Skip to main content
NCBI home page
ACS Pharmacology & Translational Science logoLink to ACS Pharmacology & Translational Science
. 2024 Apr 26;7(5):1310–1319. doi: 10.1021/acsptsci.3c00377

Disease-Agnostic Electronic Adherence Aid for Subcutaneous at-Home and Self-Administration Devices—The Lowest Common Denominator Based on a Cross-Indication Survey

Beate Bittner †,*, Francisco Javier Munoz †, James Odonoghue ‡, Jose Manuel Ordonez §, Johannes Schmidt †, Kathrin Schmitt ‡, Katja Stassen ∥
PMCID: PMC11091979  PMID: 38751643

Abstract

graphic file with name pt3c00377_0002.jpg

The value of connected devices and health apps with features such as adherence trackers, dosing reminders, and remote communication tools for users and healthcare providers has been assessed to support home-based subcutaneous administration. A comprehensive survey was conducted with 605 participants, including users and caregivers, from eight countries. Medical conditions encompassed ankylosing spondylitis, asthma, cerebral palsy, cluster headaches, Crohn’s disease, hemophilia, lupus, migraine, multiple sclerosis, Parkinson’s disease, plaque psoriasis, psoriatic arthritis, rheumatoid arthritis, spasticity, spondyloarthritis, and ulcerative colitis. Utilizing a maximum difference scaling methodology, the survey gauged participant preferences regarding specific attributes and features of connected drug delivery devices. Irrespective of demographic factors like age, gender, nationality, or the specific medical condition, the device’s ability to verify a successful injection stood out as universally valued. The second and third most valued attributes pertained to temperature-related indicators or warnings. These features do not necessitate the use of a connected device and can be integrated into existing autoinjector platforms. The survey findings support the development of a universal adherence tool for at-home subcutaneous dosing, independent of a specific medical condition. This tool may be gradually improved with disease-specific features. Once established as a platform, manufacturers can launch any subcutaneous medication and later integrate real-world evidence for enhanced educational, treatment, and diagnostic capabilities. This approach is crucial for advancing connected adherence tools in decentralized healthcare, aligning with user and healthcare system needs while translating scientific innovation into practical solutions.

Keywords: connected devices, health apps, adherence, subcutaneous, decentralized care, self-administration

Addressing medication adherence remains a substantial healthcare challenge.1,2 The World Health Organization’s findings indicate that only 50% of chronically ill patients in developed countries adhere to prescribed medications.3 Despite numerous attempts, prevailing interventions often exhibit complexity and suboptimal efficacy.4 Nonadherence correlates with inferior health outcomes and increased healthcare costs for both individuals and society at large.5 Especially for biotherapeutics that are self-administered in the home setting, adherence with the dosing regimen can be impacted by drug administration errors, injection–site reactions, or by fear of injections and needle phobia.6,7

At the same time, healthcare systems are struggling with the rising costs of medical management while concurrently striving to enhance treatment outcomes and expand access to care.8 Notably, the administration of biotherapeutics through parenteral routes has been shown to contribute to healthcare institutional expenditures and increased resource demands.9 To address these challenges, there is a growing interest in shifting care away from controlled clinical settings, in order to reduce healthcare expenditures and cater to individual patient preferences and capabilities.10,11 The COVID-19 pandemic has further underscored the advantages of moving toward patient self-administration outside of a controlled clinical environment. In this regard, subcutaneous ready-to-use autoinjectors and pen devices have proven to be valuable tools for establishing flexible care settings.12

More recently, in response to the documented challenges associated with the ongoing use of subcutaneous self-administration at home,13,14 manufacturers have taken steps to address these issues by introducing electronic adherence aids5 either as a stand-alone app or in conjunction with an injection device.15 These telemedicine tools are equipped with a range of features, including dosing reminders, adherence tracking capabilities, educational tools for users, and patient outcome diaries. This technological integration is developed to not only enhance supervision and counseling within the home setting but also to foster efficient communication between users and their primary physician as well as between healthcare providers.16 Connected drug delivery devices provide an interface through which users can transmit dosing data in real-time directly from the device to a companion application.17

Supplying the connected drug delivery device and accompanying health apps right from the initial launch of novel medicinal products can pose a challenge for manufacturers. This challenge arises from the need for comprehensive assessments to determine the suitability of these tools for the specific user population.18 While it is recognized that certain connected features may be tailored to the condition being treated, there are others that may be universally valuable to all individuals who are willing to use such devices regardless of their specific medical condition.

An ongoing question in this context pertains to the necessity of electronic adherence measures and, if deemed necessary, whether they should be integrated with the device or offered as a separate stand-alone health app.

To enable the timely availability of an injection device equipped with the aforementioned connected features, this research was undertaken involving users of autoinjectors and pen devices as well as lay care partners, encompassing a diverse range of indications. The primary objective was to identify the basic tools necessary to meet the needs of a broader user base, irrespective of the patient’s specific medical condition. Such a versatile tool could then be applied across a manufacturer’s pipeline of devices and even extended beyond. Subsequently, the unique requirements of specific populations and indications were evaluated on a case-by-case basis and incorporated as necessary.

Results and Discussion

Participants

The study was conducted with a heterogeneous population from eight countries. Participants (n = 605) (n = 409 females; n = 190 males; n = 4 did not identify as male or female, and n = 2 preferred not to answer) could be divided into two age groups, below and above the median of 42 years (n = 308 between 18 and 41 years; n = 297 above 42 years). Across countries, the majority of participants were female (67.6%), except in China, where the majority was male (67.5%). The average age was 42.3.

On average, participants had been on their current subcutaneous treatment for 5.8 years. Forty-five percent received weekly or more frequent injections, and 52% used an auto- or pen-injector for their treatment.

