This qualitative study was conducted at the New Hampshire (NH) site of the multistate randomized controlled trial, Long-Acting Buprenorphine vs Naltrexone Opioid Treatments in CJS-Involved Adults (EXIT-CJS) [45]. EXIT-CJS is an open-label, noninferiority trial comparing extended-release buprenorphine (XR-B, Sublocade), extended-release naltrexone (XR-NTX; Vivitrol), and enhanced treatment as usual among adults with criminal legal system involvement and moderate-to-severe OUD. Participants were either currently incarcerated with an expected release date within 6 months or living in the community with a history of incarceration or supervision within the prior 6 months. Participants in the medication arms were offered study medication injections monthly for 24 weeks post-release under the care of a local substance use treatment clinic, after which monthly study follow-up visits for survey assessments continued until week 48. This qualitative study recruited participants with a history of recent incarceration from the NH EXIT-CJS study sample. To be eligible for an interview, NH EXIT-CJS participants had to be at least 2 months post-release from jail or prison and still within the 12-month EXIT-CJS study period. Reincarcerated EXIT-CJS participants remained eligible once they had spent at least two cumulative months in the community and were still within the 12-month EXIT-CJS study window. The EXIT-CJS study launched during the COVID-19 pandemic, during which most partnering carceral sites increased their use of telehealth, including for the treatment of SUDs [46].

To reduce in-person study activities during the COVID-19 pandemic, the NH EXIT-CJS study team gave all NH site participants a smartphone upon release with 1 year of unlimited talk, text, and data. Participants engaged in monthly EXIT-CJS study visits primarily via HIPAA (Health Insurance Portability and Accountability Act)–compliant Zoom to complete follow-up survey assessments. For recruitment into the interview substudy, the study team recruited incarcerated EXIT-CJS participants post-release during study visits 2 to 8 (weeks 4‐48) and recruited formerly incarcerated EXIT-CJS participants during the baseline study visit through the final week 48 study visit.

The qualitative study principal investigator (MFS) and the EXIT-CJS team met weekly to identify newly recruited EXIT-CJS participants who were eligible for an interview. The team aimed to recruit approximately 12 participants per EXIT-CJS study arm and purposively sample across the 3 study arms to maximize variation in biological sex, race, and ethnicity. During EXIT-CJS study visits with eligible participants, a team member described the qualitative study and provided an informational recruitment flyer. MFS then contacted interested persons via telephone to confirm eligibility, review study details, schedule Zoom teleconference interviews, and advise participating in a private setting. During the Zoom teleconference, the interviewer described the principal investigator’s professional background, shared motivations for conducting the study, and read aloud the study information sheet shared on screen. Interviewers assessed participants’ comprehension and addressed questions before obtaining verbal consent. Before beginning, interviewers assessed participants’ comfort with their level of privacy. Two female researchers conducted all interviews (MFS and KDB). Both researchers had completed training on qualitative interviewing. One researcher (KDB) engaged with some participants as part of the larger EXIT-CJS study team, while the other interviewer (MFS) had no prior relationship with participants. Interviews lasted approximately 90 minutes, were audio recorded, and transcribed verbatim by Rev.com. MFS reviewed the transcripts for quality and removed any potentially identifying information (eg, person names). Recruitment continued until thematic saturation was reached. Saturation was determined through team discussions assessing for emergence of new sentiments in interviews and reached by team consensus that no new themes were being expressed by participants. Participant recruitment for interviews occurred between August 2021 and February 2024.

The semistructured interview guide covered three domains: (1) substance use and treatment experiences post release, (2) medication and treatment preferences during reentry, and (3) perceptions of technology use for substance use treatment during incarceration and reentry (the focus of this analysis; Multimedia Appendix 1). Domains 1 and 2 were informed by the Dynamic Biopsychosocial Model and 2 prior qualitative studies on MOUD experiences during reentry, both of which were grounded in the Social Cognitive Theory[47,48]. The Dynamic Biopsychosocial Model considers how health is dynamically influenced by interactions between an individual’s biomedical, psychological, and socioenvironmental contexts [49]. Biopsychosocial factors and the weight of their impact on health change as individuals face new major life challenges (eg, reentry) that may demand different sets of resources. The 2 prior qualitative studies included (1) Velasquez et al’s study of reentry experiences and perceptions of XR-NTX among adults with OUD released from New York City jails [48], and (2) Fox et al’s study of reentry experiences and perceptions of sublingual formulations of buprenorphine among formerly incarcerated adults with OUD engaged in addiction treatment in New York City[47]. Velasquez and Fox drew upon the Social Cognitive Theory to ask questions about how these key determinants of behavior change (ie, self-efficacy, outcome expectations, perceived barriers and facilitators, behavioral patterns, observational learning, and the environment) influenced treatment engagement, retention, and post-incarceration opioid and other drug use [50].

