Physical activity and mobility are critical for healthy aging and predict diverse health outcomes. While wrist-worn accelerometers are widely used to monitor physical activity, estimating gait metrics from wrist data remains challenging. We extend ElderNet, a self-supervised deep-learning model previously validated for walking-bout detection, to estimate gait metrics from wrist accelerometry. Validation involved 819 older adults (Rush-Memory- and-Aging-Project) and 85 individuals with gait impairments (Mobilise-D), from six medical centers. In Mobilise-D, ElderNet achieved an absolute error of 8.82 cm/s and an intra-class correlation of 0.87 for gait speed, outperforming state-of-the-art methods (p < 0.001) and models using a lower-back sensor. ElderNet outperformed (percentage error; p < 0.01) competing approaches in estimating cadence and stride length, and better (p < 0.01) classified mobility disability (AUC = 0.80) than conventional gait or physical activity metrics. These results render ElderNet a scalable tool for gait assessment using wrist-worn devices in aging and clinical populations.

Background Fatigue, impaired sleep quality and daytime sleepiness, common in neurodegenerative and immune-mediated diseases, are debilitating and have serious societal and economic implications. Currently, measurement of these symptoms largely relies on self-reported questionnaires, which are burdensome for patients and lack sensitivity, granularity and reliability. Methods Building on a preceding feasibility study and qualification advice of the European Medicines Agency, the Clinical Observational Study of the European project Identifying Digital Endpoints to Assess FAtigue, Sleep and acTivities of daily living in Neurodegenerative disorders and Immune-mediated inflammatory diseases (IDEA-FAST) investigates the relationship between digital and clinical parameters of the target concepts of fatigue, reduced sleep quality and daytime sleepiness. Results Between 2022 and 2025, 2000 people are being recruited at 24 European sites – 500 with Parkinson's disease, 500 with inflammatory bowel disease, 200 with each of the following diseases: Huntington's disease, rheumatoid arthritis, systemic lupus erythematosus, primary Sjögren's syndrome and 200 healthy volunteers. Participants are followed over a 24-week period with four visits, each including a 1-week assessment phase at home using CE-certified digital health (including active and passive) technologies. The latter collect information on physical activity, physiology, cognition as well as social interaction and behaviour as core dimensions of the target concepts. Conclusion This study will help to develop reliable, valid and efficient digital endpoints of fatigue, impaired sleep quality and daytime sleepiness for use in future clinical studies and trials.

BackgroundMobile health (mHealth) apps are useful tools for research and disease management. However, implementation of mHealth apps is lacking in many areas. While mHealth apps offer various advantages to researchers and patients, their effectiveness depends on their actual use. Barriers to using mHealth apps are often due to human factors such as usability or technology acceptance. Although prior studies have examined the acceptance of mHealth apps in patient treatment, the key factors driving or hindering the use of mHealth apps in research remain unclear. ObjectiveThis study explores user perceptions of 2 mHealth apps in the setting of an observational technology evaluation study using the unified theory of acceptance and use of technology. We aim to evaluate the technology acceptance of these specific apps and to investigate challenges in choosing suitable mHealth apps in research. The apps were intended for data collection; no effect on health was expected. MethodsPatients with chronic diseases as well as healthy participants used a symptom tracking app and a cognitive test app over the course of 4 weeks within the feasibility study of the project “Identifying Digital Endpoints to Assess Fatigue, Sleep and Activities of Daily Living in Neurodegenerative Disorders and Immune-Mediated Inflammatory Diseases.” Thereafter, 61 qualitative interviews were conducted, recorded, and transcribed. A qualitative content analysis using the unified theory of acceptance and use of technology was performed. ResultsAn important aspect of motivation for participants was feedback on their health data and performance in the cognitive tests. Effort played a significant role in app use. Patients rated the apps as easy to use and quick. Using the app multiple times per day at fixed times was perceived as disruptive. Participants preferred using their own phone. Social influence as well as facilitating conditions played a lesser role in intention to use the apps. Data security was no concern for most participants. They stressed the importance of good relations with the study team. ConclusionsIn choosing suitable apps, one size will certainly not fit all. For medical research, pretesting of all materials with the potential users is of utmost importance. If the positive effects of the app on users’ health are not immediately apparent, other factors may motivate use, for example, feedback, gamification, adjustable functions, applicability on all smartphone operating systems, and good relations to the study team.

