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Journal Description

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, Scopus, and Science Citation Index Expanded (SCIE), and in June 2018 received an Impact Factor of 4.541, which ranks the journal #2 (behind JMIR) out of 25 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.


Recent Articles:

  • Walking group. Source: Pixabay; Copyright: Mario Ohibsky; URL:; License: Public Domain (CC0).

    Key Elements of mHealth Interventions to Successfully Increase Physical Activity: Meta-Regression


    Background: Mobile technology gives researchers unimagined opportunities to design new interventions to increase physical activity. Unfortunately, it is still unclear which elements are useful to initiate and maintain behavior change. Objective: In this meta-analysis, we investigated randomized controlled trials of physical activity interventions that were delivered via mobile phone. We analyzed which elements contributed to intervention success. Methods: After searching four databases and science networks for eligible studies, we entered 50 studies with N=5997 participants into a random-effects meta-analysis, controlling for baseline group differences. We also calculated meta-regressions with the most frequently used behavior change techniques (behavioral goals, general information, self-monitoring, information on where and when, and instructions on how to) as moderators. Results: We found a small overall effect of the Hedges g=0.29, (95% CI 0.20 to 0.37) which reduced to g=0.22 after correcting for publication bias. In the moderator analyses, behavioral goals and self-monitoring each led to more intervention success. Interventions that used neither behavioral goals nor self-monitoring had a negligible effect of g=0.01, whereas utilizing either technique increased effectiveness by Δg=0.31, but combining them did not provide additional benefits (Δg=0.36). Conclusions: Overall, mHealth interventions to increase physical activity have a small to moderate effect. However, including behavioral goals or self-monitoring can lead to greater intervention success. More research is needed to look at more behavior change techniques and their interactions. Reporting interventions in trial registrations and articles need to be structured and thorough to gain accurate insights. This can be achieved by basing the design or reporting of interventions on taxonomies of behavior change.

  • Source: Pixabay; Copyright: Kathy Bugajsky; URL:; License: Public Domain (CC0).

    Using Mobile Phones to Examine and Enhance Perceptions of Control in Mildly Depressed and Nondepressed Volunteers: Intervention Study


    Background: Perceived control is strongly linked to healthy outcomes, mental healthiness, and psychological well-being. This is particularly important when people have little control over things that are happening to them. Perceived control studies have been performed extensively in laboratory settings and show that perceived control can be increased by experimental manipulations. Although these studies suggest that it may be possible to improve people’s mental health by increasing their perceived control, there is very little evidence to date to suggest that perceived control can also be influenced in the real world. Objective: The first aim of this study was to test for evidence of a link between noncontrol situations and psychological well-being in the real world using a mobile phone app. The second and arguably more important aim of the study was to test whether a simple instructional intervention on the nature of alternative causes would enhance people’s perceptions of their own control in these noncontrol situations. Methods: We implemented a behavioral action-outcome contingency judgment task using a mobile phone app. An opportunity sample of 106 healthy volunteers scoring low (n=56, no depression) or high (n=50, mild depression) on a depression scale participated. They were given no control over the occurrence of a low- or high-frequency stimulus that was embedded in everyday phone interactions during a typical day lasting 8 hours. The intervention involved instructions that either described a consistent alternative cause against which to assess their own control, or dynamic alternative causes of the outcome. Throughout the day, participants rated their own control over the stimulus using a quantitative judgment scale. Results: Participants with no evidence of depression overestimated their control, whereas those who were most depressed were more accurate in their control ratings. Instructions given to all participants about the nature of alternative causes significantly affected the pattern of perceived control ratings. Instructions describing discrete alternative causes enhanced perceived control for all participants, whereas dynamic alternative causes were linked to less perceived control. Conclusions: Perceptions of external causes are important to perceived control and can be used to enhance people’s perceptions. Theoretically motivated interventions can be used to enhance perceived control using mobile phone apps. This is the first study to do so in a real-world setting.

  • Source: Pxhere; Copyright: Pxhere; URL:; License: Public Domain (CC0).

    Usability Study of Mainstream Wearable Fitness Devices: Feature Analysis and System Usability Scale Evaluation


