Skip to main content
Download PDF
Download ePub
  • Study Protocol
  • Open access
  • Published:

Integrated telehealth intervention to reduce chronic pain and unhealthy drinking among people living with HIV: protocol for a randomized controlled trial

Addiction Science & Clinical Practice volume 19, Article number: 64 (2024) Cite this article

Abstract

Background

Unhealthy alcohol use represents a significant risk for morbidity and mortality among people living with HIV (PLWH), in part through its impact on HIV management. Chronic pain, a common comorbidity, exacerbates suboptimal engagement in the HIV care continuum and has reciprocal detrimental effects on alcohol outcomes. There are no integrated, accessible approaches that address these comorbid conditions among PLWH to date. This paper describes a research study protocol of an integrated telehealth intervention to reduce unhealthy drinking and chronic pain among PLWH (Motivational and Cognitive-Behavioral Management for Alcohol and Pain [INTV]).

Methods

Two-hundred and fifty PLWH with unhealthy drinking and chronic pain will be recruited nationally via online advertisement. Informed consent and baseline assessments occur remotely, followed by 15 days of ecological momentary assessment to assess alcohol use, chronic pain, functioning, and mechanisms of behavior change. Next, participants will be randomized to either the INTV or Control (CTL) condition. Individuals in both conditions will meet with a health counselor through videoconferencing following randomization, and those in the INTV condition will receive 6 additional sessions. At 3- and 6-months post-baseline, participants will complete outcome assessments. It is hypothesized that the INTV condition will result in reduced unhealthy alcohol use and pain ratings compared to the CTL condition.

Conclusion

This protocol paper describes a randomized controlled trial which tests the efficacy of a novel, integrated telehealth approach to reduce unhealthy alcohol use and chronic pain for PLWH, two common comorbid conditions that influence the HIV treatment cascade.

ClinicalTrials.gov identifier

NCT05503173.

Highlights

Unhealthy alcohol use and chronic pain are common in people living with HIV.

An integrated telehealth intervention may improve alcohol, pain, and HIV outcomes.

Ecological momentary assessment will examine processes underlying behavior change.

Introduction

Unhealthy alcohol use represents a significant risk for morbidity and mortality among people living with HIV (PLWH) and is estimated to occur among 10 to 38% of patients depending on sample characteristics [1,2,3]. Unhealthy alcohol use refers to the consumption of alcohol that places individuals at risk for negative health outcomes and ranges from consumption of amounts that represent increased risk through severe alcohol use disorders [4]. Heavy drinking, defined as consuming more than 14 standard drinks per week or more than 4 standard drinks on any day for men and more than 7 standard drinks per week or more than 3 standard drinks on any day for women, is commonly identified as an amount that represents risk to health [5]. However, the amount of alcohol associated with mortality and physiologic injury may be lower among PLWH [6]. Both suboptimal viral suppression and faster HIV disease progression are associated with unhealthy alcohol use, [5, 7] which may be mediated directly through biological mechanisms (e.g., inflammatory processes) or indirectly through engagement in a variety of behaviors including poor linkage to care, missed healthcare appointments, suboptimal or non-adherence to antiretroviral therapy (ART), unprotected sexual activity, and injection drug use [1, 8,9,10,11]. Unhealthy alcohol use is also associated with the likelihood and severity of several comorbid conditions among PLWH impacting mental and physical health [12, 13].

Common comorbidities among PLWH, including chronic pain, exacerbate unhealthy drinking and its impact on HIV outcomes. Among patients presenting to HIV clinics, 35–50% experience chronic pain [12, 14]. Moreover, as the population of PLWH ages, pain has become more common, originating as a complication of HIV itself (e.g., neuropathy; [15]) and from common age-related conditions (e.g., arthritis). Not only is chronic pain associated with more severe HIV-related symptoms and reduced engagement with HIV care [16], but it also increases negative alcohol-related consequences among patients meeting criteria for unhealthy drinking across medical settings [17, 18]. Pain influences alcohol use through a variety of pathways (e.g., alcohol use to cope with pain; [19]) and compounds the impact of unhealthy alcohol use on HIV-related health outcomes such as frailty and poor physical functioning [20,21,22]. Given that pain commonly co-occurs with unhealthy drinking among PLWH, that they have reciprocal influences on one another [17], they impact mental and physical functioning, and they have additive effects on HIV-disease progression [14, 23], it is critical to develop and test approaches to address these concerns among this population.

While pharmacological treatments are available for pain, they are often inadequate for long-term pain management and complicated by comorbid conditions, including substance use history [24]. It is therefore often necessary to identify effective non-pharmacological treatments to adequately manage pain over time. Efforts to address unhealthy drinking among PLWH present other challenges, as the majority of individuals are unaware of the health risks associated with their alcohol use and do not seek alcohol treatment resources [25]. Furthermore, there are few alcohol services routinely offered in HIV care settings [26]. The need for multiple in-person clinic visits compounds challenges related to access, utilization, and adherence to behavioral treatments for unhealthy drinking and pain. Providing alternative intervention modalities that do not require repeated in-person medical visits is critical for reducing patient burden, increasing access to care, and improving treatment efficacy.

This paper describes a study protocol for a randomized controlled trial (RCT) that aims to test the efficacy of an integrated telehealth intervention for reducing unhealthy drinking and chronic pain among PLWH that may be implemented in a fully remote context. The secondary aims are to examine the moderators of the Motivational and Cognitive-Behavioral Management for Alcohol and Pain (INTV) effect and the mediational pathways of the INTV effect. Baseline interview-administered self-report and ecological momentary assessment (EMA) will be used to identify potential individual difference factors that moderate intervention efficacy. Post-intervention EMA, which provides the advantage of real-time assessment of self-regulatory processes and contextual influences on those processes, will be used to identify potential mediators of the intervention and explore mechanisms of behavior change [27, 28].

Method

This RCT is a project component of ARCHER (Addressing Related Comorbidities for HIV by Employing Remote technologies), an HIV Center funded by National Institute on Alcohol Abuse and Alcoholism (P01AA029546). The trial is registered on ClinicalTrials.gov (NCT05503173). This study is conducted in compliance with the protocol and applicable regulatory requirements approved by the Boston University Medical Campus/Boston Medical Center Institutional Review Board and Human Research Protection Program.

Overall design

Participants will be PLWH who engage in unhealthy drinking and experience moderate or greater chronic pain recruited nationally via online advertisement. Those who are eligible based on an initial set of online prescreening questions will be contacted by study staff for a more comprehensive phone screening. Those who remain eligible based on the more extensive screening will be scheduled for baseline assessment during which they will provide informed consent, confirm HIV diagnosis, and complete baseline questionnaires administered in an interview format. Staff will also orient participants to the EMA procedures whereby they will complete 5 surveys per day for the following 15 days. After EMA data collection, participants will undergo 1:1 randomization into either the intervention group (INTV) or the control (CTL) group. The INTV condition is a 7-session program designed for weekly delivery through videoconferencing by a health coach. Those in the CTL group meet virtually with the health coach for a single session and be provided with information and resources about alcohol and pain. At three-month outcome assessment, staff will administer questionnaires via interview, prior to another 15 days of EMA data collection. The final study assessment occurs six months after randomization and will include a staff-administered interview and a self-collected blood draw to measure phosphatidylethanol (PEth), a biological marker of alcohol use (see [29]). Participants complete self-collected blood draws at home and with guidance through videoconferencing (Fig. 1).

