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Abstract

PURPOSE:

To examine the relationship between clinic-based walking capacity measures and community-based walking activity in ambulatory children with cerebral palsy (CP).

METHODS:

A secondary analysis of a cross-sectional cohort was employed at tertiary care children’s hospital; $n=$ 128, ages 2–9 years, Gross Motor Function Classification System (GMFCS) I–III. Walking capacity was captured with 1- and 6-minute walk tests (1MWT, 6MWT), Gross Motor Function Measure-walk/run/jump score (GMFM-E), and Activity Scale for Kids performance version (ASKp-30). Walking activity performance in the community was quantified by StepWatch (SW).

RESULTS:

Moderate correlations were documented for 6MWT to SW outputs of walking level, moderate high intensity, 60-minute peak and peak activity index ( $r=$ 0.55–0.58, $p<$ 0.01). GMFM-E correlated with all SW outputs ( $r=$ 0.55–0.69, $p<$ 0.01) except 1-minute peak walking rate. Per regression modeling, GMFM-E was associated with walking level and intensity ( $p<$ 0.02) and 6MWT related to high intensity walking ( $p<$ 0.4, R ${}^{2}=$ 0.28–0.48).

CONCLUSION:

6MWT and GMFM-E have the strongest associations with level, amount and intensity of walking in daily life. Results suggest that the 6MWT and GMFM-E can be employed to estimate community walking activity in ambulatory children with CP. Future studies should focus on environmental and personal factors that influence community walking performance.

Keywords

Cerebral palsy walking capacity performance accelerometry

1. Introduction

The impact of gross motor functional impairments on physical activity in children with cerebral palsy (CP) is well documented [1, 2, 4, 5, 6]. Children with CP walk significantly less time each day and achieve fewer high step rates than typically developing youth, displaying greater proportions of lower walking intensity levels within total daily step activity [1]. Ambulatory children with CP ages 7–13 years have also been reported to take significantly more steps during school days than during weekend days [2]. Rehabilitation management strategies for youth with CP are focused to optimize their walking in order to support participation in daily life. Currently, the effect of these clinical management strategies on walking is evaluated largely on clinic-based measures and/or parental and community provider reports.

Accelerometers and pedometers allow sampling of day-to-day walking and physical activity, yet are not practical to be used as a clinical measure due to time constraints within clinical visits, costs of the devices and logistics of accessing and processing the walking activity data. Levels and intensity of community-based walking activity and physical activity have been well documented in children with CP [3, 4, 5, 6, 7, 8, 9]. Despite the known health benefits of regular physical activity, only 25% of independently ambulatory children with CP performed a sufficient level of physical or walking activity to meet public health recommendations of 60 minutes of moderate to vigorous activity per day [5]. Mitchell and colleagues documented in 2015 that physical activity was positively associated with the clinic based six-minute walk test (6MWT) [7]. Yet, to date, there are no published documentation of the relationship of clinic-based walking tests to direct validated accelerometry measures of walking in the community in children with CP.

Activity capacity and performance are related but distinctly different concepts in understanding the walking activity of ambulatory children with CP within the International Classification of Functioning and Disability (ICF) framework [10]. Walking activity encompasses both capacity (executing a task in a controlled environment such as a clinical setting) and performance (executing a task in the natural environment; what the child actually does in his/her home, school, or community setting). Participation is only partially reflected by walking and physical activity capacity levels in young children with CP, suggesting that physical activity performance as well as other physical, social, environmental and personal factors also play a role [7].

The SW has been found to be reliable in capturing steps taken as compared to observation of the heel leaving the ground. The SW is reported to have superior accuracy for step counts as compared with waist-mounted pedometers during treadmill walking in lean and obese youth ages 10–12 years [11]. Foster’s group compared adults wearing a spring-levered Accusplit pedometer, a piezoelectric Omron HF-100, and a SW accelerometer [12]. The SW produced negligible variance over all speeds and counts were virtually identical to manual counts. In pediatric patients, excellent accuracy and precision of the SW have been reported for treadmill walking speeds up to 1.8 m/sec [13].

