Abstract
BACKGROUND:
Normative data on Hand grip strength has a wide range of application and is of great value.
OBJECTIVE:
To establish gender and age-specific reference data concerning hand grip strength of Iranian office workers, to explore possible relationships with demographic and anthropometric factors and to develop appropriate predictive models.
METHODS:
The study population included 418 (220 males and 198 females) Iranian office employees. They were divided into 5-year age-groups. Hand length, palm width, palm length, forearm length, wrist circumference, and forearm circumference were measured by means of a digital Caliper (±0.01 mm) and a tape meter (±0.1 cm). The value of hand grip strength was measured by JAMAR hydraulic dynamometer respecting the American Society of Hand Therapy recommendations.
RESULTS:
The average of grip strength for dominant and non-dominant hands (in Kg) respectively was 51.10±9.50 and 46.90±9.89 for male and 28.76±4.47 and 25.89±4.60 for female. Age was curvilinearly related to hand grip strength. All selected hand dimensions were highly correlated with grip strength; palm width, palm length and hand length being the most correlated ones, respectively. Prediction equations of hand grip strength were developed for dominant and non-dominant hands of both genders.
CONCLUSIONS:
Suggested norms would provide more accuracy for ergonomic designers as well as health practitioners especially with regards to proposed prediction models with which grip strength could be estimated faster and easier.
Introduction
Reference values have a vital importance in today’s various scientific aspects. Ergonomic designing needs population norms to guarantee health, safety, productivity and pleasure for all types of users including workforces as well as consumers [1]. Normative data of Hand Grip Strength (HGS) is required to determine design parameters concerning activation forces in hand tools [2]. HGS is defined as the maximum voluntary contraction (MVC) force that human hands and forearm muscles can attain [3]. It is also introduced as a main indicator for evaluating neuromuscular disorders [4, 5], functionality of upper extremities and severity of musculoskeletal diseases [6, 7], cardiovascular disorders [8]; and ageing consequences like general illness and inabilities [9, 10].
HGS test is typically conducted by JAMAR hydraulic dynamometer which is claimed to be highly valid and reliable [11, 12]. The hydraulic dynamometer records HGS in kilograms or pounds of force [13].
Previous studies have demonstrated that HGS can be influenced by several factors. Age, gender, height, weight, body mass index (BMI), muscle size, and handedness are among the most investigated factors. There is a general accordance that HGS has a curvilinear relationship with age, increases linearly with height, weight and BMI; and is significantly greater in males than females and in dominant hand compared to the non-dominant one [14–17]. Hand anthropometric dimensions are also found to be related with grip strength. A general assumption exists that hand grip strength increases with hand size [18, 19]. More precisely, palm length, palm width, hand length, wrist circumference and forearm circumference are found to be related with HGS [20–23].
Remarkable differences are also reported between the hand grip capacities of different populations. For example, a couple of studies have already found that manual workers have higher grip strength compared to non-manual workers [24, 25]. Indeed, it seems that hand grip capacity varies depending on ethnicity and nationality [26]. Therefore, in order to ensure the best possible design to fit the required manual force during the activity to the operators grip capacity, it is logical to use appropriate grip strength norms for each population.
Since office employees are handling most of their activities with their hands, where these activities are mainly along with hand grip, thus hand grip strength evaluation is known as one of the significant component of the individual performance ability. This measurement and investigation are highlighted when office employees constitute a high population percentage of the occupational group in such societies. Since, the large number of Iranian occupational population is office employees; therefore performance evaluation of this occupational group can give useful information for industrial designers, ergonomists, and occupational medicine experts.
To the best of the authors’ knowledge, HGS measurements of Iranian office employees are not performed up to yet. Therefore, In order to cover the shortcomings of essential reference values, the first aim of the present study is to establish hand grip norms of Iranian office workers stratified by gender, age-group and hand dominancy. Latterly, we are looking for the correlation between hand grip strength and different demographic (age, gender, hand dominancy) and anthropometric (height, weight, BMI, hand length, palm width, palm length, forearm length, wrist and forearm circumference) variables. We further aim to investigate the predictors of HGS among the study population and to develop the appropriate predictive equations.
Material and methods
Subjects
A minimum sample size of 384 was reached at using the Cochran’s formula, n = t2pq/d2, the required sample size, where t = standard deviation of 1.96 (corresponding to a 95% confidence interval), p = 0.50, q = (1–p), and d is the degree of accuracy desired (0.05 for an acceptable error margin of 5%) [27]. The sample size was increased by around 10% to account for possibly missing and/or incorrect data. Finally, a total of 418 office employees (220 males and 198 females) volunteered to participate in this study. Subjects were recruited from a variety of local organizations and agencies through convenience sampling on the basis of spending most of their working time on routine office work. Employees’ duties were assigned in accordance with the office procedures and included as typing or word processing, computer using, answering telephones, book keeping, stenography, office machine operation, and filling. The under study sample was classified into five year interval age-groups (i.e. 20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59).
