Clinically Meaningful Improvement in Depression and Anxiety Among Psychiatry Patients Within a Measurement-Based Care Digital Mental Health Intervention: A Retrospective Analysis of Real-World Data from Rula Health

Abstract

Depression and anxiety are common, disabling conditions that often require sustained psychiatric care. While digital mental health interventions (DMHIs) offer scalable access, few integrate measurement-based care (MBC) to track outcomes such as minimal clinically important difference (MCID) and remission. The purpose of this study was to evaluate the rate and timing of MCID and remission in depression and anxiety among patients with elevated baseline depression and anxiety scores receiving psychiatry services through Rula Health, a MBC-based DMHI that connects patients with psychiatric care. Symptoms were assessed prior to psychiatric visits over a 24-week period and used to evaluate effect sizes, as well as rates of MCID and remission. Kaplan-Meier and Cox proportional hazards models were used to estimate the timing of MCID and remission, and to identify demographic and clinical factors associated with achieving each outcome. A total of 7124 adults with elevated depression symptoms and 7628 with elevated anxiety symptoms at baseline were included. Depression and anxiety symptoms decreased with large effect sizes (d’s = −1.17 to −1.62). The median survival time to MCID in depression was 12 weeks and remission 22 weeks. The median survival time to MCID in anxiety was 11 weeks and remission 19 weeks. Several demographic and clinical characteristics were associated with time to MCID and remission. MBC-based digital psychiatry services can support sustained, clinically meaningful change. Faster improvement among patients with varying clinical and demographic characteristics highlights Rula Health’s ability to address a range of patient needs.

Keywords

depression anxiety telepsychiatry digital mental health measurement based care

Introduction

Depression and anxiety are leading contributors to the global burden of disease, affecting more than 300 million adults and accounting for a substantial share of global disability.¹ In the United States (US) alone, nearly 1 in 5 adults report symptoms of anxiety or depression.^2,3 These conditions are not only prevalent but often chronic and recurrent, with high rates of comorbidity and functional impairment.^4
-6 Depression and anxiety are associated with reduced quality of life, difficulty maintaining employment and relationships, increased risk of substance use, and elevated suicide risk.^7
-9 On a systems level, they drive significant healthcare utilization and contribute to work loss.^10,11 This widespread impact of depression and anxiety underscores the need for accessible and effective psychiatric care that can address the chronic and multifaceted nature of these symptoms.

Psychiatric care, including pharmacotherapy and ongoing clinical management, is a key treatment pathway for individuals with moderate-to-severe depression and anxiety. Unlike short-term interventions, psychiatric treatment typically involves sustained engagement, allowing providers to adjust medications, monitor response, and manage co-occurring conditions over time. In-person psychiatric care provides valuable therapeutic relationships and structure, though access and continuity remains an ongoing challenge in many care settings.^12,13 Importantly, only about half of adults in the US who experience a mental illness (~50.6%) receive care,¹⁴ primarily due to limited provider availability, financial burdens, and stigma surrounding mental health treatment.¹⁵ Shortages in mental health professionals, particularly for US adults living in rural areas, further limits access to psychiatric care.¹⁶ Even when traditional psychiatric care is available, outcome monitoring is rarely systematic, leaving clinicians without clear data to guide treatment adjustments.

To address these gaps, digital mental health interventions (DMHIs) have expanded rapidly in recent years. DMHIs encompass a wide range of tools, from self-guided apps and chatbots to telehealth platforms. Virtual care platforms that connect patients directly with licensed clinicians have become a particularly important model.^17,18 Many commercial and academic DMHIs incorporate features of measurement-based care (MBC), which involves routinely collecting standardized symptom measures to guide treatment decisions and monitor response.¹⁹ MBC has been shown to improve outcomes and increase patient engagement,^20,21 but its use varies across DMHI types. In self-guided or blended programs, assessments are often collected but not consistently integrated into clinician workflows. Barriers, such as misaligned systems, limited time, or lack of feedback loops, limit their impact on real-time care.^22,23 Even in academic DMHIs – such as mobile apps, text-message systems, and digital clinics offering telehealth services – MBC has typically been studied under controlled conditions rather than scaled in routine practice.²⁴ DMHIs like virtual care platforms have the potential to embed MBC directly into ongoing treatment at scale.

