Measuring Upper Extremity Activity of Children With Unilateral Cerebral Palsy Using Wrist-Worn Accelerometers: A Pilot Study

Abstract

Importance: Children with unilateral cerebral palsy (UCP) have poor strength and movement control on one side of their body, leading to impaired bimanual coordination skills.

Objective: To compare duration and intensity of all-day habitual movement of the dominant and nondominant upper extremities (UEs) in typically developing (TD) children and children with UCP.

Design: Two-group observational study.

Setting: Children’s naturalistic settings.

Participants: Convenience sample of 9 TD children and 9 children with UCP.

Intervention: Children wore accelerometers on both wrists all day and night for 1 wk.

Outcomes and Measures: We compared the extent of asymmetry in bilateral arm use (intensity and duration) between the 2 groups.

Results: Compared with TD children who use both UEs equally, children with UCP were more likely to use their dominant or unaffected UE than their nondominant or affected UE during daily activities. There were no differences between groups in dominant UE activity. However, children with UCP engaged in lower levels of moderate to vigorous activity and greater levels of light activity with their nondominant or affected UE than their TD peers.

Conclusions and Relevance: Wrist-worn accelerometry can provide objective information on real-world habitual activity with both arms in children. Accelerometers are nonintrusive, easy to use, and well tolerated by children, and they allow prolonged monitoring of UE activity outside therapeutic contexts. Occupational therapists can use wrist-worn accelerometers as sensitive tools to assess asymmetries in UE use at baseline and as an outcome measure to assess the efficacy of behavioral interventions and carryover into real-world settings among children with UCP.

Plain-Language Summary: This pilot study provides promising evidence that supports the use of wrist-worn accelerometry as an accurate, easy-to-use, and objective assessment tool for children with unilateral cerebral palsy (UCP) to detect asymmetries in bilateral real-world arm activity at baseline and after intensive occupational therapy interventions to improve arm function. The authors used wrist-worn accelerometry for one week with 9 typically developing (TD) children and 9 children with UCP to compare dominant or unaffected versus nondominant or affected upper extremity (UE) use for intensity and duration of activity. Compared with TD children, children with UCP had lower relative intensity of activity in the nondominant UE than the dominant UE. Wrist-worn accelerometers seem to be a sensitive measure to detect asymmetries in bilateral all-day UE use in children with UCP. The findings have implications for the use of wrist-worn accelerometers as an outcome measure to assess the efficacy of intensive therapies to improve real-world affected UE activity and bimanual function among children with UCP.

The findings of this pilot study support the use of wrist-worn accelerometry as an accurate, easy-to-use, and objective assessment tool for children with unilateral cerebral palsy to detect asymmetries in bilateral real-world arm activity and to use at baseline and after intensive occupational therapy interventions to improve arm function.

Bimanual coordination is the ability to coordinate the use of both upper extremities (UEs) simultaneously in a controlled and organized manner. For instance, self-care activities, such as using a fork and knife, tying a shoelace, or opening a bottle of water; academic activities, such as opening a pencil box or stabilizing a piece of paper while writing; and recreational activities, such as playing catch or baseball with peers, require coordinated and synchronous use of both hands (Abd El Wahab & Hamed, 2015; Do et al., 2016; Gordon et al., 2007; Wang et al., 2021; Woytowicz et al., 2016). Bimanual function is strongly associated with independence in activities of daily living (ADLs) and participation across a variety of physical and social contexts, ultimately contributing to a better quality of life (Arnould et al., 2004; Fedrizzi et al., 2003; Poitras et al., 2021).

In typical development, children are able to engage in skillful bimanual tasks that require asymmetrical and differentiated use of both UEs on the basis of task and environmental requirements by the end of the 2nd yr (Babik & Michel, 2016; Goble et al., 2005; Kimmerle et al., 2010; Michel et al., 2016; Shumway-Cook & Woollacott, 2022; Woytowicz et al., 2016). In contrast to age-matched neurotypical peers, children with unilateral cerebral palsy (UCP) have significant impairments in bimanual coordination (Charles & Gordon, 2006; Gordon, 2010; Sköld et al., 2004; Utley & Steenbergen, 2006). Early damage to unilateral neural substrates leads to poor strength, impaired muscle tone, deficits in movement control and precision, motor planning difficulties, as well as tactile and proprioceptive disturbances on one side of the body, termed the paretic or affected side (Abd El Wahab & Hamed, 2015 ; Arnould et al., 2014; Bleyenheuft & Gordon, 2013). Such deficits in sensorimotor control of one UE lead to obvious difficulties with bimanual tasks that require skilled and coordinated use of both arms (Charles & Gordon, 2006; Michel et al., 2016; Wang et al., 2021). Moreover, difficulties with bimanual coordination also stem from brain damage extending to the neural structures that underlie bimanual coordination and motor planning skills (Charles & Gordon, 2006; Michel et al., 2016; Wang et al., 2021).

As a result of these impairments and the developmental nature of the condition, children with UCP learn to compensate over time by relying on their relatively unaffected UE (which becomes their dominant UE) and do not use their more affected UE (nondominant UE) during daily activities, leading to developmental nonuse and disregard (Bleyenheuft & Gordon, 2013; Clouatre et al., 2020; Eliasson et al., 2022). Developmental disregard is defined as learning to use the unaffected or dominant UE to successfully perform functional patterns by using compensatory movements, body mechanics, and external objects to assist with task completion while continuing to neglect the affected or nondominant UE (Hoare et al., 2007; Taub et al., 2006). From a clinical perspective, it is crucial to assess the extent of bimanual asymmetries during daily activities among children with UCP compared with typically developing (TD) children to better plan interventions that encourage use of the nondominant UE in naturalistic settings and also to assess intervention efficacy in addressing these asymmetries.

Current measures to assess bimanual UE use and function of children with UCP include standardized clinician-delivered tests (Davids et al., 2006; Eliasson et al., 2006; Tedesco et al., 2015), observational measures of UE use (Speth et al., 2013), and caregiver-report questionnaires (Arnould et al., 2004; Elvrum et al., 2016; James et al., 2014; Wagner & Davids, 2012). Although these measures are valid and reliable for use with children with UCP, they have significant limitations. Standardized and observational tests rely on measurement of the child’s function at a single timepoint, are usually conducted in controlled clinic- or lab-based settings, and may not reflect the child’s habitual and spontaneous UE use across a variety of real-world everyday contexts. Children with UCP may perform significantly differently in lab-based settings compared with naturalistic daily settings (James et al., 2017; Lai et al., 2019; Sokal et al., 2015). Although questionnaire-based measures obtain information from caregivers about the child’s UE use through the day, they may be limited by parental recall and do not provide a quantitative measure of children’s daily nondominant UE use (Klingels et al., 2010; Wagner & Davids, 2012). Therefore, quantitative, objective, and accurate measures are needed that assess habitual bilateral UE use among children with UCP.

