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Published on 04.10.19 in Vol 7, No 10 (2019): October

Preprints (earlier versions) of this paper are available at http://preprints.jmir.org/preprint/13691, first published Feb 13, 2019.

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

    Original Paper

    End User–Informed Mobile Health Intervention Development for Adolescent Cannabis Use Disorder: Qualitative Study

    1Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States

    2Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, CA, United States

    3Neuropsychiatric Institute, University of California, Los Angeles, Los Angeles, CA, United States

    4University of California, San Diego, La Jolla, CA, United States

    *all authors contributed equally

    Corresponding Author:

    Kara Bagot, MD

    University of California, San Diego

    Department of Psychiatry

    9500 Gilman Drive

    MC 0405

    La Jolla, CA, 92093

    United States

    Phone: 1 8582462553

    Email: kbagot@ucsd.edu


    ABSTRACT

    Background: The rates of cannabis use continue to increase among adolescents and the current interventions have modest effects and high rates of relapse following treatment. There is increasing evidence for the efficacy of mobile technology–based interventions for adults with substance use disorders, but there is limited study of this technology in adolescents who use cannabis.

    Objective: The goal of our study was to elucidate elements of an app-based adjunctive intervention for cannabis cessation that resonate with adolescents who use cannabis.

    Methods: Adolescents, aged between 14 and 17 years, who used cannabis were recruited from San Diego County high schools. Semistructured focus groups (6 total; N=37) were conducted to examine the ways in which participants used smartphones, including the use of any health behavior change apps, as well as to elicit opinions about elements that would promote engagement with an app-based intervention for adolescent cannabis cessation. An iterative coding structure was used with first cycle structural coding, followed by pattern coding.

    Results: Themes that emerged from the analysis included (1) youth valued rewards to incentivize the progressive reduction of cannabis use, which included both nontangible rewards that mimic those obtained on social media platforms and prosocial activity-related rewards, (2) having the ability to self-monitor progression, (3) peer social support, (4) privacy and confidentiality discrete logo and name and usernames within the app, and (5) individualizing frequency and content of notifications and reminders.

    Conclusions: Integrating content, language, interfaces, delivery systems, and rewards with which adolescents who use cannabis are familiar, engage with on a day-to-day basis, and identify as relevant, may increase treatment engagement and retention for adolescents in substance use treatment. We may increase treatment effectiveness by adapting and individualizing current evidence-based interventions, so that they target the needs of adolescents and are more easily incorporated into their everyday routines.

    JMIR Mhealth Uhealth 2019;7(10):e13691

    doi:10.2196/13691

    KEYWORDS



    Introduction

    Cannabis is the most prevalent drug of abuse among adolescents, with nearly 50% of 12th graders, 30% of 10th graders, and 15% of 8th graders reporting lifetime use [1]. Although the rates of use of many substances have been on the decline among youth, cannabis use has failed to decline [1]. Greater than 25% of adolescents who use cannabis meet criteria for cannabis use disorder (CUD) [2]. The likelihood of developing cannabis dependence is linearly associated with the frequency of use and inversely associated with age, such that early onset users are the most susceptible for later dependence [3]. This is of concern as adolescents’ brains are developing and more susceptible to perturbations because of substance exposure. Early exposure to high levels of delta-9-tetrahydrocannabinol through cannabis use triggers repeated activation of the endogenous mesolimbic dopaminergic system, which may in turn lead to sensitization of this system and progressive enhancement of acquired susceptibility to psychiatric illness [4]. Early regular cannabis use is associated with psychosocial consequences that increase burden of illness and decrease functional outcomes [5,6], including increased likelihood of other illicit drug use and poorer academic outcomes [7].

    To date, the evidence for efficacious interventions targeting problematic cannabis use, especially in youth, are sparse, with some behavioral interventions demonstrating short-term abstinence during active treatment, but high rates of relapse at follow-up. Specifically, randomized controlled trials of behavioral interventions for adolescent cannabis use show moderate effect sizes posttreatment for motivational enhancement therapy (MET), cognitive behavioral therapy (CBT), family support therapy, case management, and contingency management (CM), with few adolescents maintaining abstinence through follow-up [6,8]. In a study evaluating CM plus MET and/or CBT on cannabis use outcomes in adolescents, those youth assigned to active treatment demonstrated longer durations of continuous abstinence during treatment and higher rates of point prevalence posttreatment abstinence as compared with behavioral treatment alone [9]. Further study of adjunctive CM has yielded conflicting results [10]. In addition, 5-week and 12-week MET and/or CBT, family support with home visits, psychoeducation, case management and referral to self-help groups, adolescent community reinforcement approach (operational skills training+social systems support), and multidimensional skills therapy have all demonstrated initial within-intervention efficacy, with reduction of use and increased abstinence. However, posttreatment, adolescents experienced frequent cycles of recovery and relapse; many were unable to maintain abstinence and the majority continued to report substance-related problems at follow-up [11]. This suggests that continued monitoring and intervention following active treatment may be important in maintaining gains during treatment.

