Prospective Prediction of Lapses in Opioid Use Disorder: Protocol for a Personal Sensing Study

Abstract

Background Successful long-term recovery from opioid use disorder (OUD) requires continuous lapse risk monitoring and appropriate use and adaptation of recovery-supportive behaviors as lapse risk changes. Available treatments often fail to support long-term recovery by failing to account for the dynamic nature of long-term recovery. Objective The aim of this protocol paper is to describe research that aims to develop a highly contextualized lapse risk prediction model that forecasts the ongoing probability of lapse. Methods The participants will include 480 US adults in their first year of recovery from OUD. Participants will report lapses and provide data relevant to lapse risk for a year with a digital therapeutic smartphone app through both self-report and passive personal sensing methods (eg, cellular communications and geolocation). The lapse risk prediction model will be developed using contemporary rigorous machine learning methods that optimize prediction in new data. Results The National Institute of Drug Abuse funded this project (R01DA047315) on July 18, 2019 with a funding period from August 1, 2019 to June 30, 2024. The University of Wisconsin-Madison Health Sciences Institutional Review Board approved this project on July 9, 2019. Pilot enrollment began on April 16, 2021. Full enrollment began in September 2021. Conclusions The model that will be developed in this project could support long-term recovery from OUD—for example, by enabling just-in-time interventions within digital therapeutics. International Registered Report Identifier (IRRID) DERR1-10.2196/29563

mHealth and social mediation: Mobile support among stigmatized people living with HIV and substance use disorder

“…Future research could investigate other types of digital trace data such as language features to predict and deploy real-time interventions for relapse prevention (Kornfield et al, 2018a, 2018b). This study suggests future mobile communication research should focus on digital phenotyping—that is, “moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices” (Moshontz et al, 2021; Torous et al, 2016). Mobile devices can collect a wide range of actual behaviors due to their omnipresence in everyday life, their powerful sensors capable of collecting granular data (e.g.…”

“…Improvement in recovery outcomes after using the app would be indicative of effective implementation of this app among its targeted populations. Identifying the relations between use patterns and health benefits can inform the development of sensing systems for digital phenotyping—moment-by-moment quantification of individuals’ situational status using data from their digital devices (Moshontz et al, 2021; Torous et al, 2016). Signal detected by mobile devices can be used to predict adverse health outcomes, potentially providing the basis for alert systems that trigger support “just-in-time” (Ling and Oppegaard, 2021).…”

“…system log) and active (e.g. surveys) data to build lapse prediction models (Kornfield et al, 2018a; Moshontz et al, 2021).…”

Acceptability of Personal Sensing Among People With Alcohol Use Disorder: Observational Study

“…More recently, research using personal sensing of raw data streams other than self-reporting is emerging for mental health, including alcohol and other substance use disorders. This includes methods to sense geolocation [ 13 - 16 ], cellular communication [ 14 - 16 ], sleep [ 17 ], and physiology [ 15 , 16 , 18 ], for example. These alternative personal sensing methods provide benefits and opportunities that are not possible with EMA.…”

“…These methods can also be used to monitor psychiatric symptoms or even predict the future risk of symptom recurrence or other harmful behaviors (eg, suicide attempts and risky or otherwise harmful drinking episodes) [ 24 - 27 ]. For alcohol and other substance use disorders, there is emerging research on using sensed data to predict craving [ 13 , 18 ]; alcohol [ 15 , 27 - 29 ], cannabis [ 16 ], or opioid use [ 14 ]; and lapses or relapse [ 14 , 30 , 31 ]. Personal sensing measures or risk indicators may be shared, with patient consent, to health care providers to allow for cost-effective, targeted allocation of limited mental health resources to patients with the greatest or most urgent need [ 32 ].…”