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BACKGROUND: Environmental exposure to trichloroethylene (TCE), a carcinogenic dry-cleaning chemical, may be linked to Parkinson's disease (PD). OBJECTIVE: The objective of this study was to determine whether PD and cancer were elevated among attorneys who worked near a contaminated site. METHODS: We surveyed and evaluated attorneys with possible exposure and assessed a comparison group. RESULTS: Seventy-nine of 82 attorneys (96.3%; mean [SD] age: 69.5 [11.4] years; 89.9% men) completed at least one phase of the study. For comparison, 75 lawyers (64.9 [10.2] years; 65.3% men) underwent clinical evaluations. Four (5.1%) of them who worked near the polluted site reported PD, more than expected based on age and sex (1.7%; P = 0.01) but not significantly higher than the comparison group (n = 1 [1.3%]; P = 0.37). Fifteen (19.0%), compared to four in the comparison group (5.3%; P = 0.049), had a TCE-related cancer. CONCLUSIONS: In a retrospective study, diagnoses of PD and TCE-related cancers appeared to be elevated among attorneys who worked next to a contaminated dry-cleaning site. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Neoplasias , Doença de Parkinson , Tricloroetileno , Masculino , Humanos , Idoso , Feminino , Doença de Parkinson/epidemiologia , Doença de Parkinson/etiologia , Doença de Parkinson/diagnóstico , Estudos Retrospectivos , Tricloroetileno/análiseRESUMO
Sensor data from digital health technologies (DHTs) used in clinical trials provides a valuable source of information, because of the possibility to combine datasets from different studies, to combine it with other data types, and to reuse it multiple times for various purposes. To date, there exist no standards for capturing or storing DHT biosensor data applicable across modalities and disease areas, and which can also capture the clinical trial and environment-specific aspects, so-called metadata. In this perspectives paper, we propose a metadata framework that divides the DHT metadata into metadata that is independent of the therapeutic area or clinical trial design (concept of interest and context of use), and metadata that is dependent on these factors. We demonstrate how this framework can be applied to data collected with different types of DHTs deployed in the WATCH-PD clinical study of Parkinson's disease. This framework provides a means to pre-specify and therefore standardize aspects of the use of DHTs, promoting comparability of DHTs across future studies.
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Metadados , Doença de Parkinson , HumanosRESUMO
PURPOSE OF REVIEW: Digital technology affords the opportunity to provide objective, frequent, and sensitive assessment of disease outside of the clinic environment. This article reviews recent literature on the application of digital technology in movement disorders, with a focus on Parkinson's disease (PD) and Huntington's disease. RECENT FINDINGS: Recent research has demonstrated the ability for digital technology to discriminate between individuals with and without PD, identify those at high risk for PD, quantify specific motor features, predict clinical events in PD, inform clinical management, and generate novel insights. Digital technology has enormous potential to transform clinical research and care in movement disorders. However, more work is needed to better validate existing digital measures, including in new populations, and to develop new more holistic digital measures that move beyond motor features.
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Doença de Huntington , Doença de Parkinson , Tecnologia Digital , Humanos , Doença de Parkinson/diagnóstico , Doença de Parkinson/terapiaRESUMO
BACKGROUND: Digital health technologies show promise for improving the measurement of Parkinson's disease in clinical research and trials. However, it is not clear whether digital measures demonstrate enhanced sensitivity to disease progression compared to traditional measurement approaches. METHODS: To this end, we develop a wearable sensor-based digital algorithm for deriving features of upper and lower-body bradykinesia and evaluate the sensitivity of digital measures to 1-year longitudinal progression using data from the WATCH-PD study, a multicenter, observational digital assessment study in participants with early, untreated Parkinson's disease. In total, 82 early, untreated Parkinson's disease participants and 50 age-matched controls were recruited and took part in a variety of motor tasks over the course of a 12-month period while wearing body-worn inertial sensors. We establish clinical validity of sensor-based digital measures by investigating convergent validity with appropriate clinical constructs, known groups validity by distinguishing patients from healthy volunteers, and test-retest reliability by comparing measurements between visits. RESULTS: We demonstrate clinical validity of the digital measures, and importantly, superior sensitivity of digital measures for distinguishing 1-year longitudinal change in early-stage PD relative to corresponding clinical constructs. CONCLUSIONS: Our results demonstrate the potential of digital health technologies to enhance sensitivity to disease progression relative to existing measurement standards and may constitute the basis for use as drug development tools in clinical research.
