Prediction of Hand Grip Strength among Elderly Farmers of Odisha in India

Mukherjee, Saikat; Mishra, Debesh

doi:10.1016/j.matpr.2020.04.281

Cited by 3 publications

(5 citation statements)

References 17 publications

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“…In the multiple linear regression model developed, the coefficient of determination of the FF variable was high only in the HGS test regression model [57.708 − (9.602 × sex male=1; female=2 ) + (0.315 × age) − (0.256 × percent body fat) − (0.528 × BMI)] with a mean explanatory power of 77.3% (adjusted R 2 ). This HGS mean explanatory power was higher than the ones of previously developed equations in India (52.25%; Mukherjee et al, 2020 ) and Taiwan [53.3% ( Pan et al, 2020 )] indicating that the multiple linear regression model formulation by Kim et al was more accurate and straightforward than the predictive power of previous studies. However, the coefficients of determination in the 30-s chair stand, chair sit-and-reach, figure of 8 walk, timed up-and-go, and 2-min step tests were significantly low to moderate, indicating difficulty in predicting the remaining FF variables (i.e., lower body strength and flexibility, coordination, agility/dynamic balance, and aerobic endurance, respectively) in older adults using easy to measure independent variables.…”

Section: Predicting Functional Fitness Through a Multiple Linear Regr...contrasting

confidence: 59%

Editorial: ICT-based training intervention for healthy aging: ITIHA

Ammar

Trabelsi²,

Glenn³

2022

Front. Physiol.

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Section: Predicting Functional Fitness Through a Multiple Linear Regr...contrasting

confidence: 59%

Editorial: ICT-based training intervention for healthy aging: ITIHA

Ammar

Trabelsi²,

Glenn³

2022

Front. Physiol.

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“…The mean explanatory power of HGS without outlier data was 78.4%, which was the highest value in the DNN model (MLR: 77.3%, SVM: 78.4%, RF: 74.2%, XGBoost: 78.3%, DNN: 78.4%, MDN: 78.3%). Our proposed regression model’s explanatory power of HGS was improved by approximately 25% compared with previous studies [ 25 , 26 ]. In our previous study, we developed a linear regression model for predicting FF variables of South Korean older adults [ 17 ].…”

Section: Discussionmentioning

confidence: 78%

Estimation of Functional Fitness of Korean Older Adults Using Machine Learning Techniques: The National Fitness Award 2015–2019

Lee

Kim

et al. 2022

IJERPH

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Measuring functional fitness (FF) to track the decline in physical abilities is important in order to maintain a healthy life in old age. This paper aims to develop an estimation model of FF variables, which represents strength, flexibility, and aerobic endurance, using easy-to-measure physical parameters for Korean older adults aged over 65 years old. The estimation models were developed using various machine learning techniques and were trained with the National Fitness Award datasets from 2015 to 2019 compiled by the Korea Sports Promotion Foundation. The machine-learning-based nonlinear regression models were employed to improve the performance of the previous linear regression models. To derive the optimal estimation model that showed the best estimation accuracy, we developed five different machine-learning-based estimation models and compares the estimation accuracy not only among the machine learning models, but also with the previous linear regression model. The coefficient of determination of the FF variables was used to compare the performance of each model; the mean absolute percentage error (MAPE) and standard error of estimation (SEE) were used to evaluate the model performance. The deep neural network (DNN) model presented the best performance among the regression models for the estimation of all of the FF variables. The coefficient of determination in the HGS test was 0.784, while those of the others were less than 0.5 meaning that the HGS of older adults can be reliably estimated using easy-to-measure independent variables.

