BACKGROUND: Long-term follow-up of weight loss interventions is essential, but collecting weights can be difficult, and self-reports inaccurate. We examined the relationship between weight measures obtained in the context of a weight loss trial and in routine clinical care. METHODS: Body weight data from a trial of behavioral obesity treatment among 88 obese women and 203 women age 40 to 65 years with comorbid obesity and depression were compared against weight data entered into an electronic medical record (EMR) during routine clinical care. Study and EMR weights and weight changes were then compared at 6 and 12 months using scatterplots, Pearson's correlations, and t-tests. RESULTS: The 12-month follow-up rate for this trial was 77%. Among the 224 12-month completers, 142 women (63%) had an EMR weight within 90 days of their 12-month study weight. Study and EMR weights were highly correlated (0.99), with a mean difference of 0.1 kg. The correlation between two measures of 12-month weight change using study and EMR weights was 0.96. These results were robust to sensitivity analyses that explored the impact of different-sized windows for matching clinical weights with study weights. Among the 67 women who were missing study weights at 12 months, 33 (49%) had an EMR weight available within 90 days of their missed follow-up appointment. CONCLUSIONS: Weight measures routinely obtained in clinical care are highly correlated with those obtained by trained research staff and may be used, without statistical correction, to achieve higher rates of long-term follow-up in weight loss studies.
RCT Entities:
BACKGROUND: Long-term follow-up of weight loss interventions is essential, but collecting weights can be difficult, and self-reports inaccurate. We examined the relationship between weight measures obtained in the context of a weight loss trial and in routine clinical care. METHODS: Body weight data from a trial of behavioral obesity treatment among 88 obesewomen and 203 women age 40 to 65 years with comorbid obesity and depression were compared against weight data entered into an electronic medical record (EMR) during routine clinical care. Study and EMR weights and weight changes were then compared at 6 and 12 months using scatterplots, Pearson's correlations, and t-tests. RESULTS: The 12-month follow-up rate for this trial was 77%. Among the 224 12-month completers, 142 women (63%) had an EMR weight within 90 days of their 12-month study weight. Study and EMR weights were highly correlated (0.99), with a mean difference of 0.1 kg. The correlation between two measures of 12-month weight change using study and EMR weights was 0.96. These results were robust to sensitivity analyses that explored the impact of different-sized windows for matching clinical weights with study weights. Among the 67 women who were missing study weights at 12 months, 33 (49%) had an EMR weight available within 90 days of their missed follow-up appointment. CONCLUSIONS: Weight measures routinely obtained in clinical care are highly correlated with those obtained by trained research staff and may be used, without statistical correction, to achieve higher rates of long-term follow-up in weight loss studies.
Authors: Jennifer A Linde; Robert W Jeffery; Emily A Finch; Gregory E Simon; Evette J Ludman; Belinda H Operskalski; Laura Ichikawa; Paul Rohde Journal: Prev Med Date: 2007-03-31 Impact factor: 4.018
Authors: Jennifer A Linde; Gregory E Simon; Evette J Ludman; Laura E Ichikawa; Belinda H Operskalski; David Arterburn; Paul Rohde; Emily A Finch; Robert W Jeffery Journal: Ann Behav Med Date: 2011-02
Authors: Yeyi Zhu; Margo A Sidell; David Arterburn; Matthew F Daley; Jay Desai; Stephanie L Fitzpatrick; Michael A Horberg; Corinna Koebnick; Emily McCormick; Caryn Oshiro; Deborah R Young; Assiamira Ferrara Journal: Diabetes Care Date: 2019-09-19 Impact factor: 19.112
Authors: Gregory E Simon; Paul Rohde; Evette J Ludman; Robert W Jeffery; Jennifer A Linde; Belinda H Operskalski; David Arterburn; Emily A Finch Journal: Obes Res Clin Pract Date: 2010-10-06 Impact factor: 2.288
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
Authors: David E Arterburn; Gwen L Alexander; Josephine Calvi; Laura A Coleman; Matthew W Gillman; Rachel Novotny; Virginia P Quinn; Margaret Rukstalis; Victor J Stevens; Elsie M Taveras; Nancy E Sherwood Journal: Clin Med Res Date: 2010-08-03
Authors: Evan L Thacker; Barbara McKnight; Bruce M Psaty; W T Longstreth; Sascha Dublin; Paul N Jensen; Katherine M Newton; Nicholas L Smith; David S Siscovick; Susan R Heckbert Journal: J Gen Intern Med Date: 2012-09-13 Impact factor: 5.128
Authors: Bruce C M Wang; Edwin S Wong; Rafael Alfonso-Cristancho; Hao He; David R Flum; David E Arterburn; Louis P Garrison; Sean D Sullivan Journal: Eur J Health Econ Date: 2013-03-24
Authors: Evette J Ludman; Joan E Russo; Wayne J Katon; Gregory E Simon; Lisa H Williams; Elizabeth H B Lin; Susan R Heckbert; Paul Ciechanowski; Bessie A Young Journal: J Gerontol A Biol Sci Med Sci Date: 2009-10-12 Impact factor: 6.053
Authors: Alanna V Rigobon; Richard Birtwhistle; Shahriar Khan; David Barber; Suzanne Biro; Rachael Morkem; Ian Janssen; Tyler Williamson Journal: Can J Public Health Date: 2015-04-30