JMIR mHealth and uHealth
Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.
JMIR mhealth and uhealth (mobile and ubiquitous health) (JMU, ISSN 2291-5222) is a new spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2015: 4.532). JMIR mHealth and uHealth has a projected impact factor (2015) of about 2.03. The journal focusses 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/developmental than what would be published in the Journal of Medical Internet Research.
In addition to peer-reviewing paper submissions by researchers, JMIR mHealth and uHealth offers peer-review of medical apps itself (developers can submit an app for peer-review here).
JMIR mHealth and uHealth 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.
Sep 22, 2016
Sep 21, 2016
Sep 19, 2016
Sep 16, 2016
Sep 15, 2016
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Sep 1, 2016
Aug 31, 2016
Aug 23, 2016
Aug 22, 2016
Aug 19, 2016
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Latest Submissions Open for Peer-Review:View All Open Peer Review Articles
Perceptions of Mobile Health Technology of Midlife Adults with Chronic Conditions
Date Submitted: Sep 23, 2016
Open Peer Review Period: Sep 24, 2016 - Nov 19, 2016
Background: The growth in mobile health (mHealth) technology is intersecting the demographic shift to an aging society. This presents unprecedented opportunity to maximize healthy aging. Regular physi...
A call to digital health practitioners: New guidelines can help improve the quality of digital health evidence
Date Submitted: Sep 10, 2016
Open Peer Review Period: Sep 12, 2016 - Nov 7, 2016
In the recent years, there has been rapid increase in the number of mobile phone supported health interventions and accompanying literature assessing the efficacy of these interventions. The quality o...
In the recent years, there has been rapid increase in the number of mobile phone supported health interventions and accompanying literature assessing the efficacy of these interventions. The quality of reporting of this evidence has been largely variable. Though the field has expanded in its scope and scale, the quality of the supporting evidence is still in its infancy. The mHealth Evidence Reporting and Assessment (mERA) checklist, led by the WHO, and developed in partnership with several institutions, aims to standardize the quality of mHealth evidence reporting. mERA was published as an original manuscript in the British Medical Journal in March 2016. The attached commentary provides a brief overview of the checklist, with a call to the digital help community to use the checklist in the reporting of evidence on digital health interventions.
Remote Monitoring of Hypertension Diseases in Pregnancy
Date Submitted: Aug 26, 2016
Open Peer Review Period: Aug 30, 2016 - Oct 25, 2016
Background: Although remote monitoring has proven its added value in various healthcare domains, little is known about the remote follow-up of pregnant women diagnosed with a gestational hypertensive...
Background: Although remote monitoring has proven its added value in various healthcare domains, little is known about the remote follow-up of pregnant women diagnosed with a gestational hypertensive disorder (GHD). Objective: To evaluate the added value of a remote follow-up program for pregnant women diagnosed with GHD. Methods: A one year retrospective study was performed in the outpatient clinic of a 2nd level prenatal center where pregnant women with GHD received remote monitoring (RM) or conventional care (CC). Study endpoints include number of prenatal visits and admissions to the prenatal observation ward, gestational outcome, mode of delivery, neonatal outcome and admission to neonatal intensive care (NIC). Differences in continuous and categorical variables were tested using Student’s two sampled t-test and the χ² test, respectively, at nominal level α = 0.05. Results: Of 166 patients diagnosed with GHD, 53 received RM and 113 CC. After excluding 9 patients in the RM group and 15 in de CC group because of missing data, 44 patients in RM group and 98 in CC group were taken into final analysis. Both groups had similar demographics. The RM group had more women diagnosed with gestational hypertension but less with pre-eclampsia when compared with CC (79.55% versus 42.86% and 15.91% versus 43.88%). The RM group had less hospital and NIC admissions together with less hospital stay until delivery when compared with CC (31.81% versus 74.47%; 11.36% versus 31.63% and 18.18% versus 64.24%). A spontaneous start of the birth process was more likely and less inductions occurred in RM than in CC (56.81% versus 31.63% and 27.27% versus 48.98%). Conclusions: A RM follow – up of women with GHD is a promising tool in the obstetrician care. It opens the perspectives to reverse the current evolution of antenatal interventions leading to more interventions and as such to ever increasing medicalized antenatal care.
Analyzing mHealth: Joint Models for Intensively Collected User Engagement Data
Date Submitted: Aug 19, 2016
Open Peer Review Period: Aug 26, 2016 - Oct 21, 2016
Background: Evaluating engagement with an intervention is a key component of understanding its efficacy. With an increasing interest in developing behavioral interventions in the mobile health (mHealt...
Background: Evaluating engagement with an intervention is a key component of understanding its efficacy. With an increasing interest in developing behavioral interventions in the mobile health (mHealth) space, appropriate methods for evaluating engagement in the mHealth context is necessary. Data collected to evaluate mHealth interventions are often collected much more frequently than those for clinic-based interventions. Additionally, missing data on engagement is closely linked to level of engagement resulting in the potential for informative missingness. Thus, models that can accommodate intensively collected data and can account for informative missingness are required for unbiased inference when analyzing engagement with an mHealth intervention. Objective: The objective of this paper is to demonstrate the utility of a joint modeling approach to longitudinal engagement data in mHealth research. Methods: Engagement data from an evaluation of an mHealth intervention designed to support illness management among people with schizophrenia is analyzed. A joint model is applied to the longitudinal engagement outcome and time-to-dropout to allow unbiased inference on the engagement outcome. Results are compared to separate naïve models that do not account for the relationship between drop-out and engagement. Results: The joint model shows a strong relationship between engagement and reduced risk of dropout. Using the mHealth app one day more per week was associated with a 33% decreased risk of dropout (P<.001). The decline in engagement over time was steeper when the joint model was used in comparison with the naïve model. Conclusions: Naïve longitudinal models that do not account for informative missingness in mHealth data produce biased results. Joint models are appropriate for modeling intensively collected engagement outcomes in mHealth intervention research. Clinical Trial: Trial Registration: ClinicalTrials.gov NCT02364544.