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There is a high prevalance of burnout and mental health illness among trainees. Through structured meetings, Program Directors (PDs) have an opportunity to screen and aid residents that may be affected by mental health concerns. However, barriers to this process exist. This study sought to evaluate the perspectives of PDs regarding mental health screening for trainees.A 13-item survey-based study.Electronic distribution of the survey was performed via three individualized requests sent via e-mail to PDs.PDs of 5 ACGME specialties, including Internal Medicine, Pediatrics, Emergency Medicine, General Surgery, and Psychiatry were invited to participate.In total, 595 PDs responded to the survey (response rate = 40.0%) In general, PDs expressed dissatisfaction with the management of burnout and mental health. Most PDs supported periodic screening of residents for burnout (87.0%) and mental health (73.9%). For a resident that could screen positive for mental illness, most PDs were concerned about the possibility of harm to a patient (70.7%) and implications for future licensing (65.7%). Only 30.2% of PDs currently use some form of standardized screening to identify residents struggling with mental health and burnout concerns.The majority of PDs across 5 ACGME specialties support the use of periodic screening of residents for burnout and mental health. However, concerns exist regarding such screening such as the implications for future licensing. Additional work needs to be done to address PD concerns and destigmatizate mental health wellbeing and care among trainees.

Intensive longitudinal methods (ILMs) for collecting self-report (e.g., daily diaries, ecological momentary assessment) and passive data from smartphones and wearable sensors provide promising avenues for improved prediction of depression and suicidal ideation (SI). However, few studies have utilized ILMs to predict outcomes for at-risk, non-clinical populations in real-world settings.Medical interns (N = 2881; 57 % female; 58 % White) were recruited from over 300 US residency programs. Interns completed a pre-internship assessment of depression, were given Fitbit wearable devices, and provided daily mood ratings (scale: 1-10) via mobile application during the study period. Three-step hierarchical logistic regressions were used to predict depression and SI at the end of the first quarter utilizing pre-internship predictors in step 1, Fitbit sleep/step features in step 2, and daily diary mood features in step 3.Passively collected Fitbit features related to sleep and steps had negligible predictive validity for depression, and no incremental predictive validity for SI. However, mean-level and variability in mood scores derived from daily diaries were significant independent predictors of depression and SI, and significantly improved model accuracy.Work schedules for interns may result in sleep and activity patterns that differ from typical associations with depression or SI. The SI measure did not capture intent or severity.Mobile self-reporting of daily mood improved the prediction of depression and SI during a meaningful at-risk period under naturalistic conditions. Additional research is needed to guide the development of adaptive interventions among vulnerable populations.

The primary objective of this study was to establish the feasibility and acceptability of an intensive data collection protocol that involves the delivery of a personalized just-in-time adaptive intervention (JITAI) in three distinct groups of care partners (care partners of persons with spinal cord injury [SCI], Huntington's disease [HD], or hematopoietic cell transplantation [HCT]).Seventy care partners were enrolled in this study (n = 19 SCI; n = 21 HD, n = 30 HCT). This three-month (90 day) randomized control trial involved wearing a Fitbit to track sleep and steps, providing daily reports of health-related quality of life (HRQOL), and completing end of month HRQOL surveys. Care partners in the JITAI group also received personalized pushes (i.e., text-based phone notifications that include brief tips or suggestions for improving self-care). At the end of three-months, care partners in both groups completed a feasibility and acceptability questionnaire.Most (98.6%) care partners completed the study, average compliance was 88% for daily HRQOL surveys, 96% for daily steps, and 85% for daily sleep (from wearing the Fitbit), and all monthly surveys were completed with the exception of one missed 3-month assessment. The acceptability of the protocol was high; ratings exceeded 80% agreement for the different elements of the study. Improvements were seen for the majority of the HRQOL measures. There was no evidence of measurement reactivity.Findings provide strong support for the acceptability and feasibility of an intensive data collection protocol that involved the administration of a JITAI. Although this trial was not powered to establish efficacy, findings indicated improvements across a variety of different HRQOL measures (~1/3 of which were statistically significant). (PsycInfo Database Record (c) 2022 APA, all rights reserved).

