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Effects of videoconferencing use on momentary changes in disordered eating urges, body dissatisfaction, and mood.

Jade PortingaleJesy KennyMatthew Fuller-TyszkiewiczIsabel Krug
Published in: European eating disorders review : the journal of the Eating Disorders Association (2023)
The coronavirus disease 2019 (COVID-19) pandemic led to a global surge in videoconferencing use for work/study-related reasons. Although these platforms heighten exposure to one's image, the implications of videoconferencing use on body image and eating concerns remain scantly examined. This study sought to investigate, in an Australian sample, whether videoconferencing for work/study-related reasons predicted increases in body dissatisfaction (BD), urge to engage in disordered eating (DE; restrictive eating, exercise, overeating/purging), and negative mood at the state level. Participants (N = 482, 78.8% women, M age  = 20.5 years [SD = 5.3]) completed baseline demographic measures, accompanied by an ecological momentary assessment (EMA) of videoconferencing for work/study-related reasons, BD, DE urges, and negative mood six times a day for 7 days via a smartphone application. Most participants (n = 429; 89.0%) reported state-based videoconferencing use during the EMA phase. Consistent with expectations, state-based videoconferencing use was associated with an increase in state-level urges to engage in exercise. However, contrary to predictions, state-based videoconferencing use was linked to a decrease in state-level BD at the next assessment point and failed to predict negative mood and urges to engage in restrictive eating or overeating/purging at the state level. Given the simplified measure of videoconferencing use, the current research is considered preliminary and future replication and extension, using more nuanced measures, is warranted.
Keyphrases
  • physical activity
  • weight loss
  • coronavirus disease
  • bipolar disorder
  • type diabetes
  • pregnant women
  • sleep quality
  • machine learning
  • adipose tissue
  • deep learning
  • human health
  • skeletal muscle