3
Work 57 (2017) 3–8 DOI:10.3233/WOR-172537 IOS Press
Investigation of the relationship between anthropometric measurements and maximal handgrip strength in young adults Christopher A. Eidson, Gavin R. Jenkins, Hon K. Yuen∗ , Anne M. Abernathy, Mary Beth Brannon, Anna R. Pung, Kiara D. Ward and Tara E. Weaver Department of Occupational Therapy, School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, USA Received 11 May 2016 Accepted 5 November 2016
Abstract. BACKGROUND: To identify physical measures that predict maximal handgrip strength (MHGS) and provide evidence for identifying lack of sincerity of effort when assessing upper extremity weakness. OBJECTIVE: This study investigated anthropometric measurements associated with MHGS of healthy young adults. METHODS: A convenience sample of 150 healthy adults ages 19 to 34 years old completed the MHGS assessment, which was measured using a Jamar dynamometer according to the protocol of the American Society of Hand Therapists, for both dominant and non-dominant hands. Several anthropometric data were collected, which included height, body weight, forearm length, forearm circumference, hand length, and hand width. RESULTS: Multivariable linear regression analysis indicated gender and hand width were uniquely and significantly associated with participants’ MHGS for dominant and non-dominant hand and accounted for more than 60% of the variance, with R2 = 0.60, P < 0.001 for the dominant hand model and R2 = 0.64, P < 0.001 for the non-dominant hand model. CONCLUSIONS: Among the forearm and hand anthropometric measures, hand width is the best predictor of MHGS in both the non-dominant and dominant hands for healthy young adults. Keywords: Regression models, millennials, malingering
1. Introduction The measurement of maximal handgrip strength (MHGS) is commonly used to evaluate the effectiveness of treatment strategies for upper extremity dysfunction [1, 2], to assess the ability of the patients to return to work [3], and to serve as evidence against feigning upper extremity weakness [4]. However, MHGS is a psycho-physiological measure, as ∗ Address
for correspondence: Hon K. Yuen, PhD., OTR/L, Professor & Director of Research, Department of Occupational Therapy, School of Health Professions, University of Alabama at Birmingham, 1530 3rd Avenue South, Birmingham, AL 35294, USA. Tel.: +1 205 934 6301; Fax: +1 205 975 7787; E-mail: [email protected].
opposed to a purely physical measure, in that it depends in part on sincerity of effort from the person being assessed. Sincerity of effort cannot be reliably measured, and as a result, detection of feigned weakness in MHGS during physical assessment for workers’ compensation is difficult [4]. To overcome this limitation, researchers have attempted to propose statistical models with physical measures that can be easier to administer; more accurate (valid) and precise (reliable) in estimating MHGS. Gender and age are the two main factors influencing MHGS, where gender accounts for the largest proportion of the overall variance [5]. Studies have shown that MHGS also varies by body and forearm and hand anthropometric traits in healthy
1051-9815/17/$35.00 © 2017 – IOS Press and the authors. All rights reserved
4
C.A. Eidson et al. / Anthropometric traits
people [6]. Body anthropometric traits, such as height and body weight [5, 7–11] and body mass index (BMI) [7, 9] have been shown to be significant independent predictors for MHGS. Several forearm and hand anthropometric traits, such as forearm circumference [11–13], hand length [8, 11, 14], and hand width or circumference [8, 11, 14, 15] were also found to be significant independent predictors for MHGS. Studies [13, 16] suggest that measurement of the forearm and/or hand were found to provide more accurate estimates and serve as better predictors for MHGS than general body anthropometric traits [17]. Two decades ago, Desrosiers et al. [8] showed that age, hand length, and hand circumference are significant