Recruitment and Retention Challenges in Opioid Use Disorder Studies: Insights and Strategies from a Pilot Digital Monitoring Study

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Opioid use disorder (OUD) affects millions in the United States. Emerging technologies like home motion sensors offer the potential for relapse prediction. The study evaluates the feasibility and acceptability of such technology in OUD patients. Methods . Participants were recruited through local OUD treatment centers in Columbus, Ohio. The study involved installing passive monitoring sensors in participants' homes and required participants to wear a Fitbit and complete daily surveys. The target was to enroll 25 patients, with incentives provided for participation. Results . Out of 170 evaluated records, 50 met the inclusion criteria, and only 14 consented to participate, with four completing the study. Main recruitment challenges included housing instability, privacy concerns, and the COVID-19 pandemic's impact. Most participants were willing to use sensor devices, especially in less private home areas. Conclusions . The study faced significant barriers in recruiting and retaining participants, highlighting the complexities of OUD research. Despite methodological adaptations like virtual follow-ups, the retention rate remained low. This suggests the need for more flexible, patient-centric approaches in future research, particularly for populations experiencing instability or distrust. The study underscores the potential of technology in treatment but emphasizes the importance of building trust and understanding within target communities. Figures Figure 1 Background Opioid use disorder (OUD) is a chronic medical condition affecting an estimated 2.4 million individuals in the United States [ 1 ]. Effective treatment for OUD includes the use of evidence-based medications such as buprenorphine, naltrexone, or methadone. It can also include behavioral therapies and support systems [ 1 , 2 ]. However, relapse is common among this patient population, and overdose deaths in the U.S. are rising, mainly due to fentanyl[ 3 ]. Emerging technologies may offer additional opportunities to support the recovery of OUD patients in the community. For example, home motion sensors can be placed in individuals’ home environments to detect living patterns remotely and have the potential to reveal deviations from typical behaviors that might indicate an elevated risk of relapse. In addition, these monitoring devices are discreet and do not burden individuals with the need to report their behaviors continuously. Home motion sensors have been used in a different patient population - the older adults - in helping these patients and their caregivers establish daily living patterns, and when there is a deviation in their pattern, to alert their providers or caregivers, as this may signal a medical problem [ 4 ]. However, the feasibility and acceptability of this technology for OUD patients is unknown and merits evaluation. Based on previous research using smart home sensors, we proposed a small study to gather data from these sensors in participants' homes [ 5 ]. In this study, we planned to assess whether the technology would be acceptable to patients and whether it was feasible to identify potential behavioral markers that would predict the risk of relapse among adults with OUD who were engaged in treatment. Unfortunately, we encountered many issues in recruiting and retaining this patient population. This brief discusses the challenges in recruiting OUD patients to complete a pilot study involving digital monitoring, and the strategies that increased recruitment and retention. We hope this will inform the design of future technology-based interventions and the considerations needed to develop effective recruitment strategies. Methods Study Design The Establishing a Digital Heartbeat of the Household for the Drug Abuse Population Study (also known as the Digital Heartbeat Study or DHS) was a pilot study to evaluate whether home activity sensors can be used in patients with OUD to establish patterns of daily living, referred to in this study as the “home digital heartbeat.” The goal was to investigate whether changes in these patterns could predict relapse, so interventions could be offered quickly. We partnered with EmPowerYu, a digital healthcare company, to collect home monitoring data. Passive monitoring sensors were installed in participants’ homes for 60 days. These sensors consist of door sensors that trigger upon the opening and closing of a door in the household, appliance sensors that trigger when an appliance is being used (e.g., Coffee maker, TV, etc.), and motion sensors that trigger upon movement detection. Those sensors are limited to motion detection and cannot distinguish between individuals triggering the sensor, resulting in data not linked to specific persons in the household. Participants were also asked to wear a Fitbit during their time in the study, and take a once-daily ecological momentary assessment (EMA) survey sent to their cellphones via a mobile app (ilumivu) asking about their sleep and drug use/cravings. EMA is a methodology that has been used successfully among populations misusing substances where subjects are asked to report on substance use patterns within their environment or context at pre-specified survey response times [ 6 ]. For the present study, subjects were sent a once daily, end-of-day survey over the study period to capture patient-reported outcomes and symptoms, including any substance use and cravings. Participants were also followed up every two weeks to perform urine toxicology screening and ask about their drug cravings and sleep. Incentives for subjects included allowing them to keep the Fitbit after the study period, and debit cards for the number of surveys and follow-up study visits completed. We expected to enroll 25 participants. Potential participants were given an optional survey on their attitudes towards sensor technologies (Technology Attitudes Survey). The survey asked about their willingness to have these technologies in their home and their comfort level with specific sensor locations (sensor in the bedroom or bathroom, wearing a fitness tracker, etc.) Setting This study was done at Nationwide Children’s Hospital (NCH) Substance Use Treatment and Recovery Program and The Ohio State University’s Medication Assisted Treatment program within their Primary Care Clinic. Both are in Columbus, Ohio, and serve many counties in central Ohio. Each facility can see around 100 patients annually and has approximately 50 active patients at any given time. We also recruited from local addiction treatment centers in the Central Ohio area. These facilities are smaller, with more patients struggling with substances other than opioids. Study Population To be eligible for the study, patients must be diagnosed with OUD and be 18 years of age or older. They must also live in the Greater Columbus Metropolitan Area (Delaware, Fairfield, Franklin, Hocking, Licking, Madison, Morrow, Perry, Pickaway, and Union Counties) and have stable housing for at least 3–6 months. The selection of these specific counties is due to the necessity of installing sensors in participants' homes and the logistical convenience of conducting follow-up visits. They needed to be active patients or participants with a clinic or sober living house connected with our study. They must also have been willing to install the sensors in their home, agree to wear a Fitbit and answer surveys, and provide a urine sample for study visits. Results The recruitment for this study started in December 2021. By the end of 2023, we had evaluated 170 patient records. Of the 50 who met inclusion criteria, 33 talked to a study recruiter and completed the technology attitude survey. 