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:

  • Follow Texting in the Rain. Image source: Author: Garry Knight. License:

    Text Message-Based Intervention Targeting Alcohol Consumption Among University Students: Findings From a Formative Development Study


    Background: Drinking of alcohol among university students is a global phenomenon; heavy episodic drinking is accepted despite several potential negative consequences. There is emerging evidence that short message service (SMS) text messaging interventions are effective to promote behavior change among students. However, it is still unclear how effectiveness can be optimized through intervention design or how user interest and adherence can be maximized. Objective: The objective of this study was to develop an SMS text message-based intervention targeting alcohol drinking among university students using formative research. Methods: A formative research design was used including an iterative revision process based on input from end users and experts. Data were collected via seven focus groups with students and a panel evaluation involving students (n=15) and experts (n=5). Student participants were recruited from five universities in Sweden. A semistructured interview guide was used in the focus groups and included questions on alcohol culture, message content, and intervention format. The panel evaluation asked participants to rate to what degree preliminary messages were understandable, usable, and had a good tone on a scale from 1 (very low degree) to 4 (very high degree). Participants could also write their own comments for each message. Qualitative data were analyzed using qualitative descriptive analysis. Quantitative data were analyzed using descriptive statistics. The SMS text messages and the intervention format were revised continuously in parallel with data collection. A behavior change technique (BCT) analysis was conducted on the final version of the program. Results: Overall, students were positive toward the SMS text message intervention. Messages that were neutral, motivated, clear, and tangible engaged students. Students expressed that they preferred short, concise messages and confirmed that a 6-week intervention was an appropriate duration. However, there was limited consensus regarding SMS text message frequency, personalization of messages, and timing. Overall, messages scored high on understanding (mean 3.86, SD 0.43), usability (mean 3.70, SD 0.61), and tone (mean 3.78, SD 0.53). Participants added comments to 67 of 70 messages, including suggestions for change in wording, order of messages, and feedback on why a message was unclear or needed major revision. Comments also included positive feedback that confirmed the value of the messages. Twenty-three BCTs aimed at addressing self-regulatory skills, for example, were identified in the final program. Conclusions: The formative research design was valuable and resulted in significant changes to the intervention. All the original SMS text messages were changed and new messages were added. Overall, the findings showed that students were positive toward receiving support through SMS text message and that neutral, motivated, clear, and tangible messages promoted engagement. However, limited consensus was found on the timing, frequency, and tailoring of messages.

  • Person using smartphone, created and copyright owned by authors.

    A Review of Persuasive Principles in Mobile Apps for Chronic Arthritis Patients: Opportunities for Improvement


    Background: Chronic arthritis (CA), an umbrella term for inflammatory rheumatic and other musculoskeletal diseases, is highly prevalent. Effective disease-modifying antirheumatic drugs for CA are available, with the exception of osteoarthritis, but require a long-term commitment of patients to comply with the medication regimen and management program as well as a tight follow-up by the treating physician and health professionals. Additionally, patients are advised to participate in physical exercise programs. Adherence to exercises and physical activity programs is often very low. Patients would benefit from support to increase medication compliance as well as compliance to the physical exercise programs. To address these shortcomings, health apps for CA patients have been created. These mobile apps assist patients in self-management of overall health measures, health prevention, and disease management. By including persuasive principles designed to reinforce, change, or shape attitudes or behaviors, health apps can transform into support tools that motivate and stimulate users to achieve or keep up with target behavior, also called persuasive systems. However, the extent to which health apps for CA patients consciously and successfully employ such persuasive principles remains unknown. Objective: The objective of this study was to evaluate the number and type of persuasive principles present in current health apps for CA patients. Methods: A review of apps for arthritis patients was conducted across the three major app stores (Google Play, Apple App Store, and Windows Phone Store). Collected apps were coded according to 37 persuasive principles, based on an altered version of the Persuasive System Design taxonomy of Oinas-Kukkonen and Harjuma and the taxonomy of Behavior Change Techniques of Michie and Abraham. In addition, user ratings, number of installs, and price of the apps were also coded. Results: We coded 28 apps. On average, 5.8 out of 37 persuasive principles were used in each app. The most used category of persuasive principles was System Credibility with an average of 2.6 principles. Task Support was the second most used, with an average of 2.3 persuasive principles. Next was Dialogue Support with an average of 0.5 principles. Social Support was last with an average of 0.01 persuasive principles only. Conclusions: Current health apps for CA patients would benefit from adding Social Support techniques (eg, social media, user fora) and extending Dialogue Support techniques (eg, rewards, praise). The addition of automated tracking of health-related parameters (eg, physical activity, step count) could further reduce the effort for CA patients to manage their disease and thus increase Task Support. Finally, apps for health could benefit from a more evidence-based approach, both in developing the app as well as ensuring that content can be verified as scientifically proven, which will result in enhanced System Credibility.

