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. 2025 Feb 22;25(3):e12258. doi: 10.1002/ejsc.12258

Effect of Daily Social Media Use on Smartphones Before Training on Attack Efficiency and Repeated Vertical Jump Ability in Young Male Volleyball Players: A Randomized and Crossover Trial

Carlos Freitas‐Junior 1,✉, Fabio Nakamura 2, Yago Costa 1,3, Gilmário Batista 1, Leonardo Fortes 1
PMCID: PMC11845908  PMID: 39985325

ABSTRACT

This investigation observed the repeated effect of using social media on smartphones immediately before training sessions on attack efficiency (AE) and repeat‐vertical jumping ability (RVJA) in young volleyball athletes. A crossover study involved 14 volleyball athletes (17.57 ± 0.65 years of age). For three weeks, the athletes participated in their training routines under two experimental conditions: using social media (SMA) and watching documentaries (DOC), both for 30 min before the training sessions. Before and after the three weeks, the athletes had their AE and RVJA evaluated. The AE test consisted of performing 6 attacks, with the total score obtained by adding the product of the score and the speed of each attempt. RVJA was assessed using the intermittent vertical jump test of four sets of 15 s (IJT60), with the average heights of the best series of 15 s (Hpeak) and the total time (Hmean) used for analysis. The visual analog scale revealed an increase in subjective mental fatigue for both conditions (p < 0.05), with SMA having higher levels compared to DOC (p = 0.02). There were improvements in AE for DOC compared to SMA (p = 0.03). No differences were found between the experimental conditions for Hpeak and Hmean. The results demonstrated that using social media on smartphones immediately before training sessions caused mental fatigue and impaired AE in young volleyball athletes.

Keywords: cognition, fatigue, performance, team sport

Summary.

  • Using social media on smartphones for 30 min causes mental fatigue.

  • Attack efficiency is impaired after three weeks of using social networks on smartphones before the start of training sessions.

  • Technical committees must manage the time spent using social media on athletes' smartphones before training sessions begin.

1. Introduction

Volleyball is a sport that alternates periods of exertion with rest (Costa et al. 2018; Sánchez‐moreno et al. 2016). Furthermore, the majority of actions involved in the game (i.e., serve reception, set, attack, dig, and block) are open motor skills, necessitating players' capacity to discern crucial environmental cues and integrate them into tactical decision‐making (Nuri et al. 2013). In this sense, the success of a match is most closely linked with the effectiveness of the attack actions (Castro, Souza, and Mesquita 2011; Drikos and Vagenas 2011). Moreover, these actions are positively related to the quality of the vertical jump (Berriel et al. 2021) and perception–cognitive skill (Alves et al. 2013; Montuori et al. 2019; Trecroci et al. 2021). Consequently, the training of volleyball players is typically oriented toward enhancing their jumping ability and the perceptual–cognitive skills that are integral to technical–tactical actions (de Faria et al. 2020; Freitas, Miloski, and Bara Filho 2015). In another direction, factors, such as mental fatigue, have stimulated interest among researchers and coaches because it is hypothesized that they impede the development of players (Costa et al. 2022; Russell et al. 2019).

The term “mental fatigue” is used to describe a psychobiological state that is characterized by a sensation of tiredness and a reduction in cognitive skills, particularly in inhibitory control (Lopes et al. 2023; Marcora, Staiano, and Manning 2009). In general terms, the magnitude of mental fatigue is determined by the cognitive load, which depends on the duration and complexity of the task (Lopes et al. 2023). Accordingly, when initiating a complex or prolonged cognitive undertaking, the capacity for ATP resynthesis is surpassed, resulting in the accumulation of adenosine predominantly within the anterior cingulate cortex (Smith et al. 2018), a region strongly linked to inhibitory control (Khan et al. 2020; Lorist, Boksem, and Ridderinkhof 2005). Consequently, the detrimental impact of mental fatigue on executive functions and fatigue perception results in a decline in athletic performance (Martin et al. 2018; Smith et al. 2018).

