Background: Remote monitoring of symptoms in Parkinson's disease (PD) using body-worn sensors would assist treatment decisions and evaluation of new treatments. To date, a rigorous, systematic evaluation of the acceptability of body-worn sensors in PD has not been undertaken. Materials and Methods: Thirty-four participants wore bilateral wrist-worn sensors for 4 h in a research facility and then for 1 week at home. Participants' experiences of wearing the sensors were evaluated using a Likert-style questionnaire after each phase. Qualitative data were collected through free-text responses. Differences in responses between phases were assessed by using the Wilcoxon rank-sum test. Content analysis of qualitative data was undertaken. ''Non-wear time'' was estimated via analysis of accelerometer data for periods when sensors were stationary. Results: After prolonged wearing there was a negative shift in participants' views on the comfort of the sensor; problems with the sensor's strap were highlighted. However, accelerometer data demonstrated high patient concordance with wearing of the sensors. There was no evidence that participants were less likely to wear the sensors in public. Most participants preferred wearing the sensors to completing symptom diaries. Conclusions: The finding that participants were not less likely to wear the sensors in public provides reassurance regarding the ecological validity of the

IntroductionCurrent PD assessment methods have inherent limitations. There is need for an objective method to assist clinical decisions and to facilitate evaluation of treatments. Accelerometers, and analysis using artificial neural networks (ANN), have shown potential as a method of motor symptom evaluation. This work describes the development of a novel PD disease state detection system informed by algorithms based on data collected in an unsupervised, home environment. We evaluated whether this approach can reproduce patient-completed symptom diaries and clinical assessment of disease state. Methods34 participants with PD wore bilateral wrist-worn accelerometers for 4 h in a research facility (phase 1) and for 7 days at home whilst completing symptom diaries (phase 2). An ANN to predict disease state was developed based on home-derived accelerometer data. Using a leave-one-out approach, ANN performance was evaluated against patient-completed symptom diaries and against clinician rating of disease state. ResultsIn the clinical setting, specificity for dyskinesia detection was extremely high (0.99); high specificity was also demonstrated for home-derived data (0.93), but with low sensitivity (0.38). In both settings, sensitivity for on/off detection was sub-optimal. ANN-derived values of the proportions of time in each disease state showed strong, significant correlations with patient-completed symptom diaries.

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there was widespread failure to address attitudes on delirium within teaching, to evaluate the impact of sessions, to involve patients and the public in teaching and to guarantee exposure to delirium. Future teaching interventions should be directed at attitudinal outcomes, using a synthesis of clinical experience with multidisciplinary interaction and supportive technologies.