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BACKGROUND: The Mnemonic Similarity Task (MST) is a popular memory task designed to assess hippocampal integrity. We assessed whether analyzing MST performance using a multinomial processing tree (MPT) cognitive model could detect individuals with elevated Alzheimer's disease (AD) biomarker status prior to cognitive decline. METHOD: We analyzed MST data from >200 individuals (young, cognitively healthy older adults and individuals with mild cognitive impairment [MCI]), a subset of which also had existing cerebrospinal fluid (CSF) amyloid beta (Aβ) and phosphorylated tau (pTau) data using both traditional and model‐derived approaches. We assessed how well each could predict age group, memory ability, MCI status, Aβ, and pTau status using receiver operating characteristic analyses. RESULTS: Both approaches predicted age group membership equally, but MPT‐derived metrics exceeded traditional metrics in all other comparisons. DISCUSSION: A MPT model of the MST can detect individuals with AD prior to cognitive decline, making it a potentially useful tool for screening and monitoring older adults during the asymptomatic phase of AD. Highlights: The MST, along with cognitive modeling, identifies individuals with memory deficits and cognitive impairment.Cognitive modeling of the MST identifies individuals with increased AD biomarkers prior to changes in cognitive function.The MST is a digital biomarker that identifies individuals at high risk of AD. [ABSTRACT FROM AUTHOR]

This study examined the association between in vivo skeletal mitochondrial function and digital free-living physical activity patterns—a measure that summarizes biological, phenotypic, functional, and environmental effects on mobility. Among 459 participants (mean age 68 years; 55% women) in the Baltimore Longitudinal Study of Aging, mitochondrial function was quantified as skeletal muscle oxidative capacity via post-exercise phosphocreatine recovery rate (τPCr) in the vastus lateralis muscle of the left thigh, using 31P magnetic resonance spectroscopy. Accelerometry was collected using a 7-day, 24-h wrist-worn protocol and summarized into activity amount, intensity, endurance, and accumulation patterning metrics. Linear regression, two-part linear and logistic (bout analyses), and linear mixed effects models (time-of-day analyses) were used to estimate associations between τPCr and each physical activity metric. Interactions by age, sex, and gait speed were tested. After covariate adjustment, higher τPCr (or poorer mitochondrial function) was associated with lower activity counts/day (β = − 6593.7, SE = 2406.0; p = 0.006) and activity intensity (− 81.5 counts, SE = 12.9; p < 0.001). For activity intensity, the magnitude of association was greater for men and those with slower gait speed (interaction p < 0.02 for both). Conversely, τPCr was not associated with daily active minutes/day (p = 0.15), activity fragmentation (p = 0.13), or endurance at any bout length (p > 0.05 for all). Time-of-day analyses show participants with high τPCr were less active from 6:00 a.m. to 12:00 a.m. than those with low τPCr. Results indicate that poorer skeletal mitochondrial function is primarily associated with lower engagement in high intensity activities. Our findings help define the connection between laboratory-measured mitochondrial function and real-world physical activity behavior. [ABSTRACT FROM AUTHOR]

Abstract Background Digital speech assessment has potential relevance in the earliest, preclinical stages of Alzheimer’s disease (AD). We evaluated the feasibility, test-retest reliability, and association with AD-related amyloid-beta (Aβ) pathology of speech acoustics measured over multiple assessments in a remote setting. Methods Fifty cognitively unimpaired adults (Age 68 ± 6.2 years, 58% female, 46% Aβ-positive) completed remote, tablet-based speech assessments (i.e., picture description, journal-prompt storytelling, verbal fluency tasks) for five days. The testing paradigm was repeated after 2–3 weeks. Acoustic speech features were automatically extracted from the voice recordings, and mean scores were calculated over the 5-day period. We assessed feasibility by adherence rates and usability ratings on the System Usability Scale (SUS) questionnaire. Test-retest reliability was examined with intraclass correlation coefficients (ICCs). We investigated the associations between acoustic features and Aβ-pathology, using linear regression models, adjusted for age, sex and education. Results The speech assessment was feasible, indicated by 91.6% adherence and usability scores of 86.0 ± 9.9. High reliability (ICC ≥ 0.75) was found across averaged speech samples. Aβ-positive individuals displayed a higher pause-to-word ratio in picture description (B = -0.05, p = 0.040) and journal-prompt storytelling (B = -0.07, p = 0.032) than Aβ-negative individuals, although this effect lost significance after correction for multiple testing. Conclusion Our findings support the feasibility and reliability of multi-day remote assessment of speech acoustics in cognitively unimpaired individuals with and without Aβ-pathology, which lays the foundation for the use of speech biomarkers in the context of early AD.

