By the numbers: ratings and utilization of behavioral health mobile applications

Carlo, Andrew D.; Ghomi, Reza Hosseini; Renn, Brenna N.; Areán, Patricia A.

doi:10.1038/s41746-019-0129-6

Cited by 156 publications

(119 citation statements)

References 32 publications

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“…As the MARS has proven its feasibility and psychometric quality in existing works and evaluations [14]- [16], the authors of the current work decided to develop a database solution that shall support making the MARS expert ratings for mHealth apps available to the public more easily and comprehensively. This is necessary, since consumers are particularly vulnerable right now, since the mHealth market is not well regulated and most of the currently available mHealth apps are not scientifically evaluated [15]- [17]. Thus, there is an urgent need to inform users, patients, and healthcare providers about the quality of mHealth apps [17].…”

Section: Introductionmentioning

confidence: 99%

Mobile Health App Database - A Repository for Quality Ratings of mHealth Apps

Stach

Probst

Meßner

et al. 2020

2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)

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The utilization of mobile technology in the field of medicine and healthcare has become a decisive aspect. The entire field is denoted as mobile health (mHealth). For mHealth, the development and use of mobile applications are crucial. The purposes and goals of mHealth apps, in turn, are manifold. As a consequence, a plethora of mHealth apps can be found in the app stores. Interestingly, for patients, users, and health care providers that consider to use mHealth apps one aspect has been less pursued so far: Systematic and standardized ways that help about the quality of an app or its medical evidence are mainly missing. The Mobile App Rating Scale (MARS) is a standardized instrument that aims at the systematic and comparable evaluation of the quality of mobile health apps as well as categorizing their goals and functions. It comprises 23 items, which are utilized to calculate a rating scale. Having MARS in mind, a database was developed that is called Mobile Health App Database (MHAD). The latter offers technical features to systematically utilize the MARS for researchers as well as clinicians and end-users that (i) want to evaluate apps as well as (ii) want an interactive and easy-to-use web interface that shows the results of the rating procedure. MHAD comprises a rating platform that supports the conduction of MARS ratings and their release process. With the information platform, a web application was developed that prepares the data stored in the rating platform for being freely viewed and studied by users, patients, and health care providers. The goal of MHAD constitutes to be an open science repository that encourages researchers to release their MARS ratings to a broader audience. Such repositories become more and more important in many fields, especially in the field of mHealth.

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Section: Introductionmentioning

confidence: 99%

Mobile Health App Database - A Repository for Quality Ratings of mHealth Apps

Stach

Probst

Meßner

et al. 2020

2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)

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“…The reported articles already demanded more observational study, validation of effectiveness, clinical trial and more empirical evidence [66], [70]. Our paper also finds that there is a clear gap to understand and link the need and user adoption to adequately evaluate the digital mental health support on the mobile platform.…”

Section: Assessment Of the Digital Supportmentioning

confidence: 79%

“…A critical exploration of 25 behavioral health apps, [66] revealed the need for standardization of the quality of such apps and a more effective metric to inform the users. [66] urged for better guidance for the apps that not only include evidence but also integrate disclosed consumer choices.…”

Section: Assessment Of the Digital Supportmentioning

confidence: 99%

Arts and Science of Digital Mental Health Support

Tania

Andreev

Temdee

2020

2020 Joint International Conference on Digital Arts, Media and Technology With ECTI Northern Section Conference on Electrical,

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Globally, over 600 million people are affected by mental health-related issues, according to the recent statistics. Similar to the global scenario, these issues are the single source of disease burden in the UK as well. On the bright side, the boom in the mobile application or 'app' market and the paradigm shift in the global disease burden have promoted a growing interest for mental health-related apps. This paper critically explores the mobile application for mental health support to understand what apps are available in the market and their merits and capabilities to meet the need of users' demand. This paper also provides a recommendation for future directions.

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“…This new framework enables a detailed examination of the stability of memory and its relationship to fluctuations in clinical state within the individual, and as such may generate the critical assays for personalized medicine purposes 4 . Furthermore, this approach offers a practically viable method in ambulatory settings where mobile technology is increasingly used for clinical purposes 23 .…”

Section: Discussionmentioning

confidence: 99%

Applying speech technologies to assess verbal memory in patients with serious mental illness

et al. 2020

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Verbal memory deficits are some of the most profound neurocognitive deficits associated with schizophrenia and serious mental illness in general. As yet, their measurement in clinical settings is limited to traditional tests that allow for limited administrations and require substantial resources to deploy and score. Therefore, we developed a digital ambulatory verbal memory test with automated scoring, and repeated self-administration via smart devices. One hundred and four adults participated, comprising 25 patients with serious mental illness and 79 healthy volunteers. The study design was successful with high quality speech recordings produced to 92% of prompts (Patients: 86%, Healthy: 96%). The story recalls were both transcribed and scored by humans, and scores generated using natural language processing on transcriptions were comparable to human ratings (R = 0.83, within the range of human-to-human correlations of R = 0.73-0.89). A fully automated approach that scored transcripts generated by automatic speech recognition produced comparable and accurate scores (R = 0.82), with very high correlation to scores derived from human transcripts (R = 0.99). This study demonstrates the viability of leveraging speech technologies to facilitate the frequent assessment of verbal memory for clinical monitoring purposes in psychiatry.

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By the numbers: ratings and utilization of behavioral health mobile applications

Cited by 156 publications

References 32 publications

Mobile Health App Database - A Repository for Quality Ratings of mHealth Apps

Mobile Health App Database - A Repository for Quality Ratings of mHealth Apps

Arts and Science of Digital Mental Health Support

Applying speech technologies to assess verbal memory in patients with serious mental illness

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