Search

Major depressive disorder (MDD) is a leading cause of disease-associated disability, with much of the increased burden due to psychiatric and medical comorbidity. This comorbidity partly reflects common genetic influences across conditions. Integrating molecular-genetic tools with health records enables tests of association with the broad range of physiological and clinical phenotypes. However, standard phenome-wide association studies analyze associations with individual genetic variants. For polygenic traits such as MDD, aggregate measures of genetic risk may yield greater insight into associations across the clinical phenome.We tested for associations between a genome-wide polygenic risk score for MDD and medical and psychiatric traits in a phenome-wide association study of 46,782 unrelated, European-ancestry participants from the Michigan Genomics Initiative.The MDD polygenic risk score was associated with 211 traits from 15 medical and psychiatric disease categories at the phenome-wide significance threshold. After excluding patients with depression, continued associations were observed with respiratory, digestive, neurological, and genitourinary conditions; neoplasms; and mental disorders. Associations with tobacco use disorder, respiratory conditions, and genitourinary conditions persisted after accounting for genetic overlap between depression and other psychiatric traits. Temporal analyses of time-at-first-diagnosis indicated that depression disproportionately preceded chronic pain and substance-related disorders, while asthma disproportionately preceded depression.The present results can inform the biological links between depression and both mental and systemic diseases. Although MDD polygenic risk scores cannot currently forecast health outcomes with precision at the individual level, as molecular-genetic discoveries for depression increase, these tools may augment risk prediction for medical and psychiatric conditions.

Summary: Millions of wearable-device users record their heart rate (HR) and activity. We introduce a statistical method to extract and track six key physiological parameters from these data, including an underlying circadian rhythm in HR (CRHR), the direct effects of activity, and the effects of meals, posture, and stress through hormones like cortisol. We test our method on over 130,000 days of real-world data from medical interns on rotating shifts, showing that CRHR dynamics are distinct from those of sleep-wake or physical activity patterns and vary greatly among individuals. Our method also estimates a personalized phase-response curve of CRHR to activity for each individual, representing a passive and personalized determination of how human circadian timekeeping continually changes due to real-world stimuli. We implement our method in the “Social Rhythms” iPhone and Android app, which anonymously collects data from wearable-device users and provides analysis based on our method. Motivation: The exploding popularity of wearable devices, now a multi-billion dollar industry, provides a new opportunity for real-world data collection. Here, we propose a statistical method for analysis of ambulatory wearable-device data that can estimate circadian rhythms. Accounting for circadian rhythms in HR will allow more accurate measurement of other physiological parameters, e.g., basal HR, how activity increases HR, and changes in HR due to infection.

BACKGROUND Care partners (ie, informal family caregivers) of individuals with health problems face considerable physical and emotional stress, often with a substantial negative impact on the health-related quality of life (HRQOL) of both care partners and care recipients. Given that these individuals are often overwhelmed by their caregiving responsibilities, low-burden self-management interventions are needed to support care partners to ensure better patient outcomes. OBJECTIVE The primary objective of this study is to describe an intensive data collection protocol that involves the delivery of a personalized just-in-time adaptive intervention that incorporates passive mobile sensor data feedback (sleep and activity data from a Fitbit [Fitbit LLC]) and real time self-reporting of HRQOL via a study-specific app called CareQOL (University of Michigan) to provide personalized feedback via app alerts. METHODS Participants from 3 diverse care partner groups will be enrolled (care partners of persons with spinal cord injury, care partners of persons with Huntington disease, and care partners of persons with hematopoietic cell transplantation). Participants will be randomized to either a control group, where they will wear the Fitbit and provide daily reports of HRQOL over a 3-month (ie, 90 days) period (without personalized feedback), or the just-in-time adaptive intervention group, where they will wear the Fitbit, provide daily reports of HRQOL, and receive personalized push notifications for 3 months. At the end of the study, participants will complete a feasibility and acceptability questionnaire, and metrics regarding adherence and attrition will be calculated. RESULTS This trial opened for recruitment in November 2020. Data collection was completed in June 2021, and the primary results are expected to be published in 2022. CONCLUSIONS This trial will determine the feasibility and acceptability of an intensive app-based intervention in 3 distinct care partner groups: care partners for persons with a chronic condition that was caused by a traumatic event (ie, spinal cord injury); care partners for persons with a progressive, fatal neurodegenerative disease (ie, Huntington disease); and care partners for persons with episodic cancer conditions that require intense, prolonged inpatient and outpatient treatment (persons with hematopoietic cell transplantation). CLINICALTRIAL ClinicalTrials.gov NCT04556591; https://clinicaltrials.gov/ct2/show/NCT04556591 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/32842

Advancing our ability to predict who is likely to develop depression in response to stress holds great potential in reducing the burden of the disorder. Large-scale genome-wide association studies (GWAS) of depression have, for the first time, provided a basis for meaningful depression polygenic risk score construction (MDD-PRS). The Intern Health Study utilizes the predictable and large increase in depression with physician training stress to identify predictors of depression. Applying the MDD-PRS derived from the PGC2/23andMe GWAS to 5,227 training physicians, we found that MDD-PRS predicted depression under training stress (beta=0.082, p=2.1×10−12) and that MDD-PRS was significantly more strongly associated with depression under stress than at baseline (MDD-PRS × stress interaction - beta=0.029, p=0.02). While known risk factors accounted for 85.6% of the association between MDD-PRS and depression at baseline, they only accounted for 55.4% of the association between MDD-PRS and depression under stress, suggesting that MDD-PRS can add unique predictive power to existing models of depression under stress. Further, we found that low MDD-PRS may have particular utility in identifying individuals with high resilience. Together, these findings suggest that polygenic risk score holds promise in furthering our ability to predict vulnerability and resilience under stress.

Multiple studies suggest that the risks of depression and suicide increase with increasing altitude of residence, but no studies have assessed whether changing altitude changes these risks. To address this gap, we used data from the Intern Health Study, which follows students from the end of medical school through the first year of residency, recording depression via the 9-item Patient Health Questionnaire (PHQ-9), anxiety via the 7-item Generalized Anxiety Disorder Questionnaire (GAD-7), and multiple risk factors for these symptoms. Data from 3,764 medical students representing 46 schools and 282 residencies were available. Odds ratios (OR) representing the effects of altitude on psychiatric symptoms were estimated using generalized linear models. After excluding participants with missing altitude data, 3731 medical students were analyzed. High altitude residence (>900m) was significantly associated with PHQ-9 total score (OR=1.32, 95% CI=1.001-1.75, p

This cross-sectional study examines the frequency of actions taken against physician licenses in the US because of substance use and psychological or physical impairment from 2004 to 2020.

Consumer-grade wearables are needed to track disease, especially in the ongoing pandemic, as they can monitor patients in real time. We show that decomposing heart rate from low-cost wearable technologies into signals from different systems can give a multidimensional description of physiological changes due to COVID-19 infection. We find that the separate physiological features of basal heart rate, heart rate response to physical activity, circadian variation in heart rate, and autocorrelation of heart rate are significantly altered and can classify symptomatic versus healthy periods. Increased heart rate and autocorrelation begin at symptom onset, while the heart rate response to activity increases soon after symptom onset and increases more in individuals exhibiting cough. Symptom onset is associated with a blunting of circadian variation in heart rate, as measured by the uncertainty in the phase estimate. This work establishes an innovative data analytic approach to monitor disease progression remotely using consumer-grade wearables.

