We are scheduled to perform a server upgrade on Thursday, November 30, 2017 between 4 and 6 PM Eastern Time.
Please refrain from submitting support requests related to server downtime during this window.
Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.
JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a sister journal of JMIR, the leading eHealth journal. JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received an impressive inaugural Impact Factor of 4.636, which ranks the journal #2 (behind JMIR) out of over 20 journals in the medical informatics category indexed by the Science Citation Index Expanded (SCIE) by Thomson Reuters/Clarivate.
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 since 2013 and was the first mhealth journal in Pubmed. It 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.
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.
Right click to copy or hit: ctrl+c (cmd+c on mac)
Background: Wearable technology is finding its way in clinical practices. Physical Activity (PA) describes patient’s functional status after cardiac surgery and in remote monitoring by use of PA tr...
Background: Wearable technology is finding its way in clinical practices. Physical Activity (PA) describes patient’s functional status after cardiac surgery and in remote monitoring by use of PA trackers. Objective: The aim of this work is to assess the usability of a wearable fitness tracker, to monitor patients who underwent coronary artery bypass surgery, by either the conventional Off-Pump procedure (OPCAB) or robotically assisted CABG (RA-MIDCAB). We hypothesized a faster recovery of functional status after RA-MIDCAB in the first weeks after discharge. Methods: Patients undergoing RA-MIDCAB or OPCAB were included. Each patient received a Fitbit Charge Heart Rate PA tracker (Fitbit Inc., San Francisco, CA, USA) following discharge. Rehabilitation progress was assessed by measuring the number of steps and physical activity level (PAL) daily. PAL was calculated as energy expenditure divided by basic metabolic rate. Results: 10 RA-MIDCAB patients with a median age of 68 (IQR: [60;76]) and 12 OPCAB patients with a median age of 69 (IQR: [65;76]) were included. Baseline characteristics were comparable except for BMI (RA-MIDCAB: 26 Kg/m² [24;27] versus OPCAB: 29 Kg/m² [27;31]; P<0.001, respectively). Intubation time (P<0.05) was significantly lower in the RA-MIDCAB group. A clear trend, although not statistically significant, was observed towards a higher number of steps in RA-MIDCAB patients in the first week following discharge. Conclusions: Wearable PA trackers can describe functional status in a cardiac rehabilitation setting after surgery. RA-MIDCAB patients have an advantage in recovery in the first weeks of revalidation reflected by the number of steps and PAL, measured by the Fitbit Charge HR, compared to OPCAB patients. However, unsupervised assessment of daily PA varied greatly and this could involve consequences for the use of these trackers as research tools. Clinical Trial: S59757
Background: Since January 2013 the New York City (NYC) Health Department TB Program has offered persons diagnosed with latent tuberculosis infection (LTBI) the 3-month, once-weekly isoniazid and rifap...
Background: Since January 2013 the New York City (NYC) Health Department TB Program has offered persons diagnosed with latent tuberculosis infection (LTBI) the 3-month, once-weekly isoniazid and rifapentine (3HP) treatment regimen. Patients on this treatment are monitored in-person under directly observed therapy (DOT). To address patient and provider barriers to in-person DOT, we piloted the used a videoconferencing software application to remotely conduct DOT (VDOT) for patients on 3HP. Objective: We evaluated the implementation of VDOT for patients on 3HP and whether treatment completion for patients on 3HP increased when monitored using VDOT compared to using the standard in-person DOT. Methods: Between February and October 2015, patients diagnosed with LTBI at any of the four NYC Health Department TB clinics who met eligibility criteria for treatment with 3HP under VDOT (V3HP) were followed-up until treatment completion or 16 weeks after treatment initiation, whichever came first. Treatment completion of V3HP patients were compared to historical data on treatment completion of patients on 3HP under clinic-based in-person DOT. Outcomes of video sessions with V3HP patients were collected and analyzed. Results: During the pilot period, 50 (70%) of 71 eligible patients were place on V3HP. Treatment completion among V3HP patients was 88% (44/50) compared to 65% (196/302) among 3HP patients on clinic DOT (P < .001). A total of 360 video sessions were conducted for V3HP patients with a median of 8 sessions per patient (range: 1-11), and a median time of 4 minutes (range: 1-59 minutes) per session. Adherence issues (e.g. >15 minutes late) during video sessions occurred 104 times. No major side effects were reported by V3HP patients. Conclusions: The NYC TB program observed higher treatment completion among patients on 3HP with VDOT than historically seen with clinic DOT. Expanding the use of VDOT may improve treatment completion and corresponding outcomes for LTBI.