65% of the survey participants intended to accept using home sensor devices in their homes, and over 56% of the respondents agreed to wear a smart fitness watch most of the time (Table 1 ). People were more accepting of placing sensors in the living room or the kitchen than in the bedroom or the bathroom. After learning about the specific home sensor devices, 14 consented to participate in the study. However, only four participants completed the study (Fig. 1). Table 1 Willingness to use home sensor devices in current living setting to support recovery. Sensor Type Somewhat or completely willing Neither willing or unwilling Somewhat or completely unwilling n % n % n % Motion Sensors Overall sensor 21 65.6 6 18.8 5 15.6 In the living room 17 53.1 6 18.8 9 28.1 In the kitchen & dining room 16 50.0 6 18.8 10 31.3 In the bedroom 13 40.6 6 18.8 13 40.6 In the bathroom 9 28.1 8 25.0 15 46.9 On a bed 12 37.5 7 21.9 13 40.6 On a chair 14 43.8 7 21.9 11 34.4 On a refrigerator 15 46.9 8 25.0 9 28.1 On a utensil drawer or cabinet 14 43.8 9 28.1 9 28.1 Smart Switch TV or other entertainment devices 16 50.0 9 28.1 7 21.9 Kitchen appliances 14 43.8 8 25.0 10 31.3 Lighting (lamps) 17 53.1 7 21.9 8 25.0 Separation Sensors Front door and other exit doors 20 62.5 5 15.6 7 21.9 Medication cabinet 16 50.0 8 25.0 8 25.0 Fitness tracker Wear most of the time 18 56.3 8 25.0 6 18.8 We identified some main issues with the recruitment of participants for the study. Housing instability One of the requirements for our study was that participants have stable housing for at least 2–3 months since the sensors are meant to be incorporated into the home. However, housing instability presents a significant challenge for patients with OUD, exacerbating their struggle for recovery and stability. This instability can lead to a vicious cycle where the lack of stable housing aggravates the disorder, making securing stable housing more difficult. Moreover, housing instability often coexists with other social determinants of health, such as unemployment and lack of social support. The Coronavirus Disease 2019 (COVID-19) pandemic worsened housing instability in this population. The economic fallout led to increased unemployment, and social distancing measures often limited access to essential services and support systems[ 7 , 8 ]. We kept track of participants who initially declined the study due to lacking appropriate housing and kept in touch with their physicians at the clinic. By establishing relationships with the physicians, we were quickly notified when participants who were initially not eligible but were interested in the study acquired stable housing. Finding eligible participants We have also been limited in where we can recruit for our study. We initially recruited at the NCH Substance Use Treatment and Recovery Program. While we have succeeded in finding eligible participants, there were many that we could not initially approach due to them not meeting eligibility criteria (Fig. 1). Many patients were under the age of 18, and some were being seen for other substance use disorders. We also initially limited ourselves to recruiting only people actively taking medication treatment. Once we identified this issue, we expanded our recruitment sites. We got in contact with OSU and were able to connect with their medication treatment clinic. While not every patient at this clinic had OUD, most patients were over 18, addressing one of our more significant issues with finding eligible participants. We expanded outside the clinic to sober living homes in the Columbus area. To do this, we expanded our eligibility criteria to include patients not taking medication for OUD, but who were compliant with the requirements of the sober living home they were staying in. Most sober living homes require total abstinence from all substances along with other strict criteria (ex, curfews, therapy, etc.). We also attempted to reach out to medication treatment providers in the city and other sober living recovery facilities but could not secure partnerships. Challenges involving privacy concerns about the sensors One common reason for declining participation in the study was privacy concerns. This population has a history of previous encounters with law enforcement and may have experienced a heightened security environment if they stayed in a residential treatment facility. While the sensors do not have cameras, some patients felt the technology was too invasive. Our Technology Attitudes survey found that the patients were less willing to have the sensors installed in home areas that were considered private (e.g., bedroom, bathroom) (Table 1 ). In some cases, patients were interested in the study but had other members of their household who felt that the sensors were too invasive. Our analysis required implicit consent from other household members and the participant’s informed consent, meaning that even interested patients could not participate in the study if other household members did not want the sensors in their shared homes. We worked closely with the clinic staff at potential recruitment sites to carefully explain the technology to participants to alleviate these concerns. We described the sensors in detail and noted that these sensors contained no cameras and could not individually identify a person. We made bathroom sensors optional for sensor installation, which helped us recruit more study participants. We also decided not to install sensors on the bed. Facilitating enrollment and study visits Other barriers to enrollment and retention of subjects included the overall burden on participants due to the required frequency of study visits and a high degree of non-responsiveness post-consent. Obtaining consent and enrolling participants in the study occurred on a different day than sensor installation, due to logistical constraints. Thus, some subjects who were originally consented to the project were lost to follow up before sensors could be installed. Additionally, we encountered difficulties with participants not responding to follow-up appointments and made changes to our protocol to offer a virtual meeting option so that participants did not have to have study visits in person. This virtual option let participants do follow-up visits without having to be seen by a researcher every two weeks and reduced travel burden for in-person appointments. We also started gathering multiple forms of contact (email, phone, contact of friend or sober house advisor, etc.) to keep in touch with the participants when they consented to the study. Impact of the COVID-19 Pandemic Our original plan was to commence study recruitment in March or April 2020. However, the onset of the COVID-19 pandemic necessitated a delay in these plans. Our institution paused new study recruitments until safe operating procedures could be established in response to the pandemic. Furthermore, the delay extended beyond this revised timeline due to the prolonged process of obtaining IRB approval for expanded recruitment strategies. Consequently, our