Introduction: Essential tremor is a common movement disorder. Numerous validated clinical rating scales exist to quantify essential tremor severity by employing rater-dependent visual observation but have limitations, including the need for trained human raters and the lack of precision and sensitivity compared to technology-based objective measures. Other continuous objective methods to quantify tremor amplitude have been developed, but frequently provide unitless measures (e.g., tremor power), limiting real-world interpretability. We propose a novel algorithm to measure kinetic tremor amplitude using digital spiral drawings, applying the V3 framework (sensor verification, analytical validation, and clinical validation) to establish reliability and clinical utility. Methods: Archimedes spiral drawings were recorded on a digitizing tablet from participants (n = 7) enrolled in a randomized placebo control double-blinded crossover pilot trial evaluating the efficacy of oral cannabinoids in reducing essential tremor. We developed an algorithm to calculate the mean and maximum tremor amplitude derived from the spiral tracings. We compared the digitally measured tremor amplitudes to manual measurement to evaluate sensor reliability, determined the test-retest reliability of the digital output across two short-interval repeated measures, and compared the digital measure to kinetic tremor severity graded using The Essential Tremor Rating Assessment Scale (TETRAS) score for spiral drawings. Results: This algorithm for automated assessment of kinetic tremor amplitude from digital spiral tracings demonstrated a high correlation with manual spot measures of tremor amplitude, excellent test-retest reliability, and a high correlation with human ratings of the TETRAS score for spiral drawing severity when the tremor severity was rated “slight tremor” or worse. Discussion: This digital measure provides a simple and clinically relevant evaluation of kinetic tremor amplitude that shows promise as a potential future endpoint for use in clinical trials of essential tremor.

Easily applied diagnostic tools such as digital biomarkers for Alzheimer's disease (AD) are urgently needed due to the recent approval of disease‐modifying therapies. We aimed to determine the diagnostic performance of hand‐held, quantitative light reflex pupillometry (qLRP) in patients with AD in a proof‐of‐concept, cross‐sectional study. Participants underwent qLRP at a university memory clinic from August 2022 to October 2023. We fitted multivariable logistic regression models with qLRP, sex, and age as predictors evaluated with area under the receiver operating characteristics curve (AUROC). In total, 107 patients with AD, 44 patients with mixed AD and vascular cognitive dysfunction (VCD), 53 patients with dementia with Lewy bodies (DLB), and 50 healthy controls (HCs) were included. Our diagnostic models showed similar discriminatory ability (AUROC range 0.74‐0.81) when distinguishing patients with AD from HCs and other dementias. The qLRP seems promising as a bedside digital biomarker to aid in diagnosing AD. Highlights: We demonstrated the diagnostic performance of qLRP in Alzheimer's disease.The diagnostic models were robust in sensitivity analyses.qLRP may assist in the bedside diagnostic evaluation of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusion Affective states influence the sympathetic nervous system, inducing variations in electrodermal activity (EDA), however, EDA association with bipolar disorder (BD) remains uncertain in real‐world settings due to confounders like physical activity and temperature. We analysed EDA separately during sleep and wakefulness due to varying confounders and potential differences in mood state discrimination capacities.We monitored EDA from 102 participants with BD including 35 manic, 29 depressive, 38 euthymic patients, and 38 healthy controls (HC), for 48 h. Fifteen EDA features were inferred by mixed‐effect models for repeated measures considering sleep state, group and covariates.Thirteen EDA feature models were significantly influenced by sleep state, notably including phasic peaks (p < 0.001). During wakefulness, phasic peaks showed different values for mania (M [SD] = 6.49 [5.74, 7.23]), euthymia (5.89 [4.83, 6.94]), HC (3.04 [1.65, 4.42]), and depression (3.00 [2.07, 3.92]). Four phasic features during wakefulness better discriminated between HC and mania or euthymia, and between depression and euthymia or mania, compared to sleep. Mixed symptoms, average skin temperature, and anticholinergic medication affected the models, while sex and age did not.EDA measured from awake recordings better distinguished between BD states than sleep recordings, when controlled by confounders. [ABSTRACT FROM AUTHOR]

