JMIR mHealth and uHealth

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

The Use of Smartphone and Medical Related Apps among General Practitioners

Background: Smartphone has drastically changed the way we work and live today. Smartphones and smartphone applications (apps) have expanded new forms of health professionals’ work. There are a lot of studies on the usage of smartphone apps for different specialists. However, there is no study on current use of smartphone apps among General practitioners (GPs). Objective: To investigate the extent to GPs own smartphones and use them to aid their clinical medical activities, and how often they use apps to acquire medical information and support clinical decision. Methods: A questionnaire survey of GPs was undertaken in Hangzhou, China. Data probing the GPs’ current use of medical apps in their clinical activities and its influencing factors, including: the numbers who owned a smartphone; type of smartphone; the number of medical apps owned; how often medical apps were actually referred to during working hours; and the clinical environment in which the non-medical smartphone app was used, were collected and analyzed Results: 125 GPs anticipated the survey. 90.4% (n = 113/125) of GPs owned a smartphone, with 48.7% (n = 55/113) of GPs owning an iPhone and 47.8% (n = 54/113) owning an android platform smartphone. Most smartphone user of GPs owned 1-3 medical related applications, with very few owning more than 4, and there is no difference between iPhone and android smartphone owners (χ2 = 1.388, P = 0.846). 36% of GPs reported using medical related apps on a daily basis. The majority of doctors reported using apps daily less than 30 minutes. Conclusions: A high level of smartphone ownership and usage among GPs is found in this study, but few people choose apps to support their clinical practice. Several non-medical apps were frequently used to aid GPs’ medical activity during working hours.

2014-08-17

JMIR mHealth and uHealth (JMU) http://mhealth.jmir.org is now the first #mhealth journal listed in PubMed: http://www.ncbi.nlm.nih.gov/pubmed?term=%22JMIR+Mhealth+Uhealth%22[jour] and PubMed Central: http://www.ncbi.nlm.nih.gov/pmc/journals/2531/ Please follow @JMedInternetRes on Twitter for JMU content updates and sign up for email alerts by creating a profile at http://mhealth.jmir.org/user/profile.

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Journal Description

JMIR mhealth and uhealth (mobile and ubiquitous health) (JMU, ISSN 2291-5222) is a new spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2013: 4.7). 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 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.
In addition to peer-reviewing paper submissions by researchers, JMIR mHealth and uHealth offers peer-review of medical apps itself (developers can submit an app for peer-review here).

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 is indexed in PubMed Central/PubMed).

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.

 

Recent Articles:

  • option.

    Public Health Guidelines for Physical Activity: Is There an App for That? A Review of Android and Apple App Stores

    Abstract:

    Background: Physical activity participation is an important behavior for modifying lifestyle-related disease risk. Mobile health apps for chronic disease management and prevention are being developed at a rapid rate. However, it is unclear whether these apps are evidence-based. Current public health recommendations for physical activity participation for adults highlight the importance of engaging in 150 minutes weekly of purposeful exercise, and muscle strengthening activities on at least 2 days of the week. Objective: The aims of the present review were to (1) identify available evidence-based physical activity apps, and (2) identify technological features that could be leveraged to improve health outcomes. Methods: iTunes and Google Play mobile app stores were searched using keyword and category searching during a single day (February 18, 2014) for physical activity apps available in English. The description pages of eligible apps were reviewed by 4 independent reviewers for evidence-based content, technological, and descriptive features. An a priori subset of apps was downloaded for further review (n=6 affiliated with a non-commercial agency; n=10 top rated; n=10 random selection), and developers were contacted for information regarding evidence-informed content. Results: The initial search yielded 2400 apps, of which 379 apps (n=206 iTunes; n=173 Google Play) were eligible. Primary results demonstrated no apps (n=0) adhering to evidence-based guidelines for aerobic physical activity, and 7 out of 379 implementing evidence-based guidelines for resistance training physical activity. Technological features of apps included social networking (n=207), pairing with a peripheral health device (n=61), and measuring additional health parameters (n=139). Secondary results revealed 1 app that referenced physical activity guidelines (150 minutes/weekly of exercise), and demonstrated that apps were based on various physical activity reports (n=4) or personal expertise (n=2). Conclusions: The present study demonstrated a shortage of evidence-based physical activity apps. This gap underscores the need for development of evidence-informed mobile apps. Results highlight the opportunity to develop evidence-informed mobile apps that can be used clinically to enhance health outcomes.

