Sex-Based Kinanthropometric and Health Metric Analysis in Amateur Athletes Across Various Disciplines: A Comparative Study

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The establishment of sports and health interventions based on different anthropometric profiles, considering the differences between the sexes, and a demonstration that physical activity is beneficial for health.

Abstract

Kinanthropometry is the study of body dimensions and composition measurements, which are influenced by factors such as age and nutritional status, establishing a relationship between static measurements and dynamic performance. This study aimed to evaluate the kinanthropometric differences among 403 individuals (aged 18–42), categorized by biological sex and the recreational sport they practiced. The main objective of this study was to clarify whether or not there were statistically significant differences between these groups. All of the measurements and indices were obtained following the International Society for the Advancement of Kinanthropometry (ISAK) protocol. Significant differences were found in most variables among the different sports. In general, the men showed higher values in terms of weight, height, body circumference, body mass index (BMI), relative index of the lower limbs (RILLs), percentage of muscle mass (%M), and percentage of residual mass (%R). The women exhibited higher values in terms of skinfold thicknesses, Cormic index (CI), body density index (BDI), percentage of fat mass (%F), and percentage of bone mass (%B). These findings can guide individuals in selecting sports based on their morphotype, optimizing their physical performance in recreational activities and improving their health and quality of life.

1. Introduction

Anthropometry is a vital tool for identifying differences in body dimensions and compositions among various population groups, providing critical insights into physical health and performance [1]. The World Health Organization (WHO) recognizes anthropometry as an inexpensive, non-invasive method to monitor health, and it is widely used in both clinical and research settings [2]. Everyone possesses unique morphological characteristics, influenced by genetics, age, sex, and lifestyle, that define their anatomical and functional traits [3].

The International Society for the Advancement of Kinanthropometry (ISAK) offers a standardized methodology for the collection and interpretation of anthropometric data, ensuring reliable measurements across different populations [4]. Using a specific research methodology is crucial because it provides guidelines for investigators to define their questions and objectives, making it possible to correlate their results and draw conclusions. This approach enables a detailed understanding of how body structure impacts physical performance and health outcomes. In this study, we assessed the kinanthropometric differences among amateur athletes grouped by biological sex and their recreational sport of choice, as defined by Piercy’s criteria [5]. That is, we classified an amateur athlete as someone who engaged in at least 150 min of moderate-intensity aerobic activity or 90 min of high-intensity activity per week.

Physical activity has been well-established as a cornerstone of public health, offering numerous benefits that extend beyond physical fitness. Sedentary lifestyles are strongly associated with negative health outcomes including cardiovascular disease, diabetes, and mental health disorders [6,7]. Regular exercise has been shown to significantly reduce the risk of more than 25 chronic conditions including hypertension, type 2 diabetes, and certain cancers and lower the risk of premature mortality by 20–30% [8,9,10]. These findings highlight the importance of physical activity in both the prevention and management of chronic diseases [11].

The application of anthropometry in sports science provides valuable insights into how body composition can influence performance and susceptibility to injury [12]. Kinanthropometric data are crucial for understanding the physical demands of various sports and how different body types are better suited for specific activities [13,14]. This is particularly relevant for amateur athletes, as tailored training programs based on individual body metrics may help optimize performance and reduce the risk of injury [15].

Furthermore, sex-based differences in body composition, such as muscle mass, fat distribution, and bone density, play a significant role in health and performance outcomes [16]. Men typically exhibit greater muscle mass and strength, while women tend to have higher body fat percentages and distinct patterns of fat distribution, which are important considerations in both sport and health contexts [17]. Understanding these differences can help develop more targeted interventions that promote optimal health and performance for both sexes.

Given the broad implications of physical activity for public health and the potential of anthropometric data to guide personalized exercise and health recommendations, it is crucial to continue research in this field. By analyzing kinanthropometric differences among recreational athletes, we can better understand how body composition influences sport participation and long-term health outcomes. This knowledge could also aid in the development of interventions aimed at promoting healthy living and reducing the risk of various pathologies [18]. Therefore, we decided to analyze the anthropometric differences between the sexes and among different sport modalities in a heterogeneous population, as we believe that these insights could be used as a reference for many health and sport professionals.

2. Materials and Methods

2.1. Legal Documents

The sample for our study was obtained voluntarily from different kinds of sports complexes in Malaga and at the University Podiatry Clinic facilities of the University of Extremadura, which are both located in Spain. We received approval from the Bioethics Committee of the University of Extremadura before we started our investigation (reference 169/2019). All participants were informed of the procedure and had to sign the corresponding informed consent form before their measurements were taken.

2.2. Inclusion Criteria

The participants had to be between 18 and 42 years of age and free from any pathology or injury at the time of the measurements. We also determined that they should be following a Mediterranean diet, which is commonly consumed in the study region [19].

We followed Piercy’s criteria to define a person as an amateur athlete [5]. According to the criteria, they should be completing at least 150 min of moderate-intensity physical exercise or 90 min of vigorous-intensity activity per week (Figure 1).

2.3. Exclusion Criteria

The exclusion criteria were as follows: the presence of any injury or medical condition at the time of the study; the use of special diets or dietary supplements; failing to meet Piercy’s criteria; and participation in multiple sports or competing at a professional or federative level.

2.4. Study Sample

This study analyzed 403 individuals, with an average age of 25.03 ± 4.65 years. The participants were grouped according to their biological sex and the recreational sport they regularly practiced (

Table 1. Categories of the subjects studied and their biological sex.

Sport	Total		Men		Women
	N	%	N	%	N	%
Basketball	53	13.15%	27	10.93%	26	16.67%
Handball	48	11.91%	26	10.53%	22	14.10%
Walking	64	15.88%	32	12.96%	32	20.51%
Running	49	12.16%	40	16.19%	9	5.77%
CrossFit	49	12.16%	38	15.38%	11	7.05%
Soccer	69	17.12%	52	21.05%	17	10.90%
Gym	31	7.69%	16	6.48%	15	9.62%
Volleyball	40	9.93%	16	6.48%	24	15.38%
TOTAL	403	100.00%	247	100.00%	156	100.00%

N = population size; % = percentage of population.

).

2.5. Study Variables

The following anthropometric measurements were taken:

-

Weight (kg);

-

Height (cm) in standing and sitting positions;

-

Arm span (cm);

-

Perimeters (cm) of contracted arm, waist, hip, thigh, and calf;

-

Diameters (cm) of wrist styloid process and femoral bicondylar;

-

Skinfold thicknesses (mm) of the biceps, triceps, subscapular, abdominal, suprailiac, thigh, and calf. The pectoral skinfold was only measured in men to avoid creating discomfort among the female participants as it was not deemed critical for this study’s objectives.

From these measurements, several kinanthropometric indices were calculated:

-

Body mass index (BMI);

-

Ponderal index (PI);

-

Cormic index (CI);

-

Relative index of lower limbs (RILLs);

-

Body density index (BDI).

Additionally, the following body composition parameters were estimated:

-

Fat percentage (F%);

-

Muscle percentage (M%);

-

Bone percentage (B%);

-

Residual percentage (R%).

2.6. Protocol

The participants provided their informed consent prior to their enrollment, acknowledging that their data would be used for research purposes while ensuring that their privacy was protected. Ethical approval for this study was obtained from the Bioethics Committee of the University of Extremadura (169/2019).

