ABSTRACT
Quantification of any biomechanical factor requires several steps: definition of characteristics of a motor system as a physical variable, development or implementation of a measurement method, technical and clinical validation of the measurement method, establishment of a normal database, optimization of the number of parameters, development of the report, and establishment of a patients’database. A common problem that must be addressed in any measurement method is the development of so-called “normal” databases. The term “normal database” is used in this chapter as a distribution of variables described by a mean value and a standard deviation, obtained from a representative, able-bodied population. In ergonomics, the prediction of developmentally induced changes of biomechanical factors is critical for optimal design of the technological environment. In medicine, the development of normal databases of instrumental outcome measures, that is often biomechanical factors (such as strength, ranges of motion, etc.), is important to quantify biomechanical deficits, to help establish goals for treatment, and to evaluate the progress of treatment. Motor system dysfunction during childhood and adolescence should be treated with special care as structural deformations may become permanent in a very short time. The consequences of treatment applied during these phases determine the quality of life. The establishment of such ranges is necessary to compare the variables of an individual patient or a group of patients to the variables from able-bodied population (“normal”). The timeconsuming and expensive normal database collection is more complex in children and adolescents due to their growth and the changes in their body shape and size. The fact that children undergo physical growth creates another problem in the application of standard age grouping in rehabilitation. If during the same calendar year a child undergoes rehabilitation and an increase in the outcome measure is noticed, it is unknown if it is a result of intervention or growth. It is also difficult using the age-group
design in clinical trials involving patients with rare diseases, because it may be difficult to enrol children in the same age group. The standard approach to the problem of creating a normative database in children originates in anthropometrics. It requires data collection from at least 30 children in each age group (Wolanski 1975). This means that at least 360 children aged 6-18 years, should undergo measurements. The distance of a data point of an individual patient from the mean age is defined with a z-score or growth graphs. A z-score is calculated as a ratio between the difference of a data point obtained in a patient and the mean value in an age group divided by the standard deviation of the age group. The mean is equal to zero. When the result in a patient is within 2 they are considered “normal.” The distance between the means in two groups can be quantified in a similar way using a larger standard deviation. The growth graph presentation of the data usually involves percentile calculations for individual children. The large number of motor system parameters at the range of its structure and function makes the process of normal database collection extremely difficult and time consuming. Contrary to anthropometrics, the research infrastructure for rehabilitation is still under development. It is clear that various measurement methods must be applied to evaluate the effectiveness of rehabilitation. The popular view that each laboratory should establish its own database was acceptable when one laboratory was involved in one or two types of measurement. Instead an effort should be made to establish the rules allowing normalization, transfer, and the updating of existing databases.
