The relationship between sleep and AD is unclear: sleep problems may contribute to AD pathogenesis, but the spreading of AD pathology across the brain may also de-regulate sleep. What aspect of sleep is relevant in which disease phase is also unclear, as many studies are based on questionnaires. We study sleep efficiency and rapid eye movement (REM) sleep, objectively measured using an activity tracker to shed light on sleep disturbances across the AD spectrum. The RADAR-AD is a cross-sectional study (N=204), including healthy controls (HC, n=67), preclinical AD (preAD, n=34), prodromal AD (proAD, n=56) and mild-to-moderate AD (MMAD, n=47) participants (Table 1). Participants wore a Fitbit Charge-3 activity tracker on their non-dominant wrists for eight weeks, measuring REM sleep (in hours) and sleep efficiency (sleep duration/total time in bed). CSF amyloid and phosphorylated Tau (pTau) levels were determined in the subset (N=117) and stratified into biomarker groups (A-T-, A+T-, and A+T+). We evaluated the difference in these mean sleep features among AD stages and biomarker groups. Compared to HC, we observed reductions in daily mean REM sleep for proAD (Estimate±SE=-0.24±0.08, p=0.002) and MMAD (Estimate±SE=-0.23±0.08, p=0.001) (Figure 1), and in sleep efficiency for preAD (Estimate±SE=-0.012±0.005, p=0.016), proAD (Estimate±SE=-0.011±0.004, p=0.015), and MMAD (Estimate±SE=-0.009±0.004, p=0.046) participants. Compared to A-T- participants, sleep efficiency was reduced for A+T- (Estimate±SE=-0.016±0.006, p=0.022) and A+T+ (Estimate±SE=-0.013±0.006, p=0.019) participants (Figure 2), while REM sleep was lower for A+T+ participants only (Estimate±SE=-0.32±0.11, p=0.004). Sleep efficiency is altered across the AD spectrum, including the early pre-clinical AD stage. There is a trend toward lower REM sleep among subjects at more advanced stages of AD and elevated amyloid and tau that may suggest an association between AD pathology and REM sleep. 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.

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.

Alzheimer’s Disease (AD) is associated with sleep disturbances. Moreover, individuals with sleep disturbances have been reported to have a higher risk for developing AD. The measurement of sleep behavior therefore opens the opportunity for a potential digital biomarker of AD. We modeled sleep patterns coming from the RADAR-AD cohort from two sleep monitoring devices (Table 1). We applied a stochastic modeling approach, multi-state models, and analyzed the times spent in each sleep state before transitioning and transition probabilities in and between sleep states. We further applied statistical analysis of sleep monitoring data and sleep questionnaires (ESS, PSQI) from the RADAR-AD study (Fitbit Charge 3, DREEM) and preliminary data from the ADIS study (MotionWatch8) (Table 1), using a likelihood ratio test with the aim to assess the diagnostic potential of sleep monitoring devices compared to traditional sleep questionnaires. Modeling of digital device data showed that preclinial (preAD), prodromal (proAD) and mild-to-moderate (mildAD) AD patients spent more time in the light sleep and awake state, and less time in the REM sleep states compared to healthy controls (HC) before transitioning to the next state, showing non-linear associations between diagnostic stage and sojourn times (Figure 1A). ProAD and mildAD patients had a higher probability to transit to a light sleep phase compared to HC and to subsequently wake up (Figure 1B). Based on our current and partially still preliminary data, only digital sleep monitoring via Fitbit allowed for a separation between HC and preclinical AD at nominal significance (preAD) but findings were not significant when adjusting for multiple testing (Table 2A). A significant distinction between HC and proAD (p < 0.001) as well as between HC and mildAD (p < 0.01) was possible using Fitbit sleep monitoring data, whereas traditional sleep questionnaires were only able to distinguish HC from mildAD (p < 0.05) (Table 2). Sleep patterns assessed via tested digital devices were able to separate proAD from HC – in case of Fitbit - which was not possible by traditional sleep questionnaires. Digital sleep monitoring has thus the potential to support the early diagnosis of dementia.

