Criterios centrales vs. periféricos: Explorando la sobrevaloración de redes sociales y el uso problemático en un estudio longitudinal con adolescentes Core vs. peripheral: Exploring social media overvaluation and problematic use in a longitudinal adolescent study

Autores/as

  • Víctor Ciudad-Fernández Departamento de Personalidad, Evaluación y Tratamientos Psicológicos, Universidad de Valencia / Instituto Polibienestar, Universidad de Valencia
  • Alfredo Zarco-Alpuente Departamento de Psicología Básica, Universidad de Valencia
  • Tamara Escrivá-Martínez Departamento de Personalidad, Evaluación y Tratamientos Psicológicos, Universidad de Valencia / Instituto Polibienestar, Universidad de Valencia / CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid
  • Marcos Romero-Suárez Departamento de Psicología Social y Metodología, Universidad Autónoma de Madrid
  • Rosa Baños Departamento de Personalidad, Evaluación y Tratamientos Psicológicos, Universidad de Valencia / Instituto Polibienestar, Universidad de Valencia / CIBER de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid

DOI:

https://doi.org/10.20882/adicciones.2237

Palabras clave:

Sobrevaloración de redes sociales, uso problemático de redes sociales, fiabilidad test-retest, invarianza longitudinal, modelo de componentes de la adicción Overvaluation of social media, problematic social media use, test-retest reliability, longitudinal invariance, components model of addiction

Resumen

Las personas con Sobrevaloración de la Utilidad Relativa de las Redes Sociales (ORUSM) sobreestiman el valor de las redes sociales para satisfacer necesidades, priorizándolas sobre otras actividades alternativas. La ORUSM es un mecanismo clave en el desarrollo del Uso Problemático de Redes Sociales (UPRS), frecuentemente asociado a problemas de salud mental. La Plan-net 25, mide la ORUSM pero carece de evaluación de sus propiedades psicométricas longitudinales. El UPRS se evalúa utilizando seis criterios derivados de los trastornos por consumo de sustancias, reclasificados en criterios centrales (modificación del estado de ánimo, abstinencia, conflicto, recaída) como indicadores de uso problemático, y criterios periféricos (saliencia, tolerancia) que reflejan alta implicación sin patología. Este estudio examinó la estabilidad temporal y la invarianza longitudinal de la Plan-net 25, así como sus asociaciones con los criterios de UPRS y diversos indicadores de salud mental durante seis semanas. Una muestra de 294 adolescentes (14-20 años) completó medidas de ORUSM, UPRS, depresión, ansiedad, soledad y satisfacción con la vida. Los resultados mostraron una estabilidad temporal baja a moderada de las subescalas e invarianza longitudinal de la Plan-net 25. Los dominios de ORUSM relacionados con la regulación emocional, la expresión social y el manejo del aburrimiento se asociaron positivamente con criterios centrales y periféricos de UPRS. Los criterios centrales predijeron peores resultados en salud mental, mientras que los criterios periféricos se asociaron positivamente con satisfacción con la vida. Estos hallazgos destacan la importancia de distinguir entre los criterios centrales y periféricos del UPRS.
Individuals with Overvaluation of the Relative Utility of Social Media (ORUSM) overestimate the value of social media to meet needs, prioritizing it over alternative activities. ORUSM is a key mechanism in the development of Problematic Social Media Use (PSMU), frequently associated with mental health issues. The Plan-net 25 measures ORUSM but lacks evaluation of its longitudinal psychometric properties. PSMU is assessed using six criteria derived from substance use disorders, reclassified into core criteria (mood modification, withdrawal, conflict, relapse) as indicators of problematic use, and peripheral criteria (salience, tolerance) that reflect high engagement without pathology. This study examined the temporal stability and longitudinal invariance of the Plan-net 25, as well as its associations with PSMU criteria and various mental health indicators over six weeks. A sample of 294 adolescents (14–20 years old) completed measures of ORUSM, PSMU, depression, anxiety, loneliness, and life satisfaction. The results showed low to moderate temporal stability of the subscales and longitudinal invariance of the Plan-net 25. ORUSM domains related to emotional regulation, social expression, and boredom management were positively associated with both core and peripheral PSMU criteria. Core criteria predicted worse mental health outcomes, while peripheral criteria were positively associated with life satisfaction. These findings highlight the importance of distinguishing between core and peripheral PSMU criteria.

