Associations between absolute and relative handgrip strength with fitness and fatness

Abstract

The main purpose of this study was to assess relationships between absolute and relative handgrip strength (HGS) versus other markers of health (body composition) and physical fitness (VO₂ max, vertical jump) in 220 (112 male) healthy young adults (25±10 years). HGS was measured using a hand dynamometer. Absolute HGS represented the highest grip strength measurement (kg) of the right and left hand combined, while relative HGS represented the absolute HGS divided by body weight (kg/kg). Body composition (lean and fat mass) was measured using dual energy x-ray absorptiometry. VO₂ max was measured using a treadmill peak speed protocol (ml/kg/min), while vertical jump was assessed using a countermovement jump (cm). Absolute HGS (mean=86±22 kg) was positively related with lean mass (r=0.82, p<0.001) and vertical jump (r=0.63, p<0.001), while relative HGS (mean=1.2±0.2 kg/kg) was negatively related with body fat (r=–0.69, p<0.001), but positively correlated with VO₂ max (r=0.47, p<0.001), and vertical jump (r=0.45, p<0.001). Linear models suggest that lean mass, body fat, and vertical jump predicted 69% of variance for absolute HGS (adjusted R²=0.71, p<0.001), while lean mass and body fat predicted 49% of variance for relative HGS (adjusted R²=0.49, p<0.001). Lower relative HGS scores (<1.0 kg/kg) were associated with higher body fat levels and may represent a quick, simple, marker of health.

Keywords

body composition - muscle strength - fitness assessment

Introduction

Handgrip strength (HGS) is a quick, simple, and portable measure of generalized muscle strength and physical functioning, particularly in elderly populations [1] [2] [3]. However, the measurement of maximum isometric handgrip force is often applied in a vague and inconsistent manner across populations. Epidemiological studies, largely conducted in older populations, confirm that low absolute HGS is a surrogate marker for muscle weakness, which are then strongly associated with cardiovascular disease [2], dementia [4], cancer [5], disability [3], and all-cause mortality [1] [2]. More clinically relevant HGS thresholds remain under-developed, however, due to a lack of both clarity and standardization of measurement. For example, maximum HGS thresholds of 42 kg for males and 25 kg for females were estimated to reduce all-cause mortality in a prospective study performed on ~64-year-old adults from 28 countries [6]. Whether or not these mortality thresholds represent a single HGS or both HGSs combined remains unclear.

Thus, the difficulty interpreting HGS – and the practical application of these values – is largely due to the high variability in reporting. While the reporting of absolute HGS values is common, the reporting of single hand (right or left) [7] [8] versus double hand (right plus left hand) [4] [5] or using the average score [4] [5] versus the highest score [9] alter the practical utility of HGS across studies. Furthermore, HGS has also been reported in Newtons [3] [10], quartiles [4] [5] [11], quintiles [3], and/or normalized by dividing absolute HGS by body mass index (BMI) [11] [12] and body weight [13] [14]. Thus, although evidence supports that low HGS is associated with death, disease, and disability the clinical utility of providing a unified, practical, metric to assess health in real time is lacking.

In contrast to the robust amount of data obtained from older populations, the utility of HGS to predict health and disease in younger populations remains under-investigated. In (young) elite athletic populations, absolute HGS is related with upper and lower body strength, impulsive (jumping) ability, body mass, lean muscle mass, age, and training experience [15]. Of greater clinical utility, the normalization of HGS, by dividing absolute HGS by body weight, has produced cut-off thresholds that predict both diabetes risk [13] and metabolic syndrome (MetS) [14] in middle-aged adults. Thus, relative HGS (rather than absolute HGS) may be a more practical and universally applied metric to assess fitness, health and/or disease risk in non-elderly populations.

Therefore, the main purpose of this study is to assess relationships between both absolute and relative HGS versus markers of metabolic health (blood pressure, resting heart rate, fasted blood glucose, and visceral adipose tissue), physical health (body composition), and performance (VO₂ max, vertical jump) in a convenience sample of young, healthy, adults. A secondary purpose is to establish a predictive model, for both absolute and relative HGS, utilizing these select markers of health (physical and metabolic) and (fitness) performance. A tertiary purpose is to evaluate mean thresholds for HGS that would translate across average health and fitness metrics in young adults.