Abstract 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.

Remote monitoring technologies (RMTs), such as smartphone apps, smartwatches, and in-home sensors, are rapidly changing the way functional and cognitive performance is measured in Alzheimer’s disease (AD) patients. Here, we present results from the European RADAR-AD study on the use of multimodal data streams for the identification of functional deficits across all 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. The RMT features (gait measures from Timed up and Go (TUG), Dual Task Effect (DTE) using physilog, acoustic features from Speech task in Mezurio, neurocognitive function using Altoida, managing finances with Banking app) with in-clinic neuropsychological (NP) tests, activities of daily living with Amsterdam IADL and demographics (age, gender, education years) were analyzed for different combinations of the multimodal digital biomarker of disease stage in AD across different pairwise comparisons. The analysis includes data from 175 participants (HC = 67, Pre.AD = 26, Pro.AD = 50, Mild AD = 32) collected for 8 weeks. An extreme gradient boosting (XGBoost) machine learning model was trained to obtain a multimodal biomarker of AD disease stage with repeated cross-validation (5-fold with 4 repeats) (Figure 1). This investigation is part of the ongoing RADAR-AD study. The multimodal combination of RMTs achieved a mean AUC of > 0.60 in all pairwise comparisons with a mean AUC > 0.65 for HC vs (Pre.AD, Pro.AD and Mild) (Figures 2 and 3). The addition of NP tests increases the performance considerably across all pairwise comparisons except HC vs Pre.AD. Our results highlight the advantage of combining RMTs to identify functional deficits in the early stage of AD. In particular, in prodromal and mild AD patients, a combined signal shows much more strength compared to individual tests. 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 .

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. 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. 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. Our results demonstrate the potential of RMTs and the Amsterdam I-ADL questionnaire for identifying patients in prodromal stage in primary care settings.

Abstract Background/Aims Psoriatic arthritis (PsA) is a form of inflammatory arthritis affecting up to 30% of those with psoriasis. It has a considerable impact on patients’ functional capacity and quality of life. Studies have shown an increased prevalence of depression in patients with PsA which can contribute to reduced likelihood of disease remission. Smartphone applications have been developed to monitor symptoms in patients with rheumatic diseases. We used experience sampling to assess the relationship between daily PsA symptom burden using the PsAID-9 questionnaire and mood components utilising the validated Mood Zoom (MZ) questionnaire. Methods Participants (n = 25) with PsA aged ≥18 and <80 years, meeting the CASPAR criteria were recruited from rheumatology clinics at a single centre. At baseline, disease impact was assessed using the Psoriatic Arthritis Impact of Disease-9 (PsAID-9) questionnaire. Participants used the Mezurio smartphone application daily for 28 days to collect data on the PsAID-9 and Mood Zoom questionnaires. MZ comprises 10-items assessing mood symptoms on a 5-point Likert scale. The relationship between daily fluctuations in PsA symptoms and each mood component was assessed using linear mixed-effect (LMER) models. Likelihood ratio testing and Chi-square tests were used to identify statistical significance. Each model controlled for gender and PsAID-9 remission at baseline. Results Mean (SD) age was 49.6 (12.05) years. Sixteen had co-morbid psoriasis. Mean (SD) PsAID-9 scores were 3.28 (1.92). There was a bi-directional association between the mood components of MZ and total PsAID-9 score for each day, as shown in Table 1. Conclusion This study has demonstrated a relationship between daily fluctuations in disease burden and mood. This is the first study to pilot a daily assessment of disease impact versus traditional longer interval collections. Whilst these data demonstrate an association between mood and disease symptoms, they do not allow us to make inferences on the causal effect of mood on symptoms and vice versa. Further research into the relationship between mood and disease burden in PsA are warranted. Disclosure D. McGagh: None. N. McGowan: None. Y. Wu: None. C. Hinds: None. K.E.A. Saunders: None. L.C. Coates: None.

