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Enabling Precision Behavior Change
- 1. Enabling Precision Behavior Change @ehekler Dr. Eric Hekler Arizona State University November 19, 2015 Talk given at the University of North Carolina, Chapel Hill
- 2. Outline • Precision behavior change • Requirements for precision behavior change • Agile science • UbiHealthy Cup @ehekler
- 4. Behavior at the center Hovell M, Wahlgren D, Adams M. Emerging theories in health promotion practice and research. 2009;2:347-85.@ehekler
- 5. Behaviors explain most variability in health Flickr – Stuck in Customs@ehekler 40 15 5 10 30 Sub-Optimal Health behaviors Social Circumstances Environmental Exposures Healthcare Genetics McGinnis, et al. 2002 Health Affairs
- 6. Why now? Personal, pervasive, & powerful technologies Flickr – Stuck in CustomsPatrick, Hekler, Estrin, Godino, Crane, Riper, & Mohr, Riley, Manuscript in Prep@ehekler
- 7. Why now? Behavioral meteorology Flickr-Bart Everson Patrick, Hekler Estrin, Godino, Crane, Riper, & Mohr, & Riley, Manuscript in Prep@ehekler
- 8. Why now? The world needs us… Flickr – Stuck in Customs http://youtu.be/QPKKQnijnsM Flickr – just.Luc Flickr-meanMrmustard
- 9. Just in Time Adaptive Interventions Just in Time • State of opportunity or vulnerability • Receptive • Key target behavior does not have to happen now Adaptive • Responsive to: – micro-scale changes (e.g., weather, stress) – Meso-scale changes (e.g., season, motivational waves) – Macro-scale life transitions (e.g., retirement, becoming a parent) @ehekler Nahum-Shani, Hekler, & Spruijt-Metz, (2016) Health Psychology
- 10. Just in Time: State of vulnerability Flickr - Rob Marquardt @ehekler Nahum-Shani, Hekler, & Spruijt-Metz, (2016) Health Psychology
- 11. Just in Time: State of opportunity Flickr - Miroslav Petrasko @ehekler Nahum-Shani, Hekler, & Spruijt-Metz, (2016) Health Psychology
- 12. Just in Time: Receptive Flickr-Jonathan Powell Nahum-Shani, Hekler, & Spruijt-Metz, (2016) Health Psychology@ehekler
- 13. Adaptive: Series of “Just in Time” moments @ehekler Flickr - Dave Gray
- 14. Precision behavior change spectrum Individual/User Controlled System Controlled Individual/System Balanced Control @ehekler
- 15. System controlled “Giving the fish” NSF IIS-1449751: EAGER: Defining a Dynamical Behavioral Model to Support a Just in Time Adaptive Intervention, PIs, Hekler & Rivera @ehekler
- 16. System model Riley, Martin, Rivera, Hekler, et al. 2016; Martin, Riley, Rivera, Hekler, et al. 2014@ehekler
- 17. Idiographic trajectory models Hekler, et al. 2013 Health Education and Behavior@ehekler
- 18. Martin, Rivera, & Hekler Manuscript Submitted for Publication Model-predictive controller @ehekler
- 19. Individual controlled “Teaching to fish” Eric Hekler, Jisoo Lee, Erin Walker, Winslow Burleson, Arizona State University; Bob Evans, Google Flickr Juhan Sonin @ehekler
- 20. Measure success towards goal Results Self-experimentation Plan + Implement for 1 week @ehekler
- 21. @ehekler
- 22. Requirements for precision behavior change • Interoperability/communication – Robust system architectures • Ecologically-valid data streams – Smartphone, wearable, data and digital trace inference • Data standardization – Schemas, ontologies, and other knowledge structuring tools • Behavior change tools – Codified evidence-based and usable behavior change modules • Predictive computational models – Multi-level & multi-time scale mathematical models about health and behavior • Personalization algorithms – Recommender system, model-predictive controller, or other translations of data into useful adaptation decisions • Test-bed for iterative optimization – Data , “ground truth” definitions, and participants @ehekler
- 24. Interoperable systems https://www.apple.com/ios/whats-new/health/ http://researchkit.github.io/ http://sagebase.org/