predictors for MHGS among older Canadian adults ages 60 to 94. However, the prediction of MHGS should not be extrapolated beyond the age range being studied. In addition, the nature of demographic and anthropometric traits as predictors for MHGS differed between age groups (e.g., female under 30 years old versus those 30 and above) and gender [7, 8]. As a result, more restricted age stratification may provide more precise prediction of MHGS. Recently, Fain and Weatherford [18] have updated the normative MHGS values for young American adults (millennials), and a logical extension of this line of scientific inquiry is to identify predictors for MHGS in this age group. Therefore, the purpose of this study was to investigate anthropometric measurements associated with MHGS of healthy adults ages 19 to 34 years old. 2. Methods 2.1. Design This descriptive study involved a cross-sectional research design. The study was approved by the Institutional Review Board of the University of Alabama at Birmingham. 2.2. Participants Participants were recruited via convenience sampling mainly from Birmingham, Alabama, United States (US). The inclusion criteria for this study were: (1) community-living adults ages 19 to 34 years old, (2) able to follow at least 1-2 step verbal instructions to complete a task, (3) demonstrating 90◦ elbow flexion from full extension, and (4) full pronation and supination of forearms as indicated by turning palms facing up and down when elbow in
90◦ flexion. Exclusion criteria were: (1) self-reported current or previous history of injuries, diseases or surgeries affecting the upper extremity resulting in pain, abnormal sensation, weakness or limited range of movement while at rest or in motion, (2) history of life-threatening traumatic medical events such as stroke, head injury or cardiac arrest, or (3) significant physical, sensory or cognitive impairments. There were 150 participants (114 women and 36 men) who met the study criteria. Twenty (13%) participants were left-hand dominant with twelve (11%) female participants being left-handed dominant, and eight (22%) male participants were left-handed dominant. Demographic and anthropometric characteristics of the participants are shown in Table 1. MHGS for both the dominant and non-dominant hand stratified by gender and age is shown in Table 2. 2.3. Procedures Prior to beginning the grip strength assessment, eligible participants were asked to provide their gender, date of birth, height, body weight, and which hand they used for writing, which was identified as their dominant hand. MHGSs were obtained using the Jamar dynamometer with a standardized testing position and instructions as recommended by the American Society of Hand Therapists [19]. To begin the assessment, participants were instructed to sit upright (i.e., hips in 90◦ flexion and knees in 90◦ flexion) with feet flat on the floor, testing arm at side, not touching the body, elbow flexed at 90◦ , forearm in neutral position, wrist slightly extended between 0◦ and 30◦ and ulnar deviated between 0◦ and 15◦ , and the non-testing arm relaxed at side. This testing position was shown to produce accurate and reliable MHGS measurements [20]. The MHGS measurement was conducted at the second handle position of the dynamometer, in which the highest strengths have been recorded [21]. The examiner demonstrated the procedure and then gave the dynamometer to the participant. The examiner also warned the participant not to move their arm or body while squeezing the handle. After the participant was positioned appropriately, the participant was instructed to squeeze the handle of the dynamometer once as hard as he/she could while the examiner supported the dynamometer. Instructions given to each participant were as follows [20]: “I want you to hold the handle like this and squeeze as hard as you can and relax.” High inter-rater and testretest reliability have been demonstrated for these standardized measurement procedures [22].
