ABSTRACT
Goal: This paper introduces DISPEL, a Python framework to facilitate development of sensor-derived measures (SDMs) from data collected with digital health technologies in the context of therapeutic development for neurodegenerative diseases. Methods: Modularity, integrability and flexibility were achieved adopting an object-oriented architecture for data modelling and SDM extraction, which also allowed standardizing SDM generation, naming, storage, and documentation. Additionally, a functionality was designed to implement systematic flagging of missing data and unexpected user behaviors, both frequent in unsupervised monitoring. Results: DISPEL is available under MIT license. It already supports formats from different data providers and allows traceable end-to-end processing from raw data collected with wearables and smartphones to structured SDM datasets. Novel and literature-based signal processing approaches currently allow to extract SDMs from 16 structured tests (including six questionnaires), assessing overall disability and quality of life, and measuring performance outcomes of cognition, manual dexterity, and mobility. Conclusion: DISPEL supports SDM development for clinical trials by providing a production-grade Python framework and a large set of already implemented SDMs. While the framework has already been refined based on clinical trials' data, ad-hoc validation of the provided algorithms in their specific context of use is recommended to the users.
ABSTRACT
BACKGROUND: Parkinson's disease (PD) is the second most common neurodegenerative disorder and, according to the Global Burden of Disease estimates in 2015, was the fastest growing neurological disorder globally with respect to associated prevalence, disability, and deaths. Information regarding the awareness, diagnosis, phenotypic characteristics, epidemiology, prevalence, risk factors, treatment, economic impact and lived experiences of people with PD from the African perspective is relatively sparse in contrast to the developed world, and much remains to be learned from, and about, the continent. METHODS: Transforming Parkinson's Care in Africa (TraPCAf) is a multi-faceted, mixed-methods, multi-national research grant. The study design includes multiple sub-studies, combining observational (qualitative and quantitative) approaches for the epidemiological, clinical, risk factor and lived experience components, as appropriate, and interventional methods (clinical trial component). The aim of TraPCAf is to describe and gain a better understanding of the current situation of PD in Africa. The countries included in this National Institute for Health and Care Research (NIHR) Global Health Research Group (Egypt, Ethiopia, Ghana, Kenya, Nigeria, South Africa and Tanzania) represent diverse African geographies and genetic profiles, with differing resources, healthcare systems, health and social protection schemes, and policies. The research team is composed of experts in the field with vast experience in PD, jointly led by a UK-based and Africa-based investigator. DISCUSSION: Despite the increasing prevalence of PD globally, robust data on the disease from Africa are lacking. Existing data point towards the poor awareness of PD and other neurological disorders on the continent and subsequent challenges with stigma, and limited access to affordable services and medication. This multi-site study will be the first of its kind in Africa. The data collected across the proposed sub-studies will provide novel and conclusive insights into the situation of PD. The selected country sites will allow for useful comparisons and make results relevant to other low- and middle-income countries. This grant is timely, as global recognition of PD and the public health challenge it poses builds. The work will contribute to broader initiatives, including the World Health Organization's Intersectoral global action plan on epilepsy and other neurological disorders. TRIAL REGISTRATION: https://doi.org/10.1186/ISRCTN77014546 .
Subject(s)
Global Health , Parkinson Disease , Humans , Parkinson Disease/epidemiology , Parkinson Disease/therapy , Delivery of Health Care , South Africa , NigeriaABSTRACT
Free-living ambulation with accelerometer-based devices is an attractive methodology to assess habitual behaviour within Parkinson's disease (PD). However, slowness of movement can contribute to difficulty in quantifying ambulatory/walking outcomes within this group by these devices. This study investigates the use of a commercial accelerometer device (activPAL™) in those with moderate PD to understand its proprietary software (inbuilt algorithm) limitations. The values provided by the proprietary software are evaluated in comparison to novel algorithms on the same raw data to examine limitations for use within this cohort. The bespoke algorithms help to alter sensitivity in outcomes stemming from the same accelerometer data while also highlighting how slight changes in algorithms can drastically inflate/deflate values. In general, results show that the proprietary software generally quantifies lower values of outcomes (step and bout count), which is similar to previous findings. Variations in algorithm functionality highlight large heterogeneity in bout and step counts resulting from a lack of how they are defined within the literature. The novel alternative ambulatory algorithms presented here should be considered for use on raw data from the activPAL™ in those with moderate/severe PD.
