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The relationship between young football players' psychological health resources and the psychological quality of their football experiences: A cross-sectional study.

Yngvar OmmundsenAndreas IvarssonBente WoldSiv GjesdalBård Erlend Solstad
Published in: PloS one (2024)
Studies taking a person-centred statistical approach when examining young peoples` psychological experiences in sport is scarce. The main aim of the present study was to examine the relationships between young football players' psychological health resources and the psychological quality of their football-specific experiences. Data for this cross-sectional study was collected as part of the [BLINDED] arm of the larger Promoting Adolescence Physical Activity (PAPA) multi-centre project [1]. The sample consisted of young [BLINDED] male (n = 814), female (n = 576), grassroots football players between the ages of 10 and 15 years (M = 12.5 years, SD = 1.1 years). We performed a latent profile analysis using Mplus 8.4 using a robust maximum likelihood estimator (MLR). Players with the most resourceful psychological health profile experienced more coach social support (mean = 4.38) than did those with a less well-off resourceful profile (mean = 3.79) and those with the least well-off profile (mean = 3.28). Players with the most resourceful profile also felt a stronger sense of unity among their teammates and they enjoyed football more than those least well off (mean = 4.43 vrs. mean = 3.12 and mean = 4.74 vrs 3.50. respectively). Parallel between-profile differences were also found for the players' general health resources including perceived life satisfaction, general health and family affluence as covariates. Findings suggest that variations in young players' psychological health profiles and their general health resources play a role in the quality of their football-specific psychological experiences.
Keyphrases
  • mental health
  • public health
  • high school
  • healthcare
  • social support
  • physical activity
  • depressive symptoms
  • sleep quality
  • health promotion
  • clinical trial
  • body mass index
  • machine learning
  • social media
  • big data