C.A. Eidson et al. / Anthropometric traits
5
Table 1 Participants’ demographic and anthropometric characteristics (N = 150) Variable Age (yr) Height (cm) Weight (kg) BMI (kg/m2 ) Forearm length (mm) Forearm length (mm) Forearm circumference (mm) Forearm circumference (mm) Hand length (mm) Hand length (mm) Hand width (mm) Hand width (mm) Handgrip strength (kg) Handgrip strength (kg)
Hand dominance
Dominant Non-dominant Dominant Non-dominant Dominant Non-dominant Dominant Non-dominant Dominant Non-dominant
Male (n = 36) Mean SD 25.64 3.60 179.56 7.65 85.19 17.13 26.43 5.21 291.61 29.28 289.36 28.23 281.17 26.97 279.36 26.60 191.06 13.30 189.47 14.69 88.22 11.51 87.61 11.38 46.34 10.68 45.02 9.92
Female (n = 114) Mean SD 24.65 2.80 165.78 6.59 64.67 13.14 23.49 4.39 260.23 19.04 260.08 18.70 235.56 26.13 234.72 25.95 175.22 17.09 175.16 17.26 76.15 10.53 75.68 10.59 27.45 5.04 25.38 5.02
Note. SD = standard deviation. Table 2 Maximal handgrip strength (kg) for both the dominant and non-dominant hand stratified by gender and age (N = 150) Age
Hand dominance n
19–24 19–24 25–29 25–29 30–34 30–34
Dominant Non-dominant Dominant Non-dominant Dominant Non-dominant
15 15 15 15 6 6
Male (n = 36) Mean 49.37 47.50 40.83 39.89 52.50 51.67
SD
n
11.71 10.74 7.70 7.80 9.22 6.64
76 76 28 28 10 10
Female (n = 114) Mean 26.65 24.79 28.71 26.37 30.00 27.04
SD 4.62 4.63 5.31 4.93 6.25 7.46
Note. SD = standard deviation.
To reset the dynamometer, the examiner then turned the red peak-hold needle on the dynamometer counter-clockwise to zero after each measurement. Handgrip strength was measured three times with each hand (always started with the right hand first) consecutively with 10-second pauses between each measurement [23]. To repeat the three trials of measurement on the left hand, the examiner instructed the participant to keep the body in the same position and then positioned the left arm in the same manner as the previously tested right arm. The average of three measurements on each hand was considered the MHGS [24]. The MHGS was used for statistical analysis as high repeatability and reproducibility has been reported [25]. Several forearm and hand anthropometric measures were taken, including forearm length, forearm circumference, hand length, and hand width. Forearm length was measured as the distance between the lateral humeral epicondyle and radial styloid process using a flexible tape measure when the elbow flexed to 90◦ [26]. Forearm circumference was measured as the perimeter of the largest part of the forearm, located over the bulk of the brachioradialis muscle, at about the proximal quarter of forearm length, using
a flexible tape measure when the elbow flexed to 90◦ [12, 15]. The tape was applied closely around the skin of the forearm without compression when the measurement was taken. Hand length was measured as the straight distance between the midline of the distal wrist crease and the tip of middle finger using a flexible tape measure when the forearm was supported on a table [27]. Hand width was measured as the distance between the radial side of the index finger to the ulnar side of the little finger at the level of the metacarpophalangeal joints with the fingers adducted [28]. To measure the hand width, participants placed their hand (volar side) flat on a ruler. All anthropometric data were measured to the nearest millimeter for both dominant and non-dominant hands. Examiners were graduate students in the Masters of Occupational Therapy program and had received one two-hour orientation and practice training on conducting the protocol. All examiners were provided with a written protocol for collecting data that included photographs for anthropometric measures, and were required to demonstrate the data collection procedure without prompts or error, before collecting data from participants. The dynamometer was purposefully purchased new prior to the
6
C.A. Eidson et al. / Anthropometric traits
commencement of the study and calibrated by the manufacturer. 2.4. Data analysis In the preliminary analysis, we performed unadjusted bivariate analysis to explore any significant associations between the outcome (i.e., MHGS) and explanatory variables. Potential explanatory variables included gender, age, BMI, forearm length, forearm circumference, hand length, and hand width. To reduce the number of variables included in the regression model and avoid multicollinearity (i.e., linear correlation among explanatory variables making it difficult to separate their effects on MHGS), BMI was used instead of height and body weight. BMI = body weight [kg]/(height*height) [m2 ]. In addition, forearm length has been suggested as a surrogate for height [29]. For the adjusted analysis, two multivariable linear regression models were fit with the MHGS as the outcome variable, one for the dominant hand and the other for the non-dominant hand. We considered variables, for inclusion in the final model, if they were significantly associated with MHGS (P < 0.05) in the unadjusted analysis. Stepwise and forward selection procedures for model building were used to obtain the most parsimonious model. Explanatory variables whose regression coefficients had P-values less than 0.05 were retained in the multivariable models. All data analyses were conducted using the IBM Statistics Package for Social Sciences (SPSS) for Windows, version 22 (www.spss.com).
