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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: Obesity is one of the largest drivers of healthcare spending, but nearly half of the population with obesity demonstrate suboptimal readiness for weight loss treatment. Black women are dis...
Background: Obesity is one of the largest drivers of healthcare spending, but nearly half of the population with obesity demonstrate suboptimal readiness for weight loss treatment. Black women are disproportionately likely to have both obesity and limited weight loss readiness. However, they have been shown to be receptive to strategies that prevent weight gain. Objective: This work evaluates the costs and cost-effectiveness of a digital weight gain prevention intervention (Shape) for Black women. Methods: A cost and cost-effectiveness analysis of Shape was conducted from the payer perspective. Costs included those of program delivery and were summarized by program element: self-monitoring, skills training, coaching, and administration. Effectiveness was measured in quality-adjusted life years (QALYs). The primary outcome was the incremental cost per QALY of Shape relative to usual care. Results: Shape cost an average of $758 per participant. The base-case model, in which quality of life benefits decay linearly to zero five years post intervention cessation, generated an incremental cost-effectiveness ratio (ICER) of $55,264/QALY. Probabilistic sensitivity analyses suggest an ICER below $50,000/QALY and $100,000/QALY in 39% and 98% of simulations, respectively. Results are highly sensitive to durability of benefits, rising to $165,730 if benefits end 6 months post intervention. Conclusions: Results suggest the Shape intervention is cost effective based on established benchmarks, indicating it can be part of a successful strategy to address the nation’s growing obesity epidemic in low-income at-risk communities. Clinical Trial: The trial was registered with the ClinicalTrials.gov database (NCT00938535)
Background: Our goal is to improve psychosocial and spiritual care outcomes for elderly patients with cancer by optimizing an intervention focused on dignity conservation tasks such as settling relati...
Background: Our goal is to improve psychosocial and spiritual care outcomes for elderly patients with cancer by optimizing an intervention focused on dignity conservation tasks such as settling relationships, sharing words of love, and preparing a legacy document. These tasks are central needs for elderly patients with cancer. Dignity Therapy (DT) has proven efficacy and can be led by nurses or chaplains, the two disciplines within palliative care that may be most available to provide DT. DT is well accepted by patients in studies, but not widely used; it remains unclear how best it can work in real life settings. Objective: We propose a randomized clinical trial whose aims are to: (1) compare usual palliative care for elderly patients with cancer and usual palliative care with DT groups for effects on: a) patient outcomes (dignity impact, existential tasks, and cancer prognosis awareness); and b) processes of delivering palliative spiritual care services (satisfaction and unmet spiritual needs); and (2) explore the influence of physical symptoms and spiritual distress on the dignity impact and existential tasks effects of usual palliative care and nurse- or chaplain-led DT. We hypothesize that, controlling for pretest scores, each of the DT groups will have higher scores on the dignity impact and existential tasks measures than the usual care group; each of the DT groups will have better peaceful awareness and treatment preference more consistent with their cancer prognosis than the usual care group. We also hypothesize that physical symptoms and spiritual distress will significantly affect intervention effects. Methods: We are conducting a 3-arm, pre/posttest, randomized, controlled 4-step, stepped-wedge design to compare the effects of usual outpatient palliative care and usual outpatient palliative care along with either nurse or chaplain led DT on patient outcomes (dignity impact, existential tasks, and cancer prognosis awareness). We will include 560 elderly patients with cancer from 6 outpatient palliative care services across the U.S. Using multilevel analysis with site, provider (nurse, chaplain), and time (step) included in the model, we will compare usual care and DT groups for effects on patient outcomes and spiritual care processes and determine the moderating effects of physical symptoms and spiritual distress Results: Results are expected in 4 years. Conclusions: This rigorous trial of DT will constitute a landmark step in palliative care and spiritual health services research. Clinical Trial: NCT03209440
Background: Search engines display helpline notices when people query for suicide-related information. Objective: Here we aim to examine if these notices and other information displayed in response to...
