BACKGROUND: Periodic limb movements (PLMs) are repetitive, stereotypical and unconscious movements, typically of the legs, that occur in sleep and are associated with several sleep disorders. The gold standard for detecting PLMs is overnight electromyography which, although highly sensitive and specific, is time and labour consuming. The current generation of smart phones is equipped with tri-axial accelerometers that record movement. AIM: To develop a smart phone application that can detect PLMs remotely. METHOD: A leg movement sensing application (LMSA) was programmed in iOS 5x and incorporated into an iPhone 4S (Apple INC.). A healthy adult male subject underwent simultaneous EMG and LMSA measurements of voluntary stereotypical leg movements. The mean number of leg movements recorded by EMG and by the LMSA was compared. RESULTS: A total of 403 leg movements were scored by EMG of which the LMSA recorded 392 (97%). There was no statistical difference in mean number of leg movements recorded between the two modalities (p = 0.3). CONCLUSION: These preliminary results indicate that a smart phone application is able to accurately detect leg movements outside of the hospital environment and may be a useful tool for screening and follow up of patients with PLMs.
BACKGROUND: Periodic limb movements (PLMs) are repetitive, stereotypical and unconscious movements, typically of the legs, that occur in sleep and are associated with several sleep disorders. The gold standard for detecting PLMs is overnight electromyography which, although highly sensitive and specific, is time and labour consuming. The current generation of smart phones is equipped with tri-axial accelerometers that record movement. AIM: To develop a smart phone application that can detect PLMs remotely. METHOD: A leg movement sensing application (LMSA) was programmed in iOS 5x and incorporated into an iPhone 4S (Apple INC.). A healthy adult male subject underwent simultaneous EMG and LMSA measurements of voluntary stereotypical leg movements. The mean number of leg movements recorded by EMG and by the LMSA was compared. RESULTS: A total of 403 leg movements were scored by EMG of which the LMSA recorded 392 (97%). There was no statistical difference in mean number of leg movements recorded between the two modalities (p = 0.3). CONCLUSION: These preliminary results indicate that a smart phone application is able to accurately detect leg movements outside of the hospital environment and may be a useful tool for screening and follow up of patients with PLMs.