Journal of Medical Internet Research

The popularity of the magnetic vaccine conspiracy theory and other conspiracy theories of a similar nature creates challenges to promoting vaccines and disseminating accurate health information.

Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer.

The promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data has proven elusive, largely because of the quality of the data captured. Despite the importance of data quality (DQ), an agreed-upon DQ taxonomy evades literature. When consolidated frameworks are developed, the dimensions are often fragmented, without consideration of the interrelationships among the dimensions or their resultant impact.

Personal health information (PHI) is created on behalf of and by health care consumers to support their care and wellness. Available tools designed to support PHI management (PHIM) remain insufficient. A comprehensive understanding of PHIM work is required, particularly for older adults, to offer more effective PHIM tools and support.

Scientists often make cognitive claims (eg, the results of their work) and normative claims (eg, what should be done based on those results). Yet, these types of statements contain very different information and implications. This randomized controlled trial sought to characterize the granular effects of using normative language in science communication.

The collection, storage, and analysis of large data sets are relevant in many sectors. Especially in the medical field, the processing of patient data promises great progress in personalized health care. However, it is strictly regulated, such as by the General Data Protection Regulation (GDPR). These regulations mandate strict data security and data protection and, thus, create major challenges for collecting and using large data sets. Technologies such as federated learning (FL), especially paired with differential privacy (DP) and secure multiparty computation (SMPC), aim to solve these challenges.

Turning during walking is a relevant and common everyday movement and it depends on a correct top-down intersegmental coordination. This could be reduced in several conditions (en bloc turning), and an altered turning kinematics has been linked to increased risk of falls. Smartphone use has been associated with poorer balance and gait; however, its effect on turning-while-walking has not been investigated yet. This study explores turning intersegmental coordination during smartphone use in different age groups and neurologic conditions.

Internet- or web-based research is rapidly increasing, offering multiple benefits for researchers. However, various challenges in web-based data collection have been illustrated in prior research, particularly since the onset of the COVID-19 pandemic. To add to the literature on best practices for web-based qualitative data collection, we present 4 case studies in which each research team experienced challenges unique to web-based qualitative research and had to modify their research approaches to preserve data quality or integrity. The first 2 case examples describe issues with using social media to recruit hard-to-reach populations, the third example demonstrates the challenge in engaging adolescents in sensitive conversations on the web, and the final example discusses both the issues in recruitment and the use of different modalities in collecting data to accommodate the medical needs of study participants. Based on these experiences, we provide guidance and future directions for journals and researchers in collecting qualitative data on the web.

Brauneck and colleagues have combined technical and legal perspectives in their timely and valuable paper “Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review.” Researchers who design mobile health (mHealth) systems must adopt the same privacy-by-design approach that privacy regulations (eg, General Data Protection Regulation) do. In order to do this successfully, we will have to overcome implementation challenges in privacy-enhancing technologies such as differential privacy. We will also have to pay close attention to emerging technologies such as private synthetic data generation.

Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease. It is characterized by a broad spectrum of manifestations, depending on the affected organs and the severity of the inflammation at the time of presentation. Despite improvements in management, treatments are required on a chronic, cyclical basis; have high potential for unpleasant side effects; and deliver variable efficacy. Patients require care from multiple specialists, which can be delivered simultaneously and sporadically. Our fragmented health care system further exacerbates the disconnect between intermittent medical care and the lived experiences of patients with SLE. The goals of this research are to (1) assess the current standard of care for patients with SLE through the review of medical literature, including clinical consensus guidelines and systematic reviews; (2) assess the lived experiences of patients with lupus through the review of peer-reviewed literature on social listening, structured interviews, and data available from the open-access digital health platform PatientsLikeMe; and (3) present the perspective that the medical community has an opportunity to acknowledge and review the use of digital health interventions (DHIs) with their patients. The results of this research indicate that patients are incorporating DHIs, such as the internet and social media platforms, as critical components of their care for even the most basic of support. Although patients with SLE are depending on this support to shape their care, it is not considered a primary source of care by clinicians. Integrating the voices of patients brings valuable dimension to understanding the lived experiences of patients with SLE and the impacts of mutually dependent patient needs as patients navigate the disease in daily life. The medical community has a meaningful opportunity to leverage and recommend existing DHIs, such as web-based community platforms and web-based patient registries, at every stage of the patient journey to help patients better manage their condition. This has the potential to proactively build patient trust and well-being, reduce the underreporting of symptoms, increase shared decision-making, inform and shape clinical guidelines and future research, and improve patient outcomes.

Telehealth seems feasible for use in home-based palliative care (HBPC). It may improve access to health care professionals (HCPs) at patients’ homes, reduce hospital admissions, enhance patients’ feelings of security and safety, and increase the time spent at home for patients in HBPC. HBPC requires the involvement of various HCPs such as nurses, physicians, allied health professionals, dietitians, psychologists, religious counselors, and social workers. Acceptance of the use of technology among HCPs is essential for the successful delivery of telehealth in practice. No scoping review has mapped the experiences and perspectives of HCPs regarding the use of telehealth in HBPC.

Regular medical care is important for people living with HIV. A no-show predictive model among people with HIV could improve clinical care by allowing providers to proactively engage patients at high risk of missing appointments. Epic, a major provider of electronic medical record systems, created a model that predicts a patient’s probability of being a no-show for an outpatient health care appointment; however, this model has not been externally validated in people with HIV.