Post on 22-Jan-2018
Enabling Precision Behavior Change
@ehekler
Dr. Eric HeklerArizona State University
November 19, 2015
Talk given at the University of North Carolina, Chapel Hill
Outline
• Precision behavior change
• Requirements for precision
behavior change
• Agile science
• UbiHealthy Cup@ehekler
@ehekler http://www.nih.gov/precisionmedicine/
Behavior at the center
Hovell M, Wahlgren D, Adams M. Emerging theories in health promotion practice and research. 2009;2:347-85.@ehekler
Behaviors explain most variability in health
Flickr – Stuck in Customs@ehekler
40
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Sub-Optimal Health behaviors
Social Circumstances
Environmental Exposures
Healthcare
Genetics
McGinnis, et al. 2002 Health Affairs
Why now? Personal, pervasive, &
powerful technologies
Flickr – Stuck in CustomsPatrick, Hekler, Estrin, Godino, Crane, Riper, & Mohr, Riley, Manuscript in Prep@ehekler
Why now? Behavioral meteorology
Flickr-Bart Everson
Patrick, Hekler Estrin, Godino, Crane, Riper, & Mohr, & Riley, Manuscript in Prep@ehekler
Why now? The world needs us…
Flickr – Stuck in Customs
http://youtu.be/QPKKQnijnsM
Flickr – just.Luc
Flickr-meanMrmustard
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
Just in Time: State of vulnerability
Flickr - Rob Marquardt
@ehekler Nahum-Shani, Hekler, & Spruijt-Metz, (2016) Health Psychology
Just in Time: State of opportunity
Flickr - Miroslav Petrasko
@ehekler Nahum-Shani, Hekler, & Spruijt-Metz, (2016) Health Psychology
Just in Time: Receptive
Flickr-Jonathan Powell
Nahum-Shani, Hekler, & Spruijt-Metz, (2016) Health Psychology@ehekler
Adaptive: Series of “Just in Time” moments
@ehekler Flickr - Dave Gray
Precision behavior change spectrum
Individual/User
ControlledSystem
ControlledIndividual/System
Balanced Control
@ehekler
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
System model
Riley, Martin, Rivera, Hekler, et al. 2016; Martin, Riley, Rivera, Hekler, et al. 2014@ehekler
Idiographic trajectory models
Hekler, et al. 2013 Health Education and Behavior@ehekler
Martin, Rivera, & Hekler Manuscript Submitted for Publication
Model-predictive controller
@ehekler
Individual controlled
“Teaching to fish”
Eric Hekler, Jisoo Lee, Erin Walker, Winslow Burleson, Arizona State University; Bob Evans, Google
Flickr Juhan Sonin
@ehekler
Measure
success
towards
goal
Results
Self-experimentation
Plan
+Implement for 1 week
@ehekler
@ehekler
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
Interoperable systems
@ehekler
LeadSecondary
Secondary
Secondary
SecondarySecondary
Interoperable systems
https://www.apple.com/ios/whats-new/health/ http://researchkit.github.io/ http://sagebase.org/
Interoperable systems
www.openmhealth.org
Ecologically-valid data streams
@ehekler
Lead Secondary
Secondary
Co-Lead
Turning “noise” into information
https://ubicomplab.cs.washington.edu/
Data standardization
@ehekler
LeadCo-Lead
Secondary
Secondary
Secondary
Data standardization
www.openmhealth.org
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
Pre-agile software “waterfall”
@ehekler
Agile (XP Scrum) development
@ehekler
Agile science philosophical assumptions
https://en.wikipedia.org/wiki/Philosopher#/media/File:The_School_of_Athens.jpg@ehekler
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
Evidence & logic defines truth
https://en.wikipedia.org/wiki/Phases_of_Venus#/media/File:Phases-of-Venus.svg@ehekler
Rigor achieved via trial & error
https://en.wikipedia.org/wiki/Incandescent_light_bulb#/media/File:Edison_incandescent_lights.jpg@ehekler
