A Remote Digital Monitoring Platform to Assess Cognitive and Motor Symptoms in Huntington Disease: Cross-sectional Validation Study.
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ABSTRACT: Background
Remote monitoring of Huntington disease (HD) signs and symptoms using digital technologies may enhance early clinical diagnosis and tracking of disease progression, guide treatment decisions, and monitor response to disease-modifying agents. Several recent studies in neurodegenerative diseases have demonstrated the feasibility of digital symptom monitoring.Objective
The aim of this study was to evaluate a novel smartwatch- and smartphone-based digital monitoring platform to remotely monitor signs and symptoms of HD.Methods
This analysis aimed to determine the feasibility and reliability of the Roche HD Digital Monitoring Platform over a 4-week period and cross-sectional validity over a 2-week interval. Key criteria assessed were feasibility, evaluated by adherence and quality control failure rates; test-retest reliability; known-groups validity; and convergent validity of sensor-based measures with existing clinical measures. Data from 3 studies were used: the predrug screening phase of an open-label extension study evaluating tominersen (NCT03342053) and 2 untreated cohorts-the HD Natural History Study (NCT03664804) and the Digital-HD study. Across these studies, controls (n=20) and individuals with premanifest (n=20) or manifest (n=179) HD completed 6 motor and 2 cognitive tests at home and in the clinic.Results
Participants in the open-label extension study, the HD Natural History Study, and the Digital-HD study completed 89.95% (1164/1294), 72.01% (2025/2812), and 68.98% (1454/2108) of the active tests, respectively. All sensor-based features showed good to excellent test-retest reliability (intraclass correlation coefficient 0.89-0.98) and generally low quality control failure rates. Good overall convergent validity of sensor-derived features to Unified HD Rating Scale outcomes and good overall known-groups validity among controls, premanifest, and manifest participants were observed. Among participants with manifest HD, the digital cognitive tests demonstrated the strongest correlations with analogous in-clinic tests (Pearson correlation coefficient 0.79-0.90).Conclusions
These results show the potential of the HD Digital Monitoring Platform to provide reliable, valid, continuous remote monitoring of HD symptoms, facilitating the evaluation of novel treatments and enhanced clinical monitoring and care for individuals with HD.
SUBMITTER: Lipsmeier F
PROVIDER: S-EPMC9277525 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Monitoring mental health outcomes has traditionally been based on heuristic decisions, often based on scarce, subjective evidence, making the clinical decisions made by professionals, as well as the monitoring of these diseases, subject to flaws. However, the digital phenotype, which refers to the analysis of data collected by measuring human behavior with mobile sensors and smart bracelets, is a promising tool for filling this gap in current clinical practice. OBJECTIVE:The objectives of this study are to develop the digital phenotyping of patients with alcohol use disorder and anxiety symptoms using data collected from a mobile device (ie, smartphone) and a wearable sensor (ie, Fitbit) and to analyze usability and patient satisfaction with the data collection service provided by the app. METHODS:We propose to conduct a study among a group of 60 participants split into two subgroups-experimental and control-of 30 participants each. The experimental group will be recruited by physicians from the Hospital Clínic de Barcelona, and the control group will be recruited on a volunteer basis through fliers and social media. All participants will go through pretraining to ensure technological capability and understanding of tasks, then each participant will download the HumanITcare app and will be given a wearable sensor (ie, Fitbit). Throughout the 4-month period, participants will be monitored on a range of factors, including sleep cycle, heart rate, movement patterns, and sociability. All data from the wearable sensors and the mobile devices will be saved and sent to the HumanITcare server. Participants will be asked to complete weekly questionnaires about anxiety, depression, and alcohol use disorder symptoms. Research assistants will ensure timely responses. The data from both sensors will then be compared to the questionnaire responses to determine how accurately the devices can predict the same symptoms. RESULTS:The recruitment phase was completed in November 2019 and all the data were collected by the end of December 2019. Data are being processed; this process is expected to be completed by October 2020. CONCLUSIONS:This study was created and conducted as a pilot study with the Hospital Clínic de Barcelona, with the purpose of exploring the feasibility of our approach. The study is focused on patients diagnosed with anxiety and alcohol use disorder, but participants were also monitored for depressive symptoms throughout the trial, although these were not part of the initial inclusion criteria. A limitation to our study was the exclusive use of Android smartphones over iOS devices; this could result in a potential selection bias, due to the accessibility and affordability of Android phones as opposed to iOS-based phones. Another limitation might be that reviews of usability and satisfaction could be confounded by factors such as age and familiarity. An additional function that we might add in future studies is the ability for patients to manage their own data. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):DERR1-10.2196/16964.