Due to the lack of prior studies assessing technology use and perceptions among incarcerated and reentry populations, the team developed domain 3 interview questions drawing from the Dynamic Biopsychosocial Model, the domain’s research objective (ie, to assess how recently incarcerated adults with OUD perceive digital health tools and to identify barriers to and facilitators of engaging with these technologies during incarceration and reentry), and team member expertise in digital health research (ECS and LAM). Instead of pilot testing, all authors reviewed and revised the interview guide. Participants received a brief orientation to relevant technologies (ie, smartphones, laptops, and computers) and digital tools (eg, telemedicine and health apps) before discussing the third domain.

Qualitative analysis of the third interview domain (ie, technology perceptions) used a deductive-inductive approach to content analysis [51] using Atlas.ti software. The coding team (MFS, ECS, SKM, and KDB) developed an initial codebook for this interview domain based on the Dynamic Biopsychosocial Model [49] and the concepts targeted in each interview question. The team coded small batches of 3 to 5 transcripts at a time, with analysts independently applying deductive codes and identifying new emergent codes. The team met regularly to revise codes and refine the codebook. Discrepancies in coding were resolved through discussion. The team applied a final code structure across all transcripts using a constant comparison method. Subtheme analysis involved grouping excerpts by code and developing categories. Codes were exported to separate Microsoft Word documents. For each code, 2 analysts independently reviewed the emergent categories across all code groups and synthesized these into overarching themes and subthemes, which were finalized through team consensus. The COREQ (Consolidated Criteria for Reporting Qualitative Research) criteria guided the reporting of the study methods and findings (Checklist 1) [52]. The team conducted the qualitative analysis from February 2022 to December 2025.

The NH EXIT-CJS team collected baseline survey data from all EXIT-CJS participants, including demographics, sentencing length, educational attainment, and health care engagement. The team calculated descriptive statistics (ie, counts with proportions and medians with IQRs) in Microsoft Excel to summarize these baseline characteristics for interview participants.

Ethical approval for the study was obtained from the Dartmouth College Institutional Review Board (STUDY00032371). The project was conducted in accordance with the Declaration of Helsinki guidelines and declarations. Verbal informed consent was collected from participants before each interview, and they were informed of their right to end the interview, pause, or skip questions at any time. The study team maintained participants’ privacy and confidentiality throughout recruitment, data collection, data analysis, and reporting. Participants remained deidentified using an assigned study number and removal of all mentioned names from interview transcripts. All data were stored securely on a HIPAA-compliant Google Drive maintained by Dartmouth College. Participants were compensated U.S. $50 via reloadable study debit cards for their contribution to the study.

A total of 40 EXIT-CJS trial participants were approached for an interview, among whom 39 agreed to participate, including 15 (38.5%) randomized to the XR-B arm, 11 (28.2%) randomized to the XR-NTX arm, and 13 (33.3%) in the enhanced treatment as usual arm. Table 1 describes the participant characteristics. Participants were predominantly men (n=23, 59%), non-Hispanic (n=37, 94.9%), and White (n=37, 94.9%). The median reported sentencing length was 328 (IQR 822.5) days. When asked an open-ended question in the interviews about access to internet-capable devices (Table 2), nearly half of participants (n=18, 46.2%) reported that the study-issued smartphone was their only functional internet-capable device, and only 11 (28.2%) participants expressed confidence that they would have been able to access a phone at release if the study had not provided one. Challenges to accessing a reliable device during reentry included uncertain access to items that were left in storage or placed under the care of others during incarceration, limited functional capability of their existing devices, the need to rely on public resources to access devices (eg, library computer and Lifeline-issued phone), limitations of borrowing devices from others, and device theft.

