Neng Wan

Background: Physical inactivity is prevalent among adults in the United States and is related to various health disparities. The search for effective policies and interventions to promote physical activity (PA) is severely hampered by the paucity of research on the mechanisms underlying PA behavior change. Objective: This paper describes a research protocol that uses mobile health technology to examine the influence of contextual and environmental factors and acute momentary precipitants on PA adoption and maintenance among Pacific Islanders in the United States. Methods: The study is guided by an overarching conceptual framework derived from models of the social and environmental determinants of health, social cognitive theories of behavior change, and prior empirical findings. Participants will be assessed using real-time, field-based, state-of-the-art methodologies consisting of MotionSense, ecological momentary assessment, and GPS tracking. MotionSense tracks behavioral and physiological data in real time and can objectively detect PA behaviors of participants. GPS tracking permits real-time mapping of an individual’s space-time trajectories and relevant environmental exposures and characteristics (eg, proximity to PA facilities and neighborhood safety) using Geographic Information System data. Principal outcomes of interest are PA adoption and PA maintenance. Results: This study was funded by the National Cancer Institute of the National Institutes of Health in August 2023. Data collection started on May 23, 2025, and is expected to finish by March 2028. As of August 10, 2026, the project has recruited all 150 participants. Data analysis is ongoing, and results are expected to be published in May 2028. Conclusions: This is among the first studies to link objective and momentary indexes of PA to key environmental and psychosocial factors in PA behavior studies. The comprehensive, multi-method approach addresses 2 long-standing limitations in PA research: the reliance on self-reported outcome measures and the use of static residential locations as proxies for neighborhood exposure. In addition, this study is among the first to apply dynamic prediction models, a novel statistical approach well suited to the high-frequency, intensive longitudinal data generated by real-time mobile health assessment. The findings will provide actionable evidence to inform policies and interventions aimed at reducing PA-related health disparities among Pacific Islanders and other racial and ethnic groups that experience similar health problems.

Background: Social determinants of health continue to drive persistent disparities in perioperative care. Our team has previously demonstrated racial and socioeconomic disparities in perioperative processes, notably in the administration of antiemetic prophylaxis, in several large perioperative registries. Given how neighborhoods are socially segregated in the United States, we examined geospatial clustering of perioperative antiemetic disparities. Objective: The study aimed to determine whether disparities in perioperative antiemetic prophylaxis exhibit geographic clustering based on neighborhood-level disadvantage and whether patients from disadvantaged communities are more likely to be undertreated after adjusting for individual postoperative nausea and vomiting risk. Methods: We conducted a retrospective cohort study of anesthetic records from the University of Utah Hospital involving 19,477 patients who met the inclusion criteria. We geocoded patient home addresses and combined them with the census block group–level neighborhood disadvantage, a composite index from the National Neighborhood Data Archive. We stratified our patients by antiemetic risk score and calculated the number of antiemetic interventions. We used Poisson spatial scan statistics, implemented in SaTScan (Information Management Services, Inc), to detect geographic clusters of undertreatment. Results: We identified 1 significant cluster ( P <.001) of undertreated perioperative antiemetic prophylaxis cases. The relative risk of the whole cluster was 1.44, implying that patients within the cluster were 1.44 times more likely to receive fewer antiemetics after controlling for antiemetic risk. Patients from more disadvantaged neighborhoods were more likely to receive below-median antiemetic prophylaxis after controlling for risk. Conclusions: To our knowledge, this is the first geospatial cluster analysis of perioperative process disparities; we leveraged innovative geostatistical methods and identified a spatially defined, geographic cluster of patients whose home address census-tract level neighborhood deprivation index predicted disparities in risk-adjusted antiemetic prophylaxis.

