Skip to main content
NCBI home page
BMC Pregnancy and Childbirth logoLink to BMC Pregnancy and Childbirth
. 2026 Jul 24;26:1188. doi: 10.1186/s12884-026-09370-0

A novel digital hybrid care model for maternal mental health: preliminary findings from a matched retrospective cohort study

Elombe Calvert 1,2, Kelly Chen 2, Khatiya Chelidze Moon 3, Margaret R Emerson 4, Natalie Feldman 5, Catherine Lager 2, John Torous 2,6,✉
PMCID: PMC13681939  PMID: 42897493

Abstract

Background

Perinatal mood and anxiety disorders are among the most common complications of pregnancy and the postpartum period, while access to timely, evidence-based mental health care remains limited. Although smartphone applications may help extend access to care, recent research highlights notable limitations, including variable quality and unclear effectiveness. Hybrid care models, which integrate asynchronous app use with synchronous clinician-delivered care, have therefore emerged as a promising alternative. This study examined one such model, the Digital Clinic, to assess its potential to help bridge this treatment gap by analyzing clinical outcomes among peripartum women receiving care for anxiety and depressive symptoms.

Methods

Pregnant and postpartum women referred for anxiety and depression of peripartum onset received 8 weeks of synchronous, virtual, evidence-based CBT from a trained clinician. This treatment was complemented by the asynchronous use of the mindLAMP app, providing digital phenotyping, psychoeducation, and CBT skills, with the support of a Digital Health Navigator. Preliminary clinical outcomes were assessed by comparing GAD-7 and PHQ-9 scores from intake to the completion of the treatment course.

Results

This preliminary matched retrospective cohort study included 13 peripartum and 39 matched non-peripartum patients receiving care through the Digital Clinic. Among the peripartum cohort, GAD-7 scores decreased from 14.4 (SD = 5.0) at intake to 5.5 (SD = 4.3) at treatment completion, corresponding to a significant mean reduction of 8.9 points (SD = 4.8; p < 0.01; d = -1.91, 95% CI: -2.89 to -0.93). PHQ-9 scores decreased from 9.3 (SD = 5.9) to 5.6 (SD = 4.8), corresponding to a mean reduction of 3.7 points (SD = 6.3); however, this change did not reach statistical significance (p = 0.055; d = -0.68, 95% CI: -1.41 to 0.05).

Conclusion

The Digital Clinic, a hybrid care model, was associated with reductions in anxiety symptoms and less conclusive reductions in depressive symptoms among pregnant and postpartum women. This approach to maternal mental health holds promise for delivering accessible, evidence-based care to peripartum patients in real-world settings. Future research efforts should further evaluate its effectiveness in larger, more diverse patient populations, including those with more moderate to severe symptoms.

Keywords: Maternal, Digital phenotyping, Hybrid care model, Perinatal mood and anxiety disorders, Effect size

Introduction

Perinatal mood and anxiety disorders (PMADs) are among the most common complications during pregnancy, significantly contributing to maternal morbidity and mortality [1, 2]. The effects of maternal PMADs also extend to infants, who may develop behavioral or learning difficulties [3]. Despite the considerable prevalence of PMADs, access to quality, evidence-based mental health care remains limited. Recent data shows that only a fraction of patients with PMADs receive adequate treatment [4, 5]. Consequently, there is a growing interest in effective, low-risk, and rapid-acting interventions to bridge this treatment gap and improve the quality of care for those affected.

While antidepressants are widely recognized as effective and supported by years of safety data for use during pregnancy and lactation, uncertainties remain regarding the risks associated with perinatal medication use. Due to ethical constraints on conducting research in pregnant and lactating populations, no double-blind, randomized controlled trials (RCTs) of antidepressants have been conducted in these groups. The American College of Obstetricians and Gynecologists (ACOG) recommends psychotherapy as a first-line treatment for mild to moderate perinatal depressive disorders [6]. While psychotherapy is an effective treatment for many, this treatment can take many weeks to show efficacy, and patient engagement in psychotherapy may be variable. Given these limitations, there is a growing interest in novel approaches that augment PMADs care to address treatment gaps while minimizing potential risks.

One such novel approach is the use of smartphone apps, which have great potential to improve the quality of care for perinatal patients. Apps geared towards improving the well-being of perinatal people are readily accessible, however, reviews of these apps suggest concerns about their data safety and variable efficacy [7, 8]. Numerous studies have shown that apps are acceptable and of interest to both women with perinatal depression and their providers [9]. However, a 2022 review showed that despite the availability of apps for peripartum mental health, choosing the right one is an emerging challenge [10]. This challenge is compounded by recent reviews suggesting that the clinical quality of these apps is often low [11, 12]. This is not to say that all apps focused on this population are unhelpful, as a 2024 meta-analysis of 176 randomized controlled trials of apps found an effect size of g = 0.28 for depression [13], highlighting the potential of these apps to deliver effective care.

Given the limitations of standalone apps, there has been a move toward integrating them into hybrid models of care, which leverage a combination of in-person and app-based approaches to improve the scale and delivery of evidence-based treatments. Hybrid models of care combine self-guided app use with traditional clinical visits, offering a blend of in-person (or telehealth) care augmented by app use between sessions. Recent evidence suggests that hybrid models of care can be effective for depression even when offered over a shorter timeframe than traditional therapy [14]. Thus, hybrid models of care may present a novel means to meet ACOG’s recommendation for offering evidence-based psychotherapy to treat perinatal depressive disorders [6].

The hybrid care model is particularly well-suited for the peripartum population, whose demanding schedules necessitate flexible, remote options that reduce logistical barriers such as transportation and childcare required for traditional appointments. Perinatal individuals frequently experience distressing symptoms at various times throughout the day and night, underscoring the importance of interventions that are accessible at all hours. Such on-demand approaches provide support and accessibility that traditional care models, constrained to scheduled appointments during standard business hours, cannot offer. By addressing these unique temporal and logistical challenges, hybrid care models represent an innovative and practical solution tailored to the specific needs of the perinatal population, potentially enhancing engagement and improving clinical outcomes. Emerging research already suggests such hybrid care models are acceptable, but less is known about their clinical impact.

