The link between social networks and messaging apps with addictive behaviors and academic performance in adolescents: the EHDLA study.

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Andrea Aquino-Blanco, Fiorella Quiroz-Cárdenas, José Adrián Montenegro-Espinosa, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7124293/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Dec, 2025 Read the published version in Italian Journal of Pediatrics → Version 1 posted 5 You are reading this latest preprint version Abstract Background Academic performance in adolescence is a key predictor of future educational, occupational, and health outcomes. While social networks (SNs) and messaging apps are increasingly integrated into adolescent´s daily lives, their impact on academic achievement remains debated. This study aimed to evaluate associations between the use of SN, messaging apps, addictive behaviors, and academic performance in Spanish adolescents. Materials and methods A cross-sectional analysis was conducted using data from the Eating Healthy and Daily Life Activities (EHDLA) study, which included 583 adolescents aged 12–17 years from three secondary schools in Valle de Ricote , Spain, during the 2021–2022 academic year. SNs and messaging app use were assessed via a self-report scale. Addictive behaviors were measured using the Short Social Networks Addiction Scale-6 Symptoms (SNAddS-6S). Academic performance was evaluated using grade point average (GPA) and subject-specific grades obtained from school records. Associations were analyzed using generalized linear models adjusted for important covariates. Results The median age of participants was 14 years, with 57% female. TikTok and WhatsApp were the most frequently used platforms. No statistically significant association was found between the overall use of SNs and GPA. However, higher levels of addictive behaviors related to SN use were significantly associated with lower academic performance (B = − 0.15, 95% CI: − 0.26 to − 0.03, p = 0.014). Conclusions Our findings show a relationship between the use of SNs and GAP, however, this was not significant. On the contrary, we found a significant relationship between addictive behaviors related to SNs and GAP. These findings suggest the need for educational interventions that promote mindful technology use while preserving the benefits of digital connectivity. Psychological well-being adolescent behavior academic performance social media addiction youth Figures Figure 1 Figure 2 Introduction Academic performance is the evaluation of a student’s accomplishments based on factors such as grade point average, standardized test results, and educational goals ( 1 ). Academic performance broadly involves acquiring knowledge, developing skills and competencies, attaining high grades and other academic milestones, striving for a successful career, and exhibiting dedication and persistence in education ( 2 ). Studies performed in England and Slovakia on adolescent´s educational process have identified key factors influencing academic performance. These factors include motivation, stress levels and coping mechanisms; anxiety, self-esteem and self-efficacy; time management; and the methods and strategies employed to acquire academic knowledge ( 2 , 3 ). Academic performance is a critical indicator of adolescent´s success and achievement. It plays a vital role in child development, as skills such as reading and mathematics impact a wide range of outcomes, including educational attainment, career performance and income, physical and mental health, and overall life expectancy ( 4 ). Social networks (SNs) refer to online platforms and digital tools that facilitate user interactions by allowing individuals to share information, express opinions, and engage with shared interests ( 5 ). Instagram, Telegram, Facebook, Twitter (now X), and WhatsApp are some of the most popular SNs and messaging applications among users ( 6 ). Adolescents and young adults are increasingly spending time on SNs activities ( 7 ), as a result, engaging with SNs requires the dedication of various resources, including time, cost, and effort, which can significantly impact daily life in both positive and negative ways ( 8 ). According to a report by the Carat ( 9 ) agency on social media usage among young people aged 8–17 years, 91% access social media regularly. Usage is intensive, with more than 3 hours per day from Monday to Friday and more than 5 hours on weekends. Children typically had a smartphone at the age of 11 years, which is the primary device used for accessing social media and it has been associated with more problematic patterns of use ( 10 ). According to the report by Carat agency, Instagram is their preferred platform, followed by the growth of TikTok and gaming platforms. While social media offers new ways of communication, evidence suggests connections between issues related to body image, isolation, depression, addictive behaviors and bullying ( 9 ). Researchers have explored whether SNs have a positive or negative relationship with academic performance ( 11 , 12 ). SNs usage among students can play a role in collaborative learning by helping teachers address educational challenges and increasing student engagement ( 11 ). Platforms such as YouTube and X can enhance interactive learning by supporting knowledge transfer through visual and auditory means while fostering engagement and collaboration among students, teachers, and peers, respectively ( 13 , 14 ). Additionally, SNs help students share ideas and research globally, supporting academic problem solving ( 15 ). In contrast, some studies suggest that SNs have a negative effect on academic performance, especially when they are overused ( 16 – 18 ). In this line, studies have shown that students experience distractions and lack focus due to excessive SN use. Additionally, they report having less time to study, submitting assignments late, and struggling with poor spelling and grammar as a result of heavy SNs consumption ( 19 , 20 ). Studies have shown that overconsumption of SNs is linked to higher levels of procrastination and decreased productivity, impacting overall academic performance ( 21 ). Adolescents and young adults are often the most vulnerable to these patterns of use, with some individuals developing addictive behaviors characterized by a persistent and uncontrollable urge to engage with social networking platforms. This addictive use of social networks represents the more severe end of a spectrum known as problematic media use, which encompasses behaviors ranging from occasional overuse to compulsive engagement that disrupts daily functioning. Unlike casual or even frequent use, addictive use is distinguished by symptoms such as loss of control, withdrawal-like experiences when access is restricted, and prioritization of online interactions over offline responsibilities and relationships ( 22 ). This widespread use of SNs and the design features commonly used, while fostering connectivity and engagement with online communities, also has the potential to manipulate and influence young people, ultimately affecting their well-being in negative ways. As such, the impact of SNs on student´s academic and personal lives is complex, requiring a balanced approach to harness its benefits while mitigating its risks ( 15 ). Based on this, there is talk of possible addiction to SNs, which is especially worrisome among the younger generations, who spend a significant part of their daily lives on these platforms ( 23 ). In this context, the present study aims to evaluate the link between SNs and academic performance in Spanish adolescents. Specifically, we hypothesize that higher frequency of SN use will be associated with lower academic performance, and that adolescents exhibiting addictive or compulsive patterns of SN use will show even greater risk of academic difficulties. While SNs can enhance learning and communication, excessive use may lead to distractions and procrastination and diminished academic outcomes. Materials and methods Study design and population This is a cross-sectional analysis using data from the Eating Healthy and Daily Life Activities (EHDLA) study, which was carried out in three secondary schools in Valle de Ricote , within the Region of Murcia (Spain), during the 2021–2022 academic year. This secondary study involved a sample of 583 adolescents aged 12–17 years. The detailed methodology of the EHDLA study is described elsewhere ( 24 ). The inclusion criteria for participants were as follows: ( 1 ) they had to be between 12 and 17 years and ( 2 ) they had to be registered or reside in the Valle de Ricote . The exclusion criteria were as follows: ( 1 ) adolescents who were exempt from physical education at school, as the tests and questionnaires were conducted during physical education classes; ( 2 ) those with any condition that contraindicated physical activity or required special attention; ( 3 ) those undergoing pharmacological treatment; ( 4 ) those whose parents or legal guardians had not given consent for participation; or ( 5 ) those who did not agree to participate in the research project. Approval for the EHDLA project was granted by the Bioethics Committee of the Universidad de Murcia (ID 2218/2018, approved on 18 February 2019) and the Ethics Committee of the Complejo Hospitalario Universitario de Albacete and the Gestión Asistencial Integrada de Albacete (ID 2021–86, approved on 23 November 2021). The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki by the World Medical Association. With respect to participation in this research project, parents or legal guardians of the adolescents received a written informed consent form to sign in advance. In addition, both the parents or guardians and the participants were given an information sheet outlining the objectives of the study, along with details about the tests and questionnaires that would be administered. Adolescents were also asked about their willingness to take part in the study ( 24 ). Variables Social network and messaging applications The use of SNs (Facebook, X, Instagram, Snapchat, TikTok, WhatsApp) was evaluated by a single-item scale asking adolescents what type of SN they used, on the basis of five possible responses: (a) I never or rarely use it (b) I am a low consumer, (c) I am a medium consumer, (d) I am a fairly high consumer, or (e) I