Abstract
Adherence to antiretroviral therapy (ART) is important in people with HIV (PWH). This study explores perceptions of a novel adherence measurement system (TruTag) using silica-based microparticles in ART pills that when scanned with a smartphone camera, indirectly measure adherence. Twenty PWH on ART were recruited from a community health center and operated the TruTag system during a semi-structured interview. Qualitative data were analyzed using a framework matrix to understand perceptions and recommendations for the use of the TruTag system. Participants ranged in age from 26 to 68 (median 46 years), were White (60%), and homosexual or gay (70%). Most participants (70%) self-reported 100% ART adherence over the past 6 months. After instruction, all participants successfully operated the TruTag system, with many participants willing to consider it as a platform for their own adherence measurement. Key facilitators included the ability to verify medication, track adherence over time, and share data with healthcare providers. Some participants expressed concerns about scanning difficulties and perceived the system as an unnecessary step in their adherence routine. Participants suggested refinements to enhance scanning and emphasized the importance of reminders to support adherence. These findings suggest that this system may be a strategy for measuring ART adherence in PWH.
Keywords: Medication adherence, antiretroviral therapy, HIV, adherence technology
SUSTAINABLE DEVELOPMENT GOALS: SDG 3: Good health and well being; SDG 4: Quality education; SDG 9: Industry, innovation and infrastructure; SDG 10: Reduced inequalities
Introduction
Despite significant advancements in pharmacotherapy, the HIV epidemic persists in the United States (U.S.), given the lack of a cure (Centers for Disease Control and Prevention, 2025). One of the reasons for new and insufficiently treated HIV infections is suboptimal adherence to antiretroviral therapy (ART), which increases the risk of HIV transmission (Benjamin, 2012; Madi et al., 2013; Van Wyk & Davids, 2019). Adherence to ART is a complex challenge influenced by various biobehavioral factors, including access to medication, HIV-related stigma, substance use, and mental health challenges. Exacerbations of these co-occurring factors (e.g., syndemics) can happen at any time and result in ART nonadherence (Mehta et al., 1997). Therefore, developing personalized interventions to optimize ART adherence is a critical strategy to end the HIV epidemic.
Measuring adherence to ART is essential for developing interventions to support optimal adherence and address factors that contribute to nonadherence. Various strategies for measuring ART adherence can be broadly categorized into direct and indirect methods. Direct measures include biological confirmation of drug concentrations in hair, urine, or blood; directly observed therapy; and the use of ingestible sensors that verify medication ingestions (Bell & Haberer, 2018; Chai et al., 2016; Spinelli et al., 2020). While these methods provide accurate data, they can be burdensome, require medical infrastructure and laboratory processing equipment, and/or may be costly to sustain over extended periods. Indirect measures include self-reporting, pharmacy refill records, artificial intelligence-driven video adherence monitoring, and electronic pill bottles that record the opening of pill bottle caps (Ellsworth et al., 2021; Liu et al., 2022; Simoni et al., 2006). Although these measures infer adherence rather than confirming it directly, they remain clinically useful given the low threshold for implementation.
Technologically enabled strategies to measure adherence have gained traction over the past decade in concert with the increasing adoption of smartphones and wearable devices. These societal shifts have facilitated the deployment of digital health tools designed to enhance adherence through real-time feedback (Castillo-Mancilla & Haberer, 2018; Chai et al., 2022; Spinelli et al., 2020). In HIV treatment, digital health innovations have ushered in new strategies to measure and support adherence, contributing to efforts to end the HIV epidemic. Previous studies have demonstrated that video-based pill monitoring and smartphone-captured photos of pills prior to ingestion are acceptable strategies for measuring ART adherence (Kamal et al., 2020; Kordy et al., 2023).
An innovative advancement in this field involves using a microparticle taggant to verify medication ingestion. Taggant systems, comprising small particles or drug metabolites, have been previously integrated into xenobiotics as a detectable marker of adherence, as well as used to track medications from their origin (i.e., manufacturing) to dispensing (Deisingh, 2005; Fayazpour et al., 2007). In this study, we were interested in understanding the user perception of utilizing a silica-based microparticle system (TruTag®, Emeryville, CA), which is classified as “generally regarded as safe” (GRAS) by the U.S. Food and Drug Administration (FDA) to measure ART adherence. This system incorporates microparticles into ART pills; when scanned with a smartphone camera, the unique refraction pattern of the microparticles confirms the pill as ART and provides an indirect method to record adherence (TruTag® Technologies). In order to understand formative perceptions of this system, we conducted semi-structured qualitative interviews covering early use and incorporation of this technology into ART, leveraging taggant-based adherence data as a tool to support adherence and potential challenges to scaling the technology.
