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

Peter T Katzmarzyk, Emily F Mire, Corby K Martin, Robert L Newton Jr, John W Apolzan, Eboni G Price-Haywood, Kara D Denstel, Ronald Horswell, San T Chu, William D Johnson, PROPEL Research Group, Peter T Katzmarzyk, Robert L Newton Jr, Corby K Martin, John W Apolzan, William D Johnson, Kara D Denstel, Emily F Mire, Robert K Singletary Jr, Cheryl Lewis, Phillip Brantley, Ronald Horswell, Betty Kennedy, Dachuan Zhang, Stephanie Authement, Shiquita Brooks, Danielle S Burrell, Leslie Forest-Everage, Angelle Graham Ullmer, Laurie Murphy, Cristalyn Reynolds, Kevin Sanders, Stephen Bower, Hillary Gahagan, Tabitha K Gray, Jill Hancock, Marsha Herrera, Brittany Molinere, Georgia Morgan, Brittany Neyland, Stephanie Rincones, Deanna Robertson, Ekambi Shelton, Russell J Tassin, Kaili Williams, Benjamin F Springgate, Terry C Davis, Connie L Arnold, Eboni Price-Haywood, Carl J Lavie, Jewel Harden-Barrios, Vivian A Fonseca, Tina K Thethi, Jonathan Gugel, Kathleen B Kennedy, Daniel F Sarpong, Amina D Massey, Peter T Katzmarzyk, Emily F Mire, Corby K Martin, Robert L Newton Jr, John W Apolzan, Eboni G Price-Haywood, Kara D Denstel, Ronald Horswell, San T Chu, William D Johnson, PROPEL Research Group, Peter T Katzmarzyk, Robert L Newton Jr, Corby K Martin, John W Apolzan, William D Johnson, Kara D Denstel, Emily F Mire, Robert K Singletary Jr, Cheryl Lewis, Phillip Brantley, Ronald Horswell, Betty Kennedy, Dachuan Zhang, Stephanie Authement, Shiquita Brooks, Danielle S Burrell, Leslie Forest-Everage, Angelle Graham Ullmer, Laurie Murphy, Cristalyn Reynolds, Kevin Sanders, Stephen Bower, Hillary Gahagan, Tabitha K Gray, Jill Hancock, Marsha Herrera, Brittany Molinere, Georgia Morgan, Brittany Neyland, Stephanie Rincones, Deanna Robertson, Ekambi Shelton, Russell J Tassin, Kaili Williams, Benjamin F Springgate, Terry C Davis, Connie L Arnold, Eboni Price-Haywood, Carl J Lavie, Jewel Harden-Barrios, Vivian A Fonseca, Tina K Thethi, Jonathan Gugel, Kathleen B Kennedy, Daniel F Sarpong, Amina D Massey

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.

Conflict of interest statement

Competing Interests: The authors report no conflicts of interest.

© 2022. The Author(s), under exclusive licence to Springer Nature Limited.

Figures

Figure 1.
Figure 1.
Scatterplot of association between EMR-derived and researcher-measured body weights (n = 1 939).
Figure 2.
Figure 2.
Bland and Altman plot of mean differences and limits of agreement for EMR-derived and researcher-measured weights in the PROPEL trial (n=1 936).
Figure 3.
Figure 3.
Patient flow through the PROPEL trial among clinics with electronic medical record (EMR) data available.
Figure 4.
Figure 4.
Mean percent change in weight from baseline in the usual care (UC) group and the intensive lifestyle intervention (ILI) group) using data collected by trained research technicians versus data obtained from electronic medical records (EMR). The error bars represent standard errors.