3. Results From the results of the univariable analysis (see Table 3), variables included in the multivariable linear regression model were: gender, age, BMI, forearm length, forearm circumference, hand length, and hand width. Two multivariable models are presented in Table 3, one for the dominant hand (model 1) and the other for the non-dominant hand (model 2). Both final models indicated that gender and hand width were significantly and uniquely associated with the participants’ MHGS. Model 1: MHGS of the dominant hand = 52.21 – 17.32*gender + 0.13* dominant hand width Model 2: MHGS of the non-dominant hand = 53.45 – 18.30*gender + 0.11* non-dominant hand width
The multiple linear regression model for the MHGS of the dominant hand with the two explanatory variables (gender and hand width) produced R2 = 0.60, F(2, 147) = 112.24, P < 0.001, whereas the model for the MHGS of the non-dominant hand produced R2 = 0.64, F(2, 147) = 130.62, P < 0.001. The adjusted R2 for the final models 1 and 2 were 0.60 and 0.64 respectively. The coefficient of each explanatory variable with significant effect on the MHGS for young adult participants ages 19 to 34 years old is shown in Table 3. When the predictive regression model was applied to women ages 19 to 24 years old (n = 76), the only predictor for MHGS was hand width for both dominant and non-dominant hands. Model 1F: MHGS of the dominant hand in female ages 19–24 years old = 14.48 + 0.16*dominant hand width, F(1, 74) = 10.15, P = 0.002; R2 = 0.12. Model 2F: MHGS of the non-dominant hand in female ages 19–24 years old = 13.22 + 0.16*nondominant hand width, F(1, 74) = 9.54, P = 0.003; R2 = 0.11.
4. Discussion Findings were consistent with previous studies [8, 11, 14–16] that hand width or circumference is the best forearm and hand anthropometric measurement to estimate MHGS. The predictive equations (regression models) derived from the participants’ hand width provide a more precise estimation of the expected MHGS than would be obtained from the normative values based on their gender and age. Results were also consistent with Li et al.’s study that hand width was the sole predictor of MHGS for young adults ages 19 to 24 years old [15], in which 71% of the participants in Li et al.’s study were male. Participants of Li et al.’s study were composed of undergraduate students from a university in France [15]. Whereas, 83% of the present study participants in this age category (19 to 24 years old) were female and the majority of them were graduate students from a university in the USA. Results from the present and Li et al.’s studies suggest that hand width (or hand circumference) is a valid robust predictor for MHGS in adults ages 19 to 24 years old regardless of their gender and nationality. Furthermore, this study validated that hand width can be used to predict MHGS in adults ages up to 34 years old, beyond the age category of 19–24 years old adults investigated in Li et al.’s study.
