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28. Visualizing knowledge evolution trends and research hotspots of personal health data research: a bibliometric review (Preprint): Bibliometric Analysis

Author

Gong, Jianxia, Sihag, Vikrant, Kong, Qingxia, Zhao, Lindu, and Department of Technology and Operations Management

Subjects
SDG 3 - Good Health and Well-being, ComputingMethodologies_GENERAL
Abstract

Background: The recent surge in clinical and nonclinical health-related data has been accompanied by a concomitant increase in personal health data (PHD) research across multiple disciplines such as medicine, computer science, and management. There is now a need to synthesize the dynamic knowledge of PHD in various disciplines to spot potential research hotspots. Objective: The aim of this study was to reveal the knowledge evolutionary trends in PHD and detect potential research hotspots using bibliometric analysis. Methods: We collected 8281 articles published between 2009 and 2018 from the Web of Science database. The knowledge evolution analysis (KEA) framework was used to analyze the evolution of PHD research. The KEA framework is a bibliometric approach that is based on 3 knowledge networks: reference co-citation, keyword co-occurrence, and discipline co-occurrence. Results: The findings show that the focus of PHD research has evolved from medicine centric to technology centric to human centric since 2009. The most active PHD knowledge cluster is developing knowledge resources and allocating scarce resources. The field of computer science, especially the topic of artificial intelligence (AI), has been the focal point of recent empirical studies on PHD. Topics related to psychology and human factors (eg, attitude, satisfaction, education) are also receiving more attention. Conclusions: Our analysis shows that PHD research has the potential to provide value-based health care in the future. All stakeholders should be educated about AI technology to promote value generation through PHD. Moreover, technology developers and health care institutions should consider human factors to facilitate the effective adoption of PHD-related technology.

Published
2021

34. Visualizing Knowledge Evolution Trends and Research Hotspots of Personal Health Data Research: Bibliometric Analysis

Author

Gong, Jianxia, Sihag, Vikrant, Kong, Qingxia, Zhao, Lindu, Gong, Jianxia, Sihag, Vikrant, Kong, Qingxia, and Zhao, Lindu

Abstract

BACKGROUND: The recent surge in clinical and nonclinical health-related data has been accompanied by a concomitant increase in personal health data (PHD) research across multiple disciplines such as medicine, computer science, and management. There is now a need to synthesize the dynamic knowledge of PHD in various disciplines to spot potential research hotspots.OBJECTIVE: The aim of this study was to reveal the knowledge evolutionary trends in PHD and detect potential research hotspots using bibliometric analysis.METHODS: We collected 8281 articles published between 2009 and 2018 from the Web of Science database. The knowledge evolution analysis (KEA) framework was used to analyze the evolution of PHD research. The KEA framework is a bibliometric approach that is based on 3 knowledge networks: reference co-citation, keyword co-occurrence, and discipline co-occurrence.RESULTS: The findings show that the focus of PHD research has evolved from medicine centric to technology centric to human centric since 2009. The most active PHD knowledge cluster is developing knowledge resources and allocating scarce resources. The field of computer science, especially the topic of artificial intelligence (AI), has been the focal point of recent empirical studies on PHD. Topics related to psychology and human factors (eg, attitude, satisfaction, education) are also receiving more attention.CONCLUSIONS: Our analysis shows that PHD research has the potential to provide value-based health care in the future. All stakeholders should be educated about AI technology to promote value generation through PHD. Moreover, technology developers and health care institutions should consider human factors to facilitate the effective adoption of PHD-related technology. These findings indicate opportunities for interdisciplinary cooperation in several PHD research areas: (1) AI applications for PHD; (2) regulatory issues and governance of PHD; (3) education of all stake

Published
2021

35. Visualizing knowledge evolution trends and research hotspots of personal health data research: a bibliometric review (Preprint):Bibliometric Analysis

Author

Gong, Jianxia, Sihag, Vikrant, Kong, Qingxia, Zhao, Lindu, Gong, Jianxia, Sihag, Vikrant, Kong, Qingxia, and Zhao, Lindu