    Background: Wearable devices have the potential to promote a healthy lifestyle because of their real-time data monitoring capabilities. However, device usability is a critical factor that determines whether they will be adopted on a large scale. Usability studies on wearable devices are still scarce. Objective: This study aims to compare the functions and attributes of seven mainstream wearable devices and to evaluate their usability. Methods: The wearable devices selected were the Apple Watch, Samsung Gear S, Fitbit Surge, Jawbone Up3, Mi Band, Huawei Honor B2, and Misfit Shine. A mixed method of feature comparison and a System Usability Scale (SUS) evaluation based on 388 participants was applied; the higher the SUS score, the better the usability of the product. Results: For features, all devices had step counting, an activity timer, and distance recording functions. The Samsung Gear S had a unique sports track recording feature and the Huawei Honor B2 had a unique wireless earphone. The Apple Watch, Samsung Gear S, Jawbone Up3, and Fitbit Surge could measure heart rate. All the devices were able to monitor sleep, except the Apple Watch. For product characteristics, including attributes such as weight, battery life, price, and 22 functions such as step counting, activity time, activity type identification, sleep monitoring, and expandable new features, we found a very weak negative correlation between the SUS scores and price (r=−.10, P=.03) and devices that support expandable new features (r=−.11, P=.02), and a very weak positive correlation between the SUS scores and devices that support the activity type identification function (r=.11, P=.02). The Huawei Honor B2 received the highest score of mean 67.6 (SD 16.1); the lowest Apple Watch score was only 61.4 (SD 14.7). No significant difference was observed among brands. The SUS score had a moderate positive correlation with the user’s experience (length of time the device was used) (r=.32, P<.001); participants in the medical and health care industries gave a significantly higher score (mean 61.1, SD 17.9 vs mean 68.7, SD 14.5, P=.03). Conclusions: The functions of wearable devices tend to be homogeneous and usability is similar across various brands. Overall, Mi Band had the lowest price and the lightest weight. Misfit Shine had the longest battery life and most functions, and participants in the medical and health care industries had the best evaluation of wearable devices. The perceived usability of mainstream wearable devices is unsatisfactory and customer loyalty is not high. A consumer’s SUS rating for a wearable device is related to their personal situation instead of the device brand. Device manufacturers should put more effort into developing innovative functions and improving the usability of their products by integrating more cognitive behavior change techniques.

  • Source: The Authors / Placeit; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    A Tool to Measure Young Adults’ Food Intake: Design and Development of an Australian Database of Foods for the Eat and Track Smartphone App


    Background: Dietary assessment is reliant on the collection of accurate food and beverage consumption data. Technology has been harnessed to standardize recording and provide automatic nutritional analysis to reduce cost and researcher burden. Objective: To better assess the diet of young adults, especially relating to the contribution of foods prepared outside the home, a database was needed to support a mobile phone data collection app. The app also required usability testing to assure ease of entry of foods and beverages. This paper describes the development of the Eat and Track app (EaT app) and the database underpinning it. Methods: The Australian Food and Nutrient Database 2011-13, consisting of 5740 food items was modified. Four steps were undertaken: (1) foods not consumed by young adults were removed, (2) nutritionally similar foods were merged, (3) foods available from the 30 largest ready-to-eat food chains in Australia were added, and (4) long generic food names were shortened and simplified. This database was used to underpin the EaT app. Qualitative, iterative usability testing of the EaT app was conducted in three phases using the “Think Aloud” method. Responses were sorted and coded using content analysis. The System Usability Scale (SUS) was administered to measure the EaT app’s perceived usability. Results: In total, 1694 (29.51%) foods were removed from the Australian Food and Nutrient Database, including 608 (35.89%) ingredients, 81 (4.78%) foods already captured in the fast food chain information, 52 (3.07%) indigenous foods, 25 (1.48%) nutrients/dietary supplements, and 16 (0.94%) child-specific foods. The remaining 912 (53.84%) foods removed were not consumed by young adults in previous surveys or were “not defined” in the Australian Food and Nutrient Database. Another 220 (3.83%) nutritionally similar foods were combined. The final database consisted of 6274 foods. Fifteen participants completed usability testing. Issues identified by participants fell under six themes: keywords for searching, history list of entered foods, amounts and units, the keypad, food names, and search function. Suggestions for improvement were collected, incorporated, and tested in each iteration of the app. The SUS of the final version of the EaT app was rated 69. Conclusions: A food and beverage database has been developed to underpin the EaT app, enabling data collection on the eating-out habits of 18- to 30-year-old Australians. The development process has resulted in a database with commonly used food names, extensive coverage of foods from ready-to-eat chains, and commonly eaten portion sizes. Feedback from app usability testing led to enhanced keyword searching and the addition of functions to enhance usability such as adding brief instructional screens. There is potential for the features of the EaT app to facilitate the collection of more accurate dietary intake data. The database and the app will be valuable dietary assessment resources for researchers.

  • Most available apps for cardiopulmonary resuscitation are medically incorrect or user-unfriendly. Source: The Authors; Copyright: The Authors; URL:; License: Licensed by JMIR.

    Medical Correctness and User Friendliness of Available Apps for Cardiopulmonary Resuscitation: Systematic Search Combined With Guideline Adherence and...


