Cognitive Behavioral Therapy Plus a Serious Game as a Complementary Tool for a Patient With Parkinson Disease and Impulse Control Disorder: Case Report
Bibliographic record
Abstract
BACKGROUND: Impulse control disorders (ICDs) are commonly developed among patients who take dopamine agonist drugs as a treatment for Parkinson disease (PD). Gambling disorder and hypersexuality are more frequent in male patients with PD, with a prevalence over 4% in dopamine agonists users. Although impulsive-compulsive behaviors are related to antiparkinsonian medication, and even though ICD symptomatology, such as hypersexuality, often subsides when the dopaminergic dose is reduced, sometimes ICD persists in spite of drug adjustment. Consequently, a multidisciplinary approach should be considered to address these comorbidities and to explore new forms of complementary interventions, such as serious games or therapies adapted to PD. OBJECTIVE: The aim of this study is to present the case of a patient with ICD (ie, hypersexuality) triggered by dopaminergic medication for PD. A combined intervention was carried out using cognitive behavioral therapy (CBT) for ICD adapted to PD, plus an intervention using a serious game-e-Estesia-whose objective is to improve emotion regulation and impulsivity. The aim of the combination of these interventions was to reduce the harm of the disease. METHODS: After 20 CBT sessions, the patient received the e-Estesia intervention over 15 sessions. Repeated measures, before and after the combined intervention, were administered to assess emotion regulation, general psychopathology, and emotional distress and impulsivity. RESULTS: After the intervention with CBT techniques and e-Estesia, the patient presented fewer difficulties to regulate emotion, less emotional distress, and lower levels of impulsivity in comparison to before the treatment. Moreover, the frequency and severity of the relapses also decreased. CONCLUSIONS: The combined intervention-CBT and a serious game-showed positive results in terms of treatment outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".