This document summarizes a seminar presentation about research at the intersection of behavioral science, human-computer interaction, and engineering to develop behavior change technologies. The presenter discusses their background and projects using a combined HCI/behavioral science process to create smartphone apps and other digital tools to promote physical activity. Example projects described include developing and testing multiple smartphone apps in a randomized controlled trial with older adults, validating smartphone-based activity monitors, and developing "just-in-time adaptive interventions". The presentation outlines a nine-step process for behavior change technology development and concludes by discussing opportunities for collaboration.
Pooja 9892124323, Call girls Services and Mumbai Escort Service Near Hotel Hy...
The Design and Evaluation of Beahvior Change Tech
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. 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. 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
6. 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
10. 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
12. 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
13. 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
14. 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
15. 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
16. 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
19. 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:
20. 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
23. 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
24. 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
28. 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
29. 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
30. 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,
31. ∆ 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
32. 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…
33.
34. Improved Passive Sensing
Matthew Buman
Activity/Sleep Validation
Geotagging
Matt Buman, ASU;
Max Utter, Jawbone
David Mohr, Northwestern
35. Just in Time Adaptive
mHealth Intervention
Daniel Rivera
Co-PI: Daniel Rivera, ASU
Other Collaborators: Matthew Buman, Marc Adams, & Pedrag
36. Secondary Analysis – System Identification
Hekler, Buman, Rivera, et al, 2013, Health Education & Behavior.
37. Secondary Analysis – System Identification
Hekler, Buman, Rivera, et al, 2013, Health Education & Behavior.
38. Dynamical Model Social Cognitive Theory
Riley, Martin, Rivera, Hekler, et al. Manuscript Submmited for Publication
40. 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
41. Just in Time Adaptive
mHealth Intervention
Daniel Rivera
Co-PI: Daniel Rivera, ASU
Other Collaborators: Matthew Buman, Marc Adams, & Pedrag
42. A DIY Self-Experimentation
Toolkit for Behavior Change
Win Burleson
Eric Hekler
Winslow Burleson
Jisoo Lee
Arizona State University
Bob Evans
Google
51. 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
53. 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
56. 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
57. 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
58. Thank you!
Flickr – veo_
For these slides visit:
www.designinghealth.org
ehekler@asu.edu
@ehekler
Hinweis der Redaktion
Thank you very much, particularly Ross, for inviting me to give this talk today!
Here is a quick outline of what I’d like to talk about today. A quick introduction about me, a discussion on what behavior change technologies are and why they are interesting. Next, I will discuss a working development model for behavior change technologies that merges lessons from behavioral science and human computer-interaction research. I will then get into some more details on a few projects I’ve been working on to give you a sense of the breadth of my research along with some of the other BCT-related projects that are being conducted by other faculty at the School of Nutrition and Health Promotion.
I completed my undergrad at the University at Albany in psychology and then went on to complete my masters and PhD in Clinical Health Psychology at Rutgers. While on my clinical internship, I started to get more and more interested in the idea of using technology to promote healthful behavior change because their unique strengths. Based on this, I moved on to complete my postdoctoral training at Stanford. At Stanford, while I was housed in the medical school. I also worked closely with faculty and students from the Computer Science, particularly the HCI faculty such as Scott Klemmer, and also worked with folks in the Stanford Design or d.School. As such, while I was there, I really started to develop my current research program that focuses on utilizing technology to promote healthful behavior change through a melding of lessons from behavioral science and human computer-interaction research.
Beyond just me though, I also just wanted to acknowledge the many other faculty that are a part of the School of Nutrition and Health Promotion here at ASU. In particular, all of these folks that are still highlighted have, as part of their research, an interest in using technology to promote health. This is a point I’ll return to at the end of the talk. I also want to acknowledge that we actually have a full time software developed, Kevin Hollingshead, who has over 20 years of programming experience running his own software development group. He joined our team partially because he is very interested in mentoring students. I mention all of this to say that we’ve got a lot of good resources, skills, and interest for doing behavior change technology research and many of us are very interested in collaborating with CIDSE folks. Again, I’ll come back to this later in the talk though.