Based on self-reported medical history, the following inclusion criteria was respected: (i) no history of any surgery in hand, neck or upper extremities, (ii) no neurological disorders, (iii) no history of fracture in hand/arm areas, and upper limb injury/deformities over the past 6 months, (iv) no manual hard working during the day.
Procedure
The study was approved by the regional ethical committee (IR.SBMU.REC.1396.83). Prior to the testing, all subjects were informed from the purpose of the study and the ethical consent was signed by each subject thereafter. All measurements were performed by a sole expert previously trained in the administration of HGS test and anthropometric measurements. Participants were asked to wear comfortable cloths and to remove all extra objects such as jewelry or wrist watch during the test. Subjects with long fingernails had been excluded from testing in order to avoid any bias in results [28].
The age of subjects was obtained from registered data available in their relevant organizations. The standard techniques recommended in the NASA anthropometric source book [29] were respected for measuring the selected hand anthropometric dimensions as following: Hand length (the straight distance between the crease of the wrist and the tip of the middle finger); Palm width (measured from the edge of the hand on one side, across the palm to the edge of the hand on the other side, at the level of the metacarpophalangeal joints with the fingers parallel and extended); Palm length (the distance from the base of the hand at the wrist crease to the furrow at the base of the middle finger); Forearm length (Length of the forearm or middle (mesomelic) segment of the arm); Wrist circumference (the circumference of the wrist, measured at the level of the tip of the styloid process of the radius); Forearm circumference (the circumference of the forearm at approximately 3.8–5 cm below the distal crease of elbow).
Grip strength test was conducted according to the methodology proposed by American Society of Hand Therapy (ASHT) [30]. So that, subjects were instructed to sit on a chair and rest on it, with their shoulders adducted and neutrally rotated, elbow flexed to 90 degrees, forearm and wrist in neutral position (0–15 degrees of extension and 0–15 degrees of ulnar deviation). The HGS evaluating test was repeated 3 times for dominant and non-dominant hands of each participant (with a 1 minute interval as a rest time), respectively, whereas the average of them was calculated and recorded as HGS value for the analysis.
In order to avoid any residual effect of fatigue, participants were asked whether or not they were subjected to any heavy manual work before testing. If yes, they were excluded from the study.
Equipment
The value of hand grip strength was measured by means of a JAMAR hydraulic dynamometer (Hersteller/manufactures; SEHAN Corporation, Masan-Korea; Distributer Rehaforum Medical GmbH, Elmshorn-Germany). A digital caliper (±0.01 mm) was used to measure hand length, palm width and length; while a tape meter (±0.1 cm) was served for measuring forearm length, wrist and forearm circumferences. The same instruments were used for all tests. Using a stadiometer, height was nearly recorded to 0.1 cm and weight was measured to around 0.1 kg. Accordingly, Body Mass Index (BMI) was calculated in kg/m2. The calibrations of equipment was conducted prior and throughout testing by setting the Dynamometer dial to zero in order to ensure the accuracy of procedure.
Statistical analysis
The mean values and standard deviation (SD) of age, weight, height, and BMI were calculated for each age-groups and genders. Independent sample t-tests were used to examine HGS differences between genders. Prior to t-testing, the normality assumption and homogeneity of variance were carried out using Kolmogorov-Smirnov tests. To compare the differences between dominant and non-dominant grip strength, the paired sample t-test was performed. The effect of the handedness between different age-groups was analyzed by univariate analysis of variance (ANOVA). Also, the variations of HGS in various age-groups were tested by one-way ANOVA with Scheffe’s post hoc contrast. Pearson’s correlation coefficient was performed to examine the relationships between demographic characteristics and hand selected dimensions with HGS for dominant and non-dominant hands in both males and females subjects (P < 0.05).
To solve the problem of multicollinearity, for formulating prediction equations of HGS, anthropometric variables (height, weight, BMI, hand width, hand length, wrist circumference, forearm length, forearm circumference) were initially reduced via principal components analysis. Factors with eigenvalues more than 1 were then extracted. To produce a rotated component matrix, varimax rotation algorithm was utilized. For each rotated component, the original variables with the highest factor loading as “lead variables” were identified to be included in the linear regression models. Variable selection relied on a p-based forward selection algorithm. Separate models were formulated for dominant and non-dominant hand grip strength. The significance level was set at 0.05. SPSS version 23 (SPSS Inc., Chicago, IL) was used for statistical analysis.