All data collection followed the protocol established by the International Society for the Advancement of Kinanthropometry (ISAK) [4]. Sociodemographic data including biological sex, age, training frequency (hours and days per week), and years of recreational sport participation were collected.

All measurements were performed by a single evaluator with ISAK Level 1 accreditation to ensure consistency and accuracy. To further minimize measurement errors, each measurement was repeated three times, and the average was used for analysis. The equipment used included a SECA701^® electronic scale with a height rod (Hamburg, Germany), a Lawton18-0160^® tape measure (Franconia, NH, USA), and a Trimmeter H3001^® digital caliper, all of which have been internationally approved and calibrated.

2.7. Statistical Analysis

Data analysis was performed using IBM-SPSS Statistics 25.0^® software. A significance level of 5% (p ≤ 0.05) was set, with high significance defined at 1% (p ≤ 0.001). The qualitative variables were analyzed using the Chi-square test or Fisher’s exact test, depending on the sample distribution [20]. For the quantitative variables, normality was assessed using the Shapiro–Wilk test, while the homogeneity of variances was evaluated with Levene’s test. Depending on the results, the Student’s t-test or Mann–Whitney U test was employed to compare the groups. Additionally, to analyze whether there was an association between age and the different variables (anthropometrics and kinanthropometrics), we used Pearson’s coefficient to analyze whether there was a mathematical correlation between them and linear regression to analyze how age could influence these parameters. A negative result of the mathematical correlation indicates that the older one is, the smaller the variable. Meanwhile, a positive correlation indicates that the older one is, the higher the variable.

3. Results

3.1. Sociodemographic and Lifestyle Differences

3.1.1. Sociodemographic and Lifestyle Differences According to Sex

The sociodemographic and lifestyle results of the participants showed several statistically significant differences depending on sex in terms of age and hours of weekly physical activity. In both cases, higher values were found among the men than among the women (

Table 2. Sociodemographic and lifestyle results of the participants.

Variable Analyzed		Study Groups
		T	B	H	W	R	C	S	G	V
Age	T	25.03 ± 4.65	22.15 ± 3.08	22.79 ± 2.74	25.36 ± 2.54	30.18 ± 6.00	26.59 ± 4.14	25.14 ± 4.34	25.39 ± 4.98	22.28 ± 3.34
	M	25.90 ± 4.89	22.89 ± 2.89	23.08 ± 2.76	25.38 ± 2.34	31.65 ± 5.33	26.50 ± 4.09	25.35 ± 4.67	24.69 ± 5.61	23.88 ± 2.47
	F	23.64 ± 3.86	21.38 ± 3.14	22.45 ± 2.76	25.34 ± 2.77	23.67 ± 4.36	26.91 ± 0.48	24.53 ± 3.12	26.13 ± 4.27	21.21 ± 3.46
		p ≤ 0.001 *	p = 0.075	p = 0.440	p = 0.961	p ≤ 0.001 *	p = 0.776	p = 0.504	p = 0.428	p = 0.011 *
Training hours per week	T	4.65 ± 1.91	5.45 ± 1.08	5.60 ± 0.49	2.27 ± 0.78	3.63 ± 1.87	5.18 ± 0.86	5.75 ± 1.97	3.26 ± 1.34	6.00 ± 1.75
	M	4.68 ± 1.90	5.37 ± 1.45	5.62 ± 0.50	2.28 ± 0.68	3.93 ± 1.82	5.24 ± 0.94	5.35 ± 2.11	2.88 ± 0.89	7.00 ± 0.00
	F	4.60 ± 1.94	5.54 ± 0.51	5.59 ± 0.50	2.25 ± 0.88	2.33 ± 1.58	5.00 ± 0.45	7.00 ± 0.00	3.67 ± 1.63	5.33 ± 2.01
		p = 0.023 *	p = 0.578	p = 0.583	p ≤ 0.001 *	p ≤ 0.001 *	p = 0.142	p = 0.650	p ≤ 0.001 *	p ≤ 0.001 *
Training days per week	T	3.65 ± 1.36	3.62 ± 0.95	4.21 ± 0.99	2.30 ± 0.81	4.65 ± 1.61	3.41 ± 0.61	4.04 ± 1.42	2.81 ± 1.08	4.25 ± 1.32
	M	3.73 ± 1.41	3.67 ± 1.21	4.23 ± 0.99	2.25 ± 0.72	4.85 ± 1.51	3.45 ± 0.60	3.73 ± 1.51	2.63 ± 1.02	5.00 ± 0.00
	F	3.53 ± 1.28	3.58 ± 0.58	4.18 ± 1.01	2.34 ± 0.90	3.78 ± 1.86	3.27 ± 0.65	5.00 ± 0.00	3.00 ± 1.13	3.75 ± 1.51
		p = 0.138	p = 0.733	p = 0.866	p = 0.071	p = 0.071	p = 0.408	p = 0.001 *	p = 0.341	p = 0.002 *
Years of training	T	5.02 ± 4.99	7.31 ± 3.79	7.40 ± 4.75	0.26 ± 0.59	3.04 ± 2.54	1.49 ± 0.56	9.19 ± 6.10	1.81 ± 2.54	6.11 ± 3.44
	M	5.14 ± 5.18	7.50 ± 4.00	7.04 ± 4.50	0.23 ± 0.51	3.47 ± 2.47	1.57 ± 0.57	8.94 ± 6.85	1.00 ± 1.56	7.38 ± 2.63
	F	4.83 ± 4.68	7.12 ± 3.63	7.89 ± 5.16	0.29 ± 0.66	1.22 ± 2.11	1.00 ± 0.00	9.94 ± 2.97	2.55 ± 3.08	5.10 ± 3.73
		p = 0.584	p = 0.724	p = 0.569	p = 0.015 *	p = 0.015 *	p = 0.035 *	p = 0.563	p = 0.170	p = 0.047 *

* = statistically significant, T = total, M = male, F = female, p = probability value, B = basketball, H = handball, W = walking, R = running, C = CrossFit, S = soccer, G = gym, V = volleyball.

).

3.1.2. Sociodemographic and Lifestyle Differences According to Sex in Different Sports Modalities

When evaluating the differences based on sex in each of the sports modalities, we did not observe statistically significant differences in the sociodemographic values between those people who practiced basketball and handball, but we did observe them in the rest of the sports analyzed. For the age variable, differences were observed between the people who ran and those who practiced volleyball, with values being higher among the men. Regarding the hours of physical activity per week, differences were observed among the people who walked, those who ran, those who went to the gym, and those who played volleyball. The values were higher among the men in all cases, except in the case of those that went to the gym. When analyzing the days of training per week, we observed statistically significant differences between those who played soccer and volleyball, with higher values among the women and men, respectively. Regarding years of regular training, statistically significant differences were observed between those who walked, those who ran, those who did CrossFit, and those who played volleyball, with higher values among the men in all cases, except among the group of those who walked (Table 2).

3.2. Anthropometric Differences

3.2.1. Anthropometric Differences According to Sex

According to sex, we observed significantly higher values in the men in terms of weight, standing height, sitting height, arm span, arm perimeter, waist perimeter, calf perimeter, and styloid diameter of the wrist. The women had significantly higher values in most of the skinfold measurements such as the triceps, subscapular, abdominal, suprailiac, and thigh (

Table 3. Anthropometric results of the participants.

