ClinicalTrials.gg
RecruitingNCT06676059Updated Nov 10, 2026

SMART-r: Substance Monitoring and Active Relapse Tracking Repository

An observational study in Alcoholism and Substance-Related Disorders, sponsored by National Institute on Drug Abuse (NIDA). Recruiting at 1 site in United States. Open to participants aged 18 Years to 120 Years. Per ClinicalTrials.gov, last updated 2026-11-10.

Sponsored by National Institute on Drug Abuse (NIDA) · Observational

Updated Nov 10, 2026Start date movedGo to Updates ↓
Study type
Observational
Model
Other
Time perspective
Prospective
Enrollment
10,000
Ages
18 Years to 120 Years
Sex
All
01

Study summary

Background:

About 1.5 million adults in the US enter alcohol or substance use treatment programs each year. Unfortunately, more than half of patients do not finish their program. For those who start treatment, about 70% return to substance use within weeks or months after starting treatment. To discover why patients drop out of treatment and return to substance use - and what can be done about it - researchers need to learn more about people who use drugs and alcohol.

Objective:

To create a data repository by gathering survey and smartphone data from adults who use drugs and alcohol in order to conduct future research.

Eligibility:

Adults who have used drugs or alcohol in the past and have a Android smartphone. The researchers will recruit targeted demographics at different times throughout the duration of the study period.

Design:

Data will be collected for up to 6 months. All research activities will be online.

Participants will download a smartphone app called TTRU-Curtis AWARE and keep it active on their phone. The app will run in the background and collect participant data, including: screen unlocks, duration of time the screen is on; apps used; words typed (except passwords); duration and time of phone calls; estimated location (exact location is not collected); and movement, such as how many steps are taken in a day. All personally identifying information is automatically removed before the data is stored (including phone numbers, names, or locations described in messages).

Each day, participants will receive a text with a link to a survey. They will answer questions about their mood, behavior, and substance use from the day before. This survey should take less than 5 minutes to complete.

Every 30 days, participants will complete a longer survey. They will answer questions about their personal relationships, risky behaviors, mood, substance use, and feelings. They can skip any questions they do not feel comfortable answering. These surveys should take about 30 minutes to complete.

Participants may opt to allow researchers to access their social media posts.

Read the detailed description

Description:

This project aims to establish a comprehensive data repository for conducting secondary research, including the analysis of digital phenotype data from individuals who have historically used drugs, including alcohol. Our hypothesis is that digital phenotyping can reveal unique behavioral patterns and risk factors associated with substance use. By collecting data such as smartphone app usage, social media interactions, language data, phone sensor measurements, historical conversational AI data, and wearable device metrics, the TTRU research lab will be able to conduct secondary analyses and uncover insights that could inform prevention and intervention strategies with individuals who use substances.

Objectives

Primary Objective:

The primary objective is to establish the NIDA-IRP Technology and Translational Research Unit (TTRU) Substance Monitoring and Active Relapse Tracking Repository (SMART-r) for collection, storage, and analysis of the human data from individuals who use substances. The secure, high-quality resource of digital, sensor, social media, conversational AI data, self-report, and clinical data (Repository Materials) will be created. Core variables of interest include sensor data, language, and relevant self-report measures. The intention to collect digital phenotyping data is to aid in the efficiency and efficacy of secondary research investigating health outcomes for individuals who use substances by creating a repository of de-identified data.

02

Conditions studied

  • Alcoholism
  • Substance-Related Disorders

Keywords

  • Alcohol
  • Substance use
  • digital phenotyping
03

In context

Alcoholism

1,674 studies on the registry are indexed under Alcoholism; 331 are open to participants now.

This study's planned enrollment of 10,000 is above the median of 159 across 183 observational studies indexed under Alcoholism.

Browse Alcoholism studies →

Lead sponsor

National Institute on Drug Abuse (NIDA) is the lead sponsor of 393 studies on the registry; 19 are open to participants now.

