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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:

  • A randomized controlled trial with a cell phone intervention and a personal coaching intervention for young adults (montage). Source: Unsplash / The Authors; Copyright: The Authors; URL: http://mhealth.jmir.org/2018/10/e10471/; License: Creative Commons Attribution (CC-BY).

    The Association Between Engagement and Weight Loss Through Personal Coaching and Cell Phone Interventions in Young Adults: Randomized Controlled Trial

    Abstract:

    Background: Understanding how engagement in mobile health (mHealth) weight loss interventions relates to weight change may help develop effective intervention strategies. Objective: This study aims to examine the (1) patterns of participant engagement overall and with key intervention components within each intervention arm in the Cell Phone Intervention For You (CITY) trial; (2) associations of engagement with weight change; and (3) participant characteristics related to engagement. Methods: The CITY trial tested two 24-month weight loss interventions. One was delivered with a smartphone app (cell phone) containing 24 components (weight tracking, etc) and included prompting by the app in predetermined frequency and forms. The other was delivered by a coach via monthly calls (personal coaching) supplemented with limited app components (18 overall) and without any prompting by the app. Engagement was assessed by calculating the percentage of days each app component was used and the frequency of use. Engagement was also examined across 4 weight change categories: gained (≥2%), stable (±2%), mild loss (≥2% to <5%), and greater loss (≥5%). Results: Data from 122 cell phone and 120 personal coaching participants were analyzed. Use of the app was the highest during month 1 for both arms; thereafter, use dropped substantially and continuously until the study end. During the first 6 months, the mean percentage of days that any app component was used was higher for the cell phone arm (74.2%, SD 20.1) than for the personal coaching arm (48.9%, SD 22.4). The cell phone arm used the apps an average of 5.3 times/day (SD 3.1), whereas the personal coaching participants used them 1.7 times/day (SD 1.2). Similarly, the former self-weighed more than the latter (57.1% days, SD 23.7 vs 32.9% days, SD 23.3). Furthermore, the percentage of days any app component was used, number of app uses per day, and percentage of days self-weighed all showed significant differences across the 4 weight categories for both arms. Pearson correlation showed a negative association between weight change and the percentage of days any app component was used (cell phone: r=−.213; personal coaching: r=−.319), number of apps use per day (cell phone: r=−.264; personal coaching: r=−.308), and percentage of days self-weighed (cell phone: r=−.297; personal coaching: r=−.354). None of the characteristics examined, including age, gender, race, education, income, energy expenditure, diet quality, and hypertension status, appeared to be related to engagement. Conclusions: Engagement in CITY intervention was associated with weight loss during the first 6 months. Nevertheless, engagement dropped substantially early on for most intervention components. Prompting may be helpful initially. More flexible and less intrusive prompting strategies may be needed during different stages of an intervention to increase or sustain engagement. Future studies should explore the motivations for engagement and nonengagement to determine meaningful levels of engagement required for effective intervention. Trial Registration: ClinicalTrials.gov NCT01092364; https://clinicaltrials.gov/ct2/show/NCT01092364 (Archived by WebCite at http://www.webcitation.org/72V8A4e5X)

  • TOC_digital_health_trasforming_healtcare. Source: Image created by the Authors; Copyright: The Authors; URL: http://mhealth.jmir.org/2018/10/e11040/; License: Creative Commons Attribution (CC-BY).

    Design Rationale and Performance Evaluation of the Wavelet Health Wristband: Benchtop Validation of a Wrist-Worn Physiological Signal Recorder

    Abstract:

    Background: Wearable and connected health devices along with the recent advances in mobile and cloud computing provide a continuous, convenient-to-patient, and scalable way to collect personal health data remotely. The Wavelet Health platform and the Wavelet wristband have been developed to capture multiple physiological signals and to derive biometrics from these signals, including resting heart rate (HR), heart rate variability (HRV), and respiration rate (RR). Objective: This study aimed to evaluate the accuracy of the biometric estimates and signal quality of the wristband. Methods: Measurements collected from 35 subjects using the Wavelet wristband were compared with simultaneously recorded electrocardiogram and spirometry measurements. Results: The HR, HRV SD of normal-to-normal intervals, HRV root mean square of successive differences, and RR estimates matched within 0.7 beats per minute (SD 0.9), 7 milliseconds (SD 10), 11 milliseconds (SD 12), and 1 breaths per minute (SD 1) mean absolute deviation of the reference measurements, respectively. The quality of the raw plethysmography signal collected by the wristband, as determined by the harmonic-to-noise ratio, was comparable with that obtained from measurements from a finger-clip plethysmography device. Conclusions: The accuracy of the biometric estimates and high signal quality indicate that the wristband photoplethysmography device is suitable for performing pulse wave analysis and measuring vital signs.

  • Source: Flickr; Copyright: Joi Ito; URL: https://www.flickr.com/photos/joi/420211012; License: Creative Commons Attribution (CC-BY).

