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. Author manuscript; available in PMC: 2023 Mar 9.
Published in final edited form as: Psychol Addict Behav. 2020 Mar 16;34(6):641–649. doi: 10.1037/adb0000570

Smartphone application plus brief motivational intervention reduces substance use and sexual risk behaviors among homeless young adults: Results from a randomized controlled trial

Ronald G Thompson Jr 1, Christina Aivadyan 2, Malki Stohl 3, Efrat Aharonovich 4, Deborah S Hasin 5
PMCID: PMC9997652  NIHMSID: NIHMS1566504  PMID: 32175752

Abstract

Homeless young adults are more likely than their never-homeless counterparts to abuse substances and engage in sexual risk behaviors. This study evaluated the feasibility and preliminary effectiveness of OnTrack, a smartphone application to self-monitor substance use and sexual risk behaviors, plus a brief motivational intervention (BMI), in reducing substance use and sexual risk among homeless young adults. A randomized controlled pilot trial (N=60) compared OnTrack+BMI to treatment as usual (TAU) at an inner-city crisis shelter for homeless young adults (18 to 21 years). Participants were assessed at baseline, 2 weeks, 4 weeks, and 6 weeks after baseline to evaluate substance use and sexual risk behaviors. Kruskal-Wallis tests determined differences between baseline and post-intervention assessments. Logistic regression models examined treatment effect on change in outcomes between baseline and post-intervention follow-up assessment, controlling for baseline levels. Participants in OnTrack+BMI significantly reduced past 2-week number of drinks (p=.023), times used marijuana (p=.046), times engaged in unprotected sex (p=.012), and times used drugs before sexual activity (p=.019). No reductions of substance use or sexual risk behaviors were found among participants in TAU (all ps>.05). After adjusting for baseline levels of substance use and sexual risk, participants in OnTrack+BMI had significantly lower odds than those in TAU for drinking alcohol (AOR=0.14; p=0.01) and having unprotected sex (AOR=0.151; p=0.032). OnTrack+BMI is feasible and effective in reducing past 2-week alcohol use and unprotected sex among homeless young adults. A larger randomized trial is warranted to replicate and extend present results.

Keywords: brief intervention, substance use, sexual risk behavior, smartphone application, homeless young adults

An estimated 750,000 to 2 million Americans 18–24 years of age experience homelessness in any given year (Ammerman et al. 2004; National Alliance to End Homelessness, 2012; National Coalition for the Homeless, 2008; Morton et al., 2018; Morton et al., 2017; Department of Housing and Urban Development, 2018). Homeless young adults are significantly more likely to endure high rates of physical and sexual victimization, family estrangement, and loss of economic and social supports than their residentially stable counterparts (Thompson & Hasin, 2011; Thompson & Hasin 2012). Such experiences often result in serious behavioral, medical, and psychosocial problems, including substance abuse. The prevalence of substance use disorders among these young adults is 3 to 4 times higher than that of their never-homeless counterparts (Reardon, Burns, Preist, Sachs-Ericsson, & Lang, 2003; Shelton, Taylor, Bonner, & van den Bree, 2009; Gomez, Thompson, & Barczyk, 2010), with about half having a history of alcohol disorders (Koegel, Burnam, & Baumohl, 1996; Thompson & Hasin, 2011) and up to 75% engaging in daily and heavy marijuana use (Thompson & Hasin, 2011). HIV infection rates are also substantially higher among homeless persons than those with stable housing, with 2.3% to 16% of homeless young adults being HIV-positive (Beech, Myers, Beech, & Kemick, 2003; Greene, Ennett, & Ringwalt, 1997; Logan et al., 2013; Rotheram-Borus & Koopman, 1991; Stricof, Kennedy, Nattell, Weisfuse, & Novick, 1991; Tyler, 2008), versus an estimated 0.1% of young adults in the general population (Centers for Disease Control and Prevention, 2008; Morris et al., 2014). Substance use among homeless young adults significantly predicts sexual risk behaviors and poor prospects for eventual housing. Therefore, reducing the alcohol and marijuana use among homeless young adults might not only be critical to preventing HIV infection among this vulnerable population but also be crucial to improving their prospects of becoming stably housed. However, few individual-level interventions to reduce both substance use and sexual risk behaviors for this vulnerable population have been systematically designed and evaluated (Logan et al., 2013; Naranbhai, Abdool Karim, & Meyer-Weitz, 2011).

Treatment providers and researchers have limited opportunities to intervene with homeless young adults, who are by definition in their care on only a transient basis. As such, recruitment and retention in longer-term treatment efforts, especially those aiming for immediate and major behavioral changes, is challenging. Thus, brief interventions to reduce substance use and sexual risk for homeless young adults are needed that are targeted toy the population and their environmental context and tailored at the individual level. However, only one study (Thompson et al., 2017) has reported on the development and testing of a brief, individual-level intervention to reduce substance use (alcohol only) and sexual risk behaviors among this underserved population. Specifically, the study tested a two-session (1-hour per session), semi-structured, individual-level, intervention that significantly increased participant readiness to change alcohol use but did not significantly decrease actual alcohol or sexual risk behaviors, either in pre-post analyses within the intervention condition or relative to an individual-level, time-matched education control. Unfortunately, adding additional sessions is not feasible given the transient nature of homeless young adults and the limited time for staff to provide such an intervention (or the time-matched education-only intervention) in real world settings.

