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Multilevel Interventions Targeting Obesity: Research Recommendations for Vulnerable Populations

Published:October 26, 2016DOI:https://doi.org/10.1016/j.amepre.2016.09.011

      Introduction

      The origins of obesity are complex and multifaceted. To be successful, an intervention aiming to prevent or treat obesity may need to address multiple layers of biological, social, and environmental influences.

      Methods

      NIH recognizes the importance of identifying effective strategies to combat obesity, particularly in high-risk and disadvantaged populations with heightened susceptibility to obesity and subsequent metabolic sequelae. To move this work forward, the National Heart, Lung, and Blood Institute, in collaboration with the NIH Office of Behavioral and Social Science Research and NIH Office of Disease Prevention convened a working group to inform research on multilevel obesity interventions in vulnerable populations. The working group reviewed relevant aspects of intervention planning, recruitment, retention, implementation, evaluation, and analysis, and then made recommendations.

      Results

      Recruitment and retention techniques used in multilevel research must be culturally appropriate and suited to both individuals and organizations. Adequate time and resources for preliminary work are essential. Collaborative projects can benefit from complementary areas of expertise and shared investigations rigorously pretesting specific aspects of approaches. Study designs need to accommodate the social and environmental levels under study, and include appropriate attention given to statistical power. Projects should monitor implementation in the multiple venues and include a priori estimation of the magnitude of change expected within and across levels.

      Conclusions

      The complexity and challenges of delivering interventions at several levels of the social−ecologic model require careful planning and implementation, but hold promise for successful reduction of obesity in vulnerable populations.
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      References

        • Singh G.K.
        • Siahpush M.
        • Hiatt R.A.
        • Timsina L.R.
        Dramatic increases in obesity and overweight prevalence and body mass index among ethnic-immigrant and social class groups in the United States, 1976–2008.
        J Commun Health. 2011; 36: 94-110https://doi.org/10.1007/s10900-010-9287-9
        • Gordon-Larsen P.
        • Harris K.M.
        • Ward D.S.
        • Popkin B.M.
        Acculturation and overweight-related behaviors among Hispanic immigrants to the U.S.: the National Longitudinal Study of Adolescent Health.
        Soc Sci Med. 2003; 57: 2023-2034https://doi.org/10.1016/S0277-9536(03)00072-8
        • Ogden C.L.
        • Carroll M.D.
        • Lawman H.G.
        • et al.
        Trends in obesity prevalence among children and adolescents in the United States, 1988–1994 through 2013–2014.
        JAMA. 2016; 315: 2292-2299https://doi.org/10.1001/jama.2016.6361
        • Warnecke R.B.
        • Oh A.
        • Breen N.
        • et al.
        Approaching health disparities from a population perspective: the National Institutes of Health Centers for Population Health and Health Disparities.
        Am J Public Health. 2008; 98: 1608-1615https://doi.org/10.2105/AJPH.2006.102525
        • Clauser S.B.
        • Taplin S.H.
        • Foster M.K.
        • Fagan P.
        • Kaluzny A.D.
        Multilevel intervention research: lessons learned and pathways forward.
        J Natl Cancer Inst Monogr. 2012; 2012: 127-133https://doi.org/10.1093/jncimonographs/lgs019
        • Gorin S.S.
        • Badr H.
        • Krebs P.
        • Prabhu Das I.
        Multilevel interventions and racial/ethnic health disparities.
        J Natl Cancer Inst Monogr. 2012; 2012: 100-111https://doi.org/10.1093/jncimonographs/lgs015
      1. NIH. NIH-Supported Centers for Population Health and Health Disparities (CPHHD) (P50). http://grants.nih.gov/grants/guide/rfa-files/RFA-CA-09-001.html. Accessed February 16, 2016.