    An mHealth Diabetes Intervention for Glucose Control: Health Care Utilization Analysis

    Abstract:

    Background: Type 2 diabetes (T2D) is a major chronic condition requiring management through lifestyle changes and recommended health service visits. Mobile health (mHealth) is a promising tool to encourage self-management, but few studies have investigated the impact of mHealth on health care utilization. Objective: The objective of this analysis was to determine the change in 2-year health service utilization and whether utilization explained a 1.9% absolute decrease in glycated hemoglobin (HbA1c) over 1-year in the Mobile Diabetes Intervention Study (MDIS). Methods: We used commercial claims data from 2006 to 2010 linked to enrolled patients’ medical chart data in 26 primary care practices in Maryland, USA. Secondary claims data analyses were available for 56% (92/163) of participants. In the primary MDIS study, physician practices were recruited and randomized to usual care and 1 of 3 increasingly complex interventions. Patients followed physician randomization assignment. The main variables in the analysis included health service utilization by type of service and change in HbA1c. The claims data was aggregated into 12 categories of utilization to assess change in 2-year health service usage, comparing rates of usage pre- and posttrial. We also examined whether utilization explained the 1.9% decrease in HbA1c over 1 year in the MDIS cluster randomized clinical trial. Results: A significant group by time effect was observed in physician office visits, general practitioner visits, other outpatient services, prescription medications, and podiatrist visits. Physician office visits (P=.01) and general practitioner visits (P=.02) both decreased for all intervention groups during the study period, whereas prescription claims (P<.001) increased. The frequency of other outpatient services (P=.001) and podiatrist visits (P=.04) decreased for the control group and least complex intervention group but increased for the 2 most complex intervention groups. No significant effects of utilization were observed to explain the clinically significant change in HbA1c. Conclusions: Claims data analyses identified patterns of utilization relevant to mHealth interventions. Findings may encourage patients and health providers to discuss the utilization of treatment-recommended services, lab tests, and prescribed medications. Trial Registration: ClinicalTrials.gov NCT01107015; https://clinicaltrials.gov/ct2/show/NCT01107015 (Archived by Webcite at http://www.webcitation.org/72XgTaxIj)

  • Healthy.me Gout (montage). Source: The Authors; Copyright: The Authors; URL: http://mhealth.jmir.org/2018/10/e182; License: Licensed by JMIR.

    mHealth App Patient Testing and Review of Educational Materials Designed for Self-Management of Gout Patients: Descriptive Qualitative Studies

    Abstract:

    Background: Gout is a form of chronic arthritis caused by elevated serum uric acid (SUA) and culminates in painful gout attacks. Although effective uric acid-lowering therapies exist, adherence is low. This is partly due to the lack of support for patients to self-manage their disease. Mobile health apps have been used in the self-management of chronic conditions. However, not all are developed with patients, limiting their effectiveness. Objective: The objective of our study was to collect feedback from gout patients to design an effective gout self-management app. Methods: Two descriptive qualitative studies were conducted. In Study 1, researchers developed a short educational video and written materials about gout management, designed to be embedded into an app; 6 interviews and 1 focus group were held with gout patients to gather feedback on these materials. Usability testing in Study 2 involved additional gout patients using a pilot version of Healthy.me Gout, a gout self-management app, for 2 weeks. Following the trial, patients participated in an interview about their experiences using the app. Results: Patients viewed the gout educational material positively, appreciating the combined use of video, text, and images. Patients were receptive to using a mobile app to self-manage their gout. Feedback about Healthy.me Gout was generally positive with patients reporting that the tracking and diary features were most useful. Patients also provided suggestions for improving the app and educational materials. Conclusions: These studies involved patients in the development of a gout self-management app. Patients provided insight to improve the app’s presentation and usability and general lessons on useful features for chronic disease apps. Gout patients enjoyed tracking their SUA concentrations and gout attack triggers. These capabilities can be translated into self-management apps for chronic diseases that require monitoring of pathological values, medication adherence, or symptoms. Future health app design should integrate patient input and be developed iteratively to address concerns identified by patients.

  • Trialist N-of-1 app (montage). Source: The Authors; Copyright: The Authors; URL: https://mhealth.jmir.org/2018/10/e10291; License: Licensed by JMIR.

    Patient Perceptions of Their Own Data in mHealth Technology–Enabled N-of-1 Trials for Chronic Pain: Qualitative Study

    Abstract:

    Background: N-of-1 (individual comparison) trials are a promising approach for comparing the effectiveness of 2 or more treatments for individual patients; yet, few studies have qualitatively examined how patients use and make sense of their own patient-generated health data (PGHD) in the context of N-of-1 trials. Objective: The objective of our study was to explore chronic pain patients’ perceptions about the PGHD they compiled while comparing 2 chronic pain treatments and tracking their symptoms using a smartphone N-of-1 app in collaboration with their clinicians. Methods: Semistructured interviews were recorded with 33 patients, a consecutive subset of the intervention group in a primary study testing the feasibility and effectiveness of the Trialist N-of-1 app. Interviews were transcribed verbatim, and a descriptive thematic analysis was completed. Results: Patients were enthusiastic about recording and accessing their own data. They valued sharing data with clinicians but also used their data independently. Conclusions: N-of-1 trials remain a promising approach to evidence-based decision making. Patients appear to value their roles as trial participants but place as much or more importance on the independent use of trial data as on comparative effectiveness results. Future efforts to design patient-centered N-of-1 trials might consider adaptable designs that maximize patient flexibility and autonomy while preserving a collaborative role with clinicians and researchers.

  • Source: Rawpixel; Copyright: Rawpixel; URL: https://www.rawpixel.com/image/380110/aerial-view-doctor-stethoscope-and-computer-laptop; License: Public Domain (CC0).