    Studies suggest that the effectiveness of interventions may be increased by developing selective, highly specific interventions that are presented on interactive platforms and include content addressing specific risk factors in at-risk youth [12]. However, few interventions have done so to date [13]. Mobile phones are a highly promising and palatable intervention platform for adolescents as access to mobile technology is ubiquitous; nearly 75% of adolescents own or have regular access to a smartphone (85% African American and 71% Hispanic and Caucasian) and 91% access the internet through a smartphone. Furthermore, 90% of adolescents with mobile phones use text messaging, sending and receiving an average of 30 messages daily [14]. Minority adolescents appear more likely to own smartphones and use apps, and those who are most likely to own a smartphone are also those in need of behavioral change interventions [15]. Therefore, there is enormous potential for leveraging mobile technologies to widely disseminate interventions for adolescent substance use. Furthermore, the ubiquity of mobile technology use among African American and Hispanic teenagers, provides the opportunity for increasing substance use treatment and public health intervention among historically underserved populations.

    Although, adolescents spend a significant amount of time using technology, are expert users by virtue of exposure and use from childhood, and interactive platforms have been shown to be effective for treatment delivery in youth [12,14], few mobile health (mHealth) interventions (internet/mobile phone–based platforms) have been developed for use in adolescent substance users and none for adolescent cannabis use [16]. As such, we look to the adult data for guidance in examining the potential applicability of these types of interventions for at-risk adolescents. CBT4CBT (a computer-based CBT program supplementing therapist-delivered CBT has been shown to have greater efficacy (more days abstinent and fewer positive drug screens) than therapist-delivered CBT alone, for adults with SUDs up to 6 months posttreatment [17,18]. Web-based self-help interventions have also been shown to be efficacious in reducing alcohol use in adults [19-21]. Limited study of Web- and computer-based interventions in cannabis-using young adults and adults have demonstrated efficacy in correcting misperceptions about use and increasing knowledge but demonstrate mixed results for cessation outcomes [22-25]. Adults in a study of the Therapeutic Education System (self-guided, Web-based CBT modules+CM for completing modules and abstinence), exhibited higher rates of abstinence and greater treatment retention at study completion as compared with treatment-as-usual (TAU) [26]. A Web-based, self-guided intervention called Reduce Your Use: How to Break the CannabisHabit, which comprises modules based on CBT, MI, and behavioral self-management, demonstrated significantly greater rates of retention and reduction of cannabis use, frequency and quantity, during intervention as compared with controls [27]. The Quit the Shit intervention (52 weeks; Web-based questionnaires, weekly cannabis use diary with feedback, baseline and posttreatment therapist chat) also demonstrated a reduction in frequency and quantity of cannabis use in adults [28].

    The goal of this study is to increase our understanding of adolescent cannabis use behaviors in the context of near universal availability of mobile, wireless, and wearable technology, with the overall aim of developing a user-informed mHealth intervention for high-risk cannabis-using adolescents engaged in outpatient substance use treatment. Specifically, we aimed to (1) examine adolescent use of various mobile platforms (ie, short message service [SMS] text, apps), social media, and wearable devices to determine the use of mobile technologies as sources of health and substance use related information, and the effect on risk-taking behaviors and behavioral change, and (2) examine salience of components of existing behavioral interventions for youth.


    Methods

    Recruitment

    We employed a stratified purposeful sampling strategy. A total of 37 adolescents aged between 14 and 18 years of age were recruited from high schools and substance use treatment centers in San Diego County, and San Diego Unified School District (SDUSD), which serves >130,000 students and is the second largest district in California. The racial/ethnic makeup of the students in the SDUSD is 46.5% Hispanic, 23.4% Caucasian, and 10.2% African American. Eligibility criteria for the focus groups included being 14 to 18 years of age, owning a smartphone and/or having internet access, speaking fluent English, and cannabis use one or more times in the past 30 days. If eligible, adolescents were invited to a focus group held either on their school campus, in the outpatient treatment center, or in the lab.

