My presentation in a panel focusing on how we can use novel technologies to build better computational models of behavior change.

1. Implications for future connected and mobile
health promotion and disease prevention research
Ilkka Korhonen
mHealth Summit 2013

2. Images: frog design ©
Digital ‘footprints’ of health, behavior and context
Quantification and modeling of real behaviors in context

3. Self-monitoring is an intervention
© Jacob Arnold, Jung Hong, and Shelten Yuen, R&D Fitbit Inc
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3

4. Non-adherence to regular self-weighing
associates with weight gain
Helander E, Orsama A-L, Wansink B, Korhonen I. Breaks in regular self-weighing associate with weight gain. Submitted
•
•
37 individuals, 1y follow-up,
instructed to self-weigh daily
Comparison of temporal nonadherence and weight change

Non-adherence to self-monitoring
is information – not just missing
data!
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5. Weight increases during weekends and
decreases during weekdays –
especially in weight losers
Orsama A-L, Mattila E, Ermes M, van Gils M, Wansink B, Korhonen I. Obesity Facts. In press.
80 subjects
1y follow-up
instructed to
self-weigh
daily
 There is hidden information in variability!
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6. When new wearable monitoring meets
physiological modelling
100
90
80
Percentage
70
Stress
Relax
Heavy actitity
Light activity
Exercise recovery
Unrecognized
60
50
40
30
WORK
20
WORK
SLEEP
10
0
www.firstbeat.fi
8
12 16 20
Thu
0
4
SLEEP
8
12 16 20
Fri
0
4
SLEEP
8
12 16 20
Sat
0
4
HRV analysis based on
physiological model and big data
based calibration  classification
of physiological state and
quantification of physical activity
8
Sun
10.12.2013
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7. Crowd data  what really happens
Physical activity (>3MET & >10min)
(based on HRV analysis)
32
Jan
59724
Individuals, #
17715
Age
44±10
(18-65)
Feb
30
Mar
Apr
BMI
26±4
(18-40)
Males [%]
47
Activity class
4.9±2.0
(0-10)
28
May
Jun
26
Jul
24
Aug
Sep
22
Oct
Nov
20
Dec
Weekday
mean
18
Mon
Tue
Wed
Thu
Fri
Sat
Sun
Month
mean
>3MET from 10- minute bouts, background (age,
gender, BMI, activity class) controlled
Minutes
Measurement days #

8. When physiological monitoring meets
behavioral measures
Alcohol and physiological
recovery during sleep
Physiological recovery during
sleep during different weekdays
0 (7352)
Alcohol portions
1 (2714)
2 (1998)
3 (1233)
4-5 (1313)
6-7 (551)
>7 (561)
0
50
100
150
200
250
Relaxation minutes during sleep
300
350
Based on ~30.000 monitoring days &
HRV analysis

9. 24/7 HRV monitoring combined with diary (=personal
context)  personally relevant discoveries!
Physiological Stress (red) and recovery (green)
Day 1 – Wed 4th of Apr, 2012
Telcos
F2f mtg
Running
Delayed
recovery
Nap
Ice hockey
game on TV
(play-off)
Day 2 – Thu 5th of Apr, 2012
Sleep

10. Computational Modelling of Behaviors…
© Rivera and Jimison, 2013
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11. …and Optimization of Interventions
© Rivera and Jimison, 2013
10.12.2013
11

12. Feedback and support via
crowdsourcing – Eatery example
(eatery.massivehealth.com)
Power of peers:
• Free
• Real-time
• Social support
Q: is the feedback valid?

13. Crowd feedback is valid!
Correlation coefficients
Rater 2
Rater 3
Peers
0.75
0.73
0.78
0.84
0.84
0.75
0.88
Rater 1
Rater 2
Rater 3
Raters' average
•
•
•
•
•
•
•
•
Fast food
Refined grains
Red meat (beef, pork, lamb)
Cheese and high-fat dairy
Savory snacks
Sweets/Desserts
Sugar sweetened beverages
Alcohol
Decreased the
healthiness score
(more unhealthy)
• Fruits
• Vegetables
Three U.S nutritionist students assess the
contents and healthiness of foods in 450
randomly chosen Eatery pictures according to
U.S dietary guidelines (things to avoid and things
to include)
•
•
•
•
•
•
•
Increased the
healthiness score
(more healthy)
Whole grains
Fat-free and low-fat dairy
Seafood
Beans, peas, lentils, nuts, or
seeds
Water or unsweetened
beverage
Processed food
Chicken or chicken mixed
dishes
Eggs and egg mixed dishes
•
Did not affect
In co-operation with VTT and University of South Carolina

14. Real-Time Intervention to decrease
Sedentary Time
© Donna Spuijt-Metz

15. Your Activity Meter
Active Time in the Last
60 Minutes
Each bar = 30 seconds
20 bars = 10 minutes
Sedentary Time
(since the last reset)
Total Active
Time
Total Elapsed
Time
Battery Indicator
for Each Device
Sedentary = lying down, sitting, sitting & fidgeting, standing, standing & fidgeting
Active = standing playing Wii, slow walking, brisk walking, running

16. Counts
Differences in Accelerometry Counts
in 10 minute periods after being prompted
6000
5500
5000
4500
4000
3500
3000
2500
2000
1500
1000
500
0
No prompt vs. Prompt
No Prompt
Prompt
• Accelerometer counts
were 1,066 counts
higher
• in the following 10
minute period
• compared to when
SMS prompts were not
sent (p<0.0001)
© Donna Spuijt-Metz

17. Future of computational modelling
of behaviors??
•
•
Data acquired with modern wearable and ubiquitous technologies reveals
novel patterns and relationships between different factors which can help us
to develop dynamic computational models of behavior.
Future:
– dynamic, personalizable, adaptable, contextualized models of health
behavior and behavior change based on real data
 behavioral risk factor quantification
 intervention optimization
– Real-time interventions and social support from peers
 personalized behavioral interventions
 cost-benefit, cost-utility
10.12.2013
17

18. Competitive landscape

19. Thank you!
Ilkka Korhonen
Personal Health Informatics/Tampere University of Technology
&
Personalized ICT for Health, VTT
ilkka.korhonen@tut.fi