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Amplifying humanity via precision behavior change
- 1. Amplifying humanity via precision behavior change Dr. Eric Hekler Director Designing Health Lab Arizona State University @ehekler Talk @ Apple 5/19/2015 Flickr -B.S. Wise
- 4. Behavior is complex… Hovell M, Wahlgren D, Adams M. The logical and empirical basis for the behavioral ecological model. Emerging theories in health promotion practice and research. 2009;2:347-85.
- 5. @ehekler
- 6. 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 Behavior
- 9. Flickr – Stuck in Customs@ehekler Personal, powerful, & pervasive https://www.apple.com/iwatch/
- 10. What is precision behavior change? @ehekler Just in time Adaptive Flickr -Casgen
- 14. Meaningful moments Flickr - Dave Gray
- 15. Flickr - Rob Marquardt@ehekler Opportunity
- 16. Receptivity 0 1 2 3 4 5 6 7 8 9 10 1 4 7 10 13 16 19 22 Value/Burden(10=high) Interactions with the system Value/burden ratio Value Burden
- 17. Eric Hekler (ASU)Aaron Coleman (Fitabase) Sayali Phatak (ASU) Exploring Strategies to Improve Acceptability and Usability of a Just In Time Adaptive Intervention via Incorporation of Proximity Sensors and a Smartwatch
- 18. Why Context Matters (fictitious hourly activity graph)
- 19. Why Context Matters Nightstand Nightstand Nightstand Nightstand Nightstand Nightstand Nightstand Nightstand Nightstand Nightstand Desk Desk Desk Desk Desk Desk Desk Desk Desk Desk Home Home Home Home Home Home Home Home HomeHome Home
- 20. The honeybee is nature’s best collector and communicator of data. Project HoneyBee explores the transformational promise of continuous physiological measurement to sustain health through the prevention and early detection of disease. HoneyBee’s Clinical Validation Network for Observational Clinical Trials (OCTs)
- 22. Precision adaptation Individual/User Controlled System Controlled Individual/System Balanced Control @ehekler www.agilescience.orgHeartSteps NIH-Funded (PI, Klasnja)
- 23. System-controlled – “Giving a fish” NSF IIS-1449751: EAGER: Defining a Dynamical Behavioral Model to Support a Just in Time Adaptive Intervention, PIs, Hekler & Rivera @ehekler
- 24. 0 2000 4000 6000 8000 10000 12000 14000 1000 3000 5000 7000 9000 11000 13000 15000 ActualDailySteps Recommended Goal Dynamic "Sweet Spot"
- 25. Impacting the sweet spot @ehekler
- 26. Personal trajectory predictions Hekler, et al. 2013 Health Education and Behavior @ehekler
- 27. Message selection based on: - time of day - location - weather - current activity Two types of messages: - promote activity - interrupt sedentary behavior Klasnja, P. (PI), Hekler, E.B., (Co-I), Tewari, A., (Co-I), Jackson, E., (Co-I)), Murphy, S., (Co-I)) NHLBI, R01HL125440
- 28. Eric Hekler, Jisoo Lee, Erin Walker, Winslow Burleson, Arizona State University; Bob Evans, Google Flickr Juhan Sonin Individual-controlled – “Teach to Fish”
- 29. Goal Behavior Change Technique(s) Create a plan Plan = + @ehekler
- 30. Measure success towards goal Results Evaluate your plan Plan + Implement for 1 week @ehekler
- 32. http://commons.wikimedia.org/wiki/File:Symptoms_of_multiple_sclerosis.svg Dynamic "Sweet Spot" Chiauzzi (PI), Hekler (Co-I), Rodarte(Co-I),
- 33. Enabling precision behavior change https://www.apple.com/iwatch/
- 34. Building the ecosystem of precision https://www.apple.com/ios/whats-new/health/ http://researchkit.github.io/
- 35. Academia now Flickr-alsotop What we should be Flickr-Stuck in Customs
- 36. Classic evidence pipeline Phase 1 Define & Design Phase 2 Pilot and Proof of Concept Phase 3 Efficacy Trial (RCT) Phase 4 Effectiveness Trial @ehekler
- 37. What do we get from RCTs? Stead LF, Lancaster T. Group behaviour therapy programmes for smoking cessation. Cochrane Database of Systematic Reviews 2005, Issue 2. Art. No.: CD001007. DOI: 10.1002/14651858.CD001007.pub2.@ehekler
- 38. What do we get from RCTs? Mohr DC, Ho J, Hart TL, Baron KG, Berendsen M, Beckner V, et al. Translational Behavioral Medicine. 2014:1-17.@ehekler
- 39. What do we get from RCTs? @ehekler
- 40. @ehekler What are useful products from science?
