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.

Frailty among older adults heightens their risk of negative health outcomes, and medication use plays a major role in this increased vulnerability. Various aspects of medication use elevate the risk of poor outcomes in individuals with frailty. The current scoping review was designed to explore medication use in older adults with frailty in primary care, focusing on the prevalence of potentially inappropriate medications (PIMs), polypharmacy, medication adherence, and their role in contributing to adverse drug events. This scoping review was conducted using the Arksey and O’Malley, supplemented by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Extension for Scoping Reviews (PRISMA-ScR) guidelines. A search of the literature was conducted from inception to November 2023 in Ovid EMBASE, PubMed (MEDLINE), Scopus, EBSCOhost CINAHL, and Ovid International Pharmaceutical Abstracts. Studies which met the eligibility criteria included older adults with frailty (≥65 years) living at home, defined frailty criteria, and assessment of medication use. Out of the 4726 studies screened, 223 were included, conducted across 39 countries. Frailty prevalence varied widely from 0.9% to 89.2%. Polypharmacy (5–9 medications) and hyper-polypharmacy (≥10 medications) were notably more common among individuals with frailty, with polypharmacy rates ranging from 1.3% to 96.4%. Twelve studies reported PIM prevalence among individuals with varying levels of frailty, ranging from 2.4% to 95.9%. This scoping review highlights the challenges and complexities involved in understanding the relationship between medication use and frailty in older adults.

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.

Background: Medication adherence technologies (MATs) offer innovative solutions to support older adults in managing complex medication regimens, yet usability challenges can prevent their successful use. Older adults often face cognitive, physical, sensory, motivational, and environmental barriers, which can influence how they interact with these devices. Therefore, performance-based usability testing is essential for identifying usability issues. Objective: This study aimed to evaluate the performance-based usability and user experience of 13 MATs among older adults with diverse capabilities. Methods: A prospective mixed methods design was used, with 96 participants aged 60 years and older testing smart and electronic MATs using cognitive walkthroughs and predefined usability tasks without formal training to simulate first-use conditions. This manuscript focuses specifically on the performance-based usability metrics collected as part of the mixed methods study. Validated tools, including the Self-Medication Assessment Tool (SMAT), Daily Living Tasks Dependent on Vision (DLTV), Whisper Test, Self-Efficacy for Appropriate Medication Use Scale (SEAMS), and Martin and Park Environmental Demands (MPED) Questionnaire, were used to assess individual barriers. Poisson generalized estimating equations (GEE) was used to determine predictors of unassisted task success and error rates while accounting for repeated observations. Results: A total of 96 participants (mean age 75.1, SD 7.7, range 61‐95 y) were included, with 37.5% (36/96) male and 62.5% (60/96) female. Poisson GEE models identified significant predictors of unassisted task success rate, including cognitive score ( P <.001), physical score ( P <.001), vision score ( P <.001), motivational score ( P <.001), and lower environmental busyness ( P =.049). Female sex was associated with a 9% lower success rate ( P =.002). Predictors of total error rate included age ( P =.02), sex ( P ≤.001), physical score ( P ≤.001), vision score ( P =.004), DLTV vision score ( P =.01), MPED routine score (environmental; P =.05), and interaction between physical score and sex ( P =.004). Conclusions: Both device design and user characteristics strongly influence the usability of MATs. Performance-based evaluations provide actionable findings for user-centered design and selection, guiding manufacturers and health care providers toward developing and choosing accessible technologies that could improve medication adherence among older adults.

Background: Older adults frequently experience cognitive, physical, sensory, motivational, and environmental barriers that affect medication management. Medication adherence technologies (MATs) can support adherence, but their usability varies widely depending on individual abilities and device features. Prior research has largely focused on overall adherence or user experience, providing limited insight into feature-level usability challenges. Objective: The aim of the study is to develop and internally validate a personalized predictive model to predict the success of MAT subtasks for older adults with diverse cognitive, physical, sensory, motivational, and environmental capabilities. Methods: A mixed methods approach was used, incorporating the assessment of impairments using various standardized questionnaires, measurement of usability metrics through cognitive walkthroughs, and one-on-one semistructured interviews. For this study, we used “subtasks” as the representative of features of the devices. A subtask is a discrete, individual action that forms part of a larger task, specifically designed to achieve a step in the overall process. Participants tested between 1 and 7 devices from a selection of 13 devices. The proportion of subtask success was taken as the outcome measure. Predictors included demographic, clinical, cognitive, physical, sensory, motivational, and environmental characteristics. Personalized predictive modeling using cosine similarity and generalized linear models were compared with nonpersonalized and naive models. Model performance was evaluated using mean square error (MSE) through cross-validation and held-out validation. Results: A total of 117 participants (mean age 74.6, SD 7.9 years) were recruited, including 96 participants for usability testing and 21 for the validation, all varying in cognitive, physical, sensory, motivational, and environmental abilities. Both personalized ( m =0.25) and nonpersonalized models ( m =1.0) outperformed naive predictions ( m =1.21), demonstrating that subtask-level success can be predicted using routinely measurable demographic and functional characteristics. During cross-validation, personalized models achieved optimal performance at a matching proportion of m =0.25, with MSEs lower than those observed at higher matching levels, although differences compared with nonpersonalized models were not statistically significant (Self-Medication Assessment Tool [SMAT]: P =.50; Daily Living Tasks Dependent on Vision [DLTV]: P =.43). In the held-out validation cohort, personalized models achieved MSEs of 0.89 (SMAT-based) and 1.16 (DLTV-based) at m=0.20, whereas nonpersonalized models demonstrated better performance with MSEs of 0.726 (SMAT-based) and 0.815 (DLTV-based). Models incorporating performance-based vision measures (SMAT-based) consistently outperformed those using self-reported vision scores (DLTV-based) across both personalized and nonpersonalized settings. Conclusions: This study demonstrates the feasibility of predicting subtask success of MATs in older adults. While personalization showed limited added benefit in this dataset, the subtask-focused model provides clinically meaningful insights to support evidence-informed selection of medication technologies, reduce usability-related medication errors, and improve adherence outcomes.