About

Dr. Yuqing Guo is a nursing scientist specializing in the study of how perinatal health impacts the emotional development of infants and young children in underserved communities. Her research team collaborates with BIPOC communities and incorporates technology such as wearable devices and mobile applications to enhance understanding and support for early childhood development. Her work focuses on addressing health disparities and leveraging innovative tools to improve outcomes for vulnerable populations.

Research topics

• Medicine
• Psychology
• Computer Science
• Nursing
• Sociology
• Political Science
• Gerontology
• Data science
• Engineering
• Physical therapy
• Endocrinology
• Environmental health
• Psychiatry
• Knowledge management
• Social psychology
• Internal medicine
• Family medicine
• Developmental psychology
• Clinical psychology
• Immunology

Selected publications

• SHERO: A Community-Driven, Technology-Enabled, and Human-Led Initiative to Advance Black Perinatal Health Equity in the U.S.

  Journal of Health Care for the Poor and Underserved · 2025-08-01

  articleSenior author

  Shaping Healthy Equitable Reproductive Outcomes (SHERO) is a community-driven, technology-enabled, and human-led initiative designed to address Black maternal and infant health disparities. It integrates culturally concordant care teams, digital health education, and hybrid social support into a technology-enhanced, coordinated perinatal care model. Evaluated by mixed methods, this initiative seeks to enhance maternal and infant health equity nationwide.

  PublisherDOI

• 0399 Risk and Protective Factors for Sleep Disturbances Among Black Reproductive-Aged Women in the US

  SLEEP · 2025-05-01

  articleOpen access1st authorCorresponding

  Abstract Introduction Black women disproportionately experience sleep disturbances often linked to chronic stress including racial discrimination. Limited research explores both the risk and protective factors affecting sleep quality amongst Black reproductive-aged women. Understanding these factors is crucial for advancing sleep equity and health in this population. Methods A retrospective design was used. Data was collected at the 2024 Essence Festival using a tech-based platform. A total of 769 Black women who had given birth in the past 10 years consented to participate. The survey included measures of demographics (e.g., education, marital status), adverse experiences (e.g., domestic violence, adverse childhood experiences), mental health (e.g., perinatal depression/anxiety), coping levels, social support, the Everyday Discrimination Scale, and the PROMIS Sleep Disturbance Short Form. Descriptive statistics and regression analyses were conducted. Results Among participants, 95% self-identified as non-Hispanic Black, with 48% from New Orleans. The average age was 35.8 years (SD = 6.5). Approximately 43% graduated high school or had some college, while 55% obtained a bachelor’s or graduate degree; 43% were married; 36% (n=274) reported adverse experiences and 43% (n=432) reported perinatal depression/anxiety. Notably, 85% reported sleep problems: 70% woke up tired, 65% did not get enough sleep, 52% had trouble staying asleep, 47% had difficulty falling asleep, and 33% snored. Adjusting for covariates, regression analysis identified that greater racial discrimination (β = 0.14, p< 0.001), adverse experiences (β = 0.10, p=0.005), and perinatal depression/anxiety (β = 0.12, p=0.001) were associated with higher sleep disturbances. Conversely, higher coping ability (β = -0.20, p< 0.001) and social support (β = -0.11, p=0.003) were associated with lower sleep disturbances. Conclusion This study highlights chronic stressors (racial discrimination, social adversity, and perinatal mental health challenges) are risk factors for sleep disturbances, whereas higher coping ability and social support serve as critical protective factors amongst Black reproductive-aged women. Our findings emphasize the need for multi-level interventions that enhance individual coping mechanisms, increase social support networks, and address systemic discrimination and adversity. Such interventions may advance sleep equity and overall health of Black reproductive-aged women. Support (if any) Department of Health and Human Services, Office of Minority Health (CPIMP231373).

  PublisherOA PDFDOI

• Sleep Quality of Persons with Dementia and Family Caregivers in Korean Americans: Wearable Technology to Study the Dyadic Association