In this direction, a meta‐analysis of the literature demonstrated a negative (small‐moderate) impact on endurance performance (as assessed by the Yo‐Yo test), passing (as evaluated by the Loughborough soccer passing test), and shooting (as assessed by the Loughborough soccer shooting test) of football players (Grgic, Mikulic, and Mikulic 2022). In addition, mental fatigue led to an increased number of turnovers in basketball players during simulated matches (Moreira et al. 2018) and negatively affected on attack performance of beach volleyball athletes (Domingos‐Gomes et al. 2024). Although these studies have contributed to the existing literature on mental fatigue, it is important to acknowledge some limitations. Primarily, the authors focused on the acute effects of mental fatigue. Secondly, the cognitive tasks used to induce mental fatigue, such as the Stroop Task, have limited ecological validity, as it is unlikely that athletes would perform such tasks before training sessions and/or matches.

In order to address these limitations, some studies have explored alternative approaches, such as inducing mental fatigue through repeated social media use, which is an activity widely carried out by young athletes (Thompson et al. 2020). In this sense, Fortes et al. (2021) observed that in a training period of four weeks, the repeated use of Instagram for a period of 30 min prior to sessions harmed the decision‐making skill of volleyball players but no effect was observed on endurance capacity (assessed using the Yo‐Yo test) or lower limb power (assessed using a vertical jump with countermovement). Moreover, another two‐week study, also conducted with volleyball players, concluded that prolonged Instagram use impaired visuomotor skills (Fortes et al. 2022). However, the aforementioned studies did not assess the athletes' repeated vertical jump ability and attack technical efficiency, which are essential elements of volleyball performance.

An interesting method for assessing repeated jumping ability in volleyball players is the four series 15 s vertical jump test (Hespanhol, Silva Neto, and Arruda 2006). This method is justified by the high demand for vertical jumping in volleyball during both training and competitive settings (Skazalski, Whiteley, and Bahr 2018). Furthermore, the attack can be evaluated in relation to the accuracy of the action and the speed of the ball (Gabbett and Georgieff 2006; Tabor, Urbanik, and Mastalerz 2018; Wang, Qin, and Wei 2024). Consequently, these tests enable the evaluation of whether the recurrent use of social networks adversely affects the development of key elements essential for volleyball performance throughout a training period. This study aimed to examine the effects of daily social media use on smartphones immediately before training sessions on repeated vertical jumping ability (RVJA) and attack efficiency (AE) in young male volleyball players after three weeks of training. The hypothesis was that the utilization of social media prior to the training sessions would hurt the development of the physical and technical skills under analysis. The findings can guide volleyball coaches in understanding the importance of regulating social media use before training to maintain athletes' optimal performance.

2. Methods

2.1. Participants

Initially, 16 young male volleyball athletes volunteered to participate in the research. The athletes had more than 5 years of experience training in the sport and participated in national‐level competitions. The athletes trained regularly on the same team and were not injured at the beginning of the investigation. Due to missing more than 15% of training sessions, two athletes were excluded from the study leaving the final number of participants at 14 athletes (age = 17.57 ± 0.65 years; body mass = 76.97 ± 15.35 kg; and height = 187.57 ± 7.42 cm). Moreover, the players were in a precompetitive period, and the methods employed in this study were conducted between September and October.

The power of the a‐posteriori analysis was calculated using the G*Power software (version 3.1.9.2, University of Kiel, Kiel, Germany) based on the total number of participants in this research. The statistical power of the repeated measures ANOVA within‐factors was observed to be 0.98 to effect size = 0.62 [(η p 2  = 0.28), based on the systematic review study by Sun et al. (2022), on FM and technical effectiveness performance in athletes], p = 0.05, number of groups = 2, and number of measurements = 2. A statistical power of 0.98 was determined.

Regarding ethical aspects, all participants were informed about the aims, methods, and risks of the study. Those who consented to participate in the study did so voluntarily as evidenced by their signature on the consent form. From younger participants (i.e., age < 18 years), written informed consent was obtained from their parents/legal guardians and written informed consent was obtained from young participants before their participation. The research was approved by the local ethics committee and followed the principles of the Declaration of Helsinki.

