Medication Adherence Technologies: A Classification Taxonomy Based on Features

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.

According to the United Nation’s World Population Prospects 2022, the global population aged 65 years and over is expected to increase from 10% in 2022 to 16% by 2050.^1,2 This demographic shift demands a focus on the health care needs of the aging population.³ Aging is associated with decline in functions such as swallowing, motor skills, vision, hand–eye coordination, hearing, cognition, health literacy, and self-care, along with an increasing prevalence of chronic conditions.4, 5, 6 The 2017-2018 Canadian Community Health Survey showed that more than one-third of older adults experience multimorbidity (coexistence of 2 or more chronic conditions) with the prevalence rising with age.⁷ Over one-third of people aged 65 years and older have at least 2 chronic conditions, and this number increases to nearly half among those 85 years and older.⁷

High prevalence of multimorbidity, along with age-related changes, contribute to functional limitations and associated disabilities, complex medication regimens, and a high risk of polypharmacy in older adults.8, 9, 10, 11 Polypharmacy, often defined as taking 5 or more medications simultaneously, is common among those with multiple chronic conditions.^12,13 A number of adverse health outcomes are associated with it, including a higher mortality rate, falls, drug interactions, nonadherence, hospitalization, and higher health care costs.^13,14 Furthermore, multimorbidity and related functional disabilities, complex medication regimens, polypharmacy, and age-related changes can negatively affect an older adult’s ability to manage their medications.15, 16, 17, 18 The challenges presented by these factors also increase the risk of medication errors, adverse drug reactions, hospitalization, and medication nonadherence.^19,20 Medication nonadherence, when a patient fails to take a prescribed medication or follow the health care provider’s instructions for its administration, can often result from challenges in managing medications owing to a lack of ability.²¹ Medication nonadherence not only prevents the achievement of treatment goals, quality of life, and productivity, but also elevates health care costs due to avoidable hospitalizations, with the added risk of mortality.22, 23, 24, 25, 26

Several strategies have been designed to address medication taking and nonadherence in older adults.^25,27 Among the various solutions, assistive medication adherence technologies are emerging as one of the key interventions to address medication taking issues and enhance adherence in older adults, offering potential improvements in treatment outcomes.28, 29, 30 Advancements in data processing, electronics, and wireless communication have led to a rapid increase in available medication adherence technologies in recent years.28, 29, 30 A systematic search conducted in 2016 identified 80 electronic adherence devices available in Canada, while another scoping review published in 2023 identified 114 smart products (defined as those with both connectivity and automaticity) designed to improve medication adherence.^31,32 These devices assist with medication self-management by organizing and dispensing medications as well as by providing reminders for taking medications.³³

Various cognitive, sensory, and motor impairments associated with aging can significantly affect how older people interact with medication organizing and dispensing devices.34, 35, 36 User testing with older individuals is a vital step to ensure these devices are designed to meet the diverse needs of older individuals and to identify potential challenges resulting from aging-related limitations.^36,37 Usability testing is the process of understanding how a product can be used to achieve a desired goal, while taking the user’s needs and capacity into consideration.²⁸ Because various medication adherence technologies available on the market differ significantly in their design and features, their usability can vary from individual to individual.³⁷ An inefficient or ineffective medication adherence product that is not user-friendly, complicated to use, socially unacceptable, or has limited learnability may negatively affect adherence rather than improve it.³⁸ For example, a person who is visually impaired may not be able to comprehend information from a pill bottle that produces a visual alert to take medication, but a device that produces an audio alert may be more effective. From the perspective of behavioral science, the selection of a medication adherence technologies has to take into account both the product features and the characteristics of the user, such as the users’ capabilities as well as their limitations.^34,39

Classification, a key cognitive process, involves organizing objects based on their characteristics.⁴⁰ Taxonomy, a type of classification, is crucial in both research and practical applications.^41,42 It structures concepts and their interrelations, aiding in understanding diverse research outcomes.40, 41, 42, 43 This method of classification helps in describing, comprehending, and analyzing relevant objects.^41,42,44,45 In case of medication adherence technologies, although numerous products with diverse and unique design, function, and features are being developed and marketed, there is no widely accepted definition of medication adherence technology; electronic reminder systems, electronic monitoring systems, digital health, wearable sensors, and ingestible sensors have been included in technological interventions for adherence.46, 47, 48 Moreover, medication organization and dispensing products are often identified as automated dispensers, pill boxes, smart vials or vial caps, blister packaging, or storage boxes, but no system of classification by which these devices may be differentiated for use by older adults has been developed yet.^29,31