In a review of pedometers and accelerometers in 2010, the SW was reported to be the most accurate pedometer ever designed for walking and is capable of capturing actual strides taken to within $\pm$ 3% for speeds from 0.45 to 2.2 m/sec [14, 15]. Thus, the StepWatch device has one of the highest levels of accuracy for detecting the movement of stepping across walking speeds of the commercially available monitors today. This high level of accuracy and ability to be individually calibrated to slow and/or unusual walking patterns makes the StepWatch ideally suited for populations, such as cerebral palsy, with various pathological gait patterns. The individualized calibration of the SW to the unique and changing walking pattern of the patient makes this device suitable for pre- and post-testing of interventions expected to impact walking activity levels and intensity.

Mudge and Susan examined the relationship of four clinical measures of walking capacity and community walking performance in patients with chronic stroke using the SW, a two-dimensional accelerometer [16]. Participants completed clinical walking tests and then wore a SW activity monitor for a three-day period. The authors reported moderate to high correlations between most SW variables and both the 6MWT and 10-meter walk test. Findings suggested that SW monitoring was able to capture aspects of walking performance that were not able to be measured in the clinical setting, including diverse behavioral, social, environmental and personal factors, such as things like motivation, neighborhood accessibility, and family lifestyle [16].

To date, the relationship between clinical measures of walking activity (capacity) to directly measured community walking (performance) as measured with the SW has not been examined in children with CP. Such information has implications for rehabilitation management, by focusing the use of clinical measures that have the strongest relationship to day-to-day walking and potentially meaningful participation of the child in daily life. The aim of the study is to examine the relationship of clinic-based walking capacity measures to community-based walking activity as measured with the StepWatch (SW) in ambulatory children with CP.

2. Methods

A secondary analysis of data collected within a cross-sectional cohort study examining the relationships of motor capacity to participation in daily life in ambulatory children with CP was conducted. With local institutional review board (IRB) approval, the recruitment strategies and summary of participant recruitment has been previously published [17]. A total of 128 participants were enrolled and were considered eligible for the study with the inclusion criteria of: (1) Gross Motor Functional Classification System (GMFCS) levels I to III (34% level I, 42% level II, 24% level III), (2) between the ages of 2 to $<$ 10 years, and (3) diagnosis of CP. Participants had either spastic unilateral motor involvement ( $n=$ 70) or spastic bilateral diplegia/quadriplegia ( $n=$ 58). Participants were excluded if there had (1) visual impairments limiting independent mobility, (2) lower extremity injection therapy (spasticity treatment) within the last 3 months, (3) uncontrolled seizure disorder affecting independent walking, and (4) neurological or orthopedic surgery within the last 6 months. Once informed consent/assent was obtained, demographic and clinical motor function information was collected during a single study visit. Participant characteristics including age, sex, cognition and functional motor level were sampled through interview and direct observation. Functional motor skills were classified using the GMFCS [18].

All participants had the following measures administered by the third author, KB (physical therapist, with $>$ 25 years combined research/clinical experience): one-minute walk test (1MWT), 6MWT, Gross Motor Function Measure (GMFM-E; walk, run and jump score). The Activity Scale for Kids Performance questionnaire (ASKp-30) was completed by a parent.

Walking activity capacity was captured with the 1MWT [19, 20, 21] and the 6MWT [22, 23] which are defined as the distance in meters walked in 1 and 6 minutes, respectively. Both measures were collected within a single 6MWT trial session, to prevent increased burden for the child. The 1MWT was documented due to clinicians anticipating some children would not be able to complete the full 6MWT due to compliance, fatigue, cognition, and/or developmental level. However, all children included in the analysis were able to complete both measures.

The 1MWT and 6MWT were performed on a flat, carpeted floor over a 20-meter oval track (10 meters/ side) of which no child demonstrated difficulty with dizziness as the set up replicated their typical clinical testing setting. Participants received verbal instructions to walk around the oval track as fast as they could (no running) for 6 minutes. The distance walked in meters for 1 and 6 minutes was recorded to the nearest meter. The Gross Motor Function Measure-66-Item Set (GMFM-66-IS), the validated gold standard for measuring gross motor function for children with CP, was administered as an indirect clinical measure of walking activity capacity [24]. In order to decrease the burden of testing all 66 items; the GMFM-66-IS was developed to allow valid measurement of the GMFM-66 score from a subset of items. A set of three decision items were administered to determine which of the four-item sets to employ with the GMFM-E (dimension E, walk/run/jump) score then calculated using the Gross Motor Ability Estimator software [24, 25, 26].

