Objectives/Goals: Using k-means clustering, we analyzed data from the All of Us Research Program to 1) identify distinct social connectedness clusters (based on social support and neighborhood social cohesion) and 2) examine their associations with objectively measured physical activity (PA) and daily steps among adults in the USA. Methods/Study Population: PA is an important modifiable factor for preventing noncommunicable diseases, yet <50% of US adults meet moderate-to-vigorous PA (MVPA) guidelines. While social connectedness may influence PA, its relationship with objectively measured PA remains understudied. We conducted a secondary analysis of All of Us data from participants who completed the Social Determinants of Health Survey and provided 7 days of objective PA data (Fitbit). Using k-means clustering, we identified social connectedness profiles based on social support and neighborhood cohesion. We determined the optimal number of clusters using partition criteria and ran logistic regression models (adjusted for demographic, clinical, and neighborhood factors) to examine associations with meeting MVPA guidelines and achieving 10,000 steps/day. Results/Anticipated Results: Among 8,406 participants (mean age 55.6±14.7 years; 70.0% women; 80.1% non-Hispanic White), we identified three clusters: high social support/high neighborhood cohesion (HSS-HSC; 28%), high social support/low neighborhood cohesion (HSS-LSC; 47%), and low social support/low neighborhood cohesion (LSS-LSC; 24%). Compared to HSS-HSC, participants in HSS-LSC and LSS-LSC clusters had lower odds of meeting MVPA guidelines (HSS-LSC: AOR=0.87, 95% CI=0.78–0.98; LSS-LSC: AOR=0.81, 95% CI=0.70–0.93) and achieving 10,000 steps/day (HSS-LSC: AOR=0.82, 95% CI=0.72–0.92; LSS-LSC: AOR=0.84, 95% CI=0.72–0.98). Discussion/Significance of Impact: Social connectedness is associated with greater PA. Nearly 50% of our sample had low neighborhood cohesion despite high social support, highlighting gaps in community-level connectedness. Findings support the need for multilevel interventions to address determinants of PA among adults.

Understanding mobile Health (mHealth) user engagement patterns and its association to adherence in chronic disease populations is critical for data quality and clinical outcomes. Yet, this remains unexplored and a missed opportunity, given the unique value of mHealth data for traditionally under-documented conditions. Methods: We analyzed 13,997 total days of data from 131 female participants with chronic pelvic pain disorders (CPPDs) and 72 healthy controls. Participants tracked daily symptoms using a research App for 90 days. Using mixed-effects regression, we investigated predictors of adherence and consistency in App engagement, with moderators of CPPD burden and temporal accessibility. Results: Later-day engagement was the strongest predictor of user consistency, independent of pain interference or CPPD. Habitual evening tracking was associated with better adherence for those with CPPD vs healthy controls. Consistency was the strongest predictor of adherence. Conclusion: Prioritizing habit and preference over fixed-time assessments could improve temporal accessibility and data completeness.