        • Pratt C.A.
        • Arteaga S.
        • Loria C.
        Forging a future of better cardiovascular health: addressing childhood obesity.
        J Am Coll Cardiol. 2014; 63: 369-371https://doi.org/10.1016/j.jacc.2013.07.088
        • Story M.
        • Kaphingst K.M.
        • Robinson-O’Brien R.
        • Glanz K.
        Creating healthy food and eating environments: policy and environmental approaches.
        Annu Rev Publ Health. 2008; 29: 253-272https://doi.org/10.1146/annurev.publhealth.29.020907.090926
        • Pratt C.A.
        • Boyington J.
        • Esposito L.
        • et al.
        Childhood Obesity Prevention and Treatment Research (COPTR): interventions addressing multiple influences in childhood and adolescent obesity.
        Contemp Clin Trials. 2013; 36: 406-413https://doi.org/10.1016/j.cct.2013.08.010
        • Robinson T.N.
        • Matheson D.
        • Desai M.
        • et al.
        Family, community and clinic collaboration to treat overweight and obese children: Stanford GOALS—A randomized controlled trial of a three-year, multi-component, multi-level, multi-setting intervention.
        Contemp Clin Trials. 2013; 36: 421-435https://doi.org/10.1016/j.cct.2013.09.001
        • Flegal K.M.
        • Carroll M.D.
        • Ogden C.L.
        • Johnson C.L.
        Prevalence and trends in obesity among U.S. adults, 1999-2000.
        JAMA. 2002; 288: 1723-1727https://doi.org/10.1001/jama.288.14.1723
        • Ogden C.L.
        • Flegal K.M.
        • Carroll M.D.
        • Johnson C.L.
        Prevalence and trends in overweight among U.S. children and adolescents, 1999–2000.
        JAMA. 2002; 288: 1728-1732https://doi.org/10.1001/jama.288.14.1728
        • Fakhouri T.H.
        • Ogden C.L.
        • Carroll M.D.
        • Kit B.K.
        • Flegal K.M.
        Prevalence of obesity among older adults in the United States, 2007–2010.
        NCHS Data Brief. 2012; : 1-8
        • Befort C.A.
        • Nazir N.
        • Perri M.G.
        Prevalence of obesity among adults from rural and urban areas of the United States: findings from NHANES (2005–2008).
        J Rural Health. 2012; 28: 392-397https://doi.org/10.1111/j.1748-0361.2012.00411.x
      2. Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults—The Evidence Report. National Institutes of Health.
        Obes Res. 1998; 6: 51s-209s
        • Duffey K.J.
        • Popkin B.M.
        Energy density, portion size, and eating occasions: contributions to increased energy intake in the United States, 1977–2006.
        PLoS Med. 2011; 8: e1001050https://doi.org/10.1371/journal.pmed.1001050
        • Powell L.M.
        • Nguyen B.T.
        Fast-food and full-service restaurant consumption among children and adolescents: effect on energy, beverage, and nutrient intake.
        JAMA Pediatr. 2013; 167: 14-20https://doi.org/10.1001/jamapediatrics.2013.417
        • Piernas C.
        • Popkin B.M.
        Food portion patterns and trends among U.S. children and the relationship to total eating occasion size, 1977-2006.
        J Nutr. 2011; 141: 1159-1164https://doi.org/10.3945/jn.111.138727
        • Sallis J.F.
        • Glanz K.
        Physical activity and food environments: solutions to the obesity epidemic.
        Milbank Q. 2009; 87: 123-154https://doi.org/10.1111/j.1468-0009.2009.00550.x
        • Neckerman K.M.
        • Lovasi G.S.
        • Davies S.
        • et al.
        Disparities in urban neighborhood conditions: evidence from GIS measures and field observation in New York City.
        J Public Health Policy. 2009; 30: S264-S285https://doi.org/10.1057/jphp.2008.47
        • Duncan D.T.
        • Johnson R.M.
        • Molnar B.E.
        • Azrael D.
        Association between neighborhood safety and overweight status among urban adolescents.
        BMC Public Health. 2009; 9: 289https://doi.org/10.1186/1471-2458-9-289
        • Taylor W.
        • Lou D.
        Do all children have places to be active? Disparities in access to physical activity environments in racial and ethnic minority and lower-income communities.
        Active Living Research, a National Program of the Robert Wood Johnson Foundation, Princeton, NJNovember 2011 (Accessed September 28, 2016)
        • Harvey J.R.
        • Ogden D.E.
        Obesity treatment in disadvantaged population groups: where do we stand and what can we do?.
        Prev Med. 2014; 68: 71-75https://doi.org/10.1016/j.ypmed.2014.05.015
        • Young D.R.
        • Johnson C.C.
        • Steckler A.
        • et al.
        Data to action: using formative research to develop intervention programs to increase physical activity in adolescent girls.
        Health Educ Behav. 2006; 33: 97-111https://doi.org/10.1177/1090198105282444
        • Linde J.A.
        • Sevcik S.M.
        • Petrich C.A.
        • et al.
        Translating a health behavior change intervention for delivery to 2-year college students: the importance of formative research.
        Transl Behav Med. 2014; 4: 160-169https://doi.org/10.1007/s13142-013-0243-y
        • Moore S.M.
        • Borawski E.A.
        • Cuttler L.
        • Ievers-Landis C.E.
        • Love T.E.
        IMPACT: a multi-level family and school intervention targeting obesity in urban youth.
        Contemp Clin Trials. 2013; 36: 574-586https://doi.org/10.1016/j.cct.2013.08.009
        • Czajkowski S.M.
        • Powell L.H.
        • Adler N.
        • et al.
        From ideas to efficacy: the ORBIT model for developing behavioral treatments for chronic diseases.
        Health Psychol. 2015; 34: 971-982https://doi.org/10.1037/hea0000161
      3. UK Government Office for Science. Foresight. Tackling obesities: future choices—project report. 2nd ed. www.gov.uk/government/uploads/system/uploads/attachment_data/file/287937/07-1184x-tackling-obesities-future-choices-report.pdf. Published October 2007. Accessed September 28, 2016.