Objective Parkinson’s disease can impair gait and stability, leading to reduced independence and increased fall risk. While speed dependent treadmill training (SDTT) is clinically effective, the specific biomechanical and neurophysiological mechanisms driving these improvements remain unclear. The “StepuP” multicenter randomized controlled trial aims to elucidate these mechanisms and determine whether training enriched with virtual reality or mechanical perturbations (SDTT+) enhances gait efficacy and transfer to daily life. Methods We will recruit 126 individuals with Parkinson’s disease across four clinical sites and 21 healthy older adults as a reference group. Participants will be randomized to receive either standard SDTT or SDTT+ for 12 sessions. To capture the trajectory of recovery and retention, assessments will occur at three distinct timepoints: baseline, post-intervention, and a 12-week follow-up, each assessment including synchronized 64-channel electroencephalography (EEG), electromyography (EMG), and 3D kinematics. This multimodal setup allows for the quantification of cortical beta-band activity, corticomuscular coherence, and stability-related foot placement control. Furthermore, we will assess participant’s satisfaction, usability, and engagement through questionnaires and interviews to understand individual adherence and barriers to training. Significance The primary clinical endpoint is comfortable overground walking speed. We hypothesize that gait improvements are mediated by improved stability-related foot placement and cortical sensorimotor integration. By correlating lab-based mechanistic changes with real-world mobility patterns and participant experiences, this study seeks to identify specific pathophysiological mechanisms engaged during the treadmill training. These insights will help distinguish responders from non-responders, facilitating the development of personalized, acceptable, and effective rehabilitation strategies.

Mobile EEG has become popular in investigating brain dynamics during gait in recent years. Within this development, new preprocessing pipelines have been introduced and refined. The diversity of approaches, however, complicates comparisons across studies. To provide clarity, we reviewed studies that combined mobile EEG with gait measurements to map the preprocessing pipelines used in the field. Our review identified substantial heterogeneity in pipeline steps, their order, combinations, and the level of reporting detail. We visualized this heterogeneity as a map, tracing pathways from raw data to outcomes such as Power spectral density (PSD), Event-related spectral perturbations (ERSP), Event-related (de-) synchronization (ERD/ERS), and Corticomuscular coherence (CMC), along with a subsequent analysis highlighting unique pipelines. Notably, artifact rejection varied across studies in both the tools used and reporting practices. While differences in hardware, setup, and experimental paradigms can justify this variability, they also challenge comparability across findings. These results emphasize the need for transparent reporting standards and provide a foundation for future efforts toward developing shared standards in the mobile EEG community.

BACKGROUND:Wearable devices can measure real-world mobility and generate metrics known as digital mobility outcomes (DMOs). For people with multiple sclerosis (pwMS), these DMOs have not been clinically validated. This study aimed to evaluate the construct (convergent, divergent, and known groups) validity of a comprehensive panel of 24 DMOs in pwMS. METHODS:PwMS were recruited to the Mobilise-D Clinical Validation Study, which involved 7-days of real-world mobility monitoring with a lower-back-worn wearable device. DMOs of walking amount, pattern, pace, rhythm, and variability domains were assessed. Convergent validity was evaluated against established clinical constructs (Expanded Disability Status Scale (EDSS), Timed 25-Foot Walk, Multiple sclerosis (MS) Walking Scale-12, and Patient-Determined Disease Steps) using a priori hypotheses. Divergent validity was tested against systolic blood pressure. Known-groups validity was assessed across three EDSS strata. An expert panel completed a standardised consensus process. RESULTS:We included 556 participants: 65% women, mean (SD) age of 52.3 (10.7) years, and median (p25-p75) EDSS 5 (4-6). Convergent, divergent, and known-groups validity were supported for 19, 24, and 23 DMOs, respectively. Nineteen DMOs, covering all domains, reached consensus agreement. CONCLUSION:This study confirms the construct validity of 19 DMOs across all walking domains in pwMS, supporting their use in clinical research and practice. www.isrctn.com/ISRCTN12051706.