Rula Health, a digital platform that connects individuals with licensed therapists and psychiatric providers across the US, is 1 DMHI that integrates MBC. Since its launch in 2019, Rula Health has demonstrated rapid growth, with over 10 000 in-network providers and offering coverage to over 120 million US adults.²⁵ Rula Health incorporates MBC into its treatment model to promote personalized care and improve clinical outcomes. Providers have access to real-time patient-reported outcome data, supporting ongoing symptom monitoring and the evaluation of treatment effectiveness. This approach positions Rula Health as a scalable model for delivering mental health care.

Despite the rapid expansion of digital approaches, such as Rula Health, and increasing interest in MBC, real-world evidence on psychiatric outcomes in virtual care platforms remains limited. Most research has examined psychotherapy apps or self-guided interventions,^26,27 which differ substantially from clinician-delivered psychiatric care. In contrast, little evidence focuses on outcomes for patients receiving structured psychiatric treatment on virtual platforms in real-world settings. Virtual care platforms often emphasize average symptom reduction rather than clinically meaningful change.^28,29 Using benchmarks such as minimal clinically important difference (MCID) and remission provides insights that are more actionable for clinicians and meaningful to patients. In addition, the timing of symptom improvement is seldom examined, despite its importance for treatment planning and patient expectations. Finally, few studies explore whether time to clinical improvement varies by patient characteristics (eg, demographics, clinical) in real-world digitally-delivered psychiatric settings, limiting personalized care.

The purpose of this study was to evaluate the rate and timing of MCID and remission in depression and anxiety among patients with elevated baseline depression and anxiety scores receiving psychiatry services through Rula Health, a MBC-based DMHI that connects patients with psychiatric care. We aimed to (1) describe depression and anxiety symptom changes among psychiatry patients with clinically elevated symptoms of depression and anxiety, (2) examine the timing of MCID and remission within the first 24 weeks of psychiatric treatment, and (3) investigate whether time to MCID and remission differ by demographic or clinical characteristics.

Methods

Study Design and Participants

This retrospective observational study analyzed de-identified secondary data from Rula Health, and followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for cohort studies (See Supplemental Material). Rula Health is a national DMHI in the United States that connects individuals with licensed therapists and psychiatric providers through a network of over 10 000 licensed professionals. Rula Health’s care model emphasizes personalized treatment and is structured around routine implementation of MBC to support ongoing clinical decision-making. Participants were identified retrospectively using consecutive sampling (ie, inclusion of all eligible patients who received care during the specified time period) from Rula Health records. The study sample included adult patients (aged ≥ 18 years) who received outpatient psychiatry services through Rula Health between 1/20/2022 and 4/30/2025. Patients were included for analysis if they had at least moderate depression or anxiety symptoms at baseline,^30,31 and if they received only psychiatry services from Rula Health (ie, did not concurrently participate in therapy services). Patients were excluded if they were <18 years old, did not meet symptom severity thresholds at baseline, did not engage with Rula Health’s psychiatry services, and/or additionally engaged in therapy services during the study period. Each patient record was assigned a unique de-identified code to link all observations from the same individual.

Ethical Considerations

Prior to initiating treatment, all patients receiving care through Rula Health provide written informed consent for care, which includes information on the scope of services, confidentiality protections, and data use. This consent process is separate from and in addition to agreeing to Rula Health’s privacy policy. Patients also review and agree to Rula Health’s privacy policy prior to treatment, which states that their de-identified data may be used for analysis. This study did not involve prospective recruitment of participants or the collection of data specifically for research purposes; all data were collected as part of routine care delivery.

Because this study involved a retrospective analysis of de-identified data, it qualified for exemption from additional informed consent under U.S. human subjects research regulations. Therefore, no research-specific consent procedures (written or verbal) were required or obtained. All records were linked using unique patient identifiers to ensure that each individual was represented only once in the dataset. The Biomedical Research Alliance of New York (BRANY) Institutional Review Board (Study ID: 25-035-2061) reviewed the study and approved the exemption determination. No participant compensation was provided for this research.

Treatment

Patients access Rula Health via a variety of referral sources including primary care providers, insurance partners, employer-sponsored mental health benefits, and healthcare organizations, as well as through direct online search and paid marketing channels. Majority of Rula Health use in-network insurance coverage, though self-pay options are available for those without insurance coverage. The Rula Health platform provides education to patients on the differences between psychiatric care and therapy, and allows patients to meet with a psychiatrist if they choose to. After completing a brief intake questionnaire, individuals are matched with a curated list of psychiatric providers based on their mental health concerns, insurance coverage, and personal preferences (eg, provider specialty, background, and availability).