In this qualitative study of recently released adults with OUD, participants described digital health tools as an acceptable modality for engaging in health care during reentry. Only 2 of the 39 participants expressed strong personal preferences against engaging in digital health care. However, even these participants viewed digital health technologies as highly convenient and easy to integrate into their lives amidst the abundant socioecological challenges encountered during reentry. Most participants emphasized that the digital delivery of health care reduced or eliminated many of the practical barriers that routinely block access to in-person care during reentry, including transportation costs and logistics, lost wages for time off work, and fragile social supports. In doing so, digital health technologies allow health care to be delivered any place and any time that participants could feasibly receive it and enable more autonomy to act on their care preferences. Digital health technologies support care models that meet reentering patients where they are rather than requiring them to overcome excessive resource constraints to reach care.

Our findings align with prior evidence that telemedicine and other virtual care modalities can increase care access and patient satisfaction and overcome travel-related barriers, particularly for populations facing geographic or resource constraints [53-55]. The high acceptability of digital health during reentry suggests the potential to develop digital health tools that target reentry-specific overdose risks and health needs, such as delivering reentry planning and resource mapping, overdose prevention interventions and education, and linkage facilitation to addiction treatment and recovery supports.

However, participants emphasized that the benefits of digital health care are contingent on a set of enabling resources: a reliable internet-capable device, an active data service, the financial means to sustain connectivity, and a baseline of digital literacy and comfort with technology. These findings are consistent with studies assessing the feasibility of engaging in digital health for OUD [55]. Without these resources, digital care is selectively accessible and risks exacerbating disparities among people reentering the community [28]. The risk of widening the digital divide is particularly relevant for settings with limited broadband infrastructure, such as rural communities in the NH study setting, as well as during periods of public disinvestment in connectivity supports. For example, the recent wind-down of the Affordable Connectivity Program eliminated a widely adopted subsidy that reduced the cost of internet-capable devices and broadband service for low-income households [24,56]. Our study adds to the well-documented literature on the digital divide among marginalized populations by highlighting the unique vulnerabilities and contexts that shape access to digital health care specific to adults with OUD during reentry and the need for investments in connectivity, device access, affordability, and knowledge transfer to realize equitable benefits.

Participants nearly universally described a high value for in-person, human connection when receiving health care, even those who preferred digital care for convenience. Consistent with findings in the general population, in-person care was seen as important for facilitating diagnostic accuracy [57], and prosocial human connection was regarded as therapeutic and positively reinforcing. Some participants described the tension between desiring in-person care and the perceived higher level of accountability but lacking the resources necessary to participate in an in-person care delivery model. This emphasis on human connections is consistent with literature linking social capital and healthy interpersonal support to recovery outcomes [58-61], and it suggests that any digital health care models that wholly eliminate in-person connection and human presence may risk eroding important recovery supports. This signals a need to address the upstream structural barriers to reaching in-person care models during reentry, such as restricted access to adequate housing and employment, limited transportation, debt, and insufficient financial assistance [30,31,62], which may go unaddressed and therefore be reinforced through a focus on digital innovation alone [63,64]. Structural reforms in combination with care model redesigns that make care less provider-centric and more patient-centered may strengthen patients’ capacity to feasibly engage in care in person.

Participants’ accounts also indicate the desirability and potential acceptability of hybrid models that intentionally blend digital tools with preserved or enhanced human touch. These models rethink where, how, and when we deliver in-person services to align with patients’ existing resources for access instead of the current model of providing access contingent on patient resource acquisition. One example of a potentially acceptable model being tested with patients with OUD, including reentry populations, is the InMOTION mobile pharmacy and clinic, which brings a low-barrier mobile SUD and infectious disease treatment into community settings via hybrid in-person and telehealth encounters [65]. Such a model may provide a practical template for patient-centered hybrid care approaches. Other potentially acceptable models not yet tested with incarcerated and reentering populations may include device distribution programs or telehealth access points located in accessible, trusted, community-based locations (eg, community centers and pharmacies) equipped with the necessary technologies, which might extend access where connectivity is unreliable, as has been demonstrated in other populations [66,67]. Future research should explore design features of digital tools that preserve and strengthen therapeutic human relationships.

A less commonly expressed but notable sentiment expressed by a few participants included the role of internalized and enacted stigmas in shaping preferences for in-person versus digital health care. Regarding internalized stigma, 2 participants described recovery as necessarily effortful, made valid by the exertion of willpower, and characterized by experiencing triumph amid adversity. These sentiments reflect the broader discourse on willpower-driven recovery and biases against approaches perceived as requiring less willpower (eg, MOUD), which can be dismissed as a “crutch” not representative of true recovery [68,69]. The internalization of the willpower-over-adversity recovery trope may discourage some people with SUD from engaging in emerging resources that reduce the effortful burden of recovery (eg, digital modalities of care delivery), regardless of their effectiveness. Regarding enacted stigma, 2 participants who preferred digital health care described how it has helped to avoid experiencing stigma from health care staff when circumstances make it impossible to adhere to traditional care norms and expectations (eg, arriving on time when Medicaid ride is unpredictably late). Previous studies have described how enacted stigma by health care providers can negatively impact care experience and engagement for people with SUD [70-72]. Altogether, our findings suggest that digital interventions for SUD may benefit from integrating stigma prevention and mitigation strategies to facilitate reach, adoption, and engagement.