intended start date was postponed to December 2021. Additionally, the rapid expansion of telehealth services during the pandemic further complicated our recruitment efforts, as fewer potential participants were physically present in clinics, a key recruitment venue. Discussion This pilot study aimed to explore the feasibility and acceptability of using home sensors to monitor patients with OUD, intending to identify behavioral markers to predict relapse. However, the challenges encountered in recruiting and retaining participants highlighted significant barriers, offering valuable insights into the complexities of conducting research within this population. Due to the small number of participants who completed the study, we cannot draw any conclusions from the EMA and Fitbit data. Anecdotally, the Fitbit data set was impacted by participants not wearing the device throughout the study due to skin irritation, or forgetting to put on or charge the device. EMA response appeared to be improved by reminders that incentive payments depended on participation, but those amounts may not have been sufficiently compelling to maintain engagement in this study. Recruitment and Retention Challenges One of the primary challenges faced was the requirement for stable housing among participants. Our findings echoed existing literature indicating that housing instability is a pervasive issue among individuals with OUD, often intertwined with their struggle for recovery [ 9 , 10 ]. The COVID-19 pandemic exacerbated this instability, aligning with studies that have shown an increase in substance use disorders and a decrease in accessible healthcare services during this period [ 8 ]. Another critical challenge encountered in our study was the high rate of loss to follow-up. This phenomenon is not unique to our research; it reflects a broader issue prevalent in studies involving OUD patients. Individuals with OUD often face numerous life stressors, including unstable housing, financial instability, and social marginalization, which can disrupt their participation in longitudinal research [ 10 ]. Moreover, the relapsing-remitting nature of OUD contributes to this challenge, as periods of relapse can lead to disengagement from the study and subsequent loss of follow-up. Among patients receiving medications, only 40% continue their medication for more than six months [ 11 ]. To address this, future studies should consider implementing more robust engagement strategies, such as frequent check-ins, building strong community relationships, flexible scheduling, and using incentives tailored to this population's needs and preferences to increase recruitment and engagement. Privacy Concerns and Ethical Considerations Privacy concerns of participants and housemates significantly influenced willingness to join the study. Previous studies have evaluated the desire to wear an electronic overdose detection device and found positive responses [ 12 , 13 ]. Based on our technology attitude survey results, more than half of the participants were willing to wear a smartwatch most of the time, whereas willingness to have sensors depended on location in the home. In residential settings, motion sensors are static installations designed solely to detect movement, thereby eliminating the necessity for occupants to wear any additional devices. These sensors are devoid of photographic capabilities, precluding the recording of non-movement-based activities. Conversely, wearable devices akin to smartwatches possess the capability to monitor geographical positioning via Global Positioning System (GPS). Consequently, home motion sensors present a less intrusive approach to privacy and facilitate the seamless acquisition of uninterrupted data streams. Nevertheless, our empirical survey reveals prevailing privacy apprehensions amongst individuals regarding the placement of these sensors in more private domestic spaces, such as bathrooms and bedrooms. Future research should focus on transparent communication regarding data use and privacy for home motion sensors to mitigate these concerns. Methodological Adjustments and Impact The adaptations to our study protocol, including the introduction of virtual follow-ups and the provision of multiple contact options, slightly improved recruitment, but retention rates remained low. These changes highlight the necessity of flexible and patient-centric approaches in research involving vulnerable populations. However, despite these adjustments, the overall retention rate remained low, suggesting that further innovation in study design and participant engagement strategies is required. Conclusions In conclusion, this brief has highlighted the multifaceted challenges encountered during our study's recruitment and enrollment phases. Our adaptive strategies have been instrumental in partially overcoming these barriers, including the introduction of virtual study options, expansion of eligibility criteria, and thorough communication to address privacy concerns. Importantly, this experience underscores the need for flexibility and sensitivity to participant circumstances in clinical research, particularly in populations experiencing instability or distrust. Substance use disorder is a significantly growing problem with a heterogeneous patient population, which requires a lot of research and an essential public health force committed to prevention and treatment. Technology is rapidly growing and should be used to increase treatment opportunities. Future studies should focus on building trust and understanding within target communities, especially when introducing new technologies, to facilitate successful participant recruitment and retention. Abbreviations OUD: Opioid use disorder EMA: Ecological momentary assessment NCH: Nationwide Children’s Hospital COVID-19: Coronavirus Disease 2019 GPS: Global Positioning System Declarations Funding This project received support from Ohio Department of Higher Education. The opinions, views, or comments expressed in this paper are those of the authors and do not necessarily represent the official positions of funding agency. Further, the funding body had no input on any aspect of this study. Authors’ contributions MZ and AB conceptualized the study. KH recruited participants and collected data. YP and KH drafted the article, with LM, TB, LC and AB substantively revised it. All authors interpreted the data, read and approved the final manuscript. Ethics declarations Ethics approval and consent to participate The Nationwide Children’s Hospital Institutional Review Board reviewed and approved the analysis protocol (STUDY00000745) prior to data collection and analysis. Informed consent was obtained from all subjects. Consent for publication Not applicable. Availability of Data and Materials Not applicable. Competing interests The authors declare the following competing interests: Laura McIntosh, one of the authors, holds the position of Chief Executive Officer at EmPowerYu, Inc., the manufacturer of the home sensor devices used in this study. The involvement of Laura McIntosh in the involvement of this research could represent a potential conflict of interest. No financial support was received from EmPowerYu, Inc. for the conduct of this study, and the involvement of Laura McIntosh was strictly within the realms of research design, analysis, and interpretation of the data. All findings and conclusions presented in this paper are the result of unbiased scientific inquiry, and efforts have been made to ensure the integrity of the research process. References Shulman, M., J.M. Wai, and E.V. Nunes, Buprenorphine Treatment for Opioid Use Disorder: An Overview. CNS Drugs, 2019. 