- 26. Ecologically-valid data streams @ehekler Lead Secondary Secondary Co-Lead
- 27. Turning “noise” into information https://ubicomplab.cs.washington.edu/
- 30. Agile science targets • Interoperability/communication – Robust system architectures • Ecologically-valid data streams – Smartphone, wearable, data and digital trace inference • Data standardization – Schemas, ontologies, and other knowledge structuring tools • Behavior change tools – Codified evidence-based and usable behavior change modules • Predictive computational models – Multi-level & multi-time scale mathematical models about health and behavior • Personalization algorithms – Recommender system, model-predictive controller, or other translations of data into useful adaptation decisions • Test-bed for iterative optimization – Data , “ground truth” definitions, and participants @ehekler@ehekler
- 32. Agile (XP Scrum) development @ehekler
- 33. Agile science philosophical assumptions https://en.wikipedia.org/wiki/Philosopher#/media/File:The_School_of_Athens.jpg@ehekler
- 34. Target =“Idiographic generalization” Analytic Perspective Focus Mixed Model Analogy Between-person On-average effects across participants Fixed effect (centered) Within-person On average effects over time Fixed effect (daily variation) Idiographic Individualized responses Random effect (error term) @ehekler
- 35. Evidence & logic defines truth https://en.wikipedia.org/wiki/Phases_of_Venus#/media/File:Phases-of-Venus.svg@ehekler
- 36. Rigor achieved via trial & error https://en.wikipedia.org/wiki/Incandescent_light_bulb#/media/File:Edison_incandescent_lights.jpg@ehekler
- 37. Knowledge accumulation via effective curation www..google.com@ehekler
- 38. Agile science products • Modules • System models • Personalization algorithms @ehekler
- 39. Modules Smallest, meaningful, repurposable,& concrete “Perfect” intervention package Components Flickr - Paul Swansen Flickr - Benjamin Esham @ehekler
- 43. System models: Meteorology analogy Flickr-Bart Everson Patrick, Hekler, Estrin, Godino, Crane, Riper, & Mohr, & Riley Manuscript in Prep @ehekler
- 44. System models Riley, Martin, Rivera, Hekler, et al. 2016; Martin, Riley, Rivera, Hekler, et al. 2014@ehekler
- 45. Dynamic hypotheses- “Sweet Spot” Hekler (PI), Rivera (Co-PI), NSF IIS-1449751 -15 -10 -5 0 5 10 15 20 0 2000 4000 6000 8000 10000 12000 14000 AveChangeSelfEffficacy ActualDailySteps Recommended Goal Actual Steps Δ Self-Efficacy @ehekler
- 47. Martin, Rivera, & Hekler Am. Control Conference (2015) Personalization algorithms @ehekler
- 49. Sprint • GOAL: Discovery and resource-efficient vetting of promising new approaches @ehekler
- 50. Amy Luginbill; Samantha Quagliano; Sepideh Zohreh S=Stop M=Move I= I statement; I can do it! L=Love (positivity) E=Exhale SMS: “If you are stressed today, try one of the following options, Deep breathing, Stretching, get up move around.” MOBILECAR MAIDSERVICES GREEN CLEAN Prototype 1: S.M.I.L.E. Prototype 2: Facial Wave Prototype 3: SMS Intervention Prototype 4: De-stress your carScrappy Trials @ehekler
- 51. Phoenix Proposition 104 John Harlow, Erik Johnston, Zoe Yeh@ehekler
- 52. Phoenix Proposition 104 John Harlow, Erik Johnston, Zoe Yeh@ehekler http://movephx.org/get-the-facts/maps/
- 53. Phoenix Proposition 104 John Harlow, Erik Johnston, Zoe Yeh@ehekler http://movephx.org/get-the-facts/maps/
- 54. Optimization • GOAL: Translation of a promising resources into useful & evidence-based tools for real-world use. @ehekler
- 55. Linda M. Collins The Methodology Center Penn State methodology.psu.edu@ehekler
- 56. Micro-randomization design • Sequential, full factorial designs • Randomize intervention component • Each time we might deliver component • Multiple components can be randomized • Randomized 100s or 1000s of times Klasnja, Hekler, Shiffman, Boruvka, Almirall, Tewari, Murphy, Health Psych, 2016@ehekler
