This paper is in the following e-collection/theme issue:

RCTs - Protocols/Proposals (funded, already peer-reviewed, eHealth) (343) HIV/AIDS/STI Prevention and Care (819) LGBTQ Issues (415) Usability of Apps and User Perceptions of mHealth (900) Design and Formative Evaluation of Mobile Apps (662) Online Dating, Sexual Health Behavior (149) Risky Sexual Behavior (137) mHealth for Wellness, Behavior Change and Prevention (2746)

Published on in Vol 10, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22572, first published .
A Theory-Based mHealth Intervention (Getting Off) for Methamphetamine-Using Men Who Have Sex With Men: Protocol for a Randomized Controlled Trial

A Theory-Based mHealth Intervention (Getting Off) for Methamphetamine-Using Men Who Have Sex With Men: Protocol for a Randomized Controlled Trial

A Theory-Based mHealth Intervention (Getting Off) for Methamphetamine-Using Men Who Have Sex With Men: Protocol for a Randomized Controlled Trial

Authors of this article:

Cathy J Reback1, 2 Author Orcid Image ;   Jesse B Fletcher1 Author Orcid Image ;   Raymond P Mata1 Author Orcid Image
Article Authors Cited by (8) Tweetations (3) Metrics

    Protocol

    1Friends Research Institution, Inc, Los Angeles, CA, United States

    2UCLA Center for HIV Identification, Prevention, and Treatment Services, University of California, Los Angeles, Los Angeles, CA, United States

    *all authors contributed equally

    Corresponding Author:

    Raymond P Mata

    Friends Research Institution, Inc

    6910 Santa Monica Blvd

    Los Angeles, CA, 90038

    United States

    Phone: 1 323 463 1601

    Fax:1 323 463 0126

    Email: rmata@friendsresearch.org


    Background: Methamphetamine (meth) use among men who have sex with men (MSM) is associated with increased HIV prevalence and transmission and substandard advancement along the HIV prevention and care continuum. Given the growth of mobile health (mHealth) technologies, it is no longer necessary to limit meth treatment options to physical, brick-and-mortar sites, and administration using generic, nontailored content.

    Objective: In a 2-arm randomized controlled trial (RCT; N=300), we aim to evaluate the use of an mHealth intervention (Getting Off) to assess the impact and noninferiority of a cross-platform app (developed from a manualized meth treatment intervention) to help MSM reduce meth use and HIV sexual risk behaviors and improve their advancement along the HIV prevention and care continuum (HIV testing, pre-exposure prophylaxis uptake and persistence, and antiretroviral therapy uptake and adherence).

    Methods: Participants will be randomized into 2 arms: arm A, with immediate access to the app (immediate delivery: n=150), or arm B, with delayed access to the app after a 30-day period (delayed delivery: n=150). Participants in both arms will use the same Getting Off app and will have 30 days to complete the 24 sessions. Participants will be assessed at the 1-, 2- (delayed delivery arm only), 3-, 6-, and 9-month timepoints to determine observed treatment effects and will be compared with a historical matched sample of participants (n=~600) who received the brick-and-mortar group-based Getting Off intervention.

    Results: Recruitment began in January 2019 for phase 1, the formative phase. In January and February 2019, 4 focus groups (N=36) were formed to provide input on the adaptation of the group-based manual intervention to a mobile app. Data collection for phase 2, the RCT, is expected to be completed in January 2023. The final results are anticipated in April 2023.

    Conclusions: By creating a culturally responsive mobile app, Getting Off aims to reduce meth use and improve sexual health outcomes among meth-using MSM. The Getting Off app could have significant public health impact by greatly expanding access to effective, affordable, private, culturally competent, and highly scalable meth treatment for MSM.

    Trial Registration: Clinicaltrials.gov NCT03884946; https://clinicaltrials.gov/ct2/show/NCT03884946

    International Registered Report Identifier (IRRID): DERR1-10.2196/22572

    JMIR Res Protoc 2021;10(2):e22572

    doi:10.2196/22572

    Keywords

    HIV; AIDS; methamphetamine; mHealth; mobile app; ART; mobile phone 


    Background

    Men who have sex with men (MSM) have elevated rates of methamphetamine (meth) use relative to non-MSM [1-3], as meth use is deeply integrated into the sexual identities and sexual behaviors of MSM in the United States [3-7] and permeates the venues most often associated with high-risk sexual behaviors among MSM [8-10]. Use of meth by MSM before or during sex is associated with decreased behavioral inhibition and increased engagement in HIV risk behaviors [11-14], including condomless anal intercourse (CAI) [15-22] with serodiscordant or HIV status unknown sexual partners [23-25].

    HIV prevalence is significantly higher among MSM who use meth [14,25-29] and increases in concert with the intensity of meth use [30]. Meth use has thus been identified by the Centers for Disease Control and Prevention (CDC) as a driving force of the HIV epidemic among MSM in the United States [13]. Meth use is associated with poor antiretroviral therapy (ART) adherence and outcomes [31-34] and reduced adherence to HIV postexposure prophylaxis (PEP) and pre-exposure prophylaxis (PrEP) among HIV-positive/-negative MSM, respectively [35]. Providing meth treatment to MSM is a public health imperative for addressing HIV/AIDS in the 21st century.

    MSM use smartphones for sexual partner selection, sexual health information, and sexual identity expression at a higher rate than non-MSM [36-41], and they also use smartphone apps to facilitate GPS-based sexual partner selection (eg, Scruff, Grindr, and Jack’d) [42-44]. Such behaviors increase the odds of both meth use and HIV sexual risk behavior [45]. Young MSM report using such apps daily [37,39,46], and young, racial minority MSM are simultaneously both the group most at risk for meth use and HIV infection [47] as well as the group most likely to use smartphones [38,39,46]. MSM living in rural areas rely on internet resources and GPS-enabled smartphones to locate sexual partners [48]. High rates of smartphone use by young racial or ethnic minority and rural MSM dovetail cleanly with the current meth treatment and HIV risk reduction deficits evidenced in the United States health care system [38]. Psychosocial factors (eg, stigma) are the primary barriers discouraging MSM from accessing meth treatment [49,50], obstacles obviated through technology-based delivery. Given the severe personal and public health consequences of meth use, the ability to access treatment from a smartphone would eliminate embarrassment, homophobic prejudice, and/or any stigma associated with meth use and/or HIV sexual risk behaviors [50,51].

    Providing theory-driven, MSM-specific meth treatment, which integrates HIV risk reduction programming, reminders about HIV testing, PrEP, PEP, and information about HIV care (including ART reminders), will address a range of HIV-related health deficits and address key priorities set by the National Institutes of Health HIV/AIDS research priorities and the National HIV/AIDS Strategy. Furthermore, the overall public health benefits could be tremendous, as meth use has also been associated with major physical harm [52], dental disease [53], psychological harm [15,54-56], and neurological damage [57]. The broader scientific community would benefit substantially from the knowledge that meth use and HIV sexual risk behaviors can successfully be reduced via remote intervention, and clinical practice could face a potential paradigm shift toward mobile content delivery for difficult-to-reach and/or stigmatized populations.

    Given the growth of mobile technology, it is no longer reasonable or necessary to limit meth treatment to physical, brick-and-mortar sites. Only 1% of app-using MSM express a preference to participate in programs delivered in person, whereas 70% prefer content delivered via smartphones [36]. The Getting Off intervention is particularly well-situated for translation into a mobile app–driven format, as it has already been adapted to be carried out in community settings with peer counselors and does not require delivery via masters’ level cognitive behavioral therapy (CBT) clinicians [4,58].