Background: Progressive loss of standing balance is a feature of Friedreich's ataxia (FRDA). Objectives: This study aimed to identify standing balance conditions and digital postural sway measures that best discriminate between FRDA and healthy controls (HC). We assessed test–retest reliability and correlations between sway measures and clinical scores. Methods: Twenty‐eight subjects with FRDA and 20 HC completed six standing conditions: feet apart, feet together, and feet tandem, both with eyes opened (EO) and eyes closed. Sway was measured using a wearable sensor on the lumbar spine for 30 seconds. Test completion rate, test–retest reliability with intraclass correlation coefficients, and areas under the receiver operating characteristic curves (AUCs) for each measure were compared to identify distinguishable FRDA sway characteristics from HC. Pearson correlations were used to evaluate the relationships between discriminative measures and clinical scores. Results: Three of the six standing conditions had completion rates over 70%. Of these three conditions, natural stance and feet together with EO showed the greatest completion rates. All six of the sway measures' mean values were significantly different between FRDA and HC. Four of these six measures discriminated between groups with >0.9 AUC in all three conditions. The Friedreich Ataxia Rating Scale Upright Stability and Total scores correlated with sway measures with P‐values <0.05 and r‐values (0.63–0.86) and (0.65–0.81), respectively. Conclusion: Digital postural sway measures using wearable sensors are discriminative and reliable for assessing standing balance in individuals with FRDA. Natural stance and feet together stance with EO conditions suggest use in clinical trials for FRDA. © 2024 International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Significant advances have been made in the diagnosis and treatment of multiple sclerosis in recent years yet challenges remain. The current classification of MS phenotypes according to disease activity and progression, for example, does not adequately reflect the underlying pathophysiological mechanisms that may be acting in an individual with MS at different time points. Thus, there is a need for clinicians to transition to a management approach based on the underlying pathophysiological mechanisms that drive disability in MS. A Canadian expert panel convened in January 2023 to discuss priorities for clinical discovery and scientific exploration that would help advance the field. Five key areas of focus included: identifying a mechanism-based disease classification system; developing biomarkers (imaging, fluid, digital) to identify pathologic processes; implementing a data-driven approach to integrate genetic/environmental risk factors, clinical findings, imaging and biomarker data, and patient-reported outcomes to better characterize the many factors associated with disability progression; utilizing precision-based treatment strategies to target different disease processes; and potentially preventing disease through Epstein-Barr virus (EBV) vaccination, counselling about environmental risk factors (e.g. obesity, exercise, vitamin D/sun exposure, smoking) and other measures. Many of the tools needed to meet these needs are currently available. Further work is required to validate emerging biomarkers and tailor treatment strategies to the needs of individual patients. The hope is that a more complete view of the individual's pathobiology will enable clinicians to usher in an era of truly personalized medicine, in which more informed treatment decisions throughout the disease course achieve better long-term outcomes. [ABSTRACT FROM AUTHOR]