Our team has developed one such hybrid care model for the treatment of depression and anxiety that has been well studied across diverse populations [14, 15]. This model, called the Digital Clinic, offers an 8-week treatment program that combines therapist-led clinical sessions, self-guided use of the mindLAMP smartphone app, and engagement and troubleshooting support from a trained coach-like figure known as a Digital Health Navigator (DHN). Since its implementation in 2022, the model has been associated with favorable clinical outcomes in prior real-world evaluations [16] and has been used to treat patients with PMADs. However, there has been limited examination of clinical outcomes associated with this model among peripartum patients, nor have outcomes been compared with those of non-peripartum patients.

Thus, in this preliminary matched retrospective cohort study, we examined the use of a hybrid care model, the Digital Clinic, for the treatment of anxiety and depressive symptoms among peripartum women, compared with a matched non-peripartum cohort. Specifically, we aimed to: (1) characterize engagement and treatment adherence; (2) assess changes in psychosocial outcomes across treatment; (3) estimate changes in anxiety and depressive symptoms from baseline to treatment completion; and (4) explore associations between treatment engagement, psychosocial outcomes, and clinical symptom change. These analyses aim to generate preliminary evidence on the use of hybrid care models for treating PMADs, laying the groundwork for designing randomized controlled trials in this underserved peripartum population.

Methods

Study design

This study was a preliminary matched retrospective cohort study conducted using data from patients treated in the Digital Clinic, an app-augmented hybrid care model for depression and anxiety based at a single academic medical center. The study was designed to examine preliminary clinical outcomes for anxiety and depression symptoms among peripartum patients receiving care through the Digital Clinic. A matched non-peripartum cohort was included to provide a comparative clinical reference group for symptom outcomes. Throughout this manuscript, the Digital Clinic refers to the specific 8-week intervention evaluated in this study. Hybrid care model refers to the broader treatment framework integrating synchronous clinician-delivered care with asynchronous app-based use and Digital Health Navigator assistance [17].

Setting and timeframe

The Digital Clinic is based at Beth Israel Deaconess Medical Center (BIDMC), a tertiary care teaching hospital affiliated with Harvard Medical School in Boston, Massachusetts, United States. The clinic operates within the Beth Israel Lahey Health system and accepts referrals from outpatient clinics across the network. The peripartum cohort included patients who participated in the Digital Clinic between August 2022 and December 2024. Recruitment occurred continuously throughout this period, with patients enrolling on a rolling basis as referrals were received.

Participants and recruitment

Peripartum patients were eligible if they met the following criteria: (1) aged at least 18 years; (2) pregnant or up to one year postpartum; (3) referred for generalized anxiety disorder or major depressive disorder with peripartum onset; (4) residing in Massachusetts; (5) English-speaking; and (6) ownership of an Android or Apple smartphone. Patients were excluded if they had severe intellectual or attentional deficits that would interfere with participation in therapy, were acutely suicidal and required a higher level of care, or were currently enrolled in a more intensive treatment program, including inpatient treatment, partial hospitalization, or detoxification rehabilitation. Diagnoses of generalized anxiety disorder or major depressive disorder with peripartum onset were established by the referring clinician based on clinical evaluation in accordance with DSM-5 criteria before referral to the Digital Clinic. No additional structured diagnostic interview was administered at enrollment.

Participants were recruited through clinician referrals from outpatient clinics across the Beth Israel Lahey Health system. A psychiatrist and member of the research team held informational sessions for primary care providers from Beth Israel Deaconess Medical Center and affiliated hospitals, describing the Digital Clinic, eligibility criteria, and referral procedures. Referrals were made directly from providers to the Digital Clinic team; no direct patient outreach was conducted. After referral, potential participants completed a web-based screening questionnaire. Once eligibility was confirmed, participants met virtually with a trained Digital Health Navigator, who explained the intervention, assisted with the mindLAMP app installation, reviewed data collection procedures, and obtained informed consent. Of note, no monetary or non-monetary incentives were provided for participation in the Digital Clinic intervention.

Matched non-peripartum comparison cohort

A matched non-peripartum cohort was selected from patients treated in the Digital Clinic during the same clinical implementation period. Non-peripartum patients received the same Digital Clinic model of care but did not meet peripartum status criteria. Matching was conducted to provide a more comparable clinical reference group for symptom outcomes and to address potential imbalance in demographic and clinical characteristics. Peripartum patients were matched to non-peripartum patients in a 1:3 ratio, resulting in 13 peripartum and 39 non-peripartum patients. Matching variables included baseline demographic and clinical characteristics available in the clinical dataset, including age, race, education, and baseline symptom severity. The matched cohort was used for comparisons of anxiety and depressive symptom change only. All other analyses including treatment engagement, psychosocial outcome changes, and the exploratory engagement-symptom correlations were done within the peripartum cohort.

Intervention: the digital clinic

The Digital Clinic is an 8-week app-augmented hybrid care model that integrates three components [18]: (1) synchronous virtual cognitive-behavioral therapy delivered by a trained clinician; (2) asynchronous use of the mindLAMP smartphone app for symptom monitoring, psychoeducation, and skills practice; and (3) weekly support from a Digital Health Navigator. All therapy sessions and Digital Health Navigator check-ins were conducted virtually using Zoom. Virtual delivery was selected to reduce common access barriers for peripartum patients, including transportation challenges, childcare demands, and the logistical burden of in-person attendance during the peripartum period. Research suggests telehealth is acceptable [19], with brief evidence-based treatment being noninferior [20].

Clinician-delivered therapy

Participants received an 8-week course of brief, transdiagnostic, evidence-based cognitive-behavioral therapy based on the Unified Protocol for Transdiagnostic Treatment of Emotional Disorders [21]. The Unified Protocol (UP) was selected because it targets shared mechanisms across anxiety and depressive disorders, including emotional reactivity, avoidance, cognitive inflexibility, and maladaptive responses to distress. The clinical program included an initial intake session followed by weekly therapy sessions. Therapy sessions were approximately 45 to 50 min and followed a structured but flexible manual developed for the Digital Clinic. Core session topics included psychoeducation about the function of emotions, mindfulness, cognitive flexibility, countering avoidance, exposure-based practice, and relapse prevention through continued skills practice. During therapy sessions, clinicians review data from the app with the patient and assign relevant skills practice as homework through the app between sessions.