    Mobile apps are available for download and use 24 hours a day, 7 days a week, making it easier to attempt treatment and access information in a contextualized and need-based manner; such immediacy cannot be achieved at a brick-and-mortar facility. In addition, the adaptation of a group-based intervention to a self-directed and individualized format provides the opportunity for novel delivery modalities such as gamifying formerly group-based intervention activities; insights and information will be conveyed to participants through activities such as sorting, matching, and interactive board games.

    Objectives

    The overall goal of this research study is to (1) adapt the Getting Off meth treatment intervention from a physical, brick-and-mortar facility to computerized delivery; a counselor-delivered intervention to self-directed; and a group-based to an individualized format and (2) assess the impact and noninferiority of the Getting Off app. The final Getting Off app will provide meth-using MSM and service providers with a culturally competent, free-to-own, cross-platform mobile health (mHealth) smartphone app that can be broadly disseminated.


    Research Aims

    The project builds upon the established efficacy of our manualized meth treatment intervention, Getting Off: A Behavioral Treatment Intervention for Gay and Bisexual Male Methamphetamine Users, and the highly promising findings from our successful stage 1 proof-of-concept study to complete translation of Getting Off into a cross-platform (iOS and Android) mHealth smartphone app and to assess the app’s efficacy and noninferiority in a scientifically rigorous randomized trial. The stage 2 development of the cross-platform Getting Off app will be based on user feedback from the feasibility pilot test, current literature on app preferences among MSM, and state-of-the-art technology. The app will be an interactive presentation of the Getting Off manual. Health content, behavioral self-assessments, homework assignments, and multimedia content will be integrated in a walk-through (ie, step-by-step) manner, where the consumer will be presented dynamic content, all under the guidance of a culturally competent, user-friendly, and attractive interface. The app could broadly disseminate culturally competent meth use and HIV risk reduction content to large numbers of demographically and geographically diverse users who otherwise could not access such services, particularly racial or ethnic minority MSM and MSM living in rural areas [38,47,59].

    The aims of the research include the following:

    • Primary aim 1: Refine and enhance the first 8 sessions of the Getting Off meth-use treatment intervention developed in stage 1 based on feasibility pilot test user feedback.
      • Approach: Integrate findings from stage 1, and refine or enhance the first 8 sessions with the technology team and input from our community advisory board.
    • Primary aim 2: Conduct formative research to develop the remaining 16 sessions of the Getting Off meth-use treatment intervention into a cross-platform computerized mobile app targeted to reduce meth use and HIV sexual risk behaviors and increase advancement along the HIV prevention or care continuum.
      • Approach: Design and develop the complete app by guiding technology team revisions using focus group and community advisory board feedback; conduct a usability pilot study to assess the feasibility, acceptability, and preliminary effects of the Getting Off app; and refine app iterations through community input.
    • Primary aim 3: Conduct a randomized controlled trial (RCT) to evaluate reductions in meth use and HIV sexual risk behaviors (eg, CAI, engagement in sex work, and sex while feeling the effects of alcohol or drugs) and increase advancement along the HIV prevention (repeat HIV testing, PEP or PrEP linkage and uptake, PEP adherence, and PrEP adherence and persistence) or care continuum (linkage to HIV care, ART adherence, and virological suppression).
      • Approach 3a: A 2-arm RCT to determine intervention effects through comparison of the immediate delivery (ID; n=150) and delayed delivery (DD; n=150) arms
        • Aim 3a hypothesis: Exposure to the Getting Off app will produce significant reductions in meth use and HIV sexual risk behaviors as well as increased odds of advancement along the HIV prevention or care continuum.
      • Approach 3b: An observed treatment effects analysis powered for prospective subgroup contrasts to compare longitudinal pre- or postdata from the pooled ID and DD arms (N=300)
        • Aim 3b hypothesis: Exposure to the Getting Off app will be associated with significant reductions in meth use and HIV sexual risk behaviors as well as increased odds of advancement along the HIV prevention or care continuum.
      • Approach 3c: A 2-arm historical matched comparison design to evaluate the outcomes of the Getting Off app (ID+DD; N=300) relative to a matched sample of participants who previously attended the brick-and-mortar group-based Getting Off intervention (N=~600; total N=900).
        • Aim 3c hypothesis: Reductions in meth use and HIV sexual risk behaviors as well as increased advancement along the HIV prevention or care continuum will be statistically noninferior to those observed in the brick-and-mortar group-based Getting Off intervention.
    • Secondary aim 1: Determine the impact of structural- (eg, housing insecurity, food scarcity, educational attainment, and access to health care) and individual-level (eg, homophobia, stigma, and discrimination) factors as moderators of intervention outcomes.

    Ethics Statement

    All study procedures were approved by the Western Institutional Review Board (IRB Study #1248891; IRB Tracking #20182737). The study was registered as a clinical trial (Clinical Trials #NCT03884946). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

    Study Procedures

    Aim 1

    App refinement of the first 8 sessions addressed the areas identified through the feasibility pilot test. The research and app development teams designed and developed a cross-platform mobile app using a cross-platform framework (eg, Ionic) that will permit downloading the app on both the iOS and Android platforms. The cross-platform framework approach promotes extensive code reusability between the 2 platforms and ensures a consistent user experience. The app will be for the latest versions of iOS and Android operating systems and will include backward compatibility for one operating system version of each.

    Aim 2

    The research team conducted formative research to translate the remaining 16 sessions from the group-based manual-driven intervention to a computerized app, conducted alpha-phase postdevelopment bug testing, conducted beta-phase usability pilot testing, and refined the app according to alpha- and beta-phase testing.

    Four focus groups (N=36) provided input on the development of the Getting Off app: (1) out-of-treatment, meth-using MSM (n=10); (2) meth-using MSM who are currently in outpatient treatment in the Getting Off program (n=10); (3) prior meth-using MSM with a minimum of 1 year recovery who have completed a minimum of 18 out of 24 sessions (75%) of the brick-and-mortar Getting Off program (n=6); and (4) prior meth-using MSM with a minimum of 1 year of recovery who have had no previous experience or knowledge of the Getting Off intervention (n=10). The focus groups were structured to provide guidance on translating Getting Off into an app that is responsive to culture (eg, sexuality, HIV prevention or care including PrEP uptake, adherence and persistence, ART adherence, and viral load suppression) and meth treatment needs. Additional input was obtained through the ongoing community advisory board and usability pilot testing.

    Following app development, alpha-phase testing has uncovered and removed unwanted bugs. Beta-phase usability pilot testing will be conducted with members of the target population (N=30) to test the feasibility and acceptability of the Getting Off app. Furthermore, pilot testing will be used to ensure the functionality of the app (ie, that the app is user-friendly, and all features function appropriately). Inclusion criteria for the pilot test were as follows: (1) self-identified MSM, (2) prior meth user but no meth use in the past 365 days, (3) aged between 18 and 65 years, and (4) able and willing to provide informed consent. The behavioral assessments will be consistent with those of the RCT. Although the group-based Getting Off intervention is delivered over 8 weeks (24 sessions at 3 groups per week), it is expected that it will take far less time to progress through the self-directed app, and because of the interactive features, participants may choose to replay treatment modules multiple times in the 30-day period. Behavioral assessments will be conducted at baseline and at the 1-month follow-up. In addition, at the 1-month follow-up visit, an open-ended, in-person, qualitative user experience exit interview will be collected to collect feedback on the app, including the perceived benefits, concerns, and suggestions for improvements. Qualitative interviews will be digitally recorded and analyzed using the same methodology as the focus groups.

