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The leading peer-reviewed journal for digital medicine, and health & healthcare in the Internet age
The Journal of Medical Internet Research (JMIR), now in its 20th year, is the pioneer open access eHealth journal and is the flagship journal of JMIR Publications. It is the leading digital health journal globally in terms of quality/visibility (Impact Factor 2017: 4.671, ranked #1 out of 22 journals) and in terms of size (number of papers published). The journal focuses on emerging technologies, medical devices, apps, engineering, and informatics applications for patient education, prevention, population health and clinical care. As leading high-impact journal in its' disciplines (health informatics and health services research), it is selective, but it is now complemented by almost 30 specialty JMIR sister journals, which have a broader scope. Peer-review reports are portable across JMIR journals and papers can be transferred, so authors save time by not having to resubmit a paper to different journals.
As open access journal, we are read by clinicians, allied health professionals, informal caregivers, and patients alike, and have (as all JMIR journals) a focus on readable and applied science reporting the design and evaluation of health innovations and emerging technologies. We publish original research, viewpoints, and reviews (both literature reviews and medical device/technology/app reviews).
We are also a leader in participatory and open science approaches, and offer the option to publish new submissions immediately as preprints, which receive DOIs for immediate citation (eg, in grant proposals), and for open peer-review purposes. We also invite patients to participate (eg, as peer-reviewers) and have patient representatives on editorial boards.
Be a widely cited leader in the digitial health revolution and submit your paper today!
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Background: Clinical trials are key to advancing evidence-based medical research. The medical research literature has identified the impact and risks of publication bias in clinical trials. Selective...
Background: Clinical trials are key to advancing evidence-based medical research. The medical research literature has identified the impact and risks of publication bias in clinical trials. Selective publication for positive outcomes or non-publication of negative results could lead to misdirect subsequent research, justify further research, and result in literature reviews lean towards positive outcomes. Digital health randomized clinical trials face specific challenges, including high attrition rate, usability issues, and insufficient prior formative research. These challenges may become contributing factors to non-publication of trials results. To our knowledge, there exists no study that has analyzed and reported the characteristics of non-publication rates within the domain of digital health trials. Objective: The primary research objective was to examine the prevalence and characteristics of non-published digital health randomized clinical trials, including eHealth, mHealth and telehealth clinical trials, registered in ClinicalTrials.gov Methods: To identify digital health trials, a list of 47 search terms and phrases was developed through an iterative process and applied to the “Title”, “Interventions” and “Outcome Measures” fields of registered clinical trials with completion dates between April 1st, 2010 and April 1st, 2013. The search was based on the full dataset exported from the ClinlicalTrials.gov database with 265,657 registered clinical trials entries downloaded on February 10th, 2018, to allow for up to nearly 5 years for the publication of the study after trial completion. To identify publications related to the results of the trials, we extracted the complete registered randomized clinical trials content from the ClinicalTrials.gov website in XML format and identified relevant publications through a comprehensive approach that included an automated as well as a manual publication identification process. Results: In total, 6717 articles matched the priori search terms and phrases, of which 803 trials matched our latest completion date criteria. 556 randomized trials were included in this study after screening. We found that 150 (27%) of all included trials remain unpublished five years after the trials’ completion data. In bivariate analyses, statistically significant differences in trial characteristics between published and unpublished trials were found for the intervention target condition (cancer having the largest non-publication rate at 45%, while addiction/smoking cessation trials having the lowest non-publication rate at 16%), country (US at 33% vs non US at 18%), trial size (small trials at 52%, larger trials at 30%), clinical trial phases, and recruitment. In multivariate analyses, trial characteristics differences between published and unpublished trials remained statistically significant for the intervention target condition, country, trial size, trial phases and recruitment, with the odds of publication for non-US based trials being significant and 2.8 (CI:1.690-4.758) times more likely to be published compared to the reference group of the US based trials. Conclusions: In the realm of digital health, non-publication of registered clinical trials results is prevalent at 27%, which is lower than published non-publication rates in other fields. There are substantial differences in publication rates between US versus non-US based trials and whether, or not, the trials were funded by industry sponsors. Further research is required to define further determinants and reasons for non-publication, and more importantly to articulate the impact and risk of publication bias in the field of digital health clinical trials.
Background: Obesity is an endemic problem with significant health and financial consequences. Text messaging has been shown to be a simple and effective method of facilitating weight reduction. Additi...
