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Anales de Psicología

On-line version ISSN 1695-2294Print version ISSN 0212-9728

Anal. Psicol. vol.35 n.1 Murcia Jan./Mar. 2019

http://dx.doi.org/10.6018/analesps.35.1.324311 

Clinical and Health Psychology

MULTICAGE CAD-4 for behavioral addiction screening: structural validity after inclusion of a scale on smartphone abuse

MULTICAGE CAD-4 para la detección de adicciones conductuales: validez estructural después de la inclusión de una escala en el abuso de teléfonos inteligentes

Mª Teresa Rodríguez-Monje1  , Eduardo J. Pedrero-Pérez2  , Elías Rodríguez-Alonso1  , Mercedes Fernández-Girón1  , Victoria Pastor-Ramos1  , Alicia Mateo-Madurga1  , Isabel del Cura-González1  , Teresa Sanz-Cuesta1  , Celina Arana-Cañedo1  , Mª Paz Sánchez-Salgado1  , Rosa Ana Escriva-Ferrairo1 

1 Primary Care Management Madrid Health Service. Community of Madrid (Spain)

2 Madrid Salud. City Council of Madrid (Spain)

Abstract

Addictive behaviors are not limited to drugs use, but also include certain daily behaviors that can cause gratification. Their progression to more severe pathological patterns entails grave consequences for the individual, including multiple psychopathological manifestations. The early detection of this type of behavior is of concern to primary health care. Therefore, in order to detect risk at early stages, reliable and valid tools for daily practice are essential. The MULTICAGE CAD-4 questionnaire is a screening tool for simultaneously detecting addictive behaviors. This study includes a new scale for the detection of smartphone abuse. The objective is to evaluate the adequacy of its psychometric properties. A sample of 2,074 subjects that were recruited from primary care centers of Madrid (Spain) completed the MULTICAGE CAD-4 questionnaire. A confirmatory factor analysis, using unweighted least squares method, was performed. The test showed good internal consistency both at item and scale levels. The questionnaire structure was consistent with theoretical expectations. The MULTICAGE CAD-4, including the new smartphone scale, is a robust, reliable tool with a valid structure for assessing the presence of dysfunctional or potentially addictive behaviors, and especially useful in primary health care services.

Keywords: Behavioral addictions; Drug addiction; Screening; Questionnaire; Factor analysis; Psychometric properties

Resumen

Los comportamientos adictivos no se limitan al uso de drogas, sino que también incluyen ciertos comportamientos diarios que pueden causar gratificación. Su progresión a patrones patológicos más severos conlleva graves consecuencias para el individuo, incluidas múltiples manifestaciones psicopatológicas. La detección temprana de este tipo de comportamiento es de interés para la atención primaria de salud. Por lo tanto, para detectar riesgos en etapas tempranas, las herramientas confiables y válidas para la práctica diaria son esenciales. El cuestionario MULTICAGE CAD-4 es una herramienta de detección para detectar simultáneamente conductas adictivas. Este estudio incluye una nueva escala para la detección del abuso de teléfonos inteligentes. El objetivo es evaluar la adecuación de sus propiedades psicométricas. Una muestra de 2.074 sujetos reclutados en centros de atención primaria de Madrid (España) completó el cuestionario MULTICAGE CAD-4. Se realizó un análisis factorial confirmatorio, utilizando el método de mínimos cuadrados no ponderados. La prueba mostró una buena consistencia interna tanto a nivel del ítem como de las escalas. La estructura del cuestionario fue consistente con las expectativas teóricas. El MULTICAGE CAD-4, incluida la nueva escala para teléfonos inteligentes, es una herramienta robusta y confiable con una estructura válida para evaluar la presencia de comportamientos disfuncionales o potencialmente adictivos, y especialmente útil en servicios de atención primaria de salud.

Palabras clave: Adicciones conductuales; Drogadicción; Cribado; Cuestionario; Análisis factorial; Propiedades psicométricas

Introduction

Addictive behaviors represent a complex problem for public health that entails adverse consequences for the individual's personal health and growth, family dynamics, and social development and stability. Talking about addictions refers to a complex, multi-determined behavioral phenomenon that involves diverse factors: genetic, epigenetic, development-related, educational, psychological, environmental, and cultural. This phenomenon is characterized by the creation of new habits or repetitive behaviors that escape brain mechanisms supervising conduct, so that they get repeated because of short-term reinforcement, even if they later cause negative consequences (Lewis, 2015; Volkow, & Baler, 2014).