Psychiatric visits are held virtually via secure videoconferencing with the provider. During the initial visit (intake), the provider collects the patient’s medical and psychiatric history, and discusses the patient’s concerns and goals for treatment. The provider then utilizes the information collected at intake to create a personalized treatment plan. Session frequency varies and is based on the needs of the patient. Rula Health’s care model is grounded in MBC, and patients complete standardized symptom assessments throughout treatment. These assessments inform clinical decisions such as medication adjustments or referrals to other care (eg, therapy). Providers are subject to quality assurance protocols, ensuring consistent and effective psychiatric care delivery across the Rula Health network.

Study Measures

At intake, patients self-report demographic information including age and gender. Providers utilize the information collected at intake to assign the patient an initial primary diagnosis. Between January 20, 2022, and December 7, 2024, patients were sent standardized symptom assessments prior to each scheduled visit, up to once per week. Patients were sent a link by email to complete assessments 48 h before the scheduled visit. Starting on December 8, 2024, the assessment frequency was reduced to a maximum of once every 14 days to reduce survey burden. That is, patients attending weekly appointments only received assessments on a bi-weekly basis. No further changes were made to assessment frequency thereafter. The current study focuses on patient-reported depression and anxiety symptoms collected through these assessments.

Mental Health Symptom Measures

Symptoms of depression are assessed using the PHQ-9,³⁰ a 9-item self-report measure of the severity of depressive symptoms over the last 2 weeks. Items are rated on a 4-point Likert scale from 0 (“Not at all”) to 3 (“Nearly every day”), and total scores range from 0 to 27. The MCID in depression is defined as a reduction of ≥5 points on the PHQ-9, and is the smallest change in symptom severity that patients perceive as meaningful.³² Remission is defined as attaining a score of <5, which represents no or minimal depression.³⁰

Symptoms of anxiety are assessed using the GAD-7,³¹ a 7-item self-report measure of the severity of generalized anxiety symptoms over the last 2 weeks. Items are rated on a 4-point Likert scale from 0 (“Not at all”) to 3 (“Nearly every day”), and total scores range from 0 to 21. MCID in anxiety is defined as a reduction of ≥4 points on the GAD-7,³³ and remission is defined as achieving a score of <5, indicating no or minimal anxiety.³¹

Statistical Analysis

Data were limited to 24 weeks of psychiatry treatment in order to account for the variability in the time it takes for psychotropic medications to take effect.³⁴ Outcomes were assessed over 2 timeframes: (1) through the first 12 weeks (~3 months), and (2) between 13 and 24 weeks (~6 months). The last available score within the 2 timeframes was used in outcomes calculations. Two subsamples were created for subsequent analysis because medication management is often recommended for patients with at least moderate symptoms.³⁵ One subsample with moderate to severe depression symptoms at baseline (PHQ-9 ≥ 10³⁰) and a second subsample with moderate to severe anxiety symptoms at baseline (GAD-7 ≥ 10³¹). Patients with both moderate-to-severe depression and anxiety symptoms at baseline could appear in both subsamples; thus, the 2 groups are not mutually exclusive. Depression and anxiety outcomes, including MCID and remission, were evaluated in their respective subsamples with elevated baseline symptoms.

Demographic and descriptive statistics were calculated for each subsample. Raw proportions of symptom improvement metrics, including MCID and remission, were calculated for each subsample over the 12- and 24-week timeframes. Change scores were calculated and effect sizes (Cohen’s d) were estimated for depression and anxiety over the 12- and 24-week timeframes.³⁶ Separate Kaplan-Meier survival analyses were conducted in each subsample and were used to estimate time to 2 terminal events: MCID and remission through 24 weeks of treatment. For each analysis, the mean survival time and median survival time (ie, the time for 50% of patients to achieve the terminal event) were estimated. The survival probabilities (ie, the probability that depression or anxiety MCID or remission was achieved each week up to 24 weeks) were calculated at each week of treatment and represented in separate survival curves for each outcome, representing the average treatment course after accounting for baseline score, age, gender, and diagnosis group. Cox proportional hazard models were used to estimate the impact of baseline score, age, gender, and diagnosis group on treatment course.

