Abstract
Importance
Healthcare workers (HCWs), particularly those identifying as female or Black, face disproportionate mental health strain. Digital mental health platforms have grown in popularity and, for health systems, may offer scalable solutions, but their differential impact across demographic groups remains understudied.
Design, Setting, and Participants
This secondary analysis of a randomized controlled trial enrolled 1,275 HCWs from an urban academic health system between January and May 2022. Participants were randomized to usual care or proactive digital engagement via the Cobalt platform. Female and Black HCWs were oversampled to assess subgroup effects.
Intervention
Monthly digital outreach, including mental health symptom screening and linkage to resources via the Cobalt platform, compared with usual care.
Main Outcomes and Measures
Primary outcomes were changes in depression (PHQ-9) and anxiety (GAD-7) scores at 6 and 9 months. Secondary outcomes included well-being (WHO-5, WBI-9) and work productivity (LEAPS). Generalized linear models assessed HTE by gender and race.
Results
Of 1,275 randomized participants (mean age 38.6 years; 83.4% female; 25.1% Black), both intervention and control groups showed significant reductions in anxiety and depression scores over time. No significant HTE was observed by gender or race for primary outcomes. Female HCWs receiving the intervention reported significantly greater improvement in work productivity at 6 months (LEAPS score difference: 1.70; p=0.03). Black HCWs in the intervention arm showed a sustained improvement in depression scores at 9 months (−2.21; p<0.001), though adjusted models did not confirm statistical significance.
Conclusions and Relevance
A proactive digital mental health strategy coupled with a well-being platform improved mental health outcomes across HCWs, with modest differential effects in productivity and depression among female and Black participants. These findings support the scalability of digital interventions and highlight the need for culturally tailored approaches to enhance equity and impact.
Introduction:
Health care workers (HCWs) continue to face work-related challenges affecting their mental health and career longevity.1,2 Burnout, clinician suicide, and workforce depletion continue to increase despite health system efforts, with additional attention following the pandemic.3,4 The optimal way to develop and deploy strategies to support the mental health of HCWs remains unknown, and supporting groups of HCWs with differential risk is essential.
Female and Black HCWs experience disproportionate mental health strain and hindered career development and growth,5,6 as well as longstanding differences in health outcomes.7–10 Black HCWs experience greater levels of anxiety and insomnia compared to non-Black colleagues.11,12 Female HCWs, who historically play outsized roles in caring for their own families, often face stress related to dependent or family care and experience higher anxiety, depression, and burnout than male HCWs.13
The pandemic accelerated the development and launch of digital health platforms and strategies for employees to navigate mental health and well-being resources.14,15 Yet, less is known about the effect of these platforms or the specific ability of these platforms to support female and Black HCWs, given the known differential risks to these groups. Research has begun to demonstrate that culturally aligned health interventions may improve these populations’ physical and mental health outcomes. Yet, the expansion to digital mental health remains understudied.16,17
This study is a secondary analysis of a randomized controlled trial (RCT) testing the effect of passive versus proactive digital engagement with a health system developed mental health and well-being platform (e.g., Cobalt) for HCWs.18 The parent RCT demonstrated improved anxiety and depression assessment scores among a broader cohort of HCWs over a 9-month period. The objective of this analysis was to evaluate the heterogeneity of treatment effect of the proactive digital engagement on female and Black HCWs’ mental health and well-being. These subgroup analyses were exploratory in nature. The hypothesis was that there would be a differential improved impact for female and Black HCWs, as the digital engagement strategy may provide a format to overcome stigma and access to mental health care.
Methods:
Overview
1,275 HCWs at an urban academic health system were recruited and enrolled in a previously reported RCT.18 This RCT compared usual care, whereby HCWs navigate to and access mental health resources on their own, versus proactive digital engagement in which HCWs received monthly text messages, mental health symptom screening, and connection to mental health resources via “Cobalt.” Cobalt is an open-source digital wellness and mental health platform for HCWs that provides mental health assessments, linkage to asynchronous support resources, and connection to mental health expertise care. The trial protocol was approved by the University of Pennsylvania’s Institutional Review Board (#848844).
