|Year : 2020 | Volume
| Issue : 2 | Page : 200-208
A detailed study on morphological profile, hand grip strength, flexibility, and working postures in housemaids of Kolkata
Piyali Mukherjee1, Anindita Singha Roy2, Amit Bandyopadhyay2, Somnath Gangopadhyay1
1 Occupational Ergonomics Laboratory, Department of Physiology, University of Calcutta, University Colleges of Science and Technology, Kolkata, West Bengal, India
2 Sports and Exercise Physiology Laboratory, Department of Physiology, University of Calcutta, University Colleges of Science and Technology, 92 APC Road, Kolkata, West Bengal, India
|Date of Submission||30-May-2020|
|Date of Decision||13-Jul-2020|
|Date of Acceptance||18-Aug-2020|
|Date of Web Publication||18-Dec-2020|
Dr. Somnath Gangopadhyay
Occupational Ergonomics Laboratory, Department of Physiology, University of Calcutta, University Colleges of Science and Technology, 92 A. P. C. Road, Kolkata - 700 009, West Bengal
Source of Support: None, Conflict of Interest: None
BACKGROUND: House maids belonging to the unorganised sector jobs in the developing countries suffer from serious health consequences due to their awkward working posture.
AIMS AND OBJECTIVES: The study was aimed to evaluate the morphological profile, hand grip strength, flexibility and working posture in housemaids of Kolkata.
MATERIALS AND METHODS: 94 female house maids of Kolkata were recruited in the study and standard procedures were adopted to evaluate the parameters.
RESULTS: Morphological and cardiovascular parameters were within the normal range but hand grip strength was lower than normal reference range. Significant differences in physical parameters, conicity index (CI), waist and hip circumferences, lean body mass (LBM), hand grip strength (HGS) was found in different age groups of house maids. Depending on significant correlation (r=0.89, P<0.001) between percentage of fat and BMI, a linear regression equation has been computed. CI score depicted the tendency of developing central obesity from 30 years onwards while it was evident in 51–60 years' age–group. These indicated their proneness to cardiovascular diseases. Evaluation of centre of gravity (COG) and assessment of working posture depicted erroneous working posture during utensils cleaning, sweeping and mopping. Such changes in COG during different activities revealed unstable and awkward working postures which require immediate attention and correction among the house maids.
CONCLUSION: It is concluded that although the morphological parameters were within the normal range in housemaids of Kolkata but their working postures were erroneous and awkward with poor HGS and they are prone to develop cardiovascular diseases.
Keywords: %fat, body mass index, central obesity, center of gravity, conicity index, unorganized sector
|How to cite this article:|
Mukherjee P, Roy AS, Bandyopadhyay A, Gangopadhyay S. A detailed study on morphological profile, hand grip strength, flexibility, and working postures in housemaids of Kolkata. BLDE Univ J Health Sci 2020;5:200-8
|How to cite this URL:|
Mukherjee P, Roy AS, Bandyopadhyay A, Gangopadhyay S. A detailed study on morphological profile, hand grip strength, flexibility, and working postures in housemaids of Kolkata. BLDE Univ J Health Sci [serial online] 2020 [cited 2021 Apr 14];5:200-8. Available from: https://www.bldeujournalhs.in/text.asp?2020/5/2/200/303972
Approximately 60% of the workers are engaged in jobs at unorganized or informal sectors in the developing countries, and occupational health hazards and discomfort due to awkward working posture are commonly experienced problems among them. This is the chief cause of their health problems which lead to deterioration of work performance., Studies revealed that social and economic factors are also responsible in affecting the performance of workers., Job pattern of housemaids imposes serious health consequences because they work in awkward working posture. The most prevalent one is the musculoskeletal disorders (MSDs) that not only destruct the health but also cause more absenteeism and reduced performance.
Body mass index (BMI) is a good predictor of an individual's body fatness, and waist hip ratio (WHR) gives an impression about the health and the risk of developing serious health conditions. On the other hand, conicity index (CI), an index of abdominal obesity that was developed based on a model of geometric reasoning, proved to be a sensitive and better indicator of risk for hyperlipidemia in Western population than the WHR.