The ASKp-30 was developed and validated by Young and colleagues in 2000 to examine the physical activity performance of youth age 5–15 years in daily life [27]. The ASK-p30 was completed by the parents to capture physical activity in daily life. The performance versions of this measure were developed to query what a youth ‘usually does,’ taking into account the day-to-day life experiences and environment in which the child has functioned over the past seven days. Parents completed the ASKp-30 after their child had worn the SW monitor for seven days, consistent with protocol of previous study [3], and were returned to the study team by mail. ASKp-30 total summary score was computed to sample physical activity.

Direct measurement of community walking activity (performance) was captured with the SW accelerometer (Modus: https://modushealth.com/) worn on the ankle by participants. The SW is a two-dimensional device developed to detect when the heel leaves the ground and is the size of a pager (75 $\times$ 50 $\times$ 20 mm). The SW monitor has been evaluated in chronic stroke patients against three-dimensional gait analysis and footswitches in indoor and outdoor walking conditions, showing intraclass correlation coefficients (ICCs) of 0.989 for total step count [27]. Test-retest reliability of the SW in stroke patients over a three-day period demonstrated high ICCs for all SW outputs (0.830–0.989) but were lowest for the one-day monitoring period [28]. Average step count per day, highest step rate in 1 minute, highest step rate in 5 minutes, and peak activity index were reported to have good test-retest reliability [28]. The test-retest reliability of the SW has also been validated in the pediatric population for both typically developing youth and ambulatory youth with CP [29, 30].

Table 1
Study sample characteristics

Demographics ( $n=$ 128 total participants)
	n (%)
Females	52 (41%)
GMFCS
Level I	44 (34%)
Level II	54 (42%)
Level III	30 (23%)
Unilateral motor impairment	70 (55%)
Bilateral motor impairment	58 (45%)
	Years (SD)	Range
Age, mean	6.1 (2.3)	2.2–9.9
Clinical tests
	Mean (SD)	Range
1MWT (meter)	57.7 (25.3)	8–152
6MWT (meter)	331.9 (151.9)	28–920
GMFM-E	66.6 (11.3)	40–100
ASKp-30 summary score	53.6 (24.8)	10.7–99
StepWatch output
	Mean (SD)	Range
Average steps per day	5043 (2650)	433–13948
% of waking time spent walking	0.48 (0.14)	0.1–0.8
Number of steps at $>$ 30 steps/minute	2214 (1935)	18–9473
Highest step rate in 1 minute (steps/minute)	60 (11)	29–83
Highest average step rate in 60 minutes (steps/minute)	19 (8)	3–39
Peak activity index (steps/minute)	42 (13)	10–61

Participants were instructed to wear the SW laterally on their left ankle with a knit cuff during all waking hours (except bathing or swimming). For all participants, a total of 5 days of walking activity data (4 weekdays and 1 weekend day) were collected. Ishikawa et al. in 2013 documented that an average of 6, 5 and 4 days were required to reach stable measures of step activity with SW for GMFCS levels I, II, and III respectively [31]. Only the waking hours of the 24-hour period were analyzed with SW proprietary software to calculate the walking level and intensity variables of average number of steps per day, percentage of waking time spent walking, number of steps taken each day at rate greater than 30 steps per minute, highest step rate per 1 minute and per 60 minutes, and peak activity index (the average of the top 30 one-minute step rates).

2.1 Data analysis

Participant characteristics were summarized by descriptive statistics as appropriate for type of data. All variables were examined for normality of distribution using the Shapiro-Wilk statistic. The level of relationship between the clinic-based measures to each of the SW variables was first examined by Pearson correlation coefficient for normally distributed data. The Spearman rank correlation coefficient was used for the number of steps greater than 30 per minute, as it was not a normally distributed. A correlation of $>$ 0.70 is interpreted as high, 0.50 to 0.70 moderate, 0.30 to 0.50 low and $<$ 0.30 considered little or no correlation [32]. Backward multiple linear regressions were carried out with SW outputs as dependent variables and all significant independent variables which correlated at a level of $p<$ 0.20 initially included (1MWT, 6MWT, GMFM-E and ASKp-30), controlling for age, sex and motor impairment (unilateral or bilateral). Linear regression models were applied to test the strength of the relationship between the clinic-based measures of walking capacity and the community-based walking activity. Backwards linear regression was employed due to the relative limited number of variables to be initially entered into the model as this was a secondary data analysis.

