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Understanding what matters to people with Parkinson’s and their family is essential to derive relevant clinical outcome measures and guide clinical care. The purpose of this study was to explore what is important to people with Parkinson’s disease vs. family over time. A qualitative content-analysis of online survey data collected by Parkinson’s UK was conducted to identify types and frequencies of important symptoms and impacts of Parkinson’s for people with the disease vs. family of people with Parkinson’s. Independent T-tests were used to identify significance of between group differences for patients vs. family at < 2, 2–5, 6–10, 11–20, > 20-year durations. ANOVA was used to assess for within group differences by disease duration. We found that symptom priority changed significantly over time with longer disease duration. Tremor was reported less often later on, whereas mobility, dyskinesias, gait and speech/communication symptoms gained priority. In general, patients identified movement-related symptoms (e.g., walking, bradykinesia) as the most bothersome at all durations while family more strongly prioritized the physical and psychosocial impacts of disease (e.g., mobility, safety, interpersonal interactions, independence, and family impact). We conclude that important differences exist between family and patient perspectives of what matters and change over time with longer duration of disease.

The Huntington’s Disease Cognitive Assessment Battery (HD-CAB) is frequently used in clinical trials; however, it is unclear whether the HD-CAB captures meaningful symptoms from patients’ perspectives. We aimed to explore whether HD-CAB tests are considered meaningful to people with Huntington’s disease (HD) by mapping them to relevant symptoms and impacts. Eighteen people with HD before and after clinical motor diagnosis completed a semi-structured interview with symptom mapping to hierarchically rank the importance and bothersomeness of cognitive symptoms and discussed the relevance of each test to meaningful symptoms. Reflexive Thematic Analysis (RTA) was used to explore participant perceptions of the relevance of HD-CAB tests for assessing disease progression. Content coding was used to identify the frequency of important symptoms and impacts, and we used binomial tests to determine whether there was statistically significant consensus (agreement > 50%). Our findings showed that all HD-CAB tests were viewed as meaningful by a significant majority of participants and all subtests were agreed to assess important or bothersome symptoms ( p  < .05). Participants identified two factors contributing to the importance of the HD-CAB tests for measuring HD changes: (1) The tests of the HD-CAB provide participants with knowledge about their abilities and disease; and (2) The tests of the HD-CAB capture changes that are important for everyday life. Overall, the HD-CAB was a meaningful cognitive assessment tool from the perspective of people with HD.

Remote health assessments that gather real-world data (RWD) outside clinic settings require a clear understanding of appropriate methods for data collection, quality assessment, analysis and interpretation. Here we examine the performance and limitations of smartphones in collecting RWD in the remote mPower observational study of Parkinson’s disease (PD). Within the first 6 months of study commencement, 960 participants had enrolled and performed at least five self-administered active PD symptom assessments (speeded tapping, gait/balance, phonation or memory). Task performance, especially speeded tapping, was predictive of self-reported PD status (area under the receiver operating characteristic curve (AUC) = 0.8) and correlated with in-clinic evaluation of disease severity ( r  = 0.71; P  < 1.8 × 10 −6 ) when compared with motor Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Although remote assessment requires careful consideration for accurate interpretation of RWD, our results support the use of smartphones and wearables in objective and personalized disease assessments. Smartphone sensors that monitor disease symptoms enable remote assessment of Parkinson’s patients.

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.

Metastasis underlies the majority of cancer-related deaths yet remains poorly understood due, in part, to the lack of models in vivo. Here we show that expression of the EMT master inducer Snail in primary adult Drosophila intestinal tumors leads to the dissemination of tumor cells and formation of macrometastases. Snail drives an EMT in tumor cells, which, although retaining some epithelial markers, subsequently break through the basal lamina of the midgut, undergo a collective migration and seed polyclonal metastases. While metastases re-epithelialize over time, we found that early metastases are remarkably mesenchymal, discarding the requirement for a mesenchymal-to-epithelial transition for early stages of metastatic growth. Our results demonstrate the formation of metastases in adult flies, and identify a key role for partial-EMTs in driving it. This model opens the door to investigate the basic mechanisms underlying metastasis, in a powerful in vivo system suited for rapid genetic and drug screens. Modelling and visualizing tumor metastasis in Drosophila has been a challenge. Here, the authors show that constitutive expression of Sna in primary adult Drosophila intestinal tumors drives EMT and dissemination of tumor cells, induces collective cell migration and formation of polyclonal metastases.

While speech biomarkers of disease have attracted increased interest in recent years, a challenge is that features derived from signal processing or machine learning approaches may lack clinical interpretability. As an example, Mel frequency cepstral coefficients (MFCCs) have been identified in several studies as a useful marker of disease, but are regarded as uninterpretable. Here we explore correlations between MFCC coefficients and more interpretable speech biomarkers. In particular we quantify the MFCC2 endpoint, which can be interpreted as a weighted ratio of low- to high-frequency energy, a concept which has been previously linked to disease-induced voice changes. By exploring MFCC2 in several datasets, we show how its sensitivity to disease can be increased by adjusting computation parameters.

Background Longitudinal qualitative data on what matters to people with Parkinson’s disease are lacking and needed to guide patient-centered clinical care and development of outcome measures. Objective To evaluate change over time in symptoms, impacts, and relevance of digital measures to monitor disease progression in early Parkinson’s. Methods In-depth, online symptom mapping interviews were conducted with 33 people with early Parkinson’s at baseline and 1 year later to evaluate (A) symptoms, (B) impacts, and (C) relevance of digital measures to monitor personally relevant symptoms. Maps and transcripts were coded for frequencies, Likert scale rankings (0 = not present to 4 = most bothersome), and thematic findings. Wilcoxon Signed Rank test was used to evaluate change over time. Results Other than walking and balance, most motor symptoms did not change significantly from baseline to 1 year later. Multiple significant changes were observed in non-motor areas (cognition, speech, sleep, mood, fatigue, pain; p  < 0.05) and functional impacts (mobility, effort to do usual activities, personal comfort; p  < 0.05). Thematic analysis revealed ability to cope with and compensate for actual or anticipated symptoms reduced disruptions to well-being and changed how bothersome symptoms were. All digital measures targeted symptoms that were personally important to most participants (> 80%). Conclusion Non-motor and walking/balance symptoms changed sooner than other motor symptoms during the course of 1 year. Evaluation of coping and compensatory mechanisms may be essential to understanding symptom bothersomeness at a given point in time. Smartphone and smartwatch digital measures were relevant to personally meaningful symptoms of early PD.

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.

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.

Purpose of ReviewDigital 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 FindingsRecent 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.SummaryDigital 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.