33 (6): p. 567-580. Bell, J. and J. Strang, Medication Treatment of Opioid Use Disorder. Biological Psychiatry, 2020. 87 (1): p. 82-88. Hedegaard, H., et al., Drug overdose deaths in the United States, 1999–2020. 2021. Peetoom, K.K., et al., Literature review on monitoring technologies and their outcomes in independently living elderly people. Disability and Rehabilitation: Assistive Technology, 2015. 10 (4): p. 271-294. Walsh, L., et al. Inferring health metrics from ambient smart home data . in 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) . 2014. Shiffman, S., Ecological momentary assessment (EMA) in studies of substance use. Psychological Assessment, 2009. 21 (4): p. 486-497. Genberg, B.L., et al., The health and social consequences during the initial period of the COVID-19 pandemic among current and former people who inject drugs: A rapid phone survey in Baltimore, Maryland. Drug Alcohol Depend, 2021. 221 : p. 108584. Aronowitz, S.V., et al., "We have to be uncomfortable and creative": Reflections on the impacts of the COVID-19 pandemic on overdose prevention, harm reduction & homelessness advocacy in Philadelphia. SSM Qual Res Health, 2021. 1 : p. 100013. Upshur, C.C., et al., Homeless women's service use, barriers, and motivation for participating in substance use treatment. Am J Drug Alcohol Abuse, 2018. 44 (2): p. 252-262. Hoffman, K.A., et al., Barriers and facilitators to recruitment and enrollment of HIV-infected individuals with opioid use disorder in a clinical trial. BMC Health Serv Res, 2019. 19 (1): p. 862. Krawczyk, N., et al., Who stays in medication treatment for opioid use disorder? A national study of outpatient specialty treatment settings. Journal of Substance Abuse Treatment, 2021. 126 : p. 108329. Kanter, K., et al., Willingness to use a wearable device capable of detecting and reversing overdose among people who use opioids in Philadelphia. Harm Reduction Journal, 2021. 18 (1): p. 1-14. Ahamad, K., et al., Factors associated with willingness to wear an electronic overdose detection device. Addiction Science & Clinical Practice, 2019. 14 (1). Additional Declarations Competing interest reported. The authors declare the following competing interests: Laura McIntosh, one of the authors, holds the position of Chief Executive Officer at EmPowerYu, Inc., the manufacturer of the home sensor devices used in this study. The involvement of Laura McIntosh in the involvement of this research could represent a potential conflict of interest. No financial support was received from EmPowerYu, Inc. for the conduct of this study, and the involvement of Laura McIntosh was strictly within the realms of research design, analysis, and interpretation of the data. All findings and conclusions presented in this paper are the result of unbiased scientific inquiry, and efforts have been made to ensure the integrity of the research process. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3921917","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272841827,"identity":"027c7bfe-068b-4e6d-a85f-e56acc1b0ca7","order_by":0,"name":"Yuhan Pan","email":"","orcid":"","institution":"The Ohio State University","correspondingAuthor":false,"prefix":"","firstName":"Yuhan","middleName":"","lastName":"Pan","suffix":""},{"id":272841828,"identity":"dae4c0d4-f0e6-4342-bd95-c9a9b7377fe7","order_by":1,"name":"Kayleigh Humphries","email":"","orcid":"","institution":"Nationwide Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kayleigh","middleName":"","lastName":"Humphries","suffix":""},{"id":272841829,"identity":"dd27ab1d-990b-4786-ad7c-f3a06842223f","order_by":2,"name":"Laura McIntosh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"McIntosh","suffix":""},{"id":272841830,"identity":"b9fa6382-59d5-48dc-a41e-92fd9185e300","order_by":3,"name":"Traci Bouchard","email":"","orcid":"","institution":"Nationwide Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Traci","middleName":"","lastName":"Bouchard","suffix":""},{"id":272841831,"identity":"f6df23af-c624-4593-86ca-2ebb9ebbc889","order_by":4,"name":"Laura Chavez","email":"","orcid":"","institution":"Nationwide Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Chavez","suffix":""},{"id":272841832,"identity":"8ca3ad2b-a49a-42dc-91d9-afd0821b4f5a","order_by":5,"name":"Martin Fried","email":"","orcid":"","institution":"The Ohio State University","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Fried","suffix":""},{"id":272841833,"identity":"3f99d9d5-8990-4bdc-8bab-ed03e3d0225f","order_by":6,"name":"Orman Hall","email":"","orcid":"","institution":"The Ohio State University","correspondingAuthor":false,"prefix":"","firstName":"Orman","middleName":"","lastName":"Hall","suffix":""},{"id":272841834,"identity":"07e4dd75-e178-428e-afd7-0c934efa0ae0","order_by":7,"name":"Andrea Bonny","email":"","orcid":"","institution":"Nationwide Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Bonny","suffix":""},{"id":272841835,"identity":"302107bb-5e2a-40ec-b57b-948dcb324b25","order_by":8,"name":"Motao Zhu","email":"data:image/png;base64,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","orcid":"","institution":"Nationwide Children's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Motao","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2024-02-02 19:14:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3921917/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3921917/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51237040,"identity":"19bad556-e960-4948-b036-7c50bbf6f3a8","added_by":"auto","created_at":"2024-02-16 16:44:11","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":54396,"visible":true,"origin":"","legend":"\u003cp\u003eDigital Heartbeat Study Consort Diagram\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3921917/v1/919670088f9449c0a817028c.jpg"},{"id":55264701,"identity":"3e00d1dd-d80a-4e3b-9764-e84f52ad0972","added_by":"auto","created_at":"2024-04-25 01:47:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":572245,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3921917/v1/45f156d4-4670-42be-b5ae-54a78d053cc9.pdf"}],"financialInterests":"Competing interest reported. The authors declare the following competing interests: Laura McIntosh, one of the authors, holds the position of Chief Executive Officer at EmPowerYu, Inc., the manufacturer of the home sensor devices used in this study. The involvement of Laura McIntosh in the involvement of this research could represent a potential conflict of interest. No financial support was received from EmPowerYu, Inc. for the conduct of this study, and the involvement of Laura McIntosh was strictly within the realms of research design, analysis, and interpretation of the data. All findings and conclusions presented in this paper are the result of unbiased scientific inquiry, and efforts have been made to ensure the integrity of the research process.","formattedTitle":"Recruitment and Retention Challenges in Opioid Use Disorder Studies: Insights and Strategies from a Pilot Digital Monitoring Study","fulltext":[{"header":"Background","content":"\u003cp\u003eOpioid use disorder (OUD) is a chronic medical condition affecting an estimated 2.4\u0026nbsp;million individuals