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.

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.

Aim: To develop a shiny app for doctors to investigate breast cancer treatments through a new approach by incorporating unsupervised clustering and survival information. Materials & methods: Analys...

Frontotemporal Dementia (FTD) encompasses a diverse group of progressive neurodegenerative diseases that impact speech production and comprehension, higher-order cognition, behavior, and motor control. Traditional acoustic speech markers have been extensively studied in FTD, as have assessments capturing apathy and impairments in recognizing and expressing emotion. This work leverages machine learning to track changes in emotional content within the speech of individuals with FTD and healthy controls. The aim of the project is to develop tools for assessing and monitoring emotional changes in individuals with FTD, quantifying these subtle aspects of the disease and thus potentially providing insights for assessing future therapeutic interventions. A retrospective analysis was conducted on a dataset comprising standard elicited speech tasks performed by 78 individuals diagnosed with FTD and 55 healthy elderly controls. We employed an ensemble-based convolutional neural network (CNN) classifier trained on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset to extract emotion scores from processed speech samples. The classifier was applied with a sliding window to the FTD and healthy control narratives to facilitate a granular examination of emotional changes throughout longer speech samples. Analysis of variance (ANOVA) was used to test for group differences in average emotion scores as well as emotional variability over the duration of the speech samples. Compared to healthy controls, people with FTD demonstrated reduced emotional change in a monologue task describing a happy experience, as measured by the interquartile range (IQR) (p ¡ 0.005) and slope of “happy” emotion scores vs. time (p ¡ 0.005). During a picture description task, people with FTD displayed a slightly elevated average level of frustration (p ¡ 0.005). Increased frustration levels in individuals with FTD could potentially indicate their difficulties in accomplishing the task. This study introduced the application of a pre-trained Speech Emotion Recognition (SER) model on overlapping short segments of extended speech samples, allowing for a detailed examination of emotional changes over time. Capturing the temporal evolution of emotional content offers a nuanced understanding of communication in individuals with FTD. Our findings lay the groundwork for further development of digital biomarkers to refine the assessment, monitoring, and understanding of the emotional and social communication impacts of FTD.

Kidney failure is a critical health condition with significant impact on patient well-being and healthcare systems worldwide. Analyzing the longitudinal trajectory of kidney function is crucial for understanding disease progression, predicting outcomes, and personalizing treatment strategies. This paper proposes a novel approach utilizing latent longitudinal trajectory clustering techniques by incorporating survival information to analyze kidney failure and explore patterns within patient populations. Besides, we also developed a web application to provide visualize and intuitive way to explore the relationship between estimated glomerular filtration rate (EGFR) progression and survival outcomes, helping researchers and clinicians gain valuable insights. By identifying distinct subgroups, this analysis can aid in early detection, risk stratification, and treatment optimization. The proposed methodology holds promise for improving patient care and outcomes in the field of nephrology.