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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: All organisms on Earth have their own circadian rhythm, and humans are no exception. Circadian rhythms are associated with various human states, especially mood disorders and disturbance o...
Background: All organisms on Earth have their own circadian rhythm, and humans are no exception. Circadian rhythms are associated with various human states, especially mood disorders and disturbance of circadian rhythm is known to be very closely related. Attempts have also been made to derive clinical implications associated with mood disorders using the acquiring vast amounts of digital log as digital technologies develop and using computational analysis techniques. Objective: The present study was conducted to evaluate the mood state/episode, activity, sleep, light exposure, and heart rate during a period of about two years by acquiring various digital log data through wearable devices and smartphone applications as well as conventional clinical assessments. We investigated a mood prediction algorithm developed with machine learning using passive data phenotypes based on circadian rhythms. Methods: We performed a prospective observational cohort study on sixty patients with mood disorders (major depressive disorder, bipolar disorder type 1 and 2; MDD, BD I, and BD II, respectively) for two years. A smartphone application for self-recording daily mood scores and detecting light exposure (using installed sensor) were provided. From daily worn activity trackers, digital log data of activity, sleep, and heart rate were collected. Passive digital phenotypes were processed into 130 features based on circadian rhythms, and a mood prediction algorithm was developed by random forest. Results: The mood state prediction accuracies in all patients, MDD, BD I, and BD II were 76, 78, 76, and 79%, with 0.83, 0.84, 0.84, and 0.81 area under the curve (AUC) values, respectively. The accuracies of all patients for no episode (NE), depressive episode (DE), manic episode (ME) and hypomanic episode (HME) were 91.3, 91.2, 99.3, and 98.2%, with 0.972, 0.965, 1, and 0.999 of AUCs, respectively. The prediction accuracy in BD II patients was distinctively balanced high showing 92.1, 93.1, and 96.8% of the accuracies, with 0.975, 0.98, and 0.997 of the AUCs for NE, DE, and HME, respectively. Conclusions: Based on the theoretical basis of chronobiology, this study proposed a good model of future research by developing a mood prediction algorithm using machine learning by processing and reclassifying digital log data. In addition to academic value, it is expected that this study will be of practical help to improve the prognosis of patients with mood disorder by making it possible to apply actual clinical application due to rapid expansion of digital technology. Clinical Trial: ClinicalTrials.gov: NCT03088657
Background: Data visualization experts have identified core principles to follow when creating visual displays of data that facilitate comprehension. Such principles can be applied to creating effecti...
Background: Data visualization experts have identified core principles to follow when creating visual displays of data that facilitate comprehension. Such principles can be applied to creating effective reports for clinicians that display compliance with quality improvement protocols. A basic tenet of implementation science is continuous monitoring and feedback. Applying best practices for data visualization to reports for clinicians can catalyze implementation and sustainment of new protocols. Objective: To apply best practices for data visualization to create reports that clinicians find clear and useful. Methods: Using an evidence-based fall prevention program, Fall TIPS (Tailoring Interventions for Patient Safety), we created a report showing program compliance. First, we conducted a systematic literature review to identify best practices for data visualization. We applied these findings to a monthly report displaying compliance with the Fall TIPS protocol. We refined the Fall TIPS Monthly Report (FTMR) based on feedback collected via a questionnaire we developed. This questionnaire was based on the requirements for effective data display suggested by expert Stephen Few. We then evaluated usability of the FTMR using a 15-item Health Information Technology Usability Evaluation Scale (Health-ITUES). Items were rated on a 5-point Likert scale from strongly disagree (1) to strongly agree (5). Results: The results of the systematic literature review emphasized that the ideal data display maximizes the information communicated while minimizing the cognitive efforts involved with data interpretation. Factors to consider include selecting the correct type of display (e.g. line vs bar graph) and creating simplistic reports. The pre (n=79) and post (n=72) qualitative and quantitative evaluations of the final FTMR revealed improved perceptions of the visual display of the reports and their usability. Themes that emerged from the staff interviews emphasized the value of simplified reports, meaningful data, and usability to clinicians. The mean (SD) rating on the Health-ITUES scale in the pre-modification period was 3.86 (.19) and increased to 4.29 (0.11) in the post-modification survey period (Mann Whitney U Test, z=-12.25, P<0.001). Conclusions: Best practices identified through a systematic review can be applied to create effective reports for clinician use. The lessons learned from evaluating FTMR perceptions and measuring usability can be applied to creating effective reports for clinician use in the context of other implementation science projects.