  • Image Source: Samsung Galaxy Tab 3 10.1, copyright Kārlis Dambrāns., Licensed under Creative Commons Attribution cc-by 2.0

    Breadth of Coverage, Ease of Use, and Quality of Mobile Point-of-Care Tool Information Summaries: An Evaluation


    Background: With advances in mobile technology, accessibility of clinical resources at the point of care has increased. Objective: The objective of this research was to identify if six selected mobile point-of-care tools meet the needs of clinicians in internal medicine. Point-of-care tools were evaluated for breadth of coverage, ease of use, and quality. Methods: Six point-of-care tools were evaluated utilizing four different devices (two smartphones and two tablets). Breadth of coverage was measured using select International Classification of Diseases, Ninth Revision, codes if information on summary, etiology, pathophysiology, clinical manifestations, diagnosis, treatment, and prognosis was provided. Quality measures included treatment and diagnostic inline references and individual and application time stamping. Ease of use covered search within topic, table of contents, scrolling, affordance, connectivity, and personal accounts. Analysis of variance based on the rank of score was used. Results: Breadth of coverage was similar among Medscape (mean 6.88), Uptodate (mean 6.51), DynaMedPlus (mean 6.46), and EvidencePlus (mean 6.41) (P>.05) with DynaMed (mean 5.53) and Epocrates (mean 6.12) scoring significantly lower (P<.05). Ease of use had DynaMedPlus with the highest score, and EvidencePlus was lowest (6.0 vs 4.0, respectively, P<.05). For quality, reviewers rated the same score (4.00) for all tools except for Medscape, which was rated lower (P<.05). Conclusions: For breadth of coverage, most point-of-care tools were similar with the exception of DynaMed. For ease of use, only UpToDate and DynaMedPlus allow for search within a topic. All point-of-care tools have remote access with the exception of UpToDate and Essential Evidence Plus. All tools except Medscape covered criteria for quality evaluation. Overall, there was no significant difference between the point-of-care tools with regard to coverage on common topics used by internal medicine clinicians. Selection of point-of-care tools is highly dependent on individual preference based on ease of use and cost of the application.

  • Using knowledge translation to craft ‘sticky’ social media health messages. Image created and copyright owned by author Dr Sanchia Shibasaki.

    Using Knowledge Translation to Craft “Sticky” Social Media Health Messages That Provoke Interest, Raise Awareness, Impart Knowledge, and Inspire Change


    Background: In Australia, there is growing use of technology supported knowledge translation (KT) strategies such as social media and mobile apps in health promotion and in Indigenous health. However, little is known about how individuals use technologies and the evidence base for the impact of these health interventions on health behavior change is meager. Objective: The objective of our study was to examine how Facebook is used to promote health messages to Indigenous people and discuss how KT can support planning and implementing health messages to ensure chosen strategies are fit for the purpose and achieve impact. Methods: A desktop audit of health promotion campaigns on smoking prevention and cessation for Australian Indigenous people using Facebook was conducted. Results: Our audit identified 13 out of 21 eligible campaigns that used Facebook. Facebook pages with the highest number of likes (more than 5000) were linked to a website and to other social media applications and demonstrated stickiness characteristics by posting frequently (triggers and unexpected), recruiting sporting or public personalities to promote campaigns (social currency and public), recruiting Indigenous people from the local region (stories and emotion), and sharing stories and experiences based on real-life events (credible and practical value). Conclusions: KT planning may support campaigns to identify and select KT strategies that are best suited and well-aligned to the campaign’s goals, messages, and target audiences. KT planning can also help mitigate unforeseen and expected risks, reduce unwarranted costs and expenses, achieve goals, and limit the peer pressure of using strategies that may not be fit for purpose. One of the main challenges in using KT systems and processes involves coming to an adequate conceptualization of the KT process itself.