Results

Demographic and Descriptive Statistics

Demographic statistics are presented in Table 1, and descriptive statistics for baseline and outcome scores are presented in Table 2. Demographic characteristics were similar across those with elevated depression symptoms at baseline (N = 7124, baseline depression score mean = 15.95) and those with elevated anxiety symptoms at baseline (N = 7506, baseline anxiety score mean = 15.13). Patients in both samples were on average 34 years old and mostly female. The most common primary diagnosis in both samples was an anxiety disorder (32.9% in the elevated depression sample, and 39% in the elevated anxiety sample), followed by a depressive disorder (31.6% in the elevated depression sample, and 28.5% in the elevated anxiety sample).

Table 1.

Demographic Characteristics of Patients with Elevated Depression and Anxiety Symptoms.

Demographic characteristics	Elevated depression sample (N = 7124)		Elevated anxiety sample (N = 7506)
Categorical variable	N	%	N	%
Gender
Female	4638	65.1	4888	65.1
Male	2202	30.9	2333	31.1
Missing	284	4.0	285	3.8
Primary diagnosis
Anxiety disorder	2346	32.9	2930	39
Depressive disorder	2250	31.6	2141	28.5
Neurodevelopmental disorder	833	11.7	731	9.7
Other	1429	20.1	1446	19.3
Missing	266	3.7	258	3.4
Continuous variable	Mean	SD	Mean	SD
Age (n = 7124)	33.52	11.09	33.88	11.09
Week of last available assessment up to 12 weeks	6.96	2.94	7.00	2.98
Week of last available assessment between 13 and 24 weeks	18.35	3.63	18.68	3.61

Table 2.

Descriptive Statistics of Baseline and Outcome Scores in Patients with Elevated Depression and Anxiety Symptoms.

Baseline and outcome scores	Elevated depression sample (N = 7124) – PHQ-9 measures		Elevated anxiety sample (N = 7506) – GAD-7 measures
Continuous variable	Mean	SD	Mean	SD
Baseline score	15.95	4.41	15.13	3.39
Up to 12 week score	8.91	5.83	7.43	5.26
13-24 week score	7.69	5.67	6.21	4.94
Change in up to 12 weeks	−7.02	6.00	−7.61	5.42
Change in 13-24 weeks	−8.03	6.27	−8.62	5.34
Categorical variable	N	%	N	%
MCID in up to 12 weeks	2354	67	3024	78
MCID in 13-24 weeks	801	73	1081	83
Remission in up to 12 weeks	911	26	1330	34
Remission in 13-24 weeks	367	33	587	45

Patients in both samples improved markedly over both timelines assessed. Among patients with elevated depression symptoms at baseline, patients’ depression symptoms were, on average, 15.95 (SD = 4.41) at baseline, 8.91 (SD = 5.83) within 12 weeks, and 7.69 (SD = 5.67) between 13 and 24 weeks, corresponding to large-to-very large effect sizes of d = −1.17 (95% CI: [−1.21, −1.13]), and d = −1.28 (95% CI: [−1.36, −1.20]), respectively. Among patients with elevated anxiety symptoms at baseline, patients’ anxiety symptoms were, on average, 15.13 (SD = 3.39) at baseline, 7.43 (SD = 5.26) within 12 weeks, and 6.17 (SD = 4.94) between 13 and 24 weeks, corresponding to very large effect sizes of d = −1.40 (95% CI:[−1.45, −1.36]) and d = −1.62 (95% CI:[−1.70, −1.53]), respectively. Large proportions of patients in both samples achieved MCID in depression (67% in up to 12 weeks, 73% in 13-24 weeks) and anxiety (78% in up to 12 weeks, 83% in 13-24 weeks).

Kaplan-Meier Survival Analysis

Survival analysis was used to evaluate the probability of Rula Health psychiatric patients achieving MCID and remission in up to 24 weeks of treatment, controlling for age, gender, primary diagnosis, and baseline depression or anxiety score. Cumulative incidence curves are in Figure 1. The probabilities of achieving MCID and remission in both depression and anxiety increase with each week in treatment. The mean survival time to MCID in depression was 13.11 weeks (SE = 0.14), and the median was 12 weeks. The mean survival time to remission in depression was 18.43 weeks (SE = 0.16), and the median was 22 weeks. The mean survival time to MCID in anxiety was 12.57 weeks (SE = 0.13), and the median was 11 weeks. The mean survival time to remission in anxiety was 16.84 weeks (SE = 0.14), and the median was 19 weeks.

Figure 1.

Cumulative incidence curves of MCID and remission in depression (A) and anxiety (B) symptoms.