C.A. Eidson et al. / Anthropometric traits
7
Table 3 Results of univariable and multivariable linear regressions on maximal handgrip strength for each explanatory variable in young adults age 19 to 34 years old (N = 150) Explanatory Variables Constant Gender Age BMI Forearm length Forearm circumference Hand length Hand width R2 Adj R2
Univariate Dominant Non-dominant Beta SE Beta SE –18.89 0.72 0.57 0.18 0.18 0.17 0.40
1.30 0.28 0.18 0.03 0.02 0.05 0.07
–19.65 0.65 0.64 0.19 0.19 0.17 0.40
1.25 0.28 0.18 0.03 0.02 0.05 0.07
Beta
Multivariate Dominant Non-dominant SE P-value Beta SE P-value
52.21 –17.32
5.62 1.42
Work 57 (2017) 3–8 DOI:10.3233/WOR-172537 IOS Press
Investigation of the relationship between anthropometric measurements and maximal handgrip strength in young adults Christopher A. Eidson, Gavin R. Jenkins, Hon K. Yuen∗ , Anne M. Abernathy, Mary Beth Brannon, Anna R. Pung, Kiara D. Ward and Tara E. Weaver Department of Occupational Therapy, School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, USA Received 11 May 2016 Accepted 5 November 2016
Abstract. BACKGROUND: To identify physical measures that predict maximal handgrip strength (MHGS) and provide evidence for identifying lack of sincerity of effort when assessing upper extremity weakness. OBJECTIVE: This study investigated anthropometric measurements associated with MHGS of healthy young adults. METHODS: A convenience sample of 150 healthy adults ages 19 to 34 years old completed the MHGS assessment, which was measured using a Jamar dynamometer according to the protocol of the American Society of Hand Therapists, for both dominant and non-dominant hands. Several anthropometric data were collected, which included height, body weight, forearm length, forearm circumference, hand length, and hand width. RESULTS: Multivariable linear regression analysis indicated gender and hand width were uniquely and significantly associated with participants’ MHGS for dominant and non-dominant hand and accounted for more than 60% of the variance, with R2 = 0.60, P < 0.001 for the dominant hand model and R2 = 0.64, P < 0.001 for the non-dominant hand model. CONCLUSIONS: Among the forearm and hand anthropometric measures, hand width is the best predictor of MHGS in both the non-dominant and dominant hands for healthy young adults. Keywords: Regression models, millennials, malingering
1. Introduction The measurement of maximal handgrip strength (MHGS) is commonly used to evaluate the effectiveness of treatment strategies for upper extremity dysfunction [1, 2], to assess the ability of the patients to return to work [3], and to serve as evidence against feigning upper extremity weakness [4]. However, MHGS is a psycho-physiological measure, as ∗ Address
for correspondence: Hon K. Yuen, PhD., OTR/L, Professor & Director of Research, Department of Occupational Therapy, School of Health Professions, University of Alabama at Birmingham, 1530 3rd Avenue South, Birmingham, AL 35294, USA. Tel.: +1 205 934 6301; Fax: +1 205 975 7787; E-mail: [email protected].
opposed to a purely physical measure, in that it depends in part on sincerity of effort from the person being assessed. Sincerity of effort cannot be reliably measured, and as a result, detection of feigned weakness in MHGS during physical assessment for workers’ compensation is difficult [4]. To overcome this limitation, researchers have attempted to propose statistical models with physical measures that can be easier to administer; more accurate (valid) and precise (reliable) in estimating MHGS. Gender and age are the two main factors influencing MHGS, where gender accounts for the largest proportion of the overall variance [5]. Studies have shown that MHGS also varies by body and forearm and hand anthropometric traits in healthy
1051-9815/17/$35.00 © 2017 – IOS Press and the authors. All rights reserved
4
C.A. Eidson et al. / Anthropometric traits