Fig. 1
figure 1

Study flow diagram

Full size image

Rationale for study design

The study is designed to test the efficacy of INTV, but has several additional objectives including, (1) the use of a nationally representative sample, (2) implementation of all study procedures remotely, (3) being able to identify potentially important moderators of intervention efficacy, and (4) providing detailed analyses of the processes through which the intervention may impact outcomes. Recruitment through social media will target PLWH across the country in areas defined by the CDC as having high HIV prevalence rates. This fully remote approach reduces participant burden and increases study efficiency. Finally, the study design incorporates the use of EMA. Multilevel modeling allows for an examination of how treatment may change associations between contextual cues (e.g., mood) and outcomes of interest (e.g., drinking, pain) over time. This approach enables fine-grained analysis of how the intervention may impact pain, alcohol use, and their interaction, in addition to providing insight into psychological processes (e.g., self-efficacy) through which these influences may occur. Such information will provide insight into how and for whom the interventions may reduce heavy drinking and pain.

Study aims and hypotheses

Primary aim

This study aims to test the efficacy of INTV for reducing unhealthy drinking and chronic pain among PLWH. The primary alcohol outcomes will be the average number of drinks per week and number of heavy drinking episodes over the past month. The primary pain outcome will be a composite measure of pain intensity and interference (i.e., pain average, interference with enjoyment of life, and interference with general activity) known as the PEG; [30]). Primary analyses will focus on alcohol and pain outcomes assessed at 6-month follow-up. It is hypothesized that those randomized to INTV will show fewer drinks per week, fewer episodes of heavy episodic drinking (primary alcohol outcomes), and lower ratings of pain at the 6-month assessment compared to the those in the CTL group, controlling for corresponding baseline values.

Secondary aims

Secondary aims seek to identify potential moderators and mediators of INTV, using data from assessment interviews and EMA. It is hypothesized that the influence of the intervention will be moderated by baseline readiness-to-change (for alcohol) and baseline GSAB questionnaire ratings of self-efficacy and planning (for both alcohol and pain outcomes). Secondary aims pertaining to alcohol use will be addressed by using baseline interview measures and parallel analyses will be conducted using baseline daily EMA ratings of readiness-to-change, alcohol coping self-efficacy, and alcohol-related planning. Mediational analyses will also be conducted using EMA at the 3-month outcome to test hypothesis that changes in self-regulatory processes mediate the effect of the INTV on alcohol use and chronic pain.

Inclusion and exclusion criteria

Inclusion criteria will consist of the following: (1) at least 18 years of age; HIV + with confirmation via visual evidence of antiretroviral (ART) medication or medical record; (2) engaged in unhealthy drinking defined as: a) having more than 7 drinks for women or 14 drinks for men per week; or b) using an HIV specific index of heavy episodic drinking, having 3 or more drinks for women or 4 or more drinks for men on one occasion in the past month [31,32,33]; (3) experiencing moderate or greater chronic pain (a score of 4 or greater on the Numerical Pain Rating Scale [NPRS]) for at least three months per self-report); (4) own a smart phone; and live in the United States with a United States mailing address. Exclusion criteria are as follows: (1) a history of bipolar disorder, schizoaffective disorder, or schizophrenia per self-report; (2) an unstable dose of psychoactive medication for pain or alcohol/substance use [i.e., if on medication, participant has not been on same dose for least 2 months]; (3) a history of withdrawal-related seizures or delirium tremens per self-report; current non-pharmacological treatment for alcohol use disorder or chronic pain per self-report; (4) acute life-threatening illness that requires treatment or intention to have surgery for a pain-related condition in the next 6 months; (5) current cancer-related pain per self-report; (6) unwilling to provide sex at birth; (7) limited or non-reader; or (8) unable to provide one or more contacts to facilitate follow-up in case the participant cannot be reached.

Recruitment, consent, and randomization

Recruitment will take place nationally via online advertisement in virtual patient communities, Google, Facebook, Instagram, health websites, and medical applications. BuildClinical (BC), a company that delivers study specific advertisements to various online platforms, regularly monitors metrics (i.e. percentage of potential participants who view and subsequently click an advertisement) to assess effectiveness and optimize the campaign to improve reach as necessary. When potential participants click on a study advertisement, a website populates containing general information about the trial and a link to a BC pre-screening form for those who are interested. BC pre-screening forms ask for contact information (name, e-mail, telephone number) and assess a preliminary subset of study eligibility criteria (e.g., HIV status, drinking habits, pain level).

If potential participants are eligible based on their responses to the preliminary BC pre-screening, research staff will contact the individual via phone call to confirm pre-screening questions and ask additional eligibility questions. At the time of consent, study staff will visually confirm HIV diagnosis via medical record or ART medication. Eligible and interested participants will take part in an informed consent process via videoconference or phone call and those who provide their consent electronically sign a REDCap [34, 35] form.

Randomization occurs after the baseline assessment phase is completed (described below) and is stratified by both gender and frequency of heavy drinking episodes. The project statistician will generate a list of randomization assignments to be used sequentially based on a permuted blocks strategy.

Assessments

Assessment procedures

Assessments will include interview-administered instruments at baseline, 3 months post-randomization, and 6 months post-randomization. In addition, there will be two 15-day EMA data collection periods (one at baseline and one at 3-months), a treatment satisfaction measure, and a blood draw to test a biological marker of alcohol use, phosphatidylethanol (PEth; [29]) self-administered 6 months-post randomization.

Participants will complete all assessment interviews (baseline, 3-month follow-up, and 6-month follow-up) virtually through a videoconferencing platform or by phone. Interviews will take approximately 90 to 120 min and are each associated with $50 compensation for time and effort.

In addition to research interviews, participants will complete two phases of EMA data collection over 15-day periods. The first 15-day EMA period begins following the baseline interview while the second 15-day EMA period takes place following the 3-month assessment interview. Participants will learn about EMA procedures following the baseline interview, including how to use the EMA iOS or Android mobile application (MetricWire; [36]. Each day during the EMA periods, MetricWire will prompt participants to complete a morning survey and four additional surveys which arrive randomly throughout the day.

Upon completing the 15 days of EMA surveys at the 3-month time point, participants will be notified to complete one final survey on intervention satisfaction and acceptability for which they will be compensated $5. Across all study activities, participants will be able to earn up to $480 over the 6 months. Payments are made via electronic gift certificates.

Finally, at the 6-month outcome assessment interview, participants will be able to earn an additional $25 for completing and sending a self-administered blood sample to the collaborating laboratory [29].

