Journal of Medical Internet Research

The current methods of evaluating cognitive functioning typically rely on a single time point to assess and characterize an individual’s performance. However, cognitive functioning fluctuates within individuals over time in relation to environmental, psychological, and physiological contexts. This limits the generalizability and diagnostic utility of single time point assessments, particularly among individuals who may exhibit large variations in cognition depending on physiological or psychological context (eg, those with type 1 diabetes [T1D], who may have fluctuating glucose concentrations throughout the day).

Patient online access to medical records is assumed to facilitate patient empowerment and advance patient-centered health care. However, to date, the actual effects of online access to medical records perceived by patients and other outcomes are insufficiently empirically tested.

eHealth literacy describes the ability to locate, comprehend, evaluate, and apply web-based health information to a health problem. In studies of eHealth literacy, researchers have primarily assessed participants’ perceived eHealth literacy using a short self-report instrument, for which ample research has shown little to no association with actual performed eHealth-related skills. Performance-based measures of eHealth literacy may be more effective at assessing actual eHealth skills, yet such measures seem to be scarcer in the literature.

Social media platforms have been increasingly used to express suicidal thoughts, feelings, and acts, raising public concerns over time. A large body of literature has explored the suicide risks identified by people’s expressions on social media. However, there is not enough evidence to conclude that social media provides public surveillance for suicide without aligning suicide risks detected on social media with actual suicidal behaviors. Corroborating this alignment is a crucial foundation for suicide prevention and intervention through social media and for estimating and predicting suicide in countries with no reliable suicide statistics.

In the context of a deepening global shortage of health workers and, in particular, the COVID-19 pandemic, there is growing international interest in, and use of, online symptom checkers (OSCs). However, the evidence surrounding the triage and diagnostic accuracy of these tools remains inconclusive.

Direct-acting antiviral medications have the potential to eliminate the hepatitis C virus (HCV) epidemic among people who inject drugs; yet, suboptimal adherence remains a barrier. Directly observed treatment (DOT), an effective strategy for optimizing adherence, has been frequently implemented in opioid treatment programs but less commonly in community health settings due to the heavy burden of daily visits. An alternative is video-observed therapy (VOT), which uses mobile health technology to monitor adherence. VOT has not been widely studied among people who inject drugs with HCV.

The growing public interest and awareness regarding the significance of sleep is driving the demand for sleep monitoring at home. In addition to various commercially available wearable and nearable devices, sound-based sleep staging via deep learning is emerging as a decent alternative for their convenience and potential accuracy. However, sound-based sleep staging has only been studied using in-laboratory sound data. In real-world sleep environments (homes), there is abundant background noise, in contrast to quiet, controlled environments such as laboratories. The use of sound-based sleep staging at homes has not been investigated while it is essential for practical use on a daily basis. Challenges are the lack of and the expected huge expense of acquiring a sufficient size of home data annotated with sleep stages to train a large-scale neural network.

Májovský and colleagues have investigated the important issue of ChatGPT being used for the complete generation of scientific works, including fake data and tables. The issues behind why ChatGPT poses a significant concern to research reach far beyond the model itself. Once again, the lack of reproducibility and visibility of scientific works creates an environment where fraudulent or inaccurate work can thrive. What are some of the ways in which we can handle this new situation?

Given the importance of proactively supporting women trainees in medicine to address gender inequities, we draw on the experience of a well-established professional development initiative to provide a framework for other institutions seeking to create similar trainee-focused programs.

Artificial intelligence (AI) has advanced substantially in recent years, transforming many industries and improving the way people live and work. In scientific research, AI can enhance the quality and efficiency of data analysis and publication. However, AI has also opened up the possibility of generating high-quality fraudulent papers that are difficult to detect, raising important questions about the integrity of scientific research and the trustworthiness of published papers.

Despite the benefits of digital health technology use, older adults with cancer (ie, aged 65 years) have reported challenges to technology adoption. However, there has been a lack of a good understanding of their digital health technology use patterns and the associated influential factors in the past few years.

Digital cognitive behavioral therapy (CBT) interventions can effectively prevent and treat depression and anxiety, but engagement with these programs is often low. Although extensive research has evaluated program use as a proxy for engagement, the extent to which users acquire knowledge and enact skills from these programs has been largely overlooked.