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Abstract

Objective

This open pilot study examines the feasibility, acceptability, and qualitative outcomes of an interactive web- and text message-delivered personalized feedback intervention aimed at cultivating motivation and tolerance of distress for adults initiating outpatient buprenorphine treatment.

Methods

Patients (n = 10) initiating buprenorphine within the past 8 weeks first completed a web-based intervention focused on enhancing motivation and providing psychoeducation on distress tolerance skills. Participants then received 8 weeks of daily personalized text messages that provided reminders of salient motivational factors and recommended distress tolerance-oriented coping skills. Participants completed self-report measures to assess intervention satisfaction, perceived usability, and preliminary efficacy. Additional perspectives were captured via qualitative exit interviews.

Results

In total, 100% of retained participants (n = 9) engaged with the text messages throughout the entire 8-week period. Mean scores of 27 (SD = 5.05) on the Client Satisfaction Questionnaire at the end of 8-week period indicated a high degree of satisfaction with the text-based intervention. The average rating on the System Usability Scale was 65.3 at the end of the 8-week program, suggesting that the intervention was relatively easy to use. Participants also endorsed positive experiences with the intervention during qualitative interviews. Clinical improvements were observed across the intervention period.

Conclusions

Preliminary findings from this pilot suggest that the content and delivery method of this combined web- and text message-based personalized feedback intervention is perceived by patients as feasible and acceptable. Leveraging digital health platforms to augment buprenorphine has potential for high scalability and impact to reduce opioid use, increase adherence/retention to treatment, and prevent future incidence of overdose. Future work will evaluate the efficacy of the intervention in a randomized clinical trial design.

Keywords

Opioid mobile health substance use

Introduction

The prevalence of opioid use disorder (OUD) has reached epidemic rates in the United States,¹ resulting in a dramatic rise in morbidity and mortality.^2,3 In 2018, over 46,800 people died from an opioid-involved overdose, accounting for 70% of all drug overdose deaths.^2,3 The emergence of coronavirus disease 2019 (COVID-19) has further exacerbated this issue with rates of fatal overdoses increasing substantially in 2020.⁴

Medications for OUD (MOUD) such as buprenorphine-naloxone (suboxone), methadone, and naltrexone are currently the most efficacious treatments for OUD.⁵ Due to its safety profile and flexible administration, buprenorphine has grown in popularity over the last decade.⁶ Although buprenorphine is highly effective in reducing mortality and morbidity,^7,9 maintaining adherence to and retention in treatment is a significant challenge. A substantial portion of patients receiving buprenorphine discontinue treatment prematurely and/or return to illicit opioid use.^10,12 Recent studies suggest that approximately 50% of individuals who initiate buprenorphine will discontinue medication by 6-months.¹³ This is notable as current recommendations highlight the importance of adhering to buprenorphine for at least 6 months to confer its full benefits and stabilize the overall functioning of the individual (e.g., return to work).¹⁴ These findings emphasize the need to develop novel interventions aimed at improving engagement and retention in traditional medication-based treatments for OUD.

Prior research has examined factors that contribute to treatment non-adherence and reduced retention for buprenorphine and documented the role of numerous multi-level barriers such as transportation challenges,¹⁵ family conflict,¹⁶ stigma towards OUD and MOUD¹⁷ and cost of MOUD¹⁷ as contributing factors. Whereas addressing each of these factors requires a multi-level approach, addressing underlying factors may be one way to promote retention in face of these challenges. Negative affective experiences are a product of all these barriers. Consistent with the negative reinforcement model of substance use, individuals use opioids to alleviate aversive internal states, such as depression, anxiety, or frustration.^11,18 Indeed, negative affect is a well-established precipitant of a return to opioid use.¹⁹ Distress tolerance (DT), which reflects the actual or perceived ability to tolerate or manage negative affect,²⁰ has been linked to a variety of substance use outcomes, including frequency and severity of drug use, premature treatment discontinuation, and return to illicit substance use.^21,24 According to the functional model of DT, it involves a behavioral inhibition in face of negative reinforcement and is understood as a propensity to respond to immediate (negative) reinforcement or reward.²⁵ Preliminary evidence also demonstrates that increasing DT is associated with decreased opioid use,²⁶ by promoting the ability to persist in goal-directed activity (e.g., medication adherence), even when experiencing negative affect.²⁷

Personalized feedback interventions, such as decisional balance frameworks, also show promising effects in reducing problematic substance use.²⁸ Consistent with a motivational enhancement perspective, decisional balance procedures involve an evaluation of the pros/cons of engaging in a certain behavior (e.g., opioid use), compared to the pros/cons of alternative behavior (e.g., recovery), and also offer strategies to support behavior change (e.g., DT skills training).^29,30 Decisional balance procedures can be utilized as a part of multi-component interventions,^31,32 but are also effective when used as a stand-alone intervention.^29,33,34 Stand-alone decisional balance procedures have demonstrated effectiveness for reducing substance use³⁵ among various populations including non-treatment seeking college student drinkers²⁹ and non-treatment-seeking smokers.^36,37 Whereas limited research has examined the effectiveness of decisional-balance procedures for reducing opioid use, one qualitative study on youth engaged in treatment for OUD noted that decisional balance was useful in realizing that help was needed for their opioid use.³⁸ Decisional-balance procedures, embedded with other motivation interviewing techniques, are recommended as best practices for OUD treatment.³⁵ However, no integrated protocol exists that seek to both enhance motivation and improve DT skills among individuals with OUD.