Assessment instruments

Sociodemographic characteristics

Sociodemographic information will be collected during screening and baseline interview and includes the following: age, state and zip code, sex assigned at birth, gender identity, height, weight, sexual orientation and whom they have sex with in their life (men only, women only, both men and women, not had sex), marital status, current spouse/partner, ethnicity, race, primary language, education, employment, insurance, difficulty paying bills, and unhoused status.

Alcohol assessment measures

The primary outcomes for alcohol use include number of alcoholic drinks consumed per week and number of heavy episodic drinking days in the past month (assessed via Alcohol Timeline Followback – 30 days [TLFB-30]; [37, 38]). Alcohol-related consequences will also be assessed using the Short Inventory of Problems-Revised (SIP-R; [39]) as a secondary outcome. A series of measures related to the self-regulation of alcohol use will also be administered, including the Readiness to Change Questionnaire (RTCQ; [40] and Goal Systems Assessment Battery (GSAB; [41, 42]), which provides assessment of value, self-efficacy, self-monitoring, and planning related the goal of moderating alcohol use.

Pain assessment measures

The primary outcome for pain is a composite measure of pain severity and interference (pain average, interference with enjoyment of life, and interference with general activity [PEG]; [30]). More comprehensive indices of pain intensity and pain interference will also be assessed using the Brief Pain Inventory- Short Form (BPI; [43]). The GSAB for pain will also be administered to assess self-regulatory components related to the goal of pain management, specifically planning and self-efficacy [41, 42].

EMA

As noted above, participants will complete two 15-day periods of EMA: one following the baseline interview and one following the 3-month interview assessment. Each day, participants will complete a morning survey and a set of 4 shorter random surveys. Compensation for completing EMA surveys is $3 for each morning survey and $1 for each random survey. Those who complete all 5 EMA surveys on any given day receive a $3 bonus. Participants can earn up to $10 per day for completing EMA surveys, with a maximum total of $150 per 15-day EMA span ($300 for both timepoints). EMA surveys were modified from previous EMA studies (e.g., [27, 44, 45]) to include questions about alcohol use [46, 47], chronic pain [30], physical and mental functioning [48,49,50], affect [51, 52], sleep [53], other substance use (i.e., cannabis and tobacco; [54, 55]), and self-regulatory processes [41, 42] that underlie pain management and drinking. EMA data will be collected via MetricWire, a smartphone application that is downloaded onto participants’ phones. Participants will be given the option to use their own email and private password or use a study provided email and password to access the program. The EMA surveys will consist of two assessment types, a morning survey and random survey. Morning surveys will be available from 4am to 10:30am and are initiated by the participant to report aggregate experiences over the past 24 h. Two reminders will be sent within that block to confirm the availability of the morning survey which will take approximately 5 min to complete. Random surveys are signal-contingent recordings whereby participants respond to prompts about their current pain, physical activity, alcohol and other substance use, and affect. They occur randomly in 3-hour blocks (e.g., 9am-noon; noon-3pm; 3-6pm; 6-9pm) and will take approximately 3 min to complete.

Phosphatidylethanol

PEth biomarker tests will be used as a marker of unhealthy alcohol use, and is an additional outcome to supplement self-report outcome measures [56]. Blood samples will be collected by the participants using a Tasso-M-20 device [57] with guidance from research assistants. The device uses a button that sticks to the skin, typically on the shoulder. When the instrument is pressed, a lancet pierces the skin and draws blood from the capillaries into a container at the bottom of the device. This procedure produces little discomfort and can be used with minimal training.

The full set of assessments is presented in Table 1.

Table 1 Enrollment, intervention, and assessment schedule
Full size table

Study conditions

Intervention

Rationale for integrated videoconferencing intervention

There would be considerable benefit to have an integrated approach to treat unhealthy alcohol use and pain for utilization across a variety of health care and community settings. Self-management interventions grounded in CBT have proven efficacious in reducing alcohol use and chronic pain across a wide range of populations [81, 82]. In the context of chronic medical conditions, these approaches emphasize strategies for addressing motivation to change and developing cognitive-behavioral skills for initiating and maintaining change [83]. Integrated interventions provide the opportunity to condense the number of sessions through common underlying treatment targets (e.g., stress coping) as well as helping patients understand the associations between pain and alcohol use in their own lives.

Telehealth through a videoconferencing platform represents an increasingly available method of intervention delivery that can enhance treatment access and adherence [84]. Telehealth has proven particularly advantageous among populations facing significant barriers to treatment, such as non-urban and low-income individuals, and has gained similar levels of treatment satisfaction compared to in-person treatment [85,86,87,88,89]. Second, the web-based formatting facilitates patient connection to supplemental materials (e.g., web-based assessments, EMA, video-skills training) that may extend interventions [84, 85]. Overall, this approach provides greater flexibility in terms of how and when interventions may be delivered.

INTV structure and content

The INTV utilizes a self-regulation framework [90] to integrate evidence-based approaches for chronic pain and unhealthy drinking. Specifically, INTV integrates Motivational Interviewing (MI; [91]) and cognitive-behavioral skill training [92, 93] for unhealthy drinking with cognitive-behavioral and self-management approaches for chronic pain [82, 94]. These approaches have proven efficacious for reducing unhealthy drinking and chronic pain as individual treatment targets among PLWH [95, 96]. The central goals of INTV are to increase motivation and self-efficacy to change and provide cognitive and behavioral skills to manage pain and reduce alcohol use. The intervention will be delivered through internet-based videoconferencing and supplemented with web-based content. The initial treatment session provides a rationale for addressing alcohol and pain together in the context of HIV management, and initiates MI related to alcohol use. Over the subsequent weeks, participants receive 6 additional treatment sessions, each lasting 45–60 min, for a total of 7 sessions (see Table 2). The intervention content was adapted for PLWH through a mixed methods approach and underwent preliminary testing to assess feasibility and acceptability [95, 96].

Table 2 Overview of intervention content
Full size table

Control

Participants randomized to CTL will meet once with an interventionist (“health counselor”) via videoconferencing for 20–30 min and receive psychoeducation about the effects of alcohol and pain on HIV symptoms, recommendations for alcohol use reduction, and a list of treatment resources for alcohol and chronic pain. CTL participants complete the same research assessments as those in INTV.

Rationale for control condition

The choice of this control condition was based on our goal to obtain an estimate for the impact of INTV compared to the current standard of care. We believe that psychoeducation and brief advice serves as an optimal control condition at this stage of intervention efficacy testing because it provides a test of whether the proposed intervention provides benefit compared to the type of intervention that may be commonly provided as part of an HIV-care visit (treatment-as-usual) or community setting. We recognize that different control conditions offer different types of information relevant to the efficacy and effectiveness of an intervention and that alternative control conditions (e.g., weekly contact) may be utilized in the next stages of research should this approach prove to demonstrate evidence of efficacy.

