The Design and Evaluation of
Behavior Change Technologies:
Research at the Intersection of Behavioral Science, HCI/CS, and...
Outline
Who am I?
Why Behavior Change Technologies?
BCT Development Model
Projects
mHealth Physical Activity
Interventions...
Outline
Who am I?
Why Behavior Change Technologies?
BCT Development Model
Projects
mHealth Physical Activity
Interventions...
Who we are…

Software Programming
and Development Core
Lead Programmer
Outline
Who am I?
Why Behavior Change Technologies?
HCI/Behavioral Science Combined
Process
Projects
mHealth Physical Acti...
http://youtu.be/QPKKQnijnsM
Why care about BCTs?

Flickr – just.Luc

Flickr-meanMrmustard
Digital technologies are…

Pervasive
Interconnected
Powerful enough to tackle the complexity?

Flickr – Stuck in Customs
Health is complex…

Individual
characteristics
Over the lifespan
We want Behavior Change Technologies
that are…
• Evidence-based
• Cost-effective
• Personalized
• Easy to disseminate
• Pr...
Behaviora
l
Science

Behavior
Change
Technologies

Engineering

Human
ComputerInteraction

Hekler, Klasnja, Froehlich, & B...
Outline
Who am I?
Why Behavior Change Technologies?
BCT Development Model
Projects
mHealth Physical Activity
Interventions...
Mind the Theoretical Gap: Interpreting, Using, and
Developing Behavioral Theory in HCI Research

Eric Hekler

Predrag Klas...
Goal

HCI/Design
The design and
creation of useful
and usable
technologies and
interactions.

Behavioral Science
The syste...
The design, creation, and evaluation
of useful and usable technologies to
effectively promote behavior change
for positive...
Nine Questions for BCT Development
DEFINE THE PROBLEM
1) What behavior(s) are you trying to change for
whom?
2) What influ...
Defining the Behavior
Designing Behavior Change Class

Syllabus:
Class Projects

wk6

wk18

Develop an SMS
health behavior
Intervention.

Use theory to
make yourself
healthier.

Use previ...
Outline
Who am I?
Why Behavior Change Technologies?
BCT Development Model
Projects
mHealth Physical Activity
Interventions...
Projects

Individual
characteristics

Over the lifespan

*

*

*
*

*

*

*PI/ Co-PI
mHealth Physical Activity Interventions
MILES Study
• Develop theoretically meaningful
smartphone apps for midlife & older
adults
•

Physical activity &
behavior
...
Activity Monitor Validation
N=15, Men & Women, Mean Age=55
12 laboratory-based activities 3-4 min each
Hip- and pocket-wor...
Validation Results

Hekler et al. Manuscript in Preparation
Validation Results

Hekler et al. Manuscript in Preparation
The smartphone apps
mTrack

mSmiles

mConnect

Calorific

Hekler et al. 2011, Personal Informatics Workshop at CHI – Desig...
Components

study arms
mConnec
mTrack mSmiles
t
Calorific
Push component
X
X
X
X
Pull component
X
X
X
X
"Glance-able" disp...
MILES Study Design
Prestudy

Baseline
Week1
Visit1

Follow up

Feedback
Week2

Week8

Visit2, check in

Visit3
mTrack (Ana...
min/week of activity at study completion

Physical Activity - 8wk Results

300
250
200
150

Brisk
walking
(min/week)

100
...
∆ Food Consumption

15

†

Servings pre day

10

5

**

**

†

**

Physical
Activity Apps
Control

0

-5

Processed Sweets...
Lessons learned…
• Activity monitoring by phone only is
difficult
• Each intervention had merit, not potent
• “Right” inte...
Improved Passive Sensing
Matthew Buman

Activity/Sleep Validation

Geotagging

Matt Buman, ASU;
Max Utter, Jawbone

David ...
Just in Time Adaptive
mHealth Intervention

Daniel Rivera

Co-PI: Daniel Rivera, ASU
Other Collaborators: Matthew Buman, M...
Secondary Analysis – System Identification

Hekler, Buman, Rivera, et al, 2013, Health Education & Behavior.
Secondary Analysis – System Identification

Hekler, Buman, Rivera, et al, 2013, Health Education & Behavior.
Dynamical Model Social Cognitive Theory

Riley, Martin, Rivera, Hekler, et al. Manuscript Submmited for Publication
Dynamical Model Simulations