Citas

Abal, F. J. P., Sánchez González, J. F. & Attorresi, H. F.

(2024). Adaptation of the Bergen Instagram addiction scale in Argentina: calibration with item response theory. Current Psychology, 43(2), 1794-1805. https://doi.org/10.1007/s12144-023-04257-1

Aksoy, M. E. (2018). A qualitative study on the reasons for social media addiction. European Journal of Educational Research, 7(4), 861-865. https://doi.org/10.12973/eu-jer.7.4.861

Amendola, S. (2023). Discussing evidence on the components model of addiction. A commentary on Fournier et al. (2023). Addictive Behaviors, 145, 107764. https://doi.org/10.1016/j.addbeh.2023.107764

Andrade, B., García, I. G. & Rial Boubeta, A. (2021). Estudio sobre el impacto de la tecnología en la adolescencia. www.unicef.es/infancia-tecnologia.

Andreassen, C. S., Billieux, J., Griffiths, M. D., Kuss, D. J., Demetrovics, Z., Mazzoni, E. & Pallesen, S. (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychology of addictive behaviors, 30(2), 252-262. https://doi.org/10.1037/adb0000160

Andreassen, C. S., Torsheim, T., Brunborg, G. S. & Pallesen, S. (2012). Development of a Facebook addiction scale. Psychological Reports, 110(2), 501-517. https://doi.org/10.2466/02.09.18.PR0.110.2.501-517

Aparicio-Martínez, P., Ruiz-Rubio, M., Perea-Moreno, A. J., Martínez-Jiménez, M. P., Pagliari, C., Redel-Macías, M. D. & Vaquero-Abellán, M. (2020). Gender differences in the addiction to social networks in the Southern Spanish university students. Telematics and Informatics, 46, 101304. https://doi.org/10.1016/j.tele.2019.101304

Ballou, N. & Van Rooij, A. J. (2021). The relationship between mental well-being and dysregulated gaming: A specification curve analysis of core and peripheral criteria in five gaming disorder scales. Royal Society Open Science, 8(5), 201385. https://doi.org/10.1098/rsos.201385

Beeres, D. T., Andersson, F., Vossen, H. G. & Galanti, M. R. (2021). Social media and mental health among early adolescents in Sweden: A longitudinal study with 2-year follow-up (KUPOL study). Journal of Adolescent Health, 68(5), 953-960. https://doi.org/10.1016/j.jadohealth.2020.07.042

Billieux, J., Flayelle, M., Rumpf, H. J. & Stein, D. J. (2019). High involvement versus pathological involvement in video games: A crucial distinction for ensuring the validity and utility of gaming disorder. Current Addiction Reports, 6, 323-330. https://doi.org/10.1007/s40429-019-00259-x

Boyd, D. R., Bee, H. L. & Johnson, P. A. (2012). Lifespan development. Pearson.

Brailovskaia, J. & Margraf, J. (2024). Addictive social media use during Covid-19 outbreak: Validation of the Bergen Social Media Addiction Scale (BSMAS) and investigation of protective factors in nine countries. Current Psychology, 43(14), 13022-13040. https://doi.org/10.1007/s12144-022-03182-z

Castro‐Calvo, J., King, D. L., Stein, D. J., Brand, M., Carmi, L., Chamberlain, S. R.,... Billieux, J. (2021). Expert appraisal of criteria for assessing gaming disorder: An international Delphi study. Addiction, 116(9), 2463-2475. https://doi.org/10.1111/add.15411

Cataldo, I., Billieux, J., Esposito, G. & Corazza, O. (2022). Assessing problematic use of social media: Where do we stand and what can be improved? Current Opinion in Behavioral Sciences, 45, 101145. https://doi.org/10.1016/j.cobeha.2022.101145

Charlton, J. P. & Danforth, I. D. (2007). Distinguishing addiction and high engagement in the context of online game playing. Computers in human behavior, 23(3), 1531-1548. https://doi.org/10.1016/j.chb.2005.07.002

Chegeni, M., Shahrbabaki, P. M., Shahrbabaki, M. E., Nakhaee, N. & Haghdoost, A. (2021). Why people are becoming addicted to social media: A qualitative study. Journal of Education and Health Promotion, 10(1). https://doi.org/10.4103/jehp.jehp_1109_20

Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural equation modeling: a multidisciplinary journal, 14(3), 464-504. https://doi.org/10.1080/10705510701301834