Interventionist training, Supervision, and Fidelity Assessment

Interventionists will be clinical psychologists and advanced level PhD clinical psychology students trained in both INTV and CTL condition content. Training will include MI in medical settings, [91] CBT for pain, [94] and alcohol self-management [92, 93]. Interventionists will participate in trainings on the INTV manual, supplemented by video recordings, role plays, and structured training cases. Supervision will occur on a biweekly basis, during which individual cases will be discussed. Interventionists also will conduct post-intervention fidelity assessments. Sessions will be digitally recorded with participant consent and select cases may be reviewed by interventionists and supervisors as needed.

Statistical design and analysis

Sample size determination

A sample size of 250 (n = 125 to each condition) will be randomized. With a conservative estimate of a 20% attrition rate by 6-month follow-up, this sample size gives power (80%) to detect medium effects (Cohen’s d = 0.40) at the 2-tailed significance level (0.05) for INTV versus CTL at 6-month follow-up on both pain and alcohol outcomes. The anticipated effect size is based on the following: (1) meta-analyses of CBT interventions compared to assessment and minimal interventions suggesting medium post-intervention effects on alcohol and pain; [98,99,100] and (2) results of our pilot study which showed medium-to-large pre-to-post changes (Cohen’s d from 0.79 to 1.0) within participants receiving INTV [95, 96]. Moreover, the availability of repeated measurements (to include a baseline covariate) increases statistical power to detect intervention effects for the analysis.

Primary and secondary analyses

Descriptive statistics will be provided for the baseline characteristics of the full sample and by condition (i.e., INTV vs. CTL). Multiple linear regression analyses will be used to test whether those in the INTV group consume fewer drinks per week, engage in fewer episodes of heavy episodic drinking, and report lower PEG ratings than those in the CTL group at 6-month outcome. Covariates will include age, sex at birth, and corresponding baseline measures of alcohol use and pain. Secondary analyses will examine related outcomes (i.e. alcohol related consequences, pain intensity, pain severity) using a parallel approach. Exploratory analyses will examine INTV effects on outcomes including substance use frequency, depressive symptoms, anxiety symptoms, HIV symptom index scores, HIV risk-taking behaviors, mental functioning, and physical functioning at 6 months using multivariate regression models. For PEth analyses, we will use the lower limit of quantification as a cut-off of > 50 as a measure of unhealthy alcohol use and a cutoff of PEth > 8 ng/ml) as a marker of any alcohol use. Logistic multiple regression with the age and race/ethnicity will be used to examine intervention effects on 6-month PEth outcomes [101]. Secondary aims will be addressed, in part, by using baseline interview measures of readiness-to-change (for alcohol) and baseline GSAB questionnaire ratings of self-efficacy and planning (for both alcohol and pain outcomes). Planned covariates include those described under primary aims and will include baseline values of the questionnaire measure being evaluated. The Condition x Moderator term will be added to the full models to examine interaction effects, which will be followed with simple slopes analyses to determine the direction of effects.

EMA data analysis

Consistent with previous work using a highly individualized approach [102] to treatment and assessment, self-regulatory responses from EMA periods will also be used to conduct moderation and mediation analyses. Moderation analyses will test the extent to which daily ratings of readiness-to-change, alcohol coping self-efficacy, and alcohol-related planning, moderate the effect of the INTV intervention on alcohol use. Mediational analyses will test the extent to which the intervention reduces both alcohol use and chronic pain through changes in self-regulatory processes when exposed to relevant contexts. Analyses will use aggregated scores from events in which the participant indicates that they were exposed to relevant contexts, including moderate or greater pain, or the opportunity to use alcohol [102]. As with the questionnaire data, we will use structural equation modeling (SEM) to test the hypothesis that increases in context-specific self-efficacy and planning will mediate the relationship between condition (INTV vs. CTL) and 6-month outcomes. Bias-corrected bootstrap confidence interval estimates will be used to test the mediation hypotheses [103]. As described, power of > 80% is estimated when the standardized effect of path a and path b represents a small-to-medium effect. Covariates in the EMA analyses will include the number of event monitoring events completed, as well as the number of relevant pain or drinking opportunity events identified.

EMA data will also contribute to several exploratory analyses which address event level questions, including how the intervention influences associations between cues (e.g., mood) and pain and alcohol use outcomes. It is hypothesized that, when day is included as a covariate to control for potential systematic changes over time, higher negative mood earlier in the day will be associated with higher chronic pain or unhealthy drinking later in the day. Within-subject associations between mood and chronic pain or unhealthy drinking may vary by treatment group, and it is hypothesized that those randomized to INTV will demonstrate attenuated associations relative to the CTL group.

Discussion

Unhealthy drinking and chronic pain are common comorbidities among PLWH that have reciprocal influences on one another and negatively impact HIV outcomes. The development of an efficacious, integrated, telehealth approach is an important step toward addressing treatment uptake challenges common in these comorbid conditions. This protocol describes an RCT for PLWH with co-occurring unhealthy alcohol use and chronic pain. The INTV telehealth intervention integrates MI and cognitive-behavioral skill training to target unhealthy drinking and cognitive-behavioral self-management approaches for chronic pain management. EMA before and after intervention delivery allows for identification of moderators and mediators of the intervention effect.

Although this protocol makes an important contribution to the literature, it has limitations. The intervention aims to improve access and utilization of treatments for pain and unhealthy drinking, however, the recruitment and enrollment procedures for this study occur online and therefore require internet access. Moreover, inclusion criteria also require that participants own a smartphone or other device (e.g., tablet, computer) with internet access to allow for videoconferencing with the health counselor and completion EMA surveys. Individuals who do not have internet access are therefore unable to engage with the study advertisements or enroll to participate. Similarly, those who are unable to provide a contact may not participate. These criteria may reduce the generalizability of findings. Finally, blood collection for PEth analysis is both self-administered and requires mailing the sample to the laboratory. Although this procedure is designed to increase the capacity to assess PEth with a national sample, its feasibility among this population is largely unknown.

Notwithstanding these limitations, the INTV trial will test the efficacy of a novel, integrated telehealth intervention for chronic pain and unhealthy drinking. This integrated video telehealth program has the potential to address structural barriers to the reduction of chronic pain and unhealthy drinking among PLWH.

Data availability

Data collected for this study will be submitted to the NIAAA Data Archive (NIAAA DA) repository. Data will be de-identified, individual-level data. To facilitate data sharing, we plan to use data collection instruments/data dictionaries that have already been defined rather than create new versions of those data dictionaries, whenever possible. We will also provide the required supporting documentation when sharing our data, including assessment clinical trial protocol, schedule of visits, manual of operations, case report forms/assessment instruments, and analytic/statistical algorithms used to derive variables in publications. We will adhere to all required data collection instruments for data harmonization purposes, and all subjects in the dataset will have a global unique identifier (GUIDs). We will link any relevant publications to our study in the Data Archive.

References

  1. Williams EC, Joo YS, Lipira L, Glass JE. Psychosocial stressors and alcohol use, severity, and treatment receipt across human immunodeficiency virus (HIV) status in a nationally representative sample of US residents. Subst Abus. 2017;38(3):269–77.