References

    1. Tunis SR, Stryer DB, Clancy CM. Practical clinical trials: increasing the value of clinical research for decision making in clinical and health policy. JAMA. 2003;290(12):1624–32.
    1. Patsopoulos NA. A pragmatic view on pragmatic trials. Dialogues Clin Neurosci. 2011;13(2):217–24.
    1. Tsai AG, Remmert JE, Butryn ML, Wadden TA. Treatment of obesity in primary care. Med Clin North Am. 2018;102(1):35–47.
    1. Carvajal R, Wadden TA, Tsai AG, Peck K, Moran CH. Managing obesity in primary care practice: A narrative review Ann NY Acad Sci. 2013;1281:191–206.
    1. Tronieri JS, Wadden TA, Chao AM, Tsai AG. Primary care interventions for obesity: Review of the evidence. Curr Obes Rep. 2019;8(2):128–36.
    1. Befort CA, VanWormer JJ, Desouza C, Ellerbeck EF, Gajewski B, Kimminau KS, et al. Effect of behavioral therapy with in-clinic or telephone group visits vs in-clinic individual visits on weight loss among patients with obesity in rural clinical practice: A randomized clinical trial. JAMA. 2021;325(4):363–72.
    1. Katzmarzyk PT, Martin CK, Newton RL Jr., Apolzan JW, Arnold CL, Davis TC, et al. Weight loss in underserved patients - A cluster-randomized trial. N Engl J Med. 2020;383(10):909–18.
    1. Katzmarzyk PT, Apolzan JW, Gajewski B, Johnson WD, Martin CK, Newton RL Jr., et al. Weight loss in primary care: A pooled analysis of two pragmatic cluster-randomized trials. Obesity. 2021;29(12):2044–54.
    1. Ellenberg SS, Ellenberg JH. Proceedings of the University of Pennsylvania 12th annual conference on statistical issues in clinical trials: Electronic health records (EHR) in randomized clinical trials-Challenges and opportunities. Clin Trials. 2020;17(4):343–5.
    1. Sherman RE, Anderson SA, Dal Pan GJ, Gray GW, Gross T, Hunter NL, et al. Real-world evidence - What is it and what can it tell us? N Engl J Med. 2016;375(23):2293–7.
    1. Arterburn D, Ichikawa L, Ludman EJ, Operskalski B, Linde JA, Anderson E, et al. Validity of clinical body weight measures as substitutes for missing data in a randomized trial. Obes Res Clin Pract. 2008;2(4):277–81.
    1. Gallis JA, Kusibab K, Egger JR, Olsen MK, Askew S, Steinberg DM, et al. Can electronic health records validly estimate the effects of health system interventions aimed at controlling body weight? Obesity (Silver Spring). 2020;28(11):2107–15.
    1. Xiao L, Lv N, Rosas LG, Au D, Ma J. Validation of clinic weights from electronic health records against standardized weight measurements in weight loss trials. Obesity (Silver Spring). 2017;25(2):363–9.
    1. Katzmarzyk PT, Martin CK, Newton RL Jr., Apolzan JW, Arnold CL, Davis TC, et al. Promoting Successful Weight Loss in Primary Care in Louisiana (PROPEL): Rationale, design and baseline characteristics. Contemp Clin Trials. 2018;67:1–10.
    1. Lawrence IKL. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989;45:255–68.
    1. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1:307–10.
    1. Stevens VJ, Wagner EL, Rossner J, Craddick S, Greenlick MR. Validity and usefulness of medical chart weights in the long-term evaluation of weight loss programs. Addict Behav. 1988;13(2):171–5.
    1. DiMaria-Ghalili RA. Medical record versus researcher measures of height and weight. Biol Res Nurs. 2006;8(1):15–23.
    1. Leo MC, Lindberg NM, Vesco KK, Stevens VJ. Validity of medical chart weights and heights for obese pregnant women. EGEMS (Wash DC). 2014;2(1):1051.
    1. Foley P, Steinberg D, Levine E, Askew S, Batch BC, Puleo EM, et al. Track: A randomized controlled trial of a digital health obesity treatment intervention for medically vulnerable primary care patients. Contemp Clin Trials. 2016;48:12–20.
    1. Bennett GG, Foley P, Levine E, Whiteley J, Askew S, Steinberg DM, et al. Behavioral treatment for weight gain prevention among black women in primary care practice: a randomized clinical trial. JAMA Intern Med. 2013;173(19):1770–7.
    1. Richesson RL, Marsolo KS, Douthit BJ, Staman K, Ho PM, Dailey D, et al. Enhancing the use of EHR systems for pragmatic embedded research: lessons from the NIH Health Care Systems Research Collaboratory. J Am Med Inform Assoc. 2021.

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