Accelerometry has predominantly been used to assess habitual use of the affected UE in the population of adults who have experienced a stroke (Bailey & Lang, 2013; de Niet et al., 2007; Lang et al., 2017; Poitras et al., 2022). Wrist-worn accelerometers offer the advantage of being compact, lightweight, and economical devices that are sensitive to even small movements in different directions and provide highly accurate objective estimates of UE activity (Beani et al., 2019; Hayward et al., 2016; Lang et al., 2017; Migueles et al., 2017; Uswatte et al., 2000). They are also nonintrusive and able to collect information on both duration and intensity of UE activity throughout the day. Although accelerometers have been validated for use with adults with stroke (Braito et al., 2018; Noorkõiv et al., 2014; Uswatte et al., 2005; van der Pas et al., 2011), these data cannot be assumed to hold true for children with UCP because (1) in contrast to adults with stroke, children with UCP have never experienced typical movement patterns on their affected or nondominant side (Dawe et al., 2019; Deluca et al., 2006), (2) children engage in more nonfunctional movements with their UEs during the day compared with adults, and (3) children may demonstrate potential compliance issues with wearing the activity monitors for prolonged periods of data collection (∼1 wk).

As a result, normative data from neurotypical children are needed to aid interpretation of wrist-worn accelerometry results from children with UCP (Dawe et al., 2019; Sokal et al., 2015). Although few studies have used accelerometers as an outcome measure to assess changes in asymmetry between dominant and nondominant UEs among children with UCP after behavioral interventions (Coker-Bolt et al., 2017; Goodwin et al., 2020; Gordon et al., 2007; Hwang & Kwon, 2020), foundational research on the sensitivity of accelerometers to measure asymmetry in arm movement among children as well as normative comparisons of bimanual UE activity between TD children and children with UCP are scarce (Beani et al., 2019; Dawe et al., 2019; Goodwin et al., 2020; Hollis et al., 2021; Hoyt et al., 2020; Sokal et al., 2015). A majority of these studies assessed UE activity during a single session of UE activity or over a short duration of 1 to 3 days. For example, a recent study compared bilateral arm activity during 20 min of seated play in a controlled lab setting among children and adolescents who were TD and those with UCP using accelerometry and video-based measures (Dawe et al., 2019). They found good agreement (81%–89%) between accelerometer and video-based measures of arm function in both groups. However, the authors recommended replication of their findings, data collection over a longer period of time, and extension of their findings to naturalistic settings (Dawe et al., 2019).

Our study replicates previous work and expands it to comparing all-day habitual movement of dominant and nondominant UEs over 1 wk between TD children and children with UCP measured using wrist-worn activity monitors. We hypothesized that wrist-worn accelerometers would be a viable and easy-to-implement means of collecting quantitative measures of children’s habitual UE activity. We also hypothesized that children with UCP will have significantly lower duration and intensity of activity with their nondominant UE but not their dominant UE compared with TD children. These data will serve as a foundation for our ongoing and future intervention-related work to assess the efficacy of behavioral interventions in improving affected UE use in children with UCP.

Method

Study Design

This two-group observational study compared differences in the habitual activity levels of the dominant and nondominant UEs measured over 1 wk among TD children and children with UCP. Children were asked to wear accelerometers throughout their routine day except when they were in prolonged contact with water. The study received approval from the Institutional Review Board at the University of Connecticut.

Participants

Nine TD children and 9 children with a medical diagnosis of UCP ages 3 to 12 yr were recruited through convenience sampling (Table 1). Children were recruited through fliers distributed at events in the local community, by contacting families from a university database of community members who had volunteered to be contacted for studies related to child development, and through an annually held summer camp for children with CP. Interested families contacted us and underwent a screening procedure that assessed children’s eligibility for study participation. Children were excluded if their parents indicated that they had sensory sensitivities that would interfere with wearing the activity monitors on their wrists. We used the Manual Ability Classification System (MACS; Eliasson et al., 2006) to assess children’s use of their hands to manipulate objects during ADLs. The MACS assesses children’s spontaneous ability to use their hands to handle different objects and the amount of assistance or adaptation they require to perform daily activities. Scores range from Level I to Level V, with higher levels indicating more severe impairment. Children with UCP in our study had mild to moderate levels of impairment as determined by MACS scoring (see Table 1). We obtained parental permission, as well as oral or written assent from the children, before commencement of the study.

Table 1.

Participant Demographics

Characteristic	Sample		Between-Group Comparisons (p)
Characteristic	TD Children (n = 9)	Children With UCP (n = 9)	Between-Group Comparisons (p)
Age, yr, M (SD)	7.37 (1.7)	5.9 (1.8)	.11 (ns)
Gender			.62 (ns)
Male	4	3
Female	5	6
Dominance			.009*
Left	0	3
Right	9	6
MACS score, M (SD)	—	2.14 (0.66)	—
Race			—
White non-Hispanic	8	6
White Hispanic	0	1
White Asian	0	1
Mixed	1 a	1 b

Note. Dash indicates not applicable. MACS = Manual Ability Classification System; ns = not significant; TD = typically developing; UCP = unilateral cerebral palsy.

Asian and White.

Korean, Puerto Rican, Irish, Polish.

*p < .05.

Measures

Children wore ActiGraph (Pensacola, FL; https://actigraphcorp.com/) activity monitors (wGT3X-BT) on both wrists for 1 wk (both awake and sleep hours). Children were asked to remove the monitors only before prolonged contact with water (taking a shower, going swimming, etc.). The wGT3X-BT accelerometer is a small (4.6 × 3.3 × 1.5 cm), lightweight (19 g), three-axis accelerometer similar to a wristwatch that collects raw acceleration data in all three directions, with a dynamic range of ±8 gravitational units. The accelerometers collected data at a sampling frequency of 30 Hz. On the basis of previous research, we recommended wear-time guidelines of a minimum of 4 days (3 weekdays and 1 weekend day), for a minimum period of 7 to 10 hr/day (Airlie et al., 2022; Chinapaw et al., 2014; Migueles et al., 2017).

