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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.

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

Gamification, the application of gaming elements to increase enjoyment and engagement, has the potential to improve the effectiveness of digital health interventions, while the effectiveness of competition gamification components remains poorly understood on residency. To address this gap, we evaluate the effect of smartphone-based gamified team competition intervention on daily step count and sleep duration via a micro-randomized trial on medical interns. Our aim is to assess potential improvements in the factors (namely step count and sleep) that may help interns cope with stress and improve well-being. In 1779 interns, team competition intervention significantly increases the mean daily step count by 105.8 steps (SE 35.8, p = 0.03) relative to the no competition arm, while does not significantly affect the mean daily sleep minutes (p = 0.76). Moderator analyses indicate that the causal effects of competition on daily step count and sleep minutes decreased by 14.5 steps (SE 10.2, p = 0.16) and 1.9 minutes (SE 0.6, p = 0.003) for each additional week-in-study, respectively. Intra-institutional competition negatively moderates the causal effect of competition upon daily step count by −90.3 steps (SE 86.5, p = 0.30). Our results show that gamified team competition delivered via mobile app significantly increases daily physical activity which suggests that team competition can function as a mobile health intervention tool to increase short-term physical activity levels for medical interns. Future improvements in strategies of forming competition opponents and introducing occasional competition breaks may improve the overall effectiveness.

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.

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.

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.

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

Mobile measures of human circadian rhythms (CR) are needed in the age of chronotherapy. Two wearable measures of CR have recently been validated: one that uses heart rate to extract circadian rhythms that originate in the sinoatrial node of the heart, and another that uses activity to predict the laboratory gold standard and central circadian pacemaker marker, dim light melatonin onset (DLMO). We first find that the heart rate markers of normal real-world individuals align with laboratory DLMO measurements when we account for heart rate phase error. Next, we expand upon previous work that has examined sleep patterns or chronotypes during the COVID-19 lockdown by studying the effects of social distancing on circadian rhythms. In particular, using data collected from the Social Rhythms app, a mobile application where individuals upload their wearable data and receive reports on their circadian rhythms, we compared the two circadian phase estimates before and after social distancing. Interestingly, we found that the lockdown had different effects on the two ambulatory measurements. Before the lockdown, the two measures aligned, as predicted by laboratory data. After the lockdown, when circadian timekeeping signals were blunted, these measures diverged in 70% of subjects (with circadian rhythms in heart rate, or CRHR, becoming delayed). Thus, while either approach can measure circadian rhythms, both are needed to understand internal desynchrony. We also argue that interventions may be needed in future lockdowns to better align separate circadian rhythms in the body.

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

Informal family care partners of persons with traumatic brain injury (TBI) often experience intense stress resulting from their caregiver role. As such, there is a need for low burden, and easy to engage in interventions to improve health-related quality of life (HRQOL) for these care partners. This study is designed to evaluate the effectiveness of a personalized just-in-time adaptive intervention (JITAI) aimed at improving the HRQOL of care partners. Participants are randomized either to a control group, where they wear the Fitbit® and provide daily reports of HRQOL over a six-month (180 day) period (without the personalized feedback), or the JITAI group, where they wear the Fitbit®, provide daily reports of HRQOL and receive personalized self-management pushes for 6 months. 240 participants will be enrolled (n = 120 control group; n = 120 JITAI group). Outcomes are collected at baseline, 1-, 2-, 3-, 4-, 5- & 6-months, as well as 3- and 6-months post intervention. We hypothesize that the care partners who receive the intervention (JITAI group) will show improvements in caregiver strain (primary outcome) and mental health (depression and anxiety) after the 6-month (180 day) home monitoring period. Participant recruitment for this study started in November 2020. Data collection efforts should be completed by spring 2025; results are expected by winter 2025. At the conclusion of this randomized control trial, we will be able to identify care partners at greatest risk for negative physical and mental health outcomes, and will have demonstrated the efficacy of this JITAI intervention to improve HRQOL for these care partners. Trial registration: ClinicalTrial.gov NCT04570930; https://clinicaltrials.gov/ct2/show/NCT04570930.

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.

Circadian rhythms drive the timing of many physiological events in the 24-hour day. When individuals undergo an abrupt external shift (e.g., change in work schedule or travel across multiple time zones), circadian rhythms become misaligned with the new time and may take several days to adjust. Chronic circadian misalignment, e.g., as a result of shift work, has been shown to lead to several physical and mental health problems. Despite the serious health implications of circadian misalignment, relatively little is known about how genetic variation affects an individual’s ability to shift to abrupt external changes. Accordingly, we use the one-hour advance from the onset of daylight saving time (DST) as a natural experiment to comprehensively study how individual heterogeneity affects the shift of sleep-wake rhythms in response to an abrupt external time change. We find that individuals genetically predisposed to a morning tendency adjust to the advance in a few days, while genetically predisposed evening-inclined individuals have not shifted. Observing differential effects by genetic disposition after a one-hour advance underscores the importance of heterogeneity in adaptation to external schedule shifts, and these genetic differences may affect how individuals adjust to jet lag or shift work as well.

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.

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

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

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.