There are a growing number of mobile apps available for monitoring and management of mental health symptoms or disorders. However, clinically validated evidence for most of them is unclear; their ...benefits to patients on long term use are thus debatable.
This updated review aimed to systematically appraise the available research evidence of the efficacy and acceptability of mobile apps for mental health in all ages.
A comprehensive literature search (May 2013 to December 2017) in PubMed, Cochrane Library, EMBASE, Web of Science, and Google Scholar was conducted. Abstracts were included if they described mental health apps (targeting depression, anxiety, bipolar disorder, psychosis, post-traumatic stress disorders (PTSD), substance use disorders, sleep disorders, and suicidal behaviors) delivered on mobile devices for all ages.
In total, 1501 abstracts were identified. Of these, 17 publications describing 16 apps targeting anxiety/stress, alcohol disorder, sleep disorder, depression, suicidal behaviors, and PTSD met the inclusion criteria. Five studies randomized individuals to trial conditions, and 14 apps were proven to have clinically validated evidence in reducing mental health symptoms or disorder.
Mental health apps have potentials in improving the monitoring and management of mental health symptoms or disorders. However, majority of the apps that are currently available lack clinically validated evidence of their efficacy. Given the number and pace at which mobile Health (mHealth) apps are being released, further robust research is warranted to develop and test evidence-based programs.
Purpose/significance User reviews are helpful for developers to realize mobile application innovation. This paper summarizes the literature related to mobile application review mining and provides ...references for mobile application development and review mining. Method/process This study reviewed the researches related to mobile application review mining into three key themes of review classification, review clustering and review feature extraction by using the text analysis method, and expounded on the development status of this field according to this framework. Result/conclusion At present, the methods of review classification have begun to evolve from machine learning to deep learning; review clustering mainly uses K-Means and DBSCAN; feature extraction is still focused on the explicit features of APP reviews. In the future, there are still three issues worth exploring in mobile application review mining: domain dependence, multi-source information fusion and review value evaluation.
A growing body of literature affirms the usefulness of mobile technologies, including mobile applications (apps), in the primary prevention field. The quality of health apps, which today number in ...the thousands, is a crucial parameter, as it may affect health-related decision-making and outcomes among app end-users. The mobile application rating scale (MARS) has recently been developed to evaluate the quality of such apps, and has shown good psychometric properties. Since there is no standardised tool for assessing the apps available in Italian app stores, the present study developed and validated an Italian version of MARS in apps targeting primary prevention.
The original 23-item version of the MARS assesses mobile app quality in four objective quality dimensions (engagement, functionality, aesthetics, information) and one subjective dimension. Validation of this tool involved several steps; the universalist approach to achieving equivalence was adopted. Following two backward translations, a reconciled Italian version of MARS was produced and compared with the original scale. On the basis of sample size estimation, 48 apps from three major app stores were downloaded; the first 5 were used for piloting, while the remaining 43 were used in the main study in order to assess the psychometric properties of the scale. The apps were assessed by two raters, each working independently. The psychometric properties of the final version of the scale was assessed including the inter-rater reliability, internal consistency, convergent, divergent and concurrent validities.
The intralingual equivalence of the Italian version of the MARS was confirmed by the authors of the original scale. A total of 43 apps targeting primary prevention were tested. The MARS displayed acceptable psychometric properties. The MARS total score showed an excellent level of both inter-rater agreement (intra-class correlation coefficient of .96) and internal consistency (Cronbach's α of .90 and .91 for the two raters, respectively). Other types of validity, including convergent, divergent, discriminative, known-groups and scalability, were also established.
The Italian version of MARS is a valid and reliable tool for assessing the health-related primary prevention apps available in Italian app stores.
•An app was developed to help infectious diseases prevention and management in pregnancy.•Two modes are available “healthcare provider” and “patient”, with tailored content.•Over 2,500 downloads ...since August 2022 launch, with positive user feedback.•Future focus: continuous updates, wider adoption, and language expansion.
To develop and assess the GAIA! app, designed to assist pregnant women and healthcare professionals in managing infectious diseases during pregnancy, and to bridge the information gap between health professionals and expectant mothers.
This collaborative initiative in Italy involved partnerships with the University of Florence, Careggi University Hospital, and other institutions. The app, built on the Ionic framework, is available on both Apple and Google App Stores. It offers two distinct modes: “healthcare providers” and “patients.” Content for the app was derived from extensive literature reviews and clinical guidelines.
Since its August 2022 launch, the GAIA! app has garnered over 2,500 downloads, indicating its effectiveness and acceptance within the community. The app differentiates itself from others, such as the Sanford Guide, by focusing specifically on the needs of pregnant women. It ensures cross-platform compatibility, a user-friendly interface, and offline functionality.