| S-EPMC7381016 | biostudies-literature
Project description:Importance:Brain hypometabolism is associated with the clinical consequences of the degenerative process, but little is known about regional hypermetabolism, sometimes observed in the brain of patients with clinically manifest Huntington disease (HD). Studying the role of regional hypermetabolism is needed to better understand its interaction with the motor symptoms of the disease. Objective:To investigate the association between brain hypometabolism and hypermetabolism with motor scores of patients with early HD. Design, Setting, and Participants:This study started in 2001, and analysis was completed in 2016. Sixty symptomatic patients with HD and 15 healthy age-matched control individuals underwent positron emission tomography to measure cerebral metabolism in this cross-sectional study. They also underwent the Unified Huntington's Disease Rating Scale motor test, and 2 subscores were extracted: (1) a hyperkinetic score, combining dystonia and chorea, and (2) a hypokinetic score, combining bradykinesia and rigidity. Main Outcomes and Measures:Statistical parametric mapping software (SPM5) was used to identify all hypo- and hypermetabolic regions in patients with HD relative to control individuals. Correlation analyses (P?<?.001, uncorrected) between motor subscores and brain metabolic values were performed for regions with significant hypometabolism and hypermetabolism. Results:Among 60 patients with HD, 22 were women (36.7%), and the mean (SD) age was 44.6 (7.6) years. Of the 15 control individuals, 7 were women (46.7%), and the mean (SD) age was 42.2 (7.3) years. In statistical parametric mapping, striatal hypometabolism was significantly correlated with the severity of all motor scores. Hypermetabolism was negatively correlated only with hypokinetic scores in the cuneus (z score?=?3.95, P?<?.001), the lingual gyrus (z score?=?4.31, P?<?.001), and the crus I/II of the cerebellum (z score?=?3.77, P?<?.001), a region connected to associative cortical areas. More severe motor scores were associated with higher metabolic values in the inferior parietal lobule, anterior cingulate, inferior temporal lobule, the dentate nucleus, and the cerebellar lobules IV/V, VI, and VIII bilaterally corresponding to the motor regions of the cerebellum (z score?=?3.96 and 3.42 in right and left sides, respectively; P?<?.001). Conclusions and Relevance:Striatal hypometabolism is associated with clinical disease severity. Conversely, hypermetabolism is likely compensatory in regions where it is associated with decreasing motor scores. Hypermetabolism might be detrimental in other structures in which it is associated with more severe motor symptoms. In the cerebellum, both compensatory and detrimental contributions seem to occur. This study helps to better understand the motor clinical relevance of hypermetabolic brain regions in HD.
| S-EPMC5710184 | biostudies-literature
Project description:Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor impairments such as tremor, bradykinesia, dyskinesia, and gait abnormalities. Current protocols assess PD symptoms during clinic visits and can be subjective. Patient diaries can help clinicians evaluate at-home symptoms, but can be incomplete or inaccurate. Therefore, researchers have developed in-home automated methods to monitor PD symptoms to enable data-driven PD diagnosis and management. We queried the US National Library of Medicine PubMed database to analyze the progression of the technologies and computational/machine learning methods used to monitor common motor PD symptoms. A sub-set of roughly 12,000 papers was reviewed that best characterized the machine learning and technology timelines that manifested from reviewing the literature. The technology used to monitor PD motor symptoms has advanced significantly in the past five decades. Early monitoring began with in-lab devices such as needle-based EMG, transitioned to in-lab accelerometers/gyroscopes, then to wearable accelerometers/gyroscopes, and finally to phone and mobile & web application-based in-home monitoring. Significant progress has also been made with respect to the use of machine learning algorithms to classify PD patients. Using data from different devices (e.g., video cameras, phone-based accelerometers), researchers have designed neural network and non-neural network-based machine learning algorithms to categorize PD patients across tremor, gait, bradykinesia, and dyskinesia. The five-decade co-evolution of technology and computational techniques used to monitor PD motor symptoms has driven significant progress that is enabling the shift from in-lab/clinic to in-home monitoring of PD symptoms.