Of its 14 completed or terminated interventional studies of FDA-regulated products, 6 (43%) have results posted.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years to 120 Years
Sexes eligible
All
Accepts healthy volunteers
No
Sampling method
Non-probability sample

Study population

Adults who have used drugs or alcohol in the past and have a Android smartphone. The researchers will recruit targeted demographics at different times throughout the duration of the study period.

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

Inclusion criteria

In order to be eligible to participate in the repository, an individual must meet all of the following criteria:

  • Must understand and be willing to complete an online process.
  • Be an adult aged 18 or older.
  • Self-report alcohol or other drug use within the past 30 days.
  • Have a smartphone as their primary mobile phone.

    --For the AWARE app data monitoring procedure, there may be times during this study period where, due to technical or OS limitations, the app is only available to users of certain devices (e.g., Android, iOS). If there is such a limitation, this information will be evaluated during screening. For example, if the AWARE app is not available for iOS, then the screening form may exclude iPhone users for that time period where the limitation exists, if the only procedures offered are daily diary surveys and AWARE data monitoring.

  • Be willing to adhere to the procedures, such as downloading the AWARE app onto their smartphone and keeping it active throughout the data collection period, completing baseline questionnaires, daily diary EMAs, open-ended survey questions with the TTR Chatbot, uploading historical conversational AI data, and/or follow-up surveys.
  • Understand and write in English.

    --This exclusion criterion is included because future research on the data collected will include linguistic analysis. all linguistic analyses, we remove rare words (e.g., words must be said by at least 95% of participants)95. Additionally, there are cultural differences across languages and, thus, interpreting language results across more than one language becomes difficult.96 As a result, we must keep analysis limited to a single language (English), since words in other languages will not meet that criteria.

  • Live in the United States.

Exclusion criteria

EXCLUSION CRITERIA:

An individual who meets any of the following criteria will be excluded from participation in this repository:

1. Any impairment severe enough to preclude or valid self-report.

Terms in these criteria, in plain language(1)

Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.

Informed consent

Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.

You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.

05

Study design

Observational model
Other
Time perspective
Prospective
Enrollment
10,000 participants (estimated)

Groups and cohorts

  • Adults who use substances

    Adults 18 years or older who use drugs or alcohol

06

What researchers measure

Primary outcomes

  1. SMARTr data repository

    The primary objective is to establish the NIDA-IRP Technology and Translational Research Unit (TTRU) Substance Monitoring and Active Relapse Tracking Repository (SMART-r) for collection, storage, and analysis of the human data from individuals who use substances.