The SW outputs measure different constructs of walking activity such as intensity, endurance, and burst of intensity. Various outputs have separate clinical applications and could be uniquely valuable to clinicians, which was the rationale for choosing the multiple regression model over a single model. Analyses were completed through SPSS Version 19 software (SPSS Inc., Chicago, IL, USA) with a significance level of $p<$ 0.05. The regression model fit was checked by change in the R ${}^{2}$ (percent of variability of dependent variables explained by model) as variables were removed with goal of optimizing the R ${}^{2}$ relative to the degrees of freedom. Criterion for removal from the backwards regression model was F greater than or equal to 0.10.

Table 2
Correlation coefficients for StepWatch outputs, and clinic-based tests

StepWatch output	1MWT	6MWT	GMFM-E	ASKp-30
Average steps per day	0.52	0.55	0.63	0.54
% waking time spent walking	0.48	0.46	0.55	0.49
Number steps $>$ 30 steps/minute‡	0.54	0.59	0.69	0.54
Highest step rate in 1 minute	0.42	0.45	0.46	0.47
Highest average step rate in 60 minutes	0.49	0.55	0.60	0.53
Peak activity index	0.53	0.57	0.62	0.59

$p<$ 0.01 for all relationships. ‡Spearman correlation coefficient used; all other correlations reported are Pearson. 1MWT: one minute walk test. 6MWT: six minute walk test. GMFM-E: Gross Motor Function Measure: run, jump, and walk score. ASKp-30: Activity Scale for Kids Performance questionnaire.

Table 3

Backwards linear regression models of community walking activity performance (StepWatch) to clinic-based walking capacity measures (1MWT, 6MWT, GMFM-E, ASKp-30) ${}^{*}$

StepWatch output/predictors	Regression coefficients	Constant	R ${}^{2}$	R ${}^{2}$ Change	Adjusted R ${}^{2}$	P
Average total steps per day
6MWT	2.85					0.095
GMFM-E	92.6	$-$ 3232.2	0.482	0.011	0.465	$<$ 0.001
% of time spent walking
1MWT	0.001					0.079
GMFM-E	0.005	0.059	0.337	0.08	0.321	$<$ 0.001
# steps $>$ 30 steps/minute
6MWT	1.82					0.08
GMFM-E	50.9	$-$ 2540.6	0.461	0.009	0.443	0.001
Highest step rate in 1 minute
6MWT	0.02					0.009
ASKp-30	0.156	50.09	0.278	0.013	0.261	0.001
Highest average step rate in 60 minutes
6MWT	0.012					0.028
GMFM-E	0.324	$-$ 4.3	0.472	0.008	0.450	$<$ 0.001
Peak Activity Index
6MWT	0.017					0.043
GMFM-E	0.334					0.018
ASKp-30	0.108	11.3	0.471	0.008	0.449	0.063

${}^{*}$ Controlled for age, sex and motor impairment distribution (unilateral or bilateral).

3. Results

A total of 128 participants (ages 2 to 10 years; 52 females) completed the protocol (Table 1). Gross motor function distribution was 34% GMFCS level I, 42% GMFCS level II, and 23% GMFCS level III. The sample was comprised of 45% with bilateral motor impairment and 55% with unilateral motor impairment.

A broad range of walking distance was documented for 1 and 6 minutes, average 58 (range 8–152 meters, SD 25) and 332 meters (range 28–920 meters, SD 152). The GMFM-E and ASKp-30 scores also varied consistent with spread of GMFCS levels (Table 1). Community walking levels (average number of steps/day, percent time walking) and intensity (average number of steps $>$ 30 steps/min, highest 1- and 60-minute step rate, peak activity index) are summarized in Table 1. Participants walked an average of 5043 steps per day (SD 2650), were walking for an average of 48% of waking time, and with a mean peak activity index of 42 steps per minute [17].

