The German Version of the Mobile App Rating Scale (MARS-G): Development and Validation Study

Background The number of mobile health apps (MHAs), which are developed to promote healthy behaviors, prevent disease onset, manage and cure diseases, or assist with rehabilitation measures, has exploded. App store star ratings and descriptions usually provide insufficient or even false information about app quality, although they are popular among end users. A rigorous systematic approach to establish and evaluate the quality of MHAs is urgently needed. The Mobile App Rating Scale (MARS) is an assessment tool that facilitates the objective and systematic evaluation of the quality of MHAs. However, a German MARS is currently not available. Objective The aim of this study was to translate and validate a German version of the MARS (MARS-G). Methods The original 19-item MARS was forward and backward translated twice, and the MARS-G was created. App description items were extended, and 104 MHAs were rated twice by eight independent bilingual researchers, using the MARS-G and MARS. The internal consistency, validity, and reliability of both scales were assessed. Mokken scale analysis was used to investigate the scalability of the overall scores. Results The retranslated scale showed excellent alignment with the original MARS. Additionally, the properties of the MARS-G were comparable to those of the original MARS. The internal consistency was good for all subscales (ie, omega ranged from 0.72 to 0.91). The correlation coefficients (r) between the dimensions of the MARS-G and MARS ranged from 0.93 to 0.98. The scalability of the MARS (H=0.50) and MARS-G (H=0.48) were good. Conclusions The MARS-G is a reliable and valid tool for experts and stakeholders to assess the quality of health apps in German-speaking populations. The overall score is a reliable quality indicator. However, further studies are needed to assess the factorial structure of the MARS and MARS-G.

[1]  R. Dellavalle,et al.  Mobile medical and health apps: state of the art, concerns, regulatory control and certification , 2014, Online journal of public health informatics.

[2]  Mobin Yasini,et al.  Criteria for assessing the quality of mHealth apps: a systematic review , 2018, J. Am. Medical Informatics Assoc..

[3]  Gerhard Andersson,et al.  Adherence to Internet-Based and Face-to-Face Cognitive Behavioural Therapy for Depression: A Meta-Analysis , 2014, PloS one.

[4]  Klaas Sijtsma,et al.  A tutorial on how to do a Mokken scale analysis on your test and questionnaire data. , 2017, The British journal of mathematical and statistical psychology.

[5]  Molly E Waring,et al.  Evaluating and selecting mobile health apps: strategies for healthcare providers and healthcare organizations , 2014, Translational behavioral medicine.

[6]  Stoyan R. Stoyanov,et al.  Review and Evaluation of Mindfulness-Based iPhone Apps , 2015, JMIR mHealth and uHealth.

[7]  Michael Clyne,et al.  The Janus face of monolingualism: a comparison of German and Australian language education policies , 2010 .

[8]  Sarah Iribarren,et al.  Review and Analysis of Existing Mobile Phone Apps to Support Heart Failure Symptom Monitoring and Self-Care Management Using the Mobile Application Rating Scale (MARS) , 2016, JMIR mHealth and uHealth.

[9]  Harald Baumeister,et al.  Internet and mobile-based psychological interventions: Applications, efficacy and potential for improving mental health. A report of the EFPA E-Health Taskforce (vol 23, pg 167, 2018) , 2018 .

[10]  Harald Baumeister,et al.  Effectiveness and cost-effectiveness of a guided internet- and mobile-based depression intervention for individuals with chronic back pain: protocol of a multi-centre randomised controlled trial , 2017, BMJ Open.

[11]  Oksana Zelenko,et al.  Mobile App Rating Scale: A New Tool for Assessing the Quality of Health Mobile Apps , 2015, JMIR mHealth and uHealth.

[12]  Alexander Domnich,et al.  Development and validation of the Italian version of the Mobile Application Rating Scale and its generalisability to apps targeting primary prevention , 2016, BMC Medical Informatics and Decision Making.

[13]  Charles Abraham,et al.  A review and content analysis of engagement, functionality, aesthetics, information quality, and change techniques in the most popular commercial apps for weight management , 2016, International Journal of Behavioral Nutrition and Physical Activity.

[14]  Ke-Hai Yuan,et al.  Robust Coefficients Alpha and Omega and Confidence Intervals With Outlying Observations and Missing Data , 2016, Educational and psychological measurement.

[15]  Klaas Sijtsma,et al.  Mokken Scale Analysis for Dichotomous Items Using Marginal Models , 2007, Psychometrika.

[16]  Rubén Martín Payo,et al.  Spanish adaptation and validation of the Mobile Application Rating Scale questionnaire , 2019, Int. J. Medical Informatics.

