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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.
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Background: Personal electronic health devices, such as fitness trackers, heart rate monitors, blood glucose meters, blood pressure monitors and stress level meters, and related smartphone-based healt...
Background: Personal electronic health devices, such as fitness trackers, heart rate monitors, blood glucose meters, blood pressure monitors and stress level meters, and related smartphone-based health applications are increasing in usage and popularity. These Internet-based medical technologies, which this paper refers to as mHealth systems, may be prescribed by a healthcare professional or purchased over-the-counter and make it easier for an individual to collect, access and monitor information relevant to their own health and well-being. However, as with many Internet-based technologies, and especially so with sensitive, personal health information, privacy is a significant concern. Actual or a perceived risk of privacy intrusions may delay the wider adoption of mHealth systems and even generate mistrust that reduces their long-term effectiveness. This paper contributes to the understanding of users’ perspectives on information privacy in mHealth systems. Objective: To gain an understanding of current usage patterns and how important users perceive privacy, we have conducted a national survey in Australia. Understanding consumers’ preferences and expectations provide directions for developers, lawmakers and researchers in creating an improved mHealth ecosystem. Methods: As part of the National Social Survey by Population Research Laboratory of CQUniversity, participants who were 18 years or older were randomly selected from across Australia for telephone interviews. The participants were asked 10 questions about usage and privacy of mobile health systems. The collected data was tabulated, cleaned and analysed using SPSS and the resultant data set contained 1,225 cases with a total of 187 variables for each case. Results: The survey reveals users of mHealth systems have a strong desire for privacy, e.g. more than 80% rate privacy important or very important and more than 60% think no personal information should be released to developers. The survey also shows around 70% of users never or rarely review privacy policies, and that they perceive the significant potential impact of intrusions, including increased health insurance costs, embarrassment and financial loss. Conclusions: While the survey results show users desire privacy and have low trust of telecommunications and IT organisations, this conflicts with the technical design of mHealth systems: in many cases application developers, device manufacturers and telecommunication companies may have access to sensitive health information. The lack of standardization and guidelines for data processing by mHealth systems, as well as ineffectiveness of privacy policies, need to be addressed to avoid users’ confusion and potential invasions of privacy. Clinical Trial: This research is undertaken as part of our CQUniversity Population Research Grant Scheme (PRGS). NSS-2016 received approval by the Human Ethics Research Review Panel at CQUniversity before administration to the general public. Project: H14/09-203, NATIONAL SOCIAL SURVEY 2016.
Background: Fitness devices have spurred the development of applications that not only monitor physical activity (PA) but also aim to motivate users, through interventions, to increase their PA. Perso...
Background: Fitness devices have spurred the development of applications that not only monitor physical activity (PA) but also aim to motivate users, through interventions, to increase their PA. Personalization in the interventions is essential as the target users are diverse with respect to their activity levels, requirements, preferences, and behavior. Objective: This review aims to: 1) identify different kinds of personalization in interventions for promoting PA; 2) identify user models used for providing personalization; and 3) identify gaps in the current literature and suggest future research directions. Methods: A systematic review was undertaken using the PRISMA guidelines. PsycINFO, PubMed and Web of Science databases were searched. The main inclusion criteria were: 1) studies that aimed to promote PA among target users as (at least one of) their objectives; 2) studies that had user personalization, with the intention of promoting PA (e.g., activity recommendations or motivational messages); 3) studies that described user models for personalization. Results: The literature search resulted in 28 eligible studies. Of these, 68% (19 studies) focused solely on increasing PA, while the remaining studies had other objectives, like healthcare (18%), weight loss (7%), rehabilitation (7%). The reviewed studies provide personalization in six categories related to recommendation and feedback: goal recommendation, activity recommendation, fitness partner recommendation, educational content, motivational content, and intervention timing. With respect to the mode of generation, interventions were found to be manual, semi-automated, or automatic. Of these, the automatic systems were either knowledge-based or data-driven or both. User models in the studies were constructed with parameters from five categories: PA profile, demographics, medical data, behavior change technique (BCT) parameters, and contextual information. Only 15 of the eligible studies evaluated the interventions and 67% of these 15 concluded that the interventions to increase PA are more effective when they are personalized. Conclusions: This systematic review investigates personalization of interventions, in the form of recommendations or feedback for increasing PA. Based on the review and gaps identified, research directions for improving the efficacy of personalized interventions are proposed. First, data-driven prediction techniques can facilitate effective personalization of automated and semi-automated interventions. Second, use of BCTs in automated interventions, and in combinations with PA guidelines, are yet to be explored and preliminary studies in this direction are promising. Third, systems with automated interventions also need to be suitably adapted to serve specific needs of patients with clinical conditions. Fourth, previous user models focus on single metric evaluations of PA instead of a, potentially more effective, holistic and multidimensional view. Fifth, with the widespread adoption of activity monitoring devices and smartphones, personalized and dynamic user models can be created using the available user data, including the user’s social profile. Finally, the long-term effects of such interventions need to be evaluated rigorously.
