Why Assess Measures Used with the SCI population?

Why is there a need to assess the psychometric or clinometric properties of an outcome measure in different clinical populations?

  • Many outcome measures have a considerable body of research suggesting it is valid and reliable. Do we really need to test it in different diagnostic populations?
    The short answer is ‘absolutely’. The long answer is a bit academic, but important all the same.
  • Gold standard measures used across populations may be deficient in measuring characteristics in the SCI population.
  • Measures not made specifically for SCI may contain items that do not apply, and may affect an individual’s seriousness to answer, confound the data and prevent meaningful interpretation of data.

Example: Functional Independence Measure (FIM)

  • Gold standard for the assessment of basic function.
  • Despite its popularity and its universal recognition, attempts to use it across a broad range of disabling physical disorders, including SCI, has revealed deficiencies and inadequacies.

In response, Catz and colleagues (1997) created the Spinal Cord Independence Measure (SCIM). The results demonstrate that the responsiveness, or the ability to detect change, is better in the SCIM than the FIM. Now in its third version, the SCIM III is gaining international acceptance as the measure to use to assess functioning after SCI.

Example: Short Form-36 and the Short-Form-12.

  • Extremely popular generic surveys of health related Quality of Life (QOL).
  • Includes items oriented around activity limitation at the personal level, as well as participation/restriction at a societal level (e.g. can you lift and carry and object; can you climb stairs?). It seems obvious that a good proportion of the SCI population would not be able to complete many of these activities.

This is why it is critical to assess that each survey item is first and foremost appropriate for the level of SCI being assessed, as unacceptable items can alter the individual’s response (seriousness to answer) or confound the data from each study cohort.

This stance does not mean that new measures should be created for every diagnosis, health condition or situation, but it does make sense that existing measures must be validated for each study population so they are both sufficiently accurate and sensitive to detect a meaningful difference in a functionally significant clinical endpoint between the experimental and control groups of the trial.