Addictions have traditionally been associated with substance use, but the characteristics described above can also appear in relation to other specific behaviors, the so-called behavioral addictions (Grant, Potenza, Weinstein, & Gorelick, 2010). Recent research has found evidence that the brain circuits implied in addiction, whether involving substances or not, are essentially the same (Brand, Young, & Laier, 2014; Huang, 2017), which corresponds to previous findings that both conditions share genetic predispositions, personality traits, behavior patterns, and complications in diverse daily-life activities. (Grant, Brewer, & Potenza, 2006).

In recent years, the emergence of smartphones has brought about a new revolution, as it has become the most widely used device for Internet access, largely displacing personal computers. Its easy portability and information accumulation capacity have turned the smartphone into a handheld computer, with unlimited utilities (Pedrero-Pérez, Rodríguez-Monje, & Ruiz-Sánchez de León, 2012).

The limit between use, overuse, and addiction is by no means easy to draw. As previously noted, addiction is currently regarded as a progressing process: from an initial reinforced response, to a habit where some stimulus-response associations prevail that escape the conscious cognitive control (Kuss, & Lopez-Fernandez, 2016). This loss of control makes the individual embrace compulsive, constraining behaviors that can interfere with brain, personal, and social development, and is frequently also associated with mood disorders or other psychopathological expressions (Kaess et al., 2014). Therefore, it is essential to detect risk at initial stages of this process, which would in many cases stop the evolution of the loss of control (Lee, & Lee, 2017), especially in the case of new technologies, which primarily affect subjects with increasingly lower ages (Carbonell et al, 2012) and are linked to the emergence of behaviors derived from high social risk (Arnaiz, Cerezo, Giménez, & Maquilón, 2016).

In order to effectively detect addictions at early stages, it is essential to depend on reliable, validated tools that can be applied in the daily practice of primary care. A study that assesses available resources (Rowan, & Galasso, 2000) suggests that current means are inadequate for clinicians, both for detecting drug consumption and pathological gambling, and that access to quick-to-use, validated, reliable tools would be of major help for their detection, analogously to the CAGE for alcohol-abuse disorders. Following these premises, MULTICAGE CAD-4 (Pedrero-Pérez et al., 2007) was proposed as an initial screening tool for the si-multaneous detection of addictive behaviors involving substances (alcohol and drugs) or not (Internet and videogames addictions, or pathological gambling), as well as other impulse control disorders (eating disorders, compulsive spending, or sex addiction). The MULTICAGE CAD-4 questionnaire, formerly aimed at the population suffering addictions and impulse control disorders, has been later validated for the primary care setting (Rodríguez-Monje, Pedrero-Pérez, Fernández-Girón, Gallardo-Alonso, & Sanz-Cuesta, 2009) and has proven to possess adequate psychometric and diagnostic capacities in both cases. The questionnaire, which is based on the CAGE and follows its 4-question original model, evaluates each of the eight studied addictive disorders, and hence comprises 32 items. A study performed on drug-addicted populations has proven its usefulness for the early diagnosis of these behaviors, allowing for the detection of related addictive behaviors that were neither acknowledged by patients nor identified by the health professionals (Pedrero-Pérez, 2010). Additionally, other studies (Calvo-Francés, 2016; Del Prete et al., 2017; Estevez, Herrero-Fernández, Sarabia, & Jauregui. 2015; Estévez, Ozerinjauregi, Herrero-Fernández, & Jauregui, 2016; Garrido-Elustondo, Reneses, Navalón, Martín, Ramos, & Fuentes, 2016; Jauregui, Estevez, & Urbiola, 2016; Navas et al., 2017; Navas, Verdejo-Garcia, Lopez-Gomez, Maldonado, & Perales, 2016; Pedrero-Pérez et al., 2018; Reneses et al., 201525-32) have shown its utility as a screening tool.

Given the relevance of smartphone overuse and the high prevalence found in several studies (Pedrero-Pérez et al., 2012), it has been decided to include a new scale to assess this problem, which will also allow to complete the profile of addictions to new technologies. The questions included in the smartphone scale follow, as in all other cases, CAGE's basic scheme, asking about the inability to limit the time of use, complaints from significant others about excessive time spent, the need to maintain permanent access to the device and failures in attempts to control behavior. This study aims to assess the adequacy of the psychometric characteristics of the new scale, as well as the preservation of the structural validity of the questionnaire after its inclusion.

Method

Design and procedure

A descriptive, transversal, multicentric study to validate diagnostic tests was conducted in the primary care setting. It was performed in 5 urban primary health care centers, within the public health care system of Madrid, which services a population of 162,461 citizens. The study population comprised subjects older than 14 years who attended primary care consultation for any reason in any of the health centers included in this study and selected by random, non-consecutive sampling. Patients suffering from any moderate to severe mental deficiency or disorder were excluded, as well as those who had problems to read, write, or understand Spanish language. Participants were informed about the study and asked for written consent. In the case of minors, further consent was obtained from their parents or guardians.

