1. An iterative approach to optimization of a behavioral intervention to reduce STIs on college campuses
Kari Kugler & David Wyrick
OBBI Training
May 20th, 2016

2. Outline
Rationale for itMatters Preparation phase Optimization phase Evaluation phase Practical considerations Summary

3. Outline
Rationale for itMatters Preparation phase Optimization phase Evaluation phase Practical considerations Summary

4. An important health issue
College students have high rates of STIs
Sexual risk-taking is common
Inconsistent condom use
Multiple concurrent partners
Casual sex (hook-ups)
Alcohol use is associated with
Increase in number of partners and hook-ups
Decrease in condom use

5. Numerous factors of influence
Proximal factors
Perceived norms
Expectancies
Perceived benefits
Self-efficacy
Contextual factors
Gender
Relationship status
Campus environment

6. Few interventions target the intersection
Combine separate components
Personalized normative feedback
Most are in online format
Limitations
Focus on changing norms only
Diversity of students (race/ethnicity)
Only sexually active students

7. What is the goal of itMatters study?
To engineer an optimized online intervention to prevent sexually transmitted infections among college students
Aimed uniquely at the intersection of alcohol use and sexual risk behaviors
Every component of itMatters has an empirically detectable effect
Effectiveness of itMatters approaches that of other STI preventive interventions

8. Interdisciplinary team of investigators

9. Outline
Rationale for itMatters Preparation phase Optimization phase Evaluation phase Practical considerations Summary

10. itMatters applied to MOST
Preparation phase
Component 1
Component 2
Component 4
Component 3
Component 5

11. Conceptual model
Situational Context
Appraisal of sex potential & risk
Sexual arousal
Cognitive impairment/disinhibition
Sexual Risk Behaviors
STIs
Moderators
Gender
Other individual factors
Relational
Institutional context
Alcohol Use
Protective Behavioral Strategies
Descriptive Norms
(e.g., perceptions of how many people have hook-ups due to
alcohol use)
Injunctive Norms
(e.g., perceptions of acceptability of sex after drinking alcohol)
Proximal Factors
Conceptual Pathway
Self-efficacy to
Prevent Harm
(e.g., self-efficacy to negotiate condom use when intoxicated)
Outcome Expectancies
(e.g., expect alcohol use to enhance or diminish sex experience)
Perceived Benefit
(e.g., perceived benefits of using PBSs related to alcohol and sexual behaviors)
We have had 25 other iterations of this model to try to get it right!

12. Identification of components
Situational Context
Appraisal of sex potential & risk
Sexual arousal
Cognitive impairment/disinhibition
Sexual Risk Behaviors
STIs
Moderators
Gender
Other individual factors
Relational
Institutional context
Alcohol Use
Protective Behavioral Strategies
Descriptive Norms
(e.g., perceptions of how many people have hook-ups due to
alcohol use)
Injunctive Norms
(e.g., perceptions of acceptability of sex after drinking alcohol)
Proximal Factors
Conceptual Pathway
Self-efficacy to
Prevent Harm
(e.g., self-efficacy to negotiate condom use when intoxicated)
Outcome Expectancies
(e.g., expect alcohol use to enhance or diminish sex experience)
Perceived Benefit
(e.g., perceived benefits of using PBSs related to alcohol and sexual behaviors)
Intervention Components
Perceived Benefits

13. Intervention components
Develop online modules for five candidate intervention components
Descriptive norms
Injunctive norms
Expectancies
Perceived benefits
Self-efficacy
Component levels: On/Off

14. Program modules Descriptive norms Injunctive norms
Correct misperceptions about alcohol use and sexual behaviors with and without alcohol
Strategy: Personalized feedback comparing prevalence to own behaviors
Injunctive norms
Correct misperceptions about other college students’ approval of alcohol and sexual behaviors (with and without alcohol)
Strategy: Personalized feedback perceptions of approval to actual approval

15. Program modules Outcome expectancies Perceived benefits
Increase understanding that expectancies drive behavior and alcohol shouldn’t be given the “credit”
Strategy: bar lab simulation and visual representation of how alcohol effects key elements for fun and safe sex
Perceived benefits
Increase perceptions of perceived benefits of using protective behavioral strategies (PBS) and value of reducing risk
Strategy: Valuation of reducing risk of STI and risk assessment of vaious PBS

16. Program modules Self efficacy to use protective behavioral strategies
Increase self-efficacy to use alcohol and sex protective behavioral strategies
Strategy: Modeling; Know boundaries, have a plan, practice
Knowledge (everybody gets)
Increase knowledge about alcohol impairment, condom use skills, STIs
Strategy: Decisional balance activity; interactive presentation of important facts

17. Module development This has been an iterative process working with:
eLearning designers
External Advisory Panel
Students

18. Example module

19. Optimization criterion
Include only “active” components in the optimized intervention
“Active” = d≥.15
How did we decide on this?
Meta-analyses suggest alcohol and sexual risk reduction interventions have overall effects of d ~.35
.35/5 components = .07

20. Outline
Rationale for itMatters Preparation phase Optimization phase Evaluation phase Practical considerations Summary

21. Optimization phase
Goal: to have the most effective STI preventive intervention possible Constraints: to include only “active” components Use MOST to accomplish this …taking an iterative approach!

