Can the sedentary behavior of young male basketball and volleyball players impact the bone mass and bone geometry?

doi:10.21203/rs.3.rs-51216/v1

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Can the sedentary behavior of young male basketball and volleyball players impact the bone mass and bone geometry?

https://doi.org/10.21203/rs.3.rs-51216/v1

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Background: Sedentary behavior is considered a health risk independent of physical activity. We evaluated the relationship between sedentary behavior, bone mass, and bone geometry among young male basketball and volleyball players.

Methods: Fifty-five athletes (basketball n=21; volleyball n=34) aged 14 to 17 years old were included. Body composition and bone mass were measured by dual-energy X-ray absorptiometry, comprising bone mineral density, bone mineral content at the lumbar spine (L1-L4), and femoral neck. Bone quality was evaluated by bone geometry considering the femur strength index, section modulus, cross-sectional moment of inertia, and cross-sectional area. Information on all foods and beverages were obtained by a nutritionist through a 24-hour food recall and a semi-quantitative food frequency questionnaire. The sedentary behavior was assessed using the Adolescent Sedentary Activity Questionnaire. A series of multilevel linear regression models were fitted to explore whether there was variation for players' body composition, bone parameters, diary nutrient intake and sedentary behavior by sport. All models were fitted using Bayesian methods.

Results: Body composition and bone mass values were high for both basketball and volleyball players. However, there was no substantial variation between players by sport for body composition. Adjusting for age, there was no association of sedentary behavior on both bone mass and geometry among the athletes. Except for femoral strength index, age had a substantial moderate to large association with all bone mass and geometry indicators. Lastly, there was no substantial influence of sport (level-2 unit) on the estimates of the association between sedentary behavior and age with bone mass and geometry, as uncertainty estimates for group-level effects were high.

Conclusions: In conclusion, there is no association between sedentary behaviour and bone mass and bone geometry, showing that accumulated training loads (15+ h/week) among young basketball and volleyball players are critical; they produce a positive stimulus on bone mass and bone geometry development.

Orthopedic Surgery

Orthopedics

Sports Medicine and Kinesiology

Bone density

athletes

basketball

sedentary behavior

Childhood and adolescence make up a period in which significant changes in bone tissue occur. During adolescence, several interrelated events occur in sequence, although time is highly variable. At any age, there is a wide variation among children in body size, body composition, growth rate and biological maturation [(e.g. in boys, peak velocities in lean mass, bone mineral and strength always follow peak linear growth (peak height speed)] [1]. The lifetime bone mineral density acquisition occurs primarily during the second decade of life [2].

These changes occur mainly due to the linear increase in bone tissue, which reflects the predominance of bone deposition over its resorption [3]. The structural integrity of the bone depends on several parameters, such as bone mass, tissue properties, constitution, and bone geometry [4].

Regular physical activity and sports, especially those related to body overload, play an essential role in the development and maintenance of bone mass and its resistance [5]. Bone tissue integrity is also influenced by factors such as genetics, hormonal status, sun exposure, and diet.

The growth phase induces important mineral accumulation helping the bone constitution [6], while nutrition, especially calcium and vitamin D [7] intake, influences bone geometry and metabolism. However, the excess of some nutrients, such as protein and sodium, can promote urinary excretion of calcium and contribute negatively to bone health [8]. Excessive phosphorus intake can also disturb the hormonal regulation of calcium and vitamin D, causing deleterious effect that increases when calcium intake is low [8].

Sports is one of the factors among adolescents that bear a direct or indirect influence in the process of bone growth and development [9]. Studies that evaluated the bones’ quality in athletes have focused mainly on bone mineral density (BMD) and bone mineral content (BMC) provided by dual-energy X-ray absorptiometry (DXA) [10–12]. Even though BMD is the gold standard for diagnosis of osteoporosis, this measure can only provide information on the amount of bone tissue. However, the degree of bone mineralization, size, and heterogeneity of hydroxyapatite crystals, collagen properties, osteocyte density, trabecular and cortical microarchitecture as well as total bone geometry are also determinants of bone strength [13]. Furthermore, each of these can independently contribute to increased or decreased fracture risk, even if significant changes in BMD and BMC are not perceived [14]. The effects of different sports activities on bone mass are not yet fully understood, as they vary according to the intensity, impact, and whether body overload is present or not (gymnastics, soccer, and basketball versus swimming and running, respectively) [15–17]. Moreover, the osteogenic activity frequency (i.e. vigorous and muscular strengthening related activities) as opposed to the volume of physical activity, is vital in limitating the negative effects presented by sedentary behavior[18].

