Factors affecting the implementation of employee whole health in the veterans health administration: a qualitative evaluation.

BACKGROUND: There is increasing recognition of the need to focus on the health and well-being of healthcare employees given high rates of burnout and turnover. Employee wellness programs are effective at addressing these issues; however, participation in these programs is often a challenge and requires large scale organizational transformation. The Veterans Health Administration (VA) has begun to roll out their own employee wellness program-Employee Whole Health (EWH)-focused on the holistic needs of all employees. This evaluation's goal was to use the Lean Enterprise Transformation (LET) model for organizational transformation to identify key factors-facilitators and barriers-affecting the implementation of VA EWH. METHODS: This cross-sectional qualitative evaluation based on the action research model reflects on the organizational implementation of EWH. Semi-structured 60-minute phone interviews were conducted in February-April 2021 with 27 key informants (e.g., EWH coordinator, wellness/occupational health staff) knowledgeable about EWH implementation across 10 VA medical centers. Operational partner provided a list of potential participants, eligible because of their involvement in EWH implementation at their site. The interview guide was informed by the LET model. Interviews were recorded and professionally transcribed. Constant comparative review with a combination of a priori coding based on the model and emergent thematic analysis was used to identify themes from transcripts. Matrix analysis and rapid turnaround qualitative methods were used to identify cross-site factors to EWH implementation. RESULTS: Eight common factors in the conceptual model were found to facilitate and/or hinder EWH implementation efforts: [1] EWH initiatives, [2] multilevel leadership support, [3] alignment, [4] integration, [5] employee engagement, [6] communication, [7] staffing, and [8] culture. An emergent factor was [9] the impact of the COVID-19 pandemic on EWH implementation. CONCLUSIONS: As VA expands its EWH cultural transformation nationwide, evaluation findings can (a) enable existing programs to address known implementation barriers, and (b) inform new sites to capitalize on known facilitators, anticipate and address barriers, and leverage evaluation recommendations through concerted implementation at the organization, process, and employee levels to jump-start their EWH program implementation.

Changes in Burnout and Moral Distress Among Veterans Health Administration (VA) Physicians Before and During the COVID-19 Pandemic.

BACKGROUND: We investigated the impacts of workload, resources, organizational satisfaction, and psychological safety on changes in physician burnout and moral distress among physicians during the early pandemic. METHODS: We obtained national administrative and survey data on burnout, moral distress, organizational satisfaction, psychological safety, COVID-19 burden, and state-level restrictions for 11,877-14,246 Veterans Health Administration (VA) physicians from 2019 and 2020. We regressed the changes in burnout and moral distress on the changes in reasonable workload, appropriate job resources, organizational satisfaction, and psychological safety, controlling for COVID-19 burden and restrictions, and individual and medical center characteristics. RESULTS: Burnout and moral distress were not related to COVID-19 cases or restrictions but were reduced by improvements in workload, organizational satisfaction, and psychological safety. CONCLUSIONS: Health systems should be conscious of factors that can harm or improve physician well-being, especially in the context of external stressors.

Organizational Readiness for Patient-Facing Mobile Teledermatology to Care for Established Veteran Patients in the United States.

Purpose: To improve patient access to skin care, the Department of Veterans Affairs (VA) developed a patient-facing asynchronous mobile teledermatology application (app), which allows patients to follow up remotely with dermatologists. To understand how the app would be received in VA, we examined Organizational Readiness for Change (ORC), an important prelude to effective implementation, which includes the shared resolve and collective ability of organizational members to implement a change. Methods: We used a mixed-methods multiple case study approach to assess ORC at three VA facilities. Data derived from a site process call, surveys, and semistructured telephone interviews of VA staff, field notes, and administrative data. Results: Participants at all three facilities supported the intervention and recognized the value of using the app to increase patients' access to dermatologists, but expressed concerns largely related to disruption of the pre-existing clinical workflow. Participants at the facility most actively using the app had the highest overall ORC score and reported the most facilitators. Facility leadership support when guided by a clinical champion minimized barriers by recognizing the complexities of health care provision at specialty clinics. Discussion: While provider buy-in remained a barrier, leadership, guided by the clinical champion, played a critical role instituting implementation strategies. The strong association between the ORC survey score and the presence of facilitators and barriers suggests that the ORC survey may be a rapid, convenient, and effective tool for health care systems to identify favorable sites for wider implementation of mobile telehealth care. Clinical Trials Identifier: NCT03241589.