Objectives: Neighborhood physical activity environments (PAEs)—including walkability, recreational facilities, green space, and civic infrastructure—support active living and population health but are often inequitably distributed. This study examines racial/ethnic and socioeconomic disparities in neighborhood PAEs and assesses variation by urbanicity. Methods: We used population-weighted ordinary least squares regression models with county fixed effects using the latest available 2018 data from 69,889 census tracts in the contiguous United States. Models assessed associations between racial/ethnic and poverty composition and four PAE dimensions—built, physical facilities, natural, and social environments. Sensitivity analyses compared models with and without county fixed effects and population weighting. Urbanicity-stratified models examined disparities across urban, suburban, town, rural, and mixed settings. Results: Non-Hispanic White and low-poverty populations had greater access to PAEs across all domains, except that low-poverty populations lived in areas with lower walkability. Disparities were largest in urban and suburban areas. Rural high-poverty populations had more natural resources but less infrastructure and civic support. County-fixed effects reversed the walkability advantages for non-Hispanic Black observed in unadjusted models. Conclusions: PAE disparities disproportionately affect racial/ethnic minority and high-poverty populations, especially in urban areas. Findings support equity-focused, context-specific interventions for environmental justice in health policy.

Background: A key challenge for schools throughout the COVID-19 pandemic was finding ways to monitor and prevent COVID-19 cases. While diagnostic testing and connecting students and their families to appropriate resources to mitigate the spread of COVID-19 were recommended, few schools had scalable infrastructure, including information technology systems, to implement these types of measures. Objective: This study tested a new approach to COVID-19 testing (SCALE-UP Counts) in school settings that used automated bidirectional text messages provided to the school community that alerted parents of students to COVID-19 testing options and guidance on when to test. Methods: The SCALE-UP Counts trial was designed as a Sequential Multiple Assignment Randomized Trial and final analyses compared results from parents who received intensive, fully automated, bidirectional text messaging about COVID-19 testing or usual care (control; fully automated unidirectional text messaging about COVID-19 testing), unblinded interventions. From the 16 selected schools, we enrolled all eligible participants who did not opt out of the study. The study provided schools from both arms of the trial with free at-home COVID-19 test kits. The primary outcome was the proportion of parents whose households tested for COVID-19, and the secondary outcome was the number of missed school days. The study asked parents to respond to self-report measures on testing outcomes and missed school days through web-based questionnaires. Results: The study included 7122 parents of students from 16 schools, half of which were title 1 schools; 2588 were randomized to usual care or control and 4534 to bidirectional text messaging. The SCALE-UP Counts intervention led to increased self-reported testing when compared with the control condition (22.8% vs 13.5%, relative testing rate=1.64, 95% CI 1.31-2.02; P<.001). There was no observed difference in missed school days between the study arms (0.43 per month vs 0.28 in usual care, relative missed days rate=1.55, 95% CI 0.98-2.45; P=.06). Conclusions: SCALE-UP Counts worked closely with schools and the state’s public health system to implement and test a scalable health information technology approach that delivered automated text messages to students’ parents around COVID-19 testing and provided access to free at-home test kits. Such an approach can help facilitate COVID-19 testing among school communities, including those that provide education and resources to students and their families from racial or ethnic minorities and with low socioeconomic status. Similar health information technology approaches could be used to increase ease of access to testing, reduce testing burden, and provide tailored information on health measures in school communities for a variety of illnesses or public health concerns. Trial Registration: ClinicalTrials.gov NCT05112900; http://clinicaltrials.gov/ct2/show/NCT05112900