Methods
Participants
Participants were recruited through targeted electronic health record outreach at Fenway Health, a Boston-based clinical and research center dedicated to the care of LGBTQ+ individuals, serving more than 1500 people with HIV (PWH). Additional recruitment methods included social media, flyers, and community outreach events in partnership with local HIV advocacy groups and organizations serving LGBT populations. All participants met the following inclusion criteria: (1) 18 years or older; (2) living with HIV; (3) currently prescribed and taking ART for at least 3 months; (4) owned a smartphone; and (5) able to complete study procedures in English.
Procedures
Potential participants were screened via phone or email by a member of the study team. Eligible and interested individuals were scheduled for a one-time study visit, where they completed the informed consent process and signed written consent. During the visit, participants were introduced to the taggant technology (TruTag®) and interacted with a mock version of the pills and associated smartphone app (Figure 1). Successful operation of the system was defined as the ability to scan a pill and record the mock ingestion within the smartphone app during the study visit.
Figure 1.
Overview of the TruTag® System. (A) Users scan antiretroviral therapy (ART) pills using the smartphone app. The embedded silica-based microparticles are detected through the app’s camera, confirming the medication identity. (B) Ingestion data are recorded on a calendar interface within the app, allowing users to track their adherence over time.
Participants then completed a brief quantitative assessment, followed by a semi-structured qualitative interview, conducted in a private location by a trained member of the study team with experience conducting interviews among sexual and gender minorities and PWH. Interviews lasted approximately 30 min. Participants were remunerated for their time upon completion of all study visit procedures. Institutional Review Board (IRB) approval was obtained from the Fenway Community Health IRB. Qualitative interviews were conducted between July 2023 and April 2024.
Measures
Quantitative assessment
Sociodemographics.
Participants self-reported sociodemographic information, including age, biological sex, gender, race, ethnicity, sexual orientation, relationship status, education, income, and primary care provider information.
Health information.
Participants self-reported history of HIV diagnosis and ART treatment, and sexual activity history.
Mental health.
Mental health was assessed using the Patient Health Questionnaire (PHQ-8) (Kroenke et al., 2009) and the Generalized Anxiety Disorder 7-Item Scale (GAD-7) (Spitzer et al., 2006). The PHQ-8 evaluates depression using a 4-point Likert scale (0 = not at all, 3 = nearly every day). Scores are calculated by summing responses to the eight items, with a total score of ≥10 indicating major depression and a score of ≥20 indicating severe major depression (Kroenke et al., 2009). The GAD-7 assesses anxiety through seven items scored on a 4-point Likert scale (0 = not at all, 3 = nearly every day). Scores range from 0–21, with a total score of ≥8 indicating probable anxiety disorder (Spitzer et al., 2006).
Substance use.
An adapted version of the World Health Organization (WHO) Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) measure was used to assess substance use (WHO ASSIST Working Group, 2002). The ASSIST utilizes questions about lifetime and recent (i.e., past three months) substance use, covering alcohol, tobacco, marijuana, cocaine, amphetamine, inhalants, sedatives, hallucinogens, opioids and other substances (WHO ASSIST Working Group, 2002). Respondents indicated recent substance use on a 5-point Likert scale (0 = never, 4 = daily or almost daily).
Medical mistrust.
To evaluate mistrust in research and medical settings, participants completed a modified version of the adapted Group-Based Medical Mistrust Scale (GBMMS) (Knopf et al., 2021). The GBMMS is composed of six items scored using a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). Total scores range from 6–30, with higher mean scores indicating greater mistrust (Knopf et al., 2021).
Attitudes toward technology.
Participants completed four attitude subscales of the Media and Technology Usage and Attitudes Scale (MTUAS): the positive attitudes subscale (six items, e.g., “With technology, anything is possible”), the negative attitudes subscale (three items, e.g., “New technology makes life more complicated”), the anxiety or dependence on technology subscale (three items, e.g., “I get anxious when I don’t have my cell phone”), and the preference for task switching subscale (four items, e.g., “I like to finish one task completely before focusing on anything else”) (Rosen et al., 2013). Responses are scored on a 5-point Likert-scale (1 = strongly disagree, 5 = strongly agree). Ranges were 1–5 for each subscale, with higher scores indicating more positive attitudes, more negative attitudes, and more technological anxiety and dependence, and lower scores indicating increased preference for task switching (Rosen et al., 2013).
Qualitative interview
A semi-structured qualitative interview guide was developed by the study team members with expertise in the deployment of novel technologies (PRC, GRG), qualitative interviewing techniques (GRG), and HIV treatment science (CO, KHM). Questions were grounded in the Technology Acceptance Model (TAM) (Venkatesh & Davis, 2000), and explored participants’ initial perceptions of taggant technology and perceived barriers to use of the system. Additional questions were adapted from commercial partners (BM) to understand how these technologies might be scaled and implemented for ART adherence measurement in the future. The guide was piloted among study team members to ensure clarity.
The final interview guide covered the following key content areas: overall perceptions of taggant technology, its use in the context of ART treatment, potential applications for the technology and associated adherence data, privacy and health-related concerns, and future recommendations (Table 1). We adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ) in the design, conduct, and analysis of individual interviews (Rosen et al., 2023).
