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Papers of the Week

2022 Jun

Data Brief


Dataset for the performance of 15 lumbar movement control tests in nonspecific chronic low back pain.


Adelt E, Schöttker-Königer T, Luedtke K, Hall T, Schäfer A
Data Brief. 2022 Jun; 42:108063.
PMID: 35372650.


The ability to actively control movements of the lumbar spine (LMC) is believed to play an important role in non-specific chronic low back pain (NSCLBP). However, because NSCLBP is a multifactorial problem and LMC a complex ability, different aspects of LMC are still debated including the influence of pain, the question whether LMC is a cause or consequence of NSCLBP or whether differences in LMC are due to population variance. The complexity of LMC is reflected in the large number of described tests, hence it is not possible to evaluate LMC by a single test. LMC ability should be understood as a latent construct. The structure of LMC and how to summarize results of different single LMC tests is unknown. The dataset provided in this article was used to analyse the structural validity of LMC in NSCLBP. 277 participants (age 42.4 years (± 15.8), 61% female) performed 15 different test movements. 21 experienced physiotherapists rated the performance of each test movement on a nominal scale (correct/incorrect including the direction of test movement). A test was rated as "incorrect" if movement in the lumbar spine occurred prematurely and/or excessively based on the visual observation of a trained physiotherapist. In addition to the judgement whether the test performance was correct/incorrect the direction of test movement and the presence of pain was noted. For statistical analysis, raw data was converted to a binary scale (correct/incorrect). Item response theory (IRT) is recommended to analyse the data because the underlying statistical model is reflective, the single LMC tests are binary scaled (correct/incorrect) and the underlying ability (LMC) measured on a continuous scale. First dimensionality and local independence were analysed, followed by selection of the best fitting IRT model. Finally, IRT modelling was used to describe the psychometric properties of each item and each battery of tests. The datasets provided in this article are useful for calibration and for group comparisons. Besides they support a better understanding of LMC. ***Link to publication of original article in "musculoskeletal science and practice"**.