Abstract

Background: The recent surge in clinical and nonclinical health-related data has been accompanied by a concomitant increase in personal health data (PHD) research across multiple disciplines such as medicine, computer science, and management. There is now a need to synthesize the dynamic knowledge of PHD in various disciplines to spot potential research hotspots. Objective: The aim of this study was to reveal the knowledge evolutionary trends in PHD and detect potential research hotspots using bibliometric analysis. Methods: We collected 8281 articles published between 2009 and 2018 from the Web of Science database. The knowledge evolution analysis (KEA) framework was used to analyze the evolution of PHD research. The KEA framework is a bibliometric approach that is based on 3 knowledge networks: reference co-citation, keyword co-occurrence, and discipline co-occurrence. Results: The findings show that the focus of PHD research has evolved from medicine centric to technology centric to human centric since 2009. The most active PHD knowledge cluster is developing knowledge resources and allocating scarce resources. The field of computer science, especially the topic of artificial intelligence (AI), has been the focal point of recent empirical studies on PHD. Topics related to psychology and human factors (eg, attitude, satisfaction, education) are also receiving more attention. Conclusions: Our analysis shows that PHD research has the potential to provide value-based health care in the future. All stakeholders should be educated about AI technology to promote value generation through PHD. Moreover, technology developers and health care institutions should consider human factors to facilitate the effective adoption of PHD-related technology.

Published
2021

41. Technology outage risk and independent research and development investment decision in global supply chains

Author

Chang, Xiaoxiao, Sun, Guohao, Zhou, Junhe, and Zhao, Lindu

Abstract

Under the new global situation of the world pattern, the global industrial chain and supply chain are being reconstructed, and the safe and stable operation is facing a large number of risks and challenges. As the internal driving force of the reconstruction of the global supply chain, key core technologies have become an important part of world economic competition. This paper addresses the issue that what product manufacturer should do to prevent the risk of key core technology outage in the context of international competition. This paper first constructs a duopoly Hotelling model of two competitive products from two countries and the game models under different circumstances where enterprises in the core technology outage whether or not implement the independent research and development (IR&D). We establish four scenarios: non-IR&D benchmark before and after technology outage, IR&D before and after technology outage. Furthermore, outage risk is evaluated by comparing the impact of outage initiated by the government on the profit change of the initiator country firms and the firm in which supply is cut off. When the enterprises in technology outage implement IR&D, we compare the scale of profit changes of enterprises between the two countries and analyze the effectiveness of IR&D investment of enterprises in the country suffering from technology outage to cope with the technology outage risk. Our analysis indicates that: outage maybe make the country that initiates the core technology outage lose more profits and the core technology country's profit change may be bigger than the country investing IR&D. Moreover, the enterprise's IR&D investment can establish certain game space and rights for their country. When the country can reduce technological disadvantages, IR&D strategy, to some extent, can reduce the urgency due to the technology outage risk and weaken the absolute control from the rival country that initiates the technology outage.

Published
2024
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42. A comparative study of public-health emergency management

Author

Jiaxiang Hu, Zeng, Amy Z., and Zhao, Lindu

Subjects
Public health -- Management, Emergency medical services -- Management, Company business management, Business, Business, general, Computers and office automation industries
Abstract

The article studies the managing of emergencies pertinent to public health that is important to the welfare of a society. Results suggest the deficient areas in Chinese public-health management mechanism as well as issues linked with supply chain design and coordination for emergency supplies in the context of large-scale public emergencies with low frequency.

Published
2009

43. Emergency logistics in large-scale disaster context: achievements and challenges

Author

Jiang, Yiping, Yuan, Yufei, Huang, Kai, Zhao, Lindu, McMaster eBusiness Research Centre (MeRC), and None

Subjects
Large-scale disaster, Emergency logistics, Emergency response, Operations research, Humanitarian logistics
Abstract

28 p. ; Includes bibliographical references. ; "June 2013." Different from normal business operations, emergency response for large scale disaster is complex and there are many challenges arise. Research on emergency logistics is still in its infancy stage. Understanding the challenges and new research directions becomes imperative. In this paper, we present a literature review of emergency logistics in large-scale disaster context. First, we identify key characteristics of large-scale disasters and assess their challenges to emergency logistics. Second, we analyze and summarize current literatures on how to deal with these challenges. Finally, we discuss existing gaps in relevant research and suggest future research directions.

