This paper evaluates the privacy policies of AI-powered mHealth apps, focusing on their availability, readability, transparency, and scope. We replicate the methodology of Sunyaev et al. 2015, for iOS apps and compile a dataset of 2,231 AI-focused health apps. Our analysis reveals that only 68.04% of these apps have publicly accessible privacy policies. On average, a privacy policy contains 2,784.25 words, with a mean readability score of 13.48. Regarding transparency, aspects such as "type of information collected" and "sharing of information" are more frequently addressed, whereas "rationale for collection" is less commonly discussed. Additionally, only 11.2% of the privacy policies mention the use of user health data for training AI systems. In terms of scope, over 60% of app privacy policies cover the single app, and 25% cover no app-related scope., , # iOS AI Mobile Health Application Privacy Policies

This dataset comprises privacy policies collected from mobile health applications available on the iOS App Store that utilize Artificial Intelligence (AI).

Dataset Structure

The dataset is provided in a JSON format. Each entry in the JSON array represents an individual mobile health application and contains the following fields:

• title: The name of the mobile health application.
• privacy_policy: The full text of the application's privacy policy. In cases where a privacy policy could not be found, this field is explicitly marked as "None Found".

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IntroductionAnxiety and depression are major causes of disability in Arab countries, yet resources for mental health services are insufficient. Mobile devices may improve mental health care delivery (mental m-Health), but the Arab region's mental m-Health app landscape remains under-documented. This study aims to systematically assess the features, quality, and digital safety of mental m-Health apps available in the Arab marketplace. We also contrast a set of recommended Australian apps to benchmark current strategies and evidence-based practices and suggest areas for improvement in Arabic apps.MethodsFifteen Arab country-specific iOS Apple Stores and an Android Google Play Store were searched. Apps that met the inclusion criteria were downloaded and evaluated using the Mobile App Rating Scale (MARS) and the Mobile App Development and Assessment Guide (MAG).ResultsTwenty-two apps met the inclusion criteria. The majority of apps showed no evidence of mental health experts being involved in the app design processes. Most apps offered real-time communication with specialists through video, text, or audio calls rather than evidence-based self-help techniques. Standardized quality assessment showed low scores for design features related to engagement, information, safety, security, privacy, usability, transparency, and technical support. In comparison to apps available in Australia, Arabic apps did not include evidence-based interventions like CBT, self-help tools and crisis-specific resources, including a suicide support hotline and emergency numbers.DiscussionIn conclusion, dedicated frameworks and strategies are required to facilitate the effective development, validation, and uptake of Arabic mental mHealth apps. Involving end users and healthcare professionals in the design process may help improve app quality, dependability, and efficacy.

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.

m-health applications categories and re-categorization as per WHO.

FC m-health applications adoption and environmental determinants.

FC m-health application adoption and technological determinants.

PC m-health application adoption and technological determinants.

PC m-health applications adoption and environmental determinants.

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