        • Huang T.T.
        • Drewnosksi A.
        • Kumanyika S.
        • Glass T.A.
        A systems-oriented multilevel framework for addressing obesity in the 21st century.
        Prev Chronic Dis. 2009; 6: A82
        • Finegood D.T.
        • Merth T.D.
        • Rutter H.
        Implications of the foresight obesity system map for solutions to childhood obesity.
        Obesity (Silver Spring). 2010; 18: S13-S16https://doi.org/10.1038/oby.2009.426
        • Michie S.
        • Wood C.E.
        • Johnston M.
        • et al.
        Behaviour change techniques: the development and evaluation of a taxonomic method for reporting and describing behaviour change interventions (a suite of five studies involving consensus methods, randomised controlled trials and analysis of qualitative data).
        Health Technol Assess. 2015; 19: 1-188https://doi.org/10.3310/hta19990
        • Michie S.
        • van Stralen M.M.
        • West R.
        The behaviour change wheel: a new method for characterising and designing behaviour change interventions.
        Implement Sci. 2011; 6: 42https://doi.org/10.1186/1748-5908-6-42
        • Perry C.
        Creating Health Behavior Change: How to Develop Community-Wide Programs for Youth.
        Sage Publications, Thousand Oaks, CA1999
        • Bartholomew L.K.
        • Markham C.
        • Mullen P.
        • Fernandez M.E.
        Planning models for theory-based health-promotion interventions.
        in: Glanz K. Rimer B. Viswanath K. Health Behavior: Theory, Research, and Practice. 5th ed. Jossey-Bass, San Franciso, CA2015: 359-388
        • Stevens J.
        • Taber D.R.
        • Murray D.M.
        • Ward D.S.
        Advances and controversies in the design of obesity prevention trials.
        Obesity (Silver Spring). 2007; 15: 2163-2170https://doi.org/10.1038/oby.2007.257
        • Almirall D.
        • Nahum-Shani I.
        • Sherwood N.E.
        • Murphy S.A.
        Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research.
        Transl Behav Med. 2014; 4: 260-274https://doi.org/10.1007/s13142-014-0265-0
        • Collins L.M.
        • Nahum-Shani I.
        • Almirall D.
        Optimization of behavioral dynamic treatment regimens based on the sequential, multiple assignment, randomized trial (SMART).
        Clin Trials. 2014; 11: 426-434https://doi.org/10.1177/1740774514536795
        • Wyrick D.L.
        • Rulison K.L.
        • Fearnow-Kenney M.
        • Milroy J.J.
        • Collins L.M.
        Moving beyond the treatment package approach to developing behavioral interventions: addressing questions that arose during an application of the Multiphase Optimization Strategy (MOST).
        Transl Behav Med. 2014; 4: 252-259https://doi.org/10.1007/s13142-013-0247-7
        • Michie S.
        • Richardson M.
        • Johnston M.
        • et al.
        The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions.
        Ann Behav Med. 2013; 46: 81-95https://doi.org/10.1007/s12160-013-9486-6
        • Guzman A.
        • Richardson I.M.
        • Gesell S.
        • Barkin S.L.
        Recruitment and retention of Latino children in a lifestyle intervention.
        Am J Health Behav. 2009; 33: 581-586https://doi.org/10.5993/ajhb.33.5.11
        • UyBico S.J.
        • Pavel S.
        • Gross C.P.
        Recruiting vulnerable populations into research: a systematic review of recruitment interventions.
        J Gen Intern Med. 2007; 22: 852-863https://doi.org/10.1007/s11606-007-0126-3
        • 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
        • Brueton V.C.
        • Tierney J.
        • Stenning S.
        • et al.
        Strategies to improve retention in randomised trials.
        Cochrane Database Syst Rev. 2013; 12: MR000032https://doi.org/10.1002/14651858.mr000032.pub2
      4. Task Force on Community Preventive Services. 2016 Annual Report to Congress, Federal Agencies and Prevention Stakeholders. Using evidence to improve health outcomes. Atlanta, GA: Centers for Disease Control and Prevention. www.thecommunityguide.org/annualreport/2016-congress-report-full.pdf. Accessed September 28, 2016.