Biopsychosocial Response to the COVID-19 Lockdown in People with Major Depressive Disorder and Multiple Sclerosis.

BACKGROUND: Changes in lifestyle, finances and work status during COVID-19 lockdowns may have led to biopsychosocial changes in people with pre-existing vulnerabilities such as Major Depressive Disorders (MDDs) and Multiple Sclerosis (MS). METHODS: Data were collected as a part of the RADAR-CNS (Remote Assessment of Disease and Relapse-Central Nervous System) program. We analyzed the following data from long-term participants in a decentralized multinational study: symptoms of depression, heart rate (HR) during the day and night; social activity; sedentary state, steps and physical activity of varying intensity. Linear mixed-effects regression analyses with repeated measures were fitted to assess the changes among three time periods (pre, during and post-lockdown) across the groups, adjusting for depression severity before the pandemic and gender. RESULTS: Participants with MDDs (N = 255) and MS (N = 214) were included in the analyses. Overall, depressive symptoms remained stable across the three periods in both groups. A lower mean HR and HR variation were observed between pre and during lockdown during the day for MDDs and during the night for MS. HR variation during rest periods also decreased between pre- and post-lockdown in both clinical conditions. We observed a reduction in physical activity for MDDs and MS upon the introduction of lockdowns. The group with MDDs exhibited a net increase in social interaction via social network apps over the three periods. CONCLUSIONS: Behavioral responses to the lockdown measured by social activity, physical activity and HR may reflect changes in stress in people with MDDs and MS. Remote technology monitoring might promptly activate an early warning of physical and social alterations in these stressful situations. Future studies must explore how stress does or does not impact depression severity.

A qualitative examination of the implementation of a perinatal collaborative care program.

AIM: To identify implementation strategies for collaborative care (CC) that are successful in the context of perinatal care. BACKGROUND: Perinatal depression is one of the most common complications of pregnancy and is associated with adverse maternal, obstetric, and neonatal outcomes. Although treating depressive symptoms reduces risks to mom and baby, barriers to accessing psychiatric treatment remain. CC has demonstrated benefit in primary care, expanding access, yet few studies have examined the implementation of CC in perinatal care which presents unique characteristics and challenges. METHODS: We conducted qualitative interviews with 20 patients and 10 stakeholders from Collaborative Care Model for Perinatal Depression Support Services (COMPASS), a perinatal collaborative care (pCC) program implemented since 2017. We analyzed interview data by employing the Exploration, Preparation, Implementation, Sustainment (EPIS) framework to organize empirically selected implementation strategies from Expert Recommendations for Implementing Change (ERIC) to create a guide for the development of pCC programs. FINDINGS: We identified 14 implementation strategies used in the implementation of COMPASS. Strategies were varied, cutting across ERIC domains (eg, plan, educate, finance) and across EPIS contexts (eg, inner context - characteristics of the pCC program). The majority of strategies were identified by patients and staff as facilitators of pCC implementation. In addition, findings show opportunities for improving the implementation strategies used, such as optimal dissemination of educational materials for obstetric clinicians. The implementation of COMPASS can serve as a model for the process of building a pCC program. The identified strategies can support the implementation of this evidence-based practice for addressing postpartum depression.

Psychometric validity and reliability of the 10- and 2-item Connor-Davidson resilience scales among a national sample of Americans responding to the Covid-19 pandemic: an item response theory analysis.

