Arthrogryposis multiplex congenita (AMC) is a birth defect that involves congenital joint contractures in two or more joints including the limbs, spine, and jaw. The purpose of our study was to identify long-term outcomes of adults with AMC. We recruited 177 participants from over 15 countries, making this the largest international study of adults with AMC. Participants provided demographic information including living situation and mobility and completed two standardized outcome measures, of quality of life and physical activity, using an online survey format. The data were compiled and descriptive analyses were performed. The study group consisted of 72% females and a mean age of 39 years. Over 90% of participants had upper and lower limb involvement, 35% had scoliosis or lordosis while 16% had jaw problems. Participants had an average of nine (0-70) surgeries at the time of the study. The majority (75%) of respondents lived independently of family members (on their own or with a partner). Participants were nearly three times more likely to have a graduate degree than the general US population. Participants reported lower physical function scores than the general US population; however, they reported similar or higher scores for the other quality of life domains of the SF-36. They were considerably less physically active than able-bodied individuals. Half of participants experienced chronic back pain and 60% reported joint pain. Additionally, almost half of the participants took regular pain medications.

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SUMMARYActigraphy can assist in the detection of periodic limb movements in sleep. Although several actigraphs have been previously reported to accurately detect periodic limb movements, many are no longer available; of the existing actigraphs, most sample too infrequently to accurately detect periodic limb movements. The purpose of this study was to use advanced signal analysis to validate a readily available actigraph that has the capability of sampling at relatively high frequencies. We simultaneously recorded polysomnography and bilateral ankle actigraphy in 96 consecutive patients presenting to our sleep laboratory. After pre-processing and conditioning, the bilateral ankle actigraphy signals were then analysed for 14 simple time, frequency and morphology-based features. These features reduced the signal dimensionality and aided in better representation of the periodic limb movement activity in the actigraph signals. These features were then processed by a Na€ ıve-Bayes binary classifier for distinguishing between normal and abnormal periodic limb movement indices. We trained the Na€ ıve-Bayes classifier using a training set, and subsequently tested its classification accuracy using a testing set. From our experiments, using a periodic limb movement index cut-off of 5, we found that the Na€ ıve-Bayes classifier had a correct classification rate of 78.9%, with a sensitivity of 80.3% and a specificity of 73.7%. The algorithm developed in this study has the potential of facilitating identification of periodic limb movements across a wide spectrum of patient populations via the use of bilateral ankle actigraphy.