BACKGROUND: Cardiovascular disease (CVD) is a major global health concern, the leading cause of death and disability. Thus, preventive interventions targeting modifiable risk factors are essential. Mobile-health technologies have emerged as promising tools for improving prevention by modifying risk factors. We created the \"Green Heart\" mobile app to help coronary artery disease (CAD) patients control their risk factors. The app has three modules: smoking cessation, dyslipidemia (DLP) control, and blood pressure (BP) management. This study evaluated the app\'s performance in monitoring hypertension (HTN) and DLP among known CAD cases.
METHODS: A randomized controlled trial enrolled 1590 CAD subjects, including 1114 hypertensive patients and 1488 subjects with DLP, and assigned them randomly to paper-based education or application-based groups.
RESULTS: Regarding HTN, after 6 months, we finally analyzed 545 and 546 hypertensive patients, assigned to the conventional and app groups, respectively. Patients in the app group were more likely to have their BP managed successfully (88.6% vs. 78.5%; P<0.001). The app group showed higher odds of successful BP management (odds ratio [OR]: 2.13; 95% CI: 1.51 - 3.03). Regarding DLP, we analyzed 728 patients in the conventional and 714 patients in the app group. A higher percentage of patients in the app group (24.8%) had low-density lipoprotein cholesterol (LDL-C) levels less than 70 mg/dL (16.1%; P<0.001). The app group showed higher odds of reducing LDL-C (OR: 1.72; 95% CI: 1.32-2.26).
CONCLUSIONS: We found that using the Green Heart app in the self-monitoring setting significantly improved BP and DLP management across the study population.

BACKGROUND: Support care aims to improve the experience of patients. m-health is one of the tools recently developed to promote patient empowerment. The objective of this study was to evaluate the appreciation of an m-health application to enhance prostatectomy path for patients suffering from prostate cancer.
METHODS: A prospective monocentric study was conducted in the urology department of the University Hospital of Rennes from February to April 2023. MyCHU application was optimized by integrating information sheets in the postoperative period after prostatectomy on sphincter rehabilitation exercises, erectile dysfunction and urinary incontinence. The questionnaire used to evaluate the usability of \"MyCHU\" application was the System Usability Scale (SUS). Semi-structured interviews explored the patients\' feelings about the content of the information sheets and the impact on their empowerment regarding sexual disorders.
RESULTS: Twelve patients participated in this study and 7 agreed to complete an interview The average SUS score was 75.58, which indicate an high usability. Patients appreciated the fact that the application structured their healthcare pathway by centralizing information. The information sheets were clear and accurate. The impact on their empowerment was positive, with a gain in their ability to take ownership of the therapies.
CONCLUSIONS: The role of digital technology in health care has been growing in recent years. Our study has shown the interest that mobile application can bring to the patient who undergoes prostatectomy. It increases his empowerment and favor the dialogue with his surgeon.
METHODS:

BACKGROUND: Cardiovascular diseases (CVDs) pose a significant global health concern and are the most common cause of death and disability, necessitating preventive interventions targeting modifiable risk factors. Recently, mobile-health technology has been developed to improve the delivery of cardiovascular prevention by risk factor modification. The \"Green Heart\" mobile application (app) was designed to aid in risk factor control among coronary artery disease (CAD) patients.
METHODS: This parallel-group, single-blinded randomized controlled trial enrolled 1590 CAD patients, including 668 current smokers, randomly assigned to control (paper-based education) and intervention (application-based) groups. The app encompassed three modules targeting smoking cessation, dyslipidemia control, and blood pressure management. This study evaluated the impact of the smoking cessation module on behavioral change among current smokers. Green Heart assesses nicotine dependence, offering personalized quit plans, educational content, motivational messages, and automated progress tracking. The odds of smoking behavior changes during the 24-week follow-up underwent assessment.
RESULTS: The intention-to-treat analysis highlighted significantly elevated rates of smoking cessation and reductions in the intervention group versus the control group. Adherence to the app (per-treatment analysis) also demonstrated significantly more favorable smoking behavior changes among the application users. Logistic regression emphasized higher odds of quitting and reduction in smoking in the application group, showing an odds ratio of 2.14 (95% CI: 1.16-3.97) compared to those not using the app (P=0.015).
CONCLUSIONS: Our results confirmed that complete adherence to the app for at least 24 weeks was linked to alterations in cigarette smoking behavior among CAD patients. Trial Registration Number: IRCT20221016056204N1.