in the United States [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Effective treatment for OUD includes the use of evidence-based medications such as buprenorphine, naltrexone, or methadone. It can also include behavioral therapies and support systems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, relapse is common among this patient population, and overdose deaths in the U.S. are rising, mainly due to fentanyl[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEmerging technologies may offer additional opportunities to support the recovery of OUD patients in the community. For example, home motion sensors can be placed in individuals\u0026rsquo; home environments to detect living patterns remotely and have the potential to reveal deviations from typical behaviors that might indicate an elevated risk of relapse. In addition, these monitoring devices are discreet and do not burden individuals with the need to report their behaviors continuously. Home motion sensors have been used in a different patient population - the older adults - in helping these patients and their caregivers establish daily living patterns, and when there is a deviation in their pattern, to alert their providers or caregivers, as this may signal a medical problem [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the feasibility and acceptability of this technology for OUD patients is unknown and merits evaluation.\u003c/p\u003e \u003cp\u003eBased on previous research using smart home sensors, we proposed a small study to gather data from these sensors in participants' homes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In this study, we planned to assess whether the technology would be acceptable to patients and whether it was feasible to identify potential behavioral markers that would predict the risk of relapse among adults with OUD who were engaged in treatment.\u003c/p\u003e \u003cp\u003eUnfortunately, we encountered many issues in recruiting and retaining this patient population. This brief discusses the challenges in recruiting OUD patients to complete a pilot study involving digital monitoring, and the strategies that increased recruitment and retention. We hope this will inform the design of future technology-based interventions and the considerations needed to develop effective recruitment strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThe Establishing a Digital Heartbeat of the Household for the Drug Abuse Population Study (also known as the Digital Heartbeat Study or DHS) was a pilot study to evaluate whether home activity sensors can be used in patients with OUD to establish patterns of daily living, referred to in this study as the \u0026ldquo;home digital heartbeat.\u0026rdquo; The goal was to investigate whether changes in these patterns could predict relapse, so interventions could be offered quickly. We partnered with EmPowerYu, a digital healthcare company, to collect home monitoring data. Passive monitoring sensors were installed in participants\u0026rsquo; homes for 60 days. These sensors consist of door sensors that trigger upon the opening and closing of a door in the household, appliance sensors that trigger when an appliance is being used (e.g., Coffee maker, TV, etc.), and motion sensors that trigger upon movement detection. Those sensors are limited to motion detection and cannot distinguish between individuals triggering the sensor, resulting in data not linked to specific persons in the household. Participants were also asked to wear a Fitbit during their time in the study, and take a once-daily ecological momentary assessment (EMA) survey sent to their cellphones via a mobile app (ilumivu) asking about their sleep and drug use/cravings. EMA is a methodology that has been used successfully among populations misusing substances where subjects are asked to report on substance use patterns within their environment or context at pre-specified survey response times [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For the present study, subjects were sent a once daily, end-of-day survey over the study period to capture patient-reported outcomes and symptoms, including any substance use and cravings. Participants were also followed up every two weeks to perform urine toxicology screening and ask about their drug cravings and sleep. Incentives for subjects included allowing them to keep the Fitbit after the study period, and debit cards for the number of surveys and follow-up study visits completed. We expected to enroll 25 participants.\u003c/p\u003e \u003cp\u003ePotential participants were given an optional survey on their attitudes towards sensor technologies (Technology Attitudes Survey). The survey asked about their willingness to have these technologies in their home and their comfort level with specific sensor locations (sensor in the bedroom or bathroom, wearing a fitness tracker, etc.)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSetting\u003c/h2\u003e \u003cp\u003eThis study was done at Nationwide Children\u0026rsquo;s Hospital (NCH) Substance Use Treatment and Recovery Program and The Ohio State University\u0026rsquo;s Medication Assisted Treatment program within their Primary Care Clinic. Both are in Columbus, Ohio, and serve many counties in central Ohio. Each facility can see around 100 patients annually and has approximately 50 active patients at any given time. We also recruited from local addiction treatment centers in the Central Ohio area. These facilities are smaller, with more patients struggling with substances other than opioids.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eTo be eligible for the study, patients must be diagnosed with OUD and be 18 years of age or older. They must also live in the Greater Columbus Metropolitan Area (Delaware, Fairfield, Franklin, Hocking, Licking, Madison, Morrow, Perry, Pickaway, and Union Counties) and have stable housing for at least 3\u0026ndash;6 months. The selection of these specific counties is due to the necessity of installing sensors in participants' homes and the logistical convenience of conducting follow-up visits. They needed to be active patients or participants with a clinic or sober living house connected with our study. They must also have been willing to install the sensors in their home, agree to wear a Fitbit and answer surveys, and provide a urine sample for study visits.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe recruitment for this study started in December 2021. By the end of 2023, we had evaluated 170 patient records. Of the 50 who met inclusion criteria, 33 talked to a study recruiter and completed the technology attitude survey. 65% of the survey participants intended to accept using home sensor devices in their homes, and over 56% of the respondents agreed to wear a smart fitness watch most of the time (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). People were more accepting of placing sensors in the living room or the kitchen than in the bedroom or the bathroom. After learning about the specific home sensor devices, 14 consented to participate in the study. However, only four participants completed the study (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWillingness to use home sensor devices in current living setting to support recovery.