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Applying control systems engineering to behavioral interventions
- 1. Making decisions for complex, dynamic problems with imperfect knowledge The application of control systems engineering to a behavioral intervention @ehekler Eric Hekler, PhD Arizona State University August 18, 2016 Flickr -Pat Castaldo 1
- 3. Outline • Epistemological target • Control Systems Engineering • Our work – Encapsulate previous knowledge – Define dynamic decisions of an intervention – Devise a system ID experiment – Examine individual differences (ARX) – Examine mechanistic model (semi-physical modeling) – Devise model-predictive controller @ehekler 3
- 4. From generally useful to useful for me Epistemological target 4 @ehekler
- 5. Embracing (plausibly) meaningful variability @ehekler “In General” ~50% “Personalization/ Matchmaking” ~35% Idiosyncratic/ Subjective ~15% Hekler, et al. 2016, Agile Science, Translational Behavioral Medicine 5
- 6. 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 6 @ehekler
- 7. 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 Process Individualization Science 7 @ehekler
- 8. 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 Process Individualization Science Citizen/Patient-led 8 @ehekler
- 9. 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 Process Individualization Science 9 @ehekler
- 10. Making decisions in complex, dynamic systems with imperfect knowledge Control Systems Engineering 10 @ehekler
- 11. Control Systems Engineering NSF IIS-1449751: EAGER: Defining a Dynamical Behavioral Model to Support a Just in Time Adaptive Intervention, PIs, Hekler & Rivera @ehekler 11
- 12. Describe & predict: System identification -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 12
- 13. Martin, Rivera, & Hekler Am. Control Conference (2015) Control: Model-predictive control @ehekler 13
- 14. Continuous improvement: Adaptive control @ehekler Flickr - Dave Gray 14
- 15. Systematically managing and mitigating imperfect knowledge to support dynamic evidence- based decisions Our work 15 @ehekler
- 16. Encapsulate previous knowledge (theory) @ehekler 16
- 17. Dynamical model of Social Cognitive Theory Riley, Martin, Rivera, Hekler, et al. 2016; Martin, Riley, Rivera, Hekler, et al. 2014@ehekler 17
- 18. One inventory Riley, Martin, Rivera, Hekler, et al. 2016; Martin, Riley, Rivera, Hekler, et al. 2014@ehekler 18
- 19. Differential equations (first order shown) Riley, Martin, Rivera, Hekler, et al. 2016; Martin, Riley, Rivera, Hekler, et al. 2014@ehekler 19
- 20. Simulation: Low vs. high self-efficacy Riley, Martin, Rivera, Hekler, et al. 2016; Martin, Riley, Rivera, Hekler, et al. 2014@ehekler Low Self-Efficacy High Self-Efficacy 20
- 21. Simulation: Habituation Riley, Martin, Rivera, Hekler, et al. 2016; Martin, Riley, Rivera, Hekler, et al. 2014@ehekler 21
- 22. Secondary data analysis: Validation Riley, Martin, Rivera, Hekler, et al. 2016; Martin, Riley, Rivera, Hekler, et al. 2014@ehekler 22
- 23. Define dynamic decision(s) of intervention @ehekler 23
- 24. Daily “ambitious but doable” step goals 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 24
- 25. Intervention decisions Martin, Rivera, & Hekler, 2015; 2016, American Control Conference 25 @ehekler
- 26. Simulation Martin, Rivera, & Hekler, 2016, American Control Conference 26 @ehekler
- 27. Devise a system identification experiment @ehekler Need: replicable (for estimation & validation), random/pseudo-random, excitation over time 27
- 28. Experimental design: System identification 28 @ehekler
- 29. Multisine “pseudo-random” signals Martin, Rivera, & Hekler, 2015, American Control Conference 29 @ehekler
- 30. Pilot study: “Just Walk” Fitbit Zip 30 @ehekler
- 31. “Black box” modeling to develop descriptive models & examine individual differences @ehekler 31
- 32. Participants • 22 inactive, overweight Android users – BMI 33.7 ± 6.7 – 47 ± 6.2 years – 87% women Living anywhere in the US Average Baseline Median Steps: 4972 steps/day (SE = 482) 32 @ehekler
- 33. Preliminary results: Average effects 6,827 (SE = 647) Average median steps in the last cycle 45% (SD = 36) Average increase in median steps/day from baseline to final cycle 69% (SD = 24) Average goals met >90% Adherence to daily self-report 33 @ehekler
- 34. Multiple-Input Single-Output AutoRegressive with eXternal inputs model (ARX model) 34 @ehekler
- 35. Preliminary ARX parametric estimation 35 @ehekler
- 36. Preliminary dynamical modeling results 0 6000 External cues (Goals) 0 200 400 Outcome Expectancy for Reinforcement (Available points) 0 200 400 Reinforcement (Granted Points) 0 5 Predicted Busyness 0 5 Predicted Stress 0 5 Predicted Typical 0 1 Weekday (0) - Weekend (1) Time (days) 0 10 20 30 40 50 60 70 80 90 100 Steps 2000 4000 6000 8000 10000 12000 Baseline Estimation Validation Goals (98 =u8 ) Predicted Steps (28.66% fit) Actual Steps Figure 4. System ID “open-loop” experiment w/ predicted & actual steps from one participant from Just Walk 36 @ehekler
- 38. 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 38 @ehekler
- 39. Semi-physical modeling: Examine mechanistic dynamical models idiographically @ehekler 39
- 40. Almost done… Stay tuned! @ehekler 40
- 41. Devise model predictive controller @ehekler 41
- 42. Idiographic trajectory model predictions Hekler, et al. 2013 Health Education and Behavior@ehekler 42
- 43. Martin, Rivera, & Hekler Am. Control Conference (2015; 2016) Model-predictive controller @ehekler 43
- 44. “Closing the loop” Open Loop MPC 44 @ehekler
- 45. Simulation: MPC robustness Martin, Rivera, & Hekler (2016) 45 @ehekler
- 47. 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 Process Individualization Science 47 @ehekler
- 48. From “in general” to “for me” Decisions for complex, dynamic problems Manage & mitigate imperfect knowledge @ehekler 48
- 49. Feedback and questions welcome! Dr. Eric Hekler, Arizona State University ehekler@asu.edu, @ehekler 49
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.