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“…In our study, the mean explanatory power of the HGS test regression model [57.708 − (9.602 × sex male = 1; female = 2 ) + (0.315 × age) − (0.256 × percent body fat) − (0.528 × BMI)] was 77.3% (adjusted R 2 ). In a previous study, 14 anthropometric dimension variables (i.e., weight, stature, eye, mid-shoulder, acromion, cervical, mid-shoulder, elbow rest, knee height, popliteal, hand length, palm length, hand breadth with thumb, and grip inside diameter maximum) predicted 52.25% (adjusted R 2 ) of the HGS of elderly individuals ( n = 38) in India ( Mukherjee et al, 2020 ). In another study, the independent variables of height, sex, age, exercise time, weight, waist circumference, diabetes mellitus, heart disease, and living status accounted for 53.3% (R 2 ) of the variance of the HGS in elderly individuals (age: 65 years and older; total: n = 2,470; men: n = 998; women: n = 1,472) in Taiwan ( Pan et al, 2020 ).…”

Section: Discussionmentioning

confidence: 88%

“…In terms of healthcare, it would be helpful to develop tools that can measure and evaluate FF in daily life. In previous studies, most of the FF variables in older adults were compared with FF measurement parameters developed or regression equations targeting relatively small numbers of samples ( Mukherjee et al, 2020 ; Pan et al, 2020 ; Yee et al, 2021 ). Herein, we aimed to develop a multiple linear regression model for estimating FF variables in Korean older adults using easy-to-measure independent variables.…”

Section: Discussionmentioning

confidence: 99%

“…The European Working Party on Sarcopenia in Older People recommends the HGS as the most common practical measure of muscle strength available in clinical practice ( Cruz-Jentoft et al, 2010 ). The HGS has been used to evaluate various anthropometric factors ( Mukherjee et al, 2020 ). In our study, the mean explanatory power of the HGS test regression model [57.708 − (9.602 × sex male = 1; female = 2 ) + (0.315 × age) − (0.256 × percent body fat) − (0.528 × BMI)] was 77.3% (adjusted R 2 ).…”

Section: Discussionmentioning

confidence: 99%

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Development of Functional Fitness Prediction Equation in Korean Older Adults: The National Fitness Award 2015–2019

et al. 2022

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The main advantage of measuring functional fitness (FF) in older adults is that individual tests can estimate and track the rate of decline with age. This study aimed to develop a multiple linear regression model for predicting FF variables using easy-to-measure independent variables (e.g., sex, age, body mass index, and percent body fat) in Korean older adults. National Fitness Award datasets from the Republic of Korea were used in this analysis. The participants were aged ≥65 years and included 61,465 older men and 117,395 older women. The FF variables included the hand grip strength, lower body strength (30-s chair stand), lower body flexibility (chair sit-and-reach), coordination (figure of 8 walk), agility/dynamic balance (timed up-and-go), and aerobic endurance (2-min step test). An estimation multiple linear regression model was developed using a stepwise technique. In the regression model, the coefficient of determination in the hand grip strength test (adjusted R2 = 0.773, p < 0.001) was significantly high. However, the coefficient of determination in the 30-s chair stand (adjusted R2 = 0.296, p < 0.001), chair sit-and-reach (adjusted R2 = 0.435, p < 0.001), figure of 8 walk (adjusted R2 = 0.390, p < 0.001), timed up-and-go (adjusted R2 = 0.384, p < 0.001), and 2-min step tests (adjusted R2 = 0.196, p < 0.001) was significantly low to moderate. Our findings suggest that easy-to-measure independent variables can predict the hand grip strength in older adults. In future studies, explanatory power will be further improved if multiple linear regression analysis, including the physical activity level and nutritional status of older adults, is performed to predict the FF variables.

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Prediction of Hand Grip Strength among Elderly Farmers of Odisha in India

Cited by 3 publications

References 17 publications

Editorial: ICT-based training intervention for healthy aging: ITIHA

Editorial: ICT-based training intervention for healthy aging: ITIHA

Estimation of Functional Fitness of Korean Older Adults Using Machine Learning Techniques: The National Fitness Award 2015–2019

Development of Functional Fitness Prediction Equation in Korean Older Adults: The National Fitness Award 2015–2019

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