    A total of 37 adolescents were enrolled in one of 6 focus groups, as that was the number needed to reach saturation (themes were repeated, no new information obtained) [29]. Groups were stratified on the basis of age (early adolescence: 14-16 years/8th-10th grades; late adolescence: 17-18 years/11th-12th grades) so that younger adolescents would not feel intimidated to speak in the presence of older teens. Age stratification was also done on the basis of data that marijuana use differs significantly by adolescent age, and the belief that differing rates of use and exposure to methods and strains of marijuana use might influence their experiences and ideas about a cannabis intervention. The groups included a maximum of 10 and a minimum of 5 adolescents per group so that the groups were small enough to give each participant the opportunity to speak and share ideas but large enough for some diversity. Adolescents were compensated with a US $25 gift card and travel to/from the focus group site was provided, if needed. Institutional Review Board approval and a Certificate of Confidentiality from the National Institutes of Health were obtained.

    Design

    Focus groups were conducted in English and followed a semistructured interview guide developed by the study principal investigator, a child and adolescent psychiatrist. Participants answered questions regarding characteristics of their smartphones, how they use their phones, what they use them for (including apps downloaded and native apps), time spent on their phones, use of related mobile technologies such as activity trackers, and use of mobile technology and Web-based platforms for accessing, monitoring, and/or changing health or substance related behaviors. Participants also worked as a group to design, and arrive at a consensus among group members, a mock app-based cannabis intervention for cannabis-using adolescents. They were prompted to discuss aspects of interest, including the following:

    1. Content
      • Health-related content for in-app messages and/or push notifications
      • Access to psychoeducation and prior measured personal data (eg, cannabis use/treatment adherence)
      • Static (same intervention for all users) versus dynamic (individualized/tailored) messaging
      • Theory: learning (temporal feedback on individual level data to enhance engagement and behavior change) and social cognitive (increased self-efficacy with goal setting and defined expectations, positively reinforce behavior change)
    2. Usage
      • Frequency of content delivery (exposure) via prompts, cue-related, global positioning system (GPS) location triggered
      • Format of content: text, video, pictures, and/or links to external sources
    3. Duration: active (brief, extended) with or without continuing care (time limited vs indefinite access)
    4. Contact
      • Clinicians via SMS text and Skype/video for confirmation of abstinence at routine intervals and in response to psychiatric or high-risk behavioral concerns
      • Peer supports via in-app social media
    5. Accessibility
      • Navigational features
      • Design of user interface
      • Environments (feasibility of continuous monitoring/access to intervention across academic, social, and familial settings).

    Measures

    Participants were administered 4 self-report questionnaires: the Customary Drinking and Drug Use Record [30] to measure current and past substance use, an internet and a social media addiction scale (based on the Bergen Facebook Addiction Scale) [31], a smartphone attachment index (scale from 1 to 10 of the query How attached are you to your smartphone? with 10 being the highest), and a social media engagement questionnaire (queries related to time spent and actions taken on various platforms, including Instagram, Snapchat, YouTube, and Facebook). Table 1 presents participant characteristics, mean cannabis use, and phone attachment. Group discussions provided qualitative reports of how adolescents use mobile technology to communicate and track health behaviors, in addition to app development input. Audio recordings were transcribed verbatim and coded individually.

    Table 1. Participant characteristics.
    View this table

    Statistical Analysis

    Qualitative software, ATLAS.ti (Scientific Software Development GmbH) was used to aid in data management for thematic analyses of transcripts and coding [32]. We used a team coding approach with iterative coding. Coding was framed by the focus group guide developed following individual interviews of high-risk cannabis-using teens that helped to triangulate information obtained from the focus groups. Themes were selected a priori to correspond to the guide. These themes included health behavior change technology and components of platform design for mobile technology intervention development. A total oftwoindependent coders (KB and EH) determined the coding structures, coded the 6 focus group transcripts, and engaged in intensive discussion of the coded transcripts to refine the coding guide and arrive at consensus in instances of discrepancy. Code cooccurrence tables were analyzed for connections between codes and participant statements. We ensured rigor by checking coding through qualitative software (ATLAS.ti); staff were trained in qualitative methods and coders arrived at a consensus greater than 90% of the time.


