Abstract

Lapse risk when trying to stop or reduce harmful substance use is idiosyncratic, dynamic and multi-factorial. Just-in-time adaptive interventions (JITAIs) aim to deliver tailored support at moments of need or opportunity. We aimed to synthesize evidence on decision points, tailoring variables, intervention options, decision rules, study designs, user engagement and effectiveness of technology-mediated JITAIs for reducing harmful substance use. Systematic review of empirical studies of any design with a narrative synthesis. We searched Ovid MEDLINE, Embase, PsycINFO, Web of Science, the ACM Digital Library, the IEEE Digital Library, ClinicalTrials.gov, the ISRCTN register and dblp using terms related to substance use/mHealth/JITAIs. Outcomes were user engagement and intervention effectiveness. Study quality was assessed with the mHealth Evidence Reporting and Assessment checklist. We included 17 reports of 14 unique studies, including two randomized controlled trials. JITAIs targeted alcohol (S = 7, n = 120 520), tobacco (S = 4, n = 187), cannabis (S = 2, n = 97) and a combination of alcohol and illicit substance use (S = 1, n = 63), and primarily relied on active measurement and static (i.e. time-invariant) decision rules to deliver support tailored to micro-scale changes in mood or urges. Two studies used data from prior participants and four drew upon theory to devise decision rules. Engagement with available JITAIs was moderate-to-high and evidence of effectiveness was mixed. Due to substantial heterogeneity in study designs and outcome variables assessed, no meta-analysis was performed. Many studies reported insufficient detail on JITAI infrastructure, content, development costs and data security. Current implementations of just-in-time adaptive interventions (JITAIs) for reducing harmful substance use rely on active measurement and static decision rules to deliver support tailored to micro-scale changes in mood or urges. Studies on JITAI effectiveness are lacking.

Highlights

  • With improved mobile hardware, software and computational power, individual-level data on substance use triggers can be collected, processed and actioned in or near real-time

  • Current implementations of just-in-time adaptive interventions (JITAIs) for reducing harmful substance use rely on active measurement and static decision rules to deliver support tailored to micro-scale changes in mood or urges

  • A large body of research using technology-mediated ecological momentary assessments (EMAs) in people’s daily lives indicates that lapse risk in people attempting to quit or reduce harmful substance use is idiosyncratic, dynamic and multifactorial [1,2,3,4,5,6,7]

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Summary

Introduction

Software and computational power, individual-level data on substance use triggers can be collected, processed and actioned in or near real-time. A large body of research using technology-mediated ecological momentary assessments (EMAs) in people’s daily lives indicates that lapse risk in people attempting to quit or reduce harmful substance use is idiosyncratic (i.e. it differs between individuals), dynamic (i.e. it fluctuates over time) and multifactorial (i.e. it is driven by multiple variables, such as urge to smoke, negative affect and contextual cues) [1,2,3,4,5,6,7]. Dynamic and multi-factorial nature of lapse risk in individuals attempting to quit or reduce harmful substance use, JITAIs are poised as suited to the delivery of lapse prevention support. We aimed to synthesise evidence on decision points, tailoring variables, intervention options, decision rules, study designs, user engagement and effectiveness of technology-mediated JITAIs for reducing harmful substance use

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