Participants
Participants were recruited via email, postings in clinical settings, and meetings across an urban academic health system from January - May 2022. Participants represented a variety of clinical roles, including but not limited to physicians, nurses, technicians, medical assistants, pharmacists, and social workers. Eligible participants were 18 years or older, working within the University Health System, able to provide informed consent, working at least four hours per week in a hospital or outpatient setting, and had daily access to a smartphone.
Because an explicit aim of the underlying study was to investigate distinctive challenges, Black and female HCWs were oversampled in the original RCT enrollment. Participants self-reported demographics including gender identity (e.g., male and female) and race, choosing from options such as American Indian, Alaska Native, Asian, Black or African American, White, Native Hawaiian or Other Pacific Islander, or multiple races.
Outcomes:
Primary Outcomes
The primary outcomes were changes in depression and anxiety symptom scores from baseline to six months using the Patient Health Questionnaire (PHQ-9) and General Anxiety Disorder (GAD-7) total scores, respectively – both are validated measures for depression and anxiety screening, and have been used within HCWs.19,20 Higher scores on both self-report measures indicate the presence of more severe symptoms.19,21
Secondary Outcomes
Well-being was measured using the World Health Organization-Five Well-Being Index (WHO-5) scale, work productivity was measured using the Lam Employment Absence and Productivity Scale (LEAPS), and overall burnout was measured using the well-being index (WBI-9).22,23 The WHO-5 has been validated across diverse populations, including health professionals, during the pandemic.24 LEAPS and WBI-9 have been validated in research exploring the impact of mental health on employment.
Analysis
We analyzed heterogeneity of treatment effects (HTE). The race and gender moderation analyses were pre-specified as secondary analyses and objectives within the parent RCT. The specific subgroup comparisons reported here are exploratory in nature and the results therefore are hypothesis-generating rather than confirmatory. We calculated that if 20% of the initial RCT participants self-identified as Black and 50% as women, the study would achieve 80% power (alpha 0.05) to detect the following: 1) a 1.8 Black-non-Black difference in mean GAD-7 score change and a 2.0 difference in mean PHQ-9 score change between arms and 2) a 1.2 male-female difference in mean GAD-7 score change and a 1.3 difference in mean PHQ-9 score change at 6 months.25
We used a generalized linear model (GLM) to evaluate whether a subgroup variable (gender, race) had a statistically significant interaction with the treatment indicator (primary outcome). The GLM model was adjusted by participant demographics such as age, ethnicity, and marital status. Models used an identity link function with normal distributional assumptions (Gaussian GLM); robust standard errors (HC3) were used to account for heteroskedasticity. Baseline outcome scores were included as covariates in the adjusted models. Adjusted models for female vs. male comparison included arm, gender, arm-gender interaction, race, ethnicity, and partnership status as covariates. Adjusted models for Black vs. non-Black comparison included the same. A subgroup analysis was then conducted to estimate treatment effects separately at each level of the categorical variable used to define mutually exclusive subgroups (e.g., male and female; Black and non-Black). We used all available scores on eligible patients from randomization through the last observation. To address missing outcome data, we performed multiple imputation using chained equations under a missing at random assumption, generating 20 imputed datasets and combining estimates using Rubin’s rules. A p<0.05 was deemed statistically significant. Analyses were completed using SAS software (Version 9.4).
Results
1,854 interested HCWs were screened and 1,275 (68.7%) were randomized: 642 to the intervention arm and 633 to usual care (control group). Participants’ demographics (e.g., age, gender, race, ethnicity, marital status) and professional characteristics (e.g., clinical role) were similarly distributed between intervention and control groups. Participants had a mean age of 38.6 (SD 10.9) years, the majority were female (n=1,063 (83.4%), with n=793 (62.2%) identifying as White and n=320 (25.1%) as Black. Primary outcome measures of anxiety and depression were balanced across the arms with no significant differences (Table 1).
