JMIR mHealth and uHealth

Editor-in-Chief:

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


Impact Factor 4.31

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 is indexed in PubMed, PubMed Central, MEDLINE and Science Citation Index Expanded (SCIE), and in June 2020 received an Impact Factor of 4.31.

The journal focuses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. JMIR mHealth and uHealth publishes even faster and has a broader scope with including papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research. JMIR mHealth and uHealth journal features a rapid and thorough peer-review process, professional copyediting, professional production of PDF, XHTML, and XML proofs. JMIR mHealth and uHealth adheres to the same quality standards as JMIR and all articles published here are also cross-listed in the Table of Contents of JMIR, the worlds' leading medical journal in health sciences / health services research and health informatics (http://www.jmir.org/issue/current).

We are looking for papers covering for example the following themes:

  • Ubiquitous Health (uHealth)
  • mHealth for Data Collection and Research
  • Usability and user perceptions of mHealth
  • mHealth in the Developing World and for Global Health
  • mHealth in a Clinical Setting
  • mHealth for Symptom and Disease Monitoring
  • mHealth for Wellness, Behavior Change and Prevention
  • mHealth for Screening
  • Text-messaging (SMS)-Based Interventions
  • Design and Formative Evaluation of Mobile Apps
  • Security and Privacy of mHealth and uHealth
  • Quality Evaluation and Descriptive Analysis of Multiple Existing Mobile Apps
  • mHealth for Treatment Adherence
  • Use and User Demographics of mHealth
  • mHealth for Telemedicine and Homecare
  • mHealth for Patient Education
  • mHealth in Medical Education and Training
  • Evaluation and Research Methodology for mHealth
  • Wearable Devices and Sensors
  • Fitness Trackers and Smart Pedometers/Accelerometers
  • Google Glass and Augmented Reality Applications
  • Product Reviews and Tutorials in mHealth

Recent Articles

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

Mobile health applications have been developed to support diabetes self-management, but their effectiveness could depend on patient engagement. Therefore, patient engagement must be examined through multifactorial tailored behavioral interventions from an individual perspective.

|
Article Thumbnail
Theme Issue 2020-2021: Apps for COVID-19 (#Apps4Covid)

COVID-19 has significantly altered health care delivery, requiring clinicians and hospitals to adapt to rapidly changing hospital policies and social distancing guidelines. At our large academic medical center, clinicians reported that existing information on distribution channels, including emails and hospital intranet posts, was inadequate to keep everyone abreast with these changes. To address these challenges, we adapted a mobile app developed in-house to communicate critical changes in hospital policies and enable direct telephonic communication between clinical team members and hospitalized patients, to support social distancing guidelines and remote rounding.

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

Clinical evaluation of a pressure ulcer is based on quantitative and qualitative evaluation. In clinical practice, acetate tracing is the standard technique used to measure wound surface area; however, it is difficult to use in daily practice (because of material availability, data storage issues, and time needed to calculate the surface area). Planimetry techniques developed with mobile health (mHealth) apps can be used to overcome these difficulties.

|
Article Thumbnail
mHealth for Wellness, Behavior Change and Prevention

The growing epidemic of opioid use disorder (OUD) and associated injection drug use has resulted in a surge of new hepatitis C virus (HCV) infections. Approximately half of the people with HCV infection are unaware of their HCV status. Improving HCV awareness and increasing screening among people with OUD are critical. Addiction-Comprehensive Health Enhancement Support System (A-CHESS) is an evidence-based, smartphone-delivered relapse prevention system that has been implemented among people with OUD who are receiving medications for addiction treatment (MAT) to improve long-term recovery.

|
Article Thumbnail
mHealth in the Developing World/LMICs, Underserved Communities, and for Global Health

Lack of trained health care workers and nonadherence to national guidelines are key barriers to achieving high-quality newborn care in health care facilities in low- and middle-income countries. Traditional didactic approaches addressing these barriers fail to account for high staff turnover rates and result in temporary behavior change. NoviGuide, a clinical decision support software designed to standardize neonatal care through point-of-care assessments, has the potential to align bedside practice to national guidelines in settings lacking subspecialty neonatal providers.

|
Article Thumbnail
mHealth for Rehabilitation

Parkinson disease (PD) is a common movement disorder. Patients with PD have multiple gait impairments that result in an increased risk of falls and diminished quality of life. Therefore, gait measurement is important for the management of PD.

|
Article Thumbnail
mHealth for Wellness, Behavior Change and Prevention

Technology has provided a new way of life for the adolescent population. Indeed, strategies aimed at improving health-related behaviors through digital platforms can offer promising results. However, since it has been shown that peers are capable of modifying behaviors related to food and physical exercise, it is important to study whether digital interventions based on peer influence are capable of improving the weight status of adolescents.

|
Article Thumbnail
Evaluation and Research Methodology for mHealth

There is certain evidence on the efficacy of smartphone-based mental health interventions. However, the mechanisms of action remain unclear. Placebo effects contribute to the efficacy of face-to-face mental health interventions and may also be a potential mechanism of action in smartphone-based interventions.

|
Article Thumbnail
mHealth for Wellness, Behavior Change and Prevention

A significant proportion of cancer survivors have overweight or obesity. Although this has negative implications for health, weight management is not a standard component of oncology aftercare. Mobile health (mHealth) technology, in combination with behavior change techniques (BCTs), has the potential to support positive lifestyle changes. Few studies have been carried out with cancer survivors; therefore, the acceptability of these tools and techniques requires further investigation.

|
Article Thumbnail
Reviews

Globally, the number of HIV cases continue to increase, despite the development of multiple prevention strategies. New cases of HIV have been reported disproportionately more in men who have sex with men and other vulnerable populations. Issues such as internalized and structural homophobia prevent these men from accessing prevention strategies such as postexposure prophylaxis (PEP). Mobile health (mHealth) interventions are known to be one of the newest and preferred options to enhance PEP knowledge and access.

|
Article Thumbnail
mHealth for Data Collection and Research

Given the established links between an individual’s behaviors and lifestyle factors and potentially adverse health outcomes, univariate or simple multivariate health metrics and scores have been developed to quantify general health at a given point in time and estimate risk of negative future outcomes. However, these health metrics may be challenging for widespread use and are unlikely to be successful at capturing the broader determinants of health in the general population. Hence, there is a need for a multidimensional yet widely employable and accessible way to obtain a comprehensive health metric.

|
Article Thumbnail
Reviews

Peripheral artery disease (PAD) affects over 236 million people worldwide, and exercise interventions are commonly used to alleviate symptoms of this condition. However, no previous systematic review has evaluated the effects of mobile health (mHealth)–based exercise interventions for patients with PAD.

|

Preprints Open for Peer-Review

There are no preprints available for open peer-review at this time. Please check back later.

We are working in partnership with