  • Photo credits: FAYEZ NURELDINE/AFP/Getty Images.

    What Overweight Women Want From a Weight Loss App: A Qualitative Study on Arabic Women

    Abstract:

    Background: Overweight and obesity are international public health issues. With mobile and app use growing globally, the development of weight loss apps are increasing along with evidence that interventions using technology have been effective in the treatment of obesity. Although studies have been conducted regarding what content health professionals would recommend within weight loss apps, there are limited studies that explore users’ viewpoints. There is specifically a paucity of research that takes the cultural background of the user into consideration, especially in Middle Eastern countries where the lives and weight loss intervention needs of women not only vary vastly from the West, but the obesity rate is also increasing exponentially. Objective: The current study sought to explore the proposed features of an Arabic weight loss app by seeking the experiences and opinions of overweight and obese Saudi Arabian users in order to design a mobile phone app to fit their needs. Methods: Focus group discussions were conducted with a purposive sample of volunteer overweight and obese Saudi women (BMI ≥ 25) who were older than 18 years and who owned a mobile phone. The most common Arabic and English weight loss mobile apps were downloaded to initiate dialogue about app usage and to get their opinions on what an ideal weight loss app would look like and the features it would include. All transcribed, translated discussions were thematically analyzed, categorized for each of the main topics of the discussion, and specific quotations were identified. Results: Four focus groups were conducted with a total of 39 participants. Most participants owned an Android mobile phone and only a few participants were aware of the availability of health-related apps. Barriers to weight loss were identified including: motivation, support (social and professional), boring diets, customs, and lifestyle. Diverse themes emerged as suggestions for an ideal weight loss app including: Arabic language and culturally sensitive; motivational support and social networking; dietary and physical activity tools; and a tailorable, user-friendly interface. Conclusions: This study identifies weight loss app features from the users’ perspective, which should be considered in the development of a weight loss app for this population.

  • MyBehavior app screenshots: (a) taking photo of a food item, (b) searching for foods from the USDA database, and (c) Life Log, a chronological list of activity and food log events.

    Automated Personalized Feedback for Physical Activity and Dietary Behavior Change With Mobile Phones: A Randomized Controlled Trial on Adults

    Abstract:

    Background: A dramatic rise in health-tracking apps for mobile phones has occurred recently. Rich user interfaces make manual logging of users’ behaviors easier and more pleasant, and sensors make tracking effortless. To date, however, feedback technologies have been limited to providing overall statistics, attractive visualization of tracked data, or simple tailoring based on age, gender, and overall calorie or activity information. There are a lack of systems that can perform automated translation of behavioral data into specific actionable suggestions that promote healthier lifestyle without any human involvement. Objective: MyBehavior, a mobile phone app, was designed to process tracked physical activity and eating behavior data in order to provide personalized, actionable, low-effort suggestions that are contextualized to the user’s environment and previous behavior. This study investigated the technical feasibility of implementing an automated feedback system, the impact of the suggestions on user physical activity and eating behavior, and user perceptions of the automatically generated suggestions. Methods: MyBehavior was designed to (1) use a combination of automatic and manual logging to track physical activity (eg, walking, running, gym), user location, and food, (2) automatically analyze activity and food logs to identify frequent and nonfrequent behaviors, and (3) use a standard machine-learning, decision-making algorithm, called multi-armed bandit (MAB), to generate personalized suggestions that ask users to either continue, avoid, or make small changes to existing behaviors to help users reach behavioral goals. We enrolled 17 participants, all motivated to self-monitor and improve their fitness, in a pilot study of MyBehavior. In a randomized two-group trial, investigators randomly assigned participants to receive either MyBehavior’s personalized suggestions (n=9) or nonpersonalized suggestions (n=8), created by professionals, from a mobile phone app over 3 weeks. Daily activity level and dietary intake was monitored from logged data. At the end of the study, an in-person survey was conducted that asked users to subjectively rate their intention to follow MyBehavior suggestions. Results: In qualitative daily diary, interview, and survey data, users reported MyBehavior suggestions to be highly actionable and stated that they intended to follow the suggestions. MyBehavior users walked significantly more than the control group over the 3 weeks of the study (P=.05). Although some MyBehavior users chose lower-calorie foods, the between-group difference was not significant (P=.15). In a poststudy survey, users rated MyBehavior’s personalized suggestions more positively than the nonpersonalized, generic suggestions created by professionals (P<.001). Conclusions: MyBehavior is a simple-to-use mobile phone app with preliminary evidence of efficacy. To the best of our knowledge, MyBehavior represents the first attempt to create personalized, contextualized, actionable suggestions automatically from self-tracked information (ie, manual food logging and automatic tracking of activity). Lessons learned about the difficulty of manual logging and usability concerns, as well as future directions, are discussed. Trial Registration: ClinicalTrials.gov NCT02359981; https://clinicaltrials.gov/ct2/show/NCT02359981 (Archived by WebCite at http://www.webcitation.org/6YCeoN8nv).