Cox Regression Results

Cox regression analysis was used to evaluate the impact of demographic variables and diagnosis on overall survival to MCID and remission for depression and anxiety symptoms (Table 3). Higher baseline depression score was associated with faster time to MCID but slower time to remission in depression. Greater age was associated with slower time to depression MCID. Having a diagnosis of a neurodevelopmental disorder was associated with a faster depression MCID and remission compared to having a depressive disorder.

Table 3.

Summary of Cox Regression Comparing Effects of Demographic Variables and Diagnosis on Overall Survival for Depression and Anxiety MCID and Remission Outcomes.

	Depression outcomes^a
Demographic and diagnosis predictors	MCID			Remission
Variable	Hazard ratio	95% CI	P-value	Hazard ratio	95% CI	P-value
Baseline depression score	1.05	[1.04, 1.06]	<.0001	0.94	[0.93, 0.96]	<.0001
Gender (male)	1.01	[0.92, 1.10]	.86	1.08	[0.95, 1.24]	.24
Age	0.996	[0.99, 1.00]	.044	1.0	[0.99, 1.00]	.30
Diagnosis (reference: depressive disorder)
Anxiety disorder	1.07	[0.97, 1.18]	.19	1.02	[0.87, 1.19]	.79
Neurodevelopmental disorder	1.25	[1.09, 1.43]	.0010	1.39	[1.14, 1.69]	.00089
Other diagnosis	1.11	[0.99, 1.24]	.083	1.02	[0.83, 1.23]	.87
	Anxiety outcomes^b
	MCID			Remission
Variable	Hazard ratio	95% CI	P-value	Hazard ratio	95% CI	P-value
Baseline anxiety score	1.05	[1.03, 1.06]	<.0001	0.96	[0.94, 0.97]	<.0001
Gender (male)	1.04	[0.96, 1.00]	.35	1.15	[1.03, 1.29]	.011
Age	0.995	[0.99, 0.999]	.0075	1.00	[0.99, 1.00]	.42
Diagnosis (reference: anxiety disorder)
Depressive disorder	0.995	[0.85, 1.01]	.086	0.81	[0.71, 0.92]	.00085
Neurodevelopmental disorder	1.10	[0.97, 1.25]	.15	0.96	[0.80, 1.14]	.61
Other diagnosis	1.01	[0.91, 1.13]	.86	0.99	[0.85, 1.16]	.93

CI = confidence interval; MCID = minimal clinically important difference.

Analyses for the depression outcomes were conducted among patients with elevated depression symptoms at baseline.

Analyses for the anxiety outcomes were conducted among patients with elevated anxiety symptoms at baseline (GAD-7 ≥ 10).

Higher baseline anxiety was associated with a faster time to anxiety MCID, but a slower time to anxiety remission. Greater age was also associated with a slower time to anxiety MCID. Male gender was associated with faster time to anxiety remission. Having a diagnosis of a depressive disorder was associated with slower times to anxiety MCID and remission compared to having an anxiety disorder.

Discussion

The purpose of this study was to evaluate the rate and timing of MCID and remission in depression and anxiety among patients with elevated baseline depression and anxiety. Patients received psychiatry services through Rula Health, a MBC DMHI. We aimed to (1) describe depression and anxiety symptom changes among psychiatry patients with clinically elevated symptoms of depression and anxiety, (2) examine the timing of MCID and remission within the first 24 weeks of psychiatric treatment, and (3) investigate whether time to MCID and remission differed by demographic or clinical characteristics. Psychiatry patients with elevated depression and anxiety symptoms at baseline improved substantially within the first 12 weeks, with symptoms decreasing by nearly half, and experienced continued gains between 13 and 24 weeks. The likelihood of achieving meaningful improvement and remission in both depression and anxiety increased steadily over time, with most patients achieving MCID in depression or anxiety in 11 to 12 weeks, and most achieving remission by 19 to 22 weeks. Higher baseline symptoms predicted faster MCID but slower remission. Older age was associated with slower time to depression and anxiety improvement, and male gender was linked to faster anxiety remission. Depressive disorder diagnoses were associated with slower anxiety symptom improvement and remission, and neurodevelopmental diagnoses were associated with faster improvement and remission in depression symptoms.

