JMIR Publications

JMIR mHealth and uHealth

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


Journal Description

JMIR mhealth and uhealth (mobile and ubiquitous health) (JMU, ISSN 2291-5222) is a new spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2015: 4.532). JMIR mHealth and uHealth has a projected impact factor (2015) of about 2.03. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics.

JMIR mHealth and uHealth publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.
In addition to peer-reviewing paper submissions by researchers, JMIR mHealth and uHealth offers peer-review of medical apps itself (developers can submit an app for peer-review here).

JMIR mHealth and uHealth features a rapid and thorough peer-review process, professional copyediting, professional production of PDF, XHTML, and XML proofs.

JMIR mHealth and uHealth is indexed in PubMed Central/PubMed, and Thomson Reuters' Science Citation Index Expanded (SCIE).

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:; CC BY 2.0, Attribution K. Kendall.

    An Evaluation of a Smartphone–Assisted Behavioral Weight Control Intervention for Adolescents: Pilot Study


    Background: The efficacy of adolescent weight control treatments is modest, and effective treatments are costly and are not widely available. Smartphones may be an effective method for delivering critical components of behavioral weight control treatment including behavioral self-monitoring. Objective: To examine the efficacy and acceptability of a smartphone assisted adolescent behavioral weight control intervention. Methods: A total of 16 overweight or obese adolescents (mean age=14.29 years, standard deviation=1.12) received 12 weeks of combined treatment that consisted of weekly in-person group behavioral weight control treatment sessions plus smartphone self-monitoring and daily text messaging. Subsequently they received 12 weeks of electronic-only intervention, totaling 24 weeks of intervention. Results: On average, participants attained modest but significant reductions in body mass index standard score (zBMI: 0.08 standard deviation units, t (13)=2.22, P=.04, d=0.63) over the in-person plus electronic-only intervention period but did not maintain treatment gains over the electronic-only intervention period. Participants self-monitored on approximately half of combined intervention days but less than 20% of electronic-only intervention days. Conclusions: Smartphones likely hold promise as a component of adolescent weight control interventions but they may be less effective in helping adolescents maintain treatment gains after intensive interventions.

  • The technology provided included a Wi-Fi–enabled scale, activity tracker, and access to a private dashboard. The dashboard was accessible via Web and mobile apps.

    Retrofit Weight-Loss Outcomes at 6, 12, and 24 Months and Characteristics of 12-Month High Performers: A Retrospective Analysis


    Background: Obesity is the leading cause of preventable death costing the health care system billions of dollars. Combining self-monitoring technology with personalized behavior change strategies results in clinically significant weight loss. However, there is a lack of real-world outcomes in commercial weight-loss program research. Objective: Retrofit is a personalized weight management and disease-prevention solution. This study aimed to report Retrofit’s weight-loss outcomes at 6, 12, and 24 months and characterize behaviors, age, and sex of high-performing participants who achieved weight loss of 10% or greater at 12 months. Methods: A retrospective analysis was performed from 2011 to 2014 using 2720 participants enrolled in a Retrofit weight-loss program. Participants had a starting body mass index (BMI) of >25 kg/m² and were at least 18 years of age. Weight measurements were assessed at 6, 12, and 24 months in the program to evaluate change in body weight, BMI, and percentage of participants who achieved 5% or greater weight loss. A secondary analysis characterized high-performing participants who lost ≥10% of their starting weight (n=238). Characterized behaviors were evaluated, including self-monitoring through weigh-ins, number of days wearing an activity tracker, daily step count average, and engagement through coaching conversations via Web-based messages, and number of coaching sessions attended. Results: Average weight loss at 6 months was −5.55% for male and −4.86% for female participants. Male and female participants had an average weight loss of −6.28% and −5.37% at 12 months, respectively. Average weight loss at 24 months was −5.03% and −3.15% for males and females, respectively. Behaviors of high-performing participants were assessed at 12 months. Number of weigh-ins were greater in high-performing male (197.3 times vs 165.4 times, P=.001) and female participants (222 times vs 167 times, P<.001) compared with remaining participants. Total activity tracker days and average steps per day were greater in high-performing females (304.7 vs 266.6 days, P<.001; 8380.9 vs 7059.7 steps, P<.001, respectively) and males (297.1 vs 255.3 days, P<.001; 9099.3 vs 8251.4 steps, P=.008, respectively). High-performing female participants had significantly more coaching conversations via Web-based messages than remaining female participants (341.4 vs 301.1, P=.03), as well as more days with at least one such electronic message (118 vs 108 days, P=.03). High-performing male participants displayed similar behavior. Conclusions: Participants on the Retrofit program lost an average of −5.21% at 6 months, −5.83% at 12 months, and −4.09% at 24 months. High-performing participants show greater adherence to self-monitoring behaviors of weighing in, number of days wearing an activity tracker, and average number of steps per day. Female high performers have higher coaching engagement through conversation days and total number of coaching conversations.