Correlations of SW variables to clinic-based measures are displayed in Table 2. Age was weakly correlated with only the percent of wake time spent walking. The 1MWT, 6MWT, GMFM-E, and ASKp-30 all showed moderate correlations with SW output of average steps per day, number of steps $>$ 30 steps/minute, and peak activity index. Highest average step rate in 60 minutes also showed moderate correlations with 6MWT, GMFM-E, and ASKp-30, whereas percentage of waking time spent walking moderately correlated only with GMFM-E. Highest step rate in 1 minute showed low correlations with all clinic-based measures.

All SW variables were significantly associated with the GMFM-E (all p values $<$ 0.02) except for highest step rate in 1 minute (Table 3). The highest step rate in 1 and 60 minutes as well as Peak Activity Index were significantly associated with the 6MWT ( $p=$ 0.009–0.04). The highest step rate for 1 minute was associated ASKp-30 ( $p<$ 0.001). Variance explained by the models (R ${}^{2}$ ) developed ranged from 0.28 to 0.48 (Table 3). This suggests the independent variables and covariates (i.e. 6MWT, GMFM-E, sex, age, etc.) explain between 28 to 48 percent of the relationship with the dependent variables of interest the community walking performance the SW variables (i.e. average steps/day).

4. Discussion

All clinic-based measures of walking capacity had low to moderate correlations with all SW outputs. However, of all clinic-based measures examined in this study, the GMFM-E demonstrated the strongest consistent relationship with SW output measures of community walking performance level and intensity. The 6MWT appears to be associated with the SW measures of high intensity walking (i.e. highest step rate in 1 and 60 minutes, Peak Activity Index).

In a similar study, clinical performance of children with CP at GMFCS levels I–II on the 6MWT was compared with community physical activity (versus walking activity) measured with a waist mounted ActiGraph accelerometer over a four-day period. Younger age, male sex, and better performance on the 6MWT were significantly associated with higher physical activity counts [33]. Wilson and colleagues documented the relationship between walking capacity (measured by the 1MWT, 6MWT, and self-selected walking speed) and walking performance (measured by the Functional Mobility Scale [FMS]) [34]. The 6MWT was an independent positive predictor of the FMS. For intermediate distances, significant associations were reported for FMS to the 1MWT, 6MWT, and self-selected walking speed. However, this only accounted for 20–27% of the variance modeled in the FMS distances [34], compared to 27–47% of the variance in walking performed explained by the models with 6MWT of our study. The difference in variance may reflect the use of different monitors (Actigraph vs SW) and/or use of a categorical outcome (FMS) to capture community walking. Both studies suggest that there are likely many other factors at play, including environment (rural vs. urban), socioeconomic status, family dynamics, individual patient characteristics, behaviors, and motivation, which may potentially influence walking activity performance across various settings.

Within this study, parental report of walking performance as assessed by the ASK-p revealed small to moderate correlations with community performance measured by SW outputs. This is in contrast with work by Chong et al., in which ABILICO-Kids scores were compared to the 1MWT and the 6MWT [35]. The ABILICO-kids was a Rasch based questionnaire specifically designed to examine the locomotion abilities of children with CP. Per parental report, the questionnaire assesses the difficulty with 10 locomotion activities (i.e. up/down stairs without railing, walking backwards, etc.) [36]. The ABILICO-Kids scores were significantly correlated with the 1MWT ( $\rho=$ 0.70, $p<$ 0.01) and the 6MWT ( $\rho=$ 0.70, $p<$ 0.01) [35]. The difference in correlation strength between the findings of Chong’s work and those of the study we conducted may be due to the inclusion of several more self-care items in the ASKp-30 as compared to the ABILOCO-Kids, therefore evaluating different skillsets of the children.

Age is hypothesized to play a role in the relationship between motor capacity and performance [2]. Our work documented that age was only weakly associated with the percent of time spent walking ( $r=$ 0.20, $p<$ 0.05). In a prospective longitudinal study of young children and adolescents with CP, changes seen in motor capacity over time did not consistently translate to changes in motor performance and that relationships between measures were stronger for toddlers than for school-age children and adolescents with CP [1]. In adults with spastic CP, 40% declined by one GMFCS level from adolescence to adult age, and slower Timed Up and Go scores were most strongly related to lower scores on the 6MWT [37]. Changes in motor capacity with age may be influenced by factors such as motivation, family/caregiver involvement, pain, fatigue, and secondary musculoskeletal issues negatively influencing walking activity.

