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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 2016: 5.175, 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: Traditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with one- to three-week delay. Accurate real-time monitoring systems of influenza ou...
Background: Traditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with one- to three-week delay. Accurate real-time monitoring systems of influenza outbreaks could be useful for public health decisions. Several works have investigated the possibility to use internet-users’ activity data and different statistical models to predict influenza epidemics in near real-time. However, very few studies have investigated hospital big data. Objective: Here, we compared internet and electronic health records (EHR) data and different statistical models to identify the best approach (data type and statistical model) for ILI estimates in real time. Methods: We used Google Data for internet data and the clinical data warehouse eHOP, that includes all EHR from Rennes University Hospital (France), for hospital data. We compared three statistical models, Random Forest (RF), Elastic Net, and Support Vector Machine (SVM). Results: For national ILI incidence rate, the best correlation was 0.98 and the mean squared error (MSE) was 866 obtained with hospital data and the SVM model. For the Brittany region, the best correlation was 0.923 and MSE was 2364 with hospital data and the SVM model. Conclusions: For national ILI incidence rate, the best correlation was 0.98 and the mean squared error (MSE) was 866 obtained with hospital data and the SVM model. For the Brittany region, the best correlation was 0.923 and MSE was 2364 with hospital data and the SVM model.
Background: Degenerative Cervical Myelopathy (DCM) is a prevalent and progressively disabling condition. Treatment is currently limited to surgery, the timing of which is not without controversy. New...
Background: Degenerative Cervical Myelopathy (DCM) is a prevalent and progressively disabling condition. Treatment is currently limited to surgery, the timing of which is not without controversy. New international guidelines advise all patients should undergo lifelong surveillance, with moderate to severe or progressive disease offered surgery. Long-term surveillance will place substantial burden on health services. Moreover, clinic assessments may risk misrepresenting disease severity. The use of smart technology to monitor disease progression could provide an invaluable opportunity to lessen this burden and improve patient care. However, given the older demographic of DCM the feasibility of smart technology use is unclear. Objective: The aim of this study is to investigate current usage of smart technology in patients with self-reported DCM to inform design of smart technology applications targeted at monitoring DCM disease progression. Methods: Google Analytics from the patient section of Myelopathy.org, an international DCM charity with a large online patient community, were analysed over a one-year period. A total of 15,761 sessions were analysed. Results: In total, 39.6% of visitors accessed the website using desktop computer, 35.1% mobile and 25.3% tablet. Of the mobile visitors, 98.2% utilised a touchscreen device. A total of 53.7% of mobile visitors used iOS and 43.2% Android operating systems. Apple and Samsung were the most popular devices, utilised by 53.7% and 25.8% of visitors, respectively. Overall visitor age was representative of DCM trials. Smart technology was widely used by older visitors: 24.0% of mobile visitors and 60.5% of tablet visitors were 55 years or older. Conclusions: Smart technology is commonly used by DCM patients. DCM applications need to be iOS and Android compatible to be available to all patients.
The medical interview is one of the most challenging topics in medicine. The ways in which the interview is implemented varies greatly depending on the clinician. The ways in which a.) the interview i...
The medical interview is one of the most challenging topics in medicine. The ways in which the interview is implemented varies greatly depending on the clinician. The ways in which a.) the interview is evaluated and, b.) the outcomes are determined, are also dependent on the bias of the clinician. For those reasons, the process of the medical interview is thought of as an art, rather than a science. In this study, clinicians were asked "how do you categorize the outcomes of the medical interview?” in relation to: a patient’s own disease history, a patients maternal and paternal disease history, and a patient’s current occupation. To obtain a sample representative of all clinicians in Turkey, we invited participants from 18 university hospitals dispersed through 14 providences. 1,270 clinicians representing specializations of otology, general surgery, internal medicine, cardiology, pulmonology and psychiatry were invited to participate in the study. Of those 1,270 clinicians, 77 of them responded to the survey. We obtained participation from clinicians in six of the 18 clinics. Our representative sample size was approximately 6% of the intended population.
Background: Prevention of depression is a priority to reduce its global disease burden. Targeting specific risk factors, such as rumination, may increase the efficacy of preventive interventions. Rumi...