Background: Physical activity data provides important information on disease onset, progression, and treatment outcomes. Although analyzing physical activity data in conjunction with other clinical an...
Background: Physical activity data provides important information on disease onset, progression, and treatment outcomes. Although analyzing physical activity data in conjunction with other clinical and microbiological data will lead to new insights crucial for improving human health, it has been hampered partly due to the large variations in the way the data are collected and presented. Objective: The goal of this study was to develop a Physical Activity Concept Ontology (PACO) to support structuring and standardizing heterogeneous descriptions of physical activities. Methods: We prepared a corpus of 1140 unique questions collected from various physical activity questionnaires and scales, as well as existing standardized terminologies and ontologies. We extracted concepts relevant to physical activity from the corpus using MUTT (Multipurpose Text processing Tool). The target concepts were formalized into an ontology using Protégé (version 4). Evaluation of PACO was performed along two aspects: structural consistency and structural cohesiveness. Evaluations were conducted using the Ontology Debugger plugin of Protégé and OntOlogy Pitfall Scanner (OOPS!). A use case application of PACO was demonstrated by structuring and standardizing 36 exercise habit statements and then automatically classifying them to a defined class of either sufficiently active or insufficiently active using FaCT++, an ontology reasoner available in Protégé. Results: PACO was constructed using the 268 unique concepts extracted from the questionnaires and assessment scales. PACO contains 225 classes including 9 defined classes, 8 object properties, 1 data property, and 23 instances (excluding 36 exercise statements). The maximum depth of classes is 4 and the maximum number of siblings is 38. The evaluations with ontology auditing tool confirmed that PACO is structurally consistent and cohesive. We showed in a small sample of 36 exercise habit statements that we could map text segments to relevant PACO concepts (e.g., exercise type class, intensity, and total minutes exercised per week) and infer from these concepts output determinations of sufficiently active or insufficiently active, using the FaCT++ reasoner. Conclusions: As a first step toward standardizing and structuring heterogeneous descriptions of physical activities for integrative data analyses, PACO was built with the concepts collected from physical activity questionnaires and scale. PACO was evaluated to be structurally consistent and cohesive, and also demonstrated to be potentially useful in standardizing heterogeneous physical activity descriptions and classifying them into clinically meaningful categories that reflect adequacy of exercise. Clinical Trial: NA
Background: The potential for machine learning to disrupt the medical professions is the subject of ongoing debate within biomedical informatics and related fields. Objective: To explore GPs’ opinio...
Background: The potential for machine learning to disrupt the medical professions is the subject of ongoing debate within biomedical informatics and related fields. Objective: To explore GPs’ opinions about the potential impact of future technology on key tasks in primary care. Methods: Context and Setting: A web-based survey of 720 UK GPs’ opinions about the likelihood of future technology to fully replace GPs in performing six key primary care tasks; and if respondents considered replacement for a particular task likely, to estimate how soon the technological capacity might emerge. Qualitative descriptive analysis of written responses (‘comments’) to an open-ended question. Results: Comments were classified into three major categories in relation to primary care: (i) limitations of future technology; (ii) potential benefits of future technology; and (iii) social and ethical concerns. Perceived limitations included the beliefs that communication and empathy are exclusively human competencies; many GPs also considered clinical reasoning, and the ability to provide value-based care as necessitating physicians’ judgements. Perceived benefits of technology included expectations about improved efficiencies in particular with respect to the reduction of administrative burdens on physicians. Social and ethical concerns encompassed multiple, divergent themes including the need to train more doctors to overcome workforce shortfalls, and misgivings about the acceptability of future technology to patients. However, some GPs believed that the failure to adopt technological innovations could incur harms to both patients and physicians. Conclusions: This study presents timely information on physicians’ views about the scope of artificial intelligence in primary care. Overwhelmingly, GPs considered the potential of artificial intelligence to be limited. These views differ from the predictions of biomedical informaticians. More extensive, stand-alone qualitative work would provide a more in-depth understanding of GPs’ views. Clinical Trial: (Not applicable)
Background: The telehealth program is diverse with mixed results. A comprehensive and integrated approach is needed to evaluate who gets benefits from the program to improve clinical outcomes. Objecti...
