Medication Adherence Technologies: A Classification Taxonomy Based on Features
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.
Authors
- Tejal Patel (ORCID: https://orcid.org/0000-0003-3002-8306)
- Jasdeep Gill
- Ryan H. Griffin (ORCID: https://orcid.org/0000-0003-4173-023X)
- Kirk Patterson
- SooMin Park (ORCID: https://orcid.org/0009-0003-5420-616X)
- Feng Chang (ORCID: https://orcid.org/0000-0003-4661-6911)
- Sara J. T. Guilcher (ORCID: https://orcid.org/0000-0002-9552-9139)
- Catherine M. Burns (ORCID: https://orcid.org/0000-0002-6182-958X)
- Annette McKinnon (ORCID: https://orcid.org/0000-0002-7887-3122)
- Sadaf Faisal (ORCID: https://orcid.org/0000-0002-4090-2194)
- Bincy Baby (ORCID: https://orcid.org/0009-0004-6778-4708)
- Ghada Elba
- Linda Lee (ORCID: https://orcid.org/0000-0001-9774-9568)
Institutions
- University of Waterloo (CA)
- University of Toronto (CA)
- Canadian Pharmacists Association (CA)
- Canadian Patient Safety Institute (CA)
- Research Institute for Aging (CA)
- National Research Council Canada (CA)
- McMaster University (CA)
Publication Details
- Journal
- Mayo Clinic Proceedings Digital Health
- Published
- 2025-06-09
- DOI
- https://doi.org/10.1016/j.mcpdig.2025.100237
- Primary Topic
- Medication Adherence and Compliance
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Government of Canada
- Canadian Institutes of Health Research