The conundrum remains of how to strengthen the effectiveness of brief individualized interventions to translate readiness to change into actual behavioral change, while simultaneously minimizing staff time and training, decreasing participant time and inconvenience, and ensuring ease of dissemination and lower cost. Theoretically, the components of information, motivation, and behavioral skills should be incorporated to reduce substance use and sexual risk (Fisher & Fisher, 1992). Realistically, this must be done in an acceptable format that addresses the transient nature and particular needs of homeless young adults. Thus, moving beyond traditional prevention methods that are entirely in-person and embracing more contemporary technological advances might offer a solution (Rice, Lee, & Taitt, 2011; Rice, Monro, Barman-Adhikari, & Young, 2010).

User-friendly smartphone application technology increasingly has been shown to extend involvement in, and improve the effectiveness of, in-person behavioral interventions (Cohn, Hunter-Reel, Hagman, & Mitchell, 2011; Muessig, Pike, Legrand, & Hightow-Weidman, 2013). Wide expansion of U.S. smartphone use suggests that capitalizing on this technology to provide theoretically informed interventions could provide low-cost, engaging, and effective substance use and sexual risk interventions. This is especially true given that smartphone use among homeless young adults is similar to the general population’s (Eyrich-Garg, 2010; Pollio, Batey, Bender, Ferguson, & Thompson, 2013; Rice et al., 2011; Rice et al., 2010), mitigating concern that such intervention platforms might be less feasible, engaging, or relevant for such resource-limited populations. In fact, studies examining cell phone use among homeless populations have found surprisingly high rates of ownership. For example, a study of East Coast homeless youth found that 78% owned a cell phone with half receiving the phone as a gift and 44% purchasing the phone themselves (Jennings et al., 2016). Among a sample of West Coast homeless youth, 62% owned a cell phone and of these, 40% reported having a working phone. Similarly, rates of mobile phone ownership among homeless youth in Colorado revealed that approximately 47% owned a phone (Harpin, Davis, Low, & Gilroy, 2016). In terms of phone payment plans, 38% have reported having a monthly contract for their phone while 23% purchased minutes as needed (Rice et al., 2011). Therefore, we developed OnTrack+BMI, a smartphone application to self-monitor substance use and sexual risk behaviors plus a brief motivational intervention to reduce alcohol use, marijuana use, and sexual risk behaviors among homeless young adults.

To determine whether the targeted population would be engaged by OnTrack and use it on a daily basis and if such use would result in decreased alcohol and marijuana use and sexual risk behaviors, a randomized controlled pilot trial (N=60) was conducted at an inner-city crisis shelter for homeless young adults (age 18-21 years) to compare two conditions: OnTrack+BMI and treatment as usual (TAU). It was hypothesized that participants in OnTrack+BMI would decrease substance use and sexual risk behaviors compared to TAU.

Procedures

A sample of eligible homeless young adults were provided a complete oral and written description of the study and invited to participate. Those willing to participate and who provided written informed consent were scheduled to complete a baseline assessment within two days of screening and informed consent. Using a random-number generator, participants were then assigned to one of two conditions: (1) OnTrack+BMI (n = 30) or (2) TAU (n = 30). All participants were assessed at baseline, 2 weeks, 4 weeks, and 6 weeks after baseline to evaluate alcohol consumption, marijuana use, sexual risk behaviors, and other relevant variables. A 6 week time interval from baseline to final assessment was selected because that is the length of time clients are encouraged to stay at the shelter. Participants also completed a self-administered questionnaire via an Audio-Computer Administered Self Interview (A-CASI), which permitted assessments to be conducted in privacy and enabled participants with reading difficulties to self-complete the computerized assessment. Two weeks after the second session, participants completed post-intervention assessment. The Institutional Review Board at Columbia University approved all procedures.

Participants

Young adults from an urban, northeastern crisis shelter were eligible for the study if they were homeless, 18-21 years old, engaged in unprotected vaginal, anal, or oral sex one or more times per week in the past month, binge drank (4 or more drinks on one occasion; National Institute on Alcohol Abuse and Alcoholism, 2005) in the past month, and used marijuana 4 or more days per week in the past month. The shelter provides housing and services to young adults with substance use and psychiatric disorders. However, those who seek shelter while actively psychotic, suicidal, homicidal, or intoxicated are not permitted shelter access. They are transported to appropriate emergency services. Similarly, no young adults were included in the study if they presented as actively psychotic, suicidal, homicidal, or intoxicated.

Of 279 homeless young adults screened, 63 were eligible and 60 completed baseline assessments and were randomized to either OnTrack+BMI (N=30) or TAU (N=30). Forty young adults (20 per condition) participated in all intervention sessions and assessments and were included in study evaluation. The final sample had an average age of 19.2 (s.d.=0.84) [range = 18–21] years, 75% were male, 51.7% were Hispanic, 66.7% were Black, 10.0% were White, and 23.3% were of other race/ethnicity. The crisis shelter did not allow the collecting of information about gender identity or sexual orientation.