Riley, Martin, Rivera, Hekler, et al. Manuscript Submmited for Publication
Informative Experiment for a Controller
14000

12000

Steps per Day

8000

Week Average
Intervention 4
6000

Intervention ...
Just in Time Adaptive
mHealth Intervention

Daniel Rivera

Co-PI: Daniel Rivera, ASU
Other Collaborators: Matthew Buman, M...
A DIY Self-Experimentation
Toolkit for Behavior Change

Win Burleson

Eric Hekler
Winslow Burleson
Jisoo Lee
Arizona State...
Quantified Self
Context Matters
GaLLaG- Home Sensing & Feedback

Sensors

Wireless
Communication
The DIY Self-Experimentation Toolkit

Paco

GaLLaG

?
Design Portal
Improving Online Support Groups

David McDonald

Erika Poole
Other BCT-related projects

Software Programming
and Development Core
Lead Programmer
Activity “ground truth”
validation app
Matthew Buman
Round-the-Clock
Activity Logging App
Matthew Buman
Environmental Context
Assessment App
Matthew Buman

• Harness technology to improve
neighborhood designs for
physical acti...
Environmental Context
Assessment App
Matthew Buman

Data
collection

Review
Responses

Complete a
survey

Buman, Winter, S...
EMA App & Sun Card Study
Assessing network behaviors in the
here and now
Meg Bruening

• Purpose: Determine mechanisms and...
Adaptive SMS Intervention
Marc Adams
National Campaign SMS
Jen Huberty
Local Food Safety App
• Food safety regulations

Chris Wharton

– Important for safe distribution in
wholesale markets
– D...
Conclusions
BCTs=Exciting Transdisciplinary Research
Great promise for combatting societal
problems
How to create useful &...
Thank you!

Flickr – veo_

For these slides visit:
www.designinghealth.org
ehekler@asu.edu
@ehekler
The Design and Evaluation of Beahvior Change Tech
The Design and Evaluation of Beahvior Change Tech
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The Design and Evaluation of Beahvior Change Tech

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This talk discusses my current program of research focused on behavior change technologies.

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The Design and Evaluation of Beahvior Change Tech