Parkinson's disease can impact a person's ability to move, which can result in slow or rigid movements. Wearable sensors can be used to measure these symptoms and could be particularly useful to detect changes early in the course of the disease when symptoms may be subtle. We developed a wearable sensor-based method to measure movement in people with early Parkinson's disease that uses wrist and foot-worn sensors. Our results demonstrate that our sensor-based measurements can accurately quantify progressive changes in movement function. Such measurements may allow researchers to more accurately evaluate how well treatments designed to slow the course of Parkinson's disease are working in the future.
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Background: Speech changes are an early symptom of Huntington disease (HD) and may occur prior to other motor and cognitive symptoms. Assessment of HD commonly uses clinician-rated outcome measures, which can be limited by observer variability and episodic administration. Speech symptoms are well suited for evaluation by digital measures which can enable sensitive, frequent, passive, and remote administration. Methods: We collected audio recordings using an external microphone of 36 (18 HD, 7 prodromal HD, and 11 control) participants completing passage reading, counting forward, and counting backwards speech tasks. Motor and cognitive assessments were also administered. Features including pausing, pitch, and accuracy were automatically extracted from recordings using the BioDigit Speech software and compared between the three groups. Speech features were also analyzed by the Unified Huntington Disease Rating Scale (UHDRS) dysarthria score. Random forest machine learning models were implemented to predict clinical status and clinical scores from speech features. Results: Significant differences in pausing, intelligibility, and accuracy features were observed between HD, prodromal HD, and control groups for the passage reading task (e.g., p < 0.001 with Cohen'd = -2 between HD and control groups for pause ratio). A few parameters were significantly different between the HD and control groups for the counting forward and backwards speech tasks. A random forest classifier predicted clinical status from speech tasks with a balanced accuracy of 73% and an AUC of 0.92. Random forest regressors predicted clinical outcomes from speech features with mean absolute error ranging from 2.43-9.64 for UHDRS total functional capacity, motor and dysarthria scores, and explained variance ranging from 14 to 65%. Montreal Cognitive Assessment scores were predicted with mean absolute error of 2.3 and explained variance of 30%. Conclusion: Speech data have the potential to be a valuable digital measure of HD progression, and can also enable remote, frequent disease assessment in prodromal HD and HD. Clinical status and disease severity were predicted from extracted speech features using random forest machine learning models. Speech measurements could be leveraged as sensitive marker of clinical onset and disease progression in future clinical trials.
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Huntington's disease (HD), like many other neurological disorders, affects both lower and upper limb function that is typically assessed in the clinic - providing a snapshot of disease symptoms. Wearable sensors enable the collection of real-world data that can complement such clinical assessments and provide a more comprehensive insight into disease symptoms. In this context, almost all studies are focused on assessing lower limb function via monitoring of gait, physical activity and ambulation. In this study, we monitor upper limb function during activities of daily living in individuals with HD (n = 16), prodromal HD (pHD, n = 7), and controls (CTR, n = 16) using a wrist-worn wearable sensor, called PAMSys ULM, over seven days. The participants were highly compliant in wearing the sensor with an average daily compliance of 99% (100% for HD, 98% for pHD, and 99% for CTR). Goal-directed movements (GDM) of the hand were detected using a deep learning model, and kinematic features of each GDM were estimated. The collected data was used to predict disease groups (i.e., HD, pHD, and CTR) and clinical scores using a combination of statistical and machine learning-based models. Significant differences in GDM features were observed between the groups. HD participants performed fewer GDMs with long duration (> 7.5 seconds) compared to CTR (p-val = 0.021, d = -0.86). In velocity and acceleration metrics, the highest effect size feature was the entropy of the velocity zero-crossing length segments (HD vs CTR p-val <0.001, d = -1.67; HD vs pHD p-val = 0.043, d=-0.98; CTR vs pHD p-val = 0.046, d=0.96). In addition, this same variable showed a strongest correlation with clinical scores. Classification models achieved good performance in distinguishing HD, pHD and CTR individuals with a balanced accuracy of 67% and a 0.72 recall for the HD group, while regression models accurately predicted clinical scores. Notably the explained variance for the upper extremity function subdomain scale of Unified Huntington's Disease Rating Scale (UHDRS) was the highest, with the model capturing 60% of the variance. Our findings suggest the potential of wearables and machine learning for early identification of phenoconversion, remote monitoring in HD, and evaluating new treatments efficacy in clinical trials and medicine.