Table 1 depicts the demographics of the study cohort in the total sample and per country.

Table 1. Demographics Including Participant Categories According to the Main Medical Condition for Which the Subcutaneous Treatment Was Given.

  total (n = 605) USA (n = 80) Germany (n = 75) France (n = 75) Italy (n = 74) Spain (n = 70) UK (n = 76) China (n = 80) Brazil (n = 75)
mean age in years 42.3 45.1 44.5 42.0 40.0 40.9 44.3 38.4 41.4
% female 67.6 82.5 73.3 65.3 64.9 78.6 71.1 32.5 74.7
mean time in years on current SC injection 5.8 8.5 7.3 5.6 6.1 6.2 5.2 1.7 5.8
% who inject weekly or more often 44.6 46.3 54.7 41.3 50.0 51.4 47.4 28.8 38.7
participants with neurological conditions: multiple sclerosis, Parkinson’s disease, cerebral palsy, spasticity, migraines, and cluster headaches 308 54 43 39 53 45 33 13 28
participants with rheumatoid arthritis and related conditions: psoriatic arthritis, ankylosing spondylitis, and lupus 373 70 54 38 33 37 51 44 46
participants with other conditions: hemophilia, asthma, and migraine 207 33 17 26 30 27 19 36 19
Open in a new tab

Three categories according to the type of administration included self-injection at home (n = 413), injection at home by a visiting healthcare professional or by a lay care partner (n = 69, 58 patients received their injection with the support of an HCP or a lay care partner and 11 of the participants supported a close one with their injection at home), and injection at a healthcare facility (i.e., hospital, office, or community center) (n = 123).

Table 1 shows the number of participants across the main medical conditions for which the subcutaneous treatment was given. Among the participants, 308 were undergoing treatment for neurological conditions, 373 were undergoing treatment for rheumatological conditions, and 207 were undergoing treatment for other conditions. Because participants with multiple comorbidities could fall into one or more categories according to the subgroups of rheumatology, neurology, and other conditions, there is a 30% overlap across these subgroups.

Main Results

General Questions

The study revealed that the current utilization of connected subcutaneous devices is low. Less than 2.6% of participants indicated that their current injection device is connected to another device, such as a smartphone, smartwatch, or tablet. More than half of the participants (52.9%) do not employ any tools, whether digital or not, to assist with at-home injections. About a quarter (24.8%) utilize medication reminder apps on their smartphone or smartwatch, which is the most prevalent tool. A smaller portion relies on phone call reminders (11.2%), while a similar proportion relies on SMS reminders (10.1%). In Italy, more than a third (35.1%) utilize phone call reminders, representing a statistically significant difference compared with most other countries. These trends remain consistent across different medical conditions. Table 2 shows current use and interest levels in using connected adherence tools.

Table 2. Current Use and Interest Level in Using Connected Adherence Tools (Percentage Respondents).

subgroup analysis by country total (n = 605) USA (n = 80) Germany (n = 75) France (n = 75) Italy (n = 74) Spain (n = 70) UK (n = 76) China (n = 80) Brazil (n = 75)
medication reminder app on a smartphone/smartwatch/tablet 24.8% 28.8% 25.3% 29.3% 16.2% 30.0% 31.6% 21.3% 16.0%
phone call reminders when it is time to take the medication/injection 11.2% 10.0% 17.3% 1.3% 35.1% 8.6% 2.6% 6.3% 9.3%
SMS reminders when it is time to take the medication/injection 10.1% 16.3% 2.7% 4.0% 6.8% 17.1% 6.6% 15.0% 12.0%
medication reminder received directly from the connected injection device 1.3% 1.3% 0.0% 2.7% 4.1% 1.4% 0.0% 0.0% 1.3%
not using any tools that support with the at-home injection 52.9% 52.5% 45.3% 54.7% 43.2% 45.7% 57.9% 62.5% 60.0%
subgroup analysis by condition total (n = 605) neurology (n = 265) rheumatology (n = 267) other (n = 103)
medication reminder app on a smartphone/smartwatch/tablet 24.8% 23.4% 21.7% 34.0%
phone call reminders when it is time to take the medication/injection 11.2% 13.2% 11.2% 7.8%
SMS reminders when it is time to take the medication/injection 10.1% 9.4% 9.4% 13.6%
medication reminder received directly from the connected injection device 1.3% 1.1% 1.5% 1.0%
not using any tools that support with the at-home injection 52.9% 52.8% 54.3% 49.5%
Open in a new tab

When participants who received injections outside of clinical settings (n = 482) were asked to describe unmet needs regarding subcutaneous injection devices used at home, the top three spontaneous responses were

  • 1.

    “nothing to improve” (22.6%),

  • 2.

    “less pain” (12.7%), and

  • 3.

    “easier to use/handle/apply” (9.1%).

Interestingly, none of the participants spontaneously mentioned features related to device connectivity, such as “smart technology,” “integration of injection devices with other technology,” or “data sharing functionality.” Less than 4% of participants did suggest aspects that could be improved that would require smart technology, digital features, or connectivity (Table 3).

Table 3. Unmet Needs for Subcutaneous Injection Devices Related to Smart, Digital, or Connected Features.

  total (n = 482)
more instructions/guidance 3.1%
support/monitoring from a nurse/healthcare provider 2.1%
add reminder function 1.5%
more high-tech/connected device 0.8%
help with finding the correct injection site 0.8%
Open in a new tab

When prompted, a majority of respondents (81.7%) expressed an interest in using an injection device with connected features, such as dosing reminders, in the future. This desire is notably higher in Brazil and China, where 94.7 and 88.8% of the population, respectively, can envision using an injector with connected features in the future (Table 4), with no discernible variation across different medical indications.

