DEVELOPMENTAL MEDICINE & CHILD NEUROLOGY
ORIGINAL ARTICLE
Characteristics associated with physical activity among independently ambulant children and adolescents with unilateral cerebral palsy LOUISE E MITCHELL 1
| JENNY ZIVIANI 2,3 | ROSLYN N BOYD 1
1 Queensland Cerebral Palsy and Rehabilitation Research Centre, The University of Queensland, Herston, Qld; 2 Children’s Health Queensland, Queensland Health, Brisbane, Qld; 3 School of Health and Rehabilitation Sciences, The University of Queensland, St Lucia, Qld, Australia. Correspondence to Louise Mitchell at The University of Queensland – Queensland Cerebral Palsy and Rehabilitation Research Centre Level 7, Block 6 Royal Brisbane & Women’s Hospital Herston, Herston, Queensland 4029, Australia. E-mail: [email protected] This article is commented on by Peterson. To view this paper visit http://dx.doi.org/10.1111/dmcn.12578.
PUBLICATION DATA
Accepted for publication 4th June 2014. Published online ABBREVIATIONS
LIFE-H MobQues28 MVPA PEM-CY
SEIFA 6MWT
Assessment of Life Habits 28-item Mobility Questionnaire Moderate to vigorous intensity physical activity Participation and Environment Measure for Children and Youth Socio-Economic Indexes for Areas Six-minute walk test
AIM This study aimed to quantify the contribution of physical, personal and environmental characteristics to physical activity among independently ambulant children with unilateral cerebral palsy (CP). METHOD One-hundred and two children with unilateral CP (52 males, 50 females; 52 right hemiplegia; mean age 11y 3mo, range 8–17y [SD 2y 4mo]) classified at Gross Motor Function Classification System (GMFCS) levels I = 44 and II = 58 participated. Physical activity was measured over 4 days using ActiGraph accelerometers recording as activity counts. GMFCS, functional strength, 6-minute walk test (6MWT), mobility limitations (MobQues28), age, sex, Assessment of Life-Habits recreation domain, Participation and Environment Measure for Children and Youth (PEM-CY) and environmental characteristics were considered for selection in a linear regression model. These served as independent variables which were determined using a backwards selection procedure. RESULTS Younger age, male sex, increased performance on the 6MWT, and increased participation in the home and community measured using the PEM-CY were significantly associated with activity counts (p8h of data were recorded each day) were included
in the analysis and where activity counts values of zero occurred for >20 minutes, this was deemed as non-wear time and excluded from the analysis using ActiLife software algorithms. Vertical axis activity counts were converted to activity intensity using Evenson cut points20 to provide a total of daily time spent inactive (≤100 vertical counts/min), at light intensity (101–2295 vertical counts/ min) and at moderate to vigorous intensity (MVPA; ≥2296 vertical counts/min). To account for differences in wear time between participants, wear time was standardized to 12h using the formula: (time spent in activity state/wear time)*12. Average activity counts/minute, standardized inactive time and MVPA over 4 days were used for the linear regression modelling. Data were collated in Microsoft Excel 2010 (Microsoft Corporation, Redmond, WA, USA) and analyzed using IBM SPSS v22 (IBM Corp., New York, NY, USA). Continuous data were examined for normality by visually inspecting histograms and with the Shapiro–Wilk test. All of these variables were normally distributed. As physical activity data were normally distributed and variances were equal, differences in physical activity between groups were calculated using independent t-tests and linear regression was performed. Standard multiple linear regression was calculated using the following steps: variables were correlated with activity counts using Pearson or where nonnormally distributed, Spearman correlation coefficients (q); variables with q>0.20 were entered as independent variables into a direct linear regression model; since no a priori hypotheses had been made to determine the order of entry of predictor variables, a backward selection procedure was employed which selected up to five variables with significant regression co-efficient values p
ORIGINAL ARTICLE
Characteristics associated with physical activity among independently ambulant children and adolescents with unilateral cerebral palsy LOUISE E MITCHELL 1
| JENNY ZIVIANI 2,3 | ROSLYN N BOYD 1
1 Queensland Cerebral Palsy and Rehabilitation Research Centre, The University of Queensland, Herston, Qld; 2 Children’s Health Queensland, Queensland Health, Brisbane, Qld; 3 School of Health and Rehabilitation Sciences, The University of Queensland, St Lucia, Qld, Australia. Correspondence to Louise Mitchell at The University of Queensland – Queensland Cerebral Palsy and Rehabilitation Research Centre Level 7, Block 6 Royal Brisbane & Women’s Hospital Herston, Herston, Queensland 4029, Australia. E-mail: [email protected] This article is commented on by Peterson. To view this paper visit http://dx.doi.org/10.1111/dmcn.12578.
PUBLICATION DATA
Accepted for publication 4th June 2014. Published online ABBREVIATIONS
LIFE-H MobQues28 MVPA PEM-CY
SEIFA 6MWT
Assessment of Life Habits 28-item Mobility Questionnaire Moderate to vigorous intensity physical activity Participation and Environment Measure for Children and Youth Socio-Economic Indexes for Areas Six-minute walk test
AIM This study aimed to quantify the contribution of physical, personal and environmental characteristics to physical activity among independently ambulant children with unilateral cerebral palsy (CP). METHOD One-hundred and two children with unilateral CP (52 males, 50 females; 52 right hemiplegia; mean age 11y 3mo, range 8–17y [SD 2y 4mo]) classified at Gross Motor Function Classification System (GMFCS) levels I = 44 and II = 58 participated. Physical activity was measured over 4 days using ActiGraph accelerometers recording as activity counts. GMFCS, functional strength, 6-minute walk test (6MWT), mobility limitations (MobQues28), age, sex, Assessment of Life-Habits recreation domain, Participation and Environment Measure for Children and Youth (PEM-CY) and environmental characteristics were considered for selection in a linear regression model. These served as independent variables which were determined using a backwards selection procedure. RESULTS Younger age, male sex, increased performance on the 6MWT, and increased participation in the home and community measured using the PEM-CY were significantly associated with activity counts (p8h of data were recorded each day) were included
in the analysis and where activity counts values of zero occurred for >20 minutes, this was deemed as non-wear time and excluded from the analysis using ActiLife software algorithms. Vertical axis activity counts were converted to activity intensity using Evenson cut points20 to provide a total of daily time spent inactive (≤100 vertical counts/min), at light intensity (101–2295 vertical counts/ min) and at moderate to vigorous intensity (MVPA; ≥2296 vertical counts/min). To account for differences in wear time between participants, wear time was standardized to 12h using the formula: (time spent in activity state/wear time)*12. Average activity counts/minute, standardized inactive time and MVPA over 4 days were used for the linear regression modelling. Data were collated in Microsoft Excel 2010 (Microsoft Corporation, Redmond, WA, USA) and analyzed using IBM SPSS v22 (IBM Corp., New York, NY, USA). Continuous data were examined for normality by visually inspecting histograms and with the Shapiro–Wilk test. All of these variables were normally distributed. As physical activity data were normally distributed and variances were equal, differences in physical activity between groups were calculated using independent t-tests and linear regression was performed. Standard multiple linear regression was calculated using the following steps: variables were correlated with activity counts using Pearson or where nonnormally distributed, Spearman correlation coefficients (q); variables with q>0.20 were entered as independent variables into a direct linear regression model; since no a priori hypotheses had been made to determine the order of entry of predictor variables, a backward selection procedure was employed which selected up to five variables with significant regression co-efficient values p
