JOURNAL ARTICLE

Usability evaluation and reporting for mobile health apps targeting patients with skin diseases: a systematic review.

  • Published In: Clinical & Experimental Dermatology, 2025, v. 50, n. 2. P. 387 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Kounidas, Georgios; Cleer, Iona; Harriss, Elinor; Harrison, Rachel; Matin, Rubeta N 3 of 3

Abstract

This article systematically reviews the usability evaluation and reporting of patient-facing mobile health (mHealth) smartphone apps targeting skin diseases. Analyzing nine qualitative studies covering apps for conditions such as skin cancer, sun protection, chronic pruritus, and cutaneous leishmaniasis, the review found that most apps were perceived as useful and easy to use, with common usability themes including ease of navigation, comprehensibility, security/privacy, data sharing, customizability, cost, and self-monitoring capabilities. The studies primarily employed usability questionnaires and semistructured interviews but varied in methodology and sample size, often limited to single centers and younger, smartphone-competent users. The review highlights a significant gap between the rapid proliferation of dermatology apps and the limited rigorous usability evaluation, emphasizing the need for user-centred design, comprehensive usability assessment, and inclusion of diverse populations to optimize long-term adoption and equitable access.

Additional Information

  • Source:Clinical & Experimental Dermatology. 2025/02, Vol. 50, Issue 2, p387
  • Document Type:Article
  • Subject Area:Complementary and Alternative Medicine
  • Publication Date:2025
  • ISSN:0307-6938
  • DOI:10.1093/ced/llae378
  • Accession Number:182905013
  • Copyright Statement:Copyright of Clinical & Experimental Dermatology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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