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

Abstract

The aim of the study was to evaluate the relationship between sedentary behavior, bone mass, and bone geometry among young male basketball and volleyball players. This cross-sectional study included 55 adolescent basketball (n = 21) and volleyball (n = 34) players (14–17 years). Body composition (body mass index, fat mass, and lean mass) was measured by dual-energy X-ray absorptiometry, comprising bone mineral density, bone mineral content at the lumbar spine, and femoral neck. Bone geometry considered the femur strength index, section modulus, cross-sectional moment of inertia, and cross-sectional area. Dietary intake was obtained through a semiquantitative questionnaire, and the sedentary behavior, by the Adolescent Sedentary Activity Questionnaire. Linear regression models, fitted by Bayesian methods, explored the variation of the variables by sport. Body composition and bone mass values were high for both sports, but there was no variation for body composition. Adjusting for age, there was no association of sedentary behavior on bone parameters. For femoral strength index, age had a moderate to large association with all bone indicators. Lastly, there was influence of sport (level-2 unit) on the estimates of the association between sedentary behavior and age with bone indicators, as uncertainty estimates for group-level effects were high. There is no association between sedentary behavior and bone parameters, showing that accumulated training loads (15+ h/wk) among young basketball and volleyball players are critical; producing a positive stimulus on bone parameters development.

Keywords

Body composition femur strength lumbar spine moment of inertia youth sport

Introduction

For adolescents, sport is one of the factors that can directly or indirectly influence bone growth and the process of development. Therefore, most studies have analyzed the effect of its practice on bone mass, considering the intensity, impact, and the presence or absence of body overload.^1–3 In addition, it is known that the frequency of stimuli, especially jumping, has a positive impact on deposition and resorption activity between osteoblasts and osteoclasts in remodeling,⁴ which applies to sports such as volleyball⁵ and basketball,⁶ as both involve biomechanical aspects and osteogenic impact.

Training in these two sports aims to improve physical performance; thus, for athletes to meet the demands imposed by the game and by competition, the training stimuli must promote a certain degree of fatigue that can be offset during recuperation.⁷ This recovery may be active, proactive, or passive, the latter characterized by the application of external methods (e.g., massage) or a state of rest characterized by inactivity,⁸ as the adaptation rate (rest) is an integral part of the training stimulus composed of the triad intensity, duration, and frequency of stimulus.^9,10

Thus, physical activity levels and sedentary behavior [that is, light activity, which occurs with the body in a seated or reclined position and which expends energy close to that observed in a state of rest (<1.5MET)]^11,12 of athletes on rest days, can indicate the quality of the rest and the state of their health.¹³

Even when adolescent athletes perform moderate to vigorous physical activity (MVPA) beyond the minimum recommendation (i.e., 60 min of daily physical activity¹⁴) they must remain active outside training hours.¹³ Studies analyzing the profiles of physical activity and sedentary behavior during nontraining hours in elite soccer players¹⁵ and rowers⁹ show higher levels of inactivity during this period when compared to nonathletes. Hence, time spent in MVPA is independent of sedentary time.

In this regard, few studies have investigated the behavior of activities performed by adolescent athletes during their nontraining hours, especially sedentary time, which seems to be essential since adolescents’ habits are different from those of professional athletes (e.g., during school hours where students remain seated).

Therefore, this study hypothesizes that the frequency of osteogenic activity (i.e., activities related to muscle strengthening, vigorous intensities, and impact¹⁶) induced by basketball and volleyball training in adolescents, as opposed to the volume of physical activity, is vital for limiting the adversities and effects of sedentary behavior on bone mass and bone geometry. Thus, the study aimed to verify the relationship between sedentary behavior and bone parameters in elite athletes in basketball and volleyball.

Material and methods

This study was approved by the local Research Ethics Committee (CAAE: 79718417.0.0000.5404). All participants and their parents or legal guardians signed an informed consent form. All of the procedures were conducted following 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 players, between 14 and 17 years old, who had participated for three years or more in official competitions promoted by the respective confederations. In addition, all athletes must have been training over 15 h a week. Finally, athletes injured at the time of observations were excluded.

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

Body composition and bone parameters

Body mass (kg) was determined using digital weighing scales. Stature (cm) was determined using a vertical stadiometer. Body mass index (BMI kg/m²) was also calculated.