Background: The telehealth program is diverse with mixed results. A comprehensive and integrated approach is needed to evaluate who gets benefits from the program to improve clinical outcomes. Objective: The CHA2DS2-VASc score has been widely used for the prediction of stroke in patients with atrial fibrillation. This study adopts the predictive concept of the CHA2DS2-VASc score and investigated this score for risk stratification in hospital admission in patients with cardiovascular diseases receiving a fourth-generation synchronous telehealth program. Methods: This was a retrospective cohort study. We recruited patients with cardiovascular disease who received the fourth-generation synchronous telehealth program at the National Taiwan University Hospital between October 2012 and June 2015. We enrolled 431 patients who had joined a telehealth program and compared them with 1549 control patients. Cardiovascular hospitalization was estimated with Kaplan-Meier curves. The CHA2DS2-VASc score was used as the composite parameter to stratify the severity of the patients. The association between baseline characteristics and the clinical outcomes was assessed via the Cox proportional hazard model. Results: The mean follow-up duration was 886.1 ± 531.0 days in patients receiving the fourth-generation synchronous telehealth program and 707.1 ± 431.4 days in the control group. (p<0.0001). The telehealth group had more comorbidities at baseline than the control group. Patients with higher CHA2DS2-VASc score (≥ 4) were associated with a lower estimated rate of free from cardiovascular hospitalization (46.5% vs. 54.8%, log-rank test p = 0.0028). Patients receiving the telehealth program with CHA2DS2-VASc score ≥ 4 were less likely to be admitted for cardiovascular disease (61.5% vs. 41.8%, log-rank test p = 0.010). The telehealth program remains a significant prognostic factor after multivariable Cox analysis in patients with CHA2DS2-VASc score ≥ 4 (HR=0.36 [CI: 0.22 -0.62], p < 0.0001) Conclusions: A higher CHA2DS2-VASc score is associated with higher cardiovascular admission. Patients with CHA2DS2-VASc ≥4 benefits most for free from cardiovascular hospitalization after accepting the fourth-generation telehealth program. Clinical Trial: N/A
Background: The amount of medical and clinical-related information on the Web is increasing. Among the various types of information on the Web, social media-based data obtained directly from people ar...
Background: The amount of medical and clinical-related information on the Web is increasing. Among the various types of information on the Web, social media-based data obtained directly from people are particularly valuable and garnering much attention. To encourage medical natural language processing research exploiting social media data, the NTCIR-13 MedWeb (Medical Natural Language Processing for Web Document) provides pseudo-Twitter messages in a cross-language and multi-label corpus, covering three languages (Japanese, English, and Chinese), and annotated with eight symptom labels (e.g., cold, fever, flu, and so on). Then, participants classify each tweet into one of two categories: those containing a patient’s symptom, and those that do not. Objective: We aim to present the results of groups participated in the Japanese subtask, the English subtask, and the Chinese subtask along with discussions, in order to clarify the issues that need to be resolved in the field of medical natural language processing. Methods: The performance of participant systems is assessed using the exact match accuracy, F-measure based on precision and recall, and Hamming loss. Results: In all, eight groups (19 systems) participated in the Japanese subtask, four groups (12 systems) participated in the English subtask, and two groups (six systems) participated in the Chinese subtask. The best system achieved .880 in exact match accuracy, .920 in F-measure, and .019 in Hamming loss. Conclusions: This paper presented and discussed the performance of systems participated in the NTCIR-13 MedWeb task. Because the MedWeb task settings can be formalized as the factualization of text, the achievement of this task could be applied directly to practical clinical applications.