Digital health navigator support

Digital Health Navigators [22] provided non-clinical engagement and technical support throughout treatment. Before treatment began, the Digital Health Navigator helped participants install and log into the mindLAMP app, explained app features and data collection procedures, and reviewed privacy considerations. During treatment, participants had weekly Digital Health Navigator check-ins of up to 30 min, typically before therapy sessions. During these check-ins, Digital Health Navigators addressed technical or motivational barriers to app use, reviewed active and passive data summaries with patients, and helped patients understand app-generated data visualizations. Digital Health Navigators also monitored app engagement and data quality and shared clinically relevant insights with the treating clinician when needed. This structure was intended to support app engagement without increasing clinician workload.

mindLAMP app

The mindLAMP app (Fig. 1) is an open-source mental health smartphone app designed to support digital phenotyping, symptom monitoring, psychoeducation, and skills practice [23]. In the Digital Clinic, mindLAMP served two main purposes: to facilitate between-session practice of CBT-related skills and to collect self-report and behavioral data that could inform care. The app includes four primary therapeutic sections. The Learn section provides psychoeducational content. The Assess section includes self-report questionnaires and other assessments. The Manage section includes interactive skills and coping tools. The Portal section provides data visualizations and feedback. The app can also collect passive behavioral data, such as time spent at home, steps walked, screen time, and sleep-related estimates, although the present analysis focused primarily on app-based engagement, survey completion, and clinical outcomes.

Fig. 1.

Fig. 1

Open in a new tab

mindLAMP app. This figure shows the main app sections available to patients during treatment, including Feed, Learn, Assess, Manage, and Portal. The app supported between-session care through scheduled activities, psychoeducational materials, symptom assessments, CBT-based skill practice and survey completion

Treatment schedule

The 8-week program was structured to support both orientation to the app and delivery of therapy. During the initial phase, patients were introduced to the app, completed baseline measures, and received Digital Health Navigator support to promote familiarity with daily and weekly assessments. During the treatment phase, patients attended weekly therapy sessions while continuing app use and Digital Health Navigator check-ins. App-based skills and assessments were intended to reinforce therapy content between sessions and support measurement-based care.

Clinician and digital health navigator training

Clinicians included mental health counseling trainees (5 master’s and 3 doctoral), a postdoctoral-level clinical psychologist, and a licensed psychiatrist. Clinicians received 8 h of structured training in conducting therapy based on our Digital Clinic manual and understanding UP principles. Training thus focused on core principles of transdiagnostic CBT, session structure, app integration, and use of app-derived data to support care. Clinical adherence and competency were monitored through weekly supervision with a psychologist or psychiatrist. Digital Health Navigators (16) were non-clinician volunteers or trainees with an interest in digital mental health interventions. They received 10 h structured training in app setup, patient onboarding, weekly check-in procedures, technical troubleshooting, digital phenotyping data interpretation, data quality monitoring, and procedures for escalating safety concerns to clinical supervisors. Digital Health Navigators completed 2 live supervised training appointments before being cleared to conduct patient check-ins independently. Training protocol for clinic staff have been published [22].

Quality control

Therapist adherence to the Digital Clinic manual was supported through weekly supervision and review of clinical notes. Digital Health Navigator adherence to check-in procedures was supported through the use of standardized protocols and checklists. Engagement and data quality were monitored throughout treatment, and issues affecting app use or survey completion were addressed during the Digital Health Navigator check-ins with patients before sessions.

Study measures – clinical outcomes

The primary clinical outcomes were changes in anxiety and depressive symptoms from intake to treatment completion, measured using the GAD-7 and PHQ-9, respectively.

Depressive symptoms

The 9-item Patient Health Questionnaire (PHQ-9) has been validated for use in perinatal depression [24] and was used at baseline and at follow-up visits to assess the presence and severity of depressive symptoms over the preceding two weeks. Each of the nine items is scored on a scale from 0 (not at all) to 3 (nearly every day) yielding a total possible score of 0 to 27, with higher scores indicating greater depressive symptom severity.

Anxiety symptoms

The 7-item Generalized Anxiety Disorder scale (GAD-7) has been validated in the pregnant population [25] and was used at baseline and at follow-up visits. to assess the presence and severity of anxiety symptoms over the preceding two weeks. Each of the seven items is scored on a scale from 0 (not at all) to 3 (nearly every day) yielding a total possible score of 0 to 21, with higher scores indicating greater anxiety symptom severity.

Study measures – psychosocial outcomes

Self-efficacy at managing emotions

Self-efficacy in managing emotions was measured using the PROMIS Item Bank v1.0 – Self-Efficacy for Managing Emotions Short Form 8a [26]. Items are rated on a scale of 1 (I am not at all confident) to 5 (I am very confident), with a total possible score of 8–40. Higher scores indicated higher levels of self-efficacy for managing negative emotions.

Emotional self-awareness

Perceived emotional self-awareness was measured using a single question, “I feel as though I am aware of and in tune with emotions”, which was rated from 1 (I am not at all confident) to 5 (I am very confident). Higher scores indicated higher levels of self-awareness [14].

Perceived social support

The Multidimensional Scale of Perceived Social Support (MSPSS) [27] was used to measure levels of perceived social support. Items were rated on a scale of 1 (very strongly disagree) to 7 (very strongly agree), with a possible score range of 12–84. Higher scores indicated higher levels of perceived social support.

Functional impairment

The Sheehan Disability Scale (SDS) [28], a 5-item assessment of impairment in 3 domains: work/school, social life, and family life. Three items assessing these three domains are rated from 0 (not at all) to 10 (extremely) and yield a total summed score of 0 (unimpaired) to 30 (highly impaired).

Self-management knowledge and behaviors

The Partners in Health Scale (PIH) [29], a 12-item assessment with 3 subscales: knowledge, adherence and symptom management. Item responses were rated from 0 (Very little, Never, Not very well) to 8 (A lot, Always, Very well) and yield a total summed score 0 to 96; higher scores indicated more motivation.

Digital literacy

The Digital Health Care Literacy Scale [30], a 3-item assessment, was used to measure digital health care literacy. The items are rated from 0 (strongly disagree) to 4 (strongly agree) and yield a total summed score 0 to 12. Scores that are closer to 12 indicate higher digital health literacy.

Study measures – engagement features

mindLAMP engagement

App engagement was assessed among peripartum patients with available mindLAMP engagement data. Engagement metrics included number of days the app was used, total number of logins, total time spent in the app, average daily and weekly app use, time of day of app use, and use of specific app features. Feature utilization analyses focused on therapeutic app components, including Assess, Manage, Learn, and Portal pages, while excluding general navigation pages such as Home and Feed.