Background: Dementia prevalence is predicted to triple to 152 million globally by 2050. Alzheimer's disease (AD) constitutes 70% of cases. There is an urgent need to identify individuals with preclinical AD, a 10–20-year period of progressive brain pathology without noticeable cognitive symptoms, for targeted risk reduction. Current tests of AD pathology are either too invasive, specialised or expensive for population-level assessments. Cognitive tests are normal in preclinical AD. Emerging evidence demonstrates that movement analysis is sensitive to AD across the disease continuum, including preclinical AD. Our new smartphone test, TapTalk, combines analysis of hand and speech-like movements to detect AD risk. This study aims to [1] determine which combinations of hand-speech movement data most accurately predict preclinical AD [2], determine usability, reliability, and validity of TapTalk in cognitively asymptomatic older adults and [3], prospectively validate TapTalk in older adults who have cognitive symptoms against cognitive tests and clinical diagnoses of Mild Cognitive Impairment and AD dementia. Methods: Aim 1 will be addressed in a cross-sectional study of at least 500 cognitively asymptomatic older adults who will complete computerised tests comprising measures of hand motor control (finger tapping) and oro-motor control (syllabic diadochokinesis). So far, 1382 adults, mean (SD) age 66.20 (7.65) years, range 50–92 (72.07% female) have been recruited. Motor measures will be compared to a blood-based AD biomarker, phosphorylated tau 181 to develop an algorithm that classifies preclinical AD risk. Aim 2 comprises three sub-studies in cognitively asymptomatic adults: (i) a cross-sectional study of 30–40 adults to determine the validity of data collection from different types of smartphones, (ii) a prospective cohort study of 50–100 adults ≥ 50 years old to determine usability and test-retest reliability, and (iii) a prospective cohort study of ~1,000 adults ≥ 50 years old to validate against cognitive measures. Aim 3 will be addressed in a cross-sectional study of ~200 participants with cognitive symptoms to validate TapTalk against Montreal Cognitive Assessment and interdisciplinary consensus diagnosis. Discussion: This study will establish the precision of TapTalk to identify preclinical AD and estimate risk of cognitive decline. If accurate, this innovative smartphone app will enable low-cost, accessible screening of individuals for AD risk. This will have wide applications in public health initiatives and clinical trials. Trial registration: ClinicalTrials.gov identifier: NCT06114914, 29 October 2023. Retrospectively registered. [ABSTRACT FROM AUTHOR]

Background Dementia often results in postural control impairment, which could signify central nervous system dysfunction. However, no studies have compared postural control characteristics among various types of dementia. This study aimed to compare static postural control in patients with Alzheimer's disease (AD), dementia with Lewy bodies (DLB), and vascular dementia (VaD). Methods Cross-sectional relationship between the clinical diagnoses (AD, DLB, VaD, or normal cognition [NC]) of outpatients at a memory clinic and their upright postural control characteristics were examined. In the postural control test, participants were instructed to maintain a static upright standing on a stabilometer for 60 seconds under the eyes-open and eyes-closed conditions. Forty postural control parameters, including distance, position, and velocity in the anterior–posterior and medio–lateral directions, derived from the trajectory of the center of mass sway, were calculated. The characteristics of each type of dementia were compared to those of NC, and the differences among the 3 types of dementia were evaluated using linear regression models. Results The study included 1 789 participants (1 206 with AD, 111 with DLB, 49 with VaD, and 423 with NC). Patients with AD exhibited distinct postural control characteristics, particularly in some distance and velocity parameters, only in the eyes-closed condition. Those with DLB exhibited features in the mean position in the anterior–posterior direction. In patients with VaD, significant differences were observed in most parameters, except the power spectrum. Conclusions Patients with AD, DLB, and VaD display disease-specific postural control characteristics when compared to cognitively normal individuals. [ABSTRACT FROM AUTHOR]

Background: Disorders associated with cognitive impairment impose a significant burden on both families and society. Previous studies have indicated that gait characteristics under dual-task as reliable markers of early cognitive impairment. Therefore, digital gait detection has great potential for future cognitive screening. However, research on digital biomarkers based on smart devices to identify cognitive impairment remains limited. The aim of this study is to explore digital gait biomarkers by utilizing intelligent wearable devices for discriminating mild cognitive impairment and dementia. Methods: This study included 122 subjects (age: 74.7 ± 7.7 years) diagnosed with normal cognition (NC, n = 38), mild cognitive impairment (MCI, n = 42), or dementia (n = 42). All subjects underwent comprehensive neuropsychological assessments and cranial Magnetic Resonance Imaging (MRI). Gait parameters were collected using validated wearable devices in both single-task and dual-task (DT). We analyzed the ability of gait variables to predict MCI and dementia, and examined the correlations between specific DT-gait parameters and sub-cognitive functions as well as hippocampal atrophy. Results: Our results demonstrated that dual-task could significantly improve the ability to predict cognitive impairment based on gait parameters such as gait speed (GS) and stride length (SL). Additionally, we discovered that turn velocity (TV and DT-TV) can be a valuable novel digital marker for predicting MCI and dementia, for identifying MCI (DT-TV: AUC = 0.801, sensitivity 0.738, specificity 0.842), and dementia (DT-TV: AUC = 0.923, sensitivity 0.857, specificity 0.842). The correlation analysis and linear regression analysis revealed a robust association between DT-TV and memory function, as well as the hippocampus atrophy. Conclusion: This study presents a novel finding that DT-TV could accurately identify varying degrees of cognitive impairment. DT-TV is strongly correlated with memory function and hippocampus shrinkage, suggests that it can accurately reflect changes in cognitive function. Therefore, DT-TV could serve as a novel and effective digital biomarker for discriminating cognitive impairment. [ABSTRACT FROM AUTHOR]