Table 1.
Sample interview content areas, questions and probes.
| Content area | Sample questions and probes |
|---|---|
| Overall perceptions of taggant technology |
|
| Taggants in the context of ART treatment |
|
| Taggant applications and adherence data |
|
| Privacy and health-related concerns |
|
| Future recommendations |
|
Analysis
Descriptive statistics were calculated for sociodemographics and health information, as well as measures of mental health, substance use, medical mistrust, and attitudes toward technology.
Audio-recorded interviews were professionally transcribed, de-identified, and reviewed by study staff to confirm accuracy. Participant checking was not conducted for transcript review. Our primary aim was to optimize the technology and prepare for its deployment in potential clinical trials. To achieve this, we leveraged a framework matrix analysis, a strategy previously used to explore the adoption of novel health technologies. In the analysis, matrix rows represented participant IDs (one per row) and columns represented key themes identified from interviews based on the qualitative interview guide. Columns were organized into thematic areas, including: (1) overall perceptions of technology; (2) technology in the context of ART treatment; (3) technology applications and adherence data; (4) privacy and health-related concerns; and (5) future recommendations. Two study team members (LL, AF) independently read transcripts and summarized participant comments in the appropriate thematic cells within the matrix. The matrix allowed for a comprehensive view of participant responses, with rows summarizing individual feedback and columns highlighting thoughts on specific themes. A brief summary was added to the final cell of each column to identify common themes and recommendations across participants. All transcripts were double-coded to ensure consistency and accuracy. Study team members met regularly to review coding, discuss discrepancies, and iteratively refine the matrix by adding new codes as necessary.
Results
During the study period, 31 individuals were pre-screened, of whom 30 were eligible (96.8%). One individual was deemed ineligible due to not owning a smartphone. Twenty of the 30 eligible individuals attended the study visit and provided informed consent (66.7%). The remaining ten participants did not follow up to schedule an in-person study visit. All participants who consented successfully completed the study procedures.
Sociodemographic characteristics for study completers (N = 20) are provided in Table 2. The median age was 46 years old (range 26–68). Participants primarily self-identified as White (60%), not Hispanic or Latino (75%), and homosexual or gay (70%). Forty percent had completed a college degree or higher.
Table 2.
Sociodemographic characteristics (N = 20).
| Variable | N | % |
|---|---|---|
| Age (years) | ||
| Median (IQR) | 46 (19) | – |
| Range | 26–68 | – |
| Gender | ||
| Male | 17 | 85% |
| Nonbinary/gender fluid/genderqueer | 2 | 10% |
| Prefer not to answer | 1 | 5% |
| Race | ||
| Black | 4 | 20% |
| White | 12 | 60% |
| Other | 1 | 5% |
| More than one race | 3 | 15% |
| Ethnicity | ||
| Not Hispanic or Latino | 15 | 75% |
| Hispanic or Latino | 5 | 25% |
| Sexual orientation | ||
| Bisexual | 1 | 5% |
| Homosexual or gay | 14 | 70% |
| Heterosexual | 3 | 15% |
| Other term used | 1 | 5% |
| Prefer not to answer | 1 | 5% |
| Relationship status | ||
| Single | 13 | 65% |
| Committed relationship | 2 | 10% |
| Domestic partnership | 2 | 10% |
| Married | 2 | 10% |
| Separated | 1 | 5% |
| Education | ||
| High school or GED | 7 | 35% |
| Some college | 5 | 25% |
| College degree | 6 | 30% |
| Graduate/professional degree | 2 | 10% |
| Annual income | ||
| Less than $6000 | 5 | 25% |
| $6000 to $11,999 | 5 | 25% |
| $12,000 to $17,999 | 1 | 5% |
| $24,000 to $29,999 | 1 | 5% |
| $30,000 to $59,999 | 4 | 20% |
| $60,000 or more | 4 | 20% |
Note: GED, General Educational Development.
Quantitative results
At enrollment, most participants had been on ART for more than five years (85%), and 70% self-reported 100% adherence to their ART over the past six months. The most commonly self-reported substances used were alcohol (60%) and cannabis (60%). Participants reported a mean score of 7.9 (SD 6.1) on the PHQ-8 and 6.0 (SD 6.22) on the GAD-7, indicating moderate levels of depressive and anxiety symptoms, respectively. The mean score for medical mistrust, as measured by the GBMMS, was 12.6 (SD 3.9), suggesting a high degree of trust in the medical sphere. On the MTUAS, participants reported positive attitudes toward technology (mean 3.7, SD 0.9), and a moderate level of anxiety or dependence on technology (mean 3.3, SD 1.1) (Table 3). Most participants reported being sexually active over the past three months (60%). One fifth (20%) reported history of STI, and the majority (60%) reported almost never or never using condoms during sexual intercourse during the past three months.