Survey completion rate

Survey completion was calculated using each patient’s observed treatment period as the denominator. EMA completion was defined as the proportion of treatment days with at least one valid daily mood, anxiety, or functioning survey response. Weekly clinical survey completion was defined as the proportion of treatment weeks with at least one completed PHQ-9 or GAD-7 assessment. Overall survey completion was calculated as the patient-level average across EMA and weekly clinical survey completion percentages. This approach counted unique days or weeks with valid responses rather than raw survey entries, preventing inflated completion rates when multiple responses.

Study measures – demographic features

Demographic characteristics

Demographic data was collected at intake from patients and included age, race, and biological sex assigned at birth, educational attainment, and peripartum status while receiving care in the clinic.

Statistical analysis

Analyses were conducted in R version 4.4.1. Descriptive statistics were used to summarize demographic characteristics, baseline clinical severity, app engagement, survey completion, and psychosocial outcomes. Continuous variables were summarized using means and standard deviations, and categorical variables were summarized using frequencies and percentages. Baseline characteristics were compared between peripartum and matched non-peripartum patients to assess group comparability. Welch two-sample t-tests were used for continuous variables, and Fisher’s exact test was used for categorical variables. For primary clinical outcomes, changes in GAD-7 and PHQ-9 scores from intake to treatment completion were examined separately among peripartum and matched non-peripartum patients using paired t-tests. Mean change scores, standard deviations, p-values, and Cohen’s d effect sizes [31] with 95% confidence intervals were reported. Because the study was preliminary and not powered to establish comparative effectiveness, group-specific changes were interpreted cautiously.

For secondary psychosocial outcomes, paired t-tests were used to compare intake and treatment completion scores among peripartum patients with available paired data. Results were reported using means, standard deviations, t statistics, degrees of freedom, p-values, and descriptive interpretation of direction and magnitude. Exploratory Pearson correlations were conducted among peripartum patients to examine associations between app engagement, survey completion, psychosocial outcome changes, and symptom change scores. Benjamini-Hochberg correction was applied to account for multiple comparisons, with statistical significance evaluated using a two-sided α level of less than 0.05 for all tests. Because of the small sample size, correlation analyses were interpreted as exploratory and hypothesis-generating. Confidence intervals were reported to emphasize uncertainty around the estimates. Missing data were not imputed; analyses were conducted using available cases for each outcome.

Ethical aspect of research

The operation of our hybrid model and the use of data from the program for research was reviewed and approved by the BIDMC’s Institutional Review Board (IRB) as part of a quality improvement project. Patients provided informed consent after meeting with a Digital Health Navigator, who explained the treatment structure, app use, digital data collection, privacy considerations, and the use of de-identified data for research. Data from patients were stored in HIPAA-compliant systems.

Results

Sample characteristics

The matched cohort comprised 52 patients, including 13 peripartum and 39 non-peripartum women (Table 1). The mean age was 35.4 years (SD = 11.2), with similar ages among peripartum (M = 35.8, SD = 5.1) and non-peripartum patients (M = 35.2, SD = 12.6). All participants identified as female (100%). The cohort was predominantly White (75%), with Black or African American (17%), Asian (5.8%), and Native Hawaiian/Pacific Islander (1.9%) patients also represented. Educational attainment was high across both groups, with the majority holding a postgraduate degree (44%) or college degree (33%), and a smaller proportion reporting some college education (23%). Baseline clinical severity was comparable between groups, with mean PHQ-9 scores of 9.3 (SD = 5.9) and 10.3 (SD = 4.4) for peripartum and non-peripartum patients respectively, and mean GAD-7 scores of 14.4 (SD = 5.0) and 14.0 (SD = 5.3).

Table 1.

Baseline characteristics for the peripartum and non-peripartum matched cohort

Variable Overall
N = 521 		Peripartum
N = 131 		Non-peripartum
N = 391 		p-value2
Age, mean (SD) 35.4 (11.2) 35.8 (5.1) 35.2 (12.6) 0.8
Sex, n (%) -
 Female 52 (100) 13 (100) 39 (100)
Race, n (%) 0.4
 Asian 3 (5.8) 1 (7.7) 2 (5.1)
 Black or African American 9 (17.3) 2 (15.4) 7 (17.9)
 Native Hawaiian/Pacific Islander 1 (1.9) 1 (7.7) 0
 White 39 (75.0) 9 (69.2) 30 (76.9)
Education, n (%) 0.7
 College Graduate 17 (32.7) 4 (30.8) 13 (33.3)
 Postgraduate Degree 23 (44.2) 7 (53.8) 16 (41.0)
 Some College 12 (23.1) 2 (15.4) 10 (25.6)
PHQ-9 Baseline, mean (SD) 10.1 (4.8) 9.3 (5.9) 10.3 (4.4) 0.6
GAD-7 Baseline, mean (SD) 14.1 (5.2) 14.4 (5.0) 14.0 (5.3) 0.8
Open in a new tab

1Mean (SD); n (%)

2Welch Two Sample t-test; Fisher’s exact test

Engagement and treatment adherence

Use frequency and duration

Peripartum patients with available app engagement data (n = 9) demonstrated high adherence, using the mindLAMP app on an average of 44.8 days (SD = 7.2), corresponding to 86% of their treatment period. Patients logged into the app a mean of 77.3 times over the course of treatment (SD = 38.4), averaging approximately 2.0 logins per day (SD = 0.7). Total time spent in the app was 5.2 h (SD = 3.7), translating to a mean of 7.2 min per day (SD = 5.4) and 39.7 min per week (SD = 30.3). App use was predominantly in the evenings (34.5%), followed by the afternoon (25.2%), morning (20.4%) and night (19.8%).

App feature utilization

Analyses of feature utilization focused on therapeutic app components (Assess, Manage, Learn, Portal), excluding general navigation pages (Home, Feed). Among therapeutic features, the Assess page was the most frequently visited, accounting for 43% of all page visits (n = 1,882), followed by Manage (9%, n = 411), Learn (8%, n = 337), and Portal (4%, n = 161) pages. Patients spent the most time on the Assess (M = 1.2 h, SD = 0.89) and Manage pages (M = 0.44 h, SD = 0.60). The evening hours were the most common period of the day for Assess page visits (34%), whereas Manage page visits were mostly in the morning (34%) and Learn page visits were in the afternoon (37%).

