dc.contributor.author | Jiménez Murcia, Susana | |
dc.contributor.author | Perales López, José César | |
dc.contributor.author | Navas, Juan F. | |
dc.date.accessioned | 2020-05-13T11:57:18Z | |
dc.date.available | 2020-05-13T11:57:18Z | |
dc.date.issued | 2019-03-29 | |
dc.identifier.citation | Jiménez-Murcia S, Granero R, Fernández-Aranda F, Stinchfield R, Tremblay J, Steward T, Mestre-Bach G, Lozano-Madrid M, Mena-Moreno T, Mallorquí-Bagué N, Perales JC, Navas JF, Soriano-Mas C, Aymamí N, Gómez-Peña M, Agüera Z, del Pino-Gutiérrez A, Martín-Romera V and Menchón JM (2019) Phenotypes in Gambling Disorder Using Sociodemographic and Clinical Clustering Analysis: An Unidentified New Subtype? Front. Psychiatry 10:173. | es_ES |
dc.identifier.uri | http://hdl.handle.net/10481/62033 | |
dc.description.abstract | Background: Gambling disorder (GD) is a heterogeneous disorder which has clinical
manifestations that vary according to variables in each individual. Considering the
importance of the application of specific therapeutic interventions, it is essential to
obtain clinical classifications based on differentiated phenotypes for patients diagnosed
with GD.
Objectives: To identify gambling profiles in a large clinical sample of n = 2,570 patients
seeking treatment for GD.
Methods: An agglomerative hierarchical clustering method defining a combination of
the Schwarz Bayesian Information Criterion and log-likelihood was used, considering a
large set of variables including sociodemographic, gambling, psychopathological, and
personality measures as indicators.
Results: Three-mutually-exclusive groups were obtained. Cluster 1 (n = 908
participants, 35.5%), labeled as “high emotional distress,” included the oldest patients
with the longest illness duration, the highest GD severity, and the most severe levels of
psychopathology. Cluster 2 (n = 1,555, 60.5%), labeled as “mild emotional distress,”
included patients with the lowest levels of GD severity and the lowest levels of
psychopathology. Cluster 3 (n = 107, 4.2%), labeled as “moderate emotional distress,” included the youngest patients with the shortest illness duration, the highest level of
education and moderate levels of psychopathology.
Conclusion: In this study, the general psychopathological state obtained the highest
importance for clustering. | es_ES |
dc.description.sponsorship | Financial support was received through the Ministerio de
Economía y Competitividad (grant PSI2015-68701-R) and
the Investigación subvencionada por la Delegación del
Gobierno para el Plan Nacional sobre Drogas (2017I067).
FIS PI14/00290, FIS PI17/01167, and 18MSP001 - 2017I067
received aid from the Ministerio de Sanidad, Servicios Sociales e
Igualdad. CIBER Fisiología Obesidad y Nutrición (CIBERobn)
and CIBER Salud Mental (CIBERSAM), both of which are
initiatives of ISCIII. GM-B is supported by a predoctoral
AGAUR grant (2018 FI_B200174), grant co-funded by the
European Social Fund (ESF) ESF, investing in your future.
With the support of the Secretariat for Universities and
Research of the Ministry of Business and Knowledge of the
Government of Catalonia. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Frontiers in Media | es_ES |
dc.rights | Atribución 3.0 España | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Gambling Disorder | es_ES |
dc.subject | Personality traits | es_ES |
dc.subject | Clustering | es_ES |
dc.subject | Psychopathology | es_ES |
dc.subject | Severity | es_ES |
dc.title | Phenotypes in Gambling Disorder Using Sociodemographic and Clinical Clustering Analysis: An Unidentified New Subtype? | es_ES |
dc.type | journal article | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.identifier.doi | 10.3389/fpsyt.2019.00173 | |