- 41. What are useful products from science?
- 42. Flickr – Metrix X 2005 2006 2007 2008 2009 2010 2011 2012 Conceive of a study Gather Pilot Data Submit Grant Receive Funding Conduct the study Submit publications for review 500,000th App Accepted on App Store @ehekler
- 43. Horst Rittel’s “wicked problems” Rith C, Dubberly H. Why Horst WJ Rittel matters. Design Issues. 2007;23(1):72-91. @ehekler
- 45. Smaller meaningful tools of precision @ehekler
- 46. Linda M. Collins The Methodology Center Penn State methodology.psu.edu@ehekler
- 47. 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, under review
- 48. System identification “informative” experiment -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: EAGER: Defining a Dynamical Behavioral Model to Support a Just in Time Adaptive Intervention, PIs, Hekler & Rivera
- 50. Agile science products • Modules • System architectures • Matchmaking algorithms @ehekler www.agilescience.org
- 51. Modules Smallest, meaningful, repurposable, and concrete Flickr - Benjamin Esham@ehekler www.agilescience.org
- 54. Getting scientists to amplify creativity @ehekler “Perfect” intervention Tools for meaningful moments Flickr - Paul Swansen Flickr - Benjamin Esham www.agilescience.org
- 59. Build & iterate together www.pacoapp.com@ehekler
- 60. Ecosystem of precision https://www.apple.com/ios/whats-new/health/ http://researchkit.github.io/
- 61. Enabling precision behavior change @ehekler
- 62. Move scientists from building & evaluating @ehekler this, to this. Flickr - Paul Swansen Flickr - Benjamin Esham
- 63. To amplify creativity of individuals to create this Flickr- Tony Fischer@ehekler
- 64. Thank you! Dr. Eric Hekler Arizona State University ehekler@asu.edu @ehekler Flickr- Tony Fischer
Notas del editor
- Thank you so much for inviting me here to give a talk about behavior change. A key theme for this talk is to find ways to use the technologies that Apple in particular creates to amplify our collective humanity via a concept I’ve been calling precision behavior change. Goal of talk: -Link Apple Watch with Precision Behavior Change -Link Health & Research Kits with Agile Science Thank you for inviting me to give this talk. I am truly honored and look forward to some interesting conversation. Briefly, I’m a clinical psychologist by training who has increasinglystarted exploring ways to promote health behavior change through digital technologies.
- And I think a discussion about this with Apple is particularly important because of your collective track-record at amplifying the creativity of others. As a personal example, I am a clinical psychologist by training, though I have gained expertise in a wide of other disciplines as well, but we’ll get to that. In many ways what started my interest in using digital technologies to promote behavior change was the iPhone. I was at a bar with a friend in 2008 playing around and discussing the iPhone and I had the very dorky thought while chatting with my friend that I could turn this phone into a tool for operant conditioning, which is basically what you do to train a dog to do new behaviors. You reward good behaviors. In many ways, my life has been shaped by that small epiphany, which was made possible by the possibility that I could create an app on the iPhone. You amplified my creativity and for that I am eternally grateful. Acknowledge Apple’s incredible ability to enable and promote cultural behavior change Building the ecosystem that enables the creativity of others
- In my view, there is no better time to start understanding how we can amplify the creativity of others related to behavior change. There are so many major societal problems that have a strong behavioral roots like environmental sustainability, obesity, or even wealth inequality that, without careful amplification of our collective creativity, will we be able to enable them.