Survey completion

Overall survey completion averaged 66.5% (SD = 18.5%) across all measures. EMA completion, which included daily mood, anxiety, and functioning surveys, averaged 51.5% of treatment days (SD = 18.6%). Completion of weekly clinical surveys was higher, with patients completing 81.6% of expected PHQ-9 and GAD-7 assessments (SD = 25.7%).

Psychosocial outcomes across treatment

Paired t-tests comparing available intake scores to those at the completion of treatment (n = 10) revealed significant improvements across several psychosocial domains (Fig. 2). Perceived social support (MSPSS) had the largest statistically significant improvement increasing from 59.1 (SD = 12.1) at intake to 71.8 (SD = 16.0) at completion (t(9) = 6.10, p < 0.01). Self-management knowledge and behaviors (PIH) similarly demonstrated significant improvement, increasing from 78.1 (SD = 13.6) to 95.2 (SD = 16.1; t(9) = 7.16, p < 0.01). Emotional self-awareness (ESA) also improved significantly from 2.5 (SD = 0.97) to 3.9 (SD = 0.99; t(9) = 2.69, p = 0.025). While not statistically significant, functional impairment (SDS) decreased from 16.7 (SD = 9.9) to 11.6 (SD = 9.6; t(9) = -2.26, p = 0.050), and self-efficacy at managing emotions (SEME) showed a positive trend from 24.0 (SD = 5.2) to 27.2 (SD = 8.0; t(9) = 1.84, p = 0.100). Digital literacy (DHLS) remained largely unchanged across the treatment period (t(9) = -0.51, p = 0.619).

Fig. 2.

Fig. 2

Open in a new tab

Psychosocial outcomes across treatment timepoints. Higher scores indicate greater self-efficacy in managing emotions (SEME), emotional self-awareness (ESA), self-management knowledge and behaviors (PIH), perceived social support (MSPSS), and digital literacy (DHLS). Lower scores indicate reduced functional impairment (SDS)

Matched comparative clinical symptom change

Both peripartum and non-peripartum patients showed reductions in anxiety and depressive symptoms following treatment, with the strongest evidence observed for anxiety symptoms (see Table 2). GAD-7 scores improved significantly in both cohorts, with a larger within-group reduction among peripartum patients (M = -8.92, SD = 4.8; d = -1.91, 95% CI: -2.89 to -0.93) than among non-peripartum patients (M = -5.05, SD = 5.1; d = -0.89, 95% CI: -1.23 to -0.56). PHQ-9 scores also decreased in both groups, although these reductions did not reach statistical significance. For depressive symptoms, peripartum patients showed a moderate effect-size estimate (M = -3.69, SD = 6.3; d = -0.68, 95% CI: -1.41 to 0.05), while non-peripartum patients showed a small effect-size estimate (M = -1.97, SD = 6.6; d = -0.36, 95% CI: -0.76 to 0.03). Given the small sample size, null PHQ-9 findings should be interpreted with caution.

Table 2.

Baseline and Post-treatment Changes in Anxiety and Depressive Symptoms Among Matched Peripartum (PP) and Non-peripartum (NP) Patients

Variable & Group Baseline,
mean (SD)		 Follow-up,
mean (SD)		 Change,
mean (SD)		 p-value1 Effect size2 (95% CI)
GAD-7a
 PP (n = 13) 14.38 (5.0) 5.46 (4.3) -8.92 (4.8) < 0.01 -1.91 (-2.89, -0.93)
 NP (n = 39) 14.03 (5.3) 8.97 (5.9) -5.05 (5.1) < 0.01 -0.89 (-1.23, -0.56)
PHQ-9b
 PP (n = 13) 9.31 (5.9) 5.62 (4.8) -3.69 (6.3) 0.055 -0.68 (-1.41, 0.05)
 NP (n = 39) 10.31 (4.4) 8.33 (6.2) -1.97 (6.6) 0.068 -0.36 (-0.76, 0.03)
Open in a new tab

Negative change scores and negative effect sizes from the table indicate symptom reduction from intake to treatment completion

1Paired t-test comparing intake and completion scores

2Cohen’s d for within-group change

bPHQ-9: 9-item Patient Health Questionnaire

aGAD-7: Generalized Anxiety Disorder-7

Engagement, psychosocial and symptom associations

No associations remained statistically significant after correction, and wide confidence intervals reflected substantial uncertainty. Accordingly, correlation estimates are reported descriptively and should be interpreted as exploratory. Because symptom improvement was coded as a more negative change score, negative correlations indicate associations in the direction of greater symptom reduction. For GAD-7 change, psychosocial variables showed the largest associations in the expected direction, with self-efficacy (r = -0.40, 95% CI: -0.82 to 0.30), emotional self-awareness (r = -0.35, 95% CI: -0.80 to 0.36), and perceived social support (r = -0.35, 95% CI: -0.80 to 0.36) showing moderate negative associations. Survey completion metrics showed small negative associations in the expected direction (r = -0.12 to -0.18). However, app engagement metrics showed weak positive associations in the unexpected direction (r = 0.25 to 0.37). For PHQ-9 change, EMA completion showed the largest association (r = -0.48, 95% CI: -0.81 to 0.10), followed by overall survey completion (r = -0.45, 95% CI: -0.80 to 0.13) and total time in the app (r = -0.45, 95% CI: -0.86 to 0.31). Psychosocial variables showed weaker associations with depression change (r = -0.10 to -0.36).

Discussion

In this preliminary matched retrospective cohort study, peripartum patients receiving care through the Digital Clinic demonstrated good engagement with our app-augmented hybrid model of mental health care, improvements in several psychosocial outcomes, and reductions in anxiety and depressive symptoms over the course of treatment. These preliminary findings suggest that a hybrid care model employing an open-source smartphone app may be a feasible and potentially useful approach for supporting care for PMADs. This is important given persistent gaps in access to timely, evidence-based mental health care for peripartum patients. Unlike proprietary digital interventions [32], the structure of this model may support more scalable, reproducible, and collaborative implementation efforts. By combining clinician-delivered care, app-based symptom monitoring, psychoeducational resources, and Digital Health Navigator support, the Digital Clinic may help extend care beyond scheduled visits while creating opportunities for measurement-based and data-informed treatment [33–35].