Body composition is an important aspect of health as excess body fat is associated with risks of hypertension, hyperlipidemia, Type 2 diabetes, and cardiovascular diseases. Skeletal muscle is the largest nonadipose tissue component at the tissue-system level of the body composition in humans, and it plays an important role in physical activity and many biochemical processes. Fat-free mass had commonly been used as a surrogate measure of muscle mass but did not always accurately reflect specific changes in muscle mass or differences in muscle mass between individuals.
The center of gravity (COG) is the point in a body or system around which its mass or weight is evenly distributed or balanced and force of gravity acts through this point. In other words, COG of an object is the point at which the whole weight of the object can be considered to act and thus it is a useful tool to analyze human movement and enumerate postural balance. Working patterns of housemaids involve different postures which need to be assessed to enumerate their body's stability during the different phases of their work.
Hand grip strength (HGS) is used clinically in the rehabilitation area and has been recommended as a basic measure in the determination of musculoskeletal function, as well as of weakness and disability., It provides a measure of isometric strength that not only identifies muscle weakness but also provides an impression of overall muscular strength of the upper limb. HGS is considered a good health marker, and it is measured by manual dynamometry which is a simple, fast, inexpensive, and noninvasive test.
It is essential to evaluate the working posture of workers to analyze their working pattern because incorrect working posture leads to MSDs. Rapid upper limb assessment (RULA) that analyzes posture with particular attention to the upper part of the body, i.e., neck, trunk, and upper limbs, has gained immense popularity in survey-based ergonomic investigations, probably for its simplicity.,
Considering the above facts, the present study was aimed to evaluate the morphological profile, HGS, flexibility, and working posture analysis in housemaids of Kolkata, India.
| Materials and Methods|| |
Selection of subjects
Ninety-four housemaids of 20–60 years of age were recruited in this cross-sectional study by random sampling method from different parts of the city of Kolkata, India. They were divided into four age groups, i.e., 20–30 years (n = 38), 31–40 years (n = 33), 41–50 years (n = 9), and 51–60 years (n = 14), to compare their demographic data. They were also divided according to their working experience, i.e., 3–5 years (n = 46), 6–10 years (n = 28), 11–15 years (n = 8), 16–20 years (n = 8), and 21–26 years (n = 4) to find out the relationship of working experience with MSDs in different body parts.
Participants having <3 years of working experience in household works were excluded from this study. The age of each participant was calculated from the “Aadhar Card,” which is the national identification issued by the Government of India. They were paid lost-time compensation for the activities of the study. Individuals under medication, those suffering from chronic orthopedic or cardiorespiratory disorders, or those suffering from any other major diseases were excluded from the study. Informed consent was obtained from each worker. Ethical approval for this study was obtained from the Human Research Ethics Committee of Department of Physiology, University of Calcutta.
Measurement of physical parameters
The body height and body weight were measured by using the weighing machine and anthropometric rod, respectively. The BMI and body surface area (BSA) of the participants were calculated by using the standard formulae., The waist and hip circumferences were measured by a measuring tape and the values were used to calculate WHR as well as CI by using the following formulae:
WHR = Waist circumference (cm): hip circumference (cm)
CI = Waist circumference (m)/0.109 v weight (kg)/height (m)
Determination of flexibility
Flexibility was measured by modified sit and reach test. The participant stretched out the leg by sitting on the floor without shoes. The soles of the feet were placed flat against a box. Both knees were locked and pressed flat to the floor. With the palms facing downward, and the hands on the top of each other or side by side, the participant reached forward along the measuring line as far as possible. It was ensured that the hands remain at the same level, not one reaching further forward than the other. After some practice trials, the participant reached out and held in that position for about 2 s, while the distance was recorded. No jerky movement was allowed during this measurement.
Determination of body composition
Body composition was determined by skinfold measurement with the help of a skinfold caliper with constant tension (Holtain Limited, Crosswell, UK). The following formula was used to determine the fat percentage.
%body fat or %fat= (0.41563 × X1) - (0.00112 × X12) + (0.03661 × X2) + 4.03653
(X1 = sum of triceps, abdominal and suprailliac skinfolds and X2 = age in nearest years).