    Assessing the Attitudes and Perceptions Regarding the Use of Mobile Health Technologies for Living Kidney Donor Follow-Up: Survey Study

    Abstract:

    Background: In 2013, the Organ Procurement and Transplantation Network began requiring transplant centers in the United States to collect and report postdonation living kidney donor follow-up data at 6 months, 1 year, and 2 years. Despite this requirement, <50% of transplant centers have been able to collect and report the required data. Previous work identified a number of barriers to living kidney donor follow-up, including logistical and administrative barriers for transplant centers and cost and functional barriers for donors. Novel smartphone-based mobile health (mHealth) technologies might reduce the burden of living kidney donor follow-up for centers and donors. However, the attitudes and perceptions toward the incorporation of mHealth into postdonation care among living kidney donors are unknown. Understanding donor attitudes and perceptions will be vital to the creation of a patient-oriented mHealth system to improve living donor follow-up in the United States. Objective: The goal of this study was to assess living kidney donor attitudes and perceptions associated with the use of mHealth for follow-up. Methods: We developed and administered a cross-sectional 14-question survey to 100 living kidney donors at our transplant center. All participants were part of an ongoing longitudinal study of long-term outcomes in living kidney donors. The survey included questions on smartphone use, current health maintenance behaviors, accessibility to health information, and attitudes toward using mHealth for living kidney donor follow-up. Results: Of the 100 participants surveyed, 94 owned a smartphone (35 Android, 58 iPhone, 1 Blackberry), 37 had accessed their electronic medical record on their smartphone, and 38 had tracked their exercise and physical activity on their smartphone. While 77% (72/93) of participants who owned a smartphone and had asked a medical question in the last year placed the most trust with their doctors, nurses, or other health care professionals regarding answering a health-related question, 52% (48/93) most often accessed health information elsewhere. Overall, 79% (74/94) of smartphone-owning participants perceived accessing living kidney donor information and resources on their smartphone as useful. Additionally, 80% (75/94) perceived completing some living kidney donor follow-up via mHealth as useful. There were no significant differences in median age (60 vs 59 years; P=.65), median years since donation (10 vs 12 years; P=.45), gender (36/75, 36%, vs 37/75, 37%, male; P=.57), or race (70/75, 93%, vs 18/19, 95%, white; P=.34) between those who perceived mHealth as useful for living kidney donor follow-up and those who did not, respectively. Conclusions: Overall, smartphone ownership was high (94/100, 94.0%), and 79% (74/94) of surveyed smartphone-owning donors felt that it would be useful to complete their required follow-up with an mHealth tool, with no significant differences by age, sex, or race. These results suggest that patients would benefit from an mHealth tool to perform living donor follow-up.

  • Example of an mHealth app for employees. Source: BrandNewHealth; Copyright: BrandNewHealth; URL: http://www.brandnewhealth.com/jmir_mhealth_uhealth.html; License: Licensed by the authors.

    Behavior Change Techniques in mHealth Apps for the Mental and Physical Health of Employees: Systematic Assessment

    Abstract:

    Background: Employees remain at risk of developing physical and mental health problems. To improve the lifestyle, health, and productivity many workplace interventions have been developed. However, not all of these interventions are effective. Mobile and wireless technology to support health behavior change (mobile health [mHealth] apps) is a promising, but relatively new domain for the occupational setting. Research on mHealth apps for the mental and physical health of employees is scarce. Interventions are more likely to be useful if they are rooted in health behavior change theory. Evaluating the presence of specific combinations of behavior change techniques (BCTs) in mHealth apps might be used as an indicator of potential quality and effectiveness. Objective: The aim of this study was to assess whether mHealth apps for the mental and physical health of employees incorporate BCTs and, if so, which BCTs can be identified and which combinations of BCTs are present. Methods: An assessment was made of apps aiming to reduce the risk of physical and psychosocial work demands and to promote a healthy lifestyle for employees. A systematic search was performed in iTunes and Google Play. Forty-five apps were screened and downloaded. BCTs were identified using a taxonomy applied in similar reviews. The mean and ranges were calculated. Results: On average, the apps included 7 of the 26 BCTs (range 2-18). Techniques such as “provide feedback on performance,” “provide information about behavior-health link,” and “provide instruction” were used most frequently. Techniques that were used least were “relapse prevention,” “prompt self-talk,” “use follow-up prompts,” and “provide information about others’ approval.” “Stress management,” “prompt identification as a role model,” and “agree on behavioral contract” were not used by any of the apps. The combination “provide information about behavior-health link” with “prompt intention formation” was found in 7/45 (16%) apps. The combination “provide information about behavior-health link” with “provide information on consequences,” and “use follow-up prompts” was found in 2 (4%) apps. These combinations indicated potential effectiveness. The least potentially effective combination “provide feedback on performance” without “provide instruction” was found in 13 (29%) apps. Conclusions: Apps for the occupational setting might be substantially improved to increase potential since results showed a limited presence of BCTs in general, limited use of potentially successful combinations of BCTs in apps, and use of potentially unsuccessful combinations of BCTs. Increasing knowledge on the effectiveness of BCTs in apps might be used to develop guidelines for app developers and selection criteria for companies and individuals. Also, this might contribute to decreasing the burden of work-related diseases. To achieve this, app developers, health behavior change professionals, experts on physical and mental health, and end-users should collaborate when developing apps for the working context.