Background: Anticoagulants are major contributors to preventable adverse drug events, and their optimal management in the peri-procedural period is particularly challenging. Traditional methods of dis...
Background: Anticoagulants are major contributors to preventable adverse drug events, and their optimal management in the peri-procedural period is particularly challenging. Traditional methods of disseminating clinical guidelines and tools cannot keep pace with the rapid expansion of available therapeutic agents, approved indications for use, and published medical evidence, so a mobile application, Management of Anticoagulation in the Peri-Procedural Period (MAPPP), was developed and disseminated to provide clinicians with guidance reflecting the most current medical evidence. Objective: To assess the global, national, and state-level acquisition of a mobile app since its initial release and characterize individual episodes of use based on drug selection, procedural bleeding risk, and patient thromboembolic risk. Methods: Data was extracted from a mobile app usage tracker (Google Analytics) to characterize new users and completed episodes temporally (by calendar quarter) and geographically (globally, nationally, and in the targeted US state of New York) for the period from April 1, 2016, through September 30, 2017. Results: The app was acquired by 2,866 new users in the measurement period, and users completed nearly 10,000 individual episodes of use. Acquisition and utilization spanned 51 countries globally, predominantly in the United States and particularly in New York State. Warfarin and rivaroxaban were the most frequently selected drugs, and completed episodes most frequently included the selection of the high bleeding risk (49.06%) and high thromboembolic risk categories (45.17%). Conclusions: The MAPPP app is a successful means of disseminating current guidance on peri-procedural anticoagulant use, as indicated by broad global uptake and upward trends in utilization. Limitations in access to provider and patient-specific data preclude objective evaluation of the clinical impact of the app. An ongoing study incorporating app logic into electronic health record systems at participant health systems will provide a more definitive evaluation of the clinical impact of the app logic.
Background: Extended intervention contact after an initial, intensive intervention is becoming accepted as best practice in behavioral weight control interventions. What is not clear is whether extend...
Background: Extended intervention contact after an initial, intensive intervention is becoming accepted as best practice in behavioral weight control interventions. What is not clear is whether extended contact mitigates weight regain in the longer-term, or whether it simply delays weight regain until after the extended intervention contact ceases. Objective: To evaluate maintenance following the Get Healthy, Stay Healthy (GHSH) extended contact intervention, by comparing: the intervention and control group averages at 12 months (the traditional comparison of maintained intervention effects); the intervention and control group averages over the first six months of non-contact (a novel approach which directly compares the relapse effects between groups over the same duration of time); and, the individual participant changes over the first six months of non-contact (to explore the extent to which the group average is reflective of directions of changes for individuals). Methods: Clients completing the Get Healthy Service (GHS) lifestyle telephone coaching program were randomised to receive extended contact via tailored text messages (GHSH, n=114) or standard care (no additional contact, n=114) and were assessed at baseline (following completion of GHS), six months (following completion of GHSH) and 12 months (no-contact maintenance follow-up). At all three assessments participants self-reported their body weight, waist circumference, physical activity (walking, moderate and vigorous sessions/week) and dietary behaviors (fruit and vegetable serves/day, cups of sweetened drinks per day, takeaway meals per week; Fat, Fiber and Total indices from the Fat and Fiber Behavior Questionnaire). Moderate-vigorous physical activity (MVPA) was also assessed via accelerometry. Results: Retention over the 12-month trial was high (93%, 211/228). Participants had a mean (±standard deviation) age of 53.4±12.3 years and baseline BMI of 29.2±5.9 kg/m2. The between-group differences detected at 6 months were still present and statistically significant at 12 months for body weight (-1.33kg (-2.61, -0.05)) and accelerometer-assessed MVPA (24.9 minutes/week (5.8, 44.0)). None of the other outcomes were significantly favoured compared to the control group at 12 months. Changes over their first six months of non-contact for the GHSH group were significantly better than the control group in terms of accelerometer-measured MVPA and self-reported moderate activity (other differences between the groups were all non-significant). In addition to the maintenance seen in the group averages, most intervention participants had maintained their behavioral outcomes during the first six months of non-contact. Conclusions: The GHSH participants were better off relative to where they were initially, and relative to their counterparts not receiving extended contact in terms of MVPA. However, based on the between group difference in bodyweight over the first six months of non-contact, GHSH does appear to simply delay the ‘inevitable’ weight regain. However, this delay in weight regain, coupled with sustained improvements in MVPA, has public health benefit.