Table 1:
Participant Demographics
| Control (n=633) |
Intervention (n=642) |
Total (n=1275) |
SMD | |
|---|---|---|---|---|
| Age, mean (SD) | 38.6 (11.1) | 38.6 (10.6) | 38.6 (10.9) | 0.001 |
| Age group, n (%) | ||||
| 18–35 | 305 (48.2) | 296 (46.1) | 601 (47.1) | −0.042 |
| 36–50 | 219 (34.6) | 249 (38.8) | 468 (36.7) | 0.087 |
| 51–64 | 100 (15.8) | 86 (13.4) | 186 (14.6) | −0.068 |
| >=65 | 9 (1.4) | 11 (1.7) | 20 (1.6) | 0.024 |
| Female, n (%) | 529 (83.6) | 534 (83.2) | 1,063 (83.4) | −0.011 |
| Race, n (%) | ||||
| Asian | 54 (8.5) | 55 (8.6) | 109 (8.5) | 0.001 |
| Black | 160 (25.3) | 160 (24.9) | 320 (25.1) | −0.008 |
| Other | 25 (3.9) | 28 (4.4) | 53 (4.2) | 0.021 |
| White | 394 (62.2) | 399 (62.1) | 793 (62.2) | −0.002 |
| Hispanic, n (%) | 38 (6.0) | 39 (6.1) | 77 (6.0) | 0.003 |
| Married or with partner, n (%) | 352 (55.6) | 367 (57.2) | 719 (56.4) | 0.031 |
| Shiftwork, n (%) | 283 (44.7) | 267 (41.6) | 550 (43.1) | −0.063 |
| Manager, n (%) | 137 (21.6) | 153 (23.8) | 290 (22.7) | 0.052 |
| Profession Role, n (%) | ||||
| Physician | 85 (13.4) | 89 (13.9) | 174 (13.6) | 0.013 |
| Nurse | 210 (33.2) | 206 (32.1) | 416 (32.6) | −0.023 |
| Other | 338 (53.4) | 347 (54.0) | 685 (53.7) | 0.013 |
| Baseline survey score, mean (SD) | ||||
| Anxiety (GAD-7) | 5.79 (4.77) | 6.03 (4.88) | 5.91 (4.83) | 0.050 |
| Depression (PHQ-9) | 5.71 (4.74) | 5.92 (5.19) | 5.81 (4.97) | 0.042 |
| Work productivity (LEAPS) | 4.43 (4.00) | 4.51 (4.28) | 4.47 (4.14) | 0.0188 |
| Well-being (WBI-9) | 2.70 (2.33) | 2.72 (2.23) | 2.71 (2.28) | 0.0073 |
| Well-being (WHO-5) | 13.13 (5.33) | 13.10 (5.26) | 13.12 (5.29) | −0.0048 |
SMD: standard mean difference
There were substantial mean decreases from baseline PHQ-9 total scores within the intervention group at 6 months for both men (−2.16, p<0.001) and women (−1.10, p<0.001), which held at nine months. Regarding anxiety symptoms, women in the intervention group had a significant change at 6 months (−1.01, p<0.001). At nine months, women in both control (−0.78, p=0.0003) and intervention (−1.92, p<0.001) arms had significant decreases in mean anxiety scores (Table 2). The most considerable mean depression score reduction at 6 months and 9 months was among Black intervention participants (Table 3).
Table 2.