Body composition in terms of fat mass (FM) and lean mass (LM) was assessed using dual-energy X-ray absorptiometry (DXA) (iDXA—GE Healthcare Lunar, Madison, WI, USA) and enCore ™ 2011 software (GE Lunar Healthcare), version 13.6.

Bone mineral density (BMD), bone mineral content (BMC), and geometry parameters

Whole-body BMD (g/cm²) and BMC (g) were obtained using the iDXA equipment. The acquisition, positioning, and outcome analysis were performed following the International Society of Clinical Densitometry.¹⁷ These parameters were obtained through a scan of the following regions: total body less head (TBLH), lumbar spine (L1–L4), and right femoral neck (neck).

The bone geometry, that is, 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 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), that is, an indicator of the risk of fracture caused by a heavy fall on the trochanter in relation to the CSA.¹⁸

Thus, the parameters BMD, BMC, L1-L4-BMD, L1-L4-BMC, Neck-BMD, Neck-BMC, CSMI, CSA, Z, and FSI were obtained. All measurements were performed by a single evaluator, following the manufacturer's recommended protocol.

Sedentary lifestyle behavior

Sedentary behavior was evaluated through a questionnaire that determined the average adolescent's sedentary activities during the week (Adolescent Sedentary Activity Questionnaire—ASAQ validated for the Brazilian population).¹⁹ The Brazilian version of ASAQ consists of 13 items, divided into five categories, in which participants report their time spent in sedentary activities (e.g., sitting time during school, sitting time during other activities such as screen time at home, including video games), on each day of the week and during a typical weekend. For this study, the results were expressed in minutes considering a seven-day week (i.e., including weekend behavior). The Brazilian National Education Guidelines and Framework Law establish a maximum of 240 min of sitting time in school.²⁰

Dietary intake assessment

A nutritionist obtained information on all foods and beverages through a 24-h food recall²¹ and a semiquantitative food frequency questionnaire (FFQ).²² The semiquantitative FFQ, with standard portions of foods rich in calcium, was only used to estimate calcium intake due to its essential role in bone health. The estimates of the other nutrients were based on the 24-h dietary recall. All the nutrient estimates 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, as follows: 0.85 g and 0.80 g protein/kg,²³ 1300 mg of calcium,²⁴ 5 μg of vitamin D,²⁴ 1250 mg of phosphorus, and 1500 mg sodium and between 400 mg and 410 mg of magnesium.²⁴

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 any variation in players’ body composition, bone parameters, daily nutrient intake, and sedentary behavior by sport. Hence, we assumed players (level-1) nested by sports, that is, basketball and volleyball (level-2). A varying-intercept null hypothesis model was used to measure the proportion of total variance between the two sports (i.e., variance partition coefficient). For all outcomes, we observed variance partition coefficients lower than.05, implying 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 linear regression slope to be interpreted as a correlation or partial correlation. All models were fitted using Bayesian methods, implemented using R statistical language, with the “brms” package, which calls Stan. Since we standardized all outcomes, we used weakly informative priors for population-level effects and, for group-level, normal priors (0,1). We ran four chains for 2000 iterations with a warm-up length of 1000 iterations for each model. The convergence of the Markov chains was examined with trace plots, and the validity of the models was analyzed using posterior predictive checks.

The multilevel models explicitly consider variations 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 by sport, we report our sample estimates.

Results

Sixty-seven elite basketball and volleyball players were recruited; nine were excluded due to injuries that prevented them from participating in all the evaluation stages. As a result, 55 athletes were able to complete all the tests.

There was no substantial variation between players when grouped by sport for most of the outcomes. BMI and BMI z-score values are slightly higher for basketball players than for volleyball players as there was a small overlap between credible intervals. For dietary intake variables, the volleyball players showed higher values for magnesium consumption, as there was a small overlap between credible intervals. Although there were no substantial differences between the other nutrients, as uncertainty estimates indicated a considerable overlap between groups, protein intake and sodium were two times higher than the recommended values. In both groups, calcium and vitamin D were below (almost half), and phosphorus and magnesium were slightly above the recommended values. The sample's calcium/phosphorus ratio was 1:1.9, a low calcium intake and a high phosphorus intake (Table 1). The descriptive results of bone mass for the total sample and by sport category are displayed in Table 2.

Table 1.

Means and 95% credible intervals of sedentary behavior, body composition, and nutritional analyses for the total sample, basketball players, and volleyball players.

