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

older people experiencing homelessness can have mental and physical indicators of aging several decades earlier than the general population and experience premature mortality due to age-related chronic conditions. Digital interventions could positively impact the health and well-being of homeless people. However, increased reliance on digital delivery may also perpetuate digital inequalities for socially excluded groups. The potential triple disadvantage of being older, homeless, and digitally excluded creates a uniquely problematic situation warranting further research. Few studies have synthesized available literature on digital interventions for older people experiencing homelessness.

Depression affects 32% of older adults. Loneliness and social isolation are common risk factors for depression in older adults. Mobile apps can connect users and are also effective in depression management in the general population. However, older adults have specific needs in terms of the content of depression self-care interventions and their accessibility. It remains unknown whether existing apps for depression self-care are applicable to older adults.

Despite excitement around artificial intelligence (AI)–based tools in health care, there is work to be done before they can be equitably deployed. The absence of diverse patient voices in discussions on AI is a pressing matter, and current studies have been limited in diversity. Our study inquired about the perspectives of racial and ethnic minority patients on the use of their health data in AI, by conducting a cross-sectional survey among 230 participants who were at least 18 years of age and identified as Black, Latinx, Indigenous, or Asian. While familiarity with AI was high, a smaller proportion of participants understood how AI can be used in health care (152/199, 76.4%), and an even smaller proportion understood how AI can be applied to dermatology (133/199, 66.8%). Overall, 69.8% (139/199) of participants agreed that they trusted the health care system to treat their medical information with respect; however, this varied significantly by income (P=.045). Only 64.3% (128/199) of participants felt comfortable with their medical data being used to build AI tools, and 83.4% (166/199) believed they should be compensated if their data are used to develop AI. To our knowledge, this is the first study focused on understanding opinions about health data use for AI among racial and ethnic minority individuals, as similar studies have had limited diversity. It is important to capture the opinions of diverse groups because the inclusion of their data is essential for building equitable AI tools; however, historical harms have made inclusion challenging.

Patient-centered care promotes the involvement of patients in decision-making related to their health care. The adoption and implementation of shared decision-making (SDM) into routine care are constrained by several obstacles, including technical and time constraints, clinician and patient attitudes and perceptions, and processes that exist outside the standardized clinical workflow.


Health care wearable devices can transform health care delivery by enabling real-time, continuous monitoring that facilitates early disease detection, personalized treatments, and improved patient engagement. The COVID-19 pandemic has heightened awareness of the importance of health technology, accelerating interest in wearables as tools for monitoring health and managing chronic conditions. As we navigate the postpandemic era, understanding the adoption and data-sharing behaviors associated with wearable devices has become increasingly critical. Despite their potential, challenges and low adoption rates persist, with significant gaps in understanding the impact of sociodemographic factors, health conditions, and digital literacy on the use and data-sharing behaviors of these devices.


The emergence of new SARS-CoV-2 variants, the resulting reinfections, and post–COVID-19 condition continue to impact many people’s lives. Tracking websites like the one at Johns Hopkins University no longer report the daily confirmed cases, posing challenges to accurately determine the true extent of infections. Many COVID-19 cases with mild symptoms are self-assessed at home and reported on social media, which provides an opportunity to monitor and understand the progression and evolving trends of the disease.

Online record access (ORA) is being increasingly implemented internationally. Despite reported benefits for patients, health care professionals (HCPs) have raised concerns about potential disadvantages. To date, no review has examined the empirical evidence on whether and how documentation changes following the introduction of patients’ ORA.
Preprints Open for Peer-Review
Open Peer Review Period:
-
Open Peer Review Period:
-
Open Peer Review Period:
-
Open Peer Review Period:
-
Open Peer Review Period:
-