The observed reductions in anxiety symptoms, and to a lesser extent depressive symptoms, are clinically relevant given the burden of PMADs and the need for flexible, accessible, and low-risk treatment options during pregnancy and the postpartum period. Peripartum patients often face logistical barriers to traditional psychotherapy, including childcare demands, transportation challenges, unpredictable schedules and sleep disruption. These barriers are clinically important because delays in treating PMADs may affect maternal functioning, bonding and infant well-being [36]. The Digital Clinic was designed to address these challenges through an eight-week model that combines weekly virtual cognitive-behavioral therapy, app-based tools, symptom monitoring, and Digital Health Navigator support. In this context, the observed symptom reductions may reflect the value of an eight-week hybrid care model for supporting PMAD care in real-world settings by extending therapeutic support beyond scheduled appointments while preserving access to clinician-delivered treatment.

Several components of the Digital Clinic may plausibly have contributed to the observed symptom changes. First, weekly clinician-delivered cognitive behavioral therapy provided structured, evidence-based treatment for anxiety and depressive symptoms. Second, the mindLAMP app supported between-session engagement through symptom assessments, psychoeducational content, and self-management tools. Third, Digital Health Navigator support may have helped sustain engagement by addressing technical barriers, reinforcing app use, and helping patients integrate digital tools into care. This combination of synchronous clinical care, asynchronous app use, and navigator-assisted engagement may be particularly relevant for peripartum patients, whose symptoms and care needs may not align neatly with traditional weekly appointments.

The psychosocial findings provide additional context for understanding the clinical changes observed in this study. Peripartum patients showed significant improvements in perceived social support, self-management knowledge and behaviors, and emotional self-awareness across treatment. Functional impairment and self-efficacy in managing emotions also changed in favorable directions, although these changes did not reach statistical significance. These patterns are clinically relevant in the context of PMADs, where symptoms may be accompanied by reduced perceived support, diminished confidence in managing emotions, and impaired daily functioning. The breadth of improvement across psychosocial domains suggests that the Digital Clinic may support patients through multiple pathways rather than through a single mechanism. This is consistent with the transdiagnostic CBT approach underpinning the intervention, which targets shared processes across anxiety and depressive symptoms. In addition to weekly therapy sessions, Digital Health Navigator support [37] may have contributed to these psychosocial gains by reviewing app data with patients, reinforcing skill practice between sessions, and helping integrate digital insights into clinical care.

Engagement results suggest that peripartum patients were able to use the app consistently during treatment. Among patients with available app data, participants used mindLAMP for most of their treatment period and completed a substantial proportion of expected weekly clinical assessments. Feature-level analyses indicated that the Assess page was the most frequently used therapeutic component, suggesting that symptom monitoring was the most consistently adopted app function. Patients also used Manage and Learn features, although less frequently, indicating some engagement with psychoeducational and self-management components. These patterns help clarify how the digital components of the hybrid care model were used in practice and how patients engaged with the intervention [38].

These findings should be interpreted in light of several limitations. First, the retrospective observational design precludes causal inference. Symptom improvements cannot be definitively attributed to the Digital Clinic rather than regression to the mean, spontaneous improvement, or concurrent treatment. Second, the peripartum sample was small, limiting statistical power and producing inconclusive estimates, particularly for depression outcomes and engagement-symptom correlations. Third, the sample may reflect selection bias. Patients referred to and enrolled in our hybrid care model may have had greater interest in digital care, higher baseline digital literacy, more reliable access to technology, or fewer barriers to virtual treatment than the broader population of peripartum patients [39, 40]. Fourth, the cohort was predominantly White and highly educated, limiting generalizability to patients with lower educational attainment, lower socioeconomic backgrounds, lower digital literacy, or reduced access to stable internet and smartphones. This is especially important because peripartum mental health disparities are often greatest among patients experiencing structural and socioeconomic adversity [41]. Fifth, outcomes were based on self-report measures, which may be affected by reporting bias, social desirability, or changes in symptom awareness over treatment. Sixth, engagement data were available for only a subset of peripartum patients, and app-use metrics may not fully capture the quality of therapeutic engagement or skill practice. Finally, the Digital Clinic’s multi-component model makes it difficult to determine which components were most responsible for observed changes.

Thus, future research should build on these findings using prospective, adequately powered, RCT designs. A larger trial should test whether the Digital Clinic improves anxiety and depressive symptoms compared with usual care, teletherapy alone, or app access with or without Digital Health Navigator support. Future studies should also examine which patients benefit most, including pregnant versus postpartum patients, patients with higher baseline symptom severity, and patients facing socioeconomic or digital access barriers. In addition, future work should evaluate mechanisms of change, including whether improvements in self-management, emotional awareness, social support, or functional impairment mediate symptom improvement. Finally, implementation studies are needed to determine how digital navigator support, app-based monitoring, and therapist-delivered care can be adapted for more diverse peripartum populations, particularly those with lower digital literacy or reduced access to specialty mental health care.

In conclusion, this preliminary matched retrospective cohort study provides early evidence that the Digital Clinic, a hybrid care model for maternal mental, may support treatment for anxiety and depressive symptoms in pregnant and postpartum women. This approach shows promise for delivering accessible, evidence-based care to peripartum patients in real-world settings. Future studies should evaluate its effectiveness in larger, more diverse patient populations, including those with moderate to severe symptoms.

Authors’ contributions

All authors contributed equally, helped write, reviewed, and approved the final manuscript.

Funding Statement

No funding was received for conducting this research study.

Data availability

The datasets generated and analyzed during the current study are not publicly available as they contain identifiable patient information.