Ciudad-Fernández, V., Zarco-Alpuente, A., Escrivá-Martínez, T., Gomis-Vicent, E., Espejo, B., Lecuona, O., Perales, J. C., Lopez-Fernandez, O. & Baños, R. M. (2024a). The seven deadly sins: Measuring overvaluation of social media with the Plan-net 25 scale. OSF Preprints. https://doi.org/10.17605/OSF.IO/WC4EV

Ciudad-Fernández, V., Zarco-Alpuente, A., Escrivá-Martínez, T., Herrero, R. & Baños, R. (2024b). How adolescents lose control over social networks: A process-based approach to problematic social network use. Addictive Behaviors, 154, 108003. https://doi.org/10.1016/j.addbeh.2024.108003

Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037/0033-2909.112.1.155

Crockett, M. A., Martínez, V. & Ordóñez-Carrasco, J. L. (2022). Propiedades psicométricas de la escala Generalized Anxiety Disorder 7-Item (GAD-7) en una muestra comunitaria de adolescentes en Chile. Revista Médica de Chile, 150(4), 458-464. http://dx.doi.org/10.4067/S0034-98872022000400458

Diez-Quevedo, C., Rangil, T., Sanchez-Planell, L., Kroenke, K. & Spitzer, R. L. (2001). Validation and utility of the patient health questionnaire in diagnosing mental disorders in 1003 general hospital Spanish inpatients. Psychosomatic Medicine, 63(4), 679-686. https://doi.org/10.1097/00006842-200107000-00021

Eisinga, R., Grotenhuis, M. T. & Pelzer, B. (2013). The reliability of a two-item scale: Pearson, Cronbach, or Spearman-Brown?. International journal of public health, 58, 637-642. https://doi.org/10.1007/s00038-012-0416-3

Fineberg, N. A., Demetrovics, Z., Potenza, M. N., Mestre-Bach, G., Ekhtiari, H., Roman-Urrestarazu, A.,... Stein, D. J. (2024). Global action on problematic usage of the internet: Announcing a Lancet Psychiatry Commission. The Lancet Psychiatry.

Flayelle, M., Brevers, D., King, D. L., Maurage, P., Perales, J. C. & Billieux, J. (2023). A taxonomy of technology design features that promote potentially addictive online behaviours. Nature Reviews Psychology, 2(3), 136-150. https://doi.org/10.1038/s44159-023-00153-4

Flayelle, M., Schimmenti, A., Starcevic, V. & Billieux, J. (2022). The pitfalls of recycling substance-use disorder criteria to diagnose behavioral addictions. In Evaluating the brain disease model of addiction (pp. 339-349). Routledge.

Fournier, L., Schimmenti, A., Musetti, A., Boursier, V., Flayelle, M., Cataldo, I.,... Billieux, J. (2023). Deconstructing the components model of addiction: An illustration through “addictive” use of social media. Addictive Behaviors, 143, 107694. https://doi.org/10.1016/j.addbeh.2023.107694

Fournier, L., Schimmenti, A., Musetti, A., Boursier, V., Flayelle, M., Cataldo, I.,... Billieux, J. (2024). Further evidence for the bidimensionality of the components model of addiction: A reply to Amendola (2023). Addictive Behaviors, 150, 107914. https://doi.org/10.1016/j.addbeh.2023.107914

Gamer, M. (2019). irr: Various coefficients of interrater reliability and agreement (Version 0.84. 1). Computer Software and Manual.

Gioia, F., Rega, V. & Boursier, V. (2021). Problematic internet use and emotional dysregulation among young people: A literature review. Clinical Neuropsychiatry, 18(1), 41. https://doi.org/10.36131/cnfioritieditore20210104

Griffiths, M. (2005). A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 10(4), 191-197. https://doi.org/10.1080/14659890500114359

Griffiths, M. D. (2019). The evolution of the’components model of addiction’and the need for a confirmatory approach in conceptualizing behavioral addictions. Düşünen Adam: The Journal of Psychiatry and Neurological Sciences, 32, 179-184.