  • Mobile herbs (montage). Source: Pixabay; Copyright: kerdkanno; URL: http://mhealth.jmir.org/2018/9/e181; License: Licensed by JMIR.

    Social Media Users’ Perception of Telemedicine and mHealth in China: Exploratory Study

    Abstract:

    Background: The use of telemedicine and mHealth has increased rapidly in the People’s Republic of China. While telemedicine and mHealth have great potential, wide adoption of this technology depends on how patients, health care providers, and other stakeholders in the Chinese health sector perceive and accept the technology. Objective: To explore this issue, we aimed to examine a social media platform with a dedicated focus on health information technology and informatics in China. Our goal is to utilize the findings to support further research. Methods: In this exploratory study, we selected a social media platform—HC3i.cn—to examine the perception of telemedicine and mHealth in China. We performed keyword analysis and analyzed the prevalence and term frequency–inverse document frequency of keywords in the selected social media platform; furthermore, we performed qualitative analysis. Results: We organized the most prominent 16 keywords from 571 threads into 8 themes: (1) Question versus Answer; (2) Hospital versus Clinic; (3) Market versus Company; (4) Doctor versus Nurse; (5) Family versus Patient; (6) iPad versus Tablet; (7) System versus App; and (8) Security versus Caregiving. Social media participants perceived not only significant opportunities associated with telemedicine and mHealth but also barriers to overcome to realize these opportunities. Conclusions: We identified interesting issues in this paper by studying a social media platform in China. Among other things, participants in the selected platform raised concerns about quality and costs associated with the provision of telemedicine and mHealth, despite the new technology’s great potential to address different issues in the Chinese health sector. The methods applied in this paper have some limitations, and the findings may not be generalizable. We have discussed directions for further research.

  • SmartIntake for alcohol. Source: Image created by the Authors; Copyright: The Authors; URL: http://mhealth.jmir.org/2018/9/e10460/; License: Creative Commons Attribution (CC-BY).

    The Remote Food Photography Method and SmartIntake App for the Assessment of Alcohol Use in Young Adults: Feasibility Study and Comparison to Standard...

    Abstract:

    Background: Heavy drinking is prevalent among young adults and may contribute to obesity. However, measurement tools for assessing caloric intake from alcohol are limited and rely on self-report, which is prone to bias. Objective: The purpose of our study was to conduct feasibility testing of the Remote Food Photography Method and the SmartIntake app to assess alcohol use in young adults. Aims consisted of (1) quantifying the ability of SmartIntake to capture drinking behavior, (2) assessing app usability with the Computer System Usability Questionnaire (CSUQ), (3) conducting a qualitative interview, and (4) comparing preference, usage, and alcohol use estimates (calories, grams per drinking episode) between SmartIntake and online diet recalls that participants completed for a parent study. Methods: College students (N=15) who endorsed a pattern of heavy drinking were recruited from a parent study. Participants used SmartIntake to send photographs of all alcohol and food intake over a 3-day period and then completed a follow-up interview and the CSUQ. CSUQ items range from 1-7, with lower scores indicating greater usability. Total drinking occasions were determined by adding the number of drinking occasions captured by SmartIntake plus the number of drinking occasions participants reported that they missed capturing. Usage was defined by the number of days participants provided food/beverage photos through the app, or the number of diet recalls completed. Results: SmartIntake captured 87% (13/15) of total reported drinking occasions. Participants rated the app as highly usable in the CSUQ (mean 2.28, SD 1.23). Most participants (14/15, 93%) preferred using SmartIntake versus recalls, and usage was significantly higher with SmartIntake than recalls (42/45, 93% vs 35/45, 78%; P=.04). Triple the number of participants submitted alcohol reports with SmartIntake compared to the recalls (SmartIntake 9/15, 60% vs recalls 3/15, 20%; P=.06), and 60% (9/15) of participants reported drinking during the study. Conclusions: SmartIntake was acceptable to college students who drank heavily and captured most drinking occasions. Participants had higher usage of SmartIntake compared to recalls, suggesting SmartIntake may be well suited to measuring alcohol consumption in young adults. However, 40% (6/15) did not drink during the brief testing period and, although findings are promising, a longer trial is needed.

  • Source: Pexels; Copyright: Fabian Hurnaus; URL: https://www.pexels.com/photo/black-amazon-echo-on-table-977296/; License: Public Domain (CC0).

    Health and Fitness Apps for Hands-Free Voice-Activated Assistants: Content Analysis

    Abstract:

