Publications by authors named "Alan Whone"

Background: Sleep 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.

Objective: This 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.

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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.

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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.

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There is currently mixed evidence on the effect of Parkinson's disease on motor adaptation. Some studies report that patients display adaptation comparable to age-matched controls, while others report a complete inability to adapt to novel sensory perturbations. Here, early to mid-stage Parkinson's patients were recruited to perform a prism adaptation task.

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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.

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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.

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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 F-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.

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Article Synopsis
  • - The study investigates the potential of turning parameters, captured through video, to serve as biomarkers for assessing disease progression in Parkinson's disease (PD) and examines the impact of clinician observation on turning outcomes.
  • - Conducted over 5 days with 11 PD participants and 11 control participants in a home-like environment, the research analyzed 3869 turns from high-resolution video, finding significant differences in turning metrics between medication states.
  • - Results indicate that turning parameters correlate positively with established clinical rating scales, suggesting that real-world turning behavior can effectively distinguish PD medication states and provides a method for validating digital health outcomes.
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Importance: Current treatments manage symptoms of Parkinson disease (PD), but no known treatment slows disease progression. Preclinical and epidemiological studies support the potential use of statins as disease-modifying therapy.

Objective: To determine whether simvastatin has potential as a disease-modifying treatment for patients with moderate PD.

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Background: Parkinson 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.

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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.

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Background: Procedural aspects and complications of gastrojejunostomy insertion are important considerations in the use of levodopa-carbidopa intestinal gel therapy (LCIG) and may limit uptake. We describe our experience of using per-oral image guided gastrojejunostomy (PIG-J) which avoids the need for endoscopy and routine sedation in percutaneous endoscopic gastrojejunostomy (PEG-J) and allows more secure tube placement than radiologically inserted gastrojejunostomy techniques.

Methods: We describe a case series of 32 patients undergoing PIG-J insertion for LCIG therapy in a single centre.

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Parkinson's disease (PD) is a chronic neurodegenerative condition that affects a patient's everyday life. Authors have proposed that a machine learning and sensor-based approach that continuously monitors patients in naturalistic settings can provide constant evaluation of PD and objectively analyse its progression. In this paper, we make progress toward such PD evaluation by presenting a multimodal deep learning approach for discriminating between people with PD and without PD.

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Background: Frailty and Parkinson's disease (PD) are both highly prevalent in older people, but few studies have studied frailty in people with Parkinson's. Identifying frailty in this population is vital, to target new interventions to those who would most benefit.

Methods: Data were collected as part of the double-blind randomised controlled rivastigmine to stabilise gait ReSPonD trial in 130 people with Hoehn and Yahr 2-3, idiopathic PD who had fallen in the year prior to enrolment.

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There is an increasing need for improved endpoints to assess clinical trial effects in Parkinson's disease. We propose the Parkinson's Disease Comprehensive Response as a novel weighted composite endpoint integrating changes measured in three established Parkinson's outcomes, including: OFF state Movement Disorder Society Unified Parkinson's Disease Rating Scale Motor Examination scores; Motor Experiences of Daily Living scores; and total good-quality ON time per day. The data source for the initial development of the composite described herein was a recent Phase II trial of glial cell line-derived neurotrophic factor.

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Introduction: The impact of disease-modifying agents on disease progression in Parkinson's disease is largely assessed in clinical trials using clinical rating scales. These scales have drawbacks in terms of their ability to capture the fluctuating nature of symptoms while living in a naturalistic environment. The SPHERE (Sensor Platform for HEalthcare in a Residential Environment) project has designed a multi-sensor platform with multimodal devices designed to allow continuous, relatively inexpensive, unobtrusive sensing of motor, non-motor and activities of daily living metrics in a home or a home-like environment.

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Background: Recent advances in methods used for deep brain stimulation (DBS) include subthalamic nucleus electrode implantation in the "asleep" patient without the traditional use of microelectrode recordings or intraoperative test stimulation.

Objective: To examine the clinical outcome of patients who have undergone "asleep" DBS for the treatment of Parkinson disease using robot-assisted electrode delivery.

Methods: This is a retrospective review of clinical outcomes of 152 consecutive patients.

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REM Sleep Behavior Disorder (RBD) is a chronic sleep condition characterized by dream enactment and loss of REM atonia. Individuals often present to clinic with complaints of injury to themselves or their bed-partner due to violent movements during sleep. RBD patients have a high risk of developing one of the neurodegenerative α-synucleinopathy diseases: over 70% will develop parkinsonism or dementia within 12 years of their diagnosis.

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The concept of repairing the brain with growth factors has been pursued for many years in a variety of neurodegenerative diseases including primarily Parkinson's disease (PD) using glial cell line-derived neurotrophic factor (GDNF). This neurotrophic factor was discovered in 1993 and shown to have selective effects on promoting survival and regeneration of certain populations of neurons including the dopaminergic nigrostriatal pathway. These observations led to a series of clinical trials in PD patients including using infusions or gene delivery of GDNF or the related growth factor, neurturin (NRTN).

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Background: The emergence of new technologies measuring outcomes in Parkinson's disease (PD) to complement the existing clinical rating scales has introduced the possibility of measurement occurring in patients' own homes whilst they freely live and carry out normal day-to-day activities.

Objective: This systematic review seeks to provide an overview of what technology is being used to test which outcomes in PD from free-living participant activity in the setting of the home environment. Additionally, this review seeks to form an impression of the nature of validation and clinimetric testing carried out on the technological device(s) being used.

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Background: Freezing of gait (FOG) is a common symptom of Parkinson's disease (PD) which can result in falls and fall related injuries, poor quality of life and reduced functional independence. It is a heterogeneous phenomenon that is difficult to quantify and eludes a unified pathophysiological framework.

Objective: Our aim was to document the occurrence and nature of freezing, cognitive stops and stumbles in people with PD during walks with varying cognitive loads and conditions designed to elicit FOG.

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Introduction: Parkinson's disease (PD) is a progressive neurodegenerative condition affecting approximately 185,000 people in the UK. No drug has been proven to slow disease progression. Epidemiological and pre-clinical data support simvastatin, a widely used cholesterol-lowering drug with a well-established safety profile, having neuroprotective properties.

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