  Western Journal of Nursing Research · 2025-03-17 · 3 citations

  article

  BACKGROUND: Sleep disturbances, such as difficulty in falling asleep and multiple awakenings at night, are prevalent among persons with Alzheimer's disease and related dementias (hereafter dementia), resulting in advanced cognitive impairment and increased behavioral problems. Additionally, family caregivers (eg, spouses or offspring) suffer from reduced sleep quality as a result of sleep disturbances in the persons with dementia (PWDs) they care for. Relatively little is known about the interaction of sleep parameters in dyads (PWD-caregiver) as paired units among understudied immigrant minorities, particularly Korean Americans. OBJECTIVES: To describe dyads' sleep parameters (ie, total/deep/rapid eye movement/light sleep, awake duration, latency duration, sleep efficiency) using wearable technology (smart-rings) and sleep diaries, and to identify interrelationships among sleep parameters between PWD and caregivers. METHODS: This 4-week observational study used smart-ring monitoring, a sleep survey, and self-reported sleep diaries to explore sleep associations of Korean American dyads recruited from the community. Pearson correlations were performed. RESULTS: A total of 11 dyads participated in the study. The mean age of PWD was 82.7 years (standard deviation (SD) = 2.3); of caregivers 69.1 years (SD = 10.2). Nine PWD (81.8%) were male, all caregivers (100%) were female, and 4 dyads (36.4%) slept in the same bed. Sleep parameters of PWD were significantly correlated with those of caregivers. CONCLUSION: Our findings demonstrate that PWD's sleep parameters (eg, deep sleep) were significantly correlated with those of caregivers' parameters. This study shows the feasibility of using wearable device to measure dyadic sleep quality for both PWD and their caregivers, particularly within immigrant populations.

  PublisherDOI

• Individualized Time‐Varying Nonparametric Model With an Application in Mobile Health

  Statistics in Medicine · 2025-02-18

  articleOpen access

  Individualized modeling has become increasingly popular in recent years with its growing application in fields such as personalized medicine and mobile health studies. With rich longitudinal measurements, it is of great interest to model certain subject-specific time-varying covariate effects. In this paper, we propose an individualized time-varying nonparametric model by leveraging the subgroup information from the population. The proposed method approximates the time-varying covariate effect using nonparametric B-splines and aggregates the estimated nonparametric coefficients that share common patterns. Moreover, the proposed method can effectively handle various missing data patterns that frequently arise in mobile health data. Specifically, our method achieves subgrouping by flexibly accommodating varying dimensions of B-spline coefficients due to missingness. This capability sets it apart from other fusion-type approaches for subgrouping. The subgroup information can also potentially provide meaningful insight into the characteristics of subjects and assist in recommending an effective treatment or intervention. An efficient ADMM algorithm is developed for implementation. Our numerical studies and application to mobile health data on monitoring pregnant women's deep sleep and physical activities demonstrate that the proposed method achieves better performance compared to other existing methods.

  PublisherOA PDFDOI

• PERFECT: Personalized Exercise Recommendation Framework and architECTure

  ACM Transactions on Computing for Healthcare · 2024-10-03 · 4 citations

  article

  Background : The health benefits of regular physical activity (PA) are well-established and widely acknowledged. Through the integration of wearable trackers, the Internet of Things (IoT)—a network of interconnected devices capable of collecting and exchanging data—coupled with mobile health (mHealth), which refers to the use of mobile devices to support medical and public health practices, it is now feasible to systematically gather and present individual exercise behaviors. This advanced approach enables the precise correlation of users’ physiological data and daily activities with their specific fitness needs, offering a personalized pathway to improving health outcomes. Objective : This study aims to enhance PA levels among individuals by developing a personalized exercise recommendation system. Utilizing reinforcement learning, the system proposes tailored exercise plans based on biomarkers and the user’s specific context. Methods : In this study, we developed applications for smartphones and smartwatches designed to gather, monitor, and recommend exercise routines through the application of a contextual multi-arm bandit algorithm. To evaluate the efficacy of this mHealth exercise regimen, we enlisted the participation of twenty female college students. Results : The outcomes of our investigation revealed a significant enhancement in the average daily duration of exercise (P \({\lt}\) . 001). Participants expressed high levels of satisfaction with both the walking program and the recommendation system, achieving average ratings of 4.31 (SD \(=\) 0.60) and 3.69 (SD \(=\) 0.95), respectively, on a 5-point scale. Furthermore, the average scores for participants’ confidence in safely performing the recommended walking exercises, as well as their perception of the study’s effectiveness in meeting their PA needs, were both above 4, indicating a positive reception and confidence in the program’s design and implementation. Conclusions : The evolution of the IoT and wearable technology has marked the beginning of a new era for mHealth systems, particularly in the personalization of health interventions. Such advancements enable the precise personalization of PA recommendations, potentially enhancing user engagement and performance outcomes. This paper introduces a novel exercise recommendation system that utilizes reinforcement learning to personalize walking exercises based on the user’s biomarkers and context, aiming to improve the user’s aerobic capacity significantly.

