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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:

  • Source: Image created by the Authors; Copyright: Benjamin Marent; URL:; License: Creative Commons Attribution (CC-BY).

    Development of an mHealth platform for HIV Care: Gathering User Perspectives Through Co-Design Workshops and Interviews


    Background: Despite advances in testing and treatment, HIV incidence rates within European countries are at best stable or else increasing. mHealth technology has been advocated to increase quality and cost-effectiveness of health services while dealing with growing patient numbers. However, studies suggested that mHealth apps are rarely adopted and often considered to be of low quality by users. Only a few studies (conducted in the United States) have involved people living with HIV (PLWH) in the design of mHealth. Objective: The goal of this study was to facilitate a co-design process among PLWH and clinicians across 5 clinical sites in the European Union to inform the development of an mHealth platform to be integrated into clinical care pathways. We aimed to (1) elicit experiences of living with HIV and of working in HIV care, (2) identify mHealth functionalities that are considered useful for HIV care, and (3) identify potential benefits as well as concerns about mHealth. Methods: Between January and June 2016, 14 co-design workshops and 22 semistructured interviews were conducted, involving 97 PLWH and 63 clinicians. Data were analyzed thematically and iteratively, drawing on grounded theory techniques. Results: Findings were established into 3 thematic clusters: (1) approaching the mHealth platform, (2) imagining the mHealth platform, and (3) anticipating the mHealth platform’s implications. Co-design participants approached the mHealth platform with pre-existing concerns arising from their experiences of receiving or providing care. PLWH particularly addressed issues of stigma and questioned how mHealth could enable them to manage their HIV. Clinicians problematized the compatibility of mHealth with existing information technology systems and questioned which patients should be targeted by mHealth. Imagining the potential of mHealth for HIV care, co-design participants suggested medical functionalities (accessing test results, managing medicines and appointments, and digital communication channels), social functionalities (peer support network, international travel, etc), and general features (security and privacy, credibility, language, etc). Co-design participants also anticipated potential implications of mHealth for self-management and the provision of care. Conclusions: Our approach to co-design enabled us to facilitate early engagement in the mHealth platform, enabling patient and clinician feedback to become embedded in the development process at a preprototype phase. Although the technologies in question were not yet present, understanding how users approach, imagine, and anticipate technology formed an important source of knowledge and proved highly significant within the technology design and development process.

  • Doctor using the Vula app. Source: The Authors; Copyright: The Authors; URL:; License: Licensed by JMIR.

    Teleconsultation Using Mobile Phones for Diagnosis and Acute Care of Burn Injuries Among Emergency Physicians: Mixed-Methods Study


    Background: The referral process in acute care remains challenging in many areas including burn care. Mobile phone apps designed explicitly for medical referrals and consultations could streamline the referral process by using structured templates and integrating features specific to different specialties. However, as these apps are competing with commercial chat services, usability becomes a crucial factor for successful uptake. Objective: The aim of this study was to assess the usability of a mobile phone app for remote consultations and referrals of burn injuries. Methods: A total of 24 emergency doctors and 4 burns consultants were recruited for the study. A mixed-methods approach was used including a usability questionnaire and a think-aloud interview. Think-aloud sessions were video-recorded, and content analysis was undertaken with predefined codes relating to the following 3 themes: ease of use, usefulness of content, and technology-induced errors. Results: The users perceived the app to be easy to use and useful, but some problems were identified. Issues relating to usability were associated with navigation, such as scrolling and zooming. Users also had problems in understanding the meaning of some icons and terminologies. Sometimes, some users felt limited by predefined options, and they wanted to be able to freely express their clinical findings. Conclusions: We found that users faced problems mainly with navigation when the app did not work in the same way as the other apps that were frequently used. Our study also resonates with previous findings that when using standardized templates, the systems should also allow the user to express their clinical findings in their own words.

  • Source: Shutterstock; Copyright: Tyler Olson; URL:; License: Licensed by the authors.

