#24706 | AsPredicted

'Reliability of a new dynamic proprioceptive accuracy measure'
(AsPredicted #24706)

Created:       06/12/2019 06:55 AM (PT)

This is an anonymized version of the pre-registration.  It was created by the author(s) to use during peer-review.
A non-anonymized version (containing author names) should be made available by the authors when the work it supports is made public.

1) Have any data been collected for this study already?
No, no data have been collected for this study yet.

2) What's the main question being asked or hypothesis being tested in this study?
The test-retest reliability of a new dynamic proprioceptive accuracy test (using the HapticMaster, a 3 degrees of freedom robotic arm) will be investigated and compared with an existing method (joint position reproduction test with the elbow).

3) Describe the key dependent variable(s) specifying how they will be measured.
The dependent variables are the absolute error (AE) and variable error (VE) on the dynamic accuracy and position reproduction tasks. The mean absolute deviation from the expected trajectory/angle will serve as AE. Variance within subjects on the deviation will serve as VE.

4) How many and which conditions will participants be assigned to?
2 x 2 within-subjects conditions that are crossed within participant:
- Session, which will contain two levels: test (first session) and retest (second session 24 +/- 3 hours later).
- Visual feedback, which will contain two levels: no visual feedback (participants wear blindfold) and visual feedback (participants are not blindfolded).

5) Specify exactly which analyses you will conduct to examine the main question/hypothesis.
The intraclass correlation coefficient (ICC) (Shrout & Fleiss, 1979) between repeated measures will be used to test agreement between test and retest sessions for AE and VE for both tasks separately. RM ANOVAs will be used to test for differences between sessions. Further RM ANOVAs will be used to test for training effects (main effect of trial) within each task per condition. We expect no differences between sessions and no training effect to occur.
Pearson correlation coefficients (r) will be calculated to test the relationship between the AE of both tasks and VE of both tasks. When data is not normally distributed, non-parametric tests will be used, e.g. Spearman rank-order correlation, rho. We expect a significant correlation between the two tasks.
Alpha level of 5% for both the Pearson’s correlation coefficients and the RM ANOVAs. Bonferroni corrections will be used in case of multiple testing. The ICC categories of reliability used for reference will be as follows: 0.0–0.4 (poor), 0.4–0.75 (fair to good), and 0.75–1.00 (good to excellent) (Fleiss, 1986).

6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
Participants who perform completely different movement patterns (e.g., triangles) will be excluded based on visual inspection of the movement trajectories.

7) How many observations will be collected or what will determine sample size?
No need to justify decision, but be precise about exactly how the number will be determined.

We will test 25 participants. If people drop out between sessions, we will continue recruitment until full data set of 25 participants on 2 sessions is reached.

8) Anything else you would like to pre-register?
(e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?)

RM ANOVAs will be used to test for the effect of visual feedback. We expect a higher mean deviation when no visual feedback is available.