PRESSURE ALGOMETRY IN HEALTHY-SUBJECTS - INTER-EXAMINER VARIABILITY

Citation
F. Antonaci et al., PRESSURE ALGOMETRY IN HEALTHY-SUBJECTS - INTER-EXAMINER VARIABILITY, Scandinavian journal of rehabilitation medicine, 30(1), 1998, pp. 3-8
Citations number
24
Categorie Soggetti
Rehabilitation,"Sport Sciences
ISSN journal
00365505
Volume
30
Issue
1
Year of publication
1998
Pages
3 - 8
Database
ISI
SICI code
0036-5505(1998)30:1<3:PAIH-I>2.0.ZU;2-R
Abstract
The purpose of this study was to estimate inter-examiner reliability o f head and neck algometry, Pain perception thresholds were assessed wi th a mechanical pressure algometer in 21 healthy individuals, Threshol ds were assessed at 13 symmetrical points on each side of the head and neck, at the deltoid muscle and at the median finger, The pressure ra nge of the instrument proved insufficient to study the pain perception threshold on the finger, however, Two different examiners carried out one or two examinations in each subject during one day, The sequence of investigations was varied randomly, The inter-examiner reliability was found to be good, with a mean intra-class correlation coefficient (ICC) of 0.75, Intra-examiner reproducibility was excellent (mean ICC= 0.84). The mean inter-examiner coefficient of variation was 18.7%, whi le the mean coefficient of repeatability (CR) was 1.60 kg/cm(2), In co mparison, the mean intra-examiner coefficient of variation was 15% whi le the mean CR was 1.29 kg/cm(2), Statistically significant difference s between examiners were found for the frontal point (p<0.01), while a trend towards lower thresholds in one of the two observers was seen i n 10 of the 13 non-significant points, Inter-examiner reliability of s ide differences was excellent, with CR=1.23 kg/cm(2). In conclusion, m anual algometry with a rather inexpensive mechanical device has a good to excellent inter-rater reliability, When studying patients, however , the possible bias introduced by different examiners should be taken into account, both regarding study design and data analysis.