Introduction: Pelvic pain (dysmenorrhea and non-menstrual) is the most common presentation of adolescent endometriosis, but symptoms vary between and within patients. Other presentations, such as gastrointestinal (GI) symptoms, are often misattributed, leading to diagnostic delays. Patients incur frequent primary and specialty care visits, generating multiple and diverse clinical notes. These offer insights into disease trajectory and symptom heterogeneity, which can be rigorously investigated using clustering methods. This study aims to 1) evaluate phenotypes using electronic health records (EHRs) and 2) compare two clustering models (note- vs patient-level) for their ability to identify symptom patterns. Methods: We queried the Mount Sinai Data Warehouse for clinical notes from patients aged 13-19 years with a SNOMED endometriosis diagnosis, yielding an initial sample of 7,221 notes. A randomly selected subsample was annotated with 12 disease-relevant labels, including symptoms, hormone use, and medications. The final analytic sample included 695 notes from 26 unique patients. Pelvic pain, dysmenorrhea, chronic pain, and GI symptoms were selected as model predictors based on principal component analysis. Two unsupervised machine learning (ML) methods were then applied for note- vs patient-level analyses: Partitioning Around Medoid (PAM) and Multivariate Mixture Models (MGM). Results: The PAM model identified K=3 clusters with average silhouette width of 0.76, indicating strong between-cluster separation. The "feature-absent" (abs) phenotype (76%) was distinct for absence of all 4 features. The "classic" phenotype (8%) exhibited pelvic pain, dysmenorrhea, and chronic pain. The "GI" phenotype (16%) was dominated by GI symptoms. The MGM identified K=2 stable patient-level clusters (Δweighted model deviance = -224.93 from K=2 to 3) with a mean cluster membership probability of 0.97: A "classic" phenotype (50%), characterized by pelvic pain and chronic pain, and a "non-classic" phenotype (50%), defined by the absence of these features. PAM-based classic phenotype had significantly higher rates of hormonal intervention (78% vs 26% abs, 49% GI) and pain medication (68% vs 9% abs, 14% GI). For the patient-level, the classic phenotype also had higher average rates per person of hormonal therapy (26% vs 7%) and prescription pain medications (27% % vs 9%) (p<0.01 for all). Conclusions: Both methods captured classic and non-classic phenotypes, with the note-level model uniquely identifying a feature-absent group. The classic phenotype's link to higher hormonal and pain intervention underscores the importance of recognizing non-classic symptoms. This study, the first to directly compare note- and patient-level clustering of EHR notes in endometriosis, demonstrates the ability to detect the less clinically recognizable phenotypes. This proof-of-concept can be applied to larger datasets to refine phenotype identification, aiding in earlier diagnosis. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The first author was supported by funds from Mount Sinai's Diversity Innovation Hub. ### 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: The Institutional Review Board at Icahn School of Medicine at Mount Sinai 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 All data produced in the present study are not publicly available due to patient privacy, security, and the Health Insurance Portability and Accountability Act of 1996 (HIPAA) requirement.

Sexual and gender minority (SGM) adults experience significant health disparities linked to chronic exposure to minority stressors (e.g., discrimination), and could be reciprocally associated with physical activity (PA) behavior. While PA is a health-protective factor, research on PA patterns in SGM adults is limited. Identifying potential latent PA profiles can inform tailored behavior change approaches. To investigate latent profiles (i.e., clusters) of daily PA trajectories among sexual and gender minority (SGM; lesbian, gay, bisexual, transgender, queer) adults using functional latent block models (FLBMs), a co-clustering technique that simultaneously accounts for variation at the individual- and day-level. The study included 42 Black and Latinx SGM adults who wore Fitbit trackers for up to 30 days of PA data collection as a part of a sleep health study, yielding 1,209 person-level days of step count data. Each 24-h period of step counts was smoothed using Fourier-transform to create the functional data matrix and fit the FLBMs. The optimal number of clusters was determined using the integrated completed likelihood (ICL) criterion. The best-fitting model identified 3 individual-level clusters (K) based on the daily step count patterns (ICL = -88,495.88). Low activity cluster (n = 11) was characterized with the lowest overall PA, slightly later bedtimes, and the least intra-day and hourly variability. Steady moderate activity cluster (n = 23) was characterized by a gradual increase in step counts that spread over the course of the day, with a small peak in the afternoon. Fluctuating high activity cluster was characterized by a peak in activity earlier in the day, compared to other clusters. Cluster 3 membership was also associated with the highest volume of PA overall, along with hourly and daily variability in step counts and higher intensities of PA. The model secondarily identified 2 day-level clusters (L), representing weekday and weekend PA patterns. We identified distinct habitual daily PA trajectories among SGM adults based on daily volume and variability. Analyzing individual PA variances can help identify inactive periods and individuals at higher risk, which can inform the design of tailored interventions and self-management strategies to promote PA.