    Background: Hands-free voice-activated assistants and their associated devices have recently gained popularity with the release of commercial products, including Amazon Alexa and Google Assistant. Voice-activated assistants have many potential use cases in healthcare including education, health tracking and monitoring, and assistance with locating health providers. However, little is known about the types of health and fitness apps available for voice-activated assistants as it is an emerging market. Objective: This review aimed to examine the characteristics of health and fitness apps for commercially available, hands-free voice-activated assistants, including Amazon Alexa and Google Assistant. Methods: Amazon Alexa Skills Store and Google Assistant app were searched to find voice-activated assistant apps designated by vendors as health and fitness apps. Information was extracted for each app including name, description, vendor, vendor rating, user reviews and ratings, cost, developer and security policies, and the ability to pair with a smartphone app and website and device. Using a codebook, two reviewers independently coded each app using the vendor’s descriptions and the app name into one or more health and fitness, intended age group, and target audience categories. A third reviewer adjudicated coding disagreements until consensus was reached. Descriptive statistics were used to summarize app characteristics. Results: Overall, 309 apps were reviewed; health education apps (87) were the most commonly occurring, followed by fitness and training (72), nutrition (33), brain training and games (31), and health monitoring (25). Diet and calorie tracking apps were infrequent. Apps were mostly targeted towards adults and general audiences with few specifically geared towards patients, caregivers, or medical professionals. Most apps were free to enable or use and 18.1% (56/309) could be paired with a smartphone app and website and device; 30.7% (95/309) of vendors provided privacy policies; and 22.3% (69/309) provided terms of use. The majority (36/42, 85.7%) of Amazon Alexa apps were rated by the vendor as mature or guidance suggested, which were geared towards adults only. When there was a user rating available, apps had a wide range of ratings from 1 to 5 stars with a mean of 2.97. Google Assistant apps did not have user reviews available, whereas most of Amazon Alexa apps had at least 1-9 reviews available. Conclusions: The emerging market of health and fitness apps for voice-activated assistants is still nascent and mainly focused on health education and fitness. Voice-activated assistant apps had a wide range of content areas but many published in the health and fitness categories did not actually have a clear health or fitness focus. This may, in part, be due to Amazon and Google policies, which place restrictions on the delivery of care or direct recording of health data. As in the mobile app market, the content and functionalities may evolve to meet growing demands for self-monitoring and disease management.

  • A new T1DM (type 1 diabetes mellitus) management mobile app. Source: Image created by the Authors; Copyright: The Authors; URL: http://mhealth.jmir.org/2018/9/e11400/; License: Licensed by JMIR.

    Exploration of Users’ Perspectives and Needs and Design of a Type 1 Diabetes Management Mobile App: Mixed-Methods Study

    Abstract:

    Background: With the popularity of mobile phones, mobile apps have great potential for the management of diabetes, but the effectiveness of current diabetes apps for type 1 diabetes mellitus (T1DM) is poor. No study has explored the reasons for this deficiency from the users’ perspective. Objective: The aims of this study were to explore the perspectives and needs of T1DM patients and diabetes experts concerning a diabetes app and to design a new T1DM management mobile app. Methods: A mixed-methods design combining quantitative surveys and qualitative interviews was used to explore users’ needs and perspectives. Experts were surveyed at 2 diabetes conferences using paper questionnaires. T1DM patients were surveyed using Sojump (Changsha ran Xing InfoTech Ltd) on a network. We conducted semistructured, in-depth interviews with adult T1DM patients or parents of child patients who had ever used diabetes apps. The interviews were audio-recorded, transcribed, and coded for theme identification. Results: The expert response rate was 63.5% (127/200). The respondents thought that the reasons for app invalidity were that patients did not continue using the app (76.4%, 97/127), little guidance was received from health care professionals (HCPs; 73.2%, 93/127), diabetes education knowledge was unsystematic (52.8%, 67/127), and the app functions were incomplete (44.1%, 56/127). A total of 245 T1DM patient questionnaires were collected, of which 21.2% (52/245) of the respondents had used diabetes apps. The reasons for their reluctance to use an app were limited time (39%, 20/52), complicated operations (25%, 13/52), uselessness (25%, 13/52), and cost (25%, 13/52). Both the experts and patients thought that the most important functions of the app were patient-doctor communication and the availability of a diabetes diary. Two themes that were useful for app design were identified from the interviews: (1) problems with patients’ diabetes self-management and (2) problems with current apps. In addition, needs and suggestions for a diabetes app were obtained. Patient-doctor communication, diabetes diary, diabetes education, and peer support were all considered important by the patients, which informed the development of a prototype multifunctional app. Conclusions: Patient-doctor communication is the most important function of a diabetes app. Apps should be integrated with HCPs rather than stand-alone. We advocate that doctors follow up with their patients using a diabetes app. Our user-centered method explored comprehensively and deeply why the effectiveness of current diabetes apps for T1DM was poor and what T1DM patients needed for a diabetes app and provided meaningful guidance for app design.

  • DARWIN login page screenshot (montage). Source: The Authors / Placeit.net; Copyright: JMIR Publications; URL: http://mhealth.jmir.org/2018/9/e11187/; License: Creative Commons Attribution (CC-BY).

    The Effectiveness of Near-Field Communication Integrated with a Mobile Electronic Medical Record System: Emergency Department Simulation Study

    Abstract:

    Background: Improved medical practice efficiency has been demonstrated by physicians using mobile device (mobile phones, tablets) electronic medical record (EMR) systems. However, the quantitative effects of these systems have not been adequately measured. Objective: This study aimed to determine the effectiveness of near-field communication (NFC) integrated with a mobile EMR system regarding physician turnaround time in a hospital emergency department (ED). Methods: A simulation study was performed in a hospital ED. Twenty-five physicians working in the ED participated in 2 scenarios, using either a mobile device or personal computer (PC). Scenario A involved randomly locating designated patients in the ED. Scenario B consisted of accessing laboratory results of an ED patient at the bedside. After completing the scenarios, participants responded to 10 questions that were scored using a system usability scale (SUS). The primary metric was the turnaround time for each scenario. The secondary metric was the usability of the system, graded by the study participants. Results: Locating patients from the ED entrance took a mean of 93.0 seconds (SD 34.4) using the mobile scenario. In contrast, it only required a mean of 57.3 seconds (SD 10.5) using the PC scenario (P<.001). Searching for laboratory results of the patients at the bedside required a mean of only 25.2 seconds (SD 5.3) with the mobile scenario, and a mean of 61.5 seconds (SD 11.6) using the PC scenario (P<.001). Sensitivity analysis comparing only the time for login and accessing the relevant information also determined mobile devices to be significantly faster. The mean SUS score of NFC-mobile EMR was 71.90 points. Conclusions: NFC integrated with mobile EMR provided for a more efficient physician practice with good usability.

