Advances in Integrative Medicine 2 (2015) 96–102
Research article
Estimating the prevalence of use of kinesiology-style manual muscle testing: A survey of educators
Anne M. Jensen
Background: Manual muscle testing (MMT) is a non-invasive assessment method used by a variety of manual therapists to evaluate neuromusculoskeletal integrity. Goodheart developed a technique, Applied Kinesiology, where muscles are tested, not to evaluate muscular strength, but neural control. Following Goodheart’s work, a third type of MMT emerged, often referred to colloquially as ‘‘muscle testing’’ or ‘‘kinesiology.’’ This type of muscle testing, kinesiology-style MMT (kMMT) typically only uses one muscle, tested repeatedly, to scan for the presence of target conditions, such as stress or food allergies. While AK-MMT has been found to be used by approximately 40% of American chiropractors, little is known about the prevalence of use of kMMT. The aim of this study was to investigate the prevalence of use of kinesiology-style manual muscle testing (kMMT).
Methods: First, a search of Internet databases, textbooks, and expert opinion were used to compile a list of known technique systems that use kMMT. Direct contact was attempted to representatives of each kMMT technique system. Once contacted, the representative was asked to provide a conservative estimate of the number trained in their form of kMMT. For those organisations unable to provide an estimate, expert opinion was sought to approximate the numbers trained. From this data, an estimation of the prevalence of use of kMMT was made.
Results: Seventy-nine kMMT technique systems were identified, 46 of which provided an estimate and 33 did not (for various reasons). From information provided, kMMT was then estimated to be used by over 1 million people worldwide.
Summary: With the prevalence of use at over 1 million people worldwide, kMMT merits further consideration and investigation into its usefulness in clinical settings. This estimation might be amplified due to the possibility of redundancies or attrition. Likewise, it might be low due to misclassification or too narrow search methods.