Presentation on theme: "Michael P. O’Donnell, PhD, MBA, MPH"— Presentation transcript:
1Michael P. O’Donnell, PhD, MBA, MPH AMSO & His POSSE: A Framework to Develop Effective Organization and Individual Behavior Change ProgramsMichael P. O’Donnell, PhD, MBA, MPH
2What Works Best? Strategy to Develop the Framework SystematicBackgroundBenchmarking studyGood, very good, best programsSystematic literature reviewsHealth impact of programsFinancial impact of programsRefining frameworkC Everett Koop AwardHealth impactFinancial impactComposite reviews1800+ manuscriptsDesign/manage programs100+ organizations
4Sampling of Theories Not to mention the statistics! Individual levelLearning TheoriesInformation processingHealth Belief ModelProtection Motivation Theory; Extended Parallel Process ModelTheories of Reasoned Action, Planned Behavior, and Integrated Behavior ModelGoal-SettingGoal goal-directed behaviorAutomatic behavior, impulse behavior, habitsTranstheoretical Model of Behavior ChangePrecaution Adoption Process Model and risk communicationAttribution Theory and Relapse PreventionCommunication-Persuasion MatrixElaboration Likelihood ModelSelf RegulationInterpersonal environmentSocial Cognitive TheoryStigma and DiscriminationDiffusion of InnovationSocial networks and social supportMulti-levelSystemsPowerEmpowermentOrganization levelStage Theory of Organization ChangeStakeholder TheoryCommunity levelCoalition TheorySocial Capital TheorySocial normsConscientizationCommunity OrganizationSociety and government levelAgenda-buildingMultiple StreamsAdvocacy CoalitionSource: Bartholomew LK, Parcel GS, Kok G, Gottleib NH, Fernandez ME, Planning Health Promotion Programs, 3rd 2011, Jossey-Bass
9Trends in Consumption of Five or More Recommended Vegetable and Fruit Servings for Adults 18 and Older, US,Note: Data from participating states and the District of Columbia were aggregated to represent the United States.Source: Behavioral Risk Factor Surveillance System CD-ROM ( , 1996, 1998) and Public Use Data Tape (2000, 2003, 2005, 2007), National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, 1997, 1999, 2000, 2001, 2004, 2006, 2008.
10Trends in Prevalence (%) of No Leisure-Time Physical Activity, by Educational Attainment, Adults 18 and Older, US,Adults with less than a high school educationAll adultsNote: Data from participating states and the District of Columbia were aggregated to represent the United States. Educational attainment is for adults 25 and older.Source: Behavioral Risk Factor Surveillance System CD-ROM ( , 1996, 1998) and Public Use Data Tape (2000, 2002, 2004, 2005, 2006, 2007), National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, 1997, 1999, 2000, 2001, 2003, 2005, 2006, 2007, 2008.
36Effects of Integrated Medication and Behavioral Interventions 51015202530NoBehavioralTherapyBriefAdviceNo MedicationMedicationRates doubled with brief advice & triple combining pharmacological and behavioral inventionsTypical Long Term Quit RatesHughes JR. CA Cancer J Clin. 2000; 50:
37Best Science for Tobacco Treatment Meta-analyses of 27 different topicsCombined approaches: Brief MD advice+ behavior therapy + medicationMinutes of therapy: 300Number sessions: 8Type and number of staff: 2-3 including one physicianMedication type: outcomes by medicationBehavioral therapy type: outcomes by typeTreating Tobacco Use and Dependence: 2008 Update, Fiore, et al, HHS
39Workplace Health Promotion Overall Processes What Works in Worksite Health Promotion: Systematic Review Findings and Recommendations from the Task Force on Community Preventive ServicesRobin E. Soler, Nicholaas Pronk, Ron GoetzelAmerican Journal of Preventive MedicineVolume 38(2) Supplement 2, February, 2010The Community Guide
40Methodology Search databases: Medline, Employees Benefits,NTIS, Sports Information Resource Guide, Cambridge Scientific Abstracts, Business Week, ABI Inform, Health Promotion and Education, Cumulative Index to Nursing and Allied Health Literature, Office of Smoking and Health, AIDSLine, PsychInfo, and Sociological AbstractsInclusion CriteriaPrimary research in peer review journal or technical reportPublished January 1980-June 2005Meet research quality criteriaEvaluate impact of workplace health promotion programMeasure change in one or more outcomes of interestStudies foundAbstracts and titles: 4,584Studies examined in detail: 334Studies meeting all criteria: 86RatingsStudy design: threats to internal validity: greatest, moderate, leastQuality of execution: good, fair, limitedEffect size: quantitative, qualitative
