Posttraumatic stress disorder (PTSD) is a critical occupational health concern among health care workers (HCWs). Quantifying global prevalence and identifying risk factors is critical for guiding intervention and policy strategies. A systematic review and meta-analysis was performed following PRISMA guidelines (PROSPERO CRD42024587810). A literature search of PubMed, Embase, PsycINFO, Web of Science, and Cochrane Library databases was performed from database inception to March 11, 2025. Observational studies reporting PTSD prevalence and odds ratios (ORs) for PTSD risk factors among HCWs were included. The primary outcome was p ooled prevalence of PTSD and ORs for risk factors among HCWs. A total of 308 studies from 60 countries were included, comprising 371,211 HCWs. The pooled PTSD prevalence was 27.2% (95% CI, 25.3%–29.2%). Higher prevalence was observed among female (31.5%), nurses (28.6%), HCWs in low- and middle-income countries (30.0%), and those in Africa (40.8%). Prevalence increased from 20.2% (95% CI, 14.4%–26.0%) before COVID-19 to 27.8% (95% CI, 25.8%–29.8%) after its onset, with meta-regression showing a significant upward trend over time (β = 9.94*10 -4 , P = 0.012). The strongest risk factors for PTSD included a history of mental disorder (OR, 2.08; 95% CI, 1.54–2.80), nursing occupation (OR, 1.60; 95% CI, 1.41–1.82), and symptomatic family or friends (OR, 1.53; 95% CI, 1.22–1.90). These findings indicate a substantial psychological burden among HCWs and identify subgroups with higher vulnerability across settings.

Background:Mobile apps and wearable devices may help to facilitate early detection of mental health conditions by providing objective, real-time data to supplement other forms of feedback and diagnoses. Few studies have investigated the acceptability and feasibility of using a mobile app to track survey- and wearable-based data in mental health research in Sub-Saharan Africa. Objective:This pilot study evaluated the feasibility and acceptability of using a mobile app and wearables to capture mental health-based survey data and passively sensed data among Kenyan health care workers. Methods:A mixed methods study was conducted among health care workers employed at 4 hospitals in Nairobi, Kenya, over 30 days. A mobile app was used to collect and integrate active (baseline questionnaire and daily mood) and passive (wearable) data. The baseline questionnaire gathered information on sociodemographics, work environment, and mental health assessments on depression, anxiety, personality, early family environment, posttraumatic stress disorder, and substance use. A wearable device was used to gather data on steps, heart rate, and sleep. Qualitative interviews were conducted post trial to gain in-depth insights into participants' experiences during the study. Results:Fifty-one participants enrolled in the pilot study. They were primarily nurses (47%) and female (70%), with a median (IQR) age of 32 (29-36) years. Attrition over 30 days was low, with only one participant dropping out due to device malfunction, which was a broken screen. Completeness of the baseline survey was high, with participants completing 96.1% of the questions. Further, 58% of the daily mood ratings were completed over the 30 days. For the wearable measures, participants submitted steps, heart rate, and sleep data on 93%, 73%, and 51% of study days, respectively. The proportion of days the wearable was worn for over 10 hours was 63%. Interviews revealed 2 primary themes. The first was intrinsic and extrinsic motivation; participants indicated that they liked having their health metrics tracked and receiving congratulatory messages from the app, encouraging increased step counts. The second theme was technical and usability challenges; 48% (10/21) of the participants reported discomfort wearing the watch while sleeping and challenges with synchronization of data due to the nonautomated nature of the process. Participants suggested additional prompts to remind them to complete the daily mood question. Conclusions:This pilot study demonstrates the feasibility of deploying mental health surveys, collecting data through wearable devices, and integrating such data within a single mobile platform under real-world infrastructure constraints. Health care workers in Kenya were willing to provide sensitive information through mental health assessments using a mobile app. To improve adherence, future studies should consider addressing some contextual factors such as daily prompts, enhanced data synchronization methods, and comfort concerns to improve adherence, especially during sleep.

