JMIR Formative Research

Poor mental health among higher education students is a global public health concern. Learning analytics, which involves collecting and analysing big data to support learning, could detect changes in behaviour, learning patterns, as well as mental health and well-being. This could help inform mental health interventions in university settings. However, research has yet to explore students' perspectives on using learning analytics for mental health and well-being purposes.

The rise of short-video platforms, such as TikTok (Douyin in China) and Bilibili, has significantly influenced how health information is disseminated to the public. However, the quality, reliability, and effectiveness of health-related content on these platforms, particularly regarding uterine fibroids, remain underexplored. Uterine fibroids are a common medical condition that affects a substantial proportion of women worldwide. While these platforms have become vital sources of health education, misinformation and incomplete content may undermine their efficacy.

Climate change is a pressing public health issue, with the U.S. healthcare sector contributing about 479 million tons of carbon dioxide (CO2) annually. Online continuing medical education (CME) offers an alternative solution to increase global education delivery while reducing CO2 emissions associated with traditional teaching methods

There is growing interest in applying generative artificial intelligence (GenAI) to respond to electronic patient portal messages, particularly in primary care where message volumes are highest. However, evaluations of GenAI as an inbox communication tool are limited. Qualitative analysis of when and how often GenAI responses achieve communication goals can inform estimates of impact and guide continuous improvement.

Despite evidence that use of patient portals and telehealth is associated with increased health knowledge, patient satisfaction, and preventive services use, disparities exist in awareness, adoption, and use. Populations with lower digital literacy access and skills largely overlap with the populations known to experience poor health outcomes, and these problems can be mutually exacerbating. Understanding factors and strategies specific to these populations is key to achieving digital equity and better health.

Adverse drug reactions (ADRs) are a major concern in drug safety and the FDA Adverse Event Reporting System (FAERS) provides valuable ADR data. However, analyzing FAERS data is complex and requires bioinformatics expertise. Despite the vast amount of ADR data available, there is a lack of user-friendly tools that enable efficient visualization and comparison of ADRs for researchers and healthcare professionals.

Artificial intelligence (AI) models are increasingly being developed to improve the efficiency of pathological diagnoses. Rapid technological advancements are leading to more widespread availability of AI models that can be used by domain-specific experts (ie, pathologists and medical imaging professionals). This study presents an innovative AI model for the classification of colon polyps, developed using AutoML algorithms that are readily available from cloud-based machine learning platforms. Our aim was to explore if such AutoML algorithms could generate robust machine learning models that are directly applicable to the field of digital pathology.

Social media listening can be leveraged to obtain authentic perceptions about events, their impact, guidelines and policies. There has been to date no research that has examined coronavirus disease 2019 (COVID-19) patients’ experiences from diagnosis to treatment using social media listening in the United Kingdom.

Every year, around 1.8 million people in the UK are referred to NHS Talking Therapies services, predominantly for Cognitive Behavioral Therapy (CBT) which is the first-line treatment for common affective and anxiety disorders. However, more than a million of these do not complete their course. Supporting this ‘missing million’ to attend and complete CBT is a policy priority.

Wearable Internet of Things (IoT) devices are powerful tools for remotely collecting intensive longitudinal data. The TDK Silmee W22, a wristband-type wearable IoT device with a built-in 3-axis acceleration sensor, provides minute-by-minute physical activity data such as estimated metabolic equivalents (METs) and step counts. These measurements can be aggregated to daily estimates; however, their accuracies have not been fully explored in adults under free-living settings.

Consumer-grade wearables, such as Fitbits, are a promising, cost-effective methodology for objectively assessing sleep and physical activity in youth with pain.

Older adult mistreatment occurs in many as one-half of dementia care partners. Psychological mistreatment is the most common form of older adult mistreatment by family caregivers and is known to create mental health morbidities among care recipients. The Knowledge and Interpersonal Skills to Develop Enhanced Relationships (KINDER) intervention is among the first older adult mistreatment prevention interventions focused on family caregivers. KINDER was designed to prevent psychological mistreatment of older adults. Caregivers found the initial asynchronous web-based version (KINDER 1.0) to be acceptable but expressed a desire to engage with other family caregivers. KINDER was revised to integrate 3 facilitated small group discussion sessions conducted by videoconference. This study examines the acceptability of a revised KINDER intervention. This research addresses the extent to which caregivers find a novel approach to older adult mistreatment prevention to be acceptable.