Thus, investigating how the bone quality is affected by different types of sports is relevant and necessary, mainly due to the different biomechanical aspects and osteogenic impact.

Besides, the evolution of technology has, unfortunately, induced a growing tendency of sedentary behavior amongst young people. Longitudinal studies show that lack of physical activity in school children is negatively associated with lower extremity bone development [19, 20] and that elite athletes have little concern in meeting recommended physical activity guidelines. However, sedentary behavior is considered a health risk independent of physical activity and is only recognized in public health guidelines that warn against prolonged sedentary time. Few studies explore the sedentary behavior among athletes when they are not practicing elite sports [21]. Therefore, the study aimed to verify the relationship of sedentary behavior on bone parameters in male basketball and volleyball elite athletes.

This study was approved by the local Research Ethics Committee from State University of Campinas (UNICAMP) (CAAE: 79718417.0.0000.5404). All participants and their parents or legal guardians signed an informed consent form. All of the procedures were conducted per the Helsinki Declaration for Human Studies.

Design of the Study

The study was cross-sectional; the evaluations were conducted in November 2018.

Inclusion criteria consisted of male basketball and volleyball athletes, with ages ranging from 14 to 17 years, who participated for 3 years or more in official competitions promoted by the basketball and volleyball confederations. All athletes had to train over 15 hours a week.

Exclusion criteria consisted of female athletes, athletes that presented injuries at the time of the evaluations, and those that did not participate in one or more stages of the study.

All athletes were submitted to tests related to body composition and bone mass evaluation. Furthermore, the athletes were required to answer a questionnaire related to sedentary behavior and dietary intake, which are described below.

Peak of Height Velocity (PHV)

The PHV was assessed according to the formula (for boys) [22]: PHV = -7.999994+0.0036124*(age*stature)), where R² = 0.896, and SEE = 0.542.

Body Composition

The body mass (kg) was determined using a digital scale. Stature (cm) was determined (cm) using a vertical stadiometer. Body mass index (BMI kg/m²) was calculated.

Body composition in fat mass (FM) and lean mass (LM) was assessed by dual-energy X-ray absorptiometry (DXA) (iDXA - GE Healthcare Lunar, Madison, WI, USA) and version 13.6 software enCore ™ 2011 (GE Lunar Healthcare). The reproducibility of the variables estimated by the DXA was determined by the coefficient of variation (CV%) and by the technical measurement error (TEM), based on a test-retest study performed with 23 subjects (adolescents and young adults) using the formula:

Where: D is the difference between the two measurements and n is the sample size. CV% were 0.74% and 0.26% for FM and LM, respectively; and TEM were 0.25 kg and 0.25 kg for FM and LM respectively.

Bone Mineral Density (BMD), Bone Mineral Content (BMC) and Geometry Parameters

BMD (cm²) and BMC (g) were obtained using the iDXA equipment, with acquisition, positioning, and outcome analysis performed according to the International Society of Clinical Densitometry [23]. These parameters were obtained by scanning: total body less head (TBLH); lumbar spine (L1-L4); and right femoral neck (neck).

The bone geometry, i.e., hip structural analysis data was obtained with the Advanced Hip Assessment software [version 13.6 in software enCore ™ 2011 (GE Healthcare Lunar)]. This software automatically derives geometric properties of the proximal femur, such as a) Cross-sectional moment of inertia [CSMI (mm²)], which is an estimate of resistance and weight forces directed along the length of the bone in a cross-section; b) Transverse cross-sectional area of the femoral neck [CSA (mm²)], which measures the resistance to loads directed along the bone axis; c) Section modulus [Z (mm³)], which is a measure of the maximum bending strength in a cross-section and, d) Femoral strength index (FSI), i.e., an indicator of the risk of a fracture caused by severe fall on the trochanter in relation to the CSA [24].