Declarations

Consent for publication

Informed consent was obtained from all participants included in the study to publish project results.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Carnevali GS, Buoli M. The role of epigenetics in perinatal depression: Are there any candidate biomarkers? J Affect Disord. 2021;280(Pt B):57–67. 10.1016/j.jad.2020.11.056 . Epub 2020 Nov 13. PMID: 33221608. [DOI] [PubMed] [Google Scholar]
  • 2.Putnick DL, Sundaram R, Bell EM, Ghassabian A, Goldstein RB, Robinson SL, Vafai Y, Gilman SE, Yeung E. Trajectories of Maternal Postpartum Depressive Symptoms. Pediatrics. 2020;146(5):e20200857. 10.1542/peds.2020-0857 . PMID: 33109744; PMCID: PMC7772818. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Faisal-Cury A, Tabb KM, Ziebold C, Matijasevich A. The impact of postpartum depression and bonding impairment on child development at 12 to 15 months after delivery. J Affect Disord Rep. 2021;4:100125. 10.1016/j.jadr.2021.100125. [DOI] [Google Scholar]
  • 4.Reinert M, Fritze D, Nguyen T. The State of Mental Health in America 2024. Mental Health America; 2024 Jul. Available from: https://mhanational.org/research/state-of-mental-health-in-america-2024.
  • 5.Cox EQ, Sowa NA, Meltzer-Brody SE, Gaynes BN. The Perinatal Depression Treatment Cascade: Baby Steps Toward Improving Outcomes. J Clin Psychiatry. 2016;77(9):1189–1200. 10.4088/JCP.15r10174. PMID: 27780317. [DOI] [PubMed]
  • 6.Weingarten SJ, Osborne LM. Review of the Assessment and Management of Perinatal Mood and Anxiety Disorders. Focus (Am Psychiatr Publ). 2024;22(1):16–24. 10.1176/appi.focus.20230023. Epub 2024 Jan 12. PMID: 38694149; PMCID: PMC11058917. [DOI] [PMC free article] [PubMed]
  • 7.Miura Y, Ogawa Y, Shibata A, Kamijo K, Joko K, Aoki T. App-based interventions for the prevention of postpartum depression: a systematic review and meta-analysis. BMC Pregnancy Childbirth. 2023;23(1):441. 10.1186/s12884-023-05749-5 . PMID: 37316768; PMCID: PMC10265566. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Carrandi A, Hayman M, Harrison CL. Safety considerations for assessing the quality of apps used during pregnancy: A scoping review. Digit Health. 2023;9:20552076231198683. 10.1177/20552076231198683 . PMID: 37675058; PMCID: PMC10478559. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Varma DS, Mualem M, Goodin A, Gurka KK, Wen TS, Gurka MJ, Roussos-Ross K. Acceptability of an mHealth App for Monitoring Perinatal and Postpartum Mental Health: Qualitative Study With Women and Providers. JMIR Form Res. 2023;7:e44500. 10.2196/44500 . PMID: 37285185; PMCID: PMC10285622. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Evans K, Donelan J, Rennick-Egglestone S, Cox S, Kuipers Y. Review of Mobile Apps for Women With Anxiety in Pregnancy: Maternity Care Professionals’ Guide to Locating and Assessing Anxiety Apps. J Med Internet Res. 2022;24(3):e31831. 10.2196/31831 . PMID: 35319482; PMCID: PMC8987965. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Emerson M, Perret S, Chinn H, Alon N, Watanabe-Galloway S, Johnson DJ, Dinkel D, Torous J. A systematic review and exploration of smartphone app interventions for perinatal depression with case study. Curr Treat Options Psychiatry. 2023;10(3):136–66. 10.1007/s40501-023-00301-w. [DOI] [Google Scholar]
  • 12.Feldman N, Back D, Boland R, Torous J. A systematic review of mHealth application interventions for peripartum mood disorders: trends and evidence in academia and industry. Arch Womens Ment Health. 2021;24(6):881–92. 10.1007/s00737-021-01138-z . Epub 2021 Apr 30. PMID: 33929636; PMCID: PMC8085644. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Linardon J, Torous J, Firth J, Cuijpers P, Messer M, Fuller-Tyszkiewicz M. Current evidence on the efficacy of mental health smartphone apps for symptoms of depression and anxiety. A meta-analysis of 176 randomized controlled trials. World Psychiatry. 2024;23(1):139–49. 10.1002/wps.21183 . PMID: 38214614; PMCID: PMC10785982. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Calvert E, Cipriani M, Chen K, Dhima A, Burns J, Torous J. Evaluating clinical outcomes for anxiety and depression: A real-world comparison of the digital clinic and primary care. J Affect Disord. 2025;377:275–83. Epub 2025 Feb 21. PMID: 39988138. [DOI] [PubMed] [Google Scholar]
  • 15.Calvert E, Hackett K, Torous J, Giovannetti T. Barriers and facilitators to usability of a smartphone-based digital mental health tool in older adults: Insights from a secondary analysis of mindLAMP. Int Psychogeriatr. 2026;38(2):100123. 10.1016/j.inpsyc.2025.100123 . Epub 2025 Sep 8. PMID: 40925804. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Chang S, Gray L, Torous J. Smartphone app engagement and clinical outcomes in a hybrid clinic. Psychiatry Res. 2023;319:115015. 10.1016/j.psychres.2022.115015 . Epub 2022 Dec 18. PMID: 36549096. [DOI] [PubMed] [Google Scholar]
  • 17.Chen K, Huang JJ, Torous J. Hybrid care in mental health: a framework for understanding care, research, and future opportunities. NPP Digit Psychiatry Neurosci. 2024;2(1):16. 10.1038/s44277-024-00016-7 . PMID: 41360926; PMCID: PMC12624923. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Macrynikola N, Chen K, Lane E, Nguyen N, Pinto J, Yen S, Torous J. Testing the Feasibility, Acceptability, and Potential Efficacy of an Innovative Digital Mental Health Care Delivery Model Designed to Increase Access to Care: Open Trial of the Digital Clinic. JMIR Ment Health. 2025;12:e65222. 10.2196/65222 . PMID: 39879612; PMCID: PMC11822323. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Polinski JM, Barker T, Gagliano N, Sussman A, Brennan TA, Shrank WH. Patients’ Satisfaction with and Preference for Telehealth Visits. J Gen Intern Med. 2016;31(3):269–75. 10.1007/s11606-015-3489-x . Epub 2015 Aug 13. PMID: 26269131; PMCID: PMC4762824. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Swartz HA, Bylsma LM, Fournier JC, Girard JM, Spotts C, Cohn JF, Morency LP. Randomized trial of brief interpersonal psychotherapy and cognitive behavioral therapy for depression delivered both in-person and by telehealth. J Affect Disord. 2023;333:543–52. 10.1016/j.jad.2023.04.092 . Epub 2023 Apr 28. PMID: 37121279; PMCID: PMC10228570. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Barlow DH, Harris BA, Eustis EH, Farchione TJ. The unified protocol for transdiagnostic treatment of emotional disorders. World Psychiatry. 2020;19(2):245–6. 10.1002/wps.20748 . PMID: 32394551; PMCID: PMC7215073. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Chen K, Lane E, Burns J, Macrynikola N, Chang S, Torous J. The Digital Navigator: Standardizing Human Technology Support in App-Integrated Clinical Care. Telemed J E Health. 2024;30(7):e1963-e1970. 10.1089/tmj.2024.0023. Epub 2024 Apr 4. PMID: 38574251. [DOI] [PubMed]