    Article  PubMed  Google Scholar 

  2. Satre DD, Levine-Hall T, Sterling SA, Young-Wolff KC, Lam JO, Alexeeff S, et al. The relationship of smoking and unhealthy alcohol use to the HIV care continuum among people with HIV in an integrated health care system. Drug Alcohol Depend. 2021;219:108481.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Lesko CR, Gnang JS, Fojo AT, Hutton HE, McCaul ME, Delaney JA, et al. Alcohol use and the longitudinal HIV care continuum for people with HIV who enrolled in care between 2011 and 2019. Ann Epidemiol. 2023;85:6–12.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Saitz R. Unhealthy Alcohol Use. N Engl J Med. 2005;352(6):596–607.

    Article  CAS  PubMed  Google Scholar 

  5. Chander G, Lau B, Moore RD. Hazardous alcohol use: a risk factor for non-adherence and lack of suppression in HIV infection. J Acquir Immune Defic Syndr. 2006;43(4):411–7.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Justice AC, McGinnis KA, Tate JP, Braithwaite RS, Bryant KJ, Cook RL, et al. Risk of mortality and physiologic injury evident with lower alcohol exposure among HIV infected compared with uninfected men. Drug Alcohol Depend. 2016;161:95–103.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Hendershot CS, Stoner SA, Pantalone DW, Simoni JM. Alcohol use and antiretroviral adherence: review and Meta-analysis. JAIDS J Acquir Immune Defic Syndr. 2009;52(2):180–202.

    Article  PubMed  Google Scholar 

  8. Williams EC, McGinnis KA, Edelman EJ, Matson TE, Gordon AJ, Marshall BDL, et al. Level of Alcohol Use Associated with HIV Care Continuum targets in a National U.S. Sample of Persons Living with HIV receiving Healthcare. AIDS Behav. 2019;23(1):140–51.

    Article  PubMed  PubMed Central  Google Scholar 

  9. the MASH Research Team, Scott-Sheldon LAJ, Walstrom P, Carey KB, Johnson BT, Carey MP. Alcohol use and sexual risk behaviors among individuals infected with HIV: a systematic review and Meta-analysis 2012 to early 2013. Curr HIV/AIDS Rep. 2013;10(4):314–23.

    Article  PubMed Central  Google Scholar 

  10. Samet JH, Horton NJ, Traphagen ET, Lyon SM, Freedberg KA. Alcohol consumption and HIV disease progression: are they related? Alcohol Clin Exp Res. 2003;27(5):862–7.

    Article  PubMed  Google Scholar 

  11. Shuper PA, Joharchi N, Irving H, Rehm J. Alcohol as a correlate of unprotected sexual behavior among people living with HIV/AIDS: review and meta-analysis. AIDS Behav. 2009;13(6):1021–36.

    Article  PubMed  Google Scholar 

  12. Uebelacker LA, Weisberg RB, Herman DS, Bailey GL, Pinkston-Camp MM, Stein MD. Chronic Pain in HIV-Infected patients: relationship to Depression, Substance Use, and Mental Health and Pain Treatment. Pain Med. 2015;16(10):1870–81.

    Article  PubMed  Google Scholar 

  13. Crawford TN, Thornton AC. Alcohol Use and Multimorbidity among individuals living with HIV. AIDS Behav. 2019;23(1):152–60.

    Article  PubMed  Google Scholar 

  14. Merlin JS, Walcott M, Kerns R, Bair MJ, Burgio KL, Turan JM. Pain self-management in HIV-infected individuals with chronic pain: a qualitative study. Pain Med. 2015;16(4):706–14.

    Article  PubMed  Google Scholar 

  15. Slawek DE. People living with HIV and the emerging field of chronic pain-what is known about epidemiology, etiology, and management. Curr HIV/AIDS Rep. 2021;18(5):436–42.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Berg KM, Cooperman NA, Newville H, Arnsten JH. Self-efficacy and depression as mediators of the relationship between pain and antiretroviral adherence. AIDS Care. 2009;21(2):244–8.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Zale EL, Maisto SA, Ditre JW. Interrelations between pain and alcohol: an integrative review. Clin Psychol Rev. 2015;37:57–71.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Alford DP, German JS, Samet JH, Cheng DM, Lloyd-Travaglini CA, Saitz R. Primary care patients with drug Use Report Chronic Pain and Self-Medicate with Alcohol and other drugs. J GEN INTERN MED. 2016;31(5):486–91.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Parisi CE, Gracy HR, Bush NJ, Cook RL, Wang Y, Chichetto N. Does treating pain with alcohol affect drinking reduction among women with HIV enrolled in a clinical trial of naltrexone? Alcohol Clin Exp Res (Hoboken). 2023;47(10):1917–25.

    Article  CAS  PubMed  Google Scholar 

  20. Derry-Vick HM, Johnston CD, Brennan-Ing M, Burchett CO, Glesby N, Zhu YS, et al. Pain is Associated with depressive symptoms, inflammation, and poorer physical function in older adults with HIV. Psychosom Med. 2022;84(8):957–65.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Levett TJ, Vera JH, Jones CI, Bremner S, Leon A, Begovac J, et al. A cross-sectional assessment of frailty, falls and perceptions of ageing in people living with HIV using an mHealth platform. HIV Med. 2023;24(4):431–41.

    Article  CAS  PubMed  Google Scholar 

  22. Serota DP, Capozzi C, Lodi S, Colasanti JA, Forman LS, Tsui JI, et al. Predictors of pain-related functional impairment among people living with HIV on long-term opioid therapy. AIDS Care. 2021;33(4):507–15.

    Article  PubMed  Google Scholar 

  23. Galvan FH, Bing EG, Fleishman JA, London AS, Caetano R, Burnam MA, et al. The prevalence of alcohol consumption and heavy drinking among people with HIV in the United States: results from the HIV cost and services utilization study. J Stud Alcohol. 2002;63(2):179–86.

    Article  PubMed  Google Scholar 

  24. Uebelacker LA, Weisberg RB, Herman DS, Bailey GL, Pinkston-Camp MM, Garnaat SL, et al. Pilot randomized trial of collaborative behavioral treatment for Chronic Pain and Depression in persons living with HIV/AIDS. AIDS Behav. 2016;20(8):1675–81.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Edelman EJ, Hansen NB, Cutter CJ, Danton C, Fiellin LE, O’Connor PG, et al. Implementation of integrated stepped care for unhealthy alcohol use in HIV clinics. Addict Sci Clin Pract. 2016;11(1):1.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Armstrong ML, LaPlante AM, Altice FL, Copenhaver M, Molina PE. Advancing behavioral HIV Prevention: adapting an evidence-based intervention for people living with HIV and Alcohol Use disorders. AIDS Res Treat. 2015;2015:879052.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Kuerbis A, Reid MC, Lake JE, Glasner-Edwards S, Jenkins J, Liao D, et al. Daily factors driving daily substance use and chronic pain among older adults with HIV: an exploratory study using ecological momentary assessment. Alcohol. 2019;77:31–9.