Background: Search engines display helpline notices when people query for suicide-related information. Objective: Here we aim to examine if these notices and other information displayed in response to suicide-related queries are correlated with subsequent searches for suicide prevention rather than harmful information. Methods: Anonymous suicide-related searches made to Bing and Google in the United States, the United Kingdom, Hong Kong, and Taiwan during ten months were extracted. Descriptive analyses and regression models were fit to the data to assess the correlation with observed behaviours. Results: Display of helpline notices was not associated with an observed change in click behaviour or future searches. Pages with higher rank, being neutral to suicide, and those shown among more anti-suicide pages were more likely to be clicked on. Having more anti-suicide webpages displayed was the only factor associated with further searches for suicide prevention information. Conclusions: Helpline notices are not associated with harm. If they cause positive change, it is small. This is possibly due to the variability in intent of users seeking suicide-related information. Nonetheless, helpline notice should be displayed but more efforts should be made to improve the visibility and ranking of suicide prevention webpages.
Background: Worldwide, the burden of chronic diseases is growing, necessitating novel approaches that complement and go beyond evidence-based medicine. In this respect a promising avenue is the second...
Background: Worldwide, the burden of chronic diseases is growing, necessitating novel approaches that complement and go beyond evidence-based medicine. In this respect a promising avenue is the secondary use of Electronic Health Records (EHR) data, where clinical data are analysed to conduct basic and clinical and translational research. Methods based on machine learning algorithms to process EHR are resulting in improved understanding of patients’ clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, wealth of patients’ clinical history remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on development of Natural Language Processing (NLP) methods to automatically transform clinical text into structured clinical data that can be directly processed using machine learning algorithms. Objective: To provide a comprehensive overview of the development and uptake on NLP methods applied to free-text clinical notes related to chronic diseases, including investigation of challenges faced by NLP methodologies in understanding clinical narratives. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed and searches were conducted in 5 databases using “clinical notes”, “natural language processing” and “chronic disease” as keywords as well as their variations to maximise coverage of the articles. Results: Of the 2646 articles considered, 100 met the inclusion criteria. Review of the included papers resulted in identification of 42 chronic diseases, which were then further classified into 10 diseases categories using ICD-10. Majority of the studies focused on diseases of circulatory system (N=38) while endocrine and metabolic diseases were fewest (N=12). This was due to the structure of clinical records related to metabolic diseases that typically contain much more structured data than medical records for diseases of circulatory system, which focus more on unstructured data and consequently have seen a stronger focus of NLP. The review has shown that there is a significant increase in the use of machine learning methods compared to rule-based approaches, however deep learning methods remain emergent (N=3). Consequently, majority of works focus on classification of disease phenotype, while only a handful of papers concern the extraction of comorbidities from the free-text or the integration of clinical notes with structured data. There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods. Finally, scarcity of publicly available data may also have contributed to insufficient development of more advanced methods, such as extraction of word embeddings from clinical notes. Conclusions: Efforts are needed to improve (1) progression of clinical NLP methods from extraction towards understanding; (2) recognition of relations among entities, rather than entities in isolation; (3) temporal extraction to understand past, current and future clinical events; (4) exploitation of alternative sources of clinical knowledge; and (5) availability of large-scale, de-identified clinical corpora.
Background: The onset of mental health problems peaks between adolescence and young adulthood, however young people face barriers to treatment and are often reluctant to seek professional help. Many a...