    Time frame: 20 years

07

Study locations

1 of 1 sites recruiting
08

References and documents

Publications

  • Sinha R. New findings on biological factors predicting addiction relapse vulnerability. Curr Psychiatry Rep. 2011 Oct;13(5):398-405. doi: 10.1007/s11920-011-0224-0. PubMed 21792580 ↗
  • Bauer LO. Predicting relapse to alcohol and drug abuse via quantitative electroencephalography. Neuropsychopharmacology. 2001 Sep;25(3):332-40. doi: 10.1016/S0893-133X(01)00236-6. PubMed 11522462 ↗
  • Claus RE, Kindleberger LR. Engaging substance abusers after centralized assessment: predictors of treatment entry and dropout. J Psychoactive Drugs. 2002 Jan-Mar;34(1):25-31. doi: 10.1080/02791072.2002.10399933. PubMed 12003110 ↗
  • Darke S, Campbell G, Popple G. Retention, early dropout and treatment completion among therapeutic community admissions. Drug Alcohol Rev. 2012 Jan;31(1):64-71. doi: 10.1111/j.1465-3362.2011.00298.x. Epub 2011 Mar 22. PubMed 21426420 ↗
  • Evans E, Li L, Hser YI. Client and program factors associated with dropout from court mandated drug treatment. Eval Program Plann. 2009 Aug;32(3):204-12. doi: 10.1016/j.evalprogplan.2008.12.003. Epub 2008 Dec 11. PubMed 19150133 ↗
  • Lejuez CW, Zvolensky MJ, Daughters SB, Bornovalova MA, Paulson A, Tull MT, Ettinger K, Otto MW. Anxiety sensitivity: a unique predictor of dropout among inner-city heroin and crack/cocaine users in residential substance use treatment. Behav Res Ther. 2008 Jul;46(7):811-8. doi: 10.1016/j.brat.2008.03.010. Epub 2008 Mar 28. PubMed 18466878 ↗
  • Lopez-Goni JJ, Fernandez-Montalvo J, Arteaga A. Addiction treatment dropout: exploring patients' characteristics. Am J Addict. 2012 Jan-Feb;21(1):78-85. doi: 10.1111/j.1521-0391.2011.00188.x. Epub 2011 Dec 1. PubMed 22211350 ↗
  • Odenwald M, Semrau P. Dropout among patients in qualified alcohol detoxification treatment: the effect of treatment motivation is moderated by Trauma Load. Subst Abuse Treat Prev Policy. 2013 Mar 21;8:14. doi: 10.1186/1747-597X-8-14. PubMed 23514277 ↗
  • Epstein DH, Marrone GF, Heishman SJ, Schmittner J, Preston KL. Tobacco, cocaine, and heroin: Craving and use during daily life. Addict Behav. 2010 Apr;35(4):318-24. doi: 10.1016/j.addbeh.2009.11.003. Epub 2009 Nov 12. PubMed 19939575 ↗
  • Epstein DH, Willner-Reid J, Vahabzadeh M, Mezghanni M, Lin JL, Preston KL. Real-time electronic diary reports of cue exposure and mood in the hours before cocaine and heroin craving and use. Arch Gen Psychiatry. 2009 Jan;66(1):88-94. doi: 10.1001/archgenpsychiatry.2008.509. PubMed 19124692 ↗
  • Preston KL, Vahabzadeh M, Schmittner J, Lin JL, Gorelick DA, Epstein DH. Cocaine craving and use during daily life. Psychopharmacology (Berl). 2009 Dec;207(2):291-301. doi: 10.1007/s00213-009-1655-8. Epub 2009 Sep 24. PubMed 19777216 ↗
  • Koob G, Kreek MJ. Stress, dysregulation of drug reward pathways, and the transition to drug dependence. Am J Psychiatry. 2007 Aug;164(8):1149-59. doi: 10.1176/appi.ajp.2007.05030503. PubMed 17671276 ↗
  • McHugh RK. Treatment of co-occurring anxiety disorders and substance use disorders. Harv Rev Psychiatry. 2015 Mar-Apr;23(2):99-111. doi: 10.1097/HRP.0000000000000058. PubMed 25747923 ↗
  • Berenz EC, Coffey SF. Treatment of co-occurring posttraumatic stress disorder and substance use disorders. Curr Psychiatry Rep. 2012 Oct;14(5):469-77. doi: 10.1007/s11920-012-0300-0. PubMed 22825992 ↗
  • Crosby R, Holtgrave DR, Stall R, Peterson JL, Shouse L. Differences in HIV risk behaviors among black and white men who have sex with men. Sex Transm Dis. 2007 Oct;34(10):744-8. doi: 10.1097/OLQ.0b013e31804f81de. PubMed 17565334 ↗