2.2. Experimental Design

A randomized study with a crossover design was carried out using the split technique for six weeks (Figure 1). During this period, the athletes participated in their regular training routines, being subjected to two experimental protocols every three weeks. The protocols were activities with greater and lesser cognitive effort. In the condition of greater cognitive effort, athletes used social media on smartphones for 30 min (SMA). In the lower cognitive effort condition, athletes watched documentaries for 30 min (DOC). Both protocols were applied before the training sessions.

FIGURE 1.

FIGURE 1

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Experimental design. AE = attack efficiency test; DOC = watch documentaries; RVJA = repeated‐vertical jumping ability test; session‐RPE = session rating of perceived exertion; SMA = use of social media on smartphones; VAS = visual analog scale.

In the first three weeks (period 1), seven athletes started in the SMA condition and another seven in the DOC condition. In the last three weeks (period 2), the athletes reversed the experimental conditions. The order of participation in SMA and DOC was defined using a random number table generator (www.randomizer.org). The athletes were exempt from knowing the purpose of the study. Before and after the protocols, MF was assessed using the visual analog scale (VAS). Athletes had three to five weekly training sessions with external training loads applied similarly for each period. The times of the training sessions in periods 1 and 2 were not different (p = 0.18) (Table 1). Before and after the three weeks, the athletes were evaluated for AE and RVJA. Internal training load (ITL) was analyzed session‐by‐session using the session rating of the perceived exertion method (session‐RPE). The athletes were instructed not to participate in any other physical exercise schedule, not to drink alcoholic beverages, and to maintain their usual sleeping and eating routines throughout the experiment. Athletes were asked not to drink coffee or use social media for up to 3 h before the start of training sessions. The times spent using social media on smartphones for SMA (3426.57 ± 2051.12 min) and DOC (3640.29 ± 1917.40 min) were not different (p = 0.78). Before the protocols began, the researchers asked whether the athletes followed the guidelines (checklist). If not, the athlete would not participate in the protocols that day and a foul would be assessed.

TABLE 1.

Features of the training schedule.

Period 1 Period 2
Week 1 2 3 1 2 3
Number of sessions (days) 5 5 3 5 5 3
Time (min)
Technical–tactical 335 335 150 310 270 180
Physical 150 130 90 140 130 90
Duration of the training session (min) 117.23 ± 26.40 100.90 ± 24.27
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2.3. Experimental Conditions

SMA used the social media Facebook, WhatsApp, and e Instagram for 30 min before the training sessions. The use of smartphones was supervised by the researchers to ensure that the athletes only used the recommended social media applications and were encouraged to use them continuously. For the same time and period, DOC watched documentaries, which were rear‐projected on a two‐dimensional screen (size: 2.50 m high x 3.20 m). The content of the videos included the history of the Olympic Games, highlighting the social and cultural aspects from its creation to the present day. Moreover, it also highlighted the main competition results, mainly of Brazilian athletes. During the protocols, SMA and DOC athletes remained in different rooms and were not allowed to speak to each other. Previous studies adopted the same procedures and were successful in experimental manipulation (Fortes et al. 2021, 2023).

2.4. Manipulation Check for Mental Fatigue

Mental fatigue levels were assessed using a visual analog scale [VAS (Smith et al. 2019)] both before and after the intervention protocols. Participants were asked to place a mark on a 100 mm line with bipolar terminal anchors (0 mm = no fatigue at all and 100 mm = extremely fatigued) to indicate how they were feeling. The VAS score was determined by measuring (in mm) the distance from the left side of the line to the mark made by the participant. The daily average value for the three weeks was used for analysis. In addition, the concept of mental fatigue was introduced to the athletes accompanied by illustrative examples of cognitive activities (e.g., school examinations) and symptoms (e.g., experiencing fatigue despite a lack of physical exertion). This was done with the objective of enhancing the athletes' metacognitive abilities following the recommendations put forth by Thompson et al. (2019).