Technological platforms, such as text-based modalities, offer the advantage of delivering evidence-based strategies to participants’ natural environments, with individually tailored content, timing, and frequency, that may overcome challenges with drug cues and/or negative affect states. Indeed, text-based interventions are effective in promoting various health and behavior changes, with personalized text messaging showing the greatest benefit.³⁹ There is growing evidence that decisional balance procedures can be effectively delivered in digital format,²⁹ however, DT-based interventions designed to target substance use have predominately been delivered in a structured, in-person, format.²⁷ Thus, there is a greater need in research to explore the integration of DT-based interventions with decisional balance procedures (DT + Decisional Balance), and translation of these intervention procedures into a digital health platform to promote greater reach and access.

We developed and refined a web- and text message-based intervention that is grounded in DT theory and motivation enhancement theory. It combines DT skills training with decisional balance procedures to promote engagement in and adherence to buprenorphine treatment.^40,41 The pilot phase of this intervention was conducted among individuals engaged in buprenorphine at two outpatient treatment centers. The primary objective of this pilot intervention was to examine preliminary indices of feasibility, acceptability, and qualitative outcomes of the developed intervention.

Methods

Participants and procedures

The current study is an open pilot development and trial that was conducted at Rhode Island Hospital. Ten adults were recruited to participate from two hospital-affiliated clinics in Rhode Island that offer buprenorphine to treat OUD. Data was collected from October 2020 to September 2021. Participants were eligible for the study if they were over 18 years of age, met the Diagnostic and Statistical Manual of Mental Health Disorders (Fifth Edition)⁴² criteria for OUD per treating provider, initiated buprenorphine within the past 8 weeks, and endorsed having access to a cell phone with text message capability. Exclusion criteria included: active suicidality and/or psychosis that would interfere with the ability to participate in the intervention; not fluent in English; limited mental capacity or inability to provide informed written consent. Participants were between the ages of 25 and 68 (M_age = 40.2, SD = 13.5), predominately male (n = 8), non-Hispanic (n = 7), White (n = 7), and living with a spouse or partner (n = 5). Four of the participants had engaged in treatment prior to the current episode, with detox (n = 3) and residential (n = 3) endorsed as the most attended programs. One participant reported previously using online support for OUD. All participants reported having access to a smartphone with an unlimited text messaging plan and provided informed written consent. See Table 1 for an overview of sample characteristics.

Table 1.

Participant demographics at baseline.

	n	%
Gender
Female	2	20
Male	8	80
Age
18–35	4	40
36–50	4	40
51–65	2	20
Race
White	7	70
More than one race	1	10
Black/African American	0	0
American Indian/Alaskan Native	0	0
Other	2	20
Ethnicity
Hispanic/Latino	3	30
Non-Hispanic/Latino	7	70
Relationship status
Single/never married	1	10
Dating/seeing someone	1	10
Living with spouse/partner	5	50
Married	0	0
Divorced/widowed	3	30
Education
Finished high school/received GED	4	40
Some college	2	20
Trade/tech school	2	20
College/university degree or higher	2	20

N = 10. Participants were on average 40.2 years old (SD = 13.5).

As a result of the COVID-19 pandemic, “routine clinic visits” involved virtual and in-person appointments, thus the majority of recruitment and enrollment procedures were conducted remotely. To be in compliance with the recruitment site's COVID-19 safety measures, study recruitment efforts relied on provider referrals and limited contact with the few individuals being seen in-person, posing a challenge to enrollment numbers and attrition from initial contact (socially-distanced, in person) to the baseline assessment (remotely). See Figure 1 for an overview of study recruitment, enrollment, and follow-up procedures. The acceptability, feasibility, and preliminary efficacy results presented below are based on 10 participants who completed the intervention.

Figure 1.

Overview of study procedures.

Enhancing distress tolerance to uplift motivation in recovery intervention

The two-part enhancing distress tolerance to uplift motivation in recovery (iENDURE) intervention focused on enhancing motivational processes in combination with strategies for tolerating distress through a single, brief, web-based session followed by 8 weeks of personalized text messages.

Web-based intervention

The initial intervention phase was delivered online via Qualtrics, a web-based survey platform. The goals of this phase were as follows: (a) engage participants in a decisional balance exercise to evaluate the perceived advantages/disadvantages of making a behavior change, related to their opioid use, to enhance motivation and (b) provide concrete strategies to better tolerate emotional and physical discomfort to persist with behavioral goals while initiating treatment. Throughout the web-based intervention, participants were presented with predetermined lists of ‘pros’ and ‘cons’ for discontinuing opioid use and initiating treatment, from which they self-selected personally relevant items or wrote in personal motivators. Additionally, individuals were prompted by the intervention to enter their short-term and long-term recovery goals; examples were provided for guidance. Lastly, participants were presented with psychoeducation on DT-based coping strategies, including distraction, self-soothing, changing body chemistry, evaluating pros/cons, and improving the moment. Similarly, participants were encouraged to select strategies that were most useful/relevant to their recovery. Participant responses were then summarized and embedded within future text messages. See Figure 2 for an example of the web-based intervention.