- 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
- 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.
- 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.
- 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.
- 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.
- Coming to how the daily goal signal was designed: Then developed an experimental design In control systems world, this methodology is called system identification. It is to test this hypothesis, Estimate and validate the dynamical model. Focus is on idiographic modeling, individual model per participant. System ID experiments are specifically designed to estimate and validate the dynamical model, and the focus is on idiographic models, meaning individual models per participant or user. Every day, a step goal (external cues), and points is assigned to the participant (outcome expectancy for reinforcement). Step goals range from doable (baseline median), to ambitious (up to 2.5x baseline). Each individual has her own unique randomization signal This strategy also uses “cycles” of the intervention, for us, we used 16 day cycles. So the same randomization signal repeats every 16 days. The randomization signal is determined using multisine wave design strategies, which maximizes the signal to noise, delievered orthogonally in frequency, and is useful for progressively testing model fit, thus making it valuable for understanding how dynamics change over time for an individual. Multisine signal design utilizes periodic signals defined in the frequency domain to implement an open-loop experiment (see C.2.1). A useful analogy is an audio equalizer whereby different frequencies like bass or treble can be emphasized; “frequencies” occurring as cycles across time can be used to design an experimental signal (e.g., daily goal variations). One thing to note here, this was not a perpetually adaptive or personalized intervention, it was mainly designed to understand the dynamics and build individualized computational models. Pseudo-randomly assigns daily goals and points to every participant
- We ran our pilot study from June to December 2015, and the quick summary so that everything makes sense is that the study was 14 weeks long, participants received a Fitbit, an Android app, and a daily step goal and we measured many contextual variables that were informed from the SCT…on a daily/weekly/monthly level depending on the variables. Just Walk is the system that we developed for running the experiment. It includes a front-end Android app Notifications of daily step goal and corresponding points Notifications when user achieves goals Integration with Fitbit which is used to measure the steps
- Daily Morning and evening self-report We also collected weather, and location data but have not analysed that yet. We recruited 22 inactive, overweight Android users (one lost her Fitibit during last three weeks but was willing to continue but this compromised system ID analyses for her as our power calculations required a minimum of 5 cycles; final sample N=21; 90% women; M = 47.0 ± 6.2 years, BMI 33.7 ± 6.7). Baseline median steps averaged 4,972 steps/day (SE = 482), and median steps in the last cycle were 6,827 steps/day (SE = 647). By design, there was an average 45% (SD = 36%) increase in steps/day from baseline to the last cycle, and participants met 69% (SD = 24%) of goals. Results from a nonlinear mixed effects model indicated a significant, on average, increase in steps from baseline to the first intervention cycle of 1,500 steps (t=-5.52, p<.001) with a significant quadratic effect (t=-5.01, p<.001), suggesting the increased steps largely leveled off by the 3rd cycle, which, again, is according to design, which did not include any progressive increase in step goals. Exit interviews and follow-up surveys suggested that participants liked getting different daily goals (100%), perceived the app to be easy-to-use (85%) and expressed interest in continued use of the app (88%). The most common problem was a time lag in syncing between Fitbit and Just Walk, which will be addressed in the next version. Adherence to EMA was above 90% for both morning and evening surveys.
- Blackbox modeling is the first step in the system id analyses. We used goals, points, and some of our self-report measures as inputs to predict daily steps in this procedure. The primary interest here is to fit the data regardless of a particular structure of the model. So this is not considering the SCT model structure when conducting the analyses Typically a trial and error process where you estimate the parameters of various structures and compare results Minimal knowledge of the structure is used – so used an autoregressive model structure. (consistent estimation with probability of 1) What we have been currently doing as part of the blackbox modeling is finding the best fitting model for all participants...as I mentioned earlier, there are various ways to go about this and we carried out an exhaustive search looking over every possible ARX structure (output and input lags), and this is a trial and error process... Used all combinations of cycles for estimation and validation, and then obtained We have been trying to find ties to the statistical methods we use in the social sciences for this process..such as checking assumptions. To try and bring structure into interpreting and choosing the best models for each participants in a way that they are also reliable. This is our first pass at this... In choosing these models, we have looked at the best average validation fits (using roughly 50-50 estimation/validation), and cross-correlations between the inputs. We looked at cross-correlations amongst the inputs to try and use only orthogonal signals. So we have removed those signals that were highly correlated to choose the most parsimpnious models. We also tried to maintain inter-rater reliability by having two different individuals go over the model-choosing process. We will be able to properly validate these models only when they enter a controller/ when we do the semi-physical modeling which uses the SCT model structure. Orthogonal inputs Autoregressive A portion of the Only 1 participant below 10% model fit, suggesting “good enough” model fit for 95% of our sample For all combinations of cycles as esti and vali. Sets, you chose the best ARX structure (most predictive) for that combination. Model fit per cycle (in the validation set), and then average over that.
- illustrates the results obtained from a specific participant utilizing (60%) of cycles for estimation and (40%) for validation. A parsimonious ARX model with 5 inputs and 12 parameters, with model fit corresponding to 49% of validation output variance. More accurate results are expected using semi-physical model estimation that incorporates the SCT model structure
- 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.
- 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.