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Accepted for/Published in: Online Journal of Public Health Informatics

Date Submitted: Feb 24, 2025
Date Accepted: Sep 23, 2025

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

Evidence-Based Mental Health at Scale: Benchmarking Retrospective Cohort Study of a Digital Employee Benefits Program for Depression and Anxiety

Ward EJ, Hawrilenko M, Brown M, Chekroud AM

Evidence-Based Mental Health at Scale: Benchmarking Retrospective Cohort Study of a Digital Employee Benefits Program for Depression and Anxiety

Online J Public Health Inform 2025;17:e72999

DOI: 10.2196/72999

PMID: 41161337

PMCID: 12612640

Evidence-Based Mental Health at Scale: A Benchmarking Retrospective Cohort Study of a Digital Employee Benefits Program for Depression and Anxiety

  • Emily J Ward; 
  • Matt Hawrilenko; 
  • Millard Brown; 
  • Adam M Chekroud

ABSTRACT

Background:

Depression and anxiety affect millions worldwide, yet many people face barriers to timely and effective mental healthcare, underscoring the need for scalable, high-quality interventions.

Objective:

To evaluate the clinical effectiveness and quality of a centralized, employer-sponsored mental health program in treating depression and anxiety during a period of rapid growth in access to mental healthcare.

Methods:

This retrospective cohort study included participants using a digital mental health benefit (Spring Health), sponsored by 412 US employers from 2021-2024. Participants had access to therapists, psychiatrists, and care navigators. Primary measures were clinical effectiveness (treatment duration, PHQ-9 depression scale, GAD-7 anxiety scale) and clinical outcomes (reliable change, recovery, remission). Outcomes were benchmarked to meta-analytic results of evidence-based therapy.

Results:

53,757 adult participants started therapy from among 6,864 providers during the study period, scored positive for depression or anxiety, and had at least one mental health assessment before and during treatment. Depression symptoms decreased with each log-day in treatment, resulting in a total reduction of 6.69 points (95% CI, -6.62 to -6.76) at one-week post-treatment, corresponding to a large effect size (d = 1.56; 95% CI, 1.54 to 1.57), significantly greater than the meta-analytic pre-post benchmark for psychotherapy (effect size difference = 0.08, z = 10.5, p<0.001). Anxiety symptoms also decreased, resulting in a total reduction of 5.86 points (95% CI, -5.79 to -5.92), corresponding to a large effect size (d = 1.77; 95% CI, 1.75 to 1.79), significantly greater than the meta-analytic benchmarks (effect size difference = 0.36, z = 70.56 , p<0.001). White participants and participants of color had similar outcomes. 90% (95% CI, 90%–91%) of participants’ symptoms (in depression or anxiety) reliably improved, 87% (95% CI, 87%–88%) achieved recovery, and 60% (95% CI, 59%–60%) achieved remission by 1-week post-treatment.

Conclusions:

Among a large and diverse sample, using a digital mental health benefit with a centralized system of care produces clinical outcomes in depression and anxiety significantly greater than what is typically observed through meta-analyses of psychotherapy. By using data to monitor, incentive, and improve quality of care, the clinical outcomes outperform or equal benchmarks as a growing number of individuals across race, gender, and age access mental healthcare.


 Citation

Please cite as:

Ward EJ, Hawrilenko M, Brown M, Chekroud AM

Evidence-Based Mental Health at Scale: Benchmarking Retrospective Cohort Study of a Digital Employee Benefits Program for Depression and Anxiety

Online J Public Health Inform 2025;17:e72999

DOI: 10.2196/72999

PMID: 41161337

PMCID: 12612640

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