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

BMC Medical Informatics and Decision Making

Volumn 16, Issue 1, 2016, Pages

  Domnich, Alexander ^a   Arata, Lucia ^a   Amicizia, Daniela ^a   Signori, Alessio ^a   Patrick, Bernard ^b   Stoyanov, Stoyan ^c,d   Hides, Leanne ^c,d   Gasparini, Roberto ^a   Panatto, Donatella ^a  

^a UNIVERSITY OF GENOA   (Italy)

^b UNIVERSITY OF GENOA   (Italy)

^c QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

^d Young and Well   (Australia)

Author keywords

mHealth; Mobile application; Mobile application rating scale; Mobile health; Prevention

Indexed keywords

CONCURRENT VALIDITY; CORRELATION COEFFICIENT; CRONBACH ALPHA COEFFICIENT; HUMAN; HUMAN EXPERIMENT; INSTRUMENT VALIDATION; INTERNAL CONSISTENCY; INTERRATER RELIABILITY; MOBILE APPLICATION; PRIMARY PREVENTION; PUBLICATION; RATING SCALE; SAMPLE SIZE; VALIDATION PROCESS; ITALY; PSYCHOMETRY; QUESTIONNAIRE; REPRODUCIBILITY; STANDARDS; TELEMEDICINE;

HUMANS; ITALY; MOBILE APPLICATIONS; PRIMARY PREVENTION; PSYCHOMETRICS; REPRODUCIBILITY OF RESULTS; SURVEYS AND QUESTIONNAIRES; TELEMEDICINE;

EID: 84977273217     PISSN: None     EISSN: 14726947     Source Type: Journal    
DOI: 10.1186/s12911-016-0323-2     Document Type: Article

References (51)