  1. 1. The Design and Evaluation of Behavior Change Technologies: Research at the Intersection of Behavioral Science, HCI/CS, and Engineering Eric Hekler Assistant Prof, Nutrition & Health Promotion Arizona State University ehekler@asu.edu www.designinghealth.org Seminar for CIDSE 11/1/2
  2. 2. Outline Who am I? Why Behavior Change Technologies? BCT Development Model Projects mHealth Physical Activity Interventions DIY Self-Experimentation Toolkit Online Support Group Other BCT-related Projects at SNHP
  3. 3. Outline Who am I? Why Behavior Change Technologies? BCT Development Model Projects mHealth Physical Activity Interventions DIY Self-Experimentation Toolkit Online Support Group Other BCT-related Projects at SNHP
  4. 4. Who we are… Software Programming and Development Core Lead Programmer
  5. 5. Outline Who am I? Why Behavior Change Technologies? HCI/Behavioral Science Combined Process Projects mHealth Physical Activity Interventions DIY Self-Experimentation Toolkit Online Support Group Other BCT-related Projects at SNHP
  6. 6. http://youtu.be/QPKKQnijnsM Why care about BCTs? Flickr – just.Luc Flickr-meanMrmustard
  7. 7. Digital technologies are… Pervasive Interconnected Powerful enough to tackle the complexity? Flickr – Stuck in Customs
  8. 8. Health is complex… Individual characteristics Over the lifespan
  9. 9. We want Behavior Change Technologies that are… • Evidence-based • Cost-effective • Personalized • Easy to disseminate • Promote maintenance • Fit into a person’s daily life • Financially self-sustaining
  10. 10. Behaviora l Science Behavior Change Technologies Engineering Human ComputerInteraction Hekler, Klasnja, Froehlich, & Buman, 2013, SIGCHI; Hekler, Klasnja, Travers, & Hendriks, 2013 IEEE Pulse
  11. 11. Outline Who am I? Why Behavior Change Technologies? BCT Development Model Projects mHealth Physical Activity Interventions DIY Self-Experimentation Toolkit Online Support Group Other BCT-related Projects at SNHP
  12. 12. Mind the Theoretical Gap: Interpreting, Using, and Developing Behavioral Theory in HCI Research Eric Hekler Predrag Klasnja Jon Froehlich Matthew Buman Assistant Prof, Nutrition & Health Promotion Arizona State University ehekler@asu.edu Assistant Prof, iSchool U. of Michigan klasnja@umich.edu Assistant Prof, CS U. of Maryland jonf@cs.umd.edu Assistant Prof, Nutrition & Health Promotion Arizona State University mbuman@asu.edu
  13. 13. Goal HCI/Design The design and creation of useful and usable technologies and interactions. Behavioral Science The systematic search for generalizable truths about behavior to create effective interventions. Hekler, Klasnja, Froehlich, & Buman, 2013, SIGCHI flickr Amyn Kassam
  14. 14. The design, creation, and evaluation of useful and usable technologies to effectively promote behavior change for positive societal change. Flickr ecstaticist Hekler, Klasnja, Froehlich, & Buman, 2013, SIGCHI
  15. 15. Nine Questions for BCT Development DEFINE THE PROBLEM 1) What behavior(s) are you trying to change for whom? 2) What influences target behavior(s) for this user group? 3) How can we change the target behavior(s)? DESIGN THE TECHNOLOGY & USER EXPERIENCE 4) How can technology support behavior change? 5) How should individual features work? 6) How is technology used and experienced? DETERMINE IF IT WORKS 7) How are features working to change the target behavior? Klasnja, Hekler, Froehlich, & Buman, Manuscript Submitted for Publication
  16. 16. Defining the Behavior
  17. 17. Designing Behavior Change Class Syllabus:
  18. 18. Class Projects wk6 wk18 Develop an SMS health behavior Intervention. Use theory to make yourself healthier. Use previous work, theory, and UX Design to iterate on a health intervention. Family & Friends Self Targeted User Group Pre/Post Comparison Baseline – Intervention – Baseline Study Iterate at least 3 times Test with A vs. B experiments Methods Focus wk4 Who? wk1 Syllabus:
  19. 19. Outline Who am I? Why Behavior Change Technologies? BCT Development Model Projects mHealth Physical Activity Interventions DIY Self-Experimentation Toolkit Online Support Group Other BCT-related Projects at SNHP
  20. 20. Projects Individual characteristics Over the lifespan * * * * * * *PI/ Co-PI
  21. 21. mHealth Physical Activity Interventions
  22. 22. MILES Study • Develop theoretically meaningful smartphone apps for midlife & older adults • Physical activity & behavior sedentary • Passively assess PA & SB • Feedback for behavior change Abby King
  23. 23. Activity Monitor Validation N=15, Men & Women, Mean Age=55 12 laboratory-based activities 3-4 min each Hip- and pocket-worn Android phones Compared to Actigraph & Zephyr Bioharness Hekler et al. Manuscript in Preparation
  24. 24. Validation Results Hekler et al. Manuscript in Preparation
  25. 25. Validation Results Hekler et al. Manuscript in Preparation
  26. 26. The smartphone apps mTrack mSmiles mConnect Calorific Hekler et al. 2011, Personal Informatics Workshop at CHI – Design paper King, Hekler, et al. 2013 PLoS One, King, Hekler, et al. Manuscript in Preparation