am a very high consumer. Each response was assigned a score from 0 (never or rarely) to 4 (very high consumer). The total SN use score was calculated by summing the scores across all six platforms, resulting in a composite measure that reflects the overall frequency of social network use for each participant ( 25 ). Addictive behaviors toward social networks The Short Social Networks Addiction Scale-6 Symptoms (SNAddS-6S) was used to assess SN addiction ( 26 ). The SNAddS-6S is a validated tool in Spanish designed to measure the degree of SN addiction in individuals. It consists of six items that evaluate key aspects of SN addiction, focusing on behaviors and psychological symptoms associated with excessive use. These aspects include: salience (social networking use becomes the individual’s primary concern and motivation), tolerance (the need to spend increasing amounts of time on SNs), mood modification (using SNs to alter mood, either by excitement or relaxation), withdrawal (experiencing psychological or physical symptoms when unable to access SNs), conflict (SN use interfering with other social or daily activities), and relapse (returning to excessive use after attempts to control or reduce it). For each item, participants respond using dichotomous (yes/no) options, indicating whether they have experienced each symptom during the past year. The total score is calculated by summing the number of “yes” responses, with higher scores reflecting a greater degree of addictive social network use. The scale is structured as a unidimensional factor ( 26 ). Academic performance School records were provided by each high school at the end of the academic year. Academic performance was assessed in two ways: first, the grade point average (GPA) was calculated as the mean of all academic subject scores for each student; second, individual evaluations were conducted in language, mathematics, and English as a foreign language ( 27 ). In the Spanish education system, all grades are awarded on a scale from 0 to 10, and a higher grade means greater academic performance. Covariates The relationship between academic performance and SNs in adolescents could be influenced by various factors that may mediate or moderate the effects of SNs, such as age, sex, socioeconomic status, physical activity, body mass index (BMI), sleep duration, energy intake, and sedentary behavior ( 28 , 29 ). The adolescents self-reported their age and sex. Socioeconomic status was assessed via the Family Affluence Scale (FAS-III) ( 30 ), which evaluates six items: whether adolescents have their own bedroom, family ownership of a car and a dishwasher, the number of bathrooms in the home, the number of vacations taken outside Spain in the past year, and the number of household computers. The FAS-III scores ranges from 0 to 13 points. Data on sedentary behavior and physical activity were collected via the Spanish-Youth Activity Profile (YAP-S) ( 31 ), in which scores are calculated by summing points from each section. Anthropometric measurements, such as height and body weight, were taken via a portable stadiometer (Leicester Tanita HR 001, Tokyo, Japan) and an electronic scale (Tanita BC-545, Tokyo, Japan), respectively. BMI was calculated by dividing weight in kilograms by the square of height in meters. Total sleep duration was assessed by asking participants the same questions for weekdays and weekends separately: “What time do you go to bed?” and “What time do you typically wake up?”. The average daily sleep duration for each participant was calculated in the following manner: [(average nocturnal sleep duration on weekdays × 5) + (average nocturnal sleep duration on weekends × 2)]/7 ( 24 ). Energy intake was assessed via a self-reported dietary habits questionnaire that has been validated for the Spanish population ( 32 ). Statistical analysis To assess whether the variables followed a normal distribution, visual methods such as density plots and quantile‒quantile (Q‒Q) plots were utilized, along with the Shapiro‒Wilk test. This study presents the median and interquartile range (IQR) for quantitative variables, whereas frequencies (n) and percentages (%) are reported for qualitative variables, both for the overall sample and those classified by SNs use. A generalized linear model was employed to examine the association between SNs use and GPA among adolescents as a continuous variable, adjusting for relevant covariates, such as sex, age, socioeconomic status, BMI, physical activity, sedentary behaviors, sleep duration, and energy intake. Additionally, unstandardized beta coefficients ( B ) were estimated to quantify the relationship between SNs use and GPA, with 95% confidence intervals (CIs) indicating the precision of the estimates. The significance level was set at p < 0.05. All the statistical analyses were performed via R software (version 4.3.2) from the R Core Team in Vienna, Austria, and RStudio (version 2023.12.1 + 402) from Posit in Boston, USA. Results Table 1 shows the study participant´s sociodemographic, lifestyle, and anthropometric characteristics. Among the total sample of 583 participants, 330 were females (57%). The median age of the sample was 14 years (IQR = 13.0–15.0). The median FAS-III score was 8 points (IQR = 7.0–9.0). With respect to the independent variables, the sample presented a median SNs use of 14.0 points (IQR = 11.0–16.0), and the applications most used were TikTok and WhatsApp, both of which presented median scores of 4.0 (IQR for TikTok = 2.0 to 5.0; IQR for WhatsApp = 3.0 to 4.0). The median GPA was 6.3 points (IQR = 4.7 to 8.0), and the highest grade was for Language, with a median score of 7.0 (IQR = 5.0 to 8.0). Table 1 Descriptive data of the study participants. Variable N = 583 1 Age (years) 14.0 (13.0–15.0) Sex Males 253 (43%) Females 330 (57%) FAS-III (score) 8.0 (7.0–9.0) BMI (kg/m 2 ) 21.6 (19.3–25.1) YAP-S physical activity (score) 2.6 (2.2–3.1) YAP-S sedentary behaviors (score) 2.6 (2.2-3.0) Overall sleep duration (minutes) 501.4 (458.6-531.4) Energy intake (kcal) 2,617.3 (1,973.1- 3,468.9) Facebook use (points) 1.0 (1.0–1.0) Twitter use (points) 1.0 (1.0–2.0) Instagram use (points) 4.0 (3.0–5.0) Snapchat use (points) 1.0 (1.0–2.0) TikTok use (points) 4.0 (2.0–5.0) WhatsApp use (points) 4.0 (3.0–4.0) SN use (points) 14.0 (11.0–16.0) Tolerance 361 (62%) Salience 138 (24%) Mood modification 259 (44%) Relapse 203 (35%) Withdrawal 138 (24%) Conflict 130 (22%) Addictive behaviors to SN use 2.0 (1.0–3.0) GPA (score) 6.3 (4.7-8.0) Language (score) 7.0 (5.0–8.0) Mathematics (score) 6.0 (4.0–8.0) Foreign language (score) 6.0 (5.0–8.0) 1 Median (IQR) or number (%); BMI = body mass index; FAS-III = Family Influence Scale-III; GPA = grade point average; SN = social network; YAP-S = Spanish Youth Active Profile. Figure 1 shows the estimated marginal means of the GPA in relation to the SNs score in the sample of adolescents examined after adjusting for sex, age, socioeconomic status, BMI, physical activity, sedentary behaviors, sleep duration, and energy intake. This result indicates that for each additional point in SN use, the GPA decreases by an average of 0.05 points. The association did not reach statistical significance ( B = -0.05, 95% CI -0.10 to 0.01, p = 0.092). The specific data can be found in Table S1. Figure 2 shows the estimated marginal means of the GPA in relation to SNs addictive behaviors. After adjusting for sex, age, socioeconomic status, BMI, physical activity, sedentary behaviors, sleep duration, and energy intake, for each unit increase in addictive behaviors to SN use, there is an associated decrease of 0.15 points in GPA. This negative association is statistically significant ( B = -0.15, 95% CI -0.26, -0.03; p = 0.014). The specific data can be found in Table S2. Table 2 presents the results of a generalized linear model examining associations between specific SN use and GPA among adolescents. It also highlights the relationship between various symptoms of addictive behavior related to SN use and GPA. All analyses were adjusted for multiple covariates, including age, sex, socioeconomic status, BMI, physical activity, sedentary behavior, sleep duration, and energy intake. A statistically significant negative association was found between GPA and Instagram ( p = 0.010), Snapchat ( p = 0.001), and Facebook ( p = 0.014) use, suggesting that greater use of these three SNs is associated with a lower GPA. The impact of TikTok, X, and WhatsApp use on GPA were not significant. With respect to addictive behavior symptoms, a significant negative association was found between GPA and relapse ( p = 0.019) and mood modification ( p = 0.008). Table 2 Generalized linear model examining the association between individual social networks or addictive behaviors towards their use and grade point average (and covariates) among adolescents. Predictors B 95% CI p -value SN use Facebook use (per one point) -0.33 -0.60, -0.07 0.014 Twitter use (per one point) -0.05 -0.32, 0.08 0.229 Instagram use (per one point) -0.19 -0.34, -0.05 0.010 Snapchat use (per one point) -0.36 -0.57, -0.15 0.001 TikTok use (per one point) -0.12 -0.13, 0.13 0.941 Messaging application use WhatsApp use (per one point) 0.14 -0.04, 0.31 0.125 Addictive behaviors to SN use Salience (yes) -0.004 -0.39, 0.39 0.983 Tolerance (yes) -0.41 -0.85, 0.02 0.063 Mood modification (yes) -0.51 -0.88, -0.13 0.008 Withdrawal (yes) -0.15 -0.53, 0.23 0.447 Relapse (yes) -0.52 -0.95, -0.09 0.019 Conflict (yes) -0.22 -0.67, 0.22 0.319 B , unstandardized beta coefficient; CI, confidence interval; SN, social network. Adjusted for age, sex, socioeconomic status, body mass index, physical activity, sedentary behavior, sleep duration, and energy intake. Discussion Our study provides evidence that the use of SNs, specifically, when they become addictive, is associated with lower academic performance among adolescents. The findings reveal a significant negative relationship between addictive behaviors related to SNs use and GPA, suggesting that higher levels of SNs addiction are linked to lower academic performance, independent of sociodemographic, anthropological and lifestyle factors. Among the platforms studied, Instagram, Snapchat, and Facebook showed a statistically significant association with lower GPA and the use of these SNs. In contrast, X and WhatsApp did not show a significant association. Addictive behaviors, such as