Background Digital mental health interventions (DMHIs) have shown promise improving depression, anxiety, and psychiatric distress, yet real-world engagement remains low. Increasing engagement has great potential to improve the impact of DMHIs, but little is known about the drivers of engagement in naturalistic settings. To better understand predictors of engagement, we examined sociodemographic and clinical characteristics associated with DMHI usage among a large clinical sample of adults. Method 1223 adults (74% White, 68% women, Mage = 36.8 years) with scheduled intake appointments for outpatient psychiatric services were randomized to either a mindfulness-based app (Headspace) or a CBT-based app (SilverCloud). Usage data were automatically collected, and participants were neither required nor compensated to use the apps. Results Participants engaged with their assigned DMHIs a median of 8 days, with 88.2% of participants using their assigned DMHI at least once. Participants engaged with Headspace for more than twice as many days [IRR (95% CI) = 2.4 (2.1, 2.7)] as SilverCloud. Female sex, white race, a college degree, and older age up to 60 predicted greater engagement. Further, depression severity was associated with engagement in a non-linear manner for those assigned to Headspace, with less engagement at minimal/mild and severe symptoms compared to moderate and moderately-severe symptoms. Conclusions These findings indicate meaningful differences in engagement between DMHIs based on sociodemographic and clinical characteristics. There may be opportunities to improve engagement by tailoring DMHI offerings, with a particular emphasis on meeting the needs of less-engaged populations.

Introduction:Mental health problems among college students have increased significantly and barriers to care contribute to a substantial treatment gap. Digital mental health interventions (DMHIs) show promise for overcoming barriers, but engagement with DMHIs is challenging, underscoring the need for low-burden strategies. Objective:This pilot trial evaluated the feasibility and acceptability of a six-week, low-burden, preventative DMHI that delivered supportive text messages and personalized feedback (PF) to first-semester college students. Method:Students (N = 120, 64% women, 55% non-Hispanic White) who had mild-to-moderate depressive symptoms (PHQ-9 scores between 5 and 14) and were not engaged in formal mental health care were randomized to intervention (n = 90) or assessment-only (n = 30) conditions. Those in the intervention condition received a weekly PF report and/or supportive text messages at random intervals as part of an embedded micro-randomized trial (MRT). Primary outcomes were feasibility and acceptability of the intervention components. Exploratory analyses examined 1) clinical outcomes after six weeks for the intervention and assessment-only conditions, and 2) weekly clinical outcomes within the intervention group based on the MRT. Results:The trial demonstrated high feasibility (95% enrollment; 87% retention) and strong intervention acceptability, especially for PF and assessment components. Exploratory analyses did not reveal consistent patterns in between- and within-group comparisons. Conclusions:Low-burden strategies for assessment and intervention are feasible and acceptable to first-year college students at risk for depression. There is significant potential for integrating these lower-intensity strategies into a full-scale trial that adaptively delivers higher-intensity DMHIs and/or integrate human-delivered components in response to needs over time.

Mental fatigue undermines workplace safety and productivity. Early detection of subtle declines in objective alertness and cognitive performance in workers can enable timely interventions to prevent costly errors and safeguard employee health. However, conventional assessments often require controlled laboratory conditions and prolonged testing, limiting their real-world applicability. Here, we demonstrate an approach that utilizes smartphone keyboard metrics in everyday use to provide a more scalable, continuous, and ambulatory method for evaluating mental fatigue. We examined the adjusted association between novel yet widely available SensorKit typing performance metrics and wearable-derived time since waking from a most recent major sleep episode or napping among 366 first-year training physicians in the United States who generated 45,042 typing sessions over a two-month period. Typing performance, especially typing speed, has a significant non-linear adjusted relationship with time awake. At the population-level, typing speed increases and peaks around 7.5 hours since awake and has a substantial decrease around 15.3 hours of time awake, highly consistent with classical lab-based active task Psychomotor Vigilance Test findings that showed increased response lapses beyond 15.8 hours of wake period. Our findings are relevant for developing a ubiquitous and unobtrusive tool to assess, monitor, and manage mental fatigue on a continuous basis in everyday life, especially for populations in high-risk and high-stake settings.

