Jennifer E. Phipps

Background: Community engagement that emphasizes shared leadership is essential in clinical and translational science, and language, naming, and framing have the potential to shape power dynamics. This study explored how renaming and restructuring a Community Advisory Board (CAB) into a Community Leadership Board (CLB) could strengthen a trauma-informed network of care (TINoC) by elevating community power, cultural responsiveness, and equitable participation. Methods: Guided by the Trauma and Resilience Informed Research Principles and Practice(TRIRPP) framework, we established a paid CLB in Yolo County, California, composed of six individuals who identified as members of groups underrepresented in science. We reviewed timesheets and TINoC products and conducted an inductive thematic analysis of meeting minutes to determine the CLB’s main areas of influence. Results: The CLB met 25 times over two years, provided iterative feedback on more than a dozen educational materials, clinical workflows, trauma-informed trainings, and communication strategies, and co-presented at community meetings. Eight recurring areas of influence were identified: trauma-informed ACE screening, accessibility, workflow feasibility, community- and patient-centered feedback, health communication, participant compensation, engagement, and post-screening navigation. CLB members highlighted gaps not identified by the academic and community members of the TINoC, including translation accuracy, time allowed for ACE screening, and ensuring voluntary patient participation. Conclusions: Renaming the CLB as a “leadership” body signaled a shift in accountability, deepened engagement, and underscored how naming practices can drive more equitable translational research. Virtual-only meetings potentially limited the representativeness of the CLB; however, results suggest naming is a critical component of trauma-informed community-engaged research(CEnR).

Background: The population of adults aged 65 and older is rapidly increasing, while the availability of caregivers is declining. Smart homes that provide unobtrusive, continuous monitoring and alerting on clinically relevant changes in daily activity patterns offer a potentially innovative solution for aging in place. Objective: This study aims to evaluate the barriers and facilitators to the adoption of a low-cost smart home embedded within a community-based approach to health monitoring for older adults with multiple chronic conditions and who are experiencing poverty. Methods: Using a prospective, mixed methods design and iterative community co-design, 46 older adults from 7 different language groups were continuously monitored for 6 months with ambient sensors installed in their homes. Two older adults were monitored for 4 and 5 months, respectively, resulting in a total sample of 48. The system generated alerts based on movement pattern changes and escalated notifications to participants, support persons, community health workers, and nurses. Sensor data were analyzed descriptively to quantify alert patterns and response rates, while written text-based data from in-the-moment surveys, community health workers’ and registered nurses’ notes, and semistructured interviews underwent qualitative descriptive analysis and reflexive thematic coding. Results: The system generated 37 million sensor readings condensed into 1.2 million high-level events and 4719 novel alerts. Qualitative data comprised 34,086 words of text. Participants responded to 1.57% (74) of the initial email alerts and 7.79% (368) of the follow-up SMS text message alerts sent when no email response was received. Community health workers and registered nurses responded to 78.36% (n=3698) of the escalated alerts, resulting in 1060 contacts with participants in response to alerts. Clinical contacts resulted in 72 interventions. Three major qualitative themes emerged: (1) Alone, (2) Trust, and (3) Human Connection. Subthemes included Safety, Personalization, and Digital Distress defined as stress associated with interacting with digital health-monitoring systems. Participants rated the system highly (mean likelihood-to-recommend rating 8.68/10, SD 1.68); however, they expressed a strong preference for phone calls over automated alerts. Cultural expectations influenced adoption, particularly in multigenerational households. Conclusions: Communities can effectively engage in technology-delivered health care. Future research is needed to improve technical aspects of smart home monitoring systems, including accurate alerting using machine learning, data visualizations for older adults and health care workers, and culturally sensitive features. Additional work should address how and when to communicate automated messaging, engage older adults with their own data, and integrate sensor-based monitoring into health care workflows. Research should also explore personalization through advanced computational approaches such as machine learning and strategies to reduce digital distress.