BACKGROUND Although evidence supporting the feasibility of large-scale mobile health (mHealth) systems continues to grow, privacy protection remains an important implementation challenge. The potential scale of publicly available mHealth applications and the sensitive nature of the data involved will inevitably attract unwanted attention from adversarial actors seeking to compromise user privacy. Although privacy-preserving technologies such as federated learning (FL) and differential privacy (DP) offer strong theoretical guarantees, it is not clear how such technologies actually perform under real-world conditions. OBJECTIVE Using data from the University of Michigan Intern Health Study (IHS), we assessed the privacy protection capabilities of FL and DP against the trade-offs in the associated model’s accuracy and training time. Using a simulated external attack on a target mHealth system, we aimed to measure the effectiveness of such an attack under various levels of privacy protection on the target system and measure the costs to the target system’s performance associated with the chosen levels of privacy protection. METHODS A neural network classifier that attempts to predict IHS participant daily mood ecological momentary assessment score from sensor data served as our target system. An external attacker attempted to identify participants whose average mood ecological momentary assessment score is lower than the global average. The attack followed techniques in the literature, given the relevant assumptions about the abilities of the attacker. For measuring attack effectiveness, we collected attack success metrics (area under the curve [AUC], positive predictive value, and sensitivity), and for measuring privacy costs, we calculated the target model training time and measured the model utility metrics. Both sets of metrics are reported under varying degrees of privacy protection on the target. RESULTS We found that FL alone does not provide adequate protection against the privacy attack proposed above, where the attacker’s AUC in determining which participants exhibit lower than average mood is over 0.90 in the worst-case scenario. However, under the highest level of DP tested in this study, the attacker’s AUC fell to approximately 0.59 with only a 10% point decrease in the target’s R2 and a 43% increase in model training time. Attack positive predictive value and sensitivity followed similar trends. Finally, we showed that participants in the IHS most likely to require strong privacy protection are also most at risk from this particular privacy attack and subsequently stand to benefit the most from these privacy-preserving technologies. CONCLUSIONS Our results demonstrated both the necessity of proactive privacy protection research and the feasibility of the current FL and DP methods implemented in a real mHealth scenario. Our simulation methods characterized the privacy-utility trade-off in our mHealth setup using highly interpretable metrics, providing a framework for future research into privacy-preserving technologies in data-driven health and medical applications.

BackgroundWhile evidence supporting the feasibility of large scale mHealth systems continues to grow, privacy protection continues to be an important implementation challenge. The potential scale of publicly available mHealth applications and the sensitive nature of the data involved will inevitably attract unwanted attention from adversarial actors seeking to compromise user privacy. Although privacy-preserving technologies such as Federated Learning and Differential Privacy offers strong theoretical guarantees, it is not clear how such technologies actually perform under real-world conditions.ObjectiveUsing data from the University of Michigan Intern Health Study (IHS), we assess the privacy protection capabilities of Federated Learning and Differential Privacy against the associated tradeoffs in model accuracy and training time using simulation methods. Specifically, our objectives are to (1) construct a “target” mHealth system using the demographic and sensor data available in the IHS (2) mount a simulated privacy attack that attempts to compromise IHS participant privacy (3) measure the effectiveness of such an attack under various levels of privacy protection on the target mHealth system, and (4) measure the costs to algorithmic performance associated with the chosen levels of privacy protection.MethodsFor (1), we perform simple data processing/imputation and construct a neural network classifier that attempts to predict participant daily mood EMA score from sensor data. For (2) we make certain assumptions of the attacker’s capabilities and construct an attack intended to uncover statistical properties of private participant data based on techniques proposed in the literature. For (3) and (4), we report a collection of conventional metrics to evaluate the success of the privacy attack and performance of the original mHealth system under Federated Learning and various levels of Differential Privacy.ResultsWe find that Federated Learning alone does not provide adequate protection against the privacy attack proposed above, where the attacker’s success rate in identifying private data attributes is over 90% in the worst case. However, under the highest level of Differential Privacy tested in this paper, the attacker’s success rate falls to around 59.6% with only a 10 percentage point decrease in model R2and a 42% increase in model training time. Finally, we show that those participants in the IHS most likely to require strong privacy protection are also most at risk from this particular privacy attack and subsequently stand to benefit the most from these privacy-preserving technologies.ConclusionsOur results demonstrate both the necessity of proactive privacy protection research and the feasibility of current Federated Learning and Differential Privacy methods implemented in a real mHealth scenario. Our simulation methods for privacy protection measurement provide a novel framework for characterizing the privacy-utility tradeoff and serve as a potential foundation for future research.

