Feasibility Of Wearable Devices Research Articles

Non-motor symptoms in dystonia: from diagnosis to treatment

The Dystonia Medical Research Foundation organized an expert virtual workshop in March 2023 to review the evidence on non-motor symptoms across the spectrum of dystonia, discuss existing assessment methods, need for their harmonisation and roadmap to achieve this, and evaluate potential treatment approaches. Albeit the most investigated non-motor domains, experts highlighted the need to identify the most accurate screening procedure for depression and anxiety, clarify their mechanistic origin and quantify their response to already available therapies. Future exploration of sleep disruption in dystonia should include determining the accuracy and feasibility of wearable devices, understanding the contribution of psychotropic medication to its occurrence, and defining the interaction between maladaptive plasticity and abnormal sleep patterns. Despite recent advances in the assessment of pain in dystonia, more research is needed to elucidate the relative importance of different mechanisms called into play to explain this impactful sensory feature and the most appropriate treatments. Amongst the different non-motor features investigated in dystonia, cognitive dysfunction and fatigue require an in-depth observation to evaluate their functional impact, their clinical profile and assessment methods and, in the case of cognition, whether impairment represents a prodrome of dementia. Finally, experts identified the development and field validation of a self-rated screening tool encompassing the full spectrum of non-motor symptoms as the most urgent step towards incorporating the management of these features into routine clinical practice.

Feasibility of wearable devices and machine learning for sleep classification in children with Rett syndrome: A pilot study.

Sleep is vital to many processes involved in the well-being and health of children; however, it is estimated that 80% of children with Rett syndrome suffer from sleep disorders. Caregiver reports and questionnaires, which are the current method of studying sleep, are prone to observer bias and missed information. Polysomnography is considered the gold standard for sleep analysis but is labor and cost-intensive and limits the frequency of data collection for sleep disorder studies. Wearable digital health technologies, such as actigraphy devices, have shown potential and feasibility as a method for sleep analysis in Rett syndrome, but have not been validated against polysomnography. Furthermore, the collected accelerometer data has limitations due to the rigidity, periodic limb movement, and involuntary muscle contractions prevalent in Rett syndrome. Heart rate and electrodermal activity, along with other physiological signals, have been linked to sleep stages and can be utilized with machine learning to provide better resistance to noise and false positives than actigraphy. This research aims to address the gap in Rett syndrome sleep analysis by comparing the performance of a machine learning model utilizing both accelerometer data and physiological data features to the gold-standard polysomnography for sleep analysis in Rett syndrome. Our analytical validation pilot study ( = 7) found that using physiological and accelerometer features, our machine learning models can differentiate between awake, non-rapid eye movement sleep, and rapid eye movement sleep in Rett syndrome children with an accuracy of 85.1% when using an individual model. Additionally, this work demonstrates that it is feasible to use digital health technologies in Rett syndrome, even at a young age, without data loss or interference from repetitive movements that are characteristic of Rett syndrome.

Determination of optimal electroencephalography recording locations for detecting drowsy driving

Early detection of drowsy driving is an important issue for driving safety. Quantitative electroencephalography (EEG) is an attractive method for detecting brain activity changes. However, further study is still needed to evaluate the feasibility of wearable devices that can detect drowsy driving in real-world settings. This study sought to determine whether convenient EEG recording locations are sensitive in detecting brain activity changes associated with drowsy driving and to characterise these EEG changes. Twenty-two healthy adult subjects were recruited to participate in a car-following task using a driving simulator. EEG data were recorded from four locations, two frontals (Fp1, Fp2) and two temporals (T3, T4) of the brain while driving. The results showed that the increase of δ activity, decrease of θ and α activity and a decrease of spectral edge frequency at 90% were found in the drowsy state compared to the alert state (paired t -tests, p <; 0.05). Effect sizes for EEG changes were larger at the temporal locations compared to frontal locations. This suggests temporal locations can be feasible recording locations for wearable monitoring devices to detect drowsy driving.