Background: Female chronic pelvic pain disorders (CPPD) are marked by unpredictable symptom flares, involving both pain and non-pain symptoms that affect the gastrointestinal (GI) and genitourinary (GU) systems. Management is challenging due to limited understanding of pathophysiology and lack of reliable predictors. Heart rate variability (HRV), the measure of small-time differences between heartbeats and an indicator of autonomic nervous system function, has shown promise as a digital biomarker for pain and inflammation. Research Question: This study evaluates whether real-time wearable and patient-tracked data can predict CPPD symptom flares. Methods: The final analytic sample comprised of 311,308 HRV measurements, characterized as RMSSD (root mean square of successive differences) and LF/HF (low frequency/high frequency), across 4,166 person-days from 87 females with CPPD in an observational study using an mHealth application (ehive iOS and android) and Fitbit tracker (model Inspire 3). The primary outcome was the “flare week” score, defined as a 7-day period with more days of disease-specific symptom flares than baseline. The daily “flare score” is the product of the number of CPPD-related pain and GI/GU symptoms with their intensity. Cosinor mixed-effects regression was applied to HRV circadian features: midline-estimating statistic of rhythm (MESOR), amplitude and acrophase. Covariates included age, body mass index (BMI), daily steps, sleep efficiency, and menstrual period. Participant ID and person-level amplitude and acrophase were included as random effects. Results: HRV circadian patterns significantly differed in the week prior to a flare week (Figure 1). RMSSD’s MESOR and amplitude decreased in the preceding week (Table 1), while the acrophase increased (all p<0.05). For LF/HF, the MESOR significantly decreased in a week preceding a flare. Menstrual period was positively associated with RMSSD, while menstrual period, daily step count, and sleep efficiency were inversely associated with LF/HF. Significant interactions for both metrics included obesity with amplitude, period with amplitude and steps with acrophase. There was significant variance in the HRV between-participants based on the significant random effects. Conclusions: We present initial evidence that HRV metrics can predict CPPD symptom fares via non-linear estimation, supporting a promising use of real-time mHealth and wearable data in the context of CPPDs.

BACKGROUND:Cardiovascular health (CVH) disparities have been documented among sexual minority adults, yet prior research has focused on individual CVH metrics. We sought to examine sexual identity differences in CVH using the American Heart Association's composite measure of ideal CVH, which provides a more comprehensive assessment of future CVD risk. METHODS:Data from the All of Us Research Program were analyzed. Sexual identity was categorized as heterosexual, gay/lesbian, bisexual, or other. Individual CVH health metrics and cumulative ideal CVH (range 0-100) were assessed. We ran sex-stratified multiple linear regression models to estimate differences across individual CVH metrics and cumulative ideal CVH between sexual minority and heterosexual adults. We also explored differences in CVH across racial/ethnic and age groups. RESULTS:The sample included 11 047 cisgender adults with a mean age of 61.1 years (± 13.85); 80% were non-Hispanic White. Lesbian women, gay men, and bisexual women reported greater nicotine exposure than their heterosexual counterparts. Compared to heterosexual men, gay men (B [95% CI] = -8.95 [-14.50, -3.39]) had worse physical activity scores. Gay men also had better body mass index scores than heterosexual men (B [95% CI] = 3.21 [0.09, 6.33]). Bisexual women and men had lower cumulative ideal CVH scores than heterosexual adults. Exploratory analyses revealed several differences in individual CVH metrics and cumulative ideal CVH across racial/ethnic and age groups. CONCLUSIONS:Clinical interventions to improve the CVH of bisexual adults are needed. Findings can inform the design of interventions that are tailored for specific subgroups of sexual minority adults.