Variable Analyzed		Study Groups
		T	B	H	W	R	C	S	G	V
Weight	T	73.70 ± 11.86	80.69 ± 8.83	76.85 ± 15.80	71.79 ± 11.56	69.57 ± 8.90	78.99 ± 11.03	71.93 ± 11.12	70.85 ± 11.23	67.52 ± 8.71
	M	78.92 ± 10.44	86.16 ± 6.35	86.29 ± 5.07	79.23 ± 7.65	71.52 ± 8.07	83.09 ± 8.57	75.46 ± 9.84	77.45 ± 9.11	75.39 ± 4.51
	F	65.43 ± 8.90	75.02 ± 7.37	65.68 ± 6.86	64.35 ± 9.92	60.90 ± 7.39	64.82 ± 5.29	61.12 ± 7.24	63.81 ± 8.86	62.27 ± 6.60
		p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p = 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *
Standing height	T	173.98 ± 9.38	180.75 ± 6.61	177.10 ± 9.31	168.78 ± 7.36	174.16 ± 8.23	174.41 ± 6.87	174.65 ± 9.26	170.52 ± 9.06	170.40 ± 7.12
	M	178.10 ± 7.97	184.02 ± 6.13	184.73 ± 12.16	173.86 ± 4.68	176.10 ± 7.64	176.45 ± 5.97	178.02 ± 7.69	177.44 ± 4.76	175.63 ± 4.30
	F	167.47 ± 7.60	177.35 ± 5.30	168.09 ± 7.94	163.70 ± 5.93	165.56 ± 0.45	167.35 ± 4.97	164.35 ± 5.17	163.13 ± 6.21	166.92 ± 6.50
		p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *
Sitting height	T	90.60 ± 5.18	93.15 ± 4.65	92.90 ± 6.94	88.54 ± 3.87	86.57 ± 5.08	91.94 ± 4.17	90.79 ± 4.54	90.16 ± 3.93	171.05 ± 9.38
	M	92.18 ± 5.10	93.59 ± 5.87	96.85 ± 5.58	90.88 ± 2.72	87.08 ± 5.15	93.09 ± 3.83	92.24 ± 4.04	92.63 ± 2.47	177.94 ± 6.73
	F	88.11 ± 4.28	92.69 ± 2.96	88.23 ± 5.35	86.19 ± 3.42	84.33 ± 4.33	87.95 ± 2.53	86.35 ± 2.76	87.53 ± 3.48	163.70 ± 5.29
		p ≤ 0.001 *	p = 0.486	p ≤ 0.001 *	p ≤ 0.001 *	p = 0.146	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *
Arm span	T	174.78 ± 11.68	182.70 ± 7.45	178.27 ± 9.71	169.45 ± 8.14	174.95 ± 10.14	175.54 ± 8.37	176.58 ± 10.93	91.10 ± 4.41	167.28 ± 16.94
	M	179.37 ± 9.47	186.93 ± 6.55	186.35 ± 12.40	174.66 ± 5.83	177.13 ± 9.27	177.75 ± 7.55	179.96 ± 10.08	94.69 ± 2.94	173.63 ± 4.57
	F	167.52 ± 11.17	178.31 ± 5.60	168.73 ± 7.83	164.23 ± 6.68	165.28 ± 8.27	167.91 ± 6.50	166.24 ± 5.73	88.71 ± 3.53	163.04 ± 8.65
		p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p = 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p = 0.052
Arm perimeter	T	32.25 ± 4.40	32.76 ± 3.78	33.77 ± 5.91	31.77 ± 4.18	29.54 ± 3.12	36.56 ± 2.79	31.93 ± 3.75	30.48 ± 3.89	30.46 ± 3.27
	M	33.54 ± 4.32	33.31 ± 3.46	36.58 ± 6.11	33.22 ± 4.01	29.89 ± 3.11	37.22 ± 2.14	32.61 ± 3.87	33.09 ± 2.74	33.56 ± 2.34
	F	30.20 ± 3.71	32.19 ± 4.08	30.45 ± 3.53	30.33 ± 3.89	28.00 ± 2.83	34.27 ± 3.61	29.88 ± 2.47	27.70 ± 2.89	28.40 ± 1.84
		p ≤ 0.001 *	p = 0.284	p ≤ 0.001 *	p = 0.005 *	p = 0.102	p ≤ 0.001 *	p = 0.008 *	p ≤ 0.001 *	p ≤ 0.001 *
Waist perimeter	T	83.83 ± 9.55	88.64 ± 8.71	84.00 ± 6.44	88.19 ± 11.48	78.86 ± 8.77	86.29 ± 7.80	84.33 ± 8.30	77.65 ± 8.84	77.33 ± 7.05
	M	85.57 ± 8.89	91.93 ± 7.64	85.92 ± 6.14	93.00 ± 7.01	80.28 ± 8.46	85.74 ± 7.85	85.87 ± 8.78	79.88 ± 5.26	77.06 ± 5.11
	F	81.08 ± 9.92	85.23 ± 8.58	81.73 ± 6.16	83.38 ± 13.08	72.56 ± 7.63	88.18 ± 7.68	79.65 ± 3.98	75.27 ± 11.22	77.50 ± 8.20
		p ≤ 0.001 *	p = 0.004 *	p = 0.023 *	p ≤ 0.001 *	p = 0.015 *	p = 0.366	p = 0.006 *	p = 0.150	p = 0.850
Hip perimeter	T	94.10 ± 6.67	97.28 ± 5.19	95.17 ± 8.08	97.16 ± 8.17	90.94 ± 5.63	93.00 ± 5.22	92.30 ± 5.33	92.84 ± 6.67	93.00 ± 5.17
	M	94.20 ± 7.13	97.00 ± 6.18	97.96 ± 9.40	98.53 ± 8.51	90.50 ± 5.65	93.58 ± 5.54	92.19 ± 5.99	93.88 ± 6.14	92.25 ± 4.28
	F	93.94 ± 5.87	97.58 ± 4.01	91.86 ± 4.42	95.78 ± 7.69	92.88 ± 5.40	91.00 ± 3.38	92.65 ± 2.47	91.73 ± 7.25	93.50 ± 5.72
		p = 0.707	p = 0.690	p = 0.008 *	p = 0.180	p = 0.256	p = 0.151	p = 0.762	p = 0.381	p = 0.461
Thigh perimeter	T	50.62 ± 6.38	55.16 ± 4.05	47.77 ± 6.31	49.55 ± 6.61	51.24 ± 3.53	52.87 ± 3.64	48.7 ± 7.90	52.21 ± 4.08	48.30 ± 8.06
	M	50.15 ± 6.28	53.43 ± 4.09	50.77 ± 6.19	48.75 ± 6.60	50.83 ± 2.81	53.29 ± 3.68	47.35 ± 8.56	52.72 ± 4.41	43.81 ± 3.80
	F	51.35 ± 6.48	56.96 ± 3.19	44.23 ± 4.39	50.36 ± 6.62	53.11 ± 5.60	51.41 ± 3.22	52.72 ± 2.93	51.67 ± 3.77	51.29 ± 8.79
		p = 0.065	p ≤ 0.001 *	p ≤ 0.001 *	p = 0.334	p = 0.079	p = 0.133	p = 0.014 *	p = 0.483	p = 0.003 *
Calf perimeter	T	35.34 ± 3.47	36.07 ± 2.49	34.78 ± 2.78	36.40 ± 4.38	35.07 ± 3.11	35.32 ± 3.38	35.04 ± 3.31	34.97 ± 5.19	34.53 ± 2.39
	M	35.70 ± 3.77	35.72 ± 2.30	35.65 ± 2.95	37.38 ± 4.65	34.93 ± 3.06	35.84 ± 3.52	35.74 ± 3.41	36.03 ± 7.00	33.56 ± 2.99
	F	34.77 ± 2.84	36.42 ± 2.67	33.75 ± 2.20	35.42 ± 3.92	35.72 ± 3.47	33.50 ± 2.06	32.88 ± 1.73	33.83 ± 1.57	35.17 ± 1.66
		p = 0.008 *	p = 0.310	p ≤ 0.001 *	p = 0.074	p = 0.494	p = 0.042 *	p = 0.002 *	p = 0.245	p = 0.035 *