people [6]. Body anthropometric traits, such as height and body weight [5, 7–11] and body mass index (BMI) [7, 9] have been shown to be significant independent predictors for MHGS. Several forearm and hand anthropometric traits, such as forearm circumference [11–13], hand length [8, 11, 14], and hand width or circumference [8, 11, 14, 15] were also found to be significant independent predictors for MHGS. Studies [13, 16] suggest that measurement of the forearm and/or hand were found to provide more accurate estimates and serve as better predictors for MHGS than general body anthropometric traits [17]. Two decades ago, Desrosiers et al. [8] showed that age, hand length, and hand circumference are significant predictors for MHGS among older Canadian adults ages 60 to 94. However, the prediction of MHGS should not be extrapolated beyond the age range being studied. In addition, the nature of demographic and anthropometric traits as predictors for MHGS differed between age groups (e.g., female under 30 years old versus those 30 and above) and gender [7, 8]. As a result, more restricted age stratification may provide more precise prediction of MHGS. Recently, Fain and Weatherford [18] have updated the normative MHGS values for young American adults (millennials), and a logical extension of this line of scientific inquiry is to identify predictors for MHGS in this age group. Therefore, the purpose of this study was to investigate anthropometric measurements associated with MHGS of healthy adults ages 19 to 34 years old. 2. Methods 2.1. Design This descriptive study involved a cross-sectional research design. The study was approved by the Institutional Review Board of the University of Alabama at Birmingham. 2.2. Participants Participants were recruited via convenience sampling mainly from Birmingham, Alabama, United States (US). The inclusion criteria for this study were: (1) community-living adults ages 19 to 34 years old, (2) able to follow at least 1-2 step verbal instructions to complete a task, (3) demonstrating 90◦ elbow flexion from full extension, and (4) full pronation and supination of forearms as indicated by turning palms facing up and down when elbow in
90◦ flexion. Exclusion criteria were: (1) self-reported current or previous history of injuries, diseases or surgeries affecting the upper extremity resulting in pain, abnormal sensation, weakness or limited range of movement while at rest or in motion, (2) history of life-threatening traumatic medical events such as stroke, head injury or cardiac arrest, or (3) significant physical, sensory or cognitive impairments. There were 150 participants (114 women and 36 men) who met the study criteria. Twenty (13%) participants were left-hand dominant with twelve (11%) female participants being left-handed dominant, and eight (22%) male participants were left-handed dominant. Demographic and anthropometric characteristics of the participants are shown in Table 1. MHGS for both the dominant and non-dominant hand stratified by gender and age is shown in Table 2. 2.3. Procedures Prior to beginning the grip strength assessment, eligible participants were asked to provide their gender, date of birth, height, body weight, and which hand they used for writing, which was identified as their dominant hand. MHGSs were obtained using the Jamar dynamometer with a standardized testing position and instructions as recommended by the American Society of Hand Therapists [19]. To begin the assessment, participants were instructed to sit upright (i.e., hips in 90◦ flexion and knees in 90◦ flexion) with feet flat on the floor, testing arm at side, not touching the body, elbow flexed at 90◦ , forearm in neutral position, wrist slightly extended between 0◦ and 30◦ and ulnar deviated between 0◦ and 15◦ , and the non-testing arm relaxed at side. This testing position was shown to produce accurate and reliable MHGS measurements [20]. The MHGS measurement was conducted at the second handle position of the dynamometer, in which the highest strengths have been recorded [21]. The examiner demonstrated the procedure and then gave the dynamometer to the participant. The examiner also warned the participant not to move their arm or body while squeezing the handle. After the participant was positioned appropriately, the participant was instructed to squeeze the handle of the dynamometer once as hard as he/she could while the examiner supported the dynamometer. Instructions given to each participant were as follows [20]: “I want you to hold the handle like this and squeeze as hard as you can and relax.” High inter-rater and testretest reliability have been demonstrated for these standardized measurement procedures [22].