Table 3.
Descriptive statistics for select quantitative measures (N = 20).
| Variable | N | % |
|---|---|---|
| Length of ART treatment | ||
| 6–12 months | 1 | 5% |
| 3–5 years | 2 | 10% |
| More than 5 years | 17 | 85% |
| Self-reported ART adherence over the past 6 months | ||
| 2 out of 7 days per week | 1 | 5% |
| 3 out of 7 days per week | 1 | 5% |
| 6 out of 7 days per week | 3 | 15% |
| 7 out of 7 days per week | 14 | 70% |
| Prefer not to answer | 1 | 5% |
| Ever had an STI | ||
| Yes | 4 | 20% |
| No | 16 | 80% |
| Sexually active in past 3 months | ||
| Yes | 12 | 60% |
| No | 8 | 40% |
| Number of sexual partners in past 3 monthsa | ||
| Median (IQR) | 4 (4) | – |
| Range | 1–18 | – |
| Condom use in past 3 monthsa | ||
| Almost never | 4 | 20% |
| Never | 8 | 40% |
| Not applicable | 8 | 40% |
| Stimulant use before sex in past 3 monthsa | ||
| Sometimes | 1 | 5% |
| Almost every time | 2 | 10% |
| Every time | 1 | 5% |
| Never | 8 | 40% |
| Not applicable | 8 | 40% |
| Lifetime substance use | ||
| Alcohol | 15 | 75% |
| Tobacco | 12 | 60% |
| Cannabis | 15 | 75% |
| Cocaine | 12 | 60% |
| Hallucinogens | 10 | 50% |
| Amphetamines | 10 | 50% |
| Inhalants | 6 | 30% |
| Sedative hypnotics | 6 | 30% |
| Opioids | 5 | 25% |
| Other | 2 | 10% |
| Mental health, mean (SD) | ||
| Depression (PHQ-8 score) | 7.9 (6.1) | – |
| Anxiety (GAD-7 score) | 6 (6.22) | – |
| GBMMS Score, mean (SD) | 12.6 (3.9) | |
| MTUAS Attitudes Subscale, mean (SD) | ||
| Positive attitudes toward technology subscale | 3.7 (0.9) | – |
| Negative attitudes toward technology subscale | 3.5 (1.1) | |
| Anxiety or dependence on the technology subscale | 3.3 (1.1) | |
| Preference for the task switching subscale | 3.1 (1.1) |
Note: ART, antiretroviral therapy; STI, sexually transmitted infections; PHQ-8, Patient Health Questionnaire-8; GAD-7, Generalized Anxiety Disorder 7-Item Scale; GBMMS, Group-Based Medical Mistrust Scale; MTUAS, Media and Technology Usage and Attitudes Scale.
Denominator: participants who reported being sexually active in the past 3 months.
Qualitative results
Key facilitators and barriers to the adoption of the technology were identified across several thematic areas. These included: (1) perceptions of the technology as an adherence measurement system; (2) verification of both ART formulation and, for those on multiple medications, confirmation of the correct medication being taken; (3) uses of adherence data generated by the system; (4) preliminary specifications for adherence reminders and app infrastructure; and (5) overall suggestions for future iterations of the system (Table 4).
Table 4.
Content areas and selected participant quotes.
| Theme | Exemplar Quotes |
|---|---|
| Overall perceptions of technology | “I think it’s a little invasive just tracking it and seeing-having somebody else track it for you.” (Age 39, >5 years on ART, 100% self-reported adherence) “I honestly think it’s awesome. I think it has a lot of great applications.” (Age 54, >5 years on ART, 100% self-reported adherence) |
| Verification of medications | “People are afraid…You go to the doctor, and the doctor prescribes medication for you, but you wanna make sure that it’s the right medication that you’re getting…That it’s not a fake pill, and again, to go back to TruTag® by being able to scan, you can see what you’re taking.” (Age 67, >5 years on ART, adherence not reported) |
| Uses of adherence data from system | “I think it would help me understand about the medication that I’m takin’. It could provide more information for me; how I’m takin’ it, how much am I takin’, and when I’m usin’ it.” (Age 60, >5 years on ART, 100% self-reported adherence) “There’s no measure that I actually took the pill…It’s not actually a measure of adherence. It’s just a trust measure that I’m taking the pill after, so it is a step toward adherence.” (Age 39, >5 years on ART, 86% self-reported adherence) |
| Preliminary app and reminder structure | “It’s extremely useful and only to the benefit of improving the use of the app and your medication scheduling. Software interface design will make or break this and the more polish that can be put on that and the more supportive it can be of helping folks know where in the process they are of identifying and taking their medications regularly, the better.” (Age 56, > 5 years on ART, 100% self-reported adherence) “The reminders would be helpful for adherence and remembering to take medications; the reminders could have affirmations, such as ‘Congratulations, you’ve taken your meds 20 days in a row.” (Age 42, 6–12 months on ART, 86% self-reported adherence) |
| Adoption of technology | “It would be convenient. It would help me mentally, emotionally ‘cause I have a problem with havin’ to take medication. I take it, religiously. Don’t get me wrong, but it would be fabulous if I could go on the app.” (Age 67, >5 years on ART, adherence not reported) “I’d say it’s 50/50 at present. It would’ve to be a good user interface solution for me to actually adopt it…My one-a-day 24-hour adherence on my ARTs is already pretty darn good. Maybe a missed dose a year. This doesn’t provide a lot of extra benefit at present, but it could absolutely be very useful.” (Age 56, >5 years on ART, 100% self-reported adherence) |
| Recommendations | “The app should have a user interface that reinforces use and provides good feedback movements to the user. The app should also have a confirmation that the pill has been taken as a separate action, and an additional entry for pill that could not scan.” (Age 56, >5 years on ART, 100% self-reported adherence) “The camera didn’t, or the app rather, didn’t pick up on a couple of the pills at first. I scanned two pills and then the third one picked up. I don’t know if it’s got some polishing that needs to be done.” (Age 45, >5 years on ART, 29% self-reported adherence) |
