Zenghui Ding

Prof. Dr. Zenghui Ding

orcid

Research Focus & Metrics

Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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5 April 2024 in JMIR mHealth and uHealth
Verified
Journal Article
Open access
TSTing Sun
Ting Sun
XXXuejie Xu
Xuejie Xu
ZDZenghui Ding
Zenghui Ding
HXHui Xie
Hui Xie
LMLinlin Ma
Linlin Ma
ZMZuchang Ma
Zuchang Ma
Background: The effectiveness of timely medication, physical activity (PA), a healthy diet, and blood pressure (BP) monitoring for promoting health outcomes and behavioral changes among patients with hypertension is supported by a substantial amount of literature, with “adherence” playing a pivotal role. Nevertheless, there is a lack of consistent evidence regarding whether digital interventions can improve adherence to healthy behaviors among individuals with hypertension. Objective: The aim was to develop a health behavioral digital intervention for hypertensive patients (HBDIHP) based on an intelligent health promotion system and WeChat following the behavior change wheel (BCW) theory and digital micro-intervention care (DMIC) model and assess its efficacy in controlling BP and improving healthy behavior adherence. Methods: A 2-arm, randomized trial design was used. We randomly assigned 68 individuals aged >60 years with hypertension in a 1:1 ratio to either the control or experimental group. The digital intervention was established through the following steps: (1) developing digital health education materials focused on adherence to exercise prescriptions, Dietary Approaches to Stop Hypertension (DASH), prescribed medication, and monitoring of BP; (2) using the BCW theory to select behavior change techniques; (3) constructing the intervention's logic following the guidelines of the DMIC model; (4) creating an intervention manual including the aforementioned elements. Prior to the experiment, participants underwent physical examinations at the community health service center's intelligent health cabin and received intelligent personalized health recommendations. The experimental group underwent a 12-week behavior intervention via WeChat, while the control group received routine health education and a self-management manual. The primary outcomes included BP and adherence indicators. Data analysis was performed using SPSS, with independent sample t tests, chi-square tests, paired t tests, and McNemar tests. A P value <.05 was considered statistically significant. Results: The final analysis included 54 participants with a mean age of 67.24 (SD 4.19) years (n=23 experimental group, n=31 control group). The experimental group had improvements in systolic BP (–7.36 mm Hg, P=.002), exercise time (856.35 metabolic equivalent [MET]-min/week, P<.001), medication adherence (0.56, P=.001), BP monitoring frequency (P=.02), and learning performance (3.23, P<.001). Both groups experienced weight reduction (experimental: 1.2 kg, P=.002; control: 1.11 kg, P=.009) after the intervention. The diet types and quantities for both groups (P<.001) as well as the subendocardial viability ratio (0.16, P=.01) showed significant improvement. However, there were no statistically significant changes in other health outcomes. Conclusions: The observations suggest our program may have enhanced specific health outcomes and adherence to health behaviors in older adults with hypertension. However, a longer-term, larger-scale trial is necessary to validate the effectiveness. Trial Registration: Chinese Clinical Trial Registry ChiCTR2200062643; https://www.chictr.org.cn/showprojEN.html?proj=172782 International Registered Report Identifier (IRRID): RR2-10.2196/46883

1 June 2017 in Drug and Alcohol Dependence
Verified
Journal Article
Closed access
ZDZenghui Ding
Zenghui Ding
YCYanyan Chen
Yanyan Chen
XWXi Wang
Xi Wang
XZXu Zhou
Xu Zhou
YXYang Xu
Yang Xu
MJMing Jiang
Ming Jiang

5 December 2024 in JMIR mHealth and uHealth
Verified
Journal Article
Open access
XXXuejie Xu
Xuejie Xu
GZGuoli Zhang
Guoli Zhang
YXYuxin Xia
Yuxin Xia
HXHui Xie
Hui Xie
ZDZenghui Ding
Zenghui Ding
HXHui Xie
Hui Xie
Background: Personalized intelligent exercise prescriptions have demonstrated significant benefits in increasing physical activity and improving individual health. However, the health benefits of these prescriptions depend on long-term adherence. Therefore, it is essential to analyze the factors influencing adherence to personalized intelligent exercise prescriptions and explore the intrinsic relationship between individual behavioral motivation and adherence. This understanding can help improve adherence and maximize the effectiveness of such prescriptions. Objective: This study aims to identify the factors influencing adherence behavior among middle-aged and older community residents who have been prescribed personalized exercise regimens through an electronic health promotion system. It also explores how these factors affect the initiation and maintenance of adherence behavior. Methods: We used purposive sampling to conduct individual, face-to-face semistructured interviews based on the Transtheoretical Model (TTM) with 12 middle-aged and older community residents who had been following personalized exercise regimens for 8 months. These residents had received detailed exercise health education and guidance from staff. The interviews were recorded, transcribed verbatim, and analyzed using NVivo software through grounded theory. We then applied the TTM and multibehavioral motivation theory to analyze the factors influencing adherence. Additionally, the relationship between behavioral motivations and adherence was explored. Results: Using the behavior change stages of the TTM, open coding yielded 21 initial categories, which were then organized into 8 main categories through axial coding: intrinsic motivation, extrinsic motivation, benefit motivation, pleasure motivation, achievement motivation, perceived barriers, self-regulation, and optimization strategies. Selective coding further condensed these 8 main categories into 3 core categories: “multitheory motivation,” “obstacle factors,” and “solution strategies.” Using the coding results, a 3-level model of factors influencing adherence to intelligent personalized exercise prescriptions was developed. Based on this, an implementation path for promoting adherence to intelligent personalized exercise prescriptions was proposed by integrating the model with the TTM. Conclusions: Adherence to personalized exercise prescriptions is influenced by both facilitating factors (eg, multibehavioral motivation, optimization strategies) and obstructive factors (eg, perceived barriers). Achieving and maintaining adherence is a gradual process, shaped by a range of motivations and factors. Personalized solutions, long-term support, feedback mechanisms, and social support networks are essential for promoting adherence. Future efforts should focus on enhancing adherence by strengthening multibehavioral motivation, optimizing solutions, and addressing barriers to improve overall adherence.