Styloid diameter of the wrist	T	7.76 ± 0.78	8.17 ± 0.78	8.00 ± 0.65	7.50 ± 0.77	7.80 ± 0.68	7.68 ± 0.73	7.86 ± 0.79	7.50 ± 0.85	7.45 ± 0.72
	M	7.89 ± 0.76	8.46 ± 0.83	8.00 ± 0.63	7.67 ± 0.74	7.83 ± 0.67	7.76 ± 0.64	8.03 ± 0.80	7.71 ± 0.72	7.31 ± 0.70
	F	7.57 ± 0.77	7.87 ± 0.59	8.00 ± 0.69	7.34 ± 0.79	7.69 ± 0.78	7.41 ± 0.97	7.35 ± 0.49	7.28 ± 0.94	7.54 ± 0.74
		p ≤ 0.001 *	p = 0.004 *	p = 1.000	p = 0.084	p = 0.595	p = 0.161	p = 0.002 *	p = 0.160	p = 0.333
Bicondylar diameter of the femur	T	14.15 ± 1.25	14.48 ± 1.16	14.46 ± 0.85	13.60 ± 1.43	14.42 ± 1.50	13.92 ± 1.05	13.97 ± 1.27	13.93 ± 0.90	14.66 ± 1.22
	M	14.24 ± 1.26	14.83 ± 1.08	14.588 ± 0.95	13.92 ± 1.41	14.44 ± 1.56	14.11 ± 1.05	14.01 ± 1.33	13.99 ± 0.94	14.19 ± 0.98
	F	14.00 ± 1.22	14.12 ± 1.14	14.32 ± 0.72	13.27 ± 1.39	14.33 ± 1.30	13.27 ± 0.79	13.88 ± 0.73	13.85 ± 0.89	14.98 ± 1.27
		p = 0.056	p = 0.024 *	p = 0.298	p = 0.067	p = 0.846	p = 0.019 *	p = 0.578	p = 0.673	p = 0.042 *
Biceps skinfold	T	8.83 ± 367	9.66 ± 3.92	9.50 ± 3.34	11.36 ± 3.98	8.41 ± 4.06	6.51 ± 2.65	8.67 ± 3.15	8.26 ± 3.04	6.93 ± 1.76
	M	8.72 ± 3.76	8.93 ± 3.67	10.46 ± 3.65	12.16 ± 3.51	8.10 ± 4.17	6.05 ± 2.47	8.37 ± 3.09	9.56 ± 3.24	6.88 ± 1.71
	F	8.99 ± 3.54	10.42 ± 4.09	8.36 ± 2.57	10.56 ± 4.31	9.78 ± 3.38	8.09 ± 2.77	9.59 ± 3.26	6.87 ± 2.13	6.96 ± 1.83
		p = 0.468	p = 0.166	p = 0.028 *	p = 0.110	p = 0.267	p = 0.202	p = 0.167	p = 0.011 *	p = 0.886
Triceps skinfold	T	14.15 ± 5.37	16.38 ± 5.20	13.31 ± 4.54	18.16 ± 5.11	12.67 ± 3.99	11.86 ± 4.78	12.00 ± 4.99	15.19 ± 6.05	13.35 ± 4.73
	M	12.41 ± 4.73	12.93 ± 4.27	12.42 ± 4.09	16.78 ± 4.44	11.80 ± 3.77	11.84 ± 4.80	10.19 ± 4.08	12.63 ± 5.60	12.69 ± 5.00
	F	16.91 ± 5.18	19.96 ± 3.32	14.36 ± 4.90	19.53 ± 5.42	16.56 ± 2.30	11.91 ± 4.93	17.53 ± 3.14	17.93 ± 5.40	13.79 ± 4.60
		p ≤ 0.001 *	p ≤ 0.001 *	p = 0.142	p = 0.030 *	p ≤ 0.001 *	p = 0.968	p ≤ 0.001 *	p = 0.012 *	p = 0.477
Pectoral skinfold	T	12.02 ± 4.95	13.93 ± 5.19	12.42 ± 4.18	13.88 ± 3.85	13.95 ± 4.99	7.29 ± 2.66	10.65 ± 4.52	12.38 ± 5.52	15.00 ± 4.10
Sub- scapular skinfold	T	16.55 ± 7.07	20.04 ± 5.79	17.00 ± 5.69	23.75 ± 7.36	13.78 ± 4.42	12.69 ± 5.00	12.49 ± 4.89	18.16 ± 8.22	13.70 ± 5.03
	M	14.78 ± 6.15	17.93 ± 6.38	14.69 ± 4.25	22.91 ± 4.92	13.20 ± 4.00	12.29 ± 5.43	11.42 ± 4.47	14.13 ± 6.34	14.75 ± 5.21
	F	19.35 ± 7.52	22.23 ± 4.20	19.73 ± 6.06	24.59 ± 9.18	16.33 ± 5.50	14.09 ± 2.81	15.76 ± 4.76	22.47 ± 7.95	13.00 ± 4.89
		p ≤ 0.001 *	p = 0.006 *	p = 0.002 *	p = 0.363	p = 0.054	p = 0.297	p = 0.001 *	p = 0.003 *	p = 0.287
Abdominal skinfold	T	16.95 ± 7.23	20.08 ± 6.47	15.94 ± 5.43	23.36 ± 7.61	14.71 ± 4.90	14.35 ± 6.17	13.06 ± 0.624	19.19 ± 8.46	14.65 ± 4.56
	M	15.14 ± 6.58	17.19 ± 7.03	13.27 ± 4.38	22.66 ± 5.18	14.25 ± 4.96	14.16 ± 6.70	12.13 ± 6.39	14.69 ± 5.20	14.50 ± 5.03
	F	19.81 ± 7.30	23.08 ± 4.17	19.09 ± 4.90	24.06 ± 9.48	16.78 ± 4.27	15.00 ± 4.02	15.88 ± 4.90	24.00 ± 8.76	14.75 ± 4.33
		p ≤ 0.001 *	p = 0.001 *	p ≤ 0.001 *	p = 0.464	p = 0.164	p = 0.695	p = 0.031 *	p = 0.001 *	p = 0.868
Suprailiac skinfold	T	16.68 ± 7.47	21.94 ± 7.13	16.56 ± 5.23	23.38 ± 8.51	12.63 ± 4.26	12.33 ± 4.43	13.23 ± 6.20	17.61 ± 7.11	14.68 ± 4.84
	M	14.60 ± 6.51	20.30 ± 8.38	14.73 ± 5.05	21.19 ± 5.80	11.63 ± 3.40	11.88 ± 4.35	11.52 ± 5.56	14.88 ± 5.44	15.31 ± 4.57
	F	19.99 ± 7.72	23.65 ± 5.17	18.73 ± 4.66	25.56 ± 10.18	17.11 ± 4.99	14.09 ± 4.44	18.47 ± 5.11	20.53 ± 7.67	14.25 ± 5.07
		p ≤ 0.001 *	p = 0.087	p = 0.007 *	p = 0.039 *	p ≤ 0.001 *	p = 0.135	p ≤ 0.001 *	p = 0.024 *	p = 0.504
Thigh skinfold	T	16.44 ± 8.03	46.79 ± 6.41	13.35 ± 6.63	21.69 ± 8.36	19.88 ± 6.24	14.14 ± 8.05	13.93 ± 7.70	20.81 ± 8.20	10.85 ± 4.88
	M	15.69 ± 7.44	16.15 ± 5.94	12.81 ± 6.03	20.63 ± 6.78	19.10 ± 6.06	14.66 ± 8.20	14.60 ± 8.07	14.50 ± 5.39	8.44 ± 4.15
	F	17.63 ± 8.77	17.46 ± 6.91	14.00 ± 7.36	22.75 ± 9.69	23.33 ± 6.18	12.36 ± 7.61	11.88 ± 6.18	27.53 ± 4.37	12.46 ± 4.74
		p = 0.018 *	p = 0.461	p = 0.540	p = 0.313	p = 0.065	p = 0.411	p = 0.209	p ≤ 0.001 *	p = 0.009 *
Calf skinfold	T	12.78 ± 4.97	13.77 ± 4.60	12.54 ± 3.98	15.61 ± 5.85	14.14 ± 4.83	10.96 ± 3.97	1.36 ± 5.57	11.55 ± 2.86	11.18 ± 3.86
	M	12.40 ± 5.04	12.56 ± 4.68	13.31 ± 3.84	15.25 ± 5.35	13.68 ± 4.67	10.92 ± 4.23	11.33 ± 6.06	11.38 ± 3.34	9.81 ± 4.04
	F	13.38 ± 4.81	15.04 ± 4.24	11.64 ± 4.04	15.97 ± 6.38	16.22 ± 5.24	11.09 ± 3.08	11.47 ± 3.81	11.73 ± 2.34	12.08 ± 3.53
		p = 0.054	p = 0.048 *	p = 0.149	p = 0.627	p = 0.155	p = 0.902	p = 0.927	p = 0.734	p = 0.067

