Enabling Precision Behavior Change

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

155

10

30

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


Just in Time: State of opportunity

Flickr - Miroslav Petrasko


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


https://www.apple.com/ios/whats-new/health/ http://researchkit.github.io/ http://sagebase.org/


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

10

15

20

0

2000

4000

6000

8000

10000

12000

14000

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


John Harlow, Erik Johnston, Zoe Yeh@ehekler http://movephx.org/get-the-facts/maps/


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

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

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


Paco


Paco


Fundamental problem

@ehekler

We each build “optimized”

packages for one-off

problems

We need to build inter-operable

modular resources


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

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


Flickr – just.Luc

Flickr-meanMrmustard


First step…

@ehekler

Stop building “perfect”

packages…

Start building interoperable

modules


www.agilescience.org

Next step, organize and share!

Dr. Eric Hekler, Arizona State University

[email protected], @ehekler

mailto:[email protected]

Health & Medicine

Enabling Precision Behavior Change