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Canine distemper virus (CDV) has recently emerged as an extinction threat for the endangered Amur tiger (Panthera tigris altaica). CDV is vaccine-preventable, and control strategies could require vaccination of domestic dogs and/or wildlife populations. However, vaccination of endangered wildlife remains controversial, which has led to a focus on interventions in domestic dogs, often assumed to be the source of infection. Effective decision making requires an understanding of the true reservoir dynamics, which poses substantial challenges in remote areas with diverse host communities. We carried out serological, demographic, and phylogenetic studies of dog and wildlife populations in the Russian Far East to show that a number of wildlife species are more important than dogs, both in maintaining CDV and as sources of infection for tigers. Critically, therefore, because CDV circulates among multiple wildlife sources, dog vaccination alone would not be effective at protecting tigers. We show, however, that low-coverage vaccination of tigers themselves is feasible and would produce substantive reductions in extinction risks. Vaccination of endangered wildlife provides a valuable component of conservation strategies for endangered species.

Background: Alzheimer's Disease (AD) impairs the ability to carry out daily activities, reduces independence and quality of life and increases caregiver burden. Our understanding of functional decline has traditionally relied on reports by family and caregivers, which are subjective and vulnerable to recall bias. The Internet of Things (IoT) and wearable sensor technologies promise to provide objective, affordable, and reliable means for monitoring and understanding function. However, human factors for its acceptance are relatively unexplored. Objective: The Public Involvement (PI) activity presented in this paper aims to capture the preferences, priorities and concerns of people with AD and their caregivers for using monitoring wearables. Their feedback will drive device selection for clinical research, starting with the study of the RADAR-AD project. Method: The PI activity involved the Patient Advisory Board (PAB) of the RADAR-AD project, comprised of people with dementia across Europe and their caregivers (11 and 10, respectively). A set of four devices that optimally represent various combinations of aspects and features from the variety of currently available wearables (e.g., weight, size, comfort, battery life, screen types, water-resistance, and metrics) was presented and experienced hands-on. Afterwards, sets of cards were used to rate and rank devices and features and freely discuss preferences. Results: Overall, the PAB was willing to accept and incorporate devices into their daily lives. For the presented devices, the aspects most important to them included comfort, convenience and affordability. For devices in general, the features they prioritized were appearance/style, battery life and water resistance, followed by price, having an emergency button and a screen with metrics. The metrics valuable to them included activity levels and heart rate, followed by respiration rate, sleep quality and distance. Some concerns were the potential complexity, forgetting to charge the device, the potential stigma and data privacy. Conclusions: The PI activity explored the preferences, priorities and concerns of the PAB, a group of people with dementia and caregivers across Europe, regarding devices for monitoring function and decline, after a hands-on experience and explanation. They highlighted some expected aspects, metrics and features (e.g., comfort and convenience), but also some less expected (e.g., screen with metrics).

Background Functional decline in Alzheimer’s disease (AD) is typically measured using single-time point subjective rating scales, which rely on direct observation or (caregiver) recall. Remote monitoring technologies (RMTs), such as smartphone applications, wearables, and home-based sensors, can change these periodic subjective assessments to more frequent, or even continuous, objective monitoring. The aim of the RADAR-AD study is to assess the accuracy and validity of RMTs in measuring functional decline in a real-world environment across preclinical-to-moderate stages of AD compared to standard clinical rating scales. Methods This study includes three tiers. For the main study, we will include participants ( n  = 220) with preclinical AD, prodromal AD, mild-to-moderate AD, and healthy controls, classified by MMSE and CDR score, from clinical sites equally distributed over 13 European countries. Participants will undergo extensive neuropsychological testing and physical examination. The RMT assessments, performed over an 8-week period, include walk tests, financial management tasks, an augmented reality game, two activity trackers, and two smartphone applications installed on the participants’ phone. In the first sub-study, fixed sensors will be installed in the homes of a representative sub-sample of 40 participants. In the second sub-study, 10 participants will stay in a smart home for 1 week. The primary outcome of this study is the difference in functional domain profiles assessed using RMTs between the four study groups. The four participant groups will be compared for each RMT outcome measure separately. Each RMT outcome will be compared to a standard clinical test which measures the same functional or cognitive domain. Finally, multivariate prediction models will be developed. Data collection and privacy are important aspects of the project, which will be managed using the RADAR-base data platform running on specifically designed biomedical research computing infrastructure. Results First results are expected to be disseminated in 2022. Conclusion Our study is well placed to evaluate the clinical utility of RMT assessments. Leveraging modern-day technology may deliver new and improved methods for accurately monitoring functional decline in all stages of AD. It is greatly anticipated that these methods could lead to objective and real-life functional endpoints with increased sensitivity to pharmacological agent signal detection.

