Predicting states of elevated negative affect in adolescents from smartphone sensors: a novel personalized machine learning approach.

ABSTRACT:

Background

Adolescence is characterized by profound change, including increases in negative emotions. Approximately 84% of American adolescents own a smartphone, which can continuously and unobtrusively track variables potentially predictive of heightened negative emotions (e.g. activity levels, location, pattern of phone usage). The extent to which built-in smartphone sensors can reliably predict states of elevated negative affect in adolescents is an open question.

Methods

Adolescent participants (n = 22; ages 13-18) with low to high levels of depressive symptoms were followed for 15 weeks using a combination of ecological momentary assessments (EMAs) and continuously collected passive smartphone sensor data. EMAs probed negative emotional states (i.e. anger, sadnes

SUBMITTER: Ren B 

PROVIDER: S-EPMC10650966 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature