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Competing Interests: Declaration of competing interest Dr. Schneider: Dr. Schneider has received research funding from the NIH/NINDS, the Michael J. Fox Foundation for Parkinson's Research, Biohaven Phamaceuticals, Acadia Pharmaceuticals, and Bial and has received funding from Clintrex for her role on a DSMB. Dr. Phillips: Dr. Phillips has no conflicts of interest to declare. Dr. Kalia: Dr. Kalia has received research support from Canadian Institutes of Health Research (CIHR), Cure Parkinson's, Krembil Foundation, Michael J. Fox Foundation for Parkinson's Research (MJFF), Natural Sciences and Engineering Research Council of Canada (NSERC), Parkinson Canada; consultancy fees from Cure Ventures, Ipsen, Knight Therapeutics, Right Brain Bio, UCB; and, honoraria from Canadian Movement Disorders Society (CMDS), Critical Path for Parkinson's (CPP), International Parkinson and Movement Disorder Society (MDS), IOS Press.

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., Competing Interests: Additional Declarations: There is NO Competing Interest.

Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Gregory Pontone reports a relationship with ACADIA Pharmaceuticals Inc that includes: consulting or advisory. Gregory Pontone reports a relationship with GE Healthcare that includes: consulting or advisory. Kelly Mills reports a relationship with Tilosia that includes: consulting or advisory. Ruth Schneider reports a relationship with ACADIA Pharmaceuticals Inc that includes: funding grants. Ruth Schneider reports a relationship with Pfizer Inc that includes: funding grants. Ruth Schneider reports a relationship with Biohaven Ltd that includes: funding grants. Ruth Schneider reports a relationship with ESCAPE Bio that includes: consulting or advisory. none.

Background: Minor phenomena, including passage phenomena, feeling of presence, and illusions, are common and may represent a prodromal form of psychosis in Parkinson's disease (PD). We examined the prevalence and clinical correlates of minor phenomena, and their potential role as a risk factor for PD psychosis., Methods: A novel questionnaire, the Psychosis and Mild Perceptual Disturbances Questionnaire for PD (PMPDQ), was completed by Fox Insight cohort participants with and without PD. Additional assessments included the Non-Motor Symptoms Questionnaire (NMSQuest), REM Sleep Behavior Disorder Single Question Screen (RBD1Q), Movement Disorder Society-Unified Parkinson Disease Rating Scale Part II, demographic features, and medication usage. For participants with PD, we used regression models to identify clinical associations and predictors of incident psychosis over one year of follow-up., Results: Among participants with PD (n = 5950) and without PD (n = 1879), the prevalence of minor phenomena was 43.1% and 31.7% ( P < .001). Of the 3760 participants with PD and no baseline psychosis, independent correlates of minor phenomena included positive responses on the NMSQuest apathy/attention/memory (OR 1.7, 95% CI 1.3-2.1, P < .001) or sexual function domain (OR 1.3, 95% CI 1.1-1.6, P = .01) and positive RBD1Q (OR 1.3, 95% CI 1.05-1.5, P = .01). Independent risk factors for incident PD psychosis included the presence of minor phenomena (HR 3.0, 95% CI 2.4-3.9, P < .001), positive response on the NMSQuest apathy/attention/memory domain (HR 1.8, 95% CI 1.3-2.6, P < .001), and positive RBD1Q (HR 1.5, 95% CI 1.1-1.9, P = .004)., Conclusions: Minor phenomena are common, associated with specific non-motor symptoms, and an independent predictor of incident psychosis in PD., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: RS: Has received research support from Acadia Pharmaceuticals, Biohaven Pharmaceuticals, and Pfizer and consulted for Escape Bio. PA: Nothing to disclose. RD: Nothing to disclose. KM: Has consulted for Tilosia. CK: Nothing to disclose. TM: Nothing to disclose. MK: Nothing to disclose. GP: Has consulted for Acadia Pharmaceuticals and receives funding from the NIH. MJN: Nothing to disclose.

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., Competing Interests: AN, RM, and AV were employed by BioSensics. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Nunes, Pawlik, Mishra, Waddell, Coffey, Tarolli, Schneider, Dorsey, Vaziri and Adams.)

Digital health technologies are growing at a rapid pace and changing the healthcare landscape. Our current understanding of digital health literacy in Parkinson's disease (PD) is limited. In this review, we discuss the potential challenges of low digital health literacy in PD with particular attention to telehealth, deep brain stimulation, wearable sensors, and smartphone applications. We also highlight inequities in access to digital health technologies. Future research is needed to better understand digital health literacy among individuals with PD and to develop effective solutions. We must invest resources to evaluate, understand, and enhance digital health literacy for individuals with PD.