Selecting the right (measurement) tool for the job

“New study reveals the average height of Australian adults is 100 cm…


…when measured with a metre stick.”


While the flaw in this fictitious study is easy to address, selecting an appropriate measurement tool to capture something as complex as motor impairment is not so easy. Many health conditions can lead to motor impairment (Australia Bureau of Statistics, 2013; Centers for Disease Control and Prevention, 2014), and clinicians and researchers often seek to measure different aspects of human health that contribute to or are affected by these deficits (Baker et al, 2011). For example:

  • How weak are this person’s leg muscles?
  • How does this weakness compare to a healthy individual of the same age?
  • Does this weakness impact the person’s ability to safely go up and down a flight of stairs?
  • Did the 6-week strength training program reduce the person’s level of dependence on family members for daily self-care?

There are many things we may want to know about a person’s level of motor impairment, and there are often numerous measurement tools that can be used to capture each aspects. This begs the question, what should we measure and with what tool? This question is not trivial as these data may ultimately influence patient care, intervention and service prescription (Hobart, 2003). More broadly, policy makers and research funding agencies increasingly require the use of scientifically sound measures that capture all relevant aspects of an individuals health status (Hobart et al., 2007).


Recently, Dr. Kathleen Norman (Queen’s University, Kingston, Canada) and I prepared a brief overview of factors that need to be considered when selecting a measure to assess fine motor skills in individuals with tremor disorders (Norman & Héroux, 2013). Some measurement tools focus on specific disease or impairment features – e.g., motor skills or dexterity, slowness in movement execution associated with parkinsonian bradykinesia, or magnitude of tremor – that may or may not be relevant to the end user. Less obviously, some tools may be better suited than others for specific goals such as detecting subtle dysfunction early in disease, revealing aspects of brain function affected by disease, or tracking changes expected from treatment or disease progression. Based on our appraisal, no single measure of fine motor dexterity possesses all the attributes (e.g., validity, responsiveness, interpretability, scope of hand function) that would make it optimal across a wide range of research and clinical situations and tremor disorders. Each tool had advantages and disadvantages, and ultimately a choice must be made based on a well defined measurement purpose and a clear understanding of each measure’s strengths and weaknesses.

Video of study participant doing line-tracing component of the Fahn-Tolosa-Marìn Tremor Rating Scale.



While our example focused on fine motor skills and movement disorders, a similar exercise could be carried out for almost any aspect of motor impairment. This is true for ‘basic science’ questions such as changes in spinal reflex excitability as well as more ‘global health’ questions such as a person’s level of perceived functional limitation. Poorly chosen measures can lead to data that are difficult or impossible to interpret. Worse yet, such measures may fail to capture meaningful changes in a person’s health status.

The selection of appropriate measurement tools is important to both clinicians and researchers, and resources are available to guide us through this decision process. It is definitely unwise to rush this process, if you do you may select the wrong tool for the job…like a metre stick!



Norman KE, Héroux ME (2013). Measures of fine motor skills in people with tremor disorders: appraisal and interpretation. Front Neurol 10,4:50.


Australia Bureau of Statistics (2013, November 13). Disability, Ageing and Carers, Australia: Summary of Findings, 2012. Retrieved from

Baker K, Cano SJ, Playford ED (2011). Outcome measurement in stroke: a scale selection strategy. Stroke 42,1787-94.

Centers for Disease Control and Prevention (2014, May 14). Fast Stats. Retrieved from

Hobart J (2003). Rating scales for neurologists. J Neurol Neurosurg Psychiatry 74, Suppl 4:iv22-iv26.

Hobart JC, Cano SJ, Zajicek JP, Thompson AJ (2007). Rating scales as outcome measures for clinical trials in neurology: problems, solutions, and recommendations. Lancet Neurol 6,1094-105.

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