Yaguang Zheng

Faculty

Yaguang Zheng headshot

Yaguang Zheng

PhD RN

Assistant Professor

1 212 998 5170

433 1st Ave.
New York, NY 10010
United States

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

Obesity
Diabetes
Chronic disease
Informatics

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)

Publications

Applying Real-World Data to Inform Continuous Glucose Monitoring Use in Clinical Practice

Zheng, Y., Siminerio, L. M., Krall, J., Anton, B. B., Hodges, J. C., Bednarz, L., Li, D., & Ng, J. M. (2021). Journal of Diabetes Science and Technology, 1932296821997403. 10.1177/1932296821997403

Actual use of multiple health monitors among older adults with diabetes: Pilot study

Zheng, Y., Weinger, K., Greenberg, J., Burke, L. E., Sereika, S. M., Patience, N., Gregas, M. C., Li, Z., Qi, C., Yamasaki, J., & Munshi, M. N. (2020). Journal of Medical Internet Research, 22(3). 10.2196/15995
Abstract
Abstract
Background: Previous studies have reported older adults perceptions of using health monitors; however, no studies have examined the actual use of multiple health monitors for lifestyle changes over time among older adults with type 2 diabetes (T2D). Objective: The primary aim of this study was to examine the actual use of multiple health monitors for lifestyle changes over 3 months among older adults with T2D. The secondary aim was to explore changes in caloric intake and physical activity (PA) over 3 months. Methods: This was a single-group study lasting 3 months. The study sample included participants who were aged .65 years with a diagnosis of T2D. Participants were recruited through fliers posted at the Joslin Diabetes Center in Boston. Participants attended five 60-min, biweekly group sessions, which focused on self-monitoring, goal setting, self-regulation to achieve healthy eating and PA habits, and the development of problem-solving skills. Participants were provided with the Lose It! app to record daily food intake and devices such as a Fitbit Alta for monitoring PA, a Bluetooth-enabled blood glucose meter, and a Bluetooth-enabled digital scale. Descriptive statistics were used for analysis. Results: Of the enrolled participants (N=9), the sample was white (8/9, 89%) and female (4/9, 44%), with a mean age of 76.4 years (SD 6.0; range 69-89 years), 15.7 years (SD 2.0) of education, 33.3 kg/m2 (SD 3.1) BMI, and 7.4% (SD 0.8) hemoglobin A1c. Over the 84 days of self-monitoring, the mean percentage of days using the Lose It!, Fitbit Alta, blood glucose meter, and scale were 82.7 (SD 17.6), 85.2 (SD 19.7), 65.3 (SD 30.1), and 53.0 (SD 34.5), respectively. From baseline to completion of the study, the mean daily calorie intake was 1459 (SD 661) at week 1, 1245 (SD 554) at week 11, and 1333 (SD 546) at week 12, whereas the mean daily step counts were 5618 (SD 3654) at week 1, 5792 (SD 3814) at week 11, and 4552 (SD 3616) at week 12. The mean percentage of weight loss from baseline was 4.92% (SD 0.25). The dose of oral hypoglycemic agents or insulin was reduced in 55.6% (5/9) of the participants. Conclusions: The results from the pilot study are encouraging and suggest the need for a larger study to confirm the outcomes. In addition, a study design that includes a control group with educational sessions but without the integration of technology would offer additional insight to understand the value of mobile health in behavior changes and the health outcomes observed during this pilot study.

Psychometric Evaluation of the Barriers to Healthy Eating Scale: Results from Four Independent Weight Loss Studies

Sun, R., Rohay, J. M., Sereika, S. M., Zheng, Y., Yu, Y., & Burke, L. E. (2019). Obesity, 27(5), 700-706. 10.1002/oby.22414
Abstract
Abstract
Objective: The purpose of this study was to evaluate the psychometric properties of the 22-item Barriers to Healthy Eating (BHE) scale in four independent weight loss studies conducted over 13 years. Methods: Principal axis factoring with promax rotation was performed to reveal the underlying factor structure. Internal consistency was assessed using Cronbach α, and convergent validity was assessed by correlating the baseline BHE with the Weight Efficacy Lifestyle questionnaire total and subscale scores. Predictive validity was examined by the association of BHE change with weight loss over 6 months. Results: The four studies had similar gender (82.9%-89.9% female) and race (70.5%-81.4% white) distributions. Factor analyses suggested removal of two items and a three-factor structure: self-control and motivation (10 items), daily mechanics (7 items), and social support (3 items). The Cronbach α for the 20-item BHE ranged from 0.849 to 0.881 across the four studies. The BHE and Weight Efficacy Lifestyle questionnaire total and subscale scores were all negatively correlated with each other, showing good convergent validity (r = 0.120-0.544, P < 0.05). BHE change was associated with weight loss from 0 to 6 months (r = 0.282-0.450, P < 0.05). Conclusions: The BHE scale showed very good psychometric properties over time, supporting its use in measuring barriers to one’s ability to adopt or maintain a healthy eating plan.

