This study determined the minimum number of trials needed to reach good performance stability of kinematic variables obtained during the drinking task both in non-disabled persons and in a sample of individuals with chronic stroke. The results revealed that for most kinematic variables only 2 to 3 trials were required to reach sufficient performance stability. Small but significant trends were noted for shorter movement times in the non-disabled group for the last 3 trials compared to the first 3 trials. In the stroke sample, a good to excellent test-retest reliability was reached for many variables when less than 3 trials from each occasion were used in the analysis. However, more trials were needed for movement time in reaching and returning as well as for wrist angle. Only moderate reliability was reached for the time to peak velocity and poor reliability was observed for the variable peak velocity in the stroke group.
Number of trials needed to reach good performance stability
The current study is the first to demonstrate that only 2 to 3 trials are required to reach good performance stability for most kinematic variables of the drinking task. This finding was valid both for non-disabled and for stroke participants and is in line with two previous studies analyzing reaching kinematics using optoelectronic systems (21, 22). Blinch et al. reported that not more than 3 trials were required to achieve good within trial reliability of movement time and peak velocity during fast visually guided pointing tasks in non-disabled participants (21). Likewise, Hansen et al. demonstrated that 5 trials were estimated to be the minimum number required to get reliable ICC estimates for most of the kinematics when reaching for low and high targets in persons with subacute stroke (22).
Similar results have also been shown with other measurement systems in non-disabled individuals. A study using a virtual reality gaming Kinect system showed that 2 to 5 trials during reaching were needed to achieve performance stability in movement time and elbow and shoulder range of motion (32). Additionally, when using an inertial sensor system, comparable results of 3 trials was considered enough to reach acceptable levels of reliability for movement time and shoulder and elbow range of motion during a drinking task in non-disabled participants (33). These results confirm that for most of the kinematic variables a set of 3 trials would be sufficient. However, more trials in a range of 4–6 and ≥ 8 trials would probably be needed for certain variables and study groups (e.g.
Even though the total movement time for the drinking task only required 2 trials to reach good performance stability, up to 5 trials were needed for movement time in reaching (stroke) and up to 8 trials for movement time during returning (stroke and non-disabled). Post-stroke, abnormal muscle activation synergies and inadequate inter-joint coordination have been suggested to be the prime contributing causes to reaching dysfunction (10, 34, 35). In addition, abnormal inter-segmental dynamics, particularly regarding suppressed interaction torque and deficient feedforward control of this torque around the elbow might significantly contribute to the dysfunction in reaching (36). Deficits in the grasp formation during reaching impact as well the reaching time (37). All these complex demands on reaching might increase the within trial variability in reaching seen in individuals with stroke.
To move the hand back to the starting position in the returning phase of the drinking task should theoretically be less challenging, however, up to 8 trials were needed to reach good performance stability in both investigated groups. One possible explanation for this finding could be that the movements in this phase did not require direct visual feedback and that the participants might have corrected the end position of the hand in some trials. To overcome this potential problem, a more standardized end of the task could be used.
The relative time to peak velocity, designating acceleration and deceleration time in reaching, showed also higher variability with 6 or more trials required to reach good performance stability in both groups.
Higher variability, characterized by lower effect sizes of discriminative validity, was also observed for this variable during the drinking task in persons with stroke in a previous study (17
). This suggests that this point in time when the peak velocity is reached may vary between trials both in persons with stroke and in those without disability.
Interestingly, in the non-disabled group more trials were needed for NMU (3 to 9 and more) and inter-joint coordination (4 trials) than in individuals with stroke (2–3 trials). The main reason for that was most likely the inherent properties of the variables themselves. In both metrics, the between-subjects’ variation was extremely low compared to participants with stroke (see Table 3). Further, the performance of non-disabled participants was also close to the extreme possible value of the metrics (ceiling or floor effect). These aspects need to be considered when interpreting the reported ICC values for these variables in the non-disabled group.
Good movement performance stability was reached after 2 trials for all joint angles and trunk displacement metrics (Fig. 1 and Table 4). This finding confirms that movement variability of the joints and segments of the body is relatively stable when repeatedly performing a well-known task (16), such as drinking from a glass, in a self-paced comfortable speed. This result is in line with previous research in non-disabled persons showing high level of automaticity of movement execution of well-learned tasks (16), and also in persons late after stroke where compensatory movement strategies have shown to be more fixed (38, 39).
