Resident physician depression: systemic challenges and possible solutions.

Resident physicians face intense stressors that significantly heighten their depression risk. This article discusses research findings on critical factors contributing to depression among resident physicians. Understanding these factors is essential to developing targeted interventions, fostering healthy work environments, and ultimately improving physician wellbeing and patient care.

Comparative Effectiveness of Three Digital Interventions for Adults Seeking Psychiatric Services: A Randomized Clinical Trial.

Importance: There is a substantial gap between demand for and availability of mental health services. Digital mental health interventions (DMHIs) are promising tools for bridging this gap, yet little is known about their comparative effectiveness. Objective: To assess whether patients randomized to a cognitive behavioral therapy (CBT)-based or mindfulness-based DMHI had greater improvements in mental health symptoms than patients randomized to the enhanced personalized feedback (EPF)-only DMHI. DESIGN,: SETTING, AND PARTICIPANTS This randomized clinical trial was conducted between May 13, 2020, and December 12, 2022, with follow-up at 6 weeks. Adult patients of outpatient psychiatry services across various clinics within the University of Michigan Health System with a scheduled or recent outpatient psychiatry appointment were recruited. Eligible patients were randomized to an intervention arm. All analyses followed the intent-to-treat principle. Interventions: Participants were randomized to 1 of 5 intervention arms: (1) EPF only; (2) Silvercloud only, a mobile application designed to deliver CBT strategies; (3) Silvercloud plus EPF; (4) Headspace only, a mobile application designed to train users in mindfulness practices; and (5) Headspace plus EPF. Main Outcomes and Measures: The primary outcome was change in depressive symptoms as measured by the Patient Health Questionnaire-9 (PHQ-9; score range: 0-27, with higher scores indicating greater depression symptoms). Secondary outcomes included changes in anxiety, suicidality, and substance use symptoms. Results: A total of 2079 participants (mean [SD] age, 36.8 [14.3] years; 1423 self-identified as women [68.4%]) completed the baseline survey. The baseline mean (SD) PHQ-9 score was 12.7 (6.4) and significantly decreased for all 5 intervention arms at 6 weeks (from -2.1 [95% CI, -2.6 to -1.7] to -2.9 [95% CI, -3.4 to -2.4]; n = 1885). The magnitude of change was not significantly different across the 5 arms (F4,1879 = 1.19; P = .31). Additionally, the groups did not differ in decrease in anxiety or substance use symptoms. However, the Headspace arms reported significantly greater improvements on a suicidality measure subscale compared with the Silvercloud arms (mean difference in mean change = 0.63; 95% CI, 0.20-1.06; P = .004). Conclusions and Relevance: This randomized clinical trial found decreases in depression and anxiety symptoms across all DMHIs and minimal evidence that specific applications were better than others. The findings suggest that DMHIs may provide support for patients during waiting list-related delays in care. Trial Registration: ClinicalTrials.gov Identifier: NCT04342494.

A review of the peak-end rule in mental health contexts.

The peak-end rule, a memory heuristic in which the most emotionally salient part of an experience (i.e., peak) and conclusion of an experience (i.e., end) are weighted more heavily in summary evaluations, has been understudied in mental health contexts. The recent growth of intensive longitudinal methods has provided new opportunities for examining the peak-end rule in the retrospective recall of mental health symptoms, including measures often used in measurement-based care initiatives. Additionally, principles of the peak-end rule have significant potential to be applied to exposure-based therapy procedures. Additional research is needed to better understand the contexts in which, and persons for whom, the peak-end rule presents a greater risk of bias, to ultimately improve assessment strategies and clinical care.

Duration of New-Onset Depressive Symptoms During Medical Residency.

