Background: Black women experience a higher prevalence of poor asthma outcomes and physical inactivity than their White counterparts. Black women comprise a particularly vulnerable group of patients with asthma, with some of the highest rates of asthma in adults, high health care use (emergency department visits and hospitalizations), and the highest crude asthma mortality rate of all race or ethnicity groups. Despite recommendations to engage in regular physical activity, fewer than 15% of Black women meet the 2008 National Physical Activity Guidelines, the lowest of all racial subgroups of adults. Given the connection between physical inactivity and poor asthma outcomes, addressing physical activity among Black women with asthma is imperative. Objective: This 2-arm randomized controlled trial aims to (1) determine the efficacy of a lifestyle walking intervention on asthma control compared to an education (control) group over 24 weeks, (2) examine the maintenance effects of the lifestyle walking intervention on asthma control at 48 weeks, (3) explore the behavioral mediators (eg, self-efficacy, social support, self-regulation, and daily physical activity levels) and contextual moderators (eg, baseline asthma severity, neighborhood environment, comorbid conditions, and social determinants of health) that contribute to treatment responsiveness, and (4) assess the reach and implementation potential of the intervention. Methods: The proposed study (ACTION [A Lifestyle Physical Activity Intervention for Minority Women with Asthma]) delivers a 24-week lifestyle walking intervention designed for and by urban Black women with asthma. Participants (n=224) will be recruited through 2 urban health care systems that care for a diverse Black population. Patients will be randomized to one of two groups: (1) ACTION intervention (group sessions, physical activity self-monitoring—Fitbit, and text-based support for step goal setting) or (2) education control (an individual asthma education session and SMS text messages related to asthma education). Outcome assessments will take place at baseline, 12, 24, and 48 weeks. The primary outcome is a change in asthma control from baseline to week 24 as assessed by the asthma control questionnaire-6 (ACQ-6). Secondary outcomes include asthma-related quality of life, health care use, and asthma exacerbations and behavioral outcomes such as self-efficacy, self-regulation, social support, and physical activity. Results: This study was funded by the National Institute of Minority Health Disparities in August 2022. We pilot-tested our recruitment and intervention procedures and began recruitment in April 2023, with the enrollment of our first participant in May 2023. The anticipated completion of the study is April 2027. Conclusions: This study will deliver a new approach to physical activity interventions in Black women with asthma and help to provide guidance for addressing physical activity within this subgroup. This study will also provide a potential framework for future studies in minoritized populations with other disease conditions associated with low levels of physical activity. Trial Registration: ClinicalTrials.gov NCT05726487; https://clinicaltrials.gov/study/NCT05726487

Introduction: The Young Children’s Participation in Environment Measure (YC-PEM) is an evidence-based and promising electronic patient-reported outcome (e-PRO) option to improve early intervention (EI) service quality, such as when designing the EI service plan. Aim: Establish the preliminary effectiveness of implementing the YC-PEM e-PRO and program-specific shared decision support tool option for EI service quality when designing a service plan. Methods: For this 2-arm pilot pragmatic trial with cluster randomization at the provider level, 76 caregivers enrolled and 57 caregivers (n = 29 intervention group; n = 28 control group) completed pre- and post-intervention measures. Intervention group caregivers completed the YC-PEM e-PRO and program-specific shared decision support tool and were compared to usual care on EI service quality indicators: (1) caregiver perceptions of family-centeredness, (2) caregiver activation for shared decision-making, (3) caregiver engagement in service design and implementation; and (4) service plan quality. Results: No significant group differences at baseline were noted. Pre-post EI service quality revealed no significant differences in the adjusted model ( P > .05). However, intervention group families had higher rates participation-focused service plans (69.2%) versus controls (51.4%), most of which met state-level criteria for quality (84%). Conclusion: For EI service quality indicators, this intervention option demonstrated comparable performance to usual care. This finding suggests the intervention promoted high quality, participation-focused service planning despite no overall differences in EI service quality, warranting further testing of implementation factors and effectiveness in various service contexts. Trial Registration Number: NCT04562038

Chronic disease symptoms fluctuate across the menstrual cycle, but such patterning is understudied in cystic fibrosis (CF), particularly in the post-CFTR modulator (CFTRm) era. This study examined how CF symptoms change across the cycle, identifying which symptoms worsen, how severe they get, and when they occur. Menstruating cisgender women with CF (ages 18-45) completed surveys and tracked 40 CF and menstrual symptoms daily using a customized app. We assessed symptom periodicity over a 28-day cycle using a cumulative link mixed-effects model and evaluated the effects of CFTRm use and cycle phase, while adjusting for multiple covariates. We interviewed 20 participants using a constructivist grounded theory approach. Sixty-four participants (89%) contributed three cycles of symptom data; most (87%) used CFTRm and reported mild symptoms. Across multiple statistical models, menstrual cycle phase significantly affected CF symptoms, including gastrointestinal (GI) symptoms (e.g., bloating, gas, stomachache, and bowel movement pain) and pulmonary/sinus symptoms (e.g., coughing up mucus, migraine, and runny, stuffy nose). A phase effect on CF symptoms remains even after adjusting for CFTRm use. Qualitative findings corroborate these patterns, with interviewees reporting improvements in coughing, coughing mucus, and joint pain with CFTRm, though some experienced intensified GI symptoms. CF symptom severity varied across the menstrual cycle, even after accounting for CFTRm. While menstruation was associated with the highest symptom burden, other cycle phases showed significantly reduced symptom severity, which varies by symptom. Clinicians should encourage females with CF to track symptoms to support phasic CF management strategies.

