Digital Engagement Strategy and Health Care Worker Mental Health: A Randomized Clinical Trial.

Importance: The health care workforce continues to experience high rates of depression and anxiety. Finding ways to effectively support the mental health and well-being of health care workers is challenging. Objective: To test the effectiveness of remote, pushed digital assessments and engagement to improve depression and anxiety among health care workers compared with usual care. Design, Setting, and Participants: This was a 9-month randomized clinical trial with a 6-month intervention period. Participants were health care workers with self-reported daily access to a smartphone and at least 4 clinical hours per week. Participants were randomized to usual care or the intervention between January 2022 and March 2023. Data analyses were conducted between May and July 2023. Interventions: All participants completed baseline, 6-month, and 9-month mental health, well-being, and burnout assessments. The control group had open access to a web-based mental health platform. Participants in the intervention group received monthly text messaging about mental health, mental health assessments, and linkages to care. Main Outcomes and Measures: The primary outcomes were mean change in depression and anxiety scores at 6 months from baseline. Secondary outcomes include mean change in well-being, burnout, and self-reported workplace productivity. Results: In this study, 1275 participants were randomized (642 [50.4%] to the intervention group and 633 [49.6%] to control group). Participants had a mean (SD) age of 38.6 (10.9) years, 1063 participants (83.4%) were female, 320 (25.1%) self-identified as Black, and 793 (62.2%) self-identified as White. Across the groups, the mean difference in depression score was significantly different at 6 months (-0.96 [95% CI, -1.52 to -0.40]) and at 9 months (-1.14 [95% CI, -1.69 to -0.58]). The mean difference in anxiety score from baseline to 6 months was statistically significantly larger for those in the intervention group vs usual care (-0.71 [95% CI, -1.25 to -0.17]) and held true at 9 months (-1.06 [95% CI, -1.59 to -0.52]). Conclusions and Relevance: In a trial of health care workers, a proactive digital engagement strategy, including pushed text messaging, mobile mental health assessments, and connection to care, improved depression and anxiety over a 6-month period compared with simply making the same resources available for individuals to find and use. Trial Registration: ClinicalTrials.gov Identifier: NCT05028075.

Qualitative analysis of a remote monitoring intervention for managing heart failure.

BACKGROUND: Heart failure (HF) is one of the most common reasons for hospital admission and is a major cause of morbidity, mortality, and increasing health care costs. The EMPOWER study was a randomized trial that used remote monitoring technology to track patients' weight and diuretic adherence and a state-of-the-art approach derived from behavioral economics to motivate adherence to the reverse monitoring technology. OBJECTIVE: The goal was to explore patient and clinician perceptions of the program and its impact on perceived health outcomes and better understand why some patients or clinicians did better or worse than others in response to the intervention. APPROACH: This was a retrospective qualitative study utilizing semi-structured interviews with 43 patients and 16 clinicians to understand the trial's processes, reflecting on successes and areas for improvement for future iterations of behavioral economic interventions. KEY RESULTS: Many patients felt supported, and they appreciated the intervention. Many also appreciated the lottery intervention, and while it was not an incentive for enrolling for many respondents, it may have increased adherence during the study. Clinicians felt that the intervention integrated well into their workflow, but the number of alerts was burdensome. Additionally, responses to alerts varied considerably by provider, perhaps because there are no professional guidelines for alerts unaccompanied by severe symptoms. CONCLUSION: Our qualitative analysis indicates potential areas for additional exploration and consideration to design better behavioral economic interventions to improve cardiovascular health outcomes for patients with HF. Patients appreciated lottery incentives for adhering to program requirements; however, many were too far along in their disease progression to benefit from the intervention. Clinicians found the amount and frequency of electronic alerts burdensome and felt they did not improve patient outcomes. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02708654.

Qualitative Analysis of a Remote Monitoring Intervention for Managing Heart Failure.

Background : Heart failure (HF) is one of the most common reasons for hospital admission and is a major cause of morbidity, mortality, and increasing health care costs. The EMPOWER study was a randomized trial that used remote monitoring technology to track patients' weight and diuretic adherence and a state-of-the-art approach derived from behavioral economics to motivate adherence to the reverse monitoring technology. Objective : The goal was to explore patient and clinician perceptions of the program and its impact on health outcomes and better understand why some patients/clinicians did better/worse than others in response to the intervention. Approach : This was a retrospective qualitative study to understand the trial's processes, reflecting on successes and areas for improvement for future iterations of behavioral economic interventions. Key Results: Many patients felt supported, and they appreciated the intervention. Many also appreciated the lottery intervention, and while it was not an incentive for enrolling for many respondents, it may have increased adherence during the study. Clinicians felt that the intervention integrated well into their workflow, but the number of alerts was burdensome. Additionally, responses to alerts varied considerably by provider, perhaps because there are no professional guidelines for alerts unaccompanied by severe symptoms. Conclusion : Those interviews offer insights into the potential reasons for the study's null result and opportunities for improvements in the future. Trial Registration: ClinicalTrials.gov Identifier: NCT02708654.