12 September 2023 in JMIR Research Protocols
Verified
Journal Article
Open access
HXHui Xie
Hui Xie
HZHuanhuan Zhao
Huanhuan Zhao
ZDZenghui Ding
Zenghui Ding
HXHui Xie
Hui Xie
LMLinlin Ma
Linlin Ma
ZMZuchang Ma
Zuchang Ma
Background: Hypertension is the most prevalent chronic condition and a significant risk factor for cardiovascular and kidney diseases. The efficacy of health behavioral interventions in blood pressure (BP) control has been demonstrated by a large and expanding body of literature, with “adherence” playing a crucial role. WeChat is the most common social communication mobile app in China, and it has been shown to be an acceptable delivery platform for delivering health interventions. The WeChat-based health behavioral digital intervention program (WHBDIP) showed high feasibility and efficacy. However, the results regarding BP improvement between the WHBDIP and control groups were inconsistent. Objective: The objective of this study is to develop a WHBDIP and assess its efficacy in controlling BP and improving adherence among patients with hypertension. Methods: A 2-arm, parallel-group, and randomized trial design was used. Patients older than 60 years and with hypertension were randomly assigned to either the control group or the experimental group, which received a 12-week intervention. The program, primarily developed based on the Behavior Change Wheel (BCW) theory, offers health education on exercise, diet, BP monitoring, and medicine adherence (MA). It also includes other behavior interventions guided by an intervention manual, incorporating behavior change techniques (BCTs). The primary outcomes encompass BP and adherence indicators, while the secondary outcomes encompass cardiovascular function indicators, body composition indicators, learning performance, satisfaction, and acceptability. The exercise and blood pressure monitoring adherence (BPMA) indicators for the WHBDIP group were assessed weekly via WeChat during the initial 3 months, while other outcome data for both groups will be collected at the baseline assessment phase, 3 months after the intervention, and 1 year after the program. Results: The trial will assess the efficacy of WHBDIP for patients with hypertension (N=68). The WHBDIP seeks to enhance participants' knowledge of healthy behaviors and assist patients in developing positive health behaviors to improve their health outcomes. Patient recruitment for individuals with hypertension commenced on September 5, 2022, and concluded on September 19, 2022. The 3-month intervention and phased data collection were finalized in January 2023. Data analysis will commence in August 2023, and the final 1-year health outcome results will be collected in September 2023. Conclusions: A successful WHBDIP will establish the management mode as a feasible approach for hypertension management in the community. Additionally, it will pave the way for the development of related mobile health programs. Trial Registration: Chinese Clinical Trial Registry ChiCTR2200062643; https://tinyurl.com/mwyv67wk International Registered Report Identifier (IRRID): PRR1-10.2196/46883

ZDZenghui Ding
Zenghui Ding
ZMZuchang Ma
Zuchang Ma
XYXianjun Yang
Xianjun Yang
YSYining Sun
Yining Sun
Objective: To evaluate the bone response to an 8 month aerobic gymnastics training program in young opioid-addicted women. Design: Randomized controlled trial (parallel design). Setting: Women’s Specific Drug Rehabilitation Center in China. Patients: One hundred and two young women with low bone quality and previous opioid addiction were divided into two groups: (a) the low bone quality intervention experimental group (n = 55; age: 30.3 ± 6.1) and (b) the low bone quality observed control group (observation group; n = 47; age: 29.0 ± 5.3). Interventions: The intervention group took aerobic gymnastics regularly for 80 min/d and 5 d/wk for 8 months and completed follow-up testing. Main Outcome Measures: Substance use history and other life habits affecting bone quality were assessed by questionnaire-based interviews. Bone quality (stiffness-index, T-score, Z-score) was examined with quantitative ultrasound. Anthropometric characteristics (body weight, fat-free mass, fat mass) were obtained by bioelectrical impedance analysis. Results: After the 8 month intervention, the stiffness index of bone quality increased significantly (before: 82 ± 6, after: 108 ± 14, p < 0.05) in the experimental group. However, the bone quality did not change significantly in the controls (before: 79 ± 10, after: 77 ± 13, p > 0.05). The bone change in the difference group was significant (experimental group: 31.7% vs observation group: -0.03%). Fat mass decreased in the experimental group (experimental group: before: 19.6 ± 3.7 kg, after: 18.8 ± 4.0 kg, p < 0.05). Meanwhile, the change in fat-free mass was the determination of the change in bone quality in the experimental group. Conclusions: Our results suggested that aerobic gymnastics intervention can be an effective strategy for the prevention and treatment of drug-induced osteoporosis in detoxification addicts.

1 January 2025
Verified
Conference Paper
Open access
PDF file
CYChengzhang Yu
Chengzhang Yu
YZYiming Zhang
Yiming Zhang
ZLZhixin Liu
Zhixin Liu
ZDZenghui Ding
Zenghui Ding
YSYining Sun
Yining Sun
ZJZhanpeng Jin
Zhanpeng Jin
Chengzhang Yu, Yiming Zhang, Zhixin Liu, Zenghui Ding, Yining Sun, Zhanpeng Jin. Findings of the Association for Computational Linguistics: ACL 2025. 2025.