* = statistically significant, T = total, M = male, F = female, p = probability value, B = basketball, H = handball, W = walking, R = running, C = CrossFit, S = soccer, G = gym, V = volleyball.

).

3.2.2. Anthropometric Differences According to Sex in Different Sports Modalities

Statistically significant results were observed in different sports modalities according to sex (Table 3):

-

Basketball: The men had higher values in weight, standing height, arm span, waist, and thigh circumference, the styloid diameter of the wrist, and bicondylar diameter of the femur. The women had higher values in some skinfolds such as the tricipital, subscapular, and abdominal folds, and the calf.

-

Handball: The men had higher values in weight, standing height, sitting height, arm span, and some circumferences such as the arm, waist, hip, thigh, calf, and biceps skinfold. The women had higher values in some skinfolds such as the subscapular, abdominal, and suprailiac folds.

-

Walking: The men had higher values in weight, standing height, sitting height, arm span, and some circumferences such as the arm and the waist. The women had higher values in some skinfolds such as the tricipital and suprailiac folds.

-

Running: The men had higher values in weight, standing height, arm span, and the circumference of the waist. The women had higher values in some skinfolds such as the tricipital and suprailiac folds.

-

CrossFit: The men had higher values in weight, standing height, sitting height, arm span, some circumferences, such as the arm and the calf, and the bicondyle diameter of the femur.

-

Soccer: The men had higher values in weight, standing height, sitting height, arm span, some circumferences such as the arm, waist, and calf, and the styloid diameter of the wrist. The women had higher values in the circumference of the thigh and some skinfolds such as the tricipital, subscapular, abdominal, and suprailiac folds.

-

Gym: The men had higher values in weight, standing height, sitting height, arm span, the circumference of the arm, and the bicipital skinfold. The women had higher values in some skinfolds such as the tricipital, subscapular, abdominal, suprailiac, and thigh folds.

-

Volleyball: The men had higher values in weight, standing height, sitting height, and circumference of the arm. The women had higher values in some circumferences such as the thigh and the calf, the bicondyle diameter of the femur, and the thigh skinfold.

3.2.3. Anthropometric Differences According to Age and Sex

Statistically significant anthropometric results were observed according to age and sex (

Table 4. Anthropometric results of the participants according to age.

Variable Analyzed		Pearson	R2	Fitted R2	p-Value
Weight	T	0.105	0.011	0.009	p = 0.035 *
	M	−0.068	0.005	0.001	p = 0.287
	F	0.062	0.004	−0.003	p = 0.441
Standing height	T	0.052	0.003	0.000	p = 0.295
	M	−0.038	0.001	−0.003	p = 0.556
	F	−0.205	0.042	0.036	p = 0.010 *
Sitting height	T	−0.132	0.017	0.015	p = 0.008 *
	M	−0.230	0.053	0.049	p ≤ 0.001 *
	F	−0.278	0.077	0.071	p ≤ 0.001 *
Arm span	T	0.056	0.003	0.001	p = 0.261
	M	−0.047	0.002	−0.002	p = 0.460
	F	−0.106	0.011	0.005	p = 0.187
Arm perimeter	T	0.116	0.014	0.011	p = 0.019 *
	M	−0.030	0.001	−0.003	p = 0.638
	F	0.179	0.032	0.026	p = 0.025 *
Waist perimeter	T	0.105	0.011	0.008	p = 0.036 *
	M	−0.009	0.000	−0.004	p = 0.887
	F	0.167	0.028	0.021	p = 0.038 *
Hip perimeter	T	−0.070	0.005	0.002	p = 0.161
	M	−0.090	0.008	0.004	p = 0.157
	F	−0.044	0.002	−0.005	p = 0.589
Thigh perimeter	T	0.025	0.001	−0.002	p = 0.620
	M	0.079	0.006	0.002	p = 0.216
	F	−0.011	0.000	−0.006	p = 0.892
Calf perimeter	T	−0.030	0.001	−0.002	p = 0.544
	M	−0.082	0.007	0.003	p = 0.198
	F	−0.014	0.000	−0.006	p = 0.867
Styloid diameter of the wrist	T	0.032	0.001	−0.001	p = 0.521
	M	0.012	0.000	−0.004	p = 0.847
	F	−0.065	0.004	−0.002	p = 0.422
Bicondylar diameter of the femur	T	0.035	0.001	−0.001	p = 0.485
	M	0.056	0.003	−0.001	p = 0.385
	F	−0.071	0.005	−0.001	p = 0.378
Biceps skinfold	T	0.021	0.000	−0.002	p = 0.675
	M	0.005	0.000	−0.004	p = 0.940
	F	0.082	0.007	0.000	p = 0.307
Triceps skinfold	T	−0.040	0.002	−0.001	p = 0.422
	M	0.030	0.001	−0.003	p = 0.636
	F	0.116	0.013	0.007	p = 0.149
Pectoral skinfold	T	0.170	0.029	0.025	p = 0.006 *
	M	0.200	0.040	0.036	p = 0.002 *
	F	-	-	-	-
Subscapular skinfold	T	−0.011	0.000	−0.002	p = 0.826
	M	−0.005	0.000	−0.004	p = 0.936
	F	0.184	0.034	0.028	p = 0.022 *
Abdominal skinfold	T	0.051	0.003	0.000	p = 0.309
	M	0.094	0.009	0.005	p = 0.141
	F	0.207	0.043	0.036	p = 0.010 *
Suprailiac skinfold	T	−0.073	0.005	0.003	p = 0.144
	M	−0.090	0.008	0.004	p = 0.158
	F	0.171	0.029	0.023	p = 0.033 *
Thigh skinfold	T	0.059	0.003	0.001	p = 0.239
	M	0.061	0.004	0.000	p = 0.343
	F	0.139	0.019	0.013	p = 0.084
Calf skinfold	T	−0.019	0.000	−0.002	p = 0.701
	M	0.013	0.000	−0.004	p = 0.841
	F	−0.017	0.000	−0.006	p = 0.835

* = statistically significant, T = total, M = male, F = female, p = probability value, R2 = coefficient of determination.