Background The use of wearables facilitates data collection at a previously unobtainable scale, enabling the construction of complex predictive models with the potential to improve health. However, the highly personal nature of these data requires strong privacy protection against data breaches and the use of data in a way that users do not intend. One method to protect user privacy while taking advantage of sharing data across users is federated learning, a technique that allows a machine learning model to be trained using data from all users while only storing a user’s data on that user’s device. By keeping data on users’ devices, federated learning protects users’ private data from data leaks and breaches on the researcher’s central server and provides users with more control over how and when their data are used. However, there are few rigorous studies on the effectiveness of federated learning in the mobile health (mHealth) domain. Objective We review federated learning and assess whether it can be useful in the mHealth field, especially for addressing common mHealth challenges such as privacy concerns and user heterogeneity. The aims of this study are to describe federated learning in an mHealth context, apply a simulation of federated learning to an mHealth data set, and compare the performance of federated learning with the performance of other predictive models. Methods We applied a simulation of federated learning to predict the affective state of 15 subjects using physiological and motion data collected from a chest-worn device for approximately 36 minutes. We compared the results from this federated model with those from a centralized or server model and with the results from training individual models for each subject. Results In a 3-class classification problem using physiological and motion data to predict whether the subject was undertaking a neutral, amusing, or stressful task, the federated model achieved 92.8% accuracy on average, the server model achieved 93.2% accuracy on average, and the individual model achieved 90.2% accuracy on average. Conclusions Our findings support the potential for using federated learning in mHealth. The results showed that the federated model performed better than a model trained separately on each individual and nearly as well as the server model. As federated learning offers more privacy than a server model, it may be a valuable option for designing sensitive data collection methods.

While 24-h total sleep time (TST) is established as a critical driver of major depression, the relationships between sleep timing and regularity and mental health remain poorly characterized because most studies have relied on either self-report assessments or traditional objective sleep measurements restricted to cross-sectional time frames and small cohorts. To address this gap, we assessed sleep with a wearable device, daily mood with a smartphone application and depression through the 9-item Patient Health Questionnaire (PHQ-9) over the demanding first year of physician training (internship). In 2115 interns, reduced TST (b = −0.11, p b = 0.068, p = 0.015), along with increased variability in TST (b = 0.4, p = 0.0012) and in wake time (b = 0.081, p = 0.005) were associated with more depressive symptoms. Overall, the aggregated impact of sleep variability parameters and of mean sleep parameters on PHQ-9 were similar in magnitude (both r2 = 0.01). Within individuals, increased TST (b = 0.06, p b = 0.09, p b = − 0.07, p b = −0.011, p b = −0.004, p

Importance Frontline health care professionals who work with patients with COVID-19 have an increased incidence of burnout symptoms. Cannabidiol (CBD) has anxiolytic and antidepressant properties and may be capable of reducing emotional exhaustion and burnout symptoms. Objective To investigate the safety and efficacy of CBD therapy for the reduction of emotional exhaustion and burnout symptoms among frontline health care professionals working with patients with COVID-19. Design, setting, and participants This prospective open-label single-site randomized clinical trial used a 1:1 block randomization design to examine emotional exhaustion and burnout symptoms among frontline health care professionals (physicians, nurses, and physical therapists) working with patients with COVID-19 at the Ribeirao Preto Medical School University Hospital in Sao Paulo, Brazil. Participants were enrolled between June 12 and November 12, 2020. A total of 214 health care professionals were recruited and assessed for eligibility, and 120 participants were randomized in a 1:1 ratio by a researcher who was not directly involved with data collection. Interventions Cannabidiol, 300 mg (150 mg twice per day), plus standard care or standard care alone for 28 days. Main outcomes and measures The primary outcome was emotional exhaustion and burnout symptoms, which were assessed for 28 days using the emotional exhaustion subscale of the Brazilian version of the Maslach Burnout Inventory-Human Services Survey for Medical Personnel. Results A total of 120 participants were randomized to receive either CBD, 300 mg, plus standard care (treatment arm; n = 61) or standard care alone (control arm; n = 59) for 28 days. Of those, 118 participants (59 participants in each arm; 79 women [66.9%]; mean age, 33.6 years [95% CI, 32.3-34.9 years]) received the intervention and were included in the efficacy analysis. In the treatment arm, scores on the emotional exhaustion subscale of the Maslach Burnout Inventory significantly decreased at day 14 (mean difference, 4.14 points; 95% CI, 1.47-6.80 points; partial eta squared [ηp2] = 0.08), day 21 (mean difference, 4.34 points; 95% CI, 0.94-7.73 points; ηp2 = 0.05), and day 28 (mean difference, 4.01 points; 95% CI, 0.43-7.59 points; ηp2 = 0.04). However, 5 participants, all of whom were in the treatment group, experienced serious adverse events: 4 cases of elevated liver enzymes (1 critical and 3 mild, with the mild elevations reported at the final 28-day assessment) and 1 case of severe pharmacodermia. In 2 of those cases (1 with critical elevation of liver enzymes and 1 with severe pharmacodermia), CBD therapy was discontinued, and the participants had a full recovery. Conclusions and relevance In this study, CBD therapy reduced symptoms of burnout and emotional exhaustion among health care professionals working with patients during the COVID-19 pandemic. However, it is necessary to balance the benefits of CBD therapy with potential undesired or adverse effects. Future double-blind placebo-controlled clinical trials are needed to confirm the present findings. Trial registration ClinicalTrials.gov Identifier: NCT04504877.

Objectives. To quantify deaths in the United States from 2010 through 2018 that were reported with an underlying cause of death as a psychiatric diagnosis, which do not indicate a clear mechanism of death, and that may be misclassified suicide and overdose deaths. Methods. We used national vital statistics data to identify rates and circumstances of deaths by specific underlying cause of death categories in the US population. Results. There were 115 442 deaths attributed to psychiatric diagnoses and 834 763 deaths attributed to suicide or overdose. The population rate of deaths attributed to psychiatric diagnoses increased from 3.26 to 4.96 per 100 000 US persons between 2010 and 2018. Conclusions. Psychiatric diagnoses may represent a fairly substantial number of misclassified overdose and suicide deaths. Improving mortality surveillance requires improving the accuracy of diagnoses reported on death certificates.

This cohort study uses survey data to assess the prevalence and development of depressive symptoms among sexual minority and heterosexual physicians during residency training.