Background: Heart sound monitor (HSM), a device suitable for home-use, can be used to record heart sounds and to store, transmit or analyze those recordings. It enables cardiac reserve telemonitoring...
Background: Heart sound monitor (HSM), a device suitable for home-use, can be used to record heart sounds and to store, transmit or analyze those recordings. It enables cardiac reserve telemonitoring which has been largely evolved and widely used in recent years. Nevertheless, the designers of HSM paid little attention to the consistency of its information model and data interaction, and because of that, the collected heart sounds data could not be shared and aggregated effectively. Thus, the device’s development and its application in telehealth service are hindered. Objective: In order to solve the above problem and to build interoperability for HSM devices, this paper proposes a HSM interoperability framework that is composed of the hierarchical information model and the transport-independent interaction model, which is constructed by using standardized modeling methods. Methods: The authors collected and studied the common device-output information of HSM involved in the cardiac reserve telemonitoring, leveraged the standardized interoperability framework defined in ISO/IEEE 11073 Personal Health Device (11073-PHD) standards to model the static data structure and dynamic interaction behaviors of HSM. Results: Via the meta analysis, the authors identified that the common device-output information of HSM mainly includes the phonocardiogram (PCG), the feature parameters of PCG, the ratio of diastolic to systolic duration (D/S), for example, and the threshold data, device status, sensor location, etc. Based on such information, an 11073-PHD-compliant domain information model has been successfully created. This enables the interoperability between HSM and aggregation device, allowing inter-device plug&play using service model and communication model. A prototype of this design has been implemented and validated via the Continua Enabling Software Library. Conclusions: The ISO/IEEE 11073-PHD standard framework has the potential to accommodate the HSM device. The standard-compliant domain information models can be established to cover the common HSM device-output information. Findings in this paper may be taken as a reference for standard developing organizations to establish a standardized interoperability framework for HSM.
Background: Nutrition-related apps are commonly used to provide information about the users’ dietary intake, but limited research has been performed on the evaluation of their reliability. Objective...
Background: Nutrition-related apps are commonly used to provide information about the users’ dietary intake, but limited research has been performed on the evaluation of their reliability. Objective: To evaluate the relative reliability of popular nutrition-related apps for the assessment of energy and available macro- and micronutrients against a standard method. Methods: Dietary analysis of 24-hour weighed food records (n = 20) were compared between five nutrition-related apps: S Health, MyFitnessPal, FatSecret, Noom Coach and Lose It!, and DietPlan6 (reference method). Estimates of energy, macronutrients (carbohydrate, protein, fat, saturated fat and fibre) and micronutrients (sodium, calcium, iron, vitamin A and vitamin C) were compared using t-tests and Wilcoxon signed-rank tests, correlation coefficients and Bland-Altman plots. 24-hour weighed food records from 20 participants (female/male, 15/5; mean age, 36.3 years; mean body mass index, 22.9 kg/m2) recruited from previous controlled studies conducted at the Hugh Sinclair Unit of Human Nutrition, University of Reading, UK. Results: No significant difference in estimation of energy and saturated fat intake between DietPlan6 and the diet apps was observed. Estimates of protein and sodium intake were significantly lower using Lose It! and FatSecret than DietPlan6. Lose It! also gave significantly lower estimates for other reported outputs: carbohydrate, fat, fibre and sodium, compared with DietPlan6. For S Health and MyFitnessPal, calcium, iron and vitamin C were all significantly under-estimated compared with DietPlan6, although there was no significant difference for vitamin A. No other significant differences were observed between DietPlan6 and the apps. Correlation coefficients ranged from -0.12 for iron (S Health vs. DietPlan6) to 0.91 for protein (FatSecret vs DietPlan6). Noom Coach was limited to energy output but it had a high correlation with DietPlan6 (r=0.91) and S Health had the greatest variation of correlation, with energy at 0.79. Bland-Altman analysis revealed potential proportional bias for vitamin A. Conclusions: The findings suggest that the apps provide comparable estimates of energy and saturated fat intake compared with DietPlan6. With the exception of Lose It!, the apps also provided comparable estimates of carbohydrate, total fat and fibre. Two apps displayed a tendency to underestimate protein and sodium (FatSecret and Lose It!). Apart from vitamin A, the estimates of micronutrient intake (calcium, iron and vitamin C) by the two apps (S Health and MyFitnessPal) were inconsistent and less reliable. Lose It! was the less comparable app in relation to DietPlan6. As the use and availability of apps grows, this study helps clinicians and researchers to make better-informed decisions about using these apps in research and practice.