    Background: In case of a cardiac arrest, start of cardiopulmonary resuscitation by a bystander before the arrival of the emergency personnel increases the probability of survival. However, the steps of high-quality resuscitation are not known by every bystander or might be forgotten in this complex and time-critical situation. Mobile phone apps offering real-time step-by-step instructions might be a valuable source of information. Objective: The aim of this study was to examine mobile phone apps offering real-time instructions in German or English in case of a cardiac arrest, to evaluate their adherence to current resuscitation guidelines, and to test their usability. Methods: Our 3-step approach combines a systematic review of currently available apps guiding a medical layperson through a resuscitation situation, an adherence testing to medical guidelines, and a usability evaluation of the determined apps. The systematic review followed an adapted preferred reporting items for systematic reviews and meta-analyses flow diagram, the guideline adherence was tested by applying a conformity checklist, and the usability was evaluated by a group of mobile phone frequent users and emergency physicians with the system usability scale (SUS) tool. Results: The structured search in Google Play Store and Apple App Store resulted in 3890 hits. After removing redundant ones, 2640 hits were checked for fulfilling the inclusion criteria. As a result, 34 apps meeting all inclusion criteria were identified. These included apps were analyzed to determine medical accuracy as defined by the European Resuscitation Council’s guidelines. Only 5 out of 34 apps (15%, 5/34) fulfilled all criteria chosen to determine guideline adherence. All other apps provided no or wrong information on at least one relevant topic. The usability of 3 apps was evaluated by 10 mobile phone frequent users and 9 emergency physicians. Of these 3 apps, solely the app “HELP Notfall” (median=87.5) was ranked with an SUS score above the published average of 68. This app was rated significantly superior to “HAMBURG SCHOCKT” (median=55; asymptotic Wilcoxon test: z=−3.63, P<.01, n=19) and “Mein DRK” (median=32.5; asymptotic Wilcoxon test: z=−3.83, P<.01, n=19). Conclusions: Implementing a systematic quality control for health-related apps should be enforced to ensure that all products provide medically accurate content and sufficient usability in complex situations. This is of exceptional importance for apps dealing with the treatment of life-threatening events such as cardiac arrest.

  • Source: Flickr; Copyright: Top Ten Alternatives; URL:; License: Creative Commons Attribution (CC-BY).

    Implementation Tells Us More Beyond Pooled Estimates: Secondary Analysis of a Multicountry mHealth Trial to Reduce Blood Pressure


    Background: The uptake of an intervention aimed at improving health-related lifestyles may be influenced by the participant’s stage of readiness to change behaviors. Objective: We conducted secondary analysis of the Grupo de Investigación en Salud Móvil en América Latina (GISMAL) trial according to levels of uptake of intervention (dose-response) to explore outcomes by country, in order to verify the consistency of the trial’s pooled results, and by each participant’s stage of readiness to change a given lifestyle at baseline. The rationale for this secondary analysis is motivated by the original design of the GISMAL study that was independently powered for the primary outcome—blood pressure—for each country. Methods: We conducted a secondary analysis of a mobile health (mHealth) multicountry trial conducted in Argentina, Guatemala, and Peru. The intervention consisted of monthly motivational phone calls by a trained nutritionist and weekly tailored text messages (short message service), over a 12-month period, aimed to enact change on 4 health-related behaviors: salt added to foods when cooking, consumption of high-fat and high-sugar foods, consumption of fruits or vegetables, and practice of physical activity. Results were stratified by country and by participants’ stage of readiness to change (precontemplation or contemplation; preparation or action; or maintenance) at baseline. Exposure (intervention uptake) was the level of intervention (<50%, 50%-74%, and ≥75%) received by the participant in terms of phone calls. Linear regressions were performed to model the outcomes of interest, presented as standardized mean values of the following: blood pressure, body weight, body mass index, waist circumference, physical activity, and the 4 health-related behaviors. Results: For each outcome of interest, considering the intervention uptake, the magnitude and direction of the intervention effect differed by country and by participants’ stage of readiness to change at baseline. Among those in the high intervention uptake category, reductions in systolic blood pressure were only achieved in Peru, whereas fruit and vegetable consumption also showed reductions among those who were at the maintenance stage at baseline in Argentina and Guatemala. Conclusions: Designing interventions oriented toward improving health-related lifestyle behaviors may benefit from recognizing baseline readiness to change and issues in implementation uptake. Trial Registration: NCT01295216; (Archived by WebCite at

  • Prototype showing the provider's view of the patient's medication information in the app (montage). Source: The Authors / Placeit; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    Simulated Clinical Encounters Using Patient-Operated mHealth: Experimental Study to Investigate Patient-Provider Communication