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  • MyLung: A mHealth Assistive System to Empower Patients with COPD

    Date Submitted: Oct 12, 2018

    Open Peer Review Period: Oct 14, 2018 - Oct 20, 2018

    Background: Chronic Obstructive Pulmonary Disease (COPD) is one of those progressive diseases that deteriorate lung functions. When patients cannot breathe, nothing else in their lives matter. The bre...

    Background: Chronic Obstructive Pulmonary Disease (COPD) is one of those progressive diseases that deteriorate lung functions. When patients cannot breathe, nothing else in their lives matter. The breathlessness has negative implications on patients’ lives that lead to physical and psychological limitations. The physical limitations impede daily life activities that are essential for patients with COPD. Psychological limitations are manifested in anxiety that results from the emotional response to breathlessness. Because patients with COPD are always overwhelmed by anxiety and depression, they are less motivated to engage in self-care and education intervention. Moreover, the lack of relevant and updated information about the causes and consequences of the disease can exacerbate the problems of health literacy, information accessibility, and medical adherence Objective: The objective of this study is to design an innovative mHealth app system called “MyLung” that provides a complete solution to increase self-awareness and to promote better self-care management. This IT artifact includes three integrative modules that are novel: education, risk reduction, and monitoring. Methods: The utility and effectiveness of the assistive mobile-based technology are evaluated using mixed methods approach. The study combines quantitative and qualitative research methods to thoroughly understand how the assistive mobile-based technology can influence patients’ behavioral intention to change their lifestyle. Thirty patients were assigned in two groups (intervention group and control group). The results from the quantitative analysis introduced several follow-up interviews using a qualitative study Results: The results of the quantitative study provide significant evidence that the design of MyLung leads to change in awareness level, self-efficacy and behavioral intention for patients with COPD. T- tests reveal significant difference before and after using mobile based app for awareness level (M = 3.28 versus 4.56, t(10) = 6.062, p < 0.001), self-efficacy (M = 3.11 versus 5.56, t(10) = 2.96, p = 0.014) and behavioral intention (M = 2.91versus 4.55, t(10) = 3.212, p = 0.009). In the same vein, independent sample t-tests reveal significant difference between groups in awareness level (M = 4.56 versus 3.31, t(19) = 4.80, p < 0.001) and self- efficacy (M = 5.56 versus 3.66, t(19) = 2.8, p < 0.012). Integrating findings from quantitative and qualitative strands introduces inferences that describe the impact of the design in a comprehensive view. These inferences are referred in this study as “meta-inferences”. Conclusions: The objective of this research is to empower patients with COPD with assistive mobile-based technology that helps increase awareness levels and to engage patients in self-care management activities. The assistive technology aims to inform patients about the risk factors of COPD, and to improve access to relevant information. Meta-inferences that are emerged from the research outputs contribute to chronic management information systems research by helping us gain more complete understanding of the potential impacts of this proposed mobile-based design on patients with chronic disease.

  • Impact of training and integration of apps into dietetic practice on dietitians' app self-efficacy and patient satisfaction: a feasibility study

    Date Submitted: Oct 5, 2018

    Open Peer Review Period: Oct 8, 2018 - Dec 3, 2018

    Background: Use of mobile health (mHealth) applications (apps) in dietetic practice could support delivery of nutrition care in medical nutrition therapy. However, apps are underutilized by dietitians...

    Background: Use of mobile health (mHealth) applications (apps) in dietetic practice could support delivery of nutrition care in medical nutrition therapy. However, apps are underutilized by dietitians in patient care. Objective: This study aimed to determine the feasibility of an intervention, comprising of education, training and integration of apps, in improving dietitians’ perceived self-efficacy with using mHealth apps. Methods: Private practice Accredited Practising Dietitians who were not regular users or recommenders of mHealth apps were recruited into the intervention. The intervention consisted of two phases: 1) a workshop that incorporated an educational lecture and skill building activities to target self-efficacy, capability, opportunity and motivation factors; 2) 12-week intervention phase allowing for the integration of an app into dietetic practice via an app platform. During the 12-week intervention phase, dietitians prescribed an Australian commercial nutrition app to new (intervention) patients receiving nutrition care. Existing (control) patients were also recruited to provide a measure of patient satisfaction before the apps were introduced. New patients completed their patient satisfaction surveys at the end of the 12 weeks. Usability feedback about the app and app platform were gathered from intervention patients and dietitians. Results: Five dietitians participated in the study. The educational and skills training workshop component of the intervention produced immediate significant improvements in dietitians’ mHealth app self-efficacy compared to baseline (P=.02), particularly with regards to ‘familiarity with apps’ factor (P<.001). The self-efficacy factor ‘integration into dietetic work systems’ achieved significant improvements from baseline to 12 weeks (P=.03). Patient satisfaction with dietetic services did not differ significantly between intervention (n=17) and control patients (n=13). Overall, dietitians and their patients indicated they would continue using the app platform and app respectively, and would recommend it to others. To improve usability, enhancing patient-dietitian communication mediums in the app platform and reducing the burden of entering in meals cooked at home should be considered. Conclusions: Administering an educational and skills training workshop in conjunction with integrating an app platform into dietetic practice were feasible methods for improving the self-efficacy of dietitians towards using mHealth apps. Further translational research will be required to determine how the broader dietetic profession respond to this intervention.

