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Development and validation of the Italian version of the Mobile Application Rating Scale and its generalisability to apps targeting primary prevention

Overview of attention for article published in BMC Medical Informatics and Decision Making, July 2016
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Title
Development and validation of the Italian version of the Mobile Application Rating Scale and its generalisability to apps targeting primary prevention
Published in
BMC Medical Informatics and Decision Making, July 2016
DOI 10.1186/s12911-016-0323-2
Pubmed ID
Authors

Alexander Domnich, Lucia Arata, Daniela Amicizia, Alessio Signori, Bernard Patrick, Stoyan Stoyanov, Leanne Hides, Roberto Gasparini, Donatella Panatto

Abstract

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.

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Mendeley readers

The data shown below were compiled from readership statistics for 106 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 <1%
Unknown 105 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 23 22%
Student > Ph. D. Student 19 18%
Student > Bachelor 11 10%
Student > Doctoral Student 11 10%
Researcher 9 8%
Other 17 16%
Unknown 16 15%
Readers by discipline Count As %
Medicine and Dentistry 19 18%
Psychology 16 15%
Computer Science 13 12%
Nursing and Health Professions 12 11%
Social Sciences 7 7%
Other 20 19%
Unknown 19 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 July 2016.
All research outputs
#9,297,341
of 11,625,761 outputs
Outputs from BMC Medical Informatics and Decision Making
#885
of 1,067 outputs
Outputs of similar age
#186,858
of 266,101 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#31
of 36 outputs
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