JMIR mHealth and uHealth

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing, and domotics for health

Editor-in-Chief:

Lorraine R. Buis, PhD, MSI, Associate Professor, Department of Family Medicine, University of Michigan, USA


Impact Factor 6.2 CiteScore 11.6

JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a leading peer-reviewed journal and one of the flagship journals of JMIR Publications. JMIR mHealth and uHealth has been published since 2013 and was the first mHealth journal indexed in PubMed. 

JMIR mHealth and uHealth focuses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. 

The journal adheres to rigorous quality standards, involving a rapid and thorough peer-review process, professional copyediting, and professional production of PDF, XHTML, and XML proofs.

Like all JMIR journals, JMIR mHealth and uHealth encourages Open Science principles and strongly encourages the publication of a protocol before data collection. Authors who have published a protocol in JMIR Research Protocols get a discount of 20% on the Article Processing Fee when publishing a subsequent results paper in any JMIR journal.

It is indexed in all major literature indices, including MEDLINEPubMedPubMed CentralScopus, Psycinfo, SCIE, JCR, EBSCO/EBSCO Essentials, DOAJ, GoOA and others.

JMIR mHealth and uHealth received a Journal Impact Factor of 6.2 according to the latest release of the Journal Citation Reports from Clarivate, 2025.

JMIR mHealth and uHealth received a Scopus CiteScore of 11.6 (2024), placing it in the 91st percentile (#13 of 153) as a Q1 journal in the field of Health Informatics. 

Recent Articles

Article Thumbnail
mHealth for Rehabilitation

Functional rehabilitation is commonly used for patients with chronic ankle instability (CAI). Digital training systems have become increasingly popular in postoperative rehabilitation; however, their effectiveness for CAI patients after modified Brostrom surgery is uncertain. Furthermore, specialized physiotherapy resources for CAI are limited in some regions, highlighting the need for effective digital home-based rehabilitation alternatives.

|
Article Thumbnail
mHealth for Wellness, Behavior Change and Prevention

Patients undergoing gastrectomy usually experience postgastrectomy syndrome and face difficulties adapting to a regular diet. Human health coaching via a mobile app has recently been applied to patients with chronic metabolic diseases, with significant improvements being observed in clinical outcomes.

|
Article Thumbnail
Usability of Apps and User Perceptions of mHealth

Digital microinterventions have strong potential to improve the lives of adults diagnosed with cancer. However, little is known about which types of digital microinterventions are most desired and how contextual factors may influence those preferences. This potentially limits guidance for personalized and timely digital microintervention delivery.

|
Article Thumbnail
Wearables and MHealth Reviews

Effective follow-up and management after organ transplantation are crucial for transplant recipients. Mobile health (mHealth) interventions have emerged as a significant approach for facilitating follow-up and management. However, there is a lack of systematic reviews and meta-analyses of their effectiveness.

|
Article Thumbnail
Usability of Apps and User Perceptions of mHealth

Medication nonadherence is a significant barrier to therapy success. Smartphone apps represent reasonable tools for simple adherence-enhancing interventions. Many adherence apps are available in app stores with diverse content, quality, and outputs. We define “output of an adherence app” as the processing and visualization of data recorded by the user and related to adherence. In 2016, Santo et al defined 5 desirable features in the output of adherence apps: tracking history, charts, statistics, rewards, and an exportable file. With this, a reference point to evaluate outputs of adherence apps was delivered. Identifying and fulfilling users’ needs are essential when developing an adherence app for patients’ self-management and professional adherence services, such as therapy support provided by health care professionals (HCPs).

|
Article Thumbnail
mHealth for Symptom and Disease Monitoring, Chronic Disease Management

Knee osteoarthritis (OA) is a prevalent cause of disability among the elderly, emphasizing the need for effective and accessible self-management strategies. Mobile application based personalized exercise programs predominantly overcome the barriers associated with traditional approaches.

|
Article Thumbnail
Usability of Apps and User Perceptions of mHealth

The adoption of mobile health (mHealth) technologies among older adults remains significantly lower than in younger populations, despite their potential to promote healthier lifestyles and mitigate age-related health risks.

|
Article Thumbnail
mHealth for Wellness, Behavior Change and Prevention

Many mobile health (mHealth) apps focus on promoting physical activity (PA) and healthy eating (HE). However, there is limited empirical evidence regarding their effectiveness in initiating and sustaining behavior change, particularly among children and adolescents. Considering that behavior is influenced by social contexts, it is essential to take core settings like family dynamics into account when designing mHealth apps.

|
Article Thumbnail
Quality Evaluation and Descriptive Analysis/Reviews of Multiple Existing Mobile Apps

Mobile visual acuity (VA) applications have emerged as valuable tools in both clinical and home settings, particularly in the context of expanding teleophthalmology. Despite the growing number of apps available to measure visual acuity, studies evaluating their overall quality, functionality, and clinical relevance are limited.

|
Article Thumbnail
mHealth for Wellness, Behavior Change and Prevention

Approximately 1 out of 5 pregnant women develops depression. Internet-based cognitive behavioral therapy (iCBT) is an effective way to treat not only depression but also mild depressive symptoms or subthreshold depression. While numerous iCBT programs have been developed and tested through randomized controlled trials for various mental health conditions and specific populations, research on their effectiveness and application in the real world remains limited.

|
Article Thumbnail
Fitness Trackers and Smart Pedometers/Accelerometers

Background: Latina adolescents report low levels of moderate-vigorous physical activity (MVPA) and high lifetime risk of lifestyle-related diseases. There is a lack of MVPA interventions targeted at this demographic despite documented health disparities. Given their high rates of using mobile technology, interventions delivered through mobile devices may be effective for this population.

|
Article Thumbnail
mHealth for Symptom and Disease Monitoring, Chronic Disease Management

Adverse events (AEs) related to cancer treatment represent a valuable source of information that can be used to adjust therapy for individual patients. The NIH developed the Common Terminology Criteria for Adverse Events (CTCAE), a comprehensive standardized terminology for healthcare providers to consistently report AEs during patient visits. mHealth technologies, in principle, also allow AEs to be self-reported by patients in-between visits; however, the terminology poses challenges for them, both in selecting the correct symptom to report and in rating its severity. NIH developed the Patient-Reported Outcomes (PRO)-CTACE as the patient-oriented companion of the CTCAE. However, it shows some weaknesses in completeness and precision when used for continuous home patient monitoring and for decision support.

|

Preprints Open for Peer-Review

We are working in partnership with