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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4
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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5
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. 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
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
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
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
19. Thank you!
Ilkka Korhonen
Personal Health Informatics/Tampere University of Technology
&
Personalized ICT for Health, VTT
ilkka.korhonen@tut.fi
Notas del editor
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