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Using new technologies for
understanding and changing
behavior
Ilkka Korhonen, Donna SprujtMetz, Niilo Saranummi, Stephen
...
Our behavior is killing us…
Proportional contribution to premature death

Genetic disposition
Social circumstances
Environ...
Our behavior is killing us…
Here’s the part we can change

Genetic disposition
Social circumstances
Environmental exposure...
Outline
• Motivation
• Lessons from “International Workshop on New
Computationally-Enabled Theoretical Models to
Support H...
International Workshop on New ComputationallyEnabled Theoretical Models to Support Health
Behavior Change and Maintenance,...
Workshop Premise
• Current understanding of human behavior is based on static
‘snapshots’ of human behavior

Riley et al 2...
Our continuous digital ‘footprints’ record our
behavior in context and real time.

This allows quantification and
modeling...
Example 1: Weight patterns
• Self-monitoring / self-weighing is
an efficient intervention for weight
loss and weight manag...
Weight increases during weekends
and decreases during weekdays –
especially in weight losers

80 subjects
1y follow-up
ins...
Example 2: HRV & ”big data”
in collaboration with Firstbeat Technologies
www.firstbeat.fi
Number of
measurement
days

2154...
HRV analysis  classification of physiological
state, quantification physical activity

Stress

Recovery

An elevated acti...
Physical activity* by weekday
and month in Finnish employees
Jan

34

Feb

32

Mar

30

Apr

28

May

24

Jul

22

Aug
Sep...
Conclusions
• Data acquired with modern wearable and
ubiquitous technologies reveals novel
patterns and relationships betw...
Thank you!

Ilkka Korhonen
Personal Health Informatics/Tampere University of Technology
&
Personalized ICT for Health, VTT...
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Using new technologies for understanding and changing behaviors

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My presentation in Wireless Health, 2013

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Using new technologies for understanding and changing behaviors

  1. 1. Using new technologies for understanding and changing behavior Ilkka Korhonen, Donna SprujtMetz, Niilo Saranummi, Stephen Intille, Wendy Nielsen, Misha Pavel Wireless Health 2013 2.11.2013
  2. 2. Our behavior is killing us… Proportional contribution to premature death Genetic disposition Social circumstances Environmental exposure Health care Behavior Schroeder NEJM 2007
  3. 3. Our behavior is killing us… Here’s the part we can change Genetic disposition Social circumstances Environmental exposure Health care Behavior Schroeder NEJM 2007
  4. 4. Outline • Motivation • Lessons from “International Workshop on New Computationally-Enabled Theoretical Models to Support Health Behavior Change and Maintenance” • Two examples of use of new technologies to understand behaviors – Getting under the hood of weight data – Big data (heart rate variability) • Conclusions 14.11.2013 4
  5. 5. International Workshop on New ComputationallyEnabled Theoretical Models to Support Health Behavior Change and Maintenance, Brussels Oct 2012 Experts from the European Union (15) and the United States (15): e-, tele- and mHealth (mental health, metabolic health, substance use), web marketing, persuasive technologies, policy, physics, systems science, economics, clinical, health & behavioral psychology, gaming, information and communications technology, artificial intelligence, human computer interactions, health informatics, computer science, biomedical engineering, health disparities, medicine (chronic disease, endocrinology, primary care), public health and public health law, art and design.
  6. 6. Workshop Premise • Current understanding of human behavior is based on static ‘snapshots’ of human behavior Riley et al 2012, Kumar et al AJPM 2013, Spruijt-Metz et al under review
  7. 7. Our continuous digital ‘footprints’ record our behavior in context and real time. This allows quantification and modeling of real behaviors in context
  8. 8. Example 1: Weight patterns • Self-monitoring / self-weighing is an efficient intervention for weight loss and weight management • Focus so far mostly in trends – but weight varies from day to day • Connected weight scales allow easy and frequent long-term monitoring • Is there information in the variability? 14.11.2013 8
  9. 9. Weight increases during weekends and decreases during weekdays – especially in weight losers 80 subjects 1y follow-up instructed to self-weigh daily  Weight variability pattern may associate with weight management success 14.11.2013 9
  10. 10. Example 2: HRV & ”big data” in collaboration with Firstbeat Technologies www.firstbeat.fi Number of measurement days 21546 Number of individuals 14190 Age 43±10 (18-65) BMI 26±4 (18-40) Percentage of males [%] 49 Activity class 4.9±1.9 (0-10) 14.11.2013 10
  11. 11. HRV analysis  classification of physiological state, quantification physical activity Stress Recovery An elevated activity level in the body caused by external or internal factors (excluding immediate physical demands). A lowered activity level in the body caused by reduction of external or internal stress factors. Low heart rate and high HRV. High heart rate and low HRV.
  12. 12. Physical activity* by weekday and month in Finnish employees Jan 34 Feb 32 Mar 30 Apr 28 May 24 Jul 22 Aug Sep 20 Oct 18 Nov 16 Dec 14 Sun Mon Tue Wed Thu Fri Sat Minutes 26 Jun *>3MET from 10minute bouts, background (age, gender, BMI, activity class) controlled
  13. 13. Conclusions • 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, conte xtualized models of health behavior and behavior change. 14.11.2013 13
  14. 14. Thank you! Ilkka Korhonen Personal Health Informatics/Tampere University of Technology & Personalized ICT for Health, VTT ilkka.korhonen@tut.fi

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