Hallquist, M. N. & Wiley, J. F. (2018). MplusAutomation: An R package for facilitating large-scale latent variable analyses in M plus. Structural equation modeling: a multidisciplinary journal, 25(4), 621-638. https://doi.org/10.1080/10705511.2017.1402334

Hu, L. T. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118

Huang, C. (2022). A meta-analysis of the problematic social media use and mental health. International Journal of Social Psychiatry, 68(1), 12-33. https://doi.org/10.1177/0020764020978434

Hughes, M. E., Waite, L. J., Hawkley, L. C. & Cacioppo, J. T. (2004). A short scale for measuring loneliness in large surveys: Results from two population-based studies. Research on Aging, 26(6), 655–672. https://doi.org/10.1177/0164027504268574

Infanti, A., Valls-Serrano, C., Perales, J. C., Vögele, C. & Billieux, J. (2023). Gaming passion contributes to the definition and identification of problematic gaming. Addictive Behaviors, 147, 107805.

Izadpanah, S., Barnow, S., Neubauer, A. B. & Holl, J. (2019). Development and validation of the Heidelberg Form for Emotion Regulation Strategies (HFERST): Factor structure, reliability, and validity. Assessment, 26(5), 880-906. https://doi.org/10.1177/1073191117720283

Jobst, L. J., Bader, M. & Moshagen, M. (2023). A tutorial on assessing statistical power and determining sample size for structural equation models. Psychological Methods, 28(1), 207–221. https://doi.org/10.1037/met0000423

Jovanović, V., Rudnev, M., Arslan, G., Buzea, C., Dimitrova, R., Góngora, V.,... Žukauskienė, R. (2022). The Satisfaction with Life Scale in adolescent samples: Measurement invariance across 24 countries and regions, age, and gender. Applied research in quality of life, 17(4), 2139-2161. https://doi.org/10.1007/s11482-021-10024-w

Kardefelt-Winther, D. (2014). A conceptual and methodological critique of internet addiction research: Towards a model of compensatory internet use. Computers in human behavior, 31, 351-354. https://doi.org/10.1016/j.chb.2013.10.059

Kelly, Y., Zilanawala, A., Booker, C. & Sacker, A. (2018). Social media use and adolescent mental health: Findings from the UK Millennium Cohort Study. EClinicalMedicine, 6, 59-68. https://doi.org/10.1016/j.eclinm.2018.12.005

Kjell, O. N. E. & Diener, E. (2021). Abbreviated three-item versions of the satisfaction with life scale and the harmony in life scale yield as strong psychometric properties as the original scales. Journal of Personality Assessment, 103(2), 183–194. https://doi.org/10.1080/00223891.2020.1737093

Koo, T. K. & Li, M. Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of Chiropractic Medicine, 15(2), 155-163. https://doi.org/10.1016/j.jcm.2016.02.012

Kroenke, K., Spitzer, R. L. & Williams, J. B. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x

Lewin, K. M., Kaur, A. & Meshi, D. (2023). Problematic social media use and impulsivity. Current Addiction Reports, 10(3), 553-562. https://doi.org/10.1007/s40429-023-00495-2

Lopez-Fernandez, O. (2018). Generalised versus specific internet use-related addiction problems: A mixed methods study on internet, gaming, and social networking behaviours. International journal of environmental research and public health, 15(12), 2913. https://doi.org/10.3390/ijerph15122913

Lyyra, N., Junttila, N., Gustafsson, J., Lahti, H. & Paakkari, L. (2022). Adolescents’ online communication and well-being: Findings from the 2018 health behavior in school-aged children (HBSC) study. Frontiers in psychiatry, 13, 976404. https://doi.org/10.3389/fpsyt.2022.976404

MacCallum, R. C., Browne, M. W. & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149. https://doi.org/10.1037/1082-989X.1.2.130

Maxwell, S. E. (2004). The Persistence of Underpowered Studies in Psychological Research: Causes, Consequences, and Remedies. Psychological Methods, 9(2), 147–163. https://doi.org/10.1037/1082-989X.9.2.147

McNeish, D. (2023). Psychometric properties of sum scores and factor scores differ even when their correlation is 0.98: A response to Widaman and Revelle. Behavior Research Methods, 55(8), 4269-4290. https://doi.org/10.3758/s13428-022-02016-x

Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58, 525-543. https://doi.org/10.1007/BF02294825

Millsap, R. E. & Cham, H. (2012). Investigating factorial invariance in longitudinal data. In B. Laursen, T. D. Little & N. A. Card (Eds.), Handbook of developmental research methods (pp. 109-127). Guilford Press.