Background: Many research domains still heavily rely on paper-based data collection procedures despite numerous drawbacks. However, the QuestionSys framework is intended to empower researchers as well...
Background: Many research domains still heavily rely on paper-based data collection procedures despite numerous drawbacks. However, the QuestionSys framework is intended to empower researchers as well as clinicians with no programming skills to develop their own smart mobile applications in order to collect data in their specific scenarios. Objective: In order to validate the feasibility of this model-driven end-user programming approach, a study with N=80 participants was conducted. Methods: Across 2 sessions (7 days between Session 1 and Session 2), participants had to model 10 data collection instruments (5 at each session) with the developed configurator component. Thereby, performance measures like time and operations needed and the resulting errors were evaluated. Participants were separated in two groups (i.e., novices vs. experts) based on prior knowledge in process modeling, which is one fundamental pillar of the QuestionSys approach. Results: T-tests revealed that novices showed significant learning effects in errors, operations, and time during the use of the configurator (all p < .05) and experts showed learning effects in operations and time (both p < .05) but not in errors as the experts' errors were already very low at the first modeling of a data collection instrument. Moreover, regarding time and operations needed, novices got significantly better after 3rd modeling task than experts were after the 1st one (t-Tests; both p < .05). With regard to errors, novices did not get significantly better at any of the 10 data collection instruments than experts were at the 1st modeling task, but novices' error rates in all 5 data collection instruments at Session 2 were not significantly different anymore from the ones of experts at the 1st modeling task. During the 7 days without using the configurator (from Session 1 to Session 2), the experts' learning effect of Session 1 remained stable, but the novices' learning effect of Session 1 lessened significantly regarding time and operations (t-Tests; both p < .05). Conclusions: In conclusion, novices are able to use the configurator properly and show fast (but still instable) learning effects so that they become as good as experts already after little experience with the configurator. Researchers and clinicians can use the QuestionSys configurator to develop data collection applications for smart mobile devices on their own.
Background: Up to 70% of lung cancer survivors are also affected by chronic obstructive pulmonary disease (COPD), a common, debilitating, comorbid disease characterized by symptoms such as breathlessn...
Background: Up to 70% of lung cancer survivors are also affected by chronic obstructive pulmonary disease (COPD), a common, debilitating, comorbid disease characterized by symptoms such as breathlessness and fatigue. These distressing symptoms detract from a survivor’s quality of life Objective: To identify and evaluate evidence-based, commercially available apps for promoting mindfulness-based strategies among adults with a COPD or lung cancer history. Methods: An interdisciplinary research team used 19 keyword combinations in the search engines of Google and iOS app stores in May 2017. Evaluations were conducted on apps’ (1) content, (2) usability heuristics, (3) grade-level readability, and (4) cultural sensitivity. Results: The search resulted in 768 apps (508 in iOS and 260 in Google stores). Nine apps met the inclusion criteria and received further evaluation. Only one app had below an eighth-grade reading level; the ninth did not have enough text to calculate a readability score. None of the 9 apps met the cultural sensitivity evaluation criteria. Conclusions: This systematic review identified critical design flaws that may affect the ease of using the apps in this study. Few mobile apps promote mindfulness-based strategies among adults with a COPD or lung cancer history, but those that exist, overall, do not meet the latest scientific evidence. Recommendations include more stringent regulation of health-related apps; use of evidence-based frameworks and participatory design processes; following evidence-based usability practices; use of culturally sensitive language and images; and ensuring that content is written in plain language. Clinical Trial: N/A