Systematic trend over a set of trials
In the non-disabled individuals, small but significant trends towards improvement were demonstrated in some temporal variables (for total movement time and for some of the movement phases) when the last three trials were compared to the first three. These trends might be caused by the learning effect. The improvements were, however, small and can therefore be considered to be of less clinical relevance.
In the stroke group, no significant trends over multiple trials were found, but even here small trends could be observed visually in some variables, e.g. increased trunk displacement in later trials (Fig. 2). Not finding significant trends in stroke data could be caused by the low power due to the small group size (n = 8), and larger studies in stroke populations are therefore warranted.
We expected to find signs of muscular fatigue in terms of declining trends in the stroke group over the set of trials, but this assumption was not supported in the results. Interestingly, from an intervention study it was reported that participants in post stroke training could conduct up to 300 repetitions (3 tasks x 100 reps)/occasion, within one hour) without experiencing increased fatigue (40). The risk of fatigue influencing motor performance after stroke has, however, been highlighted in several previous studies (12, 20, 22), and a planned rest in between trials has been recommended.
In the current study, the participants took a short break of about 5 seconds between each trial.
Test-retest reliability in a subsample of individuals with stroke
In the current study, good to excellent test-retest reliability with a mean of 2 to 3 trials was demonstrated for most of the kinematic variables in the individuals with stroke performing the drinking task at 4 different occasions. However, for two end-point variables (the peak velocity and the time to peak velocity), the reliability remained poor or moderate even after 9 trials. Our findings agree with previous research (19, 20), even though there are some methodological differences.
In a study with participants with stroke (tested at two occasions, few days apart) good to excellent test-retest reliability were found for movement time, peak velocity and trunk displacement in different reach-to-grasp tasks (different object sizes and at self-selected and fast speeds) (19). Interestingly, for non-disabled controls only moderate to good reliability was demonstrated (19). The authors proposed that the lower consistency observed in non-disabled individuals might be caused by an exploratory behavior among controls trying to find the most optimal solutions for movement execution within the existing task constraints (41). Individuals with hemiparesis after stroke often move with behavioral compensation and this altered movement performance has been reported to be less variable (11, 38, 42). From a theoretical dynamic system’s perspective, the underlying mechanisms for these more fixed movement patterns developed over time in people with stroke might explain the low observed variations (39).
Test-retest reliability of kinematic variables obtained during a pointing task, using a mean of 2 trials in persons late after stroke, showed varying ICC values (20). Good reliability (ICC > 0.75) was reported for shoulder flexion and elbow extension, moderate reliability for peak velocity, shoulder abduction and inter-joint coordination, while the ICC values for movement time, time to peak velocity and number of velocity peaks were low (20). In contrast to the Wagner et al. (20), our results showed good reliability for movement time (except for the returning phase) and NMU, while the time to peak velocity showed low reliability similarly to the abovementioned study. Plausible explanations to these inconsistent results might be the differences in tasks and that the participants in the Wagner et al. study had more impaired upper extremity function (FMA ≈ 35/66) as compared to in the current study (FMA ≈ 50/66). The time between test-retest sessions was also longer (one month) in the study of Wagner et al. compared to one week in the current study, which might have influenced the results.
Strengths and limitations
In the current study a wide range of well-established kinematic variables, covering temporal, end-point, angular and displacement kinematics, were evaluated, which is a strength of the study. The results regarding non-disabled people were based on a relatively large sample (n = 44), although the results from stroke participants need to be interpreted with caution due to the small sample size (n = 8). However, the kinematic variables analyzed in the current study in stroke participants showed a consistent pattern in line with existing research (11).
This implies that 3–5 trials per test occasion might be used as a rough guide for self-paced functional everyday reach-to-grasp tasks both in non-disabled people and in individuals with stroke.
As also experienced in the current study, not all trials might be successful during the data capture due to various reasons including obscured markers and data gaps. This might be particularly relevant for individuals with stroke where the altered movement patterns might cause obscured markers resulting in data gaps. This further suggests that even when a good performance stability might be reached with 2 to 3 trials, few extra trials are needed to ensure sufficient number of successful trials.
The results of the current study are only applicable for the kinematic motion capture systems using multiple optoelectronic cameras. The results seem, however, to be similar even when the kinematics are collected by other systems, such as Kinect camera or inertial sensors (32, 33). This is promising, taking the constant push from users (clinicians, researchers and patients) to make movement analysis more readily available with systems that can operate outside the lab.