  • Contact, email. Source: License:CC0 License.

    Baseline Motivation Type as a Predictor of Dropout in a Healthy Eating Text Messaging Program


    Background: Growing evidence suggests that text messaging programs are effective in facilitating health behavior change. However, high dropout rates limit the potential effectiveness of these programs. Objective: This paper describes patterns of early dropout in the HealthyYou text (HYTxt) program, with a focus on the impact of baseline motivation quality on dropout, as characterized by Self-Determination Theory (SDT). Methods: This analysis included 193 users of HYTxt, a diet and physical activity text messaging intervention developed by the US National Cancer Institute. Descriptive statistics were computed, and logistic regression models were run to examine the association between baseline motivation type and early program dropout. Results: Overall, 43.0% (83/193) of users dropped out of the program; of these, 65.1% (54/83; 28.0% of all users) did so within the first 2 weeks. Users with higher autonomous motivation had significantly lower odds of dropping out within the first 2 weeks. A one unit increase in autonomous motivation was associated with lower odds (odds ratio 0.44, 95% CI 0.24–0.81) of early dropout, which persisted after adjusting for level of controlled motivation. Conclusions: Applying SDT-based strategies to enhance autonomous motivation might reduce early dropout rates, which can improve program exposure and effectiveness.

  • Dr. Kent demonstrating our application to a patient. Image sourced and copyright owned by authors Authors Rebecca Gunter et al.

    Evaluating Patient Usability of an Image-Based Mobile Health Platform for Postoperative Wound Monitoring


    Background: Surgical patients are increasingly using mobile health (mHealth) platforms to monitor recovery and communicate with their providers in the postdischarge period. Despite widespread enthusiasm for mHealth, few studies evaluate the usability or user experience of these platforms. Objective: Our objectives were to (1) develop a novel image-based smartphone app for postdischarge surgical wound monitoring, and (2) rigorously user test it with a representative population of vascular and general surgery patients. Methods: A total of 9 vascular and general surgery inpatients undertook usability testing of an internally developed smartphone app that allows patients to take digital images of their wound and answer a survey about their recovery. We followed the International Organization for Standardization (ISO) 9241-11 guidelines, focusing on effectiveness, efficiency, and user satisfaction. An accompanying training module was developed by applying tenets of adult learning. Sessions were audio-recorded, and the smartphone screen was mirrored onto a study computer. Digital image quality was evaluated by a physician panel to determine usefulness for clinical decision making. Results: The mean length of time spent was 4.7 (2.1-12.8) minutes on the training session and 5.0 (1.4-16.6) minutes on app completion. 55.5% (5/9) of patients were able to complete the app independently with the most difficulty experienced in taking digital images of surgical wounds. Novice patients who were older, obese, or had groin wounds had the most difficulty. 81.8% of images were sufficient for diagnostic purposes. User satisfaction was high, with an average usability score of 83.3 out of 100. Conclusion: Surgical patients can learn to use a smartphone app for postoperative wound monitoring with high user satisfaction. We identified design features and training approaches that can facilitate ease of use. This protocol illustrates an important, often overlooked, aspect of mHealth development to improve surgical care.

  • Image Source: schizophrenia, copyright Elli Ktrizität,,
Licensed under Creative Commons Attribution cc-by 2.0

    Measuring Users’ Receptivity Toward an Integral Intervention Model Based on mHealth Solutions for Patients With Treatment-Resistant Schizophrenia...


