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Responsiveness to mHealth Intervention for Cannabis Use in Young Adults Predicts Improved Outcomes

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Abstract

Mobile health (mHealth) interventions have proliferated rapidly in part because of their advantages in reducing consumer and provider burden, but less attention has been paid to participant responsiveness to mHealth programs and how this may affect outcomes. This study adds to that literature by examining whether participant responsiveness to a text messaging-delivered intervention was predictive of treatment outcomes over baseline levels of the outcome. We analyzed data from a pilot-randomized controlled trial of a text messaging-intervention to treat young adults with cannabis use disorder (treatment arm, N = 47), examining three indicators of responsiveness (two behavioral: treatment completion and booster message participation; and one subjective: perceived helpfulness of treatment) on abstinence from cannabis use and use-related problems measured at 3-month follow-up. With the exception of completion, the indicators were positively correlated with each other. Each of the indicators was predictive of better treatment outcomes above and beyond baseline risk. Treatment completion and booster participation—measured via technical data captured during intervention administration—appeared to be stronger predictors of improved outcomes than self-reported perceived helpfulness. Results suggest that behavioral and subjective responsiveness measures appear to be valid indicators of treatment response to mHealth interventions for substance use. Responsiveness measured via technical data captured during intervention administration may be a stronger and more efficient strategy for monitoring continued engagement. We discuss implications of these findings for deploying mHealth interventions at scale and monitoring responsiveness.

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References

  • Alessi, S. M., & Rash, C. J. (2017). Treatment satisfaction in a randomized clinical trial of mHealth smoking abstinence reinforcement. Journal of substance abuse treatment, 72, 103–110. https://doi.org/10.1016/j.jsat.2016.06.013.

    Article  PubMed  Google Scholar 

  • Bentler, P. M. (1992). On the fit of models to covariances and methodology to the Bulletin. Psychological Bulletin112, 400.

  • Berkel, C., Mauricio, A. M., Sandler, I. N., Wolchik, S. A., Gallo, C. G., & Brown, C. H. (2018). The cascading effects of multiple dimensions of implementation on program outcomes: A test of a theoretical model. Prevention Science, 19, 782–94. https://doi.org/10.1007/s11121-017-0855-4.

    Article  PubMed  PubMed Central  Google Scholar 

  • Berkel, C., Mauricio, A. M., Schoenfelder, E., & Sandler, I. N. (2011). Putting the pieces together: An integrated model of program implementation. Prevention Science, 12(1), 23–33.

  • Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. Sage focus editions, 154, 136–136.

    Google Scholar 

  • Centers for Disease Control and Prevention (CDC). (2013). Youth online: High school Youth Risk Behavior Survey. Book Youth Online.

  • Durlak, J. A., & DuPre, E. P. (2008). Implementation matters: a review of research on the influence of implementation on program outcomes and the factors affecting implementation. American journal of community psychology, 41, 327–350. https://doi.org/10.1007/s10464-008-9165-0.

    Article  PubMed  Google Scholar 

  • Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6, 1–55. https://doi.org/10.1080/10705519909540118.

    Article  Google Scholar 

  • Leviton, L. C., & Lipsey, M. W. (2007). A big chapter about small theories: Theory as method: Small theories of treatments. New Directions for Evaluation, 27–62.

  • Ltd, N. (n.d.). The leading bot platform. TextIt. Retrieved January 25, 2022, from https://www.textit.com/

  • Mason, M. J., Zaharakis, N. M., Moore, M., Brown, A., Garcia, C., Seibers, A., & Stephens, C. (2018). Who responds best to text-delivered cannabis use disorder treatment? A randomized clinical trial with young adults. Psychology of Addictive Behaviors32, 699. https://doi.org/10.1037/adb0000403

  • Perski, O., Blandford, A., West, R., & Michie, S. (2017). Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Translational behavioral medicine, 7, 254–267. https://doi.org/10.1007/s13142-016-0453-1.

    Article  PubMed  Google Scholar 

  • Simons, J. S., & Carey, K. B. (2006). An affective and cognitive model of marijuana and alcohol problems. Addictive Behaviors, 31, 1578–1592. https://doi.org/10.1016/j.addbeh.2005.12.004.

    Article  PubMed  Google Scholar 

  • Taki, S., Lymer, S., Russell, C. G., Campbell, K., Laws, R., Ong, K. L., ... & Denney-Wilson, E. (2017). Assessing user engagement of an mHealth intervention: development and implementation of the growing healthy app engagement index. JMIR mHealth and uHealth5, e89. https://doi.org/10.2196/mhealth.7236

  • Turner-McGrievy, G. M., Dunn, C. G., Wilcox, S., Boutté, A. K., Hutto, B., Hoover, A., & Muth, E. (2019). Defining Adherence to Mobile Dietary Self-Monitoring and Assessing Tracking Over Time. Journal of the Academy of Nutrition and Dietetics, 119, 1516–1524. https://doi.org/10.1016/j.jand.2019.03.012.

    Article  PubMed  PubMed Central  Google Scholar 

  • Twilio. (n.d.). Communication APIs for SMS, Voice, Video & Authentication. Retrieved January 25, 2022, from https://www.twilio.com/

  • Yeager, C. M., & Benight, C. C. (2018). If we build it, will they come? Issues of engagement with digital health interventions for trauma recovery. Mhealth, 4, 37–39. https://doi.org/10.21037/mhealth.2018.08.04.

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

A postdoctoral fellowship was provided to the first author by National Institute for Drug Abuse (T32DA039772-03) through the REACH Institute, Arizona State University, and by the Human Resource Services Administration (T98HP33815) through the Center for Applied Behavioral Health Policy, Arizona State University.

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Correspondence to Nikola M. Zaharakis Ph.D..

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The Institutional Review Board of the University of Tennessee approved the study. All study procedures adhered to the tenets of the Declaration of Helsinki.

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Informed consent was obtained from all individual study participants.

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The authors declare no conflict of interest.

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Zaharakis, N.M., Mason, M.J. & Berkel, C. Responsiveness to mHealth Intervention for Cannabis Use in Young Adults Predicts Improved Outcomes. Prev Sci 23, 630–635 (2022). https://doi.org/10.1007/s11121-022-01333-z

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