Development of OnTrack+BMI

The starting point in the development of OnTrack was HealthCall for Smartphone (HealthCall-S; Hasin et al., 2014), initially developed for urban substance-abusing HIV primary care patients, most of whom were in their 30s and 40s (Hasin et al., 2013; Hasin et al., 2014; Aharonovich et al., 2017). HealthCall-S is a smartphone application designed to provide daily structured self-monitoring of alcohol and drug use, medication adherence, and sexual risk behaviors, offering personalized feedback, positive reinforcement on these behaviors, and daily tips for evidence-based skills-building. Studies of HealthCall-S participation have demonstrated that it is a feasible, engaging, and effective means to extend BMI for substance use among people living with HIV. Building upon previous HealthCall-S studies, individual qualitative interviews were conducted with 10 homeless young adults to obtain their reactions to various aspects of HealthCall-S. The purpose of these qualitative interviews was to determine how HealthCall-S could be adapted to better suit the needs and interests of homeless young adults. HealthCall-S content on substance use and sexual risk behaviors was reviewed, including automated questions about drinking amounts, desires, and goals; reasons for drinking or abstention; drug use; unprotected sex; physical and emotional well-being; and personalized, graphed feedback based on responses to questions. Based upon these qualitative findings, HealthCall-S was revised to more fully incorporate a focus on marijuana use, as well as alcohol, and to tailor it to the preferences of the homeless young adult population. Adaptations included targeting three behavioral outcomes (marijuana, alcohol, unprotected sex); removal of video counselor (who read questions to user); removal of audio reading of questions; provision of daily summary of risk behaviors; addition of links to websites that provide access to substance use and sexual risk prevention information and skills training, substance use and mental health treatment, emergency shelter, and HIV testing and pre-exposure prophylaxis (PrEP); and a new name: OnTrack.

Study Conditions

OnTrack+BMI: Overview.

OnTrack+BMI is comprised of two theory- and evidence-based components: (1) brief daily technology-supported self-monitoring of alcohol, marijuana, and sexual risk behaviors (2-3 min/day) over 28 days and (2) brief motivational sessions at weeks 0, 2, and 4 to promote use of OnTrack, encourage risk reduction, and provide graphed personalized feedback from the self-monitoring data.

Session 1 occurred within two days of baseline assessment and informed consent and included a counseling session lasting approximately 20 minutes to discuss participant alcohol and marijuana use and sexual risk behaviors and the possibility of reducing these behaviors. Two brief (20 minutes or less) check-in meetings, based on 2-week graphs of participant OnTrack data, were held two weeks (Session 2) and four weeks (Session 3) later. Participants completed self-administered questionnaires and timeline follow-back (TLFB; Sobell & Sobell, 1992) measures (administered by research staff) at the beginning of Sessions 1-3.

Participants received $25 gift cards for completing baseline assessments, $30 gift cards for Session 1 assessment, $35 gift cards for Session 2 assessment, $40 gift cards for Session 3 assessment, and $50 gift cards for post-intervention assessment. Smartphones were provided for the 30 participants in the OnTrack+BMI condition at their first session to ensure that all participants were availed the same technology and appropriate operating system and continuous and secure cellular service for data encryption. Participants had the option of keeping the smartphone or returning it at the end of Session 3 and receiving a $100 gift card. At the end of the intervention, participants in the TAU condition also had the options of receiving a smartphone or a $100 gift card.

The OnTrack Application

Upon opening OnTrack, the user is given three options: (1) to use the Tracker to answer questions about past day substance use and sexual behaviors; (2) to view Graphs of their past 7 days of behavior; or (3) to access information and treatment Resources. Tracker. Upon opening the Tracker, the user is presented with automated questions about drinking amounts, attributions, and goals, which are followed by similar questions about marijuana use and unprotected sex and items to evaluate mood (stress, anger, depression). The user is then provided with a Daily Tip (to reinforce risk reduction) and a Daily Summary of behaviors (total number of drinks, times smoked marijuana, whether engaged in unprotected sex, money spent on alcohol and marijuana). The Tracker can be started, stopped, and completed later as long as the Daily Summary has not been generated. Once it has been generated, data is automatically transmitted in encrypted form, without personal identifiers, from the smartphone to a password-protected server, and the Tracker can not be reopened until the following day. Graphs. When the Graphs button is selected, users are able to select/view bar and pie charts of their alcohol and marijuana use and unprotected sex past 7 days (to help visualize behavior over time). Resources. When selected, the Resources button gives the user three options: (1) to open the Tip Bank (to select tips that support personal risk reduction); (2) to select the Find Treatment/Services button, which provides links to locator sites for substance use and mental health treatment (SAMSHA), HIV testing (AIDS.gov), and homeless shelters and service organizations (HomelessShelterDirectory.org); and (3) to obtain substance use (NIDA, CDC) and sexual risk prevention information (CDC) and training (Planned Parenthood) from vetted and regulated sites.

OnTrack+BMI: Session 1.

After completion of self-administered questionnaires and TLFB, participants were provided an Android smartphone and shown how to operate OnTrack. Participants selected a 4-digit password and entered alcohol and marijuana use goals. Participants then practiced using OnTrack to make sure that they understood how to use it before they left the session and were asked to use OnTrack once a day for the next 28 days. Participants and the study counselor then selected an optimal time for daily use and set the OnTrack alarm to this time. To start OnTrack, participants touched an icon on the smartphone’s home screen and entered their 4-digit password. They input their answers to questions on a screen touchpad that was designed to display enlarged numbers for ease of use. OnTrack stored participant input on the smartphone, enabling its use independent of availability of network connectivity. After each use, participant responses were transmitted to a secure online server. Research staff checked the database daily for transmitted data. If no data were received for two consecutive days, participants were contacted to determine the reason and reminded to keep using OnTrack.

OnTrack+BMI: Sessions 2 and 3.