Results
Descriptive characteristics of all participants stratified by age-group and gender are presented in (Table 1). Male subjects (n = 220) were divided into 8 age-groups with mean age of 36.71 years (fallen into the range of 20–59), while female subjects (n = 198) were divided into 7 age-groups with mean age of 33.87 years (fallen into the range of 20–54).
Descriptive characteristics of participants
Descriptive characteristics of participants
According to the (Table 1), 63.6 % of male employees were over-weighted (BMI > 25). In other words, except 20–24 to 30–34 age-groups, all other male age-groups had the mean BMI of over 25. However, female employees had the mean BMI of over 25 only for two age-groups of 35–39 and 50–54 years (27.3% of all females). The majority of subjects were right hand dominant; so that only 13 females (6.6%) and 20 males (10.1%) were left-handed.
Table 2 represents HGS measurements of participants stratified by age-group, gender and hand dominancy. The mean of male’s HGS was significantly higher (P < 0.05) than female’s HGS whatever the age-group. HGS variations were also greater among males than females, as indicated by standard errors. However, the maximum values of grip strength were observed at the age-group of 35–39 years for male (53.97±9.50 kg) and 40–44 years for female (30.85±3.09 kg). On average, dominant and non-dominant hands of male subjects were 77.67% and 81.15% stronger than those of their female colleagues, respectively.
Normative hand grip strength of Iranian office employees (n = 418)
Normative hand grip strength of Iranian office employees (n = 418)
D = Dominant hand; ND = Non-dominant hand.
Figure 1 illustrates dominant and non-dominant HGS variations within different age-groups for both genders. The effect of gender on HGS is considerably observable in this figure. Considering the role of hand dominancy on grip strength, Levene’s test for equality of variances of both hands grip strengths were significant for males and females; suggesting that dominant hand was stronger than non-dominant hand whatever the gender of subjects. Nevertheless, this is not the case of 45–49 years subjects whose non-dominant hand seems to be slightly stronger than their dominant hand in both genders. However, the observed differences between mean values of dominant and non-dominant hands’ HGSs did not reached significance level nor for males or for females (p < 0.05) between the age groups.
Dominant and non-dominant hand grip strength (HGS) performance by age groups for Iranian office employees.
Table 3 represents the Pearson’s correlation coefficients between hands grip strength with studied variables for both genders. There was a significant correlation between HGS and all measured variables, except BMI; suggesting that HGS increases as height, weight, hand length, palm width, palm length, forearm length, wrist circumferences and forearm circumferences of an office worker increase. Among all selected hand dimensions, HGS had the highest correlation with palm width, following by palm length and hand length, respectively.
Pearson’s correlation coefficients between the strengths of each hand calculated for anthropometric dimensions of both genders
Pearson’s correlation coefficients between the strengths of each hand calculated for anthropometric dimensions of both genders
*Correlation is significant at the 0.05 level (2-tailed). **Correlation is significant at the 0.01 level (2-tailed).
The results of factor analysis are summerized in Tables 4 and 5. Two components with eigenvalues of above 1 were obtained; variances being nearly 85%. The first factor revealed a close correlation to height followed respectively by hand length and forearm length; describing a substitute of body length. For the second factor, BMI was correlated as the best variable followed by forearm circumference; presenting a substitute parameter for obesity. Therefore, in addition to the gender and age, height and BMI were also entered in the regression analysis [20].
Total variance of grip strength explained by extracted components
Total variance of grip strength explained by extracted components
Extraction method: Principal component analysis.
Rotated component matrix revealing anthropometric lead variables
Extraction method: principal component analysis. Rotation method: varimax with kaiser normalization. Rotation converged in 3 iterations.
Enter regression analysis was conducted using the following variables: gender, age, age2, age3, height, height2, height3, BMI, BMI2, and BMI3. Then, comparing all possible linear, squared and cubic regression equations, the linear regression equation for both dominant and non-dominant hands were selected based on the lowest standard error and highest adjusted R2 (see Table 6).
Hand grip strength regression equations
HGS = predicted hand grip strength; D = Dominant hand; ND = Non-Dominant hand; R2 = amount of variance accounted for by the model; A = Age (years); H = Height (cm); G = Gender (male = 1; female = 2); BMI = Body Mass Index (kg/m2).
