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Background: The on‐going 'Remote Assessment of Disease and Relapse – Alzheimer's Disease' (RADAR‐AD, https://www.radar‐ad.org/) international study uses remote monitoring technologies (RMT's) to continuously and objectively monitor functional decline in Alzheimer's Disease (AD). Managing finances is an Instrumental Activity of Daily Living (IADL), usually measured via traditional pen‐paper methods and interviews. To simulate this IADL, we present the 'Banking App' and preliminary results from the RADAR‐AD study. Method: In the app, users enter a PIN, an amount to withdraw and confirm all inputs, using a numpad on a tablet's touchscreen that resembles an ATM (Figure 1) provided in eight translations. Metrics collected include the duration and correctness of each step (i.e. PIN, Amount and confirmation) and attempt as a whole. The 36 participants to date (Figure 2), from four European sites, performed neuropsychological tests (e.g., MMSE, Rey) and include 20 amyloid negative cognitively normal healthy controls (HC) and 16 amyloid positive, of which 6 cognitively normal preclinical AD (PreAD), 6 cognitively impaired prodromal AD (ProAD) and 4 Mild to Moderate AD (MildAD). Clinicians provided a PIN and an amount to enter from a bill in a controlled lab setting. Statistical analysis included One Way ANOVA, t‐test and Mann‐Whitney between groups in pairs and Pearson correlation. Result: ANOVA showed significance between groups mainly in amount and confirmation durations (p=0.010, p=0.002), leading to t‐test in group pairs (Figures 3 in numbers, visualized in Figure 4), showing increasing durations for more cognitively impaired groups and significant difference between amount and confirmation durations between HC and MildAD (p=0.011, p=0.002). Pearson correlations and heat map (Figure 5) relate app metrics and neuropsychological tests (e.g., MMSE, ADCS with correct amount, p=0.005, and confirmation duration, p=0.001). Conclusion: Preliminary findings highlight that managing finances via an app relates to cognitive assessment, further supporting the potential of this IADL in early AD using technology. This promising early data including near‐significant trends (e.g., duration of tasks between PreAD and ProAD) will be investigated deeper as the study and data collection are on‐going. This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking RADAR‐AD (grant No 806999) and their associated partners. [ABSTRACT FROM AUTHOR]

Background: Higher physical activity is associated with better global cognition and a lower risk of dementia (Rojer et al., 2021). Using questionnaires to assess physical activity is difficult in an Alzheimer's disease (AD) population, since questionnaires rely on retrospective subjective recall. Wearables measure physical activity objectively and continuously. A variety exists, with some delivering raw high‐frequency research‐grade movement data, others providing summary variables only. The aim of this study is to compare simple activity metrics from two activity trackers monitoring physical activity in AD, and relate these to standard questionnaires. Method: Participants in the RADAR‐AD study (Remote Assessment of Disease and Relapse – Alzheimer's Disease) were asked to wear two activity trackers (dominant hand: Axivity AX3, non‐dominant hand: Fitbit Charge 3) for 8 weeks. Features calculated from the Axivity's raw accelerometer data were: acceleration magnitude, time spent in sedentary, light, moderate‐to‐vigorous activity, and sleeping. Fitbit features included: step count and heart rate. Self‐reported activity levels (Godin Leisure Time Questionnaire), depression (Geriatric Depression Scale), social functioning (Social Functioning Scale), global cognition (Mini‐Mental State Examination), partner‐reported function (ADCS‐ADL) and neuropsychiatric symptoms (Neuropsychiatric Inventory) were assessed and related to activity levels using regression models. Result: We included amyloid negative controls (n = 36) and amyloid positive preclinical (n = 13), prodromal (n = 15) and mild‐to‐moderate (n = 10) AD participants (Table 1). Expected associations were found between sedentary activity time and age, and between self‐reported activity and both moderate‐to‐vigorous activity time and step count (Table 2, Figure 1). Small group sizes currently make further comparisons of exploratory value only. Conclusion: Wearable devices have the potential to accurately capture clinically relevant aspects of daily activity and function of interest in AD population. On‐going research in the RADAR‐AD study will focus on developing disease‐specific methods of feature extraction based on raw movement data, and combining features from multiple devices, to explore this further. 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: Unlike traditional pen‐and‐paper clinical assessments, remote monitoring technologies (RMTs) can assess participants' function continuously and objectively during activities of daily living. Despite the potential of RMTs to assess function or assist early disease detection, there is skepticism that age or impairment may render participants unable or unwilling to comply with complex RMT protocols. The 8‐week RADAR‐AD study protocol asks Alzheimer's disease (AD) patients in all disease stages to wear, amongst others, two activity trackers (Axivity, Fitbit), a wearable camera (Autographer), and use an active smartphone app (Mezurio), with a daily schedule totaling over 400 separate tasks and a parallel schedule for caregivers. This abstract presents preliminary compliance data and participant feedback. Method: We included amyloid negative control participants (n=47) and amyloid positive preclinical (n=16), prodromal (n=19) and mild‐to‐moderate (n=16) AD participants (Table 1), based on MMSE and CDR scores, in 8 European countries. Metrics based on device data were calculated for participant commitment (study duration completion