Background: Prevention of depression is a priority to reduce its global disease burden. Targeting specific risk factors, such as rumination, may increase the efficacy of preventive interventions. Rumination-focused CBT (RFCBT) was developed to specifically target depressive rumination. A prior randomised controlled trial (RCT) in Dutch 15-22-year-olds at risk because of elevated worry and rumination found that both guided web-based RFBCT (i-RFCBT) and group-delivered RFCBT equally reduced depressive symptoms and onset of depressive cases over 1-year follow-up, relative to usual care control. Objective: The primary objective was to test whether guided i-RFCBT would prevent the incidence of major depression relative to usual care when extended to UK university students and using diagnostic interviews to improve the assessment of incidence of depression. The secondary objective was to test the feasibility and estimated effect sizes of unguided i-RFCBT. Methods: To address the primary objective, a Phase III RCT was designed and powered to compare high risk university students (N = 235), selected with elevated worry/rumination, recruited via an open access website in response to circulars within universities and internet advertisement, randomised to receive either guided i-RFCBT (an interactive web-based version of RFCBT, supported by asynchronous written web-based support from qualified and specially trained therapists), or usual care control. To address the secondary objective, participants were also randomised to an adjunct arm of unguided (self-administered) i-RFCBT. Primary outcome was onset of a major depressive episode, assessed with structured diagnostic interview at 3 (post-intervention), 6 and 15 months post-randomisation, conducted by telephone, blind to condition. Secondary outcomes of symptoms of depression and anxiety and levels of worry and rumination were self-assessed through questionnaires at baseline and the same follow-up intervals. Results: A total of 235 participants were randomised to guided i-RFCBT (N = 82), unguided i-RFCBT (N = 76) or usual care (N = 77). For the primary comparison, guided i-RFCBT reduced risk of depression by 34% relative to usual care. Participants with higher levels of baseline stress benefited most from the intervention (HR: 0.43, 95% CI [0.21, 0.87], P = .02). Significant improvements in rumination, worry and depressive symptoms were found in the short to medium term. Of six modules, guided participants completed a mean of 3.46 modules (SD = 2.25), with 46.34% (38/82) compliant (completing ≥4 modules). Similar effect sizes and compliance rates were found for unguided i-RFCBT. Conclusions: These results confirm that guided i-RFCBT can reduce the onset of depression in high-risk young people reporting high levels of worry/rumination and stress. The feasibility study argues for formally testing unguided i-RFCBT as a preventive intervention: as a scalable intervention, if the observed effect sizes are robustly replicated in a Phase III trial, it has potential to significantly address the burden of depression. Clinical Trial: Current Controlled Trials ISRCTN12683436. Date of registration: 27/10/2014
For time-series analysis of disease counts, this paper proposes a method that identifies the shortest period of measurement, while not significantly decreasing prediction performance. There is a body...
For time-series analysis of disease counts, this paper proposes a method that identifies the shortest period of measurement, while not significantly decreasing prediction performance. There is a body of literature in statistics that shows how auto-correlation can identify the best period of measurement in order to improve the performance of a time-series prediction; therefore, period of measurement plays an important role in improving the performance of disease-count predictions. However, from the operational perspective in public health surveillance, there is a length limitation in which the period of measurements can offer meaningful and valuable predictions. The proposed method is an attempt to shorten the period of prediction but not significantly decreasing the prediction performance. Our study applies change-point analysis on auto-correlations of different periods of measurement in order to identify the shortest period that has a similar time-series prediction performance to the best prediction. Our method uses Q-Score as performance indicator. The evaluation is conducted against artificial neural networks and autoregressive, integrated-moving average as time-series analysis methods. The data used in this evaluation contains disease counts from 2007 to 2017 in northern Nevada. The disease counts, including: Chlamydia, Salmonella, Respiratory syncytial virus (RSV), Gonorrhea, Meningitis Viral and Influenza A, were predicted. Auto-correlation cannot guarantee the best performance for prediction of disease counts. However, the proposed method adopting change-point analysis suggests a period of measurement that ensures an operationally acceptable prediction period and a performance not significantly different than the best prediction.
Background: Community-dwelling older adults living in rural areas are in a very unfavorable environment for health care compared to urban older adults. We thought that intermittent coaching through we...
Background: Community-dwelling older adults living in rural areas are in a very unfavorable environment for health care compared to urban older adults. We thought that intermittent coaching through wearable devices would help to optimize health care for older adults in medically limited environments. Objective: We aimed to evaluate whether a wearable device and mobile-based intermittent coaching or self-management can increase physical activity and/or health outcome of small groups of older adults in rural areas. Methods: To address the above evaluation goal, we carried out the "Smart walk" program, which is a health care model using a wearable device to promote self-exercise focused on community-dwelling older adults managed by a community health center. We randomly selected older adults who had enrolled in a population-based, prospective cohort study of aging, the Aging Study of Pyeongchang Rural Area. The "Smart Walk" program was a 13-month program from March 2017 to March 2018, consisting of six months of coaching, one month of rest, and six months of self-management. We evaluated (1) differences in physical activity and health outcome according to frailty status and (2) pre- and post-analyses of the Smart walk program. We also performed intergroup analysis according to adherence of wearable devices. Results: We recruited 22 participants (11 robust and 11 pre-frail older adults). The two groups were similar in most variables, except age, frailty index, and SPPB score associated with frailty criteria. After a six-month coaching program, the pre-frail group showed significant improvement in usual gait speed (0.73±0.11 vs. 0.96±0.27, P=0.02), IPAQ Kcal (2790.36±2224.62 vs. 7589.72±4452.52, P = 0.01), and Euroqol-5D score (0.84±0.07 vs. 0.90±0.07, P=0.02) variables, although there was no significant improvement in the robust group. The average total step count was significantly different, and that of the coaching period was approximately four times higher than that of the self-management period (5,584,295.83 vs. 1,289,084.66, P <0.001). We found that the ‘long-self’ group who used the wearable device for the longest time increased body weight and BMI by 0.65 ± 1.317 and 0.097 ± 0.513, respectively, compared to other groups. Conclusions: Our Smart walking program improved physical fitness, anthropometric measurements, and geriatric assessment categories in a small group of older adults in rural areas with limited resources for monitoring. Further validation through various rural public health centers and a large number of rural older adults is required. Clinical Trial: This study was approved by the Asan Medical Center institution’s ethics committee (IRB File No: 2015-0673)