Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions

Addictive Behaviors

Volumn 83, Issue , 2018, Pages 42-47

  Bae, Sangwon ^a   Chung, Tammy ^b   Ferreira, Denzil ^c   Dey, Anind K ^a   Suffoletto, Brian ^b  

^a Carnegie Mellon University   (United States)

^b UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

^c UNIVERSITY OF OULU   (Finland)

Author keywords

Alcohol; AWARE app; Machine learning; Smartphone sensors

Indexed keywords

ADULT; ALCOHOL CONSUMPTION; ARTICLE; BINGE DRINKING; CELL PHONE USE; CLINICAL ARTICLE; COLLEGE DRINKING; FEMALE; HIGH RISK BEHAVIOR; HUMAN; LOW RISK POPULATION; MACHINE LEARNING; MALE; MOBILE APPLICATION; MOBILE PHONE SENSOR; PHYSICAL ACTIVITY; PILOT STUDY; PROSPECTIVE STUDY; RANDOM FOREST; RECEIVER OPERATING CHARACTERISTIC; TIME; YOUNG ADULT; ALCOHOLISM; AMBULATORY MONITORING; DEVICES; ECOLOGICAL MOMENTARY ASSESSMENT; GENETIC PROCEDURES; MOBILE PHONE; PROCEDURES; QUESTIONNAIRE; SUPERVISED MACHINE LEARNING;

ADULT; ALCOHOLISM; BIOSENSING TECHNIQUES; CELL PHONE; ECOLOGICAL MOMENTARY ASSESSMENT; FEMALE; HUMANS; MALE; MONITORING, AMBULATORY; PROSPECTIVE STUDIES; SUPERVISED MACHINE LEARNING; SURVEYS AND QUESTIONNAIRES; YOUNG ADULT;

EID: 85037027900     PISSN: 03064603     EISSN: 18736327     Source Type: Journal    
DOI: 10.1016/j.addbeh.2017.11.039     Document Type: Article

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