Journal of Medical Internet Research
The leading peer-reviewed journal for digital medicine and health and health care in the internet age.
Editor-in-Chief:
Gunther Eysenbach, MD, MPH, FACMI, Founding Editor and Publisher; Adjunct Professor, School of Health Information Science, University of Victoria, Canada
Impact Factor 5.8 CiteScore 14.4
Recent Articles
Current literature is unclear on the safety and optimal timing of delivery for pregnant individuals with gestational diabetes mellitus, which inspired our study team to conduct a web-based survey study exploring patient and provider opinions on delivery options. However, an incident of fraudulent activity with survey responses prompted a shift in the focus of the research project. Unfortunately, despite the significant rise of web-based surveys used in medical research, there remains very limited evidence on the implications of and optimal methods to handle fraudulent web-based survey responses. Therefore, the objective of this viewpoint paper was to highlight our approach to identifying fraudulent responses in a web-based survey study, in the context of clinical perinatal research exploring patient and provider opinions on delivery options for pregnancies with gestational diabetes mellitus. Initially, we conducted cross-sectional web-based surveys across Canada with pregnant patients and perinatal health care providers. Surveys were available through Research Electronic Data Capture, and recruitment took place between March and October 2023. A change to recruitment introduced a US $5 gift card incentive to increase survey engagement. In mid-October 2023, an incident of fraudulent activity was reported, after which the surveys were deactivated. Systematic guidelines were developed by the study team in consultation with information technology services and the research ethics board to filter fraudulent from true responses. Between October 14 and 16, 2023, an influx of almost 2500 responses (393 patients and 2047 providers) was recorded in our web-based survey. Systematic filtering flagged numerous fraudulent responses. We identified fraudulent responses based on criteria including, but not limited to, identical timestamps and responses, responses with slight variations in wording and similar timestamps, and fraudulent email addresses. Therefore, the incident described in this viewpoint paper highlights the importance of preserving research integrity by using methodologically sound practices to extract true data for research findings. These fraudulent events continue to threaten the credibility of research findings and future evidence-based practices.
Telehealth interventions can effectively support caregivers of people with dementia by providing care and improving their health outcomes. However, to successfully translate research into clinical practice, the content and details of the interventions must be sufficiently reported in published papers.
Cognitive deterioration is common in multiple sclerosis (MS) and requires regular follow-up. Currently, cognitive status is measured in clinical practice using paper-and-pencil tests, which are both time-consuming and costly. Remote monitoring of cognitive status could offer a solution because previous studies on telemedicine tools have proved its feasibility and acceptance among people with MS. However, existing smartphone-based apps include designs that are prone to motor interference and focus primarily on information processing speed, although memory is also commonly affected.
Delayed cerebral ischemia (DCI) is a primary contributor to death after subarachnoid hemorrhage (SAH), with significant incidence. Therefore, early determination of the risk of DCI is an urgent need. Machine learning (ML) has received much attention in clinical practice. Recently, some studies have attempted to apply ML models for early noninvasive prediction of DCI. However, systematic evidence for its predictive accuracy is still lacking.
The integration of artificial intelligence (AI) into health communication systems has introduced a transformative approach to public health management, particularly during public health emergencies, capable of reaching billions through familiar digital channels. This paper explores the utility and implications of generalist conversational artificial intelligence (CAI) advanced AI systems trained on extensive datasets to handle a wide range of conversational tasks across various domains with human-like responsiveness. The specific focus is on the application of generalist CAI within messaging services, emphasizing its potential to enhance public health communication. We highlight the evolution and current applications of AI-driven messaging services, including their ability to provide personalized, scalable, and accessible health interventions. Specifically, we discuss the integration of large language models and generative AI in mainstream messaging platforms, which potentially outperform traditional information retrieval systems in public health contexts. We report a critical examination of the advantages of generalist CAI in delivering health information, with a case of its operationalization during the COVID-19 pandemic and propose the strategic deployment of these technologies in collaboration with public health agencies. In addition, we address significant challenges and ethical considerations, such as AI biases, misinformation, privacy concerns, and the required regulatory oversight. We envision a future with leverages generalist CAI in messaging apps, proposing a multiagent approach to enhance the reliability and specificity of health communications. We hope this commentary initiates the necessary conversations and research toward building evaluation approaches, adaptive strategies, and robust legal and technical frameworks to fully realize the benefits of AI-enhanced communications in public health, aiming to ensure equitable and effective health outcomes across diverse populations.
Previous studies on public compliance with policies during pandemics have primarily explained it from the perspectives of motivation theory, focusing on normative motivation (trust in policy-making institutions) and calculative motivation (fear of contracting the disease). However, the social amplification of a risk framework highlights that the media plays a key role in this process.
Mobile devices offer an emerging opportunity for research participants to contribute person-generated health data (PGHD). There is little guidance, however, on how to best report findings from studies leveraging those data. Thus, there is a need to characterize current reporting practices so as to better understand the potential implications for producing reproducible results.
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