Temporal patterns of self-weighing behavior and weight changes assessed by consumer purchased scales in the Health eHeart Study

Zheng, Y., Sereika, S. M., Burke, L. E., Olgin, J. E., Marcus, G. M., Aschbacher, K., Tison, G. H., & Pletcher, M. J. (2019). Journal of Behavioral Medicine, 42(5), 873-882. 10.1007/s10865-018-00006-z
Abstract
Abstract
Self-weighing may promote attainment and maintenance of healthy weight; however, the natural temporal patterns and factors associated with self-weighing behavior are unclear. The aims of this secondary analysis were to (1) identify distinct temporal patterns of self-weighing behaviors; (2) explore factors associated with temporal self-weighing patterns; and (3) examine differences in percent weight changes by patterns of self-weighing over time. We analyzed electronically collected self-weighing data from the Health eHeart Study, an ongoing longitudinal research study coordinated by the University of California, San Francisco. We selected participants with at least 12 months of data since the day of first use of a WiFi- or Bluetooth-enabled digital scale. The sample (N = 1041) was predominantly male (77.5%) and White (89.9%), with a mean age of 46.5 ± 12.3 years and a mean BMI of 28.3 ± 5.9 kg/m2 at entry. Using group-based trajectory modeling, six distinct temporal patterns of self-weighing were identified: non-users (n = 120, 11.5%), weekly users (n = 189, 18.2%), rapid decliners (n = 109, 10.5%), increasing users (n = 160, 15.4%), slow decliners (n = 182, 17.5%), and persistent daily users (n = 281, 27.0%). Individuals who were older, female, or self-weighed 6–7 days/week at week 1 were more likely to follow the self-weighing pattern of persistent daily users. Predicted self-weighing trajectory group membership was significantly associated with weight change over time (p <.001). In conclusion, we identified six distinct patterns of self-weighing behavior over the 12-month period. Persistent daily users lost more weight compared with groups with less frequent patterns of scale use.

The utility of traditional Chinese medicine (Shenmai) in the cardiac rehabilitation after coronary artery bypass grafting: A single-center randomized clinical trial

Zhang, C., Zheng, Y., Chen, T., Wang, S., & Xu, M. (2019). Complementary Therapies in Medicine, 47. 10.1016/j.ctim.2019.102203
Abstract
Abstract
Objective: examine the efficacy and safety of Shenmai to the cardiac rehabilitation in patients received coronary artery bypass grafting. Design: a single-center randomized, single blind clinical trial. Setting: Department of Cardiovascular Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, China. Subjects: Patients with coronary artery disease who received coronary artery bypass grafting in our center were studied. They must be competent to complete the 6-minute walking test without any assistance and without any severe comorbidity. Interventions: in Shemmai group, the participants were treated with Shenmai injection (100 ml/day) right after the surgery to discharge for 9.28 ± 3.75 days and then capsule (3.6 g/day) sequentially for 30 days in addition to the cardiac rehabilitation. In control group, only cardiac rehabilitation was conducted. Main measures: the 6-Minute Walking Test was measured at three time points: one day before operation, on the day of discharge and 30 days follow up. Results: The sample (n = 166) was predominately male (84%), with mean age was 61.12 ± 9.13 years. There was no significant difference between groups in baseline characteristics and the procedural characteristics. There was one death in control group and one stroke in Shenmai group right after the surgery. Overall, there was group (p =.005) and time effect (p <.001) on the 6-minute walking distance. Participants in the Shenmai group walked longer distance in meters compared with control group on the day of discharge (314.54 ± 64.14 vs. 271.29 ± 76.82, P <.001), while no significant differences before operation (399.72 ± 93.19 vs. 403.67 ± 91.99, p =.78) and on 30-day follow up (436.54 ± 67.64 vs. 421.64 ± 83.53, p =.21). Conclusion: Shenmai improves the exercise tolerance in the early stage of the cardiac rehabilitation for patients received coronary artery bypass grafting.

Adherence

Burke, L. E., Zheng, Y., & Wang, J. (2018). In Principles and Concepts of Behavioral Medicine (pp. 565-593). Springer New York. 10.1007/978-0-387-93826-4_19

Bidirectional Relationships Between Weight Change and Sleep Apnea in a Behavioral Weight Loss Intervention