Based on the results of this study, rehabilitation providers can use the clinical measures of walking capacity to supplement their assessment of community walking activity for children with CP. Our work suggests that if clinical assessment time is limited, the GMFM-E may serve as a surrogate measure of community walking levels and intensity. If the clinical outcome of interest is intensity of walking (i.e. for sport participation), the 6MWT has potential to adequately capture community based walking at high intensity. Published walking activity levels for children with and without CP have the potential to inform counseling of families relative their child’s expected community walking activity [3]. If a child’s community walking performance seems to be significantly less than expected based on clinical measures of walking capacity, this may prompt the care providers to explore barriers to walking activity and participation in the community and address any problematic factors identified.

Further work is warranted to clarify how particular personal, environmental, behavioral, and family factors influence this construct. For example, studying children with CP in urban vs. rural environments, different socioeconomic classes, community architectures, lifestyle and health literacy of parents, and different personality traits would help better characterize these unique influences on walking performance.

4.1 Study limitations

The results of this study should be interpreted within the context of known limitations identified. Due to secondary data analysis, the cross-sectional nature and sample size of the study, causal relationships cannot be assumed with limited covariates examined. Thus, we could not control for the differences in environmental settings that the kids encountered, leading to more variability among participants (i.e. time spent at school vs. community vs. home based on a particular family’s typical activities and lifestyle).

5. Conclusion

We aimed to examine the relationship of clinic-based walking capacity measures to community-based walking activity in ambulatory children with CP. We found that all clinic-based measures of walking capacity examined have low to moderate correlations with community walking activity and intensity as measured by the SW activity monitor. The GMFM-E has the strongest association to community walking activity and intensity in daily life, in comparison to the other clinic-based tests evaluated in this study. Relative to intensity of community walking activity performance, the 6MWT also has a significant association.

Clinically, one may consider using the SW to correlate with measures of walking capacity (i.e. 6MWT, and GMFM-E) in assessing patients’ community walking activity level and intensity. This may help supplement the current clinical standard of parental report of community-based walking activity for ambulatory children with CP. This information has the potential to inform the rehabilitation clinician of a patient’s capability for amount and intensity of community-based walking, and may assist in identifying and addressing barriers to community ambulation if the clinic-based tests and actual community walking are significantly discordant.

Footnotes

Acknowledgments

This work received funding and support from NIH K23 HD060764 and by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1RR025014.

Conflict of interest

The authors have no conflicts of interest to declare.

References

Smits

, Gorter

, van Schie

, Dallmeijer

, Ketelaar

, PERRIN+ study group. How do changes in motor capacity, motor capability, and motor performance relate in children and adolescents with cerebral palsy? Arch Phys Med Rehabil. 2014 Aug; 95(8): 1577-1584.

van Wely

, Becher

, Balemans

, Dallmeijer

. Ambulatory activity of children with cerebral palsy: which characteristics are important? Dev Med Child Neurol. 2012 May; 54(5): 436-442. doi: 10.1111/j.1469-8749.2012.04251 [doi]

Bjornson

, Zhou

, Stevenson

, Christakis

, Song

. Walking activity patterns in youth with cerebral palsy and youth developing typically. Disability & Rehabilitation. 2014; 36(15): 1279-84; PMCID: 24160855.

Bjornson

, Belza

, Kartin

, Logsdon

, McLaughlin

. Ambulatory Physical Activity Performance in Youth With Cerebral Palsy and Youth Who Are Developing Typically. Physical Therapy. 2007; 87(3): 248-57. doi: 10.2522/ptj.20060157; PMCID: PMCID: PMC1852474.

Bjornson

, Song

, Zhou

, Coleman

, Myaing

, Robinson

. Walking stride rate patterns in children and youth. Pediatr Phys Ther. 2011; 23(4): 354-63. doi: 10.1097/PEP.0b013e3182352201. PubMed PMID: 22090075.

Mitchell

, Ziviani

, Boyd

. Habitual Physical Activity of Independently Ambulant Children and Adolescents With Cerebral Palsy: Are They Doing Enough? Physical Therapy. 2015; 95(2): 202.