Gender minority (GM; e.g., transgender, nonbinary, and gender diverse) adults encounter unique challenges to meeting aerobic and muscle-strengthening physical activity (PA) recommendations, which may negatively impact their health and well-being. However, to date, there is limited evidence on factors associated with PA among GM adults. The objectives of this systematic review were to (a) examine the differences in the prevalence of PA between GM and cisgender (cis; i.e., nontransgender) adults and (b) identify factors associated with PA among GM adults. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement to conduct a systematic review focused on PA among GM adults. We included peer-reviewed quantitative empirical studies published between January 1, 2004, and March 31, 2024. We assessed the quality of the included studies using the Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies. Out of 5,290 articles retrieved, 3,139 titles/abstracts were screened after duplicates were removed, and 24 articles representing 13 unique datasets met the inclusion criteria. Fourteen articles representing nine datasets found that GM adults engaged in lower levels of aerobic and muscle-strengthening PA than their cis counterparts. GM adults with higher acute psychological stress reported lower PA ( n = 2). One article found that gender-affirming hormone therapy was associated with higher self-reported PA among GM adults. Overall, GM adults reported lower aerobic and muscle-strengthening PA than cis adults. Findings highlight the need for further research to understand these disparities in PA. This systematic review can inform future research and the development of tailored interventions aimed at increasing PA among GM adults. Registration and Protocol: This systematic review was prospectively registered in PROSPERO (CRD42024542092), and a protocol was prepared and registered as part of this process.

Endometriosis is a chronic condition associated with severe pelvic pain, dysmenorrhea, infertility, and worsening quality of life. Regular physical activity (PA) is effective for pain management and reducing chronic disease symptoms, yet individuals with endometriosis are more likely to be insufficiently active. This study investigated latent profiles of daily PA trajectories in this population via clustering. We analyzed 171 adults (4,795 person-level days) with a confirmed diagnosis of endometriosis enrolled in the All of Us Research Program. PA data were collected from participants using Fitbit wrist-worn trackers. We used 30 consecutive days of data from each individual, allowing up to 10 days of missingness, imputed using multiple imputed chained equations. Functional mixture models (FMMs) were used to identify latent PA trajectory clusters using daily step counts as the outcome variable. The optimal number of clusters was selected via Bayesian Information Criterion (BIC). Exploratory analyses of PROMIS pain and fatigue surveys were conducted in a subset of 129 participants who completed the surveys after their PA time windows. FMM-identified profiles differed both with respect to PA volume and variability. Combinatory model fit indices supported a 4-cluster (K = 4) solution. The “High Active” phenotype exhibited the highest volume and variability of daily step counts and moderate-to-vigorous PA (MVPA) minutes over the sampling period (Steps: Mean (SD) = 12918.8 (5606.4); MVPA: Mean (SD) = 75.2 (64.6)). The “High Moderate” phenotype exhibited the second highest activity (Steps = 9283.9 (3661.2); MVPA = 58.2 (59.6)), followed by “Low Moderate” (Steps = 6234.0 (2515.8); MVPA = 18.6 (32.3)), and “Insufficiently Active” (Steps = 4317.1; MVPA = 17.2 (28.9)). Exploratory analyses revealed that higher-activity phenotypes tended to report lower pain scores. However, the “High Active” phenotype had the highest proportion of individuals reporting severe to moderate fatigue. This is the first study to investigate and report distinct PA profiles among a nationally-representative sample of individuals living with endometriosis using objectively-estimated PA. Identifying phenotypes based on within- and between-individual variance may help identify those at risk and inform the development of personalized interventions aimed at promoting PA and improving health outcomes in this population.