mood modification and relapse, showed the strongest associations with low GPA. Although the exact reasons why SNs use and addictive behaviors toward it might lead to lower academic performance have not been exposed, there are some hypotheses that could explain these findings. First, research has identified a link between SN multitasking and academic performance. A study conducted by Lau ( 11 ) revealed that social media multitasking and nonacademic use of social media negatively predict academic performance among university students. Lau’s study highlighted that those students who frequently engaged in social media multitasking, such as checking SNs while studying or completing academic tasks, experienced a decline in their GPA. This is likely due to the cognitive overload caused by dividing attention between multiple tasks, which reduces the ability of the brain to process and retain information effectively ( 33 ). Similarly, a study conducted with Spanish adolescents revealed that those who engaged in social media multitasking experienced a negative impact on their executive function and academic performance. Additionally, adolescents who frequently multitasked with media while doing homework reported higher levels of executive function difficulties ( 34 ). Second, another factor that may explain the link between SNs abuse and low GPA can be attributed to decreased self-esteem and increased stress. A study conducted by Landa-Blanco et al. ( 21 ) revealed that SN addiction could undermine academic engagement by reducing self-esteem and increasing depressive symptoms. In addition, stress plays a central role in the negative impact of SN addiction on academic performance. SN use has been shown to elevate stress through mechanisms such as constant connectivity, social comparison, and pressure to maintain an idealized online image. Additionally, social pressure to obtain more followers and likes increases stress levels, anxiety, and addictive behaviors ( 35 ). Conversely, it is also possible that students who are already experiencing high levels of academic stress may turn to social media as an avoidant coping strategy or as a means of mood regulation. This perspective is supported by the prominent role of mood modification observed in our findings, suggesting that students may use SNs to temporarily escape academic pressures or negative emotions. In this way, social media use may serve as a short-term relief from stress, but over time, such coping behaviors could reinforce compulsive use patterns and further exacerbate academic difficulties. Recognizing this bidirectional relationship is important for understanding the complex interplay between social media use, stress, and academic performance ( 36 ). This continuing stress can interfere with cognitive functions like attention and memory, ultimately impairing academic performance ( 37 ). Thus, the combination of SN addiction and elevated stress can create a harmful cycle that undermines student´s ability to succeed academically ( 35 ). In addition, procrastination may be another link between SNs addiction and academic performance. Landa-Blanco et al. ( 21 ), also revealed that addiction to SN negatively affects mental health and productivity by increasing procrastination. Another study conducted by Caratiquit and Caratiquit ( 38 ), found that among secondary school students, higher levels of SN addiction were strongly associated with increased academic procrastination. This procrastination, in turn, had a significant negative impact on academic achievement, indicating that procrastination may mediate the relationship between SN addiction and academic performance. The relationship between internet or SN addiction and procrastination is further supported by studies focusing on university students. For example, research conducted by Chavez-Yacola, et al. ( 39 ) found that academic self-efficacy may explain the relationship between internet addiction and academic procrastination. Students with higher levels of self-efficacy were less likely to develop internet addiction and, consequently, less prone to procrastinate academically. These behaviors reflect a loss of control over SN use, leading to less time devoted to academic activities and an increased tendency to procrastinate. Finally, our study also highlights that not all SNs have the same impact on academic performance. This finding is consistent with research by Junco ( 40 ), who reported that certain SN platforms, particularly those designed for rapid content consumption and frequent updates (e.g., Instagram and Facebook), are more likely to distract students and reduce their academic engagement. This aligns with our observation that platforms such as Instagram, Snapchat, and Facebook, which constantly draw users back to check for new content, interfere with their study time and concentration, are more strongly associated with lower GPAs than other platforms, such as X or messaging apps like WhatsApp. It is important to highlight that our results also support the growing evidence that the use of SNs itself is not inherently associated with lower academic performance. A study ( 11 ) identified that functional use of SNs does not necessarily interfere with student´s academic responsibilities and, in some cases, may even provide benefits such as access to educational resources, enhanced digital literacy, and improved social connection. Furthermore, another study by Valkenburg et al. ( 41 ) found that the frequency or duration of SN use alone does not predict lower grades or academic disengagement, as some adolescents are able to balance their academic obligations with recreational or communicative use of these platforms. Despite these insights, our study has several limitations. The cross-sectional design limits our ability to establish causality or observe changes over time. Additionally, the reliance on self-reported data for SNs use may introduce bias, as students may underreport their SNs use. Future studies should adopt longitudinal approaches to more accurately examine the temporal dynamics linking SNs addiction with academic outcomes. On the other hand, our study also has important strengths that must be considered. We included in the analyses many covariates, such as age, sex, socioeconomic status, BMI, physical activity, sedentary behavior, sleep duration and energy intake, enhancing the validity of the statistical results. Additionally, we also analyzed various SNs separately to assess their distinct effects, providing key insights for addictive behaviors. Conclusions Our findings show a relationship between the use of SNs and GAP, however, this was not significant. On the contrary, we found a significant relationship between addictive behaviors related to SNs and GAP. These findings suggest the need for educational interventions that promote mindful technology use while preserving the benefits of digital connectivity. Future initiatives should equip students with self-regulation strategies and raise awareness about the potential academic costs of uncontrolled SN consumption. Abbreviations SNs social networks SN social network EHDLA Eating Healthy and Daily Life Activities SNAddS-6S Social Networks Addiction Scale-6 Symptoms GPA grade point average BMI body mass index FAS-III Family Affluence Scale YAP-S Spanish-Youth Activity Profile IQR interquartile range CIs confidence intervals B unstandardized beta coefficients Declarations Data Availability The datasets generated during and/or analyzed during the current study are not publicly available due that the participants are minors, privacy and confidentiality must be respected but are available from the corresponding author on reasonable request. Acknowledgments The authors wish to extend their gratitude to Ayuntamiento de Archena and all the teenagers, parents or legal guardians, physical education instructors, schools, and staff members who participated. Funding This research received no external funding. Author contributions A.A.-B., F.Q.-C. and E.J.-L. contributed to the conceptualization. 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Martinez-Zamora MD, Valenzuela PL, Pinto-Escalona T, Martinez-de-Quel Ó. The “Fat but Fit” paradox in the academic context: relationship between physical fitness and weight status with adolescents’ academic achievement. Int J Obes. 2021 Jan;45(1):95–8. Gaspar, T.; Carvalho, M.; Noronha, C.; Guedes, F.B.; Cerqueira, A.; de Matos, M.G. Healthy Social Network Use and Well-Being during Adolescence: A Biopsychosocial Approach. Children 2023, 10, 1649. https://doi.org/10.3390/ children10101649. Fumagalli E, Shrum LJ, Lowrey TM. The Effects of Social Media Consumption on Adolescent Psychological Well-Being. J Assoc Consum Res. 2024 Apr 1;9(2):119–30. Currie C, Molcho M, Boyce W, Holstein B, Torsheim T, Richter M. Researching health inequalities in adolescents: The development of the Health Behaviour in School-Aged Children (HBSC) Family Affluence Scale. Soc Sci Med. 2008 Mar;66(6):1429–36. Segura-Díaz JM, Barranco-Ruiz Y, Saucedo-Araujo RG, Aranda-Balboa MJ, Cadenas-Sanchez C, Migueles JH, et al. Feasibility and reliability of the Spanish version of the Youth Activity Profile questionnaire (YAP-Spain) in children and adolescents. J Sports Sci. 2021 Apr 3;39(7):801–7. Rodríguez IT, Ballart JF, Pastor GC, Jordà EB, Val VA. [Validation of a short questionnaire on frequency of dietary intake: reproducibility and validity]. Nutr Hosp. 2008;23(3):242–52. Ophir E, Nass C, Wagner AD. Cognitive control in media multitaskers. Proc Natl Acad Sci. 2009 Sep 15;106(37):15583–7. Martín-Perpiñá M, Viñas Poch F, Malo Cerrato S. Media multitasking impact in homework, executive function and academic performance in Spanish adolescents. Psicothema. 2019 Feb 1;1(31):81–7. Keles B, McCrae N, Grealish A. A systematic review: the influence of social media on depression, anxiety and psychological distress in adolescents. Int J Adolesc Youth. 2020 Dec 31;25(1):79–93. Shiraly R, Roshanfekr A, Asadollahi A, Griffiths MD. Psychological distress, social media use, and academic performance of medical students: the mediating role of coping style. BMC Med Educ [Internet]. 