Table 4. General Interest across All Respondents in Using Some Kind of Digital/Electronic Adherence Tool (Percentage Respondents).

subgroup analysis by country total (n = 605) USA (n = 80) Germany (n = 75) France (n = 75) Italy (n = 74) Spain (n = 70) UK (n = 76) China (n = 80) Brazil (n = 75)
yes—I can imagine using 81.7% 78.8% 74.7% 72.0% 77.0% 84.3% 82.9% 88.8% 94.7%
no—I cannot imagine using 17.5% 20.0% 25.3% 26.7% 23.0% 14.3% 17.1% 8.8% 5.3%
I am already using a connected autoinjector device today 0.8% 1.3% 0.0% 1.3% 0.0% 1.4% 0.0% 2.5% 0.0%
subgroup analysis by condition total (n = 605) neurology (n = 265) rheumatology (n = 267) other (n = 103)
yes—I can imagine using 81.7% 79.6% 82.4% 86.4%
no—I cannot imagine using 17.5% 19.6% 16.9% 12.6%
I am already using a connected autoinjector device today 0.8% 0.8% 0.8% 1.0%
Open in a new tab

All participants were asked whether or not they would generally accept a digitally connected injection device and could be categorized into those who would accept (n = 499, 81.7%) and those who would reject (n = 106, 17.5%) using such a device.

Participants who could not imagine using an autoinjector with connected features were asked to provide their reasons. The top three unprompted reasons were

  • 1.

    no perceived need for it or do not find it useful (34.9%),

  • 2.

    believe to be able to handle process independently or to remember dates and timings (17.9%), and

  • 3.

    concerns or reservations related to smart devices, including issues like malfunction, battery life, or general distrust of technology (12.3%).

Likert Scale Assessment

In addition to the general questions with the results expressed as a percentage of respondents, additional questions were analyzed based on a Likert scale. Here, respondents who currently do not utilize any adherence tool (n = 320) were asked to also judge their preferences on an 11-point Likert scale (0—not at all important to 10—very important). The comparatively high score (mean rating of 6.8) remained consistent across various medical conditions and countries, with the exception of China, where interest was notably higher at 8.5 (Table 5).

Table 5. Interest across Participants Who Currently Do Not Utilize Any Adherence Tool in Using Some Kind of Digital/Electronic Adherence Tool (Likert Scale).

subgroup analysis by country total (n = 320) USA (n = 42) Germany (n = 34) France (n = 41) Italy (n = 32) Spain (n = 32) UK (n = 44) China (n = 50) Brazil (n = 45)
mean rating (based on an 11-point Likert scale) [SD] 6.8 [3.1] 6.2 [3.2] 5.2 [3.2] 6.4 [3.0] 6.1 [2.9] 7.4 [3.1] 6.7 [3.4] 8.5 [1.3] 7.4[3.3]
subgroup analysis by condition total (n = 320) neurology (n = 140) rheumatology (n = 145) other (n = 51)
mean rating (based on an 11-point Likert scale) [SD] 6.8 [3.1] 6.6 [3.1] 6.9 [3.2] 7.7 [2.6]
Open in a new tab

In terms of disclosing their data, participants are generally comfortable sharing their personal data with healthcare professionals, with a mean comfort level of 7.9 out of a maximum of 11. However, less comfort was expressed for sharing data with the drug manufacturer (5.5) or their insurance company (4.5). While this trend holds across countries, there are variations in comfort levels in specific regions. Notably, in Brazil, participants are more at ease sharing personal data with the drug manufacturer (7.2) or the insurance company (6.2). Conversely, in Germany, participants are less comfortable sharing with the drug manufacturer (3.9), and in France, participants are uncomfortable sharing data with the insurance company (2.9), as detailed in Table 6.

Table 6. Comfort Level with Sharing Personal Data by Countrya.

  total (n = 605) USA (n = 80) Germany (n = 75) France (n = 75) Italy (n = 74) Spain (n = 70) UK (n = 76) China (n = 80) Brazil (n = 75)
mean comfort level with sharing personal data with a physician/nurse/pharmacist 7.9[2.5] 7.9[2.5] 7.0[2.8] 7.7[2.7] 7.3[2.5] 8.8[1.9] 8.8[2.2] 7.7[2.3] 8.3[2.6]
mean comfort level sharing personal data with the drug manufacturer? 5.5[3.2] 5.3[3.5] 3.9[2.9] 4.7[3.5] 5.9[2.6] 5.5[3.4] 5.5[3.4] 5.9[2.5] 7.2[2.9]
mean comfort level sharing personal data with the insurance company? 4.5[3.4] 4.5[3.6] 3.9[3.2] 2.9[3.4] 5.0[2.8] 4.1[3.7] 4.3[3.5] 5.4[2.5] 6.2[3.2]
Open in a new tab
a

Data shown as mean rating (based on an 11-point Likert scale), [SD].

MaxDiff Attribute Hierarchy

Based on the MaxDiff attribute hierarchy, “the device can capture that the injection was conducted and completed successfully” holds the top rank across all countries and subgroups (60.09) (Table 7). Following in second and third place are “the device can track the temperature of the drug and warn about temperature excursions” and “the device can confirm that the drug has the right temperature to be injected (warmed-up).″ These two attributes have nearly equal global importance scores of 47.31 and 46.20, respectively. They consistently maintain their positions as the second and third most important attributes for age groups and most country subgroups and rank within the top four attributes for rheumatology, neurology, and “other conditions”. Although their positions vary slightly in Germany, China, and Brazil, even in these countries, their lowest rank is sixth, with the lowest importance score being 40.56 for Brazil (Supporting Information, Tables S1–S6).

Table 7. Global MaxDiff Resultsa.

graphic file with name pt3c00377_0005.jpg

Open in a new tab
a

Shadowed rows indicate physical device attributes. White rows are “smart/connected” attributes.