This survey study evaluates the prevalence of depressive symptoms, as well as associated demographic, psychological, and workplace factors, among Kenyan health care workers.

PURPOSE/OBJECTIVE:Caregiving for individuals with traumatic brain injury (TBI) is often highly stressful, and traditional in-person interventions can be inaccessible given the demands of their caregiver role. Mobile health (mHealth) interventions offer a low-burden, scalable alternative by delivering personalized, real-time support. However, how effective these interventions depends on whether people perceive them as useful and engage with them. The role of perceived usability in mHealth efficacy remains underexplored in the TBI caregiver population. This study primarily evaluates the relationship between self-reported app usability and the efficacy of a fully automated mHealth intervention, delivered via personalized mobile-app messages, in reducing caregiver strain, anxiety, and depression in a TBI caregiver population. The secondary aim is to identify variables that moderate the intervention efficacy among caregivers reporting high app usability. RESEARCH METHOD/DESIGN:We analyzed data from 122 TBI caregivers assigned to receive self-monitoring plus push notifications for self-care as part of a larger randomized controlled trial. Perceived app usability was assessed via caregiver responses to a questionnaire item. We applied multivariate linear models with a weighted and centered least squares estimator to assess the moderating effects of perceived app usability and other variables on message efficacy. RESULTS:Messages were more effective in reducing depression among TBI caregivers who reported high app usability. Moreover, shorter caregiving duration, higher Fitbit step count, and lower prior-week anxiety were significantly associated with improved message efficacy among this high usability group. CONCLUSION/IMPLICATIONS:Perceived app usability plays a critical role in the mHealth message efficacy among TBI caregivers. Tailoring interventions based on perceived app usability and identified moderators may optimize health outcomes and support more personalized care for this population. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

BACKGROUND:Medical training is a high-stress period, and residents in surgical specialties face elevated risk for depression due to demanding schedules, intensity of training, persistent mistreatment, and long work hours. Obstetrics and gynecology is the only surgical specialty predominantly composed of women, who face higher rates of depression than men and greater increases in depressive symptoms during internship. These challenges, compounded by work-family conflict and policy pressures, contribute to workforce strain in a specialty already facing a projected shortage. Understanding the mental health burden is critical to supporting both trainee well-being and long-term workforce sustainability, yet depressive symptoms, risk factors, and treatment-seeking have not been systematically studied in obstetrics and gynecology trainees. OBJECTIVE:To assess the prevalence of depressive symptoms and mental health treatment-seeking among obstetrics and gynecology first-year residents and identify associated demographic, psychological, and workplace factors. STUDY DESIGN:First-year obstetrics and gynecology residents enrolled in the longitudinal Intern Health Study between 2009 and 2024 completed surveys at enrollment and quarterly throughout internship. Depressive symptoms were measured using the 9-item Patient Health Questionnaire. Pearson correlations, χ2 tests, stepwise linear regression, and generalized estimating equation models were used to identify baseline and internship-related predictors of depressive symptoms. Sample weights were applied to address nonrepresentative sampling and attrition biases. RESULTS:Of 1603 enrolled obstetrics and gynecology interns (86.5% women; median age 27 years), 1271 (79.3%) completed at least one internship assessment and were included in the analysis. During internship, 35.6% screened positive for depression on the 9-item Patient Health Questionnaire at one or more assessments, yet less than one-third of those affected sought mental health treatment. Baseline predictors of increased depressive symptoms included history of depression, higher baseline depressive symptoms, neuroticism, a difficult early family environment, and not being in a committed relationship. Internship-related factors associated with worsening symptoms included fewer sleep hours, longer work hours, and reported medical errors. CONCLUSION:More than one-third of obstetrics and gynecology residents screened positive for depression on the 9-item Patient Health Questionnaire during their first year, yet treatment-seeking remained critically low. Both preexisting vulnerabilities and modifiable workplace stressors contributed to depressive symptoms. These findings are particularly notable given the female-predominant composition of the obstetrics and gynecology field and the growing representation of women in surgical specialties. The low treatment-seeking rate highlights the urgent need for improved mental health access, including opt-out service models. Structural interventions targeting workload and sleep are essential to support trainee well-being and long-term workforce sustainability in obstetrics and gynecology.