  • ShutterStock.
Image ID: 122099695
Copyright: Sergey Nivens.
http://www.shutterstock.com/pic-122099695/stock-photo-media-technology-illustration-with-mobile-phone-and-icons.html.

    Valuable Features in Mobile Health Apps for Patients and Consumers: Content Analysis of Apps and User Ratings

    Abstract:

    Background: The explosion of mobile phones with app capabilities coupled with increased expectations of the patient-consumers’ role in managing their care presents a unique opportunity to use mobile health (mHealth) apps. Objectives: The aim of this paper is to identify the features and characteristics most-valued by patient-consumers (“users”) that contribute positively to the rating of an app. Methods: A collection of 234 apps associated with reputable health organizations found in the medical, health, and fitness categories of the Apple iTunes store and Google Play marketplace was assessed manually for the presence of 12 app features and characteristics. Regression analysis was used to determine which, if any, contributed positively to a user’s rating of the app. Results: Analysis of these 12 features explained 9.3% (R2=.093 n=234, P<.001) of the variation in an app’s rating, with only 5 reaching statistical significance. Of the 5 reaching statistical significance, plan or orders, export of data, usability, and cost contributed positively to a user’s rating, while the tracker feature detracted from it. Conclusions: These findings suggest that users appreciate features that save time over current methods and identify an app as valuable when it is simple and intuitive to use, provides specific instructions to better manage a condition, and shares data with designated individuals. Although tracking is a core function of most health apps, this feature may detract from a user’s experience when not executed properly. Further investigation into mHealth app features is worthwhile given the inability of the most common features to explain a large portion of an app’s rating. In the future, studies should focus on one category in the app store, specific diseases, or desired behavior change, and methods should include measuring the quality of each feature, both through manual assessment and evaluation of user reviews. Additional investigations into understanding the impact of synergistic features, incentives, social media, and gamification are also warranted to identify possible future trends.

  • Screenshots of the myWHI diary interface.          

2.1 Headache entry     2.2 Daily diary entry     2.3 Reports.

    Testing the Feasibility and Psychometric Properties of a Mobile Diary (myWHI) in Adolescents and Young Adults With Headaches

    Abstract:

    Background: Headaches are prevalent among teens and young adults. Self-monitoring is essential for managing headaches and can be accomplished with the help of electronic headache diaries. An increasing number of electronic headache diaries exist, yet the absence of quality standards compromises their use for research and clinical purposes. Objective: Our goal was to develop and test the usability, feasibility, and psychometric properties of an electronic diary iPhone application for self-monitoring by adolescents and young adults with headaches. Methods: We used an iterative participatory design to develop and test our electronic headache diary. Participants aged 14-28 years old with recurrent headaches were recruited internationally. Screening and consent were conducted online. Following completion of an online pre-questionnaire, participants downloaded the diary to use in their natural environment for 14 days. An online post-questionnaire was completed following testing. The diary’s usability and feasibility were tested first and determined to be complete when improvements to the diary did not result in a statistically significant impact on indicators of feasibility and adherence. Interviews were conducted with participants of usability and feasibility testing. The psychometric properties of the diary were then tested, and a case study analysis of one participant was completed. Results: Three cycles to test the usability and feasibility were conducted. Each cycle included 11-19 unique participants ranging in age from 16 to 28 years. Following the testing period for each cycle, 15% to 25% of participants took part in the post-cycle interview. Participants perceived the final version of the diary as useful, easy to learn, and efficient to use. Psychometric properties were then tested with a sample of 65 participants (6 aged 14-17 years old; 59 aged 18-28 years old). All items in the diary had substantial between- and within-subjects variability (percent of variance for the two participant groups ranged from 20.64 to 75.60 and 23.74 to 79.21, respectively). Moreover, the Migraine Disability Assessment (MIDAS) included in the diary had adequate between-subjects reliability (R1F=0.66, RKF=0.98), but low within-subjects reliability (RC=0.51). Critical elements of the diary demonstrated adequate convergent and concurrent validity, particularly in the older age group (18-28 years). The validity of some critical elements of the diary could not be explored in the younger age group due to the small subgroup size. The case study provides an example of the potential utility of the diary. Conclusions: Our electronic headache diary was shown to be a usable and feasible self-monitoring tool when used by adolescents and young adults with headaches for 14 days. This study provides preliminary support of its psychometric properties. Our diary has the potential for helping users to better understand their headaches and, consequently, to change behaviors to improve self-management of their headaches. Its effectiveness as a component of an intervention will be the focus of future research.

