ArticleComputers in human behavior2022
Using Smartphone App Use and Lagged-Ensemble Machine Learning for the Prediction of Work Fatigue and Boredom.
Article in Computers in human behavior, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The development and validation of a prognostic prediction modeling study in acute myocardial infarction patients after percutaneous coronary intervention: hemorrhea and major cardiovascular adverse events.Journal of thoracic disease · 2024Article
- A machine learning investigation into the temporal dynamics of physical activity-mediated emotional regulation in adolescents with anorexia nervosa and healthy controls.European eating disorders review : the journal of the Eating Disorders Association · 2023Article
- Applying ensemble machine learning models to predict individual response to a digitally delivered worry postponement intervention.Journal of affective disorders · 2023Article
- Generalisable machine learning models trained on heart rate variability data to predict mental fatigue.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
introAs smartphone usage becomes increasingly prevalent in the workplace, the physical and psychological implications of this behavior warrant consideration. Recent research has investigated associations between workplace smartphone use and fatigue and boredom, yet findings are not conclusive.
methodsTo build off recent efforts, we applied an ensemble machine learning model on a previously published dataset of
resultsThe ability to predict fatigue and boredom trajectories from app use information was heterogeneous and highly person-specific. Idiographic modeling reflected moderate to high correlative capacity (
conclusionA lag- specific ensemble predictive paradigm is a promising approach to leveraging high-dimensional app use behavioral data for the prediction of work fatigue and boredom. Future research will benefit from evaluating associations on densely collected data across longer time scales.
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Registered trials
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