PURPOSE: Psychometric validity/reliability of 10-item and 2-item abbreviations of the Connor-Davidson Resilience Scale (CD-RISC-10; CD-RISC-2) was investigated via item response theory and classic approaches. METHODS: We sampled 5023 adult American participants in a June/July 2020 survey on the COVID-19 pandemic's psychological effects. Our questionnaire incorporated the CD-RISC-10 with other validated measures. CD-RISC-10 items were ranked on item-to-scale correlations, loadings on a one-factor confirmatory factor analysis model, and item slope/threshold parameters plus information curves from a unidimensional graded response model. Concurrent validity of the highest ranked item pair was evaluated vis-à-vis the CD-RISC-10 and CD-RISC-2. Internal consistency, based on average variance extracted (AVE) and multiple reliability coefficients, was also compared. Convergent/divergent validity was tested by correlating anxiety, depression, fear of COVID-19, anxiety sensitivity, coping, and personality measures with both scales and the highest ranked item pair. Binary agreement/classification indexes assessed inter-rater reliability. RESULTS: Items 2 and 9 from CD-RISC-10 ranked the highest. Reliability coefficients were > 0.93, > 0.72, and > 0.82 for the CD-RISC-10, CD-RISC-2, vs summation of items 2 and 9. AVEs were 0.66, 0.67, and 0.77. CD-RISC abbreviations and the summation of items 2 and 9 correlated negatively with anxiety (> - 0.43), depression (> - 0.42), and fear of COVID-19 (> - 0.34); positively with emotional stability (> 0.53) and conscientiousness (> 0.40). Compared to the CD-RISC-2, summative scores of items 2 and 9 more efficiently classified/discriminated high resilience on the CD-RISC-10. CONCLUSION: We confirmed construct validity/reliability of copyrighted CD-RISC abbreviations. The CD-RISC-10's items 2 and 9 were psychometrically more salient than the CD-RISC-2.

Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study.

BACKGROUND: The mobility of an individual measured by phone-collected location data has been found to be associated with depression; however, the longitudinal relationships (the temporal direction of relationships) between depressive symptom severity and phone-measured mobility have yet to be fully explored. OBJECTIVE: We aimed to explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time. METHODS: Data used in this paper came from a major EU program, called the Remote Assessment of Disease and Relapse-Major Depressive Disorder, which was conducted in 3 European countries. Depressive symptom severity was measured with the 8-item Patient Health Questionnaire (PHQ-8) through mobile phones every 2 weeks. Participants' location data were recorded by GPS and network sensors in mobile phones every 10 minutes, and 11 mobility features were extracted from location data for the 2 weeks prior to the PHQ-8 assessment. Dynamic structural equation modeling was used to explore the longitudinal relationships between depressive symptom severity and phone-measured mobility. RESULTS: This study included 2341 PHQ-8 records and corresponding phone-collected location data from 290 participants (age: median 50.0 IQR 34.0, 59.0) years; of whom 215 (74.1%) were female, and 149 (51.4%) were employed. Significant negative correlations were found between depressive symptom severity and phone-measured mobility, and these correlations were more significant at the within-individual level than the between-individual level. For the direction of relationships over time, Homestay (time at home) (φ=0.09, P=.01), Location Entropy (time distribution on different locations) (φ=-0.04, P=.02), and Residential Location Count (reflecting traveling) (φ=0.05, P=.02) were significantly correlated with the subsequent changes in the PHQ-8 score, while changes in the PHQ-8 score significantly affected (φ=-0.07, P<.001) the subsequent periodicity of mobility. CONCLUSIONS: Several phone-derived mobility features have the potential to predict future depression, which may provide support for future clinical applications, relapse prevention, and remote mental health monitoring practices in real-world settings.

A call for open data to develop mental health digital biomarkers.