BACKGROUND The use of wearables facilitates data collection at a previously unobtainable scale, enabling the construction of complex predictive models with the potential to improve health. However, the highly personal nature of these data requires strong privacy protection against data breaches and the use of data in a way that users do not intend. One method to protect user privacy while taking advantage of sharing data across users is federated learning, a technique that allows a machine learning model to be trained using data from all users while only storing a user’s data on that user’s device. By keeping data on users’ devices, federated learning protects users’ private data from data leaks and breaches on the researcher’s central server and provides users with more control over how and when their data are used. However, there are few rigorous studies on the effectiveness of federated learning in the mobile health (mHealth) domain. OBJECTIVE We review federated learning and assess whether it can be useful in the mHealth field, especially for addressing common mHealth challenges such as privacy concerns and user heterogeneity. The aims of this study are to describe federated learning in an mHealth context, apply a simulation of federated learning to an mHealth data set, and compare the performance of federated learning with the performance of other predictive models. METHODS We applied a simulation of federated learning to predict the affective state of 15 subjects using physiological and motion data collected from a chest-worn device for approximately 36 minutes. We compared the results from this federated model with those from a centralized or server model and with the results from training individual models for each subject. RESULTS In a 3-class classification problem using physiological and motion data to predict whether the subject was undertaking a neutral, amusing, or stressful task, the federated model achieved 92.8% accuracy on average, the server model achieved 93.2% accuracy on average, and the individual model achieved 90.2% accuracy on average. CONCLUSIONS Our findings support the potential for using federated learning in mHealth. The results showed that the federated model performed better than a model trained separately on each individual and nearly as well as the server model. As federated learning offers more privacy than a server model, it may be a valuable option for designing sensitive data collection methods. CLINICALTRIAL

This cohort study assesses anxiety, depression, mood, and fear of workplace violence in a cohort of young physicians in China before and during the coronavirus disease 2019 outbreak.

Background Suicidal thinking during non-fatal overdose may elevate risk for future completed suicide or intentional overdose. Long-term outcomes following an intentional non-fatal overdose may be improved through specific intervention and prevention responses beyond those designed for unintentional overdoses, yet little research has assessed suicidal intent during overdoses or defined characteristics that differentiate these events from unintentional overdoses. Methods Patients with a history of opioid overdose (n = 274) receiving residential addiction treatment in the Midwestern United States completed self-report surveys to classify their most recent opioid overdose as unintentional, actively suicidal (wanted to die), or passively suicidal (didn’t care about the risks). We characterized correlates of intent using descriptive statistics and prevalence ratios. We also examined how intent related to thoughts of self-harm at the time of addiction treatment. Results Of opioid overdoses, 51 % involved suicidal intent (44 % passive and 7 % active). Active suicidal intent was positively associated with hospitalization. Active/passive intent (vs. no intent, aPR: 2.2, 95 % CI: 1.4–3.5) and use of ≥5 substances (vs. 1 substance, aPR: 3.6, 95 % CI: 1.2–10.6) at the last opioid overdose were associated with having thoughts of self-harm or suicide in the 2 weeks before survey completion in adjusted models. Participants who reported active/passive intent more commonly used cocaine or crack (27 %) with opioids during their last overdose relative to unintentional overdoses (16 %). Conclusions Over half of opioid overdoses among individuals in addiction treatment involved some degree of suicidal thinking. Identifying patients most at risk will facilitate better targeting of suicide prevention and monitoring services.

Resident physician training is associated with a substantial increase in depression in the United States, with rates increasing from about 4% before internship to 35% at least once during the first year of residency1.Here, we sought to assess whether the rate of depression among residents in China are similar to their US counterparts and identify the common and differential predictors of depression in the two training systems. We assessed 1006 residents across three cohorts (2016-2019) at 16 affiliated hospitals of Shanghai Jiao Tong University and Peking Union Medical College. In parallel, we assessed three cohorts of 7028 residents at 100+ US institutions.At the Chinese institutions, similarly, the proportion of participants who met depression criteria increased from 9% prior to residency to 35% at least once during the first year of residency (PTo gain insight into whether differences in personal predictors between the residents in China compared to the US residents were driven more by differences between cohorts, or by training system differences, we compared US residents of East Asian descent to other US and Chinese residents. We found that for most predictors (age, Neuroticism, early family environment), US residents of East Asian descent were more similar to other US residents than to the residents training in China.Overall, the magnitude of depression increase and work-related drivers of depression were similar between China and the US, suggesting a need for system reforms, and that the types of effective reforms may be similar across the two systems.

BACKGROUND Cancer patients who undergo allogeneic hematopoietic stem cell transplantation are among the most medically fragile patient populations with extreme demands for caregivers. Indeed, with earlier hospital discharges, the demands placed on caregivers continue to intensify. Moreover, an increased number of allogeneic hematopoietic stem cell transplantations are being performed worldwide, and this expensive procedure has significant economic consequences. Thus, the health and well-being of family caregivers have attracted widespread attention. Mobile health technology has been shown to deliver flexible, and time- and cost-sparing interventions to support family caregivers across the care trajectory. OBJECTIVE This protocol aims to leverage technology to deliver a novel caregiver-facing mobile health intervention named Roadmap 2.0. We will evaluate the effectiveness of Roadmap 2.0 in family caregivers of patients undergoing hematopoietic stem cell transplantation. METHODS The Roadmap 2.0 intervention will consist of a mobile randomized trial comparing a positive psychology intervention arm with a control arm in family caregiver-patient dyads. The primary outcome will be caregiver health-related quality of life, as assessed by the PROMIS Global Health scale at day 120 post-transplant. Secondary outcomes will include other PROMIS caregiver- and patient-reported outcomes, including companionship, self-efficacy for managing symptoms, self-efficacy for managing daily activities, positive affect and well-being, sleep disturbance, depression, and anxiety. Semistructured qualitative interviews will be conducted among participants at the completion of the study. We will also measure objective physiological markers (eg, sleep, activity, heart rate) through wearable wrist sensors and health care utilization data through electronic health records. RESULTS We plan to enroll 166 family caregiver-patient dyads for the full data analysis. The study has received Institutional Review Board approval as well as Code Review and Information Assurance approval from our health information technology services. Owing to the COVID-19 pandemic, the study has been briefly put on hold. However, recruitment began in August 2020. We have converted all recruitment, enrollment, and onboarding processes to be conducted remotely through video telehealth. Consent will be obtained electronically through the Roadmap 2.0 app. CONCLUSIONS This mobile randomized trial will determine if positive psychology-based activities delivered through mobile health technology can improve caregiver health-related quality of life over a 16-week study period. This study will provide additional data on the effects of wearable wrist sensors on caregiver and patient self-report outcomes. CLINICALTRIAL ClinicalTrials.gov NCT04094844; https://www.clinicaltrials.gov/ct2/show/NCT04094844 INTERNATIONAL REGISTERED REPORT PRR1-10.2196/19288

OBJECTIVE: Despite high rates of prenatal insomnia, efficacious treatment options for this population are quite limited. Early evidence from randomized controlled trials (RCTs) support the efficacy of face-to-face cognitive-behavioral therapy for insomnia (CBTI) for prenatal insomnia. Yet, as many patients are unable to access this specialist-driven care, a critical need exists to increase its accessibility. This RCT examined the efficacy internet-based digital CBTI in pregnant women with insomnia. METHODS: Single-site RCT. A total of 91 pregnant women (29.03 ± 4.16 years) nearing/entering the third trimester who screened positive for clinical insomnia on the Insomnia Severity Index (ISI) were randomized to digital CBTI or digital sleep education control. The ISI, Pittsburgh Sleep Quality Index (PSQI), Edinburgh Postnatal Depression Scale (EPDS), and Pre-Sleep Arousal Scale’s Cognitive factor (PSAS-C) served as study outcomes, which were collected before treatment and after treatment during pregnancy, then six weeks after childbirth. RESULTS: From pre to posttreatment, CBTI patients reported reductions in ISI (−4.91 points, p < 0.001) and PSQI (−2.98 points, p < 0.001) and increases in nightly sleep duration by 32 min (p = 0.008). Sleep symptoms did not change during pregnancy in the control group. After childbirth, CBTI patients, relative to controls, slept longer by 40 min per night (p = 0.01) and reported better sleep maintenance. No pre or postnatal treatment effects on depression or cognitive arousal were observed. CONCLUSIONS: Digital CBTI improves sleep quality and sleep duration during pregnancy and after childbirth. To better optimize outcomes, CBTI should be tailored to meet the changing needs of women as the progress through pregnancy and early parenting. NAME: Insomnia and Rumination in Late Pregnancy and the Risk for Postpartum Depression. URL: clinicaltrials.gov. Registration: NCT03596879.

