Catch It Report: - Web based weight loss RCT

CATCH-IT Report

Bennett GG, Herring SJ, Puleo E, Stein EK, Emmons KM, and Gillman MW. Web-based Weight Loss in Primary Care: A Randomized Controlled Trial. Obesity (2009) doi:10.1038/oby.2009.242

November 16, 2009

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Selected for CATCH-IT Review

Recent (published online August 2009) Personal Interest in Obesity RCT of Web-Based Intervention Potential Impact on Policy Makers,

Healthcare Providers, Patients

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My thoughts..

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Authors

Gary G. Bennett, Ph.D.

Associate professor in the Department of Psychology & Neuroscience at Duke University Bachelor’s degree in psychology from Morehouse College. Graduate studies in clinical psychology (with a focus in behavioral medicine) at Duke University. Internship in clinical health psychology at the Duke University Medical Center Postdoctoral studies in social epidemiology as an Alonzo Smythe Yerby Research Fellow at the

Harvard School of Public Health Was a faculty member at the Harvard School of Public Health and Dana-Farber Cancer Institute

until joining Duke University in January 2009 In 2004, he was named one of Boston's Ten Outstanding Young Leaders. 44 papers cited on PubMed

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Other authorsSharon J. Herring MD, MPH

Assistant Professor of Medicine and Public Health, Temple University School of Medicine Research Interests: Pregnancy related weight gain and obesity risk, Obesity prevention in

primary care, Health outcomes in women with gestational diabetes mellitus, Psychosocial factors in chronic disease prevention

9 papers cited on PubMed

Puleo E

Associate Dean for Research, University of Massachusetts, School of Public Health and Health Sciences

51 papers cited on PubMed Puleo E, Zapka JG, Goins KV, Yood MU, Mouchawar J, Manos M, Somkin C, Taplin S. Recommendations

for care related to follow-up of abnormal cancer screening tests: accuracy of patient report. Eval Health Prof. 2005 Sep;28(3):310-27.

Puleo E, Zapka J, White MJ, Mouchawar J, Somkin C, Taplin S. Caffeine, cajoling, and other strategies to maximize clinician survey response rates. Eval Health Prof. 2002 Jun;25(2):169-84.

Puleo E, Ory SJ, Christakos AC 45, X/46, XY gonadal dysgenesis. A case report. J Reprod Med. 1983 Mar;28(3):215-6

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Other authorsEvelyn K. Stein

The Center for Community-Based Research, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

Karen M. Emmons, PhD

Professor of Society, Harvard School of Public Health, Human Development and Health

Research Area: Community-Based Cancer Prevention 144 papers cited on PubMed

Matthew W. Gillman, M.D. S.M.

Associate Professor in the HMS Department of Ambulatory Care and Prevention Research interests: early life prevention of adult chronic disease, optimal nutrition for

children and adults, and clinical epidemiology. 178 papers cited on PubMed

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Overview

Introduction Methods Results Discussion CONSORT versus STARE-HI

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Introduction

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Why Obesity

1/3 US population are obese Comorbidities represent major challenge

to primary care setting Web-based intervention is low cost,

adaptable, scalable and efficacious

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Why web-based behaviour counselling? Reach larger population Low cost Adaptable Scalable Effective for weight loss

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Why obese patients with hypertension in primary care

Page 1 “Evidence is lacking regarding the utility of

web-based weight loss interventions in primary care; this is particularly the case for obese patients with hypertension—a population responsible for a large proportion of primary care patient visits” (Ref 19).

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Burt C, McCaig L, Rechtsteiner E . Ambulatory medical care utilization estimates for 2005. Advance data from vital and health statistics. Hyattsville, MD: National Center for Health Statistics, 2007 <http://www.cdc.gov/nchs/data/ad/ad388.pdf>.

Reference 19

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Suggestions

Reference M. V. Chakravarthy, M. J. Joyner, F. W. Booth, .An

Obligation for Primary Care Physicians to Prescribe Physical Activity to Sedentary Patients to Reduce the Risk of Chronic Health Conditions,. Mayo Clinic Proceedings 77, 2 (2002): pp. 165.173.

Access to behavioural therapy via primary care

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Study objective

Evaluate the short-term efficacy of a web-based behavioural weight loss intervention among primary care patients with obesity and hypertension

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Methods

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Participants Inclusion criteria

Age 25 to 65 years BMI 30 to 40 kg/m2 Diagnosed hypertension and utilization of hypertension

medication Non-smoker status at least 6 months prior to recruitment English language fluency Availability of computer with internet access at home or work

Exclusion: pregnant women those with history of condition that would prohibit exercise (such

as dementia, cancer, or stroke)

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Participant Enrolment

1. 390 patients identified through review of from large outpatient practice in Cambridge Massachusetts, between June 2005 and June 2006

2. Welcome letter mailed out with opt out instructions

3. Telephoned those who did not refuse additional contact

74%

26%

unclear

Contamination Bias ?

External Validity?

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Participant Flow

Baseline assessment

Follow-up assessment

Research staff collecting evaluation data blinded to randomization status

predetermined assignments enclosed in non-transparent randomization envelopes)

Dates, setting, informed consent, blinding (participant, care provider) ??

unclear

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Baseline and follow-up assessments Web-based survey Anthropometric measures and blood

pressure (Research staff) $25 for attending assessment

Reference 20

UnclearResults?