Objective:This study aims to evaluate the short form International Physical Activity Questionnaire (IPAQ) for use in women with chronic pelvic pain disorders (CPPDs) by comparing its scores against objectively-estimated physical activity (PA) outcomes. We investigated IPAQ components that are most consistently predictive of habitual PA behavior. Method:The study sample included 966 weeks of data from 112 women with CPPDs who enrolled in a 14-week mHealth-based self-tracking study. Participants wore Fitbit devices and completed the IPAQ every week. We compared the IPAQ-reported minutes of walking, total activity, sitting, light-, moderate-, and vigorous intensity PA for concordance and divergence against their corresponding Fitbit estimates. We used linear mixed-effects regression models (MLMs) for all analyses and quantified the between-participant variance in the magnitude of agreement between the two methods via random slope terms. We further evaluated temporal consistency in scores using intraclass correlation coefficients (ICCs). Results:IPAQ-reported walking minutes were strongly associated with Fitbit step counts (B = 3952.36; p = 0.006), minutes of moderate PA (B = 15.498; p = 0.0113), and moderate-to-vigorous PA (MVPA; B = 28.973; p = 0.007). IPAQ total activity minutes were associated with Fitbit minutes of vigorous PA (B = 15.183; p = 0.007) and MVPA (B = 25.658; p = 0.010). IPAQ moderate activity minutes were predictive of Fitbit vigorous PA minutes (B = 9.060; SE = 3.719; p = 0.0151). There was substantial between-individual variance in these point estimates based on the significant random-effect terms, and average weekly PA level was a significant moderator of the association between IPAQ-reported and Fitbit-estimated scores for these variables. IPAQ-reported sitting minutes were inversely associated with Fitbit step counts (B = -3125.61; p = 0.004), and minutes of MVPA (B = -21.848; p = 0.007), vigorous AP (B = -10.854; p = 0.042), and moderate PA (B = -10.985; p = 0.004). Conclusion:These findings provide support for using IPAQ-reported walking and total activity minutes to monitor several PA domains in women with CPPDs, given their concordance with several tracker-estimated PA outcomes. However, the item on "sitting time" may not be a suitable for assessing sedentary time.

Prompt-based learning involves the additions of prompts (i.e., templates) to the input of pre-trained large language models (PLMs) to adapt them to specific tasks with minimal training. This technique is particularly advantageous in clinical scenarios where the amount of annotated data is limited. This study aims to investigate the impact of template position on model performance and training efficiency in clinical note classification tasks using prompt-based learning, especially in zero- and few-shot settings. We developed a keyword-optimized template insertion method (KOTI) to enhance model performance by strategically placing prompt templates near relevant clinical information within the notes. The method involves defining task-specific keywords, identifying sentences containing these keywords, and inserting the prompt template in their vicinity. We compared KOTI with standard template insertion (STI) methods in which the template is directly appended at the end of the input text. Specifically, we compared STI with naïve tail-truncation (STI-s) and STI with keyword-optimized input truncation (STI-k). Experiments were conducted using two pre-trained encoder models, GatorTron and ClinicalBERT, and two decoder models, BioGPT and ClinicalT5, across five classification tasks, including dysmenorrhea, peripheral vascular disease, depression, osteoarthritis, and smoking status classification. Our experiments revealed that the KOTI approach consistently outperformed both STI-s and STI-k in zero-shot and few-shot scenarios for encoder models, with KOTI yielding a significant 24

BACKGROUND & AIMS:Poor sleep is associated with flares of inflammatory bowel disease (IBD). Studies often rely on subjective assessments of sleep and disease activity. Our aim is to use wearable devices to objectively assess the impact of inflammation and symptoms on sleep architecture in IBD. METHODS:Individuals ≥18 years of age, diagnosed with and on medication for IBD, were enrolled in an observational study, answered daily disease activity surveys, and wore a wearable device. Sleep architecture, sleep efficiency, and total hours asleep were collected from the devices. Inflammatory markers were collected as standard of care. Associations between sleep metrics and periods of symptomatic and inflammatory flares and combinations of symptomatic and inflammatory activity were compared with periods of symptomatic and inflammatory remission. The rate of change in sleep metrics for 45 days before and after inflammatory and symptomatic flares was explored. RESULTS:A total of 101 participants were enrolled contributing a mean duration of 228.16 ± 154.24 nights of wearable data. Periods with active inflammation were associated with a significantly smaller percentage of sleep time in rapid eye movement and a greater percentage of sleep time in light sleep. Evaluating the intersection of inflammatory and symptomatic flares, altered sleep architecture was only evident when inflammation was present, and not with symptoms. Significant differences were observed in the rate that the percentage of time spent in deep and light sleep changed before and after inflammatory and symptomatic flares. CONCLUSIONS:Impaired sleep architecture is associated with inflammatory activity in IBD, and the presence of symptomatic flares alone does not impact sleep quality.