Background Individuals in stressful work environments often experience mental health issues, such as depression. Reducing depression rates is difficult because of persistently stressful work environments and inadequate time or resources to access traditional mental health care services. Mobile health (mHealth) interventions provide an opportunity to deliver real-time interventions in the real world. In addition, the delivery times of interventions can be based on real-time data collected with a mobile device. To date, data and analyses informing the timing of delivery of mHealth interventions are generally lacking. Objective This study aimed to investigate when to provide mHealth interventions to individuals in stressful work environments to improve their behavior and mental health. The mHealth interventions targeted 3 categories of behavior: mood, activity, and sleep. The interventions aimed to improve 3 different outcomes: weekly mood (assessed through a daily survey), weekly step count, and weekly sleep time. We explored when these interventions were most effective, based on previous mood, step, and sleep scores. Methods We conducted a 6-month micro-randomized trial on 1565 medical interns. Medical internship, during the first year of physician residency training, is highly stressful, resulting in depression rates several folds higher than those of the general population. Every week, interns were randomly assigned to receive push notifications related to a particular category (mood, activity, sleep, or no notifications). Every day, we collected interns’ daily mood valence, sleep, and step data. We assessed the causal effect moderation by the previous week’s mood, steps, and sleep. Specifically, we examined changes in the effect of notifications containing mood, activity, and sleep messages based on the previous week’s mood, step, and sleep scores. Moderation was assessed with a weighted and centered least-squares estimator. Results We found that the previous week’s mood negatively moderated the effect of notifications on the current week’s mood with an estimated moderation of −0.052 (P=.001). That is, notifications had a better impact on mood when the studied interns had a low mood in the previous week. Similarly, we found that the previous week’s step count negatively moderated the effect of activity notifications on the current week’s step count, with an estimated moderation of −0.039 (P=.01) and that the previous week’s sleep negatively moderated the effect of sleep notifications on the current week’s sleep with an estimated moderation of −0.075 (P Conclusions These findings suggest that an individual’s current state meaningfully influences their receptivity to mHealth interventions for mental health. Timing interventions to match an individual’s state may be critical to maximizing the efficacy of interventions. Trial Registration ClinicalTrials.gov NCT03972293; http://clinicaltrials.gov/ct2/show/NCT03972293

While biomarkers have been used to define pathophysiological types and to optimize treatment in many areas of medicine, in psychiatry such biomarkers remain elusive. Based on previously described abnormalities of hypothalamic-pituitary-adrenal (HPA) axis function in severe forms of depression, we hypothesized that the temporal trajectory of basal cortisol levels would vary among individuals with depression due to heterogeneity in pathophysiology, and that cortisol trajectories that reflect elevated or increasing HPA activity would predict better response to electroconvulsive therapy (ECT). To test that hypothesis, we sampled scalp hair from 39 subjects with treatment-resistant depression just before ECT. Cortisol trajectory over the 12 weeks preceding ECT was reconstructed from cortisol concentrations in sequential hair segments. Cortisol trajectories varied widely between individuals, and exploratory analyses of clinical features revealed associations with melancholia and global severity. ECT non-responders showed a decreasing trajectory (mean change −25%, 95%-CI = [–1%,–43%]) during the 8 weeks preceding ECT (group-by-time interaction, p = 0.004). The association between cortisol trajectory and subsequent ECT response was independent of clinical features. A classification algorithm showed that cortisol trajectory predicted ECT response with 80% accuracy, suggesting that this biomarker might be developed into a clinically useful test for ECT-responsive depression. In conclusion, cortisol trajectory mapped onto symptoms of melancholia and independently predicted response to ECT in this severely depressed sample. These findings deserve to be replicated in a larger sample. Cortisol trajectory holds promise as a reliable, noninvasive, inexpensive biomarker for psychiatric disorders.

This systematic review and meta-analysis examines whether physician depressive symptoms are associated with the risk for perceived or observed medical errors., Key Points Question What are the magnitude and direction of associations between physician depressive symptoms and medical errors? Findings In this systematic review and meta-analysis of 11 studies involving 21 517 physicians, physicians with a positive screening for depression were highly likely to report medical errors. Examination of longitudinal studies demonstrated that the association between physician depressive symptoms and medical errors is bidirectional. Meaning This study found that physician depressive symptoms were associated with medical errors, highlighting the relevance of physician well-being to health care quality and underscoring the need for systematic efforts to prevent or reduce depressive symptoms among physicians., Importance Depression is highly prevalent among physicians and has been associated with increased risk of medical errors. However, questions regarding the magnitude and temporal direction of these associations remain open in recent literature. Objective To provide summary relative risk (RR) estimates for the associations between physician depressive symptoms and medical errors. Data Sources A systematic search of Embase, ERIC, PubMed, PsycINFO, Scopus, and Web of Science was performed from database inception to December 31, 2018. Study Selection Peer-reviewed empirical studies that reported on a valid measure of physician depressive symptoms associated with perceived or observed medical errors were included. No language restrictions were applied. Data Extraction and Synthesis Study characteristics and RR estimates were extracted from each article. Estimates were pooled using random-effects meta-analysis. Differences by study-level characteristics were estimated using subgroup meta-analysis and metaregression. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guideline was followed. Main Outcomes and Measures Relative risk estimates for the associations between physician depressive symptoms and medical errors. Results In total, 11 studies involving 21 517 physicians were included. Data were extracted from 7 longitudinal studies (64%; with 5595 individuals) and 4 cross-sectional studies (36%; with 15 922 individuals). The overall RR for medical errors among physicians with a positive screening for depression was 1.95 (95% CI, 1.63-2.33), with high heterogeneity across the studies (χ2 = 49.91; P

Background Stress plays a causal role in depression onset, perhaps via alteration of hypothalamic-pituitary-adrenal (HPA) axis functioning. HPA axis hyperactivity has been reported in depression, though inconsistently, and the nature of this relationship remains unclear, partly because cortisol measurement over time has been challenging. Development of hair cortisol assessment, a method that captures cortisol over prolonged periods of time, creates new possibilities. In this study, hair cortisol was incorporated into a prospective and longitudinal study of medical internship, stress and symptoms of depression. This provided a rare opportunity to 1) prospectively assess hair cortisol responses to stress, and 2) examine whether stress-induced changes in hair cortisol predict depressive symptom development. Methods Hair cortisol, depressive symptoms, and stress-relevant variables (work hours, sleep, perceived stress, mastery/control) were assessed in interns (n = 74; age 25–33) before and repeatedly throughout medical internship. Results Hair cortisol sharply increased with stressor onset, decreased as internship continued, and rose again at year’s end. Depressive symptoms rose significantly during internship, but were not predicted by cortisol levels. Hair cortisol also did not correlate with increased stressor demands (work hours, sleep) or stress perceptions (perceived stress, mastery/control); but these variables did predict depressive symptoms. Discussion Hair cortisol and depressive responses increased with stress, but they were decoupled, following distinct trajectories that likely reflected different aspects of stress reactivity. While depressive symptoms correlated with stressor demands and stress perceptions, the longitudinal pattern of hair cortisol suggested that it responded to contextual features related to anticipation, novelty/familiarity, and social evaluative threat.