- 57. System identification experiments -100 100 300 500 700 900 1100 1300 1500 0 2000 4000 6000 8000 10000 12000 14000 1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 Points Stepsperday Days Points Provided (100, 300, 500) Fictionalized actual steps per day Daily step goal ((Baseline Median) to (Baseline Median+100% Baseline Median)) NSF IIS-1449751: Defining a Dynamical Behavioral Model to Support a Just in Time Adaptive Intervention, PIs, Hekler & Rivera@ehekler
- 58. Release • GOAL: Share useful resources via effective curation. @ehekler
- 61. Paco (thank you, Bob Evans!) www.pacoapp.com@ehekler
- 62. Paco-read this on the website www.pacoapp.com@ehekler
- 65. Fundamental problem @ehekler We each build “optimized” packages for one-off problems We need to build inter-operable modular resources Flickr - Paul Swansen Flickr - Benjamin Esham
- 69. RoboCup Structure • Target: “developing by 2050 a Robot Soccer team capable of winning against the human team champion of the FIFA World Cup” • Rules: Change each year depending on state of the science http://www.robocup.org/about-robocup/regulations-rules/@ehekler
- 70. What is mHealth’s RoboCup? @ehekler https://upload.wikimedia.org/wikipedia/commons/e/e3/13-06-28-robocup-eindhoven-099.jpg Question generated by participants of the Schloss Dagstuhl Seminar on “Life-long Behavior Change Technologies:” June 21-26, 2015, http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=15262
- 71. UbiHealthy Cup v.2 • Target: – Actionable tool the community needs (e.g., passive measure of consumption, user burden, goal-setting module, team module) • Bracket Science – Competing teams that are winnowed down at each stage (stop getting money) – Final four tested head-to-head in an RCT • Challenges change over time Thanks to Susan Murphy & Pedja Klasnja for co-developing this idea.@ehekler
- 72. UbiHealthy Cup Bracket https://www.whitehouse.gov/assets/images/brackets2009c.jpg Final four RCT (36m, 4) Opt. 2 (24m, 8) Opt. 2 (24m)Opt. 1 (18m,16) Opt. 1 (18m)Sprint 2 (12m, 32) Sprint 2 (12m)Sprint 1 (6m, 64) Sprint 1 (6m)
- 74. RECAP @ehekler
- 75. TARGET: Precision behavior change Individual/User Controlled System Controlled Individual/System Balanced Control @ehekler
- 76. Why now? Behavioral meteorology Flickr-Bart Everson Patrick, Riley, Estrin, Hekler, Godino, Crane, Riper, & Mohr, Manuscript in Prep@ehekler
- 77. Why now? The world needs us… Flickr – Stuck in Customs http://youtu.be/QPKKQnijnsM Flickr – just.Luc Flickr-meanMrmustard
- 78. First step… @ehekler Stop building “perfect” packages… Start building interoperable modules Flickr - Paul Swansen Flickr - Benjamin Esham www.agilescience.org
- 79. Next step, organize and share! Dr. Eric Hekler, Arizona State University ehekler@asu.edu, @ehekler
Editor's Notes
- The talk will briefly set up the current context for mHealth / UbiComp / digital health research efforts as seen from various disciplinary lenses. Following this, the precision medicine initiative will be discussed followed by a discussion on one subclass of prevention interventions, labeled precision behavior change, which could fit well within the precision medicine initiative. Following the definition of precision behavior change, transdisciplinary research questions, with a particular focus on attempting to articulate intellectual merit and contributions for each discipline when exploring the research questions, will be discussed. The talk will conclude with plausible next steps to spur conversation among the webinar participants and later viewers on ways to refine this transdisciplinary research agenda to see if it is viable and, if so, how best to more actively enable it as an organizing “moon shot” agenda for the mHealth research community.