    Results

    Health Behavior Change Mobile App Use

    Group participants spoke about their use of various smartphone apps, including mHealth apps aimed at changing health behaviors such as sleep or nutrition (Table 2). Participants indicated they had used at least one mHealth app since owning a smartphone. Health behavior change app use was discontinued because of loss of interest or motivation, or dissatisfaction with the features. Wearable activity tracking devices were less popular, viewed as uncool and obsessive by some, and only useful when a person was dedicated to physical activity (ie, belonging to a sports team).

    Table 2. Smartphone use: health behavior change technology.
    View this table

    Mobile App Design Features

    During the group discussion, the following topics were considered: (1) content, (2) usage, (3) duration, (4) contact, and (5) accessibility. Themes from each area were derived through coding of focus group transcripts and are summarized in the following tables. The most prominent themes among participants were rewards, privacy, self-monitoring, peer social support, and notifications.

    Rewards

    Participants discussed creating a reward system for cannabis use cessation (Table 3). Participants proposed a contingency system of incremental rewards for sustained abstinence. Monetary rewards, such as prepaid debit cards, gift cards, coupons, or discounts, were suggested by participants across groups. In addition, participants proposed in-app rewards, such as points, emojis, pictures, exclusive app features, and games, as an alternative to monetary prizes.

    Table 3. Preferences and design recommendations for an mHealth app for cannabis cessation: reward system.
    View this table
    Privacy

    Several issues were raised about the protection of users’ private information. Privacy considerations are presented in Table 4. Participants agreed that the app name and logo should be discrete to minimize the chance of parents or friends learning about their cannabis use. Password or passcode protection was another common recommendation.

    Attitudes toward the use of location tracking in the app were divided. Supporters of location-based features liked the prospect of regional activity recommendations and a localized social support system. Dissenters argued that privacy was more important than local connection and they felt uncomfortable being monitored. A consensus was reached that the app should allow users to control location permissions.

    Table 4. Preferences and design recommendations for an mHealth app for cannabis cessation: privacy features.
    View this table
    Self-Monitoring Cannabis Use

    Participants suggested incorporating a feature for users to track their cannabis use. Various tools for collecting user information were proposed, including pop-up prompts and open-ended journal entries (Table 5). Personalized feedback in the form of text messages or graphic representations of cannabis use over time were presented as a way for users to view their progress.

    A few group members voiced a need for abstinence verification. Solutions included in-person urine screening, remote drug tests, or providing an electronic testing device that directly attaches to the user’s smartphone.

    Table 5. Preferences and design recommendations for an mHealth app for cannabis cessation: self-monitoring cannabis use.
    View this table
    Peer Social Support

    Every group proposed a social network type of messaging to anonymously connect with other app users who were actively trying to stop cannabis use. There was overall approval for the inclusion of social contact with other app users, with precautions taken to protect anonymity and location disclosure. Participants felt that including a social component served as a means of peer encouragement and distraction when cravings arise. Social support features are described in Table 6.

    Notifications

    Participants expressed a desire to have control over the frequency and content of the app notifications (Table 7). The upper limit mentioned by group participants was 3 times a day, though most preferred no more than once a day. When speaking about other mobile app use, participants expressed annoyance with receiving too frequent notifications and subsequently ignoring or turning off all notifications from the app, or deleting the app.

    Table 6. Preferences and design recommendations for an mHealth app for cannabis cessation: peer social support.
    View this table
    Table 7. Preferences and design recommendations for an mHealth app for cannabis cessation: notifications.
    View this table

    Discussion

    Principal Findings

    We queried adolescents who use cannabis about specific aspects of a mobile technology–based intervention, including content, duration, user interface, accessibility, support (peer and clinician), and current/past use of health behavior change apps, to inform development of a cannabis cessation intervention that would increase treatment engagement by youth in substance use treatment.