        • Schneider M.
        • Hall W.J.
        • Hernandez A.E.
        • et al.
        Rationale, design and methods for process evaluation in the HEALTHY study.
        Int J Obes (Lond). 2009; 33: S60-S67https://doi.org/10.1038/ijo.2009.118
        • Borrelli B.
        • Sepinwall D.
        • Ernst D.
        • et al.
        A new tool to assess treatment fidelity and evaluation of treatment fidelity across 10 years of health behavior research.
        J Consult Clin Psychol. 2005; 73: 852-860https://doi.org/10.1037/0022-006X.73.5.852
        • Young D.R.
        • Steckler A.
        • Cohen S.
        • et al.
        Process evaluation results from a school- and community-linked intervention: the Trial of Activity for Adolescent Girls (TAAG).
        Health Educ Res. 2008; 23: 976-986https://doi.org/10.1093/her/cyn029
        • Steckler A.B.
        • Linnan L.
        Process Evaluation for Public Health Interventions and Research.
        1st ed. Jossey-Bass, San Francisco, CA2002
        • Laska M.N.
        • Sevcik S.M.
        • Moe S.G.
        • et al.
        A 2-year young adult obesity prevention trial in the U.S.: process evaluation results.
        Health Promot Int. In press. Online June 30. 2015; https://doi.org/10.1093/heapro/dav066
        • Tate D.F.
        • Lytle L.A.
        • Sherwood N.E.
        • Haire-Joshu D.
        • et al.
        Deconstructing interventions: approaches to studying behavior change techniques across obesity interventions.
        Transl Behav Med. 2016; 6: 236-243https://doi.org/10.1007/s13142-015-0369-1
        • Baranowski T.
        • Stables G.
        Process evaluations of the 5-a-day projects.
        Health Educ Behav. 2000; 27: 157-166https://doi.org/10.1177/109019810002700202
        • Resnicow K.
        • Davis M.
        • Smith M.
        • et al.
        How best to measure implementation of school health curricula: a comparison of three measures.
        Health Educ Res. 1998; 13: 239-250https://doi.org/10.1093/her/13.2.239
        • Story M.
        • Lytle L.A.
        • Birnbaum A.S.
        • Perry C.L.
        Peer-led, school-based nutrition education for young adolescents: feasibility and process evaluation of the TEENS study.
        J Sch Health. 2002; 72: 121-127https://doi.org/10.1111/j.1746-1561.2002.tb06529.x
        • Melnyk B.M.
        • Morrison-Beedy D.
        • Moore S.M.
        Nuts and bolts of designing intervention studies.
        in: Melnyk B.M. Morrison-Beedy D. Intervention Research: Designing, Conducting, Analyzing, and Funding. Springer, New York2012: 37-64
        • Collins L.M.
        • Murphy S.A.
        • Bierman K.L.
        A conceptual framework for adaptive preventive interventions.
        Prev Sci. 2004; 5: 185-196https://doi.org/10.1023/B:PREV.0000037641.26017.00
        • Song M.K.
        • Lin F.C.
        • Ward S.E.
        • Fine J.P.
        Composite variables: when and how.
        Nurs Res. 2013; 62: 45-49https://doi.org/10.1097/NNR.0b013e3182741948
        • Freemantle N.
        • Calvert M.
        • Wood J.
        • Eastaugh J.
        • Griffin C.
        Composite outcomes in randomized trials: greater precision but with greater uncertainty?.
        JAMA. 2003; 289: 2554-2559https://doi.org/10.1001/jama.289.19.2554
        • Murray D.M.
        Design and Analysis of Group-Randomized Trials.
        Oxford University Press, New York1998
        • Donner A.
        • Klar N.
        Design and Analysis of Cluster Randomization Trials in Health Research.
        Arnold, London2000
        • Hayes R.J.
        • Moulton L.H.
        Cluster Randomised Trials.