Overall perceptions of the technology
Most participants found the ability to noninvasively and discreetly record ART adherence to be a key facilitator of future use of the technology. They described the process of scanning medication with a smartphone camera as a novel and innovative way to leverage smartphones as an adherence tool. Among individuals already using apps, smartphone alarms, or notes to track adherence (n = 6), taggant systems were perceived as a natural extension of their existing methods. The simplicity of the system (e.g., using the camera to scan the pill with minimal additional actions required) was identified as a potential facilitator of use. Even among participants who were already adherent to ART, the ability to verify that they had already taken their medication and to visualize adherence over time via the calendar feature in the app was viewed as significant benefits.
Participants also identified theoretical barriers to operationalizing the system. Older participants noted challenges such as hand tremors and difficulty visualizing the pill through their smartphone camera. These issues were perceived as creating an extra time burden, particularly for individuals managing multiple chronic diseases and medication regimens. Notably, despite these concerns, all participants were able to successfully scan medications after brief instruction during the qualitative interview, highlighting the system’s potential usability after minor adjustments.
Verification of medications
An emergent theme across participants was the use of taggants to verify the specific medication taken. Some participants expressed concerns about counterfeit medications, an issue often highlighted in the media and their social circles. Others mentioned the challenge of managing multiple medications, leading to confusion about which ones they had already taken. Taggant systems, especially when combined with adherence-focused smartphone apps, were consistently identified as a useful tool to confirm ART adherence on a given day. Many participants highlighted the integration of microparticles into their ART during manufacturing as a noninvasive strategy that would likely provide them with peace of mind after operating the system. This feature was particularly valued by participants who knew others who had obtained ART via online pharmacies or other nontraditional sources.
Uses of adherence data
Participants overwhelmingly viewed the ability to access on-demand ART adherence data as a facilitator for future use of the technology. Some described how the system might improve their adherence by introducing the additional step of scanning the medication prior to ingestion. Participants also noted that adherence data could be valuable during conversations with clinical care teams and in identifying potential lapses in adherence. One participant highlighted the system’s potential as an intervention for individuals newly diagnosed with HIV, suggesting it could serve as a tool to monitor and teach ART adherence at the start of pharmacotherapy.
Participants also identified potential individuals and groups with whom they would feel comfortable sharing adherence data. These included clinicians, close partners, family members, and pharmacists. There was universal agreement that prescribing clinicians should have access to adherence data to support ongoing HIV care. Most participants also identified partners as key supporters of their daily ART adherence and viewed the sharing of adherence data from the taggant systems with partners as a strategy that could enhance adherence support.
Preliminary specifications for adherence reminders and app infrastructure
During the study visit, participants were introduced to a wireframe app that enabled individuals to scan a test medication and visualize adherence patterns over time on a calendar within the app. These features were universally recognized by all participants as essential components of the app. Most participants also emphasized the importance of integrating a reminder system into the app. Recommendations included initial sessions where participants could collaborate with their clinicians to set up personalized reminder systems, which would involve tailoring both the content of reminder messages and their timing based on individual adherence patterns. Participants suggested structuring reminder messages using contingent reinforcement (e.g., positive feedback for adherence), even in instances of nonadherence.
Although many participants already had multiple reminders and alarms on their phones, they generally viewed multiple daily reminders – particularly timed close to when they usually take their medications – as an optimal strategy. Only one participant regarded the structuring of reminders as a potential barrier to using the system, stating that they perceived the system primarily as a tool for recording ingestions rather than enhancing adherence.
In addition to reminders, participants suggested that the app could serve as a repository for sexual health and HIV-related educational topics. Some participants found the idea of learning more about HIV and ART adherence within the context of scanning and recording their medications acceptable and valuable. However, others expressed concern that including additional HIV-related information unrelated to their personal adherence data could reduce the acceptability of the system, perceiving it as akin to targeted advertising.