    Thetwomost common themes in intervention development that were expressed by participants were (1) monitoring reduction of cannabis use over time and (2) providing rewards for successful reduction and cessation. Adolescents indicated that integration of CM would be appealing. Previous research suggests that CM can improve effectiveness of substance use treatment for youth and increase engagement in treatment [33-36], including for youth with CUDs [37]. Surprisingly, participants in our study indicated that nonmonetary rewards would be the most rewarding; this included receiving emojis, similar to streaks in Snapchat, which many expressed would be motivating enough to maintain abstinence. Participants also indicated that being rewarded with Groupons for prosocial activities, such as movie theater or concert tickets, where one would be unable to use substances, would be appealing. Participants also expressed preference for receiving information and/or tickets for activities and events specific to their region, as well as rides to and from these activities and indicated that they would approve of integrating geolocation tracking services into the intervention to individualize rewards. Ironically, this is despite research showing that 46% of adolescents are likely to turn off or disable geolocation features associated with downloaded apps because of concerns of privacy [15]. The difference between our findings and those previously reported may be because of expectations of privacy starting at the point of download. For our purposes, there is an expectation that personal information would be collected to treat a disorder, whereas adolescents in the Pew Research study indicated downloading free apps for entertainment purposes where there is no expectation that personal information is needed to reach the desired outcome. Furthermore, adolescents may be more willing to share personal information, including location, when it results in a reward. Other studies have found that privacy and confidentiality were of less concern to high-risk adolescents if addressed at the beginning of a mHealth intervention [38]. Behavioral interventions for adolescent substance use treatment incorporate encouragement of prosocial activities to avoid substance use, with increased efficacy of intervention, if adolescents are able to increase engagement in these activities. The adolescents enrolled in this study also independently identified engagement in alternative, prosocial activities as a way to decrease use, and indicated updates to traditional thoughts about types of activities (eg, school-based sports or clubs) to technology-accessed activities specific to their lives and communities.

    Privacy was an interesting theme that emerged in an isolated context, but also within discussion of other content areas. When asked directly about privacy and others’ potential to have access to participants’ data, knowledge of their involvement in the intervention, or location-based information available to researchers/clinicians for intervention delivery, participants almost uniformly had negative views. However, with regard to rewards and peer resources, participants voluntarily and spontaneously proposed offering personal information and inclusion of location-based services to gain psychosocial and other intangible rewards, as well as tangible rewards.

    The theme of individualizing the app was also discussed in other domains. Individualization of mHealth interventions has been identified in previous studies as important to high-risk adolescents seeking treatment [38]. In our study, adolescents indicated their desire to individualize all aspects of the app, including the user interface, frequency, timing, tone and content of notifications and messages, and the level of clinician, peer, or text-based responsiveness to fluctuations in mood states and substance use. Dynamic interventions that address these factors are currently lacking from mHealth interventions, especially those tailored to youth. The majority of mHealth interventions for substance using and/or at-risk youth are static, text message–based interventions that do not address differences in baseline severity of substance use, psychiatric symptoms, and/or adapt to changes in these behaviors or symptoms over time.

    Themes identified by adolescents who use cannabis, the target population for this app-based cannabis use intervention, are consistent with research suggesting that intervention efficacy may be increased by developing selective, highly specific interventions incorporating content that addresses risk factors in at-risk youth and are presented on interactive platforms [39].

    Limitations

    There are several limitations to this study. As we sampled adolescents who use cannabis from one city in Southern California, it was not representative of the broader adolescent population. In addition, we had a low number of racial and ethnic minorities in our sample, further limiting generalizability. With regard to uses of mobile technology, we did not query adolescents about alternate platforms for intervention other than an app delivered via smartphone. Although the literature shows that adolescents’ primary mode of mobile technology use are smartphones, we did not query participants about use or acceptability of other types of technologies. Finally, we did not query youth on general methods of access to the app, as the goal is to integrate this into extant treatment paradigms for users engaged in substance use treatment. Despite these limitations, the qualitative approach allowed us to further probe the complexities and context of components of intervention development to understand adolescent technology use, and motivations for appeal of component content, more in-depth.

    Future Directions

    This app, incorporating mobile CBT, is being developed as an adjunct to TAU for adolescent cannabis users aged between 13 and 18 years who have 9 to 12 weeks of substance use treatment remaining at the time of study entry (to ensure we enroll youth before TAU impacts substance use). Participants will be recruited from local adolescent substance use treatment clinics. The youth clinics provide case management and individual and group counseling intwotracks, outpatient drug-free groups (meet daily, Monday-Friday for 1.5 hours after school) and day care habilitative groups (intensive daily, Monday-Friday, half-day program), with adjunctive family therapy, parent and youth support meetings, and aftercare counseling, as needed. Random urine toxicology is done to confirm abstinence. Youth are enrolled in treatment for 3 months, with 1 month added to treatment length for each positive urine toxicology screen. An active cohort (TAU+biosensor) will be matched by primary substance of use to a cohort receiving TAU alone (matched comparison cohort data collected through electronic medical record [EMR]/chart review) in an external control design. We will assess and compare groups on the primary outcome of point prevalence abstinence (total number throughout treatment and number of consecutive weeks) for primary substance of use (substance for which the youth meets criteria for SUD), and secondary outcome of point prevalence abstinence for total number of substances used. These outcomes will be collected for the TAU group by reviewing their medical records. Collecting primary outcome comparison data through EMR/chart review will provide us with an important opportunity to identify systems-related considerations of cross-database information sharing, privacy, confidentiality, and other issues that must be resolved before launching a larger-scale study, in which obtaining collateral EMR/chart data will be essential to supporting treatment outcomes research trials.