22. Optimized intervention
Optimization phase
Revise components showing d<.15
Descriptive norms
component
Optimized intervention
Injunctive norms
Expectancies
Select best version of each component where d≥.15
Perceived benefits
Self efficacy
Screening experiment 2
Screening experiment 1
Optimization phase

23. Experimental Conditions in 25 Factorial Design
Screening experiment
Experimental Conditions in 25 Factorial Design
Experimental
Condition
Intervention Components
KNOW
DNORMS
INORMS
EXPECT
BENEFITS
SELFEFF 1
Include 2 √ 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Study design: 25 full factorial
Within school randomization
4 schools: public universities,
historically black colleges and
universities, rural and urban
Mode of delivery: online

24. Intervention implementation
Randomize 1st year students to experimental condition Block on gender After last data collection, students will be given access to any lessons that they were not originally assigned

25. Data collection
Students will be asked to provide consent before completing online surveys
Survey 1 (pre-test)
Survey 2 (immediate post-test)
Survey 3 (30-day follow-up)

26. Measures
Proximal factors/mediators will be used as primary outcome of the screening experiment
In general, what percentage of students at your college do you believe had penetrative sex during a hookup that also had alcohol before or during the hookup?
Measuring intervention effect on mediators is different than effect on behavioral or health outcomes
Need to develop our own measures that (a) target intersection and (b) capture intended change

27. Analytic approach Use a regression approach to ANOVA
Assess main effect of each component on hypothesized proximal mediator
Hypothesis: The effect of receiving the self-efficacy component on self-efficacy to use protective behavioral strategies was greater than or equal to d≥.15, averaged across the other components.
Conduct analyses separately for males and females

28. Decision making Any component that has effect d<.15 will be revised
Informed by focus group data
Input from External Advisory Panel
Input from eLearning experts
Any component that has d≥.15 will not be revised

29. 2nd screening experiment
We get a second shot at improving the components to make them more efficient and effective!
Use same procedures as for 1st screening experiment
Randomize, collect data, and analyze the results

30. Decision making
If component has an effect of d<.15 and does not synergistically interact with other components → do not include in optimized intervention…!
This means that we may not bring forward 5 components
If component has an effect of d≥.15 and/or synergistically interacts with other components → include in optimized intervention.

31. Outline
Rationale for itMatters Preparation phase Optimization phase Evaluation phase Practical considerations Summary

32. Optimized intervention
Evaluation phase
Revise components showing d<.15
Descriptive norms
component
Optimized intervention
Injunctive norms
Expectancies
Select best version of each component where d≥.15
Perceived benefits
Self efficacy
Screening experiment 2
Screening experiment 1
Evaluate via RCT
Evaluation phase

33. Evaluation phase
Evaluate optimized intervention compared to delayed control condition
Use same participating universities
Randomize students to experimental condition (optimized intervention v. delayed control)
Assess long-term outcomes (i.e., behaviors, STIs)

34. Outline
Rationale for itMatters Preparation phase Optimization phase Evaluation phase Practical considerations Summary

35. Practical considerations
Critical to have a partnership with the participating universities
Incentives for screening experiment and RCT needed to be different

36. Grantsmanship considerations
First submission critiques:
Include an RCT to evaluate intervention
Response: Removed one of the three fully powered screening experiments to free up time and resources to conduct an RCT.
Provide rationale for proposed research to target mediators already known to be important
Response: Targeting mediators at the intersection of alcohol and sexual risk behaviors is understudied. MOST is being used to engineer an intervention that will target some of the same mediators as previous work but will be more effective.

37. Outline
Rationale for itMatters Preparation phase Optimization phase Evaluation phase Practical considerations Summary

38. Summary Using an iterative approach to optimization
Ensures each component is achieving an effect
Maps well on to go/no-go decision making
Good approach for new intervention development
Collecting data to generate new hypotheses
Mediation analyses to better understand process of change
Moderation analyses to identify subgroups for whom intervention is working best

39. Acknowledgments
Funders: NIAAA R01 AA Penn State colleagues: UNCG colleagues: Linda Collins Amanda Tanner Jeff Milroy Brittany Chambers Alice Ma X

40. Thank you! An iterative approach to optimization of a behavioral intervention to reduce STIs on college campuses