Mitchell

, Ziviani

, Boyd

. Characteristics associated with physical activity among independently ambulant children and adolescents with unilateral cerebral palsy. Dev Med Child Neurol. 2015 Feb; 57(2): 167-174.

Keawutan

, Bell

, Davies

PSW

, Boyd

. Systematic review of the relationship between habitual physical activity and motor capacity in children with cerebral palsy. Research in Developmental Disabilities. 2014; 35(6): 1301-9. doi: http://dx.doi.org/10.1016/j.ridd.2014.03.028.

Keawutan

, Bell

, Oftedal

, Davies

PSW

, Boyd

. Validation of Accelerometer Cut-Points in Children With Cerebral Palsy Aged 4 to 5 Years. Pediatric Physical Therapy. 2016; 28(4): 427-34. doi: 10.1097/pep.0000000000000291. PubMed PMID: 00001577-201628040-00018.10.

10.

World Health Organization. International Classification of Functioning, Disability and Health (ICF). 2001.

11.

Mitre

, Lanningham-Foster

, Foster

, Levine

. Pedometer Accuracy in Children: Can We Recommend Them for Our Obese Population? Pediatrics. 2009; 123(1): e127-e31. PubMed PMID: 1426

12.

Foster

, Lanningham-Foster

, Manohar

, McCrady

, Nysse

, Kaufman

. Precision and accuracy of ankle-worn accelerometer-based pedometer in step counting and energy expenditure. Prev Med. 2005; 41: 778-783.

13.

Bjornson

, Yung

, Jacques

, Burr

, Christakis

. StepWatch stride counting: accuracy precision, and prediction of energy expenditure in children. Journal of Pediatric Rehab. 2012; 5(1): 7-14. doi: 10.3233/PRM-2011-0186 [doi]

14.

Karabulut

, Crouter

, Bassett

. Comparison of two waist-mounted and two ankle-mounted electronic pedometers. Eur J Appl Physiol. 2005; 96(3): 334-5. PubMed PMID: 1358.

15.

Bassett

, John

. Use of pedometers and accelerometers in clinical populations: Validity and reliability issues. Physical Therapy Reviews. 2010 June. doi: 10.1179/1743288X10Y.0000000004.

16.

Mudge

, Susan

. Timed Walking Tests Correlate with Daily Step Activity in Persons with Stroke. Archives of Physical Medicine and Rehabilitation. 2009; 90(2): 296-301.

17.

Bjornson

, Zhou

, Stevenson

, Christakis

. Relationship of stride activity and participation in mobility-based life habits among children with cerebral palsy. Arch Phys Med Rehabil. 2014 Feb; 95(2): 360-368. doi: 10.1016/j.apmr.2013.10.022 [doi]

18.

Palisano

, Rosenbaum

, Walter

, Russell

, Wood

, Galuppi

. Development and reliability of a system to classify gross motor function in children with cerebral palsy. Dev Med Child Neurol. 1997 Apr; 39(4): 214-223. doi: 10.1111/j.1469-8749.1997.tb07414.x [doi]

19.

Wade

. Timed Walking Tests: Measurement in neurological rehabilitation: Oxford University Place; 1992. p. 78-79.

20.

Scholtes

, Dallmeijer

, Rameckers

, Verschuren

, Tempelaars

, Hensen

, et al. Lower limb strength training in children with cerebral palsy – a randomized controlled trial protocol for functional strength training based on progressive resistance exercise principles. BMC Pediatr. 2008 Oct 8; 8: 41-2431-8-41. doi: 10.1186/1471-2431-8-41 [doi]

21.

Hassani

, Krzak

, et al. One-minute walk and modified timed up and go tests in children with cerebral palsy: performance and minimum clinically important differences. Dev Med Child Neurol. 2014; 56(5): 482-489.

22.

, Yin

, Yu

, Tsang

, So

, Wong

, et al. The six-minute walk test in healthy children: reliability and validity. Eur Respir J. 2005 Jun; 25(6): 1057-1060. doi: 10.1016/j. [doi]

23.

