Detecting delirium in Parkinson's disease: an evaluation of diagnostic accuracy of bedside tools.

Background: Delirium is a serious, acute neuropsychiatric condition associated with fluctuating attention and altered arousal. Delirium in Parkinson's disease (PD) is common but often missed in hospital due to shared clinical features. This study aimed to evaluate the accuracy of current tools used to identify delirium in inpatients with PD.

Beta-adrenoceptor drugs and progression to Parkinson's disease milestones in a large pooled incident cohort.

Beta-adrenoceptor-blockers and agonists have been associated with an increased and decreased risk of Parkinson's disease (PD), respectively. We aimed to investigate whether these medications are linked to clinical heterogeneity and progression in PD. Longitudinal data from the Parkinson's Incident Cohorts Collaboration (n = 1107) were analysed.

Continuous Assessment of Daily-Living Gait Using Self-Supervised Learning of Wrist-Worn Accelerometer Data.

Physical activity and mobility are critical for healthy aging and predict diverse health outcomes. While wrist-worn accelerometers are widely used to monitor physical activity, estimating gait metrics from wrist data remains challenging. We extend ElderNet, a self-supervised deep-learning model previously validated for walking-bout detection, to estimate gait metrics from wrist accelerometry.

Systematic review of wearables assessing medication effect on motor function and symptoms in Parkinson's disease.

To improve motor function and symptoms, people with Parkinson's (PwP) typically take dopaminergic medication. In PwP, wearable technology (WT) can provide objective insights into medication effect. This review aims to identify and explore literature which uses WT to quantify the effect of medication on motor function and symptoms in PwP (PROSPERO 2022 CRD42022310018).

Dementia risk prediction in early Parkinson's disease: Validation and genetic integration of the Montreal Parkinson risk of dementia scale (MoPaRDS).

BackgroundPrediction models for dementia in Parkinson disease (PD) are needed to better identify high-risk patients, but existing risk models often lack validation in early-stage PD, when prognosis is most challenging.ObjectiveThis study aims to validate the Montreal Parkinson Risk of Dementia Scale (MoPaRDS) in six population-based cohorts of newly diagnosed PD and to evaluate if incorporating genetic factors ( and ) enhances its performance.MethodsWe calculated MoPaRDS scores for 1108 newly diagnosed PD patients, and MoPaRDS + +  for the 941 patients with complete genetic data.

Brain network connectivity underlying neuropsychiatric symptoms in prodromal Lewy body dementia.

Neuropsychiatric symptoms (NPS) are prevalent, emerge early, and are associated with poorer outcomes in Lewy body dementia (LBD). Research suggests NPS may reflect LBD-related dysfunction in distributed neuronal networks. This study investigated NPS neural correlates in prodromal LBD using resting-state functional MRI.

Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults.

Progressive gait impairment is common among aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data.

Profiling people with Parkinson's disease at risk of cognitive decline: Insights from PPMI and ICICLE-PD data.

Introduction: A subset of people with Parkinson's disease (PD) develop dementia faster than others. We aimed to profile PD cognitive subtypes at risk of dementia based on their rate of cognitive decline.

Method: Latent class mixed models stratified subtypes in Parkinson's Progression Markers Initiative (PPMI) (= 770) and ICICLE-PD (= 212) datasets based on their decline in the Montreal Cognitive Assessment over at least 4 years.

Improving Conversations about Parkinson's Dementia.

Background: People with Parkinson's disease (PD) have an increased risk of dementia, yet patients and clinicians frequently avoid talking about it due to associated stigma, and the perception that "nothing can be done about it". However, open conversations about PD dementia may allow people with the condition to access treatment and support, and may increase participation in research aimed at understanding PD dementia.

Objectives: To co-produce information resources for patients and healthcare professionals to improve conversations about PD dementia.

Automated Gait Detection in Older Adults during Daily-Living using Self-Supervised Learning of Wrist-Worn Accelerometer Data: Development and Validation of ElderNet.