| S-EPMC8933519 | biostudies-literature
Project description:BackgroundAutoimmune limbic encephalitis (ALE) is a neurological disease characterised by inflammation of the limbic regions of the brain, mediated by pathogenic autoantibodies. Because cognitive deficits persist following acute treatment of ALE, the accurate assessment of long-term cognitive outcomes is important for clinical assessments and trials. However, evaluating cognition is costly and an unmet need exists for validated digital methods.MethodsIn this cross-sectional validation study, we investigated whether a remote digital platform could identify previously characterised cognitive impairments in patients with chronic ALE and whether digital metrics would correlate with standard neuropsychological assessment and hippocampal volume. Patients with ALE who had a chronic and stable presentation and received a clinical diagnosis of ALE were recruited for this study. The cognitive performance of 21 patients with ALE and 54 age-matched healthy controls - enrolled via the University of Oxford (UK) Cognitive Neurology Lab testing programme - was assessed with a battery of 12 cognitive tasks from the Cognitron online platform. The platform was optimised with National Institute for Health and Care Research (NIHR) support to be deliverable remotely to elderly and patient groups. The primary outcome measure was behavioural performance and corresponding neuroimaging and neuropsychological assessment metrics.FindingsBetween February 15, 2021, and April 21, 2022, 21 patients with ALE (mean age 63.01 years, 14 males) and 54 healthy controls (mean age 65.56 years, 23 males) completed the digital cognitive assessment. Patients with ALE performed significantly worse in memory, visuospatial abilities, executive function, and language. No impairments in digit & spatial span, target detection (attention) and emotion discrimination were observed. The global score on the online cognitive tasks correlated significantly with the established Addenbrooke's Cognitive Examination III (ACE) pen-and-paper test. Deficits in visuospatial processing and language were identified in ALE compared to controls using remote digital testing but not using the ACE, highlighting higher sensitivity of computerised testing to residual cognitive impairment. Finally, the hippocampal volumes of patients with ALE and healthy controls correlated with online cognitive scores.InterpretationThese findings demonstrate that subtle cognitive deficits in patients with chronic ALE, who often show full recovery in measures of disability and dependence on daily activities, are detectable using a remote online platform, which also relates to hippocampal atrophy. Such methods may facilitate the characterisation of cognitive profiles in complex neurological diseases. Future longitudinal studies designed to assess the utility of such digital methods for further clinical characterisation are needed.FundingThe Wellcome Trust, Medical Research Council, National Institute for Health Research, Rhodes Scholarship, and the Berrow Foundation Scholarship.
| S-EPMC10965407 | biostudies-literature
Project description:Genome-wide association studies (GWASs) of Huntington disease (HD) have identified six DNA maintenance gene loci (among others) as modifiers and implicated a two step-mechanism of pathogenesis: somatic instability of the causative HTT CAG repeat with subsequent triggering of neuronal damage. The largest studies have been limited to HD individuals with a rater-estimated age at motor onset. To capitalize on the wealth of phenotypic data in several large HD natural history studies, we have performed algorithmic prediction by using common motor and cognitive measures to predict age at other disease landmarks as additional phenotypes for GWASs. Combined with imputation with the Trans-Omics for Precision Medicine reference panel, predictions using integrated measures provided objective landmark phenotypes with greater power to detect most modifier loci. Importantly, substantial differences in the relative modifier signal across loci, highlighted by comparing common modifiers at MSH3 and FAN1, revealed that individual modifier effects can act preferentially in the motor or cognitive domains. Individual components of the DNA maintenance modifier mechanisms may therefore act differentially on the neuronal circuits underlying the corresponding clinical measures. In addition, we identified additional modifier effects at the PMS1 and PMS2 loci and implicated a potential second locus on chromosome 7. These findings indicate that broadened discovery and characterization of HD genetic modifiers based on additional quantitative or qualitative phenotypes offers not only the promise of in-human validated therapeutic targets but also a route to dissecting the mechanisms and cell types involved in both the somatic instability and toxicity components of HD pathogenesis.