    Background: Despite the theoretical potential of mHealth solutions in the treatment of patients with schizophrenia, there remains a lack of technological tools in clinical practice. Objective: The aim of this study was to measure the receptivity of patients, informal carers, and clinicians to a European integral intervention model focused on patients with persistent positive symptoms: Mobile Therapeutic Attention for Patients with Treatment-Resistant Schizophrenia (m-RESIST). Methods: Before defining the system requirements, a qualitative study of the needs of outpatients with treatment-resistant schizophrenia was carried out in Spain, Israel, and Hungary. We analyzed the opinions of patients, informal carers, and clinicians concerning the services originally intended to be part of the solution. A total of 9 focus groups (72 people) and 35 individual interviews were carried out in the 3 countries, using discourse analysis as the framework. Results: A webpage and an online forum were perceived as suitable to get both reliable information on the disease and support. Data transmission by a smart watch (monitoring), Web-based visits, and instant messages (clinical treatment) were valued as ways to improve contact with clinicians. Alerts were appreciated as reminders of daily tasks and appointments. Avoiding stressful situations for outpatients, promoting an active role in the management of the disease, and maintaining human contact with clinicians were the main suggestions provided for improving the effectiveness of the solution. Conclusions: Positive receptivity toward m-RESIST services is related to its usefulness in meeting user needs, its capacity to empower them, and the possibility of maintaining human contact.

  • Smartphone user. Image source: License: CC0 Public Domain.

    An mHealth Intervention Using a Smartphone App to Increase Walking Behavior in Young Adults: A Pilot Study


    Background: Physical inactivity is a growing concern for society and is a risk factor for cardiovascular disease, obesity, and other chronic diseases. Objective: This study aimed to determine the efficacy of the Accupedo-Pro Pedometer mobile phone app intervention, with the goal of increasing daily step counts in young adults. Methods: Mobile phone users (n=58) between 17-26 years of age were randomized to one of two conditions (experimental and control). Both groups downloaded an app that recorded their daily step counts. Baseline data were recorded and followed-up at 5 weeks. Both groups were given a daily walking goal of 30 minutes, but the experimental group participants were told the equivalent goal in steps taken, via feedback from the app. The primary outcome was daily step count between baseline and follow-up. Results: A significant time x group interaction effect was observed for daily step counts (P=.04). Both the experimental (P<.001) and control group (P=.03) demonstrated a significant increase in daily step counts, with the experimental group walking an additional 2000 steps per day. Conclusions: The results of this study demonstrate that a mobile phone app can significantly increase physical activity in a young adult sample by setting specific goals, using self-monitoring, and feedback.

  • Mobile Sensing and Support for People with Depression: A Pilot Trial in the Wild. Image sourced and copyright owned by makora AG.

    Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild


    Background: Depression is a burdensome, recurring mental health disorder with high prevalence. Even in developed countries, patients have to wait for several months to receive treatment. In many parts of the world there is only one mental health professional for over 200 people. Smartphones are ubiquitous and have a large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms and providing context-sensitive intervention support. Objective: The objective of this study is 2-fold, first to explore the detection of daily-life behavior based on sensor information to identify subjects with a clinically meaningful depression level, second to explore the potential of context sensitive intervention delivery to provide in-situ support for people with depressive symptoms. Methods: A total of 126 adults (age 20-57) were recruited to use the smartphone app Mobile Sensing and Support (MOSS), collecting context-sensitive sensor information and providing just-in-time interventions derived from cognitive behavior therapy. Real-time learning-systems were deployed to adapt to each subject’s preferences to optimize recommendations with respect to time, location, and personal preference. Biweekly, participants were asked to complete a self-reported depression survey (PHQ-9) to track symptom progression. Wilcoxon tests were conducted to compare scores before and after intervention. Correlation analysis was used to test the relationship between adherence and change in PHQ-9. One hundred twenty features were constructed based on smartphone usage and sensors including accelerometer, Wifi, and global positioning systems (GPS). Machine-learning models used these features to infer behavior and context for PHQ-9 level prediction and tailored intervention delivery. Results: A total of 36 subjects used MOSS for ≥2 weeks. For subjects with clinical depression (PHQ-9≥11) at baseline and adherence ≥8 weeks (n=12), a significant drop in PHQ-9 was observed (P=.01). This group showed a negative trend between adherence and change in PHQ-9 scores (rho=−.498, P=.099). Binary classification performance for biweekly PHQ-9 samples (n=143), with a cutoff of PHQ-9≥11, based on Random Forest and Support Vector Machine leave-one-out cross validation resulted in 60.1% and 59.1% accuracy, respectively. Conclusions: Proxies for social and physical behavior derived from smartphone sensor data was successfully deployed to deliver context-sensitive and personalized interventions to people with depressive symptoms. Subjects who used the app for an extended period of time showed significant reduction in self-reported symptom severity. Nonlinear classification models trained on features extracted from smartphone sensor data including Wifi, accelerometer, GPS, and phone use, demonstrated a proof of concept for the detection of depression superior to random classification. While findings of effectiveness must be reproduced in a RCT to proof causation, they pave the way for a new generation of digital health interventions leveraging smartphone sensors to provide context sensitive information for in-situ support and unobtrusive monitoring of critical mental health states.