Caregivers were asked to maintain a daily sleep and activity monitor wear-time log to document their child’s sleep and wake times as well as times when their child removed the monitors. Activity monitors and logs were collected from families at the end of 1 wk, and all data were downloaded and processed using ActiLife software. In addition, raw data from the accelerometers were processed using custom-designed code to calculate the extent of asymmetry in the duration and intensity of activity in both UEs between TD children and children with UCP. For both analyses, periods of nonwear and sleep were excluded from the analyses. Periods of sleep and nonwear were detected on the basis of visual inspection of the data in the ActiLife software and corroborated with information from caregiver logs.

Accelerometry Metrics

From Custom Software

Raw acceleration data were used to calculate activity counts (1 count = 0.001664 g, i.e., 0.0163072 m/s²; Hawk, 2015; Peach et al., 2014). Activity counts across all three axes were summed to calculate total vector magnitude (VM) counts ( $V M = \sqrt{a_{x}^{2} + a_{y}^{2} + a_{z}^{2}}$ , where a_x, a_y, and a_z are the accelerations or activity counts in the x, y, and z directions, respectively). Epoch length chosen for downsampling the data for this analysis was 1 s. We calculated three metrics to compare the duration and intensity of use of dominant and nondominant UEs across both groups: use ratio, magnitude ratio, and bilateral magnitude (Bailey et al., 2014; Coker-Bolt et al., 2017; Goodwin et al., 2020; Lang et al., 2017). To calculate the use ratio (indicative of the relative duration of use of nondominant vs. dominant UE), we summed all epochs with VM counts of 1 or more for each UE and calculated the ratio of the hours of use of nondominant versus dominant UE. A use ratio of 1 indicates equal use of both arms, <1 indicates greater use of the dominant UE, and >1 indicates greater use of the nondominant UE. The magnitude ratio (indicative of the relative intensity of nondominant vs. dominant UE activity) was calculated per epoch using VM counts for the dominant and nondominant UEs (see Lang et al., 2017). A magnitude ratio close to 0 indicates similar use of both UEs, a value <0 indicates greater use of the dominant UE, and a value >0 indicates greater use of the nondominant UE. Bilateral magnitude summed total VM counts of both UEs per epoch, and greater values indicate greater activity and use of both arms (Bailey et al., 2014; Lang et al., 2017). Given the skewness of the time-series data generated through calculations of magnitude ratio and bilateral magnitude, we report median values and interquartile ranges for these two variables. Our three dependent variables for these analyses are mean values of use ratio, median values of magnitude ratio, and median values of bilateral magnitude.

From ActiLife Software

The average VM counts/min for both dominant and nondominant UEs were calculated. We also used the in-built Freedson children algorithm (Freedson et al., 2005) to determine the average percentage of time that children spent in sedentary, light, and moderate to vigorous activity. The Freedson algorithm classifies vertical axis activity counts calculated over 60-s epochs into time spent (in minutes) in varying intensities of activity (sedentary, 0–149 counts; light activity, 150–499 counts; and moderate to vigorous activity, >500 counts; Freedson et al., 2005). Our dependent variables for this analysis include percentage of time spent in sedentary, light, and moderate to vigorous activity during the data collection period.

Statistical Analyses

Multivariate analyses of variance (MANOVAs) were used to analyze group differences in accelerometry metrics between TD children and children with UCP using IBM SPSS Statistics (Version 28). We used group (TD and UCP) and side (dominant and nondominant) as between-subjects factors. Post hoc testing was done using univariate analyses of variance. Statistical significance was set at p < .05. Effect sizes are reported as standardized mean difference (SMD) values using Hedge’s g (Hedges, 1981) along with their 95% confidence intervals (CIs). The SMD values were classified as small (0.2–0.49), medium (0.5–0.79), or large (≥0.8; Cohen, 1988; Hedges, 1981).

Results

All (100%) of the children with UCP (n = 9) and 89% (n = 8) of the TD children had activity data from 4 days (3 weekdays and 1 weekend day). For 1 TD child in the study who did not meet wear-time requirements, we considered 4 weekdays of data. Data from both UEs were available for 77.7% (n = 7) of the children with UCP and 100% (n = 9) of the TD children. The TD group had a bias for right-handed children (see Table 1).

Between-Groups Differences in Bilateral UE Use

The MANOVA for bilateral UE use indicated a significant main effect of group. We found statistically significant large group differences for the median magnitude ratio, F(1, 13) = 10.20, p = .007, η_p ² = 0.44, but not for the mean use ratio and median bilateral magnitude values (Table 2). Children with UCP were significantly more likely to use their dominant UE (indicated by more negative values on the magnitude ratio) than TD children, who tended to use both UEs equally. The relative duration of use of the nondominant and dominant UEs (use ratio) in both groups did not differ significantly. Although the median bilateral magnitude values were lower in the UCP group than in the TD group, these differences did not reach statistical significance (see Table 2).

Table 2.

Accelerometry-Based Estimates of Habitual UE Activity

Dependent Variable	Children With UCP	TD Children	p	ES Estimates [95% CI]
Activity in nondominant and dominant UEs
Magnitude ratio, Mdn (IQR)	−0.39 (0.24)	−0.04 (0.08)	.007*	−1.90 [−3.12 to −0.68]
Bilateral magnitude (activity counts), Mdn (IQR)	402.05 (243.91)	594.88 (128.05)	.35	−0.95 [−2.02 to 0.12]
Use ratio, M (SD)	0.98 (0.01)	0.99 (0.01)	.315	−0.94 [−2.01 to 0.13]
Activity in nondominant UE, M (SD)
% time sedentary	28.07 (14.62)	18.38 (7.30)	.061	0.80 [−0.16 to 1.76]
% time in light activity	15.59 (5.26)	10.79 (2.67)	.026*	1.10 [0.11 to 2.09]
% time in moderate to vigorous activity	56.33 (19.12)	70.82 (8.56)	.039*	−0.93 [−1.90 to 0.04]
VM counts/min	2,091.60 (975.05)	3,811.96 (779.93)	<.001*	−1.86 [−2.96 to −0.75]
Activity in dominant UE, M (SD)
% time sedentary	16.50 (8.17)	15.72 (5.92)	.670	0.11 [−0.88 to 1.09]
% time in light activity	11.76 (6.99)	10.59 (3.20)	.474	0.21 [−0.78 to 1.20]
% time in moderate to vigorous activity	71.74 (15.00)	73.69 (8.08)	.561	−0.16 [−1.15 to 0.83]
VM counts/min, M (SD)	3,608.08 (1,301.12)	4,126.63 (756.20)	.251	−0.48 [−1.48 to 0.52]

Note. Activity in dominant and nondominant UEs is expressed as the percentage duration of time spent in specific types of activities. ES = effect size estimates expressed as standardized mean difference values; 95% CI = 95% confidence interval; IQR = interquartile range; TD = typically developing; UCP = unilateral cerebral palsy; UE = upper extremity; VM = vector magnitude.

