Using Machine Learning to Explore the Risk Factors of Problematic Smartphone Use among Canadian Adolescents during COVID-19: The Important Role of Fear of Missing Out (FoMO)
Applied Sciences
; 13(8):4970, 2023.
Article
in English
| ProQuest Central | ID: covidwho-2292518
ABSTRACT
The goal of the present study was to use machine learning to identify how gender, age, ethnicity, screen time, internalizing problems, self-regulation, and FoMO were related to problematic smartphone use in a sample of Canadian adolescents during the COVID-19 pandemic. Participants were N = 2527 (1269 boys;Mage = 15.17 years, SD = 1.48 years) high school students from the Lower Mainland of British Columbia, Canada. Data on problematic smartphone use, screen time, internalizing problems (e.g., depression, anxiety, and stress), self-regulation, and FoMO were collected via an online questionnaire. Several different machine learning algorithms were used to train the statistical model of predictive variables in predicting problematic smartphone use. The results indicated that Shrinkage algorithms (lasso, ridge, and elastic net regression) performed better than other algorithms. Moreover, FoMO, emotional, and cognitive self-regulation made the largest relative contribution to predicting problematic smartphone use. These findings highlight the importance of FoMO and self-regulation in understanding problematic smartphone use.
Sciences: Comprehensive Works; problematic smartphone use; self-regulation; FoMO; adolescents; Pandemics; Minority & ethnic groups; Smartphones; Risk factors; Algorithms; Machine learning; Statistical models; COVID-19; Addictive behaviors; Cognitive ability; Emotions; Statistical analysis; Mathematical models; Teenagers; Learning algorithms
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Type of study:
Prognostic study
Language:
English
Journal:
Applied Sciences
Year:
2023
Document Type:
Article
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