Background Care partners (ie, informal family caregivers) of individuals with health problems face considerable physical and emotional stress, often with a substantial negative impact on the health-related quality of life (HRQOL) of both care partners and care recipients. Given that these individuals are often overwhelmed by their caregiving responsibilities, low-burden self-management interventions are needed to support care partners to ensure better patient outcomes. Objective The primary objective of this study is to describe an intensive data collection protocol that involves the delivery of a personalized just-in-time adaptive intervention that incorporates passive mobile sensor data feedback (sleep and activity data from a Fitbit [Fitbit LLC]) and real time self-reporting of HRQOL via a study-specific app called CareQOL (University of Michigan) to provide personalized feedback via app alerts. Methods Participants from 3 diverse care partner groups will be enrolled (care partners of persons with spinal cord injury, care partners of persons with Huntington disease, and care partners of persons with hematopoietic cell transplantation). Participants will be randomized to either a control group, where they will wear the Fitbit and provide daily reports of HRQOL over a 3-month (ie, 90 days) period (without personalized feedback), or the just-in-time adaptive intervention group, where they will wear the Fitbit, provide daily reports of HRQOL, and receive personalized push notifications for 3 months. At the end of the study, participants will complete a feasibility and acceptability questionnaire, and metrics regarding adherence and attrition will be calculated. Results This trial opened for recruitment in November 2020. Data collection was completed in June 2021, and the primary results are expected to be published in 2022. Conclusions This trial will determine the feasibility and acceptability of an intensive app-based intervention in 3 distinct care partner groups: care partners for persons with a chronic condition that was caused by a traumatic event (ie, spinal cord injury); care partners for persons with a progressive, fatal neurodegenerative disease (ie, Huntington disease); and care partners for persons with episodic cancer conditions that require intense, prolonged inpatient and outpatient treatment (persons with hematopoietic cell transplantation). Trial Registration ClinicalTrials.gov NCT04556591; https://clinicaltrials.gov/ct2/show/NCT04556591 International Registered Report Identifier (IRRID) DERR1-10.2196/32842

The purpose of this study was to identify individual and residency program factors associated with increased suicide risk, as measured by suicidal ideation. We utilized a prospective, longitudinal cohort study design to assess the prevalence and predictors of suicidal ideation in 6,691 (2012–2014 cohorts, training data set) and 4,904 (2015 cohort, test data set) first-year training physicians (interns) at hospital systems across the United States. We assessed suicidal ideation two months before internship and then quarterly through intern year. The prevalence of reported suicidal ideation in the study population increased from 3.0% at baseline to a mean of 6.9% during internship. 16.4% of interns reported suicidal ideation at least once during their internship. In the training dataset, a series of baseline demographic (male gender) and psychological factors (high neuroticism, depressive symptoms and suicidal ideation) were associated with increased risk of suicidal ideation during internship. Further, prior quarter psychiatric symptoms (depressive symptoms and suicidal ideation) and concurrent work-related factors (increase in self-reported work hours and medical errors) were associated with increased risk of suicidal ideation. A model derived from the training dataset had a predicted area under the Receiver Operating Characteristic curve (AUC) of 0.83 in the test dataset. The suicidal ideation risk predictors analyzed in this study can help programs and interns identify those at risk for suicidal ideation before the onset of training. Further, increases in self-reported work hours and environments associated with increased medical errors are potentially modifiable factors for residency programs to target to reduce suicide risk.

Key Points Question Are the genetic risk factors for depression the same in individuals of East Asian and European descent? Findings In this genome-wide association meta-analysis of depression in 194 548 individuals with East Asian ancestry, 2 novel genetic associations were identified, one of which is specific to individuals of East Asian descent living in East Asian countries. There was limited evidence for transferability with only 11% of depression loci previously identified in individuals of European descent reaching nominal significance levels in the individuals of East Asian descent. Meaning Caution is advised against generalizing findings about genetic risk factors for depression beyond the studied population., This genetic association study investigates the genetics of depression across multiple data sets of individuals of East Asian and European descent living in different countries and within different nongenetic cultural contexts., Importance Most previous genome-wide association studies (GWAS) of depression have used data from individuals of European descent. This limits the understanding of the underlying biology of depression and raises questions about the transferability of findings between populations. Objective To investigate the genetics of depression among individuals of East Asian and European descent living in different geographic locations, and with different outcome definitions for depression. Design, Setting, and Participants Genome-wide association analyses followed by meta-analysis, which included data from 9 cohort and case-control data sets comprising individuals with depression and control individuals of East Asian descent. This study was conducted between January 2019 and May 2021. Exposures Associations of genetic variants with depression risk were assessed using generalized linear mixed models and logistic regression. The results were combined across studies using fixed-effects meta-analyses. These were subsequently also meta-analyzed with the largest published GWAS for depression among individuals of European descent. Additional meta-analyses were carried out separately by outcome definition (clinical depression vs symptom-based depression) and region (East Asian countries vs Western countries) for East Asian ancestry cohorts. Main Outcomes and Measures Depression status was defined based on health records and self-report questionnaires. Results There were a total of 194 548 study participants (approximate mean age, 51.3 years; 62.8% women). Participants included 15 771 individuals with depression and 178 777 control individuals of East Asian descent. Five novel associations were identified, including 1 in the meta-analysis for broad depression among those of East Asian descent: rs4656484 (β = −0.018, SE = 0.003, P = 4.43x10−8) at 1q24.1. Another locus at 7p21.2 was associated in a meta-analysis restricted to geographically East Asian studies (β = 0.028, SE = 0.005, P = 6.48x10−9 for rs10240457). The lead variants of these 2 novel loci were not associated with depression risk in European ancestry cohorts (β = −0.003, SE = 0.005, P = .53 for rs4656484 and β = −0.005, SE = 0.004, P = .28 for rs10240457). Only 11% of depression loci previously identified in individuals of European descent reached nominal significance levels in the individuals of East Asian descent. The transancestry genetic correlation between cohorts of East Asian and European descent for clinical depression was r = 0.413 (SE = 0.159). Clinical depression risk was negatively genetically correlated with body mass index in individuals of East Asian descent (r = −0.212, SE = 0.084), contrary to findings for individuals of European descent. Conclusions and Relevance These results support caution against generalizing findings about depression risk factors across populations and highlight the need to increase the ancestral and geographic diversity of samples with consistent phenotyping.