Subject(s)
Accelerometry/instrumentation , Monitoring, Ambulatory/instrumentation , Parkinson Disease/diagnosis , Parkinson Disease/physiopathology , Thigh , Walking , Aged , Female , Humans , MaleABSTRACT
INTRODUCTION: Impaired postural control (PC) is common in patients with Parkinson's disease (PD) and is a major contributor to falls, with significant consequences. Mechanisms underpinning PC are complex and include motor and non-motor features. Research has focused predominantly on motor and sensory inputs. Vision and visuo-cognitive function are also integral to PC but have largely been ignored to date. The aim of this observational cross-sectional pilot study was to explore the relationship of vision and visuo-cognition with PC in PD. METHODS: Twelve people with PD and ten age-matched healthy controls (HC) underwent detailed assessments for vision, visuo-cognition and postural control. Vision assessments included visual acuity and contrast sensitivity. Visuo-cognition was measured by visuo-perception (object identification), visuo-construction (ability to copy a figure) and visuo-spatial ability (judge distances and location of object within environment). PC was measured by an accelerometer for a range of outcomes during a 2-min static stance. Spearman's correlations identified significant associations. RESULTS: Contrast sensitivity, visuo-spatial ability and postural control (ellipsis) were significantly impaired in PD (p=0.017; p=0.001; and p=0.017, respectively). For PD only, significant correlations were found for higher visuo-spatial function and larger ellipsis (r=0.64; p=0.024) and impaired attention and reduced visuo-spatial function (r=-0.62; p=0.028). CONCLUSIONS: Visuo-spatial ability is associated with PC deficit in PD, but in an unexpected direction. This suggests a non-linear pattern of response. Further research is required to examine this novel and important finding.
Subject(s)
Cognition/physiology , Parkinson Disease/physiopathology , Proprioception/physiology , Visual Perception/physiology , Aged , Attention/physiology , Cross-Sectional Studies , Female , Humans , Male , Middle Aged , Pilot Projects , Spatial Navigation/physiologyABSTRACT
UNLABELLED: Wearable technology is readily available for continuous assessment due to a growing number of commercial devices with increased data capture capabilities. However, many commercial devices fail to support suitable parameters (cut points) derived from the literature to help quantify physical activity (PA) due to differences in manufacturing. A simple metric to estimate cut points for new wearables is needed to aid data analysis. OBJECTIVE: The purpose of this pilot study was to investigate a simple methodology to determine cut points based on ratios between sedentary behaviour (SB) and PA intensities for a new wrist worn device (PRO-Diary™) by comparing its output to a validated and well characterised 'gold standard' (ActiGraph™). STUDY DESIGN: Twelve participants completed a semi-structured (four-phase) treadmill protocol encompassing SB and three PA intensity levels (light, moderate, vigorous). The outputs of the devices were compared accounting for relative intensity. RESULTS: Count ratios (6.31, 7.68, 4.63, 3.96) were calculated to successfully determine cut-points for the new wrist worn wearable technology during SB (0-426) as well as light (427-803), moderate (804-2085) and vigorous (≥ 2086) activities, respectively. CONCLUSION: Our findings should be utilised as a primary reference for investigations seeking to use new (wrist worn) wearable technology similar to that used here (i.e., PRO-Diary™) for the purposes of quantifying SB and PA intensities. The utility of count ratios may be useful in comparing devices or SB/PA values estimated across different studies. However, a more robust examination is required for different devices, attachment locations and on larger/diverse cohorts.