The questionnaire was anonymous and self-completed. In addition to the MULTICAGE CAD-4, it recorded the following socio-demographic variables: age, gender, educational level, work status, marital status, access to personal computer, access to videogame console, and social relationships.

A new version of the MULTICAGE CAD-4 was employed which dispensed with the scales regarding eating disorders, compulsive shopping, and sex addiction, and included a new 4-item scale on smartphone overuse. The final questionnaire (ANNEX I) included 24 items from 6 different scales, with 4 questions each that require dichotomous responses (Yes/No), and assess problems involving alcohol consumption, drug abuse, pathological gambling, Internet, videogames, and smartphone use.

Ethical considerations

The protocol for the present study was approved by the Comisión Central de Investigación de la Gerencia de Atención Primaria de Madrid. It has also been approved by the Ethical Committee for Clinical Research of Hospital Universitario Severo Ochoa in Leganes (Madrid).

Data analysis

A descriptive statistical analysis of the variables of inte-rest was performed. The internal consistency of items and scales was studied firstly to assess the questionnaire. Reliability of scales was tested by Standarized Cronbach's. Since the test items are dichotomous and a normal distribution of results is not expected, a confirmatory factor analysis was carried out using the unweighted least squares method. Absolute (χ 2 /df, GFI, RMR), relative (NFI, RFI), and noncentrality-based (PGFI, PNFI) fit indexes were used to test the model. The correlation between the scales was studied by Pearson's r, applying the Bonferroni correction for multiple correlations. FACTOR 10.5 for the reliability, SPSS 22 for correlation and AMOS 21 for the confirmatory and structural analysis were used.

Results

A sample of 2,074 subjects (875 men and 1,199 women) was obtained, with average age 49.7 years (SD=18.7), in a range from 14 to 94. Of them, 5.2% lacked studies, while 31,0% had elementary studies, 15.0% secondary school studies, 13.6% technical training, 14.8% high school studies, and 20.3% university studies. In total, 72.9% had at least one Internet access device for everyday use.

Table 1: Description and internal consistency of items and scales. 

Description and consistency of items of MULTICAGE CAD-4, as well as consistency of the proposed theoretic scales is shown in Table 1, where it can be observed that all scales have a coefficient ( s > 0.85 and all items have a discrimination index r it > 0.30.

A confirmatory factor analysis was carried out using the unweighted least squares method. An acceptable χ 2 /df= 3.34 was obtained, although it may result from using a very large sample size (n). Absolute adjustment indices were also adequate (GFI=0.99; AGFI=0.98; RMR=0.002) but are also affected by sample size. However, indices not sensitive to size sample were also adequate (NFI=0.97; RFI=0.96), and so were the parsimony ones (PGFI=0.78; PNFI=0.83). The resulting structure is shown in Figure 1.

All scales are significantly correlated with each other (Table 2), although the magnitude of the correlations effect varies widely. Maximal correlation is observed between the Internet and videogames scales, Internet and smartphone use, and alcohol and drug abuse.

Table 2 Correlations between the scores of each scale. 

Discussion

After controlling for the distribution of items, the internal consistency of all the scales was satisfactory, both at the item level and at the complete scales level. The new scale on smartphone use and overuse displayed good internal consistency, analogous to that of the rest of the scales, which supports the high reliability of the whole test.

Figure 1 Structure of MULTICAGE. 

The outcome of confirmatory factor analysis was fully satisfactory, and all indicators showed an adequate adjustment to the data, especially those not affected by the large sample size. The inclusion of the new scale on smartphone use did not alter the overall structure of the test, which, on the whole, shows a solid structure.

This study has strengths and limitations. Among the first, the use of a non-consecutive sample with an item/subject ratio ˃ 80 allows for applying the most stringent analysis criteria. The age range is very broad, surpassing what is normally included for this type of behavior problems, which is usually limited to samples obtained from young students. As for limitations, the observed evidences of structural validity must be complemented with studies of clinical validity. However, except for pathological gambling, there is a lack of consensus on criteria to determine the degrees of use and overuse in behavioral addictions, and this lack of a gold standard for cut-off points in health practice presents a difficult obstacle to tackle.