Now, back to behavior change technologies. What is it, and why is it important. In brief, behavior change technologies is intentionally a broad term that basically encompasses any technology, usually digital, that has, at it’s primary purpose, a focus on fostering more healthful behavior patterns. Why are these important though?
Behavior change technologies are important because of all of the complex societal problems we face. From childhood and adult obesity rates increasing, to further spreading income inequality, to environmental sustainability; many of the core problems we face as a society are largely driven by the behaviors and choices we make.
Digital technologies, which are pervasive and interconnected may provide the power to actually take on and positively influence these large behavioral problems.
But it is still an important open question on exactly HOW behavior change technologies can tackle the complexity of behaviorally driven societal problems. Just to drive this point home, there is increasing interest in the health behavior change realm in understanding the multiple levels of influence of health from individual characteristics like biology, to behaivoral factors, to how a city is designed and up to macro-level issues such as culture or federal policies. All of these influence the final outcome of behavior and health outcomes and thus can be taken into account. Based on this complexity, new tools and technologies are needed that go far beyond the current strategies for building behavior change technologies that really impact societal problems.
Beyond just the difficulty of the complexity of behavior and the behavioral problems we are trying to solve, there are a lot of demands on behavior change technologies themselves and different points that tend to thought about from different disciplines. Indeed, we want behavior change technologies that are evidence-based, cost-effective, personalized, easy to disseminate, promote maintenance, fit into a person’s life, and can, hopefully be financially self-sustained in sustaining. As can be seen just from this list, this can’t be achieved through the class disciplinary silo model of creation.
Indeed, in my view, the only way to really create behavior change technologies is through transdisciplinary research that is at the intersection of behavioral science, human computer-interaction and computer science, and the variety of engineering disciplines. Figuring out the methods and processes for getting these diffierent disciplines to “play” together though is no small feat.
And thus the development model for BCT’s has been an important underlying question within my work.
Beyond just my own projects, the articulation of how to bridge between disciplines has been a primary focus of my colleagues, PedjaKlansja, Jon Froehlich, Matt Buman and me. We started out last year with a focus on how to use behavioral theories within HCI research with a paper at last year’s CHI that won a Best Paper Award. In it, our goal was to articulate working assumptions and methods for interpretting, using, and even developing behavioral theory within an HCI context. I won’t go into full detail on this paper but if you are curious about things like the potential pitfalls of behavior theory or strategies for developing your own theories for supporting any behavior change technology you are working on, please check out the paper.
A key point I want to drive home in this talk, however, is the importance of the different, in my view, complementary goals of the two fields. With HCI, as articulated by my HCI colleagues, Pedja and Jon, the broad focus is on the design and creation of useful and usable technologies and interactions. On the flip, behavioral science’s classic focus was on the systematic search for generalizable truths about behavior to create effective interventions.
We found that an important merging of these goals sets up a nice, high-level goal for behavior change technologies. In particular, we see our collective goal as the design, creation, and evaluation of useful and usable technologies to effective promote behavior change for positive societal change. You will notice, I keep coming back to positive societal change. Not surprisingly being here at ASU, I’m very interested in user-inspired solutions-oriented research. In my view, that is not only a pragmatic focus for ensuring value is derrived from my work but I also see that as essential to doing good science as it forces us to have a metric of success or failure with which to be judged, which, sadly, I think has traditionally been lacking from our collective work. But we can discuss that more during the discussion.
Following this discussion on this, we started to think more deeply about the entire development model and, after several iterations, we landed on nine core questions, that can be chunked into three broad domains of defining the problem, designing the technology, and then determining if it works. While this might seem obvious, it is interesting that from a discplinary silo approach, many of these points are often not really properly considered. To further flesh this out. We’ve identified these questions for defiing the problem. As part of this, we’ve also articulated in our paper that is currently under review, a variety of methods from a variety discipline to help answer these questions. For designing the technology, we believe these are the important questions, and then finally, for determining if it works, we are interested in these questions.