Subject(s)
Actigraphy/instrumentation , Monitoring, Ambulatory/instrumentation , Motor Activity , Adult , Exercise Test , Female , Humans , Male , Physical Exertion , Pilot Projects , Sedentary Behavior , Technology , Young AdultABSTRACT
OBJECTIVES: The aims of this study were to (i) investigate instrumented physical capability (iCap) as a valid method during a large study and (ii) determine whether iCap can provide important additional features of postural control and gait to categorise cohorts not previously possible with manual recordings. STUDY DESIGN: Cross-sectional analysis involving instrumented testing on 74 adults who were recruited as part of a pilot intervention study; LiveWell. Participants wore a single accelerometer-based monitor (lower back) during standardised physical capability tests so that outcomes could be compared directly with manual recordings (stopwatch and measurement tape) made concurrently. MAIN OUTCOME MEASURES: Time, distance, postural control and gait characteristics. RESULTS: Agreement between manual and iCap ranged from moderate to excellent (0.649-0.983) with mean differences between methods low and deemed acceptable. Additionally, iCap successfully quantified (i) postural control characteristics which showed sensitivity to distinguish between 5 variations of the standing balance test and (ii) 14 gait characteristics known to be sensitive to age/pathology. CONCLUSIONS: Our findings show that iCap can provide robust quantitative data about physical capability during standardised tests while also providing sensitive (age/pathology) postural control and gait characteristics not previously quantifiable with manual recordings. The methodology which we propose may have practical utility in a wide range of clinical and public health surveys and studies, including intervention studies, where assessment could be undertaken within diverse settings. This will need to be tested in further validation studies in a wider range of settings.
Subject(s)
Gait/physiology , Monitoring, Physiologic/methods , Postural Balance/physiology , Aged , Cross-Sectional Studies , Female , Humans , Male , Middle Aged , Pilot ProjectsABSTRACT
Recent work has identified subdomains (tests) of physical capability that are recommended for assessment of the healthy ageing phenotype (HAP). These include: postural control, locomotion, endurance, repeated sit-to-stand-to-sit and TUG. Current assessment methods lack sensitivity and are error prone due to their lack of consistency and heterogeneity of reported outcomes; instrumentation with body worn monitors provides a method to address these potential weaknesses. This work proposes the use of a single tri-axial accelerometer-based device with appropriate algorithms (referred to here as a body worn monitor, BWM) for the purposes of instrumented testing during physicality capability assessment. In this pilot study we present 14 BWM-based outcomes across the subdomains which include magnitude, frequency and spatio-temporal characteristics. Where possible, we compared BWM outcomes with manually recorded values and found no significant differences between locomotion and TUG tasks (p ≥ 0.319). Significant differences were found for the total distance walked during endurance (p = 0.037) and times for repeated sit-to-stand-to-sit transitions (p < 0.000). We identified reasons for differences and make recommendations for future testing. We were also able to quantify additional characteristics of postural control and gait which could be sensitive outcomes for future HAP assessment. Our findings demonstrate the feasibility of this method to enhance measurement of physical capacity. The methodology can also be applied to a wide variety of accelerometer-based monitors and is applicable to a range of intervention-based studies or pathological assessment.
Subject(s)
Accelerometry/instrumentation , Monitoring, Physiologic/instrumentation , Motor Activity/physiology , Aged , Aging/physiology , Algorithms , Feasibility Studies , Humans , Locomotion , Lower Extremity/physiology , Middle Aged , Physical Endurance , Pilot Projects , Postural BalanceABSTRACT
Gait is an important clinical assessment tool since changes in gait may reflect changes in general health. Measurement of gait is a complex process which has been restricted to the laboratory until relatively recently. The application of an inexpensive body worn sensor with appropriate gait algorithms (BWM) is an attractive alternative and offers the potential to assess gait in any setting. In this study we investigated the use of a low-cost BWM, compared to laboratory reference using a robust testing protocol in both younger and older adults. We observed that the BWM is a valid tool for estimating total step count and mean spatio-temporal gait characteristics however agreement for variability and asymmetry results was poor. We conducted a detailed investigation to explain the poor agreement between systems and determined it was due to inherent differences between the systems rather than inability of the sensor to measure the gait characteristics. The results highlight caution in the choice of reference system for validation studies. The BWM used in this study has the potential to gather longitudinal (real-world) spatio-temporal gait data that could be readily used in large lifestyle-based intervention studies, but further refinement of the algorithm(s) is required.