Journal of Medical Internet Research

Patient-centered communication refers to interaction between patients and health professionals that considers patients’ preferences and empowers patients to contribute to their own care. Research suggests that patient-centered communication promotes patients’ satisfaction with care, trust in physicians, and competence in their abilities to manage their health.

There is a growing enthusiasm for machine learning (ML) among academics and health care practitioners. Despite the transformative potential of ML-based applications for patient care, their uptake and implementation in health care organizations are sporadic. Numerous challenges currently impede or delay the widespread implementation of ML in clinical practice, and limited knowledge is available regarding how these challenges have been addressed.

This viewpoint reviews the empirical evidence regarding the association between social media use and well-being, including life satisfaction and affective well-being, and the association between social media use and ill-being, including loneliness, anxiety, and depressive symptomology. To frame this discussion, this viewpoint will present 10 widely believed myths about social media, each drawn from popular discourse on the topic. In rebuttal, this viewpoint will offer a warranted claim supported by the research. The goal is to bring popular beliefs into dialogue with state-of-the-art quantitative social scientific evidence. It is the intention of this viewpoint to provide a more accurate and nuanced claim to challenge each myth. This viewpoint will bring attention to the importance of using rigorous scientific evidence to inform public debates about social media use and well-being, especially among adolescents and young adults.

Digital health innovations have emerged globally as a transformative force for addressing health system challenges, particularly in resource-constrained settings. The COVID-19 pandemic underscored the critical importance of these innovations for enhancing public health. In South and Southeast Asia, a region known for its cultural diversity and complex health care landscape, digital health innovations present a dynamic interplay of challenges and opportunities. We advocate for ongoing research built into system development and an evidence-based strategy focusing on designing and scaling national digital health infrastructures combined with a vibrant ecosystem or “marketplace” of local experiments generating shared experience about what works in which settings. As the global digital health revolution unfolds, the perspectives drawn from South and Southeast Asia—including the importance of local partnerships—may provide valuable insights for shaping future strategies and informing similar initiatives in low- and middle-income countries, contributing to effective digital health strategies across diverse global health contexts.

High systolic blood pressure is one of the leading global risk factors for mortality, contributing significantly to cardiovascular diseases. Despite advances in treatment, a large proportion of patients with hypertension do not achieve optimal blood pressure control. Arterial stiffness (AS), measured by pulse wave velocity (PWV), is an independent predictor of cardiovascular events and overall mortality. Various antihypertensive drugs exhibit differential effects on PWV, but the extent to which these effects vary depending on individual patient characteristics is not well understood. Given the complexity of selecting the most appropriate antihypertensive medication for reducing PWV, machine learning (ML) techniques offer an opportunity to improve personalized treatment recommendations.

Chronic kidney disease (CKD) is a significant public health concern. Therefore, practical strategies for slowing CKD progression and improving patient outcomes are imperative. There is limited evidence to substantiate the efficacy of mobile app–based nursing systems for decelerating CKD progression.

The high prevalence of noncommunicable diseases and the growing importance of social media have prompted health care professionals (HCPs) to use social media to deliver health information aimed at reducing lifestyle risk factors. Previous studies have acknowledged that the identification of elements that influence user engagement metrics could help HCPs in creating engaging posts toward effective health promotion on social media. Nevertheless, few studies have attempted to comprehensively identify a list of elements in social media posts that could influence user engagement metrics.

European health care systems regard information and communication technology as a necessity in supporting future health care provision by community home care services to home-dwelling older adults. Communication technology enabling synchronous communication between 2 or more human actors at a distance constitutes a significant component of this ambition, but few reviews have synthesized research relating to this particular type of technology. As evaluations of information and communication technology in health care services favor measurements of effectiveness over the experiences and dynamics of putting these technologies into use, the nuances involved in technology implementation processes are often omitted.

Systemic inflammatory response syndrome (SIRS) is a serious postoperative complication among older adult surgical patients that frequently develops into sepsis or even death. Notably, the incidences of SIRS and sepsis steadily increase with age. It is important to identify the risk of postoperative SIRS for older adult patients at a sufficiently early stage, which would allow preemptive individualized enhanced therapy to be conducted to improve the prognosis of older adult patients. In recent years, machine learning (ML) models have been deployed by researchers for many tasks, including disease prediction and risk stratification, exhibiting good application potential.

The sleep status of patients in the surgical intensive care unit (ICU) significantly impacts their recoveries. However, the effects of surgical procedures on sleep are rarely studied.

Given the complexity and diversity of lichenoid vulvar disease (LVD) risk factors, it is crucial to actively explore these factors and construct personalized warning models using relevant clinical variables to assess disease risk in patients. Yet, to date, there has been insufficient research, both nationwide and internationally, on risk factors and warning models for LVD. In light of these gaps, this study represents the first systematic exploration of the risk factors associated with LVD.

Studies show that the use of information and communications technologies (ICTs), including smartphones, tablets, computers, and the internet, varies by demographic factors such as age, gender, and educational attainment. However, the connections between ICT use and factors such as ethnicity and English proficiency, especially among Asian American older adults, remain less explored. The technology acceptance model (TAM) suggests that 2 key attitudinal factors, perceived usefulness (PU) and perceived ease of use (PEOU), influence technology acceptance. While the TAM has been adapted for older adults in China, Taiwan, Singapore, and Korea, it has not been tested among Asian American older adults, a population that is heterogeneous and experiences language barriers in the United States.