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Cited by 58Mobile Health and mHealth ApplicationsUsability and User Interface DesignMedication Adherence and Compliance

A Multi-step Usability Evaluation of a Self-Management App to Support Medication Adherence in Persons Living with HIV

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Beauchemin, Melissa, Gradilla, Melissa, Baik, Dawon, Cho, Hwayoung, Schnall, Rebecca·2019·International Journal of Medical Informatics
DOI 10.1016/j.ijmedinf.2018.11.012PMC PMC6369913
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Abstract

Purpose Adherence to antiretroviral therapy is essential to HIV management and sustaining viral suppression. Despite simplified regimens, adherence remains difficult for certain persons living with HIV (PLWH). There is evidence to support the use of mHealth apps for effective self-management in PLWH; however, a medication adherence app with real-time monitoring for this population has not been developed and rigorously evaluated by PLWH. We developed a mHealth app (WiseApp) for HIV self-management to help PLWH self-manage their health. The purpose of this study was to evaluate the usability of the WiseApp. Methods We conducted a three-step usability evaluation using 1) a traditional think-aloud protocol with end-users, 2) a heuristic evaluation with experts in informatics, and 3) a cognitive walkthrough with end-users. During the cognitive walkthrough, we tested two devices (fitness tracker and medication tracking bottle) that were linked to the WiseApp. Results The think-aloud protocol informed iterative updates to the app, specifically to make it easier to see different sections of the app. The heuristic evaluation confirmed the necessity of these design changes. The cognitive walkthrough informed additional updates and confirmed that overall, the app and the linked devices were usable for the end-users. Conclusion The results of the cognitive walkthrough with both experts and end-users informed iterative refinements to the WiseApp and finalization of a mHealth app for PLWH to better self-manage their health.