During human gait, different body segments work coherently to achieve forward propulsion in a coordinated manner. It is known that Parkinson’s disease (PD) disturbs this coordination, however, which body segments contribute most, especially with changing walking speed, has not been evaluated. To investigate in people with PD (PwPD) and healthy controls how individual body segments contribute to altered gait during different walking speeds. Twenty-nine PwPD and 29 controls walked forward along a straight walking path at three walking speeds. For each speed and movement direction, kinectomes were built by computing pairwise correlations between body segment accelerations. Network graphs were generated with anatomical segments as nodes and their co-accelerations as edges. Graph-theoretic analysis examined community organisation, modularity metrics, and network topology. Nodal strength, defined as coherence between a body segment and all other body segments, was extracted. Compared to controls, PwPD showed coherence deficits during walking, particularly at preferred speed and in the anteroposterior direction, and these deficits primarily affected the core body segments. Controls demonstrated speed-dependent modulation of coherence that was absent in PwPD, particularly in the anteroposterior direction within core body segments. Moreover, PwPD showed speed-dependent modulation of coherence primarily in lower limb segments across the mediolateral and vertical directions. In PwPD, gait coordination deficits localise primarily to the core body segments and are accompanied by a reduced ability to modulate whole-body coordination with walking speed, suggesting that trunk-focused and progressive speed-variable gait training may be a particularly effective rehabilitation strategy. The graph-theoretical framework further enables individualised identification of segmental coordination deficits for tailored treatment planning. Trial registration: The study is registered in the German Clinical Trials Register (DRKS00022998, registered on 04 Sep 2020).

Cognitive aging is shaped by genetic variation, environmental factors, and health-related conditions. Until now, it is largely unclear why some individuals maintain their cognitive function, and others show progressive cognitive decline. This study uncovered determinants of distinct cognitive aging trajectories in the older population. To approach the inter-individual variability in cognitive aging, we clustered n = 696 dementia-free individuals from the prospective TREND study based on their longitudinal changes in comprehensive cognitive testing every 2 years over 13 years on average. Identified subgroups of cognitive aging were tested for differences in general physical health, motor function, mental and neuropsychological health, personality, lifestyle, diet, and genetic and biofluid markers as observed at the phenotypes’ initial records. Comparing the best- and lowest-performing subgroups of cognitive aging, we found that individuals who maintain high cognitive function compared to those with low baseline and progressive cognitive decline showed significant faster gait speed, higher health-related quality of life, did sports and cognitive stimulating activities more frequently, and reported higher plant-based foods intake. Although there are fewer phenotypic differences involving the intermediate subgroups of cognitive aging, the best-performing subgroup compared to all other subgroups showed higher plant-based foods intake. Overall, this study identifies distinct, data-driven subgroups of long-term cognitive aging trajectories and reveals factors associated with these divergent paths using deeply phenotyped data. The findings highlight the substantial heterogeneity of cognitive aging and suggest that favorable trajectories are linked to modifiable behavioral and health-related characteristics, providing a foundation for future multidisciplinary strategies to promote healthy cognitive aging.