Background Use of intensive longitudinal methods (e.g. ecological momentary assessment, passive sensing) and machine learning (ML) models to predict risk for depression and suicide has increased in recent years. However, these studies often vary considerably in length, ML methods used, and sources of data. The present study examined predictive accuracy for depression and suicidal ideation (SI) as a function of time, comparing different combinations of ML methods and data sources. Methods Participants were 2459 first-year training physicians (55.1% female; 52.5% White) who were provided with Fitbit wearable devices and assessed daily for mood. Linear [elastic net regression (ENR)] and non-linear (random forest) ML algorithms were used to predict depression and SI at the first-quarter follow-up assessment, using two sets of variables (daily mood features only, daily mood features + passive-sensing features). To assess accuracy over time, models were estimated iteratively for each of the first 92 days of internship, using data available up to that point in time. Results ENRs using only the daily mood features generally had the best accuracy for predicting mental health outcomes, and predictive accuracy within 1 standard error of the full 92 day models was attained by weeks 7–8. Depression at 92 days could be predicted accurately (area under the curve >0.70) after only 14 days of data collection. Conclusions Simpler ML methods may outperform more complex methods until passive-sensing features become better specified. For intensive longitudinal studies, there may be limited predictive value in collecting data for more than 2 months.

Although stress is posited to play a key role in health disparities, the extent to which commonly used self-report psychosocial stress measures are related to neurobiological stress processes, especially across diverse populations, is unresolved. This study examined how two measures of psychosocial stress, perceived stress and domain-specific stress, covary with the acute neurobiological stress response.The Richmond Stress and Sugar Study includes a racially and socioeconomically diverse cohort of adults at risk for type 2 diabetes ( n = 125; mean age = 57 years, 48% Black, and 61% high neighborhood socioeconomic status [SES]). Hypothalamic-pituitary-adrenal axis reactivity was assessed by salivary cortisol response to the Trier Social Stress Test (TSST), a laboratory stressor.Higher perceived stress was associated with a lower cortisol response to the TSST (-7.5%; 95% confidence interval [CI] = -13.1% to -1.5%) but was not associated with cortisol recovery after the TSST (3%; 95% CI = -0.6% to 6.8%). In contrast, domain-specific stress was not associated with cortisol response (-2.1%; 95% CI = -20.7% to 20.9%) but was inversely associated with cortisol recovery (3.7%; 95% CI = 0.6% to 7.0%). SES modified these associations: both perceived stress and domain-specific stress were associated with TSST cortisol response only among participants from high-SES neighborhoods. There was minimal evidence of effect modification by race.Both self-report measures of psychosocial stress were associated with hypothalamic-pituitary-adrenal axis reactivity to an acute stressor. These associations varied by perceived versus domain-specific measurement scales and by neighborhood SES. Further efforts to refine stress measures and clarify biological linkages between social status and health are needed.