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

Note: SB = sedentary behavior; PHV = peak height velocity; BMI = body mass index; FM = fat mass; LM = lean mass.

Table 2.

Means and 95% credible intervals of BMD, BMC, and bone geometry parameters for total sample, basketball players, and volleyball players.

	Total (n = 55)	Basketball (n = 21)	Volleyball (n = 34)
TOTAL BMD (g/cm²)	1.28 (1.25–1.32)	1.34 (1.28–1.39)	1.25 (1.20–1.29)
BMD Z-score	1.32 (1.10–1.55)	1.68 (1.34–2.02)	1.11 (0.83–1.39)
TOTAL BMC (g)	3459.4 (3327.3–3591.5)	3585.1 (3400.5–3769.8)	3381.8 (3203.3–3560.2)
L1-L4 BMD (g/cm²)	1.31 (1.27–1.36)	1.31 (1.25–1.37)	1.31 (1.25–1.37)
L1-L4 BMC (g)	88.3 (83.8–92.9)	87.4 (80.9–93.9)	89.9 (82.7–95.2)
Neck BMD (g/cm²)	1.37 (1.31–1.43)	1.47 (1.38–1.56)	1.31 (1.25–1.37)
Neck BMC (g)	7.35 (7.03–7.67)	7.81 (7.35–8.27)	7.07 (6.67–7.48)
FSI	2.1 (1.9–2.3)	2.5 (2.2–2.8)	1.9 (1.7–2.0)
Z (mm³)	1145.6 (1073.4–1217.8)	1213.5 (1123.3–13030.6)	1103.6 (1001.9–1205.3)
CSMI (mm⁴)	20,265.4 (18654.2–21,876.7)	21,388.9 (19,422.1–23,355.6)	19,571.6 (17,256.76–21,886.3)
CSA (mm²)	234.4 (222.9–245.9)	252.5 (234.1–271.0)	223.24 (209.84–236.63)

Note: 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.

Table 3 summarizes 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. Adjusting for age, sedentary behavior was not associated with bone mass and bone geometry among the athletes. However, age had a substantial moderate to large association with all bone mass and geometry indicators except the femoral strength index. 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% credible intervals) of bone mass and bone geometry related to sedentary behavior and chronological age.

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

Note: 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.

Discussion

To the best of our knowledge, this is the first study to examine the effect of sedentary behavior on bone mineral density, mineral content, and geometry in elite adolescent basketball and volleyball players. Our observations showed no association between sedentary behavior and bone mass quality and bone geometry variables in these particular sports.

The results of growth and dietary intake were used to characterize the groups of sports. Considering the credible intervals’ probabilistic interpretation, basketball athletes exhibit slightly higher values than volleyball for BMI and BMI z-score, but not for stature, body mass, FM, and LM. Nevertheless, the variation is significant, and any interpretation must be conservative. Despite these results, athletes demonstrate BMI patterns within the normal range for their age for both sports. The magnesium consumption was a little higher than recommended, but this was not the case with vitamin D and calcium. One of the main functions of magnesium is to indirectly facilitate the absorption of these nutrients,²⁵ which, in this case, did not show a significant difference between the sports. As can be seen from this study, it is necessary to heed the recommended values of nutrients essential for bone status. A protein- and sodium-rich diet is detrimental to bone health due to increased urinary calcium excretion.²⁶ Additionally, a low calcium/phosphorus diet can lead to higher serum PTH, which could cause hypocalcemia, which increases bone resorption and decreases bone formation. The ideal ratio is 1–2:1.^27,28

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 reported on, but contrasting aspects of the sports have not been explored.^29,30

After adjustment for chronological age, associations between bone mass and geometry are rendered insignificant. Hence, bone mass and geometry changes do not appear to be influenced by sedentary behavior among young athletes after pubertal growth. It is known that, in boys, peak velocities in lean mass, bone mineral density, and strength always follow peak linear growth (peak height velocity),³¹ and the acquisition of bone mineral density occurs primarily during the second decade of life.³²