*p < .05.

The MANOVA for activity type (sedentary, light, and moderate to vigorous activity) indicated significant main effects of group, F(5, 20) = 5.37, p = .003, η_p ² = 0.57, and dominance F(5, 20) = 5.29, p = .003, η_p ² = 0.57, as well as a significant Group × Dominance interaction, F(5, 20) = 4.17, p = .009, η_p ² = 0.51. Post hoc testing indicated that children with UCP had significantly lower levels of average VM counts/min on the nondominant UE than did the TD children. In terms of activity type, we found no significant differences between groups for activity on the dominant UE. However, children with UCP engaged in lower levels of moderate to vigorous activity but greater levels of light activity on their nondominant side compared with TD children (see Table 2). Both of these findings had large effect sizes (see Table 2).

Within-Group Differences Between Dominant and Nondominant UE Use

For children with UCP, we found significant, large differences between the nondominant and dominant UEs for the time spent in sedentary activity, moderate to vigorous activity, and average VM counts/min (ps = .003–.03). Children with UCP had greater sedentary bouts, lower bouts of moderate to vigorous activity, and lower VM counts on their nondominant than their dominant UE. No such interlimb differences were significant in the TD group (ps > .05).

Discussion

This study assessed differences in duration and intensity of bilateral UE activity between TD children and children with UCP. Per our hypothesis, we found that wrist-worn accelerometry is a feasible, well-tolerated, and viable method of obtaining objective information on children’s habitual use of their UEs in real-world settings, as indicated by high levels of compliance. At the outset, we also hypothesized that children with UCP would have significantly lower duration and intensity of activity for their nondominant UE but not for their dominant UE compared with TD children. In line with our hypothesis, we found large between-groups differences in relative intensity of bilateral UE activity. Children with UCP tended to use their dominant or nonaffected UE more intensively through the day than their nondominant or affected UE, with the nondominant UE engaging in greater levels of light activity and lower levels of moderate to vigorous activity. Contrary to our initial hypothesis, we found no significant differences between groups in the relative duration of activity in nondominant versus dominant UEs, suggesting that both TD children and children with UCP tended to move their dominant and nondominant UEs for equal amounts of time during a typical week.

We found that wrist-worn accelerometry measures indicated clear differences between bilateral UE activity in TD children and children with UCP as measured during a typical week. Our findings of a lower and more negative median magnitude ratio among children with UCP compared with TD children is in line with other studies that also reported greater use of the unaffected or dominant UE than the affected or nondominant UE in children with UCP (Coker-Bolt et al., 2017; Dawe et al., 2019; Goodwin et al., 2020; Hollis et al., 2021; Hoyt et al., 2020; Sokal et al., 2015). These changes in bilateral UE use may reflect an underlying developmental disregard phenomenon in children with UCP that is attributable to their sensorimotor impairments on the nondominant side and damage to brain areas underlying motor planning and execution (Charles & Gordon, 2006; Clouatre et al., 2020; Eliasson et al., 2003; Gordon et al., 2005; Houwink et al., 2011; Taub et al., 2004; Zielinski et al., 2014). Reduced use of their affected UE can further exacerbate asymmetrical UE motor development among children (Deluca et al., 2006; Hoare et al., 2007). This phenomenon is likened to the learned nonuse seen among adult stroke patients, for whom behavioral suppression of movements on the affected side is related to negative reinforcement from unsuccessful attempts to use the affected UE and positive reinforcement from successful use of the unaffected UE, leading to a bias toward relying on the unaffected UE for daily activities (Deluca et al., 2006; Taub et al., 2002). As a result of these changes, children with UCP develop different compensatory patterns of motor coordination during bimanual tasks (Hung et al., 2004, 2010; Islam et al., 2011 ; Sköld et al., 2004). Children tend to rely as much as possible on their dominant UE with minimum reliance on the nondominant UE for the purpose of stabilization during both simple and complex bimanual tasks. These motor strategies make tasks more segmented, uncoordinated, and associated with longer completion times (Wang et al., 2021). Similar to our study, Wang et al. (2021) found no significant differences in the amount of use of the dominant UE between children with UCP and TD children. As tasks become more challenging, for instance, in terms of dexterity requirements, the preferential use of the dominant UE has also been observed to increase (Utley & Steenbergen, 2006). Overall, negative values of the magnitude ratio may be indicative of underlying processes of developmental disregard and compensatory motor strategies involving reliance on the dominant UE during ADLs by children with UCP.

We found no differences between groups in the average use ratio indicative of the relative duration of use of the affected or nondominant and unaffected or dominant UEs. Our findings contrast with those of other studies that reported lower use ratio values among children with UCP compared with TD children (Coker-Bolt et al., 2017; Dawe et al., 2019; Goodwin et al., 2020; Hoyt et al., 2020; Sokal et al., 2015). These differences in results may reflect variations in data collection periods (1 wk vs. shorter periods), heterogeneity in the assessed sample in terms of age and impairment severity, and differences in accelerometry postprocessing algorithms used across studies. In our study, in line with other researchers (Bailey et al., 2014; Lang et al., 2017), we set a low threshold for defining activity bouts (counts ≥ 1), which may have contributed to the activity monitors detecting even the smallest movements of or children’s attempts to move the nondominant UE. This sensitivity of measurement may have contributed to fairly symmetrical relative duration of bilateral UE activity.

Second, children with UCP tend to move their nondominant UE even when they move their dominant UE, what are frequently termed mirror movements (Sokal et al., 2015). Finally, there is evidence that wrist-worn accelerometers detect both functional (purposeful activities such as reaching, grasping, manipulation, gesturing, use of UE for balancing, etc.) and nonfunctional movements (UE movement during gait) of UEs, as well as compensatory patterns of the affected UE (alternative movement patterns involving UE muscles such as proximal shoulder, scapular, and elbow muscles while performing fine motor tasks; Barth et al., 2020; Lum et al., 2020). These trends may have led to comparable durations of bilateral UE activity among children with UCP. In line with these trends, we also found no statistically significant differences between groups on median bilateral magnitude values. Possible reasons include overcompensation by children with UCP for impairments of their nondominant UE through increased use of the dominant UE, accelerometry measurements capturing nonfunctional and compensatory movements of the affected and unaffected UEs, and the existence of mirror movements in UEs during daily tasks in children with UCP, as mentioned earlier (Bleyenheuft & Gordon, 2013; Clouatre et al., 2020; Eliasson et al., 2022; Hoare et al., 2007; Sokal et al., 2015; Taub et al., 2006).