  • Source:; CC0 Public Domain.

    Physical Activity Assessment Between Consumer- and Research-Grade Accelerometers: A Comparative Study in Free-Living Conditions


    Background: Wearable activity monitors such as Fitbit enable users to track various attributes of their physical activity (PA) over time and have the potential to be used in research to promote and measure PA behavior. However, the measurement accuracy of Fitbit in absolute free-living conditions is largely unknown. Objective: To examine the measurement congruence between Fitbit Flex and ActiGraph GT3X for quantifying steps, metabolic equivalent tasks (METs), and proportion of time in sedentary activity and light-, moderate-, and vigorous-intensity PA in healthy adults in free-living conditions. Methods: A convenience sample of 19 participants (4 men and 15 women), aged 18-37 years, concurrently wore the Fitbit Flex (wrist) and ActiGraph GT3X (waist) for 1- or 2-week observation periods (n=3 and n=16, respectively) that included self-reported bouts of daily exercise. Data were examined for daily activity, averaged over 14 days and for minutes of reported exercise. Average day-level data included steps, METs, and proportion of time in different intensity levels. Minute-level data included steps, METs, and mean intensity score (0 = sedentary, 3 = vigorous) for overall reported exercise bouts (N=120) and by exercise type (walking, n=16; run or sports, n=44; cardio machine, n=20). Results: Measures of steps were similar between devices for average day- and minute-level observations (all P values > .05). Fitbit significantly overestimated METs for average daily activity, for overall minutes of reported exercise bouts, and for walking and run or sports exercises (mean difference 0.70, 1.80, 3.16, and 2.00 METs, respectively; all P values < .001). For average daily activity, Fitbit significantly underestimated the proportion of time in sedentary and light intensity by 20% and 34%, respectively, and overestimated time by 3% in both moderate and vigorous intensity (all P values < .001). Mean intensity scores were not different for overall minutes of exercise or for run or sports and cardio-machine exercises (all P values > .05). Conclusions: Fitbit Flex provides accurate measures of steps for daily activity and minutes of reported exercise, regardless of exercise type. Although the proportion of time in different intensity levels varied between devices, examining the mean intensity score for minute-level bouts across different exercise types enabled interdevice comparisons that revealed similar measures of exercise intensity. Fitbit Flex is shown to have measurement limitations that may affect its potential utility and validity for measuring PA attributes in free-living conditions.

  • A Context-Sensing Mobile Phone App (Q Sense) for Smoking Cessation: A Mixed-Methods Study


    Background: A major cause of lapse and relapse to smoking during a quit attempt is craving triggered by cues from a smoker's immediate environment. To help smokers address these cue-induced cravings when attempting to quit, we have developed a context-aware smoking cessation app, Q Sense, which uses a smoking episode-reporting system combined with location sensing and geofencing to tailor support content and trigger support delivery in real time. Objective: We sought to (1) assess smokers’ compliance with reporting their smoking in real time and identify reasons for noncompliance, (2) assess the app's accuracy in identifying user-specific high-risk locations for smoking, (3) explore the feasibility and user perspective of geofence-triggered support, and (4) identify any technological issues or privacy concerns. Methods: An explanatory sequential mixed-methods design was used, where data collected by the app informed semistructured interviews. Participants were smokers who owned an Android mobile phone and were willing to set a quit date within one month (N=15). App data included smoking reports with context information and geolocation, end-of-day (EoD) surveys of smoking beliefs and behavior, support message ratings, and app interaction data. Interviews were undertaken and analyzed thematically (N=13). Quantitative and qualitative data were analyzed separately and findings presented sequentially. Results: Out of 15 participants, 3 (20%) discontinued use of the app prematurely. Pre-quit date, the mean number of smoking reports received was 37.8 (SD 21.2) per participant, or 2.0 (SD 2.2) per day per participant. EoD surveys indicated that participants underreported smoking on at least 56.2% of days. Geolocation was collected in 97.0% of smoking reports with a mean accuracy of 31.6 (SD 16.8) meters. A total of 5 out of 9 (56%) eligible participants received geofence-triggered support. Interaction data indicated that 50.0% (137/274) of geofence-triggered message notifications were tapped within 30 minutes of being generated, resulting in delivery of a support message, and 78.2% (158/202) of delivered messages were rated by participants. Qualitative findings identified multiple reasons for noncompliance in reporting smoking, most notably due to environmental constraints and forgetting. Participants verified the app’s identification of their smoking locations, were largely positive about the value of geofence-triggered support, and had no privacy concerns about the data collected by the app. Conclusions: User-initiated self-report is feasible for training a cessation app about an individual’s smoking behavior, although underreporting is likely. Geofencing was a reliable and accurate method of identifying smoking locations, and geofence-triggered support was regarded positively by participants.