In Session 2, two weeks later, participants completed self-administered questionnaires and TLFB and the study counselor provided them personalized feedback based on OnTrack data in the form of graphs of past 2-week behaviors (number of drinks; number of times smoked marijuana; unprotected sex), shown against participant drinking and drug use reduction goals. The graph page also showed summary statistics of average drinks per drinking day, times smoked marijuana per day, and reasons for substance use. Graphs were then used as the basis for a discussion of participant risk behaviors, including alcohol/marijuana use patterns, reasons for substance use and sexual risk (or not), and ways to maintain or improve risk reduction. If necessary, the study counselor reset participant goals, reminded them to use OnTrack for 14 more days, and scheduled Session 3. At 28 days, a similar discussion focused on the participant’s 14-28 day graph occurred, followed by brief termination planning (e.g., encouraging continued self-monitoring). Participants then scheduled a post-intervention assessment meeting for two weeks later.

TAU: Session Overview.

TAU included two components: (1) substance use treatment and referral and HIV testing, as regularly offered to all participants who report substance use and sexual risk behaviors at the shelter; and (2) brief meetings (20 minutes or less) with a research coordinator every two weeks. At these meetings, the research coordinator completed TLFB measures for alcohol and marijuana use and risky sexual behaviors. Participants also completed self-administered questionnaires.

Counselor Training

OnTrack+BMI.

The OnTrack+BMI counselor was a White female with a masters degree in social work and experience providing substance use and sexual risk counseling. The counselor was trained on the research protocol via 20 hours of formal didactic training and 10 hours of supervised practice sessions. Training covered study rationale and approach; detailed description of the intervention (e.g., personal risk feedback forms, session components, handouts, staff manual); matching therapeutic tasks to stage of readiness to change; explaining and obtaining informed consent from participants; confidentiality and protection of data; coding interviews; and managing any distress during and after an interview. Practice sessions were videotaped and two of these sessions were randomly selected and reviewed by the research coordinator for proficiency (adherence and competence) certification. The counselor completed training in the responsible conduct of research with human subjects through the Columbia University Office of Research Compliance and Training.

Treatment as Usual.

The researcher for TAU was an African-American male with a masters degree in social work and experience in implementing health promotion and HIV prevention. He received training (5 hours of formal didactic training and 2.5 hours supervised practice sessions) on the research protocol and how to implement the condition. Practice sessions were videotaped and reviewed by the research coordinator for proficiency (adherence and competence) certification. He also completed training in the responsible conduct of research with human subjects through the Columbia University Office of Research Compliance and Training.

Quality Assurance

Data quality was monitored by random inspection of the completed forms by the research coordinator. In addition, counselors completed a session checklist after each session to ensure that content was consistently presented for each session. Monthly statistics were compiled on participant enrollment, retention from pre-assessment to intervention and through post-assessment, and incentives provided.

Measures

Primary Outcomes.

Primary outcomes of alcohol use, marijuana use, and sexual risk behaviors were assessed with the TLFB at Sessions 1-3 and post assessment. Alcohol Use. Participants reported the number of times they drank alcohol each day in the past 2 weeks Marijuana Use. Participants reported how many times per day in the past 2 weeks they smoked marijuana. Unprotected Sex. Participants reported the number of times they engaged in unprotected sex over the prior 2 weeks. Sex-Related Substance Use. Participants also reported the number of times they drank alcohol before having sex and the number of times they used drugs prior to sex. Count outcomes for alcohol use, marijuana use, and unprotected sex were recoded as binary (yes/no) variables for final logistic regression models to account for the non-normal distribution of data.

Sociodemographics.

Participants were asked to provide the following sociodemographic characteristics: age, race, gender, education, employment status, childhood abuse, history of foster care and juvenile detention/prison, and time spent living on streets prior to shelter.

OnTrack Participant Feedback.

A crucial element in evaluating OnTrack+BMI was participant reactions to its use. After post-intervention assessment, participants in the OnTrack+BMI condition completed a 24-item participant satisfaction, acceptability, and perceived effectiveness questionnaire, developed by the research team.

Data Analysis

Sociodemographic differences between the conditions were determined via t-test for continuous variables and chi-square tests for categorical variables. Kruskal-Wallis tests, which account for the non-normal distribution of data, were conducted to determine differences between baseline and post-intervention assessments (count outcomes) for the entire sample and by condition. The timeframe for each assessment was the prior two weeks. Logistic regressions examined the treatment effect on change in (binary) outcomes between baseline and post-followup assessment, controlling for baseline level.

Results

No significant differences in baseline sociodemographics, substance use, and sexual risk behaviors were found between those randomly assigned to the two treatment conditions (Table 1). Attrition did not differ significantly by treatment condition (OnTrack+BMI, 33.3%; TAU, 33.3%). Further, no differences were found in sociodemographics or outcomes (all ps ≥ 0.05) when the 20 participants who did not complete the study were compared to the 40 who completed baseline assessment, returned for intervention sessions, and completed the study. All participants who discontinued in TAU had been discharged from the shelter and were no longer able to be located. The same was found for 6 of the 10 who discontinued in OnTrack+BMI. For the remaining 4 who discontinued in OnTrack+BMI, one was incarcerated, one hospitalized, one self-withdrew, and one relocated out of state.

Table 1.