        Taylor & Francis Group, LLC, Boca Raton, FL2009https://doi.org/10.1201/9781584888178
        • Campbell M.J.
        • Walters S.J.
        How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research.
        John Wiley & Sons Ltd, Chichester2014https://doi.org/10.1002/9781118763452
        • Pals S.L.
        • Murray D.M.
        • Alfano C.M.
        • et al.
        Individually randomized group treatment trials: a critical appraisal of frequently used design and analytic approaches.
        Am J Public Health. 2008; 98: 1418-1424https://doi.org/10.2105/AJPH.2007.127027
        • Roberts C.
        • Roberts S.A.
        Design and analysis of clinical trials with clustering effects due to treatment.
        Clin Trials. 2005; 2: 152-162https://doi.org/10.1191/1740774505cn076oa
        • Hoover D.R.
        Clinical trials of behavioural interventions with heterogeneous teaching subgroup effects.
        Stat Med. 2002; 21: 1351-1364https://doi.org/10.1002/sim.1139
        • Lee K.J.
        • Thompson S.G.
        Clustering by health professional in individually randomised trials.
        BMJ. 2005; 330: 142-144https://doi.org/10.1136/bmj.330.7483.142
        • Heo M.
        • Litwin A.H.
        • Blackstock O.
        • Kim N.
        • Arnsten J.H.
        Sample size determinations for group-based randomized clinical trials with different levels of data hierarchy between experimental and control arms.
        Stat Methods Med Res. 2014; (In press. Online August 14)
        • Baldwin S.A.
        • Bauer D.J.
        • Stice E.
        • Rohde P.
        Evaluating models for partially clustered designs.
        Psychol Methods. 2011; 16: 149-165https://doi.org/10.1037/a0023464
        • Kahan B.C.
        • Morris T.P.
        Assessing potential sources of clustering in individually randomised trials.
        BMC Med Res Methodol. 2013; 13: 58https://doi.org/10.1186/1471-2288-13-58
        • Kish L.
        Survey Sampling.
        John Wiley & Sons, New York1965
        • Cornfield J.
        Randomization by group: a formal analysis.
        Am J Epidemiol. 1978; 108: 100-102
        • Brown C.A.
        • Lilford R.J.
        The stepped wedge trial design: a systematic review.
        BMC Med Res Methodol. 2006; 6: 54https://doi.org/10.1186/1471-2288-6-54
        • Hussey M.A.
        • Hughes J.P.
        Design and analysis of stepped wedge cluster randomized trials.
        Contemp Clin Trials. 2007; 28: 182-191https://doi.org/10.1016/j.cct.2006.05.007
        • Copas A.J.
        • Lewis J.J.
        • Thompson J.A.
        • et al.
        Designing a stepped wedge trial: three main designs, carry-over effects and randomisation approaches.
        Trials. 2015; 16: 352https://doi.org/10.1186/s13063-015-0842-7
        • Hemming K.
        • Haines T.P.
        • Chilton P.J.
        • Girling A.J.
        • Lilford R.J.
        The stepped wedge cluster randomised trial: rationale, design, analysis, and reporting.
        BMJ. 2015; 350: h391https://doi.org/10.1136/bmj.h391
        • Hughes J.P.
        • Granston T.S.
        • Heagerty P.J.
        Current issues in the design and analysis of stepped wedge trials.
        Contemp Clin Trials. 2015; 45: 55-60https://doi.org/10.1016/j.cct.2015.07.006
        • Meinert C.L.
        Clinical Trials Handbook: Design, Conduct, and Analysis. 2nd ed. Wiley, New York2012https://doi.org/10.1002/9781118422878
        • Thistlethwaite D.L.
        • Campbell D.T.
        Regression-discontinuity analysis: an alternative to the ex-post facto experiment.
        J Educ Psychol. 1960; 51: 309-317https://doi.org/10.1037/h0044319
        • Shadish W.R.
        • Cook T.D.
        • Campbell D.T.