Therefore, the parameters BMD, BMC, L1-L4-BMD, L1-L4-BMC, Neck-BMD, neck-BMC, CSMI, CSA, Z, and FSI were obtained.

Sedentary Lifestyle Behavior

The sedentary behavior was determined through a questionnaire developed and validated [25]. The questionnaire determines the average adolescent’s sedentary activities during the week (Adolescent Sedentary Activity Questionnaire - ASAQ) and has been validated for the Brazilian population [26]. The Brazilian version of ASAQ consists of 13 items, divided into five categories, in which participants report their time spent in sedentary activities (sitting time during school, sitting time during other activities such as screen time at home including video games, etc.), on each day of the week and during a typical weekend. For this study, the results were expressed in minutes considering the seven days of the week (i.e. including the weekend behavior as well). The Brazilian National Education Guidelines and Law establishes a maximum of 240 minutes of sitting time in school [27].

Dietary Intake Assessment

Information on all foods and beverages were obtained by a nutritionist through a 24-hour food recall [28] and a semi-quantitative food frequency questionnaire [29,30]. The semi-quantitative FFQ, with usual portions of foods rich in calcium, was used only for the estimation of calcium intake, due to its important role in bone health. The estimation of the other nutrients was based on the 24-hour dietary recall. All the nutrients estimations were calculated using the Nutwin software. Nutrient adequacy was assessed using the recommended dietary guidelines. The daily recommendation was based on intake for ages 14-18 and 19-30 years and are as follows: 0.85g and 0.80g protein/kg [31], 1300mg of calcium [32], 5mcg of vitamin D [32], 1250mg of phosphorus, 1500mg sodium and 410mg and 400mg of magnesium [32].

Statistical analyses

Descriptive statistics are presented as means and 95% credible intervals. A series of multilevel linear regression models were fitted to explore whether there was variation for players' body composition, bone parameters, diary nutrient intake and sedentary behavior by sport. Hence, we assumed players (level-1) nested by sports, i.e., basketball and volleyball (level-2). A varying-intercept null model was used initially to measure the proportion of total variance which fell between-sport (i.e., variance partition coefficient). For all outcomes we observed variance partition coefficients smaller than 0.05, implying that there was no substantial variation between players by sport. Hence, the relationship between body composition and bone parameters with sedentary behavior was explored using single-level linear regression. We standardized all variables, allowing the slope of the linear regression to be interpreted similarly as a correlation or partial correlation. All models were fitted using Bayesian methods, which were implemented using R statistical language, with the “brms” package [33] which calls Stan [34]. Since we standardized all outcomes, we used weakly informative priors for population-level effects and group-level, normal priors (0,1). We ran four chains for 2,000 iterations with a warm-up length of 1,000 iterations for each model. The convergence of the Markov chains was examined with trace plots and validity of the models inspected using posterior predictive checks.

The multilevel models explicitly consider variation between players grouped by sport (level-2 unit). Hence, our estimates account for the potential influence of nesting by sport. Since there was no substantial variation accounted for sport, we report our estimates for the sample.

Sixty-seven elite basketball and volleyball athletes were enrolled; nine were excluded due to injuries that did not allow them to participate in all of the stages of the evaluation. Therefore, 55 athletes were able to complete all tests.

All players were classified as post-pubertal, as offset values were all positive, and beyond 1 year after PHV. There was no substantial variation between players when grouped by sport for most of the outcomes. Basketball athletes have slightly higher values than volleyball for BMI and z-score BMI, as there was small overlap between credible intervals) For dietary intake variables, the volleyball athletes showed higher values for the consumption of magnesium, as there was a small overlap between the credible intervals. Although there were no substantial differences between the other nutrients, as uncertainty estimates had a large overlap between groups, protein intake and sodium was 2-fold higher than the recommended values, calcium and vitamin D were below (almost half), phosphorus and magnesium slightly above of the recommended values, in both groups. The calcium/ phosphurus ratio of the sample was 1:1.9, that is, a low calcium intake and a high phosphorus intake (Table 1).

Table 1. Means and 95 credible intervals of sedentary behavior, body composition, and nutritional analyses for total sample, basketball, and volleyball athletes.