    Aim 3

    Following screening, informed consent, and baseline assessments, participants will be randomized into 1 of 2 arms: arm A, immediate access to the Getting Off app (ID), or arm B, participants will have access to the Getting Off app after a delayed 30-day period (DD). Participants in both arms will receive the same Getting Off app and will be given 30 days to complete the 24 sessions. The randomized 2-arm repeated measures design will assess participants at 1, 2- (DD arm only), 3-, 6-, and 9-months after randomization to determine longitudinal intervention effects, observed treatment effects, and a historical comparison with a matched sample of participants who attended the brick-and-mortar group-based Getting Off intervention (Figures 1 and 2). The study will use an intent-to-treat design; participants will be assessed regardless of participation or retention.

    ‎
    Figure 1. Schematic of study design. MSM: men who have sex with men; RCT: randomized controlled trial.
    View this figure
    ‎
    Figure 2. Randomized controlled trial design. DD: delayed delivery; ID: immediate delivery.
    View this figure

    Sample

    To ensure comparability of outcomes with those of the group-based Getting Off intervention manual, inclusion criteria must mirror that of the brick-and-mortar treatment site: (1) self-identified MSM, (2) any meth use in the past 365 days, (3) aged between 18 and 65 years, and (4) able and willing to provide informed consent. Both HIV-positive and HIV-negative participants will be eligible.

    Recruitment

    To ensure a steady stream of diverse participants, 4 recruitment strategies will be used: (1) online recruitment: banner ads or digital flyers will be placed on gay websites, apps, and social media sites that specifically target MSM, such as Scruff, Adelante, Craigslist, Adam4Adam, Jack’d, and Grindr; established relationships with web-based venues that cater to MSM will enable a successful and robust internet-based recruitment strategy. (2) Street- and venue-based outreach: a semistructured time-space sampling methodology will be used to conduct street- and venue-based outreach identified through the community advisory board and ongoing community mapping as locations where meth-using MSM congregate. (3) Poster advertisement: posters that introduce the study will be posted to inform potential participants who to contact for further information regarding the research study. (4) Long-chain referral sampling: the participants of this study will be asked to recruit potential new participants. The participants of this study participants will receive US $2 when they bring a potential participant to the site and US $18 if an eligible participant is enrolled. Potential participants who inquire about the study will be scheduled for intake within 24-48 hours. These strategies have previously been used to recruit similar samples in prior studies.

    Randomization

    Following informed consent and completion of the baseline assessment, participants will be randomized to either the ID or DD arm through a computerized variable-balanced procedure. Recent work with substance-dependent (predominantly meth) MSM revealed treatment outcomes to be associated with participant substance use histories and sociodemographics [60]. A variable-balanced procedure will thus provide a multivariate balance among the characteristics known or expected to influence outcomes. The randomization procedure will balance across age (<34 years or ≥34 years), race or ethnicity (White and all other race or ethnicities), HIV serostatus (+ or −), and stages of change (SOC; contemplation or preparation and action or maintenance) [61].

    Study Arms

    The study intervention will be delivered via the identical cross-platform Getting Off mobile app for participants in both arms. Postrandomization participants will download the app onto their phone or tablets. Those randomized into the ID arm will immediately be given a unique user passcode to access the intervention content, whereas those randomized into the DD arm receive their unique user passcode 30 days after randomization (Figure 2).

    Theoretical Foundations: Mechanism of Behavioral Change

    The SOC model [62-67] conceives of behavior change as a 5-stage process, ranging from not yet considering a particular behavior (ie, precontemplation) through ongoing, long-term maintenance of that same behavior (Figure 3). An individual moves through the stages as they become aware of the need for change, prepare for change, and implement change. Most individuals are in the action stage by the time they seek treatment at a brick-and-mortar facility. In contrast, due to the privacy and availability of a computerized intervention, the Getting Off app would be of interest and value to any meth-using MSM who has moved beyond the precontemplation stage, allowing for broader dissemination, earlier interruption of meth use and HIV sexual risk behaviors, and increased opportunities for advancement through the HIV prevention or care continuum, maximizing potential public health impacts.

    The computerized Getting Off app, like the group-based intervention before it, will be guided by CBT and will use a broad set of psychological and educational techniques to provide meth-using MSM with critical knowledge about their meth use, teaching skills to initiate abstinence and to return to abstinence should relapse occur [68]. The theoretical principles of CBT have been widely integrated into most interventions for substance use disorders in the United States (including Alcoholics Anonymous) [69] and have shown efficacy for reducing both cocaine and meth use [70-74]. The CBT model in Getting Off provides education on internal and external triggers, stages of recovery from meth use, and identification of emotional states that can signal relapse. The CBT model also teaches cognitive skills such as thought stopping, craving management, relapse analysis, and adoption of healthy lifestyle behaviors. Figure 3 displays the conceptual model of behavior change. The far-left components in the model include the participant profile, the SOC related to meth use that would encourage a MSM to download the Getting Off app, and the core elements of the Getting Off app. Outcomes include reductions in meth use and HIV sexual risk behaviors and increased advancement through the HIV prevention or care continuum.

    ‎
    Figure 3. Mechanism of behavior change. ART: antiretroviral therapy; meth: methamphetamine; HIV prevention continuum (HIV testing and pre-exposure prophylaxis/postexposure prophylaxis uptake) and HIV care continuum (link, antiretroviral therapy adherence, and virological suppression); MSM: men who have sex with men; PEP: postexposure prophylaxis; PrEP: pre-exposure prophylaxis.
    View this figure

    Measures

    All data will be collected on an Audio Computer-Assisted Self-Interview administered via the Qualtrics system. All materials will be stored in Qualtrics’ Health Insurance Portability and Accountability Act-compliant (secure and encrypted) cloud-based databases. Data from historical comparators will be sampled from participants enrolled in the brick-and-mortar Getting Off intervention beginning in 2012, as research indicates that 2012 is when up to 95% of meth-using MSM in Friends Community Center reported owning their own cellphone [75]. The following subheadings (Diagnostic Mental Health and Substance Use Disorder; Stages of Change; Sociodemographics, Familiar, Legal, and Health Status; Substance Use; Meth Use and Sexual Risks; and Biological Markers) describe the measures used to address the study’s specific aims.

    Diagnostic Mental Health and Substance Use Disorder

    Diagnostic and Statistical Manual of Mental Disorders (DSM-5) Methamphetamine Use Disorder contains the DSM-5 diagnostic items necessary to determine mild, moderate, or severe meth use disorder. This information will determine the app’s utility for consumers at various levels of meth use. It will be administered only at baseline.

    Stages of Change

    The University of Rhode Island Change Assessment (URICA) is a brief, self-administered inventory used to assess the participant’s current position regarding readiness for change (eg, precontemplation, contemplation, and action) [66]. In nontreatment seeking MSM, the participant’s motivation or readiness for change may be an important predictor of response to the computerized intervention app. The URICA will be helpful in characterizing participants’ SOC [76]. It will be administered at all time points.

    Sociodemographics, Familiar, Legal, and Health Status

    Admission or follow-up form collects demographic information, housing status, food security, educational attainment, alcohol and other drug use history, family and social history, legal status, HIV status, location on the HIV prevention or care continuum, experiences with stigma and/or discrimination, and general and mental health status. The full form will be administered at baseline, and an abbreviated version (ie, a version that excludes all “lifetime recalls” and sociodemographic characteristics) will be administered at all follow-up time points.

    Substance Use

    Substance use frequency is a brief assessment, developed by the principal investigator that assesses substance use, injection drug use, and injection protocols in the past 30 days. It will be administered at all time points.