2024 Sep 13 [cited 2025 Jul 12];24(1). Available from: https://bmcmededuc.biomedcentral.com/articles/10.1186/s12909-024-05988-w McEwen BS. Physiology and Neurobiology of Stress and Adaptation: Central Role of the Brain. Physiol Rev. 2007 Jul;87(3):873–904. Caratiquit KD, Caratiquit LJC. Influence of social media addiction on academic achievement in distance learning: Intervening role of academic procrastination. Turk Online J Distance Educ. 2023 Jan 1;24(1):1–19. Chavez-Yacolca DR, Castro-Champión RB, Cisneros-Gonzales NM, Cunza-Aranzábal DF, Morales-García M, Abanto-Ramírez CD. Relationship between academic procrastination and internet addiction in Peruvian university students: the mediating role of academic self-efficacy. Front Psychol. 2025 Jan 23;15:1454234. Junco R. Student class standing, Facebook use, and academic performance. J Appl Dev Psychol. 2015 Jan;36:18–29. Valkenburg PM, Meier A, Beyens I. Social media use and its impact on adolescent mental health: An umbrella review of the evidence. Curr Opin Psychol. 2022 Apr;44:58–68. Supplementary Files Supplementalmaterial.docx Cite Share Download PDF Status: Published Journal Publication published 29 Dec, 2025 Read the published version in Italian Journal of Pediatrics → Version 1 posted Editorial decision: Major revision 28 Aug, 2025 Reviewers agreed at journal 08 Aug, 2025 Reviewers invited by journal 08 Aug, 2025 Editor assigned by journal 28 Jul, 2025 First submitted to journal 26 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Quiroz-Cárdenas","email":"","orcid":"","institution":"Universidad de Los Lagos","correspondingAuthor":false,"prefix":"","firstName":"Fiorella","middleName":"","lastName":"Quiroz-Cárdenas","suffix":""},{"id":497540870,"identity":"8a6f06b4-b84e-4ab8-b58b-e7660d06f1b3","order_by":2,"name":"José Adrián Montenegro-Espinosa","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0004-5387-4083","institution":"Universidad de Especialidades Espíritu Santo: Universidad de 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London","correspondingAuthor":false,"prefix":"","firstName":"Brendon","middleName":"","lastName":"Stubss","suffix":""},{"id":497540877,"identity":"875e0547-5b8e-4bff-9918-badfef7f3b03","order_by":9,"name":"Lee Smith","email":"","orcid":"","institution":"Anglia Ruskin University","correspondingAuthor":false,"prefix":"","firstName":"Lee","middleName":"","lastName":"Smith","suffix":""},{"id":497540878,"identity":"d4a9c293-eae3-4482-b7c5-e55640d79b80","order_by":10,"name":"José Francisco López-Gil","email":"","orcid":"https://orcid.org/0000-0002-7412-7624","institution":"Universidad de Especialidades Espíritu Santo: Universidad de Especialidades Espiritu Santo","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Francisco","lastName":"López-Gil","suffix":""},{"id":497540879,"identity":"6df187e0-f370-43fe-a681-38f99f9a2fc2","order_by":11,"name":"Estela Jiménez-López","email":"","orcid":"","institution":"University of Castilla-La Mancha: Universidad de Castilla-La Mancha","correspondingAuthor":false,"prefix":"","firstName":"Estela","middleName":"","lastName":"Jiménez-López","suffix":""}],"badges":[],"createdAt":"2025-07-14 20:26:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7124293/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7124293/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13052-025-02180-8","type":"published","date":"2025-12-29T15:58:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88983071,"identity":"a8c7f895-ca29-4ed0-a20a-785633b81b49","added_by":"auto","created_at":"2025-08-13 11:57:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19275,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated marginal means of the GPA in relation to the SNs score.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7124293/v1/7eb6abc62accf14116114695.png"},{"id":88983072,"identity":"8574673c-7875-49de-9720-55b16f99bc05","added_by":"auto","created_at":"2025-08-13 11:57:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":22026,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated marginal means of the GPA in relation to SNs addictive behaviors.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7124293/v1/0b1433bb8af42b3355311af9.png"},{"id":99545343,"identity":"d482d2ae-f0ab-4866-8cd7-276264f4333c","added_by":"auto","created_at":"2026-01-05 16:06:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":778205,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7124293/v1/91e8d384-0815-48b9-aab2-e9fa454cd6f4.pdf"},{"id":88983069,"identity":"71a754d9-27d9-4a65-bbdc-626f461f74ed","added_by":"auto","created_at":"2025-08-13 11:57:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18602,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7124293/v1/43cad1a3a855a82892d4e0e1.docx"}],"financialInterests":"","formattedTitle":"The link between social networks and messaging apps with addictive behaviors and academic performance in adolescents: the EHDLA study.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcademic performance is the evaluation of a student\u0026rsquo;s accomplishments based on factors such as grade point average, standardized test results, and educational goals (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Academic performance broadly involves acquiring knowledge, developing skills and competencies, attaining high grades and other academic milestones, striving for a successful career, and exhibiting dedication and persistence in education (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Studies performed in England and Slovakia on adolescent\u0026acute;s educational process have identified key factors influencing academic performance. These factors include motivation, stress levels and coping mechanisms; anxiety, self-esteem and self-efficacy; time management; and the methods and strategies employed to acquire academic knowledge (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Academic performance is a critical indicator of adolescent\u0026acute;s success and achievement. It plays a vital role in child development, as skills such as reading and mathematics impact a wide range of outcomes, including educational attainment, career performance and income, physical and mental health, and overall life expectancy (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSocial networks (SNs) refer to online platforms and digital tools that facilitate user interactions by allowing individuals to share information, express opinions, and engage with shared interests (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Instagram, Telegram, Facebook, Twitter (now X), and WhatsApp are some of the most popular SNs and messaging applications among users (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Adolescents and young adults are increasingly spending time on SNs activities (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), as a result, engaging with SNs requires the dedication of various resources, including time, cost, and effort, which can significantly impact daily life in both positive and negative ways (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). According to a report by the Carat (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) agency on social media usage among young people aged 8\u0026ndash;17 years, 91% access social media regularly. Usage is intensive, with more than 3 hours per day from Monday to Friday and more than 5 hours on weekends. Children typically had a smartphone at the age of 11 years, which is the primary device used for accessing social media and it has been associated with more problematic patterns of use (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). According to the report by Carat agency, Instagram is their preferred platform, followed by the growth of TikTok and gaming platforms. While social media offers new ways of communication, evidence suggests connections between issues related to body image, isolation, depression, addictive behaviors and bullying (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eResearchers have explored whether SNs have a positive or negative relationship with academic performance (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). SNs usage among students can play a role in collaborative learning by helping teachers address educational challenges and increasing student engagement (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Platforms such as YouTube and X can enhance interactive learning by supporting knowledge transfer through visual and auditory means while fostering engagement and collaboration among students, teachers, and peers, respectively (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Additionally, SNs help students share ideas and research globally, supporting academic problem solving (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In contrast, some studies suggest that SNs have a negative effect on academic performance, especially when they are overused (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In this line, studies have shown that students experience distractions and lack focus due to excessive SN use. Additionally, they report having less time to study, submitting assignments late, and struggling with poor spelling and grammar as a result of heavy SNs consumption (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStudies have shown that overconsumption of SNs is linked to higher levels of procrastination and decreased productivity, impacting overall academic performance (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Adolescents and young adults are often the most vulnerable to these patterns of use, with some individuals developing addictive behaviors characterized by a persistent and uncontrollable urge to engage with social networking platforms. This addictive use of social networks represents the more severe end of a spectrum known as problematic media use, which encompasses behaviors ranging from occasional overuse to compulsive engagement that disrupts daily functioning. Unlike casual or even frequent use, addictive use is distinguished by symptoms such as loss of control, withdrawal-like experiences when access is restricted, and prioritization of online interactions over offline responsibilities and relationships (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This widespread use of SNs and the design features commonly used, while fostering connectivity and engagement with online communities, also has the potential to manipulate and influence young people, ultimately affecting their well-being in negative ways. As such, the impact of SNs on student\u0026acute;s academic and personal lives is complex, requiring a balanced approach to harness its benefits while mitigating its risks (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Based on this, there is talk of possible addiction to SNs, which is especially worrisome among the younger generations, who spend a significant part of their daily lives on these platforms (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this context, the present study aims to evaluate the link between SNs and academic performance in Spanish adolescents. Specifically, we hypothesize that higher frequency of SN use will be associated with lower academic performance, and that adolescents exhibiting addictive or compulsive patterns of SN use will show even greater risk of academic difficulties. While SNs can enhance learning and communication, excessive use may lead to distractions and procrastination and diminished academic outcomes.