Treatment of Internet Addiction with Anxiety Disorders: Treatment Protocol and Preliminary Before-After Results Involving Pharmacotherapy and Modified Cognitive Behavioral Therapy
Bibliographic record
Abstract
BACKGROUND: The growth of the Internet has led to significant change and has become an integral part of modern life. It has made life easier and provided innumerous benefits; however, excessive use has brought about the potential for addiction, leading to severe impairments in social, academic, financial, psychological, and work domains. Individuals addicted to the Internet usually have comorbid psychiatric disorders. Panic disorder (PD) and generalized anxiety disorder (GAD) are prevalent mental disorders, involving a great deal of damage in the patient's life. OBJECTIVE: This open trial study describes a treatment protocol among 39 patients with anxiety disorders and Internet addiction (IA) involving pharmacotherapy and modified cognitive behavioral therapy (CBT). METHODS: Of the 39 patients, 25 were diagnosed with PD and 14 with GAD, in addition to Internet addiction. At screening, patients responded to the MINI 5.0, Hamilton Anxiety Rating Scale, Hamilton Depression Rating Scale, Clinical Global Impressions Scale, and the Young Internet Addiction Scale. At that time, IA was observed taking into consideration the IAT scale (cutoff score above 50), while anxiety disorders were diagnosed by a psychiatrist. Patients were forwarded for pharmacotherapy and a modified CBT protocol. Psychotherapy was conducted individually, once a week, over a period of 10 weeks, and results suggest that the treatment was effective for anxiety and Internet addiction. RESULTS: Before treatment, anxiety levels suggested severe anxiety, with an average score of 34.26 (SD 6.13); however, after treatment the mean score was 15.03 (SD 3.88) (P<.001). A significant improvement in mean Internet addiction scores was observed, from 67.67 (SD 7.69) before treatment, showing problematic internet use, to 37.56 (SD 9.32) after treatment (P<.001), indicating medium Internet use. With respect to the relationship between IA and anxiety, the correlation between scores was .724. CONCLUSIONS: This study is the first research into IA treatment of a Brazilian population. The improvement was remarkable due to the complete engagement of patients in therapy, which contributed to the success of the treatment from a behavioral perspective, and gave patients the confidence to continue to manage Internet use in their lives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".