Unadjusted Change in Anxiety (GAD-7) and Depression (PHQ-9) among Female and Male HCWs
| Gender | 6-month change | 95% CI | P-value | 9-month change | 95% CI | P-value | |
|---|---|---|---|---|---|---|---|
| Anxiety (GAD-7) | |||||||
| Control | Male | −0.34 (3.16) | (−1.06, 0.38) | 0.348 | −0.54 (4.07) | (−1.51, 0.43) | 0.27 |
| Female | −0.27 (4.14) | (−0.66, 0.13) | 0.183 | −0.78 (4.36) | (−1.19, −0.36) | 0.001 | |
| Intervention | Male | −0.91 (4.50) | (−1.99, 0.16) | 0.094 | −1.12 (3.39) | (−1.93, −0.30) | 0.008 |
| Female | −1.01 (4.58) | (−1.46, −0.55) | <.0001 | −1.92 (4.17) | (−2.33, −1.51) | <.0001 | |
| Depression (PHQ-9) | |||||||
| Control | Male | −0.25 (3.80) | (−1.12, 0.62) | 0.568 | −0.84 (4.24) | (−1.85, 0.17) | 0.101 |
| Female | −0.31 (4.30) | (−0.72, 0.09) | 0.131 | −0.61 (4.46) | (−1.03, −0.18) | 0.001 | |
| Intervention | Male | −2.16 (4.59) | (−3.25, −1.06) | 0.001 | −1.19 (4.30) | (−2.22, −0.16) | 0.025 |
| Female | −1.10 (4.71) | (−1.57, −0.63) | <.0001 | −1.88 (4.36) | (−2.31, −1.45) | <.0001 | |
Table 3.
Unadjusted Change in Anxiety (GAD-7) and Depression (PHQ-9) among Black and non-Black HCWs
| Race | 6-month change | 95% CI | P-value | 9-month change | 95% CI | P-value | |
|---|---|---|---|---|---|---|---|
| Anxiety (GAD-7) | |||||||
| Control | Non-Black | −0.37 (3.83) | (−0.75, 0.02) | 0.065 | −0.90 (4.16) | (−1.33, −0.47) | <.0001 |
| Black | −0.03 (4.47) | (−0.79, 0.73) | 0.939 | −0.31 (4.72) | (−1.13, 0.51) | 0.457 | |
| Intervention | Non-Black | −1.13 (4.25) | (−1.59, −0.68) | <.0001 | −1.80 (4.09) | (−2.24, −1.37) | <.0001 |
| Black | −0.58 (5.37) | (−1.56, 0.40) | 0.242 | −1.80 (4.04) | (−2.52, −1.08) | <.0001 | |
| Depression (PHQ-9) | |||||||
| Control | Non-Black | −0.44 (4.20) | (−0.86, −0.01) | 0.0453 | −0.71 (4.21) | (−1.15, −0.28) | 0.001 |
| Black | 0.07 (4.28) | (−0.66, 0.80) | 0.856 | −0.44 (5.01) | (−1.31, 0.43) | 0.318 | |
| Intervention | Non-Black | −1.19 (4.43) | (−1.66, −0.72) | <.0001 | −1.62 (4.24) | (−2.07, −1.17) | <.0001 |
| Black | −1.46 (5.43) | (−2.45, −0.48) | 0.004 | −2.21 (4.65) | (−3.04, −1.38) | <.0001 | |
The adjusted analyses assessed for heterogeneity in treatment effect across gender and race. The treatment effect of the 6-month score change among women was not significantly different from that among men across outcomes, except for the LEAPS score. The difference across conditions for the 6-month work productivity score (i.e., LEAPS, where a higher score indicates greater self-reported productivity) change among women was significantly higher (1.70, p=0.03) than among men, accounting for age, race, ethnicity, and marital status (Table 4). Subsequent analyses based on multiple imputation for missing outcomes yielded consistent results for the female vs. male LEAPS finding (imputed adjusted estimate: 1.52, 95% CI 0.12–2.92, p=0.034), supporting the robustness of this finding despite missing data. Means reported in Tables 2–3 are raw unadjusted values; Tables 4–5 report adjusted difference-in-differences estimates. Analytic N per model varied across outcomes due to missing data.
Table 4.
Adjusted Female vs. Male GLM at 6- and 9-months
| 6-month score change between treatment and control arms | Estimate (Confidence Internal) | P-value |
|---|---|---|
| Depression change (PHQ-9) | 1.13 (−0.44, 2.71) | 0.16 |
| Anxiety change (GAD-7) | −0.17 (−1.69, 1.34) | 0.82 |
| Well-being change (WBI-9) | 0.42 (−0.33, 1.17) | 0.27 |
| Well-being change (WHO-5) | −0.26 (−1.90, 1.38) | 0.76 |
| Work productivity change (LEAPS) | 1.70 (0.16, 3.25) | 0.03 |
| 9-month score change between treatment and control arms | Estimate (Confidence Internal) | P-value |
| Depression change (PHQ-9) | −0.95 (−2.54, 0.64) | 0.24 |
| Anxiety change (GAD-7) | −0.61 (−2.13, 0.90) | 0.43 |
| Well-being change (WBI-9) | 0.25 (−0.57, 1.07) | 0.55 |
| Work productivity change (LEAPS) | 0.03 (−1.54, 1.61) | 0.97 |
Table 5.