Background Fatigue is the main driver of impaired quality of life in post COVID-19. Yet, fatigue overlaps with depression, anxiety, poor sleep, and cognitive complaints. Task-related neurophysiological correlates have been largely unexplored and subjective symptoms often diverge from objective cognitive performance. We investigated whether fatigue (i) is independently associated with task-related cognitive performance, (ii) has identifiable EEG correlates, and (iii) mediates the relationship between subjective cognitive symptoms and objective performance. Methods In 98 individuals with prior PCR-confirmed SARS-CoV-2 infection from the COVIDOM/NAPKON study in Kiel, we recorded 128-channel EEG during a psychomotor vigilance task. Fatigue (FACIT-Fatigue subscale), depression and anxiety (HADS), and sleep quality (PSQI) were assessed. Partial correlations, group comparisons of pre- versus post-stimulus theta and alpha peak power, and a bootstrap mediation analysis were performed. Key correlations were replicated in the population-based COVIDOM sample from Kiel (n = 2,549). We investigated the oscillatory activity by removing aperiodic components from the total signal. Results Stronger fatigue was independently associated with slower reaction times, whereas depression, anxiety, and sleep were not. This finding was replicated in the larger COVIDOM sample from Kiel. The increase in theta and alpha peak power from the pre- to the post-stimulus period was significantly smaller in participants with clinically significant fatigue. Descriptively, these participants showed slightly higher pre-stimulus theta and alpha power and lower post-stimulus peak power than participants without clinically significant fatigue. The presence of subjective cognitive symptoms (disorientation, slowed thinking, difficulties concentrating, or forgetfulness) was associated with slower reaction times. This association was substantially mediated by fatigue (39% of the effect), acknowledging potential measure overlap. Conclusions Fatigue is a construct with distinct behavioral and electrophysiological signatures and a central link between subjective cognitive complaints and objective deficits, suggesting fatigue as an essential target for clinical assessment and intervention.

Outcome assessment in Functional Motor Disorders is predominantly based on clinic-based scales. However, their ecological validity remains uncertain. Clinical improvement in Functional Motor Disorders does not parallel changes in free-living motor performance when a digitally augmented care pathway is implemented through wearable monitoring. This dissociation supports the development of objective digital endpoints in Functional Motor Disorders to quantify real-world functional recovery and fill this structural gap.

Multiple sclerosis (MS) is a common cause of disability in working age adults. Current clinical assessments are inadequate at disability assessment or predicting clinically relevant outcomes. Loss of mobility is an important functional disability to people with MS. Mobilise-D aims to develop, validate, and implement a digital mobility solution which measures unsupervised mobility performance across several chronic conditions, including MS, using a single wearable device. Six hundred two adults with MS, an Expanded Disability Status Scale (EDSS) score of 3.0–6.5, documented disability worsening over the previous 2 years and a 30-day freedom from relapses, were recruited across four European centres. Of 1416 invited, 602 participants (42

REM Sleep Behaviour Disorder (RBD) is a hallmark of the prodromal phase of α-synucleinopathies. We aimed to describe the prevalence of probable RBD and to assess its associations with demographics, cognition, and location in a large sample of older adults in Luxembourg, as a first step toward identifying individuals with RBD symptoms for future prodromal-marker assessment. In 2021, residents of Luxembourg aged 55–75 were invited to complete an online survey including the RBD Screening Questionnaire (RBDSQ); with a threshold of ≥ 7 defining screen-positive probable RBD (sppRBD). Screen-positive participants underwent a telephone interview, and those confirmed were categorised as telephone-assessed probable RBD (pRBD). Bayesian spatial mapping assessed the geographical distribution of pRBD, and logistic regression identified determinants of sppRBD and pRBD. Among 15,915 participants (54% male; median age 62 [IQR 58–67]), 12.4% had sppRBD. The telephone interview confirmed only 34.8% of these as pRBD, yielding a projected prevalence of 4.3%. Self-reported cognitive impairment, male sex, and Portuguese as questionnaire language were associated with pRBD, which showed heterogeneous geographical distribution. Online questionnaires may yield false positives, potentially reflecting e-health literacy issues; therefore, a confirmation step is essential. This analysis identifies individuals with RBD symptoms warranting further prodromal-marker assessment, a candidate group for, rather than a validated instance of, an at-risk-for-α-synucleinopathy cohort.

