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Abstract Parkinson’s disease (PD) is a neurodegenerative disorder characterised by motor symptoms such as gait dysfunction and postural instability. Technological tools to continuously monitor outcomes could capture the hour-by-hour symptom fluctuations of PD. Development of such tools is hampered by the lack of labelled datasets from home settings. To this end, we propose REMAP (REal-world Mobility Activities in Parkinson’s disease), a human rater-labelled dataset collected in a home-like setting. It includes people with and without PD doing sit-to-stand transitions and turns in gait. These discrete activities are captured from periods of free-living (unobserved, unstructured) and during clinical assessments. The PD participants withheld their dopaminergic medications for a time (causing increased symptoms), so their activities are labelled as being “on” or “off” medications. Accelerometry from wrist-worn wearables and skeleton pose video data is included. We present an open dataset, where the data is coarsened to reduce re-identifiability, and a controlled dataset available on application which contains more refined data. A use-case for the data to estimate sit-to-stand speed and duration is illustrated.

BackgroundSleep disturbances are a potentially modifiable risk factor for neurodegenerative dementia secondary to Alzheimer disease (AD) and Lewy body disease (LBD). Therefore, we need to identify the best methods to study sleep in this population. ObjectiveThis study will assess the feasibility and acceptability of various wearable devices, smart devices, and remote study tasks in sleep and cognition research for people with AD and LBD. MethodsWe will deliver a feasibility and acceptability study alongside a prospective observational cohort study assessing sleep and cognition longitudinally in the home environment. Adults aged older than 50 years who were diagnosed with mild to moderate dementia or mild cognitive impairment (MCI) due to probable AD or LBD and age-matched controls will be eligible. Exclusion criteria include lack of capacity to consent to research, other causes of MCI or dementia, and clinically significant sleep disorders. Participants will complete a cognitive assessment and questionnaires with a researcher and receive training and instructions for at-home study tasks across 8 weeks. At-home study tasks include remote sleep assessments using wearable devices (electroencephalography headband and actigraphy watch), app-based sleep diaries, online cognitive assessments, and saliva samples for melatonin- and cortisol-derived circadian markers. Feasibility outcomes will be assessed relating to recruitment and retention, data completeness, data quality, and support required. Feedback on acceptability and usability will be collected throughout the study period and end-of-study interviews will be analyzed using thematic analysis. ResultsRecruitment started in February 2022. Data collection is ongoing, with final data expected in February 2024 and data analysis and publication of findings scheduled for the summer of 2024. ConclusionsThis study will allow us to assess if remote testing using smart devices and wearable technology is a viable alternative to traditional sleep measurements, such as polysomnography and questionnaires, in older adults with and without MCI or dementia due to AD or LBD. Understanding participant experience and the barriers and facilitators to technology use for research purposes and remote research in this population will assist with the development of, recruitment to, and retention within future research projects studying sleep and cognition outside of the clinic or laboratory. International Registered Report Identifier (IRRID)DERR1-10.2196/52652

Abstract Background Sleep and circadian rhythm disorders are well recognised in both AD (Alzheimer’s Disease) dementia and MCI-AD (Mild Cognitive Impairment due to Alzheimer’s Disease). Such abnormalities include insomnia, excessive daytime sleepiness, decreased sleep efficiency, increased sleep fragmentation and sundowning. Enhancing understanding of sleep abnormalities may unveil targets for intervention in sleep, a promising approach given hypotheses that sleep disorders may exacerbate AD pathological progression and represent a contributory factor toward impaired cognitive performance and worse quality of life. This may also permit early diagnosis of AD pathology, widely acknowledged as a pre-requisite for future disease-modifying therapies. This study aims to bridge the divide between in-laboratory polysomnographic studies which allow for rich characterisation of sleep but in an unnatural setting, and naturalistic studies typically approximating sleep through use of non-EEG wearable devices. It is also designed to record sleep patterns over a 2 month duration sufficient to capture both infradian rhythm and compensatory responses following suboptimal sleep. Finally, it harnesses an extensively phenotyped population including with AD blood biomarkers. Its principal aims are to improve characterisation of sleep and biological rhythms in individuals with AD, particularly focusing on micro-architectural measures of sleep, compensatory responses to suboptimal sleep and the relationship between sleep parameters, biological rhythms and cognitive performance. Methods/design This observational cohort study has two arms (AD-MCI / mild AD dementia and aged-matched healthy adults). Each participant undergoes a baseline visit for collection of demographic, physiological and neuropsychological information utilising validated questionnaires. The main study period involves 7 nights of home-based multi-channel EEG sleep recording nested within an 8-week study period involving continuous wrist-worn actigraphy, sleep diaries and regular brief cognitive tests. Measurement of sleep parameters will be at home thereby obtaining a real-world, naturalistic dataset. Cognitive testing will be repeated at 6 months to stratify participants by longitudinal disease progression. Discussion This study will generate new insights particularly in micro-architectural measures of sleep, circadian patterns and compensatory sleep responses in a population with and without AD neurodegenerative change. It aims to enhance standards of remotely based sleep research through use of a well-phenotyped population and advanced sleep measurement technology.