Methods

Participants

This observational, cross-sectional study (IRB#073919M1E) recruited a convenience sample of both male and female participants from Detroit and the greater metropolitan area. The inclusion criteria represented any able-bodied individual (i. e., who can run on a treadmill until volitional exhaustion) between the ages of 18–100 years old. Any participant with medical conditions that may be exacerbated by running on a treadmill to volitional exhaustion (VO² max test), were asked to obtain clearance from their medical practitioner before participating in this study. The only exclusion criteria was any menstruant female who was pregnant (or thought they might be pregnant), due to radiation exposure concerns from the dual energy x-ray absorptiometry – or DXA – scan, which may be harmful to a developing fetus. This study served to provide a “snapshot” of baseline metrics, to launch ongoing longitudinal investigations (and interventions) for local community members.

All participants were asked to arrive at the laboratory in exercise attire after a 4-hour fast (i. e., no food or drink, other than water when thirsty) and sign written informed consent prior to participation. All females of child-bearing age were asked to complete a pregnancy attestation form, confirming that they were not pregnant (or think they were pregnant) prior to participation.

We assessed metabolic health through measurement of four parameters: resting blood pressure (BP), resting heart rate (RHR), fasted blood glucose (BG), and visceral adipose tissue (VAT; obtained from the DXA scan). We assessed physical health via whole body composition analyses (measuring three compartments: lean, fat, and bone mass). We assessed physical fitness (i. e., performance) using three different tests that measured: cardiorespiratory fitness (VO₂ max), muscular strength (handgrip), and muscular power (vertical jump height). The exact procedures are detailed below, as represented sequentially, in our ~60-minute study protocol.

Protocol

After written informed consent was obtained, each participant sat quietly for 5-minutes to achieve a resting, steady-state condition prior to blood pressure (BP) measurement. With both feet flat on the ground, back upright, with both forearms placed supine on the table, resting blood pressure was measured once on the right arm using an automated BP cuff (OMRON 3, Kyoto, Japan). Systolic blood pressure (SBP), diastolic blood pressure (DBP), and resting heart rate (RHR) were recorded.

Next, still in a seated position, a fingerstick fasting blood glucose (BG) was measured using a portable analyzer (GE100 Blood Glucose Monitoring Kit, Ontario, CA) and aseptic technique.

Height and weight were measured using a stadiometer and electronic scale (SECA 763, Hamburg, Germany), with participants wearing only compression shorts and a sports bra (females), without shoes. Body composition was assessed using a whole body DXA scan (Horizon A, APEX System software version 5.6.0.5, TBAR2019 calibration; Hologic, Marlborough, MA). All DXA scans were performed and analyzed by a single trained operator, according to the manufacturer’s specifications [16].

After body composition measurement, handgrip strength (HGS) was measured using a digital hand dynamometer (Handexer, South El Monte, CA). In a standing position, both elbows were simultaneously flexed to a 90º angle with the wrists held in a neutral position. Maximal HGS was measured, with verbal encouragement, per hand (3-second max contractions, with a 30-second rest between trials, alternating the dynamometer between hands). Both hands were tested three times, with the sum of the highest value (kg), for both the right and left hands, representing the absolute HGS score [17]. Relative HGS (absolute HGS in kg divided by body weight in kg) was also calculated to normalize strength per body mass, as calculated previously [13] [14].

A standing vertical jump test was then performed, using a Vertec jump trainer (Vertec Gen2, Cranston, RI). The highest of three countermovement jump attempts was recorded. We chose to report vertical jump in inches, to compare with other normative values (measured in inches) more easily but also converted to peak power output using the following equation: peak power (watts)=60.7 x (jump height [cm])+45.3 x (body mass [kg]) – 2055.

Lastly, aerobic fitness (VO₂ max) was assessed on a motorized treadmill (Lode Valiant 2 Sport, Groningen, Netherlands). Oxygen uptake was measured continuously using a metabolic cart (Parvo Medics TrueOne 2400, Sandy, UT) with maximal oxygen consumption defined as the highest value obtained before volitional exhaustion. The exercise protocol utilized was a peak treadmill speed running test, whereas all participants started at a comfortable walking or running speed. After 1 min, the treadmill speed increased 0.5 mph every minute until participants could no longer keep pace with the treadmill (volitional exhaustion) [18].