AbstractBackground.Female chronic pelvic pain disorders (CPPDs) affect 1 in 7 women worldwide and are characterized by psychosocial comorbidities, including reduced quality of life and 2-10 fold increased risk of depression and anxiety. Despite its prevalence and morbidity, CPPDs are often inadequately managed with few patients experiencing relief from any medical intervention. Characterizing mental health symptom trajectories and lifestyle predictors of mental health is a starting point to enhancing patient self-efficacy in managing symptoms. Here, we investigate the association between mental health, pain, and physical activity (PA) in females with CPPD and demonstrate a method for handling multi-modal mobile health (mHealth) data.Method.The study sample included 4,270 person-level days and 799 person-level weeks of data from CPPD participants (N=76). Participants recorded PROMIS global mental health (GMH) and physical functioning, and pain weekly for 14 weeks using a research mHealth app, and moderate-to-vigorous PA (MVPA) was passively collected via activity trackers.Data analysis.We used penalized functional regression (PFR) to regress weekly GMH-T (GMH-T) on MVPA and weekly pain outcomes, while adjusting for baseline measures, time in study, and the random intercept of the individual. We converted 7-day MVPA data into a single smooth using spline basis functions to model the potential non-linear relationship.ResultsMVPA was a significant, curvilinear predictor of GMH-T (p<0.001), independent of pain measures and prior psychiatric diagnosis. Physical functioning was positively associated with GMH-T, while pain was negatively associated with GMH-T (β=2.24, β=-1.16, respectively; p<0.05).ConclusionThese findings suggest that engaging in MVPA is beneficial to the mental health of females with CPPD. Additionally, this study demonstrates the potential of ambulatory mHealth-based data combined with functional models for delineating inter-individual and temporal variability.

Physical inactivity is a significant public health concern. Consideration of inter-individual variations in physical activity (PA) trends can provide additional information about the groups under study to aid intervention design. This study aims to identify latent profiles (“phenotypes”) based on daily PA trends among adults living in. This was a secondary analysis of 724 person-level days of accelerometry data from 133 urban-dwelling adults (89