The attributes ranked fourth, fifth, and sixth in importance are “the data can be easily accessed by my doctor,″ “I can set up individualized reminders for when my next dose is due according to my preferences,″ and “I can set up predefined reminders for when my next dose is due.″ These three attributes consistently hold high importance scores (43.18, 41.36, and 39.40, respectively). However, their positions in the hierarchy vary across different subgroups, suggesting slight variations in preferences among individuals or groups. Notably, the attribute “the data can be accessible to my doctor” holds a higher rank in China (second position, 48.05) and Brazil, or Germany (third position, 42.86 and 44.20, respectively), as seen in Table S1. It also ranks as the third most valued category in other subgroup categories, such as injections administered at home by a caregiver (48.98, Table S3), conditions other than rheumatology or neurology (46.81, Table S4), and among males (46.87, Table S5). The results in the subgroups of participants that would generally accept or reject a digitally connected injection device were consistent with the findings in the general population (Table S6).

Aligning with the general feedback, the three attributes perceived as providing the least value, irrespective of the subgroup or country, include “I can access relevant information about the patient leaflet,” “the data can be accessible to the drug manufacturer,” and “the data can be accessible to my insurance company” (with scores of 12.62, 7.28, and 6.39, respectively). Notably, the importance score for the bottom two attributes, concerning data accessibility for the drug manufacturer and insurance company, is below 10. These attributes can be considered 6 times less important than the top-ranked attribute (with an importance score of 60.09).

Other Results (Following MaxDiff Exercise)

Following the MaxDiff exercise, participants were queried about additional key factors for the ideal subcutaneous device. The top responses were as follows: “ease of use/administration” (19.8%), “reminder for administration/next injection” (14.7%), and “correct temperature indicator” (9.3%). Approximately 10% either mentioned “nothing” (7.8%) or responded with “I do not know” (1.8%). These findings exhibited consistency across various countries with slight individual variations. “Reminder for administration/next injection” was notably more frequently mentioned in China (21.3%) and Brazil (29.3%) but less so in the United Kingdom, United States of America, and Germany (3.9, 8.8, and 9.3% respectively).

This survey study assessed user perceptions of digital adherence aids, whether integrated into subcutaneous injection devices or as standalone health apps. Survey findings will inform the development of universal features for devices and apps to enhance adherence to subcutaneous self-injection in decentralized dosing regimens across medical conditions.

The survey included users and care partners with various chronic conditions. People using insulin for the treatment of diabetes were excluded, as here, subcutaneous self-administration often involves daily injections tied to meals and glucose levels.19 In contrast, participants in this survey, with other chronic conditions, have to follow a fixed dosing schedule without the need to react to changing disease parameters. Such regimen is expected to lead to higher forgetfulness compared to diabetes insulin delivery.20,21

The use of electronic adherence tools to support subcutaneous administration in a decentralized setting is not a novel concept. Outside of the diabetes domain, numerous injection devices have been approved for this use. The initial connected subcutaneous autoinjectors and pen devices approved alongside specific drugs include the BetaConnect and the RebiSmart for interferon beta treatments in multiple sclerosis,22,23 as well as the ava for Cimzia,24 the Enbrel Autotouch,25 and the Enbrel Smartclic under rheumatological conditions.26 These tools incorporate features such as visual or audible end-of-dose alerts, dosing reminders, smartphone apps for real-time adherence tracking, and storage of clinician-reported outcomes and PRO data. They also facilitate patient-provider communication. In a previous review, a comprehensive analysis of clinical trial data for various connected drug delivery devices revealed evidence of improved treatment adherence and outcomes among people utilizing these electronic tools. The assessed data focused on studies within an indication.12 The present survey was conducted to explore the needs of a more diverse population across various indications, thereby enriching and broadening the data set beyond specific disease areas.

The findings of this study indicate that there is currently minimal awareness and usage of smart injector brands. Only 2.6% of participants presently utilize a connected device for their subcutaneous injections, and merely 4.5% are using health apps connected to their injection devices. However, 47.4% of participants employ some form of tools or adherence aids (including nondigital reminders) to assist with their treatment. Notably, when prompted, 81.7% of participants expressed a willingness to adopt an autoinjector with connected features.

Respondents’ preferred attributes for a physical device included tracking successful injections, monitoring temperature, and receiving temperature-related notifications. These features can be incorporated directly into the device. Regarding apps, respondents favored features that allowed them to share their data with their primary physician and to customize medication reminders according to their personal preferences. Additional attributes mentioned as important but with a lower priority encompass various functions, including the ability to initiate remote dialogue and communication with a doctor, ensuring the security of personal data, providing visual indicators after completing a dose, maintaining an electronic diary, tracking compliance with prescriptions, offering information and reminders on injection procedures and device handling, as well as using acoustic indicators following dose completion.

In the following, the use of the survey results for the development of a hypothetical universal companion platform, referred to as “OnePlatform”, will be discussed (Figure 1).

Figure 1.

Figure 1

Open in a new tab

Schematic of hypothetical OnePlatform digital health tool, including adherence tools and treatment management features.

This digital health platform with a stepwise market introduction of different digital health applications would address the challenges related to timely access to electronic adherence aids for new drugs and ensure functionality. OnePlatform would initially be based on the described needs of a broad user population and integrate with injection devices linked to a health app or alternatively exist as a standalone app for those who prefer nonconnected devices. The tool should be applicable to various drug types, making it cost-neutral for users and healthcare providers. Depending on data protection laws and consent, data could be shared with payer organizations, with patient identifiers or in an anonymized, aggregated format. The aim would be to reimburse healthcare providers for their efforts. OnePlatform would initially gather real-world evidence on medication usage and compliance and later be augmented to include indication-specific diagnostic, genetic testing, and biomarker tracking tools.