In this real-world study, patients receiving psychiatric care through an MBC-based DMHI experienced reductions in depression and anxiety symptoms. The effect sizes observed (d = −1.17 to −1.62) were comparable to those reported in controlled research settings³⁷ and to outcomes published by other large commercial virtual psychiatry platforms (d = −1.23 to −1.64).^28,38 Notably, these effect sizes are larger than those reported for self-guided interventions, which typically produce small standardized effects for depression (g = 0.28-0.38) and anxiety (g = 0.20-0.31).³⁹ This difference may be attributed to the critical role of clinician oversight in personalized planning. In self-guided or hybrid care models, MBC data is collected but inconsistently used to guide treatment decisions. The clinician-delivered model examined here allows real-time symptom review and treatment adjustments, offering personalization not available in most self-guided platforms. These results underscore the potential of clinician-driven DMHIs that integrate MBC to support meaningful symptom improvement in real-world practice, though future prospective studies are indicated to support this claim.

Patients receiving psychiatric care delivered through Rula Health experienced timely symptom improvement and sustained progress toward remission. This finding addresses a critical gap in DMHIs, where outcomes are inconsistently tracked and long-term symptom trajectories remain understudied.⁴⁰ Consistent with prior research, the likelihood of achieving meaningful improvement and remission in both depression and anxiety increased steadily over the course of care.⁴¹ Most patients in this study reached MCID by 11 to 12 weeks and remission by 19 to 22 weeks. Our findings show the value of tracking meaningful and sustained symptom change, unlike many self-guided or blended DMHIs that report only short-term or average outcomes.^42,43 Together, our results suggest that DMHIs that facilitate psychiatric care and leverage MBC (like Rula Health) may support ongoing engagement, medication management, and timely treatment adjustments to improve clinical outcomes. Future research should examine how engagement patterns, such as visit frequency and symptom tracking, interact with diagnostic characteristics (eg, symptom severity) to optimize treatment outcomes.

Differences in time to MCID and remission were observed across patient characteristics, revealing several clinically-relevant patterns. Patients with higher baseline depression or anxiety symptoms tended to achieve MCID more quickly, but reached remission more slowly. This finding is consistent with research showing that while greater initial severity often leads to faster early gains, full remission typically results from longer-term treatment.^44
-46 This trajectory highlights the limitations of short-term care models and supports the value of longitudinal psychiatric treatment planning, especially in DMHIs where engagement can be a challenge.⁴⁷ The MBC-based care model used in this study allows providers to closely monitor symptoms and make proactive adjustments in medication type and dosage to maximize treatment effects.

Older age was associated with slower improvement in both depression and anxiety symptoms, consistent with a prior study showing that older age is associated with a slower and less intense response to pharmacotherapy for depression in adults over the age of 35.⁴⁸ Patients who are older may experience a more complex interplay of factors, such as longer illness duration, accumulated life stress, and co-occurring medical or psychosocial challenges, that could contribute to a slower initial treatment response.⁴⁸ Additionally, male gender was associated with faster remission in anxiety. Women are more likely to experience anxiety disorders across their lifetime and are 2 to 3 times more likely to have comorbid anxiety and depression.⁴⁹ This greater diagnostic complexity may delay symptom reduction and remission.⁵⁰ Compounding this, sex differences in how medications for anxiety (eg, antidepressants, benzodiazepines) are metabolized may further influence treatment response and recovery timelines.⁵¹ These findings highlight the opportunity for MBC-based DMHIs like Rula Health to improve outcomes by using demographic data to guide personalized care decisions. Future research should identify demographic variables and medication patterns that predict a positive response to further guide individualized care pathways. Doing so would help determine whether these findings hold across diverse populations and support more inclusive psychiatric care delivered through DMHIs.

Patients receiving care through Rula Health with a diagnosis of a depression disorder reached anxiety MCID marginally slower and remission significantly slower than those with an anxiety disorder. This pattern is consistent with prior research showing that anxiety symptoms often improve earlier in treatment than depressive symptoms, which may reflect differences in symptom profiles, treatment engagement, or the faster-acting nature of commonly prescribed medications for anxiety.^52
-54 In contrast, patients with a primary diagnosis of a depressive disorder may continue to endorse anxiety symptoms driven by their primary depressive symptoms such as depressive rumination or anhedonia – symptoms that are slower to respond to antidepressant medications (eg, in 4-6 weeks or longer).⁵⁵ These findings highlight the value of MBC-based DMHIs in supporting relief to individuals struggling with anxiety, and point to the need for sustained engagement in care for patients with comorbid anxiety and depression.