  • Text Messages. Image sourced and copyright held by authors Emma Cotten et al.

    Increasing Nonsedentary Behaviors in University Students Using Text Messages: Randomized Controlled Trial


    Background: Sedentary behavior (SB) has been linked to many health problems such as type 2 diabetes and heart disease. Increasing the length and frequency of breaks from sitting and increasing the time spent standing and engaged in light and moderate physical activity are ways to decrease SB. Text message-based interventions have succeeded in aiding smoking cessation and increase both physical activity and healthy eating, but they have not been shown to reduce SB. Objective: The primary purpose of this pilot study was to determine the effectiveness of a text message-based intervention in increasing nonsedentary behaviors in university students. A secondary purpose was to (1) determine whether the intervention could enhance self-efficacy beliefs for decreasing SB and (2) whether these efficacious beliefs could predict actual SB. Methods: Eighty-two university students were recruited via mass emails and randomized into intervention (SB-related text messages) or control (text messages unrelated to SB) groups. Participants received daily text messages scheduled by the researcher encouraging breaks from sitting, standing, light- and moderate-intensity physical activity (PA). They then reported various SBs via Web-based questionnaires at four time points (baseline, 2, 4, and 6 weeks). Self-efficacious beliefs toward taking breaks from sitting and decreasing the amount of time spent sitting were assessed at the same time points. Results: Last observation carried forward (LOCF) method was used for incomplete data as an intent-to-treat (ITT) analysis (intervention group n=15, control group n=11). Small-to-moderate effects favoring the text intervention group were found at 6 weeks for break frequency -14.64 minutes, break length +.59 minutes, standing +24.30 min/day, light-intensity +74.34 min/day, and moderate-intensity + 9.97 min/day PA. Only light-intensity PA approached significance (P=.07). Self-efficacy beliefs also favored the text intervention group and reached significance (P=.032) for sitting less. Significant (P<.05) relations were found between the self-efficacy constructs and breaks, standing, and light or moderate PA. Conclusions: Text messages have the potential to increase nonsedentary behaviors in university students. These messages can increase self-efficacy beliefs to take more breaks and reduce sitting time. Efficacious beliefs can predict actual SB and to a lesser extent light- and moderate-intensity PA. Trial Registration: NCT02562937; (Archived by WebCite at

  • SPAM. Image sourced and copyright owned by authors.

    “Please Don’t Send Us Spam!” A Participative, Theory-Based Methodology for Developing an mHealth Intervention


    Background: Mobile health solutions have the potential of reducing burdens on health systems and empowering patients with important information. However, there is a lack of theory-based mHealth interventions. Objective: The purpose of our study was to develop a participative, theory-based, mobile phone, audio messaging intervention attractive to recently circumcised men at voluntary medical male circumcision (VMMC) clinics in the Cape Town area in South Africa. We aimed to shift some of the tasks related to postoperative counselling on wound management and goal setting on safe sex. We place an emphasis on describing the full method of message generation to allow for replication. Methods: We developed an mHealth intervention using a staggered qualitative methodology: (1) focus group discussions with 52 recently circumcised men and their partners to develop initial voice messages they felt were relevant and appropriate, (2) thematic analysis and expert consultation to select the final messages for pilot testing, and (3) cognitive interviews with 12 recent VMMC patients to judge message comprehension and rank the messages. Message content and phasing were guided by the theory of planned behavior and the health action process approach. Results: Patients and their partners came up with 245 messages they thought would help men during the wound-healing period. Thematic analysis revealed 42 different themes. Expert review and cognitive interviews with more patients resulted in 42 messages with a clear division in terms of needs and expectations between the initial wound-healing recovery phase (weeks 1–3) and the adjustment phase (weeks 4–6). Discussions with patients also revealed potential barriers to voice messaging, such as lack of technical knowledge of mobile phones and concerns about the invasive nature of the intervention. Patients’ own suggested messages confirmed Ajzen’s theory of planned behavior that if a health promotion intervention can build trust and be relevant to the recipient’s needs in the first contacts, then the same recipients will perceive subsequent motivational messages more favorably. The health action process approach was also a useful tool for guiding the phasing of the messages. Participants were more positive and salutogenic than public health experts. Conclusions: The system showed how a process of consultation can work with a set of potential recipients of an mHealth service to ensure that their needs are included. Classic behavioral theories can and should be used to design modern mHealth interventions. We also believe that patients are the best source of messaging, ensuring that messages are culturally relevant and interesting to the recipient.