    Background: This study investigates patient-centered mobile health (mHealth) technology in terms of the secondary user experience (UX). Specifically, it examines how personal mobile technology, under patient control, can be used to improve patient-provider communication about the patient’s health care during their first visit to a provider. Common ground, a theory about language use, is used as the theoretical basis to examine interactions. A novel concept of this study is that it is one of the first empirical studies to explore the relative meaningfulness of a secondary UX for specific health care tasks. Objective: The objective of this study was to investigate the extent that patient-operated mHealth technology can be designed to improve the communication between the patient and provider during an initial face-to-face encounter. Methods: The experimental study was conducted in 2 large Midwestern cities from February 2016 to May 2016. A custom-designed smartphone app prototype was used as the study treatment. The experimental design was posttest-only control group and included video-recorded simulated face-to-face clinical encounters in which an actor role-played a patient. Experienced clinicians consisting of doctors (n=4) and nurses (n=8) were the study participants. A thematic analysis of qualitative data was performed. Quantitative data collected from time on task measurements were analyzed using descriptive statistics. Results: Three themes that represent how grounding manifested during the encounter, what it meant for communication during the encounter, and how it influenced the provider’s perception of the patient emerged from the qualitative analysis. The descriptive statistics were important for inferring evidence of efficiency and effectiveness of communication for providers. Overall, encounter and task times averaged slightly faster in almost every instance for the treatment group than that in the control group. Common ground clearly was better in the treatment group, indicating that the idea of designing for the secondary UX to improve provider outcomes has merit. Conclusions: Combining the notions of common ground, human-computer interaction design, and smartphone technology resulted in a prototype that improved the efficiency and effectiveness of face-to-face collaboration for secondary users. The experimental study is one of the first studies to demonstrate that an investment in the secondary UX for high payoff tasks has value but that not all secondary UXs are meaningful for design. This observation is useful for prioritizing how resources should be applied when considering the secondary UX.

  • Source: Photoshare; Copyright: Jodi-Ann Burey/VillageReach, Courtesy of Photoshare; URL:; License: Licensed by the authors.

    Insights From a Text Messaging–Based Sexual and Reproductive Health Information Program in Tanzania (m4RH): Retrospective Analysis


    Background: Many mobile health (mHealth) interventions have the potential to generate and store vast amounts of system-generated participant interaction data that could provide insight into user engagement, programmatic strengths, and areas that need improvement to maximize efficacy. However, despite the popularity of mHealth interventions, there is little documentation on how to use these data to monitor and improve programming or to evaluate impact. Objective: This study aimed to better understand how users of the Mobile for Reproductive Health (m4RH) mHealth intervention engaged with the program in Tanzania from September 2013 to August 2016. Methods: We conducted secondary data analysis of longitudinal data captured by system logs of participant interactions with the m4RH program from 127 districts in Tanzania from September 2013 to August 2016. Data cleaning and analysis was conducted using Stata 13. The data were examined for completeness and “correctness.” No missing data was imputed; respondents with missing or incorrect values were dropped from the analyses. Results: The total population for analysis included 3,673,702 queries among 409,768 unique visitors. New users represented roughly 11.15% (409,768/3,673,702) of all queries. Among all system queries for new users, 46.10% (188,904/409,768) users accessed the m4RH main menu. Among these users, 89.58% (169,218/188,904) accessed specific m4RH content on family planning, contraceptive methods, adolescent-specific and youth-specific information, and clinic locations after first accessing the m4RH main menu. The majority of these users (216,422/409,768, 52.82%) requested information on contraceptive methods; fewer users (23,236/409,768, 5.67%) requested information on clinic location. The conversion rate was highest during the first and second years of the program when nearly all users (11,246/11,470, 98.05%, and 33,551/34,830, 96.33%, respectively) who accessed m4RH continued on to query more specific content from the system. The rate of users that accessed m4RH and became active users declined slightly from 98.05% (11,246/11,470) in 2013 to 87.54% (56,696/64,765) in 2016. Overall, slightly more than one-third of all new users accessing m4RH sent queries at least once per month for 2 or more months, and 67.86% (278,088/409,768) of new and returning users requested information multiple times per month. Promotional periods were present for 15 of 36 months during the study period. Conclusions: The analysis of the rich data captured provides a useful framework with which to measure the degree and nature of user engagement utilizing routine system-generated data. It also contributes to knowledge of how users engage with text messaging (short message service)-based health promotion interventions and demonstrates how data generated on user interactions could inform improvements to the design and delivery of a service, thereby enhancing its effectiveness.

  • Medical apps (montage). Source: The Authors / Placeit; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    The Web-Based Physician is Ready to See You: A Nationwide Cross-Sectional Survey of Physicians Using a Mobile Medical App to Evaluate Patients With Sexually...


    Background: Web-based medical service provision is increasingly becoming common. However, it remains unclear how physicians are responding to this trend and how Web-based and offline medical services are linked. Objective: The objectives of this study were to examine physicians’ use of mobile medical apps for sexually transmitted disease (STD) consultations and identify the physicians who frequently use mobile medical apps to evaluate patients with STD. Methods: In August 2017, we conducted a nationwide cross-sectional survey among physicians registered on a mobile medical app in China. We collected data on physicians’ demographic information, institutional information, and Web-based medical practices. We compared physicians who used mobile medical apps to evaluate patients with STD frequently (at least once a week) with infrequent users. Bivariate and multivariate logistic regressions were used to identify physicians who frequently evaluated patients with STD on mobile medical apps. Results: A total of 501 physicians participated in the survey. Among them, three-quarters were men and the average age was 37.6 (SD 8.2) years. Nearly all physicians (492/501, 98.2%) recommended their last Web-based patient with STD to subsequently see a physician in the clinic. More than half (275/501, 54.9%) of physicians recommended STD testing to Web-based patients, and 43.9% (220/501) provided treatment advice to patients with STD. Of all physicians, 21.6% (108/501) used mobile medical apps to evaluate patients with STD through Web more than once a week. Overall, 85.2% (427/501) physicians conducted follow-up consultation for patients with STD using mobile medical apps. Physicians working at institutions with STD prevention materials were associated with frequent evaluation of patients with STD on mobile medical apps (adjusted odds ratio=2.10, 95% CI 1.18-3.74). Conclusions: Physicians use mobile medical apps to provide a range of services, including Web-based pre- and posttreatment consultations and linkage to offline clinical services. The high rates of referral to clinics suggest that mobile medical apps are used to promote clinic-seeking, and not replace it. Physicians’ use of mobile medical apps could benefit sexual minorities and others who avoid formal clinic-based services.