Digital biomarkers of mental health, created using data extracted from everyday technologies including smartphones, wearable devices, social media and computer interactions, have the opportunity to revolutionise mental health diagnosis and treatment by providing near-continuous unobtrusive and remote measures of behaviours associated with mental health symptoms. Machine learning models process data traces from these technologies to identify digital biomarkers. In this editorial, we caution clinicians against using digital biomarkers in practice until models are assessed for equitable predictions ('model equity') across demographically diverse patients at scale, behaviours over time, and data types extracted from different devices and platforms. We posit that it will be difficult for any individual clinic or large-scale study to assess and ensure model equity and alternatively call for the creation of a repository of open de-identified data for digital biomarker development.

Uptake and effectiveness of a self-guided mobile app platform for college student mental health.

BACKGROUND: College students endorse high rates of mental health problems. While many colleges offer on-campus services, many students who could benefit from mental health services do not receive care. Indeed, nearly half of students who screen positive for depression, for example, do not receive treatment. Digital mental health programs, such as those delivered via mobile apps, may help expand access to mental health care and resources. This mixed-methods study aims to examine the uptake and effectiveness of an implementation of IntelliCare for College Students, a self-guided app-based mental health platform, on two university campuses. METHODS: Data on counseling center utilization was collected prior to the implementation of the app (pre-implementation phase) and while the app was available on campus (implementation phase). Data on app usage was collected throughout the implementation phase. A subset of participants (n = 20), along with counseling center staff members (n = 10), completed feedback interviews. RESULTS: Overall, uptake of the app platform was low. A total of 117 participants downloaded the app and registered their study ID during the implementation phase. Approximately 24% (28/117) of participants used the app only once. The number of days between the first and last day of app use ranged from 0 to 299, with a mean of 35.01 days and a median of 14 days. A relatively small portion of the sample (26.5%; 31/117) downloaded one or more of the IntelliCare interactive apps. In examining counseling center utilization, there were no significant changes in intake appointments, individual therapy sessions, or crisis appointments observed from the pre-implementation phase to the implementation phase of the study. Feedback interviews highlighted the significant level of disruption caused by the COVID-19 pandemic and shift to remote learning, including challenges disseminating information to students and a preference to spend less time with digital devices outside of class time. CONCLUSIONS: Findings from this study indicate that there is an ongoing need to identify ways to reach college students and support student mental health and wellness for the remainder of the COVID-19 pandemic and beyond.

The relationship between text message sentiment and self-reported depression.

BACKGROUND: Personal sensing has shown promise for detecting behavioral correlates of depression, but there is little work examining personal sensing of cognitive and affective states. Digital language, particularly through personal text messages, is one source that can measure these markers. METHODS: We correlated privacy-preserving sentiment analysis of text messages with self-reported depression symptom severity. We enrolled 219 U.S. adults in a 16 week longitudinal observational study. Participants installed a personal sensing app on their phones, which administered self-report PHQ-8 assessments of their depression severity, collected phone sensor data, and computed anonymized language sentiment scores from their text messages. We also trained machine learning models for predicting end-of-study self-reported depression status using on blocks of phone sensor and text features. RESULTS: In correlation analyses, we find that degrees of depression, emotional, and personal pronoun language categories correlate most strongly with self-reported depression, validating prior literature. Our classification models which predict binary depression status achieve a leave-one-out AUC of 0.72 when only considering text features and 0.76 when combining text with other networked smartphone sensors. LIMITATIONS: Participants were recruited from a panel that over-represented women, caucasians, and individuals with self-reported depression at baseline. As language use differs across demographic factors, generalizability beyond this population may be limited. The study period also coincided with the initial COVID-19 outbreak in the United States, which may have affected smartphone sensor data quality. CONCLUSIONS: Effective depression prediction through text message sentiment, especially when combined with other personal sensors, could enable comprehensive mental health monitoring and intervention.

Investigating the impact of COVID-19 lockdown on adults with a recent history of recurrent major depressive disorder: a multi-Centre study using remote measurement technology.