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eStudy design and population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis is a cross-sectional analysis using data from the Eating Healthy and Daily Life Activities (EHDLA) study, which was carried out in three secondary schools in \u003cem\u003eValle de Ricote\u003c/em\u003e, within the Region of Murcia (Spain), during the 2021\u0026ndash;2022 academic year. This secondary study involved a sample of 583 adolescents aged 12\u0026ndash;17 years. The detailed methodology of the EHDLA study is described elsewhere (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe inclusion criteria for participants were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) they had to be between 12 and 17 years and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) they had to be registered or reside in the \u003cem\u003eValle de Ricote\u003c/em\u003e. The exclusion criteria were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) adolescents who were exempt from physical education at school, as the tests and questionnaires were conducted during physical education classes; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) those with any condition that contraindicated physical activity or required special attention; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) those undergoing pharmacological treatment; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) those whose parents or legal guardians had not given consent for participation; or (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) those who did not agree to participate in the research project.\u003c/p\u003e\u003cp\u003eApproval for the EHDLA project was granted by the Bioethics Committee of the Universidad de Murcia (ID 2218/2018, approved on 18 February 2019) and the Ethics Committee of the \u003cem\u003eComplejo Hospitalario Universitario de Albacete\u003c/em\u003e and the \u003cem\u003eGesti\u0026oacute;n Asistencial Integrada de Albacete\u003c/em\u003e (ID 2021\u0026ndash;86, approved on 23 November 2021). The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki by the World Medical Association. With respect to participation in this research project, parents or legal guardians of the adolescents received a written informed consent form to sign in advance. In addition, both the parents or guardians and the participants were given an information sheet outlining the objectives of the study, along with details about the tests and questionnaires that would be administered. Adolescents were also asked about their willingness to take part in the study (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eVariables\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSocial network and messaging applications\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe use of SNs (Facebook, X, Instagram, Snapchat, TikTok, WhatsApp) was evaluated by a single-item scale asking adolescents what type of SN they used, on the basis of five possible responses: (a) I never or rarely use it (b) I am a low consumer, (c) I am a medium consumer, (d) I am a fairly high consumer, or (e) I am a very high consumer. Each response was assigned a score from 0 (never or rarely) to 4 (very high consumer). The total SN use score was calculated by summing the scores across all six platforms, resulting in a composite measure that reflects the overall frequency of social network use for each participant (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAddictive behaviors toward social networks\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Short Social Networks Addiction Scale-6 Symptoms (SNAddS-6S) was used to assess SN addiction (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The SNAddS-6S is a validated tool in Spanish designed to measure the degree of SN addiction in individuals. It consists of six items that evaluate key aspects of SN addiction, focusing on behaviors and psychological symptoms associated with excessive use. These aspects include: \u003cem\u003esalience\u003c/em\u003e (social networking use becomes the individual\u0026rsquo;s primary concern and motivation), \u003cem\u003etolerance\u003c/em\u003e (the need to spend increasing amounts of time on SNs), \u003cem\u003emood modification\u003c/em\u003e (using SNs to alter mood, either by excitement or relaxation), \u003cem\u003ewithdrawal\u003c/em\u003e (experiencing psychological or physical symptoms when unable to access SNs), \u003cem\u003econflict\u003c/em\u003e (SN use interfering with other social or daily activities), and \u003cem\u003erelapse\u003c/em\u003e (returning to excessive use after attempts to control or reduce it). For each item, participants respond using dichotomous (yes/no) options, indicating whether they have experienced each symptom during the past year. The total score is calculated by summing the number of \u0026ldquo;yes\u0026rdquo; responses, with higher scores reflecting a greater degree of addictive social network use. The scale is structured as a unidimensional factor (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAcademic performance\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSchool records were provided by each high school at the end of the academic year. Academic performance was assessed in two ways: first, the grade point average (GPA) was calculated as the mean of all academic subject scores for each student; second, individual evaluations were conducted in language, mathematics, and English as a foreign language (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). In the Spanish education system, all grades are awarded on a scale from 0 to 10, and a higher grade means greater academic performance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCovariates\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe relationship between academic performance and SNs in adolescents could be influenced by various factors that may mediate or moderate the effects of SNs, such as age, sex, socioeconomic status, physical activity, body mass index (BMI), sleep duration, energy intake, and sedentary behavior (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe adolescents self-reported their age and sex. Socioeconomic status was assessed via the Family Affluence Scale (FAS-III) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), which evaluates six items: whether adolescents have their own bedroom, family ownership of a car and a dishwasher, the number of bathrooms in the home, the number of vacations taken outside Spain in the past year, and the number of household computers. The FAS-III scores ranges from 0 to 13 points. Data on sedentary behavior and physical activity were collected via the Spanish-Youth Activity Profile (YAP-S) (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), in which scores are calculated by summing points from each section. Anthropometric measurements, such as height and body weight, were taken via a portable stadiometer (Leicester Tanita HR 001, Tokyo, Japan) and an electronic scale (Tanita BC-545, Tokyo, Japan), respectively. BMI was calculated by dividing weight in kilograms by the square of height in meters. Total sleep duration was assessed by asking participants the same questions for weekdays and weekends separately: \u0026ldquo;What time do you go to bed?\u0026rdquo; and \u0026ldquo;What time do you typically wake up?\u0026rdquo;. The average daily sleep duration for each participant was calculated in the following manner: [(average nocturnal sleep duration on weekdays \u0026times; 5) + (average nocturnal sleep duration on weekends \u0026times; 2)]/7 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Energy intake was assessed via a self-reported dietary habits questionnaire that has been validated for the Spanish population (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eTo assess whether the variables followed a normal distribution, visual methods such as density plots and quantile‒quantile (Q‒Q) plots were utilized, along with the Shapiro‒Wilk test. This study presents the median and interquartile range (IQR) for quantitative variables, whereas frequencies (n) and percentages (%) are reported for qualitative variables, both for the overall sample and those classified by SNs use. A generalized linear model was employed to examine the association between SNs use and GPA among adolescents as a continuous variable, adjusting for relevant covariates, such as sex, age, socioeconomic status, BMI, physical activity, sedentary behaviors, sleep duration, and energy intake. Additionally, unstandardized beta coefficients (\u003cem\u003eB\u003c/em\u003e) were estimated to quantify the relationship between SNs use and GPA, with 95% confidence intervals (CIs) indicating the precision of the estimates. The significance level was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All the statistical analyses were performed via R software (version 4.3.2) from the R Core Team in Vienna, Austria, and RStudio (version 2023.12.1\u0026thinsp;+\u0026thinsp;402) from Posit in Boston, USA.