Applying the principles of classification and taxonomy to medication adherence technologies means systematically organizing these devices based on a range of features and characteristics. This structured approach is particularly beneficial when considering the specific needs of different user groups, such as older adults. For instance, older adults might benefit more from devices with simple interfaces, large buttons, or clear auditory reminders, considering potential challenges such as reduced vision, hearing, or manual dexterity.^49,50 Moreover, this classification system could aid in focused usability testing, allowing for a more in-depth understanding of how these devices function in practical settings. The combination of systematic classification and practical testing ensures the selection of technologies, which are not only theoretically suitable but also user-friendly and effective in everyday use. Therefore, the objective of this project was to develop a classification system for medication adherence technologies based on an inventory of characteristics and features of existing technology.

Participants and Methods

Study Procedure

To develop a classification system for medication adherence technologies we used a 3-stage approach: taxonomy development, taxonomy validation, and taxonomy evaluation.⁵⁰ The study was conducted from February1, 2023 to July 31, 2024.

Stage 1: Taxonomy Development

A taxonomy is a classification system that groups similar objects within a domain based on distinctive characteristics and provides a set of decision rules.^40,42,43 The taxonomy development method by Nickerson et al^41,42 was used in this study. It is a formal, systematic, and straightforward method and has been used successfully for building taxonomies in health information technologies and patient portals.^38,50,51 Various steps involved in this method are given in Supplemental Appendix 1 (available online at https://www.mcpdigitalhealth.org/). Nickerson et al⁴² has defined taxonomy as “a set of n dimensions each consisting of mutually exclusive and collectively exhaustive characteristics such that each object under consideration has only one set of characteristics for each dimension”⁴²

Following the methodology, a core research team (4 graduate students, 4 undergraduate students, and 1 researcher) and a wider research team (2 older adult knowledge users, 1 pharmacist, 1 physical therapist, 1 physician, and 2 engineers) conducted a series of steps. A detailed description of the steps followed in stage 1 is provided in Table 1, and the ending conditions established to conclude the taxonomy development are given in Table 2.

Table 1.

Detailed Description of Steps Used in Stage 1—Taxonomy Development

Steps	Description
Step 1: Define medication adherence technologies	Because there is no well-accepted definition for medication adherence technologies, the core research team defined medication adherence technologies before initiating the steps involved in the taxonomy development method. The core research team defined medication adherence technology as “any device, software or equipment that can support patients in organizing and taking their oral medications as agreed upon by their provider.”
Step 2: Determine who are the users of the taxonomy	The method by Nickerson et al41,42 method requires a precise definition of the end users of the taxonomy. Hence, the core research team determined the intended users of our taxonomy as the patient, caregiver, and health care provider, defined as follows: •Patient—The person living with an acute, chronic, or advanced illness.52 •Caregiver—A person who gives care to people who need help taking care of themselves.53 •Health care provider—A health professional whom a person sees or talks to when they need care or advice about their health. This can include a family doctor or general practitioner, pharmacist, medical specialist, and nurse practitioner.54
Step 3: Determine the meta-characteristic	Meta-characteristic is the most comprehensive characteristic that will serve as the basis for the choice of characteristics in the taxonomy.42 It is derived from the purpose and target users of the taxonomy, and all dimensions and characteristics must be a logical consequence of the meta-characteristic.42,55 The core research team determined our meta-characteristics as the definition of medication adherence technologies, “any device, software or equipment that can support patients in organizing and taking their oral medications as agreed upon by their provider.”
Step 4: Determine ending conditions	Considering that the method of developing the taxonomy is iterative, it requires both subjective and objective conditions to determine the end of the process. One of the most important objective conditions for a taxonomy is that it must be based on dimensions with mutually exclusive and collectively exhaustive characteristics.42 The subjective conditions are a set of minimum requirements that must be met in order to terminate the development process.42 The ending conditions were established based on the objective and subjective criteria outlined in the taxonomy development method by Nickerson et al41,42 (Table 2).
Step 5: Creation of taxonomy	The core research team used a combination of 2 approaches in creating the classification system: the empirical-to-conceptual and conceptual-to-empirical approaches. The empirical-to-conceptual approach is suitable when the researcher has limited domain understanding but ample data about the objects.42 In this case, the team applied this approach to identify common characteristics of 20 medication adherence technologies in our laboratory.55 This approach began with identifying a subset of well-documented technologies and selecting their common characteristics, which are inherently linked to a defined meta-characteristic, ensuring that these characteristics distinctly differentiate the technologies. Conversely, the conceptual-to-empirical approach is preferred when little data are available, but the researcher possesses significant domain understanding.42 We conducted a scoping review to understand the additional characteristics and features of devices available on the market.32 This review identified 114 Smart Medication Adherence Products, noting a wide range in their hardware, software, data management features, and cost. This review helped us in clarifying the dimensions of our taxonomy based on our theoretical understanding of what features are crucial, followed by empirical validation of these conceptual dimensions against real-world data. Subsequently, we classified characteristics identified from both approaches into dimensions and subdimensions to create the taxonomy. This dual approach facilitated the creation of a comprehensive taxonomy, integrating both practical and conceptual aspects of medication adherence technologies.42