	Total (n = 55)	Basketball (n = 21)	Volleyball (n = 34)
SB (minutes/week)	4972 (4453 to 5491)	4201 (3464 to 4938)	5448 (4784 to 6113)
Age (years)	16.7 (16.5 to 17.0)	16.9 (16.6 to 17.2)	16.6 (16.2 to 16.9)
PVH (years)	3.4 (3.2 to 3.7)	3.4 (3.1 to 3.7)	3.5 (3.1 to 3.9)
Stature (cm)	189.1 (186.8 to 191.4)	186.6 (183.1 to 190.1)	190.6 (187.8 to 193.5)
Body mass (kg)	77.8 (75.0 to 80.6)	79.9 (75.2 to 84.6)	76.5 (73.0 to 80.0)
BMI (kg/m²)	21.7 (21.0 to 22.4)	22.8 (21.9 to 23.7)	21.0 (20.2 to 21.9)
Z-score BMI	0.2 ( -0.3 to 0.4)	0.5 (0.2 to 0.8)	-0.2 (-0.32 to 0.27)
FM (kg)	13.1 (12.0 to 14.2)	13.9 (12.1 to 15.7)	12.6 (11.3 to 14.0)
FM (%)	16.7 (15.6 to 17.9)	17.3 (15.7 to 18.9)	16.4 (14.9 to 17.9)
LM (kg)	61.1 (59.1 to 63.1)	62.3 (59.2 to 65.4)	60.4 (57.7 to 63.1)
LM (%)	78.7 (77.5 to 79.9)	78.1 (76.3 to 79.9)	79.1 (77.6 to 80.7)
Energy intake (kcal)	2632.7 (2375.4 to 2890.1)	2638.7 (2235.1 to 3042.4)	2629.1 (2290.2 to 2967.9)
Protein (g/kg)	1.70 (1.4 to 1.9)	1.65 (1.2 to (2.1)	1.7 (1.47 to 2.0)
Calcium (mg)	820.6 (660.8 to 980.4)	801.4 (513.9 to 1088.9)	832.5 (642.2 to 1022.9)
Vitamin D (mcg)	2.7 (2.1 to 3.4)	2.14 (1.3 to 2.9)	3.1 (2.7 to 4.0)
Phosphorus (mg)	1526.1 (1339.9 to 1712.3)	1434.8 (1086.5 to 1783.1)	1582.6 (1371.5 to 1793.6)
Sodium (mg)	3084.9 ( 2630.5 to 3539.4)	2931.7 (2396.2 to 3467.1)	2865.7 (2417.0 to 3314.5)
Magnesium (mg)	300.0 (258.4 to 341.5)	246.5 (181.2 to 311.8)	333.0 (281.8 to 384.3)
SB: Sedentary Behavior; PVH: Peak of Height Velocity; BMI: Body Mass Index; FM: Fat Mass; LM: Lean Mass. The descriptive results of bone mass of the total sample and by modality are described in Table 2.

Table 2. Means and 95 confidence intervals of BMD, BMC and bone geometry parameters for a total sample, basketball and volleyball basketball athletes.

	Total (n = 55)	Basketball (n = 21)	Volleyball (n = 34)
TOTAL BMD (g/cm²)	1.28 (1.25 to 1.32)	1.34 (1.28 to 1.39)	1.25 (1.20 to 1.29)
Z-score BMD	1.32 (1.10 to 1.55)	1.68 (1.34 to 2.02)	1.11 (0.83 to 1.39)
TOTAL BMC (g)	3459.4 (3327.3 to 3591.5)	3585.1 (3400.5 to 3769.8)	3381.8 (3203.3 to 3560.2)
L1-L4 BMD (g/cm²)	1.31 (1.27 to 1.36)	1.31 (1.25 to 1.37)	1.31 (1.25 to 1.37)
L1-L4 BMC (g)	88.3 (83.8 to 92.9)	87.4 (80.9 to 93.9)	89.9 (82.7 to 95.2)
Neck BMD (g/cm²)	1.37 (1.31 to 1.43)	1.47 (1.38 to 1.56)	1.31 (1.25 to 1.37)
Neck BMC (g)	7.35 (7.03 to 7.67)	7.81 (7.35 to 8.27)	7.07 (6.67 to 7.48)
FSI	2.1 (1.9 to 2.3)	2.5 (2.2 to 2.8)	1.9 (1.7 to 2.0)
Z (mm³)	1145.6 (1073.4 to 1217.8)	1213.5 (1123.3 to 13030.6)	1103.6 (1001.9 to 1205.3)
CSMI (mm⁴)	20265.4 (18654.2 to 21876.7)	21388.9 (19422.1 to 23355.6)	19571.6 (17256.76 to 21886.3)
CSA (mm²)	234.4 (222.9 to 245.9)	252.5 (234.1 to 271.0)	223.24 (209.84 to 236.63)
BMD: Bone Mineral Density, BMC: Bone Mineral Content; FSI: Femoral Strength Index; Z: Section Modulus; CSMI: Cross-sectional Moment of Inertia; CSA: Cross-sectional Area.

