This document discusses a study that aimed to predict muscle power in elderly individuals using functional screening data. The researchers developed a linear regression model to predict muscle power based on variables like BMI, gait velocity, sit-to-stand time, timed up-and-go test time, and grip strength. They collected these screening data from 101 elderly participants. The initial model had good accuracy but was improved by adding the non-linear effect of sit-to-stand time. The study concluded that predicting muscle power can help detect sarcopenia, which is a major cause of functional decline in the elderly.

1. Prediction of muscle power in elderly
using functional screening data
Indian Institute of Technology Jodhpur 1
By
Dr Vivek Vijay & Brajesh Shukla

2. Ageing
What happens when people age?
A. Functional decline
B. Loss of muscle strength and power
C. Falling
2

3. Old age
fall
statistics
Ageing inWorld - 8.5%
Fall rate in elderly adults – 30-60% [1]
Injury/Death rate – 10-20 % [1]
 Reasons of fall – age, living alone, foot
problem, visual impairment, psychological
status, etc. [1]
3
[1] Rubenstein, L.Z., Falls in older people: epidemiology, risk factors and strategies for
prevention. Age and Ageing, 2006. 35(suppl 2): p. ii37-ii41

4. Old age
fall
statistics
INDIA
Ageing in India - 7.7 % > age 60 [3]
Prediction up to 2050- 19%
Prevalence of fall - 47-53% [4]
[3] Krishnaswamy, B. and U. Gnanasambandam, Falls in older people, India, in
National/ Regional review. 2007, Department of Geriatric Medicine, Madras
Medical College and government General Hospital,Chennai City,Tamil Nadu
State, India. p. 19.
4

5. Functional
screening
tests
Timed get Up and GoTest (TUG)
One Leg StanceTest (OLS)
Balance ScaleTest (BQT)
Sit to StandTest (STS)
Grip Strength
5

6. Timed getUp
andGo test
(TUG)
6http://www.strokengine.ca/indepth/tug_indepth/

7. One leg
stance
(OLS)
7http://www.myhealthyfeed.com/2016/03/19/4-signs-you-are-aging-too-fast/

8. Balance
quality test
( BQT)
8http://www.myhealthyfeed.com/2016/03/19/4-signs-you-are-aging-too-fast/

9. 5Times sit
to stand test
9
http://www.drdenizdogan.com/2012/03/5-defa-oturup-kalkma-testi.html

10. Data
collection
• 101 participants (25 females, 76 males)
• Average age 70.099 ± 5.3575 years
• No major medical conditions in last 24
months
• Ethical approval from S.N. Medical
College of Jodhpur
• Collected different functional screening
tests such as OLS, STS,TUG, Gait
Velocity, BQT, Grip strength and
different questionnaire
10

11. Explanatory
data analysis
Descriptive statistics (numerical summaries):
mean, range, variance, skewness, kurtosis
etc.
 Non-parametric tests were used to check
the normality of data.
Graphical methods:
I. frequency distribution histograms
II. scatter plots
III.Normal probability plots
11

12. Bagging
( Bootstrap
aggregating)
 It is an ensemble method: a method of combining
multiple predictors. Used most commonly to provide a
measure of accuracy of a parameter estimate,
especially when the data set is small
12
An example
X=(3.12, 0, 1.57,
19.67, 0.22, 2.20)
Mean=4.46
X1=(1.57,0.22,19.67,
0,0,2.2,3.12)
Mean=4.13
X2=(0, 2.20, 2.20,
2.20, 19.67, 1.57)
Mean=4.64
X3=(0.22, 3.12,1.57,
3.12, 2.20, 0.22)
Mean=1.74

13. Cross
Validation
Used to estimate test set prediction error
rates associated with a given learning
method to evaluate its performance.
The data set is divided into k subsets, and the
method is repeated k times.
Each time, one of the k subsets is used as the
test set and the other k-1 subsets are put
together to form a training set.
Then the average error across all k trials is
computed.
13