Total body fat, percentage of lean body mass (%LBM), and total LBM were calculated using the following equations:
Total fat or TF (kg) = %fat/100 × body mass (kg)
%LBM = 100 - %fat
LBM (kg) = Body mass (kg) - total fat (kg).
Measurement of hand grip strength
The hand-grip dynamometer was used to record the grip strength of each hand. Participants hold the dynamometer in the hand to be tested, with the arm at right angles and the elbow by the side of the body. When ready, the participant squeezed the dynamometer with maximum isometric effort. No other body movement was allowed.
Working posture analysis
Working postures were analyzed by RULA method and rapid entire body assessment (REBA) method.
Application of rapid upper limb assessment method
RULA provides an easily calculated rating of musculoskeletal loads in tasks where people have a risk of neck and upper limb loading. RULA is used to assess the posture, force, and movement associated with sedentary tasks. Such tasks include screen-based or computer tasks, manufacturing or retail tasks where the workers are seated or standing without moving apart. The four main application of RULA are to:
- Measure musculoskeletal risks, usually as a part of broader ergonomic investigation
- Compare the musculoskeletal loading of current and modified workstation designs
- Evaluate outcomes, such as productivity or suitability of equipment
- Educate workers about musculoskeletal risks created by different working postures.
Application of rapid entire body assessment method
This is the ergonomic assessment tool that is used as a systematic process to evaluate whole-body postural MSD and risks associated with job task. A single page work sheet is used to evaluate required or selected body posture, forceful exertions, type or movement of action, repetition, and coupling.
The REBA worksheet is divided into two body segment sections on the labeled A and B. Section A (left side) covers the neck, trunk, and leg. Section B (right side) covers the arm and wrist. This segmenting of the worksheet ensures that any awkward or constrained postures of the neck, trunk or legs, which might influence the postures of the arms and wrist, which are included in the assessment. Scores of trunk, neck and legs were used to compute the Score Group A. On the other hand scores of upper arms, lower arms and wrists were used to compute the Score Group B. These A and B Group scores were used to determine the C score that in turn reflected the REBA score depending on the activity level as mentioned in the REBA Worksheet. For each region, there are a posture scoring scale and additional adjustments which need to be considered and accounted for in the score.
Assessment of center of gravity
The centre of gravity (COG) was determined by segmental method. Once COG, or center of mass, is determined for the body or group of segments moving as a unit, COG can be treated as a single mass resting on that point. As the participant's segmental stats are not known, an average of these stats was used.
X = Distance of COG from fulcrum or arbitrary axis
W = Weight (percentage)
Xtotal ×× Wtotal = X1W1 + X2W2 + X3W3...XnWn
Xtotal = X1W1/Wtotal + X2W2/Wtotal + X3W3/Wtotal...XnWn/Wtotal
Xtotal = S (Wseg × Xseg)/Wtotal entire body
When determining the COG for the total body (two dimensions only):
- The procedure was started with tracing or printing the photograph of the participant in the exact position to be analyzed
- A larger image helped to reduce the measurement error
- Dots were drawn on joints representing proximal and distal points
- These dots were connected with straight lines
- COG for each body segment was distinctly marked on each line
- Overlaid grid on transparency over markings was drawn
- An arbitrary reference point was chosen as the origin of coordinate system
- Lower left point = 0
- X and Y (horizontal and vertical) axes.
- The distances of each segmental COG from X and Y axes were measured
- Each segments of weight (as percentage) was listed
- Distance between COG was calculated (distances from X and Y axes separately) (formula above)
- Each total distance from their respective axes was measured and plotted
- Two perpendicular lines parallel to their axes were drawn through their points
- The intersection of the two lines represented the total body's COG through those two dimensions.