  • Smartphone. Licensed under CC0 License. Sourced from

    Taking mHealth Forward: Examining the Core Characteristics


    The emergence of mobile health (mHealth) offers unique and varied opportunities to address some of the most difficult problems of health. Some of the most promising and active efforts of mHealth involve the engagement of mobile phone technology. As this technology has spread and as this technology is still evolving, we begin a conversation about the core characteristics of mHealth relevant to any mobile phone platform. We assert that the relevance of these characteristics to mHealth will endure as the technology advances, so an understanding of these characteristics is essential to the design, implementation, and adoption of mHealth-based solutions. The core characteristics we discuss are (1) the penetration or adoption into populations, (2) the availability and form of apps, (3) the availability and form of wireless broadband access to the Internet, and (4) the tethering of the device to individuals. These collectively act to both enable and constrain the provision of population health in general, as well as personalized and precision individual health in particular.

  • Designed by Creativeart -
Free for commercial use with attribution.

    Quantifying App Store Dynamics: Longitudinal Tracking of Mental Health Apps


    Background: For many mental health conditions, mobile health apps offer the ability to deliver information, support, and intervention outside the clinical setting. However, there are difficulties with the use of a commercial app store to distribute health care resources, including turnover of apps, irrelevance of apps, and discordance with evidence-based practice. Objective: The primary aim of this study was to quantify the longevity and rate of turnover of mental health apps within the official Android and iOS app stores. The secondary aim was to quantify the proportion of apps that were clinically relevant and assess whether the longevity of these apps differed from clinically nonrelevant apps. The tertiary aim was to establish the proportion of clinically relevant apps that included claims of clinical effectiveness. We performed additional subgroup analyses using additional data from the app stores, including search result ranking, user ratings, and number of downloads. Methods: We searched iTunes (iOS) and the Google Play (Android) app stores each day over a 9-month period for apps related to depression, bipolar disorder, and suicide. We performed additional app-specific searches if an app no longer appeared within the main search Results: On the Android platform, 50% of the search results changed after 130 days (depression), 195 days (bipolar disorder), and 115 days (suicide). Search results were more stable on the iOS platform, with 50% of the search results remaining at the end of the study period. Approximately 75% of Android and 90% of iOS apps were still available to download at the end of the study. We identified only 35.3% (347/982) of apps as being clinically relevant for depression, of which 9 (2.6%) claimed clinical effectiveness. Only 3 included a full citation to a published study. Conclusions: The mental health app environment is volatile, with a clinically relevant app for depression becoming unavailable to download every 2.9 days. This poses challenges for consumers and clinicians seeking relevant and long-term apps, as well as for researchers seeking to evaluate the evidence base for publicly available apps.

  • Lady holding a drink. Image sourced from Copyrighted by for free commercial use.

    The Quality and Accuracy of Mobile Apps to Prevent Driving After Drinking Alcohol


    Background: Driving after the consumption of alcohol represents a significant problem globally. Individual prevention countermeasures such as personalized mobile apps aimed at preventing such behavior are widespread, but there is little research on their accuracy and evidence base. There has been no known assessment investigating the quality of such apps. Objective: This study aimed to determine the quality and accuracy of apps for drink driving prevention by conducting a review and evaluation of relevant mobile apps. Methods: A systematic app search was conducted following PRISMA guidelines. App quality was assessed using the Mobile App Rating Scale (MARS). Apps providing blood alcohol calculators (hereafter “calculators”) were reviewed against current alcohol advice for accuracy. Results: A total of 58 apps (30 iOS and 28 Android) met inclusion criteria and were included in the final analysis. Drink driving prevention apps had significantly lower engagement and overall quality scores than alcohol management apps. Most calculators provided conservative blood alcohol content (BAC) time until sober calculations. None of the apps had been evaluated to determine their efficacy in changing either drinking or driving behaviors. Conclusions: This novel study demonstrates that most drink driving prevention apps are not engaging and lack accuracy. They could be improved by increasing engagement features, such as gamification. Further research should examine the context and motivations for using apps to prevent driving after drinking in at-risk populations. Development of drink driving prevention apps should incorporate evidence-based information and guidance, lacking in current apps.

  • Source:; Image purchased by Group Health Research Insititute (GHRI) under a royalty-free license through iStock.