Figure 2.

Examples from web-based iENDURE intervention.

Text message-based intervention

The second intervention phase consisted of daily text messages, delivered in partnership with Marigold Health, over a span of 8 weeks (56 days receiving messages), which focused on (a) promoting motivation for remaining in treatment and maintaining recovery and (b) providing adaptive skills for tolerating emotional and physical discomfort. Each day during the text-message intervention period, participants were provided with a morning message emphasizing their personal reasons, derived from the decisional balance exercise, to persist with their goals. This was followed by a midday “check-in” assessing for participants’ mood and presence of cravings. Participants were queried (a) “How are you feeling in general today?” and prompted to respond on a Likert scale, ranging from 1 to 5 (1 = really bad, 5 = great) and (b) “Any cravings or urges to use in the past 24 h?” and prompted to respond with “yes” or “no.” Immediately following the response, participants received a DT skill recommendation tailored to their mood/cravings assessment, which was selected from the participant-generated pool, The automated message schedule provided a reminder after two hours if no engagement with the intervention was detected and delivered recommendations for low mood/yes cravings if no response was captured within a four-hour response window. Additionally, the intervention consisted of a participant-driven text feature to provide tailored support. Participants were educated about this component at the time of enrollment. For example, if a participant texted “USE” to the automated program, indicating the need for extra support following an episode of returning to opioid use, they would receive a relevant skills-based message. On-demand keywords to obtain additional support available to participants also included “CRAVING,” “MOOD,” “GUILT,” and “PAIN.”

Measures

Appropriate copyrights and necessary permissions for all applicable tools/questionnaires in the study had been obtained prior to data collection.

Substance use measures. The online Timeline Follow-Back ⁴³ was implemented to capture self-reported daily opioid use, use of other illicit substances (e.g., cocaine), and buprenorphine adherence. Results of urine toxicology screens were collected during routine clinical care, made available through the electronic medical record (EMR), and were used to (a) screen for the presence of opioids and other illicit substances and (b) confirm adherence to buprenorphine. Retention in treatment services was captured via a retroactive EMR review.

Readiness ruler (RR). The RR is a single question assessing the current level of motivation to change opioid use, indicated by selecting a value between 0 and 10 (Please indicate to what extent you feel ready to change your opioid use.). The RR in the present study was adapted from the LaBrie et al.⁴⁴ RR, which was validated to briefly assessing motivation to change drinking behavior.

Confidence scale. This item assessed participant's confidence in meeting their recovery goals. Participants were prompted “Please indicate to what extent you feel confident in reaching your goals for treatment” and were expected to respond on a 11-point Likert scale, ranging from “0” = “I do not think I will reach my goals” and “10” = “I think I will definitely reach my goals.” Higher score indicated greater levels of confidence in meeting goals.

Opiate Craving Scale (OCS). The OCS is a 3-item measure of craving that was delivered to capture a current desire to use opioids. The first item asks participants directly about their current opiate cravings. The second and third items prompt participants to rate their opiate cravings after imagining themselves in an environment that may trigger a desire to use. Participants are expected to respond on an 11-point Likert scale, ranging from “0” = “Not at all” and “10” = “Extremely.” Higher scores indicate greater presence of cravings. The Craving Scale was validated for use in people with substance use disorders, and with opioid use.⁴⁵

DT Scale (DTS). The DTS is a 15-item self-report measure of one's ability to tolerate psychological distress.²⁰ Items are rated on a 5-point scale ranging from “1” = “Strongly agree” to “5” = “Strongly disagree. Low scores on the DTS indicate that an individual can tolerate higher levels of distress. Higher scores reflect low DT (i.e., distress intolerance). The scale has been found to demonstrate good internal consistency, good test—retest reliability, and discriminant validity with measures of negative affect.⁴⁶

Positive and Negative Affect Scale (PANAS). The PANAS is a 20-item mood scale which evaluates the extent to which participants’ experience positive and negative affectivity over the past week. The scales are highly internally consistent, largely uncorrelated, and stable over a 2-month period.⁴⁷ Items are divided to calculate a “Positive Affect Score” and “Negative Affect Score.” Adding items 1, 3, 5, 9, 10, 12, 14, 16, 17, and 19, scores can range from 10 to 50; higher scores representing higher levels of positive affect. Adding items 2, 4, 6, 7, 8, 11, 13, 15, 18, and 20, scores can range from 10 to 50; higher scores representing higher levels of negative affect.

Feasibility and Acceptability. We collected acceptability of the current intervention through the Client Satisfaction Questionnaire (CSQ-8). The CSQ-8 was validated within residential substance use treatment settings.⁴⁸ Feasibility was evaluated through (a) retention across the study period, including percent of participants who completed assessments, (b) engagement with the text messages, including participant reports of interacting with/reading messages and average response rates, and (c) perceived usability of the intervention via the System Usability Scale (SUS). The SUS has a valid measure to assess the usability of internet-based interventions when used by professionals in mental healthcare.⁴⁹

Qualitative interviews. Research staff conducted interviews over the phone or in-person (when possible) at the end of the 8-week intervention period. The purpose of these interviews was to elicit feedback on perceived acceptability of the intervention as well as ways to improve/refine intervention content and delivery for the subsequent phase of the study involving a randomized clinical trial.