Background Major Depressive Disorder (MDD) has been linked in the literature to poorer prognosis in patients with cardiovascular dysfunction, although the mechanisms of this relationship remain unclear. Underlying Sleep Disordered Breathing (SDB) serves as a potential candidate to explain this effect due to its downstream effects on inflammatory activation and decreased nitric oxide (NO) bioavailability, both of which have been shown to contribute to the pathophysiology of both MDD and cardiovascular disease (CVD). Methods This study utilizes overnight polysomnography and an inflammation panel to examine the links between cardiovascular dysfunction and sleep difficulties in control participants and patients diagnosed with SDB only, MDD only, and both SDB and MDD. Results Results demonstrate a strong positive relationship between sleep dysfunction and the nitric oxide synthesis inhibitor Symmetric Dimethyl Arginine (SDMA) in the MDD-only cohort, suggesting a link between SDMA-mediated NO dysregulation and CVD pathogenesis in individuals with MDD. Additionally, hypopneas, a form of sleep impairment characterized by partial reduction of airflow, were found to play a significant role in the relationship between SDB and cardiovascular dysfunction in MDD-only patients. Conclusions Results of this study demonstrate the need for widespread screening for SDB in MDD populations to detect predisposition to CVD, and also offer SDMA as a new potential target for CVD treatment in individuals with MDD.

Key Points Question What is the prevalence of work-related trauma exposure, and what factors are associated with posttraumatic stress disorder among intern physicians? Findings In this cohort study of 1134 interns, 56.4% reported trauma exposure during internship, and 19.0% of those experiencing trauma screened positive for posttraumatic stress disorder. Risk factors for trauma exposure included non-Hispanic White race/ethnicity, more hours worked, early family environment, and baseline stressful life experiences; risk factors for posttraumatic stress disorder included being unmarried and non-Hispanic White, concern about medical errors, stressful life experiences during internship, and depression or anxiety at 12 months. Meaning This study’s findings suggest that work-related trauma exposure and posttraumatic stress disorder are prevalent among interns, and interventions may improve physician well-being., This cohort study examines the prevalence and associations of work-related trauma exposure and posttraumatic stress disorder among physician interns in US residency programs., Importance Physicians are exposed to traumatic events during their work, but the impact and outcomes of these exposures are understudied. Objective To determine the prevalence and associations of work-related trauma exposure and posttraumatic stress disorder (PTSD) among a cohort of resident physicians in their internship year of training. Design, Setting, and Participants This cohort study involved physicians entering internship at US residency programs nationwide in 2018. Participants completed a baseline survey 1 to 2 months before commencing internship, as well as follow-up surveys at 4 time points during internship. Statistical analysis was performed from April 2020 to January 2021. Exposures Twelve months of internship. Main Outcomes and Measures Prevalence of work-related trauma and prevalence of PTSD among those who experienced work-related trauma. Trauma exposure and PTSD symptoms were assessed using the Primary Care PTSD Screen for Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (PC-PTSD-5). Risk factors assessed included depression, anxiety, early family environment, stressful life experiences, medical specialty, hours worked, and concern about medical errors. Results Among 1134 interns who completed the PC-PTSD-5 at month 12 of internship, 665 (58.6%) were female and 695 (61.6%) were non-Hispanic White; the mean (SD) age was 27.52 (2.50) years. There were 640 interns (56.4%) who reported work-related trauma exposure; among these interns with trauma exposure, 123 (19.0%) screened positive for PTSD. Overall, 123 of 1134 training physicians (10.8%) screened positive for PTSD by the end of internship year, as compared with a 12-month PTSD prevalence rate of 3.6% in the general population. Multivariable logistic regression analyses, adjusting for demographic characteristics, indicated that risk factors associated with trauma exposure included non-Hispanic White race/ethnicity (odds ratio [OR], 1.51 [95% CI, 1.14-2.01]; P = .004), more hours worked (OR, 1.01 [95% CI, 1.00-1.03]; P = .03), early family environment (OR, 1.03 [95% CI, 1.01-1.05]; P

PURPOSE: This study investigated the associations between program-level variables such as organizational structure, workload, and learning environment and residents’ development of depressive symptoms during internship. METHOD: Between 2012 and 2015, 1,276 internal medicine interns from 54 U.S. residency programs completed the Patient Health Questionnaire (PHQ-9) before internship, and then quarterly throughout the internship. The training environment was assessed via a resident questionnaire (RQ) and average weekly work hours. The authors gathered program structural variables from the American Medical Association Fellowship and Residency Electronic Interactive Database (FREIDA online); and program research rankings from Doximity. Associations between program-level variables and change in depressive symptoms were determined using stepwise linear regression modeling. RESULTS: Mean program PHQ-9 scores increased from 2.3 at baseline to 5.9 during internship (mean difference 3.6, SD 1.4, P < .001), with the mean increase ranging from −0.3 to 8.8 (interquartile range 1.1) among the programs included. In multivariable models, faculty feedback (β −0.37, 95% CI −0.62, −0.12, P = .005), learning experience in in-patient rotations (β −0.28, 95% CI −0.54, −0.02, P = .030), work hours (β 0.34, 95% CI 0.13, 0.56, P = .002), and research ranking position (β −0.25, 95% CI −0.47, −0.03, P = .036) were associated with change in depressive symptoms. CONCLUSIONS: Poor faculty feedback and inpatient learning experience, long work hours, and high institutional research rankings were associated with increased depressive symptoms among internal medicine interns. These factors may be potential targets for interventions to improve wellness and mental health among these professionals.

Background Stress is a key precipitant for many common diseases, but established biological markers to track stress and guide investigations into mechanisms linking stress and disease are lacking. Cross-sectional studies have identified correlations between stress and telomere attrition, but no large, longitudinal studies examining the impacts of chronic stress on telomere length exist. Residency training for physicians is a well-established stressful experience and can be used as a prospective stress model. Methods In a longitudinal cohort study of 250 interns (first-year residents) at 55 United States hospital systems serving during the 2015–2016 academic year, we examined associations between measures of the residency experience and saliva-measured telomere attrition. Results Telomere length shortened significantly over the course of internship year, from mean ± SD of 6465.1 ± 876.8 base pairs before internship to 6321.5 ± 630.6 base pairs at the end of internship (t246 = 2.69; p = .008). Stressful early family environments and neuroticism were significantly associated with shorter preinternship telomere length. Longer work hours were associated with greater telomere intern telomere loss over the year (p = .002). Of note, the mean telomere attrition during internship year was six times greater than the typical annual attrition rate identified in a recent meta-analysis. Conclusions This work implicates telomere attrition as a biologically measurable consequence of physician training, with the magnitude of attrition associated with workload. Identification of an objective, biological sequela of residency stress may help to facilitate the development of effective interventions. Further, the findings implicate telomere attrition as an objective biomarker to follow the pathologic effects of stress, in general.

Heterogeneity within Major Depressive Disorder (MDD) has hampered identification of biological markers (e.g., intermediate phenotypes, IPs) that might increase risk for the disorder or reflect closer links to the genes underlying the disease process. The newer characterizations of dimensions of MDD within Research Domain Criteria (RDoC) domains may align well with the goal of defining IPs. We compare a sample of 25 individuals with MDD compared to 29 age and education matched controls in multimodal assessment. The multimodal RDoC assessment included the primary IP biomarker, positron emission tomography (PET) with a selective radiotracer for 5-HT1A [(11C)WAY-100635], as well as event-related functional MRI with a Go/No-go task targeting the Cognitive Control network, neuropsychological assessment of affective perception, negative memory bias and Cognitive Control domains. There was also an exploratory genetic analysis with the serotonin transporter (5-HTTLPR) and monamine oxidase A (MAO-A) genes. In regression analyses, lower 5-HT1A binding potential (BP) in the MDD group was related to diminished engagement of the Cognitive Control network, slowed resolution of interfering cognitive stimuli, one element of Cognitive Control. In contrast, higher/normative levels of 5-HT1A BP in MDD (only) was related to a substantial memory bias toward negative information, but intact resolution of interfering cognitive stimuli and greater engagement of Cognitive Control circuitry. The serotonin transporter risk allele was associated with lower 1a BP and the corresponding imaging and cognitive IPs in MDD. Lowered 5HT 1a BP was present in half of the MDD group relative to the control group. Lowered 5HT 1a BP may represent a subtype including decreased engagement of Cognitive Control network and impaired resolution of interfering cognitive stimuli. Future investigations might link lowered 1a BP to neurobiological pathways and markers, as well as probing subtype-specific treatment targets.