BACKGROUND Wearable accelerometers have become integral to mobile health (mHealth) research, particularly for delivering real-time physical activity (PA) monitoring and applications in interventions such as Just-in-Time Adaptive Interventions (JITAIs). One critical yet underexplored factor in real-time PA monitoring is epoch length, which is the time interval over which raw accelerometry data are aggregated to classify activity intensities and levels. Shorter epochs (e.g., 1 second) enhance precision but increase computational and battery demands, while longer epochs (e.g., 60 seconds) reduce data burden but may miss brief activity bouts. Although previous studies have examined epoch effects using post-processed data, limited evidence exists regarding their influence on real-time, wrist-based PA estimates, especially for moderate-to-vigorous PA (MVPA). Identifying an optimal epoch length for real-time PA measurement remains a critical gap in supporting scalable and efficient mHealth interventions. OBJECTIVE This study determined the impact of varying epoch lengths on real-time MVPA estimates derived from a wrist-worn accelerometer to identify an optimal epoch that balances measurement accuracy with practical feasibility for mHealth applications. METHODS Twenty adults (Age: 32.5 ± 15.1 years) completed a series of carefully selected simulated free-living activities in a controlled laboratory setting. Participants wore the MotionSense HRV wristband, which computed real-time Euclidean Norm Minus One (ENMO) values, and a COSMED K5 indirect calorimetry for metabolic reference. ENMO values were aggregated into 5-, 10-, 15-, 30-, and 60-second epochs. MVPA was classified using validated ENMO cut-points. Epoch-level MVPA estimates were compared against the 1-second reference using mean absolute percent error (MAPE), Pearson’s correlations, Bland-Altman (BA) plots, and equivalence testing with a ±10% equivalence zone. RESULTS MVPA estimates from all epoch lengths were statistically equivalent to the 1-second standard. The 15-second epoch demonstrated the best trade-off between accuracy and efficiency, with minimal bias (0.05 min), low MAPE (6.3%), and strong correlation (r = 0.97). However, indicators of individual-level error increased with longer epochs; MAPE increased to 9.5% at 60 seconds, and the limits of agreement widened (from ± 2.9 min at 15s to ± 4.9 min at 60s), suggesting greater potential misclassifications in estimating MVPA with longer epochs. CONCLUSIONS Although MVPA estimates using the MotionSense HRV wristband were robust across all epoch lengths, findings from this study suggest that a 15-second epoch provides an optimal balance between measurement precision and processing efficiency, making it well-suited for mHealth interventions, such as JITAIs that rely on timely activity detection.

Real-world health studies require continuous and secure data collection from mobile and wearable devices. We introduce MotionPI, a smartphone-based system designed to collect behavioral and health data through sensors and surveys with minimal interaction from participants. The system integrates passive data collection (such as GPS and wristband motion data) with Ecological Momentary Assessment (EMA) surveys, which can be triggered randomly or based on physical activity. MotionPI is designed to work under real-life constraints, including limited battery life, weak or intermittent cellular connection, and minimal user supervision. It stores data both locally and on a secure cloud server, with encrypted transmission and storage. It integrates through Bluetooth Low Energy (BLE) into wristband devices that store raw data and communicate motion summaries and trigger events. MotionPI demonstrates a practical solution for secure and scalable mobile data collection in cyber-physical health studies.

Diet quality contributes to breast cancer (BC) risk and is shaped in part by the neighborhood food environment. Yet, the long-term impact of residential food environments on BC incidence remains largely underexplored. We linked residential histories of 7,396 BC cases and 21,900 controls in Chicago (1990–2019) to food outlet data from the National Establishment Time Series. Cumulative (time-weighted), inverse distance-weighted (IDW), food access scores were derived along with nearest distance metrics for “ healthy ” and “ less healthy” food access. Associations with incidence were stronger for walking than driving distance-based measures and for nearest distance-based measures over IDW measures. In multivariable logistic regression models, BC incidence decreased monotonically with shorter walking distance to the nearest healthy food outlet, reaching a 60% lower incidence for shorter vs. longer walking distances (OR=0.42, 95% CI=0.38, 0.48). Similarly, incidence increased monotonically with shorter walking distance to the nearest less healthy food outlet, reaching a 150% greater incidence for shorter vs. longer walking distances (OR=2.49, 95% CI=2.19, 2.83). This is the first study to use residential histories to define long-term, time-weighted geospatial food access metrics in BC epidemiology, highlighting how cumulative neighborhood environments have the potential to shape cancer risk and informing targeted interventions.