- The talk will briefly set up the current context for mHealth / UbiComp / digital health research efforts as seen from various disciplinary lenses. Following this, the precision medicine initiative will be discussed followed by a discussion on one subclass of prevention interventions, labeled precision behavior change, which could fit well within the precision medicine initiative. Following the definition of precision behavior change, transdisciplinary research questions, with a particular focus on attempting to articulate intellectual merit and contributions for each discipline when exploring the research questions, will be discussed. The talk will conclude with plausible next steps to spur conversation among the webinar participants and later viewers on ways to refine this transdisciplinary research agenda to see if it is viable and, if so, how best to more actively enable it as an organizing “moon shot” agenda for the mHealth research community.
- Discuss the lack of understanding from behavioral scientists on how to really deal with big data and opportunities for setting up “in the wild” studies that could later be harnessed for A/B testing. Nice melding of behavioral science knowledge of randomized controlled trials and HCI’s knowledge on the systems to automate those types of systems in the real-world.
- Based on this, we need to move more into an open discussion in which we explore lots and lots of different ideas if we really want to understand which ones are best. Sadly, science, particularly behavioral science doesn’t really have the sort of “maker” culture that would allow us. As such, a key emphasis.
- Based on this, we need to move more into an open discussion in which we explore lots and lots of different ideas if we really want to understand which ones are best. Sadly, science, particularly behavioral science doesn’t really have the sort of “maker” culture that would allow us. As such, a key emphasis.
- Based on this, we need to move more into an open discussion in which we explore lots and lots of different ideas if we really want to understand which ones are best. Sadly, science, particularly behavioral science doesn’t really have the sort of “maker” culture that would allow us. As such, a key emphasis.
- NOTE, this current draft is just to get a sense of timing and flow on key points to discuss. Formatting on almost all slides will not remain (e.g., likely will NOT have the titles at the top like that).
- - OK, now you’re creating a plan for your problem. For a successful plan, you should set an appropriate goal, and come up with ways to apply behavior change techniques.
- Beyond just the difficulty of the complexity of behavior and the behavioral problems we are trying to solve, there are a lot of demands on behavior change technologies themselves and different points that tend to thought about from different disciplines. Indeed, we want behavior change technologies that are evidence-based, cost-effective, personalized, easy to disseminate, promote maintenance, fit into a person’s life, and can, hopefully be financially self-sustained in sustaining. As can be seen just from this list, this can’t be achieved through the class disciplinary silo model of creation.
- The talk will briefly set up the current context for mHealth / UbiComp / digital health research efforts as seen from various disciplinary lenses. Following this, the precision medicine initiative will be discussed followed by a discussion on one subclass of prevention interventions, labeled precision behavior change, which could fit well within the precision medicine initiative. Following the definition of precision behavior change, transdisciplinary research questions, with a particular focus on attempting to articulate intellectual merit and contributions for each discipline when exploring the research questions, will be discussed. The talk will conclude with plausible next steps to spur conversation among the webinar participants and later viewers on ways to refine this transdisciplinary research agenda to see if it is viable and, if so, how best to more actively enable it as an organizing “moon shot” agenda for the mHealth research community.
