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Background: Gait is a complex everyday activity that depends upon supraspinal activity and a host of cognitive functions such as attention and executive functions. As cognition declines in neurodegenerative diseases, the interaction and competition for neuronal resources during motor‐cognitive dual‐tasking (e.g., walking while talking) might be a sensitive measure of subtle functional impairments in early Alzheimer's disease (AD). Here, we aim to identify gait deficits due to neuronal competition across the AD spectrum. Method: This investigation is part of the ongoing Remote Assessment of Disease and Relapse – Alzheimer's Disease (RADAR‐AD) study. We attached three inertial measurement units (accelerometer and gyroscope) to both feet and one hip to assess dual task effects (DTE) assessing gait performance with/without concurrent serial subtraction‐by‐1 task in four groups: 1) amyloid negative healthy controls (HC, N = 59); and 2) amyloid positive preclinical AD (PreAD, N = 30); 3) prodromal AD (ProAD, N = 51); and 4) mild‐to‐moderate AD dementia (MildAD, N = 44) (Table 1). We furthermore investigated associations of DTE with observer‐reported cognition. Result: Group comparisons showed that dual‐tasking induced lower cadence and increased stance, which were significantly different between HC and ProAD. Several DTE measures of variability differed significantly between PreAD and MildAD, with variability in the path length separating best between PreAD and ProAD (Table 2, Figure 1). DTE measures were associated with observer‐rated divided attention only in the MildAD group. Conclusion: Neuronal competition as assessed with motor‐cognitive dual‐tasking, specifically the DTE variability, might reflect functional deficits already in early AD, and could be a valuable additional measure to detect early impairments not captured by cognitive or motor tests alone. Future studies should implement an adaptive cognitive load to improve sensitivity/specificity in early AD stages and investigate the use of sensor technologies in predicting and monitoring changes in gait and fall prevention in later stages of the disease. 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. [ABSTRACT FROM AUTHOR]

Background: RADAR‐AD is a European project in the context of the Innovative Medicine Initiative (IMI) focusing on the earlier identification of patients at risk for developing Alzheimer's Disease (AD) via a panel of remote monitoring technologies (RMTs), including smartphone apps and wearable devices. Method: We examined the ability of 6 RMTs (Altoida, Axivity, Banking app, Fitbit, Physilog, and Mezurio) to distinguish between healthy controls (HC) and disease stages of preclinical (PreAD), prodromal (ProAD), and mild to moderate Alzheimer's disease (MildAD) based on 175 patients (interim analysis). We trained three machine learning classifiers (Logistic Regression, Random Forest, and XGBoost) in a pairwise setting (HC vs. PreAD, HC vs. ProAD, HC vs. MildAD, PreAD vs. ProAD, and ProAD vs. MildAD). Since the interim dataset is still limited, we performed repeated, stratified nested cross‐validation to get a robust performance estimate. Each classifier was trained with the features of the different devices and a set of baseline variables. The latter include a patient's gender, age, years of education, and body mass index (BMI) when physical conditions might play a role (Axivitiy, Fitbit, Physilog). In addition, we checked whether specific patterns of the study groups allowed discrimination of the different study groups based on the baseline variables alone. Therefore, we trained one Logistic Regression model with these variables and compared the performance of the other three models with this baseline. The models trained with the baseline and questionnaire‐based data served as the reference value in our benchmark that represents how well the discrimination of the different groups works with clinical tests. Result: Our preliminary data show that RMTs can identify patients already in a prodromal disease stage (AUC ∼69%, Figure 1). Furthermore, the pairwise combination of data from a banking app and an app monitoring functional cognitive abilities via an augmented reality game slightly increased our model's discriminative ability (Altoida ‐ Banking, Figure 2). The overall best performance was achieved when combining RMTs with the Amsterdam I‐ADL questionnaire. Conclusion: Our results demonstrate the potential of RMTs and the Amsterdam I‐ADL questionnaire for identifying patients in prodromal stage in primary care settings. [ABSTRACT FROM AUTHOR]