The results for the multilevel modeling examining the associations of sedentary behavior and chronological age with bone mass and geometry of young basketball and volleyball players are summarized in Table 3. Adjusting for age, there was no association of sedentary behavior on both bone mass and geometry among the athletes. Except for the femoral strength index, age had a substantial moderate to large association with all bone mass and geometry indicators. Note that all variables were standardized (z-score) in the models, hence estimates may be interpreted as effect sizes. Lastly, there was no substantial influence of sport (level-2 unit) on the estimates of the association between sedentary behavior and age with bone mass and geometry, as uncertainty estimates for group-level effects were high.

Table 3. Multilevel modeling estimates and uncertainty (95% confidence intervals) of bone mass and bone geometry related to sedentary behavior and chronological age.

	Population- level effects			Group-level effects
	Intercept	Sedentary behaviour, z-score	Age, z-score	Level 1 standard deviation	Level 2 standard deviation, Intercept
BMB, z-score	0.08 (-0.84 to 1.01)	-0.07 (-0.31 to 0.17)	0.51 (0.29 to 0.73)	0.83 (0.68 to 1.00)	0.69 (0.13 to 1.82)
BMC, z-score	0.04 (-0.78 to 0.89)	-0.10 (-0.33 to 0.13)	0.50 (0.26 to 0.73)	0.87 (0.72 to 1.05)	0.51 (0.03 to 1.63)
L1 – L4 BMD, z-score	0.00 (-0.90 to 0.96)	-0.12 (-0.36 to 0.13)	0.45 (0.21 to 0.69)	0.91 (0.75 to 1.10)	0.44 (0.01 to 1.58)
L1 – L4 BMC, z-score	-0.02 (-1.56 to 1.35)	-0.09 (-0.36 to 0.16)	0.45 (0.22 to 0.69)	0.92 (0.76 to 1.12)	0.82 (0.02 to 3.50)
Neck BMD, z-score	0.07 (-2.13 to 2.02)	-0.10 (-0.33 to 0.14)	0.45 (0.23 to 0.68)	0.85 (0.70 to 1.03)	1.29 (0.14 to 4.55)
Neck BMC, z-score	0.03 (-1.82 to 1.64)	-0.20 (-0.44 to 0.03)	0.48 (0.26 to 0.70)	0.82 (0.68 to 0.99)	1.03 (0.05 to 3.83)
CSMI, z-score	0.00 (-1.49 to 1.38)	-0.19 (-0.44 to 0.05)	0.40 (0.16 to 0.65)	0.91 (0.76 to 1.11)	0.83 (0.02 to 3.48)
Z, z-score	0.06 (-1.32 to 1.76)	-0.19 (-0.42 to 0.05)	0.49 (0.27 to 0.71)	0.85 (0.71 to 1.04)	0.88 (0.02 to 3.59)
CSA, z-score	0.07 (-1.89 to 1.86)	-0.22 (-0.46 to 0.01)	0.40 (0.19 to 0.63)	0.85 (0.70 to 1.03)	1.15 (0.07 to 4.35)
FI, z-score	0.07 (-2.15 to 2.28)	-0.19 (-0.44 to 0.05)	0.09 (-0.15 to 0.33)	0.88 (0.73 to 1.07)	1.52 (0.28 to 4.63)
BMD: Bone Mineral Density, BMC: Bone Mineral Content; FSI: Femoral Strength Index; Z: Section Modulus; CSMI: Cross-sectional Moment of Inertia; CSA: Cross-sectional Area.