  • Foods photographed by means of TECH. Copyright: Marie Löf.

    A New Mobile Phone-Based Tool for Assessing Energy and Certain Food Intakes in Young Children: A Validation Study

    Abstract:

    Background: Childhood obesity is an increasing health problem globally. Obesity may be established already at pre-school age. Further research in this area requires accurate and easy-to-use methods for assessing the intake of energy and foods. Traditional methods have limited accuracy, and place large demands on the study participants and researchers. Mobile phones offer possibilities for methodological advancements in this area since they are readily available, enable instant digitalization of collected data, and also contain a camera to photograph pre- and post-meal food items. We have recently developed a new tool for assessing energy and food intake in children using mobile phones called the Tool for Energy Balance in Children (TECH). Objective: The main aims of our study are to (1) compare energy intake by means of TECH with total energy expenditure (TEE) measured using a criterion method, the doubly labeled water (DLW) method, and (2) to compare intakes of fruits and berries, vegetables, juice, and sweetened beverages assessed by means of TECH with intakes obtained using a Web-based food frequency questionnaire (KidMeal-Q) in 3 year olds. Methods: In this study, 30 Swedish 3 year olds were included. Energy intake using TECH was compared to TEE measured using the DLW method. Intakes of vegetables, fruits and berries, juice, as well as sweetened beverages were assessed using TECH and compared to the corresponding intakes assessed using KidMeal-Q. Wilcoxon matched pairs test, Spearman rank order correlations, and the Bland-Altman procedure were applied. Results: The mean energy intake, assessed by TECH, was 5400 kJ/24h (SD 1500). This value was not significantly different (P=.23) from TEE (5070 kJ/24h, SD 600). However, the limits of agreement (2 standard deviations) in the Bland-Altman plot for energy intake estimated using TECH compared to TEE were wide (2990 kJ/24h), and TECH overestimated high and underestimated low energy intakes. The Bland-Altman plots for foods showed similar patterns. The mean intakes of vegetables, fruits and berries, juice, and sweetened beverages estimated using TECH were not significantly different from the corresponding intakes estimated using KidMeal-Q. Moderate but statistically significant correlations (ρ=.42-.46, P=.01-.02) between TECH and KidMeal-Q were observed for intakes of vegetables, fruits and berries, and juice, but not for sweetened beverages. Conclusion: We found that one day of recordings using TECH was not able to accurately estimate intakes of energy or certain foods in 3 year old children.

  • Evolution of the pulse oximetry module.

    Usability and Feasibility of PIERS on the Move: An mHealth App for Pre-Eclampsia Triage

    Abstract:

    Background: Pre-eclampsia is one of the leading causes of maternal death and morbidity in low-resource countries due to delays in case identification and a shortage of health workers trained to manage the disorder. Pre-eclampsia Integrated Estimate of RiSk (PIERS) on the Move (PotM) is a low cost, easy-to-use, mobile health (mHealth) platform that has been created to aid health workers in making decisions around the management of hypertensive pregnant women. PotM combines two previously successful innovations into a mHealth app: the miniPIERS risk assessment model and the Phone Oximeter. Objective: The aim of this study was to assess the usability of PotM (with mid-level health workers) for iteratively refining the system. Methods: Development of the PotM user interface involved usability testing with target end-users in South Africa. Users were asked to complete clinical scenario tasks, speaking aloud to give feedback on the interface and then complete a questionnaire. The tool was then evaluated in a pilot clinical evaluation in Tygerberg Hospital, Cape Town. Results: After ethical approval and informed consent, 37 nurses and midwives evaluated the tool. During Study 1, major issues in the functionality of the touch-screen keyboard and date scroll wheels were identified (total errors n=212); during Study 2 major improvements in navigation of the app were suggested (total errors n=144). Overall, users felt the app was usable using the Computer Systems Usability Questionnaire; median (range) values for Study 1 = 2 (1-6) and Study 2 = 1 (1-7). To demonstrate feasibility, PotM was used by one research nurse for the pilot clinical study. In total, more than 500 evaluations were performed on more than 200 patients. The median (interquartile range) time to complete an evaluation was 4 min 55 sec (3 min 25 sec to 6 min 56 sec). Conclusions: By including target end-users in the design and evaluation of PotM, we have developed an app that can be easily integrated into health care settings in low- and middle-income countries. Usability problems were often related to mobile phone features (eg, scroll wheels, touch screen use). Larger scale evaluation of the clinical impact of this tool is underway.

