Key Features of Smart Medication Adherence Products: Updated Scoping Review
Bibliographic record
Abstract
BACKGROUND: Older adults often face challenges in self-managing their medication owing to physical and cognitive limitations, complex medication regimens, and packaging of medications. Emerging smart medication dispensing and adherence products (SMAPs) offer the options of automated dispensing, tracking medication intake in real time, and reminders and notifications. A 2021 review identified 51 SMAPs owing to the rapid influx of digital technology; an update to this review is required. OBJECTIVE: This review aims to identify new products and summarize and compare the key features of SMAPs. METHODS: Gray and published literature and videos were searched using Google, YouTube, PubMed, Embase, and Scopus. The first 10 pages of Google and the first 100 results of YouTube were screened using 4 and 5 keyword searches, respectively. SMAPs were included if they were able to store and allowed for the dispensation of medications, tracked real-time medication intake data, and could automatically analyze data. Products were excluded if they were stand-alone software applications, not marketed in English, not for in-home use, or only used in clinical trials. In total, 5 researchers independently screened and extracted the data. RESULTS: This review identified 114 SMAPs, including 80 (70.2%) marketed and 34 (29.8%) prototypes, grouped into 15 types. Among the marketed products, 68% (54/80) were available for consumer purchase. Of these products, 26% (14/54) were available worldwide and 78% (42/54) were available in North America. There was variability in the hardware, software, data collection and management features, and cost of the products. Examples of hardware features include battery life, medication storage capacity, availability of types and number of alarms, locking features, and additional technology required for use of the product, whereas software features included reminder and notification capabilities and availability of manufacturer support. Data capture methods included the availability of sensors to record the use of the product and data-syncing capabilities with cloud storage with short-range communications. Data were accessible to users via mobile apps or web-based portals. Some SMAPs provided data security assurance with secure log-ins (use of personal identification numbers or facial recognition), whereas other SMAPs provided data through registered email addresses. Although some SMAPs were available at set prices or free of cost to end users, the cost of other products varied based on availability, shipping fees, and subscription fees. CONCLUSIONS: An expanding market for SMAPs with features specific to at-home patient use is emerging. Health care professionals can use these features to select and suggest products that meet their patients' unique requirements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".