Mapping risk of gambling disorder in older adults: a neural network approach.
7 affiliations
- 1Department of Psychobiology and Methodology, Universitat Autònoma de Barcelona - UAB, Barcelona, Spain.
- 2Ciber Fisiopatología Obesidad y Nutrición (CIBERobn), Instituto Salud Carlos III, Madrid, Spain.
- 3Group of Psychoneurobiology of eating and addictive behaviors, Institut d'Investigació Biomèdica de Bellvitge - IDIBELL, L'Hospitalet de Llobregat, Spain.
- 4Department of Clinical Psychology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Spain.
- 5Department of Clinical Sciences, School of Medicine and Health Sciences, Universitat de Barcelona - UB, L'Hospitalet de Llobregat, Spain.
- 6International Centre for Youth Gambling Problems and High-Risk Behaviors, McGill University, Montreal, QC, Canada.
- 7School of Psychology, Brain & Mind Centre, Science Faculty, University of Sydney, Sydney, NSW, Australia.
Abstract
Background and objectives
Gambling disorder (GD) in older adults remains insufficiently understood, despite growing concerns about gambling-related harm in older adulthood. Risk profiles and clinical presentations in this age group may differ from those typically described in younger or mixed-age samples, reflecting age-specific psychosocial contexts, health conditions, and life transitions. Traditional statistical approaches may be limited in their ability to model the complex, non-linear interplay among psychological, behavioral, and contextual factors. The present study aimed to apply neural network-based models to predict the presence and severity of GD in older adults.Methods
The study used a cross-sectional, single-center, case-control design. Data were obtained from adults aged 50 to 85 years, with 50 years adopted as a pragmatic lower threshold given the lack of consensus on defining older adulthood, comprising a clinical sample of 745 patients seeking treatment for gambling-related problems at a specialized treatment hospital unit, and a healthy control (HC) group of 136 participants without gambling-related problems, recruited at non-psychiatric hospital outpatient services.Results
A neural network (multilayer perceptron, MLP) with one hidden layer composed of eight neurons showed excellent discrimination between individuals with GD and healthy controls in the complete sample (AUC = 0.987, 90.4% specificity, 97.7% sensitivity, and 96.6% classification accuracy). In the independent holdout subset, the model yielded an AUC of 0.890, with 75.0% specificity, 96.2% sensitivity, and 93.3% classification accuracy (although these estimates should be interpreted cautiously because the holdout included only 12 healthy controls). Novelty seeking emerged as the strongest contributor to the model, followed by emotion regulation difficulties, particularly non-acceptance of emotional responses and reduced impulse control. Focusing exclusively on the clinical GD subsample, a second MLP including six hidden neurons indicated that overall psychological distress was the primary factor associated with disorder severity. Additional relevant contributors to GD included novelty seeking, gambling-related cognitive distortions, and lower self-directedness.Conclusion
The neural network models effectively classified GD and its severity in older adults, identifying a multifactorial pattern characterized by affective distress, emotion regulation difficulties, and distorted gambling-related beliefs. These findings highlight the potential of non-linear modeling to characterize patterns associated with GD in older adults, while the clinical utility of machine learning tools for age-sensitive detection and intervention remains preliminary and requires further validation.
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BibTeX
@article{granero2026mapping,
title = {{Mapping risk of gambling disorder in older adults: a neural network approach.}},
author = {Roser Granero and Fernando Fernández-Aranda and Isabel Baenas and Lucero Munguía and Anahí Gaspar-Pérez and Mónica Gómez-Peña and Jeffrey Derevensky and Sally Gainsbury and Rebeca Rodríguez-García and Susana Jiménez-Murcia},
journal = {Frontiers in psychiatry},
year = {2026},
month = oct,
doi = {10.3389/fpsyt.2026.1936618},
url = {https://europepmc.org/article/MED/42898696},
note = {CC BY},
}
RIS
TY - JOUR
TI - Mapping risk of gambling disorder in older adults: a neural network approach.
AU - Roser Granero
AU - Fernando Fernández-Aranda
AU - Isabel Baenas
AU - Lucero Munguía
AU - Anahí Gaspar-Pérez
AU - Mónica Gómez-Peña
AU - Jeffrey Derevensky
AU - Sally Gainsbury
AU - Rebeca Rodríguez-García
AU - Susana Jiménez-Murcia
PY - 2026
DA - 2026/10/05
JO - Frontiers in psychiatry
DO - 10.3389/fpsyt.2026.1936618
UR - https://europepmc.org/article/MED/42898696
AB - Background and objectives Gambling disorder (GD) in older adults remains insufficiently understood, despite growing concerns about gambling-related harm in older adulthood. Risk profiles and clinical presentations in this age group may differ from those typically described in younger or mixed-age samples, reflecting age-specific psychosocial contexts, health conditions, and life transitions. Traditional statistical approaches may be limited in their ability to model the complex, non-linear interplay among psychological, behavioral, and contextual factors. The present study aimed to apply neural network-based models to predict the presence and severity of GD in older adults.Methods The study used a cross-sectional, single-center, case-control design. Data were obtained from adults aged 50 to 85 years, with 50 years adopted as a pragmatic lower threshold given the lack of consensus on defining older adulthood, comprising a clinical sample of 745 patients seeking treatment for gambling-related problems at a specialized treatment hospital unit, and a healthy control (HC) group of 136 participants without gambling-related problems, recruited at non-psychiatric hospital outpatient services.Results A neural network (multilayer perceptron, MLP) with one hidden layer composed of eight neurons showed excellent discrimination between individuals with GD and healthy controls in the complete sample (AUC = 0.987, 90.4% specificity, 97.7% sensitivity, and 96.6% classification accuracy). In the independent holdout subset, the model yielded an AUC of 0.890, with 75.0% specificity, 96.2% sensitivity, and 93.3% classification accuracy (although these estimates should be interpreted cautiously because the holdout included only 12 healthy controls). Novelty seeking emerged as the strongest contributor to the model, followed by emotion regulation difficulties, particularly non-acceptance of emotional responses and reduced impulse control. Focusing exclusively on the clinical GD subsample, a second MLP including six hidden neurons indicated that overall psychological distress was the primary factor associated with disorder severity. Additional relevant contributors to GD included novelty seeking, gambling-related cognitive distortions, and lower self-directedness.Conclusion The neural network models effectively classified GD and its severity in older adults, identifying a multifactorial pattern characterized by affective distress, emotion regulation difficulties, and distorted gambling-related beliefs. These findings highlight the potential of non-linear modeling to characterize patterns associated with GD in older adults, while the clinical utility of machine learning tools for age-sensitive detection and intervention remains preliminary and requires further validation.
SN - 1664-0640
ER -