Data were expressed as mean ± standard deviation. One-way repeated-measures analysis of variance followed by Bonferroni post hoc analysis was adopted to compute the significance of difference in mean values obtained in different age groups. Chi-square test was employed to test the relationship between working experience and pain in different body parts. The level of significance was set at P < 0.05. The entire statistical analysis was done by using? SPSS 16 software (SPSS Software, Version 21, IBM, USA).
| Results|| |
The present study was conducted on 94 female housemaids who successfully completed the questionnaire and physical examination methods. Their physical parameters, BMI, CI, BSA, heart rate, and blood pressure, are presented in [Table 1]. The body mass (F = 3.07), body height (F = 5.48), and BSA (F = 3.81) depicted statistically significant difference (P < 0.05) in the age group of 41–50 years, while CI revealed significant difference (F = 3.36) in 31–40 years, 41–50 years and 51–60 years, respectively. The resting heart rate and blood pressure were also within the normal range in all the age groups [Table 1]. Details of the post hoc analysis of body height, body weight and BSA parameters are presented in Table 1a-c, respectively.
Values of different morphological parameters and HGS are presented in [Table 2]. Waist and hip circumferences were significantly different (F = 3.70, F = 2.71) in the age group of 41–50 years, while WHR depicted significant difference in the age group of 51–60 years. Sum of skinfolds, %fat, total fat, and %LBM did not show any significant intergroup variation. However, LBM showed significant higher value in the age group of 41–50 years. LBM showed statistically significant (P < 0.05) difference in the age group of 41–50 years. HGS of both the hands was significantly different (F = 6.59, F = 4.07) in the age group of 31–40 years, while 41–50 years and 51–60 years age groups showed statistically significantly (P < 0.05) different value of HGS in left hand only. Details of the post hoc analysis of waist circumference, hip circumference, WHR, LBM, left HGS, and right HGS are presented in [Table 2]a,[Table 2]b,[Table 2]c,[Table 2]d,[Table 2]e,[Table 2]f, respectively.
Assessment of COG by segmental method depicted different scores at different working postures of the housemaids [Table 3]. The analyses of working posture by RULA and REBA methods are presented in Tables 4 and 5, respectively. These indicated that the housemaids work in awkward working posture.
|Table 3: Center of gravity during different working postures in 20-60-year-old housemaids from Kolkata, India|
Click here to view
BMI and %fat are good predictors of body fatness. Pearson's product moment correlation was adopted to find out whether any statistically significant relationship exists between these two parameters. A statistically significant positive correlation (r = 0.89, P < 0.001) was obtained between BMI and %fat. Linear regression analysis was also conducted to compute the regression equation for prediction of %fat from BMI score in the studied population [Figure 1].
|Figure 1: Relationship between body mass index and %fat in housemaids aged 20–60 years|
Click here to view
Flexibility scores in all the age groups of housemaids were within the normal range and did not show any significant intergroup variation [Figure 2].
| Discussion|| |
The body weight and body height were significantly higher in the age group of 41–50 years and that is the reason of having significantly higher BSA in the same age range. BMI, heart rate, and blood pressure were within the normal range and did not show any significant intergroup variation [Table 1].
BMI of all the age groups in the studied population was found to be within normal range. Waist and hip circumferences were significantly higher in the age group of 41–50 years although WHR showed significant intergroup variation in 51–60 years of age group. It is difficult to postulate the exact cause of such finding [Table 2]. However, the WHR was within normal range only in the age group of 20–30 years, while all the other three age groups had WHR values higher than the normal cutoff value of 0.85. This finding is also supported with the finding of CI which is another marker of central obesity and was found to be significantly different in all the three age groups, which also showed more than cut–off value of WHR. Both these findings indicated that the prevalence of central obesity existed among housemaids from Kolkata, particularly belonging to the ages ranging between 31 and 60 years. However, BMI and %fat, which are also good predictors of obesity, were within the healthy zone. This clearly indicated that the housemaids belonging to the age group of 31–60 years, had developed abdominal fat and they may be prone to cardiovascular diseases.
Body composition parameters did not show any significant intergroup variation except in case of LBM that showed significantly higher value in 41–50 years, probably due to their higher body weight [Table 2].