  • Photo credit: Image adapted from an image by Peter Mooney shared via Flickr https://flic.kr/p/oHDm1F, licensed under Creative Commons Attribution 2.0.

    Validation of Physical Activity Tracking via Android Smartphones Compared to ActiGraph Accelerometer: Laboratory-Based and Free-Living Validation Studies

    Abstract:

    Background: There is increasing interest in using smartphones as stand-alone physical activity monitors via their built-in accelerometers, but there is presently limited data on the validity of this approach. Objective: The purpose of this work was to determine the validity and reliability of 3 Android smartphones for measuring physical activity among midlife and older adults. Methods: A laboratory (study 1) and a free-living (study 2) protocol were conducted. In study 1, individuals engaged in prescribed activities including sedentary (eg, sitting), light (sweeping), moderate (eg, walking 3 mph on a treadmill), and vigorous (eg, jogging 5 mph on a treadmill) activity over a 2-hour period wearing both an ActiGraph and 3 Android smartphones (ie, HTC MyTouch, Google Nexus One, and Motorola Cliq). In the free-living study, individuals engaged in usual daily activities over 7 days while wearing an Android smartphone (Google Nexus One) and an ActiGraph. Results: Study 1 included 15 participants (age: mean 55.5, SD 6.6 years; women: 56%, 8/15). Correlations between the ActiGraph and the 3 phones were strong to very strong (ρ=.77-.82). Further, after excluding bicycling and standing, cut-point derived classifications of activities yielded a high percentage of activities classified correctly according to intensity level (eg, 78%-91% by phone) that were similar to the ActiGraph’s percent correctly classified (ie, 91%). Study 2 included 23 participants (age: mean 57.0, SD 6.4 years; women: 74%, 17/23). Within the free-living context, results suggested a moderate correlation (ie, ρ=.59, P<.001) between the raw ActiGraph counts/minute and the phone’s raw counts/minute and a strong correlation on minutes of moderate-to-vigorous physical activity (MVPA; ie, ρ=.67, P<.001). Results from Bland-Altman plots suggested close mean absolute estimates of sedentary (mean difference=–26 min/day of sedentary behavior) and MVPA (mean difference=–1.3 min/day of MVPA) although there was large variation. Conclusions: Overall, results suggest that an Android smartphone can provide comparable estimates of physical activity to an ActiGraph in both a laboratory-based and free-living context for estimating sedentary and MVPA and that different Android smartphones may reliably confer similar estimates.

  • The screenshot of the mobile interface of ClinicalConnect.

    Mobile Access to ClinicalConnect: A User Feedback Survey on Usability, Productivity, and Quality

    Abstract:

    Background: ClinicalConnect, a federated clinical viewer for South West Ontario, Canada, launched a mobile interface in June 2012. Objective: The aim of the study was to assess usability of the mobile interface and the perceived impact on productivity of health care providers and quality of healthcare delivery. Methods: A survey was conducted using the System Usability Scale (SUS) and questionnaires designed to measure productivity and quality based on Canada Health Infoway's Benefits Evaluation framework. Results: The mean SUS score was 67 based on 77 responses. The mean scores for productivity and quality were 3.37 (N=74) and 3.62 (N=71), respectively, on a 5-point Likert scale where 3 was neutral. Conclusions: Users perceived the mobile interface of ClinicalConnect as useful but were neutral about the ease of use.

  • mPED screenshot.