Background: Obesity is an endemic problem with significant health and financial consequences. Text messaging has been shown to be a simple and effective method of facilitating weight reduction. Additionally, waist-to-hip ratio has emerged as a significant anthropometric measure. However, few studies examined the effect of serial anthropometric self-measurement combined with text messaging. Objective: The primary aim was to assess whether an eight-week program, consisting of weekly serial self-measurements of waist and hip circumference, combined with motivational text messages, could reduce waist-to-hip ratio (WHR) among Australian workers. Methods: This was a community-based, participant-blinded, staggered-entry, parallel group study. Adult workers with access to mobile phones were eligible and recruited through an open access online survey. A balanced, block randomisation was used to assign participants to receive intervention or control messages for eight weeks. Outcome data was self-assessed through an online survey. Results: Sixty participants were randomised with 30 participants allocated to each a control and an intervention group. There was no significant change in WHR (P= .43) and all secondary outcome measures did not differ between the intervention group and control group at the end of the eight-week intervention. Both groups, however, showed a significant decrease in burnout over time (mean (SE): pre 4.80 (0.39) vs. post 3.36 (0.46) P=.004). The intervention uptake followed a downward trend. Peak participant replies to weekly self-measurements were received in week three (14/23 (61%)), and the least in week eight (8/23 (35%)). No harms were found to be resulting from this study. Conclusions: This study is an innovative pilot trial using text messaging and serial anthropometric measurements in weight management. No change was detected in waist-to-hip ratios in Australian workers over 8 weeks, therefore it was unable to be concluded that the intervention affected the primary outcome. However, these results should be interpreted in the context of limited sample size and decreasing intervention uptake over the course of the study. This pilot trial is useful for informing and contributing to the design of future studies and the growing body of literature on serial self-measurements combined with text messaging. Clinical Trial: The trial is registered at ANZCTR.org.au, number: ACTRN12616001496404.
Background: With the accessibility and widespread use of mobile phones, smartphone apps targeting medication adherence may be useful tools to help patients take medications as prescribed. Objective: O...
Background: With the accessibility and widespread use of mobile phones, smartphone apps targeting medication adherence may be useful tools to help patients take medications as prescribed. Objective: Our objectives were to: 1) characterize and assess smartphone medication adherence apps guided by a conceptual framework on the focus of adherence interventions; and 2) conduct a content analysis of online reviews to explore users’ perspectives and experiences with smartphone medication adherence apps. Methods: We searched for smartphone medication adherence apps using keyword searches in Apple and Android operating systems. We characterized all apps in terms of number of downloads, ratings, languages, cost, and disease target. We categorized apps according to four key features of: 1) alerting to take medication; 2) tracking medication taking; 3) reminding to refill/indicating amount of medication left; and 4) storing medication information. We then selected representative apps from each operating system for detailed quality assessment and user testing. We also downloaded online reviews for these selected apps and conducted a qualitative content analysis using an inductive approach involving steps of initial open coding, construction of categories, and abstraction into themes. Results: We identified 704 apps (443 from Apple and 261 from Android), the majority of which having one (37.2% of Apple, 41.4% of Android) or two (38.1% of Apple, 31.4% of Android) features. Quality assessment and user testing of 20 selected apps revealed apps varied in quality and commonly focus on behavioural strategies to enhance medication adherence through alerts, reminders, and logs. A total of 1,323 eligible online reviews from these 20 selected apps were analyzed and the following themes emerged: 1) features and functions appreciated by users, which included the ability to set-up customized medication regimen details and reminders, monitor other health information (e.g. vitals, supplements, manage multiple people/pets), support health care visits (e.g. having a list of medications and necessary health information in one app); 2) negative user experiences which captured technical difficulties (glitches, confusing app navigation, poor interoperability), dosage schedule and reminder setup inflexibility; and 3) desired functions and features related to optimization of information input, improvement of reminders and upgrading app performance (better synchronization/backup of data and interoperability). Conclusions: A tremendous amount of smartphone medication adherence apps are currently available. The majority of apps have features representing a behavioural approach to intervention. Findings of the content analysis offer mostly positive feedback as well as insight into current limitations and improvements that could be addressed in current and future medication adherence apps.
Background: There are various complex reasons that influence sustainable adoption of innovations in healthcare systems. Low adoption can be caused by a lack of support from one or more stakeholders an...
