Yaguang Zheng


Yaguang Zheng Headshot

Yaguang Zheng


Assistant Professor

1 212 998 5170

NEW YORK, NY 10016
United States

Accepting PhD students

Yaguang Zheng's additional information

Yaguang Zheng is an assistant professor at NYU Rory Meyers College of Nursing. Her research focuses on cardiometabolic risk reduction by leveraging mobile health, electronic health records, and data science techniques. Zheng has explored behavioral phenotypes through the use of wireless devices in clinical trials and real-world settings and their impacts on cardiometabolic disease prevention and management. Zheng’s initial work focused on lifestyle behavior changes through mobile health, more specifically, using mobile health for self-monitoring and its impact on weight-loss outcomes. After identifying a critical knowledge gap in the area of engagement with mobile health, Zheng conducted a pilot study that found that older adults were able to use multiple mobile devices to improve diabetes self-management, debunking traditional perceptions of older adults as being skeptical of multiple mobile technologies.

Zheng has also applied machine learning algorithms to analyze data from a large real-world sample that has yielded varied patterns of use of wireless devices over the course of a year, findings which are helping to target subgroups of individuals who need long-term engagement in using mobile health devices. More recently, Zheng has worked on electronic health record data, including mobile health data from wearable devices, like continuous glucose monitors, which has real-world application for clinical practice.

Prior to joining the NYU Rory Meyers faculty, Zheng was a postdoctoral scholar supported by NIH grant T32 NR008857 Technology: Research in Chronic and Critical Illness at the University of Pittsburgh School of Nursing.

Zheng earned her PhD at the University of Pittsburgh. She also received a Nursing Informatics Certificate during her postdoctoral training.  

PhD, Nursing - University of Pittsburgh

Chronic disease

American Medical Informatics Association
American Heart Association
American Diabetes Association

Faculty Honors Awards

Post-doctoral trainee, Technology: Research in Chronic and Critical Illness (T32 NR008857) (2020)
Ruth Perkins Kuehn Scholarship, Sigma Theta Tau, Eta Chapter (2014)
New Investigator Travel Award, American Heart Association EPI/NPAM 2014 Scientific Sessions (2014)


Age differences in the effects of multi-component periodontal treatments on oral and metabolic health among people with diabetes mellitus: A meta-epidemiological study

Zhu, Z., Qi, X., Zheng, Y., Pei, Y., & Wu, B. (2023). Journal of Dentistry, 135, 104594. 10.1016/j.jdent.2023.104594
OBJECTIVE: To explore the age differences in the effects of multi-component periodontal treatments on oral and metabolic indicators among individuals with periodontitis and diabetes.DATA: Trials reporting the effects of multi-component periodontal treatments on oral and metabolic indicators among participants aged 18 and above with periodontitis and diabetes were included.SOURCES: Six databases (PubMed/Medline, Embase, CINHAL, Web of Science, Cochrane Library, and ProQuest) were searched from database inception to August 2022.STUDY SELECTION: Two reviewers selected the included studies independently. We used bivariate and multivariate meta-regression models to examine the association between age and treatment effect size. The primary outcomes were changes in probing depth (PD), clinical attachment level (CAL), and hemoglobin A1c (HbA1c).RESULTS: A total of 18,067 articles were identified in the database search. Of these, 115 trials (119 articles) met inclusion criteria. The mean age of participants was 58 years old, ranging from 35 to 73 years. The pooled evidence demonstrated that multi-component periodontal treatment significantly reduced PD (g=0.929 [0.689-1.169], I2=94.1%), CAL (g=0.879 [0.669-1.089], I2=92.1%), and HbA1c (g=0.603 [0.443-0.763], I2=87.5%). A significant decreasing trend was observed in the effect size for PD (P for trend = 0.020) and CAL (P for trend = 0.028) as age increases. Results from multivariate meta-regression showed that mean age was associated with a smaller effect size for PD (β=-0.123 [0.041], P = 0.004) and CAL (β=-0.159 [0.055], P = 0.006). Compared to their younger counterparts, the effect size for HbA1c was smaller among participants aged 55 and older (β=-0.792 [0.322], P = 0.017).CONCLUSIONS: Multi-component periodontal treatments may be more effective in younger populations in terms of effects on PD, CAL, and HbA1c.CLINICAL SIGNIFICANCE: Our study highlights the importance of early intervention and tailored treatment approaches. Clinicians should take into account the patient's age when developing periodontal treatment plans and may need to employ more aggressive or personalized strategies for older adults to achieve optimal outcomes.