This is the first study to examine the effect of sedentary behavior on bone mineral density, mineral content, and geometry in adolescent basketball and volleyball elite athletes. This investigation demonstrated that sedentary behavior does not affect the quality of bone mass and bone geometry specifically in these sport modalities.

The results of the growth and dietary intake were used to characterize the groups of sports. Considering the probabilistic interpretation of the credible intervals, basketball athletes have slightly higher values than volleyball for BMI and z-score BMI, but not to stature, body mass, FM, and LM. Nevertheless, variation is wide, and interpretations should be conservative. Despite these results, for both modalities, athletes have BMI patterns within the normal range for their age. For magnesium consumption, the same did not happen with vitamin D and calcium. It is known that one of the main functions of magnesium is to indirectly facilitate the absorption of these nutrients [35], which in turn, did not show significant differences between the modalities. As seen in this study, it is necessary to meet the recommended values of nutrients that are important to bone status. A protein and sodium-rich diet is detrimental to bone health due to increased urinary excretion of calcium [36]. And a low Ca/P diet can lead to higher serum PTH, which could cause hypocalcemia that increases bone resorption and decrease bone formation. The ideal ratio is 1–2:1 [8, 37].

Our data showed no substantial variation between players by sport for body composition, bone mass, and sedentary behavior. Body composition in young basketball and volleyball players has been described, but no contrasts between sports have been explored [38, 39].

After adjustment for chronological age, associations between bone mass and geometry become trivial. Hence, changes in bone mass and geometry after pubertal growth do not appear to be influenced by sedentary behaviours among young athletes. It is known that in boys, peak velocities in lean mass, bone mineral, and strength always follow peak linear growth (peak height speed)] [1] and the lifetime bone mineral density acquisition occurs primarily during the second decade of life [2].

The results support our hypothesis that, the changes caused by the growth and the practice of sports for long periods and with high intensities of these basketball and volleyball players were sufficient to develop and maintain their bone quality. This assumption is based on the characteristic of the modality, since bone formation, composition, and endurance depend on ground reaction forces applied to the skeleton and the muscle contractions produced during exercises [40]. Basketball and volleyball promote important effort demands related to jumping, involve a reaction to the ground of extremely high impact coupled with the application of different forces. In addition, these modalities require sprints, jumps, accelerations, and decelerations that promote skeletal loads and overloads, and have been associated with an increase in the BMC and improved bone geometric properties compared to repetitive sports [41–43]. Notwithstanding, these sports modalities vary in regard to physical dynamics. Basketball requires a large physical exertion with actions relating to sprints, changes in direction, and jumps; besides the large amount of physical contact involved as a result of the nature of the sport. On the other hand, volleyball is a sport with almost no physical contact, and its patterns of movement include a large number of purely jumps and potency-related movements, thus resulting in a larger impact over the head of the femur [44].

Therefore, despite the property of bone to react positively to the stimuli provoked by basketball and volleyball practice, as proven in some studies [45, 46] the amount of time in which the athlete engages into sedentary behavior, regardless if basketball or volleyball, could have important influences on bone mass and bone geometry parameters.

Tenforde and Fredericson [47] concluded that BMC and BMD improvement were both strongly associated with the impact of the exercise, particularly in high-impact sports, and that continued participation in sports seems to be important to maintain these benefits. Although all athletes in this study participated in training and competitions for three years or more, it was still possible to verify these differences when taking into account the sedentary behavior.

In animal models, the mechanical load influences the bone's geometry and therefore its strength; and in order to provide maximum anabolic stimulation to the bone, high levels of tension must be applied at rapid rates and of varying natures [48]. This type of stimulation effects can be observed in sports performed with balls, soccer, basketball, and volleyball.

Furthermore, an adequate consumption of nutrients and adequate neuroendocrine regulations are essential for athletes and physically active individuals. Thus, the metabolism and bone remodeling must be considered part of a complex system, which involves hormones, cytokines and the sympathetic nervous system [49].