HGS values observed in all the age groups of the currently studied population were lower than the normal value. HGS is influenced by age, sex, anthropometric variables (height, weight, hand size, and arm circumference), and hand dominance and is associated with different health outcomes, especially in elderly people.,, It plays an important role in the evaluation of clinical and surgical treatment prognoses,, functional evaluation of the elderly, identification of potential sports talents and in the composition of the battery of admission tests in different professional activities such as police, armed forces, and fire brigade.,,,, Thus, the availability of reliable and up-to-date population reference values to which individuals can be compared is paramount.
The establishment of HGS reference values for different populations makes it possible to detect differences between them but also serves to subsidizing efforts to construct more comprehensive or generalizable reference values. The understanding of the behavior of HGS in the population is important to create parameters in physical rehabilitation programs, as well as for the exploration of HGS levels, discriminating the risk of occurrence of health conditions.
There is a growing number of studies reporting HGS reference values, but their generalization is hampered by the variability of measurement instruments and protocols, differences between baseline populations, and use of nonrepresentative samples.,,,, Brazilian studies published on reference values for the population are neither comparable nor generalizable to the entire Brazilian population, which presents diverse ethnic variation. It is possible to observe very different HGS patterns from one region to another in the country.,
Human body is constructed in an irregular shape. The COG of the human body will move from its location if the body parts change their position. COG score is lowest when the human body gets the base of support on a largest area. Apparently, the stability of an object is affected by the height of the COG, size of base of support, and the weight of the object. When standing upright, the COG of an adult is located inside the body close to the level of navel (56.4% ± 2.8% of stature for women and 57.1% ± 2.3% for men) and midway between the front and the back of the body. In the present study, the observed COG in standing erect posture was 57.45% ± 0.83%, which is within the normal range of COG of human body. However, the value was lowered to 46.75% ± 2.43% and 55.83% ± 0.82% during utensils cleaning and mopping, respectively [Table 3]. On the other hand, when they were engaged in sweeping, the COG score got increased to 63.91% ± 0.70%. The lowering of COG during utensils cleaning and mopping was probably due to increase in the base of support, while the increase in COG during sweeping was attributed to their standing forward bending posture and decrease in the base of support. Location of COG also changed with the postural changes during their work. In case of mopping and utensil cleaning, the COG descended, while during sweeping, it ascended, causing unstable working posture in the latter case.
RULA index is one of the most cited and commonly used tools for evaluating ergonomic risk of work-related MSDs., RULA is a subjective observation method for posture analysis that focuses on the upper part of the body with the particular attention to the neck, trunk, and upper limbs.,, It has been proposed as a valid method for RULA.
In the present study, the working posture during utensil cleaning, mopping, and sweeping was analyzed by the method of RULA and REBA [Table 4] and [Table 5]. The stick diagrams, obtained from the still photographs of the most adopted working postures of the domestic maids, showed that they were in erroneous posture while doing their work. The RULA scores [Table 4] entail that the working postures of the domestic maids obtain the grand scores of 6 and 7 during utensil cleaning and mopping, respectively, which correspond to action levels 3 and 4, respectively. On the other hand, the REBA score [Table 5] entails that the working posture of the domestic maids obtained the grand score of 10 during sweeping, and this corresponds to required action level 4. These scores of RULA and REBA indicated that it is essential to recommend them proper correction of posture on a urgent basis. The corresponding corrective measures to be taken against each action level are indicated in [Table 4].
|Table 4: Analysis of working posture by using rapid upper limb assessment method|
Click here to view
|Table 5: Analysis of working posture by using rapid entire body assessment method|
Click here to view
BMI and %fat are good predictors of body fatness. An attempt was made to find out the relationship between BMI and %fat [Figure 1]. It was observed that highly statistically significant positive correlation (r = 0.89, P < 0.001) existed between these two parameters. Regression norm was computed for the prediction of %fat from BMI score in the studied population. The standard error of estimate was substantially small enough to recommend this regression equation for prediction of %fat from BMI in the studied population. The flexibility scores obtained in the different age groups of the housemaids were within the normal range and no significant intergroup difference was noted in this parameter [Figure 2].
