**7. Clinical analysis on the effectiveness of programs**

The clinical procedure for representing the efficiency of this treatment is very difficult because of the low number of cases. Representation of improvement using statistical analysis is limited by either parametric or non-parametric evaluation. In clinical rehabilitation, matching age and disease condition to set up a control or treated group is very difficult. Furthermore, presentation of a positive outcome in clinical improvement is very important.

Many reports of case studies from rehabilitation have shown results with explanations such as postural restoration from physical therapy (Spence, 2008). However, an interesting procedure for evaluating a single system was designed by Bloom and Fischer (1982). This system was designed basically to involve an individual or a single system by repeatedly taking recordings of dependent variables (Ottenbacher, 1986). The components of this design are composed of only sequential application and withdrawal or variation of intervention, with the use of frequent and repeated measures. Thus, this design is not a fixed procedure and can be applied in various study proposals.

The design of a case study has many models; A-B, A-B-A, A-B-A-B, and B-A-B, where A is the baseline period and B the treatment period. There is also an A-B-C model for use in different treatments. Various repeated data recordings are performed in each period, and more than 4 are enough for clinical analysis when a Bloom Table is used. Clinical explanation can be presented by visual inspection and raw data analysis. A simple line graph is an easy procedure for presenting the changes and tendency in each period. Improvement or deterious results in pre-treatment, during treatment or post-treatment can be explained from a changing or trend line. In addition, comparison of mean levels in each period is also a very important evaluation. Statistical analysis of this system can be performed using the Bloom Table (Bloom, 1975), which observes the proportion during baseline and number of treatments above or below the celeration line. Important analysis of data in each period involves changes in all parameters that must evaluate autocorrelation, which helps to separate changes between condition and treatment. Other procedures that present the statistical difference between baseline and treatment use the two standard deviation band method and C-statistic (Ottenbacher, 1986). Some researches have used this design such as the study of Cleland and Palmer (2004), who showed the effectiveness of manual physical therapy, therapeutic exercise, and patient education on bilateral disc displacement in a single-case A1 (control period) –B (intervention period) -A2 (withdrawal of the intervention) design, and also presented the results by visual analog scale and the two standard deviation band method (Cleland & Palmer, 2004). Overall, representation of effective rehabilitation or treatment in rare or few cases can be performed with a single case design.