In addition to positively influencing user behavior through reminders and educational tools, OnePlatform has the potential to enhance engagement by incorporating incentives. Health behavior studies27 have demonstrated that user incentives can significantly boost adherence, with an average increase of 20 percentage points. It is important to note that the impact may vary across different studies, with the value of the incentive showing a positive correlation with intervention effectiveness.28

These incentives can take different forms, either financial (vouchers or gift cards) or social (individual recognition or encouraging feedback).29 Integrating such motivational elements into the platform could further encourage users to stay committed to their health goals.

Another compelling aspect of this universal adherence app concept, if permissible from a data privacy standpoint, is its potential to support healthcare providers in the United States in adopting the Medicare’s Merit-based Incentive Payment System (MIPS). This system not only reimburses eligible Medicare providers but also rewards them for enhancing the quality of patient care and improving outcomes.30 This alignment with existing healthcare frameworks could contribute to the broader success and adoption of the OnePlatform solution.

To realize such a comprehensive treatment management tool, several challenges must be addressed. Primarily, integrating patient data from diverse sources, particularly diagnostic and treatment tools, necessitates strict compliance with data privacy regulations.31,32 Ensuring the security of this sensitive health information is vital to safeguarding patient privacy. Within this context, it is imperative to clearly define data ownership and sharing protocols among stakeholders including patients, providers, and researchers.

It is anticipated that payer organizations will value solutions that reduce uncertainties in adhering to parenteral dosing regimens in a remote healthcare setting. Nevertheless, manufacturers must present compelling evidence that their platform is not only cost-effective but also contributes to improved health outcomes before expecting reimbursement. In this context, the utmost importance lies in the accuracy and reliability of the data generated by these tools.33 This is critical to preventing misdiagnosis and to ensure that treatment decisions are appropriate and effective. Furthermore, robust safeguards against data breaches and ransomware attacks are essential and legally mandated to protect sensitive user information.34

In this context, given the survey results suggesting that users are reluctant to share their data with manufacturers or payer organizations, combined with the growing global emphasis on data privacy protection,35,36 the prospect of payers monitoring patient adherence at an aggregated population level seems unlikely in the near future. This may lead to an incomplete data set for adherence information that limits OnePlatform’s applicability to individual cases only.

From a regulatory perspective, depending on the OnePlatform’s functionalities, digital health tools may fall under the jurisdiction of multiple regulatory bodies, each with its own requirements and approval processes.37 As summarized by Colloud et al.,38 for medicinal product approval in the European Union, this can include the European Medicines Agency (EMA) for centralized procedure, the co-ordination Group for Mutual Recognition and Decentralised Procedures–human (CMDh) for decentralized procedure and mutual recognition, as well as National Competent Authorities (NCA) for national and clinical investigation approval. In addition, notified bodies are responsible for medical device certification. In the United States, the Food and Drug Administration (FDA) has issued numerous guidelines on the regulations of digital health technologies including premarket submissions, marketing submission regulations, aspects of cybersecurity, or usage in clinical trial settings.39 This rigorous regulatory approval process is especially applicable to software that supports healthcare decisions. Such tools are referred to as Software as Medical Device (SaMD) by regulators and categorized according to the significance of information provided and the state of healthcare situation or condition.40 While simple adherence tools with no impact on treatment decisions or the dosing regimen may not require dedicated regulatory approval,41 diagnostic and treatment software would need to be developed according to principles for demonstrating their safety, effectiveness, and performance.42,43

Another set of barriers foreseen for the implementation of the OnePlatform digital health solution pertains to logistical and operational aspects. The described OnePlatform digital health platform would necessitate seamless integration with the pre-existing healthcare systems, which frequently employ a variety of software and hardware solutions.44 This integration demands compatibility assessments and the establishment of data sharing agreements. As the platform expands and more users are incorporated, it must possess the capacity to scale effectively, accommodating the augmented data volume and user traffic without experiencing a noticeable degradation in performance. Furthermore, the assurance of interoperability between various health information systems is of paramount importance.45

Addressing the described obstacles requires a multidisciplinary approach that involves not only technology and healthcare expertise but also legal and regulatory specialists and data privacy experts.

In conclusion, the findings from this survey on the perceived value of electronic adherence tools for subcutaneous autoinjectors in decentralized healthcare settings shed light on user preferences across various medical conditions. The primary user focus revolves around enhancing the subcutaneous administration process and ensuring that devices are user-friendly and capable of monitoring temperature variations during supply, storage, and usage. Importantly, these desirable features can be seamlessly integrated into the injection devices without the necessity of connectivity to a health app. Additionally, it appears that already today, independent of the underlying disease, about a quarter of respondents utilize medication reminder apps on their smartphones or smartwatches. Applying the results of this survey, connected devices and accompanying health apps are expected to play a larger role as supportive adherence tools in areas of subcutaneous self-injection in the future.

Furthermore, the reluctance of users to share their data with manufacturers and payers represents a significant hurdle to the utilization of data derived from such tools in payment decisions. Given that a considerable portion of the population may not be inclined to use connectivity to improve adherence, it is advisible for these tools to be offered as an optional accessory that can be added to the device based on individual choice. This approach respects the diversity of user preferences and acknowledges the importance of data privacy concerns in the context of healthcare technology.

Methods

Study Population

The survey study aimed to enroll a total of 600 participants in the United States, United Kingdom, Spain, Italy, Germany, France, China and Brazil (approximately, 75 participants per country). Adults 18 years or older with either confirmed diagnosis of a medical condition that requires subcutaneous injection or who provide support to a family member or close person with subcutaneous treatment administration were eligible to participate. Participants were screened by third-party recruiters and invited to participate in the study via email, telephone, healthcare provider referrals, and patient associations and support groups on social media channels. Before entering the study, all participants were required to have read and agreed with the conditions outlined in the study informed consent form.