ImportanceEnsuring access to accommodations is critical for resident physicians and their patients. Studies show that a large proportion of medical trainees with disabilities do not request needed accommodations; however, drivers of nonrequests are unknown.ObjectiveTo assess the frequency of accommodation requests among first-year resident physicians (ie, interns) with disabilities and to identify possible drivers of nonrequest for needed accommodations.Design, Setting, and ParticipantsAs part of the Intern Health Study, a longitudinal cohort study of first-year resident physicians, residents at 86 surgical and nonsurgical residency programs in 64 US institutions provided demographic and training characteristics 2 months prior to matriculation (April-May 2021). At the end of their intern year (June 2022), participants completed a new survey with questions about disability-related information, including disability status, disability type, whether they received accommodations, and if not, reasons for nonaccommodation. Poststratification and attrition weights were used to estimate the frequency of accommodation requests and reasons for not requesting accommodations. Interns reporting at least 1 disability were included in the analysis.Main Outcomes and MeasuresPrevalence of reported disabilities, residency specialties distribution, frequency of accommodation requests, and reasons for nonaccommodation among resident physicians with disabilities.ResultsAmong the 1486 resident physicians who completed the baseline survey, 799 (53.8%) replied to the disability questions. Of those, 94 interns (11.8%; weighted number, 173 [11.9%]) reported at least 1 disability and were included in the present study (weighted numbers, 91 [52.6%] men, 82 [47.4%] women, mean [SD] age, 28.6 [3.0] years). Among interns with reported disability and need for accommodations (83 of 173 [48.0%]), more than half (42 [50.6%]) did not request them. The most frequently reported reasons for not requesting needed accommodations were fear of stigma or bias (25 [59.5%]), lack of a clear institutional process for requesting accommodations (10 [23.8%]), and lack of documentation (5 [11.9%]).Conclusions and RelevanceProgram directors should investigate cultural and structural factors within their programs that contribute to an environment where residents do not feel safe or supported in disclosing disability and requesting accommodation and review their disability policies for clarity.

Resident physicians (residents) experiencing prolonged workplace stress are at risk of developing mental health symptoms. Creating novel, unobtrusive measures of resilience would provide an accessible approach to evaluate symptom susceptibility without the perceived stigma of formal mental health assessments. In this work, we created a system to find indicators of resilience using passive wearable sensors and smartphone-delivered ecological momentary assessment (EMA). This system identified indicators of resilience during a medical internship, the high stress first-year of a residency program. We then created density estimation approaches to predict these indicators before mental health changes occurred, and validated whether the predicted indicators were also associated with resilience. Our system identified resilience indicators associated with physical activity (step count), sleeping behavior, reduced heart rate, increased mood, and reduced mood variability. Density estimation models were able to replicate a subset of the associations between sleeping behavior, heart rate, and resilience. To the best of our knowledge, this work provides the first methodology to identify and predict indicators of resilience using passive sensing and EMA. Researchers studying resident mental health can apply this approach to design resilience-building interventions and prevent mental health symptom development.

SUMMARYStress is the most important proximal precipitant of depression, yet most large genome-wide association studies (GWAS) do not include stress as a variable. Here, we review how gene × environment (G × E) interaction might impede the discovery of genetic factors, discuss two examples of G × E interaction in depression and addiction, studies incorporating high-stress environments, as well as upcoming waves of genome-wide environment interaction studies (GWEIS). We discuss recent studies which have shown that genetic distributions can be affected by social factors such as migrations and socioeconomic background. These distinctions are not just academic but have practical consequences. Owing to interaction with the environment, genetic predispositions to depression should not be viewed as unmodifiable destiny. Patients may genetically differ not just in their response to drugs, as in the now well-recognised field of pharmacogenetics, but also in how they react to stressful environments and how they are affected by behavioural therapies.

This cohort study evaluates data from the Intern Health Study and uses the 9-item Patient Health Questionnaire to estimate the prevalence of depression among surgical interns.