Statistical analysis

Unpaired t-tests were used to confirm expected sex differences in health and performance metrics. Simple regression analyses (Pearson’s r) were utilized to assess relationships between our two main outcome measures, absolute HGS and relative HGS, versus indicators of metabolic and physical health as well as physical performance. Prediction equations, for both absolute and relative HGS (dependent variables), were generated using stepwise linear regression models. The independent variables demonstrating the strongest statistical significance with the two dependent variables ([Table 3]) were sequentially added into the general linear model. The final prediction models, for both absolute and relative HGS, included only those independent variables that retained statistical significance (p<0.05) as predictors within the linear model. The adjusted R² for each linear model reflected the variance that the combined independent variables contribute to each dependent variable (i. e., absolute and relative HGS).

Table 3 Correlation coefficients (r) for analyses comparing both absolute HGS and relative HGS versus demographic (age, BMI), body composition (LM, BF, Z-score), metabolic (VAT, SBP, DBP, and BG), and physical performance (VO2, VJ) variables, for combined data (N=220). BMI=body mass index; LM=lean mass; BF=body fat; VAT=visceral adipose tissue; SBP=systolic blood pressure; DBP=diastolic blood pressure; RHR=resting heart rate; BG=blood glucose; VO2=VO2 max; VJ=vertical jump.

Handgrip Strength	Age (years)	BMI (kgm 2 )	LM (kg)	BF (%)	Z- score	VAT (cm 2 )	SBP (mmHg)	DBP (mmHg)	RHR (bpm)	BG (mg/dL)	VO2 (ml/kg/min)	VJ (cm)
Absolute (kg)	–0.05	0.34***	0.82***	–0.53***	0.22*	0.20***	0.49***	0.02	–0.09	0.08	0.21**	0.63***
Relative (kg/kg)	–0.09	–0.37***	0.20**	–0.69***	0.01	–0.35	0.22**	–0.08	–0.13	0.04	0.47***	0.45***

^*p<0.05; **p<0.01; ***p<0.001

Effect sizes to determine the magnitude of effects [19] were expressed using Cohen’s d with small (0.2), medium (0.5), and large (0.8) magnitude of effects calculated for both the unpaired t-tests (male mean – female mean/pooled SD) and regression correlations (using the r-value/ correlation coefficient) [20]. A power analysis, conducted on a similar cross-sectional study assessing relationships between HGS and VO₂ max on a convenience sample of male and female elderly outpatients, calculated a sample size of 160 participants necessary to achieve 95% power, α=0.05, β=0.05, with an anticipated effect size=0.15 (G*Power 3.1.9.2.; Heinrich-Heine-Universität, Düsseldorf, Germany) [8]. All data reported as mean±standard deviation (SD), with statistical significance set a priori at p<0.05. Both Cohen’s d and statistical significance were reported, offering two distinct perspectives on the translational (practical) interpretation of “significance.”

Results

A total of 220 (112 male, 51% and 108 female, 49%) participants completed the full testing protocol. In our reporting of means (±SD), we have separated our cohort into males and females to confirm the expected sex-specific differences well-documented in body composition and performance metrics verified and reported elsewhere [17]. The average demographic (age, height, weight, body mass index/BMI), body composition (lean, fat, and bone mass), and metabolic health (VAT, SBP, DBP, RHR, BG) metrics for males, females, and the total cohort combined are summarized in [Table 1]. As expected, large effects sizes (>0.8 for Cohen’s d) were noted between male and female participants with respect to sex-related differences in lean mass, body fat percentage, and systolic blood pressure.

Table 1 Demographic (age, height, weight, body mass index/BMI), body composition (lean, fat, and bone mass) and metabolic (VAT, resting heart rate, blood pressure, and blood glucose) data for all (total; N=220), male (n=112), and female (n=108) participants. Data expressed as mean±SD (min-max). The magnitude of difference between male and female participants are demonstrated using both the p-values (from unpaired t-tests) and effect size (from Cohen’s d). BMI=body mass index; BMD=bone mineral density; VAT=visceral adipose tissue; BP=blood pressure.