Study Design and Conduct

The two-part online survey contained survey logic as well as prompts and explanations in the respective native language to support participants in understanding the questions and exercises involved. The first part was a 3 min screener questionnaire to ensure respondents’ eligibility. Eligible participants did proceed to the second part of the online survey with questions and exercises focused on current needs and possible challenges with existing treatments that could be addressed with the implementation of connected drug delivery devices and accompanying or stand-alone health apps.

Survey design and eligibility requirements were prespecified and documented in a research protocol. A central Institutional Review Board reviewed and approved the protocol and research materials and granted an exemption for local approval. The study was conducted in compliance with the Declaration of Helsinki and European Market Research Association guidelines. The research adhered to international compliance with data protection laws, regulations, and rules. This privacy and data protection policy adopts the fundamental principles of the European Union’s General Data Protection Regulation as the minimum standard to which Ipsos Group, Roche, their employees, and suppliers adhered.

Preference Assessment (Likert Scale)

Participants were asked to judge their preferences on an 11-point Likert scale (0 - not at all important to 10 - very important).

Maximum Difference Scaling (MaxDiff) Exercise

The questionnaire included a Maximum Difference Scaling (MaxDiff) exercise, a discrete choice modeling approach to establish a ranking of attributes in the descending order of importance. The exercise included a predefined number of features of digitally connected injection devices and health apps, and participants answered several choice tasks. In each task, participants were asked to choose the most and least important feature from a set of five attributes. This process was repeated several times, with different items being shown. Each item appeared a minimum of 3 and a maximum of 5 times in each task to ensure that the effect of each attribute on respondent choice was recorded accurately and fairly across the sample. All data collected were analyzed and reported in aggregate. The variables analyzed were preference scores or outputs from MaxDiff analysis. A Hierarchical Bayes (HB) technique was applied. This fits a Multinomial Logit Model (MNL) to each individual respondent by using an iterative approach that maximizes the posterior likelihood. In other words, HB finds the optimum set of utility parameters given the observed respondent data and the knowledge about the rest of the sample.

Results from the MaxDiff exercise were analyzed at a global level, with country differences where appropriate.

Importance Score

The results of the MaxDiff exercise are expressed as an importance score. This score shows how much more important the upper ranked attributes are compared with the lower ranked attributes. If an attribute that has an importance score is double the score of a lower ranked attribute, the attribute is twice as important to patients as the lower ranked attribute and vice versa.

Subgroups

Subgroups were defined by (1) age group, (2) gender, (3) country, (4) type of administration, (5) main condition for which SC injections were given, and (6) general acceptance of an autoinjector with connected features and were used to analyze the MaxDiff results by comparing the overall hierarchy and relative difference in importance score for attributes tested.

There were no quotas set for any of these subgroups, and a natural fall out was permitted within each country. Due to the relatively small database for each country and the fact that subgroups were not evenly distributed, MaxDiff results were not analyzed for each subgroup within each country. Instead, the MaxDiff results were compared at a total and country level and for all categories within subgroups at a global level only.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsptsci.3c00377.

  • Additional detailed results of the subgroup analyses of the MaxDiff exercise (PDF)

The authors declare no competing financial interest.

Supplementary Material

pt3c00377_si_001.pdf (136KB, pdf)

