Implications for future connected and mobile
health promotion and disease prevention research
Ilkka Korhonen
mHealth Summi...
Images: frog design ©

Digital ‘footprints’ of health, behavior and context

Quantification and modeling of real behaviors...
Self-monitoring is an intervention

© Jacob Arnold, Jung Hong, and Shelten Yuen, R&D Fitbit Inc

10.12.2013

3
Non-adherence to regular self-weighing
associates with weight gain
Helander E, Orsama A-L, Wansink B, Korhonen I. Breaks i...
Weight increases during weekends and
decreases during weekdays –
especially in weight losers
Orsama A-L, Mattila E, Ermes ...
When new wearable monitoring meets
physiological modelling
100
90
80

Percentage

70
Stress
Relax
Heavy actitity
Light act...
Crowd data  what really happens
Physical activity (>3MET & >10min)
(based on HRV analysis)
32

Jan

59724

Individuals, #...
When physiological monitoring meets
behavioral measures
Alcohol and physiological
recovery during sleep

Physiological rec...
24/7 HRV monitoring combined with diary (=personal
context)  personally relevant discoveries!
Physiological Stress (red) ...
Computational Modelling of Behaviors…

© Rivera and Jimison, 2013

10.12.2013

10
…and Optimization of Interventions

© Rivera and Jimison, 2013

10.12.2013

11
Feedback and support via
crowdsourcing – Eatery example
(eatery.massivehealth.com)

Power of peers:
• Free
• Real-time
• S...
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
...
Real-Time Intervention to decrease
Sedentary Time
© Donna Spuijt-Metz
Your Activity Meter
Active Time in the Last
60 Minutes
Each bar = 30 seconds
20 bars = 10 minutes

Sedentary Time
(since t...
Counts

Differences in Accelerometry Counts
in 10 minute periods after being prompted

6000
5500
5000
4500
4000
3500
3000
...
Future of computational modelling
of behaviors??
•

•

Data acquired with modern wearable and ubiquitous technologies reve...
Competitive landscape
Thank you!

Ilkka Korhonen
Personal Health Informatics/Tampere University of Technology
&
Personalized ICT for Health, VTT...
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Implications for future connected and mobile health promotion and disease prevention research - mHealth Summit 2013

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

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  • SMS prompts sent in a 10 minute period were not associated with significant differences in light or vigorous activity during the following 10 minute period
  • Implications for future connected and mobile health promotion and disease prevention research - mHealth Summit 2013

    1. 1. Implications for future connected and mobile health promotion and disease prevention research Ilkka Korhonen mHealth Summit 2013
    2. 2. Images: frog design © Digital ‘footprints’ of health, behavior and context Quantification and modeling of real behaviors in context
    3. 3. Self-monitoring is an intervention © Jacob Arnold, Jung Hong, and Shelten Yuen, R&D Fitbit Inc 10.12.2013 3
    4. 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! 10.12.2013 4
    5. 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! 10.12.2013 5
    6. 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 6
    7. 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. 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. 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. 10. Computational Modelling of Behaviors… © Rivera and Jimison, 2013 10.12.2013 10
    11. 11. …and Optimization of Interventions © Rivera and Jimison, 2013 10.12.2013 11
    12. 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. 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. 14. Real-Time Intervention to decrease Sedentary Time © Donna Spuijt-Metz
    15. 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. 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. 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. 18. Competitive landscape
    19. 19. Thank you! Ilkka Korhonen Personal Health Informatics/Tampere University of Technology & Personalized ICT for Health, VTT ilkka.korhonen@tut.fi

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