Limitations

Our pilot study was limited by a small sample size and variation in the participants’ age range. Our data may be an overestimation of UE activity because activity monitors also detect arm movements during walking and other types of gait patterns or whole-body movements that do not represent purposeful goal-directed tasks with the UEs. There were some compliance issues with wearing the monitors; interestingly, we found greater compliance among families of children with UCP than among families of TD children. In future studies, we will assess parental burden through qualitative questionnaires to better understand issues related to adherence with wearing activity monitors and will use wrist-worn accelerometry to assess the efficacy of behavioral interventions among children with UCP.

Implications for Occupational Therapy Practice

The practice of occupational therapy is geared toward improving clients’ participation and function in daily life (American Occupational Therapy Association [AOTA], 2020). Within the domain of pediatric occupational therapy for children with UCP, therapists provide interventions that are ultimately aimed at improving children’s participation and independence in ADLs, education, play, and leisure (AOTA, 2020). Conventionally, therapists use valid and reliable standardized tests and observations of functional tasks in structured settings to assess children’s movement skills and task performance. However, previous research has suggested that performance on standardized tests of motor capacity in structured lab settings among individuals with asymmetric motor deficits may not automatically translate to actual real-world use of the affected UE (Bailey et al., 2014; Coker-Bolt et al., 2017; Hoyt et al., 2020; Sokal et al., 2015). Therefore, wrist-worn accelerometry offers an excellent opportunity to obtain objective information on real-world activity of bilateral UEs outside the traditional clinic or school setting for individuals with UE asymmetries. On the basis of our findings and previous literature, we recommend that occupational therapists incorporate accelerometry-based measurements in the clinical decision-making process, both as a sensitive assessment tool that can detect asymmetries in real-world habitual spontaneous use of the UEs across a variety of environments and as an objective outcome measure to evaluate the efficacy of behavioral occupational therapy interventions to address asymmetries in UE function in children with motor impairments (Beani et al., 2019; Coker-Bolt et al., 2017; Goodwin et al., 2020; Hwang & Kwon, 2020; Trac et al., 2018; Zoccolillo et al., 2015).

This study has the following implications for occupational therapy practice:

▪ Accelerometers are nonintrusive, easy-to-use, sensitive, and well-tolerated devices that allow for continuous and prolonged measurement of real-world UE activity across a variety of naturalistic settings and daily tasks requiring different contributions from both UEs. The proprietary software that accompanies commercially available accelerometers can readily provide clinicians with metrics that can serve as objective outcome measures to assess treatment effectiveness.

▪ Unlike standardized tests, accelerometry-based measurements of everyday UE activity are not influenced by tester expertise or effects of repeated administration or practice, may identify subtle asymmetries not detected by lab-based structured testing, and may serve as indicators of functional UE use in naturalistic settings (Beani et al., 2019; Hollis et al., 2021; Bezuidenhout et al., 2021).

▪ Overall, wrist-worn accelerometry may provide objective and accurate information on real-world UE movement that may be missed by standardized tests and parent-report measures.

Conclusion

Our pilot study assessed the differences in the intensity and duration of habitual real-world use of bilateral UEs using wrist-worn accelerometers in a group of TD children and children with UCP. Children with UCP engaged in less moderate to vigorous activity with the affected or nondominant UE than TD children. Moreover, they demonstrated significant asymmetry across the UEs, with the nondominant UE used at a lower intensity than the dominant UE. Wrist-worn accelerometers are easy-to-use, well-tolerated, nonintrusive tools that provide estimates of real-world movement of the UEs that is not captured by standardized assessments of motor capacity and can be used by clinicians to measure baseline asymmetries in UE function and to track progress after rehabilitation therapies among children with UE motor impairments.

Footnotes

Acknowledgments

We thank the children and families who participated in the study and the undergraduate and graduate students who helped with data collections and analyses.

References

Abd El Wahab, M. A. E. , & Hamed, N. E. (2015). Effect of hand-arm bimanual intensive therapy on fine-motor performance in children with hemiplegic cerebral palsy. Egyptian Journal of Medical Human Genetics, 16, 55–59. https://doi.org/10.1016/j.ejmhg.2014.07.005

Airlie

Forster

, & Birch

K. M.

(2022). An investigation into the optimal wear time criteria necessary to reliably estimate physical activity and sedentary behaviour from ActiGraph wGT3X+ accelerometer data in older care home residents. BMC Geriatrics, 22, 136. https://doi.org/10.1186/s12877-021-02725-6

American Occupational Therapy Association. (2020). Occupational therapy practice framework: Domain and process (4th ed.). American Journal of Occupational Therapy , 74(Suppl. 2), 7412410010. https://doi.org/10.5014/ajot.2020.74S2001

Arnould

Bleyenheuft

, & Thonnard

J. L.

(2014). Hand functioning in children with cerebral palsy. Frontiers in Neurology, 5, 48. https://doi.org/10.3389/fneur.2014.00048

Arnould .

Penta

, M.

Renders

, & Thonnard

J.-L.

(2004). ABILHAND-Kids: A measure of manual ability in children with cerebral palsy. Neurology , 63, 1045–1052. https://doi.org/10.1212/01.wnl.0000138423.77640.37

Babik

, & Michel

G. F.

(2016). Development of role-differentiated bimanual manipulation in infancy: Part 1. The emergence of the skill. Developmental Psychobiology, 58, 243–256. https://doi.org/10.1002/dev.21382

Bailey

R. R.

Klaesner

J. W.

, & Lang

C. E.

(2014). An accelerometry-based methodology for assessment of real-world bilateral upper extremity activity. PLoS One, 9, e103135. https://doi.org/10.1371/journal.pone.0103135

Bailey

R. R.

, & Lang

C. E.

(2013). Upper-limb activity in adults: Referent values using accelerometry. Journal of Rehabilitation Research and Development, 50, 1213–1222. https://doi.org/10.1682/JRRD.2012.12.0222

Barth

Geed

Mitchell

Lum

P. S.

Edwards

D. F.

, & Dromerick

A. W.

(2020). Characterizing upper extremity motor behavior in the first week after stroke. PLoS One, 15, e0221668. https://doi.org/10.1371/journal.pone.0221668

10.

Beani

Maselli

Sicola

Perazza

Cecchi

Dario

et al. Sgandurra, G. (2019). Actigraph assessment for measuring upper limb activity in unilateral cerebral palsy. Journal of Neuroengineering and Rehabilitation, 16, 30. https://doi.org/10.1186/s12984-019-0499-7

11.