Background: Remote monitoring technologies (RMTs), such as smartphone apps and smartwatches, are changing the way functional and cognitive performance are measured in Alzheimer's disease (AD). Due to their sensitivity, objectivity, and the option of long‐term and continuous measurement, RMTs have the potential to detect a subtle decline in the earliest stages of AD. Here, we present the results of the European RADAR‐AD project (Remote Assessment of Disease and Relapse – Alzheimer's disease), which aims to test feasibility, acceptability and validity of RMT measures across all stages of AD, from cognitively normal to mild dementia. Method: Four study groups (amyloid negative healthy controls, and amyloid positive preclinical AD, prodromal AD, mild‐to‐moderate AD) were included in this cross‐sectional study (N = 175). During 8 weeks, participants wore two activity trackers (Fitbit and Axivity) measuring physical activity, heart rate and sleep continuously, and used two interactive smartphone apps (Mezurio and Altoida's research algorithm: DNS‐MCI) measuring cognition daily/weekly. At baseline, participants underwent extensive neuropsychological, physical examinations, and did two sensor‐based tests (banking app and walk test). Features were extracted for all RMTs (Figure 1) and compared across groups using ANCOVA, with adjustment for relevant confounders. This study is part of an ongoing investigation into high‐end multimodal analyses for real‐world functional performance of continuous RMT data streams. Result: Compliance was high, but decreased with cognitive impairment (feasibility). User experience did not differ between groups but was lower for smartwatches compared to interactive smartphone apps (Table 1) (acceptability). Various individual sensors discriminated symptomatic AD participants from asymptomatic participants (p<0.05), for example the two active apps, but did not discriminate preclinical AD from healthy controls (Table 1) (validity). Conclusion: The RADAR‐AD study provides unique insights in the feasibility, acceptability, and validity of remote monitoring of functional abilities in AD and their potential to differentiate between syndromic stages. This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (grant No 806999) and their associated partners. 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. [ABSTRACT FROM AUTHOR]

Background: Augmented reality apps merge real world with virtual experiences and can be used to remotely assess complex instrumental activities of daily living (iADL) that are affected early in Alzheimer's disease (AD). Our aim was to compare standard clinical measures with an augmented reality app to assess iADL that are related to memory and spatial navigation in early AD and its feasibility in the home‐setting. Method: We administered an augmented reality app (Altoida Inc., Washington DC, USA) in an on‐going cross‐sectional study (RADAR‐AD: Remote Assessment of Disease and Relapse – Alzheimer's Disease) in three groups: 1) amyloid negative healthy controls (HC, N = 49); and amyloid positive 2) preclinical AD (PreAD, N = 17); and 3) prodromal AD (ProAD, N = 29) (Table 1). Altoida's research algorithm DNS‐MCI (Digital Neuro Signature) produces the outcome of a machine learning model trained to identify cognitively normal individuals from those with cognitive impairment). DNS‐MCI reflects performance in app‐based tasks assessing memory and visuo‐spatial function (placing and finding virtual objects, fire drill simulation) further including attention and motor performance (reaction time, finger tapping, navigational trajectory). At baseline, app‐based tasks were performed in the clinic together with a standard neuropsychological assessment and iADL questionnaires (Figure 1). Participants were furthermore given the option of using Altoida in the home environment. Result: The DNS‐MCI score could significantly distinguish HC and PreAD participants from the ProAD group and was correlated with all neuropsychological tests and iADL questionnaires (Figures 1 and 2). Participants used the app on average 3‐4 times at home (Table 1). Baseline in‐clinic assessments were strongly correlated with at‐home assessments (r = 0.53, p<.001). Conclusion: App‐based augmented reality tasks are applicable in the home setting and successful in capturing cognitive impairment in early AD. Future research should focus on fine graining algorithms to also detect possible subtle impairment in preAD. 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. [ABSTRACT FROM AUTHOR]