- The problem is that human behavior is complicated with factors “within the skin” like genetics and memory as well as factors outside the skin like culture and how your city is designed influencing behavior. Further, those factors make interact over time, thus making sustained behavior change even more complicated.
- From the scientific community though, there are sparks of hope that need to be amplified by Apple, which I see you all doing very well with the Apple Watch, HealthKit, and ResearchKit, but I will return to those in a moment. In terms of what is going in the scientific community, a great of effort, as you know, is going into the idea of precsion medicine. Central to the precision medicine initiative is the idea of taking into account a person’s individual varations in all of those factors I just highlighted about behavior change to result in more healthier options. In my view, this precision medicine initiative will only succeed, however, with a careful focus on understanding precision behavior change. Why?
- Because if you look at the factors that seem to be impacting health the most, 40% OF it is attributable to behavior, with another 15 percent 30% driven by social, environmental, and healthcare factors, which are also largely driven by behavior but at a macro scale. Taken in aggregate, it is precision behavior change that is likely going to be the real factor that moves the needle because it’s the target that explains the most variance in health outcomes.
- As I said though, behavior is a complicated and multi-faceted phenomenon. Another interesting trend occurring in the scientific community is “open” science initiatives, for which I am a firm supporter of. Why? Because if want to live up to the ideals of precision medicine and embrace the idiosyncracies of life. Embracing idiosyncrasy, however, requires a strong shift away from our we develop our knowledge; away from a model of simple creating knowledge for others to consume but instead, creating the tools, processes, and ecosystems to enable others to amplify and help themselves. As such, science needs to be opened to enable collaboration and contribution from all those that have a stake in a culture of health. I see the open science movement as an essentential step towards this type of process.
- The open science community needs help, however, particularly with the developing the tools and ecosystem to support precision behavior change. For the rest of this talk, I’m going to discuss my research in precision behavior change and an open science initiative about behavior change that I’ve been calling agile science. I’m bringing these things up though as a call for help from you all to develop the tools and ecosystem we need to amplify humanity. I am personally so very excited about some of the most recent products from Apple, particularly the Apple Watch, Healthkit and ResearchKit (and by the way, I personally love that you did not name it an iWatch! Thank you for moving away from the “I” thing; the Apple logo and word is so much cleaner in my view). I see the Apple Watch as one of the most important new tools created to support precious behavior change and see the HealthKit and ResearchKit as the start of the ecosystem that will enable open science in ways that the scientific community cannot do alone. So, partially, this talk is to say thanks but also to highlight some specific ideas and design principles for you all to think about as you progress in developing these tools and resources. So the rest of the talk is largely organized around your products, with first a focus on how Apple Watch has the potential to enable precision behavior change followed by a a focus on how Apple Kit and Research Kit can enable the ecosystem of open science and, in particular, the open scientific process about behavior change that I have called Agile Science.
- The Apple watch is a major advancement for supporting precision behavior change because of its ability to be highly personal, powerful, and eventually pervasive (though, my apologies for speaking about it without owning one yet, my order is placed; for now think of this as a discussion on what I am asprationally hoping Apple Watch can achieve, devoid of the reality beyond what I can glean from the internet). I’ll return to each one of these “p’s” when discussing precision behavior change. Before going any further, however, let me know define what I mean by precision behavior change.
- A precision behavior change intervention is an intervention that delivers support “just in time” meaning when it is needed and only when it is needed and is also adaptive, over time, to the ever changing needs of individuals.