    Article  PubMed  Google Scholar 

  28. Magill M, Longabaugh R. Efficacy combined with specified ingredients: a new direction for empirically supported addiction treatment. Addiction. 2013;108(5):874–81.

    Article  PubMed  Google Scholar 

  29. Hahn JA, Murnane PM, Vittinghoff E, Muyindike WR, Emenyonu NI, Fatch R, et al. Factors associated with phosphatidylethanol (PEth) sensitivity for detecting unhealthy alcohol use: an individual patient data meta-analysis. Alcoholism Clin Exp Res. 2021;45(6):1166–87.

    Article  CAS  Google Scholar 

  30. Krebs EE, Lorenz KA, Bair MJ, Damush TM, Wu J, Sutherland JM, et al. Development and initial validation of the PEG, a three-item Scale assessing Pain Intensity and Interference. J GEN INTERN MED. 2009;24(6):733–8.

    Article  PubMed  PubMed Central  Google Scholar 

  31. McGinnis KA, Tate JP, Williams EC, Skanderson M, Bryant KJ, Gordon AJ, et al. Comparison of AUDIT-C collected via electronic medical record and self-administered research survey in HIV infected and uninfected patients. Drug Alcohol Depend. 2016;168:196–202.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Satre D, Leibowitz A, Sterling S, Lu Y, Travis A, Weisner C. A randomized clinical trial of motivational interviewing to reduce alcohol and drug use among patients with depression. J Consult Clin Psychol. 2016;84(7):571–9.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Satre D, Sterling S. PS1-19: impact of motivational interviewing to reduce Alcohol Use among Depression patients. Clin Med Res. 2013;11(3):167.

    Article  PubMed Central  Google Scholar 

  34. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O’Neal L, et al. The REDCap consortium: building an international community of software platform partners. J Biomed Inform. 2019;95:103208.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–81.

    Article  PubMed  Google Scholar 

  36. MetricWire Breakthrough Research [Internet]. 2013. Available from: https://metricwire.com/#.

  37. Allen J, Wilson V, editors. Alcohol Timeline Followback (TLFB). In: Assessing alcohol problems: a Guide for clinicians and Researchers. 2nd ed. Bethesda, MD: National Institute on Alcohol Abuse and Alcoholism; p. 301–5.

  38. Sobell L, Sobell M. Alcohol Timeline Followback (TFLB). In: Handbook of Psychiatric measures. Washington, DC: American Psychiatric Association; p. 477–9.

  39. Kiluk BD, Dreifuss JA, Weiss RD, Morgenstern J, Carroll KM. The short inventory of problems – revised (SIP-R): psychometric properties within a large, diverse sample of substance use disorder treatment seekers. Psychol Addict Behav. 2013;27(1):307–14.

    Article  PubMed  Google Scholar 

  40. Budd RJ, Rollnick S. The structure of the readiness to Change Questionnaire: a test of Prochaska & DiClemente’s transtheoretical model. Br J Health Psychol. 1996;1(4):365–76.

    Article  Google Scholar 

  41. Karoly P, Ruehlman LS. Goal cognition and its clinical implications: development and Preliminary Validation of Four Motivational Assessment Instruments. Assessment. 1995;2(2):113–29.

    Article  Google Scholar 

  42. Karoly P, Ruehlman LS. Motivational implications of pain: chronicity, psychological distress, and work goal construal in a national sample of adults. Health Psychol. 1996;15(5):383–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Tan G, Jensen MP, Thornby JI, Shanti BF. Validation of the brief pain inventory for chronic nonmalignant pain. J Pain. 2004;5(2):133–7.

    Article  PubMed  Google Scholar 

  44. Simons RM, Walters KJ, Keith JA, Simons JS. Posttraumatic stress disorder and Conduct problems: the role of Self-Control demands. J Trauma Stress. 2021;34(2):298–308.

    Article  PubMed  Google Scholar 

  45. Sun-Suslow N, Campbell LM, Tang B, Fisher AC, Lee E, Paolillo EW, et al. Use of digital health technologies to examine subjective and objective sleep with next-day cognition and daily indicators of health in persons with and without HIV. J Behav Med. 2022;45(1):62–75.

    Article  PubMed  Google Scholar 

  46. Simons JS, Gaher RM, Oliver MNI, Bush JA, Palmer MA. An experience sampling study of associations between affect and alcohol use and problems among college students. J Stud Alcohol. 2005;66(4):459–69.

    Article  PubMed  Google Scholar 

  47. Maisto SA, Simons JS, Palfai TP, Moskal D, Luehring-Jones P. Daily associations among Alcohol Intoxication, Partner Familiarity, participant Effortful Control, Urgency, and PrEP uptake on sexual behavior in men who have sex with men. Arch Sex Behav. 2021;50(7):2843–60.

    Article  PubMed  Google Scholar 

  48. Solar C, Nansubuga A, Murillo J, Ranker L, Borrelli B, Bowen DJ, et al. Mobile health plus community health worker support for weight management among public housing residents (path to Health): a randomized controlled trial protocol. Contemp Clin Trials. 2022;119:106836.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Chen MJ, Fan X, Moe ST. Criterion-related validity of the Borg ratings of perceived exertion scale in healthy individuals: a meta-analysis. J Sports Sci. 2002;20(11):873–99.

    Article  PubMed  Google Scholar 

  50. Hays RD, Schalet BD, Spritzer KL, Cella D. Two-item PROMIS® global physical and mental health scales. J Patient Rep Outcomes. 2017;1(1):2.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Clark LA, Watson D, The. PANAS-X: Manual for the Positive and Negative Affect Schedule - Expanded Form [Internet]. 1994 [cited 2023 Dec 18]. https://iro.uiowa.edu/esploro/outputs/other/9983557488402771

  52. Posner J, Russell JA, Peterson BS. The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Develop Psychopathol [Internet]. 2005 Sep [cited 2024 May 9];17(03). http://www.journals.cambridge.org/abstract_S0954579405050340

  53. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213.

    Article  CAS  PubMed  Google Scholar 

  54. Carpenter RW, Nance M, Frumkin MR, Boissoneault J, Ellingson JM. Use and co-use of alcohol and cannabis following physical pain in the daily life of community adults engaged in regular substance use. Psychology of Addictive Behaviors [Internet]. 2024 Feb 15 [cited 2024 May 9]; https://doi.org/10.1037/adb0000992

  55. Camenga DR, Haeny AM, Krishnan-Sarin S, O’Malley SS, Bold KW. Pilot Ecological Momentary Assessment Study of subjective and contextual factors surrounding E-Cigarette and combustible Tobacco product use among young adults. IJERPH. 2021;18(21):11005.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Aradottir S, Asanovska G, Gjerss S, Hansson P, Alling C. PHosphatidylethanol (PEth) concentrations in blood are correlated to reported alcohol intake in alcohol-dependent patients. Alcohol Alcohol. 2006;41(4):431–7.

