Presentation by ScD, Adjunct Professor Harri Sievänen held in HEPA Europe Workshop "Towards objective population monitoring in the Europe: PHYSICAL ACTIVITY, SEDENTARY BEHAVIOUR AND FITNESS" on June 7-8, 2017 at UKK Institute, Tampere, Finland. Harri Sievänen works as a Research Director in the UKK Institute for Health Promotion Research in Tampere, Finland.

1. MAD-APE: the novel method for raw
data processing for both PA and SB
– how does it work and is it accurate
enough?
Harri Sievänen, ScD, Adjunct Professor
Research Director
The UKK Institute for Health Promotion Research, Tampere, Finland
E-mail: harri.sievanen@uta.fi
Twitter: @LuuHarri
HEPA Europe Workshop, June 7, 2017

2. What you can not measure,
you can not change or manage.
Bad data leads to bad science –
and bad ”evidence-based” actions.
Orientation

3. Questions?
• Physics-driven vs. brand-driven approach?
• Open vs. proprietary analysis?
• Physical/physiological vs. arbitrary unit(s)?
• What information on daily PA and SB is most
relevant?

4. Purpose of accelerometry
To translate individual of physical activity (PA) and
sedentary behaviour (SB) accurately and reliably
into palpable, meaningful outcomes:
e.g., as daily/incident energy expenditure,
met PA recommendations,
risk of NCDs,
or personal PA and SB profile.

5. Challenges and pitfalls in accelerometry
• Different devices (tech specs: sampling rate, range, resolution)
• Different placement (waist, wrist, thigh etc.)
• Different preprocessing (filtering)
• Different outcomes (proprietary algorithms, counts vs others)
• Different epoch lengths (granulated vs. averaged data)
• Different data collection times (10 h/day, 4 days -> 24/7)
• Different cut-points (fixed, purpose-dependent)
• Different interpretations (context, domain etc)
• Different target groups (children, adults, elderly etc)
Standardization is really needed – MAD-APE?

6. *Review by Migueles et al Sports Med 2017 (for ActiGraph only)
Percentage of papers not reporting key
methodological issues*
= Non-wear time

7. The MAD approach
1. For physical activity

8. Mean amplitude deviation (MAD)
X-axis
Y-axis
z-axis
R
← Epoch length →
12
9
6
3
0
• Simple to calculate
• Consistent results irrespective of the device
• Can be interpreted in physical terms
Acceleration (a) ~ Force (F) ~ change in momentum (dp/dt or m·dv/dt)

10. MAD: universal* analysis
Vähä-Ypyä et al Clin Physiol Funct Imaging 2015
Aittasalo et al BMC Sports Sci Med Rehabil 2015
MAD (mg) MAD (mg)
Adults Adolescents
* Independent of the accelerometer brand used (only triaxial raw data is needed)

11. MAD: independent of acceleration range
Vähä-Ypyä et al (unpublished data)

12. MAD: insensitive of sampling frequency
Vähä-Ypyä et al (unpublished data)

13. MAD: insensitive to waist location
Vähä-Ypyä et al PLoS One 2015

14. Vähä-Ypyä et al Clin Physiol Funct Imaging 2015
MAD: strong correlation with heart rate
Hookie 0.97
GulfCoast 0.96
Actigraph 0.95
Adults

15. Aittasalo et al BMC Sports Sci Med Rehab 2015
MAD: strong correlation with heart rate
Hookie 0.96
Actigraph 0.97
Adolescents

16. MAD, speed and oxygen consumption
Vähä-Ypyä et al PLoS One 2015

17. MAD: strong correlation with actual MET
Oxygen consumption
Vähä-Ypyä et al PLoS One 2015
Individual correlation
between MAD and
MET: from 0.93 to 0.99
Energy consumption

18. MAD: valid classification of PA intensity
Vähä-Ypyä et al PLoS One 2015
Sensitivity 100%
Specificity 96%
Sensitivity 96%
Specificity 95%
Exercise recommendations

19. The MAD-APE approach
2. For sedentary behavior

20. • Magnitude and direction
of Earth’s gravity vector is
constant
• Locomotion occurs
(always) in erect position
• Sensor is kept in a fixed
location (waist)
Body posture (~°)

21. Limit 2
Limit 1
MAD (mg)
Angle (°) 0
90
Vähä-Ypyä et al, 2017 submitted to SJMSS
MAD-APE: valid detection of body posture*
Sedentary behavior
*Angle for Posture Estimation APE

22. The MAD-APE approach
3. For 24/7 assessment of
PA and SB (and sleep)

23. 23
Characterization of individual daily physical
activity and sedentary profile
Energy consumption (PA) and body posture (SB)
as a function of time throughout the day
Unlimited number of variables can be calculated and derived - prudence must be exercised!

24. Vähä-Ypyä et al (unpublished)
Modeling of intensity
3 MET
6 MET
M
A
D
Moving average
Analysis models the physiological responses of the body while removing sporadic events

25. Individual daily physical activity
and sedentary profiles
• Several novel PA and SB features (e.g., N and duration of different intensity
classes) can be calculated from raw triaxial acceleration data.
• Statistical associations of these features with the risk of prevalent and incident
morbid events can be determined from population-based data and register data.

26. Sleep analysis
Analysis is based on wrist movements (detected as changes in sensor angle) while sleeping

27. SEDENTARY BEHAVIOR PHYSICAL ACTIVITY
BODY POSTURE ENERGY CONSUMPTION (MET)
• ACCELERATION
• HEART RATE
• STEP COUNT
• OTHER MEASUREMENTS
STANDING
H
E
A
R
T
R
A
T
E
• SENSOR ORIENTATION
ACCELERATIONSENSOR ORIENTATION
INFORMATION:
MEASUREMENT:
• PA at different intensity levels
and SB can be classified from
the acceleration signal with
high accuracy (~ 90%).
• Reasonable sleep analysis can
be done.
Adapted from Sievänen & Kujala Editorial SJMSS 2017
Summary

28. The MAD-APE approach is the method-of-choice because:
• Its calculation is simple.
• It gives consistent results with all waist-worn accelerometers
using triaxial raw data.
• It is insensitive to accelerometer technical specs.
• It is insensitive to accelerometer orientation (provided it is fixed).
• It is valid in terms of energy consumption (MET).
• It classifies accurately the PA intensity (LIPA, MVPA, VPA etc).
• It separates accurately SB from low intensity PA.
• It is able to accurately classify whether the subject is sitting,
lying, or standing (body posture).
• It provides a platform to describe daily PA and SB as summary
measures (daily means) as well as in detailed measures
(granulated data).
Summary

29. There
is method
in our MADness
Thank you!
Q & A?