  PublisherDOI

• Mindfulness and Cardiometabolic Health During Pregnancy: An Integrative Review

  Mindfulness · 2024-03-28 · 1 citations

  articleOpen access

  Objectives: Cardiometabolic health during pregnancy has potential to influence long-term chronic disease risk for both mother and offspring. Mindfulness practices have been associated with improved cardiometabolic health in non-pregnant populations. The objective was to evaluate diverse studies that explored relationships between prenatal mindfulness and maternal cardiometabolic health. Method: An integrative review was conducted in January 2023 across five databases to identify and evaluate studies of diverse methodologies and data types. Quantitative studies that examined mindfulness as an intervention or exposure variable during pregnancy and reported any of the following outcomes were considered: gestational weight gain (GWG), blood glucose, insulin resistance, gestational diabetes, inflammation, blood pressure, hypertensive disorders of pregnancy. Qualitative studies were included if they evaluated knowledge, attitudes, or practices of mindfulness in relation to the above-mentioned outcomes during pregnancy. Results: Fifteen eligible studies were identified, and 4 received a "Good" quality rating (1/7 interventional, 1/5 observational, 2/2 qualitative). Qualitative studies revealed interest among pregnant women in mindfulness-based practices for managing GWG. Some beneficial effects of mindfulness interventions on maternal glucose tolerance and blood pressure were identified, but not for other cardiometabolic outcomes. Observational studies revealed null direct associations between maternal trait mindfulness and cardiometabolic parameters, but one study suggests potential for mindful eating to mitigate excess GWG and insulin resistance. Conclusions: There currently exists limited quality evidence for mindfulness practices to support prenatal cardiometabolic health. Further rigorous studies are required to understand whether prenatal mindfulness-based interventions, either alone or in combination with other lifestyle modalities, can benefit cardiometabolic health. Preregistration: This study is not preregistered.

  PublisherOA PDFDOI

• PERFECT: Personalized Exercise Recommendation Framework and architECTure

  medRxiv · 2023-09-15 · 2 citations

  preprintOpen access

  Abstract Background There are indisputable health benefits to physical activity (PA). By collecting and displaying individual exercise behaviors via wearable trackers, the Internet of Things (IoT) and mobile health (mHealth) have made it possible to correlate users’ physiological data and daily activity information with their fitness requirements. Objective This study aimed to recommend personalized exercise to non-pregnant subjects to increase their physical activity level. Methods We developed smartphone and smartwatch applications to collect, monitor, and recommend exercises using a contextual multi-arm bandit framework. Twenty female college students were recruited to test this mHealth exercise program. Results Our findings indicated an increase in daily exercise duration ( P < .001), with average satisfaction scores for the walking and recommendation system components of 4.31 (0.60) and 3.69 (0.95), respectively, on a scale of 1 to 5. In addition, participants’ confidence in their capacity to complete the suggested walking exercises safely and the study’s ability to satisfy their needs for physical activity both received average scores of over 4. Conclusions A new era of mHealth systems has been ushered in by developments in the Internet of Things and wearable devices. Personalization of physical activity recommendations using such wearables has the potential to improve user engagement and performance. In this paper, we presented an exercise recommendation system based on reinforcement learning that uses biomarkers and the user’s context to recommend a unique walking exercise that enhances the user’s aerobic capacity.

  PublisherOA PDFDOI

• Effect of Emotional Distress on Sleep Physiology in Underserved Pregnant Women [ID: 1381497]

  Obstetrics and Gynecology · 2023-05-01

  article1st authorCorresponding

  INTRODUCTION: Pregnancy is characterized by an altered pattern of emotions and sleep. The aim of this study was to examine the effect of emotional distress on objective sleep parameters in underserved pregnant women during the COVID-19 pandemic. METHODS: Institutional review board approval was obtained for the study. This was a longitudinal observation study in which we administered weekly validated self-reported surveys (Patient Health Questionnaire-2, Generalized Anxiety Disorder-2, COVID-19-related anxiety, and life-related stressors) to consented pregnant women over the course of their second and third trimesters (n=13). The independent variable, subjective emotional distress, was derived from a sum score of these weekly surveys. A wearable device was used to measure objective sleep physiological data, such as the rapid eye movement (REM), deep, and light sleep stages. The dependent variables were obtained from weekly average scores of the sleep data. Multilevel analysis was conducted, controlling for relevant covariates. RESULTS: Adjusting for gestational age, maternal age at enrollment, and prepregnancy body mass index, higher emotional distress was associated with a shorter duration of deep sleep (b=−.65, P <.05) and longer duration of REM sleep (b=.79, P <.01). There was no significant relationship between emotional distress and light sleep. CONCLUSION: Our study appears to be the first to provide preliminary evidence that emotional distress negatively affects sleep in terms of decreased deep sleep and increased REM sleep during pregnancy. Findings suggest that further research is needed to understand the role of sleep in the relationships between emotional distress and adverse maternal and infant health outcomes.