).

In general, age was positively associated with weight, arm perimeter, waist perimeter, and the pectoral skinfold. This means that these measurements tend to increase with age. At the same time, we observed that age was negatively associated with sitting height. This means that this measurement tends to decrease with age.

In males, age was positively associated with the pectoral skinfold. This means that this measurement tends to increase with age. At the same time, we observed that age was negatively associated with sitting height. This means that this measurement tends to decrease with age.

In females, age was positively associated with arm perimeter, waist perimeter, subscapular skinfold, abdominal skinfold, and suprailiac skinfold. This means that these measurements tend to increase with age. At the same time, we observed that age was negatively associated with standing height and sitting height. This means that these measurements tend to decrease with age.

3.3. Kinanthropometric Differences

3.3.1. Kinanthropometric Differences According to Sex

Statistically significant kinanthropometric differences were observed between the men and women in the BMI, CI, RILLs, and BDI results. The BMI and RILL values were higher for the men, while the CI and BDI were higher for the women (

Table 5. Kinanthropometric results of the participants.

Variable Analyzed		Study Groups
		T	B	H	W	R	C	S	G	V
BMI	T	24.24 ± 2.65	24.67 ± 2.12	24.25 ± 2.14	25.11 ± 3.21	22.90 ± 2.28	25.90 ± 2.79	23.48 ± 2.31	24.26 ± 2.58	23.20 ± 2.18
	M	24.85 ± 2.65	25.49 ± 2.12	25.09 ± 2.07	26.26 ± 2.86	23.06 ± 2.22	26.70 ± 2.55	23.77 ± 2.41	24.53 ± 2.07	24.49 ± 2.02
	F	23.28 ± 2.36	23.82 ± 1.79	23.24 ± 1.81	23.96 ± 3.17	22.22 ± 2.54	23.14 ± 1.54	22.5 ± 1.72	23.96 ± 3.07	22.34 ± 1.87
		p ≤ 0.001 *	p = 0.003 *	p = 0.002 *	p = 0.003 *	p = 0.322	p ≤ 0.001 *	p = 0.061	p = 0.547	p = 0.001 *
PI	T	41.65 ± 1.57	41.91 ± 1.31	41.83 ± 1.27	40.80 ± 1.76	42.45 ± 1.61	40.78 ± 1.47	42.12 ± 0.30	41.35 ± 1.51	41.95 ± 1.38
	M	41.63 ± 1.60	41.70 ± 1.45	41.94 ± 1.15	40.56 ± 1.65	42.51 ± 1.61	40.51 ± 1.47	42.22 ± 1.39	41.72 ± 1.03	41.60 ± 1.34
	F	41.67 ± 1.52	42.11 ± 1.12	41.71 ± 1.42	41.03 ± 1.86	42.19 ± 1.66	41.70 ± 1.10	41.80 ± 0.95	40.97 ± 1.86	42.18 ± 1.39
		p = 0.781	p = 0.257	p = 0.538	p = 0.290	p = 0.592	p = 0.017 *	p = 0.255	p = 0.173	p = 0.205
CI	T	52.10 ± 2.00	51.54 ± 1.80	52.47 ± 1.52	52.48 ± 1.48	49.76 ± 2.94	52.72 ± 1.35	52.01 ± 1.50	52.92 ± 1.65	53.46 ± 1.04
	M	51.78 ± 2.21	50.83 ± 2.09	52.47 ± 1.47	52.28 ± 0.96	49.50 ± 3.13	52.76 ± 1.37	51.84 ± 1.68	52.22 ± 1.58	53.91 ± 0.73
	F	52.36 ± 1.47	52.27 ± 1.04	52.48 ± 1.61	52.67 ± 1.86	50.91 ± 1.52	52.57 ± 1.31	52.54 ± 0.43	53.67 ± 1.42	53.15 ± 1.11
		p ≤ 0.001 *	p = 0.003 *	p = 0.994	p = 0.290	p = 0.196	p = 0.680	p = 0.095	p = 0.012 *	p = 0.022 *
RILLs	T	92.22 ± 7.88	94.29 ± 7.53	90.73 ± 5.59	90.71 ± 5.44	101.66 ± 12.30	89.81 ± 4.87	92.42 ± 5.71	89.13 ± 5.92	87.14 ± 3.66
	M	93.52 ± 8.86	97.10 ± 9.10	90.72 ± 5.41	91.34 ± 3.49	102.81 ± 13.08	89.65 ± 4.94	93.10 ± 6.39	91.65 ± 5.82	85.53 ± 2.52
	F	90.17 ± 5.42	91.37 ± 3.80	90.74 ± 5.92	90.08 ± 6.87	96.57 ± 6.22	90.33 ± 4.83	90.34 ± 1.54	86.44 ± 4.89	88.21 ± 3.95
		p ≤ 0.001 *	p = 0.005 *	p = 0.993	p = 0.358	p = 0.172	p = 0.688	p = 0.083	p = 0.012 *	p = 0.021 *
BDI	T	18.22 ± 8.20	21.38 ± 8.00	16.39 ± 6.41	25.17 ± 9.05	17.94 ± 5.95	12.95 ± 4.74	14.34 ± 6.82	21.59 ± 10.31	15.90 ± 4.32
	M	13.85 ± 4.92	15.08 ± 4.45	11.94 ± 3.18	18.96 ± 3.76	16.08 ± 4.27	11.48 ± 3.68	1.84 ± 5.60	13.22 ± 3.68	11.82 ± 2.13
	F	25.14 ± 7.56	27.91 ± 5.03	21.66 ± 5.14	31.38 ± 8.52	26.20 ± 5.42	30.53 ± 6.90	22.11 ± 3.60	30.53 ± 6.90	18.62 ± 3.06
		p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *

* = statistically significant, T = total, M = male, F = female, p = probability value, B = basketball, H = handball, W = walking, R = running, C = CrossFit, S = soccer, G = gym, V = volleyball, BMI = body mass index, PI = Ponderal index, CI = Cormic index, RILLs = relative index of lower limbs, BDI = body density index.

).

3.3.2. Kinanthropometric Differences According to Sex in Different Sports Modalities

Statistically significant differences were observed in the different sports modalities between the sexes (Table 5):

-

Regarding the BMI, higher and more significant values were observed in the men in five sports categories (basketball, handball, walking, CrossFit, and volleyball). Significant differences did not appear among the other sports.

-

Regarding the PI, the highest and most significant values were observed in the women who practiced CrossFit.

-

Regarding the CI, higher and more significant values were observed in the men in the basketball and gym categories, while the women obtained higher values in the volleyball category.

-

Regarding the RILLs, higher and more significant values were observed in the men in two sports categories (basketball and gym).