- Digging into this a bit more the “just in time” component is all about trying to find and articulate meaningful moments. I’m intentionally being a big vague about this concept because it is likely highly idiosyncractic on when a meaningful moment might be as the meaning of the moment might be driven the competing values of the moment. For example, I have always loved Renoir’s ability to capture a moment of humans interacting. This is a meaningful moment that could be a real opportunity for someone to live healthier through development of close relationships and strengthened bonds with family and friends. This is very different from the sort of distraction that now pervades lunch and dinner conversations among friends, whereby the technology detracts from the meaningfulness of the moment. I see the Apple Watch is the start of a major shift in technology towards those that disappear when they are really not needed to help amplify these meaningful moments when they do matter.
- Technology can definitely help BUT – right now, the signal is being lost in. the noise of Ping Pollution
- A first step for the Apple Watch would be for the watch to really become a watch. A truly silent technology piece that fades into the background but is powerful and ready to be there when needed. In many ways though, all that a Apple Watch can be very tantalizing and pull us away from those meaningful moments and thus, an essential next step is to really design the Apple Watch not to be a better notification system, but actually to be a supporter of silence. Particularly knowing that James is in the room, I truly see great opportunity for the sensor team to enable the design of silence through a careful understanding on when meaningful moments that should have technology vs. should not have technology exist and to ensure the technology creates silence when it is needed. That, to me, is where the magic of the Apple Watch will likely come with v2.
- Assuming now that silence is enabled, it allows for a lowering of the noise from the ping pollution, which enables more meaningful moments of interaction with the system. Central to me is thinking of meaningful moments related to precision behavior change as those times when small micro-experiments can be tried out to slowly aid individuals in living progressively healthier and more fulfilling lives.
- To do this, the Apple Watch will likely need to detect the moments of opportunity for behavior change. These can be true opportunities or possibly vulnerabilities. For example, I personally love cheese when I get home from work but I have also tracked it long enough to know that if I eat cheese after work, I gain weight. For me, getting home from work, feeling hungry, and having cheese in the fridge is a state of vulnerability that is an opportunity for the technology to help me make a slightly better healthy option. Knowing myself though, I’ve tried lots of experiments on myself including devising games, developing trigger notifications on my smartphone, and a bunch of other options and, while I know feel comfortable and in control of that moment, I often am rotating through a wide range of solutions depending on the day. The Apple Watch, with its personal and powerful capabilities likely has the opportunity to be that companion that individuals need for behavior change.
- It’s not enough, however, to simply find those moments that there is a potential to help someone choose a healthier option. A person must also be receptive to those interactions with the technology. Sadly, with current smartphone apps, it is often not set up well to really understand and only interact with a person when they are receptive. Instead, the ‘ping pollution’ just keeps coming, resulting in this sort of value to burden ratio problem. In brief, for most health apps in particular, the most value comes from the first few times a person uses it and then preciptiously drops off. In contrast, the perceived burden of using it often increases rapidly (though, of course, there are many patterns on what this ratio might look like, this is just one example). From a design perspective, it is essential to design the interactions to ensure they are supportive a person when needed and only when needed and not overwhelming, such as a tray full of notifications that all become meaningless noise, regardless of the value. The Apple Watch, particularly with the sensors it has likely can help to enable the types of value to burden ratios that will ensure the value of interaction always remains higher than the burden of the app.
- To give you some sense on how my labe and colleagues of mine are exploring this, let me briefly touch on a few funded projects of mine. First, I’ve received a small “agile” scientific research project from the RWJF-funded Health Data Exploration Network to explore how to use proximity sensors and the Apple Watch to improve just in time adaptive interventions. We are actively working to see what signals from the phone, watch, and proximity sensors like the iBeacon, we can use to identity those just in time meaningful moments for behavior change.
- For the proximity sensors, for example, context can tell us a great deal about activity. This, for example, is fictitious data about activity visualized on my colleague Aaron Coleman’s product, Fitabase. Alone, patterns about time can be explored but not much else.
- When location is layered on, however, the logic and the interesting patterns start to make sense and emerge, thus helping to better highlight moments when a person might be able to choose slightly healthier options.
