ArticleIntelligent medicine2021
Digital health interventions for COVID-19 in China: a retrospective analysis.
Article in Intelligent medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 29 citations in OpenAlex.
- Challenges and experiences in digital health equity: a qualitative exploration of the implementation of the Health Code system in China.International journal for equity in health · 2025Article
- Applications of digital health approaches for cardiometabolic diseases prevention and management in the Western Pacific region.The Lancet regional health. Western Pacific · 2024Review
- Modern technologies and solutions to enhance surveillance and response systems for emerging zoonotic diseases.Science in One Health · 2024Review
- What motivates individuals to share information with governments when adopting health technologies during the COVID-19 pandemic?BMC public health · 2023Article
- A Personalized Avatar-Based Web Application to Help People Understand How Social Distancing Can Reduce the Spread of COVID-19: Cross-sectional, Observational, Pre-Post Study.JMIR formative research · 2023Article
- Digital intervention for public health: searching for implementing characteristics, concepts and recommendations: scoping review.Frontiers in public health · 2023Article
- A Multi-Criteria Decision-Making Model with Interval-Valued Intuitionistic Fuzzy Sets for Evaluating Digital Technology Strategies in COVID-19 Pandemic Under Uncertainty.Arabian journal for science and engineering · 2023Article
- Access to Digital Information and Protective Awareness and Practices towards COVID-19 in Urban Marginalized Communities.Healthcare (Basel, Switzerland) · 2022Article
- Demographic Characteristics and Digital Platforms for Physical Activity Among the Chinese Residents During the COVID-19 Pandemic: A Mediating Analysis.Journal of multidisciplinary healthcare · 2022Article
- Use of Digital Health Interventions in Sub-Saharan Africa for Health Systems Strengthening Over the Last 10 Years: A Scoping Review Protocol.Frontiers in digital health · 2022Article
- Acceptability and usability of a WeChat-based intervention for depression in China: A mixed-methods study.Digital healthArticle
- Telemedicine and artificial intelligence to support self-isolation of COVID-19 patients: Recent updates and challenges.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThe use of digital health technologies was an integral part to China's early response to coronavirus disease 2019 (COVID-19). Existing literatures have analyzed and discussed implemented digital health innovations from the perspective of technologies, whereas how policy mechanisms contributed to the formulation of the digital health landscape for COVID-19 was overlooked. This study aimed to examine the contexts and key mechanisms in China's rapid mobilization of digital health interventions in response to COVID-19, and to document and share lessons learned.
methodsPolicy documents were identified and retrieved from government portals and recognized media outlets. Data on digital health interventions were collected through three consecutive surveys administered between 23 January 2020 and 31 March 2020 by China Academy of Information and Communication Technology (CAICT) affiliated to the Ministry of Industry and Information Technology (MIIT). Participants were member companies of the Internet Health alliance established by MIIT and the National Health Commission (NHC) in June 2016. Self-report digital interventions focusing on social and economic recovery were excluded. Two hundred and sixty-six unique digital health interventions meeting our criteria were extracted from 175 narratives on digital health interventions submitted by 116 participating companies. Thematic analysis was conducted to describe the scope and priority of policies advocating for the use of digital health technologies and the implementation pattern of digital health interventions. Data limitations precluded an evaluation of the impact of digital health interventions over a longer time frame.
resultsBetween January and March 2020, national policy directives promoting the use of digital technologies for the containment of COVID-19 collectively advocated for use cases in emergency planning and preparedness, public health response, and clinical services. Interventions to strengthen clinical services were mentioned more than the other two themes (
conclusionsThe contextual factors and key enabling mechanisms through the use of policy instruments to promote digital health interventions for COVID-19 in China include pathway of policy directives influencing the private sector using a decentralized system, the booming digital health landscape before COVID-19, agility of the public sector in introducing regulatory flexibilities and incentives to mobilize the private sector.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.