    Prioritizing the mHealth Design Space: A Mixed-Methods Analysis of Smokers’ Perspectives


    Background: Smoking remains the leading cause of preventable disease and death in the United States. Therefore, researchers are constantly exploring new ways to promote smoking cessation. Mobile health (mHealth) technologies could be effective cessation tools. Despite the availability of commercial quit-smoking apps, little research to date has examined smokers’ preferred treatment intervention components (ie, design features). Honoring these preferences is important for designing programs that are appealing to smokers and may be more likely to be adopted and used. Objective: The aim of this study was to understand smokers’ preferred design features of mHealth quit-smoking tools. Methods: We used a mixed-methods approach consisting of focus groups and written surveys to understand the design preferences of adult smokers who were interested in quitting smoking (N=40). Focus groups were stratified by age to allow differing perspectives to emerge between older (>40 years) and younger (<40 years) participants. Focus group discussion included a “blue-sky” brainstorming exercise followed by participant reactions to contrasting design options for communicating with smokers, providing social support, and incentivizing program use. Participants rated the importance of preselected design features on an exit survey. Qualitative analyses examined emergent discussion themes and quantitative analyses compared feature ratings to determine which were perceived as most important. Results: Participants preferred a highly personalized and adaptive mHealth experience. Their ideal mHealth quit-smoking tool would allow personalized tracking of their progress, adaptively tailored feedback, and real-time peer support to help manage smoking cravings. Based on qualitative analysis of focus group discussion, participants preferred pull messages (ie, delivered upon request) over push messages (ie, sent automatically) and preferred interaction with other smokers through closed social networks. Preferences for entertaining games or other rewarding incentives to encourage program use differed by age group. Based on quantitative analysis of surveys, participants rated the importance of select design features significantly differently (P<.001). Design features rated as most important included personalized content, the ability to track one’s progress, and features designed to help manage nicotine withdrawal and medication side effects. Design features rated least important were quit-smoking videos and posting on social media. Communicating with stop-smoking experts was rated more important than communicating with family and friends about quitting (P=.03). Perceived importance of various design features varied by age, experience with technology, and frequency of smoking. Conclusions: Future mHealth cessation aids should be designed with an understanding of smokers’ needs and preferences for these tools. Doing so does not guarantee treatment effectiveness, but balancing user preferences with best-practice treatment considerations could enhance program adoption and improve treatment outcomes. Grounded in the perspectives of smokers, we identify several design considerations, which should be prioritized when designing future mHealth cessation tools and which warrant additional empirical validation.

  • Image Source: She, smoking, copyright mihi_tr,,
Licensed under Creative Commons Attribution cc-by 2.0

    Evaluating an Adaptive and Interactive mHealth Smoking Cessation and Medication Adherence Program: A Randomized Pilot Feasibility Study


    Background: Mobile health (mHealth) interventions hold great promise for helping smokers quit since these programs can have wide reach and facilitate access to comprehensive, interactive, and adaptive treatment content. However, the feasibility, acceptability, and effectiveness of these programs remain largely untested. Objective: To assess feasibility and acceptability of the My Mobile Advice Program (MyMAP) smoking cessation program and estimate its effects on smoking cessation and medication adherence to inform future research planning. Methods: Sixty-six smokers ready to quit were recruited from a large regional health care system and randomized to one of two mHealth programs: (1) standard self-help including psychoeducational materials and guidance how to quit smoking or (2) an adaptive and interactive program consisting of the same standard mHealth self-help content as controls received plus a) real-time, adaptively tailored advice for managing nicotine withdrawal symptoms and medication side-effects and b) asynchronous secure messaging with a cessation counselor. Participants in both arms were also prescribed a 12-week course of varenicline. Follow-up assessments were conducted at 2 weeks post-target quit date (TQD), 3 months post-TQD, and 5 months post-TQD. Indices of program feasibility and acceptability included acceptability ratings, utilization metrics including use of each MyMAP program component (self-help content, secure messaging, and adaptively tailored advice), and open-ended feedback from participants. Smoking abstinence and medication adherence were also assessed to estimate effects on these treatment outcomes. Results: Utilization data indicated the MyMAP program was actively used, with higher mean program log-ins by experimental than control participants (10.6 vs 2.7, P<.001). The majority of experimental respondents thought the MyMAP program could help other people quit smoking (22/24, 92%) and consistently take their stop-smoking medication (17/22, 97%) and would recommend the program to others (20/23, 87%). They also rated the program as convenient, responsive to their needs, and easy to use. Abstinence rates at 5-month follow-up were 36% in the experimental arm versus 24% among controls (odds ratio 1.79 [0.61-5.19], P=.42). Experimental participants used their varenicline an average of 46 days versus 39 among controls (P=.49). More than two-thirds (22/33, 67%) of experimental participants and three-quarters (25/33, 76%) of controls prematurely discontinued their varenicline use (P=.29). Conclusions: The MyMAP intervention was found to be feasible and acceptable. Since the study was not powered for statistical significance, no conclusions can be drawn about the program’s effects on smoking abstinence or medication adherence, but the overall study results suggest further evaluation in a larger randomized trial is warranted. ClinicalTrial: NCT02136498; (Archived by WebCite at

  • GrayMatters RCT app - Source and copyright: author Phillip J. Hartin.