Data analysis

This pilot study sought to evaluate the feasibility and acceptability of the two-part iENDURE intervention. Preliminary clinical outcomes included: opioid use, adherence to buprenorphine, opioid-related craving, treatment attendance and retention, readiness to change opioid use, confidence in the ability to achieve treatment goals, and tolerance of distress. Additionally, changes to mood (degree of positive and negative affect) were assessed as secondary outcomes. Qualitative data analysis was conducted using the Framework Matrix approach,⁵⁰ a structured process to categorize qualitative data by thematic (deductive) and data driven (inductive) codes, such that highly relevant and emergent concepts can be easily captured and compared across participants. All interviews were coded separately by two research assistants, who met regularly to establish code concordance. Any discrepancy was resolved by consultation with the Principal Investigator.

Results

Feasibility

Participants (n = 10) were administered in-person or remote follow-up assessments at 1-, 4-, and 8-weeks following enrollment in the text message component of the intervention. Seven participants completed the 1-week assessment, while 9 individuals were retained for the 4- and 8-week assessments and closing interviews. As indexed by self-report in the qualitative exit interviews, 100% of retained participants (n = 10) engaged with the text messages throughout the 8-week period. While participants received compensation for completing follow-up assessments, no incentives were administered for text message responsiveness; indicating that engagement with the iENDURE program is consistent with natural response behaviors. The level of engagement, however, varied across individuals. Analysis of text data indicated that the intervention daily response rate averaged 66.6% (SD = 34.6). While six participants responded to more than 75% of messages and two participants responded to more than 55%, two participants engaged with less than 10% of messages. During the exit interviews, the participants with the lowest response rates were prompted about barriers to engaging with the intervention. Participant 3 endorsed reading all messages, despite not responding to the check-in for current mood and cravings. Additionally, the participant admitted to not being interested in supportive resources outside medication, providers, friends, and family. This participant suggested that the iENDURE program would “benefit someone without a good support system.” Participant 7 attributed their lack of responsiveness to not having access to their cellphone throughout the workday: “Just not having my phone next to me at work. I don’t really keep my phone on me at work because it will get destroyed.” The average rating on the SUS at the end of the 8-week program was 65.3 (possible range = 0–100), suggesting that iENDURE was relatively easy to use.

Acceptability

Table 2 provides a summary of the questions and responses regarding satisfaction with various components of iENDURE. Global ratings for the quality of services and satisfaction with the program were high in participants relating to both the web-based intervention and following the 8-weeks of text messages. One participant negatively reported on iENDURE. However, this participant, nonetheless, indicated they would recommend the program to a friend. Otherwise, participants uniformly responded that iENDURE helped manage substance use more effectively and that they would come back to iENDURE if they decided to seek help again.

Table 2.

Participant satisfaction with enhancing distress tolerance to uplift motivation in recovery (iENDURE).

Question	Response option	Baseline (n = 10)	8-weeks (n = 9)
How would you rate the quality of the iENDURE computer program?	Excellent	5 (50%)	4 (44%)
	Good	5 (50%)	4 (44%)
	Fair	0	1 (11%)
	Poor	0	0
Did you get the kind of service you wanted from iENDURE?	No, definitely	0	0
	No, not really	0	1 (11%)
	Yes, generally	6 (60%)	4 (44%)
	Yes, definitely	4 (40%)	4 (44%)
To what extent has iENDURE met your needs?	Almost all of my needs have been met	6 (60%)	3 (33%)
	Most of my needs have been met	4 (40%)	5 (56%)
	Only a few of my needs have been met	0	1 (11%)
	None of my needs have been met	0	0
If a friend were in need of similar help, would you recommend iENDURE to them?	No, definitely no	0	0
	No, I don’t think so	0	0
	Yes, I think so	2 (20%)	4 (44%)
	Yes, definitely	8 (80%)	5 (56%)
How satisfied are you with the amount of help you have received from iENDURE?	Quite dissatisfied	0	0
	Indifferent or mildly dissatisfied	0	1 (11%)
	Mostly satisfied	3 (30%)	4 (44%)
	Very satisfied	7 (70%)	4 (44%)
Has iENDURE helped you to deal more effectively with substance use?	Yes, they helped a great deal	4 (40%)	4 (44%)
	Yes, they helped	5 (50%)	4 (44%)
	No, they really didn’t help	0	1 (11%)
	No, they seemed to make things worse	0	0
		1 no response
In an overall, general sense, how satisfied are you with iENDURE?	Very satisfied	5 (50%)	5 (56%)
	Mostly satisfied	4 (40%)	3 (33%)
	Indifferent or mildly dissatisfied	0	1 (11%)
	Quite dissatisfied	1 (10%)	0
If you were to seek help again, would you come back to using iENDURE?	No, definitely not	0	0
	No, I don’t think so	0	1 (11%)
	Yes, I think so	4 (40%)	3 (33%)
	Yes, definitely	6 (60%)	5 (56%)