This study allows to confirm that MULTICAGE CAD-4 is a quick-to-use, easy-to-answer, robust, and reliable questionnaire with a valid structure to value the presence of dysfunctional behaviors, whether involving substances or not. The inclusion of a scale on problematic smartphone use addresses a current issue of growing worldwide interest, both in the clinical and social realms. The use of screening instruments allows for the early detection of behaviors that can be harmful to the subject, limiting their opportunities for personal development. Just as is the case of other screening tools, it is still necessary to determine its clinical validity, cut-off points, and its sensibility and specificity to detect problematic behaviors. Future studies must draw the limits between normal use, overuse, and dependence on these technologies, find psychopathological correlations between all these addictive behaviors, and define the aims of the research as realistically as possible so that research does not always lag changes in real life.

Conflicts of interests

The authors declare that they have no conflicts of interest.

Founding

This publication has been financed by the FIIBAP through the call for grants for publications 2016.

References

Arnaiz, P., Cerezo, F., Giménez, A. M., & Maquilón, J. J. (2016). Conductas de ciberadicción y experiencias de cyberbullying entre adolescentes. Anales de Psicología//Annals of Psychology, 32, 761-769. (Spanish). DOI: (10.6018/analesps.32.3.217461 [ Links ]

Brand, M., Young, K. S., & Laier, C. (2014). Prefrontal control and Internet addiction: a theoretical model and review of neuropsychological and neuroimaging findings. Frontiers in Human Neuroscience, 8, 375. DOI: 10.3389/fnhum.2014.00375 [ Links ]

Calvo-Francés, F. (2016). Internet abusive use questionnaire: psychometric properties. Computer and Human Behavior, 59, 187-194. DOI: 10.1016/j.chb.2016.01.038 [ Links ]

Carbonell, X., Chamarro, A., Griffiths, M., Oberst, U., Cladellas, R., & Talarn, A. (2012). Problematic Internet and cell phone use in Spanish teenagers and young students. Anales de Psicología/Annals of Psychology, 28, 789-796. DOI: 10.6018/analesps.28.3.156061 [ Links ]

Del Prete, F., Steward, T., Navas, J. F., Fernández-Aranda, F., Jiménez-Murcia, S., Oei, T. P., & Perales, J. C. (2017). The role of affect-driven impulsivity in gambling cognitions: A convenience-sample study with a Spanish version of the Gambling-Related Cognitions Scale. Journal of Behavioral Addictions, 6, 51-63. DOI: 10.1556/2006.6.2017.001 [ Links ]

Estevez, A., Herrero-Fernández, D., Sarabia, I., & Jauregui, P. (2015). The impulsivity and sensation-seeking mediators of the psychological consequences of pathological gambling in adolescence. Journal of Gambling Studies, 31, 91-103. DOI: 10.1007/s10899-013-9419-0 [ Links ]

Estévez, A., Ozerinjauregi, N., Herrero-Fernández, D., & Jauregui, P. (2016). The mediator role of early maladaptive schemas between childhood sexual abuse and impulsive symptoms in female survivors of CSA. Journal of Interpersonal Violence, published online 24 April, 2016. DOI: 10.1177/0886260516645815 [ Links ]

Garrido-Elustondo, S., Reneses, B., Navalón, A., Martín, O., Ramos, I., & Fuentes, M. (2016). Ability to detect psychiatric disorders by the family physician. Atención Primaria. 49, 449-457. (Spanish). DOI: 10.1016/j.aprim.2015.09.009 [ Links ]

Grant, J. E., Brewer, J. A., & Potenza, M. N. (2006). The neurobiology of substance and behavioral addictions. CNS Spectrum, 11, 924-930. DOI: 10.1017/S109285290001511X [ Links ]

Grant, J. E., Potenza, M. N., Weinstein, A., & Gorelick, D. A. (2010). Introduction to behavioral addictions. American Journal of Drug and Alcohol Abuse, 36, 233-241. DOI: 10.3109/00952990.2010.491884 [ Links ]

Huang, A. C. W. (2017). Autonomic nervous system and brain circuitry for internet addiction. In Montag, C., & Reuter, M., Internet addiction: Neuroscientific approaches and therapeutical implications including smartphone addiction (pp. 161-180). Cham: Springer. 2010.1007/978-3-319-46276-9_10 [ Links ]

Jauregui, P., Estevez, A., & Urbiola, I. (2016). Pathological gambling and associated drug and alcohol abuse, emotion regulation, and anxious-depressive symptomatology. Journal of Behavioral Addictions, 5, 251-260. DOI: 10.1556/2006.5.2016.038 [ Links ]

Kaess, M., Durkee, T., Brunner, R., Carli, V., Parzer, P., Wasserman, C., … Wasserman, D. (2014). Pathological Internet use among European adolescents: Psychopathology and self-destructive behaviours. European Child & Adolescent Psychiatry, 23, 1093-1102. DOI: 10.1007/s00787-014-0562-7 [ Links ]