Abstract Isolated rapid eye movement sleep behavior disorder (iRBD) is a major prodromal marker of α -synucleinopathies, often preceding the clinical onset of Parkinson’s disease, dementia with Lewy bodies, or multiple system atrophy. While wrist-worn actimeters hold significant potential for detecting RBD in large-scale screening efforts by capturing abnormal nocturnal movements, they require a reliable and efficient analysis pipeline. This study presents ActiTect, a fully automated, open-source machine learning tool to identify RBD from actigraphy recordings. To ensure generalizability across heterogeneous acquisition settings, our pipeline includes robust preprocessing and automated sleep-wake detection to harmonize multi-device data and extract physiologically interpretable motion features. Model development was conducted on a cohort of 78 individuals, yielding strong discrimination under nested cross-validation (AUROC = 0.95). Generalization was confirmed on a blinded local test set ( n = 31, AUROC = 0.86) and two independent external cohorts ( n = 113, AUROC = 0.84; n = 57, AUROC = 0.94). To assess robustness, leave-one-dataset-out cross-validation across cohorts demonstrated consistent performance (AUROC range = 0.84–0.89). Complementary stability analysis showed that predictive features remained reproducible across datasets, supporting the pooled multi-center pre-trained model for broader deployment. As an open-source, easy-to-use tool, ActiTect promotes adoption, independent validation, and collaborative improvements, thereby advancing generalizable wearable-based RBD detection.

Background: Trajectories of cognitive functions and fatigue after the acute phase of the coronavirus disease 2019 (COVID-19) vary between persons. The aim of this study is to explore how these trajectories differ between different domains of cognition and fatigue, and test which trajectories reflect persisting COVID-19 symptoms. Methods: Measures of cognitive functions and fatigue were obtained from two longitudinal datasets of adults with history of COVID-19 (NAPKON-POP: MoCA, TMT-A, TMT-B, PVT, MFI General, MFI Mental, MFI Physical, n = 750, 2-4 timepoints, max. 4 years after infection; and NAPKON-SUEP: PROMIS Cognitive Function, n = 793, 1-44 timepoints, max. 1 year after infection). Growth mixture models identified trajectory classes for each functional domain. Fisher’s exact tests and multinomial logistic regression were used to assess relationships of the found classes across domains, to indicators of persistent symptoms, and to risk factors. Findings: We identified reliable classification solutions for 7/8 domains, featuring one class with low symptom burden and one to six additional classes with an unfavourable trajectory. 16 of these 21 unfavourable trajectory classes were associated with an indicator of persisting symptoms (12 constant or moderate improvements, 3 substantial improvements, 1 worsening). Female sex/gender and body mass index demonstrated consistent patterns of association with specific unfavourable symptom trajectories. Interpretation: Individual symptom trajectories after COVID-19 differ, depending on the domain of cognitive function and fatigue. Thus, we emphasise the importance of examining multivariate symptom patterns in order to better understand how the condition evolves in different individuals, and to identify the most favourable interventional approaches for specific patient clusters.

Gait abnormalities are key features of Parkinson’s disease (PD) and might be present in isolated REM Sleep Behavior Disorder (iRBD), a prodromal marker of alpha-synucleinopathy. To assess spatio-temporal gait parameters from prodromal (iRBD) to early/moderate PD stages in normal, fast and dual-task conditions. This prospective multicenter study included two independent cohorts with Polysomnography-confirmed iRBD, drug-naïve PD (naïve-PD), early and moderate treated PD (early-PD, mid-PD), and age-matched healthy controls (HC). Gait was assessed using mobile health technology. A total of 324 participants were enrolled: 194 in the Digital Neurological Assessment (DNA) cohort (23 iRBD, 57 naïve-PD, 49 mid-PD, 65 HC) and 130 in the TPA cohort (28 iRBD, 34 early-PD, 68 HC) composed from TREND, PASS-PD and ABCPD cohorts. In the two independent cohorts, iRBD and early PD (treated and non-treated) showed higher step time compared to HC, especially during dual-task gait. In fast walking, all patient groups had longer step time than HC. Step length was control-like in iRBD and early treated PD, whereas it was lower in both naïve and moderate stages PD in all different task conditions. Temporal gait parameters, especially during dual task walking, are sensitive markers of prodromal PD whereas reduced step length is occurring only in phases when PD diagnosis is already possible. These cross-sectional data need a further validation on ongoing longitudinal studies in prodromal and early stages PD.