Published
2013

49. Finite-time consensus on strongly convex balls of Riemannian manifolds with switching directed communication topologies

Author

Peng Shi, Weigong Zhang, Sheng Chen, Lindu Zhao, Chen, Sheng, Shi, Peng, Zhang, Weigong, and Zhao, Lindu

Subjects
nonsmooth analysis, Geodesic, strongly convex balls, Applied Mathematics, finite time, rotation attitudes, Riemannian manifold, Network topology, Topology, consensus algorithm, Riemannian manifolds, Consensus, Mathematics::Differential Geometry, Finite time, Convex function, Protocol (object-oriented programming), Rotation (mathematics), Analysis, Mathematics
Abstract

It is known that a consensus problem on any connected complete Riemannian manifold can be transformed into the one on its strongly convex balls via the compression–decompression along geodesics. From the viewpoint of interior metrics, this paper mainly provides a consensus protocol for strongly convex geodesic balls, in which the communication can be switching and directed. With the aid of nonsmooth analysis tools on Riemannian manifolds, our analysis shows that all dynamical points involved can achieve consensus in finite time. Meanwhile, the corresponding global algorithm is given, with its application to the consensus problem of rotation attitudes, as well as a case simulation, to demonstrate and verify our proposed techniques. Refereed/Peer-reviewed

Published
2014

50. Framework Development for Reducing Attrition in Digital Dietary Interventions: Systematic Review and Thematic Synthesis.

Author

Wang J, Mahe J, Huo Y, Huang W, Liu X, Zhao Y, Huang J, Shi F, Li Z, Jiang D, Li Y, Perceval G, Zhao L, and Zhang L

Subjects
Humans, Patient Dropouts statistics & numerical data, Diet Therapy methods, Diet Therapy statistics & numerical data
Abstract

Background: Dietary behaviors significantly influence health outcomes across populations. Unhealthy diets are linked to serious diseases and substantial economic burdens, contributing to approximately 11 million deaths and significant disability-adjusted life years annually. Digital dietary interventions offer accessible solutions to improve dietary behaviors. However, attrition, defined as participant dropout before intervention completion, is a major challenge, with rates as high as 75%-99%. High attrition compromises intervention validity and reliability and exacerbates health disparities, highlighting the need to understand and address its causes., Objective: This study systematically reviews the literature on attrition in digital dietary interventions to identify the underlying causes, propose potential solutions, and integrate these findings with behavior theory concepts to develop a comprehensive theoretical framework. This framework aims to elucidate the behavioral mechanisms behind attrition and guide the design and implementation of more effective digital dietary interventions, ultimately reducing attrition rates and mitigating health inequalities., Methods: We conducted a systematic review, meta-analysis, and thematic synthesis. A comprehensive search across 7 electronic databases (PubMed, MEDLINE, Embase, CENTRAL, Web of Science, CINAHL Plus, and Academic Search Complete) was performed for studies published between 2013 and 2023. Eligibility criteria included original research exploring attrition in digital dietary interventions. Data extraction focused on study characteristics, sample demographics, attrition rates, reasons for attrition, and potential solutions. We followed ENTREQ (Enhancing the Transparency in Reporting the Synthesis of Qualitative Research) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and used RStudio (Posit) for meta-analysis and NVivo for thematic synthesis., Results: Out of the 442 identified studies, 21 met the inclusion criteria. The meta-analysis showed mean attrition rates of 35% for control groups, 38% for intervention groups, and 40% for observational studies, with high heterogeneity (I²=94%-99%) indicating diverse influencing factors. Thematic synthesis identified 15 interconnected themes that align with behavior theory concepts. Based on these themes, the force-resource model was developed to explore the underlying causes of attrition and guide the design and implementation of future interventions from a behavior theory perspective., Conclusions: High attrition rates are a significant issue in digital dietary interventions. The developed framework conceptualizes attrition through the interaction between the driving force system and the supporting resource system, providing a nuanced understanding of participant attrition, summarized as insufficient motivation and inadequate or poorly matched resources. It underscores the critical necessity for digital dietary interventions to balance motivational components with available resources dynamically. Key recommendations include user-friendly design, behavior-factor activation, literacy training, force-resource matching, social support, personalized adaptation, and dynamic follow-up. Expanding these strategies to a population level can enhance digital health equity. Further empirical validation of the framework is necessary, alongside the development of behavior theory-guided guidelines for digital dietary interventions., Trial Registration: PROSPERO CRD42024512902; https://tinyurl.com/3rjt2df9., (©Jian Wang, Jinli Mahe, Yujia Huo, Weiyuan Huang, Xinru Liu, Yang Zhao, Junjie Huang, Feng Shi, Zhihui Li, Dou Jiang, Yilong Li, Garon Perceval, Lindu Zhao, Lin Zhang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.08.2024.)

Published
2024
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