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  1. [1]Deeks SG, Lewin SR, Havlir DV. The end of AIDS: HIV infection as a chronic disease. Lancet (London, England) 2013; 382(9903): 1525– 33. PMC4058441 24152939
  2. [2]Liu H, Golin CE, Miller LG, Hays RD, Beck CK, Sanandaji S, A comparison study of multiple measures of adherence to HIV protease inhibitors. Annals of internal medicine 2001; 134(10): 968– 77. 11352698
  3. [3]Murri R Patient-reported nonadherence to HAART is related to protease inhibitor levels. Journal of acquired immune deficiency syndromes (1999) 2000; 24(2): 123. 10935687
  4. [4]Golin CE, Liu H, Hays RD, Miller LG, Beck CK, Ickovics J, A Prospective Study of Predictors of Adherence to Combination Antiretroviral Medication. Journal of General Internal Medicine 2002; 17(10): 756– 65. 12390551 PMC1495120
  5. [5]Kumar S, Nilsen WJ, Abernethy A, Atienza A, Patrick K, Pavel M, Mobile health technology evaluation: the mHealth evidence workshop. American journal of preventive medicine 2013; 45(2): 228– 36. 23867031 PMC3803146
  6. [7]Schnall R, Cho H, Mangone A, Pichon A, Jia H. Mobile Health Technology for Improving Symptom Management in Low Income Persons Living with HIV. AIDS and behavior 2018. PMC6034982 29299790
  7. [8]Mannheimer SB. The CASE adherence index: A novel method for measuring adherence to antiretroviral therapy. AIDS care 2006; 18(7): 853– 61. 16971298 PMC2567829
  8. [10]Schnall R, Rojas M, Bakken S, Brown W, Carballo-Dieguez A, Carry M, A user-centered model for designing consumer mobile health (mHealth) applications (apps). J Biomed Inform 2016; 60: 243– 51. 26903153 PMC4837063
  9. [11]Schnall R, Bakken S, Rojas M, Travers J, Carballo-Dieguez A. mHealth Technology as a Persuasive Tool for Treatment, Care and Management of Persons Living with HIV. AIDS and behavior 2015; 19 Suppl 2: 81– 9. PMC4497931 25572830
  10. [12]Schnall R, Rojas M, Travers J, Brown W 3rd, Bakken S. Use of Design Science for Informing the Development of a Mobile App for Persons Living with HIV. AMIA Annual Symposium Proceedings 2014; 2014: 1037– 45. 25954413 PMC4419902
  11. [14]Haberer JE, Kahane J, Kigozi I, Emenyonu N, Hunt P, Martin J, Real-time adherence monitoring for HIV antiretroviral therapy. AIDS and behavior 2010; 14(6): 1340– 6. 20809380 PMC2974938
  12. [15]Maguire M Methods to support human-centred design. International Journal of Human-Computer Studies 2001; 55(4): 587– 634.
  13. [17]Shackel B. Usability—context, framework, definition, design and evaluation. In: Brian S, Richardson SJ, editors. Human factors for informatics usability: Cambridge University Press; 1991 p. 21– 37.
  14. [19]Ben-Zeev D, Kaiser SM, Brenner CJ, Begale M, Duffecy J, Mohr DC. Development and Usability Testing of FOCUS: A Smartphone System for Self-Management of Schizophrenia. Psychiatric rehabilitation journal 2013; 36(4): 289– 96. 24015913 PMC4357360
  15. [20]Hamine S, Gerth-Guyette E, Faulx D, Green BB, Ginsburg AS. Impact of mHealth chronic disease management on treatment adherence and patient outcomes: a systematic review. Journal of medical Internet research 2015; 17(2): e52. 25803266 PMC4376208
  16. [24]Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of psychiatric research 1975; 12(3): 189– 98. 1202204
  17. [25]Schnall R, Cho H, Liu J. Health Information Technology Usability Evaluation Scale (Health-ITUES) for Usability Assessment of Mobile Health Technology: Validation Study. JMIR mHealth and uHealth 2018; 6(1): e4. 29305343 PMC5775483
  18. [26]Polson PG, Lewis C, Rieman J, Wharton C. Cognitive walkthroughs: a method for theory-based evaluation of user interfaces. International Journal of Man-Machine Studies 1992; 36(5): 741– 73.
  19. [27]Jaspers MW. A comparison of usability methods for testing interactive health technologies: methodological aspects and empirical evidence. Int J Med Inform 2009; 78(5): 340– 53. 19046928
  20. [28]Kaufman DR, Patel VL, Hilliman C, Morin PC, Pevzner J, Weinstock RS, Usability in the real world: assessing medical information technologies in patients’ homes. J Biomed Inform 2003; 36(1–2): 45– 60. 14552846
  21. [29]Vaughan AS, Rosenberg E, Shouse RL, Sullivan PS. Connecting Race and Place: A County-Level Analysis of White, Black, and Hispanic HIV Prevalence, Poverty, and Level of Urbanization. American Journal of Public Health 2014; 104(7): e77– e84. PMC4056233 24832420
  22. [30]Nielsen J Usability engineering: Elsevier; 1994.
  23. [31]Yen PY, Wantland D, Bakken S. Development of a Customizable Health IT Usability Evaluation Scale. AMIA Annual Symposium proceedings AMIA Symposium 2010; 2010: 917– 21. 21347112 PMC3041285
  24. [32]Bevan N International standards for HCI and usability. International Journal of Human-Computer Studies 2001; 55(4): 533– 52.
  25. [34]Sheehan B, Lee Y, Rodriguez M, Tiase V, Schnall R. A comparison of usability factors of four mobile devices for accessing healthcare information by adolescents. Applied clinical informatics 2012; 3(4): 356– 66. 23227134 PMC3517216

Cited by

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Authors
Beauchemin, M., Gradilla, M., Baik, D., Cho, H., & Schnall, R.
Affiliations
  • Columbia University Irving Medical Center (US)
  • Columbia University (US)
Corresponding author
Beauchemin, Melissa
ORCID
Venue
International Journal of Medical Informatics
Publisher
Elsevier BV
Published
2019-02
Volume
122
Pages
37-44
ISSN
1386-5056 (ISSN-L)
DOI
10.1016/j.ijmedinf.2018.11.012
PMC
PMC6369913
OpenAlex
W2902727565
Cited by
58
References
6
Citations by year
2026: 1 · 2025: 5 · 2024: 15 · 2023: 7 · 2022: 8 · 2021: 9 · 2020: 9 · 2019: 4
FWCI
8.40
Type
journal article
Language
en
Open access
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • International Journal of Medical Informatics (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • Agency for Healthcare Research and Quality (R01HS025071)
  • Reducing Health Disparities through Informatics (RHeaDI) training
  • National Institute of Nursing Research (T32 NR007969)
  • Doctoral Degree Scholarship in Cancer Nursing (DSCN-18-068-01)
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
  • Health Sciences › Medicine › Family Practice › Medication Adherence and Compliance
Keywords
HIV, medication adherence, antiretroviral therapy, mobile health apps, self-management, usability evaluation, wearable activity trackers, think-aloud, heuristic evaluation, cognitive walkthrough, end-user feedback
MeSH terms
Self-Management, Assessment of Medication Adherence, Adult, Aged, Female, HIV, Humans, Male, Middle Aged, HIV Infections, Drug Monitoring, Telemedicine, Medication Adherence, Mobile Applications

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