Within the MBC DMHI Rula Health, distinct patterns of symptom change were observed among psychiatric patients with different primary diagnoses, including those whose primary diagnosis was not mood-related. Psychiatric patients with a primary diagnosis of a neurodevelopmental disorder (most commonly Attention Deficit/Hyperactivity Disorder [ADHD]) achieved depression remission faster than those with a primary diagnosis of a depression disorder. This finding highlights the complex interplay between ADHD and comorbid depression symptoms. One possible explanation is that ADHD treatments may improve symptoms such as restlessness and difficulty concentrating-symptoms that also commonly occur in mood disorders.^56
-58 This may help explain the faster observed improvement for patients with neurodevelopmental disorders. However, the broader pattern suggests that psychiatric care through Rula Health’s MBC-based DMHI can support meaningful transdiagnostic symptom change across diagnoses. Future studies should incorporate validated ADHD symptom measures to understand how comorbid presentations influence treatment response. Together, these findings further suggest that MBC-enabled virtual care platforms can support symptom improvement, though diagnostic differences can meaningfully influence treatment trajectories.

Findings from this retrospective study using real-world patient data offer insights relevant to improving the delivery of MBC-based DMHIs. Worldwide, mental health systems continue to face critical gaps in treatment access, workforce capacity, and coordinated implementation of evidence-based care.^59,60 Current global priorities emphasize the need for quality person-centered, rights-aligned, and scalable evidence-based care.⁶¹ The results of this study demonstrate how routine outcome monitoring can be feasibly implemented within large-scale, digitally-delivered psychiatric services. These findings suggest how national and international health systems might leverage digital infrastructure to enhance continuous quality improvement, reduce treatment variability, and extend psychiatric expertise to under-resourced regions. Together, this evidence underscores the potential of MBC-integrated DMHIs to serve as a framework for scaling equitable, data-driven, and sustainable mental health care worldwide.

Strengths and Limitations

This study had several strengths. First, this study was conducted in a large, naturalistic sample of adults with clinically significant depression or anxiety symptoms and receiving digitally-delivered psychiatric services (N’s > 7000 patients). This points to the generalizability and relevance of our findings to the broader U.S. treatment-seeking adult population. Second, this study utilized validated, patient-centered, and clinically relevant measures to assess depression and anxiety treatment response. MCID provided a nuanced view of treatment impact, while remission served as a marker of more profound clinical progress. MCID and remission were examined within the 24 week (6 month) timeframe, which also aligns well with real-world clinical goals. Finally, data were collected from a real-world, fully operational commercial DMHI, adding to a growing body of literature focused on evaluating clinical outcomes and setting benchmarks at scale.

This study also had limitations. First, the naturalistic sampling and retrospective analysis prohibited us from making causal inferences about the impact of Rula Health psychiatric treatment on patient outcomes. Our findings therefore may be impacted by selection bias or confounders (ie, patients receiving care via alternative platforms) that we were unable to control for. Future research would benefit from a prospective study to control for these confounding variables and confirm therapeutic outcomes. Additionally, as this was a retrospective analysis of real-world data, an a priori statistical power analysis was not completed. Instead, the interpretive value of the findings were presented through use of effect sizes and confidence intervals.⁶² Next, because of the way demographic data were collected, the vast majority of Rula Health psychiatry patients had missing race and ethnicity data (>95%), precluding us from including these variables in this analysis. Future studies should prioritize the systematic collection of sociodemographic factors, such as race, ethnicity, and employment status. This may enable the examination of treatment equity across a diverse population and identify potential disparities in treatment outcomes.

Similarly, we were not able to account for concurrent treatments outside of the platform (eg, psychotherapy or medication management from other providers) or engagement metrics (eg, session adherence, response rate to questionnaires), which may have influenced outcomes. Future studies should inquire about concurrent treatments and integrate backend analytics to track engagement patterns longitudinally. By doing so, future studies would be better suited to control for these confounding variables and identify engagement profiles that are associated with better outcomes.

Next, while patients’ primary diagnostic category was included in the study, granular symptom measures of important comorbid conditions (eg, ADHD) were not collected. Future studies should incorporate validated measures for comorbid conditions that are highly prevalent in psychiatric samples such as ADHD, post-traumatic stress disorder, and bipolar disorder. Similarly, platform-level variables such as patient preferences for clinician matching, clinician demographics, and therapeutic modality were not examined, despite being potentially important contributors to outcomes, and should be included in future studies. Finally, data important to the provision of psychiatric care, such as medication adherence, number of medication trials, referral uptake, and assessment completion rates, were limited or unavailable. Future studies should include these types of variables to better understand the effects digitally-delivered psychiatric services on symptom improvements.