        Experimental and Quasi-Experimental Designs for Generalized Causal Inference.
        Houghton Mifflin Company, Boston, MA2002
        • Imbens G.W.
        • Lemieux T.
        Regression discontinuity designs: a guide to practice.
        J Econ. 2008; 142: 615-635https://doi.org/10.1016/j.jeconom.2007.05.001
        • Rubin D.B.
        Assignment to treatment group on the basis of a covariate.
        J Educ Behav Stat. 1977; 2: 1-26https://doi.org/10.3102/10769986002001001
        • Pennell M.L.
        • Hade E.M.
        • Murray D.M.
        • Rhoda D.A.
        Cutoff designs for community-based intervention studies.
        Stat Med. 2011; 30: 1865-1882https://doi.org/10.1002/sim.4237
        • Bor J.
        • Moscoe E.
        • Mutevedzi P.
        • Newell M.L.
        • Barnighausen T.
        Regression discontinuity designs in epidemiology: causal inference without randomized trials.
        Epidemiology. 2014; 25: 729-737https://doi.org/10.1097/EDE.0000000000000138
        • Bor J.
        • Moscoe E.
        • Barnighausen T.
        Three approaches to causal inference in regression discontinuity designs.
        Epidemiology. 2015; 26: e28-e30https://doi.org/10.1097/EDE.0000000000000256
        • Moscoe E.
        • Bor J.
        • Barnighausen T.
        Regression discontinuity designs are underutilized in medicine, epidemiology, and public health: a review of current and best practice.
        J Clin Epidemiol. 2015; 68: 122-133https://doi.org/10.1016/j.jclinepi.2014.06.021
        • Venkataramani A.S.
        • Bor J.
        • Jena A.B.
        Regression discontinuity designs in healthcare research.
        BMJ. 2016; 352: i1216https://doi.org/10.1136/bmj.i1216
        • O’Keeffe A.G.
        • Geneletti S.
        • Baio G.
        • et al.
        Regression discontinuity designs: an approach to the evaluation of treatment efficacy in primary care using observational data.
        BMJ. 2014; 349: g5293https://doi.org/10.1136/bmj.g5293
        • Linden A.
        • Adams J.L.
        Combining the regression discontinuity design and propensity score-based weighting to improve causal inference in program evaluation.
        J Eval Clin Pract. 2012; 18: 317-325https://doi.org/10.1111/j.1365-2753.2011.01768.x
        • Murray D.M.
        • Pennell M.
        • Rhoda D.
        • Hade E.M.
        • Paskett E.D.
        Designing studies that would address the multilayered nature of health care.
        J Natl Cancer Inst Monogr. 2010; 2010: 90-96https://doi.org/10.1093/jncimonographs/lgq014
        • Rhoda D.A.
        • Murray D.M.
        • Andridge R.R.
        • Pennell M.L.
        • Hade E.M.
        Studies with staggered starts: multiple baseline designs and group-randomized trials.
        Am J Public Health. 2011; 101: 2164-2169https://doi.org/10.2105/AJPH.2011.300264
        • Cook T.D.
        • Campbell D.T.
        Quasi-Experimentation: Design & Analysis Issues for Field Settings.
        Rand McNally College Publishing Company, Chicago, IL1979
        • Hawkins N.G.
        • Sanson-Fisher R.W.
        • Shakeshaft A.
        • D’Este C.
        • Green L.W.
        The multiple baseline design for evaluating population-based research.
        Am J Prev Med. 2007; 33: 162-168https://doi.org/10.1016/j.amepre.2007.03.020
        • Biglan A.
        • Ary D.
        • Wagenaar A.C.
        The value of interrupted time-series experiments for community intervention research.
        Prev Sci. 2000; 1: 31-49https://doi.org/10.1023/A:1010024016308
        • Collins L.M.
        • Murphy S.A.
        • Strecher V.
        The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions.