-

Regarding the BDI, higher and more significant values were obtained for the women in the basketball, handball, walking, running, CrossFit, soccer, and volleyball categories.

3.3.3. Kinanthropometric Differences According to Age and Sex

Statistically significant kinanthropometric results were observed according to age and sex (

Table 6. Kinanthropometric results of the participants according to age.

Variable Analyzed		Pearson	R2	Fitted R2	p-Value
BMI	T	0.115	0.013	0.011	p = 0.021 *
	M	−0.051	0.003	−0.002	p = 0.429
	F	0.276	0.076	0.070	p ≤ 0.001 *
PI	T	−0.097	0.009	0.007	p = 0.052
	M	0.019	0.000	−0.004	p = 0.764
	F	−0.333	0.111	0.105	p ≤ 0.001 *
CI	T	−0.268	0.072	0.069	p ≤ 0.001 *
	M	−0.260	0.067	0.064	p ≤ 0.001 *
	F	−0.152	0.023	0.017	p = 0.058
RILLs	T	0.274	0.075	0.073	p ≤ 0.001 *
	M	0.264	0.070	0.066	p ≤ 0.001 *
	F	0.164	0.027	0.021	p = 0.041 *
BDI	T	−0.015	0.000	−0.002	p = 0.769
	M	−0.263	0.069	0.066	p ≤ 0.001 *
	F	−0.224	0.050	0.044	p = 0.005 *

* = statistically significant, T = total, M = male, F = female, p = probability value, R2 = coefficient of determination, BMI = body mass index, PI = Ponderal index, CI = Cormic index, RILLs = relative index of the lower limbs, BDI = body density index.

).

In general, age was positively associated with the BMI and RILLs. This means that these measurements tend to increase with age. At the same time, we observed that age was negatively associated with the CI. This means that this measurement tends to decrease with age.

In males, age was positively associated with the RILLs. This means that this measurement tends to increase with age. At the same time, we observed that age was a negative factor with the CI. This means that this measurement tends to decrease with age.

In females, age was positively associated with the BMI and RILLs. This means that these measurements tend to increase with age. At the same time, we observed that age was negatively associated with the PI and BDI.

3.4. Body Composition Differences

3.4.1. Body Composition Differences Between the Sexes

The body composition results of the participants showed statistically significant results between the men and women in all of the categories we analyzed: %F, %M, %B, and %R (p ≤ 0.001 in all of them) (

Table 7. Body composition results of the participants.

Variable Analyzed		Study Groups
		T	B	H	W	R	C	S	G	V
%F	T	15.63 ± 0.74	17.78 ± 3.07	15.39 ± 2.46	19.35 ± 3.94	14.01 ± 2.29	13.62 ± 2.51	13.55 ± 3.11	16.52 ± 4.35	14.41 ± 2.57
	M	14.49 ± 3.22	16.24 ± 3.32	14.22 ± 1.91	18.56 ± 2.53	13.57 ± 2.09	13.45 ± 2.70	12.71 ± 2.86	14.40 ± 3.17	14.54 ± 2.67
	F	17.42 ± 3.82	19.39 ± 1.72	16.79 ± 2.33	20.13 ± 4.88	16.00 ± 2.21	14.21 ± 1.69	16.13 ± 2.38	18.78 ± 4.38	14.32 ± 2.56
		p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p = 0.013 *	p = 0.003 *	p = 0.381	p ≤ 0.001 *	p = 0.003 *	p = 0.792
%M	T	35.21 ± 3.24	33.22 ± 2.59	35.06 ± 3.14	33.64 ± 2.09	34.43 ± 2.74	38.92 ± 2.51	36.24 ± 3.09	34.29 ± 3.69	35.88 ± 2.43
	M	35.82 ± 3.25	33.22 ± 2.77	36.26 ± 2.83	33.68 ± 2.15	34.58 ± 2.84	38.89 ± 2.08	36.53 ± 3.43	36.30 ± 2.79	36.81 ± 2.01
	F	34.24 ± 3.00	33.22 ± 2.44	33.64 ± 2.92	33.60 ± 2.05	33.77 ± 2.25	39.03 ± 3.75	35.37 ± 1.40	32.15 ± 3.36	35.26 ± 2.53
		p ≤ 0.001 *	p = 0.999	p = 0.003 *	p = 0.874	p = 0.432	p = 0.874	p = 0.183	p = 0.001 *	p = 0.047 *
%B	T	26.19 ± 3.29	26.44 ± 2.94	26.88 ± 2.95	24.51 ± 3.75	28.02 ± 2.48	24.01 ± 2.89	26.83 ± 2.28	25.63 ± 3.78	27.52 ± 3.23
	M	25.57 ± 2.91	26.45 ± 3.19	25.43 ± 2.44	23.66 ± 2.85	27.72 ± 2.24	23.48 ± 2.41	26.65 ± 2.29	25.17 ± 2.72	24.60 ± 2.09
	F	27.19 ± 3.60	26.44 ± 2.73	28.58 ± 2.59	25.36 ± 4.35	29.38 ± 3.15	25.85 ± 3.74	27.38 ± 2.22	26.11 ± 4.71	29.47 ± 2.23
		p ≤ 0.001 *	p = 0.993	p ≤ 0.001 *	p = 0.070	p = 0.069	p = 0.015 *	p = 0.252	p = 0.501	p ≤ 0.001 *
%R	T	22.97 ± 1.67	22.56 ± 1.59	22.67 ± 1.57	22.50 ± 1.60	23.531.30	23.44 ± 1.46	23.37 ± 1.31	23.56 ± 2.66	22.19 ± 1.56
	M	24.12 ± 0.024	24.10 ± 0.08	24.09 ± 0.10	24.09 ± 0.08	24.14 ± 0.13	24.18 ± 0.53	24.11 ± 0.15	24.13 ± 0.07	24.05 ± 0.32
	F	21.15 ± 1.30	20.96 ± 0.15	20.99 ± 0.21	20.91 ± 0.09	20.84 ± 0.23	20.90 ± 0.21	21.11 ± 0.15	22.96 ± 3.80	20.95 ± 0.16
		p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p ≤ 0.001 *	p = 0.230	p ≤ 0.001 *

* = statistically significant, T = total, M = male, F = female, p = probability value, B = basketball, H = handball, W = walking, R = running, C = CrossFit, S = soccer, G = gym, V = volleyball, F% = fat percentage, M% = muscle percentage, B% = bone percentage, %R = residual percentage.

).

3.4.2. Body Composition Differences Between the Sexes Among Different Sports Modalities

Statistically significant results were observed among the different sports modalities between the sexes (Table 7):

-

Regarding %F, higher and more significant values were observed in the men in the basketball, handball, walking, running, soccer, and gym categories. Only two sports did not show significant differences between the sexes (volleyball and CrossFit).

-

Regarding %M, higher and more significant values were observed in the men in the handball, gym, and volleyball categories. There were no differences in this measurement among the other sports between the sexes.

-

Regarding %B, higher and more significant values were observed in the men in the handball, CrossFit, and volleyball categories. Similar to the %M, there were no differences in this measurement in the other sports between the sexes.

-

Regarding %R, higher and more significant values were observed in the men in most sports categories (basketball, handball, walking, running, CrossFit, soccer, and volleyball).

3.4.3. Body Composition Differences According to Age and Sex

Statistically significant body composition results were observed according to age and sex (

Table 8. Body composition results of the participants according to age.