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eSensor Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSomewhat or completely willing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eNeither willing or unwilling\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eSomewhat or completely unwilling\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMotion Sensors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall sensor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIn the living room\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIn the kitchen \u0026amp; dining room\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e31.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIn the bedroom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e40.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIn the bathroom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOn a bed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e40.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOn a chair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e34.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOn a refrigerator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOn a utensil drawer or cabinet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmart Switch\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTV or other entertainment devices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKitchen appliances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e31.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLighting (lamps)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeparation Sensors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFront door and other exit doors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedication cabinet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFitness tracker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWear most of the time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe identified some main issues with the recruitment of participants for the study.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eHousing instability\u003c/h2\u003e \u003cp\u003eOne of the requirements for our study was that participants have stable housing for at least 2\u0026ndash;3 months since the sensors are meant to be incorporated into the home. However, housing instability presents a significant challenge for patients with OUD, exacerbating their struggle for recovery and stability. This instability can lead to a vicious cycle where the lack of stable housing aggravates the disorder, making securing stable housing more difficult. Moreover, housing instability often coexists with other social determinants of health, such as unemployment and lack of social support. The Coronavirus Disease 2019 (COVID-19) pandemic worsened housing instability in this population. The economic fallout led to increased unemployment, and social distancing measures often limited access to essential services and support systems[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe kept track of participants who initially declined the study due to lacking appropriate housing and kept in touch with their physicians at the clinic. By establishing relationships with the physicians, we were quickly notified when participants who were initially not eligible but were interested in the study acquired stable housing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFinding eligible participants\u003c/h2\u003e \u003cp\u003eWe have also been limited in where we can recruit for our study. We initially recruited at the NCH Substance Use Treatment and Recovery Program. While we have succeeded in finding eligible participants, there were many that we could not initially approach due to them not meeting eligibility criteria (Fig.\u0026nbsp;1). Many patients were under the age of 18, and some were being seen for other substance use disorders. We also initially limited ourselves to recruiting only people actively taking medication treatment.\u003c/p\u003e \u003cp\u003eOnce we identified this issue, we expanded our recruitment sites. We got in contact with OSU and were able to connect with their medication treatment clinic. While not every patient at this clinic had OUD, most patients were over 18, addressing one of our more significant issues with finding eligible participants. We expanded outside the clinic to sober living homes in the Columbus area. To do this, we expanded our eligibility criteria to include patients not taking medication for OUD, but who were compliant with the requirements of the sober living home they were staying in. Most sober living homes require total abstinence from all substances along with other strict criteria (ex, curfews, therapy, etc.). We also attempted to reach out to medication treatment providers in the city and other sober living recovery facilities but could not secure partnerships.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eChallenges involving privacy concerns about the sensors\u003c/h2\u003e \u003cp\u003eOne common reason for declining participation in the study was privacy concerns. This population has a history of previous encounters with law enforcement and may have experienced a heightened security environment if they stayed in a residential treatment facility. While the sensors do not have cameras, some patients felt the technology was too invasive. Our Technology Attitudes survey found that the patients were less willing to have the sensors installed in home areas that were considered private (e.g., bedroom, bathroom) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In some cases, patients were interested in the study but had other members of their household who felt that the sensors were too invasive. Our analysis required implicit consent from other household members and the participant\u0026rsquo;s informed consent, meaning that even interested patients could not participate in the study if other household members did not want the sensors in their shared homes.\u003c/p\u003e \u003cp\u003e We worked closely with the clinic staff at potential recruitment sites to carefully explain the technology to participants to alleviate these concerns. We described the sensors in detail and noted that these sensors contained no cameras and could not individually identify a person. We made bathroom sensors optional for sensor installation, which helped us recruit more study participants. We also decided not to install sensors on the bed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eFacilitating enrollment and study visits\u003c/h2\u003e \u003cp\u003eOther barriers to enrollment and retention of subjects included the overall burden on participants due to the required frequency of study visits and a high degree of non-responsiveness post-consent. Obtaining consent and enrolling participants in the study occurred on a different day than sensor installation, due to logistical constraints. Thus, some subjects who were originally consented to the project were lost to follow up before sensors could be installed. Additionally, we encountered difficulties with participants not responding to follow-up appointments and made changes to our protocol to offer a virtual meeting option so that participants did not have to have study visits in person. This virtual option let participants do follow-up visits without having to be seen by a researcher every two weeks and reduced travel burden for in-person appointments. We also started gathering multiple forms of contact (email, phone, contact of friend or sober house advisor, etc.) to keep in touch with the participants when they consented to the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImpact of the COVID-19 Pandemic\u003c/h2\u003e \u003cp\u003eOur original plan was to commence study recruitment in March or April 2020. However, the onset of the COVID-19 pandemic necessitated a delay in these plans. Our institution paused new study recruitments until safe operating procedures could be established in response to the pandemic. Furthermore, the delay extended beyond this revised timeline due to the prolonged process of obtaining IRB approval for expanded recruitment strategies. Consequently, our intended start date was postponed to December 2021. Additionally, the rapid expansion of telehealth services during the pandemic further complicated our recruitment efforts, as fewer potential participants were physically present in clinics, a key recruitment venue.