Development of the Thai Mobile Health Apps Rating Scale (THARS)
Article Sidebar
Main Article Content
Abstract
Objective: To develop the Thai Mobile Health Apps Rating Scale (THARS) and to examine its validity and reliability. Method: A systematic review was performed to identify comprehensive relevant quality assessment criteria for mobile health apps. The researchers developed the THARS version 1 from the complied criteria. The test for content and face validity was conducted through the review by 3 experts. Subsequently, the revised scale, the THARS version 2, was tested for face validity in 10 mobile health apps users. Internal consistency and test-retest reliability were performed in 150 volunteers using 2 selected apps. The apps were assessed twice, 14 days apart, using THARS version 3. Cronbach’s alpha and intra-class correlation coefficient were calculated from the obtained scores. Results: The systematic review identified fifteen domains for assessing mobile health apps in 66 studies. The test for content validity of the THARS version 1 revealed that I-CVI of the items ranged from 0.3-1 and S-CVI was 0.9. The scale was then revised to version 2 and 3 with 38 questions. Cronbach’s alpha coefficients of the THARS version 3 were 0.76 to 0.83. Intraclass correlation coefficient (95%CI) of the total scores obtained from 2 assessments of 2 apps were 0.84 (0.78-0.88) and 0.94 (0.91-0.96), respectively. Conclusion: The THARS demonstrated good validity, acceptable level of internal consistency and excellent level of reliability. It can be used as a tool to assess the quality of mobile health apps.
Article Details
ผลการวิจัยและความคิดเห็นที่ปรากฏในบทความถือเป็นความคิดเห็นและอยู่ในความรับผิดชอบของผู้นิพนธ์ มิใช่ความเห็นหรือความรับผิดชอบของกองบรรณาธิการ หรือคณะเภสัชศาสตร์ มหาวิทยาลัยสงขลานครินทร์ ทั้งนี้ไม่รวมความผิดพลาดอันเกิดจากการพิมพ์ บทความที่ได้รับการเผยแพร่โดยวารสารเภสัชกรรมไทยถือเป็นสิทธิ์ของวารสารฯ
References
2. Juan MG-G, Isabel de la T-D, Javier V, Montserrat R, Miguel L-C, Joel JR. Analysis of mobile health applications for a broad spectrum of consumers: A user experience approach. Health Informatics J. 2014; 20: 74-84.
3. Internet Society APAC Bureau. Mobile internet usage trend in Asia-Pacific 2016 [online]. 2016 [cited Mar 29, 2017]. Available from: www.internet society.org/wp-content/uploads/2017/08/Mobile20In ternet20Usage20Trends20in20Asia-Pacific.pdf.
4. MobiHealthNews. US regulators remove two acne medical apps [online]. 2011 [cited Sep 9, 2017]. Available from: www.mobihealthnews.com/13123/u s- regulators-remove-two-acne-medical-apps.
5. Cortez NG, Cohen IG, Kesselheim AS. FDA regulation of mobile health technologies. N Engl J Med 2014; 371: 372-9.
6. 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.
7. Stoyanov SR, Hides L, Kavanagh DJ, Wilson H. Development and validation of the user version of the mobile application rating scale (uMARS). JMIR Mhealth Uhealth 2016; 4: e72.
8. Polit DF, Hungler BP. Nursing research principles and methods. 6th ed. Philadelphia: Lippincott Will iams & Wilkins; 1999.
9. Anthoine E, Moret L, Regnault A, Sébille V, Hardouin J-B. Sample size used to validate a scale: a review of publications on newly-developed patient reported outcomes measures. Health Qual Life Outcomes 2014; 12: 30-46.
10. Ventola CL. Mobile devices and apps for health care professionals: uses and benefits. P T 2014; 39: 356-64.
11. BinDhim NF, Hawkey A, Trevena L. A systema tic review of quality assessment methods for smart phone health apps. Telemed J E Health 2015; 21: 97-104.
12.Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøt zsche PC, Ioannidis JPA, et al. The PRISMA state ment for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ 2009; 339: b2700.
13. Polit DF, Beck CT. The content validity index: Are you sure you know what's being reported? Critique and recommendations. Res Nurs Health 2006; 29: 489-97.
14. Nunnally JC. Psychometric theory. 2nd ed. New York: McGraw-Hill; 1978.
15. Devellis RF. Scale development theory and app lications. 4th ed. California: Sage; 2017.
16. Punyapas S, Pornnoppadol C, Boonyasidhi V, Likhitkiatikhachorn P. Reliability and validity of Weiss Functional Impairment Rating Scale (WFIR S)-Thai version in children and adolescents with attention deficit hyperactivity disorder. J Psychiatr Assoc Thailand 2015; 60: 111-26.
17. Ferketich S. Focus on psychometrics: aspects of item analysis. Res Nurs Health. 1991; 14: 165-8.
18. Pallant J. SPSS Survival Manual: A step by step guide to data analysis using SPSS for Windows version 15. Milton Keynes: Open University Press; 2007.
19. Koo TK, Li MY. A Guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 2016; 15: 155-63.
20. Hallgren KA. Computing inter-rater reliability for observational data: An overview and tutorial. Tutor Quant Methods Psychol 2012; 8: 23-34.
21. Parsian N, Dunning T. Developing and validating a questionnaire to measure spirituality: a psycho metric process. Glob J Health Sci 2009; 1: 2-11.
22. Streiner DL, Norman GR, Cairney J. Health measurement scales: A practical guide to their development and use. 5th ed. Oxford: Oxford University Press; 2014.
23. Keszei AP, Novak M, Streiner DL. Introduction to health measurement scales. J Psychosom Res 2010; 68: 319-23.
24. Cho MJ, Sim JL, Hwang SY. Development of smartphone educational application for patients with coronary artery disease. Healthc Inform Res 2014; 20: 117-24.
25. de la Vega R, Roset R, Castarlenas E, Sánchez-Rodríguez E, Solé E, Miró J. Development and testing of painometer: A smartphone app to assess pain intensity. J Pain 2014; 15: 1001-7.
26. Stinson JN, Jibb LA, Nguyen C, Nathan PC, Maloney AM, Dupuis LL, et al. Development and testing of a multidimensional iPhone pain assess ment application for adolescents with cancer. J Med Internet Res 2013; 15: e51.
27. Baumel A, Faber K, Mathur N, Kane JM, Muench F. Enlight: A comprehensive quality and therapeu tic potential evaluation tool for mobile and web-based eHealth interventions. J Med Internet Res 2017; 19: e82.