Introduction: Technology holds the potential to track disease progression and response to neuroprotective therapies in Parkinson’s disease (PD). The sit-to-stand (STS) transition is a frequently occurring event which is important to people with PD. The aim of this study was to demonstrate an automatic approach to quantify STS duration and speed using a real-world free-living dataset and look at clinical correlations of the outcomes, including whether STS parameters change when someone withholds PD medications. Methods: Eighty-five hours of video data were collected from 24 participants staying in pairs for 5-day periods in a naturalistic setting. Skeleton joints were extracted from the video data; the head trajectory was estimated and used to estimate the STS parameters of duration and speed. Results: 3.14 STS transitions were seen per hour per person on average. Significant correlations were seen between automatic and manual STS duration (Pearson rho − 0.419, p = 0.042) and between automatic STS speed and manual STS duration (Pearson rho − 0.780, p < 0.001). Significant and strong correlations were seen between the gold-standard clinical rating scale scores and both STS duration and STS speed; these correlations were not seen in the STS transitions when the participants were carrying something in their hand(s). Significant differences were seen at the cohort level between control and PD participants’ ON medications’ STS duration (U = 6,263, p = 0.018) and speed (U = 9,965, p < 0.001). At an individual level, only two participants with PD became significantly slower to STS when they were OFF medications; withholding medications did not significantly change STS duration at an individual level in any participant. Conclusion: We demonstrate a novel approach to automatically quantify and ecologically validate two STS parameters which correlate with gold-standard clinical tools measuring disease severity in PD.

Introduction: The earliest stages of alpha-synucleinopathies are accompanied by non-specific prodromal symptoms such as diminished sense of smell, constipation and depression, as well as more specific prodromal conditions including REM Sleep Behaviour Disorder (RBD). While the majority of RBD patients will develop an alpha-synucleinopathy, one of the greatest clinical challenges is determining whether and when individual patients will phenoconvert. Clinical evaluation of a patient presenting with RBD should therefore include robust and objective assessments of known alpha-synucleinopathy prodromes. Methods: This study compared olfactory function self-report measures with psychophysical ‘Sniffin’ Stick 16-item Identification’ test scores in Control (n = 19), RBD (n = 16) and PD (n = 17) participants. Results: We confirm that olfactory test scores are significantly diminished in RBD and PD groups compared to Controls (p

BackgroundParkinson disease (PD) symptoms are complex, gradually progressive, and fluctuate hour by hour. Home-based technological sensors are being investigated to measure symptoms and track disease progression. A smart home sensor platform, with cameras and wearable devices, could be a useful tool to use to get a fuller picture of what someone’s symptoms are like. High-resolution video can capture the ground truth of symptoms and activities. There is a paucity of information about the acceptability of such sensors in PD. ObjectiveThe primary objective of our study was to explore the acceptability of living with a multimodal sensor platform in a naturalistic setting in PD. Two subobjectives are to identify any suggested limitations and to explore the sensors’ impact on participant behaviors. MethodsA qualitative study was conducted with an inductive approach using semistructured interviews with a cohort of PD and control participants who lived freely for several days in a home-like environment while continuously being sensed. ResultsThis study of 24 participants (12 with PD) found that it is broadly acceptable to use multimodal sensors including wrist-worn wearables, cameras, and other ambient sensors passively in free-living in PD. The sensor that was found to be the least acceptable was the wearable device. Suggested limitations on the platform for home deployment included camera-free time and space. Behavior changes were noted by the study participants, which may have related to being passively sensed. Recording high-resolution video in the home setting for limited periods of time was felt to be acceptable to all participants. ConclusionsThe results broaden the knowledge of what types of sensors are acceptable for use in research in PD and what potential limitations on these sensors should be considered in future work. The participants’ reported behavior change in this study should inform future similar research design to take this factor into account. Collaborative research study design, involving people living with PD at every stage, is important to ensure that the technology is acceptable and that the data outcomes produced are ecologically valid and accurate. International Registered Report Identifier (IRRID)RR2-10.1136/bmjopen-2020-041303