2.5. Measures

2.5.1. Primary Outcome

Repeated vertical jumping ability (RVJA). The intermittent vertical jump test of four sets of 15 s (IJT60) was employed for the analysis of RVJA. This test entails the performance of countermovement vertical jumps in four series of 15 s with 10 s intervals at maximal intensity (Hespanhol, Silva Neto, and Arruda 2006). The test was previously found to have satisfactory reproducibility [ICC ranging from 0.93 to 0.99 (Hespanhol, Silva Neto, and Arruda 2006)] and was applied to volleyball players to evaluate the effect of a training program (Gantois et al. 2022), given the intermittent nature of the sport. Regarding the instructions, the players were required to maintain a vertical position for their trunks and to place their hands on their hips (Bosco et al. 2010). Verbal feedback was provided to participants during the test to encourage them to maintain their knee angle at approximately 90° and maximum performance until the end of the test. Vertical jumps were performed on a contact mat (Elite Jump, Brazil) and the average jump heights from the best series of 15 s (Hpeak) and during the 60 s (Hmean) were used for analysis. Jump heights were obtained using the S2 software (Elite Jump, Brazil). In addition, all participants were already familiar with the test procedures.

Attack efficiency (AE). The efficacy of the attack was evaluated through an adaptation of the Gabbett and Georgieff (2006) attack test, which demonstrated satisfactory reproducibility (ICC = 0.94) and a typical measurement error of 8.2%. The athletes performed six attacks, having to direct the ball to a target 1.5 m high by 1 m wide 7 m away from the athlete. To carry out the attack, each athlete had to set the ball (Mikasa VLS300; 0.270 kg; 0.2133 m in diameter) to a height of 3–4 m and then hit it. The target was divided into five 20 cm segments. If players managed to reach the middle 20 cm segment, they received five points. Three points were awarded for hitting the two 20 cm segments on either side of the middle segment and one point was awarded for hitting the 2 outer 20 cm segments of the target. Scores of two and four points were awarded if players hit the target between the one‐ and three‐point segments and the three‐ and five‐point segments, respectively. The attack speed of each attempt was analyzed. Before the preexperiment assessments, the athletes participated in two familiarization sessions, the first to learn about the procedures, without analyzing the results, and the second, in which the results were analyzed. The total score (Stotal), in arbitrary units (AU), obtained by summing the product of speed and the score of each attempt was used for analysis as previously used (Fortes et al. 2019a, 2019b). The attack speed was obtained using the Tracker software. To this end, the attempted attacks were filmed using a camera (GoPro HD Hero 3; GoPro Inc., San Mateo, CA, United States of America) that was ∼8 m from the attack location. The Tracker software has already proven effective in obtaining ball speed in volleyball (Martins, De Jesus, and Sasaki 2022).

2.5.2. Control Variable

Internal training load (ITL). The session's rating of perceived exertion (session‐RPE) method (Foster et al. 2001) was used daily to monitor the internal training load (ITL). First, 30 minutes after each training session, athletes were orally asked to answer the following question: “How difficult was your training session?” using a 10‐point RPE scale (0 = rest and 10 = maximal). Then, the total time of the training session was multiplied by the RPE (e.g., ITL = 50 min * 5 RPE = 250AU). The sum of three weeks of ITL (⅀ITL) was used for analysis.

2.5.3. Statistical Analysis

The data for all variables exhibited a normal distribution as verified by the Shapiro–Wilk test. Thus, the results were reported using mean and standard deviation (±). A two‐way repeated measures analysis of variance (ANOVA) was conducted to compare mental fatigue levels [VAS—condition (SMA x DOC); time (pre x post); and interactions] as well as AE and RVJA [conditions (SMA x DOC); time (pre x post); and interactions]. Furthermore, one‐way ANOVA was used to compare ⅀ITL between experimental conditions. Additionally, the Bonferroni post hoc test was employed for paired comparisons where appropriate, and partial eta‐squared (η p 2) was adopted to assess effect size interpreted according to its magnitude (Cohen 1992): small effect (η p 2 < 0.03); moderate effect (0.03 ≤ η p 2 < 0.10); large effect (0.10 ≤ η p 2 < 0.20); and very large effect (η p 2 ≥ 0.20).

In addition, Cohen's “d” was used to check the size of the differences between the preintervention and postintervention (paired comparisons) interpreted as (Hopkins et al. 2009): trivial—< 0.2; small—0.2 to 0.5; moderate—0.6 to 1.1; and large—1.2 to 1.9. The Statistical Package for Social Sciences (SPSS, version 20, Chicago, IL, United States of America) was used for all analyses with statistical significance established at p ≤ 0.05. The figures were created using the GraphPad Prism Software version 8.0 (California Corporation, United States of America).

