Faculty Bibliography

BACKGROUND:Health care workers (HCWs) face sustained psychological demands that place them at heightened risk for burnout and posttraumatic stress disorder (PTSD). However, assessing psychological distress in this population remains challenging because of stigma, underreporting, and the limitations of self-report tools. Although nonverbal behaviors such as facial expressions and gaze hold diagnostic promise, most approaches overlook the fine-grained, temporal fluctuations in these signals. In this study, we focused on microbehavior intervals-brief, involuntary changes in multimodal nonverbal signals-that emerge during emotion-eliciting interviews. OBJECTIVE:This study aimed to determine whether microbehavior intervals improve the discrimination of psychological distress profiles among HCWs with symptoms of burnout and PTSD. METHODS:HCWs participated in a semistructured interview that included 5 work-related, emotionally charged questions and that was recorded via Webex (online video platform). Participants also completed validated questionnaires for burnout (Maslach Burnout Inventory General Survey 9-item) and PTSD (PTSD checklist for Diagnostic and Statistical Manual, 5th edition). Recordings were analyzed with computer vision models to generate time-series data of facial expressions, head movement, gaze, body posture, and hand gestures. An unsupervised anomaly detection model (MOMENT [a Family of Open Time-Series Foundation Models]) isolated microbehavior intervals without requiring manual labels. Features derived from these intervals were used to train a deep learning classifier that predicted 4 symptom classes of psychological distress: "moderate-severe burnout," "subthreshold-provisional PTSD," "burnout+PTSD," and "resilient." We conducted an ablation study by systematically removing one behavioral data stream at a time. Finally, we conducted an explainability analysis to characterize the features driving model predictions. RESULTS:-score. The explainability analysis revealed distinct temporal patterns across symptom classes, with irregularity and variability in microbehaviors emerging as key predictors. CONCLUSIONS:Focusing on microbehavior intervals yields a scalable, interpretable, and annotation-free framework for detecting psychological distress from nonverbal signals. By moving from whole-video features to fine-grained multimodal temporal modeling, we successfully captured subtle, involuntary fluctuations in nonverbal responses to emotion-eliciting questions. This multimodal approach enables an objective, robust, and explainable assessment of psychological distress and offers a promising complement to conventional psychometric assessments.

Healthcare workers (HCWs) in emergency departments face significant mental health risk due to chronic stressors and repeated trauma, yet symptom underreporting and bias in self-reports hinder accurate assessments. Expressive flexibility, the ability to dynamically modulate and recover from stressor-related changes in emotional arousal as reflected in observable behavior, has been linked to resilience. This NIH-funded study (R01HL156134) utilized digital phenotyping and computer vision to analyze dynamic facial expressivity during video-recorded interviews about work-related stressful situations with 240 HCWs (278 assessments). Participants additionally completed validated questionnaires to assess burnout, PTSD, depression, anxiety, and resilience. Latent profile analysis revealed two clinical phenotypes: At-risk (57.6%) and Resilient/Adaptive (42.4%). Machine learning models demonstrated high classification performance (accuracy = 0.83 ± 0.06, F1-score = 0.87 ± 0.05). Our findings indicate that digital biomarkers of temporal facial dynamics may serve as objective behavioral proxies of expressive flexibility, potentially capturing dynamics consistent with underlying stress-regulatory processes. These findings highlight their potential to improve identification of resilience-related phenotypes and support well-being and mental health in HCWs.

OBJECTIVE:Health systems have finite capacity. During crises, policymakers may explicitly reallocate health system resources, or capacity limitations may necessitate implicit resource reallocation. This study modelled timing and intensity of pre-vaccination health system resource reallocation policies to predict excess mortality during the COVID-19 pandemic. METHODS:This longitudinal panel analysis included 85 countries (752 country-months, January 2020-January 2021). The predictor was resource reallocation scope, scale (summarized as intensity, 0-100), and timing. The outcome was all-cause excess mortality (percentage deaths greater than historical average/month). Covariates included COVID-19 incidence and health system parameters. RESULTS:= -0.58, 95%CI -0.93-0.23: e.g., 42,010 fewer deaths per unit increased resource reallocation, March 2020, all study countries). Effects were magnified in older populations. Health system capacity and preparedness were associated with lower mortality. CONCLUSIONS:In the pre-vaccination COVID-19 pandemic, preemptive health system resource reallocation was associated with lower mortality, whereas simultaneous resource reallocation was associated with greater mortality. This longitudinal multinational study indicates that readiness, capacity building, and proactive resource reallocation improve crisis response.