Adjusted Black vs. non-Black GLM at 6- and 9-months
| 6-month score change between treatment and control arms | Estimate (Confidence Internal) | P-value |
|---|---|---|
| Depression change (PHQ-9) | −0.72 (−2.00, 0.57) | 0.27 |
| Anxiety change (GAD-7) | 0.26 (−0.98, 1.49) | 0.68 |
| Well-being change (WBI-9) | −0.56 (−1.17, 0.05) | 0.07 |
| Well-being change (WHO-5) | 0.40 (−0.93, 1.74) | 0.55 |
| Work productivity change (LEAPS) | −1.18 (−2.45, 0.09) | 0.07 |
| 9-month score change between treatment and control arms | Estimate (Confidence Internal) | P-value |
| Depression change (PHQ-9) | −0.87 (−2.14, 0.40) | 0.18 |
| Anxiety change (GAD-7) | −0.56 (−1.77, 0.65) | 0.37 |
| Well-being change (WBI-9) | −0.41 (−1.07, 0.24) | 0.23 |
| Work productivity change (LEAPS) | −1.42 (−2.67, −0.16) | 0.03 |
Black and Non-Black HCWs
Similarly, at 6 months, there were no significant differences across conditions in depression, anxiety, or well-being scores. Across measures, no significant changes were observed at 9 months, but there were trends towards mean improvement in the LEAPS score among Black HCWs compared to non-Black HCWs (−1.42, p=0.027) (Table 5). This finding was nearly identical in imputed analyses (adjusted estimate: −1.41, 95% CI −2.59 to −0.23, p=0.019). While these LEAPS results are suggestive of a differential productivity benefit for Black HCWs, the exploratory nature of these analyses, the modest subgroup sample sizes, and the multiple outcomes tested require that they be interpreted with caution as hypothesis-generating.
Discussion
This study was a secondary analysis of an RCT investigating the impact of a passive versus proactive digital engagement with Cobalt on mental health outcomes of female and Black HCWs. These findings offer insight into differential impacts (or the lack thereof) of digital mental health resources across important groups of HCWs.14,15 HCWs continue to face a myriad of threats to their career longevity and to their mental health, given the culture and complexity of medicine.1,2 Creating modalities that are accessible and effective across the varying needs of a diverse workforce is essential yet remains unstudied. Despite known disparities in mental health burden and access among these groups, our findings suggest a largely uniform benefit of the intervention across gender or race subgroups, with a few notable exceptions.26
This study has three main findings. First, we found no significant HTE by gender or race for the primary outcomes of change in depression and anxiety across the study period. This suggests that the proactive digital engagement platform improved mental health symptoms similarly for all HCWs, regardless of gender or race. These results are promising because they support the scalability and generalizability of digital mental health tools across diverse employee populations. However, the lack of a differential or sizable effect may also indicate the need for more culturally tailored or identity-specific content to address specific groups’ unique experiences and needs. Alternative explanations of these findings include regression to the mean—particularly given that subgroups with higher baseline symptom severity (e.g., Black HCWs showing greater baseline depression scores) may show larger raw score changes independent of treatment—and the possibility that the study was underpowered to detect modest but clinically meaningful effects. The structural inequities that shape mental health burden among Black and female HCWs including workplace discrimination, disproportionate caregiving burdens, and limited access to culturally concordant care require interventions designed with these dynamics in mind, rather than universal platforms applied uniformly. The uniformity of effect across demographic groups observed here may partly reflect these structural limitations rather than true equivalence in benefit. Future research would need to identify and evaluate specific interventions tailored to sub-groups’ cultural norms and values.