14. Previous
works
(Takai etAll)
Sit-to-standTest to Evaluate Knee Extensor Muscle Size and
Strength in the Elderly: A Novel Approach– Takai et. Al
(2009) [5]
 The authors used an equation to estimate muscle power in
a 5 time sit to stand
 The variables used in the power equation were leg length,
body weight, and the time to complete the 5 STS
 Power=
𝐿−0.4 × 𝐵𝑜𝑑𝑦 𝑚𝑎𝑠𝑠 ×5𝑔
𝑡𝑖𝑚𝑒 𝑆𝑇𝑆
14

15. Previous
works
(Smith et.All)
Simple equations to predict concentric lower-body
muscle power in older adults using the 30-second
chair-rise test: a pilot study – Smith et. Al (2010) [6]
 Two variable equation - body weight and number
of stands completed in 30 sec
 Y= B0+B1X1+B2X2+B3X3+B4X4+Error
 X1 – no of stands, X2 – weight, X3 – femur length,
X4 – gender
 X3 and X4 do not contribute significantly
15

16. Prediction
of muscle
power
Linear regression to predict muscle power in
elderly using functional screening data
 Linear regression to develop a predictive model
for muscle power
 Five features – BMI, Gait velocity, STS,TUG, Grip
strength
Y= B0+B1X1+B2X2+B3X3+B4X4+ B5X5+Error
 X1 – BMI, X2 – GaitVelocity, X3 – STS, X4 –TUG,
X5- Grip strength
16

17. Prediction
of muscle
power
Estimation of parameters
 Y= 1.8657+2.3440X1+17.67X2-3.8458X3+0.8633X4+
2.040X5+Error
 X1 – BMI, X2 – GaitVelocity, X3 – STS, X4 –TUG,
X5- Grip strength
 R
2
= 0.7509 F statistics=44.01 P value=0.0000
17

18. Residual
Analysis
The linear regression model
Y= B0+B1X1+B2X2+B3X3+B4X4+Error
The residuals are defined as the n difference.
The random departures ( residuals) are
assumed
i. To have zero mean
ii. To have a constant variance
iii. Independent and follow a
normal distribution.
18

19. Prediction
of muscle
power
Regression to predict muscle power in elderly using
functional screening data
 Improving result by adding non-linearity of STS.
 Y= 82.1964+2.4144X1+5.0720X2-10.7678X3+0.1670X3
2
+
0.3995X4+1.9240X5+Error
 R
2
=0.7900, F statistics= 45.1402, P value=0.000
19
-3
-2
-1
0
1
2
3
-60 -40 -20 0 20 40 60
Q-Q Plot of Power Residual

20. Conclusion
 We use linear regression to predict muscle power
in elderly using functional screening data.
 By predicting muscle power we can easily detect
sarcopenia ( Loss of skeletal muscle mass) in
elderly.
 Sarcopenia is major reason behind functional
decline and fraility in Elderly.
20

21. Thank you
21

22. References
[1] Rubenstein, L.Z., Falls in older people: epidemiology, risk factors and strategies for
prevention. Age and Ageing, 2006. 35(suppl 2): p. ii37-ii41
[2] Heinrich, S., et al., Cost of falls in old age: a systematic review. Osteoporosis
International, 2010. 21(6): p. 891-902.
[3] Krishnaswamy, B. and U. Gnanasambandam, Falls in older people, India, in
National/ Regional review. 2007, Department of Geriatric Medicine, Madras Medical
College and government General Hospital, Chennai City,Tamil Nadu State, India. p.
19.
[4] Dsouza, S.A., et al. Falls in Indian older adults: A barrier to active ageing. Asian
Journal of Gerontology and Geriatrices, 2014. 9, 33-40.
[5]Takai,Yohei, et al. "Sit-to-stand test to evaluate knee extensor muscle size and
strength in the elderly: a novel approach." Journal of physiological anthropology 28.3
(2009): 123-128.
[6] Smith, Wesley N., et al. "Simple equations to predict concentric lower-body
muscle power in older adults using the 30-second chair-rise test: a pilot study." Clin
Interv Aging 5.5 (2010): 173-180.
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