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Precise Evidence for Specific Problems
- 1. Precise Evidence for Specific Problems @ehekler Dr. Eric Hekler Arizona State University August 4, 2016
- 2. Outline • Motivations & perspective • Precise solutions • Precise evidence • Agile science (v.2) • Citizen-led science & PLM @ehekler
- 4. Human Genome ProjectWalking on the Moon Penicillin Eric Hekler, @ehekler theamazingworldofgumball.wikia.com http://www.genome.gov/
- 8. 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
- 9. Behavior at the center Hovell M, Wahlgren D, Adams M. Emerging theories in health promotion practice and research. 2009;2:347-85.@ehekler
- 10. Core problem: Skeumorphisms Schueller et al. 2013
- 12. Personal, pervasive, & powerful technologies Flickr – Stuck in CustomsPatrick, Hekler, Estrin, Godino, Crane, Riper, & Mohr, Riley, Manuscript in Prep@ehekler
- 14. Just in Time Adaptive Interventions @ehekler
- 15. Just in time: State of vulnerability Flickr - Rob Marquardt @ehekler Nahum-Shani, Hekler, & Spruijt-Metz, (2015) Health Psychology
- 16. Just in time: State of opportunity Flickr - Miroslav Petrasko @ehekler Nahum-Shani, Hekler, & Spruijt-Metz, (2015) Health Psychology
- 17. Just in time: Receptive Flickr-Jonathan Powell Nahum-Shani, Hekler, & Spruijt-Metz, (2015) Health Psychology@ehekler
- 18. Adaptive: Series of “just in time” moments @ehekler Flickr - Dave Gray
- 19. 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
- 20. Modeling behavior Riley, Martin, Rivera, Hekler, et al. 2016; Martin, Riley, Rivera, Hekler, et al. 2014@ehekler
- 21. Three Example Individualized Computational Models via Black-Box System ID: Goals-Expected Points-Granted Points model; B: Predicted Busyness; S: Predicted Stress; T: Predicted Typical; W: Weekday-Weekend Modeling differences
- 22. Future-oriented predictions Hekler, et al. 2013 Health Education and Behavior@ehekler
- 23. Martin, Rivera, & Hekler Manuscript Submitted for Publication Future-oriented decisions @ehekler
- 24. Individual controlled “Teaching to fish” Eric Hekler, Jisoo Lee, Erin Walker, Winslow Burleson, Arizona State University; Bob Evans, Google Flickr Juhan Sonin @ehekler
- 25. Measure success towards goal Results Self-experimentation Plan + Implement for 1 week @ehekler
- 26. MS Wearables 101 Course Emil Chiauzzi, PatientsLikeMe Eric Hekler, Arizona State University Pronabesh DasMahapatra, PatientsLikeMe
- 27. Precise Evidence
- 28. Specific Solutions for Specific Problems Design & Engineering “On Average” Science “On Average” Evidence for General Problems Key Traditional pathway Emerging pathway Product Process Professional-led
- 29. Specific Solutions for Specific Problems Design & Engineering “On Average” Science “On Average” Evidence for General Problems Key Traditional pathway Emerging pathway Product Process Precise Evidence for Specific Problems Personalization Algorithm Science Professional-led
- 30. Specific Solutions for Specific Problems Design & Engineering “On Average” Science “On Average” Evidence for General Problems Key Traditional pathway Emerging pathway Product Process Precise Evidence for Specific Problems Personalization Algorithm Science Professional-led Citizen/Patient-led
- 31. Subjectivity matters @ehekler • Solving the “last mile” problem • Requires a damn good designer • AND(/OR?) patient empowerment . Mullainathan S. Solving social problems with a nudge. TEDIndia. 2009. http://www.ted.com/talks/sendhil_mullainathan.
- 32. From “on average” to algorithms @ehekler • From generally true to true for me • Requires acknowledging variance “On Average” ~50% “Personalization/Matchma king” ~35% Idiosyncratic/ Subjective ~15%
- 33. Role of the professional may change @ehekler • From solving to empowering • Professional can support – Education – Tool building – Communication – Curation
- 34. Agile Science friko-diamondsdesigns.blogspot.com HealthFoo, December 2013: https://www.youtube.com/watch?v=wY-stOXqmuw Watch this video on being a “thought leader”: https://www.youtube.com/watch?v=_ZBKX-6Gz6A
- 35. Agile science products • Modules • Computational models • Personalization algorithms @ehekler
- 36. Modules Smallest, meaningful, self-contained,& repurposable “Perfect” intervention package Components Flickr - Paul Swansen Flickr - Benjamin Esham @ehekler
- 38. Proximity sensor module @ehekler Inputs iBeacons Phone Meta-data Process Transform tagged data into a time- stamped db Output Time- stamped csv of indoor location
- 42. Computational models Riley, Martin, Rivera, Hekler, et al. 2016; Martin, Riley, Rivera, Hekler, et al. 2014@ehekler
- 43. Computational models: Ontologies Larsen, Michie, Hekler, et el. 2016, Journal of Behavioral Medicine@ehekler
- 45. Martin, Rivera, & Hekler Am. Control Conference (2015) Personalization algorithms @ehekler
- 46. Agile Science Process v0.2
- 48. Generate Design & engineer specific solutions for specific problems @ehekler
- 49. Formative research • Defining a “niche” • Defining constraints • Generating solutions IDEO
- 50. “Niche” specification IDEO: Human-Centered Design Kit
- 51. Design constraints DesignWhat fits in time & budget constraints What does your target audience think they need? What do the “experts” think your target audience needs? How might policy impact what is possible? What does your target audience ACTUALLY need? What’s fundable? What’s feasible to build? What ideas can be evaluated? What’s something that can be sustained during & after the study? What can you actually build with your tech partner?