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis pilot study aimed to explore the feasibility and acceptability of using home sensors to monitor patients with OUD, intending to identify behavioral markers to predict relapse. However, the challenges encountered in recruiting and retaining participants highlighted significant barriers, offering valuable insights into the complexities of conducting research within this population.\u003c/p\u003e \u003cp\u003eDue to the small number of participants who completed the study, we cannot draw any conclusions from the EMA and Fitbit data. Anecdotally, the Fitbit data set was impacted by participants not wearing the device throughout the study due to skin irritation, or forgetting to put on or charge the device. EMA response appeared to be improved by reminders that incentive payments depended on participation, but those amounts may not have been sufficiently compelling to maintain engagement in this study.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRecruitment and Retention Challenges\u003c/h2\u003e \u003cp\u003eOne of the primary challenges faced was the requirement for stable housing among participants. Our findings echoed existing literature indicating that housing instability is a pervasive issue among individuals with OUD, often intertwined with their struggle for recovery [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The COVID-19 pandemic exacerbated this instability, aligning with studies that have shown an increase in substance use disorders and a decrease in accessible healthcare services during this period [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnother critical challenge encountered in our study was the high rate of loss to follow-up. This phenomenon is not unique to our research; it reflects a broader issue prevalent in studies involving OUD patients. Individuals with OUD often face numerous life stressors, including unstable housing, financial instability, and social marginalization, which can disrupt their participation in longitudinal research [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Moreover, the relapsing-remitting nature of OUD contributes to this challenge, as periods of relapse can lead to disengagement from the study and subsequent loss of follow-up. Among patients receiving medications, only 40% continue their medication for more than six months [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. To address this, future studies should consider implementing more robust engagement strategies, such as frequent check-ins, building strong community relationships, flexible scheduling, and using incentives tailored to this population's needs and preferences to increase recruitment and engagement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePrivacy Concerns and Ethical Considerations\u003c/h2\u003e \u003cp\u003ePrivacy concerns of participants and housemates significantly influenced willingness to join the study. Previous studies have evaluated the desire to wear an electronic overdose detection device and found positive responses [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Based on our technology attitude survey results, more than half of the participants were willing to wear a smartwatch most of the time, whereas willingness to have sensors depended on location in the home. In residential settings, motion sensors are static installations designed solely to detect movement, thereby eliminating the necessity for occupants to wear any additional devices. These sensors are devoid of photographic capabilities, precluding the recording of non-movement-based activities. Conversely, wearable devices akin to smartwatches possess the capability to monitor geographical positioning via Global Positioning System (GPS). Consequently, home motion sensors present a less intrusive approach to privacy and facilitate the seamless acquisition of uninterrupted data streams. Nevertheless, our empirical survey reveals prevailing privacy apprehensions amongst individuals regarding the placement of these sensors in more private domestic spaces, such as bathrooms and bedrooms. Future research should focus on transparent communication regarding data use and privacy for home motion sensors to mitigate these concerns.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMethodological Adjustments and Impact\u003c/h2\u003e \u003cp\u003eThe adaptations to our study protocol, including the introduction of virtual follow-ups and the provision of multiple contact options, slightly improved recruitment, but retention rates remained low. These changes highlight the necessity of flexible and patient-centric approaches in research involving vulnerable populations. However, despite these adjustments, the overall retention rate remained low, suggesting that further innovation in study design and participant engagement strategies is required.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this brief has highlighted the multifaceted challenges encountered during our study's recruitment and enrollment phases. Our adaptive strategies have been instrumental in partially overcoming these barriers, including the introduction of virtual study options, expansion of eligibility criteria, and thorough communication to address privacy concerns. Importantly, this experience underscores the need for flexibility and sensitivity to participant circumstances in clinical research, particularly in populations experiencing instability or distrust. Substance use disorder is a significantly growing problem with a heterogeneous patient population, which requires a lot of research and an essential public health force committed to prevention and treatment. Technology is rapidly growing and should be used to increase treatment opportunities. Future studies should focus on building trust and understanding within target communities, especially when introducing new technologies, to facilitate successful participant recruitment and retention.