Ziyang Li

Dr. Ziyang Li

orcid

Research Focus & Metrics

Academic Career Length

1Year (2026-Present)

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

Representative Publications

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19 July 2026 in JMIR Preprints
Verified
Preprint
Open access
ZLZiyang Li
Ziyang Li
ZZZhuo Zeng
Zhuo Zeng
MLMinghui Li
Minghui Li
YCYiyuan Chong
Yiyuan Chong
YZYaping Zhong
Yaping Zhong
BACKGROUND Background: Exercise rehabilitation is a core component of knee osteoarthritis management, but sustained delivery may be constrained by time, geography, and limited health care resources. Digital technologies may improve access to prescribed exercise and support its delivery and monitoring; however, their overall effectiveness and the factors influencing treatment response remain uncertain. OBJECTIVE Objective:To systematically evaluate the effects of digital technology–supported exercise rehabilitation on pain, patient-reported function, performance-based physical function, and quality of life in people with knee osteoarthritis, and to investigate potential moderators of treatment effects. METHODS Methods: PubMed, Web of Science, Embase, EBSCO, the Cochrane Library, China National Knowledge Infrastructure, and Wanfang Data were systematically searched from inception to May 20, 2026. Randomized controlled trials were eligible if they evaluated exercise-based rehabilitation supported by digital technologies, including telerehabilitation, mobile applications, or virtual reality. Three-level random-effects models were used to account for dependencies among multiple effect sizes arising from different outcomes, measurement instruments, or assessment time points within the same study. Statistical inference was further supported by cluster-robust variance estimation with CR2 small-sample correction. Subgroup analyses and meta-regression were conducted to examine the potential moderating effects of knee osteoarthritis severity, type of digital technology, comparator condition, sensor use, intervention duration, and weekly intervention frequency. The certainty of evidence was assessed using the GRADE approach. RESULTS Results: Twenty randomized controlled trials involving 1,222 participants with knee osteoarthritis were included. Three-level meta-analyses showed that digital technology–supported exercise rehabilitation significantly reduced pain (Hedges’ g = 0.60, 95% CI [0.15, 1.06], 95% PI [−1.29, 2.50]) and improved patient-reported function (Hedges’ g = 0.72, 95% CI [0.24, 1.20], 95% PI [−1.42, 2.87]), performance-based physical function (Hedges’ g = 0.54, 95% CI [0.11, 0.97], 95% PI [−0.99, 2.07]), and quality of life (Hedges’ g = 0.51, 95% CI [0.16, 0.85], 95% PI [−0.46, 1.48]). These effects remained statistically significant after CR2 small-sample correction. Subgroup analyses indicated greater improvements in quality of life among participants with mild-to-moderate knee osteoarthritis. Meta-regression further showed that longer intervention duration was associated with greater pain reduction and improvements in quality of life, whereas a higher weekly intervention frequency was associated with greater improvements in patient-reported function. Sensitivity analyses supported the robustness of the findings for patient-reported function and quality of life. Although the pooled estimates consistently favored digital technology–supported exercise rehabilitation, all 95% PI crossed the line of no effect, indicating substantial variability in the treatment effects that may be observed across future clinical settings. CONCLUSIONS Conclusions: Digital technology–supported exercise rehabilitation may improve pain, function, and quality of life in people with knee osteoarthritis. Its principal clinical value may lie in expanding access to exercise rehabilitation and supporting the implementation of prescribed exercise. however, the optimal mode of delivery remains to be established through further high-quality research. CLINICALTRIAL Trial Registration: PROSPERO CRD420261404719; https://www.crd.york.ac.uk/PROSPERO/view/CRD420261404719

YCYiyuan Chong
Yiyuan Chong
MWMengli Wei
Mengli Wei
JTJun Tu
Jun Tu
ZLZiyang Li
Ziyang Li
MLMinghui Li
Minghui Li
YZYaping Zhong
Yaping Zhong
1 In recent years, major international esports events, such as the esports competition at the Hangzhou Asian Games and the Esports World Cup in Riyadh, Saudi Arabia, have been held worldwide. This ...

19 September 2026 in Nutrients
Verified
Journal Article
Open access
PDF file
XZXiaoyu Zhang
Xiaoyu Zhang
HSHao Sun
Hao Sun
Biaoxu TaoBiaoxu Tao
Biaoxu Tao
ZLZiyang Li
Ziyang Li
SLShuning Liu
Shuning Liu
JWJiatong Wang
Jiatong Wang
Background: Obesity-related metabolic dysfunction includes adipose inflammation, hepatic steatosis, insulin resistance, dyslipidemia, and intestinal disturbances. Avenanthramides are oat-specific phenolic amides, and their contribution to metabolic responses to oat foods is difficult to attribute because beta-glucan and the wider oat matrix can affect the same endpoints. This narrative review examines their mechanisms, exposure biology, evidence attribution, and translational readiness. Methods: Cell, animal, pharmacokinetic, and human studies were organized by chemical definition and intervention format, including purified congeners, characterized foods, enriched matrices, and whole-oat products. Parent compounds, circulating metabolites, target tissues, and clinical outcomes were interpreted separately. Results: In cell and animal models, avenanthramides influence NF-κB-related inflammation, redox regulation, hepatic insulin signaling, glucose production, fatty-acid metabolism, and intestinal ecology. Human studies demonstrate dietary exposure to parent compounds and metabolites, while clinical evidence comes mainly from small enriched-food exercise studies. Among the studies identified and appraised in this narrative review, none established an avenanthramide-specific effect on adiposity, liver fat, insulin sensitivity, dyslipidemia, or other cardiometabolic outcomes. Conclusions: The current literature supports measurable exposure and candidate mechanisms; a validated anti-obesity effect requires targeted clinical trials. Next studies need stable, analytically defined preparations, metabolite-informed dose selection, matched food controls, and trials that link measured exposure with prespecified clinical outcomes.