  PublisherDOI

• Longitudinal changes in objective sleep parameters during pregnancy

  Women s Health · 2023-01-01 · 13 citations

  articleOpen access1st authorCorresponding

  BACKGROUND: Sleep disturbances are associated with adverse perinatal outcomes. Thus, it is necessary to understand the continuous patterns of sleep during pregnancy and how moderators such as maternal age and pre-pregnancy body mass index impact sleep. OBJECTIVE: This study aimed to examine the continuous changes in sleep parameters objectively (i.e. sleep stages, total sleep time, and awake time) in pregnant women and to describe the impact of maternal age and/or pre-pregnancy body mass index as moderators of these objective sleep parameters. DESIGN: This was a longitudinal observational design. METHODS: Seventeen women with a singleton pregnancy participated in this study. Mixed model repeated measures were used to describe weekly patterns, while aggregated changes describe these three pregnancy periods (10-19, 20-29, and 30-39 gestational weeks). RESULTS: For the weekly patterns, we found significantly decreased deep (1.26 ± 0.18 min/week, p < 0.001), light (0.72 ± 0.37 min/week, p = 0.05), and total sleep time (1.56 ± 0.47 min/week, p < 0.001) as well as increased awake time (1.32 ± 0.34 min/week, p < 0.001). For the aggregated changes, we found similar patterns to weekly changes. Women (⩾30 years) had an even greater decrease in deep sleep (1.50 ± 0.22 min/week, p < 0.001) than those younger (0.84 ± 0.29 min/week, p = 0.04). Women who were both overweight/obese and ⩾30 years experienced an increase in rapid eye movement sleep (0.84 ± 0.31 min/week, p = 0.008), but those of normal weight (<30 years) did not. CONCLUSION: This study appears to be the first to describe continuous changes in sleep parameters during pregnancy at home. Our study provides preliminary evidence that sleep parameters could be potential non-invasive physiological markers predicting perinatal outcomes.

  PublisherDOI

• 0882 The Longitudinal Relationship between Objective Sleep and Cardiovascular Parameters During Pregnancy

  SLEEP · 2023-05-01 · 3 citations

  articleOpen access1st authorCorresponding

  Abstract Introduction Growing evidence shows the important role of sleep on cardiovascular health in pregnant women with nearly 50% self-reporting poor sleep quality and 30% insufficient sleep. These sleep disturbances have been associated with adverse perinatal outcomes (e.g., hypertension, preterm births). Many studies have only used self-reported questionnaires during pregnancy. However, a few studies have utilized sleep labs and found that subjective and objective assessments of sleep varied from poor to low associations. The aim of this study was to examine the longitudinal relationship between objective sleep and cardiovascular parameters in pregnant women. Methods Seventeen healthy pregnant women with 88% self-identifying as Hispanic were consented to participate in the longitudinal observation study. Their average age was 27.8 years (SD=4.48). A validated Oura ring was used to daily monitor objective sleep and cardiovascular parameters across the pregnancy, including resting heart rate (HR), heart rate variability (HRV) indicated by RMSSD (root mean square of successive differences) of interbeat intervals, deep, light, and Rapid EYE Movement (REM) sleep, awake time and sleep onset latency. Mixed effects models were conducted to analyze daily changing patterns of these parameters using R-4.2.0 for Mac. Results Over the pregnancy, when deep and light sleep increased by 1 hour, heart rate decreased by 5.55 bpm (p&amp;lt; 0.001) and 1.32 bpm (p = 0.01), respectively; when REM sleep increased by 1 hour, heart rate increased by 2.15 bpm (p =0.049). Conversely, when deep and light sleep increased by 1 hour, RMSSD increased by 10.32 ms (p&amp;lt; 0.001) and 2.93 ms (p&amp;lt; 0.02), respectively; when REM sleep increased by 1 hour, RMSSD decreased by 7.27 ms (p=0.001). There were no significant associations between HR/RMSSD and awake time/sleep onset latency. Conclusion This study appears to be the first to investigate the longitudinal relationship between sleep and cardiovascular parameters among primarily Hispanic pregnant women in the U.S. in a non-lab setting. Our study provides preliminary evidence that longer deep/light sleep and shorter REM sleep may improve cardiovascular health among pregnant women. Support (if any) National Science Foundation grants including the Smart and Connected Communities (CNS: 1831918) and Collaborative Research: Integrative Heterogeneous Learning for Intensive Complex Longitudinal Data (DMS: 2210640).

  PublisherOA PDFDOI

Frequent coauthors

• Pamela Pimentel

  University of California, Irvine

  20 shared

• Julie Rousseau

  University of California, Irvine

  20 shared

• Ellen Olshansky

  University of California, Irvine

  14 shared

• Priscilla Kehoe

  University of California, Irvine

  13 shared

• Nikil Dutt

  11 shared

• Brandon Brown

  French Institute for Research in Africa

  9 shared

• Janet S. Hildebrand

  9 shared

• Amir M. Rahmani

  8 shared