Knowledge accumulation via effective curation
www..google.com@ehekler
Agile science products
• Modules
• System models
• Personalization algorithms
@ehekler
ModulesSmallest, meaningful, repurposable,& concrete
“Perfect” intervention package Components
Flickr - Paul Swansen Flickr - Benjamin Esham
@ehekler
ModulesAPIs
www.yelp.com@ehekler
IFTTT
http://www.ifttt.com
ModulesTemplates
www.ifttt.com@ehekler
Modules
http://www.ifttt.comwww.ifttt.com@ehekler
System models: Meteorology analogy
Flickr-Bart Everson
Patrick, Hekler, Estrin, Godino, Crane, Riper, & Mohr, & Riley Manuscript in Prep
@ehekler
System models
Riley, Martin, Rivera, Hekler, et al. 2016; Martin, Riley, Rivera, Hekler, et al. 2014@ehekler
Dynamic hypotheses- “Sweet Spot”
Hekler (PI), Rivera (Co-PI), NSF IIS-1449751
-15
-10
-5
0
5
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Ave C
han
ge S
elf
Eff
fica
cy
Actu
al D
ail
y S
tep
s
Recommended Goal
Actual Steps Δ Self-Efficacy
@ehekler
Personalization algorithms
www.netflix.com@ehekler
Martin, Rivera, & Hekler Am. Control Conference (2015)
Personalization algorithms
@ehekler
Agile science process
@ehekler
Sprint• GOAL: Discovery and resource-efficient
vetting of promising new approaches
@ehekler
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.”
MOBILE CAR MAID SERVICES
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
Phoenix Proposition 104
John Harlow, Erik Johnston, Zoe Yeh@ehekler
Phoenix Proposition 104
John Harlow, Erik Johnston, Zoe Yeh@ehekler http://movephx.org/get-the-facts/maps/
Phoenix Proposition 104
John Harlow, Erik Johnston, Zoe Yeh@ehekler http://movephx.org/get-the-facts/maps/
Optimization
• GOAL: Translation of a promising resources
into useful & evidence-based tools for
real-world use.
@ehekler
Linda M. Collins
The Methodology Center
Penn State
methodology.psu.edu@ehekler
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
System identification experiments
-100
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0
2000
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1 8 15 22 29 36 43 50 57 64 71 78 85 92 99
Po
ints
Ste
ps
per
day
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
Release
• GOAL: Share useful resources via
effective curation.
@ehekler
Shared test-beds
@ehekler
Secondary
SecondarySecondary
LeadCo-Lead
Co-Lead
Paco (thank you, Bob Evans!)
www.pacoapp.com@ehekler
Paco-read this on the website
www.pacoapp.com@ehekler
Paco
www.pacoapp.com@ehekler
Paco
www.pacoapp.com@ehekler
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
RoboCup
@ehekler
https://upload.wikimedia.org/wikipedia/commons/2/22/Robocup_Bremen_2006_-_four_legged.JPG
RoboCup
@ehekler
https://c2.staticflickr.com/8/7410/9238794627_4be245177e_b.jpg
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
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
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
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)
RECAP
@ehekler
TARGET: Precision behavior change
Individual/User
Controlled
System
ControlledIndividual/System
Balanced Control
@ehekler
Why now? Behavioral meteorology
Flickr-Bart Everson
Patrick, Riley, Estrin, Hekler, Godino, Crane, Riper, & Mohr, Manuscript in Prep@ehekler
Why now? The world needs us…
Flickr – Stuck in Customs
http://youtu.be/QPKKQnijnsM
Flickr – just.Luc
Flickr-meanMrmustard
First step…
@ehekler
Stop building “perfect”
packages…
Start building interoperable
modules
Flickr - Paul Swansen Flickr - Benjamin Esham
www.agilescience.org
Next step, organize and share!
Dr. Eric Hekler, Arizona State University
ehekler@asu.edu, @ehekler