BACKGROUND: The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes a clinical illness Covid-19, has had a major impact on mental health globally. Those diagnosed with major depressive disorder (MDD) may be negatively impacted by the global pandemic due to social isolation, feelings of loneliness or lack of access to care. This study seeks to assess the impact of the 1st lockdown - pre-, during and post - in adults with a recent history of MDD across multiple centres. METHODS: This study is a secondary analysis of an on-going cohort study, RADAR-MDD project, a multi-centre study examining the use of remote measurement technology (RMT) in monitoring MDD. Self-reported questionnaire and passive data streams were analysed from participants who had joined the project prior to 1st December 2019 and had completed Patient Health and Self-esteem Questionnaires during the pandemic (n = 252). We used mixed models for repeated measures to estimate trajectories of depressive symptoms, self-esteem, and sleep duration. RESULTS: In our sample of 252 participants, 48% (n = 121) had clinically relevant depressive symptoms shortly before the pandemic. For the sample as a whole, we found no evidence that depressive symptoms or self-esteem changed between pre-, during- and post-lockdown. However, we found evidence that mean sleep duration (in minutes) decreased significantly between during- and post- lockdown (- 12.16; 95% CI - 18.39 to - 5.92; p <  0.001). We also found that those experiencing clinically relevant depressive symptoms shortly before the pandemic showed a decrease in depressive symptoms, self-esteem and sleep duration between pre- and during- lockdown (interaction p = 0.047, p = 0.045 and p <  0.001, respectively) as compared to those who were not. CONCLUSIONS: We identified changes in depressive symptoms and sleep duration over the course of lockdown, some of which varied according to whether participants were experiencing clinically relevant depressive symptoms shortly prior to the pandemic. However, the results of this study suggest that those with MDD do not experience a significant worsening in symptoms during the first months of the Covid - 19 pandemic.

Targeted eHealth Intervention to Reduce Breast Cancer Survivors' Fear of Recurrence: Results From the FoRtitude Randomized Trial.

BACKGROUND: Fear of recurrence (FoR) is a prevalent concern among breast cancer survivors (BCS), yet few accessible interventions exist. This study evaluated a targeted eHealth intervention, "FoRtitude," to reduce FoR using cognitive behavioral skills training and telecoaching. METHODS: BCS (N = 196) were recruited from an academic medical center and 3 National Cancer Institute Community Oncology Research Program community sites, had stage 0-III breast cancer, were 1-10 years postprimary treatment, with moderate to high FoR and familiarity with the internet. Using the Multiphase Optimization Strategy, participants were independently randomly assigned to 3 cognitive behavioral skills (relaxation, cognitive restructuring, worry practice) vs an attention control condition (health management content [HMC]) and to telecoaching (motivational interviewing) vs no telecoaching. Website content was released across 4 weeks and included didactic lessons, interactive tools, and a text-messaging feature. BCS completed the Fear of Cancer Recurrence Inventory at baseline and at 4 and 8 weeks. Fear of Cancer Recurrence Inventory scores over time were compared using mixed-effects models. All statistical tests were 2-sided. RESULTS: FCRI scores [SD] decreased statistically significantly from baseline to postintervention (T0 = 53.1 [17.4], T2 = 41.9 [16.2], P < .001). The magnitude of reduction in FCRI scores was comparable across cognitive behavior therapy (CBT) and attention control HMC conditions and was predicted by increased self-efficacy. Telecoaching was associated with lower attrition and greater website use (mean adherence score [SD] = 26.6 [7.2] vs 21.0 [10.5], P < .001). CONCLUSIONS: BCS experienced statistically significant reductions in FoR postintervention, but improvements were comparable between CBT and attention controls. Telecoaching improved adherence and retention. Future research is needed on optimal integration of CBT and HMC, dose, and features of eHealth delivery that contributed to reducing FoR. In the COVID-19 era, remote delivery has become even more essential for reaching survivors struggling with FoR.