Association Between Types of Family Support and Glycemic Control for Adults With Cognitive Impairment

Zheng, Y., Lawrence, K., Fletcher, J., Qi, X., & Wu, B. (2023). Gerontology and Geriatric Medicine, 9, 23337214231218800. 10.1177/23337214231218800
BACKGROUND: Family support is important in assisting with diabetes self-management for individuals with cognitive impairment, but what types of family support are most effective remain unknown.OBJECTIVES: We aimed to examine the association between the types of family support in diabetes self-management with glycemic control in middle-aged and older adults with cognitive impairment.METHODS: A total of 267 individuals were included with diabetes and cognitive impairment (27-point Telephone Interview for Cognitive Status score <12), using the data of 2003 Health and Retirement Study (HRS) Diabetes Study and 2004 wave of the HRS.RESULTS: Most respondents were White (68.9%), followed by Black (25.8%). The mean age was 73.4±8.4 years. Adults with strong family support (as indicated by a "strongly agree" response) in testing sugar and in handling feelings about diabetes had significantly lower A1C compared with those with less family support (mean ± standard deviation: 7.08±1.39 vs. 7.51±1.42, P=.03; 6.79±0.87 vs. 7.57±1.53; P=.007 respectively).CONCLUSIONS: Our findings indicate that family members of individuals with cognitive impairment provide critical support to patients with diabetes and cognitive impairment, and may need additional intervention to assist with diabetes self-management tasks that require unique knowledge and skills.

Dietary Self-Management Using Mobile Health Technology for Adults With Type 2 Diabetes: A Scoping Review

Zheng, Y., Campbell Rice, B., Melkus, G. D., Sun, M., Zweig, S., Jia, W., Parekh, N., He, H., Zhang, Y. L., & Wylie-Rosett, J. (2023). Journal of Diabetes Science and Technology, 17(5), 1212-1225. 10.1177/19322968231174038
Objective: Dietary self-management is one key component to achieve optimal glycemic control. Advances in mobile health (mHealth) technology have reduced the burden of diabetes self-management; however, limited evidence has been known regarding the status of the current body of research using mHealth technology for dietary management for adults with type 2 diabetes. Methods: Literature searches were conducted electronically using PubMed, CINAHL (EBSCO), Web of Science Core Collection, PsycINFO (Ovid), EMBASE (Ovid), and Scopus. Keywords and subject headings covered dietary management, type 2 diabetes, and mHealth. Inclusion criteria included studies that applied mHealth for dietary self-management for adults with type 2 diabetes and were published in English as full articles. Results: This review (N = 15 studies) revealed heterogeneity of the mHealth-based dietary self-management or interventions and reported results related to physiological, dietary behavioral, and psychosocial outcomes. Twelve studies applied smartphone apps with varied functions for dietary management or intervention, while three studies applied continuous glucose monitoring (CGM) to guide dietary changes. Among 15 reviewed studies, only three of them were two-arm randomized clinical trial (RCT) with larger sample and 12-month study duration and 12 of them were pilot testing. Nine of 12 pilot studies showed improved HbA1c; most of them resulted in varied dietary changes; and few of them showed improved diabetes distress and depression. Conclusion: Our review provided evidence that the application of mHealth technology for dietary intervention for adults with type 2 diabetes is still in pilot testing. The preliminary effects are inconclusive on physiological, dietary behavioral, and psychosocial outcomes.

Examining reactivity to intensive longitudinal ecological momentary assessment: 12-month prospective study

Cajita, M. I., Rathbun, S. L., Shiffman, S., Kline, C. E., Imes, C. C., Zheng, Y., Ewing, L. J., & Burke, L. E. (2023). Eating and Weight Disorders, 28(1), 1-5. 10.1007/s40519-023-01556-1
PurposeTo examine the association between intensive, longitudinal ecological momentary assessment (EMA) and self-reported eating behaviors.MethodsSecondary analysis of the EMPOWER study—a 12-month observational study that examined the microprocesses of relapse following intentional weight loss using smartphone-administered EMA—was conducted. Participants were asked to complete four types of EMA surveys using a mobile app. For this analysis, only the number of completed random EMA surveys was used. Using linear mixed-effects modeling, we analyzed whether the number of completed random EMA surveys was associated with changes in self-reported dietary restraint, dietary disinhibition, and susceptibility to hunger measured using the Three-Factor Eating Questionnaire (TFEQ).ResultsDuring the 12-month study, 132 participants completed a mean of 1062 random EMA surveys (range: 673–1362). The median time it took for participants to complete random EMA surveys was 20 s and 90% of random EMA surveys were completed within 46 s. The number of completed random EMA surveys was not significantly associated with the TFEQ scores.ConclusionsIntensive longitudinal EMA did not influence self-reported eating behaviors. The findings suggest that EMA can be used to frequently assess real-world eating behaviors with minimal concern about assessment reactivity. Nonetheless, care must be taken when designing EMA surveys—particularly when using self-reported outcome measures.

