatic view on pragmatic trials.

Authors:  Nikolaos A Patsopoulos
Journal:  Dialogues Clin Neurosci       Date:  2011       Impact factor: 5.986

10.  Validity of medical chart weights and heights for obese pregnant women.

Authors:  Michael C Leo; Nangel M Lindberg; Kimberly K Vesco; Victor J Stevens
Journal:  EGEMS (Wash DC)       Date:  2014-05-15
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.