    Self-Management Education Through mHealth: Review of Strategies and Structures


    Background: Despite the plethora of evidence on mHealth interventions for patient education, there is a lack of information regarding their structures and delivery strategies. Objective: This review aimed to investigate the structures and strategies of patient education programs delivered through smartphone apps for people with diverse conditions and illnesses. We also examined the aim of educational interventions in terms of health promotion, disease prevention, and illness management. Methods: We searched PubMed, Cumulative Index to Nursing and Allied Health Literature, Embase, and PsycINFO for peer-reviewed papers that reported patient educational interventions using mobile apps and published from 2006 to 2016. We explored various determinants of educational interventions, including the content, mode of delivery, interactivity with health care providers, theoretical basis, duration, and follow-up. The reporting quality of studies was evaluated according to the mHealth evidence and reporting assessment criteria. Results: In this study, 15 papers met the inclusion criteria and were reviewed. The studies mainly focused on the use of mHealth educational interventions for chronic disease management, and the main format for delivering interventions was text. Of the 15 studies, 6 were randomized controlled trials (RCTs), which have shown statistically significant effects on patients’ health outcomes, including patients’ engagement level, hemoglobin A1c, weight loss, and depression. Although the results of RCTs were mostly positive, we were unable to identify any specific effective structure and strategy for mHealth educational interventions owing to the poor reporting quality and heterogeneity of the interventions. Conclusions: Evidence on mHealth interventions for patient education published in peer-reviewed journals demonstrates that current reporting on essential mHealth criteria is insufficient for assessing, understanding, and replicating mHealth interventions. There is a lack of theory or conceptual framework for the development of mHealth interventions for patient education. Therefore, further research is required to determine the optimal structure, strategies, and delivery methods of mHealth educational interventions.

  • 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:; 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


    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: NCT01092364; (Archived by WebCite at

  • TOC_digital_health_trasforming_healtcare. Source: Image created by the Authors; Copyright: The Authors; URL:; 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


    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:; License: Creative Commons Attribution (CC-BY).

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


    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: NCT01107015; (Archived by Webcite at

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

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


    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 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 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:; License: Licensed by JMIR.

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


    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:; License: Public Domain (CC0).

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


    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:; License: Licensed by the authors.

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


    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:; License: Licensed by JMIR.

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


    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——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:; 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...


    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.

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  • Development and Testing of Pain Guard: A Randomized Trial Among Chinese Cancer Patients

    Date Submitted: Oct 18, 2018

    Open Peer Review Period: Oct 19, 2018 - Oct 27, 2018

    Background: The incidence and degree of cancer pain often progresses in discharged patients because of discontinued standard treatments and reductions in medication compliance. Motivated by the need f...

    Background: The incidence and degree of cancer pain often progresses in discharged patients because of discontinued standard treatments and reductions in medication compliance. Motivated by the need for better pain management in discharged patients, our research team developed a mobile phone application (Pain Guard) for providing continuous treatment for discharged patients suffering from pain. Objective: We aimed to design, construct, and test the Pain Guard in patients managing cancer pain, and evaluate the total remission rate of pain and improvement in quality of life (QoL), in order to realize convergence management of patients within and outside the hospital. The system’s usability, feasibility, compliance, and satisfaction were also assessed. Methods: This randomized controlled double-arm study involved 58 patients with cancer pain symptoms. Participants were randomly assigned into the group receiving care through the Pain Guard and the control group receiving only traditional pharmaceutical care. In a pretest, participants were rated using a baseline cancer pain assessment and QoL evaluation. During treatment, the consumption levels of analgesic drugs were recorded every week. After a 4-week study period, another round of cancer pain assessment and QoL evaluation was conducted. Our primary outcome was remission rate of pain, and secondary outcomes were compliance with medication, improvements in QoL, frequency of breakthrough cancer pain (BTcP), incidence of adverse reactions, and satisfaction of patients. Results: All participants (58 patients) successfully completed the study. There were no significant differences in baseline pain score or baseline QoL scores between groups (P>0.1). At the end of the study, the rate of pain remission in the trial group was significantly higher than that in the control group (P<0.01). The frequency of BTcP in the trial group was considerably lower than that in the control group (P<0.01). The rate of medication compliance in the trial group was considerably higher than that in the control group (P<0.01). Improvements in QoL scores in the trial group were also significantly higher than those in the control group (P<0.01). The incidence of adverse reactions in the trial group (7/31) was much lower than that in the control group (12/27). The 31 participants in the trial group completed a satisfaction survey regarding Pain Guard: 23 (74%) indicated that they were satisfied with receiving pharmaceutical care by Pain Guard, 8 (26%) indicated that they were somewhat satisfied, 2 (6%) indicated neutral feelings, 1 (3%) indicated that they were somewhat dissatisfied, and no participants indicated that they were very dissatisfied. Conclusions: Pain Guard can effectively resolve the management dislocation of patients with cancer pain at home, control pain steadily, reduce the incidence of adverse reactions, improve patient compliance, and significantly improve patients’ quality of life. Additionally, Pain Guard operability is good and easily accepted by patients. Clinical Trial: identifier: ChiCTR1800016066

  • 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:, 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.