Evaluation of Medicare’s Intensive Behavioral Therapy for Obesity: the BieneStar Experience

Published:February 12, 2018DOI:https://doi.org/10.1016/j.amepre.2018.01.018


      In 2011, the Centers for Medicare and Medicaid Services began to reimburse primary care providers for intensive behavior therapy for obesity. This study evaluated a Centers for Medicare and Medicaid Services intensive behavior therapy for obesity program as implemented in primary care clinics.


      Data for this retrospective cohort study were obtained between May 2012 and February 2015 and statistical analysis was performed in 2017. The sample included 643 participants who attended at least one BieneStar intensive behavior therapy for obesity program session. The primary outcome was weight, and covariates were number of sessions, age, race/ethnicity, diagnosis of hypertension and diabetes, and type of health insurance.


      Of 643 participants that initiated the BieneStar program, 641 had complete data. The median reduction in weight of participants was as follows: those who attended fewer than four sessions, 0 kg (95% CI=0, 0.11 kg); between four and eight sessions, 1.1 kg (95% CI=0.86, 1.59 kg); and more than eight sessions 3.7 kg (95% CI=3.36, 4.55 kg). Medians of weight were significantly different between each classification of session numbers (p<0.01). Participants lost on average 0.102 kg of weight per session attended.


      The BieneStar program showed that the weight of participants decreased as they attended more sessions. Further studies are needed to determine if these results can be reproduced in other office-based primary care clinics and the program’s impact on chronic disease.
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        • Dieleman J.L.
        • Baral R.
        • Birger M.
        • et al.
        U.S. spending on personal health care and public health, 1996–2013.
        JAMA. 2016; 316: 2627-2646https://doi.org/10.1001/jama.2016.16885
        • Lu Y.
        • Hajifathalian K.
        • Ezzati M.
        • Woodward M.
        • Rimm E.B.
        • Danaei G.
        Metabolic mediators of the effects of body-mass index, overweight, and obesity on coronary heart disease and stroke: a pooled analysis of 97 prospective cohorts with 1.8 million participants.
        Lancet. 2014; 383: 970-983https://doi.org/10.1016/S0140-6736(13)61836-X
        • Danaei G.
        • Finucane M.M.
        • Lu Y.
        • et al.
        National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million participants.
        Lancet. 2011; 378: 31-40https://doi.org/10.1016/S0140-6736(11)60679-X
        • Zong G.
        • Zhang Z.
        • Yang Q.
        • Wu H.
        • Hu F.B.
        • Sun Q.
        Total and regional adiposity measured by dual-energy X-ray absorptiometry and mortality in NHANES 1999–2006.
        Obesity (Silver Spring). 2016; 24: 2414-2421https://doi.org/10.1002/oby.21659
        • Santa-Maria C.A.
        • Yan J.
        • Xie X.J.
        • Euhus D.M.
        Aggressive estrogen-receptor-positive breast cancer arising in patients with elevated body mass index.
        Int J Clin Oncol. 2015; 20: 317-323https://doi.org/10.1007/s10147-014-0712-4
        • Nimptsch K.
        • Pischon T.
        Obesity biomarkers, metabolism and risk of cancer: an epidemiological perspective.
        Recent Results Cancer Res. 2016; 208: 199-217https://doi.org/10.1007/978-3-319-42542-9_11
        • Flegal K.M.
        • Kit B.K.
        • Orpana H.
        • Graubard B.I.
        Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis.
        JAMA. 2013; 309: 71-82https://doi.org/10.1001/jama.2012.113905
        • Moyer V.A.
        Screening for and management of obesity in adults: U.S. Preventive Services Task Force recommendation statement.
        Ann Intern Med. 2012; 157: 373-378https://doi.org/10.7326/0003-4819-157-5-201209040-00475
      1. Centers for Medicare & Medicaid Services. Intensive Behavioral Therapy (IBT) for Obesity. www.cms.gov/Outreach-and-Education/Medicare-Learning-Network-MLN/MLNMattersArticles/downloads/MM7641.pdf. Published 2011. Accessed February 2017.