Just to further drive this point home a bit on the importance of transdisciplinary work. Even from the the first question of defining the target behavior, it often involves having an understanding of a variety of disciplines. For example, if you are focusing on a health behavior, you will need health scientists to help define what is a useful behavior for achieving some higher level goal like weight loss. You might need an engineer though to define different ways of measuring the behavior, but then you might need more HCI methods to define what is a feasible/meaningful behavior to the target population. In our work, we have been trying to take methods from the various disciplines to try and answer these nine questions.
In addition, I also teach a grad-level class called Designing Health Behavior Change Interventions. In it, I’m constantly exploring and teaching my students various ways to building on lessons from these various disciplines for rapidly iterating on the creation of interventions.
Indeed, the class is a projects focused class that includes the design, creation, and evaluation, of a text message intervention to be tested on family and friends in the first few weeks of class. This is followed by a project focused on trying to change one’s own behavior and then finally, a group based project whereby students actively engage in creating a behavior change intervention by doing relative user experience formatic design work, utilizing small iterative tests to test their assumptions for the creation of a final project. My hope is also that some of these students might be inspired enough to even create a start-up or some other offshoot from this work. But all of this is based on this BCT development model that my colleagues and I are working on creating.
Now that I’ve establishing my philosophy for creating BCT’s, I’d now like to turn to some of my specific projects.
In particular, this is a quick visual of some projects I’m working on. I want you to notice that one of the goals I have in my research is to create behavior change technologies that span the complexity of health and thus spans the social ecological model.
First, my “Bread and butter” so to speak is really all about mHealth Interventions.
In particular, when I was at Stanford, I pitched an idea of developing theoretically meaniungful smartphone apps to increase physical activity and decrease sedentary behavior via the passive tracking of physical activity and sedentary behavior using the phone’s accelerometer to provide feedback to support behavior change. My mentor, Abby King, liked the idea so I organized a team and we ended up submitting and receiving an ARRA Stimulus grant to build this out.
The first step was then to create an activity monitor using just the accelerometer in the phone. In brief, we did that by developing “cut point” algorithms from the various “jerks” of the accelerometer.
Our results did end up supporting this approach as we could distinguish between sedentary, light, and moderate to vigorous intensity physical activity.
Further, we also found that the phones were giving comparable data to an Actigraph, a well validated activity monitor. Further, they also gave near identical data to each other.
With this passive activity monitor as the foundation, we then went out to design different smartphone apps that focused on different motivational frames for promoting behavior change. IN particular, we developed 3 apps, mTrack, mSmiles
Beyond the common elements, there are also unique elements for the three active applications, as identified here. The key idea study was to parse apart different ways to frame the information about physical activity and sedentary behavior. Rather than labor through this chart, I’m going to show you images from each of the applications to help you get a sense of how they are similar and different.
The plan for MILES is that we will conduct a small pilot study that will last 8 weeks. During these 8 weeks, we will randomize participants to receive one of four arms, what we call the cognitive, affect, or social app, or a diet tracker control app. Outside of this design, which is focused on exploring competing mechanisms of behavior change, we also plan to assess at baseline self-report behavioral measures as well as measures thought to moderate the effectiveness of each intervention. In addition, we have also built in features for gathering daily ecological momentary assessment similar to what we did in the CHART-2 trial that I described earlier. And finally, at the end of the study, we plan to assess self-report behavior again and also explore the acceptability of the applications.
We have been exploring this in the MILES project, which stands for Mobile interventions for lifestyle exercise at Stanford. As stated earlier, the study is an NIH-funded challenge grant. We are currently finalizing development of our three applications. We plan to start our pilot study in January 2011.
We have been exploring this in the MILES project, which stands for Mobile interventions for lifestyle exercise at Stanford. As stated earlier, the study is an NIH-funded challenge grant. We are currently finalizing development of our three applications. We plan to start our pilot study in January 2011.
We have been exploring this in the MILES project, which stands for Mobile interventions for lifestyle exercise at Stanford. As stated earlier, the study is an NIH-funded challenge grant. We are currently finalizing development of our three applications. We plan to start our pilot study in January 2011.