This large, multi-institutional study examines the relative contribution of residency specialty and institution to resident satisfaction with their learning environment and workload.Survey responses from 798 residents were linked to institution (N = 20) and specialty (N = 10) and to characteristics of individual residency programs (N = 126) derived from the FREIDA Online database. Hierar chical linear modeling was used to estimate relative contributions of these factors to resident satisfaction with workload and learning environment.Institution had greater influence than specialty on resident ratings of satisfaction with their workload and learning environment. Institution and specialty accounted for more variance in satisfaction with workload than with the learning environment. There is evidence that characteristics of a given residency program in a given institution have additional impact beyond these main effects. However, characteristics of institutions or programs, such as program selectivity, off-duty periods, or number of faculty, did not explain statistically significant amounts of variance in resident satisfaction ratings.This study is the first to quantify the degree to which institution and specialty contribute to differences in resident perceptions of their learning environment and workload. Although organizational and institutional cultures are presumed to influence the learning environment, estimating the size of these influences requires a multi-institutional and multispecialty dataset, such as this one. These results suggest that there is empirical justification for institutional interventions to improve the learning environment.

Background Cancer patients who undergo allogeneic hematopoietic stem cell transplantation are among the most medically fragile patient populations with extreme demands for caregivers. Indeed, with earlier hospital discharges, the demands placed on caregivers continue to intensify. Moreover, an increased number of allogeneic hematopoietic stem cell transplantations are being performed worldwide, and this expensive procedure has significant economic consequences. Thus, the health and well-being of family caregivers have attracted widespread attention. Mobile health technology has been shown to deliver flexible, and time- and cost-sparing interventions to support family caregivers across the care trajectory. Objective This protocol aims to leverage technology to deliver a novel caregiver-facing mobile health intervention named Roadmap 2.0. We will evaluate the effectiveness of Roadmap 2.0 in family caregivers of patients undergoing hematopoietic stem cell transplantation. Methods The Roadmap 2.0 intervention will consist of a mobile randomized trial comparing a positive psychology intervention arm with a control arm in family caregiver-patient dyads. The primary outcome will be caregiver health-related quality of life, as assessed by the PROMIS Global Health scale at day 120 post-transplant. Secondary outcomes will include other PROMIS caregiver- and patient-reported outcomes, including companionship, self-efficacy for managing symptoms, self-efficacy for managing daily activities, positive affect and well-being, sleep disturbance, depression, and anxiety. Semistructured qualitative interviews will be conducted among participants at the completion of the study. We will also measure objective physiological markers (eg, sleep, activity, heart rate) through wearable wrist sensors and health care utilization data through electronic health records. Results We plan to enroll 166 family caregiver-patient dyads for the full data analysis. The study has received Institutional Review Board approval as well as Code Review and Information Assurance approval from our health information technology services. Owing to the COVID-19 pandemic, the study has been briefly put on hold. However, recruitment began in August 2020. We have converted all recruitment, enrollment, and onboarding processes to be conducted remotely through video telehealth. Consent will be obtained electronically through the Roadmap 2.0 app. Conclusions This mobile randomized trial will determine if positive psychology-based activities delivered through mobile health technology can improve caregiver health-related quality of life over a 16-week study period. This study will provide additional data on the effects of wearable wrist sensors on caregiver and patient self-report outcomes. Trial Registration ClinicalTrials.gov NCT04094844; https://www.clinicaltrials.gov/ct2/show/NCT04094844 International Registered Report Identifier (IRRID) PRR1-10.2196/19288

This survey study examined data from medical schools’ disability offices to estimate the proportion of students who disclose psychological disabilities.

Study objectives While anxiety rates are alarmingly high in short sleeping insomniacs, the relationship between insomnia and anxiety symptoms has not been extensively studied, especially in comparison to the relationship between insomnia and depressive symptoms. Using residency training as a naturalistic stress exposure, we prospectively assessed the role of sleep disturbance and duration on anxiety-risk in response to stress. Methods Web-based survey data from 1336 first-year training physicians (interns) prior to and then quarterly across medical internship. Using mixed effects modeling, we examined how pre-internship sleep disturbance and internship sleep duration predicted symptoms of anxiety, using an established tool for quantifying symptom severity in generalized anxiety disorder (GAD). Results Pre-internship poor sleepers are at more than twice the odds of having short sleep (≤6 h) during internship as good sleepers (OR = 2.38, 95% CI = 1.61, 3.57). Poor sleepers were also at twice the odds for screening positive for probable GAD diagnosis (OR = 2.08, 95% CI = 1.26, 3.45). Notably, sleep onset insomnia strongly predicted anxiety development under stress (OR = 3.55, 95% CI = 1.49, 8.45). During internship, short sleep associated with concurrent anxiety symptoms (b = −0.26, 95% CI = −0.38, −0.14) and predicted future anxiety symptoms even more strongly (b = −0.39, 95% CI = −0.76, −0.03). Conclusions Poor sleepers, particularly those with sleep onset insomnia symptoms, are vulnerable to short sleep and GAD anxiety and worry during chronic stress.

Importance In the United States, approximately 1 physician dies by suicide every day. Training physicians are at particularly high risk, with suicidal ideation increasing more than 4-fold during the first 3 months of internship year. Despite this increase, to our knowledge, very few efforts have been made to prevent the escalation of suicidal thoughts among training physicians. Objective To assess the effectiveness of a web-based cognitive behavioral therapy (wCBT) program delivered prior to the start of internship year in the prevention of suicidal ideation in medical interns. Design, Setting, and Participants A randomized clinical trial conducted at 2 university hospitals with 199 interns from multiple specialties during academic years 2009-2010 or 2011-2012. The current study was conducted from May 2009 to June 2010 and May 2011 to June 2012, and data were analyzed using intent-to-treat principles, including last observation carried forward. Interventions Interns were randomly assigned to 2 study groups (wCBT and attention-control group [ACG]), and completed study activities lasting 30 minutes each week for 4 weeks prior to starting internship year. Participants assigned to wCBT completed online CBT modules and those assigned to ACG received emails with general information about depression, suicidal thinking, and local mental health professionals. Main Outcomes and Measures The Patient Health Questionnaire–9 was used to assess suicidal ideation (ie, “thoughts that you would be better off dead or hurting yourself in some way”) prior to the start of intern year and at 3-month intervals throughout the year. Results A total of 62.2% of interns (199 of 320) agreed to take part in the study; 100 were assigned to the wCBT group and 99 to the ACG. During at least 1 point over the course of internship year, 12% of interns (12 of 100) assigned to wCBT endorsed suicidal ideation compared with 21.2% of interns (21 of 99) assigned to ACG. After adjusting for covariates identified a priori that have previously shown to increase the risk for suicidal ideation, interns assigned to wCBT were less likely to endorse suicidal ideation during internship year (relative risk, 0.40; 95% CI, 0.17- 0.91; P = .03) compared with those assigned to ACG. Conclusions and Relevance This study demonstrates that a free, easily accessible, brief wCBT program is associated with reduced likelihood of suicidal ideation among medical interns. Prevention programs with these characteristics could be easily disseminated to medical training programs across the country. Trial Registration anzctr.org.au Identifier:ACTRN12610000628044