C.A. Eidson et al. / Anthropometric traits
5
Table 1 Participants’ demographic and anthropometric characteristics (N = 150) Variable Age (yr) Height (cm) Weight (kg) BMI (kg/m2 ) Forearm length (mm) Forearm length (mm) Forearm circumference (mm) Forearm circumference (mm) Hand length (mm) Hand length (mm) Hand width (mm) Hand width (mm) Handgrip strength (kg) Handgrip strength (kg)
Hand dominance
Dominant Non-dominant Dominant Non-dominant Dominant Non-dominant Dominant Non-dominant Dominant Non-dominant
Male (n = 36) Mean SD 25.64 3.60 179.56 7.65 85.19 17.13 26.43 5.21 291.61 29.28 289.36 28.23 281.17 26.97 279.36 26.60 191.06 13.30 189.47 14.69 88.22 11.51 87.61 11.38 46.34 10.68 45.02 9.92
Female (n = 114) Mean SD 24.65 2.80 165.78 6.59 64.67 13.14 23.49 4.39 260.23 19.04 260.08 18.70 235.56 26.13 234.72 25.95 175.22 17.09 175.16 17.26 76.15 10.53 75.68 10.59 27.45 5.04 25.38 5.02
Note. SD = standard deviation. Table 2 Maximal handgrip strength (kg) for both the dominant and non-dominant hand stratified by gender and age (N = 150) Age
Hand dominance n
19–24 19–24 25–29 25–29 30–34 30–34
Dominant Non-dominant Dominant Non-dominant Dominant Non-dominant
15 15 15 15 6 6
Male (n = 36) Mean 49.37 47.50 40.83 39.89 52.50 51.67
SD
n
11.71 10.74 7.70 7.80 9.22 6.64
76 76 28 28 10 10
Female (n = 114) Mean 26.65 24.79 28.71 26.37 30.00 27.04
SD 4.62 4.63 5.31 4.93 6.25 7.46
Note. SD = standard deviation.
To reset the dynamometer, the examiner then turned the red peak-hold needle on the dynamometer counter-clockwise to zero after each measurement. Handgrip strength was measured three times with each hand (always started with the right hand first) consecutively with 10-second pauses between each measurement [23]. To repeat the three trials of measurement on the left hand, the examiner instructed the participant to keep the body in the same position and then positioned the left arm in the same manner as the previously tested right arm. The average of three measurements on each hand was considered the MHGS [24]. The MHGS was used for statistical analysis as high repeatability and reproducibility has been reported [25]. Several forearm and hand anthropometric measures were taken, including forearm length, forearm circumference, hand length, and hand width. Forearm length was measured as the distance between the lateral humeral epicondyle and radial styloid process using a flexible tape measure when the elbow flexed to 90◦ [26]. Forearm circumference was measured as the perimeter of the largest part of the forearm, located over the bulk of the brachioradialis muscle, at about the proximal quarter of forearm length, using
a flexible tape measure when the elbow flexed to 90◦ [12, 15]. The tape was applied closely around the skin of the forearm without compression when the measurement was taken. Hand length was measured as the straight distance between the midline of the distal wrist crease and the tip of middle finger using a flexible tape measure when the forearm was supported on a table [27]. Hand width was measured as the distance between the radial side of the index finger to the ulnar side of the little finger at the level of the metacarpophalangeal joints with the fingers adducted [28]. To measure the hand width, participants placed their hand (volar side) flat on a ruler. All anthropometric data were measured to the nearest millimeter for both dominant and non-dominant hands. Examiners were graduate students in the Masters of Occupational Therapy program and had received one two-hour orientation and practice training on conducting the protocol. All examiners were provided with a written protocol for collecting data that included photographs for anthropometric measures, and were required to demonstrate the data collection procedure without prompts or error, before collecting data from participants. The dynamometer was purposefully purchased new prior to the
6
C.A. Eidson et al. / Anthropometric traits
commencement of the study and calibrated by the manufacturer. 2.4. Data analysis In the preliminary analysis, we performed unadjusted bivariate analysis to explore any significant associations between the outcome (i.e., MHGS) and explanatory variables. Potential explanatory variables included gender, age, BMI, forearm length, forearm circumference, hand length, and hand width. To reduce the number of variables included in the regression model and avoid multicollinearity (i.e., linear correlation among explanatory variables making it difficult to separate their effects on MHGS), BMI was used instead of height and body weight. BMI = body weight [kg]/(height*height) [m2 ]. In addition, forearm length has been suggested as a surrogate for height [29]. For the adjusted analysis, two multivariable linear regression models were fit with the MHGS as the outcome variable, one for the dominant hand and the other for the non-dominant hand. We considered variables, for inclusion in the final model, if they were significantly associated with MHGS (P < 0.05) in the unadjusted analysis. Stepwise and forward selection procedures for model building were used to obtain the most parsimonious model. Explanatory variables whose regression coefficients had P-values less than 0.05 were retained in the multivariable models. All data analyses were conducted using the IBM Statistics Package for Social Sciences (SPSS) for Windows, version 22 (www.spss.com).