Adoption of system and recommendations for future iterations
While initial concerns regarding the safety of the system were anticipated, most participants’ concerns were assuaged after discussion with the study team. Participants expressed apprehension about potential toxicity from the silica particles but were reassured by the information that these particles are categorized as GRAS compounds by the FDA. One participant voiced a general distrust of government regulatory bodies and continued to express concerns about potential long-term safety effects. Once these concerns were assuaged, participants highlighted areas for improvement in future iterations of the system, particularly around improving the reliability of scanning medications. Importantly, for individuals with poor eyesight or hand tremors, lowering the threshold for the camera to confirm and authenticate medications was identified as a critical refinement to ensure the system’s usability, especially among a target population likely to benefit from it most.
Additional recommendations included integrating a feature to manually log adherence events in cases where the scanning function might malfunction and incorporating educational resources about ART adherence and HIV treatment into the app. These enhancements were viewed as valuable ways to increase the system’s usability and perceived utility. Among individuals who reported 100% ART adherence at baseline, a robust user interface that integrates adherence coaching with other features of sexual health and HIV care was viewed as an important component of a future system.
Discussion
Despite global efforts to end the HIV epidemic, new infections continue to occur, underscoring the importance of strategies to maintain adherence to ART. Sustained ART adherence is critical for achieving undetectable viral loads in people with HIV (PWH), effectively eliminating the risk of transmission and ensuring that they receive optimal benefits from treatment (Schaecher, 2013). Various methods to accurately measure adherence have been explored, each presenting unique advantages and limitations (Haga, 2020; Mason et al., 2022). The use of a microparticle taggant integrated into the coating of ART represents a novel and significant advancement that facilitates indirect adherence detection and enhances electronic measurement capabilities. Although smart pill bottles have been widely studied as an economical strategy for tracking adherence, their clinical implementation has been hindered by issues such as high attrition rates and challenges integrating the technology into daily routines.
Given the increasing ubiquity of smartphones, leveraging smartphone cameras as “digital pill bottles” to record adherence offers an innovative and potentially more practical approach to measuring adherence among PWH. While this technology has yet to be used clinically among PWH, our formative qualitative findings suggest that this population can operate the technology effectively and view it as an acceptable strategy for measuring adherence and better understanding HIV care. Participants in our study highlighted the potential for this platform to support personalized adherence reminders and deliver general HIV-related educational content, further enhancing its value. These insights suggest that the deployment of taggant technologies paired with smartphones as digital adherence tools may be feasible in biobehavioral clinical trials. Such advances hold promise for refining adherence measurement strategies and could contribute meaningfully to efforts to end the HIV epidemic.
We found that even with brief instruction, the proposed taggant system was easily accepted and operated by PWH with little prior experience leveraging a smartphone to measure ART adherence. All participants successfully recorded a single mock adherence event during the study visit. Following this one-time interaction with the system, many participants reported they would be willing to consider it as an adherence monitoring tool. For those already accustomed to smartphone use, scanning ART pills was viewed as a natural progression of existing adherence tools. Key facilitators of system use included the ability to verify ART medication, view adherence trends over time, and share adherence data with members of their care team.
Adherence reminders emerged as a critical feature for promoting the adoption of the system. Nearly all participants agreed that reminders would be beneficial, emphasizing the importance of personalizing the frequency and timing of reminders. Interestingly, despite already receiving multiple phone notifications, many participants underscored the value of several daily reminders centered on medication-taking events. These findings suggest that the continued development of systems incorporating medication verification and digital adherence monitoring could enhance adherence support in HIV care.
Despite enthusiasm around using taggant technology to support ART adherence, some areas require additional refinement before broader deployment. First, the user experience of the app, particularly the mechanics of scanning medications with a smartphone camera, may need improvement. Individuals who are older or living with HIV may experience neuropathy or tremors that could make it more difficult to scan pills. Second, the integration of microparticle tags onto ART pills will likely need to occur at the manufacturer level. While these components are GRAS, their integration into medications may necessitate adjustments to supply chains and regulatory approvals. These key modifications may add cost to the integration of such a system into the armament of HIV treatment adherence measurement. Future work should explore the potential economic benefits of this system from both an individual level (improved HIV ART adherence) and systems level (enabled asset tracking across supply chains).
This investigation had several limitations. First, the study was conducted at a single site with expertise in HIV research, treatment, and prevention, which may limit generalizability to other clinical settings. Second, most participants were White and had been living with HIV for many years, potentially influencing their experiences and perspectives on ART adherence. Most participants reported perfect adherence and had established medication routines, which may have affected their perceived need for additional adherence support. Third, participants had low medical mistrust scores (mean GBMMS score: 12.6 (SD 3.9)), indicating high levels of trust in the medical system, as well as relatively positive attitudes toward technology (mean MTUAS positive subscale score: 3.7 (SD 0.9)). These factors may have attenuated potential concerns regarding GRAS compounds and data safety, which could differ in populations with higher levels of mistrust or lower technology acceptance.