SummaryBackgroundTwin revolutions in wearable technologies and smartphone-delivered digital health interventions have significantly expanded the accessibility and uptake of personalized interventions in multiple domains of health sciences. Gamification, the application of gaming elements to increase enjoyment and engagement, has the potential to improve the effectiveness of digital health interventions. However, the effectiveness of competition gamification components remains poorly understood, challenging informed decisions on the potential adoption of these components in future studies and trial designs. We aimed to evaluate the effect of smartphone-based gamified team competition intervention on daily step count and sleep duration via a micro-randomized trial.MethodsWe recruited first-year medical residents (interns) in the US, who downloaded the study app, provided consent, wore a wearable device, and completed a baseline survey. Teams were formed based on participating residents’ institutions and specialties, and subsequently randomized weekly to the competition or non-competition arms. In the competition arm, opponent teams and competition type (step count or sleep duration) were also randomly selected. Competition participants had access to the current competition scoreboard and competition history via the study app; they also received scheduled competition-related push notifications in a competition week. We estimated the main and moderated causal effects of competition on proximal daily step count and sleep duration. This trial is registered with ClinicalTrials.gov (NCT05106439).FindingsBetween April and June 2020, we enrolled 2,286 medical interns from 263 institutions, of whom 1,936 were formed into 191 teams that met the criteria for participation in competitions between July 6 and September 27, 2020. 1,797 participants who had pre-internship baseline information were included in the analysis. Relative to the no competition arm, competition intervention significantly increased the mean daily step count by 111·5 steps (SE 40·4, p=0·01), while competition did not significantly affect the mean daily sleep minutes (p=0·69). Secondary moderator analyses indicated that, for each additional week-in-study, the causal effects of competition on daily step count and sleep minutes decreased by 9·1 (11·6) steps (p=0·43) and 1·9 (0·6) minutes (p=0·003), respectively. Intra-institutional competition negatively moderated the causal effect of competition upon daily step count by −114.9 (93·7) steps (p=0·22).InterpretationGamified competition delivered via mobile app significantly increased daily physical activity which suggests that team competition can function as a mobile health intervention tool to increase short-term physical activity level.Research in contextEvidence before this studyWe searched PubMed for studies of mobile health intervention with gamified components: (“mobile health intervention”, “mHealth intervention”, “mobile health gamification”). We evaluated studies published before November 30, 2021. The search was not limited by language. Previous work affirmed that in mobile health interventions, gamification is effective for improving user’s physical activity and mental health. Most of previous work used feedback, reward, and progress bar as game mechanics, while none have rigorously examined the effectiveness of gamified team competition.Added value of this studyThis study provides evidence that the gamified team competition has a positive effect on physical activity. The data that was intensively collected as part of this study can be used for further investigation.Implications of all the available evidenceThe results of this study indicate that gamified team competition has the potential to improve the effectiveness of and engagement with mobile health interventions.

The coronavirus disease 2019 (COVID-19) has had a profound impact on the academic research enterprise. Over the span of just a few weeks in March 2020, most large U.S. research institutions closed the majority of their laboratories, studios, and offices, suspended travel and fieldwork, and paused the majority of human research, resulting in the halt of more than 80% of all on-site research activity (1). After months of limited operations, laboratory- and field-based research in the basic and natural sciences were among the first activities to resume. These activities presented relatively low risk for transmission with implementation of proper control measures such as face coverings, health screens, and social distancing. Performing human research during a global pandemic raises new ethical and practical challenges on a scale never before seen. To safely and ethically restart more of the human research portfolio, institutions must develop guiding principles and an explicit plan for managing human research during the pandemic. Image credit: Carey Lumeng (University of Michigan, Ann Arbor, MI). Performing human research during a global pandemic, however, raises new ethical and practical challenges on a scale never before seen. Across the clinical, social, and behavioral sciences, human research can require close contact between researchers and participants, over variable observational periods, and across a variety of locations (e.g., clinics, schools, prisons). Therefore, research with human participants has been slower to resume given the risks associated with potential direct and/or airborne transmission between and among researchers and participants. Indeed, early evidence suggests that research disciplines that rely on face-to-face human contact are among the disciplines that have seen the steepest drop in productivity during the pandemic (2), despite the fact that human research constitutes a significant fraction of the research enterprise. Not all human research paused as stay-at-home orders were implemented across the United States. … [↵][1]1To whom correspondence may be addressed. Email: stroh{at}med.umich.edu or nwigg{at}umich.edu. [1]: #xref-corresp-1-1

Objective We evaluated associations between emotional exhaustion (EE), a measure of burnout, among medical school faculty and: demographic and professional characteristics, workplace stressors, coping skills, resilience, sufficient personal time, and depressive symptoms. Respondents completed surveys in November 2017. Methods We conducted bivariate and multivariable logistic regression and recycled predictions models to estimate associations between characteristics and probability of EE. Results Of 1,401 respondents, 42% endorsed EE. Faculty with more clinical effort, more workplace stress, less resilience, less personal time, and more depressive symptoms reported statistically significantly higher probabilities of EE compared to their counterparts. Female gender, mid-career stage, and coping skills were no longer associated with EE, after accounting for stress, resilience, personal time, and depressive symptoms. Conclusions Coping skills may not mitigate physician EE when coupled with substantial time and mental health burdens.