  • Creating Patient Generated Health Data: Interviews and a Pilot Trial Exploring How and Why Patients Engage

    Date Submitted: Sep 29, 2018

    Open Peer Review Period: Oct 6, 2018 - Dec 1, 2018

    Background: Patient Generated Health Data (PGHD) is any clinically relevant data collected by patients or their carers (consumers) that may contribute to better health care outcomes. Patient generated...

    Background: Patient Generated Health Data (PGHD) is any clinically relevant data collected by patients or their carers (consumers) that may contribute to better health care outcomes. Patient generated health data, like patient reported outcome and patient experience measures, reflect the consumers perspective, promote patient centricity and can improve partnership with healthcare providers. Objective: The use of the data is also believed to encourage enhanced patient engagement and thus foster a therapeutic partnership with the healthcare provider. The aim of this study is to further identify how PGHD is used by consumers and how it influences their engagement. Methods: Study 1 used vignette-led interviews with patients, carers and doctors to test attitudes, perceptions and beliefs about the PGHD. Study 2 was a pilot trial with parents of children undergoing laparoscopic appendectomy. Parents were asked to generate post-operative surgical site photographs for 10 days and were then interviewed to deepen the understanding of parental engagement. Across both studies, interviews (n=60) were analysed to identify the themes and these were contrasted for notable differences. Results: When viewed holistically from the patient perspective PGHD can instigate an ecosystem of engagement providing clinicians with an extended view into the patient’s world. This paper proposes and validates an ‘ontological’ framework based on engagement literature which defines that categorises PGHD clarified by healthcare providers, patients and carers. A framework for understanding PGHD involves 11 themes organised into four domains; physiological, cognitive, emotional and behavioural. PGHD use is interconnected and complex but can engage and empower patients. PGHD increases reassurance, improves communication, aids sense making and can result in consumers taking on greater personal responsibility for their healthcare outcomes. Conclusions: This research demonstrates that in addition to the potential for enhanced clinical diagnosis and efficient use of healthcare resources, patient generated health data offers patients meaningful partnership with clinicians and a method of emotional empowerment, improving confidence and satisfaction in the service. Clinical Trial: ANZCTR: ACTRN12616000998448

  • Towards developing a standardized core set of outcome measures in mHealth interventions for tuberculosis management: A systematic review

    Date Submitted: Oct 2, 2018

    Open Peer Review Period: Oct 6, 2018 - Dec 1, 2018

    Background: Tuberculosis (TB) management can be challenging in low- and middle- income countries (LMICs) not only due to its high burden, but also the prolonged treatment period involving multiple dru...

    Background: Tuberculosis (TB) management can be challenging in low- and middle- income countries (LMICs) not only due to its high burden, but also the prolonged treatment period involving multiple drugs. With the rapid development in mobile technology, mHealth (Mobile Health) or using mobile device for TB has gained popularity. Despite the potential usefulness of mHealth for TB, few studies have quantitatively synthesized evidence on its effectiveness, presumably due to variability in outcome measures reported in the literature. Objective: The aim of this systematic review was to evaluate the outcome measures reported in TB mHealth literature in LMICs Methods: MEDLINE, EMBASE, and Cochrane Database of Systematic Reviews were searched to identify mHealth intervention studies for TB (published up to May 2018) which reported any type of outcome measures. Extracted information included the study setting, types of mHealth technology used, target population, study design, and categories of outcome measures. Outcomes were classified into 13 categories including treatment outcome, adherence, process measure, perception, technical outcome, and so on. The qualitative synthesis of evidence focused on the categories of outcome measures reported by type of mHealth interventions. Results: A total of 27 studies were included for the qualitative synthesis of evidence. The study designs varied widely, ranging from randomized controlled trials (RCTs) to economic evaluations. Most studies adopted short message service (SMS), while others used SMS in combination with additional technologies or mobile applications. The study population was also diverse including TB patients, TB/HIV patients, healthcare workers and general patients attending a clinic. There was a wide range of variations in the definition of outcome measures across the studies. Amongst the diverse categories of outcome measures, treatment outcomes have been reported in most of the studies, but only a few studies measured the outcome according to the standard TB treatment definitions by the World Health Organization. Conclusions: This critical evaluation of outcomes reported in mHealth studies for TB management suggests that substantial variability exists in reporting the outcome measures. To overcome challenges in evidence synthesis for mHealth interventions, this study can provide insights into the development of a core sets of outcome measures by intervention type and study design.

  • Safe and Easy Environment for frailty syndrome: a randomized controlled trial at patient home.

    Date Submitted: Sep 28, 2018

    Open Peer Review Period: Oct 6, 2018 - Dec 1, 2018

    Background: All over the world the increasing prevalence of age-related disorders such as Alzheimer’s disease (AD) and frailty and its impact on functional decline is challenging the sustainability...