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the study participant\u0026acute;s sociodemographic, lifestyle, and anthropometric characteristics. Among the total sample of 583 participants, 330 were females (57%). The median age of the sample was 14 years (IQR\u0026thinsp;=\u0026thinsp;13.0\u0026ndash;15.0). The median FAS-III score was 8 points (IQR\u0026thinsp;=\u0026thinsp;7.0\u0026ndash;9.0). With respect to the independent variables, the sample presented a median SNs use of 14.0 points (IQR\u0026thinsp;=\u0026thinsp;11.0\u0026ndash;16.0), and the applications most used were TikTok and WhatsApp, both of which presented median scores of 4.0 (IQR for TikTok\u0026thinsp;=\u0026thinsp;2.0 to 5.0; IQR for WhatsApp\u0026thinsp;=\u0026thinsp;3.0 to 4.0). The median GPA was 6.3 points (IQR\u0026thinsp;=\u0026thinsp;4.7 to 8.0), and the highest grade was for Language, with a median score of 7.0 (IQR\u0026thinsp;=\u0026thinsp;5.0 to 8.0).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive data of the study participants.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;583\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.0 (13.0\u0026ndash;15.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e253 (43%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e330 (57%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFAS-III (score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.0 (7.0\u0026ndash;9.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.6 (19.3\u0026ndash;25.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYAP-S physical activity (score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.6 (2.2\u0026ndash;3.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYAP-S sedentary behaviors (score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.6 (2.2-3.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall sleep duration (minutes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e501.4 (458.6-531.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnergy intake (kcal)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,617.3 (1,973.1- 3,468.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFacebook use (points)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0 (1.0\u0026ndash;1.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTwitter use (points)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0 (1.0\u0026ndash;2.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInstagram use (points)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.0 (3.0\u0026ndash;5.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSnapchat use (points)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0 (1.0\u0026ndash;2.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTikTok use (points)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.0 (2.0\u0026ndash;5.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhatsApp use (points)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.0 (3.0\u0026ndash;4.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSN use (points)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.0 (11.0\u0026ndash;16.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTolerance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e361 (62%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSalience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138 (24%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMood modification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e259 (44%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelapse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e203 (35%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWithdrawal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138 (24%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConflict\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130 (22%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAddictive behaviors to SN use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.0 (1.0\u0026ndash;3.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPA (score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.3 (4.7-8.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLanguage (score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.0 (5.0\u0026ndash;8.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMathematics (score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.0 (4.0\u0026ndash;8.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForeign language (score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.0 (5.0\u0026ndash;8.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eMedian (IQR) or number (%); BMI\u0026thinsp;=\u0026thinsp;body mass index; FAS-III\u0026thinsp;=\u0026thinsp;Family Influence Scale-III; GPA\u0026thinsp;=\u0026thinsp;grade point average; SN\u0026thinsp;=\u0026thinsp;social network; YAP-S\u0026thinsp;=\u0026thinsp;Spanish Youth Active Profile.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the estimated marginal means of the GPA in relation to the SNs score in the sample of adolescents examined after adjusting for sex, age, socioeconomic status, BMI, physical activity, sedentary behaviors, sleep duration, and energy intake. This result indicates that for each additional point in SN use, the GPA decreases by an average of 0.05 points. The association did not reach statistical significance (\u003cem\u003eB\u003c/em\u003e = -0.05, 95% CI -0.10 to 0.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.092). The specific data can be found in Table S1.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the estimated marginal means of the GPA in relation to SNs addictive behaviors. After adjusting for sex, age, socioeconomic status, BMI, physical activity, sedentary behaviors, sleep duration, and energy intake, for each unit increase in addictive behaviors to SN use, there is an associated decrease of 0.15 points in GPA. This negative association is statistically significant (\u003cem\u003eB\u003c/em\u003e = -0.15, 95% CI -0.26, -0.03; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014). The specific data can be found in Table S2.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of a generalized linear model examining associations between specific SN use and GPA among adolescents. It also highlights the relationship between various symptoms of addictive behavior related to SN use and GPA. All analyses were adjusted for multiple covariates, including age, sex, socioeconomic status, BMI, physical activity, sedentary behavior, sleep duration, and energy intake. A statistically significant negative association was found between GPA and Instagram (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010), Snapchat (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and Facebook (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014) use, suggesting that greater use of these three SNs is associated with a lower GPA. The impact of TikTok, X, and WhatsApp use on GPA were not significant. With respect to addictive behavior symptoms, a significant negative association was found between GPA and relapse (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019) and mood modification (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGeneralized linear model examining the association between individual social networks or addictive behaviors towards their use and grade point average (and covariates) among adolescents.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSN use\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFacebook use (per one point)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.60, -0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTwitter use (per one point)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.32, 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInstagram use (per one point)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.34, -0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSnapchat use (per one point)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.57, -0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTikTok use (per one point)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.13, 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMessaging application use\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhatsApp use (per one point)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04, 0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAddictive behaviors to SN use\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSalience (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.39, 0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTolerance (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.85, 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMood modification (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.88, -0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWithdrawal (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.53, 0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelapse (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.95, -0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConflict (yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.67, 0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e, unstandardized beta coefficient; CI, confidence interval; SN, social network. Adjusted for age, sex, socioeconomic status, body mass index, physical activity, sedentary behavior, sleep duration, and energy intake.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study provides evidence that the use of SNs, specifically, when they become addictive, is associated with lower academic performance among adolescents. The findings reveal a significant negative relationship between addictive behaviors related to SNs use and GPA, suggesting that higher levels of SNs addiction are linked to lower academic performance, independent of sociodemographic, anthropological and lifestyle factors. Among the platforms studied, Instagram, Snapchat, and Facebook showed a statistically significant association with lower GPA and the use of these SNs. In contrast, X and WhatsApp did not show a significant association. Addictive behaviors, such as mood modification and relapse, showed the strongest associations with low GPA.