- The talk will briefly set up the current context for mHealth / UbiComp / digital health research efforts as seen from various disciplinary lenses. Following this, the precision medicine initiative will be discussed followed by a discussion on one subclass of prevention interventions, labeled precision behavior change, which could fit well within the precision medicine initiative. Following the definition of precision behavior change, transdisciplinary research questions, with a particular focus on attempting to articulate intellectual merit and contributions for each discipline when exploring the research questions, will be discussed. The talk will conclude with plausible next steps to spur conversation among the webinar participants and later viewers on ways to refine this transdisciplinary research agenda to see if it is viable and, if so, how best to more actively enable it as an organizing “moon shot” agenda for the mHealth research community.
- Flipping to the second half of this talk now though, in my view, this will only be achieved by carefully building an ecosystem that supports precision behavior change and I think your HealthKit and ResearchKit are great starting points for this. To set up why though, allow me to briefly take a step back and discuss how behavioral scientists like me were told that we were supposed to do our science.
- The talk will briefly set up the current context for mHealth / UbiComp / digital health research efforts as seen from various disciplinary lenses. Following this, the precision medicine initiative will be discussed followed by a discussion on one subclass of prevention interventions, labeled precision behavior change, which could fit well within the precision medicine initiative. Following the definition of precision behavior change, transdisciplinary research questions, with a particular focus on attempting to articulate intellectual merit and contributions for each discipline when exploring the research questions, will be discussed. The talk will conclude with plausible next steps to spur conversation among the webinar participants and later viewers on ways to refine this transdisciplinary research agenda to see if it is viable and, if so, how best to more actively enable it as an organizing “moon shot” agenda for the mHealth research community.
- The talk will briefly set up the current context for mHealth / UbiComp / digital health research efforts as seen from various disciplinary lenses. Following this, the precision medicine initiative will be discussed followed by a discussion on one subclass of prevention interventions, labeled precision behavior change, which could fit well within the precision medicine initiative. Following the definition of precision behavior change, transdisciplinary research questions, with a particular focus on attempting to articulate intellectual merit and contributions for each discipline when exploring the research questions, will be discussed. The talk will conclude with plausible next steps to spur conversation among the webinar participants and later viewers on ways to refine this transdisciplinary research agenda to see if it is viable and, if so, how best to more actively enable it as an organizing “moon shot” agenda for the mHealth research community.
- The talk will briefly set up the current context for mHealth / UbiComp / digital health research efforts as seen from various disciplinary lenses. Following this, the precision medicine initiative will be discussed followed by a discussion on one subclass of prevention interventions, labeled precision behavior change, which could fit well within the precision medicine initiative. Following the definition of precision behavior change, transdisciplinary research questions, with a particular focus on attempting to articulate intellectual merit and contributions for each discipline when exploring the research questions, will be discussed. The talk will conclude with plausible next steps to spur conversation among the webinar participants and later viewers on ways to refine this transdisciplinary research agenda to see if it is viable and, if so, how best to more actively enable it as an organizing “moon shot” agenda for the mHealth research community.
- Based on this, we need to move more into an open discussion in which we explore lots and lots of different ideas if we really want to understand which ones are best. Sadly, science, particularly behavioral science doesn’t really have the sort of “maker” culture that would allow us. As such, a key emphasis.
- Central to agile science is a focus on products that will be immediately useful for non-scientists.
- Based on this, we need to move more into an open discussion in which we explore lots and lots of different ideas if we really want to understand which ones are best. Sadly, science, particularly behavioral science doesn’t really have the sort of “maker” culture that would allow us. As such, a key emphasis.
- Based on this, we need to move more into an open discussion in which we explore lots and lots of different ideas if we really want to understand which ones are best. Sadly, science, particularly behavioral science doesn’t really have the sort of “maker” culture that would allow us. As such, a key emphasis.
- Based on this, we need to move more into an open discussion in which we explore lots and lots of different ideas if we really want to understand which ones are best. Sadly, science, particularly behavioral science doesn’t really have the sort of “maker” culture that would allow us. As such, a key emphasis.
- I’ve been calling this alternative process agile science, which I’ll jump into briefly here.