NOTE, this current draft is just to get a sense of timing and flow on key points to discuss. Formatting on almost all slides will not remain (e.g., likely will NOT have the titles at the top like that).
Describe the Psychological Argument for focusing on Context
Define meta-goal of the DIY Toolkit and then the specific goal for the hackathon of building a design portal
First, here are the three “glance-able” displays for the applications. Although the information gathered is identical, minutes engaged in sedentary behavior and MVPA, the way we are displaying it is quite different in each app. For the cognitive app, we wanted to frame the information relative to goals as this model assumes that behavior change occurs through active goal-setting and problem-solving through an active “cognitive” process. For the “affect” app, we utilizing a bird “avatar” as the method of tracking your activity. In this app, as you are more active, the bird flies faster, is happier, and becomes more playful. The idea here is that we believe a person would map the bird’s mood, particularly as it feels happier to their own mood and thus create a link up between being more active and feeling better. Finally, for the social app, you will notice that there are multiple stick figures on the home screen. With this design, the idea here is that a person will be motivated to be more active based on the level of activity of other participants in the study via social norm motivations. These glance-able displays set up the differences between the three apps but now I’m going to show you some more specific elements in each.
EMA App: Brief, frequent assessments of behavior Sun Card activity Track when, where, with whom & in what contexts behaviors occur Track changes in network over time
First, here are the three “glance-able” displays for the applications. Although the information gathered is identical, minutes engaged in sedentary behavior and MVPA, the way we are displaying it is quite different in each app. For the cognitive app, we wanted to frame the information relative to goals as this model assumes that behavior change occurs through active goal-setting and problem-solving through an active “cognitive” process. For the “affect” app, we utilizing a bird “avatar” as the method of tracking your activity. In this app, as you are more active, the bird flies faster, is happier, and becomes more playful. The idea here is that we believe a person would map the bird’s mood, particularly as it feels happier to their own mood and thus create a link up between being more active and feeling better. Finally, for the social app, you will notice that there are multiple stick figures on the home screen. With this design, the idea here is that a person will be motivated to be more active based on the level of activity of other participants in the study via social norm motivations. These glance-able displays set up the differences between the three apps but now I’m going to show you some more specific elements in each.
First, here are the three “glance-able” displays for the applications. Although the information gathered is identical, minutes engaged in sedentary behavior and MVPA, the way we are displaying it is quite different in each app. For the cognitive app, we wanted to frame the information relative to goals as this model assumes that behavior change occurs through active goal-setting and problem-solving through an active “cognitive” process. For the “affect” app, we utilizing a bird “avatar” as the method of tracking your activity. In this app, as you are more active, the bird flies faster, is happier, and becomes more playful. The idea here is that we believe a person would map the bird’s mood, particularly as it feels happier to their own mood and thus create a link up between being more active and feeling better. Finally, for the social app, you will notice that there are multiple stick figures on the home screen. With this design, the idea here is that a person will be motivated to be more active based on the level of activity of other participants in the study via social norm motivations. These glance-able displays set up the differences between the three apps but now I’m going to show you some more specific elements in each.
First, here are the three “glance-able” displays for the applications. Although the information gathered is identical, minutes engaged in sedentary behavior and MVPA, the way we are displaying it is quite different in each app. For the cognitive app, we wanted to frame the information relative to goals as this model assumes that behavior change occurs through active goal-setting and problem-solving through an active “cognitive” process. For the “affect” app, we utilizing a bird “avatar” as the method of tracking your activity. In this app, as you are more active, the bird flies faster, is happier, and becomes more playful. The idea here is that we believe a person would map the bird’s mood, particularly as it feels happier to their own mood and thus create a link up between being more active and feeling better. Finally, for the social app, you will notice that there are multiple stick figures on the home screen. With this design, the idea here is that a person will be motivated to be more active based on the level of activity of other participants in the study via social norm motivations. These glance-able displays set up the differences between the three apps but now I’m going to show you some more specific elements in each.