Progressive gait impairment is common in aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data.

Early-stage idiopathic Parkinson's disease is associated with reduced circular RNA expression.

Neurodegeneration in Parkinson's disease (PD) precedes diagnosis by years. Early neurodegeneration may be reflected in RNA levels and measurable as a biomarker. Here, we present the largest quantification of whole blood linear and circular RNAs (circRNA) in early-stage idiopathic PD, using RNA sequencing data from two cohorts (PPMI = 259 PD, 161 Controls; ICICLE-PD = 48 PD, 48 Controls).

Translating digital healthcare to enhance clinical management: a protocol for an observational study using a digital health technology system to monitor medication adherence and its effect on mobility in people with Parkinson's.

Introduction: In people with Parkinson's (PwP) impaired mobility is associated with an increased falls risk. To improve mobility, dopaminergic medication is typically prescribed, but complex medication regimens result in suboptimal adherence. Exploring medication adherence and its impact on mobility in PwP will provide essential insights to optimise medication regimens and improve mobility.

Can Digital Mobility Assessment Enhance the Clinical Assessment of Disease Severity in Parkinson's Disease?

Background: Real-world walking speed (RWS) measured using wearable devices has the potential to complement the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS III) for motor assessment in Parkinson's disease (PD).

Objective: Explore cross-sectional and longitudinal differences in RWS between PD and older adults (OAs), and whether RWS was related to motor disease severity cross-sectionally, and if MDS-UPDRS III was related to RWS, longitudinally.

Methods: 88 PD and 111 OA participants from ICICLE-GAIT (UK) were included.

Predicting cognitive decline using neuropsychiatric symptoms in prodromal Lewy body dementia: A longitudinal study.

Introduction: Neuropsychiatric symptoms (NPS) in Lewy body dementias (LBD) occur frequently and early in disease progression. Such symptoms are associated with worse quality of life, caregiver burden and functional limitations. Limited evidence exists, however, outlining the longitudinal relationship between NPS and cognitive decline in prodromal LBD.

The Role of Wearable Sensors to Monitor Physical Activity and Sleep Patterns in Older Adult Inpatients: A Structured Review.

Low levels of physical activity (PA) and sleep disruption are commonly seen in older adult inpatients and are associated with poor health outcomes. Wearable sensors allow for objective continuous monitoring; however, there is no consensus as to how wearable sensors should be implemented. This review aimed to provide an overview of the use of wearable sensors in older adult inpatient populations, including models used, body placement and outcome measures.

A cholinergic contribution to postural control and freezing of gait in Parkinson's disease.

This scientific commentary refers to ‘Cholinergic system correlates of postural control changes in Parkinson's disease freezers’ by Roytman (https://doi.org/10.1093/brain/awad134).

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium.

Background: Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates.

Methods: Twenty healthy older adults, 20 people with Parkinson's disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.

A multi-sensor wearable system for the assessment of diseased gait in real-world conditions.

Accurately assessing people's gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts).

Feasibility and usability of a digital health technology system to monitor mobility and assess medication adherence in mild-to-moderate Parkinson's disease.

Introduction: Parkinson's disease (PD) is a neurodegenerative disorder which requires complex medication regimens to mitigate motor symptoms. The use of digital health technology systems (DHTSs) to collect mobility and medication data provides an opportunity to objectively quantify the effect of medication on motor performance during day-to-day activities. This insight could inform clinical decision-making, personalise care, and aid self-management.

Safety and tolerability of adjunct non-invasive vagus nerve stimulation in people with parkinson's: a study protocol.

Background: Parkinson's disease (PD) is the fastest growing neurological condition worldwide. Recent theories suggest that symptoms of PD may arise due to spread of Lewy-body pathology where the process begins in the gut and propagate transynaptically via the vagus nerve to the central nervous system. In PD, gait impairments are common motor manifestations that are progressive and can appear early in the disease course.