The GAIA! app has successfully addressed a niche in infectious disease management for pregnant women, gaining significant traction within the community. While it has seen substantial success, challenges like continuous updates and potential language expansion remain. Future endeavors will address these challenges and further evaluate the app’s impact on maternal and child health.
Currently, there is a contradiction between the growing number of mobile applications in use and the responsibility that is placed on them, on the one hand, and the imperfection of the methods and ...tools for ensuring the security of mobile applications, on the other hand. Therefore, ensuring the security of mobile applications by developing effective methods and tools is a challenging task today. This study aims to evaluate the mutual correlations and weights of factors and consequences of mobile application insecurity. We have developed a method of evaluating the weights of factors of mobile application insecurity, which, taking into account the mutual correlations of mobile application insecurity consequences from these factors, determines the weights of the factors and allows us to conclude which factors are necessary to identify and accurately determine (evaluate) to ensure an appropriate level of reliability of forecasting and assess the security of mobile applications. The experimental results of our research are the evaluation of the weights of ten OWASP mobile application insecurity factors the identification of the mutual correlations of the consequences of mobile applications’ insecurity from these factors, and the identification of common factors on which more than one consequence depends.
This paper presents a methodology and mobile application for driver monitoring, analysis, and recommendations based on detected unsafe driving behavior for accident prevention using a personal ...smartphone. For the driver behavior monitoring, the smartphone's cameras and built-in sensors (accelerometer, gyroscope, GPS, and microphone) are used. A developed methodology includes dangerous state classification, dangerous state detection, and a reference model. The methodology supports the following driver's online dangerous states: distraction and drowsiness as well as an offline dangerous state related to a high pulse rate. We implemented the system for Android smartphones and evaluated it with ten volunteers.
In the work, the authors reveal the user preferences of the mobile application market. The study is based on the Strauss–Howe generational theory. The mobile application market appears to be ...dynamic and fast-growing in an environment of exponential growth in consumer demand and a highly competitive environment. In the article, the authors classify and identify categories and subcategories of mobile applications based on their functional and value purpose. This classification formed the basis for determining the dependence based on the analysis of contingency matrices between the preference of the application for its purpose and the age category of the user, which made it possible to identify the relationship between generations of users and their choice of certain types of applications. The text provides a justification for the choice of certain applications on a daily basis by generations X, Y, Z. The relevance of the study lies in the fact that the authors examine a market, the rapid growth of which is conditioned by the global socio-economic situation. The information obtained during the analysis of user preferences of the Russian mobile application market can be used by specialists in determining the target audience of a digital product and developing marketing strategies.
Abstract Background Global smartphone penetration has brought about unprecedented addictive behaviors. Aims We report a proposed diagnostic criteria and the designing of a mobile application (App) to ...identify smartphone addiction. Method We used a novel empirical mode decomposition (EMD) to delineate the trend in smartphone use over one month. Results The daily use count and the trend of this frequency are associated with smartphone addiction. We quantify excessive use by daily use duration and frequency, as well as the relationship between the tolerance symptoms and the trend for the median duration of a use epoch. The psychiatrists' assisted self-reporting use time is significant lower than and the recorded total smartphone use time via the App and the degree of underestimation was positively correlated with actual smartphone use. Conclusions Our study suggests the identification of smartphone addiction by diagnostic interview and via the App-generated parameters with EMD analysis.
Objectives: Telehealth is promoted as a strategy to support self-management of long-term conditions. The aim of this systematic review is to identify which information and communication technology ...features implemented in mobile apps to support asthma self-management are associated with adoption, adherence to usage, and clinical effectiveness.
Methods: We systematically searched 9 databases, scanned reference lists, and undertook manual searches (January 2000 to April 2016). We include randomized controlled trials (RCTs) and quasiexperimental studies with adults. All eligible papers were assessed for quality, and we extracted data on the features included, health-related outcomes (asthma control, exacerbation rate), process/intermediate outcomes (adherence to monitoring or treatment, self-efficacy), and level of adoption of and adherence to use of technology. Meta-analysis and narrative synthesis were used.
Results: We included 12 RCTs employing a range of technologies. A meta-analysis (n = 3) showed improved asthma control (mean difference −0.25 95% CI, −0.37 to −0.12). Included studies incorporated 10 features grouped into 7 categories (education, monitoring/electronic diary, action plans, medication reminders/prompts, facilitating professional support, raising patient awareness of asthma control, and decision support for professionals). The most successful interventions included multiple features, but effects on health-related outcomes were inconsistent. No studies explicitly reported adoption of and adherence to the technology system.
Conclusion: Meta-analysis of data from 3 trials showed improved asthma control, though overall the clinical effectiveness of apps, typically incorporating multiple features, varied. Further studies are needed to identify the features that are associated with adoption of and adherence to use of the mobile app and those that improve health outcomes.
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