Table 2.

Objective and Subjective Ending Conditions⁴²

Ending conditions
Objective ending conditions •All objects or a representative sample of objects have been examined •All medication adherence technologies fall into 1 characteristic within a dimension •No new dimensions or characteristics were added in the last iteration •No dimensions or characteristics were merged or split in the last iteration •Every dimension is unique and not repeated (ie, there is no dimension duplication) •Every characteristic is unique within its dimension (ie, there is no characteristic duplication within a dimension) •Each cell (combination of characteristics) is unique and is not repeated (ie, there is no cell duplication)
Subjective ending conditions •Concise—ensure that the number of dimensions is sufficient for the taxonomy to be meaningful without becoming too large or over complex •Robust—ensure that the dimensions and characteristics of the objects are sufficient to differentiate them from each other and derive meaningful insights about sample objects by analyzing their characteristics •Comprehensive—verify that all objects within the domain of interest or sample objects within the domain are classified and confirm the identification of all dimensions of objects of interest •Extendible—ensure that the addition of a new dimension or a new characteristic to an existing dimension is an easy process •Explanatory—ensure that dimensions and characteristics give adequate explanation about an object

From Eur J Inf Syst⁴² with permission.

Stage 2: Taxonomy Validation

To validate the developed taxonomy of medication adherence technologies, we used the Delphi consensus method, involving a panel of 7 field experts (wider research team).⁵⁶ This approach is renowned for its effectiveness in achieving consensus through structured communication.^56,57 Participants in our Delphi panel were invited to assess the identified characteristics, dimensions, and subdimensions through a survey using a 4-point Likert scale: strongly disagree, disagree, agree, and strongly agree.

Survey items that achieved more than 70% agreement (agree and strongly agree) were retained within the taxonomy. Those receiving more than 70% disagreement (strongly disagree and disagree) were either modified or removed, following a thorough review and consensus among the expert panel. For items that did not meet these agreement or disagreement thresholds, a second round of surveys was conducted. In addition to rating the items, participants were given the opportunity to provide suggestions and feedback on how to improve the taxonomy, ensuring that all relevant perspectives were considered. The survey data were collected and analyzed using the Qualtrics XM Platform (Qualtrics), ensuring robust data management and analysis.

Moreover, we also focused on validating the appropriateness of each dimension and characteristic that had been identified in the previous step. As part of this verification process, 20 samples of medication adherence technologies in our laboratory were categorized based on the predefined dimensions and characteristics. Members of the core research team met multiple times to thoroughly examine and discuss the attributes and features of each device. These sessions allowed the team to apply the taxonomy directly, ensuring that each device fit into a specific dimension within our framework. During these meetings, team members discussed the features of each device, collaboratively determining their adherence to the taxonomy’s dimensions and characteristics.

Stage 3: Taxonomy Evaluation

The first 2 stages of the study led to a preliminary taxonomy, which described the dimensions and characteristics of medication adherence devices. In the third stage, to evaluate the appropriateness of the taxonomy in a real-world setting, we mapped the classification to codes identified from a qualitative study conducted among older adults. This qualitative study involved older adults aged 60 years and above, each experiencing various physical, cognitive, sensory, motivational, and environmental impairments. Participants engaged in usability testing of several smart and electronic medication adherence products, followed by 1-on-1 interviews where they provided feedback on different features of the products based on their specific needs. The interview data were analyzed using thematic content analysis, and the resulting codes were mapped to the developed classification system. This process allowed us to ensure that the taxonomy accurately reflects the real-world usability concerns and experiences of older adults, further validating its practical applicability.