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Digital measures may provide objective, sensitive, real-world measures of disease progression in Parkinson's disease (PD). However, multicenter longitudinal assessments of such measures are few. We recently demonstrated that baseline assessments of gait, tremor, finger tapping, and speech from a commercially available smartwatch, smartphone, and research-grade wearable sensors differed significantly between 82 individuals with early, untreated PD and 50 age-matched controls. Here, we evaluated the longitudinal change in these assessments over 12 months in a multicenter observational study using a generalized additive model, which permitted flexible modeling of at-home data. All measurements were included until participants started medications for PD. Over one year, individuals with early PD experienced significant declines in several measures of gait, an increase in the proportion of day with tremor, modest changes in speech, and few changes in psychomotor function. As measured by the smartwatch, the average (SD) arm swing in-clinic decreased from 25.9 (15.3) degrees at baseline to 19.9 degrees (13.7) at month 12 (P = 0.004). The proportion of awake time an individual with early PD had tremor increased from 19.3% (18.0%) to 25.6% (21.4%; P < 0.001). Activity, as measured by the number of steps taken per day, decreased from 3052 (1306) steps per day to 2331 (2010; P = 0.16), but this analysis was restricted to 10 participants due to the exclusion of those that had started PD medications and lost the data. The change of these digital measures over 12 months was generally larger than the corresponding change in individual items on the Movement Disorder Society-Unified Parkinson's Disease Rating Scale but not greater than the change in the overall scale. Successful implementation of digital measures in future clinical trials will require improvements in study conduct, especially data capture. Nonetheless, gait and tremor measures derived from a commercially available smartwatch and smartphone hold promise for assessing the efficacy of therapeutics in early PD.
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BACKGROUND: Patient perspectives on meaningful symptoms and impacts in early Parkinson's disease (PD) are lacking and are urgently needed to clarify priority areas for monitoring, management, and new therapies. OBJECTIVE: To examine experiences of people with early-stage PD, systematically describe meaningful symptoms and impacts, and determine which are most bothersome or important. METHODS: Forty adults with early PD who participated in a study evaluating smartwatch and smartphone digital measures (WATCH-PD study) completed online interviews with symptom mapping to hierarchically delineate symptoms and impacts of disease from "Most bothersome" to "Not present," and to identify which of these were viewed as most important and why. Individual symptom maps were coded for types, frequencies, and bothersomeness of symptoms and their impacts, with thematic analysis of narratives to explore perceptions. RESULTS: The three most bothersome and important symptoms were tremor, fine motor difficulties, and slow movements. Symptoms had the greatest impact on sleep, job functioning, exercise, communication, relationships, and self-concept- commonly expressed as a sense of being limited by PD. Thematically, most bothersome symptoms were those that were personally limiting with broadest negative impact on well-being and activities. However, symptoms could be important to patients even when not present or limiting (e.g., speech, cognition). CONCLUSION: Meaningful symptoms of early PD can include symptoms that are present or anticipated future symptoms that are important to the individual. Systematic assessment of meaningful symptoms should aim to assess the extent to which symptoms are personally important, present, bothersome, and limiting.