    Identifying Factors Associated With Dropout During Prerandomization Run-in Period From an mHealth Physical Activity Education Study: The mPED Trial

    Abstract:

    Background: The mobile phone-based physical activity education (mPED) trial is a randomized controlled trial (RCT) evaluating a mobile phone-delivered physical activity intervention for women. The study includes a run-in period to maximize the internal validity of the intervention trial, but little is known about factors related to successful run-in completion, and thus about potential threats to external validity. Objective: Objectives of this study are (1) to determine the timing of dropout during the run-in period, reasons for dropout, optimum run-in duration, and relevant run-in components, and (2) to identify predictors of failure to complete the run-in period. Methods: A total of 318 physically inactive women met preliminary eligibility criteria and were enrolled in the study between May 2011 and April 2014. A 3-week run-in period was required prior to randomization and included using a mobile phone app and wearing a pedometer. Cross-sectional analysis identified predictors of dropout. Results: Out of 318 participants, 108 (34.0%) dropped out prior to randomization, with poor adherence using the study equipment being the most common reason. Median failure time was 17 days into the run-in period. In univariate analyses, nonrandomized participants were younger, had lower income, were less likely to drive regularly, were less likely to have used a pedometer prior to the study, were generally less healthy, had less self-efficacy for physical activity, and reported more depressive symptoms than randomized participants. In multivariate competing risks models, not driving regularly in the past month and not having used a pedometer prior to the study were significantly associated with failure to be randomized (P=.04 and .006, respectively), controlling for age, race/ethnicity, education, shift work, and use of a study-provided mobile phone. Conclusions: Regular driving and past pedometer use were associated with reduced dropout during the prerandomization run-in period. Understanding these characteristics is important for identifying higher-risk participants, and implementing additional help strategies may be useful for reducing dropout. Trial Registration: ClinicalTrials.gov NCT01280812; https://clinicaltrials.gov/ct2/show/NCT01280812 (Archived by WebCite at http://www.webcitation.org/6XFC5wvrP).

  • Portable devices provided to participants as part of the intervention (from left: iPad Mini, bluetooth weight scale, auto-inflating blood pressure cuff).

    Patient Engagement With a Mobile Web-Based Telemonitoring System for Heart Failure Self-Management: A Pilot Study

    Abstract:

    Background: Intensive remote monitoring programs for congestive heart failure have been successful in reducing costly readmissions, but may not be appropriate for all patients. There is an opportunity to leverage the increasing accessibility of mobile technologies and consumer-facing digital devices to empower patients in monitoring their own health outside of the hospital setting. The iGetBetter system, a secure Web- and telephone-based heart failure remote monitoring program, which leverages mobile technology and portable digital devices, offers a creative solution at lower cost. Objective: The objective of this pilot study was to evaluate the feasibility of using the iGetBetter system for disease self-management in patients with heart failure. Methods: This was a single-arm prospective study in which 21 ambulatory, adult heart failure patients used the intervention for heart failure self-management over a 90-day study period. Patients were instructed to take their weight, blood pressure, and heart rate measurements each morning using a WS-30 bluetooth weight scale, a self-inflating blood pressure cuff (Withings LLC, Issy les Moulineaux, France), and an iPad Mini tablet computer (Apple Inc, Cupertino, CA, USA) equipped with cellular Internet connectivity to view their measurements on the Internet. Outcomes assessed included usability and satisfaction, engagement with the intervention, hospital resource utilization, and heart failure-related quality of life. Descriptive statistics were used to summarize data, and matched controls identified from the electronic medical record were used as comparison for evaluating hospitalizations. Results: There were 20 participants (mean age 53 years) that completed the study. Almost all participants (19/20, 95%) reported feeling more connected to their health care team and more confident in performing care plan activities, and 18/20 (90%) felt better prepared to start discussions about their health with their doctor. Although heart failure-related quality of life improved from baseline, it was not statistically significant (P=.55). Over half of the participants had greater than 80% (72/90 days) weekly and overall engagement with the program, and 15% (3/20) used the interactive voice response telephone system exclusively for managing their care plan. Hospital utilization did not differ in the intervention group compared to the control group (planned hospitalizations P=.23, and unplanned hospitalizations P=.99). Intervention participants recorded shorter average length of hospital stay, but no significant differences were observed between intervention and control groups (P=.30). Conclusions: This pilot study demonstrated the feasibility of a low-intensive remote monitoring program leveraging commonly used mobile and portable consumer devices in augmenting care for a fairly young population of ambulatory patients with heart failure. Further prospective studies with a larger sample size and within more diverse patient populations is necessary to determine the effect of mobile-based remote monitoring programs such as the iGetBetter system on clinical outcomes in heart failure.