Variable Analyzed		Pearson	R2	Fitted R2	p-Value
%F	T	0.033	0.001	−0.001	p = 0.511
	M	−0.036	0.001	−0.003	p = 0.578
	F	0.165	0.027	0.021	p = 0.040 *
%M	T	0.075	0.006	0.003	p = 0.129
	M	−0.103	0.011	0.007	p = 0.106
	F	0.007	0.000	−0.006	p = 0.927
%B	T	0.054	0.003	0.000	p = 0.278
	M	0.009	0.000	−0.004	p = 0.893
	F	−0.167	0.028	0.021	p = 0.038 *
%R	T	0.153	0.023	0.021	p = 0.002
	M	−0.068	0.005	0.001	p = 0.288
	F	0.062	0.004	−0.003	p = 0.441

* = statistically significant, T = total, M = male, F = female, p = probability value, R2 = coefficient of determination, F% = fat percentage, M% = muscle percentage, B% = bone percentage, %R = residual percentage.

).

In general, age was positively associated with %R. This means that this measurement tends to increase with age. We did not observe any negative association between age and sex.

In males, we did not observe any positive or negative association between age and sex.

In females, age was positively associated with %F. This means that this measurement tends to increase with age. At the same time, we observed that age was negatively associated with B%.

4. Discussion

The objective of this study was to define differences between biological sex and among the kinds of sports people practiced. As explained in the Results section, we did find differences in most of the anthropometric variables.

Researchers such as da Silva Rodrigues et al. (2025) [20] have conducted meta-analyses on the health benefits of regular physical exercise in sedentary adults. Their findings highlight that, in addition to improving cardiovascular health, regular exercise helps to obtain better anthropometric indices and reduce the BMI. Therefore, using anthropometric measurements could be useful for evaluating health and to promote the benefits of regular physical activity, as they provide tangible indicators of improvement.

Our main goal was to analyze whether there were anthropometrical differences between the sexes and among different sport modalities. We believe that these results could be very useful, since they could better explain which values are the most common for each sex and the sport modality practiced regularly.

In addition, the results could also be used to establish personal training plans based on an individual’s morphological characteristics, as advocated by Pedersen et al. (2015) [21]. They could also be useful for other researchers to understand what they should be looking for when recruiting sporting talent at an early age, as demonstrated by Melchiorri et al. (2017) [22] in his research on youth volleyball players.

We decided to use ISAK methodology [4] for the measurements. Additionally, in an attempt to avoid any extrinsic factor that could affect the results, we decided to follow Piercy’s criteria [5] to define the inclusion and exclusion criteria. We also considered the participants’ diet [19] and ensured the absence of any injuries during the measurements.

On average, the men tended to practice more hours of physical activity per week than the women. Other studies, such as that by Craft et al. (2014) [23], obtained different results, but this could be due to many reasons. This is likely to be a consequence of the size of the sample and the sociodemographic characteristics of the area where the data were collected. It is worth noting that one’s sex does not affect the amount of exercise that an individual can perform. Other authors, such as Palmer (2020) [24], have suggested that the amount of exercise performed depends more on variables such as age, geographical location, local culture, the existence or absence of sports facilities, and the promotion of sports by government entities and media.

Some authors, such as Markland et al. (1993) [25], believe that men tend to practice team sports to socialize and compete among themselves. Other researchers, such as Strelan et al. (2003) [26], have found that women tend to practice individual sports to achieve the goal of feeling physically well. In our opinion, this hypothesis is outdated and could even be considered sexist. It should be noted that, in this sample, a greater presence of men in team sports was observed, but we cannot claim that this is universally true, and a larger sample should be analyzed to confirm or deny this hypothesis. In addition to a larger sample size, sociocultural factors should be considered, as these may influence the results depending on the culture and environment.

Regardless of the sport modality analyzed, a pattern appeared in our sample. In general, the men tended to present higher anthropometric values in terms of weight, height, and size of their body circumference. The women tended to present higher values in the skinfolds. There are many variables to consider, ranging from biological factors, as suggested by Bredella (2017) [27], to hormonal factors, as suggested by Johnson et al. (2020) [28], and even sociocultural factors, as suggested by Kovács (2019) [29].

When focusing on the relationship of the variables analyzed with age, in our sample, we observed that the older people had higher weight and BMI values in general. Canaan Rezende et al. (2015) [30] observed the opposite. However, in their case, their sample was made up of a population over 70 years old, and ours consisted of much younger people, between 18 and 42 years old. In general, as people age, they lose their appetite and decrease their physical activity. As a result, it is normal to lose some weight, especially muscle mass, while fat and residual mass may increase, as we observed in our sample. As previously mentioned, our sample was much younger, and they tended to gain weight as they aged. This could be due to many reasons, such as gaining weight by engaging in more exercise, which leads to increased muscle mass. At the same time, as people age, many decrease their physical activity and gain fat mass. We observed this trend in our study, especially among the women, as their %F and %R increased as they became older.

We also observed that the sitting and standing height in women tended to decrease as they aged. We know that height loss may be caused by osteoporosis, vertebral fractures, disc reduction, or postural changes, and these conditions typically affect more women than men. Our findings are consistent with those of Sorkin et al. (1999) [31], who reported that height loss began at about age 30 years old and accelerated with age. Sagiv (2000) [32] found that regular physical activity attenuated height loss associated with aging; therefore, we believe that using anthropometric measurements could be useful for monitoring this occurrence.

In addition to anthropometric differences, there were also differences in the kinanthropometric measures and body composition. Mascherini et al. (2018) [33] observed that men tended to present a higher BMI index, and women usually presented a higher BDI index. This could even be related to the higher prevalence of certain types of injury in one sex compared to the other, as suggested by Domaradzki et al. (2022) [34]. However, it should be noted that there are many factors to consider, so future research is needed to further investigate this topic.

We believe that one of the greatest strengths of this study is that we used an international standardized methodology, which is what the ISAK proposes. Additionally, we used Piercy’s criteria to define a person as an amateur athlete. This will allow our results to be compared with those of futures studies, or with other researchers who use the same methodology.

Regarding the limitations of this study, although the sample was large, it is always beneficial to increase it further. It would be interesting to continue expanding the data collection and even to conduct similar research in other geographical locations, with different cultures and environments. We must also consider that the measurements were taken transversally; longitudinal research would be better to capture changes over time. More and better conclusions could be obtained with this approach, since they would provide a better perspective on the impact of sports on kinanthropometric measurements in people who practice them non-professionally. Another limitation is ensuring the accuracy of the participants’ dietary information. Although these factors would be more obvious in samples of professional athletes, we analyzed amateur athletes. Nonetheless, there are many variables to consider, and it is difficult to control for all of them. We believe that an interesting future line of research would be exploring whether these measurements could help guide students in deciding what sport to choose and providing recommendations.

5. Conclusions

Anthropometry can be used to measure differences among population groups, taking into account many variables. This study focused on the differences between biological sexes among different groups categorized by the sport modality they usually engaged in recreationally.

We believe that it is necessary to follow the guidelines stipulated by the ISAK and Piercy’s criteria, as they ensure standardized protocols and definitions among all researchers around the world.

Statistical differences were observed between men and women in all of the sports modalities studied. This approach can be useful not only for defining a group, but also for personalized training or even for advising a person on which sport modality may best suit them based on their morphology. However, this should not be an exclusive criterion; we should not forget that physical aptitude must always be accompanied by a good attitude.