41Scope of Review Incentives and Competition to Reduce Tobacco Use Health Assessment with FeedbackvsHealth Assessment with Feedback Plus InterventionIncentives and Competition to Reduce Tobacco UseSmoke-free Policies to Reduce Tobacco UsePoint of Decision Prompts to Increase Stair Use
42Health Assessment with Feedback Conclusion: Insufficient evidence to recommendReasons:Small effect sizeSmall number of studies (32)Poor study design
43Health Assessment with Feedback Plus Intervention* Conclusion: Strong evidence of effectivenessTobacco use (30) % pp prevalence % consumptionDietary fat consumption (11) % pp prevalenceBlood Pressure control (31) % pp prevalenceCholesterol management (36) % pp prevalence mg/dlAbsence from work (10) days/year lessConclusion: Sufficient evidence of effectivenessSeat belt use (10) % pp prevalenceHeavy drinking (9) % pp prevalencePhysical activity (18) % pp prevalenceHealth risk score (21)Medical utilization (7)Conclusion: Insufficient evidence of effectivenessFitness (9) positive outcomes small effect sizes, multiple measuresBody composition (27)- BMI (8) BMI unit consistent findings- Weight (17) pds small effect size- Fat (6) % small effect sizeConclusion: Not effectiveFruit and vegetable consumption (8) minimal changes observed*Numbers of studies are shown in parentheses ( )
44Incentives and Competition to Reduce Tobacco Use Conclusion: Insufficient evidence of effectivenessIncentives and Competition OnlyNumber of studies: 1, 0 qualifiedConclusion: Strong evidence of effectivenessIncentives and Competition Plus Other InterventionsNumber of studies: 26; 14 qualifiedImpact:All studies- 4.4% pp median (2.7%-9.4%) prevalence 67% improvement- 13.7% median quit rate (8% -20.5%)Incentives + skills+ social support (5)- 10% pp median (2.7%-9.4%) prevalence 168% improvement- 21% median quit rateParticipation rates (11)28% median participation of smokers (12%-84%)
53Probability That an Ego Will Become Obese According to the Type of Relationship with an Alter Who May Become Obese in Several Subgroups of the Social Network of the Framingham Heart StudyGeographic separationEffect maintained 0,.26,1.5,3.4,9.3,471 milesLikelihood & degrees of separation1: 45%2: 20%3: 10%Figure 4. Probability That an Ego Will Become Obese According to the Type of Relationship with an Alter Who May Become Obese in Several Subgroups of the Social Network of the Framingham Heart Study. The closeness of friendship is relevant to the spread of obesity. Persons in closer, mutual friendships have more of an effect on each other than persons in other types of friendships. The dependent variable in each model is the obesity of the ego. Independent variables include a time-lagged measurement of the ego's obesity; the obesity of the alter; a time-lagged measurement of the alter's obesity; the ego's age, sex, and level of education; and indicator variables (fixed effects) for each examination. Full models and equations are available in the Supplementary Appendix. Mean effect sizes and 95% confidence intervals were calculated by simulating the first difference in the contemporaneous obesity of the alter (changing from 0 to 1) with the use of 1000 randomly drawn sets of estimates from the coefficient covariance matrix and with all other variables held at their mean values.Christakis NA, Fowler JH. N Engl J Med 2007;357:
54Probability That a Subject Will Quit Smoking According to the Type of Relationship with a Contact Who Quits Smoking, in the Social Network of the Framingham Heart StudyFigure 4. Probability That a Subject Will Quit Smoking According to the Type of Relationship with a Contact Who Quits Smoking, in the Social Network of the Framingham Heart Study. The dependent variable in each model is smoking by the subject. Separate generalized-estimating-equation logit models for smoking were specified for each type of social tie. Independent variables include a time-lagged measurement of the subject's smoking status at the previous examination; the contact's smoking status; a time-lagged