Background: The onset of mental health problems peaks between adolescence and young adulthood, however young people face barriers to treatment and are often reluctant to seek professional help. Many are instead seeking support and information regarding their mental health online, especially via social networking sites (SNSs), so there is a promising opportunity to use SNSs to deliver or integrate with youth-focused online mental health interventions. Previous reviews have evaluated the effectiveness of SNSs for specific disorders in young people, however none to date have covered the breadth of SNS-based youth mental health interventions available across all mental health issues. Objective: The aim of this review was to systematically identify available evidence regarding the use of SNS-based interventions to support the mental health of young people, in order to evaluate their effectiveness, suitability and safety, and identify gaps and opportunities for future research. Methods: The PubMed and PsycInfo databases were searched using Medical Subject Headings (MeSH) terms and exploded keywords and phrases. Retrieved abstracts (n=974) were double screened, yielding 235 articles for screening at the full-text level. Of these, nine articles met the review inclusion criteria. Given the small number of studies, their exploratory nature and the variety of outcome measures used, a quantitative meta-analysis was not possible. Results: The nine articles (quantitative studies, qualitative studies and descriptions of the iterative design process) covered five separate interventions. Two interventions used purpose-built platforms based on the Moderated Online Social Therapy (MOST) model, two used Facebook, and one evaluated a purpose-built mobile app. The two MOST interventions targeted specific mental health issues (depression and psychosis), while the others focused on improving mental health literacy, social support and general wellbeing. Only three quantitative studies were identified, and all used a pre-post design (without a control group) to establish ‘proof of concept’. Of the outcome variables assessed, there were significant improvements in mental health knowledge and number of depressive symptoms, but no improvement in anxiety or psychosis symptoms. Acceptability of and engagement with the SNS platforms was generally high, as were perceptions of usefulness and safety, with no adverse incidents reported. Moderation by clinical experts was identified as a key component of the more successful interventions. When offered a choice, users showed a preference for mobile apps over web-based interfaces. Conclusions: The evidence reviewed suggests young people find SNS-based interventions highly usable, engaging and supportive. However, future studies need to address the current lack of high-quality evidence for their efficacy in reducing mental health symptoms. Given that young people are already turning to SNSs to engage in knowledge-seeking and peer-to-peer support, SNS-based youth mental health interventions provide an opportunity to address some of the barriers young people face in accessing qualified mental health support and information.
Background: Attention deficit hyperactivity disorder is a neurobehavioral disease that makes children who suffer from it display behaviors of inattention, hyperactivity or impulsivity, thus affecting...
Background: Attention deficit hyperactivity disorder is a neurobehavioral disease that makes children who suffer from it display behaviors of inattention, hyperactivity or impulsivity, thus affecting their ability to learn and establish proper family and social relationships. Various tools are currently used by child and adolescent psychiatric clinics to diagnose, evaluate, and collect information and data that then allows professional physicians to assess if patients need further treatment, following a thorough and careful clinical diagnosis process. Objective: The scientific advancements in the domain of brainwaves, e.g. electroencephalography, have been perfecting, both portable and wearable equipment have gradually been developed with the hope of determining relevant indicators of brainwave parameters in ADHD children. Methods: The sample size consists of a total of 63 subjects, 40 males and 9 females in the experimental group; while 5 males and 9 females in the control group. The brainwave sensor and wristwatch sensor were used in the experimental process, which was divided into three stages: pre_test, in_test, and post_test, with a testing interval of 20 minutes each. We use t-test and correlation analysis to investigate indicators of the disorder children. Results: The results show the disorder is significant negative correlation with brainwaves (p<0.05), the higher the concentration of brainwave activity, the less likely that the disorder is present; while, it is significant positive correlation with the activity amounts (p<0.01), the higher the amount of activity, the greater the probability that the patient will have the disorder. Two groups achieved significant differences in the independent t-test (p<0.05), indicating that the amount of activity detected by the wristwatch sensor is capable of identifying the disorder. Finally, the amount of mean activity only in in_test stage is significant differences from pre_test and post_test stages. Conclusions: The results show that when the subjects are stimulated under restricted conditions, the disorder subjects will present with different amount of activity over the unrestricted condition due to patients’ inability to exercise control over their own concentration. Further studies are required in the future to determine whether the wristwatch sensor can be used to detect the amount of activity and help physicians diagnose the disorder in order to develop more objective, rapid auxiliary diagnostic tools.