  • Screenshot of the GooDMomS website.

    Internet Use and Access Among Pregnant Women via Computer and Mobile Phone: Implications for Delivery of Perinatal Care

    Abstract:

    Background: The use of Internet-based behavioral programs may be an efficient, flexible method to enhance prenatal care and improve pregnancy outcomes. There are few data about access to, and use of, the Internet via computers and mobile phones among pregnant women. Objective: We describe pregnant women’s access to, and use of, computers, mobile phones, and computer technologies (eg, Internet, blogs, chat rooms) in a southern United States population. We describe the willingness of pregnant women to participate in Internet-supported weight-loss interventions delivered via computers or mobile phones. Methods: We conducted a cross-sectional survey among 100 pregnant women at a tertiary referral center ultrasound clinic in the southeast United States. Data were analyzed using Stata version 10 (StataCorp) and R (R Core Team 2013). Means and frequency procedures were used to describe demographic characteristics, access to computers and mobile phones, and use of specific Internet modalities. Chi-square testing was used to determine whether there were differences in technology access and Internet modality use according to age, race/ethnicity, income, or children in the home. The Fisher’s exact test was used to describe preferences to participate in Internet-based postpartum weight-loss interventions via computer versus mobile phone. Logistic regression was used to determine demographic characteristics associated with these preferences. Results: The study sample was 61.0% white, 26.0% black, 6.0% Hispanic, and 7.0% Asian with a mean age of 31.0 (SD 5.1). Most participants had access to a computer (89/100, 89.0%) or mobile phone (88/100, 88.0%) for at least 8 hours per week. Access remained high (>74%) across age groups, racial/ethnic groups, income levels, and number of children in the home. Internet/Web (94/100, 94.0%), email (90/100, 90.0%), and Facebook (50/100, 50.0%) were the most commonly used Internet technologies. Women aged less than 30 years were more likely to report use of Twitter and chat rooms compared to women 30 years of age or older. Of the participants, 82.0% (82/100) were fairly willing or very willing to participate in postpartum lifestyle intervention. Of the participants, 83.0% (83/100) were fairly willing or very willing to participate in an Internet intervention delivered via computer, while only 49.0% (49/100) were fairly willing or very willing to do so via mobile phone technology. Older women and women with children tended to be less likely to desire a mobile phone-based program. Conclusions: There is broad access and use of computer and mobile phone technology among southern US pregnant women with varied demographic characteristics. Pregnant women are willing to participate in Internet-supported perinatal interventions. Our findings can inform the development of computer- and mobile phone-based approaches for the delivery of clinical and educational interventions.

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  • The Use of Smartphone and Medical Related Apps among General Practitioners

    Date Submitted: Apr 7, 2015

    Open Peer Review Period: Apr 7, 2015 - Jun 2, 2015

    Background: Smartphone has drastically changed the way we work and live today. Smartphones and smartphone applications (apps) have expanded new forms of health professionals’ work. There are a lot o...

    Background: Smartphone has drastically changed the way we work and live today. Smartphones and smartphone applications (apps) have expanded new forms of health professionals’ work. There are a lot of studies on the usage of smartphone apps for different specialists. However, there is no study on current use of smartphone apps among General practitioners (GPs). Objective: To investigate the extent to GPs own smartphones and use them to aid their clinical medical activities, and how often they use apps to acquire medical information and support clinical decision. Methods: A questionnaire survey of GPs was undertaken in Hangzhou, China. Data probing the GPs’ current use of medical apps in their clinical activities and its influencing factors, including: the numbers who owned a smartphone; type of smartphone; the number of medical apps owned; how often medical apps were actually referred to during working hours; and the clinical environment in which the non-medical smartphone app was used, were collected and analyzed Results: 125 GPs anticipated the survey. 90.4% (n = 113/125) of GPs owned a smartphone, with 48.7% (n = 55/113) of GPs owning an iPhone and 47.8% (n = 54/113) owning an android platform smartphone. Most smartphone user of GPs owned 1-3 medical related applications, with very few owning more than 4, and there is no difference between iPhone and android smartphone owners (χ2 = 1.388, P = 0.846). 36% of GPs reported using medical related apps on a daily basis. The majority of doctors reported using apps daily less than 30 minutes. Conclusions: A high level of smartphone ownership and usage among GPs is found in this study, but few people choose apps to support their clinical practice. Several non-medical apps were frequently used to aid GPs’ medical activity during working hours.