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Doença de Parkinson , Adulto , Humanos , Doença de Parkinson/complicações , Doença de Parkinson/diagnóstico , Doença de Parkinson/terapia , Tremor , Cognição , Exercício Físico , HipocinesiaRESUMO
BACKGROUND: Adoption of new digital measures for clinical trials and practice has been hindered by lack of actionable qualitative data demonstrating relevance of these metrics to people with Parkinson's disease. OBJECTIVE: This study evaluated of relevance of WATCH-PD digital measures to monitoring meaningful symptoms and impacts of early Parkinson's disease from the patient perspective. METHODS: Participants with early Parkinson's disease (Nâ=â40) completed surveys and 1:1 online-interviews. Interviews combined: 1) symptom mapping to delineate meaningful symptoms/impacts of disease, 2) cognitive interviewing to assess content validity of digital measures, and 3) mapping of digital measures back to personal symptoms to assess relevance from the patient perspective. Content analysis and descriptive techniques were used to analyze data. RESULTS: Participants perceived mapping as deeply engaging, with 39/40 reporting improved ability to communicate important symptoms and relevance of measures. Most measures (9/10) were rated relevant by both cognitive interviewing (70-92.5%) and mapping (80-100%). Two measures related to actively bothersome symptoms for more than 80% of participants (Tremor, Shape rotation). Tasks were generally deemed relevant if they met three participant context criteria: 1) understanding what the task measured, 2) believing it targeted an important symptom of PD (past, present, or future), and 3) believing the task was a good test of that important symptom. Participants did not require that a task relate to active symptoms or "real" life to be relevant. CONCLUSION: Digital measures of tremor and hand dexterity were rated most relevant in early PD. Use of mapping enabled precise quantification of qualitative data for more rigorous evaluation of new measures.
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Doença de Parkinson , Humanos , Doença de Parkinson/complicações , Doença de Parkinson/diagnóstico , Doença de Parkinson/psicologia , TremorRESUMO
Digital health technologies can provide continuous monitoring and objective, real-world measures of Parkinson's disease (PD), but have primarily been evaluated in small, single-site studies. In this 12-month, multicenter observational study, we evaluated whether a smartwatch and smartphone application could measure features of early PD. 82 individuals with early, untreated PD and 50 age-matched controls wore research-grade sensors, a smartwatch, and a smartphone while performing standardized assessments in the clinic. At home, participants wore the smartwatch for seven days after each clinic visit and completed motor, speech and cognitive tasks on the smartphone every other week. Features derived from the devices, particularly arm swing, the proportion of time with tremor, and finger tapping, differed significantly between individuals with early PD and age-matched controls and had variable correlation with traditional assessments. Longitudinal assessments will inform the value of these digital measures for use in future clinical trials.
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Smartphones and wearables are widely recognised as the foundation for novel Digital Health Technologies (DHTs) for the clinical assessment of Parkinson's disease. Yet, only limited progress has been made towards their regulatory acceptability as effective drug development tools. A key barrier in achieving this goal relates to the influence of a wide range of sources of variability (SoVs) introduced by measurement processes incorporating DHTs, on their ability to detect relevant changes to PD. This paper introduces a conceptual framework to assist clinical research teams investigating a specific Concept of Interest within a particular Context of Use, to identify, characterise, and when possible, mitigate the influence of SoVs. We illustrate how this conceptual framework can be applied in practice through specific examples, including two data-driven case studies.
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Parkinson's disease (PD) is the fastest-growing neurological disease in the world. A key challenge in PD is tracking disease severity, progression, and medication response. Existing methods are semisubjective and require visiting the clinic. In this work, we demonstrate an effective approach for assessing PD severity, progression, and medication response at home, in an objective manner. We used a radio device located in the background of the home. The device detected and analyzed the radio waves that bounce off people's bodies and inferred their movements and gait speed. We continuously monitored 50 participants, with and without PD, in their homes for up to 1 year. We collected over 200,000 gait speed measurements. Cross-sectional analysis of the data shows that at-home gait speed strongly correlates with gold-standard PD assessments, as evaluated by the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III subscore and total score. At-home gait speed also provides a more sensitive marker for tracking disease progression over time than the widely used MDS-UPDRS. Further, the monitored gait speed was able to capture symptom fluctuations in response to medications and their impact on patients' daily functioning. Our study shows the feasibility of continuous, objective, sensitive, and passive assessment of PD at home and hence has the potential of improving clinical care and drug clinical trials.