\u003c/p\u003e\u003cp\u003eAlthough the exact reasons why SNs use and addictive behaviors toward it might lead to lower academic performance have not been exposed, there are some hypotheses that could explain these findings. First, research has identified a link between SN multitasking and academic performance. A study conducted by Lau (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) revealed that social media multitasking and nonacademic use of social media negatively predict academic performance among university students. Lau\u0026rsquo;s study highlighted that those students who frequently engaged in social media multitasking, such as checking SNs while studying or completing academic tasks, experienced a decline in their GPA. This is likely due to the cognitive overload caused by dividing attention between multiple tasks, which reduces the ability of the brain to process and retain information effectively (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Similarly, a study conducted with Spanish adolescents revealed that those who engaged in social media multitasking experienced a negative impact on their executive function and academic performance. Additionally, adolescents who frequently multitasked with media while doing homework reported higher levels of executive function difficulties (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSecond, another factor that may explain the link between SNs abuse and low GPA can be attributed to decreased self-esteem and increased stress. A study conducted by Landa-Blanco et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) revealed that SN addiction could undermine academic engagement by reducing self-esteem and increasing depressive symptoms. In addition, stress plays a central role in the negative impact of SN addiction on academic performance. SN use has been shown to elevate stress through mechanisms such as constant connectivity, social comparison, and pressure to maintain an idealized online image. Additionally, social pressure to obtain more followers and likes increases stress levels, anxiety, and addictive behaviors (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Conversely, it is also possible that students who are already experiencing high levels of academic stress may turn to social media as an avoidant coping strategy or as a means of mood regulation. This perspective is supported by the prominent role of mood modification observed in our findings, suggesting that students may use SNs to temporarily escape academic pressures or negative emotions. In this way, social media use may serve as a short-term relief from stress, but over time, such coping behaviors could reinforce compulsive use patterns and further exacerbate academic difficulties. Recognizing this bidirectional relationship is important for understanding the complex interplay between social media use, stress, and academic performance (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). This continuing stress can interfere with cognitive functions like attention and memory, ultimately impairing academic performance (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Thus, the combination of SN addiction and elevated stress can create a harmful cycle that undermines student\u0026acute;s ability to succeed academically (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn addition, procrastination may be another link between SNs addiction and academic performance. Landa-Blanco et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), also revealed that addiction to SN negatively affects mental health and productivity by increasing procrastination. Another study conducted by Caratiquit and Caratiquit (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), found that among secondary school students, higher levels of SN addiction were strongly associated with increased academic procrastination. This procrastination, in turn, had a significant negative impact on academic achievement, indicating that procrastination may mediate the relationship between SN addiction and academic performance. The relationship between internet or SN addiction and procrastination is further supported by studies focusing on university students. For example, research conducted by Chavez-Yacola, et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) found that academic self-efficacy may explain the relationship between internet addiction and academic procrastination. Students with higher levels of self-efficacy were less likely to develop internet addiction and, consequently, less prone to procrastinate academically. These behaviors reflect a loss of control over SN use, leading to less time devoted to academic activities and an increased tendency to procrastinate.\u003c/p\u003e\u003cp\u003eFinally, our study also highlights that not all SNs have the same impact on academic performance. This finding is consistent with research by Junco (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), who reported that certain SN platforms, particularly those designed for rapid content consumption and frequent updates (e.g., Instagram and Facebook), are more likely to distract students and reduce their academic engagement. This aligns with our observation that platforms such as Instagram, Snapchat, and Facebook, which constantly draw users back to check for new content, interfere with their study time and concentration, are more strongly associated with lower GPAs than other platforms, such as X or messaging apps like WhatsApp. It is important to highlight that our results also support the growing evidence that the use of SNs itself is not inherently associated with lower academic performance. A study (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) identified that functional use of SNs does not necessarily interfere with student\u0026acute;s academic responsibilities and, in some cases, may even provide benefits such as access to educational resources, enhanced digital literacy, and improved social connection. Furthermore, another study by Valkenburg et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) found that the frequency or duration of SN use alone does not predict lower grades or academic disengagement, as some adolescents are able to balance their academic obligations with recreational or communicative use of these platforms.\u003c/p\u003e\u003cp\u003eDespite these insights, our study has several limitations. The cross-sectional design limits our ability to establish causality or observe changes over time. Additionally, the reliance on self-reported data for SNs use may introduce bias, as students may underreport their SNs use. Future studies should adopt longitudinal approaches to more accurately examine the temporal dynamics linking SNs addiction with academic outcomes. On the other hand, our study also has important strengths that must be considered. We included in the analyses many covariates, such as age, sex, socioeconomic status, BMI, physical activity, sedentary behavior, sleep duration and energy intake, enhancing the validity of the statistical results. Additionally, we also analyzed various SNs separately to assess their distinct effects, providing key insights for addictive behaviors.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur findings show a relationship between the use of SNs and GAP, however, this was not significant. On the contrary, we found a significant relationship between addictive behaviors related to SNs and GAP. These findings suggest the need for educational interventions that promote mindful technology use while preserving the benefits of digital connectivity. Future initiatives should equip students with self-regulation strategies and raise awareness about the potential academic costs of uncontrolled SN consumption.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSNs\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esocial networks\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSN\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esocial network\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eEHDLA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEating Healthy and Daily Life Activities\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSNAddS-6S\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSocial Networks Addiction Scale-6 Symptoms\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eGPA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003egrade point average\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eFAS-III\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFamily Affluence Scale\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eYAP-S\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSpanish-Youth Activity Profile\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eIQR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003einterquartile range\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCIs\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econfidence intervals\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eB\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eunstandardized beta coefficients\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are not publicly available due that the participants are minors, privacy and confidentiality must be respected but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to extend their gratitude to \u003cem\u003eAyuntamiento de Archena\u003c/em\u003e and all the teenagers, parents or legal guardians, physical education instructors, schools, and staff members who participated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.A.-B., F.Q.-C. and E.J.-L. contributed to the conceptualization. J.F.L.-G. contributed to the methodology, formal analysis and data curation. A.A.-B. contributed to the writing original draft preparation. F.Q.-C., J.A.M.-E., N.M.-C., R.Y.-S., H.G.-E., J.O.-A., M.R., B.S., L.S., E.J.-L. \u0026nbsp;and J.F.L.-G. contributed to the writing review and editing. All authors have read and agreed to the published version of the manuscript. J.F.L.-G. is the guarantor of this article, and he accepts full responsibility for the work, had access to the data and controlled the decision to publish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFarb AF, Matjasko JL. 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Fear of Missing out, Social Media Engagement, Smartphone Addiction and Distraction: Moderating Role of Self-Help Mobile Apps-based Interventions in the Youth: In: Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies [Internet]. Porto, Portugal: SCITEPRESS - Science and Technology Publications; 2017 [cited 2024 Dec 12]. p. 139\u0026ndash;46. Available from: http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006166501390146\u003c/li\u003e\n\u003cli\u003eSalari N, Zarei H, Rasoulpoor S, Ghasemi H, Hosseinian-Far A, Mohammadi M. The impact of social networking addiction on the academic achievement of university students globally: a meta-analysis. Public Health Pract. 