    Meth Use and Sexual Risks

    Behavioral Questionnaire-Amphetamine (BQA)-abbreviated version gathers information on HIV-related drug and sexual risk behaviors, assesses self-efficacy for sexual behavior change, collects detailed information on discrete sexual behaviors (with primary or nonprimary partners and whether or not the behavior occurred under the influence of meth and/or other substances), and collects episodic data about participants’ most recent sexual encounters [77,78]. Although the heterogeneous nature of the material included in the BQA prevents estimation of omnibus reliability coefficients, the assessment has compared favorably in tests against similar instruments and has been validated for use in these and similar populations [70,77-79]. It will be administered at all time points.

    Biological Markers

    Urine drug screen: urine samples will be collected, monitored, and analyzed using a 5-panel Food and Drug Administration–approved urine test cup [80]. The test cup screens for metabolites of the following drugs of use at the noted cut-off levels: amphetamines (1000 ng/mL), cocaine (300 ng/mL), meths (500 ng/mL), opiates (300 ng/mL), and tetrahydrocannabinol (50 ng/mL). Urine sample validity checks will be provided by temperature and adulterant monitoring strips built into the customized test cup. Criteria for validity will be indicated by the temperature of the sample (eg, above 92 degrees Fahrenheit and below 98 degrees Fahrenheit) and the presence of normal ranges of creatinine; pH; specific gravity; and nitrates, pyridinium chlorochromate, and bleach provided by the adulterant strip. Results are coded qualitatively (above or below the threshold) and serve as the primary indicator of recent drug use. It will be administered at all time points.

    Rapid HIV antibody test: HIV-negative and status unknown participants will receive a rapid point-of-care fingerstick HIV-antibody blood test [81] at 3-month intervals, as recommended by the CDC for high-risk individuals [82]. Participants who show documentation of HIV-positive serostatus will not be given an HIV antibody test.

    Sexually transmitted infection (STI) testing: participants will be tested for Neisseria gonorrhoeae (N. gonorrhea) and Chlamydia in the urethra via urine sample. Pharyngeal and rectal swabs will be taken for N. gonorrhea and Chlamydia, and syphilis will be tested by serum red plasma reagin and confirmed by fluorescent treponemal antibody absorption testing. Positive STI results will be reported per state guidelines and will be immediately referred to care. Prior studies found high rates of undiagnosed STIs among out-of-treatment, meth-using MSM [29,83], which also serve as a marker of HIV sexual risk behaviors.

    Virologic control for HIV-positive participants: as indicated by an undetectable HIV-1 level on the COBAS AmpliPrep/COBAS TaqMan HIV test kit [84], which has a threshold for undetectability set at ≤20 copies/mL, will be performed by Foundation Laboratory. It will be conducted at each 3-month assessment.

    Dried blood spot (DBS) for HIV-negative participants who report PrEP uptake: a blood sample will be collected and a DBS analysis for intraerythrocytic tenofovir-diphosphate will be performed. It will be conducted at each 3-month assessment.

    Health study locator form: the locator form asks participants to give consent for follow-up and to provide names; addresses; email and internet site profiles, particularly social network sites; and phone numbers of 3 relatives or friends who can reach the participant. Information is also collected on where (libraries, clubs, and bars) and with whom the participant associates (ie, social network). This will be administered at baseline, each follow-up time point, and on the off months of each follow-up time point, that is, on the months when a follow-up assessment is not being conducted.

    Statistical Analysis

    All primary outcomes are operationalized and assessed in at least two discrete ways, increasing accuracy of measurements, reducing concerns of fully missing data, and allowing for posthoc comparisons of concurrent validity across assessment modalities (Table 1 shows all outcome operationalizations). Descriptive statistics will be calculated and provided for all outcomes, with specific metrics chosen based on the distributional properties of each variable (eg, counts and percentages for nominal variables, means and standard deviations for parametric continuous variables, and ranges and medians for nonparametric continuous variables). All statistical analyses will be carried out using Stata 16SE [85], although the analytical methods described are amenable to most contemporary analytical programs.

    Table 1. Instruments, targets, variable operationalizations, and minimum detectable effects (1−β=.80; α=.05, 2-tailed).
    InstrumentTargetVariable operationalizationsPower-minimum detectable effects


    		DichotomousCountContinuousModel 1—randomized controlled trial (primary aim 3a; n=150 and n=150)Model 2—Txa effects (primary aim 3b; N=300)Model 3—Matched comparison (primary aim 3c; N=300/ N=~600)
    DSM-5b (Methc)Diagnosis of meth use disorderPresence or absence of meth use disorderNumber of diagnostic criteria endorsedN/AdPotential statistical controls; exploratory subgroup analysesN/AN/A
    URICAeStage of change or readiness for changeAbove or below “Action” stageN/AURICA scoresPotential statistical controls; exploratory subgroup analysesN/AN/A
    Admissions and follow-up formSociodemographics; HIV status and Tx history, barriers, and facilitatorsSexual identity; HIV status; linked or unlinked; housing insecurityPrior drug treatment episodes; symptomologyAge; incomePotential statistical controls; HIV-related subgroup analyses; barrier or facilitator moderator analysesN/AN/A
    Admissions and follow-up formHIV prevention or care continuumfHIV test; PrEPg uptake; advance along HIV prevention or care continuumNumber of HIV prevention or care continua steps completedN/A
    • Dih: ORi=1.97
      • ‎
    • Cuj: IRRk=1.51
      • ‎
    • Di: OR=1.61
      • ‎
    • Cu: IRR=1.35
      • ‎
    • Di: OR=1.52
      • ‎
    • Cu: IRR=1.30
      • ‎
    Biomarker tests (urinalysis, viral load, dried blood spot, HIV/STIl)Meth use, HIV prevention/care continuum outcomes, sexual risk behaviorIncident STI or incident HIVLog reductions in HIV VL; DBS analysis of PrEPTreatment effectiveness scoreVariable, dependent on biomarker.Variable, dependent on biomarker.Variable, dependent on biomarker.
    Substance use frequencyMeth useUse or nonuseDays of useN/A

    • Di: OR=0.48

      • ‎

    • Cu: IRR=0.85

      • ‎

    • Di: OR=0.59

      • ‎

    • Cu: IRR=0.89

      • ‎

    • Di: OR=0.62

      • ‎

    • Cu: IRR=0.90

      • ‎
    Behavioral Questionnaire-AmphetamineHIV sexual risk behaviorCAImNumber of CAI episodesHIV sexual risk scale

    • Di: OR=0.52

      • ‎

    • Cu: IRR=0.78

      • ‎

    • Con: f2o=0.03

      • ‎

    • Di: OR=0.63

      • ‎

    • Cu: IRR=0.85

      • ‎

    • Co: f2=0.01

      • ‎

    • Di: OR=0.66

      • ‎

    • Cu: IRR=0.87

      • ‎

    • Co: f2=0.01

      • ‎
    LocatorContact informationN/AN/AN/AN/AN/AN/A

    aTx: treatment.

    bDSM: Diagnostic and Statistical Manual of Mental Disorders-5.

    cmeth: methamphetamine.

    dNA: not applicable.

    eURICA: University of Rhode Island Change Assessment.

    fHIV prevention continuum (HIV testing and pre-exposure prophylaxis or postexposure prophylaxis uptake) and HIV care continuum (link, antiretroviral therapy adherence, and virological suppression).

    gPrEP: pre-exposure prophylaxis.

    hDi: dichotomous variable.

    iOR: odds ratio; estimated 95% CIs are not reported due to the lack of concrete variance estimates to apply to the estimated mean.

    jCu: count variable.

    kIRR: incident rate ratio.

    lSTI: sexually transmitted infection.

    mCAI: condomless anal intercourse; this includes both receptive and insertive anal intercourse and will be assessed by partner type (eg, main, casual, and exchange).

    nCo: continuous variable.

    of2: multiple linear regression effect size estimation.

