Login / Signup

Correlates of Moderate-to-Vigorous Physical Activity in Children With Physical Illness and Physical-Mental Multimorbidity.

Chloe BedardSara King-DowlingJoyce ObeidBrian W TimmonsMark A Ferro
Published in: Health education & behavior : the official publication of the Society for Public Health Education (2022)
This study measured physical activity (PA) and explored its correlates among children with multimorbidity (co-occurring chronic physical and mental illness; MM) versus those with chronic physical illness only (PI). This study used baseline data from the Multimorbidity in Children and Youth Across the Life Course (MY LIFE) study, an on-going cohort study following 263 children with a PI 2 to 16 years of age (mean age: 9.8 years, SD = 4.0; 47.7% female). PA was measured using accelerometry, and demographic and psychosocial variables were collected using questionnaires. Of the 55 children with MM and the 85 with PI with valid accelerometer data, 38.1% and 41.2%, respectively, met average daily PA guidelines. Correlates of moderate-to-physical PA (MVPA) among children with MM were age, ρ(53) = -0.45, p = .001, body mass index (BMI), ρ(48) = -0.28, p = .04, self-perceived behavioral conduct, ρ(24) = -0.45, p = .02, physical health-related quality of life, ρ(51) = 0.56, p < .001, and peer support, ρ(52) = 0.27, p = .04. Correlates of MVPA among children with PI were age, ρ(83) = -0.40, p < .001, sex, ρ(83) = -0.26, p = .01, self-perceived social competence, ρ(31) = 0.42, p = .02, self-perceived athletic competence, ρ(31) = 0.48, p = .005, physical health-related quality of life, ρ(83) = 0.34, p = .001, participation in community sport, ρ(31) = 0.41, p = .02, and family functioning, ρ(83) = 0.26, p = .02. These results demonstrate that children with PI and MM are insufficiently active and their PA is correlated with demographic and psychosocial factors.
Keyphrases
  • physical activity
  • mental health
  • body mass index
  • young adults
  • mental illness
  • depressive symptoms
  • healthcare
  • machine learning
  • social support
  • electronic health record
  • weight gain