Bezuidenhout

Joseph

Einarsson

Thurston

Hagströmer

, & Moulaee Conradsson

(2021). Accelerometer assessed upper limb activity in people with stroke: A validation study considering ambulatory and non-ambulatory activities. Disability and Rehabilitation, 44 , 8463–8470. https://doi.org/10.1080/09638288.2021.2012838

12.

Bleyenheuft

, & Gordon

A. M.

(2013). Precision grip control, sensory impairments and their interactions in children with hemiplegic cerebral palsy: A systematic review. Research in Developmental Disabilities, 34, 3014–3028. https://doi.org/10.1016/j.ridd.2013.05.047

13.

Braito

Maselli

Sgandurra

Inguaggiato

Beani

Cecchi

et al. Boyd, R. (2018). Assessment of upper limb use in children with typical development and neurodevelopmental disorders by inertial sensors: A systematic review. Journal of Neuroengineering and Rehabilitation, 15, 94. https://doi.org/10.1186/s12984-018-0447-y

14.

Charles

, & Gordon

A. M.

(2006). Development of hand-arm bimanual intensive training (HABIT) for improving bimanual coordination in children with hemiplegic cerebral palsy. Developmental Medicine and Child Neurology, 48, 931–936.

15.

Chinapaw

M. J.

de Niet

Verloigne

De Bourdeaudhuij

Brug

, & Altenburg

T. M.

(2014). From sedentary time to sedentary patterns: Accelerometer data reduction decisions in youth. PLoS One, 9, e111205. https://doi.org/10.1371/journal.pone.0111205

16.

Clouatre

Campeau-Lecours

, & Flamand

(2020). Preliminary development of a wearable device to help children with unilateral cerebral palsy increase their consciousness of their upper extremity. arXiv . https://doi.org/10.48550/arXiv.2010.06082

17.

Cohen

(1988). Statistical power analysis for the behavioral sciences (2nd ed.). Erlbaum.

18.

Coker-Bolt

Downey

R. J.

Connolly

Hoover

Shelton

, & Seo

N. J.

(2017). Exploring the feasibility and use of accelerometers before, during, and after a camp-based CIMT program for children with cerebral palsy. Journal of Pediatric Rehabilitation Medicine, 10, 27–36. https://doi.org/10.3233/PRM-170408

19.

Davids

J. R.

Peace

L. C.

Wagner

L. V.

Gidewall

M. A.

Blackhurst

D. W.

, & Roberson

W. M.

(2006). Validation of the Shriners Hospital for Children Upper Extremity Evaluation (SHUEE) for children with hemiplegic cerebral palsy. Journal of Bone and Joint Surgery, 88, 326–333. https://doi.org/10.2106/JBJS.E.00298

20.

Dawe

Yang

J. F.

Fehlings

Likitlersuang

Rumney

Zariffa

, & Musselman

K. E.

(2019). Validating accelerometry as a measure of arm movement for children with hemiplegic cerebral palsy. Physical Therapy, 99, 721–729. https://doi.org/10.1093/ptj/pzz022

21.

Deluca

S. C.

Echols

Law

C. R.

, & Ramey

S. L.

(2006). Intensive pediatric constraint-induced therapy for children with cerebral palsy: Randomized, controlled, crossover trial. Journal of Child Neurology, 21, 931–938. https://doi.org/10.1177/08830738060210110401

22.

de Niet

Bussmann

J. B.

Ribbers

G. M.

, & Stam

H. J.

(2007). The stroke upper-limb activity monitor: Its sensitivity to measure hemiplegic upper-limb activity during daily life. Archives of Physical Medicine and Rehabilitation, 88, 1121–1126. https://doi.org/10.1016/j.apmr.2007.06.005

23.

J. H.

Yoo

E. Y.

Jung

M. Y.

, & Park

H. Y.

(2016). The effects of virtual reality-based bilateral arm training on hemiplegic children’s upper limb motor skills. NeuroRehabilitation, 38, 115–127. https://doi.org/10.3233/NRE-161302

24.

Eliasson

A. C.

Bonnier

, & Krumlinde-Sundholm

(2003). Clinical experience of constraint induced movement therapy in adolescents with hemiplegic cerebral palsy—A day camp model. Developmental Medicine and Child Neurology, 45, 357–359. https://doi.org/10.1111/j.1469-8749.2003.tb00409.x

25.

Eliasson

A. C.

Krumlinde-Sundholm

Rösblad

Beckung

Arner

Ohrvall

A. M.

, & Rosenbaum

(2006). The Manual Ability Classification System (MACS) for children with cerebral palsy: Scale development and evidence of validity and reliability. Developmental Medicine and Child Neurology, 48, 549–554. https://doi.org/10.1017/S0012162206001162

26.

Eliasson

A.-C.

Nordstrand

Backheden

, & Holmefur

(2022). Longitudinal development of hand use in children with unilateral spastic cerebral palsy from 18 months to 18 years. Developmental Medicine and Child Neurology, 65, 376–384. https:// 10.1111/dmcn.15370

27.

Elvrum

A. K.

Saether

Riphagen

I. I.

, & Vik

(2016). Outcome measures evaluating hand function in children with bilateral cerebral palsy: A systematic review. Developmental Medicine and Child Neurology, 58, 662–671. https://doi.org/10.1111/dmcn.13119

28.

Fedrizzi

Pagliano

Andreucci

, & Oleari

(2003). Hand function in children with hemiplegic cerebral palsy: Prospective follow-up and functional outcome in adolescence. Developmental Medicine and Child Neurology, 45, 85–91. https://doi.org/10.1111/j.1469-8749.2003.tb00910.x

29.

Freedson

Pober

, & Janz

K. F.

(2005). Calibration of accelerometer output for children. Medicine and Science in Sports and Exercise, 37(Suppl .), S523–S530. https://doi.org/10.1249/01.mss. 0000185658.28284.ba

30.

Goble

D. J.

Lewis

C. A.

Hurvitz

E. A.

, & Brown

S. H.

(2005). Development of upper limb proprioceptive accuracy in children and adolescents. Human Movement Science, 24, 155–170. https://doi.org/10.1016/j.humov.2005.05.004

31.

Goodwin

B. M.

Sabelhaus

E. K.

Pan

Bjornson

K. F.

Pham

K. L.

Walker

W. O.

, & Steele

K. M.

(2020). Accelerometer measurements indicate that arm movements of children with cerebral palsy do not increase after constraint-induced movement therapy (CIMT). American Journal of Occupational Therapy , 74, 7405205100. https://doi.org/10.5014/ajot.2020.040246

32.