| S-EPMC9118113 | biostudies-literature
Project description:BackgroundA growing number of clinical trials use various sensors and smartphone applications to collect data outside of the clinic or hospital, raising the question to what extent patients comply with the unique requirements of remote study protocols. Compliance is particularly important in conditions where patients are motorically and cognitively impaired. Here, we sought to understand patient compliance in digital trials of two such pathologies, Parkinson's disease (PD) and Huntington disease (HD).MethodsPatient compliance was assessed in two remote, six-month clinical trials of PD (n = 51, Clinician Input Study funded by the Michael J. Fox Foundation for Parkinson's Research) and HD (n = 17, sponsored by Teva Pharmaceuticals). We monitored four compliance metrics specific to remote studies: smartphone app-based medication reporting, app-based symptoms reporting, the duration of smartwatch data streaming except while charging, and the performance of structured motor tasks at home.ResultsWhile compliance over time differed between the PD and HD studies, both studies maintained high compliance levels for their entire six month duration. None (- 1%) to a 30% reduction in compliance rate was registered for HD patients, and a reduction of 34 to 53% was registered for the PD study. Both studies exhibited marked changes in compliance rates during the initial days of enrollment. Interestingly, daily smartwatch data streaming patterns were similar, peaking around noon, dropping sharply in the late evening hours around 8 pm, and having a mean of 8.6 daily streaming hours for the PD study and 10.5 h for the HD study. Individual patients tended to have either high or low compliance across all compliance metrics as measured by pairwise correlation. Encouragingly, predefined schedules and app-based reminders fulfilled their intended effect on the timing of medication intake reporting and performance of structured motor tasks at home.ConclusionsOur findings suggest that maintaining compliance over long durations is feasible, promote the use of predefined app-based reminders, and highlight the importance of patient selection as highly compliant patients typically have a higher adherence rate across the different aspects of the protocol. Overall, these data can serve as a reference point for the design of upcoming remote digital studies.Trial registrationTrials described in this study include a sub-study of the Open PRIDE-HD Huntington's disease study (TV7820-CNS-20016), which was registered on July 7th, 2015, sponsored by Teva Pharmaceuticals Ltd., and registered on Clinicaltrials.gov as NCT02494778 and EudraCT as 2015-000904-24 .
| S-EPMC6302308 | biostudies-literature
Project description:Neuroimaging studies suggest that volumetric MRI measures of specific brain structures may serve as excellent biomarkers in future clinical trials of Huntington disease (HD).Demonstration of the clinical significance of these measures is an important step in determining their appropriateness as potential outcome measures.Measures of gray- and white-matter lobular volumes and subcortical volumes (caudate, putamen, globus pallidus, thalamus, nucleus accumbens, hippocampus) were obtained from MRI scans of 516 individuals who tested positive for the HD gene expansion, but were not yet exhibiting signs or symptoms severe enough to warrant diagnosis ("pre-HD"). MRI volumes (corrected for intracranial volume) were correlated with cognitive, motor, psychiatric, and functional measures known to be sensitive to subtle changes in pre-HD.Caudate, putamen, and globus pallidus volumes consistently correlated with cognitive and motor, but not psychiatric or functional measures in pre-HD. Volumes of white matter, nucleus accumbens, and thalamus, but not cortical gray matter, also correlated with some of the motor and cognitive measures.Results of regression analyses suggest that volumes of basal ganglia structures contributed more highly to the prediction of most motor and cognitive variables than volumes of other brain regions. These results support the use of volumetric measures, especially of the basal ganglia, as outcome measures in future clinical trials in pre-HD. Results may also assist investigators in selecting the most appropriate measures for treatment trials that target specific clinical features or regions of neuropathology.
