ArticleMayo Clinic proceedings. Digital health2025
Medication Adherence Technologies: A Classification Taxonomy Based on Features.
Article in Mayo Clinic proceedings. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Development and Validation of Digital Decision Aids to Support Medication Adherence in Older Adults: A Scoping Review.Pharmacy (Basel, Switzerland) · 2026Review
- Performance-Based Usability of Medication Adherence Technologies Among Older Adults With Diverse Capabilities: Quantitative Study.JMIR aging · 2026Article
- Article
- Older adults' preferences for features of medication adherence technologies: a preference elicitation study.BMC digital health · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To develop a comprehensive classification system for medication adherence technologies based on an inventory of characteristics and features of existing technology. Participants and Methods: Using a 3-stage approach methodology-development, validation, and evaluation, the study adopted the taxonomy development method and was conducted from February 1, 2023 to July 31, 2024. In the development stage, medication adherence technologies were defined, end users were identified, and a meta-characteristic was determined; using both empirical-to-conceptual and conceptual-to-empirical approaches, dimensions and characteristics were identified. The taxonomy was validated through the Delphi consensus approach and classifying 20 sample medication adherence technologies and evaluated by mapping to codes identified from a qualitative study. Results: After undergoing 8 iterations, which included incorporating feedback from a Delphi consensus survey, the final taxonomy comprised 7 dimensions, 25 subdimensions, and 320 characteristics. These key dimensions include Physical Features, Display, Connectivity, System Alert, Data Collection and Management, Operations, and Integration. The taxonomy was considered complete and valuable once all preestablished ending conditions were met, and its applicability and comprehensiveness were verified by comparing various medication adherence technologies and mapping to codes identified from a qualitative study. Conclusion: This study successfully establishes the first comprehensive classification system for medication adherence technologies based on features, addressing a critical gap in literature. The taxonomy provides a structured framework for categorizing and evaluating technologies, supporting usability testing and the selection of appropriate devices tailored to the unique needs of older adults.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.