HIV Syndemic Factor Associations Among Adolescent Gay and Bisexual Men in the Youth Risk Behavior Survey 2015-2019: A Secondary Data Analysis

Garcia, D. R., Fletcher, J., Goldsamt, L., Bell, D. L., Zheng, Y., & Dunn Navarra, A.-M. (2023). The Journal of the Association of Nurses in AIDS Care : JANAC, 34(5), 440-458. 10.1097/JNC.0000000000000423
This analysis of the Youth Risk Behavior Survey examined HIV syndemic factor associations (substance use, violence, mental health, and HIV risk behaviors) among adolescent gay, bisexual, and other men who have sex with men-a population with the highest prevalence of undiagnosed HIV infections. The representative sample ( n = 644) exhibited low condom use (52%) and HIV testing (21%). Adjusted multivariate logistic regression models showed that Blacks were less likely to report HIV testing (adjusted odds ratio [aOR] = 0.06, 95% confidence interval [CI] [0.01-0.44], p < .01), whereas Hispanics were more likely to report four or more lifetime sexual partners (aOR = 3.75, 95% CI [1.49-9.44], p < .01), compared with Whites. A syndemic of substance use and intimate partner violence (sexual, sexual dating, and/or physical dating violence) was associated with early intercourse, multiple sexual partners, and drugs/alcohol before intercourse. Multiple syndemic factor exposures were associated with additive risk, suggesting multilevel approaches for HIV prevention.

Interindividual Variability in Self-Monitoring of Blood Pressure Using Consumer-Purchased Wireless Devices

Zheng, Y., Zhang, Y., Huang, H., Tison, G. H., Burke, L. E., Blecker, S., Dickson, V. V., Olgin, J. E., Marcus, G. M., & Pletcher, M. J. (2023). Nursing Research, 72(4), 310-318. 10.1097/NNR.0000000000000654
BACKGROUND: Engagement with self-monitoring of blood pressure (BP) declines, on average, over time but may vary substantially by individual.OBJECTIVES: We aimed to describe different 1-year patterns (groups) of self-monitoring of BP behaviors, identify predictors of those groups, and examine the association of self-monitoring of BP groups with BP levels over time.METHODS: We analyzed device-recorded BP measurements collected by the Health eHeart Study-an ongoing prospective eCohort study-from participants with a wireless consumer-purchased device that transmitted date- and time-stamped BP data to the study through a full 12 months of observation starting from the first day they used the device. Participants received no instruction on device use. We applied clustering analysis to identify 1-year self-monitoring, of BP patterns.RESULTS: Participants had a mean age of 52 years and were male and White. Using clustering algorithms, we found that a model with three groups fit the data well: persistent daily use (9.1% of participants), persistent weekly use (21.2%), and sporadic use only (69.7%). Persistent daily use was more common among older participants who had higher Week 1 self-monitoring of BP frequency and was associated with lower BP levels than the persistent weekly use or sporadic use groups throughout the year.CONCLUSION: We identified three distinct self-monitoring of BP groups, with nearly 10% sustaining a daily use pattern associated with lower BP levels.

