Wearable Sensor-Based Assessments for Remotely Screening Early-Stage Parkinson's Disease.

Prevalence estimates of Parkinson's disease (PD)-the fastest-growing neurodegenerative disease-are generally underestimated due to issues surrounding diagnostic accuracy, symptomatic undiagnosed cases, suboptimal prodromal monitoring, and limited screening access. Remotely monitored wearable devices and sensors provide precise, objective, and frequent measures of motor and non-motor symptoms. Here, we used consumer-grade wearable device and sensor data from the WATCH-PD study to develop a PD screening tool aimed at eliminating the gap between patient symptoms and diagnosis. Early-stage PD patients (n = 82) and age-matched comparison participants (n = 50) completed a multidomain assessment battery during a one-year longitudinal multicenter study. Using disease- and behavior-relevant feature engineering and multivariate machine learning modeling of early-stage PD status, we developed a highly accurate (92.3%), sensitive (90.0%), and specific (100%) random forest classification model (AUC = 0.92) that performed well across environmental and platform contexts. These findings provide robust support for further exploration of consumer-grade wearable devices and sensors for global population-wide PD screening and surveillance.

Metadata Framework to Support Deployment of Digital Health Technologies in Clinical Trials in Parkinson's Disease.

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.

Replacing sedentary time with physical activity or sleep: effects on cancer-related cognitive impairment in breast cancer survivors.

BACKGROUND: Evidence suggests reallocating daily sedentary time to physical activity or sleep confers important health benefits in cancer survivors. Despite emerging research suggesting physical activity as a treatment for cancer-related cognitive impairment (CRCI), little is known about the interactive effects of behaviors across the 24-h period. The present purpose was to examine the cognitive effects of reallocating sedentary time to light-intensity physical activity, moderate-to-vigorous physical activity (MVPA), or sleep in breast cancer survivors. METHODS: Breast cancer survivors (N = 271, Mage = 57.81 ± 9.50 years) completed iPad-based questionnaires and cognitive tasks assessing demographics, health history, executive function, and processing speed (Task-Switch, Trail Making). Participants wore an accelerometer for seven consecutive days to measure their sedentary, physical activity, and sleep behaviors. Single effects (each behavior individually) and partition (controlling for other behaviors) models were used to examine associations among behaviors and cognitive performance. Isotemporal substitution models were used to test the cognitive effects of substituting 30 min of sedentary time with 30 min of light-intensity activity, MVPA, and sleep. RESULTS: MVPA was associated with faster Task-switch reaction time in the partition models (stay: B = - 35.31, p = 0.02; switch: B = - 48.24, p = 0.004). Replacing 30 min of sedentary time with 30 min of MVPA yielded faster reaction times on Task-Switch stay (B = - 29.37, p = 0.04) and switch (B = - 39.49, p = 0.02) trials. In Trails A single effects models, sedentary behavior was associated with faster completion (B = - 0.97, p = 0.03) and light-intensity activity with slower completion (B = 1.25, p = 0.006). No single effects were observed relative to Trails B completion (all p > 0.05). Only the effect of MVPA was significant in the partition models (Trails A: B = - 3.55, p = 0.03; Trails B: B = - 4.46, p = 0.049). Replacing sedentary time with light-intensity activity was associated with slower Trails A (B = 1.55 p = 0.002) and Trails B (B = 1.69, p = 0.02) completion. Replacing light activity with MVPA yielded faster Trails A (B = - 4.35, p = 0.02) and Trails B (B = - 5.23, p = 0.03) completion. CONCLUSIONS: Findings support previous research suggesting MVPA may be needed to improve cognitive function in breast cancer survivors. Trails findings underscore the need to dissect sedentary contexts to better understand the impact of daily behavioral patterns on CRCI. Additional research investigating the cognitive impacts of behaviors across the 24-h period is warranted. TRIAL REGISTRATION: This study is registered with United States ClinicalTrials.gov ( NCT02523677 ; 8/14/2015).

Effects of physical activity on psychological well-being outcomes in breast cancer survivors from prediagnosis to posttreatment survivorship.

OBJECTIVE: The purpose of this study was to examine the effects of physical activity from prediagnosis to posttreatment survivorship on the psychological well-being (PWB) outcomes of fatigue, depression, anxiety, and quality of life (QoL) in breast cancer survivors (BCS). METHODS: Participants (N = 387) completed a questionnaire battery by using an iPad-based platform. Measures included self-reported PA (before diagnosis and currently) and perceptions of fatigue, depression, anxiety, and QoL. Multivariate analysis of covariance was used to examine differences in PWB among BCS categorized into 1 of 4 physical activity levels: (a) low-active prediagnosis, low-active currently (low-active maintainers; n = 128); (b) low-active prediagnosis, active currently (increasers; n = 74); (c) active prediagnosis, low-active currently (decreasers; n = 52); and (d) active prediagnosis, active currently (high-active maintainers; n = 136). Participants were classified as active (≥24 units) or low-active (<24 units) by using Godin Leisure-Time Exercise Questionnaire cut-points for health benefits. RESULTS: Fatigue and depression were lowest, and QoL was highest among women in the high-active maintainers category, followed by the increasers, low-active maintainers, and decreasers. No differences in anxiety were observed across categories. Women in the high-active maintainers category differed significantly in fatigue, depression, and QoL from both low-active categories (low-active maintainers and decreasers), P ≤ .001. Women in the increasers category also differed significantly in fatigue, depression, and QoL from the decreasers, P ≤ .01. CONCLUSION: Low physical activity during survivorship was associated with greater fatigue and depression and lower QoL. IMPLICATIONS FOR CANCER SURVIVORS: Efforts to help increase or maintain high levels of physical activity may be critical to helping BCS maintain their PWB.