    The Empowering Role of Mobile Apps in Behavior Change Interventions: The Gray Matters Randomized Controlled Trial


    Background: Health education and behavior change programs targeting specific risk factors have demonstrated their effectiveness in reducing the development of future diseases. Alzheimer disease (AD) shares many of the same risk factors, most of which can be addressed via behavior change. It is therefore theorized that a behavior change intervention targeting these risk factors would likely result in favorable rates of AD prevention. Objective: The objective of this study was to reduce the future risk of developing AD, while in the short term promoting vascular health, through behavior change. Methods: The study was an interventional randomized controlled trial consisting of subjects who were randomly assigned into either treatment (n=102) or control group (n=42). Outcome measures included various blood-based biomarkers, anthropometric measures, and behaviors related to AD risk. The treatment group was provided with a bespoke “Gray Matters” mobile phone app designed to encourage and facilitate behavior change. The app presented evidence-based educational material relating to AD risk and prevention strategies, facilitated self-reporting of behaviors across 6 behavioral domains, and presented feedback on the user’s performance, calculated from reported behaviors against recommended guidelines. Results: This paper explores the rationale for a mobile phone–led intervention and details the app’s effect on behavior change and subsequent clinical outcomes. Via the app, the average participant submitted 7.3 (SD 3.2) behavioral logs/day (n=122,719). Analysis of these logs against primary outcome measures revealed that participants who improved their high-density lipoprotein cholesterol levels during the study duration answered a statistically significant higher number of questions per day (mean 8.30, SD 2.29) than those with no improvement (mean 6.52, SD 3.612), t97.74=−3.051, P=.003. Participants who decreased their body mass index (BMI) performed significantly better in attaining their recommended daily goals (mean 56.21 SD 30.4%) than those who increased their BMI (mean 40.12 SD 29.1%), t80 = −2.449, P=.017. In total, 69.2% (n=18) of those who achieved a mean performance percentage of 60% or higher, across all domains, reduced their BMI during the study, whereas 60.7% (n=34) who did not, increased their BMI. One-way analysis of variance of systolic blood pressure category changes showed a significant correlation between reported efforts to reduce stress and category change as a whole, P=.035. An exit survey highlighted that respondents (n=83) reported that the app motivated them to perform physical activity (85.4%) and make healthier food choices (87.5%). Conclusions: In this study, the ubiquitous nature of the mobile phone excelled as a delivery platform for the intervention, enabling the dissemination of educational intervention material while simultaneously monitoring and encouraging positive behavior change, resulting in desirable clinical effects. Sustained effort to maintain the achieved behaviors is expected to mitigate future AD risk. Trial Registration: NCT02290912; (Archived by WebCite at

  • Source:
Licensed under Creative Commons Attribution cc-by-sa 2.0

    Popular Nutrition-Related Mobile Apps: A Feature Assessment


    Background: A key challenge in human nutrition is the assessment of usual food intake. This is of particular interest given recent proposals of eHealth personalized interventions. The adoption of mobile phones has created an opportunity for assessing and improving nutrient intake as they can be used for digitalizing dietary assessments and providing feedback. In the last few years, hundreds of nutrition-related mobile apps have been launched and installed by millions of users. Objective: This study aims to analyze the main features of the most popular nutrition apps and to compare their strategies and technologies for dietary assessment and user feedback. Methods: Apps were selected from the two largest online stores of the most popular mobile operating systems—the Google Play Store for Android and the iTunes App Store for iOS—based on popularity as measured by the number of installs and reviews. The keywords used in the search were as follows: calorie(s), diet, diet tracker, dietician, dietitian, eating, fit, fitness, food, food diary, food tracker, health, lose weight, nutrition, nutritionist, weight, weight loss, weight management, weight watcher, and ww calculator. The inclusion criteria were as follows: English language, minimum number of installs (1 million for Google Play Store) or reviews (7500 for iTunes App Store), relation to nutrition (ie, diet monitoring or recommendation), and independence from any device (eg, wearable) or subscription. Results: A total of 13 apps were classified as popular for inclusion in the analysis. Nine apps offered prospective recording of food intake using a food diary feature. Food selection was available via text search or barcode scanner technologies. Portion size selection was only textual (ie, without images or icons). All nine of these apps were also capable of collecting physical activity (PA) information using self-report, the global positioning system (GPS), or wearable integrations. Their outputs focused predominantly on energy balance between dietary intake and PA. None of these nine apps offered features directly related to diet plans and motivational coaching. In contrast, the remaining four of the 13 apps focused on these opportunities, but without food diaries. One app—FatSecret—also had an innovative feature for connecting users with health professionals, and another—S Health—provided a nutrient balance score. Conclusions: The high number of installs indicates that there is a clear interest and opportunity for diet monitoring and recommendation using mobile apps. All the apps collecting dietary intake used the same nutrition assessment method (ie, food diary record) and technologies for data input (ie, text search and barcode scanner). Emerging technologies, such as image recognition, natural language processing, and artificial intelligence, were not identified. None of the apps had a decision engine capable of providing personalized diet advice.