References

  1. Baryakova T. H.; Pogostin B. H.; Langer R.; McHugh K. J. Overcoming barriers to patient adherence: the case for developing innovative drug delivery systems. Nat. Rev. Drug Discovery 2023, 22, 387–409. 10.1038/s41573-023-00670-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Kvarnström K.; Westerholm A.; Airaksinen M.; Liira H. Factors contributing to medication adherence in patients with a chronic condition: a scoping review of qualitative research. Pharmaceutics 2021, 13, 1100. 10.3390/pharmaceutics13071100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Eduardo S.Adherence to Long-Term Therapies: Evidence for Action; World Health Organization: Geneva, Switzerland, 2003. [Google Scholar]
  4. Amankwaa I.; Boateng D.; Quansah D. Y.; Akuoko C. P.; Evans C. Effectiveness of short message services and voice call interventions for antiretroviral therapy adherence and other outcomes: A systematic review and meta-analysis. PLoS One 2018, 13, e0204091 10.1371/journal.pone.0204091. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Iuga A. O.; McGuire M. J. Adherence and health care costs. Risk Manag. Healthc. Pol. 2014, 7, 35–44. 10.2147/RMHP.S19801. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Keininger D.; Coteur G. Assessment of self-injection experience in patients with rheumatoid arthritis: psychometric validation of the Self-Injection Assessment Questionnaire (SIAQ). Health Qual. Life Outcome 2011, 9, 2. 10.1186/1477-7525-9-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Rekaya N.; Vicik S. M.; Hulesch B. T.; McDonald L. L. Enhancement of an auto-injector device for self-administration of etanercept in patients with rheumatoid arthritis confers emotional and functional benefits. Rheumatol. Ther. 2020, 7, 537–552. 10.1007/s40744-020-00216-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Fazal F.; Saleem T.; Ur Rehman M. E.; Haider T.; Khalid A. R.; Tanveer U.; Mustafa H.; Tanveer J.; Noor A. The rising cost of healthcare and its contribution to the worsening disease burden in developing countries. Ann. Med. Surg. 2022, 82 (82), 104683. 10.1016/j.amsu.2022.104683. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cronin J.; Moore S.; Lenihan N.; O’Shea M.; Woods N. The non-drug costs associated with the administration of an intravenous biologic treatment in the hospital setting. Ir. J. Med. Sci. 2019, 188, 821–834. 10.1007/s11845-018-1925-8. [DOI] [PubMed] [Google Scholar]
  10. Ritchie B.; Martins K. J. B.; Tran D. T.; Blain H.; Richer L.; Klarenbach S. W. Economic impact of self-administered subcutaneous versus clinic-administered intravenous immunoglobulin G therapy in Alberta, Canada: a population-based cohort study. Allergy, Asthma, Clin. Immunol. 2022, 18, 99. 10.1186/s13223-022-00735-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Alsina L.; Montoro J. B.; Moral P. M.; Neth O.; Pica M. O.; Sánchez-Ramón S.; Presa M.; Oyagüez I.; Casado M. Á.; González-Granado L. I. Cost-minimization analysis of immunoglobulin treatment of primary immunodeficiency diseases in Spain. Eur. J. Health Econ. 2022, 23, 551–558. 10.1007/s10198-021-01378-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Bittner B.; Schmit Chiesi C.; Kharawala S.; Kaur G.; Schmidt J. Connected drug delivery devices to complement drug treatments: potential to facilitate disease management in home setting. Med. Devices: Evidence Res. 2019, 12, 101–127. 10.2147/MDER.S198943. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Gandell D. L.; Bienen E. J.; Gudeman J. Mode of injection and treatment adherence: results of a survey characterizing the perspectives of health care providers and US women 18–45 years old. Patient Prefer. Adherence 2019, 13, 351–361. 10.2147/PPA.S187120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Keininger D.; Coteur G. Assessment of self-injection experience in patients with rheumatoid arthritis: psychometric validation of the self-injection assessment questionnaire (SIAQ). Health Qual. Life Outcome 2011, 9, 2. 10.1186/1477-7525-9-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Lohmeyer Q.; Schneider A.; Jordi C.; Lange J.; Meboldt M. Toward a new age of patient centricity? The application of eye-tracking to the development of connected self-injection systems. Expert Opin. Drug Delivery 2019, 16, 163–175. 10.1080/17425247.2019.1563070. [DOI] [PubMed] [Google Scholar]
  16. Gonçalves-Bradley D. C.; J Maria A. R.; Ricci-Cabello I.; Villanueva G.; Fønhus M. S.; Glenton C.; Lewin S.; Henschke N.; Buckley B. S.; Mehl G. L.; Tamrat T.; Shepperd S. Mobile technologies to support healthcare provider to healthcare provider communication and management of care. Cochrane Database Syst. Rev. 2020, 8, CD012927. 10.1002/14651858.CD012927.pub2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Antalfy A.; Berman K.; Everitt C.; Alten R.; Latymer M.; Godfrey C. M. The adherence and outcomes benefits of using a connected, reusable auto-injector for self-injecting biologics: a narrative review. Adv. Ther. 2023, 40, 4758–4776. 10.1007/s12325-023-02671-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Pager A.; Despa M.. A Patient-Centric, Connected Injection Device for Decentralised Clinical Trials - the Human Factors Results Are in!; ONdrugDelivery, 2022. https://www.ondrugdelivery.com/wp-content/uploads/2022/06/134_2022_Jun_Connectivity_BD.pdf.
  19. Gerada Y.; Mengistu Z.; Demessie A.; Fantahun A.; Gebrekirstos K. Adherence to insulin self administration and associated factors among diabetes mellitus patients at Tikur Anbessa specialized hospital. J. Diabetes Metab. Disord. 2017, 16, 28. 10.1186/s40200-017-0309-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Marengo M. F.; Suarez-Almazor M. E. Improving treatment adherence in patients with rheumatoid arthritis: what are the options?. Int. J. Clin. Rheumtol. 2015, 10, 345–356. 10.2217/ijr.15.39. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Cea-Calvo L.; Marín-Jiménez I.; de Toro J.; Fuster-RuizdeApodaca M. J.; Fernández G.; Sánchez-Vega N.; Orozco-Beltrán D. Different associations of intentional and non-intentional non-adherence behaviors with patient experience with healthcare and patient beliefs in medications: a survey of patients with chronic conditions. Patient Prefer. Adherence 2020, 14, 2439–2450. 10.2147/PPA.S281985. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Abbadessa G.; Brigo F.; Clerico M.; De Mercanti S.; Trojsi F.; Tedeschi G.; Bonavita S.; Lavorgna L. Digital therapeutics in neurology. J. Neurol. 2022, 269, 1209–1224. 10.1007/s00415-021-10608-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Colten S.; Cantogno E. V. D.; Jack D. Uncovering nurses’ and patients’ preferences and perceptions of rebismart 3.0 autoinjector versus other assistive devices in multiple sclerosis. Mult. Scler. Relat. Disord. 2023, 71, 104287. 10.1016/j.msard.2022.104287. [DOI] [Google Scholar]
