Selected article for: "multivariable model and school age"

Author: Nap-van der Vlist, Merel M; Dalmeijer, Geertje W; Grootenhuis, Martha A; van der Ent, Kors; van den Heuvel-Eibrink, Marry M; Swart, Joost F; van de Putte, Elise M; Nijhof, Sanne L
Title: Fatigue among children with a chronic disease: a cross-sectional study
  • Cord-id: cwzw4sxy
  • Document date: 2021_2_17
  • ID: cwzw4sxy
    Snippet: OBJECTIVE: To determine: (1) which biological/lifestyle, psychological and/or social factors are associated with fatigue among children with a chronic disease and (2) how much each of these factors contributes to explaining variance in fatigue. DESIGN AND SETTING: This was a cross-sectional study across two children’s hospitals. PATIENTS: We included children aged 8–18 years who visited the outpatient clinic with cystic fibrosis, an autoimmune disease or postcancer treatment. MAIN OUTCOME ME
    Document: OBJECTIVE: To determine: (1) which biological/lifestyle, psychological and/or social factors are associated with fatigue among children with a chronic disease and (2) how much each of these factors contributes to explaining variance in fatigue. DESIGN AND SETTING: This was a cross-sectional study across two children’s hospitals. PATIENTS: We included children aged 8–18 years who visited the outpatient clinic with cystic fibrosis, an autoimmune disease or postcancer treatment. MAIN OUTCOME MEASURES: Fatigue was assessed using the PedsQL Multidimensional Fatigue Scale. Generic biological/lifestyle, psychological and social factors were assessed using clinical assessment tools and questionnaires. Multiple linear regression analyses were used to test the associations between these factors and fatigue. Finally, a multivariable regression model was used to determine which factor(s) have the strongest effect on fatigue. RESULTS: A total of 434 out of 902 children were included (48% participation rate), with a median age of 14.5 years; 42% were male. Among these 434 children, 21.8% were severely fatigued. Together, all biopsychosocial factors explained 74.6% of the variance in fatigue. More fatigue was uniquely associated with poorer physical functioning, more depressive symptoms, more pressure at school, poorer social functioning and older age. CONCLUSIONS: Fatigue among children with a chronic disease is multidimensional. Multiple generic biological/lifestyle, psychological and social factors were strongly associated with fatigue, explaining 58.4%; 65.8% and 50.0% of the variance in fatigue, respectively. Altogether, almost three-quarters of the variance in fatigue was explained by this biopsychosocial model. Thus, when assessing and treating fatigue, a transdiagnostic approach is preferred, taking into account biological, psychological and social factors.

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