  • Millett GA, Peterson JL, Wolitski RJ, Stall R. Greater risk for HIV infection of black men who have sex with men: a critical literature review. Am J Public Health. 2006 Jun;96(6):1007-19. doi: 10.2105/AJPH.2005.066720. Epub 2006 May 2. PubMed 16670223 ↗
  • Levy ME, Wilton L, Phillips G 2nd, Glick SN, Kuo I, Brewer RA, Elliott A, Watson C, Magnus M. Understanding structural barriers to accessing HIV testing and prevention services among black men who have sex with men (BMSM) in the United States. AIDS Behav. 2014 May;18(5):972-96. doi: 10.1007/s10461-014-0719-x. PubMed 24531769 ↗
  • Shuper PA, Joharchi N, Bogoch II, Loutfy M, Crouzat F, El-Helou P, Knox DC, Woodward K, Rehm J. Alcohol consumption, substance use, and depression in relation to HIV Pre-Exposure Prophylaxis (PrEP) nonadherence among gay, bisexual, and other men-who-have-sex-with-men. BMC Public Health. 2020 Nov 25;20(1):1782. doi: 10.1186/s12889-020-09883-z. PubMed 33256651 ↗
  • Gebru NM, Benvenuti MC, Rowland BHP, Kalkat M, Chauca PG, Leeman RF. Relationships among Substance Use, Sociodemographics, Pre-Exposure Prophylaxis (PrEP) Awareness and Related Attitudes among Young Adult Men Who Have Sex with Men. Subst Use Misuse. 2022;57(5):786-798. doi: 10.1080/10826084.2022.2040030. Epub 2022 Feb 21. PubMed 35188880 ↗
  • Crozier ME, Farokhnia M, Persky S, Leggio L, Curtis B. Relationship between self-stigma about alcohol dependence and severity of alcohol drinking and craving. BMJ Ment Health. 2023 Nov 22;26(1):e300852. doi: 10.1136/bmjment-2023-300852. PubMed 37993282 ↗
  • Shiffman S. Ecological momentary assessment (EMA) in studies of substance use. Psychol Assess. 2009 Dec;21(4):486-97. doi: 10.1037/a0017074. PubMed 19947783 ↗
  • Torous J, Onnela JP, Keshavan M. New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices. Transl Psychiatry. 2017 Mar 7;7(3):e1053. doi: 10.1038/tp.2017.25. PubMed 28267146 ↗
  • Jain SH, Powers BW, Hawkins JB, Brownstein JS. The digital phenotype. Nat Biotechnol. 2015 May;33(5):462-3. doi: 10.1038/nbt.3223. No abstract available. PubMed 25965751 ↗
  • Barnett I, Torous J, Staples P, Sandoval L, Keshavan M, Onnela JP. Relapse prediction in schizophrenia through digital phenotyping: a pilot study. Neuropsychopharmacology. 2018 Jul;43(8):1660-1666. doi: 10.1038/s41386-018-0030-z. Epub 2018 Feb 22. PubMed 29511333 ↗
  • Boukhechba M, Chow P, Fua K, Teachman BA, Barnes LE. Predicting Social Anxiety From Global Positioning System Traces of College Students: Feasibility Study. JMIR Ment Health. 2018 Jul 4;5(3):e10101. doi: 10.2196/10101. PubMed 29973337 ↗
  • Giorgi S, Yaden DB, Eichstaedt JC, Ungar LH, Schwartz HA, Kwarteng A, Curtis B. Predicting U.S. county opioid poisoning mortality from multi-modal social media and psychological self-report data. Sci Rep. 2023 Jun 3;13(1):9027. doi: 10.1038/s41598-023-34468-2. PubMed 37270657 ↗
  • Son Y, Clouston SAP, Kotov R, Eichstaedt JC, Bromet EJ, Luft BJ, Schwartz HA. World Trade Center responders in their own words: predicting PTSD symptom trajectories with AI-based language analyses of interviews. Psychol Med. 2023 Feb;53(3):918-926. doi: 10.1017/S0033291721002294. Epub 2021 Jun 22. PubMed 34154682 ↗
  • Meyerhoff J, Liu T, Stamatis CA, Liu T, Wang H, Meng Y, Curtis B, Karr CJ, Sherman G, Ungar LH, Mohr DC. Analyzing text message linguistic features: Do people with depression communicate differently with their close and non-close contacts? Behav Res Ther. 2023 Jul;166:104342. doi: 10.1016/j.brat.2023.104342. Epub 2023 May 27. PubMed 37269650 ↗