The use of new technologies presents an opportunity to promote physical activity, especially among young people with type 1 diabetes (T1DM), who tend to be less active compared to their healthy counterparts. The aim of this study is to investigate the impact of a personalized resistance exercise program, facilitated by the Diactive-1 App, on insulin requirements among children and adolescents diagnosed with T1DM.
A minimum of 52 children and adolescents aged 8-18 years, who were diagnosed with T1DM at least 6 months ago, will be randomly assigned to either a group engaging in an individualized resistance exercise program at least 3 times per week over a 24-week period or a waiting-list control group. The primary outcome will be the daily insulin dose requirement. The secondary outcomes will include glycemic control, cardiometabolic profile, body composition, vascular function, physical fitness, 24-hour movement behaviors, diet, and psychological parameters. The usability of the app will also be assessed.
Ethical approval to conduct this study has been granted by the University Hospital of Navarra Research Board (PI_2020/140). Parents or legal guardians of minors participating in the study will provide written consent, while children and adolescents will sign an assent form to indicate their voluntary agreement. The trial\'s main findings will be shared through conference presentations, peer-reviewed publications, and communication directly with participating families. This study aims to offer valuable insights into the holistic management of children and adolescents with T1DM by utilizing personalized exercise interventions through an mHealth system.
NCT06048757.

In-ear acquisition of physiological signals, such as electromyography (EMG), electrooculography (EOG), electroencephalography (EEG), and electrocardiography (ECG), is a promising approach to mobile health (mHealth) due to its non-invasive and user-friendly nature. By providing a convenient and comfortable means of physiological signal monitoring, in-ear signal acquisition could potentially increase patient compliance and engagement with mHealth applications. The development of reliable and comfortable soft dry in-ear electrode systems could, therefore, have significant implications for both mHealth and human-machine interface (HMI) applications. This research evaluates the quality of the ECG signal obtained with soft dry electrodes inserted in the ear canal. An earplug with six soft dry electrodes distributed around its perimeter was designed for this study, allowing for the analysis of the signal coming from each electrode independently with respect to a common reference placed at different positions on the body of the participants. An analysis of the signals in comparison with a reference signal measured on the upper right chest (RA) and lower left chest (LL) was performed. The results show three typical behaviors for the in-ear electrodes. Some electrodes have a high correlation with the reference signal directly after inserting the earplug, other electrodes need a settling time of typically 1-3 min, and finally, others never have a high correlation. The SoftPulseTM electrodes used in this research have been proven to be perfectly capable of measuring physiological signals, paving the way for their use in mHealth or HMI applications. The use of multiple electrodes distributed in the ear canal has the advantage of allowing a more reliable acquisition by intelligently selecting the signal acquisition locations or allowing a better spatial resolution for certain applications by processing these signals independently.

Given the effective role of a mobile applications in disease management, disease monitoring, and self-care in patients with COVID-19 disease, we aimed to design, development and evaluation of a self-care Mobile app for COVID-19 patients not requiring hospitalization.
The design, development and evaluation the usability of the self-care and education mobile app for patients with COVID-19 disease were conducted in two main phases at 2021 in Northwest of IRAN; (1) Determine the features and capabilities and (2) Design, development and evaluation of self-care mobile App. JAVA programming languages and Android Operating System were used and selected to design and development of a mobile app. There were 25 participants who conducted evaluations of the mobile app\'s usability and impact using the mobile health app usability a Questionnaire of User Interface Satisfaction was administered to assess the usability of the developed application. The results were analyzed via Excel 2013.
The model of developing a mobile app as an Information System was the Waterfall model. The smartphone application based on a set of capabilities and features was designed and consists of two main parts: the login screen for user registration, and the main home menu. The user interface includes three main pages or activities; (a) Main Menu for quick access to all of the pages, (b) Symptom management and monitoring to monitor the signs and symptoms during the illness, and (c) Set Reminders and Alarms to notify patients. The users\' mean score of the application usability was calculated as 7.91 out of 9 indicating a good level of satisfaction.
This app can be a guideline and a useful tools for managing and monitoring symptoms, reminding medications, and implementing self-care instructions in outpatients. The authors suggest evaluating the efficacy and functionality test of mobile-based applications for COVID-19 in clinical trial studies.