Using Clinical Trial Data to Estimate the Costs of Behavioral Interventions for Potential Adopters: A Guide for Trialists.

Behavioral interventions involving electronic devices, financial incentives, gamification, and specially trained staff to encourage healthy behaviors are becoming increasingly prevalent and important in health innovation and improvement efforts. Although considerations of cost are key to their wider adoption, cost information is lacking because the resources required cannot be costed using standard administrative billing data. Pragmatic clinical trials that test behavioral interventions are potentially the best and often only source of cost information but rarely incorporate costing studies. This article provides a guide for researchers to help them collect and analyze, during the trial and with little additional effort, the information needed to inform potential adopters of the costs of adopting a behavioral intervention. A key challenge in using trial data is the separation of implementation costs, the costs an adopter would incur, from research costs. Based on experience with 3 randomized clinical trials of behavioral interventions, this article explains how to frame the costing problem, including how to think about costs associated with the control group, and describes methods for collecting data on individual costs: specifications for costing a technology platform that supports the specialized functions required, how to set up a time log to collect data on the time staff spend on implementation, and issues in getting data on device, overhead, and financial incentive costs.

What's behind the white coat: Potential mechanisms of physician-attributable variation in critical care.

BACKGROUND: Critical care intensity is known to vary across regions and centers, yet the mechanisms remain unidentified. Physician behaviors have been implicated in the variability of intensive care near the end of life, but physician characteristics that may underlie this association have not been determined. PURPOSE: We sought to identify behavioral attributes that vary among intensivists to generate hypotheses for mechanisms of intensivist-attributable variation in critical care delivery. METHODS: We administered a questionnaire to intensivists who participated in a prior cohort study in which intensivists made prognostic estimates. We evaluated the degree to which scores on six attribute measures varied across intensivists. Measures were selected for their relevance to preference-sensitive critical care: a modified End-of-Life Preferences (EOLP) scale, Life Orientation Test-Revised (LOT-R), Jefferson Scale of Empathy (JSE), Physicians' Reactions to Uncertainty (PRU) scale, Collett-Lester Fear of Death (CLFOD) scale, and a test of omission bias. We conducted regression analyses assessing relationships between intensivists' attribute scores and their prognostic accuracy, as physicians' prognostic accuracy may influence preference-sensitive decisions. RESULTS: 20 of 25 eligible intensivists (80%) completed the questionnaire. Intensivists' scores on the EOLP, LOT-R, PRU, CLFOD, and omission bias measures varied considerably, while their responses on the JSE scale did not. There were no consistent associations between attribute scores and prognostic accuracy. CONCLUSIONS: Intensivists vary in feasibly measurable attributes relevant to preference-sensitive critical care delivery. These attributes represent candidates for future research aimed at identifying mechanisms of clinician-attributable variation in critical care and developing effective interventions to reduce undue variation.

Active Choice Intervention Increases Advance Directive Completion: A Randomized Trial.

Background. Many people recognize the potential benefits of advance directives (ADs), yet few actually complete them. It is unknown whether an active choice intervention influences AD completion. Methods. New employees were randomized to an active choice intervention (n = 642) or usual care (n = 637). The active choice intervention asked employees to complete an AD, confirm prior AD completion, or fill out a declination form. In usual care, participants could complete an AD, confirm prior completion, or skip the task. We used multivariable logistic regression to assess the relationship between the intervention arm and both AD completion online and the return of a signed AD by mail, as well as assess interactions between intervention group and age, gender, race, and clinical degree status. Results. Participants assigned to the active choice intervention more commonly completed ADs online (35.1% v. 20.4%, P < 0.001) (odds ratio [OR] = 2.10; 95% confidence interval [CI] = 1.63-2.71; number needed to treat = 6.8) and returned signed ADs by mail (7.8% v. 3.9%, P = 0.003; number needed to treat = 25.6). The effect of the intervention was significantly greater among men (OR = 4.13; 95% CI = 2.32-7.35) than among women (OR = 1.74; 95% CI = 1.30-2.32) (interaction P value < 0.001). Responses to all eight choices made in the ADs were similar between groups (all P > 0.10). Limitations. A young and healthy participant may not benefit from AD completion as an older or seriously ill patient would. Conclusions. The active choice intervention significantly increased the proportion of participants completing an AD without changing the choices in ADs. This relationship was especially strong among men and may be a useful method to increase AD completion rates without altering choices.