  • ACT-DL in Daily Life - © 2016 Maastricht University. Image sourced and copyright owned by authors Tim Batink et al.

    Acceptance and Commitment Therapy in Daily Life Training: A Feasibility Study of an mHealth Intervention


    Background: With the development of mHealth, it is possible to treat patients in their natural environment. Mobile technology helps to bridge the gap between the therapist’s office and the “real world.” The ACT in Daily Life training (ACT-DL) was designed as an add-on intervention to help patients practice with acceptance and commitment therapy in their daily lives. The ACT-DL consists of two main components: daily monitoring using experience sampling and ACT training in daily life. Objectives: To assess the acceptability and feasibility of the ACT-DL in a general outpatient population. A secondary objective was to conduct a preliminary examination of the effectiveness of the ACT-DL. Methods: An observational comparative study was conducted. The experimental group consisted of 49 patients who volunteered for ACT-DL, and the control group consisted of 112 patients who did not volunteer. As part of an inpatient treatment program, both groups received a 6-week ACT training. Participants went home to continue their treatment on an outpatient basis, during which time the experimental group received the 4-week add-on ACT-DL. Acceptability and feasibility of the ACT-DL was assessed weekly by telephone survey. Effectiveness of the ACT-DL was evaluated with several self-report questionnaires (Flexibility Index Test (FIT-60): psychological flexibility, Brief Symptom Inventory: symptoms, Utrechtse Coping List: coping, and Quality of life visual analog scale (QoL-VAS): quality of life). Results: More than three-quarters of the participants (76%) completed the full 4-week training. User evaluations showed that ACT-DL stimulated the use of ACT in daily life: participants practiced over an hour a week (mean 78.8 minutes, standard deviation 54.4), doing 10.4 exercises (standard deviation 6.0) on average. Both ACT exercises and metaphors were experienced as useful components of the training (rated 5 out of 7). Repeated measures ANCOVA did not show significant effects of the ACT-DL on psychological flexibility (P=.88), symptoms (P=.39), avoidant coping (P=.28), or quality of life (P=.15). Conclusions: This is the first study that uses experience sampling to foster awareness in daily life in combination with acceptance and commitment therapy to foster skill building. Adherence to the ACT-DL was high for an intensive mHealth intervention. ACT-DL appears to be an acceptable and feasible mHealth intervention, suitable for a broad range of mental health problems. However, short-term effectiveness could not be demonstrated. Additional clinical trials are needed to examine both short-term and long-term effects.

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  • Assessing the contextual factors influencing RFID implementation in hospitals of a developing country: A conceptual model

    Date Submitted: Oct 18, 2016

    Open Peer Review Period: Oct 21, 2016 - Dec 16, 2016

    Background: Wasting economic resources in a developing country like Iran will have numerous negative consequences. Thus, in order to avoid possible failures, necessary infrastructures must be provided...