Baseline Sample Characteristics, Total and By Condition (N= 40)

Variable Total OnTrack TAU
(N= 20) (N= 20)
% or Mean (SD) p-value
Gender
 Male 70.0 75.0 65.0 0.49
 Female 30.0 25.0 35.0
Age (18-21) 19.1 (0.81) 19.0 (0.72) 19.0 (0.92) 0.70
Culture
 Hispanic 47.5 40.0 55.0 0.34
Race/Ethnicity
 White 10.0 15.0 5.0 0.32
 Black 65.0 60.0 70.0
 Asian 2.5 5.0 0
 Native Hawaiian/Pacific 5.0 10.0 0
Islander
 American Indian/Alaskan 15.0 10.0 20.0
Native
 Other 2.5 5.0 0
Less than High School Education 75.0 80.0 70.0 0.47
Currently Unemployed 75.0 75.0 75.0 1.0
Physically Abused 40.0 40.0 40.0 1.0
Sexually Abused 40.0 30.0 50.0 0.20
Ever Forster Care 47.5 40.0 40.0 1.0
Ever Incarcerated 52.5 40.0 55.0 0.34
Stayed on Street Prior to Shelter 80.0 60.0 45.0 0.34
Mean (SD) / Median p-value
Number of Drinks 10.9 (17.8) / 4.5 12.1 (23.1) / 4.0 9.6 (10.8) /7.5 0.35
Times Used MJ 26.6 (19.5) / 25.0 26.4 (20.3) /24.0 26.7 (19.1) /26.5 0.74
Times Unprotected Sex 2.9 (4.8) / 1.0 2.7 (4.8) /0.0 3.1 (4.8) / 1.0 0.38
Times Drank Before Sex 0.4 (1.2) / 0.0 0.4 (1.4) /0.0 0.5 (1.1) /0.0 0.43
Times Drugs Before Sex 2.1 (3.3) / 0.0 1.2 (2.7) /0.0 2.9 (3.7) / 0.5 0.09
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At post-treatment assessment, participants in OnTrack+BMI significantly reduced their number of drinks (p=.023), times used marijuana (p=.046), times engaged in unprotected sex (p=.012), and times used drugs before sexual activity (p=.019), compared to substance use and sexual behaviors reported in Session 1. No significant reductions of substance use or sexual risk behaviors were found among participants in TAU (all ps>.05). Table 2 shows preliminary study findings. After controlling for baseline levels of substance use and sexual risk (Table 3), participants in OnTrack+BMI had significantly lower odds than those who received TAU for drinking alcohol (AOR=0.14; CI=0.03-0.64; p=0.01) and having unprotected sex (AOR=0.151; CI=0.027-0.850; p=0.032).

Table 2.

Baseline and Post-Intervention Observed Means (Standard Deviation) / Medians, by Condition and Total Sample (N=40).

OnTrack (N=20) TAU (N=20) All (N=40)
Past 2 weeks Baseline
Assessment
mean (SD) /
median		 Post-Intervention
Assessment
mean (SD) /
median		 p-valuea Baseline
Assessment
mean (SD) /
median		 Post-Intervention
Assessment
mean (SD) /
median		 P-valuea Baseline
Assessment
mean (SD) /
median		 Post-Intervention
Assessment
mean (SD) /
median		 P-valuea
Number of
Drinks		 12.1 (23.1) / 4.0 4.1 (11.5) / 0.0 0.023 9.6 (10.8) / 7.5 6.2 (7.7) / 3.5 0.35 10.9 (17.8) / 4.5 5.1 (9.7) / 0.0 .024
Times Used MJ 26.4 (20.3) / 24.0 19.2 (30.8) / 5.5 0.046 26.7 (19.1) / 26.5 24.7 (24.5) / 20.5 0.54 26.6 (19.5) / 25.0 21.9 (27.6) / 9.5 .066
Times
Unprotected Sex		 2.7 (4.8) / 0.0 0.3 (0.9) / 0.0 0.012 3.1 (4.8) / 1.0 4.1 (8.3) / 0.0 0.51 2.9 (4.8) / 1.0 2.2 (6.1) / 0.0 .029
Times Drank
Before Sex		 0.4 (1.4) / 0.0 0.0 (0.0) / 0.0 0.15 0.5 (1.1) / 0.0 0.5 (1.4) / 0.0 0.47 0.4 (1.2) / 0.0 0.2 (1.0) / 0.0 0.16
Times Drugs
Before Sex		 1.2 (2.7) / 0.0 0.0 (0.0) / 0.0 0.019 2.9 (3.7) / 0.5 1.7 (3.9) / 0.0 0.12 2.1 (3.3) / 0.0 0.9 (2.8) / 0.0 .012
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a

Non-parametric Kruskal-Wallis p-value testing difference between baseline and post-intervention assessment

Table 3.

Treatment Effect on Change in Outcome between Baseline and Post-intervention Assessment, Controlling for Baseline Level

Outcome Odds ratio (95% confidence interval) p-value
(Past 2 weeks) OnTrack vs. TAU
Drank alcohol 0.14 (0.03-0.64) 0.01
Used marijuana 0.39 (0.065-2.33) 0.30
Had unprotected sex 0.151 (0.027-0.850) 0.032
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Among those assigned to OnTrack+BMI, median daily participant use of OnTrack was 78% of all possible days in the trial, indicating excellent engagement. Findings from participant evaluations, collected via a self-report questionnaire at post-assessment, revealed that participants were very enthusiastic with the overall quality, approach, and content of OnTrack+BMI. Participants reported that OnTrack was very easy to use (100%) and engaging (100%) and that using it made them more aware of their drug and alcohol use (100%) and helped them cut down on alcohol use (70%), marijuana use (50%), and unprotected sex (70%).