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Doença de Parkinson , Estudos Transversais , Progressão da Doença , Marcha , Análise da Marcha , Humanos , Doença de Parkinson/tratamento farmacológico , Ondas de Rádio , Índice de Gravidade de DoençaRESUMO
Most wearable sensor studies in Parkinson's disease have been conducted in the clinic and thus may not be a true representation of everyday symptoms and symptom variation. Our goal was to measure activity, gait, and tremor using wearable sensors inside and outside the clinic. In this observational study, we assessed motor features using wearable sensors developed by MC10, Inc. Participants wore five sensors, one on each limb and on the trunk, during an in-person clinic visit and for two days thereafter. Using the accelerometer data from the sensors, activity states (lying, sitting, standing, walking) were determined and steps per day were also computed by aggregating over 2 s walking intervals. For non-walking periods, tremor durations were identified that had a characteristic frequency between 3 and 10 Hz. We analyzed data from 17 individuals with Parkinson's disease and 17 age-matched controls over an average 45.4 h of sensor wear. Individuals with Parkinson's walked significantly less (median [inter-quartile range]: 4980 [2835-7163] steps/day) than controls (7367 [5106-8928] steps/day; P = 0.04). Tremor was present for 1.6 [0.4-5.9] hours (median [range]) per day in most-affected hands (MDS-UPDRS 3.17a or 3.17b = 1-4) of individuals with Parkinson's, which was significantly higher than the 0.5 [0.3-2.3] hours per day in less-affected hands (MDS-UPDRS 3.17a or 3.17b = 0). These results, which require replication in larger cohorts, advance our understanding of the manifestations of Parkinson's in real-world settings.
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BACKGROUND: Current Huntington's disease (HD) measures are limited to subjective, episodic assessments conducted in clinic. Smartphones can enable the collection of objective, real-world data but their use has not been extensively evaluated in HD. OBJECTIVE: Develop and evaluate a smartphone application to assess feasibility of use and key features of HD in clinic and at home. METHODS: We developed GEORGE®, an Android smartphone application for HD which assesses voice, chorea, balance, gait, and finger tapping speed. We then conducted an observational pilot study of individuals with manifest HD, prodromal HD, and without a movement disorder. In clinic, participants performed standard clinical assessments and a battery of active tasks in GEORGE. At home, participants were instructed to complete the activities thrice daily for one month. Sensor data were used to measure chorea, tap rate, and step count. Audio data was not analyzed. RESULTS: Twenty-three participants (8 manifest HD, 5 prodromal HD, 10 controls) enrolled, and all but one completed the study. On average, participants used the application 2.1 times daily. We observed a significant difference in chorea score (HD: 19.5; prodromal HD: 4.5, pâ=â0.007; controls: 4.3, pâ=â0.001) and tap rate (HD: 2.5 taps/s; prodromal HD: 8.9 taps/s, pâ=â0.001; controls: 8.1 taps/s, pâ=â0.001) between individuals with and without manifest HD. Tap rate correlated strongly with the traditional UHDRS finger tapping score (left hand: râ=â-0.82, pâ=â0.022; right hand: râ=â-0.79, pâ=â0.03). CONCLUSION: GEORGE is an acceptable and effective tool to differentiate individuals with and without manifest HD and measure key disease features. Refinement of the application's interface and activities will improve its usability and sensitivity and, ideally, make it useful for clinical care and research.
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Doença de Huntington/terapia , Aplicativos Móveis , Monitorização Ambulatorial/métodos , Smartphone , Adulto , Idoso , Feminino , Análise da Marcha , Humanos , Masculino , Pessoa de Meia-Idade , Projetos PilotoRESUMO
INTRODUCTION: Parkinson's disease (PD) research is hampered by slow, inefficient recruitment and burdensome in-person assessments that may be challenging to conduct in a world affected by COVID-19. Fox Insight is an ongoing prospective clinical research study that enables individuals to participate in clinical research from their own homes by completing online questionnaires. To date, over 45,000 participants with and without PD have enrolled. We sought to validate self-reported PD diagnosis in the Fox Insight cohort, assess the validity of other self-reported health information, and evaluate the willingness of participants to participate in video-based research studies. METHODS: Individuals with and without self-reported PD enrolled in Fox Insight were invited to participate in this virtual research study. Participants completed online questionnaires and two virtual visits, during which we conducted standard cognitive and motor assessments. A movement disorder expert determined the most likely diagnosis, which was compared to self-reported diagnosis. RESULTS: A total of 203 participants from 40 U.S. states, 159 with remote clinician-determined PD and 44 without, completed the study (59% male, mean (SD) age 65.7 (9.8)). Level of agreement between self-reported PD diagnosis in Fox Insight and clinician-determined diagnosis was very good ((kappa = 0.85, 95% CI 0.76-0.94). Overall, 97.9% of participants were satisfied with the study, 98.5% were willing to participate in a future observational study with virtual visits, and 76.1% were willing to participate in an interventional trial with virtual visits. CONCLUSION: Among the Fox Insight cohort, self-reported diagnosis is accurate and interest in virtual research studies is high.