  • Sleepsight app (montage). Source: The Authors; Copyright: The Authors; URL:; License: Licensed by JMIR.

    Capturing Rest-Activity Profiles in Schizophrenia Using Wearable and Mobile Technologies: Development, Implementation, Feasibility, and Acceptability of a...


    Background: There is growing interest in the potential for wearable and mobile devices to deliver clinically relevant information in real-world contexts. However, there is limited information on their acceptability and barriers to long-term use in people living with psychosis. Objective: This study aimed to describe the development, implementation, feasibility, acceptability, and user experiences of the Sleepsight platform, which harnesses consumer wearable devices and smartphones for the passive and unobtrusive capture of sleep and rest-activity profiles in people with schizophrenia living in their homes. Methods: A total of 15 outpatients with a diagnosis of schizophrenia used a consumer wrist-worn device and smartphone to continuously and remotely gather rest-activity profiles over 2 months. Once-daily sleep and self-rated symptom diaries were also collected via a smartphone app. Adherence with the devices and smartphone app, end-of-study user experiences, and agreement between subjective and objective sleep measures were analyzed. Thresholds for acceptability were set at a wear time or diary response rate of 70% or greater. Results: Overall, 14 out of 15 participants completed the study. In individuals with a mild to moderate symptom severity at baseline (mean total Positive and Negative Syndrome Scale score 58.4 [SD 14.4]), we demonstrated high rates of engagement with the wearable device (all participants meeting acceptability criteria), sleep diary, and symptom diary (93% and 86% meeting criteria, respectively), with negative symptoms being associated with lower diary completion rate. The end-of-study usability and acceptability questionnaire and qualitative analysis identified facilitators and barriers to long-term use, and paranoia with study devices was not a significant barrier to engagement. Comparison between sleep diary and wearable estimated sleep times showed good correspondence (ρ=0.50, P<.001). Conclusions: Extended use of wearable and mobile technologies are acceptable to people with schizophrenia living in a community setting. In the future, these technologies may allow predictive, objective markers of clinical status, including early markers of impending relapse.

  • Source: Pexels; Copyright: Kha Ruxury; URL:; License: Licensed by JMIR.

    A Mobile Sleep-Management Learning System for Improving Students’ Sleeping Habits by Integrating a Self-Regulated Learning Strategy: Randomized Controlled...


    Background: Insomnia can significantly affect students’ learning performance. Researchers have indicated the importance and challenge of coping with insomnia using nondrug treatments, such as cognitive behavioral therapy (CBT) for insomnia. However, it is easy for the traditional CBT for insomnia to be interrupted owing to the overly lengthy period of sleep therapy. Self-regulated learning (SRL) strategies are known to be an effective approach for helping students improve their time management, as well as their ability to set learning goals and adopt learning strategies. Objective: The objective of this study was to propose a mobile sleep-management learning system integrated with SRL strategies and CBT. Methods: A total of 18 undergraduate students from a university in northern Taiwan participated in the 2-week experiment of using this sleep-management system. Results: The experimental results showed that the proposed approach was useful and easy for students to use. In addition, the number of students with insomnia significantly decreased; that is, the proposed approach could help students improve their sleep quality and cultivate better sleeping habits, which is important for them to enhance their learning efficiency. Conclusions: With the assistance of this proposed approach, students can plan their daily life by setting goals, applying strategies, monitoring their life habits process, and modifying strategies to cultivate good learning and healthy lifestyle habits. Trial Registration: Government Research Bulletin MOST104-3011-E038-001; id=11568383&docId=467988 (Archived by WebCite at

  • Source: Flickr; Copyright: Remko Tanis; URL:; License: Creative Commons Attribution + Noncommercial + NoDerivatives (CC-BY-NC-ND).