    Diagnostic (ie, DSM-5), psychosocial (eg, URICA), barriers and facilitators (eg, housing insecurity, lack of transportation), and/or sociodemographic variables will be tested for significant association with study outcomes (ie, advancement through the HIV prevention or care continuum, meth use, and HIV sexual risk behaviors), with specific tests of association chosen based on the distributional properties of the outcome variables in question. All variables demonstrating significant statistical association with one or more of the study outcomes will be included as statistical covariates in all multivariate outcome analyses associated with primary aims 3a, 3b, and 3c and will additionally be included in exploratory subgroup analyses to test for moderating effects on treatment response and/or contingent effects among subsets of participants. Primary outcome analyses for primary aims 3a, 3b, and 3c will be carried out using mixed effects generalized linear model (GLM) equations.

    Advancement through the HIV prevention or care continuum will be assessed at all time points throughout the study and will be operationalized dichotomously (ie, yes or no achievement of one of the steps on either the HIV prevention or care continuum; eg, viral suppression) and as a counted variable (eg, consecutive DBS results indicating successful PrEP adherence). Power calculations related to advancement through either continuum will assume a 30% probability of achievement of at least one of the steps on either the HIV prevention or care continuum (equivalent to approximately 1:2 odds of advancement). Meth use outcomes will be assessed repeatedly (ie, at all time points) and will be operationalized dichotomously (eg, meth-metabolite-free urine sample) as a counted variable (ie, number of meth-metabolite-free urine samples provided) and as a continuous variable (ie, treatment effectiveness score [86] [total number of meth-metabolite-free samples divided by total samples possible]). Power calculations related to meth use outcomes assumed an 80% probability of meth use during the preintervention period for participants in the DD arm (equivalent to 4:1 odds of use).

    HIV sexual risk behavior outcomes will be assessed at all time points throughout the study and will be operationalized dichotomously (eg, incident STI via biomarker testing) as a counted variable (eg, number of days or times engaged in CAI in the past 30 days) and as a continuous variable (eg, an HIV risk severity index generated from multiple factor-analyzed items). Power calculations for engagement in HIV sexual risk behaviors assume a 65% probability of engagement during the preintervention period for participants in the DD arm (equivalent to approximately 2:1 odds of engagement).

    Multivariable inferential analyses of dichotomous outcomes will take the form of GLMs employing the Bernoulli family and logit link function; counted outcomes will be analyzed with GLMs employing the Poisson-log or negative binomial link functions, as distributional patterns dictate (note: if after the participant data collection period ends the data evidence an overrepresentation of zeros or an overdispersion of variance, zero-inflated Poisson and/or negative binomial analyses may be substituted). Continuous outcomes will be analyzed using GLMs employing the identity link and Gaussian family functions and assume a single covariate unless otherwise stated (note: iteratively reweighted, bootstrapped, or jackknifed estimation procedures may be used in Gaussian models if sensitivity analyses indicate an undue influence from outliers). Mixed effects GLM models are considered the best linear unbiased estimators for repeated measures data employing nonparametric and/or limited dependent variables. All power calculations are premised on tests of association across 2 time points (eg, baseline with app completion and app completion with brick-and-mortar program completion), providing the most conservative estimate of minimum detectable effect size estimations. All power calculations assume α≤.05 (2-tailed) and 1−β=.80.


    Recruitment began in January 2019 for phase 1, the formative phase. In January and February 2019, 4 separate focus groups (N=36) were conducted to provide input on the adaptation of the Getting Off group-based manual intervention to a cross-platform, computerized mobile app–based intervention. The data collection for phase 2, the RCT, is expected to be completed in January 2023. The final results are anticipated in April 2023.


    The Getting Off app is designed to expand access to effective, affordable, private, culturally competent, and highly scalable meth treatment for MSM. MSM experience higher rates of meth use relative to non-MSM, as meth use is deeply integrated into the sexual identities and sexual behaviors among MSM. Use of meth by this population, before or during sex, is associated with decreased behavioral inhibition and increased engagement in HIV risk behaviors. Given the growth of mobile technology, it is no longer reasonable to limit meth treatment options to physical sites, clustered in urban areas and administered using generic, nontailored content. A cross-platform mHealth smartphone app is well suited for engaging MSM because apps are easily accessible, widely used, private, portable, and inexpensive.

    There are limitations to the Getting Off study. Given that the study procedures are entirely virtual and that the intervention is app based, enrollment is limited to those that can afford a modern smartphone with an active monthly data plan, as the study does not provide smartphones to participants. However, given that smartphone use is ubiquitous among this population, it is expected that this will not be a major impediment to enrollment. In addition, the study will be conducted in a west coast metropolitan city, and therefore, findings may not be representative of meth-using MSM in other regions of the United States or globally. Finally, the sample may be biased toward MSM who are more likely to enroll in a research study; thus, it may not reflect meth-using MSM that do not self-select to participate in a clinical trial.

    The Getting Off app aims to reduce meth use and improve HIV prevention and care continuum outcomes among MSM by providing a culturally competent, user-friendly, and attractive interface that promotes reduced drug use and engagement in high-risk sexual behaviors. The Getting Off mobile app is highly scalable, and if successful, it can be made publicly accessible through the Apple App Store for iOS platforms or Google Play Store for Android platforms for widespread distribution. Given that meth use has also been associated with negative physical and mental health consequences [14,45,50-53], the overall public health benefits from an efficacious meth treatment app could be tremendous.

    Acknowledgments

    Funding for this study is provided by the National Institute on Drug Abuse, grant #RO1DA045562. CR acknowledges additional support from the National Institute of Mental Health (P30MH58107).

    Conflicts of Interest

    The authors were involved in the app development process and will be involved in the evaluation of the app. The authors have no financial interests in the app.

    ‎
    Multimedia Appendix 1

    Peer review report by the NIH Center for Scientific Review.