Mechanisms of Change in Cognitive Function Domains Among Older Adults in Cognitive Deterioration and Improvement Groups: Evidence From Phenotypic Network Structure

Zhu, Z., Zwerling, J. L., Qi, X., Pei, Y., Zheng, Y., & Wu, B. (2023). Journal of the American Medical Directors Association, 24(12), 2009-2016.e9. 10.1016/j.jamda.2023.08.022
OBJECTIVE: To investigate how cognitive function domains change in phenotypic networks in cognitive deterioration and improvement groups.DESIGN: Secondary data analysis.SETTING AND PARTICIPANTS: Respondents in the Aging, Demographics, and Memory Study (ADAMS) who were 70 years or older at the time of the data collection in 2000 or 2002.METHODS: This study used data from the ADAMS in Wave A and Wave B. We assessed 12 cognitive function domains. Latent profile transition analysis (LPTA) and the cross-lagged panel network model were used to the dynamic interactions of the 12 cognitive function domains over time in both the deterioration and improvement groups.RESULTS: A total of 252 participants were included in the final analysis. LPTA identified 5 subgroups and categorized all samples into 3 main categories: improvement group (n = 61), deterioration group (n = 54), and no change group (n = 137). "D9: psychomotor processing" showed the largest value of out-strength in the deterioration group (r = 0.941) and improvement group (r = 0.969). The strongest direct positive effect in the deterioration group was "C9: psychomotor processing" -> "C8: attention" (β = 0.39 [0.00, 1.13]). In the improvement group, the strongest direct positive effect was "C9 = psychomotor processing" -> "C7 = visual memory" (β = 0.69 [0.07, 1.30]).CONCLUSION AND IMPLICATIONS: Psychomotor processing affected other cognitive domains, and it played a crucial role in changes of cognitive function. The paths of psychomotor processing to attention and visual memory were found to be major factors in cognitive deterioration and improvement. Targeting psychomotor processing may lead to the development of more effective and precise interventions.

Neighborhood-Level Socioeconomic Status and Prescription Fill Patterns Among Patients With Heart Failure

Mukhopadhyay, A., Blecker, S., Li, X., Kronish, I. M., Chunara, R., Zheng, Y., Lawrence, S., Dodson, J. A., Kozloff, S., & Adhikari, S. (2023). JAMA Network Open, 6(12), e2347519. 10.1001/jamanetworkopen.2023.47519
IMPORTANCE: Medication nonadherence is common among patients with heart failure with reduced ejection fraction (HFrEF) and can lead to increased hospitalization and mortality. Patients living in socioeconomically disadvantaged areas may be at greater risk for medication nonadherence due to barriers such as lower access to transportation or pharmacies.OBJECTIVE: To examine the association between neighborhood-level socioeconomic status (nSES) and medication nonadherence among patients with HFrEF and to assess the mediating roles of access to transportation, walkability, and pharmacy density.DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study was conducted between June 30, 2020, and December 31, 2021, at a large health system based primarily in New York City and surrounding areas. Adult patients with a diagnosis of HF, reduced EF on echocardiogram, and a prescription of at least 1 guideline-directed medical therapy (GDMT) for HFrEF were included.EXPOSURE: Patient addresses were geocoded, and nSES was calculated using the Agency for Healthcare Research and Quality SES index, which combines census-tract level measures of poverty, rent burden, unemployment, crowding, home value, and education, with higher values indicating higher nSES.MAIN OUTCOMES AND MEASURES: Medication nonadherence was obtained through linkage of health record prescription data with pharmacy fill data and was defined as proportion of days covered (PDC) of less than 80% over 6 months, averaged across GDMT medications.RESULTS: Among 6247 patients, the mean (SD) age was 73 (14) years, and majority were male (4340 [69.5%]). There were 1011 (16.2%) Black participants, 735 (11.8%) Hispanic/Latinx participants, and 3929 (62.9%) White participants. Patients in lower nSES areas had higher rates of nonadherence, ranging from 51.7% in the lowest quartile (731 of 1086 participants) to 40.0% in the highest quartile (563 of 1086 participants) (P < .001). In adjusted analysis, patients living in the lower 2 nSES quartiles had significantly higher odds of nonadherence when compared with patients living in the highest nSES quartile (quartile 1: odds ratio [OR], 1.57 [95% CI, 1.35-1.83]; quartile 2: OR, 1.35 [95% CI, 1.16-1.56]). No mediation by access to transportation and pharmacy density was found, but a small amount of mediation by neighborhood walkability was observed.CONCLUSIONS AND RELEVANCE: In this retrospective cohort study of patients with HFrEF, living in a lower nSES area was associated with higher rates of GDMT nonadherence. These findings highlight the importance of considering neighborhood-level disparities when developing approaches to improve medication adherence.