Variable	Total	Male	Female	p-value	Cohen’s d
Age (years)	25±10 (18–64)	25±9	25±11	0.59	0.06
Height (m)	1.71±0.10 (1.5–2.0)	1.77±0.08	1.65±0.06	<0.001	1.2
Weight (kgs)	72.9±15.1 (43–129)	80.0±14.7	65.6±11.6	<0.001	0.95
BMI (kg/m 2 )	24.7±4.1 (17–41)	25.3±4.1	24 .0±3.9	0.01	0.35
Total lean mass (kg)	53.3±12.2 (51–85)	61.6±10.1	44.8±7.3	<0.001	1.38
Total body fat (%)	24.4±7.2 (12–47)	19.9±5.2	28.9±6.0	<0.001	1.25
BMD Z-score (no units)	–0.1±0.9 (–2.5–2.4)	–0.1±0.8	–0.1±0.9	0.75	0.05
VAT (cm 2 )	52.5±23.5 (18–195)	60.6±19.4	44.2±24.5	<0.001	0.70
Systolic BP (mmHg)	122±15 (87–168)	129±14	115±9	<0.001	0.90
Diastolic BP (mmHg)	75±9 (49–106)	76±14	76±14	0.88	0.00
Resting heart rate (bpm)	75±14 (39–118)	73±14	76±13	0.08	0.21
Blood glucose (mg/dL)	86±14 (46–136)	86±15	85±13	0.80	0.07

[Table 2] shows the mean performance metrics of all (total), male, and female participants. As expected, large effect sizes (>0.8 for Cohen’s d) between male and female participants were seen in absolute and relative HGS, vertical jump, and peak power.

Table 2 Physical performance metrics data for all (Total), male, and female participants. Data expressed as mean±SD (min-max). The magnitude of difference between male and female participants are demonstrated using both the p-values (from unpaired t-tests) and effect size (from Cohen’s d).

Variable	Total	Male	Female	p-value	Cohen’s d
Absolute handgrip (kg)	86±22 (31–144)	102±17	70±14	<0.001	1.45
Vertical jump (cm)	51±15 (15–104)	58±13	43±10	<0.001	1.00
Peak power (Watts)	4348±1275 (1304–8656)	5178±1094	3489±789	<0.001	1.32
VO2 max (ml/kg/min)	43±10 (21–83)	47±11	40±11	<0.001	0.70
Relative handgrip (kg/kg)	1.2±0.2 (0.6–1.8)	1.3±0.2	1.1±0.2	<0.001	1.00

[Table 3] details linear relationships between our two main outcome measures, absolute and relative HGS, versus demographic (age, BMI), body composition (lean mass, % body fat, and BMD Z-score), metabolic health (VAT, SPB, DBP, and BG), and performance (VO2 max, vertical jump) metrics.

[Fig. 1] demonstrates correlations between absolute HGS ([Fig. 1a]) and relative HGS ([Fig. 1b]) versus total lean mass, for the total cohort and separated by sex. [Fig. 2] demonstrates correlations between absolute HGS ([Fig. 2a]) and relative HGS ([Fig. 2b]) versus total body fat percentage, for the total cohort and separated by sex. A large effect size was noted when male and female participants were combined between absolute HGS versus total lean mass ([Fig. 1a]), while a medium to large effect size was noted between relative HGS versus body fat% ([Fig. 2b]).

Fig. 1 Relationships between absolute (total handgrip or right plus left) handgrip score versus whole body lean mass (a) and relative (total handgrip score/body weight) handgrip score versus whole body lean mass (b) for males, females, and the combined cohort.

Fig. 2 Relationships between absolute (total handgrip or right plus left) handgrip score versus total body fat percentage (a) and relative (total handgrip score/body weight) handgrip score versus total body fat percentage (b) for males, females, and the combined cohort.

Stepwise linear regression models identified three significant predictors for absolute HGS: lean mass, vertical jump height, and body fat (multiple R=0.84; adjusted R²=0.71; F=176.99; p<0.001). From this linear model, the prediction equation for absolute HGS is:

Absolute HGS (kg)=18.62–0.40 (body fat in %)+1.22 (lean mass in kg)+0.24 (vertical jump height in cm). Thus, more practically speaking, every 0.4% decrease in body fat or 1.2 kg increase in lean mass, or 0.24 cm increase in vertical jump height would result in a 1 kg increase in absolute HGS.

