Using smartphone app use and lagged-ensemble machine learning for the prediction of work fatigue and boredom

作者：

Highlights：

• A lag-specific ensemble machine learning paradigm offers promise for prediction of fatigue and boredom.

• Duration, communication, and patterns of app use frequency are among the most important features for prediction across lags.

• Future research will benefit from evaluating associations on densely collected data across longer time scales.

摘要

•A lag-specific ensemble machine learning paradigm offers promise for prediction of fatigue and boredom.•Duration, communication, and patterns of app use frequency are among the most important features for prediction across lags.•Future research will benefit from evaluating associations on densely collected data across longer time scales.

论文关键词：Passive sensing,EMA,App use,Machine learning,Lag,Digital phenotyping,Fatigue,Boredom

论文评审过程：Received 2 December 2020, Revised 17 September 2021, Accepted 21 September 2021, Available online 24 September 2021, Version of Record 28 September 2021.

论文官网地址：https://doi.org/10.1016/j.chb.2021.107029