Discussion

This study evaluated the feasibility and short-term effectiveness of OnTrack+BMI, a smartphone application plus brief motivational intervention designed to reduce substance use and sexual risk behaviors among homeless young adults. OnTrack+BMI participants were highly satisfied with its overall quality, approach, and content and used the OnTrack application consistently, particularly given the transient and chaotic nature of their lives. In pre-post analyses within each treatment group separately, participants in OnTrack+BMI significantly reduced their past 2-week number of times drank alcohol, times used marijuana, times engaged in unprotected sex, and times used drugs before sexual activity. After controlling for baseline levels of substance use and sexual risk, OnTrack+BMI significantly reduced participant odds of drinking alcohol and having unprotected sex, compared to TAU. Contrary to expectations, OnTrack+BMI did not significantly decrease marijuana use relative to TAU. No significant reductions of substance use or sexual risk behaviors were found among participants in TAU.

Descriptive norms (perceptions of others’ behaviors) and injunctive norms (perceived approval of certain behaviors) serve as social guidelines for behavior (Borsari & Carey, 2003) and highly influence young adult substance use and sexual risk behavior (Kilmer et al., 2006; LaBrie, Hummer, & Lac, 2011; Neighbors, Geisner, & Lee, 2008). Homeless young adults are often physically disconnected from their home-based positive peer networks. Subsequently, homeless peers, who are more likely to use substances and engage in sexual risk behaviors, become their primary social support network (Rice et al., 2010). This association provides frequent opportunities for substance use and engaging in sexual risk behaviors, reinforces and normalizes such behaviors, and increases the severity of substance use among homeless young adults (Rice et al., 2010; Rice, Milburn, & Monro, 2011; Young & Rice, 2011). To counter the influence of these negative peer associations and enhance pro-social influencs, the OnTrack application could be updated to include links to existing online substance use reduction and safer sex support groups for young adults and to establish a private discussion group accessible only to those currently using OnTrack to reduce their substance use and sexual risk (Rice, Milburn, & Monro, 2011; Young & Rice, 2011).

This study is strengthened by its employment of a rigorous research design, the fact that randomization resulted in two groups that did not differ significantly on baseline variables likely to be key potential confounders, and its utilization of carefully trained and supervised interventionists. However, carefully trained brief motivational interventionists, though common in the research arena, are few and far between among homelessness shelter staff. Thus, future research is needed to determine whether OnTrack remains effective paired with TAU or as a stand-alone intervention.

The present study is limited by the use of a small convenience sample. Sampling methods that might ensure a truly representative sample of the broad population of homeless young adults were not employed. Another limitation is data were obtained by self-report (which could lead to under-reporting of substance use due to recall and response bias) and during shelter intake assessment (which may lead to under-reporting due to fear of being denied services). Also, counselors differed for each treatment condition, so treatment differences could have been affected by differences in style, skill, or other individual counselor differences. Further, one-third of those enrolled in the study dropped out. This level of attrition could result in systematic demographic, psychological, or behavioral differences between study completers and those who drop out, leading to selection bias in the final sample and limiting the external validity of the study (Duan, Braslow, & Weisz, 2001; Song et al., 2006), particularly if differ in regard to intervention-targeted risk activity levels prior to the study (Siddiqui, Flay, & Hu, 1996; Rutledge et al., 2002). However, no differences were found in sociodemographics or outcomes when study non-completers were compared to completers. Furthermore, attrition did not differ significantly by treatment condition (OnTrack+BMI, 33.3%; TAU, 33.3%). Study findings are also limited by the administering of post-intervention assessments only two weeks after the intervention. A 6- week time interval from baseline to final assessment was selected because that is the average length of time clients stay at the shelter. However, the brief period of time between the end of intervention and post-assessment might not have allowed the effects of OnTrack+BMI, as well as TAU, to be fully evaluated. Thus, it is possible that further or differing behavioral changes (e.g., regarding marijuana use among OnTrack+BMI participants) might have emerged as underlying motivations and emotions regarding behavioral risks were further processed, integrated, and applied toward behavioral change. Relatedly, participants used the OnTrack application for only four weeks; whether they would continue to use it after they left the shelter, and whether continued use would result in further behavioral change, is unknown.

Further, psychiatric disorders are highly comorbid with substance disorders and sexual risk behaviors (all of which vary across sociodemographic variables) and can impair a participant’s ability to engage in treatment, which might influence treatment effects on substance abuse and sexual risk behaviors in this population (Hasin & Grant, 2004; Hasin et al., 2007; Pearson et al., 2008). Thus, future studies of OnTrack+BMI should examine whether treatment effects on outcomes are moderated by psychiatric disorders or mediated by theoretically-based mechanisms (e.g., readiness to change, commitment strength, self-efficacy).

OnTrack+BMI, however, significantly reduced participant alcohol use and unprotected sex relative to TAU, thus demonstrating its suitability and potential for further study. Therefore, a larger randomized controlled trial, among a larger, more representative sample of homeless young adults, is warranted to replicate and extend present study results. Such research should involve longer periods of OnTrack usage and longer-term follow-up assessments. Even if OnTrack+BMI, or OnTrack as a stand-alone intervention, proves to be somewhat less effective than more intensive approaches in terms of long-term behavior change, the increased coverage from ease of dissemination may eventually result in a larger overall public health effect.

Acknowledgments

Preliminary findings from this study have been presented at the 40th Annual Research Society on Alcoholism Scientific Conference, Denver, CO, June, 2017 and The College on Problems of Drug Dependence, Montreal, Canada. June, 2017. This research was supported in part by a grant from the National Institutes of Health, K23DA032323 (Ronald G. Thompson, Jr.)

Contributor Information

Ronald G. Thompson, Jr., Department of Psychiatry, University of Arkansas for Medical Sciences, Little Rock, Arkansas 72205, USA

Christina Aivadyan, Department of Psychiatry, Columbia University, New York, New York 10032, USA.

Malki Stohl, Department of Psychiatry, Columbia University, New York, New York 10032, USA.

Efrat Aharonovich, Department of Psychiatry, Columbia University, New York, New York 10032, USA; New York State Psychiatric Institute, New York, New York 10032, USA.

Deborah S. Hasin, Department of Psychiatry, Columbia University, New York, New York 10032, USA; New York State Psychiatric Institute, New York, New York 10032, USA; Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York 10032, USA

References

  1. Aharonovich E, Stohl M, Cannizzaro D, Hasin D (2017). HealthCall delivered via smartphone to reduce co-occurring drug and alcohol use in HIV-infected adults: A randomized pilot trial. Journal of Substance Abuse Treatment, 83, 15–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Ammerman SD, Ensign J, Kirzner R, Meininger ET, Tornabene M, Warf CW, … Post P (2004). Homeless young adults ages 18–24: examining service delivery adaptations. Retrieved from https://www.nhchc.org/wp-content/uploads/2011/09/101905YoungHomelessAdults.pdf. [Google Scholar]
  3. Beech BM, Myers L, Beech DJ, & Kemick NS (2003). Human immunodeficiency syndrome and hepatitis B and C infections among homeless adolescents. Seminars in Pediatric Infectious Diseases, 14(1), 12–19. [DOI] [PubMed] [Google Scholar]
  4. Borsari B, & Carey KB (2003). Descriptive and injunctive norms in college drinking: A meta-analytic integration. Journal of Studies on Alcohol, 64, 331–341. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Centers for Disease Control and Prevention. (2008). HIV Surveillance Report. Retrieved from http://www.cdc.gov/hiv/topics/surveillance/resources/reports/
  6. Cohn AM, Hunter-Reel D, Hagman BT, & Mitchell J (2011). Promoting behavior change from alcohol use through mobile technology: the future of ecological momentary assessment. Alcoholism, Clinical and Experimental Research, 35(12), 2209–2215. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Department of Housing and Urban Development (2018). The 2018 annual homeless assesment report (AHAR) to Congress: Part 1:Point-in-time estimates of homelessness.
  8. Eyrich-Garg KM (2010). Mobile phone technology: a new paradigm for the prevention, treatment, and research of the non-sheltered “street” homeless? Journal of Urban Health, 87(3), 365–380. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Fisher JD, & Fisher WA (1992). Changing AIDS-risk behavior. Psychological Bulletin, 777(3), 455–474. [DOI] [PubMed] [Google Scholar]
  10. Gomez R, Thompson SJ, & Barczyk AN (2010). Factors associated with substance use among homeless young adults. Substance Abuse, 37(1), 24–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Greene JM, Ennett ST, & Ringwalt CL (1997). Substance use among runaway and homeless youth in three national samples. American Journal of Public Health, 87(2), 229–235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Hasin DS, Aharonovich E, O'Leary A, et al. (2013). Reducing heavy drinking in HIV primary care: a randomized trial of brief intervention, with and without technological enhancement. Addiction, 708(7), 1230–1240. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Hasin DS, Aharonovic h E, Greenstein E (2014). HealthCall for the smartphone: Technology enhancement of brief intervention in HIV alcohol dependent patients. Addiction Science & Clinical Practice, 9(1), 5–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Hasin DS, & Grant BF (2004). The co-occurrence of DSM-IV alcohol abuse in DSM-IV alcohol dependence: results of the National Epidemiologic Survey on Alcohol and Related Conditions on heterogeneity that differ by population subgroup. Arch Gen Psychiatry, 67(9), 891–896. [DOI] [PubMed] [Google Scholar]
  15. Hasin DS, Stinson FS, Ogburn E, Grant BF (2007). Prevalence, correlates, disability, and comorbidity of DSM-IV alcohol abuse and dependence in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry, 64(7), 830–842 [DOI] [PubMed] [Google Scholar]
  16. Kilmer JR, Walker DD, Lee CM, Palmer RS, Mallett KA, Fabiano P, & Larimer ME (2006). Misperceptions of college student marijuana use: Implications for prevention. Journal of Studies on Alcohol, 67, 277–281. [DOI] [PubMed] [Google Scholar]
  17. Koegel P, Bumam MA, & Baumohl J (1996). In Baumohl J (Ed.), The causes of homelessness. In Baumohl J (Ed.), Homelessness in America (pp. 24–33). Phoenix: Oryx Press. [Google Scholar]
  18. LaBrie JW, Hummer JF, & Lac A (2011). Comparing injunctive marijuana use norms of salient reference groups among college student marijuana users and nonusers. Addictive Behaviors, 36, 717–720. doi: 10.1016/j.addbeh.2011.02.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Logan JL, Frye A, Pursell HO, Anderson-Nathe M, Scholl JE, & Korthuis PT (2013). Correlates of HIV risk behaviors among homeless and unstably housed young adults. Public Health Reports, 128(3), 153–160. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Morris MD, Evans J, Montgomery M, Yu M, Briceno A, Page K, & Hahn JA (2014). Intimate injection partnerships are at elevated risk of high-risk injecting: a multi-level longitudinal study of HCV-serodiscordant injection partnerships in San Francisco, CA. PLoSOne, 9(10), el09282. doi: 10.1371/journal.pone.0109282. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Morton MH, Dworsky A, Matjasko JL, Curry SR, Schlueter D, Chavez R, & Farrell AF (2018). Prevalence and correlates of youth homelessness in the United States. Journal of Adolescent Health, 62, 14–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Morton MH, Dworsky A, & Samuels GM (2017). Missed opportunities: Youth homelessness in America. National estimates. Chicago, IL: Chapin Hall at the University of Chicago. [Google Scholar]
  23. Muessig KE, Pike EC, Legrand S, & Hightow-Weidman LB (2013). Mobile phone applications for the care and prevention of HIV and other sexually transmitted diseases: a review. Journal of Medical Internet Research, 75(1), el. doi: 10.2196/jmir.2301 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Naranbhai V, Abdool Karim Q, & Meyer-Weitz A (2011). Interventions to modify sexual risk behaviours for preventing HIV in homeless youth. The Cochrane Database of Systematic Reviews, 1, CD007501. doi: 10.1002/14651858.CD007501.pub2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. National Alliance to End Homelessness. (2012). An emerging framework for ending unaccompanied youth homelessness. Retrieved from http://www.endhomelessness.org/library/entry/an-emerging-framework-for-ending-unaccompanied-youth-homelessness.
  26. National Coalition for the Homeless. (2008). Homeless youth. Retrieved from http://www.nationalhomeless.org/factsheets/youth.html
  27. National Institute on Alcohol Abuse and Alcoholism. (2005). Helping patients who drink too much: A clinician’s guide. Retrieved from https://pubs.niaaa.nih.gov/publications/practitioner/cliniciansguide2005/guide.pdf
  28. Neighbors C, Geisner IM, & Lee CM (2008). Perceived marijuana norms and social expectancies among entering college student marijuana users. Psychology of Addictive Behaviors, 22, 433–438. doi: 10.1037/0893-164X.22.3.433 [DOI] [PubMed] [Google Scholar]
  29. Pearson FS, Cleland CM, Chaple M, Hamilton Z, Prendergast ML, Rich JD (2008). Substance use, mental health problems, and behavior at risk for HIV: evidence from CJDATS. Journal of psychoactive drugs, 40(4), 459–469. [DOI] [PubMed] [Google Scholar]
  30. Pollio DE, Batey DS, Bender K, Ferguson K, & Thompson S (2013). Technology use among emerging adult homeless in two U.S. cities. Social Work, 55(2), 173–175. [DOI] [PubMed] [Google Scholar]
  31. Reardon ML, Burns AB, Preist R, Sachs-Ericsson N, & Lang AR (2003). Alcohol use and other psychiatric disorders in the formerly homeless and never homeless: prevalence, age of onset, comorbidity, temporal sequencing, and service utilization. Substance Use & Misuse, 38(3-6), 601–644. [DOI] [PubMed] [Google Scholar]
  32. Rice E, Lee A, & Taitt S (2011). Cell phone use among homeless youth: potential for new health interventions and research. Journal of Urban Health, 88(6), 1175–1182. doi: 10.1007/s11524-011-9624-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Rice E, Milburn NG, & Monro W (2011). Social networking technology, social network composition, and reductions in substance use among homeless adolescents. Prevention Science, 12, 80–88. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Rice E, Monro W, Barman-Adhikari A, & Young SD (2010). Internet use, social networking, and HIV/AIDS risk for homeless adolescents. Journal of Adolescent Health, 47(6), 610–613. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Rotheram-Borus MJ, & Koopman C (1991). Sexual risk behaviors, AIDS knowledge, and beliefs about AIDS among runaways. American Journal of Public Health, 81(2), 208–221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Shelton KH, Taylor PJ, Bonner A, & van den Bree M (2009). Risk factors for homelessness: evidence from a population-based study. Psychiatric Services, 60(4), 465–464. [DOI] [PubMed] [Google Scholar]
  37. Stricof RL, Kennedy JT, Nattell TC, Weisfuse IB, & Novick LF (1991). HIV seroprevalence in a facility for runaway and homeless adolescents. American Journal of Public Health, 81(Suppl), 50–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Thompson R, Elliott J, Hu M-C, Aivadyan C, Aharonovich E, Hasin D (2017). Short-term effects of a brief intervention to reduce alcohol use and sexual risk among homeless young adults: Results from a randomized controlled trial. Alcohol Research and Theory, 25(1), 24–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Thompson RG, & Hasin DS (2011). Cigarette, marijuana, and alcohol use and prior drug treatment among newly homeless young adults in New York City: relationship to a history of foster care. Drug and Alcohol Dependence, 117(1), 66–69. doi: 10.1016/j.drugalcdep.2010.12.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Thompson RG, & Hasin D (2012). Psychiatric disorders and treatment among newly homeless young adults with histories of foster care. Psychiatric Services, 63(9):906–912. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Tyler KA (2008). Social network characteristics and risky sexual and drug related behaviors among homeless young adults. Social Science Research, 37(2), 673–685. [DOI] [PubMed] [Google Scholar]
  42. Young SD, & Rice E (2011). Online social networking technologies, HIV knowledge, and sexual risk and testing behaviors among homeless youth. AIDS and Behavior, 15, 253–260. [DOI] [PMC free article] [PubMed] [Google Scholar]

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