This cohort study of survey responses from US physicians who recently completed residency training examines demographic and work status factors associated with preference for part-time work.

Accurately monitoring mental fatigue is critical for improving workplace safety and productivity. A recent study examined unobtrusively collected smartphone typing speed as a potential ambulatory proxy assessment of mental fatigue using data from the Intern Health Study (IHS). While population-level average typing speed patterns were found to be consistent with validated measures of mental fatigue, how these trajectories vary across participants and days may inform opportune moments for just-in-time interventions and remains an open question. Treating typing speed trajectories as sparsely observed functional data, we propose a novel sparse longitudinal functional principal component analysis (sparse LFPCA) method for decomposing variability and predicting individual curves. Specifically, sparse data are accommodated by casting covariance estimation as a structured penalized spline regression problem, enabling simultaneous estimation and smoothing of multiple covariance components while borrowing information across locations in the functional domain. Simulations show that sparse LFPCA (1) accurately estimates eigenfunctions and generates reasonable predictions for underlying curves, and (2) achieves similar or superior performance compared to existing alternatives. Our analysis of typing speed data collected from IHS reveals new and interpretable participant- and day-level patterns not captured by previous analyses and can be used to tailor behavioral interventions.

Abstract Introduction Humans exhibit circadian rhythms in sleep, but it is unclear whether they also display annual cyclical patterns. In other species, circannual rhythms often vary with latitude, with greater amplitude and predictable shifts in peak timing at higher latitudes. Demonstrating similar latitudinal clines in human sleep would provide evidence consistent with a circannual component to annual sleep variation. This study tests whether daily total sleep time (TST) and sleep midpoint exhibit annual rhythmicity and whether the amplitude and timing of these rhythms vary across geographic location. Methods This study uses yearlong wearable sleep data from the 2018–2022 cohorts of the Intern Health Study (IHS), a longitudinal study of first-year medical residents. Annual seasonality in daily sleep was tested using cosinor models. Latitude effects were evaluated in mixed-effects models that allowed the amplitude and phase of these rhythms to vary by location. Amplitude and peak timing were derived from model coefficients for interpretation. Results Both TST and sleep midpoint showed evidence of annual rhythmicity. Average TST did not differ significantly by latitude; however, peak TST was slightly greater and occurred earlier at higher latitudes. In contrast, neither sleep midpoint amplitude nor phase varied significantly across latitudes. Conclusion Annual rhythms in both TST and sleep midpoint, along with modest latitudinal differences in TST amplitude and peak timing, support the presence of latitudinal clines in annual sleep patterns, consistent with a circannual component for duration but not timing. Future research could explore whether these annual patterns reflect endogenous circannual processes, environmental influences, or their interaction by incorporating objective light exposure and physiological circadian markers to more directly assess mechanisms underlying annual sleep variation. Support (if any)

Health behaviors such as physical activity and sleep affect mental health, but the effect of each health behavior varies substantially across individuals, limiting the usefulness of generic behavioral recommendations. We collected one year of continuous wearable and ecological momentary assessment data from 3,139 participants in the Intern Health Study (2018-2023), and examined individual-level associations between wearable-derived features and mood across the internship year. The behaviors associated with mood were highly heterogeneous between individuals: the two most prevalent drivers of mood were wake-up time (the strongest driver for 34.0% of subjects) and step count (10.6% of subjects). The correlation directionality remained largely stable despite fluctuations in strength. Interestingly, 20.3% of subjects showed no significant correlations. These findings highlight the limitations of population-level recommendations and the critical need for personalized, data-driven approaches to mental health assessment and intervention. To translate these personalized insights into actionable support, we developed MoodDriver, a large language models (LLM)-powered system that generates tailored feedback emails based on each participant's behavioral and physiological patterns. This work demonstrates the feasibility of combining digital phenotyping with large language models to advance precision digital mental health for high-risk populations.