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eOUD: Opioid use disorder\u003c/p\u003e\n\u003cp\u003eEMA: Ecological momentary assessment\u003c/p\u003e\n\u003cp\u003eNCH: Nationwide Children\u0026rsquo;s Hospital\u003c/p\u003e\n\u003cp\u003eCOVID-19: Coronavirus Disease 2019\u003c/p\u003e\n\u003cp\u003eGPS: Global Positioning System\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project received support from Ohio Department of Higher Education. The opinions, views, or comments expressed in this paper are those of the authors and do not necessarily represent the official positions of funding agency. Further, the funding body had no input on any aspect of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMZ and AB conceptualized the study. KH recruited participants and collected data. YP and KH drafted the article, with LM, TB, LC and AB substantively revised it. All authors interpreted the data, read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Nationwide Children\u0026rsquo;s Hospital Institutional Review Board reviewed and approved the analysis protocol (STUDY00000745) prior to data collection and analysis. Informed consent was obtained from all subjects.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of Data and Materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare the following competing interests: Laura McIntosh, one of the authors, holds the position of Chief Executive Officer at EmPowerYu, Inc., the manufacturer of the home sensor devices used in this study. The involvement of Laura McIntosh in the involvement of this research could represent a potential conflict of interest. No financial support was received from EmPowerYu, Inc. for the conduct of this study, and the involvement of Laura McIntosh was strictly within the realms of research design, analysis, and interpretation of the data. All findings and conclusions presented in this paper are the result of unbiased scientific inquiry, and efforts have been made to ensure the integrity of the research process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShulman, M., J.M. Wai, and E.V. Nunes, \u003cem\u003eBuprenorphine Treatment for Opioid Use Disorder: An Overview.\u003c/em\u003e CNS Drugs, 2019. \u003cstrong\u003e33\u003c/strong\u003e(6): p. 567-580.\u003c/li\u003e\n\u003cli\u003eBell, J. and J. Strang, \u003cem\u003eMedication Treatment of Opioid Use Disorder.\u003c/em\u003e Biological Psychiatry, 2020. \u003cstrong\u003e87\u003c/strong\u003e(1): p. 82-88.\u003c/li\u003e\n\u003cli\u003eHedegaard, H., et al., \u003cem\u003eDrug overdose deaths in the United States, 1999\u0026ndash;2020.\u003c/em\u003e 2021.\u003c/li\u003e\n\u003cli\u003ePeetoom, K.K., et al., \u003cem\u003eLiterature review on monitoring technologies and their outcomes in independently living elderly people.\u003c/em\u003e Disability and Rehabilitation: Assistive Technology, 2015. \u003cstrong\u003e10\u003c/strong\u003e(4): p. 271-294.\u003c/li\u003e\n\u003cli\u003eWalsh, L., et al. \u003cem\u003eInferring health metrics from ambient smart home data\u003c/em\u003e. in \u003cem\u003e2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)\u003c/em\u003e. 2014.\u003c/li\u003e\n\u003cli\u003eShiffman, S., \u003cem\u003eEcological momentary assessment (EMA) in studies of substance use.\u003c/em\u003e Psychological Assessment, 2009. \u003cstrong\u003e21\u003c/strong\u003e(4): p. 486-497.\u003c/li\u003e\n\u003cli\u003eGenberg, B.L., et al., \u003cem\u003eThe health and social consequences during the initial period of the COVID-19 pandemic among current and former people who inject drugs: A rapid phone survey in Baltimore, Maryland.\u003c/em\u003e Drug Alcohol Depend, 2021. \u003cstrong\u003e221\u003c/strong\u003e: p. 108584.\u003c/li\u003e\n\u003cli\u003eAronowitz, S.V., et al., \u003cem\u003e\u0026quot;We have to be uncomfortable and creative\u0026quot;: Reflections on the impacts of the COVID-19 pandemic on overdose prevention, harm reduction \u0026amp; homelessness advocacy in Philadelphia.\u003c/em\u003e SSM Qual Res Health, 2021. \u003cstrong\u003e1\u003c/strong\u003e: p. 100013.\u003c/li\u003e\n\u003cli\u003eUpshur, C.C., et al., \u003cem\u003eHomeless women\u0026apos;s service use, barriers, and motivation for participating in substance use treatment.\u003c/em\u003e Am J Drug Alcohol Abuse, 2018. \u003cstrong\u003e44\u003c/strong\u003e(2): p. 252-262.\u003c/li\u003e\n\u003cli\u003eHoffman, K.A., et al., \u003cem\u003eBarriers and facilitators to recruitment and enrollment of HIV-infected individuals with opioid use disorder in a clinical trial.\u003c/em\u003e BMC Health Serv Res, 2019. \u003cstrong\u003e19\u003c/strong\u003e(1): p. 862.\u003c/li\u003e\n\u003cli\u003eKrawczyk, N., et al., \u003cem\u003eWho stays in medication treatment for opioid use disorder? A national study of outpatient specialty treatment settings.\u003c/em\u003e Journal of Substance Abuse Treatment, 2021. \u003cstrong\u003e126\u003c/strong\u003e: p. 108329.\u003c/li\u003e\n\u003cli\u003eKanter, K., et al., \u003cem\u003eWillingness to use a wearable device capable of detecting and reversing overdose among people who use opioids in Philadelphia.\u003c/em\u003e Harm Reduction Journal, 2021. \u003cstrong\u003e18\u003c/strong\u003e(1): p. 1-14.\u003c/li\u003e\n\u003cli\u003eAhamad, K., et al., \u003cem\u003eFactors associated with willingness to wear an electronic overdose detection device.\u003c/em\u003e Addiction Science \u0026amp;amp; Clinical Practice, 2019. \u003cstrong\u003e14\u003c/strong\u003e(1).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3921917/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3921917/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e. Opioid use disorder (OUD) affects millions in the United States. Emerging technologies like home motion sensors offer the potential for relapse prediction. The study evaluates the feasibility and acceptability of such technology in OUD patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e. Participants were recruited through local OUD treatment centers in Columbus, Ohio. The study involved installing passive monitoring sensors in participants' homes and required participants to wear a Fitbit and complete daily surveys. The target was to enroll 25 patients, with incentives provided for participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e. Out of 170 evaluated records, 50 met the inclusion criteria, and only 14 consented to participate, with four completing the study. Main recruitment challenges included housing instability, privacy concerns, and the COVID-19 pandemic's impact. Most participants were willing to use sensor devices, especially in less private home areas.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e. The study faced significant barriers in recruiting and retaining participants, highlighting the complexities of OUD research. Despite methodological adaptations like virtual follow-ups, the retention rate remained low. This suggests the need for more flexible, patient-centric approaches in future research, particularly for populations experiencing instability or distrust. The study underscores the potential of technology in treatment but emphasizes the importance of building trust and understanding within target communities.\u003c/p\u003e","manuscriptTitle":"Recruitment and Retention Challenges in Opioid Use Disorder Studies: Insights and Strategies from a Pilot Digital Monitoring Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-16 16:44:06","doi":"10.21203/rs.3.rs-3921917/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"01538638-95a8-4b90-ace1-15794922f7dd","owner":[],"postedDate":"February 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-22T11:23:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-16 16:44:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3921917","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3921917","identity":"rs-3921917","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}