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OBJECTIVE: The expanding power and accessibility of personal technology provide an opportunity to reduce burdens and costs of traditional clinical site-centric therapeutic trials in Parkinson's disease and generate novel insights. The value of this approach has never been more evident than during the current COVID-19 pandemic. We sought to (1) establish and implement the infrastructure for longitudinal, virtual follow-up of clinical trial participants, (2) compare changes in smartphone-based assessments, online patient-reported outcomes, and remote expert assessments, and (3) explore novel digital markers of Parkinson's disease disability and progression. METHODS: Participants from two recently completed phase III clinical trials of inosine and isradipine enrolled in Assessing Tele-Health Outcomes in Multiyear Extensions of Parkinson's Disease trials (AT-HOME PD), a two-year virtual cohort study. After providing electronic informed consent, individuals complete annual video visits with a movement disorder specialist, smartphone-based assessments of motor function and socialization, and patient-reported outcomes online. RESULTS: From the two clinical trials, 226 individuals from 42 states in the United States and Canada enrolled. Of these, 181 (80%) have successfully downloaded the study's smartphone application and 161 (71%) have completed patient-reported outcomes on the online platform. INTERPRETATION: It is feasible to conduct a large-scale, international virtual observational study following the completion of participation in brick-and-mortar clinical trials in Parkinson's disease. This study, which brings research to participants, will compare established clinical endpoints with novel digital biomarkers and thereby inform the longitudinal follow-up of clinical trial participants and design of future clinical trials.
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Aplicativos Móveis , Doença de Parkinson/fisiopatologia , Medidas de Resultados Relatados pelo Paciente , Projetos de Pesquisa , Smartphone , Telemedicina , Comunicação por Videoconferência , COVID-19 , Canadá , Ensaios Clínicos como Assunto , Progressão da Doença , Seguimentos , Humanos , Estudos Longitudinais , SARS-CoV-2 , Estados UnidosRESUMO
BACKGROUND: Most current measures of Huntington's disease (HD) motor symptoms are subjective, categorical, and limited to in-person visits. Wearable sensors enable objective, frequent, and remote data collection in real-world settings. However, longitudinal sensor studies in HD are lacking. OBJECTIVE: To measure motor symptoms of HD using wearable sensors in a longitudinal study. METHODS: Participants with HD, prodromal HD, and without a movement disorder wore five accelerometers, one on each limb and on the trunk, at up to four clinic visits over one year. After each visit, participants wore the sensors at home for two days. Based on the accelerometer data from the trunk, we calculated a "truncal Chorea Index" for periods when the participant was sitting. We also measured gait parameters and activity profiles. To measure group differences, track progression, and observe individual variability, statistical analysis of the data was conducted using a linear mixed-effects model. RESULTS: Fifteen individuals with HD, five with prodromal HD, and 19 controls were enrolled. The average truncal Chorea Index was higher in individuals with HD (26.6, pâ<â0.001) than in controls (15.6). For participants with HD, the truncal Chorea Index showed a high intra-day variability but minimal change over 12 months. Individuals with HD walked less (HDâ=â3818, prodromal HDâ=â6957, controlsâ=â5514 steps/day) and took longer duration steps (HDâ=â0.97, prodromal HDâ=â0.78, controlsâ=â0.85 seconds/step) than the other groups. Individuals with HD spent over half their day lying down (HDâ=â51.1%, prodromal HDâ=â38.0%, controlsâ=â37.1%). CONCLUSIONS: A novel truncal Chorea Index can assess truncal chorea at home, finds substantial variability, and suggests that truncal chorea may be present in prodromal HD. Individuals with HD walk less and slower and spend more time lying down than controls. These findings require additional investigation, could inform clinical care, and could be used to evaluate new therapies.