The use of wearable sensors allows continuous recordings of physical activity from participants in free-living or at-home clinical studies. The large amount of data collected demands automatic analysis pipelines to extract gait parameters that can be used as clinical endpoints. We introduce a deep learning-based automatic pipeline for wearables that processes tri-axial accelerometry data and extracts gait events—bout segmentation, initial contact (IC), and final contact (FC)—from a single sensor located at either the lower back (near L5), shin or wrist. The gait events detected are posteriorly used for gait parameter estimation, such as step time, length, and symmetry. We report results from a leave-one-subject-out (LOSO) validation on a pilot study dataset of five participants clinically diagnosed with Parkinson’s disease (PD) and six healthy controls (HC). Participants wore sensors at three body locations and walked on a pressure-sensing walkway to obtain reference gait data. Mean absolute errors (MAE) for the IC events ranged from 22.82 to 33.09 milliseconds (msecs) for the lower back sensor while for the shin and wrist sensors, MAE ranges were 28.56–64.66 and 40.19–72.50 msecs, respectively. For the FC-event detection, MAE ranges were 29.06–48.42, 40.19–72.70 and 36.06–60.18 msecs for the lumbar, wrist and shin sensors, respectively. Intraclass correlation coefficients, ICC(2,k), between the estimated parameters and the reference data resulted in good-to-excellent agreement (ICC ≥ 0.84) for the lumbar and shin sensors, excluding the double support time (ICC = 0.37 lumbar and 0.38 shin) and swing time (ICC = 0.55 lumbar and 0.59 shin). The wrist sensor also showed good agreements, but the ICCs were lower overall than for the other two sensors. Our proposed analysis pipeline has the potential to extract up to 100 gait-related parameters, and we expect our contribution will further support developments in the fields of wearable sensors, digital health, and remote monitoring in clinical trials.

Background: We recently showed that by employing an enhanced drug‐delivery approach, repeated administration of glial cell line–derived neurotrophic factor (GDNF) can produce a spatially distributed increased 18F‐DOPA positron emission tomography (PET) uptake, suggesting sprouting of dopaminergic terminals throughout the putamen structure. Despite this, we failed to prove a significant measurable clinical response. Since, however, we have identified a subject demonstrating a temporal relationship between repeated GDNF infusions and dyskinesia arising in the practically defined off (pracoff) state. Objectives: To describe the development of pracoff dyskinesia across our study population and consider its utility as an indicator that trophic factor–induced terminal sprouting can affect enhanced endogenous dopamine levels. Methods: This was a blinded retrospective analysis of videotaped motor assessments at eight weekly study visits. Dyskinesia in the pracoff and supramaximal on state were rated using the Clinical Dyskinesia Rating Scale. Logistic regression was employed to explore the predictors of pracoff dyskinesia. Generalized estimated equations were used to estimate the cumulative effect of repeated GDNF infusions. Results: Mild–moderate choreiform dyskinesia in the pracoff state were seen in 47 assessments in 17 (n = 41) subjects. During the 18‐month timeframe, each subsequent 8‐week period of receiving GDNF increased the risk of demonstrating pracoff state dyskinesia by 34% (odds ratio [OR], 1.34 (95% confidence interval [CI], 1.20, 1.50); P < 0.001). An increasing supramaximal on dyskinesia score (OR, 1.17 [95% CI, 1.07, 1.30]; P = 0.001) also increased the likelihood of pracoff dyskinesia at that visit. Conclusions: We report the first description of increasingly prevalent pracoff‐state dyskinesia developing during the course of a trophic factor study. This may provide a surrogate marker that GDNF can enable recovery of endogenous dopamine release even in advanced Parkinson's disease. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]