Stepwise linear regression identified two significant predictors for relative HGS: body fat and lean mass (multiple R=0.69; adjusted R²=0.49; F=104.6; p<0.001). From this linear model, the prediction equation for relative HGS is:

Relative HGS (kg/kg)=1.90–0.2 (body fat in %) – 0.002 (lean mass in kg). Thus, more practically speaking, every 0.2% decrease in body fat or 0.002 decrease in lean mass would result in an increase in relative HGS.

Discussion

The main conclusions derived from this study suggests that absolute HGS is strongly and positively related to lean mass, while relative HGS is strongly and negatively related to body fat percentage, in this cohort of young (~25 years old), metabolically healthy ([Table 1]), and physically fit ([Table 2]) individuals. The robust relationship seen in the current study between absolute HGS versus total lean mass ([Fig. 1a]) complements previous studies that confirm that low absolute HGS is associated with sarcopenia [21], muscle weakness [5], and disability later in life [3]. The present findings (from a young, healthy cohort) thereby extend previous findings (from older, less healthy cohorts) suggesting that in absolute terms, individuals with the highest HGS have greater amounts of lean mass.

In addition to absolute HGS, we measured relative HGS by dividing absolute HGS by body weight (kg/kg), to normalize muscular strength and eliminate the overall size bias. A previous study performed on>1 million Swedish young men demonstrated that the handgrip/body weight ratio was a strong predictor of disability and musculoskeletal disorders later in life [10]. Similarly, relative HGS thresholds for diabetes risk in middle-aged adults (mean age ~33 years) was estimated at 0.78 for males and 0.57 for females using data extracted from the National Health and Nutrition Examination Survey (NHANES) [13]. Relative HGS cut-offs for metabolic syndrome (MetS), from data obtained from 1795 Columbian college students, demonstrated that 34.6% of the weakest males (relative HGS<0.466) had MetS, while 18.0% of weakest females (relative HGS<0.437) had MetS [14]. Absolute grip strength failed to show similar associations with MetS [22].

Data on relative HGS from the current study extends these diabetes risk and MetS threshold data [13] [14] by identifying a threshold for optimal health rather than disease. We found that a relative HGS>1.2 (1.3 for males and 1.1 for females, whereas an individual’s absolute grip strength exceeded body weight) was congruent with markers of good metabolic health ([Table 1]) and physical fitness ([Table 2]). Moreover, the strong negative association between relative HGS versus body fat percentage ([Fig. 2b]) highlights the probability, confirmed by linear modeling in the current study, that excess body fat reduces relative HGS (<1.0) towards previously estimated cut-off thresholds that predict both diabetes [13] and MetS [14]. Thus, our data concur with prior conclusions that increased body weight is the strongest predictor of disability later in life [10] but offers additional clarity by adding that body fat compartmental expansion likely serves as the primary mediator for both metabolic disease [13] [14] [23] and disability [10].

Participants in the current study were largely young, metabolically healthy, and physically fit. The average BMI was within the normal range (<24.9 kg/m²), with males slightly over the overweight threshold (25.3 kg/m²) likely due to increased lean mass [24]. Metrics of metabolic health such as blood pressure, RHR, VAT, and BG levels were within the normal ranges [25] [26] [27], with the exception of slightly elevated resting SBP values (129 mmHg) in our male cohort. Previous studies conducted in older (~65 years) populations document inverse relationships between HGS and hypertension [9]. Our data showed unexpected positive correlations between both absolute and relative HGS versus SBP, which contradicts many studies that suggest that resistance training decreases blood pressure [28]. Any potential negative effects of increased muscle strength on blood pressure in fit young males thereby requires further study.

In addition to our participants being metabolically healthy, the average VO₂ max values for both the male (47 ml/kg/min) and female (40 ml/kg/min) cohorts would categorize their aerobic fitness between “fair” and “good” [17]. Similarly, the average vertical jump values for males (23”) and females (17”), would classify them as average or above-average in comparison with other young healthy adults [29]. Coupled with average absolute HGS values that were classified as “good” (males) and “excellent” (females), we confirm that our convenience sample was physically fit in addition to being metabolically healthy.