    Conclusions

    Adolescent cannabis use initiation is linked to negative long-term health effects [38,39], with likely greater impact because of rising delta-9-tetrahydrocannabinol concentrations [40,41]. Technology-based substance use interventions appear promising in adult and college-aged populations, but few are adapted for youth, and none specifically targeted for adolescents with CUD. Behavioral interventions for adolescent cannabis use have demonstrated limited success, with high rates of relapse and loss of gains at follow-up [11,13]. mHealth interventions allow for examination of behaviors, ecological and mental states, environments, and social networks contributing to, and resulting from, adolescent substance use in real time, with near instantaneous response and intervention. Collectively, real-time data can shape targeted treatment around individual risk factors for problematic use. Furthermore, mHealth interventions have the potential to address current barriers to treatment, including cost, stigma, and access to providers with adequate training in utilizing/implementing behavioral interventions specifically targeting substance use, as well as increasing overall reach and scalability of interventions.

    We have presented novel end user–informed data about the content, format, structure, privacy, and accessibility of an app-based substance use treatment for adolescents that may inform more successful interventions among this high-risk population. In this study, the sample of adolescents who use cannabis indicated a desire for an individualized app, with highly visual components consistent with apps that they already use and escalating rewards associated with individual progress. Furthermore, although response to use of geolocation services in the context of discussion of privacy was mixed, adolescents endorsed approval of sharing GPS information in the context of discussion pertaining to individualized rewards and connecting with peers. Identifying and developing intervention content on platforms highly utilized by target population, incorporating skill development (eg, saying no, coping with negative feeling, engagement in prosocial activities) via novel technological means, and integrating salient environmental rewards may increase intervention efficacy, and thus may improve substance use treatment outcomes. Further study of ethical and privacy implications of novel technological approaches are needed in this population. However, methods of ensuring that data are transmitted and stored in a manner that complies with Health Insurance Portability and Accountability (HIPAA) requirements are possible by ensuring that (1) all information from mobile to server will be via https/SSL (secure sockets layer), (2) stored data are encrypted and deleted when no longer required, (3) servers on the cloud are HIPAA compliant and regularly patched with security updates, stored in a secure facility, and (4) provision of a clear privacy policy and (5) strong passwords with expiration periods are mandated.

    Acknowledgments

    Susan Tapert, PhD; Suchitra Krishnan-Sarin, PhD; Dana Cavallo, PhD; Alexander Chang, MD; Emily Springfield, BA; 5K12 DA000357-17 (PIs: KB, EB); R25DA035163 (PIs: Carmen Masson, PhD, James Sorensen, PhD; subaward PI: KB).

    This work was supported by the National Institutes on Drug Abuse—American Academy of Child and Adolescent Psychiatry Physician Scientist Program in Substance Abuse K12 Award (5K12 DA00035717) awarded to KB.

    Conflicts of Interest

    None declared.

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    Abbreviations

    CBT: cognitive behavioral therapy
    CM: contingency management
    CUD: cannabis use disorder
    EMR: electronic medical record
    GPS: global positioning system
    HIPAA: Health Insurance Portability and Accountability
    MET: motivational enhancement therapy
    mHealth: mobile health
    SDUSD: San Diego Unified School District
    SMS: short message service
    TAU: treatment-as-usual


    Edited by C Dias; submitted 13.02.19; peer-reviewed by M Harris, G Cutter, J Niu; comments to author 09.04.19; revised version received 14.05.19; accepted 19.05.19; published 04.10.19

    ©Kara Bagot, Elizabeth Hodgdon, Natasha Sidhu, Kevin Patrick, Mikaela Kelly, Yang Lu, Eraka Bath. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 04.10.2019

    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 mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.