- The group studies were where the most interesting things happened. In particular, this was when the groups really took advantage of “crummy trials” for better understanding when an idea was working. For example, Amy, Sam, and Sepideh’s group was trying to reduce stress. They did a lot of empathizing work and looking into the previous literature to find the importance of breathing and stress management techniques. Sadly though, whenever they tested some of their ideas, which included mantras and other ideas to help simple triggers for relaxing, they all failed. This was particularly fascinating because in their initial brainstorming, they really loved their “S.M.I.L.E.” accronym that they came up with. When they tested it, comparing it to a control, it simply didn’t work. They perceived but ultimately found that they needed to pivot and instead ended up focusing on figuring out ways to de-stress a person’s environment. So they went and started cleaning cars and got great responses.
- Thankfully, there has been great movement away from that classic pipeline and particularly the use of a randomized trial of interventions with multiple components in it, to other strategies that are more mirrored on strategies from engineering. Central to this work is a careful understanding of how to develop the evidence around the components of the intervention, with the assumption being htat the components will be more repurposable. SO, for example, Linda Collins has been pioneering the use of fractional factorial study designs to run interventions with multiple components but with a methodology that supports understanding of how the components and how they interact might function.
- Indeed, my colleagues and I have ben extending this logic to what we’ve been calling a micor-randomization study, which is atype of factorial design but that is done within a single person. The idea is to randomize intervention componetns with a person at each time when it might help. The design allows multiple of these to work and there is great power on a single person because it is plausible to randomize hundreds and even thousands of times within person.
- Myc olleauge, Daniel Rivera, and I have been extending this further using methods fromcontrol systems engineering to develop experimental designs that take more advantage of a priori knowledge than the micro-randomization study. In the discussion section, I’d be happy to get into details on these experimental designsbut for the focus of this, the main point is to realize that this is a huge shift in the behavioral science community away from ideas like RCTs nad instead towards methods that embrace and map out idiosyncracy.
- Beyond just the difficulty of the complexity of behavior and the behavioral problems we are trying to solve, there are a lot of demands on behavior change technologies themselves and different points that tend to thought about from different disciplines. Indeed, we want behavior change technologies that are evidence-based, cost-effective, personalized, easy to disseminate, promote maintenance, fit into a person’s life, and can, hopefully be financially self-sustained in sustaining. As can be seen just from this list, this can’t be achieved through the class disciplinary silo model of creation.
- The talk will briefly set up the current context for mHealth / UbiComp / digital health research efforts as seen from various disciplinary lenses. Following this, the precision medicine initiative will be discussed followed by a discussion on one subclass of prevention interventions, labeled precision behavior change, which could fit well within the precision medicine initiative. Following the definition of precision behavior change, transdisciplinary research questions, with a particular focus on attempting to articulate intellectual merit and contributions for each discipline when exploring the research questions, will be discussed. The talk will conclude with plausible next steps to spur conversation among the webinar participants and later viewers on ways to refine this transdisciplinary research agenda to see if it is viable and, if so, how best to more actively enable it as an organizing “moon shot” agenda for the mHealth research community.
- Beyond just the difficulty of the complexity of behavior and the behavioral problems we are trying to solve, there are a lot of demands on behavior change technologies themselves and different points that tend to thought about from different disciplines. Indeed, we want behavior change technologies that are evidence-based, cost-effective, personalized, easy to disseminate, promote maintenance, fit into a person’s life, and can, hopefully be financially self-sustained in sustaining. As can be seen just from this list, this can’t be achieved through the class disciplinary silo model of creation.
- Beyond just the difficulty of the complexity of behavior and the behavioral problems we are trying to solve, there are a lot of demands on behavior change technologies themselves and different points that tend to thought about from different disciplines. Indeed, we want behavior change technologies that are evidence-based, cost-effective, personalized, easy to disseminate, promote maintenance, fit into a person’s life, and can, hopefully be financially self-sustained in sustaining. As can be seen just from this list, this can’t be achieved through the class disciplinary silo model of creation.