Parkinson's disease (PD) affects physical activity, and physical activity reduces the burden of PD. Although shown in studies using inertial measurement units (IMU), it remains unclear at which position physical activity change can best be detected in this population. Within the FAIRPARK-II trial, a subgroup of 25 newly diagnosed persons with PD (pwPD) not taking disease-specific medication yet documented their physical activity and, in parallel, wore IMUs on the most affected ankle, wrist and the lower back for two weeks. Participant-reported physical activity was transformed into Metabolic Equivalents of Tasks (METs) in 15-minute intervals using The Compendium of Physical Activities; Euclidean Norm Minus One (ENMO) values were calculated and averaged over the same intervals for the IMU data. Data of at least 3 days with at least four simultaneous 15-minute epochs of both valid IMU and diary data within the time window (9.00 to 18.00) per participant was included, resulting in a total of 8,494 15-minute epochs used for this analysis. Root mean square error (RMSE) values were calculated between scaled normalized IMU-derived ENMO and normalized MET values for each of the nine IMU-MET combinations (three IMU positions × three MET intensity levels). The wrist and lower back IMU showed comparable RMSE values across all MET intensity levels, with both IMU positions showing lower RMSE values than the ankle position. Tremor affected RMSE negatively, whereby the lower back position may be slightly favorable for the assessment of physical activity in those with tremor. This prospective longitudinal dataset from a very rare cohort provides novel insights into the assessment of physical activity during the earliest clinically evident phase of Parkinson's disease without disease-specific medication, which may inform future clinical trials and observational studies.

Background:Sustained attention is a complex cognitive function required for the successful performance of tasks such as walking, cycling, driving, conversations and other prolonged tasks. Deficits of this function are associated with frailty, falls, and general cognitive decline in older adults. Sustained attention declines with age and is impaired in many neurological disorders. However, little is known about the underlying neurophysiological characteristics of sustained attention deficits in neurogeriatric patients and their interaction with other cognitive domains. Electroencephalography (EEG) provides a non-invasive and scalable method to assess neural dynamics with high temporal resolution. EEG parameters have shown promise as objective markers of cognitive dysfunction in aging and neurodegenerative conditions, but their specific relevance as surrogate markers of sustained attention in neurogeriatric patients remains unclear. Identifying reliable EEG-based surrogate markers could facilitate early detection, risk stratification, and targeted interventions. Therefore, this study aims to investigate EEG-based parameters as potential surrogate markers of sustained attention in neurogeriatric patients. Methods/design:The study "Studying Neurocognitive Systems for Sustained Attention in Neurogeriatrics Patients" (SENSE-AGE) is a prospective, explorative, observational study and will include 120 geriatric participants. At admission, participants will perform a Go-NoGo task, a Psychomotor Vigilance Task (PVT) and resting-state condition during EEG recording, using a 32-channel system. Task-based event-related potentials (ERPs) and frequency-band power will be extracted. Neuropsychological tests characterize global and domain-specific cognition and will examine associations between sustained attention, broader cognitive performance, and EEG parameters. Questionnaires will assess fatigue, sleep, health-related quality of life, and subjective cognition. A subgroup of 50 participants will be re-evaluated at the end of the inpatient stay, after 2-3 weeks of standardized geriatric complex treatment. The main hypotheses of the study are: sustained attention (i) correlates with ERP amplitude and latency; (ii) correlates with EEG power; (iii) improves after an inpatient multiprofessional complex treatment at both the task-performance and neurophysiological level. Discussion:The study protocol describes an experimental approach to investigate sustained attention in a neurogeriatric cohort combining a behavioral approach with EEG recordings. The results may help defining objective and quantitative surrogate markers of sustained attention in this vulnerable cohort.