        • Hing E.
        • Burt C.W.
        Office-based medical practices: methods and estimates from the national ambulatory medical care survey.
        Adv Data. 2007; 383: 1-15
        • Hing E.
        • Uddin S.
        Visits to primary care delivery sites: United States, 2008.
        NCHS Data Brief. 2010; 47: 1-8
        • Newman A.B.
        • Aviles-Santa M.L.
        • Anderson G.
        • et al.
        Embedding clinical interventions into observational studies.
        Contemp Clin Trials. 2016; 46: 100-105https://doi.org/10.1016/j.cct.2015.11.017
        • Trevi-o R.P.
        • Pugh J.A.
        • Hernandez A.E.
        • Menchaca V.D.
        • Ramirez R.R.
        • Mendoza M.
        Bienestar: a diabetes risk-factor prevention program.
        J Sch Health. 1998; 68: 62-67https://doi.org/10.1111/j.1746-1561.1998.tb07192.x
        • Trevi-o R.P.
        • Yin Z.
        • Hernandez A.
        • Hale D.E.
        • Garcia O.A.
        • Mobley C.
        Impact of the Bienestar school-based diabetes mellitus prevention program on fasting capillary glucose levels: a randomized controlled trial.
        Arch Pedriatr Adolesc Med. 2004; 158: 911-917https://doi.org/10.1001/archpedi.158.9.911
        • Trevi-o R.P.
        • Yin Z.
        • Hernandez A.E.
        Impact of Bienestar Health Program on physical fitness in low-income Mexican-American children.
        Hisp J Behav Sci. 2005; 27: 120-132https://doi.org/10.1177/0739986304272359
        • Foster G.D.
        • Linder B.
        • Baronowski T.
        • et al.
        A school-based intervention for diabetes risk factors.
        N Engl J Med. 2010; 363: 443-453https://doi.org/10.1056/NEJMoa1001933
      2. National Cancer Institute. Research-tested intervention programs. http://rtips.cancer.gov/rtips/programDetails.do?programId=247904. Published 2009. Accessed January 30, 2013.

      3. Agency for Healthcare Research and Quality. Innovations exchange. https://innovations.ahrq.gov/profiles/comprehensive-school-based-program-increases-positive-health-behaviors-and-reduces-risk. Published 2011. Accessed January 30, 2013.

      4. Healthy Communities Institute. Promising Practices database. www.healthiercentraloregon.org/modules.php?op=modload&name=PromisePractice&file=promisePractice&pid=3476. Published 2014. Accessed May 30, 2016.

        • Konietschke F.
        • Placzek M.
        • Schaarschmidt F.
        • Hothorn L.
        nparcomp: An R software package for nonparametric multiple comparisons and simultaneous confidence intervals.
        J Stat Softw. 2015; 64: 1-17https://doi.org/10.18637/jss.v064.i09
        • Gao X.
        • Alvo M.
        • Chen J.
        • Li G.
        Nonparametric multiple comparison procedures for unbalanced one-way factorial designs.
        J Stat Plan Inference. 2008; 138: 2574-2591https://doi.org/10.1016/j.jspi.2007.10.015
        • Barnett A.G.
        • van der Pols J.C.
        • Dobson A.J.
        Regression to the mean: what it is and how to deal with it.
        Int J Epidemiol. 2005; 34: 215-220https://doi.org/10.1093/ije/dyh299
        • Vickers A.J.
        The use of percentage change from baseline as an outcome in a controlled trial is statistically inefficient: a simulation study.
        BMC Med Res Methodol. 2001; 1: 6https://doi.org/10.1186/1471-2288-1-6
        • Bates D.
        • Maechler M.
        • Bolker B.
        • Walker S.
        Fitting linear mixed-effects models using lme4.
        J Stat Softw. 2015; 67: 1-48https://doi.org/10.18637/jss.v067.i01
        • Thabault P.J.
        • Burke P.J.
        • Ades P.A.
        Intensive behavioral treatment weight loss program in an adult primary care practice.
        J Am Assoc Nurse Pract. 2016; 28: 249-257https://doi.org/10.1002/2327-6924.12319
        • Carvajal R.
        • Wadden T.A.
        • Tsai A.G.
        • Peck K.
        • Moran C.H.
        Managing obesity in primary care practice: a narrative review.
        Ann N Y Acad Sci. 2013; 1281: 191-206https://doi.org/10.1111/nyas.12004
        • Wadden T.A.
        • Butryn M.L.
        • Hong P.S.
        • Tsai A.G.
        Behavioral treatment of obesity in patients encountered in primary care settings: a systematic review.
        JAMA. 2014; 312: 1779-1791https://doi.org/10.1001/jama.2014.14173
        • Anderson J.W.
        • Jhaveri M.A.
        Reductions in medications with substantial weight loss with behavioral intervention.
        Curr Clin Pharmacol. 2010; 5: 232-238https://doi.org/10.2174/157488410793352030
        • Myers V.H.
        • McVay M.A.
        • Brashear M.M.
        • et al.
        Five-year medical and pharmacy costs after a medically supervised intensive treatment program for obesity.
        Am J Health Promot. 2014; 28: 364-371https://doi.org/10.4278/ajhp.120207-QUAN-80
        • Tsai A.G.
        • Felton S.
        • Wadden T.A.
        • Hosokawa P.W.
        • Hill J.O.
        A randomized clinical trial of a weight loss maintenance intervention in a primary care population.
        Obesity (Silver Spring). 2015; 23: 2015-2021https://doi.org/10.1002/oby.21224
        • Woolf S.H.
        A closer look at the economic argument for disease prevention.
        JAMA. 2009; 301: 536-538https://doi.org/10.1001/jama.2009.51
        • Medical Group Management Association
        Patient wait times, optimal panel sizes, handling no shows.
        MGMA Connex. 2011; : 10
        • Lacy N.L.
        • Paulman A.
        • Reuter M.D.
        • Lovejoy B.
        Why we don’t come: patient perceptions on no-shows.
        Ann Fam Med. 2004; 2: 541-545https://doi.org/10.1370/afm.123