- In a complementary project, I am work part of the ASU “Project HoneyBee” team, which is led by one of our Nobel Prize winning scientists as ASU, Lee Hartwell, and his business partner Michael Birt. Essential to this work is a careful partnership with clinical partners to find and identify those clinically meaningful moments from wearable and Ubiquitous Sensors to improve health care.
- We are currently in the second year and have several observational clinical trials explicitly attempting to identify these clinically meaningful moments via wearable data to enable precision behavior change intervention strategies for those moments.
- Thus far, I have largely discussed the just in time meaningful moment but there is also a need to support adaptation over time. Much of my research is focused on trying to enable this and, essential concept that we use in our lab is a clear delineation of who is in control of the adaptation, the system itself on one end, the individual/user him or herself, or some balance that might also include the clinician or other groups that can provide the right resources at the right time. I’ll briefly now touch on each of these projects.
- First, on the system-controlled side, the focus is largely on trying to find ways to give them the fish. Much like this autopilot on this boat that was set using the GPS to go in a certain direction and the boat automatically makes small adjustments, our team is working on building the methods and mathematical models that will enable this type of process for behavior change.
- For example, the problem we are trying to think through as an early start point is the right “sweet spot”goal for any given day for walking. This graph is a pretty common phenomenon. Basically, when you are in the right range for a person, they can usually hit that spot with relatively little variablity. If you push too hard though, it often happens that people actually start doing worse and what not.
- And there are a lot of factors that impact what is the right sweet spot for steps for any given day. Things like past behavior but also weather pattersn stress levels, level of busyness, etc. We are working develop the methods and mathematical models to make that sort of dynamic decision.
- Ultimately to support the creation of personalized trajectory predictions. Interestingly enough, this is from of my earlier work looking at how individuals respond to an intervention to see if a pattern emerges on what predicts maintenance. The interesting thing is that the lower visual is the general pattern of activity for those that maintained activity. It suggests the natural ebbs and flows that might actually be more robust for maintenance. We are attempting to better understand thesesorts of dynamics.
- And this is only the beginning as my colleagues and I also have al ot of other approaches to do this. For example, my colleague Pedja Klasnja from Michigan is funded by the NIH to developer Heart Steps, which is a just in time adaptive interveniton that provides context appropriate messaging and adapts over time to the types and time of messages that you respond to. All of this, as you can see though, is based largely on the adaptation occurring by the system. I definitely think there are times for this but I also think it is important to be mindful of the opposite spectrum.
- On the opposite side for supporting adaptation is allowing individuals to do the adaptation. The basic strategy is fairly simple as we are teaching individuals fundamentals of being a maker or DIY type personbut for health so that they can constantly self-experiment and adapt.
- For example, we teach them fundamentals in behavioralscience ot help them set a weekly goal for whatever behavior they want, we give them some strategies to help them achieve that goal like BJ Fogg’s tiny habit idea of having them set up an idea of whenever I_____ I will. They
- They then figure out a good metric of success or failure and try it out ofr a week. Based on the results they revise their plan and repeat.
- And just to set up why Google is interested in this, we are building this using a self-programmable UbiComp system Whereby we have self-programable and cheap sensors and feedback systems that individuals can program using a simple visual programming language that translates into a series of if/tehn conditional logic rules. Overall, we are giving folks that tools to build and constantly adapt their own solutions for themselves.
- As an example of this, I’ve got a project with the company PatientsLikeMe whereby we are helping MS patients use wearable and self-tracking data to gain a better sense on what their “sweet spot” is but for walking. For these folks, its factors like pain, fatigue, and even weather that impact how much activity like walking or chores they can do. We’ve been developing and evaluating a simple course nad process whereby individuals answer a few questions and it turns into predictions and suggestions for targeted activity such as if my pain is below 3 shoot for 4000 steps, if my pain is above y though don’t go over 3000 steps. In this, we are enabling them tochange.
- Overall, I see the Apple Watch, particularly with all of the sensors and, if you design silence into the system to allow the noise to quiet and signals to emerge, then it will become an essential component to facilitate precision behavior change.