Kuss, D. J., & Lopez-Fernandez, O. (2016). Internet addiction and problematic Internet use: A systematic review of clinical research. World Journal of Psychiatry, 6, 143-176. DOI: 10.5498/wjp.v6.i1.143 [ Links ]

Lee, B. H., & Lee, H. K. (2017). Longitudinal study shows that addictive Internet use during adolescence was associated with heavy drinking and smoking cigarettes in early adulthood. Acta Paediatrica, 106, 497-502. DOI: 10.1111/apa.13706 [ Links ]

Lewis, M. (2015). The biology of desire: why addiction is not a disease. New York: Scribe Publications. [ Links ]

Navas, J. F., Contreras-Rodríguez, O., Verdejo-Román, J., Perandrés-Gómez, A., Albein-Urios, N., Verdejo-García, A., & Perales, J. C. (2017). Trait and neurobiological underpinnings of negative emotion regulation in gambling disorder. Addiction, 112, 1086-1094. DOI: 10.1111/add.13751 [ Links ]

Navas, J. F., Verdejo-Garcia, A., Lopez-Gomez, M., Maldonado, A., & Perales, J. C. (2016). Gambling with rose-tinted glasses on: Use of emotion-regulation strategies correlates with dysfunctional cognitions in gambling disorder patients. Journal of Behavioral Addictions, 5, 271-281. DOI: 10.1556/2006.5.2016.040 [ Links ]

Pedrero-Pérez, E. J. (2010). Screening of behavioral addictions in substance-addicted patients. Trastornos Adictivos, 12, 13-18. (Spanish). DOI: 10.1016/S1575-0973(10)70005-2 [ Links ]

Pedrero-Pérez, E. J., Rodríguez-Monje, M. T., Gallardo-Alonso, F., Fernández-Girón, M., Pérez-López, M., & Chicharro-Romero, J. (2007). Validation of a tool for screening of impulse control disorders and addiction: MULTICAGE CAD-4. Trastornos Adictivos, 9, 269-278. (Spanish). DOI: 10.1016/S1575-0973(07)75656-8 [ Links ]

Pedrero-Pérez, E. J., Rodríguez-Monje, M. T., & Ruiz-Sánchez De León, J. M. (2012). Mobile phone abuse or addiction. A review of the literature. Adicciones, 24, 139-152. (Spanish). DOI: 10.20882/adicciones.107 [ Links ]

Pedrero-Pérez, E. J., Ruiz-Sánchez de León, J. M., Rojo-Mota, G., Llanero-Luque, M., Pedrero-Aguilar, J., Morales-Alonso, S., & Puerta-García, C. (2018). Information and Communications Technologies (ICT): Problematic use of Internet, video games, mobile phones, instant messaging and social networks using MULTICAGE-TIC. Adicciones, 30, 19-32. DOI: 10.20882/adicciones.806 [ Links ]

Reneses, B., Garrido, S., Navalón, A., Martín, O., Ramos, I., Fuentes, M., ... & López-Ibor J. J. (2015). Psychiatric morbidity and predisposing factors in a primary care population in Madrid. International Journal of Social Psychiatry, 61, 275-286. DOI: 10.1177/0020764014542815 [ Links ]

Rodríguez-Monje, M. T., Pedrero-Pérez, E. J., Fernández-Girón, M., Gallardo-Alonso, F., & Sanz-Cuesta, T. (2009). Early detection of addictive behaviour in primary health care. Use of MULTICAGE CAD-4. Atención Primaria, 41, 25-32. (Spanish). DOI: 10.1016/j.aprim.2008.04.004 [ Links ]

Rowan, M. S., & Galasso, C. S. (2000). Identifying office resource needs of Canadian physicians to help prevent, assess and treat patients with substance use and pathological gambling disorders. Jornal of Addictive Disorders, 19, 43-58. DOI: 10.1300/J069v19n02_04 [ Links ]

Volkow, N.D., & Baler, R. D. (2014). Addiction science: Uncovering neurobiological complexity. Neuropharmacology, 76, 235-249. DOI: 10.1016/j.neuropharm.2013.05.007 [ Links ]

Annex I

Received: October 31, 2017; Revised: April 15, 2018; Accepted: May 15, 2018

Eduardo J. Pedrero-Pérez. Departamento de Evaluación y Calidad, Unidad Técnica de Formación e Investigación. Madrid Salud, Ayuntamiento de Madrid. Avda. del Mediterráneo, 62, 28007 Madrid (Spain). E-mail: pedreropej@madrid.es

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