Background Alzheimer’s disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide, leading to cognitive and functional decline. Early detection and intervention are crucial for enhancing the quality of life of patients and their families. Remote Monitoring Technologies (RMTs) offer a promising solution for early detection by tracking changes in behavioral and cognitive functions, such as memory, language, and problem-solving skills. Timely detection of these symptoms can facilitate early intervention, potentially slowing disease progression and enabling appropriate treatment and care. Methods The RADAR-AD study was designed to evaluate the accuracy and validity of multiple RMTs in detecting functional decline across various stages of AD in a real-world setting, compared to standard clinical rating scales. Our approach involved a univariate analysis using Analysis of Covariance (ANCOVA) to analyze individual features of six RMTs while adjusting for variables such as age, sex, years of education, clinical site, BMI and season. Additionally, we employed four machine learning classifiers – Logistic Regression, Decision Tree, Random Forest, and XGBoost – using a nested cross-validation approach to assess the discriminatory capabilities of the RMTs. Results The ANCOVA results indicated significant differences between healthy and AD subjects regarding reduced physical activity, less REM sleep, altered gait patterns, and decreased cognitive functioning. The machine-learning-based analysis demonstrated that RMT-based models could identify subjects in the prodromal stage with an Area Under the ROC Curve of 73.0 %. In addition, our findings show that the Amsterdam iADL questionnaire has high discriminatory abilities. Conclusions RMTs show promise in AD detection already in the prodromal stage. Using them could allow for earlier detection and intervention, thereby improving patients’ quality of life. Furthermore, the Amsterdam iADL questionnaire holds high potential when employed remotely.

Introduction Remote monitoring technologies (RMTs) can measure cognitive and functional decline objectively at-home, and offer opportunities to measure passively and continuously, possibly improving sensitivity and reducing participant burden in clinical trials. However, there is skepticism that age and cognitive or functional impairment may render participants unable or unwilling to comply with complex RMT protocols. We therefore assessed the feasibility and usability of a complex RMT protocol in all syndromic stages of Alzheimer's disease and in healthy control participants. Methods For 8 weeks, participants (N = 229) used two activity trackers, two interactive apps with either daily or weekly cognitive tasks, and optionally a wearable camera. A subset of participants participated in a 4-week sub-study (N = 45) using fixed at-home sensors, a wearable EEG sleep headband and a driving performance device. Feasibility was assessed by evaluating compliance and drop-out rates. Usability was assessed by problem rates (e.g., understanding instructions, discomfort, forgetting to use the RMT or technical problems) as discussed during bi-weekly semi-structured interviews. Results Most problems were found for the active apps and EEG sleep headband. Problem rates increased and compliance rates decreased with disease severity, but the study remained feasible. Conclusions This study shows that a highly complex RMT protocol is feasible, even in a mild-to-moderate AD population, encouraging other researchers to use RMTs in their study designs. We recommend evaluating the design of individual devices carefully before finalizing study protocols, considering RMTs which allow for real-time compliance monitoring, and engaging the partners of study participants in the research.