Common mental health conditions such as depression, anxiety, and substance use disorders are important contributors to disability and reduced quality of life. Efforts to address these conditions have been hindered by an inadequate clinician workforce capacity. Furthermore, first-line treatments (medications and clinician-delivered counseling) have modest efficacy, and there is a paucity of data to guide treatment decisions. As a result, it takes years for many patients to find a treatment that works, and the large and growing proportion of patients needing care face long wait times. To overcome these challenges, we need scalable, innovative solutions that both increase access and tailor care to the unique needs of each patient at a specific point in time. Because of their low cost and scalability, digital interventions are a potential tool to increase treatment capacity. However, these interventions, typically delivered by apps, have not achieved robust user engagement and have produced only modest effects across a range of mental health symptoms and conditions, and as a result they have not meaningfully closed the treatment gap. Here, we outline the potential for precision approaches for the delivery of mental health interventions, both digital and conventional, to improve population-level outcomes. Mobile technology, genetics, and electronic health records provide data that capture constructs central to mental health. These data sources provide key inputs for modern data science methods that have the potential to effectively match patients to treatments as well as tailor the timing, dosage, and content within specific digital interventions.

Although adequate high-quality sleep is essential for optimal cognitive functioning, information on sleep and circadian rhythms among healthcare workers in resource-constrained settings remains scarce. We conducted a 12-month longitudinal study across five healthcare facilities using Fitbit Inspire 2™ devices. Sleep duration and irregularity were broadly comparable to higher-resource populations; observed differences in circadian metrics were largely explained by age. These findings support the generalizability of wearable-based sleep and circadian algorithms across contexts when key demographic and societal factors are considered.

Abstract Depression is associated with increased risk for a variety of medical conditions. However, the extent to which these associations reflect a causal impact of depression on medical conditions, or vice-versa, remains unresolved. We tested bidirectional causal relationships between major depressive disorder (MDD) and multiple medical conditions and symptoms, using a genetically-informed approach for causal inference. Candidate disease traits were selected based on their genetic associations with MDD, as identified in prior phenome-wide association studies that used polygenic scores for MDD and electronic health records for trait ascertainment. In total, 183 candidate traits across 15 phenome-wide association study code (phecode) categories were identified. We conducted bidirectional, two-sample Mendelian randomization using summary statistics from non-overlapping, European-ancestry genome-wide association studies (GWASs) of MDD and the disease traits. There were sufficient instrumental genetic variables to test causal effects of MDD on 182 of these traits. Genetic liability to MDD was associated with 109 (59.9%) traits, with the strongest potential causal evidence observed for 105 (57.7%) traits across 13 phecode categories: Mental disorders; digestive, genitourinary, neurological, respiratory, circulatory-system, endocrine/metabolic, musculoskeletal, sense-organ, infectious-disease, and dermatologic conditions; injuries and poisonings; and symptoms. There were 10 disease traits with sufficient instrumental genetic variables to test causal effects on MDD. Of these 10 traits, only two (20.0%)—genetically-predicted gastroesophageal reflux disease (GERD) and hypertension—were associated with MDD risk. GERD showed evidence of bidirectional associations with MDD (MDD → GERD: odds ratio (OR) = 2.02, 99% confidence interval [CI] 1.84–2.22; GERD → MDD: OR = 1.48, 99% CI 1.39–1.58). The present results are consistent with a causal effect of major depressive disorder on a broad range of medical conditions and symptoms. Prevention and treatment of MDD could benefit not only mental health but also physical health.