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Background: Ultraviolet (UV) rays are electromagnetic waves that account for about 5% of the solar light, and when overexposed, they pose malevolent effects on the human skin and health. However, with...
Background: Ultraviolet (UV) rays are electromagnetic waves that account for about 5% of the solar light, and when overexposed, they pose malevolent effects on the human skin and health. However, with recent reports on the beneficial effects of some wavelength bands of UV rays, people’s interest in UV information has increased. This has resulted in requiring, not just simple information such as the amount of UV or UV index (UVI), but detailed UV information that directly affects health, such as EUVB(Erythemally weighted UVB). However, calculating EUVB, which can be done by applying the erythemal weighted function on the intensity value in wavelength, requires specialized optical measurement devices, which cannot be easily accessed by the general public; furthermore, public institutions UV information services do not offer EUVB information for individuals. Objective: Therefore, the present study proposes a UVI sensor-based portable device, with which the general public can have easy access to UV-related information. Methods: The proposed device comprises a UVI sensor that can measure the intensity of erythemal UV radiation, a BLE module that supports communication, and a MCU for key operations. In addition, it applies the ratio of EUVB by month/time, resulting from the actual analysis of natural light to calculate the EUVB and provides the amount of UVI and EUVB to check if they meet conditions required for outdoor activities through the device and smartphone applications. Results: The applicability of the proposed device was verified by the measurement performance comparison test with the standard device, a spectrometer (CAS 140 CT), which showed an average error of 0.06 for UVI and 0.0017 W/m2. Conclusions: The proposed device’s offering of UV-related information such as UVI and EUVB to user is expected to prevent potential damage due to the exposure to UV and to support healthy outdoor activities.
Background: Young adults are rapidly adopting electronic cigarette (e-cigarette) use. E-cigarettes’ popularity among young people can be attributed to heavy industry advertising and misleading healt...
Background: Young adults are rapidly adopting electronic cigarette (e-cigarette) use. E-cigarettes’ popularity among young people can be attributed to heavy industry advertising and misleading health claims. Data indicate young e-cigarette users who never used conventional cigarettes are now progressing toward smoking combustible cigarettes. Literature documents the influence of text messaging as a delivery mode to support participants in behavioral interventions. Communicating e-cigarette risks via text messaging has not been tested. Objective: This pilot study assessed the impact of exposure to text messages on e-cigarette knowledge and risk perception outcomes. Methods: A 2-group randomized pretest and posttest study was conducted among young men and women recruited from vocational training programs. Personal phones were used to receive messages and 95 racially and ethnically diverse participants completed a pretest and posttest. Fifty percent were randomized to either receive gain- or loss-framed messages that integrated the latest scientific findings about e-cigarettes. All messages used wording suitable for audiences with low health literacy. Knowledge and risk perceptions about e-cigarettes and tobacco use were assessed pretest and posttest after message exposure. Results: The mean age of participants was 20.8 (SD = 1.7). At pretest, approximately 10.5% of the (n=10/95) participants were current e-cigarette users, and 27.4% (n=26/95) used a variety of tobacco products. Participants randomized to gain-framed messages reported a statistically significant higher risk perception for using e-cigarettes at posttest than those who received loss-framed messages (P = .018). After message exposure there was no change in use of e-cigarettes or other tobacco products. Conclusions: Young adults were informed that e-cigarette use may lead to addiction to nicotine and other consequences. Delivery of effective text messages such as those tested in this pilot can assist young consumers to evaluate and make decisions about e-cigarettes and other evolving tobacco products. Clinical Trial: This was a pilot study and not a clinical trial, thus the project was not registered.