The results support our hypothesis that the changes caused by growth and the high-intensity sports practice for long periods by these basketball and volleyball players were sufficient to develop and maintain their bone quality. This assumption is based on the characteristics of the sport since bone formation, bone composition, and endurance depend on ground reaction forces applied to the skeleton and muscle contractions produced during exercise.³³ Basketball and volleyball exert significant demands in terms of the effort related to jumping, involve a considerable high-impact ground reaction force, and they apply different forces. In addition, these sports require sprints, jumps, acceleration, and deceleration that promote skeletal loads and overload and have been associated with an increase in BMC and improved bone geometry properties compared to repetitive sports.^34–36 Notwithstanding, these two sports differ in terms of physical dynamics. Basketball requires a sizeable physical exertion with actions including sprints, changes in direction, and jumps, not to mention the high degree of physical contact involved due to the nature of the sport. On the other hand, volleyball is a sport with almost no physical contact. Its movement patterns include a large number of jumps and strength-related movements, thus resulting in a more considerable impact on the femoral head.³⁷ However, both sports have movement patterns with changes in direction performed at high intensity and short duration and jumps, considered osteogenic stimuli.³⁸

Therefore, despite the properties of the bone reacting positively to the stimuli provoked by practicing basketball and volleyball,^39,40 the length of time during which the athletes engage in sedentary behavior, regardless of the type of sport, could significantly influence bone mass and bone geometry parameters. Consequently, the control of physical activity and sedentary behavior outside of training is vital, as it may indicate the quality of rest and status of health.¹³

Tenforde and Fredericson⁴¹ concluded that improvements in BMC and BMD were strongly linked to the impact of exercise, particularly in high-impact sports. That continued participation in sports seems essential to maintaining these benefits. Although all athletes in this study participated in training and competitions for three years or more, it was still possible to confirm these differences when considering sedentary behavior.

In animal models, the mechanical load influences the bone's geometry and, therefore, its strength. Thus, to provide maximum anabolic stimulation to the bone, high strain levels must be applied at rapid rates and in different ways.⁴² These effects can be observed in sports such as soccer, basketball, and volleyball.

Furthermore, adequate consumption of nutrients and adequate neuroendocrine regulation are essential for athletes and physically active individuals. Thus, the metabolism and bone remodeling must be regarded as part of a complex system, which involves hormones, cytokines, and the sympathetic nervous system.⁴³

Furthermore, the present study observed that the athletes in the present sample, despite being physically active and attaining the recommended levels of physical activity, still engaged in high levels of sedentary behavior (mean = 4972 min) that could impair the bone quality and body composition.

Some studies involving professional athletes have shown that elite athletes have higher levels of sedentary behavior on nontraining days when compared to nonathletes.^9,15 Still, adolescent athletes need to factor in the time spent in sedentary behavior at school. Exel et al.¹³ evaluated the level of physical activity and sedentary behavior of young athletes on rest days and found different patterns for the profiles of physical activity and sedentary lifestyle of young athletes, with alarming behavior observed in the time spent in sedentary activities and most of the weekday waking hours spent at school or home, places that promote a sedentary lifestyle. However, the study was conducted on a group engaged in a minimum of six hours of training per week (with additional competition once a week), unlike the present study (15 h/wk).

Thus, an assessment of sedentary behavior can provide important information on how to rest time (i.e., outside sports training and other games requiring physical exertion) can influence bone mineral density, content, and geometry.

The interpretations in the present study are limited by the sample size and the large age range of the population. Furthermore, the length of time practicing sport and the training load were not monitored (i.e., type and intensity of training session), and the results cannot be applied to other populations. Regarding the eating behavior, the instrument is valid for a sample of athletes, but it was not valid for a sample of Brazilian athletes. However, the foods used in the semiquantitative questionnaire are also consumed by our sample of adolescent athletes. Additionally, the study's cross-sectional design does not allow for conclusions involving change between sedentary behavior and bone mass and geometry and prepares the ground 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 precise and accurate assessment of body composition, bone mass (specifically DXA), and the Advanced Hip Assessment software to assess bone geometry.

Conclusions

In conclusion, there is no association between sedentary behavior and bone mass and bone geometry, showing that cumulative training loads (15+ h/wk) among young basketball and volleyball players are key; they positively affect bone mass and bone geometry development. Consequently, maintaining bone mass at healthy levels is essential, especially in athletes with sedentary behavior.

Footnotes

Authors’ note

Humberto Moreira Carvalho, Anderson Marques de Moraes, and Gil Guerra-Júnior contributed equally to this manuscript.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Coordination for theImprovement of Higher Education Personnel (CAPES - grant number Process: 001).

ORCID iDs

Raiany Rosa Bergamo

Humberto Moreira Carvalho

Anderson Marques de Moraes

Gil Guerra-Júnior

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