  27. 27. Components study arms mConnec mTrack mSmiles t Calorific Push component X X X X Pull component X X X X "Glance-able" display X X X X Passive activity assessment X X X X Real-time feedback X X X X Self-monitoring X X X X “Help” tab X X X X Goal-setting X X Feedback about goals X X Problem-solving X X Reinforcement X X X Variable reinforcement schedule X X Attachment X "Play" X "Jack pot" random reinforcement X Hekler et al. 2011, Personal Informatics Workshop at CHI – Design paper Social norm comparison X King, Hekler, et al. 2013 PLoS One, King, Hekler, et al. Manuscript in Preparation
  28. 28. MILES Study Design Prestudy Baseline Week1 Visit1 Follow up Feedback Week2 Week8 Visit2, check in Visit3 mTrack (Analytic App) Randomize mSmiles (Affect App) mConnect (Social App) Diet Tracker Control App) Assess: Assess: Activity Assessment, Continuous Moderators Self-report Ecological Momentary Assessment, Daily PA, Sed Beh Real-time use of phone features Acceptability Self-report PA, Sed Beh King, Hekler, et al. 2013 PLoS One, King, Hekler, et al. Manuscript in Preparation
  29. 29. min/week of activity at study completion Physical Activity - 8wk Results 300 250 200 150 Brisk walking (min/week) 100 50 MVPA (min/week) 0 Analytic Social Smartphone Apps Affect Paired t [60] = 5.3, p <0.0001 King, Hekler, et al. 2013 PLoS One,
  30. 30. ∆ Food Consumption 15 † Servings pre day 10 5 ** ** † ** Physical Activity Apps Control 0 -5 Processed Sweets Fatty MeatsFatty Foods Dairy VegetablesFruits -10 Hekler, et al., Manuscript in Preparation. Diet-tracking Intervention App
  31. 31. Lessons learned… • Activity monitoring by phone only is difficult • Each intervention had merit, not potent • “Right” intervention at “right” time and place? • RCT experimental design not enough…
  32. 32. Improved Passive Sensing Matthew Buman Activity/Sleep Validation Geotagging Matt Buman, ASU; Max Utter, Jawbone David Mohr, Northwestern
  33. 33. Just in Time Adaptive mHealth Intervention Daniel Rivera Co-PI: Daniel Rivera, ASU Other Collaborators: Matthew Buman, Marc Adams, & Pedrag
  34. 34. Secondary Analysis – System Identification Hekler, Buman, Rivera, et al, 2013, Health Education & Behavior.
  35. 35. Secondary Analysis – System Identification Hekler, Buman, Rivera, et al, 2013, Health Education & Behavior.
  36. 36. Dynamical Model Social Cognitive Theory Riley, Martin, Rivera, Hekler, et al. Manuscript Submmited for Publication
  37. 37. Dynamical Model Simulations Riley, Martin, Rivera, Hekler, et al. Manuscript Submmited for Publication
  38. 38. Informative Experiment for a Controller 14000 12000 Steps per Day 8000 Week Average Intervention 4 6000 Intervention 3 Intervention 2 Intervention 1 4000 Measurement 2000 0 1 10 19 28 37 46 55 64 73 82 91 100 109 118 127 136 145 154 163 172 181 190 199 208 217 226 235 244 Steps per day 10000 Days
  39. 39. Just in Time Adaptive mHealth Intervention Daniel Rivera Co-PI: Daniel Rivera, ASU Other Collaborators: Matthew Buman, Marc Adams, & Pedrag
  40. 40. A DIY Self-Experimentation Toolkit for Behavior Change Win Burleson Eric Hekler Winslow Burleson Jisoo Lee Arizona State University Bob Evans Google
  41. 41. Quantified Self
  42. 42. Context Matters
  43. 43. GaLLaG- Home Sensing & Feedback Sensors Wireless Communication
  44. 44. The DIY Self-Experimentation Toolkit Paco GaLLaG ? Design Portal
  45. 45. Improving Online Support Groups David McDonald Erika Poole
  46. 46. Other BCT-related projects Software Programming and Development Core Lead Programmer
  47. 47. Activity “ground truth” validation app Matthew Buman
  48. 48. Round-the-Clock Activity Logging App Matthew Buman
  49. 49. Environmental Context Assessment App Matthew Buman • Harness technology to improve neighborhood designs for physical activity and healthy eating • Engage community members as auditors and advocates Buman, Winter, Sheats, Hekler, Otten, Grieco, & King, 2012
  50. 50. Environmental Context Assessment App Matthew Buman Data collection Review Responses Complete a survey Buman, Winter, Sheats, Hekler, Otten, Grieco, & King, 2012
  51. 51. EMA App & Sun Card Study Assessing network behaviors in the here and now Meg Bruening • Purpose: Determine mechanisms and contexts of how networks influence obesity & obesity-related behaviors • 3 network structures – Dorms – Dorm floors – Friends: Roommates, best friends, friend groups
  52. 52. Adaptive SMS Intervention Marc Adams
  53. 53. National Campaign SMS Jen Huberty
  54. 54. Local Food Safety App • Food safety regulations Chris Wharton – Important for safe distribution in wholesale markets – Difficult for small farms to implement • GHP/GAP app could simplify data management and reporting • Potential collab with AZ Dept. of Ag, Local Foods Lab, and local farms
  55. 55. Conclusions BCTs=Exciting Transdisciplinary Research Great promise for combatting societal problems How to create useful & usable BCTS? ASU SNHP = lots of possible collaborators interesting data opportunities for CS students contact me, ehekler@asu.edu
  56. 56. Thank you! Flickr – veo_ For these slides visit: www.designinghealth.org ehekler@asu.edu @ehekler

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