Importance: The implications of new-onset depressive symptoms during residency, particularly for first-year physicians (ie, interns), on the long-term mental health of physicians are unknown. Objective: To examine the association between and persistence of new-onset and long-term depressive symptoms among interns. Design, Setting, and Participants: The ongoing Intern Health Study (IHS) is a prospective annual cohort study that assesses the mental health of incoming US-based resident physicians. The IHS began in 2007, and a total of 105 residency programs have been represented in this national study. Interns enrolled sequentially in annual cohorts and completed follow-up surveys to screen for depression using the 9-item Patient Health Questionnaire-9 (PHQ-9) throughout and after medical training. The data were analyzed from May 2023 to March 2024. Exposure: A positive screening result for depression, defined as an elevated PHQ-9 score of 10 or greater (indicating moderate to severe depression) at 1 or more time points during the first postgraduate year of medical training (ie, the intern year). Main Outcomes and Measures: The main outcomes assessed were mean PHQ-9 scores (continuous) and proportions of physicians with an elevated PHQ-9 score (≥10; categorical or binary) at the time of the annual follow-up survey. To account for repeated measures over time, a linear mixed model was used to analyze mean PHQ-9 scores and a generalized estimating equation (GEE) was used to analyze the binary indicator for a PHQ-9 score of 10 or greater. Results: This study included 858 physicians with a PHQ-9 score of less than 10 before the start of their internship. Their mean (SD) age was 27.4 (9.0) years, and more than half (53.0% [95% CI, 48.5%-57.5%]) were women. Over the follow-up period, mean PHQ-9 scores did not return to the baseline level assessed before the start of the internship in either group (those with a positive depression screen as interns and those without). Among interns who screened positive for depression (PHQ-9 score ≥10) during their internship, mean PHQ-9 scores were significantly higher at both 5 years (4.7 [95% CI, 4.4-5.0] vs 2.8 [95% CI, 2.5-3.0]; P < .001) and 10 years (5.1 [95% CI, 4.5-5.7] vs 3.5 [95% CI, 3.0-4.0]; P < .001) of follow-up. Furthermore, interns with an elevated PHQ-9 score (≥10) demonstrated a higher likelihood of meeting this threshold during each year of follow-up. Conclusions and Relevance: In this cohort study of IHS participants, a positive depression screening result during the intern year had long-term implications for physicians, including having persistently higher mean PHQ-9 scores and a higher likelihood of meeting this threshold again. These findings underscore the pressing need to address the mental health of physicians who experience depressive symptoms during their training and to emphasize the importance of interventions to sustain the health of physicians throughout their careers.

Intensive longitudinal assessment following index trauma to predict development of PTSD using machine learning.

There are significant challenges to identifying which individuals require intervention following exposure to trauma, and a need for strategies to identify and provide individuals at risk for developing PTSD with timely interventions. The present study seeks to identify a minimal set of trauma-related symptoms, assessed during the weeks following traumatic exposure, that can accurately predict PTSD. Participants were 2185 adults (Mean age=36.4 years; 64% women; 50% Black) presenting for emergency care following traumatic exposure. Participants received a 'flash survey' with 6-8 varying symptoms (from a pool of 26 trauma symptoms) several times per week for eight weeks following the trauma exposure (each symptom assessed ∼6 times). Features (mean, sd, last, worst, peak-end scores) from the repeatedly assessed symptoms were included as candidate variables in a CART machine learning analysis to develop a pragmatic predictive algorithm. PTSD (PCL-5 ≥38) was present for 669 (31%) participants at the 8-week follow-up. A classification tree with three splits, based on mean scores of nervousness, rehashing, and fatigue, predicted PTSD with an Area Under the Curve of 0.836. Findings suggest feasibility for a 3-item assessment protocol, delivered once per week, following traumatic exposure to assess and potentially facilitate follow-up care for those at risk.

Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data.

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals; specifically the sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from behavior should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.

Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data.

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated depression symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals: sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from sensed-behaviors should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.

Trends in Sexual Harassment Prevalence and Recognition During Intern Year.

This cohort study uses Internal Health Study and Sexual Experiences Questionnaire data to assess changes in sexual harassment prevalence and recognition among training physicians.

Unraveling the interplay of circadian rhythm and sleep deprivation on mood: A Real-World Study on first-year physicians.

The interplay between circadian rhythms, time awake, and mood remains poorly understood in the real-world. Individuals in high-stress occupations with irregular schedules or nighttime shifts are particularly vulnerable to depression and other mood disorders. Advances in wearable technology have provided the opportunity to study these interactions outside of a controlled laboratory environment. Here, we examine the effects of circadian rhythms and time awake on mood in first-year physicians using wearables. Continuous heart rate, step count, sleep data, and daily mood scores were collected from 2,602 medical interns across 168,311 days of Fitbit data. Circadian time and time awake were extracted from minute-by-minute wearable heart rate and motion measurements. Linear mixed modeling determined the relationship between mood, circadian rhythm, and time awake. In this cohort, mood was modulated by circadian timekeeping (p<0.001). Furthermore, we show that increasing time awake both deteriorates mood (p<0.001) and amplifies mood's circadian rhythm nonlinearly. These findings demonstrate the contributions of both circadian rhythms and sleep deprivation to underlying mood and show how these factors can be studied in real-world settings using Fitbits. They underscore the promising opportunity to harness wearables in deploying chronotherapies for psychiatric illness.