    Background: Wasting economic resources in a developing country like Iran will have numerous negative consequences. Thus, in order to avoid possible failures, necessary infrastructures must be provided before implementing new technologies. Objective: The present study aimed at assessing the degree of readiness for implementing RFID in the hospitals of a developing country through a conceptual model. Methods: The research study adopted a descriptive design and structural equation modeling was used for data analysis. Data were collected from the hospitals affiliated to Semnan University of Medical Sciences. A questionnaire designed by the researchers was used to collect participants’ ideas about organizational readiness, cultural readiness, and human resource readiness in implementing RFID. Data were fed into Path SPSS (Version 16) and LISREL 8.8, followed by conducting path analysis. Results: The results showed that human resource readiness significantly predicted RFID implementation, with cultural readiness playing the role of a mediator variable. Cultural readiness itself was influenced by organizational readiness (p<0.01). Cultural factors play an important role in implementing RFID projects in developing countries like Iran. Conclusions: Paying more attention to these factors can reduce the risk of failure in implementing such technological projects. Hospitals should strengthen organizational factors and enhance top rank managers’ support for implementing RFID. By so doing, they will promote cultural readiness, prepare human resources, and win the cooperation of the personnel in implementing such a technological project. Clinical Trial: what is it?

  • The Malaria System MicroApp: A Low-Cost Tool for Malaria Diagnosis Using Mobile Device

    Date Submitted: Oct 6, 2016

    Open Peer Review Period: Oct 9, 2016 - Dec 4, 2016

    Background: Malaria is a worldwide public health problem which is mainly related to remote areas. In this way, low cost systems for automatic diagnosis have become a priority investigation in several...

    Background: Malaria is a worldwide public health problem which is mainly related to remote areas. In this way, low cost systems for automatic diagnosis have become a priority investigation in several research groups. Furthermore, due to climate changes, there are new cases which allow the survival of Anopheles in previously not inhabited areas. Objective: This study presents an automatic diagnostic system of malaria at low cost with mobile devices for identify Plasmodium falciparum species on ring-stage in the smears of Giemsa stained peripheral blood sample with light microscopy. Methods: This work uses image processing and artificial intelligence techniques. The proposed solution uses a known face detection algorithm to identify Plasmodium parasites. The algorithm is based on concepts such as: integral image, haar-like features and the use of weak classifiers with adaptive boosting learning. In the preprocessing step, the background is removed around blood cells reducing the search scope of the learning algorithm. Results: The best results were tested with 648 positive and 777 negative. The hit rate of the system is currently at 93%, which means that for every 100 infected, 93 parasites are identified correctly. The Receiver Operating Characteristic curve was plotted into the results section. The limitation of this work is on the parasite identification into P. falciparum specie (step trophozoite) in the ring-stage. Conclusions: Usual accessibility barriers of low-resource countries can be addressed with low cost diagnosis tools. With the development of our system for smartphones and tablets we can reach either health centers or remote communities without a specific need of expertise besides providing good accuracy for malaria detection.

  • Perceptions of Mobile Health Technology of Midlife Adults with Chronic Conditions

    Date Submitted: Sep 23, 2016

    Open Peer Review Period: Sep 24, 2016 - Nov 19, 2016

    Background: The growth in mobile health (mHealth) technology is intersecting the demographic shift to an aging society. This presents unprecedented opportunity to maximize healthy aging. Regular physi...

    Background: The growth in mobile health (mHealth) technology is intersecting the demographic shift to an aging society. This presents unprecedented opportunity to maximize healthy aging. Regular physical activity (PA) and adequate nutrition are major determinants of health, and enhance physical functioning and mental health necessary to preserve independence. A common form of communication among younger adults, mobile technology offers benefits to promote health. Objective: To assess the perceptions of midlife adults with chronic conditions in terms of use, usefulness, and ease of use of mHealth technology to promote PA. Methods: Midlife adults, age 50-64 years (n=20) diagnosed with one or more chronic conditions were randomly selected from a list generated at an academic-affiliated Internal Medicine clinic in the Midwest. Verbal consent was obtained. An adapted version of the Pew Health Survey (2012) addressing mHealth technology use to promote PA was administered by phone. Results: The majority of respondents were female, and Caucasian. Midlife women were more likely than men to access the Internet for health information. Participants were less likely to use social media sites to discuss or seek health information. They were most likely to use technology to discuss health issues with friends and family, with clinicians remaining a central resource. Despite the small sample size, these results are consistent with previous findings of all adults across the continuum. Conclusions: These findings indicate overall positive perceptions of mHealth technology among midlife adults with chronic conditions. This information will be useful to inform future mHealth interventions for healthy aging. The ultimate goal of this research is to promote health behaviors, thereby reducing the burden of chronic conditions for aging adults and society.