Individual participant data

Plan to share: No — Only aggregated data will be made available upon request.

09

Updates

32 registry updates since Sep 25, 2026
Start date
Sep 29, 2026→Nov 15, 2026
moved 32 times, Sep 25, 2026 – Nov 10, 2026
Show all 32 updates
  1. Nov 10, 2026
    Start date Nov 12, 2026→Nov 15, 2026
  2. Nov 9, 2026
    Start date Nov 11, 2026→Nov 12, 2026
  3. Nov 6, 2026
    Start date Nov 10, 2026→Nov 11, 2026
  4. Nov 5, 2026
    Start date Nov 9, 2026→Nov 10, 2026
  5. Nov 4, 2026
    Start date Nov 8, 2026→Nov 9, 2026
  6. Nov 3, 2026
    Start date Nov 5, 2026→Nov 8, 2026
  7. Nov 2, 2026
    Start date Nov 4, 2026→Nov 5, 2026
  8. Oct 30, 2026
    Start date Nov 3, 2026→Nov 4, 2026
  9. Oct 29, 2026
    Start date Nov 2, 2026→Nov 3, 2026
  10. Oct 28, 2026
    Start date Nov 1, 2026→Nov 2, 2026
  11. Oct 27, 2026
    Start date Oct 29, 2026→Nov 1, 2026
    + 1 other change: verification date
  12. Oct 26, 2026
    Start date Oct 28, 2026→Oct 29, 2026
  13. Oct 23, 2026
    Start date Oct 27, 2026→Oct 28, 2026
    + 1 other change: verification date
  14. Oct 22, 2026
    Start date Oct 26, 2026→Oct 27, 2026
    + 1 other change: verification date
  15. Oct 21, 2026
    Start date Oct 25, 2026→Oct 26, 2026
  16. Oct 20, 2026
    Start date Oct 22, 2026→Oct 25, 2026
  17. Oct 19, 2026
    Start date Oct 21, 2026→Oct 22, 2026
  18. Oct 16, 2026
    Start date Oct 20, 2026→Oct 21, 2026
  19. Oct 15, 2026
    Start date Oct 19, 2026→Oct 20, 2026
  20. Oct 14, 2026
    Start date Oct 18, 2026→Oct 19, 2026
  21. Oct 13, 2026
    Start date Oct 14, 2026→Oct 18, 2026
  22. Oct 9, 2026
    Start date Oct 13, 2026→Oct 14, 2026
  23. Oct 8, 2026
    Start date Oct 12, 2026→Oct 13, 2026
  24. Oct 7, 2026
    Start date Oct 11, 2026→Oct 12, 2026
  25. Oct 6, 2026
    Start date Oct 8, 2026→Oct 11, 2026
  26. Oct 5, 2026
    Start date Oct 7, 2026→Oct 8, 2026
  27. Oct 2, 2026
    Start date Oct 6, 2026→Oct 7, 2026
  28. Oct 1, 2026
    Start date Oct 5, 2026→Oct 6, 2026
  29. Sep 30, 2026
    Start date Oct 4, 2026→Oct 5, 2026
  30. Sep 29, 2026
    Start date Oct 1, 2026→Oct 4, 2026
  31. Sep 28, 2026
    Start date Sep 30, 2026→Oct 1, 2026
  32. Sep 25, 2026
    Start date Sep 29, 2026→Sep 30, 2026

From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗

10

Registry details

Key details

Study ID
NCT06676059
Responsible party
Sponsor
First posted
Nov 6, 2024
Start date
Nov 15, 2026 (estimated)
Primary completion
Dec 31, 2044 (estimated)
Completion
Dec 31, 2044 (estimated)
Last update
Nov 10, 2026

Study contacts

Jessica E Hemmons
Contact
jessie.hemmons@nih.gov
Not Listed
Brenda L Curtis, Ph.D.
Contact
brenda.curtis@nih.gov
(443) 740-2126
Brenda L Curtis, Ph.D.
principal investigator · National Institute on Drug Abuse (NIDA)

Oversight

FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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