Multi-ancestry genome-wide association study of major depression aids locus discovery, fine mapping, gene prioritization and causal inference.

Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry. Here we report a multi-ancestry GWAS of MD, adding data from 21 cohorts with 88,316 MD cases and 902,757 controls to previously reported data. This analysis used a range of measures to define MD and included samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic/Latin American participants (32%). The multi-ancestry GWAS identified 53 significantly associated novel loci. For loci from GWAS in European ancestry samples, fewer than expected were transferable to other ancestry groups. Fine mapping benefited from additional sample diversity. A transcriptome-wide association study identified 205 significantly associated novel genes. These findings suggest that, for MD, increasing ancestral and global diversity in genetic studies may be particularly important to ensure discovery of core genes and inform about transferability of findings.

Returning Individual Research Results from Digital Phenotyping in Psychiatry.

Psychiatry is rapidly adopting digital phenotyping and artificial intelligence/machine learning tools to study mental illness based on tracking participants' locations, online activity, phone and text message usage, heart rate, sleep, physical activity, and more. Existing ethical frameworks for return of individual research results (IRRs) are inadequate to guide researchers for when, if, and how to return this unprecedented number of potentially sensitive results about each participant's real-world behavior. To address this gap, we convened an interdisciplinary expert working group, supported by a National Institute of Mental Health grant. Building on established guidelines and the emerging norm of returning results in participant-centered research, we present a novel framework specific to the ethical, legal, and social implications of returning IRRs in digital phenotyping research. Our framework offers researchers, clinicians, and Institutional Review Boards (IRBs) urgently needed guidance, and the principles developed here in the context of psychiatry will be readily adaptable to other therapeutic areas.

Unspoken Truths: Mental Health Among Academic Surgeons.

OBJECTIVE: To characterize the current state of mental health within the surgical workforce in the United States. BACKGROUND: Mental illness and suicide is a growing concern in the medical community; however, the current state is largely unknown. METHODS: Cross-sectional survey of the academic surgery community assessing mental health, medical error, and suicidal ideation. The odds of suicidal ideation adjusting for sex, prior mental health diagnosis, and validated scales screening for depression, anxiety, post-traumatic stress disorder (PTSD), and alcohol use disorder were assessed. RESULTS: Of 622 participating medical students, trainees, and surgeons (estimated response rate=11.4%-14.0%), 26.1% (141/539) reported a previous mental health diagnosis. In all, 15.9% (83/523) of respondents screened positive for current depression, 18.4% (98/533) for anxiety, 11.0% (56/510) for alcohol use disorder, and 17.3% (36/208) for PTSD. Medical error was associated with depression (30.7% vs. 13.3%, P <0.001), anxiety (31.6% vs. 16.2%, P =0.001), PTSD (12.8% vs. 5.6%, P =0.018), and hazardous alcohol consumption (18.7% vs. 9.7%, P =0.022). Overall, 13.2% (73/551) of respondents reported suicidal ideation in the past year and 9.6% (51/533) in the past 2 weeks. On adjusted analysis, a previous history of a mental health disorder (aOR: 1.97, 95% CI: 1.04-3.65, P =0.033) and screening positive for depression (aOR: 4.30, 95% CI: 2.21-8.29, P <0.001) or PTSD (aOR: 3.93, 95% CI: 1.61-9.44, P =0.002) were associated with increased odds of suicidal ideation over the past 12 months. CONCLUSIONS: Nearly 1 in 7 respondents reported suicidal ideation in the past year. Mental illness and suicidal ideation are significant problems among the surgical workforce in the United States.

Variation by Institution in Sexual Harassment Experiences Among US Medical Interns.

This cross-sectional study investigates possible institutional and specialty variations in experiences of sexual harassment among US medical interns.

The impact of COVID-19 on a college freshman sample reveals genetic and nongenetic forms of susceptibility and resilience to stress.

Using a longitudinal approach, we sought to define the interplay between genetic and nongenetic factors in shaping vulnerability or resilience to COVID-19 pandemic stress, as indexed by the emergence of symptoms of depression and/or anxiety. University of Michigan freshmen were characterized at baseline using multiple psychological instruments. Subjects were genotyped, and a polygenic risk score for depression (MDD-PRS) was calculated. Daily physical activity and sleep were captured. Subjects were sampled at multiple time points throughout the freshman year on clinical rating scales, including GAD-7 and PHQ-9 for anxiety and depression, respectively. Two cohorts (2019 to 2021) were compared to a pre-COVID-19 cohort to assess the impact of the pandemic. Across cohorts, 26 to 40% of freshmen developed symptoms of anxiety or depression (N = 331). Depression symptoms significantly increased in the pandemic years and became more chronic, especially in females. Physical activity was reduced, and sleep was increased by the pandemic, and this correlated with the emergence of mood symptoms. While low MDD-PRS predicted lower risk for depression during a typical freshman year, this genetic advantage vanished during the pandemic. Indeed, females with lower genetic risk accounted for the majority of the pandemic-induced rise in depression. We developed a model that explained approximately half of the variance in follow-up depression scores based on psychological trait and state characteristics at baseline and contributed to resilience in genetically vulnerable subjects. We discuss the concept of multiple types of resilience, and the interplay between genetic, sex, and psychological factors in shaping the affective response to different types of stressors.