Prevalence and Incidence of Mild Cognitive Impairment in Adults with Diabetes in the United States

Zheng, Y., Ma, Q., Qi, X., Zhu, Z., & Wu, B. (2023). Diabetes Research and Clinical Practice, 205, 110976. 10.1016/j.diabres.2023.110976
BACKGROUND: Limited evidence exists about the prevalence and incidence of mild cognitive impairment (MCI) in individuals with diabetes in the U.S. We aimed to address such knowledge gaps using a nationally representative study dataset.METHOD: We conducted a secondary analysis from the Health and Retirement Study (HRS) (1996-2018). The sample for examining the prevalence of MCI was14,988, with 4,192 (28.0%) having diabetes, while the sample for the incidence was 21,824, with 1,534 (28.0%) having diabetes.RESULTS: Participants with diabetes had a higher prevalence of MCI than those without diabetes (19.9% vs. 14.8%; odds ratio [95% confidence interval] (OR[95%CI]): 1.468 [1.337, 1.611], p<.001). The incidence of MCI in participants with/without newly diagnosed diabetes was 42.9% vs. 31.6% after a mean 10-year follow-up, with the incidence rate ratio (IRR) [95%CI] (1.314 [1.213, 1.424], p<.001). Newly diagnosed diabetes was associated with elevated risks of MCI compared with non-diabetes, with the uncontrolled hazard ratio (HR) [95%CI] (1.498 [1.405, 1.597], p<.001).CONCLUSIONS: Using a nationally representative study data in the U.S., participants with diabetes had a higher prevalence and incidence of MCI than those without diabetes. Findings show the importance of developing interventions tailored to the needs of individuals with diabetes and cognitive impairment.

Associations Between Implementation of the Caregiver Advise Record Enable (CARE) Act and Health Service Utilization for Older Adults with Diabetes: Retrospective Observational Study

Zheng, Y., Anton, B. B., Rodakowski, J., Dunn, S. C. A., Fields, B., Hodges, J. C., Donovan, H., Feiler, C., Martsolf, G. R., Bilderback, A., Martin, S. C., Li, D., & James, A. E. (2022). JMIR Aging, 5(2). 10.2196/32790
Background: The Caregiver Advise Record Enable (CARE) Act is a state level law that requires hospitals to identify and educate caregivers ("family members or friends") upon discharge.Objective: This study examined the association between the implementation of the CARE Act in a Pennsylvania health system and health service utilization (ie, reducing hospital readmission, emergency department [ED] visits, and mortality) for older adults with diabetes.Methods: The key elements of the CARE Act were implemented and applied to the patients discharged to home. The data between May and October 2017 were pulled from inpatient electronic health records. Likelihood-ratio chi-square tests and multivariate logistic regression models were used for statistical analysis.Results: The sample consisted of 2591 older inpatients with diabetes with a mean age of 74.6 (SD 7.1) years. Of the 2591 patients, 46.1% (n=1194) were female, 86.9% (n=2251) were White, 97.4% (n=2523) had type 2 diabetes, and 69.5% (n=1801) identified a caregiver. Of the 1801 caregivers identified, 399 (22.2%) received discharge education and training. We compared the differences in health service utilization between pre- and postimplementation of the CARE Act; however, no significance was found. No significant differences were detected from the bivariate analyses in any outcomes between individuals who identified a caregiver and those who declined to identify a caregiver. After adjusting for risk factors (multivariate analysis), those who identified a caregiver (12.2%, 219/1801) was associated with higher rates of 30-day hospital readmission than those who declined to identify a caregiver (9.9%, 78/790; odds ratio [OR] 1.38, 95% CI 1.04-1.87; P=.02). Significantly lower rates were detected in 7-day readmission (P=.02), as well as 7-day (P=.03) and 30-day (P=.01) ED visits, among patients with diabetes whose identified caregiver received education and training than those whose identified caregiver did not receive education and training in the bivariate analyses. However, after adjusting for risk factors, no significance was found in 7-day readmission (OR 0.53, 95% CI 0.27-1.05; P=.07), 7-day ED visit (OR 0.63, 95% CI 0.38-1.03; P=.07), and 30-day ED visit (OR 0.73, 95% CI 0.52-1.02; P=.07). No significant associations were found for other outcomes (ie, 30-day readmission and 7-day and 30-day mortality) in both the bivariate and multivariate analyses.Conclusions: Our study found that the implementation of the CARE Act was associated with certain health service utilization. The identification of caregivers was associated with higher rates of 30-day hospital readmission in the multivariate analysis, whereas having identified caregivers who received discharge education was associated with lower rates of readmission and ED visit in the bivariate analysis.