1. Vita AJ, Terry RB, Hubert HB, Fries JF. Aging, health risks, and cumulative disability. N Engl J Med. 1998;338:1035-41.
2. Centers for Disease Control and Prevention (CDC). Ten great public health achievements - worldwide, 2001-2010. MMWR Morb Mortal Wkly Rep. 2011;60:814-8.
3. Kroke A, Boeing H, Rossnagel K, Willich SN. History of the concept of 'levels of evidence' and their current status in relation to primary prevention through lifestyle interventions. Public Health Nutr. 2004;7:279-84.
4. Amicizia D, Domnich A, Gasparini R, Bragazzi NL, Lai PL, Panatto D. An overview of current and potential use of information and communication technologies for immunization promotion among adolescents. Hum Vaccin Immunother. 2013;9:2634-42.
5. Klasnja P, Pratt W. Healthcare in the pocket: mapping the space of mobile-phone health interventions. J Biomed Inform. 2012;45:184-98.
6. Kharbanda EO, Stockwell MS, Fox HW, Andres R, Lara M, Rickert VI. Text message reminders to promote human papillomavirus vaccination. Vaccine. 2011;29:2537-41.
7. Vilella A, Bayas JM, Diaz MT, Guinovart C, Diez C, Simó D, et al. The role of mobile phones in improving vaccination rates in travelers. Prev Med. 2004;38:503-9.
8. Safran Naimark J, Madar Z, Shahar DR. The impact of a Web-based app (eBalance) in promoting healthy lifestyles: randomized controlled trial. J Med Internet Res. 2015;17:e56.
9. Buller DB, Berwick M, Lantz K, Buller MK, Shane J, Kane I, et al. Evaluation of immediate and 12-week effects of a smartphone sun-safety mobile application: a randomized clinical trial. JAMA Dermatol. 2015;151:505-12.
10. Fukuoka Y, Gay CL, Joiner KL, Vittinghoff E. A novel diabetes prevention intervention using a mobile app: a randomized controlled trial with overweight adults at risk. Am J Prev Med. 2015;49:223-37.
11. Cho J. The impact of post-adoption beliefs on the continued use of health apps. Int J Med Inform. 2016;87:75-83.
12. Payne HE, Lister C, West JH, Bernhardt JM. Behavioral functionality of mobile apps in health interventions: a systematic review of the literature. JMIR Mhealth Uhealth. 2015;3:e20.
13. Becker S, Miron-Shatz T, Schumacher N, Krocza J, Diamantidis C, Albrecht UV. mHealth 2.0: experiences, possibilities, and perspectives. JMIR Mhealth Uhealth. 2014;2:e24.
14. Think with Google. Mobile app marketing insights: how consumers really find and use your apps. https://think.storage.googleapis.com/docs/mobile-app-marketing-insights.pdf. Accessed 11 Nov 2015.
15. Modave F, Bian J, Leavitt T, Bromwell J, Harris Iii C, Vincent H. Low quality of free coaching apps with respect to the American college of sports medicine guidelines: a review of current mobile apps. JMIR Mhealth Uhealth. 2015;3:e77.
16. Cline RJ, Haynes KM. Consumer health information seeking on the Internet: the state of the art. Health Educ Res. 2001;16:671-92.
17. Handel MJ. mHealth (mobile health)-using apps for health and wellness. Explore (NY). 2011;7:256-61.
18. Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth. 2015;3:e27.
19. Mani M, Kavanagh DJ, Hides L, Stoyanov SR. Review and evaluation of mindfulness-based iPhone apps. JMIR mHealth uHealth. 2015;3:e82.
20. Patel R, Sulzberger L, Li G, Mair J, Morley H, Shing MNW, et al. Smartphone apps for weight loss and smoking cessation: quality ranking of 120 apps. N Z Med J. 2015;128:73-6.
21. European Commission. Special Eurobarometer 386. Europeans and their languages. http://ec.europa.eu/public-opinion/archives/ebs/ebs-386-en.pdf. Accessed 11 Nov 2015.
22. De Vito C, Nobile CG, Furnari G, Pavia M, De Giusti M, Angelillo IF, et al. Physicians' knowledge, attitudes and professional use of RCTs and meta-analyses: a cross-sectional survey. Eur J Public Health. 2009;19:297-302.
23. Herdman M, Fox-Rushby J, Badia X. A model of equivalence in the cultural adaptation of HRQoL instruments: the universalist approach. Qual Life Res. 1998;7:323-35.
24. Boudreaux ED, Waring ME, Hayes RB, Sadasivam RS, Mullen S, Pagoto S. Evaluating and selecting mobile health apps: strategies for healthcare providers and healthcare organizations. Transl Behav Med. 2014;4:363-71.
25. Schnall R, Iribarren SJ. Review and analysis of existing mobile phone applications for health care-associated infection prevention. Am J Infect Control. 2015;43:572-6.
26. Zou GY. Sample size formulas for estimating intraclass correlation coefficients with precision and assurance. Stat Med. 2012;31:3972-81.
27. D'Agostino RB. Transformation to normality of the null distribution of g1. Biometrika. 1970;57:679-81.
28. Terwee CB, Bot SD, de Boer MR, van der Windt DA, Knol DL, Dekker J, et al. Quality criteria were proposed for measurement properties of health status questionnaires. J Clin Epidemiol. 2007;60:34-42.
29. Portney LG, Watkins MP. Foundations of clinical research: applications to practice. 3rd ed. Upper Saddle River: Pearson/Prentice Hall; 2009.
30. George D, Mallery P. SPSS for Windows step by step: a simple guide and reference. 11.0 update. 4th ed. Boston: Allyn & Bacon; 2003.
31. Spearman C. Correlation calculated with faulty data. Br J Psych. 1910;3:271-95.
32. Brown W. Some experimental results in the correlation of mental abilities. Br J Psych. 1910;3:296-322.
33. Nunnally JC, Bernstein ICH. Psychometric theory. 3rd ed. New York: McGraw-Hill; 1994.
34. deVellis RF. Scale development: theory and applications. London: Sage; 1991.
35. Morales Nde M, Silva CH, Frontarolli AC, Araújo RR, Rangel VO, Pinto RM, et al. Psychometric properties of the initial Brazilian version of the CHQ-PF50 applied to the caregivers of children and adolescents with cerebral palsy. Qual Life Res. 2007;16:437-44.
36. Hankins M. Questionnaire discrimination: (re)-introducing coefficient delta. BMC Med Res Methodol. 2007;7:19.
37. Stochl J, Jones PB, Croudace TJ. Mokken scale analysis of mental health and well-being questionnaire item responses: a non-parametric IRT method in empirical research for applied health researchers. BMC Med Res Methodol. 2012;12:74.
38. Hirasawa R, Saito K, Yachi Y, Ibe Y, Kodama S, Asumi M, et al. Quality of Internet information related to the Mediterranean diet. Public Health Nutr. 2012;15:885-93.
39. Tozzi AE, Buonuomo PS, Atti ML C d, Carloni E, Meloni M, Gamba F. Comparison of quality of internet pages on human papillomavirus immunization in Italian and in English. J Adolesc Health. 2010;46:83-9.
40. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale: Lawrence Erlbaum Associates; 1988.
41. Charter RA, Feldt LS. Testing the equality of two alpha coefficients. Percept Motor Skills. 1996;82:763-8.
42. R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2014. http://www.R-project.org/. Accessed 11 Nov 2015.
43. W3Techs. Web technology surveys. Usage of content languages for websites. http://w3techs.com/technologies/overview/content-language/all. Accessed 11 Nov 2015.
44. Somers H. Computers and translation: a translator's guide (Vol. 35). Amsterdam/Philadelphia: John Benjamins Publishing; 2003.
45. O'Brien HL, Toms EG. What is user engagement? A conceptual framework for defining user engagement with technology. J Am Assoc Inf Sci Technol. 2008;59:938-55.
46. Stenalt M, Godsk M. The pleasure of e-learning: Towards aesthetic e-learning platforms. Proceedings of the 12th International Conference of European University Information Systems. Tartu, Estonia, 2006. http://www.au.dk/fileadmin/www.au.dk/e-laeringsenheden/e-laering-og-ikt/publikationer/thepleasureofe-learning.pdf. Accessed 11 Nov 2015.
47. Kuehnhausen M, Frost VS. Trusting smartphone apps? To install or not to install, that is the question. Cognitive methods in situation awareness and decision support. IEEE International Multi-Disciplinary Conference; 2013 Feb 25-28; San Diego, CA, USA. IEEE; 2013:3-7.
48. Xu W, Liu Y. mHealthApps: a repository and database of mobile health apps. JMIR Mhealth Uhealth. 2015;3:e28.
49. Geense WW, van de Glind IM, Visscher TL, van Achterberg T. Barriers, facilitators and attitudes influencing health promotion activities in general practice: an explorative pilot study. BMC Fam Pract. 2013;14:20.
50. Douglas F, Torrance N, van Teijlingen E, Meloni S, Kerr A. Primary care staff's views and experiences related to routinely advising patients about physical activity. A questionnaire survey. BMC Public Health. 2006;6:138.
51. Lambe B, Collins C. A qualitative study of lifestyle counselling in general practice in Ireland. Fam Pract. 2010;27:219-23.