| Conclusion|| |
The housemaids had normal range of BMI, CI, heart rate, and blood pressure, which apparently indicated that they were not suffering from any disease(s). However, as reflected from their CI, they had developed central obesity in the age range of 51–60 years and developed the tendency of central obesity as reflected from the significantly higher waist and hip circumferences after the age of 30 years. Although BMI and %fat were within normal range, age-wise increase in WHR indicated their proneness to cardiovascular diseases at the later phase of life. Their HGS was lower than the normal range, indicating that their muscular strength was not up to the level to sustain the workforce. Domestic maids work in erroneous posture during utensil cleaning, sweeping, and mopping. This needs attention for the correction of working posture. COG of housemaids in standing erect posture was within the normal range, but the location of COG changed during their different working postures due to changes in the base of support and standing forward-bending posture. Ascend of COG caused unstable working posture during sweeping activity. RULA and REBA scores depicted that they were working in awkward working postures and these need to be attended with remedial measures as early as possible.
Declaration of patient consent
The authors certify that they have obtained all appropriate patient consent forms. In the form the patient(s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Gangopadhyay S, Dev S. Design and evaluation of ergonomic interventions for the prevention of musculoskeletal disorders in India. Ann Occup Environ Med 2014;26:18.
Gangopadhyay S, Dev S, Das T, Ghoshal G, Ara T. An ergonomics study on the prevalence of musculoskeletal disorders among Indian bus conductors. Int J Occup Saf Ergon 2012;18:521-30.
Taieb-Maimon M, Cwikel J, Shapira B, Orenstein I. The effectiveness of a training method using self-modelling webcam photos for reducing musculoskeletal risk among office workers using computers. Appl Ergon 2012;43:376-85.
Punnett L, Wegman DH. Work-related musculoskeletal disorders: the epidemiologic evidence and the debate. J Electromyogr Kinesiol 2004;14:13-23.
Mali SC, Vyavahare RT. An ergonomic evaluation of an industrial workstation: A review. IJCET 2015;5:1820-6.
Mantzoros CS, Evagelopoulou K, Georgiadis EI, Katsilambros N. Conicity index as a predictor of blood pressure levels, insulin and triglyceride concentrations of healthy premenopausal women. Horm Metab Res 1996;28:32-4.
Douris P, Chinan A, Gomez M, Aw A, Steffens W, Weiss S. Fitness levels of middle aged martial art practitioners. Br J Sports Med 2004;38:143-7.
Andreoli A, Monteleone M, Loan MV, Promenzio L, Tarantino U, Lorenzo AD. Effects of different sports on bone density and muscle mass in highly trained athletes. Med Sci Sports Exerc 2011;33:507-11.
Andreoli A, Scalzo G, Masala S, Tarantino U, Guglielmi G. Body composition assessment by dual-energy X-ray absorptiometry (DXA). Radiol Med 2009;114:286-300.
Schlüssel MM, dos Anjos LA, de Vasconcellos MT, Kac G. Reference values of handgrip dynamometry of healthy adults: A population-based study. Clin Nutr 2008;27:601-7.
Bohannon RW, Magasi S. Identification of dynapenia in older adults through the use of grip strength t-scores. Muscle Nerve 2015;51:102-5.
Bohannon RW. Are hand-grip and knee extension strength reflective of a common construct? Percept Mot Skills 2012;114:514-8.
Bohannon RW. Hand-grip dynamometry predicts future outcomes in aging adults. J Geriatr Phys Ther 2008;31:3-10.
Guerra RS, Amaral TF, Sousa AS, Pichel F, Restivo MT, Ferreira S, et al
. Handgrip strength measurement as a predictor of hospitalization costs. Eur J Clin Nutr 2015;69:187-92.
Fernandes AA, Marins JC. Handgrip strength test: Methodological analysis and normative data in athletes. Fisioter Mov 2011;24:567-8.
Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: International survey. BMJ 2000;320:1240-3.
Banerjee S, Sen RN. Determination of surface area of body of Indians. J Appl Physiol 1955;7:585-8.
Roy AS, Bandyopadhyay A. Effect of Ramadan intermittent fasting on selective fitness profile parameters in young untrained Muslim men. BMJ Open Sport Exerc Med 2015;1:e000020.