Ethical Considerations

This study was conducted following ethical guidelines and received approval from the University of Waterloo Research Ethics Board (REB #44222). All participants provided informed consent before data collection.

Results

The results of this study outline the iterative process conducted to develop, validate, and evaluate a comprehensive taxonomy for medication adherence technologies, as detailed in Tables 3 and 4. Supplemental Appendix 2 (available online at https://www.mcpdigitalhealth.org/) presents the classification of sample medication adherence devices based on the developed taxonomy, which was done as part of the taxonomy validation process.

Table 3.

Iteration Results of Taxonomy Development, Validation, and Evaluation Stages

Stage	Iteration	Description
Stage 1: taxonomy development	First iteration	The initial taxonomy, created by the core research team after examining 20 products available in our laboratory, comprised 10 dimensions and multiple characteristics. This was based solely on the empirical-to-conceptual approach. However, ending conditions were not met, prompting a second iteration.
	Second iteration	Recognizing the unmet ending conditions, the core research team revised the classification, renaming the dimension Retrieval/Medication Access to Operation Method and introducing a new dimension, Data Collection Method with characteristics of Manual and Automatic. These changes did not meet the ending conditions. Subsequently, a conceptual-to-empirical approach was adopted, and a scoping review was conducted, whose results have been published separately.32
	Third iteration	Building on findings from the conceptual-to-empirical approach, the core research team added a new subdimension, Portability, under the dimension physical features. We also refined characteristics such as the spacing between buttons, classified displays as product display, Setting display, and Electronic display and introduced a new dimension, Ease of use, which merged the dimensions Instruction, Operation method, and Customization. Another new dimension, Data collection and Management, combining the dimensions of Data collection and Tracking, was created.
Stage 2: taxonomy validation	Fourth iteration	During the validation process, we used the Delphi consensus method and sent out a Qualtrics survey to the wider research team, consisting of 7 field experts. Five of the wider research team members responded to the survey, with the first round showing <70% agreement on certain subdimensions, including power source (within Dimension 1: Physical features), instructions (within Dimension 4: Operations), and connectivity (within Dimension 5: Connectivity) (Table 4). Based on feedback, the following modifications were made: •Removing weight (from Dimension 1: Physical features) •Reclassifying display–nonelectronic display to product display and settings display •Introducing a new dimension Operations •Adding a subdimension Reporting of Medication Intake
	Fifth iteration	We sent the second survey to the same wider research team, and at this round, only 4 members from the wider research team responded. The results showed >70% agreement on all changes (Table 4). However, minor suggestions were made, leading to further revisions, including changing the dimension display to include Transmissive, Emissive, and Reflective display types, and the addition of Screen Size.
	Sixth iteration	To further validate the taxonomy, the 20 sample products were classified by the core research team based on the developed taxonomy (Supplemental Appendix 2). No new characteristics were found, and there were no alterations in dimensions required. By classifying real medication adherence devices, iteration 6 evaluated each characteristic’s appropriateness. Through this process, we were able to prove that real medication adherence devices met all specified characteristics. It was also demonstrated that all devices examined fit into a single characteristic within a subdimension, and no device fell into multiple characteristics.
Stage 3: evaluation of taxonomy	Seventh iteration	To evaluate the taxonomy, the core research team mapped the developed classification with codes identified from the qualitative study. During this process, the team discovered an additional subdimension, Loading, which was not previously included in the taxonomy. This subdimension was placed under the sixth dimension, operations. An email was sent out to the wider research team, 6 members responded, and there was 100% agreement on the inclusion of this new subdimension. As a result, Loading was added to the taxonomy (Table 4).
	Eighth iteration	After incorporating all feedback from the panel and conducting 1 more revision, the core research team reviewed whether the taxonomy met the predefined ending conditions as listed in Table 2. On confirmation that all ending conditions were satisfied, the taxonomy development process was concluded. In the final version, no dimension or characteristic was separated, combined, or introduced.

Table 4.