The mean score on the CSQ-8 was 28.6 (SD = 2.55) following the web-based intervention and 27 (SD = 5.05) at the end of the text message program. Scores on the CSQ-8 range from 8 to 32, indicating a high degree of satisfaction with the intervention. Overall, participants endorsed positive experiences with the intervention content. Specifically, following the delivery of each text message, participants were asked to rate their satisfaction with the content on a 1–5 scale (1= “very dissatisfied,” 3= “neutral,” 5= “very satisfied”). Across the 8-week period, morning messages were, on average for each participant (n = 10), rated as 4.10 (SD = 0.76) and afternoon messages were rated as 4.22 (SD = 0.84), suggesting that participants were highly satisfied with the content delivered. This sentiment was supported by qualitative interviews with participants noting that the intervention was “…a positive experience. It kept me focused and motivated on the task at hand,” and “it's helped with my overall, … Supporting me … It was like some of my armor when I'm going into battle.”

Preliminary outcomes

To assess the impact of the intervention on treatment engagement and adherence, self-report (Timeline Follow-Back) and medical record data were utilized to examine compliance with buprenorphine, rates of opioid use, and treatment attendance. Of the 9 people who were retained at the 4-weeks follow-up period, 100% reported buprenorphine adherence and 8 individuals reported abstinence from opioids. At the end of the intervention, at the 8-week follow-up assessment, 7 participants reported buprenorphine adherence and 8 participants reported abstinence from opioids. Regarding treatment retention, 8 out of 10 participants had scheduled appointments at the 4-week follow-up, and 7 out of 10 participants had active appointments scheduled at the 8-week follow-up.

Table 3 provides the means and standard deviations of self-reported outcome variables. Participants reported high motivation for change and high confidence in their ability to reach their goals at the start of the intervention (RR: M = 8.40, SD =1.96; Confidence Scale: M = 8.20, SD = 1.99). One week following enrollment, participants reported, on average, slightly lower motivation for change and lower confidence in the ability to reach goals (RR: M = 7.86, SD = 2.27; Confidence Scale: M = 6.67, SD = 2.42). However, by the end of the 8-week intervention, participants overall motivation and confidence increased (RR: M = 8.88, SD = 1.46; Confidence Scale: M = 8.22, SD = 1.99). Participants’ cravings, as indexed by the OCS, decreased from the start of the intervention (M = 5.33, SD = 2.88) to week 1 (M = 3.67, SD = 1.91), and decreased further by week 4 (M = 3.24, SD = 2.23). By the end of the 8-week intervention, participants overall cravings remained lowered relative to the start of the program (M = 4.33, SD = 3.01).

Table 3.

Assessments.

	Baseline(Mean, SD)	1-week	4-weeks	8-weeks
OCS	5.33 (2.88)	3.67 (1.91)	3.24 (2.23)	4.33 (3.01)
Readiness ruler	8.20 (1.99)	7.86 (2.27)	8.25 (2.76)	8.88 (1.46)
Confidence Scale	8.40 (1.96)	6.67 (2.42)	8.75 (1.16)	8.22 (1.99)
DTS	2.91 (0.77)	2.59 (0.86)	2.08 (0.75)	2.24 (0.52)
PANAS positive	29.11 (7.52)	35.00 (7.96)	32.00 (8.40)	35.67 (9.04)
PANAS negative	25.78 (10.58)	24.63 (9.75)	18.00 (5.10)	20.56 (8.62)
CSQ	28.56 (2.55)			27.00 (5.05)
SUS				65.31 (12.42)
Timeline follow back	X	X	X	X

OCS: Opiate Craving Scale (range 0–30); DTS: Distress Tolerance Scale (range 1–5; higher scores reflect low tolerance); PANAS: Positive and Negative Affect Scale (range 10–50); CSQ: Client Satisfaction Questionnaire (range 8–32); SUS: System Usability Scale (range: 0%–100%).

Finally, participants demonstrated an increase in tolerance of distress throughout the first month of the intervention which was evident by a decrease in their DTS scores from baseline (M = 2.93, SD = 1.25) through week 1 (M = 2.51, SD = 1.39) and week 4 (M = 2.20, SD = 1.24) post-baseline. Gains made in the first month of the intervention were maintained throughout the 8-week follow-up period (M = 2.207, SD = 1.14). Participants also evidenced a change in affect across the study period. Specifically, levels of positive affect increased [baseline (M = 29.11, SD = 7.52); week 8 (M = 35.67, SD = 9.04)] and levels of negative affect decreased [baseline (M = 25.78, SD = 10.58); week 8 (M = 20.56, SD = 8.62)] throughout the 8-week intervention.

Discussion

Findings from this pilot development trial suggest that iENDURE, a two-part intervention designed to support buprenorphine treatment for OUD, is, in general, feasible to administer and acceptable to patients engaged in outpatient care. iENDURE differs from other existing treatments for OUD in that it (a) combines motivational enhancement with DT skill building and (b) is delivered through a highly accessible digital health platform. This unique combination of intervention content and delivery method offers patients with OUD continuous access to evidence-based and personalized support.

