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Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Just-in-time adaptive interventions (JITAIs), typically smartphone apps, learn to deliver therapeutic content when users need it. The challenge is to \u201cpush\u201d content at algorithmically chosen moments without making users trigger it with effortful input. We trained a randomForest algorithm to predict heroin craving, cocaine craving, or stress (reported via smartphone app 3x\/day) 90\u2009min into the future, using 16 weeks of field data from 189 outpatients being treated for opioid-use disorder. We used only one form of continuous input (along with person-level demographic data), collected passively: an indicator of environmental exposures along the past 5\u2009h of movement, as assessed by GPS. Our models achieved excellent overall accuracy\u2014as high as 0.93 by the end of 16 weeks of tailoring\u2014but this was driven mostly by correct predictions of absence. For predictions of presence, \u201cbelievability\u201d (positive predictive value, PPV) usually peaked in the high 0.70s toward the end of the 16 weeks. When the prediction target was more rare, PPV was lower. Our findings complement those of other investigators who use machine learning with more broadly based \u201cdigital phenotyping\u201d inputs to predict or detect mental and behavioral events. When target events are comparatively subtle, like stress or drug craving, accurate detection or prediction probably needs effortful input from users, not passive monitoring alone. 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