Nsenga Leunkeu

, Shephard

, Ahmaidi

. Six-minute walk test in children with cerebral palsy gross motor function classification system levels I and II: reproducibility, validity, and training effects. Arch Phys Med Rehabil. 2012 Dec; 93(12): 2333-2339. doi: 10.1016/j.apmr.2012.06.005 [doi]

24.

Chrysagis

, Skordilis

, Koutsouki

. Validity and clinical utility of functional assessments in children with cerebral palsy. Arch Phys Med Rehabil 2014 Feb; 95(2): 369-374. doi: 10.1016/j.apmr.2013.10.025 [doi]

25.

Russell

, Avery

, Walter

, Hanna

, Bartlett

, Rosenbaum

, et al. Development and validation of item sets to improve efficiency of administration of the 66-item Gross Motor Function Measure in children with cerebral palsy. Dev Med Child Neurol. 2010 Feb; 52(2): e48-54. doi: 10.1111/j.1469-8749.2009.03481.x [doi]

26.

Russell

, Rosenbaum

, Avery

, Lane

. Gross Motor Function Measure (GMFM-66 and GMFM-88). User’s Manual, 2002.

27.

Young

, Williams

, Yoshida

, Wright

. Measurement properties of the activities scale for kids. J Clin Epidemiol. 2000 Feb; 53(2): 125-137.

28.

Mudge

, Stott

. Test – retest reliability of the StepWatch Activity Monitor outputs in individuals with chronic stroke. Clin Rehabil. 2008 Oct–Nov; 22(10-11): 871-877. doi: 10.1177/0269215508092822 [doi]

29.

Bjornson

, Song

, Zhou

, Coleman

, Robinson

. Walking stride rate patterns in children and youth. Pediatric Physical Therapy. 23: 354-363.

30.

O’Neil

, Fragala-Pinkham

, Lennon

, George

, Forman

, Trost

. Reliability and Validity of Objective Measures of Physical Activity in Youth With Cerebral Palsy Who Are Ambulatory. Phys Ther. 2015 Jun 18. doi: ptj.20140201.

31.

Ishikawa

, Kang

, Bjornson

, Song

. Reliably measuring ambulatory activity levels of children and adolescents with cerebral palsy. Arch Phys Med Rehabil. 2013 Jan; 94(1): 132-137. doi: 10.1016/j.apmr.2012.07.027 [doi]

32.

Hinkle, Wiersma, and Jurs. Applied Statistics for the Behavioral Science (5th ed). 2003.

33.

Mitchell

, Ziviani

, Boyd

. Variability in measuring physical activity in children with cerebral palsy. Med Sci Sports Exerc. 2015 Jan; 47(1): 194-200. doi: 10.1249/MSS.0000000000000374 [doi]

34.

Wilson

, Mackey

, Stott

. How does the functional mobility scale relate to capacity-based measures of walking ability in children and youth with cerebral palsy? Phys Occup Ther Pediatr. 2014 May; 34(2): 185-196. doi: 10.3109/01942638.2013.791917 [doi]

35.

Chong

, Mackey

, Broadbent

, Stott

. Relationship between walk tests and parental reports of walking abilities in children with cerebral palsy. Arch Phys Med Rehabil. 2011 Feb; 92(2): 265-270. doi: 10.1016/j.apmr.2010.10.010 [doi]

36.

Caty

, Arnould

, Thonnard

, Lejene

. ABILOCO-Kids: a Rasch-built 10-item questionnaire for assessing locomotion ability in children with cerebral palsy. J Rehabil Med. 2008 Nov; 40(10): 823-830. doi: 10.2340/16501977-0267.

37.

Maanum

, Jahnsen

, Froslie

, Larsen

, Keller

. Walking ability and predictors of performance on the 6-minute walk test in adults with spastic cerebral palsy. Dev Med Child Neurol. 2010 Jun; 52(6): e126-32. doi: 10.1111/j.1469-8749.2010.03614.x [doi]

Are clinic-based walking measures associated with community walking activity in children with cerebral palsy?

Abstract

PURPOSE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Methods

Table 1 Study sample characteristics

Table 2 Correlation coefficients for StepWatch outputs, and clinic-based tests

4. Discussion

4.1 Study limitations

5. Conclusion

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
Study sample characteristics

Table 2
Correlation coefficients for StepWatch outputs, and clinic-based tests