The iENDURE intervention was not only feasible to administer, but also highly engaging and perceived as easy to use. Nine out of ten (90%) participants were retained in the study throughout the entire 8-week period. Additionally, participants responded to daily text messages at a rate of 66.6%, with more than half of the sample responding to messages at least 75% of the time. Interestingly, participants who did not respond to the daily queries, nonetheless, indicated that they read most of the motivational and coping skill messages. This is noteworthy as meta-analyses suggest that one-way communications are still effective in producing behavior change.³⁹

Although this study did not involve a comparison condition, nor was it powered to detect effect sizes, we observed trends in the predicted direction for levels of motivation, confidence, cravings for opioids, DT, and positive/negative affect from baseline to the 8-week follow-up assessment. At the conclusion of the study, 7 participants endorsed adherence to buprenorphine and 8 participants reported abstinence from opioids. Further, 7 participants were still actively engaged in treatment at the 8-week follow-up as evidenced by the EMR. Given that recent estimates suggest that nearly 50%–75% of patients who initiate buprenorphine will discontinue treatment within the first three months,⁵⁰ our findings provide preliminary support for improved engagement and retention in care.

While this study highlights the feasibility and acceptability of text-based interventions, such research needs to be augmented by findings on ways to improve accessibility to interventions. Specifically, the COVID-19 pandemic and the subsequent adoption of digital technologies in the provision of health services revealed stark inequalities in access to resources required for telehealth such as the internet, reliable cellphone data, or cell phones.^51,53 Research that develops digital intervention without addressing these systemic barriers will continue to exclude a significant portion, and often the most vulnerable, portions of populations from accessing care. Two aspects of the current study augmented its feasibility. Firstly, text messaging-based interventions, as opposed to cellphone application-based interventions (or apps), are accessible across various types of cellphones (e.g., smartphones, burner phones, older models) and digital literacy. Thus, research delivering intervention via text messages may have a wider reach. Secondly, incorporating this research program into standard care received by participants provided opportunities for human touch points, which then bolstered retention. For example, if a participant obtained a new cellphone number, research staff were able to collect this information during the participant's treatment visit.

There are several limitations to the current study. First, our sample was limited regarding gender, racial, and ethnic diversity as well as overall sample size. Future trials, with larger sample sizes and adequate representations of gender, racial and ethnic minorities, are needed to determine whether iENDURE is feasible and acceptable to the general population, especially those from underrepresented backgrounds. Second, given that this was a pilot development trial, the present study did not employ a control group. Thus, it is difficult to determine whether the clinical improvements observed throughout the study period, were indeed uniquely related to the iENDURE intervention or related to initial engagement in treatment for OUD, given that all participants had recently enrolled in MOUD program. Relatedly, given the study was focused on assessing feasibility and acceptability, sample size was not calculated. To fully evaluate the effectiveness of iENDURE, future work should utilize a randomized controlled trial design that incorporates an appropriate control condition and involves apriori calculation of sample size. Thirdly, we were unable to assess demographic differences among those who agreed to participate in the intervention at baseline and those who did not. Such differences would be important to investigate in future studies in order to make improvements in the intervention. And, finally, many of the studied variables were assessed via self-report. While an attempt was made to increase scientific rigor through objective verification of certain outcomes via the EMR, there is still the possibility of recall bias.

Conclusion

In conclusion, the results obtained from this pilot study demonstrate promising trends in the feasibility and acceptability of a combined web- and text message-delivered intervention in improving adherence to and engagement in buprenorphine treatment for OUD. Our sample was comprised of patients actively engaged in buprenorphine treatment in an outpatient setting. While preliminary, our data suggests that when combined with treatment as usual, incorporating motivational enhancement and distress tolerance skills training via a digital health platform, may hold promise in improving treatment outcomes for individuals with OUD.

Footnotes

Contributors

Prachi H. Bhuptani contributed to the conceptualization of study, data analysis, and writing (original and revised draft) of the manuscript. Amanda Block and Paola Jiménez Muñoz contributed to the data analysis, writing (original and revised draft) of the manuscript. Mariel S Bello contributed to the writing (review and editing) of the manuscript. Susan Ramsey, Megan Ranney, Kate Carey, and Josiah Rich contributed to the conceptualization of study, writing (review and editing). Kirsten Langdon contributed to the conceptualization of study and writing (original and revised draft).

Conflict of interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical approval

The study received approval from Rhode Island Hospital's Institutional Review Board (1777545-7).

Funding

The authors received the following financial support for the research, authorship, and/or publication of this article: This work was supported through grants from the National Institutes of Health (NIH, PI: Langdon, K23DA046482) and the National Science Foundation Graduate Research Fellowship (NSF, PI: Bello, DGE-1418060). The content is solely the responsibility of the authors and does not necessarily represent the official views of the CDC, NIH, or NSF.

Guarantor

K.L.

ORCID iDs

Prachi H Bhuptani

Kirsten Langdon

References

Substance Abuse and Mental Health Services Administration. Key substance use and mental health indicators in the United States: Results from the 2019 National Survey on Drug Use and Health. https://www.samhsa.gov/data/ (2020, accessed 1 December 2021).

Center for Behavioral Health Statistics and Quality. 2018 National survey on drug use and health: detailed tables. https://www.samhsa.gov/data/ (2019, accessed 1 December 2021).