Overall, this formative qualitative study found that using a microparticle taggant to identify medication, measure ingestions, and support ART adherence in HIV treatment may be feasible. Preliminary user feedback was largely positive, with the potential for improved accountability, smartphone-based adherence measurement and reminders, and medication verification cited as the most significant facilitators. Future user experience enhancements and detection capability improvements are likely to increase adoption even further.
Acknowledgments
We would like to thank all study participants for taking the time to share their experiences and feedback. Leanne Loo: Data Curation, Formal Analysis, Writing - original draft. Allison Frank: Investigation, Data Curation, Formal Analysis. Grace Valentine: Data Curation, Formal Analysis. Peevara Srimanus: Writing - review and editing, Project Administration. Georgia R. Goodman: Data Curation, Investigation, Methodology, Project Administration, Writing - review and editing. Kenneth H. Mayer: Conceptualization, Writing - review and editing. Barry McDonough: Conceptualization, Writing - review and editing, Funding Acquisition. Conall O’Cleirigh: Conceptualization, Writing - review and editing. Jasper S. Lee: Conceptualization, Writing - review and editing, Methodology. Peter R. Chai: Conceptualization, Data Curation, Investigation, Methodology, Project Administration, Supervision, Writing - review and editing.
Funding
This work was supported by National Institute of Allergy and Infectious Diseases [P30AI060354]; National Institute of Mental Health [K23MH135748]; National Institute on Drug Abuse [DP2DA056107, K24DA062568]; Trutag Corporation. The work was funded through an investigator-initiated research grant from TruTag® Technologies, Inc. Peter R. Chai is funded by DP2DA056107, Kenneth H. Mayer and Conall O’Cleirigh are funded through P30AI060354. Conall O’Cleirigh is funded via K24DA062568. Jasper S Lee is funded via K23MH135748.
Footnotes
Disclosure statement
Barry McDonough is the CEO of TruTag® Technologies, the company that developed the system evaluated in this study. All other authors declare no competing interests.
Data availability statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
- Bell KM, & Haberer JE (2018). Actionable adherence monitoring: Technological methods to monitor and support adherence to antiretroviral therapy. Current HIV/AIDS Reports, 15(5), 388–396. 10.1007/s11904-018-0413-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Benjamin RM (2012). Medication adherence: Helping patients take their medicines as directed. Public Health Reports, 127(1), 2–3. 10.1177/003335491212700102 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Castillo-Mancilla JR, & Haberer JE (2018). Adherence measurements in HIV: New advancements in pharmacologic methods and real-time monitoring. Current HIV/AIDS Reports, 15(1), 49–59. 10.1007/s11904-018-0377-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Centers for Disease Control and Prevention. (2025). Estimated HIV incidence and prevalence in the United States, 2018–2022 (No. 1).
- Chai PR, Rosen RK, & Boyer EW (2016). Ingestible biosensors for real-time medical adherence monitoring: MyTMed. In 2016 49th Hawaii International Conference on System Sciences (HICSS) (pp. 3416–3423). IEEE. 10.1109/HICSS.2016.426 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chai PR, Vaz C, Goodman GR, Albrechta H, Huang H, Rosen RK, Boyer EW, Mayer KH, & O’Cleirigh C (2022). Ingestible electronic sensors to measure instantaneous medication adherence: A narrative review. Digital Health, 8, 205520762210831. 10.1177/20552076221083119 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Deisingh AK (2005). Pharmaceutical counterfeiting. The Analyst, 130(3), 271. 10.1039/b407759h [DOI] [PubMed] [Google Scholar]
- Ellsworth GB, Burke LA, Wells MT, Mishra S, Caffrey M, Liddle D, Madhava M, O’Neal C, Anderson PL, Bushman L, Ellison L, Stein J, & Gulick RM (2021). Randomized pilot study of an advanced smart-pill bottle as an adherence intervention in patients with HIV on antiretroviral treatment. JAIDS Journal of Acquired Immune Deficiency Syndromes, 86(1), 73–80. 10.1097/QAI.0000000000002519 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Fayazpour F, Lucas B, Huyghebaert N, Braeckmans K, Derveaux S, Stubbe BG, Remon J-P, Demeester J, Vervaet C, & De Smedt SC (2007). Digitally encoded drug tablets to combat counterfeiting. Advanced Materials, 19(22), 3854–3858. 10.1002/adma.200602800 [DOI] [Google Scholar]
- Haga SB (2020). Toward digital-based interventions for medication adherence and safety. Expert Opinion on Drug Safety, 19(6), 735–746. 10.1080/14740338.2020.1764935 [DOI] [PubMed] [Google Scholar]