Background: There are various complex reasons that influence sustainable adoption of innovations in healthcare systems. Low adoption can be caused by a lack of support from one or more stakeholders and their needs and expectations are not always considered or aligned. Objective: To identify stakeholders’ perceptions on barriers and facilitators towards the sustainable adoption of digital health innovations. Methods: A stakeholder workshop was attended by twelve participants with a range of backgrounds on 25th August 2017, including people representing the views from patients, carers, local hospitals, pharmacy retailers, health insurers, health services researchers, engineers, and technology and pharmaceutical companies in Switzerland. Based on adoption of innovation frameworks, we asked participants to interview each other about three factors influencing the adoption of digitally-delivered health interventions: 1) facilitators and barriers in the external system; 2) needs and expectations of stakeholders; and 3) safety, quality, and usability of innovations. The worksheets and videos generated from the workshop were qualitatively analyzed and summarized. Results: Facilitators for adoption mentioned were high levels of income and education, and digital health being a high priority to stakeholders. Main common interests of different stakeholders were patient satisfaction and job protection. Healthcare spending was a misaligned interest; whilst some stakeholders were keen on spending more to obtain or provide the highest quality of care, others were focused on reducing healthcare spending to provide cost-effective services. Switzerland’s diversity and complexity in terms of the organisation with 26 cantons (administrative divisions) were barriers as this made it harder to ensure interoperability of interventions. A culture of innovation was considered a push factor, but adoption was inhibited by persistent paper-based systems, a fear of change, and unwillingness to share data. The sustainability of interventions can be promoted by making them patient-centered, meaning that patients should be involved throughout their development. Conclusions: Promoting sustainable adoption of digital health remains challenging despite various push factors being in place. Barriers related to fragmentation, patient-centeredness, data security, privacy, trust, and job security need to be addressed. A strength is that people from a wide range of backgrounds attended the workshop. A limitation is that the findings are focused on the macro level. In-depth case studies of specific issues need to be conducted in different settings. Clinical Trial: NA
Background: The health burden of type 2 diabetes can be mitigated by engaging in two key aspects of diabetes care: self-management and regular contact with health professionals. There is a clear benef...
Background: The health burden of type 2 diabetes can be mitigated by engaging in two key aspects of diabetes care: self-management and regular contact with health professionals. There is a clear benefit to integrating these two aspects of care into a single clinical tool, and as smartphone ownership increases the ‘app’ becomes a more feasible platform. However, the effectiveness of online health interventions is contingent on uptake by health care providers, which is typically low. There has been little research that focuses specifically on barriers and facilitators to health care provider uptake for interventions that link self-management apps to the user’s primary care physician (PCP). Objective: This study aimed to explore PCPs’ attitudes towards a proposed self-management app for patients with diabetes that would link to primary care services. Methods: 25 semi-structured interviews explored PCP attitudes towards a proposed diabetes app. The interview schedule discussed potential features that would link in with the patient’s primary care services. Interviews were audio-recorded, transcribed and coded using Framework Analysis to ensure rigor. Results: Our analysis indicated that PCP attitudes towards the app were underpinned by perceived roles of: (1) diabetes self-management; (2) face-to-face care; and (3) the anticipated burden of new technologies in their practice. Theme 1 explored PCPs’ perceptions about how an app could foster patient independence for self-management behaviours, but could also increase responsibility and liability for the PCP. Theme 2 identified beliefs underpinning a commonly expressed preference for face-to-face care. PCPs perceived information was more motivating, better understood, and presented with greater empathy when delivered face-to-face, rather than online. Theme 3 described how most PCPs anticipated an initial increase in workload whilst learning to use a new clinical tool. Some PCPs accepted this burden on the basis that the change was inevitable as healthcare became more integrated. Others reported potential benefits were outweighed by effort to implement the app. This study also identified how app features can be positively framed, highlighting potential benefits for PCPs to maximise PCP engagement, buy-in and uptake. For example, PCPs were more positive when they perceived that an app could facilitate communication and motivation between consultations, would focus on building capacity for patient independence, and would reinforce rather than replace in-person care. They were also more positive about app features that were automated, integrated with existing software, flexible for different patients and included secondary benefits such as improved documentation. Conclusions: This study provided insight into PCP attitudes towards a diabetes app integrated with primary care services. This was observed as more than a technological change; PCPs were concerned about changes in workload, their role in self-management, and the nature of consultations. This research highlighted potential facilitators and barriers to engaging PCPs in the implementation process.
Background: State-of-the-art classifiers based on convolutional neural networks (CNNs) generally outperform the diagnosis of dermatologists and could enable life-saving and fast diagnoses, even outsid...
Background: State-of-the-art classifiers based on convolutional neural networks (CNNs) generally outperform the diagnosis of dermatologists and could enable life-saving and fast diagnoses, even outside the hospital via installation on mobile devices. To our knowledge, at present, there is no review of the current work in this research area. Objective: This study presents the first systematic review of the state-of-the-art research on classifying skin lesions with CNNs. We limit our review to skin lesion classifiers. In particular, methods that apply a CNN only for segmentation or for the classification of dermoscopic patterns are not considered here. Furthermore, this study discusses why the comparability of the presented procedures is very difficult and which challenges must be addressed in the future. Methods: We searched the Google Scholar, PubMed, Medline, Science Direct, and Web of Science databases for systematic reviews and original research articles published in English. Only papers that reported sufficient scientific proceedings are included in this review. Results: We found 13 papers that classified skin lesions using CNNs. In principle, classification methods can be differentiated according to three principles. Approaches that use a CNN already trained by means of another large data set and then optimize its parameters to the classification of skin lesions are both the most common methods as well as display the best performance with the currently available limited data sets. Conclusions: CNNs display a high performance as state-of-the-art skin lesion classifiers. Unfortunately, it is difficult to compare different classification methods because some approaches use non-public data sets for training and/or testing, thereby making reproducibility difficult.