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Prediction Of Muscle Power In Elderly Using Functional Screening Data

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Prediction Of Muscle Power In Elderly Using Functional Screening Data by "Vivek Vijay" From IIT, Jodhpur .The presentation was done at #doppa17 DevOps++ Global Summit 2017. All the copyrights are reserved with the author.

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Prediction Of Muscle Power In Elderly Using Functional Screening Data

  1. 1. Prediction of muscle power in elderly using functional screening data Indian Institute of Technology Jodhpur 1 By Dr Vivek Vijay & Brajesh Shukla
  2. 2. Ageing What happens when people age? A. Functional decline B. Loss of muscle strength and power C. Falling 2
  3. 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. 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. 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. 6. Timed getUp andGo test (TUG) 6http://www.strokengine.ca/indepth/tug_indepth/
  7. 7. One leg stance (OLS) 7http://www.myhealthyfeed.com/2016/03/19/4-signs-you-are-aging-too-fast/
  8. 8. Balance quality test ( BQT) 8http://www.myhealthyfeed.com/2016/03/19/4-signs-you-are-aging-too-fast/
  9. 9. 5Times sit to stand test 9 http://www.drdenizdogan.com/2012/03/5-defa-oturup-kalkma-testi.html
  10. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 21. Thank you 21
  22. 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. 22

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