Participants – Demographic Data
Seventy-one patients with chronic migraine (n=56) and frequent episodic migraine (n=15) met the inclusion criteria. Overall, 89% of participants were female, 65% reported headache during examination, and 17% were examined interictally (Demographic data is shown in Table 1).
Table 1
Variables
|
Mean / number
|
SD/ percentage
|
diagnosis
|
|
|
|
CM
|
56
|
79%
|
|
EM
|
15
|
21%
|
ictal
|
|
12
|
17%
|
Age (years)
|
|
44.01
|
13.5
|
sex
|
|
|
|
female
|
63
|
89%
|
|
male
|
8
|
11%
|
BMI
|
|
24.9
|
4.9
|
Mean and standard deviation (SD) or number and percentage distribution of diagnosis and sociodemographic data. CM: chronic migraine; EM: episodic migraine; BMI: Body mass index.
Determination of the palpation points to be considered - principal component analysis and component matrix.
The principal component calculation with the items of all six palpation points from C1 to C3 showed that in the component matrix the palpation points of C3 loaded less on the principal component than the four palpation points of C1 and C2 (Table 2A). Looking at the associated eigenvalues, component two also had an eigenvalue >1.0 (Table 3) and was thus above the cut-off value for exclusion [17]. It cannot be clearly determined with only six palpation points whether one or two components are represented. Thus, the points did not clearly measure only one construct. For subgroup assignment, however, it is necessary to reduce to one construct.
After removing the items that loaded less on the first component (C3 left and right, Table 2A), i.e., considering only the palpation points of C1 and C2, the principal component analysis indicated that only component one had an eigenvalue >1.0 and was thus clearly different from the other components, whose eigenvalues were <1.0 (Table 3). Accordingly, it can be assumed that one single construct was measured by the four items. The associated component matrix showed that all four items (palpation points of C1 and C2) loaded strongly on this one component (Table 2B). Consequently, the items measured the same underlying latent construct.
Table 2
Component matrix with six and four palpation points
Component matrix A
|
Component matrix B
|
6 Palpation Points
|
Component 1
|
Component 2
|
4 Palpation Points
|
Component 1
|
Palpation C1 R
|
0.769
|
-
|
Palpation C1 R
|
0.829
|
Palpation C2 L
|
0.704
|
-
|
Palpation C2 L
|
0.750
|
Palpation C2 R
|
0.655
|
0.504
|
Palpation C1 L
|
0.701
|
Palpation C3 L
|
0.516
|
-
|
Palpation C2 R
|
0.584
|
Palpation C3 R
|
0.576
|
0.611
|
|
Palpation C1 L
|
0.584
|
-0.590
|
|
Component matrix A: loading of the six palpation points C1-3 unilaterally left (L) and right (R) on components 1 and 2, the items with the lowest loading are marked in bold; component matrix B: loading of the four palpation points C1-2 unilaterally left and right on component 1.
Thus, in the principal component analysis, it was shown that segments C1 and C2 measured the same latent construct and therefore the pain response of the four palpation points can be combined for group assignment. C3 on the other hand should not be considered for group assignment (Table 3). According to Field [17], Cronbach's alpha indicates an internal consistency for both construct measures that can be classified as acceptable (Table 3).
Table 3
Principal component analysis and eigenvalues
C1 – C3
|
C1 – C2
|
Component
|
Initial Eigenvalues
|
Component
|
Initial Eigenvalues
|
|
Eigenvalue
|
% of Varianz
|
Cummulated %
|
|
Eigenvalue
|
% of Varianz
|
Cummulated %
|
1
|
2.46
|
40.9
|
40.9
|
1
|
2.08
|
52.1
|
52.1
|
2
|
1.15
|
19.2
|
60.1
|
2
|
0.90
|
22.7
|
74.8
|
3
|
0.92
|
15.3
|
75.5
|
3
|
0.60
|
15.1
|
89.9
|
4
|
0.65
|
10.8
|
86.2
|
4
|
0.41
|
10.1
|
100
|
5
|
0.52
|
8.6
|
94.9
|
|
|
|
|
6
|
0.30
|
5.2
|
100
|
|
|
|
|
Cronbach´s Alpha
|
0.71
|
|
|
Cronbach´s Alpha
|
0.69
|
|
|
The table presents the principal component analysis of the results of manual palpation of segments C1 - C3 (left) and segments C1 - C2 (right) and the corresponding Cronbach's alpha value. (C1/C2/C3: first/second/third cervical spine segment).
Psychometric properties using IRT
The rating scale model (RSM) described by Andrich (1978) showed the best model-data fit statistics (lowest AIC and BIC values, not significant p value (p=0.4321) when compared to the partial credit model) [21]. The RSM is a parsimonious IRT model where each item shares the same discrimination parameter, and the same responses have the same meaning across all items [19].
After fitting the RSM, item hierarchy was investigated using the boundary characteristic curves (BCC) (see figure 2). The BCC displays the midpoint probabilities for choosing one response option versus the next one. The values given on the x-axis in figure 2 represent the difficulty parameters. The segment C2 had lower difficulty parameters than C1. This reflects that migraine patients of the current sample with a lower degree of cervical dysfunction (left side of the graph) were more likely to show a pain response to palpation on C2. Whereas only participants with a higher degree of dysfunction had a higher possibility to show a pain response (local pain or referred pain) on palpation of C1. Figure 2 also displays that the curves of all four items are parallel in their central part. That is because all four items share the same item discrimination parameter (discrimination value 1.24 95%CI:0.8-1.67) which is within the desirable level.
Figure 3 displays the category characteristic curve of all four items showing a clear order of the 3 categories (no pain, local pain, referred pain). Only the first option (no pain) and the last option (referred pain) are monotonically decreasing and increasing, respectively. Participants with a low dysfunction (left side of the x-axis) have the highest probability to respond with no pain on palpation of all four palpation points