The Dosing of Mobile-Based Just-in-Time Adaptive Self-Management Prompts for Caregivers: Preliminary Findings From a Pilot Microrandomized Study.

BACKGROUND: Caregivers of people with chronic illnesses often face negative stress-related health outcomes and are unavailable for traditional face-to-face interventions due to the intensity and constraints of their caregiver role. Just-in-time adaptive interventions (JITAIs) have emerged as a design framework that is particularly suited for interventional mobile health studies that deliver in-the-moment prompts that aim to promote healthy behavioral and psychological changes while minimizing user burden and expense. While JITAIs have the potential to improve caregivers' health-related quality of life (HRQOL), their effectiveness for caregivers remains poorly understood. OBJECTIVE: The primary objective of this study is to evaluate the dose-response relationship of a fully automated JITAI-based self-management intervention involving personalized mobile app notifications targeted at decreasing the level of caregiver strain, anxiety, and depression. The secondary objective is to investigate whether the effectiveness of this mobile health intervention was moderated by the caregiver group. We also explored whether the effectiveness of this intervention was moderated by (1) previous HRQOL measures, (2) the number of weeks in the study, (3) step count, and (4) minutes of sleep. METHODS: We examined 36 caregivers from 3 disease groups (10 from spinal cord injury, 11 from Huntington disease, and 25 from allogeneic hematopoietic cell transplantation) in the intervention arm of a larger randomized controlled trial (subjects in the other arm received no prompts from the mobile app) designed to examine the acceptability and feasibility of this intensive type of trial design. A series of multivariate linear models implementing a weighted and centered least squares estimator were used to assess the JITAI efficacy and effect. RESULTS: We found preliminary support for a positive dose-response relationship between the number of administered JITAI messages and JITAI efficacy in improving caregiver strain, anxiety, and depression; while most of these associations did not meet conventional levels of significance, there was a significant association between high-frequency JITAI and caregiver strain. Specifically, administering 5-6 messages per week as opposed to no messages resulted in a significant decrease in the HRQOL score of caregiver strain with an estimate of -6.31 (95% CI -11.76 to -0.12; P=.046). In addition, we found that the caregiver groups and the participants' levels of depression in the previous week moderated JITAI efficacy. CONCLUSIONS: This study provides preliminary evidence to support the effectiveness of the self-management JITAI and offers practical guidance for designing future personalized JITAI strategies for diverse caregiver groups. TRIAL REGISTRATION: ClinicalTrials.gov NCT04556591; https://clinicaltrials.gov/ct2/show/NCT04556591.

Use of mobile technology to identify behavioral mechanisms linked to mental health outcomes in Kenya: protocol for development and validation of a predictive model.

OBJECTIVE: This study proposes to identify and validate weighted sensor stream signatures that predict near-term risk of a major depressive episode and future mood among healthcare workers in Kenya. APPROACH: The study will deploy a mobile application (app) platform and use novel data science analytic approaches (Artificial Intelligence and Machine Learning) to identifying predictors of mental health disorders among 500 randomly sampled healthcare workers from five healthcare facilities in Nairobi, Kenya. EXPECTATION: This study will lay the basis for creating agile and scalable systems for rapid diagnostics that could inform precise interventions for mitigating depression and ensure a healthy, resilient healthcare workforce to develop sustainable economic growth in Kenya, East Africa, and ultimately neighboring countries in sub-Saharan Africa. This protocol paper provides an opportunity to share the planned study implementation methods and approaches. CONCLUSION: A mobile technology platform that is scalable and can be used to understand and improve mental health outcomes is of critical importance.

Posttraumatic Stress Disorder Symptoms Among First-Year Resident Physicians Working Before and During the COVID-19 Pandemic.

This cohort study investigates differences in posttraumatic stress disorder (PTSD) symptoms among first-year resident physicians training before and during the first wave of the COVID-19 pandemic (March to June 2020).