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Participation, self‐concept and motor performance of boys with developmental coordination disorder: A classification and regression tree analysis approach
Author(s) -
Poulsen Anne A.,
Johnson Helen,
Ziviani Jenny M.
Publication year - 2011
Publication title -
australian occupational therapy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.595
H-Index - 44
eISSN - 1440-1630
pISSN - 0045-0766
DOI - 10.1111/j.1440-1630.2010.00880.x
Subject(s) - psychology , motor coordination , regression , developmental psychology , regression analysis , physical medicine and rehabilitation , clinical psychology , medicine , computer science , psychiatry , machine learning , psychotherapist
Aim: Identification of relationships between participation, psycho‐social adjustment and motor performance factors in boys with developmental coordination (DCD) using a classification and regression tree approach to determine patterns of potential vulnerability. Methods: A quantitative cross‐sectional design investigating a cohort of 60 boys aged 10–13 years with DCD was employed. Classification and regression tree analysis of: (i) fundamental movement skill performance on tests of balance, ball skills and manual dexterity, (ii) self‐concept perceptions and (iii) leisure‐time activity participation, was used to define different risk groups. Results and conclusion: Five meaningful groups of boys were identified. Poor manual dexterity was the strongest discriminator of group membership in the three most severely affected groups confirming the significance of fine motor difficulties as a key grouping variable and supporting previous research using cluster analyses. Low participation in out‐of‐school informal social–physical activities was found to be a new grouping factor alongside poor peer relations self‐concept. A final group describing boys with poor motor performance across all fundamental movement skill areas who had high participation in structured social non‐physical activities, such as choir or band, was identified. The potential for future classification and regression tree analyses to inform clinical decision making was discussed.