Background: Psoriatic arthritis (PsA) is associated with sleep disturbance, depression and a lifetime risk of obesity and cardiovascular disease. To date, there have been no studies investigating the relationship between objectively-measured physical activity (PA) levels and circadian rhythm disturbance with disease activity, daily symptoms and mood in patients with PsA. Objective: This pilot study aimed to investigate the relationship between disease activity, daily symptoms and mood on PA and circadian rhythm in PsA. Design: A prospective cohort study recruiting adults with PsA from rheumatology clinics at a single centre in the UK. Methods: Participants wore an actigraph and recorded their symptoms and mood on a daily basis via a smartphone app for 28 days. Time spent in sedentary, light and moderate-to-vigorous physical activity (MVPA) and parameters reflecting the circadian rhythm of the rest-activity pattern were derived. This included the onset time of the least active 5-h (L5) and most active 10-h (M10) daily consecutive periods and the relative amplitude (RA). The relationship factors between baseline clinical status, daily symptoms, PA and circadian measures were examined using linear mixed effect regression models. Results: Nineteen participants (8/19 female) were included. Participants with active PsA spent 63.87 min (95% CI: 18.5–109.3, p = 0.008) more in inactivity and 30.78 min (95% CI: 0.4–61.1, p = 0.047) less in MVPA per day compared to those in minimal disease activity (MDA). Age, body mass index and disease duration were also associated with PA duration. Participants with worse functional impairment had an M10 onset time 1.94 h (95% CI: 0.05–3.39, p = 0.011) later than those with no reported functional impairment. No differences were detected for L5 onset time or RA. Higher scores for positive mood components such as feeling energetic, cheerful and elated were associated with less time in inactivity and greater time spent in MVPA overall. Conclusion: Our study highlights differences in PA and circadian rest-activity pattern timing based on disease activity, disability and daily mood in PsA. Reduced PA levels in patients with active disease may contribute to the observed increased risk of cardiovascular and metabolic sequelae, with further studies exploring this need.

Dementia is the most widespread form of neurodegenerative disorder and is associated with an immense societal and personal cost. Prevalence of this disorder is projected to triple worldwide by 2050 leading to an urgent need to make advances in the efficiency of both its care and therapy research. Digital technologies are a rapidly advancing field that provide a previously unavailable opportunity to alleviate challenges faced by clinicians and researchers working in this area. This clinical review aimed to summarise currently available evidence on digital technologies that can be used to monitor cognition. We identified a range of pervasive digital systems, such as smartphones, smartwatches and smart homes, to assess and assist elderly demented, prodromal and preclinical populations. Generally, the studies reported good level of agreement between the digital measures and the constructs they aimed to measure. However, most of the systems are still only in the initial stages of development with limited data on acceptability in patients. Although it is clear that the use of digital technology to monitor and support the cognitive domains affected by dementia is a promising area of development, additional research validating the efficacy, utility and cost-effectiveness of these systems in patient populations is needed.

Speech and language changes occur decades before the clinical diagnosis of Alzheimer's disease (AD). Digital tools for frequent speech assessments may have promise for early-stage AD trials. Here, we present results from the RADAR-AD study on the use of acoustic speech markers obtained from a story narration task, across AT(N) groups within the syndromic stages of AD. Four study groups (Healthy controls (HC), preclinical AD (pre. AD), prodromal AD (pro. AD), and mild AD) were included in this cross-sectional study of 8 weeks duration. The speech samples were collected using a voice-based story narration task (Story Time), deployed within the Mezurio smartphone application. Analysis of speech from this task includes data from 159 participants across 12 different European languages (HC = 47, Pre.AD = 31, Pro.AD = 49, Mild AD = 32) collected across 6 different days during the study. To understand the effect of amyloid, tau and syndromic stages of AD on speech, we used a linear mixed-effect model with participants, stories, and language as random effects and demographic variables as fixed effects and compared to A-T- healthy controls. For the preclinical subgroup, articulation rate was statically significant (FDR corrected) and was lower for A+T+ (β=-0.62; p = .03). This feature was also lower for A+T+ in both prodromal (β=-0.51; p =.03) and mild-to-moderate AD (β=-0.58; p = .01), but not for the prodromal A+T- subgroups. The prodromal subgroups had statistically significant differences in jitter and voiced segments for the A+T- subgroup, and articulation rate, speech rate, peaks of loudness, voiced segments, and F2 frequency (sd) for A+T+. Our results suggest that subtle tau driven changes in speech fluency begin as early as preclinical stage highlighting the potential utilization of speech markers in improving screening in AD clinical trials. This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (grant No 806999). www.imi.europa.eu. This communication reflects the views of the RADAR-AD consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein.