Spending time in locations outside the home and workplace (termed "third places"), has been linked to better mental health. However, studies to date have typically been cross-sectional, based on self-reported location data and employed small sample sizes, limiting their ability to assess the presence and nature of the association between third places and mental health. To overcome these limitations, we collected 18,795 person-days of objective SensorKit location data passively from a national cohort of 410 first-year medical residents across the United States, to assess visits to third places and their associations with mood and depression over the course of one year. On average, participants visited 3.3 unique locations per day (SD=1.7) and spent 17.9% of their time at third places (SD = 26.5%). Within individuals, both a higher percentage of time spent at third places (B=0.013 [per 10% increase], p<0.001) and a greater number of unique locations visited (B=0.032, p<0.001) were associated with better mood later that same day, independent of the time spent at work. These associations were partially mediated by step counts and outdoor light exposure jointly (19.2% and 27.6%). Reverse-direction associations were observed, with better mood on one day predicting both more time spent at third places (B=0.052, p<0.001) and more unique locations visited (B=0.032, p<0.001) the following day. Between subjects, depressed subjects spent less percentage of time at third places (12.3% vs. 21.2%, t=-3.7, p<0.001) and visited fewer unique places per day (2.9 vs. 3.4, t = -3.8, p<0.001) compared to non-depressed subjects. These findings demonstrate the relationship between visiting third places and well-being, and suggest that interventions and policies aimed at encouraging third places visits have the potential to improve mental health. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by the National Institutes of Mental Health (R01MH101459). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB of the University of Michigan gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes SensorKit data cannot be shared publicly because of Apple's data sharing restrictions. De-identified general study enrollment and survey data are available via the ICPSR repository (https://www.openicpsr.org/openicpsr/project/129225/version/V1/view). De-identified wearable data is available through requests to the Intern Health Study (intern_health{at}med.umich.edu). Reasonable requests will be fulfilled contingent on completion of a data use agreement with the University of Michigan.

Importance:Improving mental health through targeting behaviors like sleep and physical activity in treatment has been challenging, in part due to the challenge of measuring these factors in an accurate manner. Mobile technology can enable an understanding of the dynamic, complex associations between physical activity and sleep, but most prior mobile technology studies have had modest sample sizes and utilized cross-sectional, between-person designs, thus limiting their impact. Objective:To investigate the within-person associations between sleep, physical activity, and daily mood among individuals receiving mental health treatment to inform health behavior recommendations. Design, Setting, and Participants:This 12-month cohort study involved patients seeking mental health care at the University of Michigan academic medical center mental health care clinics. All participants were enrolled between May 13, 2020, and December 12, 2022. Data analysis was performed from September 2024 to June 2025. Exposures:Objective, wrist-based actigraphy measures of sleep (total sleep time, interrupted nighttime sleep, and napping) and physical activity (step count) were obtained. Main Outcomes and Measures:The primary outcome was participant-reported daily mood score, on a scale of 1 (worst mood) to 10 (best mood). Linear mixed-effects models were used to estimate associations among sleep, physical activity, and mood scores. Results:A total of 1476 participants (mean [SD] age, 36.5 [14.2] years; 1062 [72.0%] female) were included in the analysis. Sleep duration was associated with subsequent mood through an inverse U-shaped relationship, with both short and long sleep duration associated with poorer mood (quadratic term b = 0.027; 95% CI, -0.031 to -0.023). Notably, patients varied substantially in their optimal sleep duration (mean [SD], 6.8 [1.9] hours) for peak mood. Physical activity was positively associated with subsequent mood (linear term b = 0.160; 95% CI, 0.149 to 0.162; P < .001; quadratic term b =- 0.022; 95% CI, -0.027 to -0.017; P < .001), with diminishing associations at higher than usual activity for that individual. Conversely, daily mood was associated with subsequent sleep (linear term b = -1.377; 95% CI, -1.877 to -0.877; P < .001; quadratic term b = -0.394; 95% CI, -0.765 to -0.023; P = .037) and step count (linear term b = 0.020; 95% CI, 0.003 to 0.030; P = .02; quadratic term b = -0.010; 95% CI, -0.020 to -0.001; P = .03) in a wavelike manner. Conclusions and Relevance:This cohort study of people seeking mental health care found complex, bidirectional associations between sleep, physical activity, and mood with individual variation in optimal sleep duration for mood scores. The findings advance progress toward effectively targeting health behaviors to improve mental health.