  • Screenshot of the app. Image sourced and copyright held by authors Åsa Svensson et al.

    Overcoming Barriers: Adolescents’ Experiences Using a Mobile Phone Dietary Assessment App


    Background: The use of new technology has the potential to increase participation rates in dietary studies and improve the validity of collected dietary data. However, to evaluate the usability of developed dietary methods, qualitative studies of participants’ experiences and perceptions are needed. Objective: To explore adolescents’ experiences using a newly developed mobile phone dietary assessment app, with a focus on factors that could affect their recording of dietary intake. Methods: Focus group interviews were conducted with 75 participants who had used a newly developed mobile phone dietary assessment app in a quantitative evaluation study. The interviews were analyzed using qualitative content analysis and the theoretical framework of Self Determination Theory was applied. Results: The adolescents’ use of the mobile phone dietary assessment app was characterized by their struggle to overcome several perceived barriers. Facilitators that helped adolescents complete the method were also identified. Motivation was found to be an important facilitator, and intrinsically motivated participants completed the method because they found it fun to use. The autonomous extrinsically motivated participants completed the method for the greater good, in order to contribute to the study. The controlled extrinsically motivated participants completed the method to get a reward or avoid punishment. Amotivated participants did not complete the method. More motivated participants were assumed to be more able to overcome barriers and needed less facilitators. Conclusions: Future studies that examine the recording of food intake should include systematic efforts that aim to minimize identified barriers and promote identified facilitators. Further research should specifically aim at studying methods for (and effects of) increasing intrinsic motivation by supporting autonomy, competence, and relatedness among adolescents asked to participate in dietary studies.

Citing this Article

Right click to copy or hit: ctrl+c (cmd+c on mac)

Latest Submissions Open for Peer-Review:

View All Open Peer Review Articles
  • Formative evaluation of participant experience with mobile eConsent in the app-mediated Parkinson mPower Study

    Date Submitted: Aug 19, 2016

    Open Peer Review Period: Aug 26, 2016 - Oct 21, 2016

    Background: To fully capitalize on the promise of mobile technology to enable scalable, participant-centered research, we must develop companion self-administered electronic informed consent (eConsent...

    Background: To fully capitalize on the promise of mobile technology to enable scalable, participant-centered research, we must develop companion self-administered electronic informed consent (eConsent) processes. As we do so, we have an ethical obligation to ensure that core tenants of informed consent – informedness, comprehension, and voluntariness – are upheld. Further, we should be wary of recapitulating the pitfalls of “traditional” informed consent processes. Objective: Our objective was to describe the essential qualities of participant experience, including delineation of common and novel themes relating to informed consent, with a self-administered, smartphone-based eConsent process. We sought to identify participant responses related to informedness, comprehension, and voluntariness as well as to capture any emergent themes relating to the informed consent process in an app-mediated research study. Methods: We performed qualitative thematic analysis of participant responses to a general prompt collected over a six-month period within the Parkinson mPower app. We employed a combination of a priori and emergent codes for our analysis. A priori codes focused on the core concepts of informed consent; emergent codes were derived to capture additional themes relating to self-administered consent processes. We used self-reported demographic information from the study’s baseline survey to characterize study participants and respondents. Results: During the study period, 9846 people completed the eConsent process and enrolled in the Parkinson mPower study. 2758 participants submitted 7483 comments; initial categorization identified a subset of 3875 germane responses submitted by 1678 distinct participants. Respondents were more likely to self-report a Parkinson Disease diagnosis (30.2% vs. 11.1%), be female (28.3% vs. 20.2%), older (42.9 years vs. 34.5 years), and have completed more formal education (66.2% with a 4-year college degree or more education vs. 55.8%) as compared with all mPower participants (P<.001 for all values). Within our qualitative analysis, three conceptual domains emerged. First, consistent with traditional in-person informed consent settings, we observed a broad spectrum of comprehension of core research concepts following eConsent. Second, we identified new consent themes born out of the remote mobile research setting, for example the impact of the study design on the engagement of controls and the misconstruction of the open response field as a method for responsive communication with researchers. Finally, our findings highlight participants’ desire to be empowered as partners. Conclusions: Our study serves as a formative evaluation of participant experience with a self-administered informed consent process via a mobile app. Areas for future investigation include direct comparison of the efficacy of remote, self-administered eConsent with traditional, facilitated informed consent processes, exploring the potential benefits and pitfalls of smartphone user behavioral habits on participant engagement in research, and developing best practices to increase informedness, comprehension, and voluntariness via participant co-engagement in the research endeavor.