Conclusion

This study demonstrates that psychiatric care delivered through Rula Health’s MBC-integrated digital platform leads to meaningful and sustained improvements in depression and anxiety symptoms in adult patients. Most patients with elevated baseline symptoms achieved MCID within 11 to 12 weeks and remission by 19 to 22 weeks. These outcomes reflect not only strong clinical effectiveness but also the long-term relevance of psychiatry-led DMHIs, which are often underrepresented in the literature. Patient characteristics were associated with treatment response, as higher baseline symptoms predicted quicker initial improvement but slower remission, older age was linked to slower symptom change, and male gender was associated with faster remission. Additionally, patients with anxiety or neurodevelopmental disorders tended to reach both MCID and remission more quickly. Together, these findings show that psychiatric services delivered through MBC-based DMHIs (like Rula Health) can produce meaningful and sustained improvements in depression and anxiety symptoms in real-world care, though treatment trajectories vary across patient subgroups. Future research should examine how demographic, diagnostic, and clinical characteristics interact with engagement and treatment factors to influence outcomes and remission in real-world settings.

Supplemental Material

sj-docx-1-inq-10.1177_00469580261418135 – Supplemental material for Clinically Meaningful Improvement in Depression and Anxiety Among Psychiatry Patients Within a Measurement-Based Care Digital Mental Health Intervention: A Retrospective Analysis of Real-World Data from Rula Health

Supplemental material, sj-docx-1-inq-10.1177_00469580261418135 for Clinically Meaningful Improvement in Depression and Anxiety Among Psychiatry Patients Within a Measurement-Based Care Digital Mental Health Intervention: A Retrospective Analysis of Real-World Data from Rula Health by Kelsey L. McAlister, Lara Baez, Douglas Newton, Sam Seiniger, Allie Woodhouse and Jennifer Huberty in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

Footnotes

Acknowledgements

N/A.

ORCID iDs

Kelsey L. McAlister

Lara Baez

Sam Seiniger

Allie Woodhouse

Jennifer Huberty

Ethical Considerations

Prior to initiating treatment, all patients receiving care through Rula Health provide informed consent, which includes information on the scope of services, confidentiality protections, and data use. This consent process is separate from and in addition to agreeing to Rula Health’s privacy policy. Patients also review and agree to Rula Health’s privacy policy prior to treatment, which describes how their information may be used and protected in accordance with applicable regulations. No additional consent procedures for this research were completed, as this study involved a retrospective analysis of de-identified data and qualified for exemption from additional informed consent under U.S. human subjects research regulations. The Biomedical Research Alliance of New York (BRANY) Institutional Review Board (Study ID: 25-035-2061) reviewed and approved the study protocol. No participant compensation was provided for this research.

Author Contributions

All authors contributed substantially to the manuscript. LB, KM, and JH contributed to conceptualization and writing of the original draft. LB, KM, and JH contributed to conceptualization, formal analysis, visualization and writing of the original draft. DN, SS, AW, and JH contributed to conceptualization and supervision. DN, SS, and AW contributed to data curation. All authors reviewed, edited, and approved the final manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Rula Health.

Declaration of Conflicting Interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Douglas Newton, Sam Seiniger, and Allie Woodhouse are employed by Rula Health, and Rula Health was the behavioral health treatment used in this study. Fit Minded, Inc. provides consulting services to Rula Health. Jennifer Huberty is the Founder and CEO of Fit Minded, Inc. Kelsey McAlister is employed by Fit Minded, Inc., and Lara Baez was formerly employed by Fit Minded, Inc. To mitigate potential conflicts of interest, data analysis and interpretation were led by authors not employed by Rula Health. The analysis plan was prespecified, and results were interpreted collaboratively to ensure objectivity. The conflicts of interest have been fully disclosed. Authors’ employment status or salary are not dependent upon the results of their research.

Data Availability Statement

The data set used for this retrospective analysis is not publicly available given Rula Health’s privacy policy for patient data. However, aggregated and anonymized data may be shared with interested parties upon reasonable request.

Guarantor

Jennifer Huberty

Supplemental Material

Supplemental material for this article is available online.

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