ObjectiveTo study the effects of recent political events on mood among young physicians.DesignProspective cohort study.SettingUnited States medical centres.Participants2345 medical interns provided longitudinal mood data as part of the Intern Health Study between 2016 and 2018.Main outcome measuresMean mood score during the week following influential political and non-political events as compared with mean mood during the preceding four week control period.ResultsWe identified nine political events and eight non-political events for analysis. With the start of internship duties in July, the mean decline in mood for interns was −0.30 (95% confidence interval −0.33 to −0.27, t=−17.45, PConclusionsMacro level factors such as politics may be correlated with the mood of young doctors. This finding signals the need for further evaluation of the consequences of increasing entanglement between politics and medicine moving forward for young physicians and their patients.

BACKGROUND: Although short sleep, shift work, and physical inactivity are endemic to residency, a lack of objective, real-time information has limited our understanding of how these problems impact physician mental health. OBJECTIVE: To understand how the residency experience affects sleep, physical activity, and mood, and to understand the directional relationships among these variables. DESIGN: A prospective longitudinal study. SUBJECTS: Thirty-three first-year residents (interns) provided data from 2 months pre-internship through the first 6 months of internship. MAIN MEASURES: Objective real-time assessment of daily sleep and physical activity was assessed through accelerometry-based wearable devices. Mood scaled from 1 to 10 was recorded daily using SMS technology. Average compliance rates prior to internship for mood, sleep, and physical activity were 77.4, 80.2, and 93.7%, and were 78.8, 53.0, and 79.9% during internship. KEY RESULTS: After beginning residency, interns lost an average of 2 h and 48 min of sleep per week (t = − 3.04, p

Objective Life stress consistently increases the incidence of major depression. Recent evidence has shown that individual symptoms of major depressive disorder (MDD) differ in important dimensions such as their genetic and etiological background, but the impact of stress on individual MDD symptoms is not known. Here, we assess whether stress affects depression symptoms differentially. Method We used the chronic stress of medical internship to examine changes of the nine Diagnostic and Statistical Manual (DSM)-5 criterion symptoms for depression in 3021 interns assessed prior to and throughout internship. Results All nine depression symptoms increased in response to stress (all P

Study Objectives While short and poor quality sleep among training physicians has long been recognized as problematic, the longitudinal relationships among sleep, work hours, mood, and work performance are not well understood. Here, we prospectively characterize the risk of depression and medical errors based on preinternship sleep disturbance, internship-related sleep duration, and duty hours. Methods Survey data from 1215 nondepressed interns were collected at preinternship baseline, then 3 and 6 months into internship. We examined how preinternship sleep quality and internship sleep and work hours affected risk of depression at 3 months, per the Patient Health Questionnaire 9. We then examined the impact of sleep loss and work hours on depression persistence from 3 to 6 months. Finally, we compared self-reported errors among interns based on nightly sleep duration (≤6 hr vs. >6 hr), weekly work hours (

In this chapter, the common and often comorbid illnesses of depression and anxiety in physicians throughout their professional trajectory are discussed, through a consolidated review of the literature and through a clinical lens. The epidemiology is discussed in detail, as physicians are known to have elevated rates of depression and anxiety as compared to the general population, with particularly alarming differences during medical school and residency. Additionally, several unique features of these illnesses in the physician population are discussed, such as the psychological contributors, barriers to accessing treatment, impact on the provision of healthcare, and impact on career satisfaction and quality of life. Next, the relevant developmental concerns related to physician depression and anxiety are discussed, particularly highlighting the high-risk period of medical training. And finally, treatment interventions that address physician depression and anxiety are explored, both at individual and programmatic levels, along with potential future directions for ongoing efforts to reduce the negative footprint that physician depression and anxiety otherwise have on this field and its practitioners.

Importance Medical students are at high risk for depression and suicidal ideation. However, the prevalence estimates of these disorders vary between studies. Objective To estimate the prevalence of depression, depressive symptoms, and suicidal ideation in medical students. Data Sources and Study Selection Systematic search of EMBASE, ERIC, MEDLINE, psycARTICLES, and psycINFO without language restriction for studies on the prevalence of depression, depressive symptoms, or suicidal ideation in medical students published before September 17, 2016. Studies that were published in the peer-reviewed literature and used validated assessment methods were included. Data Extraction and Synthesis Information on study characteristics; prevalence of depression or depressive symptoms and suicidal ideation; and whether students who screened positive for depression sought treatment was extracted independently by 3 investigators. Estimates were pooled using random-effects meta-analysis. Differences by study-level characteristics were estimated using stratified meta-analysis and meta-regression. Main Outcomes and Measures Point or period prevalence of depression, depressive symptoms, or suicidal ideation as assessed by validated questionnaire or structured interview. Results Depression or depressive symptom prevalence data were extracted from 167 cross-sectional studies (n = 116 628) and 16 longitudinal studies (n = 5728) from 43 countries. All but 1 study used self-report instruments. The overall pooled crude prevalence of depression or depressive symptoms was 27.2% (37 933/122 356 individuals; 95% CI, 24.7% to 29.9%, I2 = 98.9%). Summary prevalence estimates ranged across assessment modalities from 9.3% to 55.9%. Depressive symptom prevalence remained relatively constant over the period studied (baseline survey year range of 1982-2015; slope, 0.2% increase per year [95% CI, −0.2% to 0.7%]). In the 9 longitudinal studies that assessed depressive symptoms before and during medical school (n = 2432), the median absolute increase in symptoms was 13.5% (range, 0.6% to 35.3%). Prevalence estimates did not significantly differ between studies of only preclinical students and studies of only clinical students (23.7% [95% CI, 19.5% to 28.5%] vs 22.4% [95% CI, 17.6% to 28.2%]; P = .72). The percentage of medical students screening positive for depression who sought psychiatric treatment was 15.7% (110/954 individuals; 95% CI, 10.2% to 23.4%, I2 = 70.1%). Suicidal ideation prevalence data were extracted from 24 cross-sectional studies (n = 21 002) from 15 countries. All but 1 study used self-report instruments. The overall pooled crude prevalence of suicidal ideation was 11.1% (2043/21 002 individuals; 95% CI, 9.0% to 13.7%, I2 = 95.8%). Summary prevalence estimates ranged across assessment modalities from 7.4% to 24.2%. Conclusions and Relevance In this systematic review, the summary estimate of the prevalence of depression or depressive symptoms among medical students was 27.2% and that of suicidal ideation was 11.1%. Further research is needed to identify strategies for preventing and treating these disorders in this population.

This survey study examines how gender disparities are associated with attrition from the workforce and how family considerations are associated with decisions about how much to work.