OBJECTIVE:To determine the day-to-day associations between minority stressors (i.e., anticipated and experienced discrimination) and sleep health outcomes (i.e., total sleep time (TST), sleep disturbances, and sleep-related impairment) among sexual and gender minority (SGM) people of color. METHOD:An online sample of SGM people of color living in the United States participated in a 30-day daily diary study. Daily anticipated and experienced discrimination as well as subjective sleep outcomes were assessed via electronic diaries using validated measures. Wrist-worn actigraphy was used to objectively assess TST. Multilevel linear models (MLMs) were used to estimate the independent associations of daily intersectional minority stressors with subsequent sleep outcomes, adjusted for demographic factors and lifetime discrimination. RESULTS:The sample included 43 SGM people of color with a mean age of 27.0 years (± 7.7) of which 84% were Latinx, 47% were multiracial, and 37% were bisexual. Results of MLMs indicated that greater report of daily experienced discrimination was positively associated with same-night sleep disturbances, B (SE) = 0.45 (0.10), p < .001. Daily anticipated discrimination was positively associated with sleep-related impairment on the following day, B (SE) = 0.77 (0.17), p < .001. However, daily anticipated and experienced discrimination were not associated with same-night TST. CONCLUSIONS:Findings highlight the importance of considering the differential effects of daily intersectional minority stressors on the sleep health of SGM people of color. Further research is needed to identify factors driving the link between daily minority stressors and sleep outcomes to inform sleep health interventions tailored to this population. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Background Provoked anger is associated with an increased risk of cardiovascular disease events. The underlying mechanism linking provoked anger as well as other core negative emotions including anxiety and sadness to cardiovascular disease remain unknown. The study objective was to examine the acute effects of provoked anger, and secondarily, anxiety and sadness on endothelial cell health. Methods and Results Apparently healthy adult participants (n=280) were randomized to an 8‐minute anger recall task, a depressed mood recall task, an anxiety recall task, or an emotionally neutral condition. Pre−/post‐assessments of endothelial health including endothelium‐dependent vasodilation (reactive hyperemia index), circulating endothelial cell‐derived microparticles (CD62E+, CD31+/CD42−, and CD31+/Annexin V+) and circulating bone marrow‐derived endothelial progenitor cells (CD34+/CD133+/kinase insert domain receptor+ endothelial progenitor cells and CD34+/kinase insert domain receptor+ endothelial progenitor cells) were measured. There was a group×time interaction for the anger versus neutral condition on the change in reactive hyperemia index score from baseline to 40 minutes ( P =0.007) with a mean±SD change in reactive hyperemia index score of 0.20±0.67 and 0.50±0.60 in the anger and neutral conditions, respectively. For the change in reactive hyperemia index score, the anxiety versus neutral condition group by time interaction approached but did not reach statistical significance ( P =0.054), and the sadness versus neutral condition group by time interaction was not statistically significant ( P =0.160). There were no consistent statistically significant group×time interactions for the anger, anxiety, and sadness versus neutral condition on endothelial cell‐derived microparticles and endothelial progenitor cells from baseline to 40 minutes. Conclusions In this randomized controlled experimental study, a brief provocation of anger adversely affected endothelial cell health by impairing endothelium‐dependent vasodilation.

Dysmenorrhea has been suggested as a risk factor for ischemic heart disease (IHD) in prior research; however, the evidence was limited by a single study that relied solely on coded diagnoses and lacked adjustment for key confounders. This study further explores the relationship between dysmenorrhea and IHD comparing different dysmenorrhea definition methods across three cohorts: Mount Sinai, All of Us (AoU), and the Australian Longitudinal Survey on Women's Health (ALSWH). Dysmenorrhea was alternatively identified via diagnostic codes, a large language model-EHR phenotyping algorithm (LLM-EHR), and self-report. Hazard ratios (HR) for IHD were estimated with Cox regression models and propensity score matching. Diagnostic codes had a 37.3% false negative rate. The LLM-EHR and self-reported dysmenorrhea showed higher HRs (2.5 and 3.3) compared to diagnostic code definitions (1.5). The risk was higher in women of color and those with persistent dysmenorrhea since adolescence. These findings highlight the need to recognize dysmenorrhea's impact beyond reproduction and call for greater clinical and research awareness. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This project was supported in part by a grant from the National Institute for Child Health and Human Development (NICHD) (R01HD10826), a grant from the National Library of Medicine (NLM) (R01 LM013766), and the Hasso Plattner Foundation (HPF). ### 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: This study was approved by the Icahn School of Medicine at Mount Sinai's Institutional Review Board (Institutional Review Board 19-00951). The IRB has determined that this research involves no greater than minimal risk and approved the waiver for informed consent. 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 The Australian Longitudinal Survey on Women's Health Core Dataset is available on the Australian Data Archive (https://ada.edu.au/australian-longitudinal-study-on-womens-health-alswh/) upon approval. The All of Us registered tier dataset is available upon approval at https://workbench.researchallofus.org. The Mount Sinai EHR dataset is available upon request and after Mount Sinai approval.