Gordon

A. M.

(2010). Two hands are better than one: Bimanual skill development in children with hemiplegic cerebral palsy. Developmental Medicine and Child Neurology, 52, 315–316. https://doi.org/10.1111/j.1469-8749.2009.03390.x

33.

Gordon

A. M.

Charles

, & Wolf

S. L.

(2005). Methods of constraint-induced movement therapy for children with hemiplegic cerebral palsy: Development of a child-friendly intervention for improving upper-extremity function. Archives of Physical Medicine and Rehabilitation, 86, 837–844. https://doi.org/10.1016/j.apmr.2004.10.008

34.

Gordon

A. M.

Schneider

J. A.

Chinnan

, & Charles

J. R.

(2007). Efficacy of a hand-arm bimanual intensive therapy (HABIT) in children with hemiplegic cerebral palsy: A randomized control trial. Developmental Medicine and Child Neurology, 49, 830–838. https://doi.org/10.1111/j.1469-8749.2007.00830.x

35.

Hawk

(2015). ActiGraph data conversion process. https://actigraphcorp.my.site.com/support/s/article/ActiGraph-Data-Conversion-Process

36.

Hayward

K. S.

Eng

J. J.

Boyd

L. A.

Lakhani

Bernhardt

, & Lang

C. E.

(2016). Exploring the role of accelerometers in the measurement of real world upper-limb use after stroke. Brain Impairment, 17, 16–33. https://doi.org/10.1017/BrImp.2015.21

37.

Hedges

L. V.

(1981). Distribution theory for Glass’s estimator of effect size and related estimators. Journal of Educational Statistics, 6, 107–128. https://doi.org/10.3102/10769986006002107

38.

Hoare

Imms

Carey

, & Wasiak

(2007). Constraint-induced movement therapy in the treatment of the upper limb in children with hemiplegic cerebral palsy: A Cochrane systematic review. Clinical Rehabilitation, 21, 675–685. https://doi.org/10.1177/0269215507080783

39.

Hollis

Cole

Zewdie

Metzler

M. J.

, & Kirton

(2021). Bilateral Actigraphic quantification of upper extremity movement in hemiparetic children with perinatal stroke: A case control study. Journal of Neuroengineering and Rehabilitation, 18, 172. https://doi.org/10.1186/s12984-021-00962-9

40.

Houwink

Aarts

P. B.

Geurts

A. C.

, & Steenbergen

(2011). A neurocognitive perspective on developmental disregard in children with hemiplegic cerebral palsy. Research in Developmental Disabilities, 32, 2157–2163. https://doi.org/10.1016/j.ridd.2011.07.012

41.

Hoyt

C. R.

Brown

S. K.

Sherman

S. K.

Wood-Smith

Van

A. N.

Ortega

et al. Dosenbach, N. U. F. (2020). Using accelerometry for measurement of motor behavior in children: Relationship of real-world movement to standardized evaluation. Research in Developmental Disabilities, 96, 103546. https://doi.org/10.1016/j.ridd.2019.103546

42.

Hung

Y. C.

Charles

, & Gordon

A. M.

(2004). Bimanual coordination during a goal-directed task in children with hemiplegic cerebral palsy. Developmental Medicine and Child Neurology, 46, 746–753. https://doi.org/10.1111/j.1469-8749.2004.tb00994.x

43.

Hung

Y. C.

Charles

, & Gordon

A. M.

(2010). Influence of accuracy constraints on bimanual coordination during a goal-directed task in children with hemiplegic cerebral palsy. Experimental Brain Research, 201, 421–428. https://doi.org/10.1007/s00221-009-2049-1

44.

Hwang

Y. S.

, & Kwon

J. Y.

(2020). Effects of modified constraint-induced movement therapy in real-world arm use in young children with unilateral cerebral palsy: A single-blind randomized trial. Neuropediatrics, 51, 259–266. https://doi.org/10.1055/s-0040-1702220

45.

Islam

Gordon

A. M.

Sköld

Forssberg

, & Eliasson

A. C.

(2011). Grip force coordination during bimanual tasks in unilateral cerebral palsy. Developmental Medicine and Child Neurology, 53, 920–926. https://doi.org/10.1111/j.1469-8749.2011.04040.x

46.

James

M. A.

Bagley

Vogler

J. B.

4th Davids

J. R.

, & Van Heest

A. E.

(2017). Correlation between standard upper extremity impairment measures and activity-based function testing in upper extremity cerebral palsy. Journal of Pediatric Orthopedics, 37, 102–106. https://doi.org/10.1097/BPO.0000000000000591

47.

James

Ziviani

, & Boyd

(2014). A systematic review of activities of daily living measures for children and adolescents with cerebral palsy. Developmental Medicine and Child Neurology, 56, 233–244. https://doi.org/10.1111/dmcn.12226

48.

Kimmerle

Ferre

C. L.

Kotwica

K. A.

, & Michel

G. F.

(2010). Development of role-differentiated bimanual manipulation during the infant’s first year. Developmental Psychobiology, 52, 168–180. https://doi.org/10.1002/dev.20428

49.

Klingels

Jaspers

Van de Winckel

De Cock

Molenaers

, & Feys

(2010). A systematic review of arm activity measures for children with hemiplegic cerebral palsy. Clinical Rehabilitation, 24, 887–900. https://doi.org/10.1177/0269215510367994

50.

Lai

C. H.

Sung

W. H.

Chiang

S. L.

L. H.

Lin

C. H.

Tung

Y. C.

, & Lin

C. H.

(2019). Bimanual coordination deficits in hands following stroke and their relationship with motor and functional performance. Journal of Neuroengineering and Rehabilitation, 16, 101. https://doi.org/10.1186/s12984-019-0570-4

51.

Lang

C. E.

Waddell

K. J.

Klaesner

J. W.

, & Bland

M. D.

(2017). A method for quantifying upper limb performance in daily life using accelerometers. Journal of Visualized Experiments, (122), e55673. https://doi.org/10.3791/55673

52.

Lum

P. S.

Shu

Bochniewicz

E. M.

Tran

Chang

L. C.

Barth

, & Dromerick

A. W.

(2020). Improving accelerometry-based measurement of functional use of the upper extremity after stroke: Machine learning versus counts threshold method. Neurorehabilitation and Neural Repair, 34, 1078–1087. https://doi.org/10.1177/1545968320962483

53.

Michel

G. F.

Campbell

J. M.

Marcinowski

E. C.

Nelson

E. L.

, & Babik

(2016). Infant hand preference and the development of cognitive abilities. Frontiers in Psychology, 7, 410. https://doi.org/10.3389/fpsyg.2016.00410

54.