The COVID-19 pandemic and the policy responses adopted to curtail it have drastically altered how health services are delivered around the globe. To enable the public to continue accessing health care, e-health innovations have become the most viable tool to provide convenient, timely, effective and safe care, while reducing the spread of the virus. This paper utilized data from existing literature to explore the benefits and challenges of implementing e-health technologies in Sub Saharan Africa (SSA) amid this pandemic. Evidence suggests that these technologies have the potential to strengthen public health systems in SSA as they have in First World countries. However, there are a number of challenges that need to be addressed first in order to realize the full promises of e-health on the continent. The paper proposes that African governments should come together and implement similar e-health policies, share software, expertise and other critical ICT infrastructure as it can result in the successful implementation of e-health innovations, while reducing the financial burden associated with their set up.

This paper reports the architecture of a low-cost smart crutches system for mobile health applications. The prototype is based on a set of sensorized crutches connected to a custom Android application. Crutches were instrumented with a 6-axis inertial measurement unit, a uniaxial load cell, WiFi connectivity, and a microcontroller for data collection and processing. Crutch orientation and applied force were calibrated with a motion capture system and a force platform. Data are processed and visualized in real-time on the Android smartphone and are stored on the local memory for further offline analysis. The prototype\'s architecture is reported along with the post-calibration accuracy for estimating crutch orientation (5° RMSE in dynamic conditions) and applied force (10 N RMSE). The system is a mobile-health platform enabling the design and development of real-time biofeedback applications and continuity of care scenarios, such as telemonitoring and telerehabilitation.

BACKGROUND: Since the beginning of the COVID-19 outbreak, a significant number of mobile health apps have been created around the world and in Iran to help consequence reduction of this emerging pandemic.
OBJECTIVE: This study aimed to review the characteristics of Persian Android and iOS apps related to COVID-19 and determine their use-cases based on a reference model.
METHODS: This was a cross-sectional descriptive study conducted in three main steps. First, a systematic search was conducted via Iranian mobile apps\' markets using the keywords related to COVID-19 in January 2021. Then, the retrieved apps were analyzed according to their characteristics. Finally, the use-cases of the given apps were determined and categorized based on a reference model.
RESULTS: Based on our inclusion criteria, 122 apps were selected and evaluated. Most of these apps (87.7%) was free. Small proportions (5%) of reviewed apps have been developed with participation of clinical expert and half of the apps mentioned the references they used. Furthermore, about half of the apps (50.8%) were provided contact information of the developers. The studied apps were classified into four use-case major categories, including educational (98%), fulfilling a contextual need (18%), communicating, and/or sharing the information (0.83%), and health-related management (2%).
CONCLUSIONS: The results showed that the Persian mobile apps for COVID-19 are not in a satisfying situation. Furthermore, although these apps are significant in quantity but in terms of use-cases, they are not widespread.

Observational behavior analysis plays a key role for the discovery and evaluation of risk markers for many neurodevelopmental disorders. Research on autism spectrum disorder (ASD) suggests that behavioral risk markers can be observed at 12 months of age or earlier, with diagnosis possible at 18 months. To date, these studies and evaluations involving observational analysis tend to rely heavily on clinical practitioners and specialists who have undergone intensive training to be able to reliably administer carefully designed behavioural-eliciting tasks, code the resulting behaviors, and interpret such behaviors. These methods are therefore extremely expensive, time-intensive, and are not easily scalable for large population or longitudinal observational analysis. We developed a self-contained, closed-loop, mobile application with movie stimuli designed to engage the child\'s attention and elicit specific behavioral and social responses, which are recorded with a mobile device camera and then analyzed via computer vision algorithms. Here, in addition to presenting this paradigm, we validate the system to measure engagement, name-call responses, and emotional responses of toddlers with and without ASD who were presented with the application. Additionally, we show examples of how the proposed framework can further risk marker research with fine-grained quantification of behaviors. The results suggest these objective and automatic methods can be considered to aid behavioral analysis, and can be suited for objective automatic analysis for future studies.