OBJECTIVES/OBJECTIVE:To identify stakeholder perceived challenges and facilitators for implementing a clean fuel and clean stove intervention to reduce household air pollution and hypertension in Lagos, Nigeria. DESIGN/METHODS:Qualitative study guided by the Exploration and Preparation phases of the Exploration, Preparation, Implementation, and Sustainment (EPIS) framework, using focus group discussions and in-depth semi-structured interviews with inductive and deductive thematic analysis. SETTING/METHODS:Peri-urban communities across the five administrative divisions of Lagos State, Nigeria. PARTICIPANTS/METHODS:128 stakeholders from 32 communities, including community, religious, market and youth leaders, primary healthcare staff, and household decision makers. Approximately half were female. INTERVENTIONS/METHODS:This was a pre-implementation needs assessment that included demonstrations of the clean stove and fuel. PRIMARY AND SECONDARY OUTCOME MEASURES/METHODS:Thematic domains describing barriers and enablers to adoption and implementation, mapped to EPIS inner, outer, and bridging factors. RESULTS:Stakeholders reported barriers that included stove stacking, upfront stove cost, concerns about long-term fuel price and availability, equipment durability and maintenance, safety, mistrust of new technology, and uncertainty about stove performance for dishes requiring high heat and long cooking times. Reported facilitators included payment flexibility and subsidies, opportunities to test the stove, perceived benefits of cleaner and faster cooking with less soot, endorsement by community leaders, and interest in local retail and distribution to improve access. CONCLUSIONS:Implementation planning for clean fuel and clean stove programmes should address affordability, reliable fuel supply chains, durability and service, culturally relevant cooking needs, and trust building through community leadership. These findings inform adaptation strategies for scale-up in similar low-resource settings. TRIAL REGISTRATION NUMBER/BACKGROUND:NCT05048147.

INTRODUCTION/BACKGROUND:In Africa, 75% of households are exposed to household air pollution (HAP), a key contributor to cardiovascular disease (CVD). In Nigeria, 90 million households rely on solid fuels for cooking, and 40% of adults have hypertension. Though clean fuel and clean stove (CF-CS) technologies can reduce HAP and CVD risk, their adoption in Africa remains limited. METHODS AND ANALYSIS/METHODS:Using the Exploration, Preparation, Implementation and Sustainment framework, this cluster-randomised controlled trial evaluates the implementation and effectiveness of a community mobilisation (CM) strategy versus a self-directed condition (i.e., receipt of information on CF-CS use without CM) on adoption of CF-CS technologies and systolic blood pressure (SBP) reduction among 1248 adults from 624 households across 32 peri-urban communities in Lagos, Nigeria. The primary outcome is CF-CS adoption at 12 months; secondary outcomes are SBP reduction at 12 months and sustainability of CF-CS use at 24 months. Adoption is assessed via objective monitoring of stove usage with temperature-triggered iButton sensors. SBP is assessed in 2 adults per household using validated automated blood pressure monitor. Generalised linear mixed-effects regression models will be used to assess study outcomes, accounting for clustering at the level of the peri-urban communities (unit of randomisation) and households. To date, randomisation is completed, and a total of 1248 households have enrolled in the study. The final completion of the study is expected in June 2026. ETHICS AND DISSEMINATION/BACKGROUND:The study was approved by the Institutional Review Boards (IRB) of NYU Grossman School of Medicine (primary IRB of record; protocol ID: i21-00586; Version 6.0 approved on 4 June 2024), and Lagos State University Teaching Hospital (protocol ID: LREC 06/10/1621). Written consent was obtained from all participants. Findings will inform scalable and culturally appropriate strategies for reducing HAP and CVD risk in low-resource settings. Results will be disseminated through peer-reviewed publications, conference presentations and stakeholder engagements. TRIAL REGISTRATION NUMBER/BACKGROUND:NCT05048147.

OBJECTIVES/OBJECTIVE:In mild traumatic brain injury, imaging biomarkers are needed to support clinical management. In four antecedent publications, we used two new (sodium and fingerprinting) and two established (spectroscopy and diffusion) MR techniques in a longitudinally followed patient cohort. Here we report final results and combine all data to determine which marker(s) from the four modalities offer the greatest utility for detecting injury and predicting outcomes. We also leverage the independent specificities offered by each modality to explore injury mechanisms. MATERIALS AND METHODS/METHODS:The longitudinal spectroscopy data were analysed to complete a full data set of proton (spectroscopy, fingerprinting, diffusion) and sodium MRI, acquired alongside symptomatic, cognitive, and functional assessments in 27 patients at 1, 3, and 12 months following injury. Twenty-three matched controls were scanned once. Testing for associations between nine MR markers and three outcome measures was standardized across the entire data set, and performed using Spearman correlations and logistic regression. RESULTS:from fingerprinting (marker of the cellular microenvironment). CONCLUSIONS:We identified independent, dynamic, metabolic and ionic changes, with choline and creatine from spectroscopy fulfilling the most criteria for a clinical biomarker.