    A Short Message Service Intervention for Improving Infant Feeding Practices in Shanghai, China: Planning, Implementation, and Process Evaluation


    Background: Although mobile health (mHealth) has been widely applied in health care services, few studies have reported the detailed process of the development and implementation of text messaging (short message service, SMS) interventions. Objective: Our study aims to demonstrate the process and lessons learned from a community-based text messaging (SMS) intervention for improving infant feeding in Shanghai, China. Methods: The intervention included planning and development, implementation, and process evaluation. A 3-phase process was adopted during planning and development: (1) a formative study with expectant and new mothers to explore the barriers of appropriate infant feeding practices; (2) a baseline questionnaire survey to understand potential intervention approaches; and (3) development of the text message bank. The text messaging intervention was delivered via a computer-based platform. A message bank was established before the start of the intervention containing information on the benefits of breastfeeding, preparing for breastfeeding, early initiation of breastfeeding, timely introduction of complementary foods, and establishing appropriate feeding practices, etc. An expert advisory committee oversaw the content and quality of the message bank. Process evaluation was conducted through field records and qualitative interviews with participating mothers. Results: We found that the text messaging intervention was feasible and well received by mothers because of its easy and flexible access. The weekly based message frequency was thought to be appropriate, and the contents were anticipatory and trustworthy. Some mothers had high expectations for timely response to inquiries. Occasionally, the text messages were not delivered due to unstable telecommunication transmission. Mothers suggested that the messages could be more personalized. Conclusions: This study demonstrates the feasibility and value of text messaging intervention in filling gaps in delivering health care services and promoting healthy infant feeding practices in settings where personal contact is limited.

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  • ImpulsePal: Developing a Smartphone App to Manage Food Temptations using Intervention Mapping.

    Date Submitted: Oct 29, 2018

    Open Peer Review Period: Nov 9, 2018 - Jan 4, 2019

    Background: Impulsive processes driving eating behaviour can often undermine peoples’ attempts to lose weight and maintain weight loss. Objective: To develop an impulse management intervention to su...

    Background: Impulsive processes driving eating behaviour can often undermine peoples’ attempts to lose weight and maintain weight loss. Objective: To develop an impulse management intervention to support weight loss in adults. Methods: Intervention Mapping (IM) was used to systematically develop the “ImpulsePal” intervention. The development involved: 1) a needs assessment including a qualitative study, service user workshops, a systematic review of impulse modification techniques, and consultations with intervention design and delivery experts; 2) specification of performance objectives, determinants, and change objectives; 3) selection of intervention strategies (mapping of taxonomy-related change techniques to the determinants of change); 4) creation of programme materials; 5) specification of adoption and implementation plans; 6) devising an evaluation plan. Results: Application of the IM Protocol resulted in a smartphone app-based intervention aimed at reducing unhealthy snacking, overeating, and alcoholic and sugary drink consumption. The app includes inhibition training, mindfulness techniques, implementation intentions (if-then planning), visuospatial loading, use of physical activity as a craving-management technique, and context-specific reminders. An “Emergency Button” was also included to provide access to in-the-moment support when temptation is strong. Conclusions: ImpulsePal is a novel, theory- and evidence-informed, person-centred app to improve impulse management and promote healthier eating. Intervention Mapping ensured that all app components are practical operationalisations of change techniques that target our specific change objectives and their associated theoretical determinants. Using this approach enhances transparency, provides a clear framework for analysis and increases replicability as well as the potential of the intervention to accomplish the desired outcome of supporting weight loss.

  • Is wearable technology becoming part of us? Developing and validating a measurement scale for wearable technology embodiment.

    Date Submitted: Nov 9, 2018

    Open Peer Review Period: Nov 9, 2018 - Jan 4, 2019

    Background: The increased wearability of technology has enabled individuals to access and interact with technology in such a way that it has become more and more an integral part of one’s body, mind...

    Background: The increased wearability of technology has enabled individuals to access and interact with technology in such a way that it has become more and more an integral part of one’s body, mind, and sense of self. This phenomenon, which is referred to in this paper as wearable technology embodiment, has led to extensive conceptual considerations in various research fields. These considerations and further possibilities to quantify wearable technology embodiment are of particular value to the mHealth field. For example, to predict the effectiveness of mHealth interventions, knowing the extent to which people embody the technology might be crucial. To facilitate examining wearable technology embodiment we developed a measurement scale for this construct. Objective: The wearable technology embodiment scale is a 3 dimensional scale including 9 measurement items. The items are distributed evenly between the 3 dimensions which include: body extension, cognitive extension, and self-extension. The objective of the study was to conceptualize wearable technology embodiment, create an instrument to measure it, and test the predictive validity of the scale using well known constructs related to technology adoption. Methods: Data were collected through a vignette-based survey (n=182). Each respondent was given three different vignettes, describing a hypothetical situation using a different type of mobile or wearable technology with the purpose of tracking daily activities: a smart phone, a smart wristband, and a smart watch. The scale dimensions and item reliability was tested for validity and goodness of fit. Results: Convergent validity of the three dimensions and their reliability was confirmed via the CFA factor loadings45 (> 0.70), AVE values40 (> 0.50) and minimum item to total correlations50 (> 0.40) which exceeded established threshold value. The reliability of the dimensions was also confirmed as cronbach’s alpha and composite reliability exceeded 0.70. Good fit was found within three dimensions as inter-correlated first-order factors. Predictive validity testing showed that these dimensions significantly add to multiple constructs associated with predicting the adoption of new technologies (i.e.: trust, perceived usefulness, involvement, attitude, and continuous intention). Conclusions: The wearable technology embodiment measurement instrument has shown promise as a tool to measure the extension of an individual’s body, cognition, and self, as well as predict certain aspects of technology adoption. This three dimensional tool can be applied to mixed method research and used by wearable technology developers to improve future versions through such things as: fit, improved accuracy of biofeedback data, and customizable features or fashion to connect to the users personal identity. Further research is recommended to apply this tool to multiple scenarios and technologies, and more diverse user groups.

  • Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals

    Date Submitted: Nov 9, 2018

    Open Peer Review Period: Nov 9, 2018 - Jan 4, 2019

    Background: Wearable devices have evolved as screening tools for atrial fibrillation (AF). A photoplethysmographic (PPG) AF detection algorithm was developed and applied to a convenient smartphone-bas...

    Background: Wearable devices have evolved as screening tools for atrial fibrillation (AF). A photoplethysmographic (PPG) AF detection algorithm was developed and applied to a convenient smartphone-based device with good accuracy. However, patients with paroxysmal AF frequently exhibit premature atrial complexes (PACs), which result in poor unmanned AF detection, mainly because of rule-based or hand-crafted machine learning techniques limited in terms of diagnostic accuracy and reliability. Objective: We developed deep learning (DL) classifiers using PPG data to detect AF from the sinus rhythm (SR) in the presence of PACs after successful cardioversion. Methods: We examined 75 patients with AF who underwent successful elective direct-current cardioversion (DCC). ECG and pulse oximetry data over a 15-minute period were obtained before and after DCC and labeled as AF or SR. A 1-dimensional convolutional neural network (1D-CNN) and recurrent neural network (RNN) were chosen as the two DL architectures. The PAC indicator estimated the burden of PACs on the PPG dataset. We defined a metric called the confidence level (CL) of AF or SR diagnosis and compared the CLs of true and false diagnoses. We also compared the diagnostic performance of 1D-CNN and RNN with previously developed AF detectors (root-mean square of successive difference of RR intervals + Shannon entropy, support vector machine with autocorrelation and ensemble by combining two prior methods) using a five-fold cross-validation process. Results: Among the 14,298 training samples containing PPG data, 7,157 samples were obtained during the post-DCC period. The PAC indicator estimated 2,132 out of 7,157 post-DCC samples (29.79%) had PACs. The diagnostic accuracy of AF vs. SR was 99.3% vs. 95.9% in 1D-CNN and 98.3% vs. 96.0% in RNN methods. The area under receiver operating characteristic curves of the two DL classifiers was 0.998 (95% confidence interval (CI) 0.995-1.000) for 1D-CNN and 0.996 (95% CI 0.993-0.998) for RNN, which were significantly higher than other AF detectors (P < .001). If we assumed that the dataset could emulate a sufficient number of patients in training, both DL classifiers could still correctly diagnose AF even when the PAC burden was >20% (91.1% and 91.5% for 1D-CNN and RNN, respectively). The average CLs for true vs. false classification were 98.6% vs. 80.5 % for 1D-CNN and 98.3% vs. 82.4% for RNN (P < .001 for all cases). Conclusions: New DL classifiers could detect AF using PPG monitoring signals with high diagnostic accuracy even with frequent PACs and could outperform previously developed AF detectors. Although diagnostic performance decreased as the burden of PACs increased, performance improved when samples from more patients were trained. Moreover, the reliability of the diagnosis could be indicated by the CL. Wearable devices sensing PPG signals and DL classifiers should be validated as tools to screen for AF.

  • Patient experiences and implications for consultation outcomes and treatment adherence using mHealth applications among health care professionals

    Date Submitted: Nov 5, 2018

    Open Peer Review Period: Nov 9, 2018 - Jan 4, 2019

    Background: mHealth is a broad term for the use of mobile communication devices for healthcare services delivery. The use of mobile devices by health care professionals (HCPs) has transformed many asp...

    Background: mHealth is a broad term for the use of mobile communication devices for healthcare services delivery. The use of mobile devices by health care professionals (HCPs) has transformed many aspects of clinical training and practice. However, there are still gaps in knowledge concerning patient perception of the use of mHealth technologies by HCP during secondary care consultations. Objective: To explore the impact on patient experience and implications for consultation outcomes and treatment adherence. Introduction of new technological application into interactions that have very set expectations and roles and possibility for attendant disruption of patient expectations. Methods: This paper explores, via in-depth interviews, patient opinions regarding the usage of mHealth applications by health care professionals (HCPs) during consultations, identifying the paradoxes and coping behaviors to deal with those paradoxes. This qualitative study recruited ten respondents using purposive sampling and snowballing techniques through in-depth interviews. Results: The results comprise paradoxes and coping behaviors. They showed that convenience, time savings, accuracy of diagnosis and reduction of errors are the important elements for using mHealth for both HCP and patient. In addition, respondents perceived that mobile health apps facilitate HCP engagement of patients and assist explanations and better patient understanding. Interaction and the quality of the interaction were acknowledged as significant in HCP-patient communication and patient compliance with treatment. Conclusions: To sum, many patients were responsive to the idea of mHealth, both by the doctor and themselves, but wanted to have regulation of use of apps, better involvement and explanations and not have the doctor lose focus on the patient, that is, the feeling of personalized treatment. They also were worried that the HCP might seem to ignore the patient or withdraw from the type of interaction that makes the consultation ‘human.’