Jackson AS, Pollock M. Practical assessment of body composition. Physician Sport Med 1985;13:76-90.
Cotten DJ, Johnson A. Use of the T-5 cable tensiometer grip attachment for measuring strength of college men. Res Q 1970;41:454-6.
McAtamney L, Nigel Corlett E. RULA: A survey method for the investigation of work-related upper limb disorders. Appl Ergon 1993;24:91-9.
Hignett S, McAtamney L. Rapid entire body assessment (REBA). Appl Ergon 2000;31:201-5.
Kreighbaum E, Barthels KM. Biomechanics: A Qualitative Approach for Studying Human Movement. Boston (Mass): Allyn Bacon; 1996. p. 586-7.
De K. Waist hip ratio and body mass index in assessing nutritional status and central obesity of adolescent. GJAA 2017;1:1-4.
Amaral CA, Amaral TL, Monteiro GT, Vasconcellos MT, Portela MC. Hand grip strength: Reference values for adults and elderly people of Rio Branco, Acre, Brazil. PLoS One 2019;14:e0211452.
Massy-Westropp NM, Gill TK, Taylor AW, Bohannon RW, Hill CL. Hand grip strength: Age and gender stratified normative data in a population-based study. BMC Res Notes 2011;4:127.
Chandrasekaran B, Ghosh A, Prasad C, Krishnan K, Chandrasharma B. Age and anthropometric traits predict handgrip strength in healthy normals. J Hand Microsurg 2010;2:58-61.
Flood A, Chung A, Parker H, Kearns V, O'Sullivan TA. The use of hand grip strength as a predictor of nutrition status in hospital patients. Clin Nutr 2014;33:106-14.
Mathiowetz V, Kashman N, Volland G, Weber K, Dowe M, Rogers S. Grip and pinch strength: Normative data for adults. Arch Phys Med Rehabil 1985;66:69-74.
Josty IC, Tyler MP, Shewell PC, Roberts AH. Grip and pinch strength variations in different types of workers. J Hand Surg Br 1997;22:266-9.
Kenny RA, Coen RF, Frewen J, Donoghue OA, Cronin H, Savva GM. Normative values of cognitive and physical function in older adults: findings from the Irish Longitudinal Study on Ageing. J Am Geriatr Soc 2013;61:S279-90.
Roberts HC, Denison HJ, Martin HJ, Patel HP, Syddall H, Cooper C, et al
. A review of the measurement of grip strength in clinical and epidemiological studies: towards a standardised approach. Age Ageing 2011;40:423-9.
Harrison Clark H. Objective strength test of affected muscle group involved in orthopedic disabilities. Res Q 1948;19:118-47.
Dodds RM, Syddall HE, Cooper R, Kuh D, Cooper C, Sayer AA. Global variation in grip strength: A systematic review and meta-analysis of normative data. Age Ageing 2016;45:209-16.
Dias JA, Ovando AC, Ku¨lkamp W, Junior B, Gomes N. Hand grip strength: Evaluation methods and factors influencing this measure. Rev Bras Cineantropom Desempenho Hum 2010;12:209-16.
Budziareck MB, Pureza Duarte RR, Barbosa-Silva MC. Reference values and determinants for handgrip strength in healthy subjects. Clin Nutr 2008;27:357-62.
Watkins J. Fundamental biomechanics of sport and exercise. Hoboken: Taylor and Francis; 2014.
Vignais N, Miezal M, Bleser G, Mura K, Gorecky D, Marin F. Innovative system for real-time ergonomic feedback in industrial manufacturing. Appl Ergon 2013;44:566-74.
Meksawi S, Tangtrakulwanich B, Chongsuvivatwong V. Musculoskeletal problems and ergonomic risk assessment in rubber tappers: A community-based study in southern Thailand. Int J Ind Ergonom 2012;42:129-35.
Dockrell S, O'Grady E, Bennett K, Mullarkey C, Mc Connell R, Ruddy R, et al
. An investigation of the reliability of Rapid Upper Limb Assessment (RULA) as a method of assessment of children's computing posture. Appl Ergon 2012;43:632-6.
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]