BACKGROUND The population of adults aged 65 and older is rapidly increasing, while the availability of caregivers is declining. Smart homes that provide unobtrusive, continuous monitoring and alerting on clinically relevant changes in daily activity patterns offer a potential innovative solution for aging in place. OBJECTIVE To prototype and evaluate a low-cost, community-based smart health system for monitoring the health of older adults with multiple chronic conditions and experiencing poverty, and to identify barriers and facilitators to adoption. METHODS Using a prospective, mixed-methods design and iterative community co-design, 46 older adults from 7 different language groups were continuously monitored for 6 months with ambient sensors installed in their homes. Two older adults were monitored for 4 and 5 months respectively resulting in a total sample of N=48. The system generated alerts based on movement pattern changes and escalated notifications to participants, support persons, community health workers (CHWs), and nurses. Sensor data were analyzed descriptively to quantify alert patterns and response rates, while text-based data from in-the-moment surveys, CHW and nurse notes, and semi-structured interviews underwent qualitative descriptive analysis and reflexive thematic coding. RESULTS The system generated 37 million sensor readings condensed into 1.2 million high-level events and 8,927 novel alerts. Qualitative data comprised of 34,086 words of text. Participants responded to 1.57% of initial email alerts and 7.8% of follow-up SMS alerts sent when no email response was received. CHWs responded to 83.6% of escalated alerts, resulting in 1,060 contacts with participants in response to alerts. Clinical contacts resulted in 72 interventions. Three major qualitative themes emerged: (1) mitigating aloneness, (2) building trust in technology and people, and (3) maintaining a human connection. Subthemes included safety, personalization, and digital distress. Participants rated the system highly (Net Promoter Score = 8.48/10) but expressed a strong preference for phone calls over automated alerts. Cultural expectations influenced adoption, particularly in multi-generational households. CONCLUSIONS Communities can effectively engage in technology-delivered healthcare. Future research is needed to improve technical aspects of smart health systems, including accurate alerting utilizing machine learning, data visualizations for older adults and healthcare workers, and culturally sensitive features. Additional work should address how and when to communicate automated messaging, engage older adults with their own data, and integrate sensor-based monitoring into healthcare workflows. Research should also explore personalization through advanced computer models such as machine learning and strategies to reduce digital distress. CLINICALTRIAL None

Introduction: Adverse pregnancy outcomes (APOs), including hypertensive disorders, gestational diabetes, and preterm birth, affect 10 – 20% of U.S. pregnancies and substantially increase long–term cardiovascular disease (CVD) risk. While behavioral interventions during pregnancy may reduce risk factors for APOs, evidence remains limited on which strategies are effective for high–risk women. This systematic review addresses this gap by examining patterns in intervention design, outcome reporting, and areas where research fails to meet the needs of at–risk pregnant populations. Methods: We searched PubMed and the Cochrane Central Register of Controlled Trials for articles published from January 1, 2005, to June 30, 2024. Following PRISMA guidelines, we included U.S.–based randomized controlled trials of behavioral interventions: diet, physical activity, sleep, mindfulness, or self–monitoring, delivered during pregnancy to women at high risk of APOs. Two reviewers independently performed screening, data extraction, and risk–of–bias assessment using the Cochrane Risk of Bias Tool. Results: Of 3,261 studies screened, 43 met inclusion criteria. Most interventions focused on individuals with prepregnancy overweight or obesity, often excluding those with other CVD risk factors. Thirty–six trials reported improvements in outcomes such as gestational weight gain (GWG), postpartum weight retention, blood pressure, or biomarkers. Interventions combining diet and physical activity showed the greatest benefit. However, only four trials demonstrated improvements in clinical outcomes like gestational diabetes, hypertensive disorders, or lipid profiles. Outcome measures and timelines varied widely, and few trials included postpartum follow–up or assessed outcomes beyond GWG. Discussion: Although behavioral interventions during pregnancy are growing in number, trials are limited by heterogeneous designs, narrow inclusion criteria, and inconsistent outcome reporting. The focus on GWG and exclusion of women with complex risk profiles reduce generalizability. Inclusive research with standardized outcomes is urgently needed to improve maternal health and reduce long–term CVD risk.

Background: The prevalence of hypertensive disorders of pregnancy (HDPs) in India is 11%, which is one of the highest rates globally. Existing research on HDPs in India primarily focuses on biological risk factors, with minimal research on how socioecological factors combine to increase risk of HDPs. We conducted a rapid review using Bronfenbrenner’s Ecological Model to understand the social and cultural factors associated with HDPs among Indian pregnant women to identify possible intervention targets that may uniquely improve health in this population. Bronfenbrenner’s Ecological Model is a framework that can be used to understand the complex relationship between multiple influences on health. Methods: We reviewed studies published between January 2010 and January 2024 using PubMed, Science Direct, and Scopus databases. Search terms included variants of hypertension, pregnancy, and India. Inclusion criteria were: (1) peer-reviewed journal article; (2) published between January 2010 to January 2024; (3) participants consisted of Indian women living in India; (4) studies evaluated socioecological risk factors associated with HDPs. One independent reviewer performed searches, screening, data extraction, and quality assessment. Each included study was then organized within Bronfenbrenner’s Ecological Model. Results: A total of 921 studies were generated from the initial search, with 157 exclusions due to duplicates. Following screening for inclusion and exclusion criteria at the title/abstract and full text levels, 17 studies remained in the final review. Socioecological risk factors of HDPs were identified at each level, with the most commonly identified influences including: low socioeconomic status (SES), lacking community education and knowledge on HDP management and prevention, and lacking prenatal HDP screening. Conclusion: This study determined that the high risk for HDPs in India is influenced by many intertwined socioecological factors. Women in rural and low SES areas need more health education on HDP management and prevention. There also needs to be more adequate prenatal HDP screening, with at least 4 and ideally 8 prenatal visits. Prenatal screenings should be accompanied with culturally appropriate patient education, especially for low SES women who have limited literacy, so that they can effectively make individual and microsystemic lifestyle decisions aimed at either managing or preventing HDPs.

