Background: Gait analysis with accelerometers is a relatively inexpensive and easy to use method to potentially support clinical diagnoses of Alzheimer's disease and other dementias. It is not clear, however, which gait features are most informative and how these measures relate to Alzheimer's disease pathology. Objective: In this study, we tested if calculated features of gait 1) differ between cognitively normal subjects (CN), mild cognitive impairment (MCI) patients, and dementia patients, 2) are correlated with cerebrospinal fluid (CSF) biomarkers related to Alzheimer's disease, and 3) predict cognitive decline.

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.

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.

Alzheimer’s disease (AD) and other neurodegenerative diseases such as Parkinson’s disease (PD) and Huntington’s disease (HD) are associated with progressive cognitive, motor, affective and consequently functional decline considerably affecting Activities of Daily Living (ADL) and quality of life. Standard assessments, such as questionnaires and interviews, cognitive testing, and mobility assessments, lack sensitivity, especially in early stages of neurodegenerative diseases and in the disease progression, and have therefore a limited utility as outcome measurements in clinical trials. Major advances in the last decade in digital technologies have opened a window of opportunity to introduce digital endpoints into clinical trials that can reform the assessment and tracking of neurodegenerative symptoms. The Innovative Health Initiative (IMI)-funded projects RADAR-AD (Remote assessment of disease and relapse—Alzheimer’s disease), IDEA-FAST (Identifying digital endpoints to assess fatigue, sleep and ADL in neurodegenerative disorders and immune-mediated inflammatory diseases) and Mobilise-D (Connecting digital mobility assessment to clinical outcomes for regulatory and clinical endorsement) aim to identify digital endpoints relevant for neurodegenerative diseases that provide reliable, objective, and sensitive evaluation of disability and health-related quality of life. In this article, we will draw from the findings and experiences of the different IMI projects in discussing (1) the value of remote technologies to assess neurodegenerative diseases; (2) feasibility, acceptability and usability of digital assessments; (3) challenges related to the use of digital tools; (4) public involvement and the implementation of patient advisory boards; (5) regulatory learnings; and (6) the significance of inter-project exchange and data- and algorithm-sharing.

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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.

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.

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.