This cohort study compares perceived impairment associated with depressive symptoms among physicians before intern year vs during intern year.

Background Recent studies indicate that wearable sensors can capture subtle within-person changes caused by SARS-CoV-2 infection and play a role in detecting COVID-19 infections. However, in addition to direct effects of infection, wearable sensor data may capture changes in behavior after the receipt of COVID test results. At present, it remains unclear to what extent the observed discriminative performance of the wearable sensor data is affected by behavioral changes upon receipt of the test results. Methods We conducted a retrospective study of wearable sensor data in a sample of medical interns who had symptoms and received COVID-19 test results from March to December 2020, and calculated wearable sensor metrics incorporating changes in step, sleep, and resting heart rate for interns who tested positive (cases, n = 22) and negative (controls, n = 83) after symptom onset. All these interns had wearable sensor data available for > 50% of the days in pre- and post-symptom onset periods. We assessed discriminative accuracy of the metrics via area under the curve (AUC) and tested the impact of behavior changes after receiving test results by comparing AUCs of three models: all data, pre-test-result-only data, and post-test-result-only data. Results Wearable sensor metrics differentiated between symptomatic COVID-19 positive and negative individuals with good accuracy (AUC = 0.75). However, the discriminative capacity of the model with pre-test-result-only data substantially decreased (AUC from 0.75 to 0.63; change = -0.12, p = 0.013). The model with post-test-result-only data did not produce similar reductions in discriminative capacity. Conclusions Changes in wearable sensor data, especially physical activity and sleep, are robust indicators of COVID-19 infection, though they may be reflective of a person’s behavior change after receiving a positive test result as opposed to a physiological signature of the virus. Thus, wearable sensor data could facilitate the monitoring of COVID-19 prevalence, but not yet replace SARS-CoV-2 testing.

This cohort study uses data from a survey of US medical interns to assess the prevalence of self-reported disability and program accommodations and the association between accommodations, depressive symptoms, and self-reported medical errors among resident physicians.

BACKGROUND Efforts to address the high depression rates among training physicians have been implemented at various levels of the U.S. medical education system. The cumulative effect of these efforts is unknown. OBJECTIVE To assess how the increase in depressive symptoms with residency has shifted over time and to identify parallel trends in factors that have previously been associated with resident physician depression. DESIGN Repeated annual cohort study. SETTING U.S. health care organizations. PARTICIPANTS First-year resident physicians (interns) who started training between 2007 and 2019. MEASUREMENTS Depressive symptoms (9-item Patient Health Questionnaire [PHQ-9]) assessed at baseline and quarterly throughout internship. RESULTS Among 16 965 interns, baseline depressive symptoms increased from 2007 to 2019 (PHQ-9 score, 2.3 to 2.9; difference, 0.6 [95% CI, 0.3 to 0.8]). The prevalence of baseline predictors of greater increase in depressive symptoms with internship also increased across cohorts. Despite the higher prevalence of baseline risk factors, the average change in depressive symptoms with internship decreased 24.4% from 2007 to 2019 (change in PHQ-9 score, 4.1 to 3.0; difference, -1.0 [CI, -1.5 to -0.6]). This change across cohorts was greater among women (4.7 to 3.3; difference, -1.4 [CI, -1.9 to -0.9]) than men (3.5 to 2.9; difference, -0.6 [CI, -1.2 to -0.05]) and greater among nonsurgical interns (4.1 to 3.0; difference, -1.1 [CI, -1.6 to -0.6]) than surgical interns (4.0 to 3.2; difference, -0.8 [CI, -1.2 to -0.4]). In parallel to the decrease in depressive symptom change, there were increases in sleep hours, quality of faculty feedback, and use of mental health services and a decrease in work hours across cohorts. The decrease in work hours was greater for nonsurgical than surgical interns. Further, the increase in mental health treatment across cohorts was greater for women than men. LIMITATION Data are observational and subject to biases due to nonrandom sampling, missing data, and unmeasured confounders, limiting causal conclusions. CONCLUSION Although depression during physician training remains high, the average increase in depressive symptoms associated with internship decreased between 2007 and 2019. PRIMARY FUNDING SOURCE National Institute of Mental Health.