    PDF File (Adobe PDF File), 163 KB
    1. Reback CJ, Fletcher JB, Shoptaw S, Grella CE. Methamphetamine and other substance use trends among street-recruited men who have sex with men, from 2008 to 2011. Drug Alcohol Depend 2013 Nov 01;133(1):262-265 [FREE Full text] [CrossRef] [Medline]
    2. Halkitis PN, Levy MD, Moreira AD, Ferrusi CN. Crystal methamphetamine use and HIV transmission among gay and bisexual men. Curr Addict Rep 2014 Jun 3;1(3):206-213. [CrossRef]
    3. Goldstein ND, Burstyn I, LeVasseur MT, Welles SL. Drug use among men by sexual behaviour, race and ethnicity: prevalence estimates from a nationally representative US sample. Int J Drug Policy 2016 Oct;36:148-150. [CrossRef] [Medline]
    4. Reback CJ, Shoptaw S. Development of an evidence-based, gay-specific cognitive behavioral therapy intervention for methamphetamine-abusing gay and bisexual men. Addict Behav 2014 Aug;39(8):1286-1291 [FREE Full text] [CrossRef] [Medline]
    5. Reback C. The social construction of a gay drug: methamphetamine use among gay and bisexual males in Los Angeles. www.friendscommunitycenter.org/resources. 2019.   URL: https:/​/static1.​squarespace.com/​static/​5a1dda626957daf4c4f9a3bb/​t/​5acfa4f72b6a28b299792da0/​1523557626159/​SocialConstruction_Reback.​pdf [accessed 2020-07-10]
    6. Hoenigl M, Chaillon A, Moore DJ, Morris SR, Smith DM, Little SJ. Clear links between starting methamphetamine and increasing sexual risk behavior: a cohort study among men who have sex with men. J Acquir Immune Defic Syndr 2016 Apr 15;71(5):551-557 [FREE Full text] [CrossRef] [Medline]
    7. Benotsch EG, Lance SP, Nettles CD, Koester S. Attitudes toward methamphetamine use and HIV risk behavior in men who have sex with men. Am J Addict 2012 Nov;21 Suppl 1:35-42. [CrossRef] [Medline]
    8. Grov C. HIV risk and substance use in men who have sex with men surveyed in bathhouses, bars/clubs, and on Craigslist.org: venue of recruitment matters. AIDS Behav 2012 May;16(4):807-817 [FREE Full text] [CrossRef] [Medline]
    9. Semple SJ, Strathdee SA, Zians J, Patterson TL. Social and behavioral characteristics of HIV-positive MSM who trade sex for methamphetamine. Am J Drug Alcohol Abuse 2010 Nov;36(6):325-331 [FREE Full text] [CrossRef] [Medline]
    10. Rusow JA, Fletcher JB, Reback CJ. Sexual venue choice and sexual risk-taking among substance-using men who have sex with men. AIDS Behav 2017 Apr;21(4):1149-1162 [FREE Full text] [CrossRef] [Medline]
    11. Halkitis PN, Levy MD, Solomon TM. Temporal relations between methamphetamine use and HIV seroconversion in gay, bisexual, and other men who have sex with men. J Health Psychol 2016 Jan;21(1):93-99. [CrossRef] [Medline]
    12. Methamphetamine use and risk for HIV/AIDS. Centers of Disease Control and Prevention. 2007.   URL: https://stacks.cdc.gov/view/cdc/11778 [accessed 2020-07-09]
    13. HIV and substance use in the United States. Centers for Disease Control and Prevention. 2016.   URL: https://www.cdc.gov/hiv/risk/substanceuse.html [accessed 2020-07-10]
    14. Nerlander LMC, Hoots BE, Bradley H, Broz D, Thorson A, Paz-Bailey G, NHBS Group. HIV infection among MSM who inject methamphetamine in 8 US cities. Drug Alcohol Depend 2018 Sep 01;190:216-223. [CrossRef] [Medline]
    15. Fletcher JB, Reback CJ. Depression mediates and moderates effects of methamphetamine use on sexual risk taking among treatment-seeking gay and bisexual men. Health Psychol 2015 Aug;34(8):865-869 [FREE Full text] [CrossRef] [Medline]
    16. Friedman MR, Wei C, Klem ML, Silvestre AJ, Markovic N, Stall R. HIV infection and sexual risk among men who have sex with men and women (MSMW): a systematic review and meta-analysis. PLoS One 2014;9(1):87139 [FREE Full text] [CrossRef] [Medline]
    17. Bowers JR, Branson CM, Fletcher JB, Reback CJ. Predictors of hiv sexual risk behavior among men who have sex with men, men who have sex with men and women, and transgender women. Int J Sex Health 2012;24(4):290-302 [FREE Full text] [CrossRef] [Medline]
    18. Boone MR, Cook SH, Wilson P. Substance use and sexual risk behavior in HIV-positive men who have sex with men: an episode-level analysis. AIDS Behav 2013 Jun;17(5):1883-1887. [CrossRef] [Medline]
    19. Fletcher JB, Swendeman D, Reback CJ. Associations between major depressive episode, methamphetamine use disorder severity, and engagement in sexual risk-taking among methamphetamine-using men who have sex with men. AIDS Behav 2018 May;22(5):1461-1466 [FREE Full text] [CrossRef] [Medline]
    20. Reback CJ, Fletcher JB, Swendeman D. Associations between sociodemographic characteristics and sexual risk behaviors among methamphetamine-using men who have sex with men. Subst Use Misuse 2018 Sep 19;53(11):1826-1833 [FREE Full text] [CrossRef] [Medline]
    21. Hammoud MA, Jin F, Maher L, Bourne A, Haire B, Saxton P, et al. Biomedical HIV protection among gay and bisexual men who use crystal methamphetamine. AIDS Behav 2020 May;24(5):1400-1413. [CrossRef] [Medline]
    22. Reback CJ, Fletcher JB, Swendeman DA, Metzner M. Theory-based text-messaging to reduce methamphetamine use and hiv sexual risk behaviors among men who have sex with men: automated unidirectional delivery outperforms bidirectional peer interactive delivery. AIDS Behav 2019 Jan;23(1):37-47 [FREE Full text] [CrossRef] [Medline]
    23. Colyer SP, Moore DM, Cui Z, Zhu J, Armstrong HL, Taylor M, et al. Crystal methamphetamine use and initiation among gay, bisexual, and other men who have sex with men living with HIV in a treatment as prevention environment. Subst Use Misuse 2020;55(14):2428-2437. [CrossRef] [Medline]
    24. Mimiaga MJ, Pantalone DW, Biello KB, Hughto JMW, Frank J, O'Cleirigh C, et al. An initial randomized controlled trial of behavioral activation for treatment of concurrent crystal methamphetamine dependence and sexual risk for HIV acquisition among men who have sex with men. AIDS Care 2019 Sep;31(9):1083-1095 [FREE Full text] [CrossRef] [Medline]
    25. Beyrer C, Baral SD, van Griensven F, Goodreau SM, Chariyalertsak S, Wirtz AL, et al. Global epidemiology of HIV infection in men who have sex with men. Lancet 2012 Jul 28;380(9839):367-377 [FREE Full text] [CrossRef] [Medline]
    26. Grov C, Westmoreland D, Morrison C, Carrico AW, Nash D. The crisis we are not talking about: one-in-three annual HIV seroconversions among sexual and gender minorities were persistent methamphetamine users. J Acquir Immune Defic Syndr 2020 Nov 01;85(3):272-279. [CrossRef] [Medline]
    27. Beyrer C, Sullivan P, Sanchez J, Baral SD, Collins C, Wirtz AL, et al. The increase in global HIV epidemics in MSM. AIDS 2013 Nov 13;27(17):2665-2678. [CrossRef] [Medline]
    28. Anderson-Carpenter KD, Fletcher JB, Reback CJ. Associations between methamphetamine use, housing status, and incarceration rates among men who have sex with men and transgender women. J Drug Issues 2017 Jul;47(3):383-395 [FREE Full text] [CrossRef] [Medline]
    29. Reback CJ, Fletcher JB. Elevated HIV and STI prevalence and incidence among methamphetamine-using men who have sex with men in Los Angeles county. AIDS Educ Prev 2018 Aug;30(4):350-356 [FREE Full text] [CrossRef] [Medline]