Percentage Agreement From The Surveys Conducted

First consensus survey
Dimension	Percentage (%) agreement (N=5)
Dimension 1: Physical features
Shape	80 (n = 5)
Portability	100 (n =5)
Size	100 (n=5)
Button	80 (n=4)
Power source	60 (n=3)
Compartment	100 (n=5)
Locking feature	80 (n=4)
Dimension 2: Display
Nonelectronic	80 (n=4)
Electronic	80 (n=4)
Dimension 3: System notification
Internal alert	80 (n=4)
External alert	80 (n=4)
Dimension 4: Operations
No. of steps	100 (n=5)
Instructions	60 (n=3)
Dispensing	80 (n=4)
Access	80 (n=4)
Dimension 5: Connectivity
Standalone	100 (n=5)
Connected	60 (n=3)
Dimension 6: Data collection and management
Method	75 (n=3)a
Monitoring	100 (n=5)
Reporting	100 (n=5)
Data accessibility	100 (n=5)
Dimension 7: Integration
Device	75 (n=3)a
Support	60 (n=3)

Second consensus survey
Changes	Percentage agreement (N=4)
Added new subdimensions
Holdable—Physical features	75 (n=3)
Added more characteristics
Electronic—Display	100 (n=4)
Method—Data collection and management	100 (n=4)
Data accessibility—Data collection and management	100 (n=4)
Modified subdimension
Internal alert—System notification	100 (n=4)
External alert—System notification	75 (n=3)
Instructions—Operations	100 (n=4)
Connected—Connectivity	100 (n=4)
Power source—Physical features	75 (n=3)
Support—Integration	75 (n=3)

Third consensus survey
Changes	Percentage agreement (N=6)
Added new subdimensions
Loading	100 (n=6)

^aPercentage agreements were calculated based on the number of participants who responded to each item. In the first consensus survey, 4 out of 5 participants provided a response to items relating to method and device.

The final taxonomy for medication adherence devices includes 7 key dimensions: Physical Features, Display, Connectivity, System Alert, Data Collection and Management, Operations, and Integration, each with its own subdimensions and characteristics (Figure). Under Physical Features, we consider aspects such as nonslip elements, portability, shape, and locking mechanisms. The Display dimension distinguishes between electronic and nonelectronic types, whereas Connectivity differentiates standalone from connected devices. Data Collection and Management explores methods of monitoring, reporting, and accessibility. System Alert categorizes various alert types, and Operations examines instructions, dispensing, and medication access methods. Finally, the Integration dimension considers the way in which these devices integrate with external systems or support networks, including pharmacists and caregivers. Supplemental Appendix 3 (available online at https://www.mcpdigitalhealth.org/) provides a detailed description of all dimensions and characteristics.

Figure.

Final classification system for medication adherence technologies. GPS, global positioning system; IT, information technology.

Discussion

In this study, we successfully developed, to our knowledge, the first classification system for medication adherence technologies, providing a structured framework based on the characteristics and features of existing technologies. The classification system introduced in this study fills a critical gap in the literature by providing a structured way to understand and categorize the multitude of medication adherence devices on the market.

This classification system was developed using a rigorous methodology, using the taxonomy development method by Nickerson et al.⁴² This systematic and formal approach emphasized precision in defining the end users, meta-characteristics, and ending conditions. A similar method was adopted in developing taxonomies for complex emerging technologies, mobile applications, and patient portals.^41,50,58, 59 This comprehensive methodology facilitated an iterative process that combined empirical-to-conceptual and conceptual-to-empirical approaches to ensure the inclusion of relevant features and characteristics. Nickerson et al⁴¹ also used this dual approach in creating a taxonomy for mobile applications, combining existing data with theoretical frameworks.⁴¹ Furthermore, the validation stage of our taxonomy involved the participation of a panel of experts using the Delphi consensus method.⁵⁶ This approach adds a layer of validity to the taxonomy, ensuring alignment with expert opinions in the field. The Delphi approach is widely recognized and has been effectively used in various domains, including program planning, health care interventions, policy planning, and medication adherence technologies.^56,57

In contrast to taxonomies designed for different purposes, our taxonomy, tailored for medication adherence technologies, offers a distinctive approach.^41,50,58, 59, 60 It comprises 7 dimensions, 25 subdimensions, and 320 characteristics, in contrast from other taxonomies. For instance, the taxonomy for patient portals includes 20 dimensions and 49 characteristics, whereas a mobile application taxonomy features 7 dimensions and 15 characteristics.^41,50 The extensive inclusion of subdimensions and characteristics in our taxonomy is justified by the complex and diverse aspects of medication adherence technologies, which require a more comprehensive framework to capture their varying functionalities and user-specific needs. Diverging from the taxonomy for smart health care technologies by Chaudhary et al,⁵⁹ which categorizes technologies based on application areas, our taxonomy specifically categorizes medication adherence technologies based on their characteristics and features. It highlights user needs and detailed features crucial for user interaction and technology adaptability, providing a comprehensive and user-centric framework tailored to this specific domain.