- 52. Generating solutions Stanford d.School, Bootleg Bootcamp
- 53. Complexity mapping • Finding assumptions • Defining causal pathways • Defining a research agenda
- 54. Finding assumptions via simulation Martin, Rivera, & Hekler (In preparation)
- 55. Finding assumptions via simulation Martin, Rivera, & Hekler (In preparation)
- 56. Causal pathways Antecedents Body Movement ConsequencesContext (People, place, time) Time scale Year Month Day Hour Min Bouts of MVPA Min/day MVPADaily min/day goal of MVPA Cardiovascular Fitness (vO2) Self- Management Skills Self-Identity as an “exerciser” Atherosclerotic Plaque Prevention
- 57. Research agenda
- 58. Prototyping • Testing “hunches” • Testing assumptions • Examining feasibility
- 59. 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 carTesting hunches @ehekler
- 60. Testing assumptions John Harlow, Erik Johnston, Zoe Yeh@ehekler
- 61. Phoenix Proposition 104 John Harlow, Erik Johnston, Zoe Yeh@ehekler http://movephx.org/get-the-facts/maps/
- 63. Evaluate Determine the “boundary conditions” on when, where, for whom, and in what state a tool produces its desired outcome. @ehekler
- 64. Linda M. Collins The Methodology Center Penn State methodology.psu.edu@ehekler
- 65. 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, 2015@ehekler
- 66. 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
- 67. 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
- 68. Curate Evidence-based insights for match-making of specific solutions to specific problems @ehekler
- 70. Ontologies Larsen, Michie, Hekler et al. in press
- 73. Research Kit https://www.apple.com/ios/whats-new/health/ http://researchkit.github.io/ http://sagebase.org/
- 76. eEcosphere @ehekler DISCLAIMER: On scientific advisory board w/ equity stakes in the company
- 78. PLM is a true pioneer (as you know ;)
- 79. Specific Solutions for Specific Problems Design & Engineering “On Average” Science “On Average” Evidence for General Problems Key Traditional pathway Emerging pathway Product Process Precise Evidence for Specific Problems Personalization Algorithm Science Professional-led Citizen/Patient-led
- 80. OpenAPS
- 81. OpenAPS
- 82. End-User design, engineering, & science • Target: empowering systematic patient “hacking” – Disease management (e.g., MS “sweet spot” study) – Next gen drugs (e.g., Lithium study v2.0) – Next gen medical devices (e.g., OpenAPS) • Courses on patient-led design, engineering & science • End-user programming tools (e.g., Paco) to empower patient-led design, engineering & science
- 83. What you get? • Insights on the “last mile” problem • Highly marketable (?) • Strong value back to your patients
- 84. Advocating for culture change • Target: shifting social, ethical, methodological, and regulatory change to embrace patient-led design, engineering, and science • Devise a pathway through the FDA – OpenAPS • Build communication pathways between patient- innovators and professionals – OpenAPS
- 85. Specific Solutions for Specific Problems Design & Engineering “On Average” Science “On Average” Evidence for General Problems Key Traditional pathway Emerging pathway Product Process Precise Evidence for Specific Problems Personalization Algorithm Science Professional-led Citizen/Patient-led
- 86. Thanks! What can we build together? Dr. Eric Hekler, Arizona State University ehekler@asu.edu, @ehekler
- 87. TARGET: Precision behavior change Individual/User Controlled System Controlled Individual/System Balanced Control @ehekler
- 88. Why now? Behavioral meteorology Flickr-Bart Everson Patrick, Riley, Estrin, Hekler, Godino, Crane, Riper, & Mohr, Manuscript in Prep@ehekler
- 89. Why now? The world needs us… Flickr – Stuck in Customs http://youtu.be/QPKKQnijnsM Flickr – just.Luc Flickr-meanMrmustard
- 90. First step… @ehekler Stop building “perfect” packages… Start building interoperable modules Flickr - Paul Swansen Flickr - Benjamin Esham www.agilescience.org
- 93. Ecologically-valid data streams @ehekler Lead Secondary Secondary Co-Lead
- 94. Turning “noise” into information https://ubicomplab.cs.washington.edu/
Notas do Editor
- 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.
- Decision Policies – we are talking about what this is supposed to do Citizens= Patients, Providers, and anyone else driven to solve a problem that the individual has first-hand experience with.
- Decision Policies – we are talking about what this is supposed to do Citizens= Patients, Providers, and anyone else driven to solve a problem that the individual has first-hand experience with.
- Decision Policies – we are talking about what this is supposed to do Citizens= Patients, Providers, and anyone else driven to solve a problem that the individual has first-hand experience with.
- Professionals still focus on “on average” science (even, it appears, with many precision medicine efforts) Professionals need to move towards studying the utility of personalization algorithms
- Professionals need to continually enable more “end-user” design, engineering, and Science
- FIND THE VIDEO MAKING FUN OF TED TALKS AND PUT IN A LINK HERE.
- 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.
- 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.
- 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.
- 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.
- Decision Policies – we are talking about what this is supposed to do Citizens= Patients, Providers, and anyone else driven to solve a problem that the individual has first-hand experience with.
- 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.
- 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.
- 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.
- Decision Policies – we are talking about what this is supposed to do Citizens= Patients, Providers, and anyone else driven to solve a problem that the individual has first-hand experience with.
- 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.