        Am J Prev Med. 2007; 32: S112-S118https://doi.org/10.1016/j.amepre.2007.01.022
        • Lavori P.W.
        • Dawson R.
        Introduction to dynamic treatment strategies and sequential multiple assignment randomization.
        Clin Trials. 2014; 11: 393-399https://doi.org/10.1177/1740774514527651
        • Huang X.
        • Choi S.
        • Wang L.
        • Thall P.F.
        Optimization of multi-stage dynamic treatment regimes utilizing accumulated data.
        Stat Med. 2015; 34: 3424-3443https://doi.org/10.1002/sim.6558
        • Ogbagaber S.B.
        • Karp J.
        • Wahed A.S.
        Design of sequentially randomized trials for testing adaptive treatment strategies.
        Stat Med. 2016; 35: 840-858https://doi.org/10.1002/sim.6747
        • Huseinovic E.
        • Bertz F.
        • Leu Agelii M.
        • et al.
        Effectiveness of a weight loss intervention in postpartum women: results from a randomized controlled trial in primary health care.
        Am J Clin Nutr. 2016; 104: 362-370https://doi.org/10.3945/ajcn.116.135673
        • Gans K.M.
        • Gorham G.
        • Risica P.M.
        • et al.
        A multi-level intervention in subsidized housing sites to increase fruit and vegetable access and intake: rationale, design and methods of the ‘Live Well, Viva Bien’ cluster randomized trial.
        BMC Public Health. 2016; 16: 521https://doi.org/10.1186/s12889-016-3141-7
        • Yun L.
        • Boles R.E.
        • Haemer M.A.
        • et al.
        A randomized, home-based, childhood obesity intervention delivered by patient navigators.
        BMC Public Health. 2015; 15: 506https://doi.org/10.1186/s12889-015-1833-z
        • Sherwood N.E.
        • Butryn M.L.
        • Forman E.M.
        • et al.
        The BestFIT trial: a SMART approach to developing individualized weight loss treatments.
        Contemp Clin Trials. 2016; 47: 209-216https://doi.org/10.1016/j.cct.2016.01.011
        • Naar-King S.
        • Ellis D.A.
        • Idalski Carcone A.
        • et al.
        Sequential Multiple Assignment Randomized Trial (SMART) to construct weight loss interventions for African American adolescents.
        J Clin Child Adolesc Psychol. 2016; 45: 428-441https://doi.org/10.1080/15374416.2014.971459
        • Hamad R.
        • Cohen A.K.
        • Rehkopf D.H.
        Changing national guidelines is not enough: the impact of 1990 IOM recommendations on gestational weight gain among U.S. women.
        Int J Obes (Lond). 2016; (In press. Online June 21)https://doi.org/10.1038/ijo.2016.97
        • Almond D.
        • Lee A.
        • Schwartz A.E.
        Impacts of classifying New York City students as overweight.
        Proc Natl Acad Sci U S A. 2016; 113: 3488-3491https://doi.org/10.1073/pnas.1518443113
        • Andalon M.
        Oportunidades to reduce overweight and obesity in Mexico?.
        Health Econ. 2011; 20: 1-18https://doi.org/10.1002/hec.1773
        • Cheung Y.K.
        • Chakraborty B.
        • Davidson K.W.
        Sequential multiple assignment randomized trial (SMART) with adaptive randomization for quality improvement in depression treatment program.
        Biometrics. 2015; 71: 450-459https://doi.org/10.1111/biom.12258
        • Murray D.M.
        • Varnell S.P.
        • Blitstein J.L.
        Design and analysis of group-randomized trials: a review of recent methodological developments.
        Am J Public Health. 2004; 94: 423-432https://doi.org/10.2105/AJPH.94.3.423
        • Li P.
        • Redden D.T.
        Small sample performance of bias-corrected sandwich estimators for cluster-randomized trials with binary outcomes.
        Stat Med. 2015; 34: 281-296https://doi.org/10.1002/sim.6344