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Online Behaviours during the COVID-19 Pandemic and Their Associations with Psychological Factors: An International Exploratory Study.

Julius BurkauskasNaomi A FinebergKonstantinos IoannidisSamuel R ChamberlainHenrietta Bowden-JonesInga Griškova-BulanovaAiste PranckevicieneArtemisa Rocha DoresIrene Palmares CarvalhoFernando BarbosaPierluigi SimonatoIlaria De LucaRosin MooneyMaría Ángeles Gómez-MartínezZsolt DemetrovicsKrisztina Edina ÁbelAttila SzaboHironobu FujiwaraMami ShibataAlejandra R Melero-VentolaEva María Arroyo-AnllóRicardo M Santos-LabradorKei KobayashiFrancesco Di CarloCristina MonteiroGiovanni MartinottiOrnella Corazza
Published in: International journal of environmental research and public health (2022)
This cross-sectional study aimed to explore specific online behaviours and their association with a range of underlying psychological and other behavioural factors during the COVID-19 pandemic. Eight countries (Italy, Spain, the United Kingdom, Lithuania, Portugal, Japan, Hungary, and Brazil) participated in an international investigation involving 2223 participants ( M = 33 years old; SD = 11), 70% of whom were females. Participants were surveyed for specific type of Internet use severity, appearance anxiety, self-compassion, and image and use of performance-enhancing drugs (IPEDs). Results were compared cross-culturally. The mean time spent online was 5 h ( SD = ±3) of daily browsing during the pandemic. The most commonly performed activities included social networking, streaming, and general surfing. A strong association between these online behaviours and appearance anxiety, self-compassion, and IPEDs use was found after adjustment for possible confounders, with higher scores being associated with specific online activities. Significant cross-cultural differences also emerged in terms of the amount of time spent online during the initial stages of the COVID-19 pandemic.
Keyphrases
  • health information
  • social media
  • healthcare
  • coronavirus disease
  • mental health
  • machine learning
  • deep learning
  • drug induced