http://journal.frontiersin.org/article/10.3389/fnhum.2017.00042/full?
- 1Institute of Movement Science, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
- 2Department of Kinesiology, San Francisco State University, San Francisco, CA, USA
Methods: In this preliminary study we employed three-dimensional motion recording and applied kinematic analysis in a multi-step ADL (tea-making). The trials were examined with respect to errors and sub-action structure, durations, path lengths (PLs), peak velocities, relative activity (RA) and smoothness. In order to check for specific burdens the sub-actions of the task were extracted and compared. To examine the feasibility of the approach, we determined the behavioral and kinematic metrics of the (ipsilesional) unimanual performance of seven chronic stroke patients (64a ± 11a, 3 with right/4 with left brain damage (LBD), 2 with signs of apraxia, variable severity of paresis) and compared the results with data of 14 neurologically healthy age-matched control participants (70a ± 7a).
Results: T-tests revealed that while the quantity and structure of sub-actions of the task were similar. The analysis of end-effector kinematics was able to detect clear group differences in the associated parameters. Specifically, trial duration (TD) was increased (Cohen’s d = 1.77); the RA (Cohen’s d = 1.72) and the parameters of peak velocities (Cohen’s d = 1.49/1.97) were decreased in the patient group. Analysis of the task’s sub-actions repeated measures analysis of variance (rmANOVA) revealed no impact of the different demands of the sub-actions on the relative performance of the patient group.
Conclusion: The analyses revealed kinematic peculiarities in the performance with the ipsilesional hand. These deficits apparently arose from the cognitive demands like sequencing rather than motor constraints. End-effector kinematics proved as a sensitive method to detect and quantify aspects of disturbed multi-step ADL performance after stroke. If standardized, the examination and the analysis are quick and deliver objective data supporting clinical research.
Introduction
Strokes frequently impair the ability to perform activities of daily living (ADL; Foundas et al., 1995; Forde and Humphreys, 2000; Hartmann et al., 2005; Schwartz, 2006; Wisneski and Johnson, 2007).
Stroke related syndromes like apraxia, action disorganization syndrome,
hemiparesis and neglect can cause such deficits in ADL. According to a
previous estimate 37%–55% of chronic stroke patients are impaired in ADL
(Bieńkiewicz et al., 2015).
Following stroke, the behavioral deficits in multi-step
ADL arising from impaired action planning are mostly omissions of
sub-actions and disorders in the sequencing of subsequent steps, as has
been shown in studies on food preparation (Buxbaum, 1998; Schwartz et al., 1999; Bickerton et al., 2007, 2012; Bieńkiewicz et al., 2014), dressing (Sunderland et al., 2006) or hygiene procedures (Humphreys and Forde, 1998).
Kinematic analyses do not directly address these types of errors.
Kinematics rather quantifies basic aspects of movement execution such as
speed, coordination, directness, fluency, smoothness and variability (de los Reyes-Guzmán et al., 2014),
although errors can alter kinematic parameters, e.g., the omission of
sub-actions can shorten the trajectory of a task or conceptual deficits
in handling tools can prolong trial durations (TDs). In this study we
consider kinematic measures that can be obtained with only the
positional data of the end-effectors.
Up to now, only a few studies employed the approach in
the study of multi-step ADL tasks of stroke patients. One investigated
scenario is drinking from a glass (Weiss et al., 2000; Alt Murphy et al., 2006, 2011, 2012, 2013; Thies et al., 2009; Kim et al., 2014). Alt Murphy et al. (2011)
compared the performance of stroke patients with the performance of
age-matched healthy controls. The patients revealed longer movement
times, slower peak velocities of the hand and of the elbow (angular peak
velocity), a higher number of movement units (less movement smoothness)
and an increased trunk displacement when executing the task with their
paretic arm. The task was also segmented into five single sub-actions.
An analysis of the relative movement times in the different sub-actions
showed no differences between healthy subjects and stroke patients
indicating that none of the sub-actions was specifically impaired in the
patients (Alt Murphy et al., 2011).
Notably, parameters representing the patients’ kinematics correlated
well with motor function tests like the ARAT, ABILHAND or FMA (Alt Murphy et al., 2012) and reflected changes in motor performance during the first 3 months after a stroke (Alt Murphy et al., 2013). The drinking from a glass task as a multi-step ADL is in comparison to e.g., the tea-making task of moderate complexity (Wood, 1986)
since its small set of sub-actions (component complexity) can only be
realized in one order (coordinative complexity) and only one of the
components changes one of its characteristics (weight of the glass;
dynamic complexity).
The present study aimed to examine the feasibility of
kinematic parameters when assessing the performance of stroke patients
in a complex, multi-step ADL. As a secondary goal we also wanted to gain
a better understanding of the reasons underlying impaired ADL
performance following stroke. Knowledge of the basic deficits would
offer specific targets for interventions in therapy (Bieńkiewicz et al., 2015). Partial results of a pilot of this study have been published in a conference proceeding (Gulde et al., 2014).
In order to achieve our main goal we introduced adapted kinematic
parameters: relative activity (RA) as a measure of activity, mean peak
velocity to describe the average movement speed in tasks with phases of
inactivity and number of (velocity) peaks per meter to describe
smoothness independent of the amount of executed actions. Such adapted
parameters are necessary to analyze irregular signals resulting from the
execution of many sub-actions in varying order. The examination of
tasks of higher complexity is already being used in the clinical
setting, but the assessment is so far qualitative and not quantitative,
e.g., trail making tasks or “Multistep Object Use” in the Birmingham
Cognitive Screen (BCoS; Bickerton et al., 2012).
The reaction time from the instruction to movement start
has been successfully used to quantify the duration of action planning
time during simple ADL tasks (Hermsdörfer et al., 2013).
During multi-step ADL actions, multiple phases of action planning and
movement pauses have to be expected. Consequently, movement pauses were
determined with a velocity criterion. The new parameter “RA” resulted,
which represents the composition of TD into percentages of activity and
inactivity. It is able to indicate prolongations of movement planning
and preparation. Such prolongations can be caused by slowed planning of
movement trajectories and/or slowed planning of the next action step of a
sequence, visual allocation of objects or backwards checking of already
performed steps. The second new parameter introduced is the mean peak
velocity that quantifies the general movement speed by the average value
of action-related velocity peaks.
Considering the literature we expect a number of
deviations from normal performance in stroke patients in our
ADL-scenario of preparing a cup of tea with milk and sugar, (e.g., Thies et al., 2009; Alt Murphy et al., 2011; Osu et al., 2011; Kim et al., 2014). Due to the, in comparison to e.g., the drinking from a glass task, high complexity (Wood, 1986)
of the task (in all three dimensions stated by Wood: component,
coordinative and dynamic), we expected the kinematic performance of
stroke patients to diverge from the performance of age-matched control
subjects. We anticipated increased TDs (Thies et al., 2009; Alt Murphy et al., 2011; Kim et al., 2014)
resulting from a reduced movement speed and a higher relative and
absolute amount of inactivity. We also expected reduced maximum velocity
peaks (Alt Murphy et al., 2011; Osu et al., 2011) and mean peak velocities and reduced movement smoothness (in our study an increased number of (velocity) peaks per meter; Alt Murphy et al., 2011; Osu et al., 2011).
Additionally, we hypothesized that path length (PL) may be increased
due to sequencing problems and misuse of objects in the patients. Since
the sub-actions of the task differ in physical and cognitive demands, we
also expected differences in the relative performance of stroke
patients in comparison to the age-matched control subjects between the
different sub-actions. Such differences can be detected by changes of
the relative performance of specific sub-actions (Kim et al., 2014).
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