National Institute on Drug Abuse. Overdose death rates. https://www.drugabuse.gov/related-topics/trends-statistics/overdose-death-rates (2020, accessed 1 December 2021).

Centers for Disease Control and Prevention. Multiple cause of death 1999–2020 CDC WONDER online database. https://wonder.cdc.gov/wonder/help/mcd.html?msclkid=8c86c6c2b06511ec84708c2386801f7f (2021, accessed 1 December 2021).

National Academies of Sciences, Engineering, and Medicine. Medications for opioid use disorder save lives. Washington, DC: The National Academies Press, 2019.

Substance Abuse and Mental Health Services Administration. Medication-assisted treatment buprenorphine. https://www.samhsa.gov/medication-assisted-treatment/medications-counseling-related-conditions/buprenorphine (2016, accessed 11 November 2021).

Mattick

Breen

Kimber

, et al. Buprenorphine maintenance versus placebo or methadone maintenance for opioid dependence. Cochrane Database Syst Rev. Epub ahead of print 6 February 2014. DOI: 10.1002/14651858.CD002207.pub4.

Kakko

Svanborg

Kreek

, et al. 1-year Retention and social function after buprenorphine-assisted relapse prevention treatment for heroin dependence in Sweden: a randomized, placebo-controlled trial. Lancet 2003; 361: 662–668.

Fudala

Bridge

Herbert

, et al. Office-based treatment of opiate addiction with a sublingual tablet formulation of buprenorphine and naloxone. N Engl J Med 2003; 349: 949–958.

10.

Hser

Saxon

Huang

, et al. Treatment retention among patients randomized to buprenorphine/naloxone compared to methadone in a multi-site trial. Addiction 2014; 109: 79–87.

11.

Stein

Herman

Kettavong

, et al. Antidepressant treatment does not improve buprenorphine retention among opioid-dependent persons. J Subst Abuse Treat 2010; 39: 157–166.

12.

Morgan

Walley

Murphy

, et al. Characterizing initiation, use, and discontinuation of extended-release buprenorphine in a nationally representative United States commercially insured cohort. Drug Alcohol Depend 2021; 225: 108764.

13.

O’Connor

Cousins

Durand

, et al. Retention of patients in opioid substitution treatment: a systematic review. PloS one 2020; 15: e0232086.

14.

Wakeman

Larochelle

Ameli

, et al. Comparative effectiveness of different treatment pathways for opioid use disorder. JAMA Network Open 2020; 3: e1920622–e1920622.

15.

Teruya

Schwartz

Mitchell

, et al. Patient perspectives on buprenorphine/naloxone: a qualitative study of retention during the starting treatment with agonist replacement therapies (START) study. J Psychoact Drugs 2014; 46: 412–426.

16.

Viera

Bromberg

Whittaker

, et al. Adherence to and retention in medications for opioid use disorder among adolescents and young adults. Epidemiol Rev 2020; 42: 41–56.

17.

Chan

Gean

Arkhipova-Jenkins

, et al. Retention strategies for medications for addiction treatment in adults with opioid use disorder: A rapid evidence review. 2020. doi:10.23970/ahrqepcrapidmat

18.

Novak

Burgess

Clark

, et al. Anxiety sensitivity, self-reported motives for alcohol and nicotine use, and level of consumption. J Anxiety Disord 2003; 17: 165–180.

19.

McHugh

Kneeland

. Affective vulnerability in substance use disorders. Curr Opin Psychol 2019; 30: 54–58.

20.

Simons

Gaher

. The distress tolerance scale: development and validation of a self-report measure. Motiv Emotion 2005; 29: 83–102.

21.

Daughters

Lejuez

Bornovalova

, et al. Distress tolerance as a predictor of early treatment dropout in a residential substance abuse treatment facility. J Abnormal Psychol 2005; 114: 729–734.

22.

Daughters

Lejuez

Kahler

, et al. Psychological distress tolerance and duration of most recent abstinence attempt among residential treatment-seeking substance abusers. Psychol Addictive Behaviors 2005; 19: 208.

23.

McHugh

Otto

. Profiles of distress intolerance in a substance dependent sample. Am J Drug Alcohol Abuse 2012; 38: 161–165.

24.

McHugh

Weiss

Cornelius

, et al. Distress intolerance and prescription opioid misuse among patients with chronic pain. J Pain 2016; 17: 806–814.

25.

Trafton

Gifford

. Biological bases of distress tolerance. In: Zvolensky

Bernstein

Vujanovic

(eds) Distress tolerance: Theory, research, and clinical applications. The Guilford Press, 2011, pp. 80–102.

26.

Strong

Brown

Sims

, et al. Persistence during stress-challenge predicts lapse to opiate use during buprenorphine treatment. J Addiction Med 2012; 6: 19–25.

27.

Brown

Bloom

Hecht

, et al. A pilot study of a distress tolerance treatment for opiate-dependent patients initiating buprenorphine: rationale, methodology, and outcomes. Behav Modif 2014; 38: 730–759.

28.

Bornovalova

Gratz

Daughters

, et al. Initial RCT of a distress tolerance treatment for individuals with substance use disorders. Drug Alcohol Depend 2012; 122: 70–76.

29.