    Article  CAS  PubMed  Google Scholar 

  57. Jett JD, Beck R, Tyutyunnyk D, Sanchez J, Lopez-Cruzan M, Ginsburg BC, et al. Validation of the quantification of phosphatidylethanol 16:0/18:1 concentrations in TASSO-M20 devices. Alcohol Clin Exp Res (Hoboken). 2023;47(4):748–55.

    Article  CAS  PubMed  Google Scholar 

  58. Williams LA, Garcia-Gonzalez A, Mendoza TR, Haq S, Cleeland CS. Concept domain validation and item generation for the Treatment-Induced Neuropathy Assessment Scale (TNAS). Support Care Cancer. 2019;27(3):1021–8.

    Article  PubMed  Google Scholar 

  59. Saunders JB, Aasland OG, Babor TF, De La Fuente JR, Grant M. Development of the Alcohol Use disorders Identification Test (AUDIT): WHO Collaborative Project on early detection of persons with harmful alcohol Consumption-II. Addiction. 1993;88(6):791–804.

    Article  CAS  PubMed  Google Scholar 

  60. Bush K. The AUDIT alcohol consumption questions (AUDIT-C)an effective brief screening test for Problem drinking. Arch Intern Med. 1998;158(16):1789.

    Article  CAS  PubMed  Google Scholar 

  61. Berkman LF, Glass T, Brissette I, Seeman TE. From social integration to health: Durkheim in the new millennium. Soc Sci Med. 2000;51(6):843–57.

    Article  CAS  PubMed  Google Scholar 

  62. Rosen CS, Henson BR, Finney JW, Moos RH. Consistency of self-administered and interview-based Addiction Severity Index composite scores. Addiction. 2000;95(3):419–25.

    Article  CAS  PubMed  Google Scholar 

  63. Ryan H, Trosclair A, Gfroerer J. Adult current smoking: differences in definitions and prevalence Estimates—NHIS and NSDUH, 2008. J Environ Public Health. 2012;2012:1–11.

    Article  Google Scholar 

  64. Humeniuk R, Ali R, Babor TF, Farrell M, Formigoni ML, Jittiwutikarn J, et al. Validation of the Alcohol, Smoking and Substance involvement screening test (ASSIST). Addiction. 2008;103(6):1039–47.

    Article  PubMed  Google Scholar 

  65. Coleman KJ, Ngor E, Reynolds K, Quinn VP, Koebnick C, Young DR, et al. Initial validation of an Exercise Vital sign in Electronic Medical records. Med Sci Sports Exerc. 2012;44(11):2071–6.

    Article  PubMed  Google Scholar 

  66. Lee PH, Macfarlane DJ, Lam T, Stewart SM. Validity of the international physical activity questionnaire short form (IPAQ-SF): a systematic review. Int J Behav Nutr Phys Act. 2011;8(1):115.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Justice AC, Holmes W, Gifford AL, Rabeneck L, Zackin R, Sinclair G, et al. Development and validation of a self-completed HIV symptom index. J Clin Epidemiol. 2001;54(Suppl 1):S77–90.

    Article  PubMed  Google Scholar 

  68. Darke S, Hall W, Heather N, Ward J, Wodak A. The reliability and validity of a scale to measure HIV risk-taking behaviour among intravenous drug users. AIDS. 1991;5(2):181–5.

    Article  CAS  PubMed  Google Scholar 

  69. Berger BE, Ferrans CE, Lashley FR. Measuring stigma in people with HIV: psychometric assessment of the HIV stigma scale¶. Res Nurs Health. 2001;24(6):518–29.

    Article  CAS  PubMed  Google Scholar 

  70. Taylor TR, Kamarck TW, Shiffman S. Validation of the Detroit area study discrimination scale in a community sample of older African American adults: the Pittsburgh healthy heart project. Int J Behav Med. 2004;11(2):88–94.

    Article  PubMed  Google Scholar 

  71. Williams DR, Yu Y, Jackson JS, Anderson NB. Racial Differences in Physical and Mental Health: Socio-economic status, stress and discrimination. J Health Psychol. 1997;2(3):335–51.

    Article  CAS  PubMed  Google Scholar 

  72. Krieger N, Smith K, Naishadham D, Hartman C, Barbeau EM. Experiences of discrimination: validity and reliability of a self-report measure for population health research on racism and health. Soc Sci Med. 2005;61(7):1576–96.

    Article  PubMed  Google Scholar 

  73. Russell D, Peplau LA, Cutrona CE. The revised UCLA Loneliness Scale: concurrent and discriminant validity evidence. J Personal Soc Psychol. 1980;39(3):472–80.

    Article  CAS  Google Scholar 

  74. Cohen S, Kamarck T, Mermelstein R. A Global measure of perceived stress. J Health Soc Behav. 1983;24(4):385.

    Article  CAS  PubMed  Google Scholar 

  75. Kroenke K, Strine TW, Spitzer RL, Williams JBW, Berry JT, Mokdad AH. The PHQ-8 as a measure of current depression in the general population. J Affect Disord. 2009;114(1–3):163–73.

    Article  PubMed  Google Scholar 

  76. Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092.

    Article  PubMed  Google Scholar 

  77. Kazis LE, Miller DR, Skinner KM, Lee A, Ren XS, Clark JA, et al. Applications of methodologies of the Veterans Health Study in the VA Healthcare System: conclusions and Summary. J Ambul Care Manage. 2006;29(2):182–8.

    Article  PubMed  Google Scholar 

  78. Yu L, Buysse DJ, Germain A, Moul DE, Stover A, Dodds NE, et al. Development of short forms from the PROMIS™ Sleep disturbance and sleep-related impairment item banks. Behav Sleep Med. 2012;10(1):6–24.

    Article  Google Scholar 

  79. Yin MT, Kendall MA, Wu X, Tassiopoulos K, Hochberg M, Huang JS, et al. Fractures after antiretroviral initiation. AIDS. 2012;26(17):2175–84.

    Article  CAS  PubMed  Google Scholar 

  80. Pincus DB, May JE, Whitton SW, Mattis SG, Barlow DH. Cognitive-behavioral treatment of panic disorder in adolescence. J Clin Child Adolesc Psychol. 2010;39(5):638–49.

    Article  PubMed  Google Scholar 

  81. Jamison RN, Edwards RR. Integrating pain management in clinical practice. J Clin Psychol Med Settings. 2012;19(1):49–64.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Ruehlman LS, Karoly P, Enders C. A randomized controlled evaluation of an online chronic pain self management program. Pain. 2012;153(2):319–30.

    Article  PubMed  Google Scholar 

  83. Newman S, Steed L, Mulligan K. Self-management interventions for chronic illness. Lancet. 2004;364(9444):1523–37.

    Article  PubMed  Google Scholar 

  84. Jones DJ, Anton M, Gonzalez M, Honeycutt A, Khavjou O, Forehand R, et al. Incorporating mobile phone technologies to Expand evidence-based care. Cogn Behav Pract. 2015;22(3):281–90.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Anton MT, Jones DJ, Cuellar J, Forehand R, Gonzalez M, Honeycutt A, et al. Caregiver Use of the Core Components of Technology-enhanced helping the noncompliant child: a Case Series Analysis of low-income families. Cogn Behav Pract. 2016;23(2):194–204.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Comer JS. Introduction to the Special Series: applying New technologies to extend the scope and accessibility of Mental Health Care. Cogn Behav Pract. 2015;22(3):253–7.