    30. Shoptaw S, Reback CJ. Associations between methamphetamine use and HIV among men who have sex with men: a model for guiding public policy. J Urban Health 2006 Nov;83(6):1151-1157 [FREE Full text] [CrossRef] [Medline]
    31. Carrico AW, Shoptaw S, Cox C, Stall R, Li X, Ostrow DG, et al. Stimulant use and progression to AIDS or mortality after the initiation of highly active antiretroviral therapy. J Acquir Immune Defic Syndr 2014 Dec 15;67(5):508-513 [FREE Full text] [CrossRef] [Medline]
    32. Horvath KJ, Carrico AW, Simoni J, Boyer EW, Amico KR, Petroll AE. Engagement in HIV medical care and technology use among stimulant-using and nonstimulant-using men who have sex with men. AIDS Res Treat 2013;2013:121352 [FREE Full text] [CrossRef] [Medline]
    33. Jin H, Ogunbajo A, Mimiaga MJ, Duncan DT, Boyer E, Chai P, et al. Over the influence: the HIV care continuum among methamphetamine-using men who have sex with men. Drug Alcohol Depend 2018 Nov 01;192:125-128 [FREE Full text] [CrossRef] [Medline]
    34. Lai H, Kuo Y, Kuo C, Lai Y, Chen M, Chen Y, et al. Methamphetamine use associated with non-adherence to antiretroviral treatment in men who have sex with men. Sci Rep 2020 Apr 28;10(1):7131 [FREE Full text] [CrossRef] [Medline]
    35. Landovitz RJ, Fletcher JB, Shoptaw S, Reback CJ. Contingency management facilitates the use of postexposure prophylaxis among stimulant-using men who have sex with men. Open Forum Infect Dis 2015 Jan;2(1):114 [FREE Full text] [CrossRef] [Medline]
    36. Holloway IW, Rice E, Gibbs J, Winetrobe H, Dunlap S, Rhoades H. Acceptability of smartphone application-based HIV prevention among young men who have sex with men. AIDS Behav 2014 Mar;18(2):285-296 [FREE Full text] [CrossRef] [Medline]
    37. LeGrand S, Muessig KE, Pike EC, Baltierra N, Hightow-Weidman LB. If you build it will they come? Addressing social isolation within a technology-based HIV intervention for young black men who have sex with men. AIDS Care 2014;26(9):1194-1200 [FREE Full text] [CrossRef] [Medline]
    38. Levy ME, Watson CC, Wilton L, Criss V, Kuo I, Glick SN, et al. Acceptability of a mobile smartphone application intervention to improve access to HIV prevention and care services for black men who have sex with men in the district of Columbia. Digit Cult Educ 2015;7(2):169-191 [FREE Full text] [Medline]
    39. Muessig KE, Pike EC, Legrand S, Hightow-Weidman LB. Mobile phone applications for the care and prevention of HIV and other sexually transmitted diseases: a review. J Med Internet Res 2013 Jan 04;15(1):1 [FREE Full text] [CrossRef] [Medline]
    40. Muessig KE, Nekkanti M, Bauermeister J, Bull S, Hightow-Weidman LB. A systematic review of recent smartphone, internet and Web 2.0 interventions to address the HIV continuum of care. Curr HIV/AIDS Rep 2015 Mar;12(1):173-190 [FREE Full text] [CrossRef] [Medline]
    41. Mustanski B, Greene GJ, Ryan D, Whitton SW. Feasibility, acceptability, and initial efficacy of an online sexual health promotion program for LGBT youth: the Queer Sex Ed intervention. J Sex Res 2015;52(2):220-230. [CrossRef] [Medline]
    42. Goedel WC, Duncan DT. Geosocial-networking app usage patterns of gay, bisexual, and other men who have sex with men: survey among users of grindr, a mobile dating app. JMIR Public Health Surveill 2015;1(1):4 [FREE Full text] [CrossRef] [Medline]
    43. Phillips G, Magnus M, Kuo I, Rawls A, Peterson J, Jia Y, et al. Use of geosocial networking (GSN) mobile phone applications to find men for sex by men who have sex with men (MSM) in Washington, DC. AIDS Behav 2014 Sep;18(9):1630-1637. [CrossRef] [Medline]
    44. Tampa M, Sarbu M, Georgescu S. Gay-related smartphone applications: potential and risk-a review of the medical literature in the field. J Mind Med Sci. 2014.   URL: http://scholar.valpo.edu/jmms/vol1/iss1/4 [accessed 2021-01-30]
    45. Landovitz RJ, Tseng C, Weissman M, Haymer M, Mendenhall B, Rogers K, et al. Epidemiology, sexual risk behavior, and HIV prevention practices of men who have sex with men using GRINDR in Los Angeles, California. J Urban Health 2013 Aug;90(4):729-739 [FREE Full text] [CrossRef] [Medline]
    46. Aliabadi N, Carballo-Dieguez A, Bakken S, Rojas M, Brown W, Carry M, et al. Using the information-motivation-behavioral skills model to guide the development of an HIV prevention smartphone application for high-risk MSM. AIDS Educ Prev 2015 Dec;27(6):522-537 [FREE Full text] [CrossRef] [Medline]
    47. HIV and Gay and Bisexual Men. Centers for Disease Control and Prevention. 2019.   URL: https://www.cdc.gov/hiv/group/msm/index.html [accessed 2020-07-09]
    48. White Hughto JM, Pachankis JE, Eldahan AI, Keene DE. “You Can’t Just Walk Down the Street and Meet Someone”: the intersection of social–sexual networking technology, Stigma, and health among gay and bisexual men in the small city. Am J Mens Health 2017 May;11(3):726-736 [FREE Full text] [CrossRef] [Medline]
    49. Cumming C, Troeung L, Young JT, Kelty E, Preen DB. Barriers to accessing methamphetamine treatment: a systematic review and meta-analysis. Drug Alcohol Depend 2016 Nov 01;168:263-273. [CrossRef] [Medline]
    50. Parsons JT, Vial AC, Starks TJ, Golub SA. Recruiting drug using men who have sex with men in behavioral intervention trials: a comparison of internet and field-based strategies. AIDS Behav 2013 Mar;17(2):688-699 [FREE Full text] [CrossRef] [Medline]
    51. Catalani C, Philbrick W, Fraser H, Mechael P, Israelski DM. Open AIDS J 2013;7:17-41 [FREE Full text] [CrossRef] [Medline]
    52. Panenka WJ, Procyshyn RM, Lecomte T, MacEwan GW, Flynn SW, Honer WG, et al. Methamphetamine use: a comprehensive review of molecular, preclinical and clinical findings. Drug Alcohol Depend 2013 May 01;129(3):167-179. [CrossRef] [Medline]
    53. Shetty V, Mooney LJ, Zigler CM, Belin TR, Murphy D, Rawson R. The relationship between methamphetamine use and increased dental disease. J Am Dent Assoc 2010 Mar;141(3):307-318 [FREE Full text] [CrossRef] [Medline]
    54. Peck JA, Reback CJ, Yang X, Rotheram-Fuller E, Shoptaw S. Sustained reductions in drug use and depression symptoms from treatment for drug abuse in methamphetamine-dependent gay and bisexual men. J Urban Health 2005 Mar;82(1 Suppl 1):100-108 [FREE Full text] [CrossRef] [Medline]
    55. McKetin R, Lubman DI, Lee NM, Ross JE, Slade TN. Major depression among methamphetamine users entering drug treatment programs. Med J Aust 2011 Aug 01;195(3):51-55. [CrossRef] [Medline]
    56. Fletcher JB, Swendeman D, Reback CJ. Mental health and substance use disorder comorbidity among methamphetamine-using men who have sex with men. J Psychoactive Drugs 2018;50(3):206-213 [FREE Full text] [CrossRef] [Medline]
    57. Northrop NA, Yamamoto BK. Methamphetamine effects on blood-brain barrier structure and function. Front Neurosci 2015;9:69 [FREE Full text] [CrossRef] [Medline]
    58. Reback CJ, Veniegas R, Shoptaw S. Getting Off: development of a model program for gay and bisexual male methamphetamine users. J Homosex 2014;61(4):540-553. [CrossRef] [Medline]