Lastly, significant, positive relationships (medium effect size, [Table 3]) between relative HGS verses both VO₂ max and vertical jump (lower extremity power) suggests that normalized HGS may be a surrogate marker for overall physical fitness – not just localized forearm strength. This finding aligns with previous studies conducted on older adults (>65 years), whereas HGS estimated not only aerobic fitness [7] [8] but also flexibility, balance and coordination, and overall physical fitness [7]. Therefore, we suggest that relative HGS be viewed as a more holistic marker of overall fatness and fitness than absolute HGS. Improvements in relative HGS would thereby require improvements in overall metabolic health (decrease body fat) and general physical fitness (aerobic fitness, upper and lower body strength improvements) to improve low relative HGS values (<1.0) into a healthier range (>1.0).

The limitations of the current study include a bias towards a physically fit sample. Our cross-sectional screening mainly attracted participants who were already physically active, despite our best efforts to recruit more sedentary college students. Our skewed sample of fit individuals, who exercise regularly, limits the broader interpretation of these findings to more sedentary populations. Nonetheless, our findings complement the large body of literature collected from older, diseased populations. One additional limitation was our assumption that the Handexer dynamometer was accurate (because it was FDA-approved). As such we did not perform any additional calibrations during data collection to ensure these data were accurate against a known standard.

In conclusion, our regression analyses and linear models confirm that higher absolute HGS is strongly biased towards larger, more muscular individuals. Conversely, higher relative HGS is most strongly biased towards individuals with lower body fat percentages. Our “average” data (means±SD), for all health and performance measures, would suggest that a relative HGS>1.0 (whereas an individual’s grip strength exceeds body weight) may be a quick, simple, cost-effective, and clinically useful indicator of overall metabolic health and physical fitness in young adults, worthy of further study as a holistic marker of health.

Conflict of Interest

The authors declare that they have no conflict of interest.

Acknowledgements

We would like to thank all of our enthusiastic participants for donating their time and bodies towards participating in this trial. We would also like to thank Carlissa Baker, Nakayla Adams, Matt Raezler, Marissa Smith, Sarah Rogers, Matt VanSumeren, and Mike Brown for assisting with data collection. No financial support was obtained for this study.