Second, the intervention led to a significantly greater improvement in work productivity among female HCWs compared to males. This finding may reflect the unique occupational stressors and caregiving responsibilities often borne disproportionately by women in health care roles.27 Changes in productivity scores may suggest that digital engagement offers some support for women in health care, but this study’s exact mechanism remains unknown. Future research exploring the connection between female HCW mental health and workplace self-reported productivity may have essential implications for gender-equity initiatives in the workplace.
Third, while the adjusted models did not reveal statistically significant differential effects by race, the unadjusted subgroup analysis showed a substantial and sustained reduction in depression symptoms among Black HCWs in the intervention arm. This trend suggests that Black HCWs may have derived particular benefit from the digital format, possibly due to its ability to offer private, stigma-free, and on-demand access to mental health resources. Structural barriers to mental health care—including stigma, distrust of health systems rooted in historical mistreatment, and lack of racially concordant providers—may make anonymous, self-directed digital access particularly salient for Black HCWs. The significant 9-month LEAPS difference among Black participants, which was robust across both complete-case and imputed analyses, further suggests that digital engagement may have downstream effects on perceived work productivity in this group, potentially mediated through reduced psychological burden. This warrants further study into the mechanisms through which digital platforms may support racially minoritized employees. Collectively, these findings contribute to a growing body of literature on the role of digital mental health tools in supporting a diverse health care workforce. While the current intervention appears broadly effective, future research should explore how personalization and sustained engagement enhance its impact at scale.
Limitations
This study has limitations. First, the study sample was limited to HCWs in a single extensive academic health system, which may limit the generalizability of the results to other settings. Second, self-reported measures of depression and anxiety may be subject to response bias, potentially influencing the observed outcomes. The population of this study was predominantly female and oversampled for individuals who self-identified as Black, which was intentional to investigate our study question. This study was not powered to study the intersectionality of Black, female HCWs compared to others. Lastly, although the digital platform demonstrated efficacy in this study, the long-term sustainability and impact of such interventions on a larger scale have not yet been investigated. Third, missing outcome data affected 30–40% of participants at follow-up. While missingness did not differ significantly by subgroup and multiple imputation analyses were consistent with complete-case results, the potential for unmeasured differential attrition cannot be fully excluded. Fourth, the HTE analyses reported here are exploratory; with multiple interactions tested across five outcomes, two subgroups, and two time points, the probability of false-positive findings is elevated. Results should be interpreted as hypothesis-generating and not confirmatory. Fifth, while the GLM models adjusted for key baseline demographics, residual confounding by unmeasured variables such as occupational role, shift type, prior mental health treatment, or social support cannot be ruled out.
Conclusion:
This study underscores the opportunity of a proactive digital engagement strategy to support the mental health and productivity of HCWs. The findings demonstrate that proactive digital mental health interventions can be effective and scalable, but do not show a differential impact on subsets of HCWs by demographic groupings. Future research should explore the broader application of these strategies across diverse health systems and examine the long-term sustainability of digital mental health and well-being interventions.
Disclosures and acknowledgments:
Dr. Merchant is the PI of NIH NHLBI R01HL1-141844, NIH/DHHS R01 MH127686, and NIH K24 HL157621.
Dr. Asch, MD, MBA, is a partner and part-owner of VAL Health and serves on the scientific advisory board of Thrive Global. Dr. Wolk received an administrative stipend from the University of Pennsylvania to support their effort in overseeing the Coping First Aid program, hosted on the Cobalt platform. Dr. Agarwal and Dr. Merchant had full access to all the data in the study and took responsibility for the data’s integrity and accuracy. The authors were responsible for the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.
Funding:
National Institute of Neurological Disorders and Stroke (NINDS), National Institute of Mental Health (NIMH), National Institute on Minority Health and Health Disparities (NIMHD) (Award Number: 1R01MH127686-01).
Footnotes
Human Ethics and Consent to Participate declarations:
The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the University of Pennsylvania (#848844). Participants provided informed consent for the parent and previously published study.18
Clinical Trial Number: NCT05028075
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