  • Impact of a Novel Smartphone Application “CureApp Smoking Cessation” on Nicotine Dependence: Prospective Single-arm Interventional Pilot Study

    Date Submitted: Nov 5, 2018

    Open Peer Review Period: Nov 8, 2018 - Nov 15, 2018

    Background: Mobile applications have been considered to provide active and continuous support for smoking cessation. However, it is yet to be known whether a smoking cessation smartphone application i...

    Background: Mobile applications have been considered to provide active and continuous support for smoking cessation. However, it is yet to be known whether a smoking cessation smartphone application improves long-term abstinence rates in nicotine-dependent patients. Objective: We aim to evaluate the long-term abstinence effect of a novel smartphone application, CureApp Smoking Cessation (CASC), in patients with nicotine dependence. Methods: This study was a prospective, interventional, multicenter, single-arm study. We provided the CASC application to all the participants, who used it daily for 24 weeks. The CASC application includes features to maximize the therapeutic effect of pharmacological therapies and counseling at outpatient clinics for smoking cessation. The primary endpoint was a continuous abstinence rate (CAR) from weeks 9 to 24, while secondary endpoints were CARs from weeks 9 to 12 and 9 to 52. Results: Of the 56 adult smokers recruited, one did not download the application; therefore, 55 participants constituted the full analysis sample. The CAR from weeks 9 to 24 was 63.6% (35/55; 95% confidence interval [CI]: 51.3% – 76.0%), while the CARs from weeks 9 to 12 and 9 to 52 were 76.4% (42/55; 95% CI: 65.2% – 87.5%) and 58.2% (32/55; 95% CI: 45.6% – 70.8%), respectively. These CARs were better than the results of the national survey on outpatient clinics with regard to smoking cessation under the National Health Insurance Program and that of the varenicline phase Ⅲ trial in Japan and the United States. There was only one participant who dropped out during the 12 weeks of the treatment period. This treatment decreased the scores related to withdrawal and craving symptoms. Conclusions: The addition of CASC to usual smoking cessation therapies resulted in high CARs, high patient retention rates, and improvement of cessation-related symptoms. The smartphone application CASC is a feasible and useful tool to help long-term continuous abstinence that can be combined with a standard smoking cessation treatment program. Clinical Trial: University Hospital Medical Information Network (UMIN) Clinical Trials Registry (UMIN ID: UMIN000020123).

  • Development of comprehensive personal health records integrating patient-generated health data directly from Samsung S-Health and Apple Health: An observational stud

    Date Submitted: Nov 5, 2018

    Open Peer Review Period: Nov 5, 2018 - Nov 14, 2018

    Background: Patient-generated health data (PGHD), especially lifelog data, are important for managing chronic diseases. Additionally, personal health records (PHRs) have been considered an effective t...

    Background: Patient-generated health data (PGHD), especially lifelog data, are important for managing chronic diseases. Additionally, personal health records (PHRs) have been considered an effective tool to engage patients more actively in the management of their chronic diseases. However, no PHRs currently integrate PGHD directly from Samsung S-Health and Apple Health. Objective: The purposes of this study were (1) to demonstrate the development of an electronic medical record (EMR)-tethered PHR system (Health4U) that integrates lifelog data from Samsung S-Health and Apple Health and (2) to explore the factors associated with the use rate of the functions. Methods: To upgrade conventional EMR-tethered PHRs, a task-force team (TFT) defined the functions necessary for users. After implementing a new system, we enrolled adults aged 19 years and older with prior experience accessing Health4U in the 7-month period after November 2017, when the service was upgraded. Results: Among the 17,624 users, 215 users integrated daily steps data, 175 weight data, 51 blood sugar data, and 90 blood pressure data. Among all participants, 61.95% had one or more chronic diseases. For the integration of daily steps data, 48.3% of patients used Apple Health, 43.3% used S-Health, and 8.4% entered data manually. To retrieve medical documentation, 324 users downloaded PDF files, and 31 users integrated their medical records into Samsung S-Health via the C-CDA download function. We found a consistent increase in the odds ratios for PDF downloads among patients with a higher number of chronic diseases. The age groups of 60 years and older and 80 years and older tended to use the download function less frequently. Conclusions: This is the first study examining the factors related to the integration of lifelog data from Samsung S-Health and Apple Health into EMR-tethered PHRs and the factors related to the retrieval of medical documents from PHRs. Furthermore, findings on lifelog data integration can be used to design PHRs as a platform to integrate lifelog data in the future.