• References

• 1 Soysal P, Hurst C, Demurtas J. et al. Handgrip strength and health outcomes: Umbrella review of systematic reviews with meta-analyses of observational studies. J Sport Health Sci 2021; 10: 290-295
• 2 López-Bueno R, Andersen LL, Calatayud J. et al. Associations of handgrip strength with all-cause and cancer mortality in older adults: a prospective cohort study in 28 countries. Age Ageing 2022; 51: afac117
• 3 Henriksson H, Henriksson P, Tynelius P. et al. Muscular weakness in adolescence is associated with disability 30 years later: a population-based cohort study of 1.2 million men. Br J Sports Med 2019; 53: 1221-1230
• 4 Esteban-Cornejo I, Ho FK, Petermann-Rocha F. et al. Handgrip strength and all-cause dementia incidence and mortality: findings from the UK Biobank prospective cohort study. J Cachexia Sarcopenia Muscle 2022; 13: 1514-1525
• 5 Celis-Morales CA, Welsh P, Lyall DM. et al. Associations of grip strength with cardiovascular, respiratory, and cancer outcomes and all cause mortality: prospective cohort study of half a million UK Biobank participants. Bmj 2018; 361: k1651
• 6 López-Bueno R, Andersen LL, Koyanagi A. et al. Thresholds of handgrip strength for all-cause, cancer, and cardiovascular mortality: A systematic review with dose-response meta-analysis. Ageing Res Rev 2022; 82: 101778
• 7 Kim SH, Kim T, Park JC. et al. Usefulness of hand grip strength to estimate other physical fitness parameters in older adults. Sci Rep 2022; 12: 17496
• 8 Sugie MHK, Takahashi T, Nara M. et al. Relationship between hand grip strength and peak VO2 in community-dwelling elderly outpatients. JCSM Clinical Reports 2018; 3: 1-10
• 9 Polo-López A, Calatayud J, Núñez-Cortés R. et al. Dose-response association between handgrip strength and hypertension: A longitudinal study of 76,503 European older adults. Curr Probl Cardiol 2023; 48: 101813
• 10 Ropponen A, Silventoinen K, Tynelius P. et al. Association between hand grip/body weight ratio and disability pension due to musculoskeletal disorders: a population-based cohort study of 1 million Swedish men. Scand J Public Health 2011; 39: 830-838
• 11 Yi DW, Khang AR, Lee HW. et al. Relative handgrip strength as a marker of metabolic syndrome: the Korea National Health and Nutrition Examination Survey (KNHANES) VI (2014-2015). Diabetes Metab Syndr Obes 2018; 11: 227-240
• 12 Silva CR, Saraiva B, Nascimento DDC. et al. Relative handgrip strength as a simple tool to evaluate impaired heart rate recovery and a low chronotropic index in obese older women. Int J Exerc Sci 2018; 11: 844-855
• 13 Brown EC, Buchan DS, Madi SA. et al. Grip strength cut points for diabetes risk among apparently healthy U.S. adults. Am J Prev Med 2020; 58: 757-765
• 14 Garcia-Hermoso A, Tordecilla-Sanders A, Correa-Bautista JE. et al. Muscle strength cut-offs for the detection of metabolic syndrome in a nonrepresentative sample of collegiate students from Colombia. J Sport Health Sci 2020; 9: 283-290
• 15 Cronin J, Lawton T, Harris N. et al. A brief review of handgrip strength and sport performance. J Strength Cond Res 2017; 31: 3187-3217
• 16 Centers for Disease Control (CDC) and Prevention. 2021 Body Composition Procedures Manual. 2021 May: 1–120
• 17 American College of Sports Medicine. ACSM's Health-Related Physical Fitness Assessment (American College of Sports Medicine) 5th Edition. Philadelphia PA: Wolters Kluwer Medicine; 2018
• 18 Scrimgeour AG, Noakes TD, Adams B. et al. The influence of weekly training distance on fractional utilization of maximum aerobic capacity in marathon and ultramarathon runners. Eur J Appl Physiol Occup Physiol 1986; 55: 202-209
• 19 Johnson SL, Stone WJ, Bunn JA. et al. New author guidelines in statistical reporting: Embracing an era beyond p < .05. Int J Exerc Sci 2020; 13: 1-5
• 20 Sullivan GM, Feinn R. Using effect size--or why the p value is not enough. J Grad Med Educ 2012; 4: 279-282
• 21 Lee SH, Gong HS. Measurement and interpretation of handgrip strength for research on sarcopenia and osteoporosis. J Bone Metab 2020; 27: 85-96
• 22 Byeon JY, Lee MK, Yu MS. et al. Lower relative handgrip strength is significantly associated with a higher prevalence of the metabolic syndrome in adults. Metab Syndr Relat Disord 2019; 17: 280-288
• 23 Unamuno X, Gómez-Ambrosi J, Rodríguez A. et al. Adipokine dysregulation and adipose tissue inflammation in human obesity. Eur J Clin Invest 2018; 48: e12997
• 24 Weir CB, Jan A. BMI classification percentile and cut off points. In StatPearls. Treasure Island (FL): StatPearls Publishing; 2024
• 25 Moebus S, Göres L, Lösch C. et al. Impact of time since last caloric intake on blood glucose levels. Eur J Epidemiol 2011; 26: 719-728
• 26 American Diabetes Association. 2. Classification and diagnosis of diabetes: Standards of medical care in diabetes-2018. Diabetes Care 2018; 41: S13-S27
• 27 American Heart Association. Understanding blood pressure readings. Available online https://www.heart.org/en/health-topics/high-blood-pressure/understanding-blood-pressure-readings Accessed: 10-23-2024)
• 28 Edwards JJ, Deenmamode AHP, Griffiths M. et al. Exercise training and resting blood pressure: a large-scale pairwise and network meta-analysis of randomised controlled trials. Br J Sports Med 2023; 57: 1317-1326
• 29 Patterson D, Peterson DF. Vertical jump and leg power norms for young adults. MSSE 2004; 36: S114

Publication History

Received: 30 October 2024

Accepted: 07 February 2025

Accepted Manuscript online:
11 March 2025

Article published online:
22 April 2025