mHealth for the Detection and Intervention in Adolescent and Young Adult Substance Use Disorder

Abstract

Research that explores the pairing of advanced detection methods such as wearables with real time intervention strategies is crucial to realizing the full potential of mHealth in this population.

mHealth Research Applied to Regulated and Unregulated Behavioral Health Sciences

“…Behavioral mHealth has received steady support from the NIH to identify, for example, how to influence smoking cessation among pregnant moms, 15 decrease sedentary behavior among youth, 16 increase physical activity, 17 and to control appetite, 18 to name a few. Behavioral strategies are also used in studies of mental health including substance use among adolescents, 19 management of bipolar disorder 20 and, monitoring of self-harm. 21 Over the past decade and, in response to the explosion of new technologies, behavioral scientists have developed new methods and frameworks to guide how best to use technology to optimize individual level change.…”

The Digital Health Landscape in Addiction and Substance Use Research: Will Digital Health Exacerbate or Mitigate Health Inequities in Vulnerable Populations?

“…Further, in over 90% of Americans claiming ownership of a mobile phone in the USA, 71% report a low socioeconomic status (less than $30,0000 in US dollars) and 66% report a limited level of education (no more than a high school education) [18,19]. Moreover, the use of mobile applications and mobile phones to retrieve health information is higher among racial and ethnic minorities compared to white coun- Geolocation via mobile apps may also be utilized in combination with the continuous physiological parameters collected to provide contextual basis and signal of substance use [5,23].…”

Treatment for Alcohol Use Disorder: Progress in Predicting Treatment Outcome and Validating Nonabstinent End Points

“…Future research will benefit from new tools, such as smartphones (e.g., apps to deliver surveys), wearable technology (e.g., activity trackers), and biosensors (e.g., transdermal alcohol sensors) that can provide closer to real-time monitoring across many of the AARDoC domains at the behavioral level. These new tools promise to fill in gaps in our understanding of the dynamic interplay of personal and contextual factors that enhance or erode self-efficacy to achieve and maintain drinking reduction goals (Bae et al, 2018; Carreiro et al, 2018; Tomko and McClure, 2018). Further, the use of alcohol biomarkers (e.g., serum carbohydrate-deficient transferrin, whole blood phosphatidylenthanol, urine ethyl glucuronide) will enhance the validity of self-reported drinking data, help to identify individuals who may be in need of alcohol treatment, and allow clinicians and researchers to better monitor patient progress during treatment (Helander et al, 2012; Litten et al, 2010; Piano et al, 2017).…”