Mock, T. (2023). gtExtras: Extending ‘gt’ for Beautiful HTML Tables (Version 0.5.0) [R package]. Retrieved from https://CRAN.R-project.org/package=gtExtras

Moshagen, M. & Erdfelder, E. (2016). A new strategy for testing structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 23(1), 54–60. https://doi.org/10.1080/10705511.2014.950896

Mossman, S. A., Luft, M. J., Schroeder, H. K., Varney, S. T., Fleck, D. E., Barzman, D. H.,... Strawn, J. R. (2017). The Generalized Anxiety Disorder 7-item (GAD-7) scale in adolescents with generalized anxiety disorder: Signal detection and validation. Annals of clinical psychiatry: official journal of the American Academy of Clinical Psychiatrists, 29(4), 227.

Orosz, G., Vallerand, R. J., Bőthe, B., Tóth-Király, I. & Paskuj, B. (2016). On the correlates of passion for screen-based behaviors: The case of impulsivity and the problematic and non-problematic Facebook use and TV series watching. Personality and Individual Differences, 101, 167-176.

Peng, P. & Liao, Y. (2023). Six addiction components of problematic social media use in relation to depression, anxiety, and stress symptoms: a latent profile analysis and network analysis. BMC psychiatry, 23(1), 321. https://doi.org/10.1186/s12888-023-04837-2

Perales, J. C. & Muela, I. (2019). Adicciones tecnológicas: Mitos y evidencia. In M. González de Audikana de la Hera & A. Estévez Gutiérrez (Eds.), Adicciones sin sustancia y otros trastornos del control de los impulsos (pp. 19-33). Universidad de Deusto, Servicio de Publicaciones.

Perales, J. C., King, D. L., Navas, J. F., Schimmenti, A., Sescousse, G., Starcevic, V.,... Billieux, J. (2020). Learning to lose control: A process-based account of behavioral addiction. Neuroscience & Biobehavioral Reviews, 108, 771-780. https://doi.org/10.1016/j.neubiorev.2019.12.025

Pew Research Center. (2023, December 11). Teens, social media, and technology 2023. https://www.pewresearch.org/internet/2023/12/11/teens-social-media-and-technology-2023/

Revelle, W. (2024). psych: Procedures for Psychological, Psychometric, and Personality Research (Version 2.4.3) [R package]. Northwestern University, Evanston, Illinois. Retrieved from https://CRAN.R-project.org/package=psych

Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. https://doi.org/10.18637/jss.v048.i02

Schafer, J. L. (1999). Multiple imputation: A primer. Statistical methods in medical research, 8(1), 3-15. https://doi.org/10.1177/096228029900800102

Shannon, H., Bush, K., Villeneuve, P. J., Hellemans, K. G. & Guimond, S. (2022). Problematic social media use in adolescents and young adults: Systematic review and meta-analysis. JMIR mental health, 9(4), e33450. https://doi.org/10.2196/33450

Stănculescu, E. & Griffiths, M. D. (2022). Social media addiction profiles and their antecedents using latent profile analysis: The contribution of social anxiety, gender, and age. Telematics and Informatics, 74, 101879. https://doi.org/10.1016/j.tele.2022.101879

Trucharte, A., Calderón, L., Cerezo, E., Contreras, A., Peinado, V. & Valiente, C. (2023). Three-item loneliness scale: Psychometric properties and normative data of the Spanish version. Current Psychology, 42(9), 7466-7474. https://doi.org/10.1007/s12144-021-02110-x

Twigg, L., Duncan, C. & Weich, S. (2020). Is social media use associated with children’s well-being? Results from the UK Household Longitudinal Study. Journal of Adolescence, 80, 73-83. https://doi.org/10.1016/j.adolescence.2020.02.002

van Duin, C., Heinz, A. & Willems, H. (2021). Predictors of problematic social media use in a nationally representative sample of adolescents in Luxembourg. International Journal of Environmental Research and Public Health, 18(22), 11878. https://doi.org/10.3390/ijerph 182211878

Zarate, D., Hobson, B. A., March, E., Griffiths, M. D. & Stavropoulos, V. (2023). Psychometric properties of the Bergen Social Media Addiction Scale: An analysis using item response theory. Addictive Behaviors Reports, 17, 100473. https://doi.org/10.1016/j.abrep.2022.10 0473

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Publicado

2025-09-22

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Vol. 37 Núm. 3 (2025)

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