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. 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. 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. 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 .

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. 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. 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). 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 .

Around 50 million people have dementia worldwide, with nearly 10 million new cases every year. Diagnosis is complex and often relies on expensive and invasive measures, with most patients accessing medical support when they already experience symptoms. The Early Detection of Neurodegenerative diseases (EDoN) initiative, spearheaded by Alzheimer’s Research UK, brings together over 60 experts from 49 universities, research projects, patient cohorts and technology providers to create machine learning models to detect the earliest stages of dementia-causing diseases. EDoN has reviewed behavioural and physiological modalities with the strongest association with pre-clinical disease. Over 140 modalities were identified from the review and were shortlisted to create the version 1 digital toolkit. This first version includes Mezurio and Longevity smartphone apps, a Fitbit charge 4 activity tracker and Dreem 3 sleep headband. This Toolkit was further refined through patient and public involvement studies and collects 26 measures related to 7 aspects of behaviour and physiology (cognition, neural activity, physical activity, heart rate, fine motor movement, sleep, language and speech). The Toolkit is now being used to collect digital data in four international cohorts (Boston University Alzheimer’s Disease Research Center - BU ADRC; The predictors of COgnitive DECline in attenders of memory clinic using digital devices - CODEC-2; Western Australia Memory Study - WAMS; Healthy Brain Aging - HBA), alongside prospective and retrospective clinical data, to inform the development of machine learning models. EDoN will build models with digital markers, validating them against other biomarkers to predict dementia subtypes and individualised disease trajectories. Based on the outputs of the initial models, EDoN will go through a series of iterations of cohort engagement, modality and tool refinement, and data collection. Workstreams are underway to inform data security, privacy, ethics and open policy research, as well as considering the integration of the final EDoN Toolkit into healthcare systems globally. EDoN aims to deliver a cost-effective, low burden and population-wide method for early detection of dementia-causing diseases that will benefit the public, patients, carers, researchers and clinicians, as well as the broader healthcare system and the delivery of new therapies.

Hyperactivity in the brain regions supporting episodic memory (the hippocampus and default mode network) drives the accumulation of Alzheimer’s Disease (AD) neuropathology and the resulting cascade of brain network disruption. Pharmacologically reducing hippocampal hyperactivity using the anti-seizure medication Levetiracetam improves memory in individuals with prodromal AD. Carriers of an APOE e4 genetic risk variant show patterns of brain hyperactivity from youth. This study tests if Levetiracetam can prophylactically manage hippocampal hyperactivity in mid-age APOE e4 carriers, and if such a reduction is associated with improved cognitive performance. Fifty cognitively healthy adults (aged 45-65 years), differentiated by APOE genotype (25 e4 carriers, 25 e33 carriers), are participating in a double-blind, placebo-controlled trial of Levetiracetam (125mg bidaily for two-weeks). Analyses will primarily focus on change in task-related and resting functional magnetic resonance imaging (fMRI) brain blood-oxygen-level-dependent response (BOLD) following Levetiracetam. In addition, the everyday cognitive effects of this manipulation will be evaluated, including on daily smartphone-based measures of memory and attention. To date, 43 participants are enrolled in the 8-week intervention (23 of whom have completed). Adverse events relating to the study drug have been minimal; only one participant has withdrawn in response to experiencing side-effects. APOE genotype and treatment order (Levetiracetam, placebo) will be unblinded at the end of data collection (anticipated April 2023). Conclusions from this work will help expose routes to AD risk reduction, and the potential for repurposing an existing treatment for mid-age individuals at elevated genetic risk for future dementia. This research will motivate longer-term clinical trials for risk reduction strategies across the lifespan.

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. 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. 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). 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 .

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.

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. 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. 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. 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 .

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. 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. 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). 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.

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).

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.

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.