[17]  van der Ark,et al.  Mokken Scale Analysis in R , 2007 .

[18]  S. Holm.  A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[19]  Klaas Sijtsma,et al.  Reliability of test scores in nonparametric item response theory , 1987 .

[20]  Eva-Maria Messner,et al.  German Mobile Apps in Rheumatology: Review and Analysis Using the Mobile Application Rating Scale (MARS) , 2019, JMIR mHealth and uHealth.

[21]  Alejandro Salazar,et al.  Measuring the Quality of Mobile Apps for the Management of Pain: Systematic Search and Evaluation Using the Mobile App Rating Scale , 2018, JMIR mHealth and uHealth.

[22]  Klaas Sijtsma,et al.  A Latent Class Approach to Estimating Test-Score Reliability , 2011 .

[23]  Lasse Sander,et al.  «Hilfe aus dem App-Store?»: Eine systematische Übersichtsarbeit und Evaluation von Apps zur Anwendung bei Depressionen , 2018, Verhaltenstherapie.

[24]  Stoyan R. Stoyanov,et al.  Development and Validation of the User Version of the Mobile Application Rating Scale (uMARS) , 2016, JMIR mHealth and uHealth.

[25]  Urs-Vito Albrecht.  Kapitel 8 - Gesundheits-Apps und Risiken , 2016 .

[26]  Daniel M. McNeish,et al.  Psychological Methods Thanks Coefficient Alpha , We ’ ll Take It From Here , 2022 .

[27]  Tobias Langlotz,et al.  Apps for People With Rheumatoid Arthritis to Monitor Their Disease Activity: A Review of Apps for Best Practice and Quality , 2017, JMIR mHealth and uHealth.

[28]  W. Revelle,et al.  Coefficients Alpha, Beta, Omega, and the glb: Comments on Sijtsma , 2009 .

[29]  R. J. Mokken,et al.  A Theory and Procedure of Scale Analysis: With Applications in Political Research , 1971 .

[30]  Frances Kay-Lambkin,et al.  Free smoking cessation mobile apps available in Australia: a quality review and content analysis , 2017, Australian and New Zealand journal of public health.

[31]  W. Alexander.  American psychiatric association. , 2008, P & T : a peer-reviewed journal for formulary management.

[32]  van der Ark,et al.  New Developments in Mokken Scale Analysis in R , 2012 .

[33]  H. Baumeister,et al.  Effectiveness and cost-effectiveness of a guided Internet- and mobile-based intervention for the indicated prevention of major depression in patients with chronic back pain—study protocol of the PROD-BP multicenter pragmatic RCT , 2017, BMC Psychiatry.

[34]  Myra Spiliopoulou,et al.  Prospective crowdsensing versus retrospective ratings of tinnitus variability and tinnitus–stress associations based on the TrackYourTinnitus mobile platform , 2019, International Journal of Data Science and Analytics.

[35]  J. Smyth,et al.  Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments. , 2010, British journal of health psychology.

[36]  Leslie G. Portney Dpt PhD Fapta,et al.  Foundations of Clinical Research: Applications to Practice , 2015 .

[37]  Darren George,et al.  SPSS for Windows Step by Step: A Simple Guide and Reference , 1998 .

[38]  Osman H Ahmed,et al.  Smartphone apps for the self-management of low back pain: A systematic review. , 2016, Best practice & research. Clinical rheumatology.

[39]  Elad Yom-Tov,et al.  Predicting user adherence to behavioral eHealth interventions in the real world: examining which aspects of intervention design matter most. , 2018, Translational behavioral medicine.

[40]  Yin Liu,et al.  mHealthApps: A Repository and Database of Mobile Health Apps , 2015, JMIR mHealth and uHealth.

[41]  Louis Guttman,et al.  A basis for analyzing test-retest reliability , 1945, Psychometrika.

[42]  Peter Dalgaard,et al.  R Development Core Team (2010): R: A language and environment for statistical computing , 2010 .

[43]  Amit Baumel,et al.  Enlight: A Comprehensive Quality and Therapeutic Potential Evaluation Tool for Mobile and Web-Based eHealth Interventions , 2017, Journal of medical Internet research.

[44]  H. Christensen,et al.  Adherence in Internet Interventions for Anxiety and Depression: Systematic Review , 2009, Journal of medical Internet research.

[45]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[46]  Thomas J. Dunn,et al.  From alpha to omega: a practical solution to the pervasive problem of internal consistency estimation. , 2014, British journal of psychology.

[47]  Ruth M. Masterson Creber,et al.  Review and Analysis of Existing Mobile Phone Apps to Support Heart Failure Symptom Monitoring and Self-Care Management Using the Mobile Application Rating Scale (MARS). , 2016 .