The effects of physical activity and fatigue on cognitive performance in breast cancer survivors.

PURPOSE: Research suggests that physical activity may be a promising treatment for cancer-related cognitive impairment; however, evidence is limited by small samples and self-report measures and little is known about the underlying mechanisms. The purpose of this study was to examine the effects of physical activity on cognitive function in a national sample of breast cancer survivors (BCSs) using objective measures. We hypothesized that physical activity's effects on cognition would be indirect through survivors' self-reported fatigue. METHODS: Participants (N = 299; M = 57.51 ± 9.54 years) included BCSs with access to an iPad. Participants wore an accelerometer for seven consecutive days to measure their average daily minutes of moderate-to-vigorous physical activity (MVPA) and completed a battery of questionnaires and neuropsychological tests via an iPad application to measure fatigue and cognitive function. Cognitive function was modeled as two latent factors-executive function and working memory-comprising performance across seven cognitive tasks. A structural equation modeling framework was used to test the hypotheses. RESULTS: MVPA was associated with less fatigue (γ = 0.19), which, in turn, was associated with faster times on executive function tasks (γ = -0.18) and greater accuracy on working memory tasks (γ = 0.16). The indirect paths from MVPA to cognitive performance were also significant (executive function: ß = -0.03, memory: ß = 0.03). CONCLUSIONS: Findings suggest that MVPA may be associated with greater executive function and working memory in BCSs. Further, this effect may be partially indirect through cancer-related symptoms (e.g., fatigue). Results emphasize the need for additional scientific investigation in the context of prospective and efficacy trials.

Improved measurement of disease progression in people living with early Parkinson's disease using digital health technologies.

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.

Relative Meaningfulness and Impacts of Symptoms in People with Early-Stage Parkinson's Disease.

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.

Mapping Relevance of Digital Measures to Meaningful Symptoms and Impacts in Early Parkinson's Disease.

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 (N = 40) 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.

Identifying and characterising sources of variability in digital outcome measures in Parkinson's disease.

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.

Visuomotor Transformations Are Modulated by Focused Ultrasound over Frontal Eye Field.

Neuromodulation with focused ultrasound (FUS) is being widely explored as a non-invasive tool to stimulate focal brain regions because of its superior spatial resolution and coverage compared with other neuromodulation methods. The precise effects of FUS stimulation on specific regions of the brain are not yet fully understood. Here, we characterized the behavioral effects of FUS stimulation directly applied through a craniotomy over the macaque frontal eye field (FEF). In macaque monkeys making directed eye movements to perform visual search tasks with direct or arbitrary responses, focused ultrasound was applied through a craniotomy over the FEF. Saccade response times (RTs) and error rates were determined for trials without or with FUS stimulation with pulses at a peak negative pressure of either 250 or 425 kPa. Both RTs and error rates were affected by FUS. Responses toward a target located contralateral to the FUS stimulation were approximately 3 ms slower in the presence of FUS in both monkeys studied, while only one exhibited a slowing of responses for ipsilateral targets. Error rates were lower in one monkey in this study. In another search task requiring making eye movements toward a target (pro-saccades) or in the opposite direction (anti-saccades), the RT for pro-saccades increased in the presence of FUS stimulation. Our results indicate the effectiveness of FUS to modulate saccadic responses when stimulating FEF in awake, behaving non-human primates.

Precompetitive Consensus Building to Facilitate the Use of Digital Health Technologies to Support Parkinson Disease Drug Development through Regulatory Science.

Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson's Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies.

Using transcranial direct-current stimulation (tDCS) to understand cognitive processing.

Noninvasive brain stimulation methods are becoming increasingly common tools in the kit of the cognitive scientist. In particular, transcranial direct-current stimulation (tDCS) is showing great promise as a tool to causally manipulate the brain and understand how information is processed. The popularity of this method of brain stimulation is based on the fact that it is safe, inexpensive, its effects are long lasting, and you can increase the likelihood that neurons will fire near one electrode and decrease the likelihood that neurons will fire near another. However, this method of manipulating the brain to draw causal inferences is not without complication. Because tDCS methods continue to be refined and are not yet standardized, there are reports in the literature that show some striking inconsistencies. Primary among the complications of the technique is that the tDCS method uses two or more electrodes to pass current and all of these electrodes will have effects on the tissue underneath them. In this tutorial, we will share what we have learned about using tDCS to manipulate how the brain perceives, attends, remembers, and responds to information from our environment. Our goal is to provide a starting point for new users of tDCS and spur discussion of the standardization of methods to enhance replicability.