percentage or wear time) and compliance (task completion percentage). Patient experiences were monitored using bi‐weekly semi‐structured phone interviews. Result: Mezurio average commitment and compliance were 90% and 85% respectively (Table 2) and were comparable across diagnostic groups. Wear time of Axivity, Fitbit and wearable camera was high (>86%), with 48% of those offered adopting the optional wearable camera (Table 2). Participant feedback was positive, with relatively few problems reported (Figure 1). Conclusion: RADAR‐AD shows that age and impairment are not necessarily barriers to successful deployment of complex RMT study protocols. We acknowledge that our high commitment and compliance rates benefitted from an active rapport between researchers, participants and care givers, which may not be reproducible in every scenario. Nevertheless, this study shows that AD patients are able and willing to comply and engage with technology. 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: The 'Remote Assessment of Disease and Relapse – Alzheimer's Disease' (RADAR‐AD) study is assessing functional decline in Alzheimer's disease (AD) using remote monitoring techniques (RMT's). Compared to traditional pen‐and‐paper clinical assessments, RMT's can continuously and objectively monitor function during activities of daily living (ADL), which are arguably more sensitive to the earliest stages of AD. The aim of this abstract is to compare the results of the augmented reality task 'Altoida', that recreates an ADL requiring spatial navigation and memory, implemented as a tablet application, between 1) healthy controls, preclinical AD and prodromal AD, and with 2) standard clinical tests for cognitive and functional decline. Method: We included amyloid negative cognitively normal (healthy controls, n=10), amyloid positive cognitively normal (preclinical AD, n=7) and amyloid positive mild cognitive impaired (prodromal AD, n=4) participants (Table 1) from the RADAR‐AD study. The outcome of the Altoida test, consisting of a motor task and two tasks in which participants have to hide‐and‐seek virtual objects, is the validated Neuromotor Index (NMI), with higher scores reflecting normative scoring, according to age, sex and education. Cognition was measured using a word‐list‐learning test, digit symbol substitution test (DSST), Rey complex figure, verbal fluency and Boston naming test. Functional decline was assessed using the Amsterdam Instrumented Activities of Daily Living (AIADL) questionnaire. Result: In our preliminary sub‐sample, healthy controls showed higher NMI scores compared to the preclinical AD and prodromal AD participants (p=0.02) (Figure 1). The NMI was related to the DSST only (Figure 2). Conclusion: NMI scores differed between cognitively normal healthy controls and cognitively normal preclinical AD participants, while no differences could be found in cognitive and functional tests between these groups. The sample size will increase in the coming months, but despite the currently small sample, the preliminary results are promising in evidencing that digital biomarkers are potentially more sensitive than standard clinical tests in detecting the early stages of AD, which could be helpful in developing new endpoints in clinical trials. This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking RADAR‐AD (grant No 806999) and their associated partners. [ABSTRACT FROM AUTHOR]

Background: The Remote Assessment of Disease and Relapse – Alzheimer's Disease (RADAR‐AD) study is assessing functional decline in Alzheimer's Disease (AD) using remote monitoring techniques (RMT's). RMT's allow for continuous and objective monitoring of function, potentially improving the sensitivity of assessment in AD. Gait is an important functional domain to maintain independent living. Yet, relatively little is known on gait impairments in the pre‐symptomatic stages of AD. Therefore, the aim of this study is to compare outcomes of in‐clinic gait assessments, using high frequency inertial measurement units, between healthy controls, preclinical AD patients, and cognitively impaired AD patients. Method: We included early data from amyloid negative cognitively healthy participants (controls, n=9), amyloid positive cognitively healthy participants (preclinical AD, n=6) and amyloid positive cognitively impaired participants (n=3) participants (Table 1) from the RADAR‐AD study. Participants performed a one‐minute walking task, a one‐minute dual task (walking while counting backwards from 100) and timed up‐and‐go (TUG) task, while wearing GaitUp Physilog sensors1 on the left foot, right foot, and right hip. Metrics provided by the sensors include gait speed, steps per minute, double support time, and TUG task completion time. Result: Regression analyses with models corrected for age, sex, and education revealed no between‐group differences on the main outcomes. However, the preclinical AD group showed several interesting near‐significant trends that warrant further investigation (Figure 1). This included an apparent smaller difference in gait speed between the walking task and dual task compared to controls (β=‐10.78 (5.8), p=0.09), accompanied by a smaller difference in steps per minute (β=‐9.27 (5.05), p=0.09), and a larger difference in double support time (β=14.32 (7.28), p=0.07). Conclusion: Although no between‐group differences in gait outcomes were found in this initial data, early near‐significant trends in the preclinical AD group appears to show altered gait patterns in particular during the dual task. These preliminary findings will be further investigated on possible pathological causality of such behavior as our dataset increases. Inclusion in the RADAR‐AD study is ongoing, and subsequent analyses will include larger sample sizes, more in‐depth analyses of gait behavior, and associations with rating scales of mobility. Reference: [1] https://research.gaitup.com/physilog/ [ABSTRACT FROM AUTHOR]