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Accepted for/Published in: JMIR Mental Health

Date Submitted: May 15, 2026
Date Accepted: Jul 3, 2026

The final, peer-reviewed published version of this preprint can be found here:

Clinical Outcomes and Predictors of Improvement With Virtual Behavioral Health Care for Gambling Disorder: Retrospective Cohort Study

McAlister K, Knight E, Grant C, Huberty J

Clinical Outcomes and Predictors of Improvement With Virtual Behavioral Health Care for Gambling Disorder: Retrospective Cohort Study

JMIR Ment Health 2026;13:e101517

DOI: 10.2196/101517

PMID: 42497408

Virtual Behavioral Health Care for Gambling Disorder: Clinical Outcomes and Predictors of Improvement in a Large Real-World Cohort

  • Kelsey McAlister; 
  • Elizabeth Knight; 
  • Cynthia Grant; 
  • Jennifer Huberty

ABSTRACT

Background:

Gambling disorder is associated with substantial psychiatric and functional burden, yet few individuals receive treatment. Limited real-world evidence exists evaluating outcomes of virtually-delivered behavioral health care for gambling disorder, particularly among clinically complex patients.

Objective:

The purpose of this study is to evaluate gambling symptom severity outcomes among adults with gambling disorder receiving care from Birches Health. We aimed to: 1) characterize the clinical profile of adults seeking treatment for gambling disorder, 2) quantify changes in gambling symptom severity over the initial 12 weeks of treatment and examine whether baseline clinical complexity, such as gambling symptom severity, depression severity, and psychiatric comorbidities, is associated with differences in gambling symptom severity improvement over time, and 3) estimate the timing and likelihood of achieving clinically meaningful improvement in gambling symptom severity.

Methods:

This retrospective cohort study included 1,305 adults receiving virtual behavioral health treatment for gambling disorder through Birches Health between June 2024 and April 2026. Gambling symptom severity was assessed using the Gambling Symptom Assessment Scale (G-SAS) weekly. Linear mixed-effects models evaluated changes in gambling symptom severity over 12 weeks and associations with baseline clinical characteristics. Clinically meaningful improvement was defined as a ≥4-point reduction in G-SAS score.

Results:

Participants had a mean age of 41.5 years (SD = 13.1), 65.2% were male, and baseline gambling symptom severity was moderate (mean G-SAS = 20.5, SD = 11.8). Over half of participants (56%) presented with at least one psychiatric comorbidity, most commonly anxiety disorders (26.9%) and depressive disorders (21.2%). Gambling symptom severity declined significantly over the first 12 weeks of treatment, with G-SAS scores decreasing by approximately 0.099 points per day (p < .001), corresponding to an estimated 8.3-point reduction over 12 weeks. Higher baseline depressive symptom severity was associated with faster improvement in gambling symptoms (p = .012), while depressive disorder (p = .033) and ADHD (p = .008) diagnoses were associated with slower improvement trajectories. Among patients with follow-up assessments (n = 1,071), 71.7% achieved clinically meaningful improvement in gambling symptom severity, with a median time to improvement of 14 days.

Conclusions:

Adults receiving care through a national virtual behavioral health provider demonstrated rapid and clinically meaningful reductions in gambling symptom severity across a clinically complex population. These findings highlight the potential of specialized virtual care models to expand access to gambling treatment and support symptom improvement in routine care settings. Future research should evaluate longer-term recovery trajectories and identify factors associated with sustained improvement and ongoing engagement in care.


 Citation

Please cite as:

McAlister K, Knight E, Grant C, Huberty J

Clinical Outcomes and Predictors of Improvement With Virtual Behavioral Health Care for Gambling Disorder: Retrospective Cohort Study

JMIR Ment Health 2026;13:e101517

DOI: 10.2196/101517

PMID: 42497408

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