My talk which addresses where does the science need to go in smart and connected health for obesity

1. The New Technology
Frontier in Health: Lessons
from
Behavioral Science
Sherry Pagoto, PhD
Founder, UMass Center for mHealth
and Social Media
Professor, Department of Medicine
University of Massachusetts Medical School

2. Obesity as a case
study
 Efficacy for behavioral strategies to manage obesity and
prevent diabetes was established in 2001 via the landmark
Diabetes Prevention Program Trial (DPP, 2001)
 Over 16 years, this program is still not widely available to the
public.

3. What are the
problems?
Too expensive/ burdensome (kitchen sink
approach) for patients and providers (e.g.,
numerous visits over 1 year) leading to poor reach
Clinical trials move slowly, so testing new
innovations has advanced slowly
Smaller effects when applied to real world
samples, need to increase potency

4. How can
technology help?
1. Reduce burden/cost of original intervention (i.e.,
make it cheaper and easier to implement)
2. Speed up clinical testing of strategies that could
increase potency (i.e., speed up science)
3. Advance our understanding of process of
behavior change (i.e., make it work better)

5. Make it easier and cheaper to
deliver
Prevent by Omada Health (Sepah et al 2015)
Less burdensome due to no clinic visits
Noom Health (app w/ human coaching)
(Michaelides et al 2016)
Provided by employers (not direct to individual)
Less burdensome due to no clinic visits
So far tech is being used to automate, not
innovate.

6. Speed up science
Clinical trials are the evidence gold standard
They are slow due to…
Recruitment pace and diversity relies on local population
Data collection is burdensome
Interventions are burdensome
` Research Kit –poor sample diversity, selection
bias, drop off in participation at 5 weeks.
(Dorsey et al 2016)
Need innovative platforms to solve these
problems.

7. Make it work
better
Mobile apps and wearables for self-monitoring and goal
setting
– Literature is mixed on whether apps improve diet tracking adherence
– Passive diet tracking approaches must improve accuracy, wearables
need to be acceptable to patients
– Large trial showed deleterious effect of wearable tracker on weight loss
long-term (Jakicic et al 2016)
Using online social networks to increase social support
for behavior changes
– Too early to tell
– Need data science to help us understand participant engagement

8. Make it work better
Examine how more sophisticated behavioral
strategies can be implemented with technology
Behavior change taxonomy identified 93 distinct
evidence-based behavioral strategies
(Michie et al 2015)

9. “Contextualized”
Self
Shift from “What am I doing?” to
“Why did I do it?”
Stress level
is high
You slept 4
hours
At a
restaurant
You
skipped
breakfast
With guys
from office
No exercise
in 2 days.
Predictive models of
contextual factors
surrounding healthy
and unhealthy choices

10. “Modified” Self
Which specific habits have
the biggest impact overall and for
a specific patient?
What is best time to
implement a
particular habit?
Which habit combinations are sufficient to
produce weight loss?

11. A note on team science…
Greater collaboration during the conceptual phase
Too many cases where an investigator has an
idea and then brings in collaborators; not enough
cases where investigator brings in collaborators
to generate an idea.
Collaborators ≠ service providers,
technicians, content experts

12. Check us out on Twitter
@DrSherryPagoto
@UMassmHealth
or Facebook
https://www.facebook.com/
UMASSmhealth/