  • Analyzing mHealth: Joint Models for Intensively Collected User Engagement Data

    Date Submitted: Aug 19, 2016

    Open Peer Review Period: Aug 26, 2016 - Oct 21, 2016

    Background: Evaluating engagement with an intervention is a key component of understanding its efficacy. With an increasing interest in developing behavioral interventions in the mobile health (mHealt...

    Background: Evaluating engagement with an intervention is a key component of understanding its efficacy. With an increasing interest in developing behavioral interventions in the mobile health (mHealth) space, appropriate methods for evaluating engagement in the mHealth context is necessary. Data collected to evaluate mHealth interventions are often collected much more frequently than those for clinic-based interventions. Additionally, missing data on engagement is closely linked to level of engagement resulting in the potential for informative missingness. Thus, models that can accommodate intensively collected data and can account for informative missingness are required for unbiased inference when analyzing engagement with an mHealth intervention. Objective: The objective of this paper is to demonstrate the utility of a joint modeling approach to longitudinal engagement data in mHealth research. Methods: Engagement data from an evaluation of an mHealth intervention designed to support illness management among people with schizophrenia is analyzed. A joint model is applied to the longitudinal engagement outcome and time-to-dropout to allow unbiased inference on the engagement outcome. Results are compared to separate naïve models that do not account for the relationship between drop-out and engagement. Results: The joint model shows a strong relationship between engagement and reduced risk of dropout. Using the mHealth app one day more per week was associated with a 33% decreased risk of dropout (P<.001). The decline in engagement over time was steeper when the joint model was used in comparison with the naïve model. Conclusions: Naïve longitudinal models that do not account for informative missingness in mHealth data produce biased results. Joint models are appropriate for modeling intensively collected engagement outcomes in mHealth intervention research. Clinical Trial: Trial Registration: NCT02364544.

  • Scope of mobile health (mhealth) in Indian health care system- the way forward

    Date Submitted: Aug 9, 2016

    Open Peer Review Period: Aug 12, 2016 - Oct 7, 2016

    India, the second largest populated country in the world is under demographic and environmental transition adding to the already existing high burden of communicable, non-communicable and emerging inf...

    India, the second largest populated country in the world is under demographic and environmental transition adding to the already existing high burden of communicable, non-communicable and emerging infectious diseases. The growth of health care delivery system is not in pace with the rising burden of ill health in the country. The existing health care delivery system poses many challenges due to non-existent referral/linkages between primary/secondary/tertiary health care facilities and functional inaccessibility of secondary and tertiary government health services. Technology advancements like teleconsultation strived continuously to tackle this crisis but had attained limited success. Another cost-effective alternative could be mobile based interventions as even in developing countries mobile devices have reached more people than electricity, road systems and clean piped water. Mobile eHealth or mHealth includes the use of telecommunication and multimedia technologies integrated with mobile and wireless healthcare delivery system.The mHealth services presently existing, vary in their level of sophistication from static information provision to comprehensive health care management and are in effect in many countries. Mobile technology in the present scenario has gained substantial effects on health outcomes. Using mobile technology offers a tremendous opportunity for developing countries as India to advance in health care delivery by effectively utilizing scarce resources.

  • Evaluation of Diet Related Infographics on Pinterest for use of Behavior Change Theories; a content analysis

    Date Submitted: Jul 17, 2016

    Open Peer Review Period: Jul 20, 2016 - Sep 14, 2016

    Background: There is increasing interest in Pinterest as a method of disseminating health information, however it is unclear whether the health information promoted on Pinterest is evidence-based or p...