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Acelerometria/métodos , Marcha/fisiologia , Doença de Huntington/diagnóstico , Doença de Huntington/fisiopatologia , Atividade Motora/fisiologia , Índice de Gravidade de Doença , Dispositivos Eletrônicos Vestíveis , Acelerometria/instrumentação , Adulto , Feminino , Humanos , Estudos Longitudinais , Masculino , Pessoa de Meia-Idade , Sintomas Prodrômicos , Tronco/fisiopatologia , Velocidade de Caminhada/fisiologiaRESUMO
PURPOSE OF REVIEW: The prevalence of neurodegenerative diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD), is rising as the global population ages. Access to specialist care, which improves outcomes, is insufficient and disease-related disability makes in-person physician visits burdensome. Telehealth is one potential means for improving access to care. The purpose of this manuscript is to review recent publications on telemedicine in AD and PD. RECENT FINDINGS: Telemedicine is feasible in AD and PD and acceptable to patients and their caregivers. Compared with in-person visits, telemedicine reduces visit-associated travel and time. Telemedicine can be used for rehabilitative therapies, to administer cognitive tests, and to support caregivers. Access to telemedicine results in changes in patient care including medication adjustments and referrals for therapies and supports. SUMMARY: The use of telemedicine in AD and PD stands to decrease burden on patients and increase access to specialty care. Barriers to the expansion of telemedicine care include lack of widespread broadband access, state licensure requirements, and inconsistent reimbursement. More outcomes-based prospective telemedicine studies are needed.
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BACKGROUND: Use of video research visits in neurologic conditions is rising, but their utility has not been assessed in atypical parkinsonian syndromes. We sought to evaluate the diagnostic concordance between video-based vs self-reported diagnoses of multiple system atrophy, progressive supranuclear palsy, dementia with Lewy bodies, and corticobasal syndrome. We also assessed patient satisfaction with video-based visits. METHODS: We conducted a study of video-based research visits in individuals with an atypical parkinsonian syndrome enrolled in The Michael J. Fox Foundation's Fox Trial Finder. Participants completed a recorded real-time video visit with a remote evaluator who was blinded to the participant's self-reported diagnosis. The investigator conducted a structured interview and performed standard assessments of motor function. Following the visit, the investigator selected the most likely diagnosis. The recorded visit was reviewed by a second blinded investigator who also selected the most likely diagnosis. We evaluated diagnostic concordance between the 2 independent investigators and assessed concordance between investigator consensus diagnosis and self-reported diagnosis using Cohen's kappa. We assessed participant satisfaction with a survey. RESULTS: We enrolled 45 individuals with atypical parkinsonian syndromes, and 44 completed the investigator-performed video assessment. We demonstrated excellent concordance in diagnosis between the investigators (κ = 0.83) and good reliability of self-reported diagnosis (κ = 0.73). More than 90% of participants were satisfied or very satisfied with the convenience, comfort, and overall visit. CONCLUSIONS: Video research visits are feasible and reliable in those with an atypical parkinsonian syndrome. These visits represent a promising option for reducing burden and extending the reach of clinical research to individuals with these rare and disabling conditions.
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Phenotype is the set of observable traits of an organism or condition. While advances in genetics, imaging, and molecular biology have improved our understanding of the underlying biology of Parkinson's disease (PD), clinical phenotyping of PD still relies primarily on history and physical examination. These subjective, episodic, categorical assessments are valuable for diagnosis and care but have left gaps in our understanding of the PD phenotype. Sensors can provide objective, continuous, real-world data about the PD clinical phenotype, increase our knowledge of its pathology, enhance evaluation of therapies, and ultimately, improve patient care. In this paper, we explore the concept of deep phenotyping-the comprehensive assessment of a condition using multiple clinical, biological, genetic, imaging, and sensor-based tools-for PD. We discuss the rationale for, outline current approaches to, identify benefits and limitations of, and consider future directions for deep clinical phenotyping.