    59. Sullivan PS, Carballo-Diéguez A, Coates T, Goodreau SM, McGowan I, Sanders EJ, et al. Successes and challenges of HIV prevention in men who have sex with men. Lancet 2012 Jul 28;380(9839):388-399 [FREE Full text] [CrossRef] [Medline]
    60. Reback CJ, Peck JA, Fletcher JB, Nuno M, Dierst-Davies R. Lifetime substance use and HIV sexual risk behaviors predict treatment response to contingency management among homeless, substance-dependent MSM. J Psychoactive Drugs 2012;44(2):166-172 [FREE Full text] [CrossRef] [Medline]
    61. Stout RL, Wirtz PW, Carbonari JP, Del Boca FK. Ensuring balanced distribution of prognostic factors in treatment outcome research. J Stud Alcohol Suppl 1994 Dec;12:70-75. [CrossRef] [Medline]
    62. Miller WR, Tonigan JS. Assessing drinkers' motivation for change: the Stages Of Change Readiness And Treatment Eagerness Scale (SOCRATES). Psychology of Addictive Behaviors 1996 Jun;10(2):81-89. [CrossRef]
    63. Prochaska J, DiClemente C. The transtheoretical approach : crossing traditional boundaries of therapy. In: Dorsey professional books. Homewood, IL: Dow Jones-Irwin; 1984:1-193.
    64. DiClemente C, Prochaska J. Toward a comprehensive, transtheoretical model of changetages of changeaddictive behaviors. In: Medicine & Health Sciences. Switzerland: Springer, Cham; 1998:1-374.
    65. Prochaska JO, DiClemente CC. Stages and processes of self-change of smoking: toward an integrative model of change. J Consult Clin Psychol 1983 Jun;51(3):390-395. [CrossRef] [Medline]
    66. DiClemente CC, Prochaska JO, Fairhurst SK, Velicer WF, Velasquez MM, Rossi JS. The process of smoking cessation: an analysis of precontemplation, contemplation, and preparation stages of change. J Consult Clin Psychol 1991 Apr;59(2):295-304. [CrossRef] [Medline]
    67. Prochaska JO, Velicer WF, Rossi JS, Goldstein MG, Marcus BH, Rakowski W, et al. Stages of change and decisional balance for 12 problem behaviors. Health Psychol 1994 Jan;13(1):39-46. [CrossRef] [Medline]
    68. Marlatt GA, Gordon JR. Relapse prevention: maintenance strategies in the treatment of addictive behaviors. New York, NY: Guilford Press; 1985:1-558.
    69. McKay JR, Alterman AI, Cacciola JS, Rutherford MJ, O'Brien CP, Koppenhaver J. Group counseling versus individualized relapse prevention aftercare following intensive outpatient treatment for cocaine dependence: initial results. J Consult Clin Psychol 1997 Oct;65(5):778-788. [CrossRef] [Medline]
    70. Shoptaw S, Reback CJ, Larkins S, Wang P, Rotheram-Fuller E, Dang J, et al. Outcomes using two tailored behavioral treatments for substance abuse in urban gay and bisexual men. J Subst Abuse Treat 2008 Oct;35(3):285-293. [CrossRef] [Medline]
    71. Shoptaw S, Reback CJ, Peck JA, Yang X, Rotheram-Fuller E, Larkins S, et al. Behavioral treatment approaches for methamphetamine dependence and HIV-related sexual risk behaviors among urban gay and bisexual men. Drug Alcohol Depend 2005 May 09;78(2):125-134. [CrossRef] [Medline]
    72. Carroll KM. Relapse prevention as a psychosocial treatment: a review of controlled clinical trials. Experimental and Clinical Psychopharmacology 1996 Feb;4(1):46-54. [CrossRef]
    73. Rawson RA, Huber A, McCann M, Shoptaw S, Farabee D, Reiber C, et al. A comparison of contingency management and cognitive-behavioral approaches during methadone maintenance treatment for cocaine dependence. Arch Gen Psychiatry 2002 Sep 01;59(9):817-824. [CrossRef] [Medline]
    74. Shoptaw S, Rawson RA, McCann MJ, Obert JL. The Matrix model of outpatient stimulant abuse treatment: evidence of efficacy. J Addict Dis 1994;13(4):129-141. [CrossRef] [Medline]
    75. Reback CJ, Grant DL, Fletcher JB, Branson CM, Shoptaw S, Bowers JR, et al. Text messaging reduces HIV risk behaviors among methamphetamine-using men who have sex with men. AIDS Behav 2012 Oct;16(7):1993-2002 [FREE Full text] [CrossRef] [Medline]
    76. DiClemente CC, Hughes SO. Stages of change profiles in outpatient alcoholism treatment. J Subst Abuse 1990;2(2):217-235. [CrossRef] [Medline]
    77. Twitchell G, Huber A, Reback C, Shoptaw S. Comparison of general and detailed HIV risk assessments among methamphetamine abusers. AIDS Behav Jun 2002:6-62. [CrossRef]
    78. Chesney MA, Barrett DC, Stall R. Histories of substance use and risk behavior: precursors to HIV seroconversion in homosexual men. Am J Public Health 1998 Jan;88(1):113-116. [CrossRef] [Medline]
    79. Reback CJ, Shoptaw S, Peck JA, Larkins S, Freese TE, Rawson RA. Getting Off: a behavioral treatment intervention for gay and bisexual male methamphetamine users. static1.squarespace.com. 2019.   URL: https:/​/static1.​squarespace.com/​static/​5a1dda626957daf4c4f9a3bb/​t/​5c9d549c4e17b63ec0f33799/​1553814686787/​Getting+Off+manual_final_3_15_19.​pdf [accessed 2020-07-08]
    80. HDOA-254-5 Panel Dip Card. Confirm Biosciences.   URL: https://www.confirmbiosciences.com [accessed 2020-01-12]
    81. INSTI HIV-1 / HIV-2 Antibody Test. bioLytical Laboratories Inc.   URL: http://www.insti.com/hiv-test/ [accessed 2020-06-18]
    82. Sexually transmitted diseases treatment guidelines. Centers for Disease Control and Prevention.   URL: http://www.cdc.gov/mmwr/preview/mmwrhtml/rr5912a1.htm [accessed 2021-01-30]
    83. Hernandez-Romieu AC, Sullivan PS, Sanchez TH, Kelley CF, Peterson JL, Del Rio C, et al. The comparability of men who have sex with men recruited from venue-time-space sampling and facebook: a Cohort study. JMIR Res Protoc 2014 Jul 17;3(3):e37 [FREE Full text] [CrossRef] [Medline]
    84. COBAS® AmpliPrep/COBAS® TaqMan® HIV Test. Roche Molecular Diagnostics.   URL: https://molecular.roche.com/assays/cobas-ampliprep-cobas-taqman-hiv-1-test-v2 [accessed 2018-01-12]
    85. Software for statistics and data science. StataCorp LLC.   URL: https://www.stata.com/ [accessed 2021-01-19]
    86. Ling W, Shoptaw S, Wesson D, Rawson RA, Compton M, Klett CJ. Treatment effectiveness score as an outcome measure in clinical trials. NIDA Res Monogr 1997;175:208-220. [Medline]

    ‎
    ART: antiretroviral therapy
    BQA: Behavioral Questionnaire-Amphetamine
    CAI: condomless anal intercourse
    CBT: cognitive behavioral therapy
    CDC: Centers for Disease Control and Prevention
    DBS: dried blood spot
    DD: delayed delivery
    DSM: Diagnostic and Statistical Manual of Mental Disorders
    GLM: generalized linear model
    ID: immediate delivery
    IRB: Institutional Review Board
    meth: methamphetamine
    mHealth: mobile health
    MSM: men who have sex with men
    PEP: postexposure prophylaxis
    PrEP: pre-exposure prophylaxis
    SOC: stages of change
    STI: sexually transmitted infection
    URICA: University of Rhode Island Change Assessment

    Edited by G Eysenbach; submitted 16.07.20; peer-reviewed by M Ghozali, M Ariyan; comments to author 07.12.20; revised version received 20.01.21; accepted 21.01.21; published 22.02.21

    Copyright

    ©Cathy J Reback, Jesse B Fletcher, Raymond P Mata. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 22.02.2021.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included.