Migueles

J. H.

Cadenas-Sanchez

Ekelund

Delisle Nyström

Mora-Gonzalez

Löf

et al. Ortega, F. B. (2017). Accelerometer data collection and processing criteria to assess physical activity and other outcomes: A systematic review and practical considerations. Sports Medicine, 47, 1821–1845. https://doi.org/10.1007/s40279- 017-0716-0

55.

Noorkõiv

Rodgers

, & Price

C. I.

(2014). Accelerometer measurement of upper extremity movement after stroke: A systematic review of clinical studies. Journal of Neuroengineering and Rehabilitation, 11, 144. https://doi.org/10.1186/1743-0003-11-144

56.

Peach

Van Hoomissen

, & Callender

H. L.

(2014). Exploring the ActiLife^® filtration algorithm: Converting raw acceleration data to counts. Physiological Measurement, 35, 2359–2367. https://doi.org/10.1088/0967-3334/35/12/2359

57.

Poitras

Clouâtre

Campeau-Lecours

, & Mercier

(2022). Accelerometry-based metrics to evaluate the relative use of the more affected arm during daily activities in adults living with cerebral palsy. Sensors, 22, 1022. https://doi.org/10.3390/s22031022

58.

Poitras

Martinie

Robert

M. T.

Campeau-Lecours

, & Mercier

(2021). Impact of sensory deficits on upper limb motor performance in individuals with cerebral palsy: A systematic review. Brain Sciences, 11, 744. https://doi.org/10.3390/brainsci11060744

59.

Shumway-Cook

, & Woollacott

M. H.

(2022). Motor control: Translating research into clinical practice (6th ed.). Lippincott Williams & Wilkins.

60.

Sköld

Josephsson

, & Eliasson

A. C.

(2004). Performing bimanual activities: The experiences of young persons with hemiplegic cerebral palsy. American Journal of Occupational Therapy, 58, 416–425. https://doi.org/10.5014/ajot.58.4.416

61.

Sokal

Uswatte

Vogtle

Byrom

, & Barman

(2015). Everyday movement and use of the arms: Relationship in children with hemiparesis differs from adults. Journal of Pediatric Rehabilitation Medicine, 8, 197–206. https://doi.org/10.3233/PRM-150334

62.

Speth

Janssen-Potten

Leffers

Rameckers

Defesche

Geers

et al. Vles, H. (2013). Observational skills assessment score: Reliability in measuring amount and quality of use of the affected hand in unilateral cerebral palsy. BMC Neurology, 13, 152. https://doi.org/10.1186/1471-2377-13-152

63.

Taub

Ramey

S. L.

DeLuca

, & Echols

(2004). Efficacy of constraint-induced movement therapy for children with cerebral palsy with asymmetric motor impairment. Pediatrics, 113, 305–312. https://doi.org/10.1542/peds.113.2.305

64.

Taub

Uswatte

, & Elbert

(2002). New treatments in neurorehabilitation founded on basic research. Nature Reviews Neuroscience, 3, 228–236. https://doi.org/10.1038/nrn754

65.

Taub

Uswatte

Mark

V. W.

, & Morris

D. M.

(2006). The learned nonuse phenomenon: implications for rehabilitation. Europa Medicophysica, 42, 241–256.

66.

Tedesco

A. P.

Nicolini-Panisson

R. D.

, & de Jesus

(2015). SHUEE on the evaluation of upper limb in cerebral palsy. Acta Ortopedica Brasileira, 23, 219–222. https://doi.org/10.1590/1413-78522015230400967

67.

Trac

Dawe

Likitlersuang

Musselman

, & Zariffa

(2018). Influence of upper limb movement patterns on accelerometer measurements: A pediatric case series. Physiological Measurement, 39, 04NT02. https://doi.org/10.1088/1361-6579/aab994

68.

Uswatte

Miltner

W. H.

Foo

Varma

Moran

, & Taub

(2000). Objective measurement of functional upper-extremity movement using accelerometer recordings transformed with a threshold filter. Stroke, 31, 662–667. https://doi.org/10.1161/01.STR.31.3.662

69.

Uswatte

Taub

Morris

Vignolo

, & McCulloch

(2005). Reliability and validity of the upper-extremity Motor Activity Log-14 for measuring real-world arm use. Stroke, 36, 2493–2496. https://doi.org/10.1161/01.STR.0000185928.90848.2e

70.

Utley

, & Steenbergen

(2006). Discrete bimanual co-ordination in children and young adolescents with hemiparetic cerebral palsy: Recent findings, implications and future research directions. Pediatric Rehabilitation, 9, 127–136. https://doi.org/10.1080/13638490500155573

71.

van der Pas

S. C.

Verbunt

J. A.

Breukelaar

D. E.

van Woerden

, & Seelen

H. A.

(2011). Assessment of arm activity using triaxial accelerometry in patients with a stroke. Archives of Physical Medicine and Rehabilitation, 92, 1437–1442. https://doi.org/10.1016/j.apmr.2011.02.021

72.

Wagner

L. V.

, & Davids

J. R.

(2012). Assessment tools and classification systems used for the upper extremity in children with cerebral palsy. Clinical Orthopaedics and Related Research, 470, 1257–1271. https://doi.org/10.1007/s11999-011-2065-x

73.

Wang

T. N.

Howe

T. H.

Liang

K. J.

Chang

T. W.

Shieh

J. Y.

, & Chen

H. L.

(2021). Bimanual motor performance in everyday life activities of children with hemiplegic cerebral palsy. European Journal of Physical and Rehabilitation Medicine, 57, 568–576. https://doi.org/10.23736/S1973-9087.21.06504-7

74.

Woytowicz

Whitall

, & Westlake

K. P.

(2016). Age-related changes in bilateral upper extremity coordination. Current Geriatrics Reports, 5, 191–199. https://doi.org/10.1007/s13670-016-0184-7

75.

Zielinski

I. M.

Steenbergen

Baas

C. M.

Aarts

P. B.

, & Jongsma

M. L.

(2014). Neglect-like characteristics of developmental disregard in children with cerebral palsy revealed by event related potentials. BMC Neurology, 14, 221. https://doi.org/10.1186/s12883-014-0221-0

76.

Zoccolillo

Morelli

Cincotti

Muzzioli

Gobbetti

Paolucci

, & Iosa

(2015). Video-game based therapy performed by children with cerebral palsy: A cross-over randomized controlled trial and a cross-sectional quantitative measure of physical activity. European Journal of Physical and Rehabilitation Medicine, 51, 669–676.