Kline, C. E., Burke, L. E., Sereika, S. M., Imes, C. C., Rockette-Wagner, B., Mendez, D. D., Strollo, P. J., Zheng, Y., Rathbun, S. L., & Chasens, E. R. (2018). Mayo Clinic Proceedings, 93(9), 1290-1298. 10.1016/j.mayocp.2018.04.026
Abstract
Abstract
Objective: To examine the bidirectional relationship between weight change and obstructive sleep apnea (OSA) in the context of a behavioral weight loss intervention. Patients and Methods: Adults who were overweight or obese (N=114) participated in a 12-month behavioral weight loss intervention from April 17, 2012, through February 9, 2015. The apnea-hypopnea index (AHI), a marker of the presence and severity of OSA, was assessed at baseline, 6 months, and 12 months. Linear mixed models evaluated the effect of weight change on the AHI and the effect of OSA (AHI ≥5) on subsequent weight loss. Secondary analyses evaluated the effect of OSA on intervention attendance, meeting daily calorie goals, and accelerometer-measured physical activity. Results: At baseline, 51.8% of the sample (n=59) had OSA. Adults who achieved at least 5% weight loss had an AHI reduction that was 2.1±0.9 (adjusted mean ± SE) events/h greater than those with less than 5% weight loss (P<.05). Adults with OSA lost a mean ± SE of 2.2%±0.9% less weight during the subsequent 6-month interval compared with those without OSA (P=.02). Those with OSA were less adherent to daily calorie goals (mean ± SE: 25.2%±3.3% vs 34.8%±3.4% of days; P=.006) and had a smaller increase in daily activity (mean ± SE: 378.3±353.7 vs 1060.1±377.8 steps/d; P<.05) over 12 months than those without OSA. Conclusion: Behaviorally induced weight loss in overweight/obese adults was associated with significant AHI reduction. However, the presence of OSA was associated with blunted weight loss, potentially via reduced adherence to behaviors supporting weight loss. These results suggest that OSA screening before attempting weight loss may be helpful to identify who may benefit from additional behavioral counseling.

Experiences of Daily Weighing Among Successful Weight Loss Individuals During a 12-Month Weight Loss Study

Zheng, Y., Terry, M. A., Danford, C. A., Ewing, L. J., Sereika, S. M., Goode, R. W., Mori, A., & Burke, L. E. (2018). Western Journal of Nursing Research, 40(4), 462-480. 10.1177/0193945916683399
Abstract
Abstract
The purpose of the study was to describe participants’ experience of daily weighing and to explore factors influencing adherence to daily weighing among individuals who were successful in losing weight during a behavioral weight loss intervention. Participants completed a 12-month weight loss intervention study that included daily self-weighing using a Wi-Fi scale. Individuals were eligible to participate regardless of their frequency of self-weighing. The sample (N = 30) was predominantly female (83.3%) and White (83.3%) with a mean age of 52.9 ± 8.0 years and mean body mass index of 33.8 ± 4.7 kg/m2. Five main themes emerged: reasons for daily weighing (e.g., feel motivated, being in control), reasons for not weighing daily (e.g., interruption of routine), factors that facilitated weighing, recommendations for others about daily weighing, and suggestions for future weight loss programs. Our results identified several positive aspects to daily self-weighing, which can be used to promote adherence to this important weight loss strategy.

Group-based trajectory analysis of physical activity change in a US weight loss intervention

Imes, C. C., Zheng, Y., Mendez, D. D., Rockette-Wagner, B. J., Mattos, M. K., Goode, R. W., Sereika, S. M., & Burke, L. E. (2018). Journal of Physical Activity and Health, 15(11), 840-846. 10.1123/jpah.2017-0484
Abstract
Abstract
Background: The obesity epidemic is a global concern. Standard behavioral treatment including increased physical activity, reduced energy intake, and behavioral change counseling is an effective lifestyle intervention for weight loss. Purpose: To identify distinct step count patterns among weight loss intervention participants, examine weight loss differences by trajectory group, and examine baseline factors associated with trajectory group membership. Methods: Both groups received group-based standard behavioral treatment while the experimental group received up to 30 additional, one-on-one selfefficacy enhancement sessions. Data were analyzed using group-based trajectory modeling, analysis of variance, chi-square tests, and multinomial logistic regression. Results: Participants (N = 120) were mostly female (81.8%) and white (73.6%) with a mean (SD) body mass index of 33.2 (3.8) kg/m2. Four step count trajectory groups were identified: active (>10,000 steps/day; 11.7%), somewhat active (7500-10,000 steps/day; 28.3%), low active (5000-7500 steps/day; 27.5%), and sedentary (<5000 steps/day; 32.5%). Percent weight loss at 12 months increased incrementally by trajectory group (5.1% [5.7%], 7.8% [6.9%], 8.0% [7.4%], and 13.63% [7.0%], respectively; P = .001). At baseline, lower body mass index and higher perceived health predicted membership in the better performing trajectory groups. Conclusions: Within a larger group of adults in a weight loss intervention, 4 distinct trajectory groups were identified and group membership was associated with differential weight loss.

Current theoretical bases for nutrition intervention and their uses

Zheng, Y., Mancino, J., Burke, L. E., & Glanz, K. (2017). In Nutrition in the Prevention and Treatment of Disease (pp. 185-201). Elsevier. 10.1016/B978-0-12-802928-2.00009-6
Abstract
Abstract
This chapter discusses contemporary theoretical basis for dietary interventions for disease prevention and management and their applications in practice. This chapter (1) introduces key concepts related to the application of theory in understanding and improving diet and eating-related behaviors, (2) reviews behavioral issues related to adopting healthful diets, (3) discusses dietary interventions, and (4) highlights important issues and constructs that cut across theories. Six theoretical models that are in current use and can be particularly useful for understanding the processes of changing eating habits in clinical and community settings are described: social cognitive theory, the stages of change construct from the transtheoretical model, consumer information processing, the theory of planned behavior, multiattribute utility theory, and the social ecological model. The central elements of each theory and how they can be used to guide dietary interventions are described in this chapter.