BACKGROUND Opioid overdose remains a leading cause of preventable death in the United States. Existing approaches to identify individuals at elevated risk rely on imprecise rule-based criteria that misclassify patients’ risk of this serious health outcome. Machine learning (ML) algorithms can help improve prediction performance and can be combined with electronic health record (EHR) interventions to reduce overdose risk. OBJECTIVE The Machine Learning Prediction and Reducing Overdoses With EHR Nudges (mPROVEN) clinical trial integrates a validated ML overdose risk model with behavioral economics–informed EHR nudges to test whether the combination improves evidence-based prescribing behaviors associated with lower overdose risk and, ultimately, reduces overdose among elevated-risk patients. METHODS mPROVEN is a pragmatic cluster randomized controlled trial conducted in primary care practices within a large multistate integrated health system. Eligible patients are adults (≥18 years) identified by the ML algorithm as having elevated overdose risk and seen at a primary care visit during the study period. Primary care practices serve as the unit of randomization and will be randomized into three arms: (1) usual care; (2) elevated risk flag only, where clinicians see a noninterruptive EHR flag indicating elevated overdose risk; and (3) elevated risk flag + nudges, in which active choice and accountable justification alerts are embedded within the EHR in addition to the elevated risk flag. The trial will enroll a target cohort of 800 patients for the primary analysis. The intervention period is 4 months (or until the study ends, whichever occurs later). The primary outcome is a 3‑point composite measure of safer opioid prescribing at 4 months, awarding 1 point each for active naloxone prescription, average opioid dosage of 50 morphine milligram equivalents per day or less, and absence of opioid-benzodiazepine overlap. Secondary outcomes include the composite outcome at 6 months, individual score components, and all-cause and overdose-specific emergency department or inpatient visits. Outcomes will be compared across study arms using an intention‑to‑treat approach with linear mixed‑effects models accounting for clinic-level clustering. RESULTS Funded by the National Institutes of Health, in June 2022, enrollment began on March 10, 2025. Enrollment for the primary analysis cohort (n=798) was completed in May 2025 with additional participants enrolled for secondary analyses through December 2025 (n=1662). Primary cohort analyses began in January 2026, and results are expected by mid-2027. CONCLUSIONS The mPROVEN study is among the first pragmatic randomized controlled trials to integrate ML‑based opioid overdose risk prediction with behavioral nudges within a large health system EHR. By combining advances in data science and behavioral economics, the study aims to reduce opioid overdose risk in primary care using a scalable and low-touch intervention to address a high-priority public health issue. CLINICALTRIAL ClinicalTrials.gov NCT06806163; https://clinicaltrials.gov/study/NCT06806163 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/94007

Background: Opioid overdose remains a leading cause of preventable death in the United States. Existing approaches to identify individuals at elevated risk rely on imprecise rule-based criteria that misclassify patients’ risk of this serious health outcome. Machine learning (ML) algorithms can help improve prediction performance and can be combined with electronic health record (EHR) interventions to reduce overdose risk. Objective: The Machine Learning Prediction and Reducing Overdoses With EHR Nudges (mPROVEN) clinical trial integrates a validated ML overdose risk model with behavioral economics–informed EHR nudges to test whether the combination improves evidence-based prescribing behaviors associated with lower overdose risk and, ultimately, reduces overdose among elevated-risk patients. Methods: mPROVEN is a pragmatic cluster randomized controlled trial conducted in primary care practices within a large multistate integrated health system. Eligible patients are adults (≥18 years) identified by the ML algorithm as having elevated overdose risk and seen at a primary care visit during the study period. Primary care practices serve as the unit of randomization and will be randomized into three arms: (1) usual care; (2) elevated risk flag only, where clinicians see a noninterruptive EHR flag indicating elevated overdose risk; and (3) elevated risk flag + nudges, in which active choice and accountable justification alerts are embedded within the EHR in addition to the elevated risk flag. The trial will enroll a target cohort of 800 patients for the primary analysis. The intervention period is 4 months (or until the study ends, whichever occurs later). The primary outcome is a 3‑point composite measure of safer opioid prescribing at 4 months, awarding 1 point each for active naloxone prescription, average opioid dosage of 50 morphine milligram equivalents per day or less, and absence of opioid-benzodiazepine overlap. Secondary outcomes include the composite outcome at 6 months, individual score components, and all-cause and overdose-specific emergency department or inpatient visits. Outcomes will be compared across study arms using an intention‑to‑treat approach with linear mixed‑effects models accounting for clinic-level clustering. Results: Funded by the National Institutes of Health, in June 2022, enrollment began on March 10, 2025. Enrollment for the primary analysis cohort (n=798) was completed in May 2025 with additional participants enrolled for secondary analyses through December 2025 (n=1662). Primary cohort analyses began in January 2026, and results are expected by mid-2027. Conclusions: The mPROVEN study is among the first pragmatic randomized controlled trials to integrate ML‑based opioid overdose risk prediction with behavioral nudges within a large health system EHR. By combining advances in data science and behavioral economics, the study aims to reduce opioid overdose risk in primary care using a scalable and low-touch intervention to address a high-priority public health issue. Trial Registration: ClinicalTrials.gov NCT06806163; https://clinicaltrials.gov/study/NCT06806163 International Registered Report Identifier (IRRID): DERR1-10.2196/94007