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Catch It Report: - Web based weight loss RCT
 

Catch It Report: - Web based weight loss RCT

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  • Long term weight loss difficult to achieve Multiple interventions needed including Behaviour therapy Small weight reductions (5% to 10%) associated with health benefits
  • 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.myaccess.library.utoronto.ca/nchs/data/ad/ad388.pdf >.
  • Dates defining recruitment…. EXTERNAL VALIDITY, contamination, bias 4
  • Declaration of helsinki and approved by relevant human subjects review committee blinding (participant, individual;provider, analysis) 5 Dates, setting
  • Unexpected
  • . Regular self-monitoring has been consistently demonstrated as an effective behavior change strategy (23), however, adherence wanes over time. To overcome adherence challenges, we created a dynamic, graphically rich, high usability self-monitoring tool that permitted tracking of individual progress and displayed the average performance for other program participants (see Figure 1 ).
  • We chose self-monitoring metrics that would limit complexity, relying primarily on dichotomous response options.
  • Participants randomized to the web-based intervention would demonstrate greater weight losses compared to those in usual care
  • We observed a larger reduction in BMI among intervention participants), relative to those randomized to usual care; mean difference:). We did not find any group differences for change in (mean difference), (mean difference:), or (mean difference:). We observed no gender differences in change for any of the body weight or secondary outcomes.
  • Majority of intervention participants met the website login goal through week 9 Over 40% continued to meet the login goal through week 12.
  • Figure 3 shows the association between website login quartile and weight loss in the intervention group ( P for trend = 0.0007). Compared to the lowest quartile of website logins, those in the highest quartile showed greater weight loss (mean difference:-4.16 kg; 95% CI -1.47, -6.84). Differences in weight loss were also observed between quartiles two and four (mean difference: -4.87; 95% CI -2.56, -7.17).
  • Importance of research question, originality of research, validity

Catch It Report: - Web based weight loss RCT Catch It Report: - Web based weight loss RCT Presentation Transcript

  • 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
  • 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
  • My thoughts..
  • 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
  • Other authors
    • Sharon 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
  • Other authors
    • Evelyn 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
  • Overview
    • Introduction
    • Methods
    • Results
    • Discussion
    • CONSORT versus STARE-HI
    • Introduction
  • 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
  • Why web-based behaviour counselling?
    • Reach larger population
    • Low cost
    • Adaptable
    • Scalable
    • Effective for weight loss
  • 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 ).
  • Reference 19 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 >.
  • 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
  • Study objective
    • Evaluate the short-term efficacy of a web-based behavioural weight loss intervention among primary care patients with obesity and hypertension
    • Methods
  • 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)
  • Participant Enrolment
    • 390 patients identified through review of from large outpatient practice in Cambridge Massachusetts, between June 2005 and June 2006
    • Welcome letter mailed out with opt out instructions
    • Telephoned those who did not refuse additional contact
    74% 26% unclear Contamination Bias ? External Validity?
  • 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
  • Baseline and follow-up assessments
    • Web-based survey
    • Anthropometric measures and blood pressure (Research staff)
    • $25 for attending assessment
    Reference 20 Unclear Results?
  • Reference 20 NHANES 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 >
  • Intervention vs Usual Care 84% of intervention 84% of usual care Aim for Healthy Weight material Web-based behaviour treatment + health coach ??
  • 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
  • Figure 1 W ebsite-based tracking system screenshot
  • 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
  • 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”
  • Hypothesis
    • Participants randomized to the web-based intervention would demonstrate greater weight losses compared to those in usual care
  • 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
  • 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
  • Outcome measures
    • Primary outcome:
      • Change in body weight (kg) at 12 weeks
    • Secondary outcome:
      • BMI
      • Blood pressure control
      • Waist circumference
      • Frequency of logins
    • Results
  • Primary Outcome Weight 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)
  • Secondary outcome -0.38 mm Hg (95% CI -4.03, 3.27) diastolic blood pressure -1.30 mm Hg (95% CI -3.38, 5.99) systolic blood pressure -1.87 cm (95% CI -3.97, 0.23) waist circumference -1.07 kg/m2 (95% CI -1.49, -0.64) BMI Mean Difference (Intervention versus Usual Care)
  • Secondary outcome Weekly 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
  • 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
  • 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
    • Discussion
  • Discussion (by author) Primary care setting What does study add Contributes to growing body of evidence on internet delivery of weight loss intervention Relationship to prior knowledge
    • Answers study question
    • Explanation for modest weight loss observed
    • No caloric restriction
    • Motivation not controlled
    Principal result
  • Discussion (author)
    • Small sample size
    • Generalizable only to patient with internet access in similarly structured primary care settings
    • Short duration
    • Design did not allow cost estimates
    • Unable to isolate the independent contribution of intervention components (coaching calls, raffle)
    Limitations Magnitude of intervention efficacy and coaching support What future research is needed
  • 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.
  • 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
  • Obesity guidelines (CMAJ)
  • 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?
    • STARE-HI versus CONSORT