Finally, policy levers may provide a pathway to address the digital divide for reentering populations on a wider scale. Payers and Medicaid programs should examine the cost-effectiveness of covering reliable internet-capable devices and data plans (eg, for the first year of release from incarceration). The Centers for Medicare & Medicaid Services and states should also consider using Medicaid 1115 demonstration applications to pilot coverage of internet-capable devices and data plans during reentry and explore innovations to address connectivity supports, other social determinants of health, and digital care models. Other potential state-based funding mechanisms to support digital access and innovation during reentry include Rural Health Transformation Funds and Opioid Abatement funds. Such policy levers could directly address the resource barriers our participants identified and help ensure that digital solutions reduce rather than exacerbate reentry disparities.

The findings are based on a purposive sample of adults with OUD released from NH prisons and jails, recruited from participants in a randomized clinical trial. Leveraging trial recruitment maximized the information obtained from existing participants, enhancing the return on taxpayers’ investment in research. However, several limitations exist. As a qualitative study, the findings reflect reentry experiences within the NH context with limited relevance for dissimilar populations or contexts. All participants were recruited from NH, where the population is predominantly White. While Black adults are disproportionately incarcerated in the state [73], our team was unable to recruit participants from racially and ethnically minoritized backgrounds. The lack of racial and ethnic diversity in our sample misses relevant perspectives, particularly given the overrepresentation of Black and Brown individuals in carceral systems nationwide due to systemic racism in policing, sentencing, and related structural inequities [74]. Our sample also had prior exposure to digital devices, including access to tablets during incarceration at the NH Department of Corrections, receipt of smartphones from EXIT-CJS, and experience participating in virtual study visits with the EXIT-CJS study team. Many of EXIT-CJS’s partnering carceral sites increased the use of telehealth during the COVID-19 pandemic, providing additional potential exposure to digital health care [46]. Therefore, this sample is not representative of reentering populations who have limited experience engaging with technology. Our study also describes perspectives provided over a 2.5-year period due to slow recruitment related to the larger trial’s recruitment pace, the substudy’s narrower eligibility criteria (ie, recently incarcerated only, not inclusive of community supervision alone), and attempts to balance recruitment across all 3 study arms and recruit both sexes. Given the fast pace of technological advancement and improving accessibility, later recruits may have experienced different levels of exposure to technology than earlier participants.

Future digital health research with incarcerated and reentering populations should intentionally include more diverse geographic and demographic samples to enhance the information power of the findings to reflect the needs and priorities of impacted communities. Such research should also intentionally recruit from more technology-naïve populations who have limited-to-no access to technology during incarceration or reentry to characterize their perceptions, preferences, and needs. Finally, our study launched before the mainstream emergence of AI and therefore did not assess perceptions of this new technology. In addition to enhancing personalized intervention and education on overdose prevention and substance use treatment, AI may present novel opportunities to address digital literacy needs among incarcerated and reentering populations. For example, closed-network AI models in correctional settings could provide interactive, self-paced training on basic and emerging digital tools without requiring open internet access. Such approaches could help prepare individuals for the technological advancements they will encounter post-release, reducing one barrier to engagement with digital health. Future research should assess this population’s perceptions of AI tools and ideas for innovation.

Adults with OUD returning from incarceration regard digital health as a valuable tool for expanding access to care and overcoming structural barriers to care during reentry, while emphasizing that its benefits are both contingent upon reliable, affordable access to devices and data and at odds with the strong desire for in-person therapeutic human connection. To realize the promise of digital health for improving health care access and usage during the high-risk reentry period at scale, stakeholders must partner with impacted communities to co-design acceptable and accessible technological innovations [75-78] in combination with meaningful investments in connectivity, device access, digital literacy, and hybrid care models that preserve interpersonal connection. Without these complementary investments, digital modalities risk reinforcing existing inequities among incarcerated and reentering populations rather than ameliorating them [79].