2025 Jan;100584. \u003c/li\u003e\n\u003cli\u003eTwenge JM, Campbell WK. Media Use Is Linked to Lower Psychological Well-Being: Evidence from Three Datasets. 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The Impact of Social Media on Students\u0026rsquo; Academic Performance. 2019;60(1). \u003c/li\u003e\n\u003cli\u003eMingle J, Adams M. Social Media Network Participation and Academic Performance In Senior High Schools in Ghana. \u003c/li\u003e\n\u003cli\u003eLanda-Blanco M, Garc\u0026iacute;a YR, Landa-Blanco AL, Cort\u0026eacute;s-Ramos A, Paz-Maldonado E. Social media addiction relationship with academic engagement in university students: The mediator role of self-esteem, depression, and anxiety. Heliyon. 2024 Jan;10(2):e24384. \u003c/li\u003e\n\u003cli\u003eXiao Y, Meng Y, Brown TT, Keyes KM, Mann JJ. Addictive Screen Use Trajectories and Suicidal Behaviors, Suicidal Ideation, and Mental Health in US Youths. JAMA [Internet]. 2025 Jun 18 [cited 2025 Jul 12]; Available from: https://jamanetwork.com/journals/jama/fullarticle/2835481\u003c/li\u003e\n\u003cli\u003eAlimoradi Z, Lotfi A, Lin CY, Griffiths MD, Pakpour AH. Estimation of Behavioral Addiction Prevalence During COVID-19 Pandemic: A Systematic Review and Meta-analysis. Curr Addict Rep. 2022 Sep 12;9(4):486\u0026ndash;517. \u003c/li\u003e\n\u003cli\u003eL\u0026oacute;pez-Gil JF. The Eating Healthy and Daily Life Activities (EHDLA) Study. Children. 2022 Mar 7;9(3):370. \u003c/li\u003e\n\u003cli\u003ePrefer\u0026egrave;ncies i expectatives dels adolescents relatives a la televisi\u0026oacute; a Catalunya. Barcelona: Generalitat de Catalunya. Consell de l\u0026rsquo;Audiovisual de Catalunya; 2007. \u003c/li\u003e\n\u003cli\u003eCuadrado E, Rojas R, Tabernero C. Development and Validation of the Social Network Addiction Scale (SNAddS-6S). Eur J Investig Health Psychol Educ. 2020 Jul 26;10(3):763\u0026ndash;78. \u003c/li\u003e\n\u003cli\u003eMartinez-Zamora MD, Valenzuela PL, Pinto-Escalona T, Martinez-de-Quel \u0026Oacute;. The \u0026ldquo;Fat but Fit\u0026rdquo; paradox in the academic context: relationship between physical fitness and weight status with adolescents\u0026rsquo; academic achievement. Int J Obes. 2021 Jan;45(1):95\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eGaspar, T.; Carvalho, M.; Noronha, C.; Guedes, F.B.; Cerqueira, A.; de Matos, M.G. Healthy Social Network Use and Well-Being during Adolescence: A Biopsychosocial Approach. Children 2023, 10, 1649. https://doi.org/10.3390/ children10101649. \u003c/li\u003e\n\u003cli\u003eFumagalli E, Shrum LJ, Lowrey TM. The Effects of Social Media Consumption on Adolescent Psychological Well-Being. J Assoc Consum Res. 2024 Apr 1;9(2):119\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eCurrie C, Molcho M, Boyce W, Holstein B, Torsheim T, Richter M. Researching health inequalities in adolescents: The development of the Health Behaviour in School-Aged Children (HBSC) Family Affluence Scale. Soc Sci Med. 2008 Mar;66(6):1429\u0026ndash;36. \u003c/li\u003e\n\u003cli\u003eSegura-D\u0026iacute;az JM, Barranco-Ruiz Y, Saucedo-Araujo RG, Aranda-Balboa MJ, Cadenas-Sanchez C, Migueles JH, et al. Feasibility and reliability of the Spanish version of the Youth Activity Profile questionnaire (YAP-Spain) in children and adolescents. J Sports Sci. 2021 Apr 3;39(7):801\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eRodr\u0026iacute;guez IT, Ballart JF, Pastor GC, Jord\u0026agrave; EB, Val VA. [Validation of a short questionnaire on frequency of dietary intake: reproducibility and validity]. Nutr Hosp. 2008;23(3):242\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eOphir E, Nass C, Wagner AD. Cognitive control in media multitaskers. Proc Natl Acad Sci. 2009 Sep 15;106(37):15583\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;n-Perpi\u0026ntilde;\u0026aacute; M, Vi\u0026ntilde;as Poch F, Malo Cerrato S. Media multitasking impact in homework, executive function and academic performance in Spanish adolescents. Psicothema. 2019 Feb 1;1(31):81\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eKeles B, McCrae N, Grealish A. A systematic review: the influence of social media on depression, anxiety and psychological distress in adolescents. Int J Adolesc Youth. 2020 Dec 31;25(1):79\u0026ndash;93. \u003c/li\u003e\n\u003cli\u003eShiraly R, Roshanfekr A, Asadollahi A, Griffiths MD. Psychological distress, social media use, and academic performance of medical students: the mediating role of coping style. BMC Med Educ [Internet]. 2024 Sep 13 [cited 2025 Jul 12];24(1). Available from: https://bmcmededuc.biomedcentral.com/articles/10.1186/s12909-024-05988-w\u003c/li\u003e\n\u003cli\u003eMcEwen BS. Physiology and Neurobiology of Stress and Adaptation: Central Role of the Brain. Physiol Rev. 2007 Jul;87(3):873\u0026ndash;904. \u003c/li\u003e\n\u003cli\u003eCaratiquit KD, Caratiquit LJC. Influence of social media addiction on academic achievement in distance learning: Intervening role of academic procrastination. Turk Online J Distance Educ. 2023 Jan 1;24(1):1\u0026ndash;19. \u003c/li\u003e\n\u003cli\u003eChavez-Yacolca DR, Castro-Champi\u0026oacute;n RB, Cisneros-Gonzales NM, Cunza-Aranz\u0026aacute;bal DF, Morales-Garc\u0026iacute;a M, Abanto-Ram\u0026iacute;rez CD. Relationship between academic procrastination and internet addiction in Peruvian university students: the mediating role of academic self-efficacy. Front Psychol. 2025 Jan 23;15:1454234. \u003c/li\u003e\n\u003cli\u003eJunco R. Student class standing, Facebook use, and academic performance. J Appl Dev Psychol. 2015 Jan;36:18\u0026ndash;29. \u003c/li\u003e\n\u003cli\u003eValkenburg PM, Meier A, Beyens I. Social media use and its impact on adolescent mental health: An umbrella review of the evidence. Curr Opin Psychol. 2022 Apr;44:58\u0026ndash;68. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"italian-journal-of-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"itjp","sideBox":"Learn more about [Italian Journal of Pediatrics](http://ijponline.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ITJP/default.aspx","title":"Italian Journal of Pediatrics","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Psychological well-being, adolescent behavior, academic performance, social media, addiction, youth","lastPublishedDoi":"10.21203/rs.3.rs-7124293/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7124293/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAcademic performance in adolescence is a key predictor of future educational, occupational, and health outcomes. While social networks (SNs) and messaging apps are increasingly integrated into adolescent\u0026acute;s daily lives, their impact on academic achievement remains debated. This study aimed to evaluate associations between the use of SN, messaging apps, addictive behaviors, and academic performance in Spanish adolescents.\u003c/p\u003e\u003ch2\u003eMaterials and methods\u003c/h2\u003e\u003cp\u003eA cross-sectional analysis was conducted using data from the Eating Healthy and Daily Life Activities (EHDLA) study, which included 583 adolescents aged 12\u0026ndash;17 years from three secondary schools in \u003cem\u003eValle de Ricote\u003c/em\u003e, Spain, during the 2021\u0026ndash;2022 academic year. SNs and messaging app use were assessed via a self-report scale. Addictive behaviors were measured using the Short Social Networks Addiction Scale-6 Symptoms (SNAddS-6S). Academic performance was evaluated using grade point average (GPA) and subject-specific grades obtained from school records. Associations were analyzed using generalized linear models adjusted for important covariates.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe median age of participants was 14 years, with 57% female. TikTok and WhatsApp were the most frequently used platforms. No statistically significant association was found between the overall use of SNs and GPA. However, higher levels of addictive behaviors related to SN use were significantly associated with lower academic performance (B = \u0026minus;\u0026thinsp;0.15, 95% CI: \u0026minus;\u0026thinsp;0.26 to \u0026minus;\u0026thinsp;0.03, p\u0026thinsp;=\u0026thinsp;0.014).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur findings show a relationship between the use of SNs and GAP, however, this was not significant. On the contrary, we found a significant relationship between addictive behaviors related to SNs and GAP. These findings suggest the need for educational interventions that promote mindful technology use while preserving the benefits of digital connectivity.\u003c/p\u003e","manuscriptTitle":"The link between social networks and messaging apps with addictive behaviors and academic performance in adolescents: the EHDLA study.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-13 11:57:40","doi":"10.21203/rs.3.rs-7124293/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2025-08-28T07:10:29+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-08-08T08:03:15+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-08T07:34:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-29T03:17:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Italian Journal of Pediatrics","date":"2025-07-26T10:17:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"italian-journal-of-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"itjp","sideBox":"Learn more about [Italian Journal of Pediatrics](http://ijponline.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ITJP/default.aspx","title":"Italian Journal of Pediatrics","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"24162dcc-17e0-4d2b-b206-fb425c373499","owner":[],"postedDate":"August 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T16:01:51+00:00","versionOfRecord":{"articleIdentity":"rs-7124293","link":"https://doi.org/10.1186/s13052-025-02180-8","journal":{"identity":"italian-journal-of-pediatrics","isVorOnly":false,"title":"Italian Journal of Pediatrics"},"publishedOn":"2025-12-29 15:58:18","publishedOnDateReadable":"December 29th, 2025"},"versionCreatedAt":"2025-08-13 11:57:40","video":"","vorDoi":"10.1186/s13052-025-02180-8","vorDoiUrl":"https://doi.org/10.1186/s13052-025-02180-8","workflowStages":[]},"version":"v1","identity":"rs-7124293","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7124293","identity":"rs-7124293","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}