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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]

Introduction: Clinical research with remote monitoring technologies (RMTs) has multiple advantages over standard paper-pencil tests, but also raises several ethical concerns. While several studies have addressed the issue of governance of big data in clinical research from the legal or ethical perspectives, the viewpoint of local research ethics committee (REC) members is underrepresented in the current literature. The aim of this study is therefore to find which specific ethical challenges are raised by RECs in the context of a large European study on remote monitoring in all syndromic stages of Alzheimer's disease, and what gaps remain. Methods: Documents describing the REC review process at 10 sites in 9 European countries from the project Remote Assessment of Disease and Relapse–Alzheimer's Disease (RADAR-AD) were collected and translated. Main themes emerging in the documents were identified using a qualitative analysis approach. Results: Four main themes emerged after analysis: data management, participant's wellbeing, methodological issues, and the issue of defining the regulatory category of RMTs. Review processes differed across sites: process duration varied from 71 to 423 days, some RECs did not raise any issues, whereas others raised up to 35 concerns, and the approval of a data protection officer was needed in half of the sites. Discussion: The differences in the ethics review process of the same study protocol across different local settings suggest that a multi-site study would benefit from a harmonization in research ethics governance processes. More specifically, some best practices could be included in ethical reviews across institutional and national contexts, such as the opinion of an institutional data protection officer, patient advisory board reviews of the protocol and plans for how ethical reflection is embedded within the study. [ABSTRACT FROM AUTHOR]

Background: In people with cognitive impairment, loss of social interactions has a major impact on well-being. Therefore, patients would benefit from early detection of symptoms of social withdrawal. Current measurement techniques such as questionnaires are subjective and rely on recall, in contradiction to smartphone apps, which measure social behavior passively and objectively. Objective: This study uses the remote monitoring smartphone app Behapp to assess social behavior, and aims to investigate (1) the association between social behavior, demographic characteristics, and neuropsychiatric symptoms in cognitively normal (CN) older adults, and (2) if social behavior is altered in cognitively impaired (CI) participants. In addition, we explored in a subset of individuals the association between Behapp outcomes and neuropsychiatric symptoms. Methods: CN, subjective cognitive decline (SCD), and CI older adults installed the Behapp app on their own Android smartphone for 7 to 42 days. CI participants had a clinical diagnosis of mild cognitive impairment (MCI) or Alzheimer-type dementia. The app continuously measured communication events, app use and location. Neuropsychiatric Inventory (NPI) total scores were available for 20 SCD and 22 CI participants. Linear models were used to assess group differences on Behapp outcomes and to assess the association of Behapp outcomes with the NPI. Results: We included CN (n=209), SCD (n=55) and CI (n=22) participants. Older cognitively normal participants called less frequently and made less use of apps (P<.05). No sex effects were found. Compared to the CN and SCD groups, CI individuals called less unique contacts (β=–0.7 [SE 0.29], P=.049) and contacted the same contacts relatively more often (β=0.8 [SE 0.25], P=.004). They also made less use of apps (β=–0.83 [SE 0.25], P=.004). Higher total NPI scores were associated with further traveling (β=0.042 [SE 0.015], P=.03). Conclusions: CI individuals show reduced social activity, especially those activities that are related to repeated and unique behavior, as measured by the smartphone app Behapp. Neuropsychiatric symptoms seemed only marginally associated with social behavior as measured with Behapp. This research shows that the Behapp app is able to objectively and passively measure altered social behavior in a cognitively impaired population. [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: 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. [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]

This correction notice addresses errors in the funding section of a study titled "Ethical challenges of using remote monitoring technologies for clinical research: A case study of the role of local research ethics committees in the RADAR-AD study." The correct funding statement acknowledges support from various organizations, including the Innovative Medicines Initiative 2 Joint Undertaking, the European Union's Horizon 2020 research and innovation program, and EFPIA and Software AG. The research of the Alzheimer Center Amsterdam is also mentioned, which is supported by Stichting Alzheimer Nederland and Stichting Steun Alzheimercentrum Amsterdam. Other researchers involved in the study declare support from the National Institute of Health Research, the Medical Research Council, the Susan and Charles Berghoff Foundation, and the Italian Ministry of Health. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. [Extracted from the article]

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]

Additional fixation of the palmar scapholunate interosseous ligament has been advocated to improve the long-term results of dorsal scapholunate interosseous ligament reconstruction. To investigate the validity of this approach, we determined normal scapholunate motion patterns and calculated the location of the scapholunate rotation axis. We hypothesized that the optimal location of the scapholunate interosseous ligament insertion could be determined from the scapholunate rotation axis. Four-dimensional computerized tomography was used to study the wrist motion in 21 healthy participants. During flexion-extension motions, the scaphoid rotates 38° (SD 0.6°) relative to the lunate; the rotation axis intersects the dorsal ridge of the proximal pole of the scaphoid and the dorsal ridge of the lunate. Minimal scapholunate motion is present during radioulnar deviation. Since the scapholunate rotation axis runs through the dorsal proximal pole of the scaphoid, this is probably the optimal location for attaching the scapholunate ligament during reconstructive surgery.