    Background: There is increasing interest in Pinterest as a method of disseminating health information, however it is unclear whether the health information promoted on Pinterest is evidence-based or promotes behavior change. Objective: To determine the presence of Health Behavior Theory (HBT) constructs in pins found on Pinterest and to assess the relationship between various pin characteristics and the inclusion of HBT. Methods: A content analysis was conducted on pins collected from Pinterest identified with the search terms “nutrition infographic” and “healthy eating infographic.” The coding rubric included HBT constructs, pin characteristics, and visual communication tools. Each HBT construct was coded as present or not present (yes = 1/no = 0). A total theory score was calculated by summing the values for each of the 9 constructs (range 0 - 9). Adjusted regression analysis was used to identify factors associated with the inclusion of health behavior change theory in pins (P < .05). Results: The mean total theory score was 2.03 (SD = 1.2). Perceived benefits were present most often (72.03%), followed by behavioral capacity (51.68%) and perceived severity (33.47%). The construct that appeared the least was self-regulation/self-control (0.84%). Pin characteristics associated with the inclusion of HBT included a large amount of text (P=.01), photographs of real people (P = .001), cartoon pictures of food (P = .01) and the presence of references (P = .001). The number of repins (P = .04), likes (P = .01) and comments (P = .01) were positively associated with the inclusion of HBT. Conclusions: These findings suggest that current Pinterest infographics targeting healthy eating contain little HBT elements. Health professionals and organizations need to create and disseminate infographics that contain more elements of HBT to better influence healthy eating behavior. This may be accomplished by creating pins that utilize both text and images of people and food in order to portray elements of HBT and convey nutritional information.

  • Designing a mobile health application for patients with dysphagia following head and neck cancer

    Date Submitted: Jul 13, 2016

    Open Peer Review Period: Jul 16, 2016 - Sep 10, 2016

    Background: Adherence to swallowing rehabilitation exercises is important to develop and maintain functional improvement, yet more than half of head and neck cancer (HNC) patients report having diffic...

    Background: Adherence to swallowing rehabilitation exercises is important to develop and maintain functional improvement, yet more than half of head and neck cancer (HNC) patients report having difficulty adhering to prescribed regimens. Health applications (apps) with game elements have been used in other health domains to motivate and engage patients. Understanding the factors that impact adherence may allow for more effective gamified solutions. Objective: (1) Identify self-reported factors that influence adherence to conventional home therapy without a mobile device in HNC patients, and (2) identify appealing biofeedback designs that could be used in a health app. Methods: Ten (4 females) HNC patients (M = 60.1 years) with experience completing home-based rehabilitation programs were recruited. Thematic analysis of semi-structured interviews was used to answer the first objective. Convergent interviews were used to obtain reactions to biofeedback designs. Results: Facilitators and barriers of adherence to home therapy were described through six themes: patient perceptions on outcomes and progress; clinical appointments; cancer treatment; rehabilitation program; personal factors; and connection. App visuals that provide feedback on performance during swallowing exercises should retain the chief goals of biofeedback (e.g., immediate representation of effort relative to a goal). Simple, intuitive graphics were preferred over complex, abstract ones. Conclusions: Continued engagement with the app could be facilitated by tracking progress and by using visuals that build or create structures with each subsequent use.

  • Women’s Perceptions of Participation in an Extended Contact Text Message Weight Loss Intervention

    Date Submitted: Jul 7, 2016

    Open Peer Review Period: Jul 11, 2016 - Sep 5, 2016

    Background: Extending contact with participants after the end of an initial weight loss intervention has been shown to lead to maintained weight loss and related behavior change. Mobile phone text mes...

    Background: Extending contact with participants after the end of an initial weight loss intervention has been shown to lead to maintained weight loss and related behavior change. Mobile phone text messaging offers a low cost and efficacious method to deliver extended contact. In this rapidly developing area, formative work is required to understand user perspectives of text message technology. An extended contact intervention delivered by text messages following an initial telephone-delivered weight loss intervention in breast cancer survivors provided this opportunity. Objective: To qualitatively explore women’s perceptions of participation in an extended contact intervention using text messaging to support long-term weight loss, physical activity and dietary behavior change Methods: Following the end of an initial 6-month randomized controlled trial of a telephone-delivered weight loss intervention (versus usual care), participants received a 6-month extended contact intervention via tailored text messages. Participant perceptions of the different types of text messages, the content, tailoring, timing and frequency of the texts and the length of the intervention were assessed through semi-structured interviews conducted after the extended contact intervention. The interviews were transcribed verbatim and analysed with key themes identified. Results: Participants (n=27) were Caucasian with a mean age of 56.0 years (SD 12.0), mean BMI of 30.0kg/m2 (SD 4.2) and were a mean of 16.1 months (SD 3.1) post-diagnosis at study baseline. Participants perceived the texts to be useful behavioral prompts and felt the messages kept them accountable to their behavior change goals. The individual tailoring of the text content and schedules was a key to the acceptability of the messages, however some women preferred the support and real-time discussion via telephone (during the initial intervention) compared to the text messages (during the extended contact intervention). Conclusions: Text message support was perceived as acceptable for the majority of women as a way of extending intervention contact for weight loss and behavioral maintenance. Texts supported the maintenance of healthy behaviors established in the intervention phase and kept the women accountable to their goals. A combination of phone and text support was suggested as a more acceptable option for some of the women for an extended contact intervention. Clinical Trial: Not registered