- 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.
- Scientists doing research related to health are largely reinforced for doing small incremental science. Much of our current science feels like since there is a publication history of catapults, we’re going to build a manipult to get to the moon rather than take the radical leap found to building a rocket ship.
- The manipult problrem is largely reinforced by our classic linear pipeline that illustrates evidence. It is slow and highly resource intensive but the faith was, classically, that we will land on an “answer” that will be useful. I am increasingly convinced that the final answer that comes out of this type of process is almost completely useless for behavior change though.
- First, let’s just discuss what we get from this type of experimental design. This is the ideal result, a meta-analysis about interventions to tell us the “answer” on how useful an intervention is. The problem is that this meta-analysis is nearly uninterpretable because each one of these interventions use very different strategies to achieve this. SO while this might be ‘self-help programme’, some self-help programs use strategies like giving people strategies to self-experiment, others might just give basic education, and other still might do something completely different. This heterogeneity makes it hard to know how to use this final “product” from our science.
- This is only made worse but the poor selection of control. This is a meta-analysis focused on understanding the selection of a control condition for a multicomponent intervention might impact outcomes. The results basically found that if you pick a really work control such as a wait-list control, you get an effect size that is considered large. Putting this differnetly, if you want an effective interveniton, don’t worry aobut desiging an intervnetion; just pick a weak control group.
- Last on this, this method hides heterogeneity of responses. This is a pretty standard type of graph for weight loss interventions. Technically, “on average’ this intervention worked but if you look closely, there are plenty of intervention folks that actually gained weight and plenty of control groups that lost weight. All of this is masked and thus it is not embracing that idiosycnracy that is required for precision behavior change. Overall, there is faith that the current research strategy creates robust evidence that is well vetted but, in truth, it is really very fragile evidence that is not really useful for describing a problem, predicting future responses, and ultimately supporting control, the key aims of science.
- Ultimately, this then begs an important question… what are useful products from science?
- I’m told that this is what I should be doing but I really don’t think these publications are very useful for most people if the goal is to make real-world impact. Why, because so much is lost in this and often the timeline for doing these types of publications is not at pace with the problem and context we are designing around.
- To drive this point home, think, just think about the timeline for this sort of process. This visual is a bit old but it illustrates the point (and if I changed it, it would only further emphasize the point). Basically, this is the timeline for one study if it is run perfectly, meaning no revise and resubmit or failed grant applications, which rarely happens. And these are all the technology innovations that occurred in that same timeline. I love this example from you guys. The app store went from non-existent to 500,000 apps. That to me was your great job of amplifying and enabling the creativity of others. That’s the sort of process that we need to get scientists to increasingly think is their goal, not creating sacrosanct but fragile knowledge.
- Returning to behavior though, I set up the idea that behavior is complicated and it might even be “wicked.” I imagine you know about Rittel was a professor (mostly at UC Berkeley) of design and architecture (could be conceived as a design planner). Rittel functioned to transfer knowledge from sciences and engineering to design professions. He inspired work in urban design, and, more recently computer science. Key to his formulation was this idea of “Wicked” Problem.
- (1) ill-defined formulations (i.e., successful formulations are subjectively defined and have multiple solutions with success determined on a continuum of good to bad); (2) context-dependent formulations (i.e., successful formulations are idiosyncratic to the context [e.g., time, place, cultural milieu] thus making replication and generalizability difficult); and (3) mutating formulations, often rapidly (i.e., successful formulations are “useful” for a limited time before the problem or platform the solution was built on has mutated).
- To me, the essential insight, scientifically, is a recognition that the scientific community needs to start creating smaller meaningful tools to support precision that function more like lego pieces, or in the tech realm, API’s or other plug and play tools. The idea is to get expertise out of an experts head so that someone else with no expertise can use it. That’s amplifying creativity.
- 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.
- I’ve been calling this alternative process agile science, which I’ll jump into briefly 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.
- Developing methods and processes for optimizing individual intervention components.
- 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.
- 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.