Medication adherence technologies are typically categorized based on their type, such as electronic pill bottles, smart pill dispensers, mobile applications, or wearable devices.^29,31 Although this classification provides a broad overview of the technology’s form, it does not delve into its usability aspects, and previous research has found that these technologies frequently fail to consider the diverse needs and constraints of specific user groups, such as older adults who may have cognitive, sensory, or motor impairments.²⁹ Our study addressed these issues by introducing a taxonomy that comprises multiple dimensions and covers a wide range of characteristics and features of medication adherence technologies. This multidimensional approach results in a classification system that is more comprehensive than existing classifications.

This classification system for medication adherence technology can be a valuable tool in various contexts, especially when considering the specific needs and limitations of older adults. Health care providers, caregivers, and patients can use this classification system to compare different medication adherence technologies. For example, they can assess which devices have features such as large buttons and clear auditory reminders for individuals with reduced vision or hearing. They can also compare devices based on the availability of Wi-Fi connectivity, which might be crucial for remote monitoring, adherence tracking, and data synchronization. Additionally, the classification system allows users and health care providers to identify devices that align with the specific needs of a unique user. For instance, if an older adult has limitations in manual dexterity, they can search for devices categorized under the Physical Features dimension that offer easy-to-hold options. Alternatively, if a person is managing multiple medications, they can look for devices that have a greater number of compartments with larger compartment capacity, which is covered under the compartment subdimension. This personalized approach to technology selection aligns with the principles of patient-centered care, where the individual’s needs and preferences play a key role in the decision-making process.

Usability testing guided by taxonomy can also provide a systematic approach for researchers and developers to evaluate medication adherence technologies among older adults. Test scenarios can be created that align with the taxonomy’s dimensions, allowing them to assess crucial aspects such as user interface navigation, clarity of reminders, adaptability to different dosing regimens, and the impact of connectivity features like Wi-Fi. By doing so, they can uncover potential usability challenges and areas for improvement specific to older users, ensuring that the technology is tailored to their unique needs ultimately leading to more effective and user-friendly solutions for medication management in this demographic. Furthermore, as new medication adherence technologies continue to emerge, this classification system can serve as a framework for evaluating and categorizing these innovations. It enables researchers to analyze how these new technologies fit within the existing technological environment and whether they address the unique challenges faced by older adults. Health care providers can engage in patient-centered discussions about medication management by using the classification system. They can involve older adults and their caregivers in the decision-making process, taking into account their preferences, capabilities, and limitations, and selecting the most suitable technology accordingly. With the rapid expansion of medication adherence technologies, a standardized taxonomy based on characteristics and features becomes increasingly relevant for both addressing the current gap as well as setting the stage for future advancements in the field.

Although our study contributes to a better understanding and categorization of medication adherence technologies, it is not without limitations. The inclusion of a larger group of subject matter experts at various stages may have improved the quality of the study by providing even more complete and in-depth information. Moreover, as technology continues to evolve and new devices are introduced, the classification system may require periodic updating. Additionally, the study focused on medication adherence technologies for older adults, and further study should be conducted to determine whether the classification system can be applied to other populations or age groups. Although we aimed to be comprehensive in capturing relevant features, some features, such as system lifetime (if the device is battery powered), were identified postdevelopment and were not incorporated into the original classification system. Future revisions should consider the inclusion of such emerging features.

Conclusion

This study provides the first comprehensive classification system for medication adherence technologies for older adults, filling a significant gap in literature. It provides a structured framework for categorizing and evaluating diverse technologies based on the unique challenges faced by the aging population. By combining the taxonomy development method with Delphi consensus method, this classification ensures precision and validity. With its multidimensional structure that encompasses physical features, display characteristics, system alerts, operations, connectivity, data management, and integration with devices and other supports, it provides a valuable tool for assessing usability tailored to the unique needs of older adults. Apart from its immediate applications, this taxonomy can also serve as a benchmark for objective comparisons of evolving technologies and support informed decision-making among health care stakeholders.

Potential Competing Interests

Dr Patel reports grants from National Research Council (NRC), Canadian Institutes of Health Research, Jones Healthcare Group, and Catalyst Health; travel support from National Research Council (NRC) and Canadian Institutes of Health Research; and contribution of medical adherence technology from Jones Healthcare Group and Catalyst Health. The other authors report no competing interests.

Ethics Statement

This study was conducted following ethical guidelines and received approval from the University of Waterloo Research Ethics Board (REB #44222). All participants provided informed consent before data collection.