  • 23.Vaidyam A, Halamka J, Torous J. Enabling Research and Clinical Use of Patient-Generated Health Data (the mindLAMP Platform): Digital Phenotyping Study. JMIR Mhealth Uhealth. 2022;10(1):e30557. 10.2196/30557 . PMID: 34994710; PMCID: PMC8783287. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13. 10.1046/j.1525-1497.2001.016009606.x . PMID: 11556941; PMCID: PMC1495268. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Zhong QY, Gelaye B, Zaslavsky AM, Fann JR, Rondon MB, Sánchez SE, Williams MA. Diagnostic Validity of the Generalized Anxiety Disorder – 7 (GAD-7) among Pregnant Women. PLoS ONE. 2015;10(4):e0125096. 10.1371/journal.pone.0125096 . PMID: 25915929; PMCID: PMC4411061. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Gruber-Baldini AL, Velozo C, Romero S, Shulman LM. Validation of the PROMIS® measures of self-efficacy for managing chronic conditions. Qual Life Res. 2017;26(7):1915–24. 10.1007/s11136-017-1527-3 . Epub 2017 Feb 26. PMID: 28239781; PMCID: PMC5479750. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Zimet GD, Dahlem NW, Zimet SG, Farley GK. The multidimensional scale of perceived social support. J Pers Assess. 1988;52(1):30–41. 10.1207/s15327752jpa5201_2. [DOI] [PubMed] [Google Scholar]
  • 28.Luciano JV, Bertsch J, Salvador-Carulla L, Tomás JM, Fernández A, Pinto-Meza A, Haro JM, Palao DJ, Serrano-Blanco A. Factor structure, internal consistency and construct validity of the Sheehan Disability Scale in a Spanish primary care sample. J Eval Clin Pract. 2010;16(5):895–901. 10.1111/j.1365-2753.2009.01211.x. PMID: 20626541. [DOI] [PubMed]
  • 29.Petkov J, Harvey P, Battersby M. The internal consistency and construct validity of the partners in health scale: validation of a patient rated chronic condition self-management measure. Qual Life Res. 2010;19(7):1079–85. 10.1007/s11136-010-9661-1 . Epub 2010 May 1. PMID: 20437206. [DOI] [PubMed] [Google Scholar]
  • 30.Nelson LA, Pennings JS, Sommer EC, Popescu F, Barkin SL. A 3-Item Measure of Digital Health Care Literacy: Development and Validation Study. JMIR Form Res. 2022;6(4):e36043. 10.2196/36043 . PMID: 35486413; PMCID: PMC9107049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. New York: Routledge; 1988. 10.4324/9780203771587. [DOI] [Google Scholar]
  • 32.Food US, and Drug Administration (FDA). 510(k) Premarket Notification: MamaLift Plus (K223515) [Internet]. Silver Spring (MD): FDA; 2024. Available from: https://www.accessdata.fda.gov.  Cited 2025 February 5.
  • 33.Byatt N, Levin LL, Ziedonis D, Moore Simas TA, Allison J. Enhancing Participation in Depression Care in Outpatient Perinatal Care Settings: A Systematic Review. Obstet Gynecol. 2015;126(5):1048–58. PMID: 26444130; PMCID: PMC4618720. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Koire A, Suleiman M, Teslyar P, Liu CH. Prevalence of Community Perinatal Psychiatrists in the US. JAMA Netw Open. 2024;7(8):e2426465. 10.1001/jamanetworkopen.2024.26465 . PMID: 39110463; PMCID: PMC11307129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Feldman N, Perret S. Digital mental health for postpartum women: perils, pitfalls, and promise. NPJ Digit Med. 2023;6(1):11. 10.1038/s41746-023-00756-4 . PMID: 36702915; PMCID: PMC9879244. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Carlson K, Mughal S, Azhar Y, Siddiqui W, Perinatal Depression. 2025 Jan 22. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2026 Jan–. PMID: 30085612. [PubMed]
  • 37.Dinkel D, Harsh Caspari J, Fok L, Notice M, Johnson DJ, Watanabe-Galloway S, Emerson M. A qualitative exploration of the feasibility of incorporating depression apps into integrated primary care clinics. Transl Behav Med. 2021;11(9):1708–1716. 10.1093/tbm/ibab075. PMID: 34231855. [DOI] [PubMed]
  • 38.Calvert E, Lane E, Flathers M, Torous J. LINC: a framework for maintaining high-quality passive data in digital phenotyping studies. Sci Rep. 2026;16(1):10160. 10.1038/s41598-026-41435-0 . PMID: 41724791; PMCID: PMC13022485. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Hoffman L, Wisniewski H, Hays R, Henson P, Vaidyam A, Hendel V, Keshavan M, Torous J. Digital Opportunities for Outcomes in Recovery Services (DOORS): A Pragmatic Hands-On Group Approach Toward Increasing Digital Health and Smartphone Competencies, Autonomy, Relatedness, and Alliance for Those With Serious Mental Illness. J Psychiatr Pract. 2020;26(2):80–8. PMID: 32134881; PMCID: PMC7135933. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Emerson MR, Buckland S, Lawlor MA, Dinkel D, Johnson DJ, Mickles MS, Fok L, Watanabe-Galloway S. Addressing and evaluating health literacy in mHealth: a scoping review. Mhealth. 2022;8:33. 10.21037/mhealth-22-11. PMID: 36338314; PMCID: PMC9634204. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Emerson MR, Harsh Caspari J, Notice M, Watanabe-Galloway S, Dinkel D, Kabayundo J. Mental health mobile app use: Considerations for serving underserved patients in integrated primary care settings. Gen Hosp Psychiatry. 2021;69:67–75. 10.1016/j.genhosppsych.2021.01.008. Epub 2021 Jan 27. PMID: 33571926. [DOI] [PubMed]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets generated and analyzed during the current study are not publicly available as they contain identifiable patient information.

Articles from BMC Pregnancy and Childbirth are provided here courtesy of BMC

RESOURCES