Introduction: Pregnancy is a time of increased vulnerability to mental health disorders. Additionally, the COVID-19 pandemic has increased the incidence of depression and anxiety. Thus, we aimed to assess mental health and associated healthy behaviors of pregnant people in California during the pandemic in order to contextualize prenatal well-being during the first pandemic of the twenty-first century. Methods: We conducted an online cross-sectional study of 433 pregnant people from June 6 through July 29, 2020. We explored 3 hypotheses: (1) mental health would be worse during the pandemic than in general pregnant samples to date; (2) first-time pregnant people would have worse mental health; and (3) healthy behaviors would be positively related to mental health. Results: Many of our participants (22%) reported clinically significant depressive symptoms and 31% reported clinically significant anxiety symptoms. Multiparous pregnant people were more likely to express worries about their own health and wellbeing and the process of childbirth than were primiparous pregnant people. Additionally, as pregnancy advanced, sleep and nutrition worsened, while physical activity increased. Lastly, anxious-depressive symptomology was significantly predictive of participant sleep behaviors, nutrition, and physical activity during the past week. Discussion: Pregnant people had worse mental health during the pandemic, and this was associated with worse health-promoting behaviors. Given that the COVID-19 pandemic and associated risks are likely to persist due to low vaccination rates and the emergence of variants with high infection rates, care that promotes mental and physical well-being for the pregnant population should be a public health priority.

Objective: Researchers and public health professionals need to better understand individual engagement in coronavirus disease 2019 (COVID-19) mitigation behaviors to reduce the human and societal costs of the current pandemic and prepare for future respiratory pandemics. We suggest that developing measures of individual mitigation behaviors and testing them among high-risk individuals, including pregnant people, may help to reduce overall morbidity and mortality by quickly identifying targets for messaging around mitigation until sufficient vaccination uptake is reached. Methods: We surveyed pregnant people in California over 2 waves of the COVID-19 pandemic to explore mitigation behaviors. We developed and validated a novel Viral Respiratory Illness Mitigation Scale (VRIMS). Results: Seven measures loaded onto a single factor with good psychometric properties. The overall sample scale average was high over both waves, indicating that most pregnant Californians engaged in most of the strategies most of the time. Older participants, minoritized participants, those living in more urban contexts, and those surveyed during a surge reported engaging in these strategies most frequently. Conclusions: Clinicians and researchers should consider using reliable, validated measures like the VRIMS to identify individuals and communities that may benefit from additional education on reducing risk for COVID-19, future respiratory pandemics, or even seasonal flu.

Fluorescence lifetime imaging (FLIm) was integrated with a da Vinci Surgical System (Intuitive Surgical, Inc.) for exploration of head and neck cancer (N=25 patients). In vivo results showed FLIm distinguished between tumor and healthy tissue.

Re-excision rates for breast cancer lumpectomy procedures are currently nearly 25% due to surgeons relying on inaccurate or incomplete methods of evaluating specimen margins. The objective of this study was to determine if cancer could be automatically detected in breast specimens from mastectomy and lumpectomy procedures by a classification algorithm that incorporated parameters derived from fluorescence lifetime imaging (FLIm). This study generated a database of co-registered histologic sections and FLIm data from breast cancer specimens ( N = 20) and a support vector machine (SVM) classification algorithm able to automatically detect cancerous, fibrous, and adipose breast tissue. Classification accuracies were greater than 97% for automated detection of cancerous, fibrous, and adipose tissue from breast cancer specimens. The classification worked equally well for specimens scanned by hand or with a mechanical stage, demonstrating that the system could be used during surgery or on excised specimens. The ability of this technique to simply discriminate between cancerous and normal breast tissue, in particular to distinguish fibrous breast tissue from tumor, which is notoriously challenging for optical techniques, leads to the conclusion that FLIm has great potential to assess breast cancer margins. Identification of positive margins before waiting for complete histologic analysis could significantly reduce breast cancer re-excision rates.