  24. Pouls B. P. H.; Kristensen L. E.; Petersson M.; Van den Bemt B. J. F.; Ballerini L.; Bruggraber R.; Karlen H.; Mountian I.; Van Bracht E.; Wiegratz S.; Jørgensen T. S. A pilot study examining patient preference and satisfaction for ava, a reusable electronic injection device to administer certolizumab pegol. Expert Opin. Drug Delivery 2020, 17, 705–711. 10.1080/17425247.2020.1736552. [DOI] [PubMed] [Google Scholar]
  25. Collier D. H.; Bitman B.; Coles A.; Liu L.; Kumar S.; Judd C. A novel electromechanical autoinjector, AutoTouch, for self-injection of etanercept: real-world use and benefits. Postgrad Med. 2017, 129, 118–125. 10.1080/00325481.2017.1251291. [DOI] [PubMed] [Google Scholar]
  26. Alten R.; Moss S.; Hahne S.; Muriset A. T.; Gruben D.; Latymer M. High usability and applicability ratings for the new smartclic/clicwise injection device: evidence from a health care professional opinion study. Rheumatol. Ther. 2023, 10, 477–487. 10.1007/s40744-022-00520-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Demonceau J.; Ruppar T.; Kristanto P.; Hughes D. A.; Fargher E.; Kardas P.; De Geest S.; Dobbels F.; Lewek P.; Urquhart J.; Vrijens B.; Identification and assessment of adherenceenhancing interventions in studies assessing medication adherence through electronically compiled drug dosing histories: a systematic literature review and meta-analysis. Drugs 2013, 73, 545–562. 10.1007/s40265-013-0041-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. DeFulio A.; Silverman K. The use of incentives to reinforce medication adherence. Prev. Med. 2012, 55, S86–S94. 10.1016/j.ypmed.2012.04.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Hastings T. J.; Hohmann N. S.; Jeminiwa R.; Hansen R. A.; Qian J.; Garza K. B. Perceived appropriateness of medication adherence incentives. J. Manag. Care. Spec. Pharm. 2021, 27, 772–778. 10.18553/jmcp.2021.27.6.772. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. US Centers for Medicare & Medicaid Services Quality Payment Program. Traditional MIPS Overview. https://qpp.cms.gov/mips/traditional-mips. accessed Jan 25, 2024.
  31. De Kok J. W. T. M.; de la Hoz M. Á. A.; De Jong Y.; Brokke V.; Elbers P. W. G.; Thoral P.; Castillejo A.; Trenor T.; Castellano J. M.; Bronchalo A. E.; Merz T. M.; Faltys M.; Casares C.; Jiménez A.; Requejo J.; Gutiérrez S.; Curto D.; et al. A guide to sharing open healthcare data under the general data protection regulation. Sci. Data. 2023, 10, 404. 10.1038/s41597-023-02256-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Tertulino R.; Antunes N.; Morais H. Privacy in electronic health records: a systematic mapping study. J. Public Health 2023, 10.1007/s10389-022-01795-z. [DOI] [Google Scholar]
  33. Chan A. H. Y.; Foot H.; Pearce C. J.; Horne R.; Foster J. M.; Harrison J. Effect of electronic adherence monitoring on adherence and outcomes in chronic conditions: A systematic review and meta-analysis. PLoS One 2022, 17, 0265715. 10.1371/journal.pone.0265715. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Yeo L. H.; Banfield J. Human factors in electronic health records cybersecurity breach: an exploratory analysis. Perspect. Health Inf. Manag. 2022, 19, 1i. [PMC free article] [PubMed] [Google Scholar]
  35. Pool J.; Akhlaghpour S.; Fatehi F. Towards a contextual theory of Mobile Health Data Protection (MHDP): A realist perspective. Int. J. Med. Inform. 2020, 141, 104229. 10.1016/j.ijmedinf.2020.104229. [DOI] [PubMed] [Google Scholar]
  36. Pool J.; Akhlaghpour S.; Fatehi F.; Gray L. C. Data privacy concerns and use of telehealth in the aged care context: An integrative review and research agenda. Int. J. Med. Inform. 2022, 160, 104707. 10.1016/j.ijmedinf.2022.104707. [DOI] [PubMed] [Google Scholar]
  37. Joosten C.; Gnauck N.; Banton S.; Chan S.. Regulatory Pathways for Connected Combined Products; Medtech&Pharma Platform, 2023. https://medtech-pharma.com/wp-content/uploads/2023/09/MPP-paper_Regulatory-pathways-for-connected-combined-products.pdf.
  38. Colloud S.; Metcalfe T.; Askin S.; Belachew S.; Ammann J.; Bos E.; Kilchenmann T.; Strijbos P.; Eggenspieler D.; Servais L.; Garay C.; Konstantakopoulos A.; Ritzhaupt A.; Vetter T.; Vincenzi C.; Cerreta F. Evolving regulatory perspectives on digital health technologies for medicinal product development. npj Digit. Med. 2023, 6, 56. 10.1038/s41746-023-00790-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. US Food and Drug Administration . Guidances with digital health content. https://www.fda.gov/medical-devices/digital-health-center-excellence/guidances-digital-health-content. accessed Nov 23, 2023.
  40. US Food and Drug Administration . Global approach to software as a medical device. https://www.fda.gov/medical-devices/software-medical-device-samd/global-approach-software-medical-device. accessed Nov 23, 2023.
  41. US Food and Drug Administration Guidance for Industry and Food and Drug Administration Staff; Clinical decision support software, 2022. https://www.fda.gov/medical-devices/software-medical-device-samd/your-clinical-decision-support-software-it-medical-device. accessed Nov 23, 2023.
  42. Watson A.; Chapman R.; Shafai G.; Maricich Y. A. FDA regulations and prescription digital therapeutics: Evolving with the technologies they regulate. Front. Digit. Health. 2023, 5, 1086219. 10.3389/fdgth.2023.1086219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Wade B.; Abraham J.; Coder M.. Guidance to Industry. Classification of Digital Health Technologies; Health Advances—Digital Therapeutic Alliance, 2023. https://dtxalliance.org/wp-content/uploads/2023/06/Guidance-to-Industry-Classification-of-Digital-Health-Technologies-2023Jun05.pdf.
  44. Jacob P. M.; Mani P. A reference model for testing internet of things based applications. J. Eng. Sci. Technol. 2018, 13, 2504–2519. [Google Scholar]
  45. Torab-Miandoab A.; Samad-Soltani T.; Jodati A.; Rezaei-Hachesu P. Interoperability of heterogeneous health information systems: a systematic literature review. BMC Med. Inform. Decis. Mak. 2023, 23, 18. 10.1186/s12911-023-02115-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

pt3c00377_si_001.pdf (136KB, pdf)

Articles from ACS Pharmacology & Translational Science are provided here courtesy of American Chemical Society

RESOURCES