3. Results

3.1. Manipulation Check for Mental Fatigue

A significant condition versus time interaction effect was found [F (1, 12) = 19.44; p < 0.001; and η p 2 = 0.43], with a higher subjective MF value for the SMA condition (p < 0.05) (Figure 2).

FIGURE 2.

FIGURE 2

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MF perceived by experimental groups (SMA vs. CON). DOC = watch documentaries; SMA = use of social media on smartphones; VAS = visual analog scale; *Time effect (p < 0.05) and #interaction effect (condition vs. Time) (p < 0.001).

3.2. Attack Efficiency

A interaction effect was found [F (1, 12) = 10.33; p < 0.01; and η p 2  = 0.28] for AE (Figure 3a). However, no main effect of time [F (1, 12) = 2.17; p = 0.15; and η p 2 = 0.08] or condition was found [F (1,12) = 0.10; p = 0.75; and η p 2  < 0.01]. Means and standard deviations can be found in Table 2.

FIGURE 3.

FIGURE 3

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Attack efficiency and performance of repeated‐verticsl jumping ability (Hpeak and Hmean) for the two conditions (CON vs. SMA). DOC = watch documentaries; Hmean = height during 60 s; Hpeak = height in the best series of 15 s; SMA = use of social media on smartphones; Stotal = total score. #Interaction effect (condition vs. time) (p < 0,01).

TABLE 2.

Changes in RVJA and for the two conditions (SMA vs. DOC) after three weeks.

Variables SMA DOC Effect F p η p 2
AE (UA)
Pre 559.38 ± 199.43 407.85 ± 196.27 Condition 0.1 0.75 0.004 (small)
Post 489.78 ± 224.02 595.44 ± 253.51* Time 2.17 0.15 0.08 0 (moderate)
Δ% −12.44 45.99 Interaction 10.33 0.003 0.280 (very large)
d −0.328 (small) 0.830 (moderate)
RVJA (cm)
Peak
Pre 35.16 ± 5.99 34.54 ± 6.06 Condition 0.007 0.93 0.000 (small)
Post 35.39 ± 5.23 35.68 ± 5.62 Time 0.94 0.34 0.030 (moderate)
Δ% 0.65 3.30 Interaction 0.41 0.52 0.020 (small)
d 0.041 (trivial) 0.195 (trivial)
Mean Condition 0.006 0.94 0.000 (small)
Pre 29.68 ± 4.81 29.27 ± 4.02 Time 2.80 0.11 0.100 (moderate)
Post 30.10 ± 4.22 30.27 ± 4.41 Interaction 0.47 0.50 0.020 (small)
Δ% 1.41 3.42
d 0.093 (trivial) 0.237 (small)
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Abbreviations: AE, attack effectiveness; d, Cohen's d effect size (pre vs. postexperiment); DOC, watch documentaries; Interaction, condition*time; η p 2, partial eta squared effect size; RVJA, repeated‐vertical jump capacity; SMA, use of social media on smartphones.

*Different from pre (p < 0.05).

3.3. Repeated‐Vertical Jumping Ability

The findings revealed no significant interaction effects [F (1, 12) = 0.41, p = 0.34, η p 2 = 0.02], time effects [F (1, 12) = 0.94, p = 0.34, η p 2 = 0.35], or condition effects [F (1, 12) < 0.01, p = 0.93, η p 2 < 0.001] for Hpeak (Figure 3b). There were also no condition x time interaction effects found [F (1, 12) = 0.47; p = 0.50; η p 2 = 0.02]; main effect of time [F (1, 12) = 2.80; p = 0.11; η p 2 = 0.10] and condition were also found [F (1, 12) < 0.01; p = 0.94; η p 2 < 0.001] for Hmean (Figure 3c). Means and standard deviations can be found in Table 1.

3.4. Internal Training Load

The ⅀ITL showed no difference between the experimental conditions [SMA (4513.93 ± 495.56 AU) Vs. CON (4413.57 ± 539.66 AU) p = 0.61].