Riper

van Straten

Keuken

, et al. Meta-analytic review of the effectiveness of single-session personalised normative feedback without therapeutic guidance in reducing problem drinking. Am J Prev Med 2009; 36: 247–255.

30.

Collins

Kirouac

Lewis

, et al. Randomized controlled trial of web-based decisional balance feedback and personalized normative feedback for college drinkers. J Stud Alcohol Drugs 2014; 75: 982–992.

31.

Janis

Mann

. Decision-making: A psychological analysis of conflict, choice, and commitment. New York, NY: The Free Press, 1997.

32.

Miller

Leffingwell

Claborn

, et al. Personalized feedback interventions for college alcohol misuse: an update of walters & neighbors (2005). Psychol Addict Behav 2013; 27: 909.

33.

Carey

Maisto

, et al. Brief motivational interventions for heavy college drinkers: a randomized controlled trial. J Consult Clin Psychol 2006; 74: 943.

34.

LaBrie

Pedersen

Earleywine

, et al. Reducing heavy drinking in college males with the decisional balance: analyzing an element of motivational interviewing. Addict Behav 2006; 31: 254–263.

35.

Apodaca

Longabaugh

. Mechanisms of change in motivational interviewing: a review and preliminary evaluation of the evidence. Addiction 2009; 104: 705–715.

36.

Fjalldal

Janson

Benediktsdóttir

, et al. Smoking, stages of change and decisional balance in Iceland and Sweden. Clin Respir J 2011; 5: 76–83.

37.

Okechukwu

Krieger

Sorensen

, et al. Testing hypothesized psychosocial mediators. Health Educ Behav 2011; 38: 404–411.

38.

Geller

Mendoza

Timbobolan

, et al. The decisional balance sheet to promote healthy behavior among ethnically diverse older adults. Public Health Nurs 2012; 29: 241–246.

39.

Collins

Eck

Torchalla

, et al. Decisional balance proportion: quantifying qualitative data to represent motivation to change among treatment-seeking smokers. Drug Alcohol Depend 2010; 111: 82–88.

40.

Okechukwu

Krieger

Sorensen

, et al. Testing hypothesized psychosocial mediators: lessons learned in the MassBUILT study. Health Educ Behav 2011; 38: 404–411.

41.

Moore

Guarino

Marsch

. “This is not who I want to be:” experiences of opioid-dependent youth before, and during, combined buprenorphine and behavioral treatment. Subst Use Misuse 2014; 49: 303–314.

42.

Diagnostic and statistical manual of mental disorders, 5th Edition. 2013. doi:10.1176/appi.books.9780890425596.893619.

43.

Sobell

. Timeline follow-back: a technique for assessing self-reported alcohol consumption. In: Litten

Allen

(eds) Measuring alcohol consumption: psychosocial and biological methods. Totowa, NJ: Humana Press, 1992, pp.41–72.

44.

LaBrie

Quinlan

Schiffman

, et al. Performance of alcohol and safer sex change rulers comparied with readiness to change questionnaires. Psychol Addict Behav 2005; 19: 112–115.

45.

McHugh

Trinh

Griffin

, et al. Validation of the craving scale in a large sample of adults with substance use disorders. Addict Behav 2021; 113: 106651.

46.

Anestis

Lavender

Marshall-Berenz

, et al. Evaluating distress tolerance measures: interrelations and associations with impulsive behaviors. Cognit Ther Res 2011; 36: 593–602.

47.

Watson

Clark

Tellegen

. Development and validation of brief measures of positive and negative affect: the PANAS scales. J Pers Soc Psychol 1988; 54: 1063–1070.

48.

Kelly

Kyngdon

Ingram

, et al. The Client Satisfaction Questionnaire-8: psychometric properties in a cross-sectional survey of people attending residential substance abuse treatment. Drug Alcohol Rev 2018; 37: 79–86.

49.

Gale

Heath

Cameron

, et al. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol 2013; 13: 117.

50.

Ker

Hsu

Balani

, et al. Factors that affect patient attrition in buprenorphine treatment for opioid use disorder: a retrospective real-world study using electronic health records. Neuropsychiatr Dis Treat 2021; 17: 3229–3244.

51.

Nguyen

Hargittai

Marler

, et al. Digital inequality in communication during a time of physical distancing: the case of COVID-19. Comput Human Behav 2021; 120: 106717.

52.

Anderson

Mobile technology and home broadband 2019. https://policycommons.net/artifacts/616757/mobile-technology-and-home-broadband-2019/1597443/

53.

Davies

Honeyman

Gann

, et al. Addressing the digital inverse care law in the time of COVID-19: potential for digital technology to exacerbate or mitigate health inequalities. J Med Internet Res 2021; 23: e21726.

Enhancing distress tolerance to uplift motivation in recovery: Results from an open development trial

Abstract

Objective

Methods

Results

Conclusions

Keywords

Introduction

Methods

Participants and procedures

Enhancing distress tolerance to uplift motivation in recovery intervention

Web-based intervention

Text message-based intervention

Measures

Data analysis

Results

Feasibility

Acceptability

Preliminary outcomes

Discussion

Conclusion

Footnotes

Contributors

Conflict of interests

Ethical approval

Funding

Guarantor

ORCID iDs

References