Detection of Motor Dysfunction With Wearable Sensors in Patients With Idiopathic Rapid Eye Movement Disorder

Early detection of diseases causing dementia using digital navigation and gait measures: A systematic review of evidence

Wearable digital technologies capable of measuring everyday behaviors could improve the early detection of dementia-causing diseases. We conducted two systematic reviews following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to establish the evidence base for measuring navigation and gait, two everyday behaviors affected early in AD and non-AD disorders and not adequately measured in current practice. PubMed and Web of Science databases were searched for studies on asymptomatic and early-stage symptomatic individuals at risk of dementia, with the Newcastle-Ottawa Scale used to assess bias and evaluate methodological quality. Of 316 navigation and 2086 gait records identified, 27 and 83, respectively, were included in the final sample. We highlight several measures that may identify at-risk individuals, whose quantifiability with different devices mitigates the risk of future technological obsolescence. Beyond navigation and gait, this review also provides the framework for evaluating the evidence base for future digital measures of behaviors considered for early disease detection.

Role of sleep in neurodegeneration: the consensus report of the 5th Think Tank World Sleep Forum

Sleep abnormalities may represent an independent risk factor for neurodegeneration. An international expert group convened in 2021 to discuss the state-of-the-science in this domain. The present article summarizes the presentations and discussions concerning the importance of a strategy for studying sleep- and circadian-related interventions for early detection and prevention of neurodegenerative diseases. An international expert group considered the current state of knowledge based on the most relevant publications in the previous 5 years; discussed the current challenges in the field of relationships among sleep, sleep disorders, and neurodegeneration; and identified future priorities. Sleep efficiency and slow wave activity during non-rapid eye movement (NREM) sleep are decreased in cognitively normal middle-aged and older adults with Alzheimer's disease (AD) pathology. Sleep deprivation increases amyloid-β (Aβ) concentrations in the interstitial fluid of experimental animal models and in cerebrospinal fluid in humans, while increased sleep decreases Aβ. Obstructive sleep apnea (OSA) is a risk factor for dementia. Studies indicate that positive airway pressure (PAP) treatment should be started in patients with mild cognitive impairment or AD and comorbid OSA. Identification of other measures of nocturnal hypoxia and sleep fragmentation could better clarify the role of OSA as a risk factor for neurodegeneration. Concerning REM sleep behavior disorder (RBD), it will be crucial to identify the subset of RBD patients who will convert to a specific neurodegenerative disorder. Circadian sleep-wake rhythm disorders (CSWRD) are strong predictors of caregiver stress and institutionalization, but the absence of recommendations or consensus statements must be considered. Future priorities include to develop and validate existing and novel comprehensive assessments of CSWRD in patients with/at risk for dementia. Strategies for studying sleep-circadian-related interventions for early detection/prevention of neurodegenerative diseases are required. CSWRD evaluation may help to identify additional biomarkers for phenotyping and personalizing treatment of neurodegeneration.

Sensor-based gait analysis in the premotor stage of LRRK2 G2019S-associated Parkinson's disease

INTRODUCTION

There is a need for biomarkers to monitor the earliest phases of Parkinson's disease (PD), especially in premotor stages. Here, we studied whether there are early gait alterations in carriers of the G2019S mutation of LRRK2 that can be detected by means of an inertial sensor system.

METHODS

Twenty-one idiopathic PD patients, 20 LRRK2-G2019S PD, 27 asymptomatic carriers of LRRK2-G2019S mutation (AsG2019S) and 36 controls walked equipped with 16 lightweight inertial sensors in three different experiments: i/normal gait, ii/fast gait and iii/dual-task gait. In the AsG2019S group, DaT-SPECT (¹²³I-ioflupane) with semi-quantitative analysis was carried out. Motor and cognitive performance were evaluated using MDS-UPDRS-III and MoCA scales. We employed neural network techniques to classify individuals based on their walking patterns.

RESULTS

PD patients and controls showed differences in speed, stride length and arm swing amplitude, variability and asymmetry in all three tasks (p < 0.01). In the AsG2019S group, the only differences were detected during fast walking, with greater step time on the non-dominant side (p < 0.05), lower step/stride time variability (p < 0.01) and lower step time asymmetry (p < 0.01). DaT uptake showed a significant correlation with step time during fast walking on the non-dominant side (r = -0.52; p < 0.01). The neural network was able to differentiate between AsG2019S and healthy controls with an accuracy rate of 82.5%.

CONCLUSION

Our sensor-based analysis did not detect substantial and robust changes in the gait of LRRK2-G2019S asymptomatic mutation carriers. Nonetheless, step or stride time during fast walking, supported by the observed correlation with striatal DaT binding deserves consideration as a potential biomarker in future studies.