Interestingly, the use of only the physical activity assessment questionnaire to classify these individuals as sedentary (or not) may not suffice, since it is known that in most epidemiological studies using this method, the cutoff values to discriminate sufficiently active adolescents from insufficiently active is of 300 minutes of activity per week (min/week < 300) [50]. If these athletes had been evaluated according to the mentioned cutoff points, they would all have been classified as active. The results of this investigation lead to the reflection that the athletes of the present sample, despite being physically active and reaching the recommended levels of physical activity, still present high levels of sedentary behavior (mean = 4972 minutes) that could compromise the bone quality and body composition. Thus, the sedentary behavior assessment can provide important information on how the rest of the time (i.e. outside sports training and other games requiring physical exertion) can influence bone mineral density and content as well as bone geometry.

The interpretations in the present study are limited by the sample size and large age range of the population. We focused our study on athletes, which are physically very active and substantially different in body composition of age-matched non-athletes. The time of sports practice and the training load was not monitored (i.e. type and intensity of training session) and the results cannot be applied to other populations. Furthermore, even though the questionnaires comprise a natural error due to the possibility of underestimating or overestimating sedentary behavior because they were based on self-reported responses, the literature shows accuracy among self-reported measurements for estimating sedentary behavior compared with motion sensors [51]. Additionally, the cross-sectional design of the study does not allow for conclusions regarding the chance between sedentary behavior and bone mass and geometry and opens perspectives for future research to explore this relationship in prospective and experimental studies.

Key strengths of this study include the use of cutting-edge methods for precision and accuracy assessment of body composition, bone mass (specifically DXA), and the Advanced Hip Assessment software for the assessment of bone geometry.

In conclusion, there is no association between sedentary behaviour and bone mass and bone geometry, showing that accumulated training loads (15+ h/week) among young basketball and volleyball players are critical; they produce a positive stimulus on bone mass and bone geometry development. Subsequently, maintaining bone mass at healthy levels is important especially in athletes with sedentary behavior.

Acknowledgments:

The authors thank the Coordination for the Improvement of Higher Education Personnel (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – CAPES – Process: 001) for funding this study.

Author contributions:

Designed: ME, AMM, GGJ; Data collection: ME, RRB, AMM; Analytical measures: RRB, HJMC, AMM; Data analysis: AMM, HJMC; Wrote manuscript: ME, HJMC, AMM; Revised: ME, RRB, HJMC, AMM, GGJ. None of the authors had a conflict of interest. All authors approved the final version of the manuscript before submission and are accountable for data accuracy and integrity

Funding:

This research received no external funding

Availability of data and materials

The datasets generated and analyzed during the current study are not publicly available due to ethical restrictions but are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

All research activities were reviewed and approved by the local Research Ethics Committee from State University of Campinas (UNICAMP) (CAAE: 79718417.0.0000.5404). All participants and their parents or legal guardians signed an informed consent form. All of the procedures were conducted per the Helsinki Declaration for Human Studies.

Consent for publication

Not applicable.

Competing Interests:

The authors declare that they have no competing interests.

Author information

Affiliations

¹ American School of Campinas, Campinas, Sao Paulo, Brazil; ²Laboratory of Growth and Development, Center for Investigation in Pediatrics, School of Medical Sciences,State University of Campinas (UNICAMP), Campinas, Sao Paulo, Brazil; ³School of Sports, Federal University of Santa Catarina, Florianopolis, Santa Catarina, Brazil; ⁴School of Physical Education, Pontifical Catholic University of Campinas (PUCCAMP), Campinas, Sao Paulo, Brazil.

Corresponding author*

Correspondence to Gil Guerra-Júnior ([email protected]; Tel.: +55-19-35217353)

BMD - Bone mineral density

BMC - Bone mineral content

DXA - Dual-energy X-ray absorptiometry

PHV - Peak of Height Velocity

BMI - Body mass index

FM – Fat mass

LM – Lean mass

CV - Coefficient of variation

TEM - Technical measurement error

CSMI - Cross-sectional moment of inertia

CSA - Transverse cross-sectional area of the femoral neck

Z - Section modulus

FSI - Femoral strength index

ASAQ - Adolescent Sedentary Activity Questionnaire

FFQ - Semi-quantitative food frequency questionnaire

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Can the sedentary behavior of young male basketball and volleyball players impact the bone mass and bone geometry?

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