- Kamal S, Rosen MI, Lazar C, Siqueiros L, Wang Y, Daar ES, & Liu H (2020). Perceptions of people living with HIV and HIV healthcare providers on real-time measuring and monitoring of antiretroviral adherence using ingestible sensors: A qualitative study. AIDS Research and Treatment, 2020, 1–10. 10.1155/2020/1098109 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Knopf AS, Krombach P, Katz AJ, Baker R, & Zimet G (2021). Measuring research mistrust in adolescents and adults: Validity and reliability of an adapted version of the group-based medical mistrust scale. PLoS One, 16(1), e0245783. 10.1371/journal.pone.0245783 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kordy F, Nashid N, Khan S, Read S, Macdougall G, Louch D, Arneson C, & Bitnun A (2023). Video directly observed therapy to improve adherence of human immunodeficiency virus infected adolescents to combination antiretroviral therapy: A proof-of-concept study. AIDS Care, 35(3), 417–424. 10.1080/09540121.2022.2104794 [DOI] [PubMed] [Google Scholar]
- Kroenke K, Strine TW, Spitzer RL, Williams JBW, Berry JT, & Mokdad AH (2009). The PHQ-8 as a measure of current depression in the general population. Journal of Affective Disorders, 114(1–3), 163–173. 10.1016/j.jad.2008.06.026 [DOI] [PubMed] [Google Scholar]
- Liu H, Wang Y, Huang Y, Xiong D, Shen J, Siqueiros L, Bala V, Savage GM, Guerrero M, Corado K, Rosen MI, Fletcher CV, & Daar ES (2022). Ingestible sensor system for measuring, monitoring and enhancing adherence to antiretroviral therapy: An open-label, usual care-controlled, randomised trial. eBioMedicine, 86, Article 104330. 10.1016/j.ebiom.2022.104330 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Madi D, Bhaskaran U, Ramapuram J, Rao S, Mahalingam S, & Achappa B (2013). Adherence to antiretroviral therapy among people living with HIV. North American Journal of Medical Sciences, 5(3), 220. 10.4103/1947-2714.109196 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mason M, Cho Y, Rayo J, Gong Y, Harris M, & Jiang Y (2022). Technologies for medication adherence monitoring and technology assessment criteria: Narrative review. JMIR MHealth and UHealth, 10(3), e35157. 10.2196/35157 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mehta S, Moore RD, & Graham NMH (1997). Potential factors affecting adherence with HIV therapy. AIDS, 11(14), 1665–1670. 10.1097/00002030-199714000-00002 [DOI] [PubMed] [Google Scholar]
- Rosen LD, Whaling K, Carrier LM, Cheever NA, & Rokkum J (2013). The media and technology usage and attitudes scale: An empirical investigation. Computers in Human Behavior, 29(6), 2501–2511. 10.1016/j.chb.2013.06.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rosen RK, Gainey M, Nasrin S, Garbern SC, Lantini R, Elshabassi N, Sultana S, Hasnin T, Alam NH, Nelson EJ, & Levine AC (2023). Use of framework matrix and thematic coding methods in qualitative analysis for mHealth: The FluidCalc app. International Journal of Qualitative Methods, 22, 16094069231184123. 10.1177/16094069231184123 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Schaecher KL (2013). The importance of treatment adherence in HIV. The American Journal of Managed Care, 19(12 Suppl), s231–s237. [PubMed] [Google Scholar]
- Simoni JM, Kurth AE, Pearson CR, Pantalone DW, Merrill JO, & Frick PA (2006). Self-report measures of antiretroviral therapy adherence: A review with recommendations for HIV research and clinical management. AIDS and Behavior, 10(3), 227–245. 10.1007/s10461-006-9078-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Spinelli MA, Haberer JE, Chai PR, Castillo-Mancilla J, Anderson PL, & Gandhi M (2020). Approaches to objectively measure antiretroviral medication adherence and drive adherence interventions. Current HIV/AIDS Reports, 17(4), 301–314. 10.1007/s11904-020-00502-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Spitzer RL, Kroenke K, Williams JBW, & Löwe B (2006). A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine, 166(10), 1092. 10.1001/archinte.166.10.1092 [DOI] [PubMed] [Google Scholar]
- TruTag® Technologies. https://TruTag®s.com
- Van Wyk BE, & Davids L-AC (2019). Challenges to HIV treatment adherence amongst adolescents in a low socioeconomic setting in Cape Town. Southern African Journal of HIV Medicine, 20(1), 1002. 10.4102/sajhivmed.v20i1.1002 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Venkatesh V, & Davis FD (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. 10.1287/mnsc.46.2.186.11926 [DOI] [Google Scholar]
- WHO ASSIST Working Group. (2002). The Alcohol, Smoking and Substance Involvement Screening Test (ASSIST): Development, reliability and feasibility. Addiction, 97(9), 1183–1194. 10.1046/j.1360-0443.2002.00185.x [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.