Mental health screening and diagnostic apps can provide an opportunity to reduce strain on mental health services, improve patient well-being, and increase access for underrepresented groups. Despite promise of their acceptability, many mental health apps on the market suffer from high dropout due to a multitude of issues. Understanding user opinions of currently available mental health apps beyond star ratings can provide knowledge which can inform the development of future mental health apps. This study aimed to conduct a review of current apps which offer screening and/or aid diagnosis of mental health conditions on the Apple app store (iOS), Google Play app store (Android), and using the m-health Index and Navigation Database (MIND). In addition, the study aimed to evaluate user experiences of the apps, identify common app features and determine which features are associated with app use discontinuation. The Apple app store, Google Play app store, and MIND were searched. User reviews and associated metadata were then extracted to perform a sentiment and thematic analysis. The final sample included 92 apps. 45.65% (n = 42) of these apps only screened for or diagnosed a single mental health condition and the most commonly assessed mental health condition was depression (38.04%, n = 35). 73.91% (n = 68) of the apps offered additional in-app features to the mental health assessment (e.g., mood tracking). The average user rating for the included apps was 3.70 (SD = 1.63) and just under two-thirds had a rating of four stars or above (65.09%, n = 442). Sentiment analysis revealed that 65.24%, n = 441 of the reviews had a positive sentiment. Ten themes were identified in the thematic analysis, with the most frequently occurring being performance (41.32%, n = 231) and functionality (39.18%, n = 219). In reviews which commented on app use discontinuation, functionality and accessibility in combination were the most frequent barriers to sustained app use (25.33%, n = 19). Despite the majority of user reviews demonstrating a positive sentiment, there are several areas of improvement to be addressed. User reviews can reveal ways to increase performance and functionality. App user reviews are a valuable resource for the development and future improvements of apps designed for mental health diagnosis and screening.

Code sheet: Metrics and parameters.

ANN sensitivity (excluding description text data).

This paper focuses on the evaluation and recommendation of healthcare applications in the mHealth field. The increase in the use of health applications, supported by an expanding mHealth market, highlights the importance of this research. In this study, a data set including app descriptions, ratings, reviews, and other relevant attributes from various health app platforms was selected. The main goal was to design a recommendation system that leverages app attributes, especially descriptions, to provide users with relevant contextual suggestions. A comprehensive pre-processing regime was carried out, including one-hot encoding, standardisation, and feature engineering. The feature, “Rating_Reviews”, was introduced to capture the cumulative influence of ratings and reviews. The variable ‘Category’ was chosen as a target to discern different health contexts such as ‘Weight loss’ and ‘Medical’. Various machine learning and deep learning models were evaluated, from the baseline Random Forest Classifier to the sophisticated BERT model. The results highlighted the efficiency of transfer learning, especially BERT, which achieved an accuracy of approximately 90% after hyperparameter tuning. A final recommendation system was designed, which uses cosine similarity to rank apps based on their relevance to user queries.

The global market for drug interaction checkers is experiencing robust growth, driven by increasing prescription drug usage, a rising elderly population with complex medication regimens, and a growing emphasis on patient safety and medication adherence. The market's expansion is further fueled by advancements in technology, enabling the development of more sophisticated and user-friendly interaction checkers that integrate seamlessly with electronic health records (EHRs) and mobile health (mHealth) applications. This integration facilitates quicker access to crucial information for both healthcare professionals and patients, reducing the risk of adverse drug events (ADEs) and improving overall healthcare outcomes. Key players in this market are leveraging AI and machine learning algorithms to enhance the accuracy and efficiency of their checkers, continually updating their databases with the latest drug information and interaction data to maintain reliability. However, despite considerable growth, the market faces challenges. Data privacy and security concerns related to patient medication information remain significant hurdles. Furthermore, the need for ongoing database maintenance and updates, to keep up with the continuously evolving pharmaceutical landscape, presents a cost and resource constraint for many providers. Despite these challenges, the substantial benefits of preventing ADEs—cost savings in healthcare, improved patient outcomes, and reduced liability for healthcare professionals—are powerful incentives for continued market expansion. The projected Compound Annual Growth Rate (CAGR) suggests a substantial increase in market value over the forecast period, highlighting the increasing importance of these tools in modern healthcare. Competition among established players like Medscape, WebMD, and DrugBank, alongside newer entrants, is expected to intensify, leading to further innovation and the development of more sophisticated drug interaction checkers.

Dataset for a novel study which examines technologically-assisted interventions in South Africa for psychological and neurological treatments from 1996 to 2021. Using a PRISMA-ScR search across 14 databases, 13 studies were selected, revealing trends: concentration in populous or economically influential provinces, increasing accessibility, diverse participant considerations, and a notable gap in South African literature, primarily focusing on telemedicine. The prevalence of telemedicine underscores its role in mobile health (mHealth) interventions, addressing healthcare delivery challenges in rural areas. This study provides a concise overview of technologically-assisted interventions in South Africa, highlighting current trends and suggesting avenues for further research.

Introduction: It has been 4 months since the discovery of COVID-19, and there have been many measures introduced to curb movements of individuals to stem the spread. There has been an increase in the utilization of web-based technologies for counseling, and for supervision and training, and this has been carefully described in China. Several telehealth initiatives have been highlighted for Australian residents. Smartphone applications have previously been shown to be helpful in times of a crisis. Whilst there have been some examples of how web-based technologies have been used to support individuals who are concerned about or living with COVID-19, we know of no studies or review that have specifically looked at how M-Health technologies have been utilized for COVID-19.Objectives: There might be existing commercially available applications on the commercial stores, or in the published literature. There remains a lack of understanding of the resources that are available, the functionality of these applications, and the evidence base of these applications. Given this, the objective of this content analytical review is in identifying the commercial applications that are available currently for COVID-19, and in exploring their functionalities.Methods: A mobile application search application was used. The search terminologies used were “COVID” and “COVID-19.” Keyword search was performed based on the titles of the commercial applications. The search through the database was conducted from the 27th March through to the 18th of April 2020 by two independent authors.Results: A total of 103 applications were identified from the Apple iTunes and Google Play store, respectively; 32 were available on both Apple and Google Play stores. The majority appeared on the commercial stores between March and April 2020, more than 2 months after the first discovery of COVID-19. Some of the common functionalities include the provision of news and information, contact tracking, and self-assessment or diagnosis.Conclusions: This is the first review that has characterized the smartphone applications 4 months after the first discovery of COVID-19.

PC m-health applications adoption and organizational determinants.

FC m-health applications on adoption and combined TOE effect variables.

FC m-health applications adoption and environmental determinants.

PC m-health applications adoption and environmental determinants.