    Article  Google Scholar 

  87. Frueh BC, Henderson S, Myrick H. Telehealth service delivery for persons with alcoholism. J Telemed Telecare. 2005;11(7):372–5.

    Article  PubMed  Google Scholar 

  88. King VL, Stoller KB, Kidorf M, Kindbom K, Hursh S, Brady T, et al. Assessing the effectiveness of an internet-based videoconferencing platform for delivering intensified substance abuse counseling. J Subst Abuse Treat. 2009;36(3):331–8.

    Article  PubMed  Google Scholar 

  89. Myers K, Comer JS. The case for Telemental Health for improving the accessibility and Quality of Children’s Mental Health Services. J Child Adolesc Psychopharmacol. 2016;26(3):186–91.

    Article  PubMed  Google Scholar 

  90. O’Donohue WT, Fisher JE, editors. Cognitive behavior therapy: core principles for practice. Hoboken, N.J: Wiley; 2012. p. 451.

    Google Scholar 

  91. Miller WR, Rollnick S. Motivational interviewing: Helping people change, 3rd edition. 3rd ed. Guilford Press; 2013.

  92. Morgenstern J, Irwin TW, Wainberg ML, Parsons JT, Muench F, Bux DA, et al. A randomized controlled trial of goal choice interventions for alcohol use disorders among men who have sex with men. J Consult Clin Psychol. 2007;75(1):72–84.

    Article  PubMed  Google Scholar 

  93. Sobell M, Sobell L. Problem drinkers: guided self-change treatment. The Guilford Press; 1996.

  94. Otis JD. Managing Chronic Pain: a cognitive-behavioral Therapy Approach. 1st ed. Oxford University Press; 2007.

  95. Palfai TP, Taylor JL, Saitz R, Kratzer MPL, Otis JD, Bernstein JA. Development of a tailored, telehealth intervention to address chronic pain and heavy drinking among people with HIV infection: integrating perspectives of patients in HIV care. Addict Sci Clin Pract. 2019;14(1):35.

    Article  PubMed  PubMed Central  Google Scholar 

  96. Palfai TP, Saitz R, Kratzer MPL, Taylor JL, Otis JD, Bernstein JA. An integrated videoconferencing intervention for chronic pain and heavy drinking among patients in HIV-care: a proof-of-concept study. AIDS Care. 2020;32(9):1133–40.

    Article  PubMed  PubMed Central  Google Scholar 

  97. Comer JS, Furr JM, Kerns CE, Miguel E, Coxe S, Elkins RM, et al. Internet-delivered, family-based treatment for early-onset OCD: a pilot randomized trial. J Consult Clin Psychol. 2017;85(2):178–86.

    Article  PubMed  Google Scholar 

  98. Glombiewski JA, Sawyer AT, Gutermann J, Koenig K, Rief W, Hofmann SG. Psychological treatments for fibromyalgia: a meta-analysis. Pain. 2010;151(2):280–95.

    Article  PubMed  Google Scholar 

  99. Magill M, Ray L, Kiluk B, Hoadley A, Bernstein M, Tonigan JS, et al. A meta-analysis of cognitive-behavioral therapy for alcohol or other drug use disorders: treatment efficacy by contrast condition. J Consult Clin Psychol. 2019;87(12):1093–105.

    Article  PubMed  PubMed Central  Google Scholar 

  100. Richmond H, Hall AM, Copsey B, Hansen Z, Williamson E, Hoxey-Thomas N, et al. The effectiveness of cognitive behavioural treatment for non-specific low back Pain: a systematic review and Meta-analysis. PLoS ONE. 2015;10(8):e0134192.

    Article  PubMed  PubMed Central  Google Scholar 

  101. Hahn JA, Emenyonu NI, Fatch R, Muyindike WR, Kekiibina A, Carrico AW, et al. Declining and rebounding unhealthy alcohol consumption during the first year of HIV care in rural Uganda, using phosphatidylethanol to augment self-report: Alcohol use in HIV care in Uganda. Addiction. 2016;111(2):272–9.

    Article  PubMed  Google Scholar 

  102. Litt MD, Kadden RM, Kabela-Cormier E. Individualized assessment and treatment program for alcohol dependence: results of an initial study to train coping skills. Addiction. 2009;104(11):1837–8.

    Article  PubMed  PubMed Central  Google Scholar 

  103. Fritz MS, Mackinnon DP. Required sample size to detect the mediated effect. Psychol Sci. 2007;18(3):233–9.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors wish to thank Amanda Flanagan and Skylar Karzhevsky for their insights on ways to refine protocol procedures.

Funding

This work was supported by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) of the National Institutes of Health under Award Number P01AA029546. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Authors and Affiliations

  1. Department of Psychological and Brain Sciences, Boston University, 900 Commonwealth Ave, 2nd Floor, Boston, MA, 02215, USA

    Tibor P Palfai, Lauren B Bernier, Maya PL Kratzer & John D Otis

  2. Department of Community Health Sciences, Boston University School of Public Health, Boston, MA, USA

    Kara M Magane & Sarah Fielman

  3. Biostatistics and Epidemiology Data Analytics Center, Boston University School of Public Health, Boston, MA, USA

    Timothy C Heeren & Michael R Winter

  4. Department of Health Law, Policy and Management, Boston University School of Public Health, Boston, MA, USA

    Michael D Stein

Authors
  1. Tibor P Palfai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lauren B Bernier
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maya PL Kratzer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kara M Magane
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sarah Fielman
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. John D Otis
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Timothy C Heeren
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Michael R Winter
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Michael D Stein
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

TPP developed the initial study rationale and design. TPP, MPK, KMM, SF, JDO, TCH, MRW, and MDS each contributed to the methods and procedures described in the manuscript. LBB and TPP contributed to manuscript development and MPK, KMM, SF, and MDS each contributed to manuscript writing and editing. All authors reviewed and provided approval of the final manuscript.

Corresponding author

Correspondence to Tibor P Palfai.

Ethics declarations

Ethics approval and consent to participate

This study is conducted in compliance with the protocol and applicable regulatory requirements approved by the Boston University Medical Campus/Boston Medical Center Institutional Review Board and Human Research Protection Program.

Consent for publication

Not applicable.

Competing interests

The authors have no competing interests to declare.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Palfai, T.P., Bernier, L.B., Kratzer, M.P. et al. Integrated telehealth intervention to reduce chronic pain and unhealthy drinking among people living with HIV: protocol for a randomized controlled trial. Addict Sci Clin Pract 19, 64 (2024). https://doi.org/10.1186/s13722-024-00493-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13722-024-00493-3

Keywords

Addiction Science & Clinical Practice

ISSN: 1940-0640