| S-EPMC4412155 | biostudies-other
Project description:ObjectiveTo assess the prevalence, timing, and functional impact of psychiatric, cognitive, and motor abnormalities in Huntington disease (HD) gene carriers, we analyzed retrospective clinical data from individuals with manifest HD.MethodsClinical features of patients with HD were analyzed for 6,316 individuals in an observational study of the European Huntington's Disease Network (REGISTRY) from 161 sites across 17 countries. Data came from clinical history and the patient-completed Clinical Characteristics Questionnaire that assessed 8 symptoms: motor, cognitive, apathy, depression, perseverative/obsessive behavior, irritability, violent/aggressive behavior, and psychosis. Multiple logistic regression was used to analyze relationships between symptoms and functional outcomes.ResultsThe initial manifestation of HD is increasingly likely to be motor and less likely to be psychiatric as age at presentation increases and is independent of pathogenic CAG repeat length. The Clinical Characteristics Questionnaire captures data on nonmotor symptom prevalence that correlate specifically with validated clinical measures. Psychiatric and cognitive symptoms are common in HD gene carriers, with earlier onsets associated with longer CAG repeats. Of patients with HD, 42.4% reported at least 1 psychiatric or cognitive symptom before motor symptoms, with depression most common. Each nonmotor symptom was associated with significantly reduced total functional capacity scores.ConclusionsPsychiatric and cognitive symptoms are common and functionally debilitating in HD gene carriers. They require recognition and targeting with clinical outcome measures and treatments. However, because it is impossible to distinguish confidently between nonmotor symptoms arising from HD and primary psychiatric disorders, particularly in younger premanifest patients, nonmotor symptoms should not be used to make a clinical diagnosis of HD.Trial registration informationClinicalTrials.gov Identifier: NCT01590589.
| S-EPMC8166441 | biostudies-literature
Project description:BackgroundSmartphone-based symptom monitoring has gained increased attention in psychiatric research as a cost-efficient tool for prospective and ecologically valid assessments based on participants' self-reports. However, a meaningful interpretation of smartphone-based assessments requires knowledge about their psychometric properties, especially their validity.ObjectiveThe goal of this study is to systematically investigate the validity of smartphone-administered assessments of self-reported affective symptoms using the Remote Monitoring Application in Psychiatry (ReMAP).MethodsThe ReMAP app was distributed to 173 adult participants of ongoing, longitudinal psychiatric phenotyping studies, including healthy control participants, as well as patients with affective disorders and anxiety disorders; the mean age of the sample was 30.14 years (SD 11.92). The Beck Depression Inventory (BDI) and single-item mood and sleep information were assessed via the ReMAP app and validated with non-smartphone-based BDI scores and clinician-rated depression severity using the Hamilton Depression Rating Scale (HDRS).ResultsWe found overall high comparability between smartphone-based and non-smartphone-based BDI scores (intraclass correlation coefficient=0.921; P<.001). Smartphone-based BDI scores further correlated with non-smartphone-based HDRS ratings of depression severity in a subsample (r=0.783; P<.001; n=51). Higher agreement between smartphone-based and non-smartphone-based assessments was found among affective disorder patients as compared to healthy controls and anxiety disorder patients. Highly comparable agreement between delivery formats was found across age and gender groups. Similarly, smartphone-based single-item self-ratings of mood correlated with BDI sum scores (r=-0.538; P<.001; n=168), while smartphone-based single-item sleep duration correlated with the sleep item of the BDI (r=-0.310; P<.001; n=166).ConclusionsThese findings demonstrate that smartphone-based monitoring of depressive symptoms via the ReMAP app provides valid assessments of depressive symptomatology and, therefore, represents a useful tool for prospective digital phenotyping in affective disorder patients in clinical and research applications.
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| S-EPMC10161985 | biostudies-literature