Background Speech and language changes occur decades before the clinical diagnosis of Alzheimer's disease (AD). Digital tools for frequent speech assessments may have promise for early‐stage AD trials. Here, we present results from the RADAR‐AD study on the use of acoustic speech markers obtained from a story narration task, across AT(N) groups within the syndromic stages of AD. Method Four study groups (Healthy controls (HC), preclinical AD (pre. AD), prodromal AD (pro. AD), and mild AD) were included in this cross‐sectional study of 8 weeks duration. The speech samples were collected using a voice‐based story narration task (Story Time), deployed within the Mezurio smartphone application. Analysis of speech from this task includes data from 159 participants across 12 different European languages (HC = 47, Pre.AD = 31, Pro.AD = 49, Mild AD = 32) collected across 6 different days during the study. To understand the effect of amyloid, tau and syndromic stages of AD on speech, we used a linear mixed‐effect model with participants, stories, and language as random effects and demographic variables as fixed effects and compared to A‐T‐ healthy controls. Result For the preclinical subgroup, articulation rate was statically significant (FDR corrected) and was lower for A+T+ (β=‐0.62; p = .03). This feature was also lower for A+T+ in both prodromal (β=‐0.51; p =.03) and mild‐to‐moderate AD (β=‐0.58; p = .01), but not for the prodromal A+T‐ subgroups. The prodromal subgroups had statistically significant differences in jitter and voiced segments for the A+T‐ subgroup, and articulation rate, speech rate, peaks of loudness, voiced segments, and F2 frequency (sd) for A+T+. Conclusion Our results suggest that subtle tau driven changes in speech fluency begin as early as preclinical stage highlighting the potential utilization of speech markers in improving screening in AD clinical trials. This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (grant No 806999). www.imi.europa.eu. This communication reflects the views of the RADAR‐AD consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein.

The True Colours remote mood monitoring system was developed over a decade ago by researchers, psychiatrists, and software engineers at the University of Oxford to allow patients to report on a range of symptoms via text messages, Web interfaces, or mobile phone apps. The system has evolved to encompass a wide range of measures, including psychiatric symptoms, quality of life, and medication. Patients are prompted to provide data according to an agreed personal schedule: weekly, daily, or at specific times during the day. The system has been applied across a number of different populations, for the reporting of mood, anxiety, substance use, eating and personality disorders, psychosis, self-harm, and inflammatory bowel disease, and it has shown good compliance. Over the past decade, there have been over 36,000 registered True Colours patients and participants in the United Kingdom, with more than 20 deployments of the system supporting clinical service and research delivery. The system has been adopted for routine clinical care in mental health services, supporting more than 3000 adult patients in secondary care, and 27,263 adolescent patients are currently registered within Oxfordshire and Buckinghamshire. The system has also proven to be an invaluable scientific resource as a platform for research into mood instability and as an electronic outcome measure in randomized controlled trials. This paper aimed to report on the existing applications of the system, setting out lessons learned, and to discuss the implications for tailored symptom monitoring, as well as the barriers to implementation at a larger scale.