Being the first attempt to establish hand strength references for Iranian office employees, this study reported normative data specific to age-group, gender and handedness. The predictive value of some demographic (age and gender) and anthropometric (height, weight, hand length, palm width, palm length, writs circumference, forearm circumference, and forearm length) factors was also determined. Normative data on HGS is of great importance since it helps not only designers to produce much usable products but also health practitioners to evaluate specific diseases or treatment process [31, 32]. For clinical evaluation purposes, patient HGS measurements are compared with the validated normative tables. Ideally, an HGS normative reference table requires a large and randomly selected number of subjects, reflecting the heterogeneity within the population to achieve a validated reference tool. The mean grip strength of Iranian office employees (the present study) seems to be notably greater than that previously reported for Iranian general population [33]. However, this difference is somewhat evident since the study [33] comprised subjects up to 107 years. Nevertheless, it is difficult to compare this study’s strength data with those of other populations. For example, in the case of Australian automotive workers, the observed disparity may be attributed to the different demographic and/or occupational pools from which the populations were drawn [34].
Our results were in accordance with previous researches to find gender as the most important parameter in estimating HGS [35, 36]; since male office workers were significantly much stronger in hand grip than their female counterparts. It is assumed that over the age of sixteen, females have approximately two-thirds muscle strength of the age-matched males. Male muscle fibers are larger than those of female. This fact could result in a higher muscle strength and subsequently a greater hand grip strength [37–40]. In addition, male anthropometric dimensions are mostly larger than females [41]; the principle that could partly explain the greater HGS in men.
It is well known that HGS varies across the life span. Several studies found that the relationship between HGS and age is curvilinear and suggested that the maximum HGS usually occurs in three age ranges of 35–39, 40–44, 45–49 [19, 35]. In line with mentioned researches, we found the 35–39 as the age-band in which male subjects had the highest grip strength and the 40–44 as the HGS upper-band for female participants. Due to a major reduction in skeletal muscle mass, grip strength decreases obviously with aging. However, this pattern may more or less be modulated by some other factors such as level of physical activity and chronic diseases [42, 43]. This fact may also be true in the present study.
Being always stronger than non-dominant hand [15, 44], dominant hand was 9% (for male) to 11% (for female) stronger than non-dominant one. This finding confirms the “10% rule”, which states that dominant hand possesses a 10% greater grip strength than non-dominant hand [45]. Two explanations could account for this result: First, the muscles of dominant hand are more often used forcefully in many daily activities than non-dominant ones; and hence dominant muscles get bigger and thus stronger. Second, being in use more predominantly in everyday activities resulted in larger forearm circumference; the hand dimension which is well known to be correlated with grip strength [46, 47].
Many other body dimensions especially those of upper extremities have already been found to be related to grip strength. HGS increases with hand length, palm width, palm length, wrist circumference, forearm circumference, and forearm length in males as well as females [21, 23]. However, palm width seems to be the best predictor of HGS in both genders, probably because of strong massive muscle as well as big hand skeleton which lead to grip manual equipment, properly [48, 49]. Similar studies reported significant relationships between HGS with hand length and forearm circumference [46, 47]. For example, in another study carried out on Iranian adults, a positive and significant correlation was observed between HGS with forearm circumference, hand length, and palm width [33]. Concerning wrist circumference, while some studies found it to be related positively with HGS [18, 21], others failed to report any significant relationship [20]. Nevertheless, we could claim that at least for Iranian office workers, among other related factors, HGS increased as a function of wrist circumference.
However, it is notable that some other anthropometric dimensions such as hand circumference, arm circumference, thumb circumference, and shoulder-elbow length as well as other supplementary variables (e.g. nutritional status, leisure activities, and race/ethnic group and lifestyle) could influence grip strength [18] which have not been taken into account in this study and may be considered as a limitation for it. It is also questionable if the same HGS profile observed in Iranian office workers exists for other professionals. Further studies are required to examine this question.
In conclusion, presented norms may serve as the reference values for many purposes: (1) better designing for grip strength of various hand tools, equipment or products used by general non-manual young Iranian; (2) biomechanical modeling and development of ergonomic and/or rehabilitation evaluation tools; (3) better scheduling return-to-work programs; (4) diagnosis of some neuromuscular and cardiovascular diseases and monitoring the progress of treatment. Moreover, proposed equations made it possible to predict grip strength of healthy Iranian, not engaged to heavy manual works, just by having some readily available human parameters (i.e. age, gender, height and BMI). In other word, using proposed equations, the measurement of neither grip strength nor hand anthropometric dimensions are needed for estimating HGS of any comparable population. It is proposed to validate these equations in further studies.
Conflict of interest
None to report.
Footnotes
Acknowledgments
This study was supported financially by research deputy of Shahid Beheshti University of Medical Sciences, Tehran, Iran. Authors would like to express special thanks to all the participants for giving up their time for this research.