4. Discussion

The current investigation aimed to examine the effects of daily social media use on smartphones immediately before training sessions on repeated vertical jumping ability (RVJA) and attack efficiency (AE) in young male volleyball players after three weeks of training. In this regard, the repeated utilization of social media prior to training did not compromise the development of repeated jumping ability. However, attack effectiveness was significantly impaired. Therefore, the hypotheses were partially confirmed. Furthermore, the training loads were similar between the conditions, thereby ruling out any confounding effect of this variable. To our knowledge, this is the first study to verify the effect of recurrent social media use prior to training sessions on the development of repeated jumping and attacking skills in volleyball.

In the present study, young volleyball players were unable to enhance their attacking performance, particularly the technique employed, when they used social media before training sessions. These results corroborate with studies of acute and repeated effects. For instance, mental fatigue caused by the use of social media impaired the performance of football players (L. S. Fortes et al. 2019a, 2019b, 2020). Moreover, social media use before training sessions was detrimental to improving decision‐making in attack and visuomotor skills (L. S. Fortes et al. 2021, 2022). In this sense, this body of evidence suggests that the use of social media by athletes may impair performance, especially in tasks where perceptual–cognitive skills (e.g., decision‐making and technique accuracy) are required. On the other hand, in activities, such as vertical jumping, where performance is less dependent on the central nervous system, no evidence has been found of a detrimental effect of social media use. The results of our study showed no impact on repeated jumping ability after three weeks, whereas Fortes et al. (2021) reported an increase in countermovement vertical jump height, regardless of whether participants used social media before training. This set of results about AE and RVJA can be explained by two main reasons.

First, about RVJA, the intervention may not have been long enough to show a statistically significant difference. This assumption is supported by the slightly larger effect size in the control condition compared to the social media condition [0.237 (small) versus 0.093 (trivial)]. Furthermore, Smith et al. (2018) suggest that the physical impairment caused by mental fatigue is due to an increased subjective perception of effort under conditions of mental fatigue; however, this variable is not determinant for short‐duration tasks such as maximum and repeated jump tests. Thus, it can be hypothesized that mental fatigue does not significantly impact this particular type of activity.

Secondly, perceptual–cognitive impairments appear to result from increased adenosine levels in the anterior cingulate cortex (Smith et al. 2018). This region is responsible for functions such as attention allocation and inhibitory control (Khan et al. 2020; Lorist, Boksem, and Ridderinkhof 2005). Furthermore, the use of social media requires selective attention and constant decision‐making regarding content and processes associated with neural activity in the anterior insula, anterior cingulate cortex, and the mesolimbic dopaminergic system (Sherman et al. 2018). Consequently, recurrent training under conditions where these cognitive functions are compromised leads to poorer skill development in athletes, particularly when evaluating tasks that require perceptual–cognitive processes.

Therefore, in terms of the practical implementation of the present findings, it is advised that coaches working with young male volleyball players endeavor to regulate the use of social media, particularly in the two hours preceding training sessions. This can be achieved through the utilization of dialogs, which elucidate the consequences of social media and the advantages of adhering to restrictions. Although this study represents an advancement in the literature on the impact of social media use and its long‐term effects on key sports skills in volleyball athletes, certain limitations must be acknowledged. The findings are specific to young male athletes and should be extrapolated with caution to female players or those at different levels of athletic development (e.g., high‐performance professional athletes). Furthermore, mental fatigue was assessed solely through subjective measures, and the influence of social media content on emotions was not examined. Lastly, although the tests used are closely related to volleyball‐specific skills, caution is warranted when generalizing these findings to technical and tactical performance. Future studies are encouraged to explore whether these results are replicable in female athletes, athletes of varying skill levels, and across technical and tactical variables. Furthermore, it is recommended that mental fatigue also be assessed using objective measures (e.g., EEG or Stroop Task) and that adjustments to the training routine be considered to mitigate the effects of social media use prior to training sessions.

5. Conclusion

After three weeks, although RVJA was not changed, it is important to highlight that AE was negatively affected by MF, induced by the use of social media on smartphones immediately before the training sessions. Athletes are vulnerable to having MF before training sessions and the daily use of social media on smartphones, especially before training sessions, can cause long‐term performance impairment.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding: The authors received no specific funding for this work.

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