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Reference 20NHANES Food Questionnaire

Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey Questionnaire. Hyattsville, MD, 2004 <http://www.cdc.gov/nchs/data/nhanes/nhanes_03_04/tq_fpq_c.pdf>

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Intervention vs Usual Care

84% of intervention 84% of usual care

Aim for Healthy Weight material

Web-based behaviour treatment + health coach

??

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Intervention Group

Web-based intervention

- 3 month access to website Interactive weight loss approach (iOTA)- Aims to create energy deficit through modification of routine obesogenic lifestyle

behaviours- Purpose to facilitate easy, daily self-monitoring of adherence to obesogenic

behaviour change goals

Four counselling sessions with health coach

- Two 20-min in-person motivational coaching session- Algorithm to select four obesogenic behaviour change goals (week 1)- participant select new obesogenic behaviour change goals (week 6)

- Two, 20-min biweekly session via telephone (week 3 and week 9)

?

Unclear

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Figure 1 Website-based tracking system screenshot

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Step Up, Trim Down Trial

Goal: login at least 3 times weekly Raffle entry for each login. Two raffles for $50 over 3 month period. Website presented behavioural skills need to effectively

adhere to set of obesogenic behaviour change goals (e.g., stimulus control, portion control, label reading, eating out) and updated biweekly

Website also included social networking forum, recipes, messaging feature for direct communication with coach

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iOTA behaviour change goals

- “Watch 2 hr or less of TV every day”- “Avoid sugar-sweetened beverages”- “Avoid fast food”- “Eat breakfast every day”- “No late night meals and snacks”

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Hypothesis

Participants randomized to the web-based intervention would demonstrate greater weight losses compared to those in usual care

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Sample size

Two sided type 1 error rate of 5% 100 participants (50 per group) need to

detect at least 5 kg mean weight difference between groups with 80% power

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Statistical methods

Univariate analyses of variables of interest to test for baseline group differences, outliers and distributional assumptions

Analysis of variance, regression models and nonparametric tests as necessary to test group differences in each of the study outcomes

For intent-to-treat analyses, baseline carried forward imputation approach was used

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Outcome measures

Primary outcome: Change in body weight (kg) at 12 weeks

Secondary outcome:BMI Blood pressure controlWaist circumferenceFrequency of logins

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Results

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Primary OutcomeWeight loss by condition in the Step Up Trim Down weight loss trial (n = 101)

Suggestion to include % weight loss

25.6% of intervention participants lost >5% body weight by week 12 (none in control)

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Secondary outcome

Mean Difference

(Intervention versus Usual Care)BMI -1.07 kg/m2 (95% CI -1.49, -0.64)

waist circumference

-1.87 cm (95% CI -3.97, 0.23)

systolic blood pressure

-1.30 mm Hg (95% CI -3.38, 5.99)

diastolic blood pressure

-0.38 mm Hg (95% CI -4.03, 3.27)

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Secondary outcomeWeekly website logins among participants with >= 1 login

participant who did not log in once omitted from analysis??

Those who met login goal for >= 6 weeks lost more wt (-3.30 +/- 3.78 kg) than those who did not (-0.42 +/- 1.78 kg) Mean diff: -2.88 kg (95% CI -1.56, -4.60)

Those meeting login goal for >=10 weeks loss more wt (-4.50 +/-3.29 kg) than those who did not (-0.60 +/- 1.87 kg); Mean diff: -3.90 kg; 95% CI -2.43, -5.36).

declining

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Association between frequency of website logins and weight loss

(20.99-2 =18.99) (50.49-21=29.49) (71.99-50.50=21.49) (130-72=58.0)0 – 2 missing?

Data intervals not proportional

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Coaching sessions and weight loss

80.4% received all coaching sessions within 1 week of scheduled appointment

No association between participation in all 4 coaching sessions and weight loss

Likely due to high adherence rates

Unclear

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Discussion

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Discussion (by author)Principal result Answers study question

Explanation for modest weight loss observedNo caloric restrictionMotivation not controlled

Relationship to prior knowledge

Contributes to growing body of evidence on internet delivery of weight loss intervention

What does study add Primary care setting

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Discussion (author)

Limitations Small sample sizeGeneralizable only to patient with internet access in similarly structured primary care settingsShort durationDesign did not allow cost estimatesUnable to isolate the independent contribution of intervention components (coaching calls, raffle)

What future research is needed

Magnitude of intervention efficacy and coaching support

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Funding

Unrestricted grant from Sanofi Aventis. G.G.B. is also supported by grant from National Cancer

Institute. S.J.H. was supported by an institutional Ruth L.

Kirschstein National Research Service Award (5 T32 HP11001-19) from the Health Resources and Services Administration and the Department of Ambulatory Care and Prevention at Harvard Medical School and Harvard Pilgrim Health Care in Boston, Massachusetts.

K.M.E. is supported by grant from the National Cancer Institute.

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Public health approach to prevent obesity

effectiveness of programs for weight loss via lifestyle change (controlled energy intake, increased physical activity) is highly variable and infrequent result in sustainable weight loss

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Obesity guidelines (CMAJ)

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Questions

Was it a weight loss intervention or behaviour modification to promote healthy lifestyle?

Why only a behavioural intervention? Studies suggest the need for multiple interventions

Why 12 weeks? Doesn’t address the challenges facing obesity interventions (long term sustainability)

Does research of a based tool in the primary care setting provide new knowledge?

How generalizable is study with raffle to increase web login? Why was attrition rate higher than those reported in literature?

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STARE-HI versus CONSORT