measurement of the contact's smoking status; the subject's age, sex, and level of education; and fixed effects for each examination. Full models and equations are available in the Supplementary Appendix. Mean effect sizes and 95% confidence intervals were calculated by simulating the first difference in the contemporaneous smoking of the contact (changing from 1 to 0) with the use of 1000 randomly drawn sets of estimates from the coefficient covariance matrix and with all other variables held at their mean values. "Coworkers in small firms" means that six or fewer Framingham Heart Study participants worked at the same physical location.Christakis NA, Fowler JH. N Engl J Med 2008;358:
64Sprawl Is Associated with More Health Problems I would reverse the order of this and the previous slide--show this result first and the the information in the prior slideEwing, AJHP, 2003
65The Impact of Sprawl on Health and Behavior Urban SprawlUtilitarian walkingLeisure time walkingIncreases BMIIncreases in BPSource: Ewing et al. (2003) AJHP
66A national study of US adolescents (N=20,745) A national study of US adolescents (N=20,745)* found a greater number of physical activity facilities is directly related to increased physical activity and inversely related to risk of overweightOdds of having 5 or more bouts of MVPA1.26ReferentOdds of being overweight.68*using Add Health dataGordon-Larsen P, Nelson MC, Page P, Popkin BM. Inequality in the built environment underlies key health disparities in physical activity and obesity. Pediatrics 2006; 117(2):
67The Effect of Mixed Use on Obesity Participants were divided into four groups based on the level of land use mixEach quartile increase in land use mix was associated with a 12.20% reduction in the odds of being obese.The difference in weight for an average 5’ 10” white males in the lowest quartile of mixed use and the highest quartile of mixed use was 10 pounds.Frank, L., Andresen, M., and Schmid, T., Obesity Relationships With Community Design, Physical Activity, and Time Spent in Cars. American Journal of Preventive Medicine. June 2004.
68Data collected in from 3,161 Atlanta children show 5 to 18 year olds were more likely to walk for transportation if they lived in mixed-used neighborhoods with parks, schools, and commercial destinations nearby.Frank L, Kerr J, Chapman J, Sallis J. Urban form relationships with walk trip frequency and distance among youth. American Journal of Health Promotion 2007; 21(4S): 305.ALR Funded
69Pedometer data collected from over 100 New Jersey train and car commuters revealed that those who commuted by train walked 30% more steps a day and were 4 times more likely to meet recommended 10,000 steps daily than car commuters.Wener RE, Evans GW. A morning stroll: Levels of physical activity in car and mass transit commuting. Environment and Behavior 2007; 39(1):
70A study of 33 California cities found that adults who drove the most had obesity rates (27%) that were three times higher than those who drove the least (9.5%).Lopez-Zetina J, Lee H, Friis R. The link between obesity and the built environment. Evidence from an ecological analysis of obesity and vehicle miles of travel in California. Health & Place 2006; 12(4):
71The Impact of Driving and Walking on Obesity Every additional 30 minutes spent driving per day translates into a 3% increase in the odds of being obesityEvery additional Kilometer (.6 miles) walked translates into nearly a 5% reduction in the odds of being obeseFrank, L., Andresen, M., and Schmid, T., Obesity Relationships With Community Design, Physical Activity, and Time Spent in Cars. American Journal of Preventive Medicine. June 2004.
72Built Environment and Physical Activity Research Conclusions Living in Activity FriendlyCommunities could…Generate 2 more walk/bike trips per person per weekPrevent up to 1.7 pounds of weight gain per yearPositively affect walking/cycling for transportationPositively impact the total number of minutes of physical activity (40% more physical activity)Decrease amount of time spent in a car. Each hour spent in a car is associated with a 6% increase in the likelihood of obesity.Increase life expectancy by 4 years.Ewing et al 2003, Saelens et al 2003, Giles-Corti 2003, Frank et al 2003, Sturm et al 2004, Frank et al 2004, Lopez 2004