This cohort study evaluates the association of the COVID-19 pandemic with gender differences in work-family factors and mental health among physician parents., Key Points Question Has the COVID-19 pandemic been associated with differences in careers and mental health between physician mothers and fathers? Findings In this cohort study of 276 physicians during the COVID-19 pandemic, mothers were more likely than fathers to be responsible for childcare or schooling and household tasks, to work primarily from home, to reduce their work hours, and to experience work-to-family conflict, family-to-work conflict, and depressive and anxiety symptoms. A gender difference in depressive symptoms was observed among physician parents during the COVID-19 pandemic that was not present before the pandemic. Meaning This study suggests that pandemic conditions are associated with an increase in gender inequalities within medicine and signals the importance of further attention and resources to mitigate the potential adverse consequences for the careers and well-being of physician mothers., Importance The COVID-19 pandemic has placed increased strain on health care workers and disrupted childcare and schooling arrangements in unprecedented ways. As substantial gender inequalities existed in medicine before the pandemic, physician mothers may be at particular risk for adverse professional and psychological consequences. Objective To assess gender differences in work-family factors and mental health among physician parents during the COVID-19 pandemic. Design, Setting, and Participants This prospective cohort study included 276 US physicians enrolled in the Intern Health Study since their first year of residency training. Physicians who had participated in the primary study as interns during the 2007 to 2008 and 2008 to 2009 academic years and opted into a secondary longitudinal follow-up study were invited to complete an online survey in August 2018 and August 2020. Exposures Work-family experience included 3 single-item questions and the Work and Family Conflict Scale, and mental health symptoms included the Patient Health Questionnaire–9 (PHQ-9) and Generalized Anxiety Disorder–7 scale. Main Outcomes and Measures The primary outcomes were work-to-family and family-to-work conflict and depressive symptoms and anxiety symptoms during August 2020. Depressive symptoms between 2018 (before the COVID-19 pandemic) and 2020 (during the COVID-19 pandemic) were compared by gender. Results Among 215 physician parents who completed the August 2020 survey, 114 (53.0%) were female and the weighted mean (SD) age was 40.1 (3.57) years. Among physician parents, women were more likely to be responsible for childcare or schooling (24.6% [95% CI, 19.0%-30.2%] vs 0.8% [95% CI, 0.01%-2.1%]; P

During their first year of medical residency (internship), 35% of training physicians in the United States suffer at least one depression episode. We assessed whether there is a similar increase of depression among first year residents in China, and identified predictors of depression in the two systems. 1006 residents across three cohorts (2016–2017, 2017–2018 and 2018–2019) at Shanghai Jiao Tong University and Peking Union Medical College were assessed in parallel with three cohorts of 7028 residents at 100 + US institutions. The Patient Health Questionnaire-9 (PHQ-9) depressive symptoms were measured at baseline and quarterly. Demographic, personal and residency factors were assessed as potential predictors of PHQ-9 depression scores. Similar to training interns in the US, the proportion of participants in China who met depression criteria at least once during the first year of residency increased substantially, from 9.1 to 35.1%. History of depression and symptoms at baseline were common factors significantly associated with depression during residency. By contrast, neuroticism, early family environment, female gender and not being coupled were associated with depression risk only in the US, while young age was a predictor of depression only in China. Fear of workplace violence also was a predictor in China. Long duty hours and reduced sleep duration emerged as training predictors of depression in both countries. The magnitude of depression increase and work-related drivers of depression were similar between China and the US, suggesting a need for effective system reforms in both systems.