    Background: All over the world the increasing prevalence of age-related disorders such as Alzheimer’s disease (AD) and frailty and its impact on functional decline is challenging the sustainability of health care systems. In the field of AD and related disorders, Information and Communication technologies (ICT) showed promising results in improving clinical assessment and implementing interventions to delay functional decline and decrease the burden of behavioral symptoms. Objective: The SafEE (Safe Easy Environment) project, is a collaborative French-Taiwanese project aiming to develop: 1/ an ICT-based behavior analysis platform able to automatically detect, recognize and assess daytime and nighttime behavioral patterns, and 2/ adapted tailored non pharmacological interventions. Partners of the projects include clinicians, research engineers and industrials. Methods: This study was designed as a randomized controlled trial. We recruited 3 patients with cognitive frailty syndrome [≥ 60 years, MMSE ≥ 26, CDR ≤ 0.5] randomized either to the intervention group (ICT-based therapeutic solutions, N=1) or to the control group (care as usual, N=2). The 6-month intervention included detection of daytime and nighttime behaviors based on 2D and 3D video cameras (for both groups), and tailored therapeutic solutions based on serious-games, aromatherapy and music therapy for the intervention group. The primary outcome is the acceptability of the solutions measured by the frequency of use and self-reports. The secondary outcome is the solution efficacy, measured by the results on neuropsychological tests. Results: This project made it possible to develop a communicating platform between the automatic recognition of activity and the non-pharmacological solutions developed. This platform is thus able to 1) provide healthcare professionals with continuous feedback on immediate and long-term risk events; 2) Automatically combine an online assessment with non-pharmacological interventions that can act on the detected disorders; 3) obtain relevant information in the context of an early diagnosis at home of frail people at risk of developing Alzheimer's disease. Conclusions: Building a global system aiming to detect and prevent loss of autonomy in frail people is a rather complicated task, involving numerous ICT solutions which are not always easy to use in everyday life. The innovation of the project lies in a new methodological approach to deal with care of elderly people, based on an innovative use of ICT based on the association of assessment and intervention for specific cognitive and behavioral patterns. The results of this trial may have important implications for future interventions, and provide relevant information for the general transferability of this platform as part of the AD prevention. Clinical Trial: ClinicalTrials.gov, NCT02288221. First received: August 19, 2014. Last updated: November 7, 2014. Last verified: June 2014.

  • A Randomized controlled trial of SMS Intervention in Inner Mongolia with Type 2 Diabetes

    Date Submitted: Sep 29, 2018

    Open Peer Review Period: Oct 6, 2018 - Dec 1, 2018

    Background: Nonadherence to self-management is common among patients with type 2 diabetes (T2D) and often leads to severe complications. Short messages service (SMS) technology provides a practical me...

    Background: Nonadherence to self-management is common among patients with type 2 diabetes (T2D) and often leads to severe complications. Short messages service (SMS) technology provides a practical medium for delivering content to address patients’ barriers to adherence. Objective: The aim of this study was to design a series of SMS intervention templates, and to evaluate the feasibility of the SMS through a short message quality evaluation questionnaire and to explore the intervention effect. Methods: 1. The SMS evaluation was assessed through the 10-point scale SMS Quality Assessment Questionnaire. 2. A randomized controlled trial was conducted. The patients in SMS intervention were randomly divided into intervention group (IG) and control group (CG), which received evaluated messages education and regular education, respectively. The intervention was divided into four phases, a telephone interview was conducted to evaluate the effectiveness of the intervention after each phase. The main outcome were changes in blood glucose and blood pressure (BP) and their control rates, and secondary outcomes were changes in diet, physical activity, weight control and other health-related behaviors. Results: 1. SMS design: 42 SMS text messages were designed to promote healthy behaviors in different stages of behavior change, covering four key domains: healthy knowledge, diet, physical activity, living habits and weight control. 2. SMS evaluation: The average score for healthy knowledge, diet, physical activity, living habits, weight control were 8.0 (SD 0.7), 8.5 (SD 0.6), 7.9 (SD 1.0), 8.0 (SD 0.7), and 8.4 (SD 0.9), respectively. 3. SMS intervention: A total of 146 people completed the four-phase intervention, including 72 in the CG and 74 in the IG. At the end of the intervention period, in the IG, the decrease in fasting blood glucose (FBG, mean 1.5mg/l [SD 3.0] vs 0.4 mg/l [SD 2.8], P=0.011), postprandial blood glucose (PBG, mean 5.8mg/l [SD 5.1] vs 4.2 mg/l [SD 4.7], P=0.028), systolic blood pressure (SBP, mean 9.1mmHg [SD 15.8] vs 2.2mmHg [SD 13.3], P=0.025), FBG control rate (45.9% vs 31.0%, P=0.046) and PBG control rate (57.8% vs 33.7%, P=0.002) were better than the CG. In self-behavior management, the changes of the weight control, diet and physical activity in the IG were better than those in the CG, and the average score of the IG was greater than that of the CG (1.1 vs [-0.3] ), P0.001). Conclusions: The overall quality of SMS content is higher to meet the needs of patients; Diet, physical activity and weight control message need to be focused on push. SMS interventions contribute to the management of blood glucose and BP, and help to promote a series of healthy-related behaviors.

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