3. Results From the results of the univariable analysis (see Table 3), variables included in the multivariable linear regression model were: gender, age, BMI, forearm length, forearm circumference, hand length, and hand width. Two multivariable models are presented in Table 3, one for the dominant hand (model 1) and the other for the non-dominant hand (model 2). Both final models indicated that gender and hand width were significantly and uniquely associated with the participants’ MHGS. Model 1: MHGS of the dominant hand = 52.21 – 17.32*gender + 0.13* dominant hand width Model 2: MHGS of the non-dominant hand = 53.45 – 18.30*gender + 0.11* non-dominant hand width
The multiple linear regression model for the MHGS of the dominant hand with the two explanatory variables (gender and hand width) produced R2 = 0.60, F(2, 147) = 112.24, P < 0.001, whereas the model for the MHGS of the non-dominant hand produced R2 = 0.64, F(2, 147) = 130.62, P < 0.001. The adjusted R2 for the final models 1 and 2 were 0.60 and 0.64 respectively. The coefficient of each explanatory variable with significant effect on the MHGS for young adult participants ages 19 to 34 years old is shown in Table 3. When the predictive regression model was applied to women ages 19 to 24 years old (n = 76), the only predictor for MHGS was hand width for both dominant and non-dominant hands. Model 1F: MHGS of the dominant hand in female ages 19–24 years old = 14.48 + 0.16*dominant hand width, F(1, 74) = 10.15, P = 0.002; R2 = 0.12. Model 2F: MHGS of the non-dominant hand in female ages 19–24 years old = 13.22 + 0.16*nondominant hand width, F(1, 74) = 9.54, P = 0.003; R2 = 0.11.
4. Discussion Findings were consistent with previous studies [8, 11, 14–16] that hand width or circumference is the best forearm and hand anthropometric measurement to estimate MHGS. The predictive equations (regression models) derived from the participants’ hand width provide a more precise estimation of the expected MHGS than would be obtained from the normative values based on their gender and age. Results were also consistent with Li et al.’s study that hand width was the sole predictor of MHGS for young adults ages 19 to 24 years old [15], in which 71% of the participants in Li et al.’s study were male. Participants of Li et al.’s study were composed of undergraduate students from a university in France [15]. Whereas, 83% of the present study participants in this age category (19 to 24 years old) were female and the majority of them were graduate students from a university in the USA. Results from the present and Li et al.’s studies suggest that hand width (or hand circumference) is a valid robust predictor for MHGS in adults ages 19 to 24 years old regardless of their gender and nationality. Furthermore, this study validated that hand width can be used to predict MHGS in adults ages up to 34 years old, beyond the age category of 19–24 years old adults investigated in Li et al.’s study.
C.A. Eidson et al. / Anthropometric traits
7
Table 3 Results of univariable and multivariable linear regressions on maximal handgrip strength for each explanatory variable in young adults age 19 to 34 years old (N = 150) Explanatory Variables Constant Gender Age BMI Forearm length Forearm circumference Hand length Hand width R2 Adj R2
Univariate Dominant Non-dominant Beta SE Beta SE –18.89 0.72 0.57 0.18 0.18 0.17 0.40
1.30 0.28 0.18 0.03 0.02 0.05 0.07
–19.65 0.65 0.64 0.19 0.19 0.17 0.40
1.25 0.28 0.18 0.03 0.02 0.05 0.07
Beta
Multivariate Dominant Non-dominant SE P-value Beta SE P-value
52.21 –17.32
5.62 1.42