This study investigates the prediction of mental well-being factors—depression, stress, and anxiety—using the NetHealth dataset from college students. The research addresses four key questions, exploring the impact of digital biomarkers on these factors, their alignment with conventional psychology literature, the time-based performance of applied methods, and potential enhancements through multitask learning. The findings reveal modality rankings aligned with psychology literature, validated against paper-based studies. Improved predictions are noted with temporal considerations, and further enhanced by multitasking. Mental health multitask prediction results show aligned baseline and multitask performances, with notable enhancements using temporal aspects, particularly with the random forest (RF) classifier. Multitask learning improves outcomes for depression and stress but not anxiety using RF and XGBoost. [ABSTRACT FROM AUTHOR]

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Abstract Easily applied diagnostic tools such as digital biomarkers for Alzheimer's disease (AD) are urgently needed due to the recent approval of disease‐modifying therapies. We aimed to determine the diagnostic performance of hand‐held, quantitative light reflex pupillometry (qLRP) in patients with AD in a proof‐of‐concept, cross‐sectional study. Participants underwent qLRP at a university memory clinic from August 2022 to October 2023. We fitted multivariable logistic regression models with qLRP, sex, and age as predictors evaluated with area under the receiver operating characteristics curve (AUROC). In total, 107 patients with AD, 44 patients with mixed AD and vascular cognitive dysfunction (VCD), 53 patients with dementia with Lewy bodies (DLB), and 50 healthy controls (HCs) were included. Our diagnostic models showed similar discriminatory ability (AUROC range 0.74‐0.81) when distinguishing patients with AD from HCs and other dementias. The qLRP seems promising as a bedside digital biomarker to aid in diagnosing AD. Highlights We demonstrated the diagnostic performance of qLRP in Alzheimer's disease. The diagnostic models were robust in sensitivity analyses. qLRP may assist in the bedside diagnostic evaluation of Alzheimer's disease.

Sarcopenia is a comprehensive degenerative disease with the progressive loss of skeletal muscle mass with age, accompanied by the loss of muscle strength and muscle dysfunction. Individuals with unmanaged sarcopenia may experience adverse outcomes. Periodically monitoring muscle function to detect muscle degeneration caused by sarcopenia and treating degenerated muscles is essential. We proposed a digital biomarker measurement technique using surface electromyography (sEMG) with electrical stimulation and wearable device to conveniently monitor muscle function at home. When motor neurons and muscle fibers are electrically stimulated, stimulated muscle contraction signals (SMCSs) can be obtained using an sEMG sensor. As motor neuron activation is important for muscle contraction and strength, their action potentials for electrical stimulation represent the muscle function. Thus, the SMCSs are closely related to muscle function, presumptively. Using the SMCSs data, a feature vector concatenating spectrogram-based features and deep learning features extracted from a convolutional neural network model using continuous wavelet transform images was used as the input to train a regression model for measuring the digital biomarker. To verify muscle function measurement technique, we recruited 98 healthy participants aged 20–60 years including 48 [49%] men who volunteered for this study. The Pearson correlation coefficient between the label and model estimates was 0.89, suggesting that the proposed model can robustly estimate the label using SMCSs, with mean error and standard deviation of -0.06 and 0.68, respectively. In conclusion, measuring muscle function using the proposed system that involves SMCSs is feasible.

Objective: Parkinson's disease (PD) impairs motor and non-motor functions. Driver strategies to compensate for impairments, like avoiding driving in risky environments, may reduce on-road risk at the cost of decreasing driver mobility, independence, and quality of life (QoL). It is unclear how PD symptoms link to driving risk exposure, strategies, and QoL. We assessed associations between PD symptoms and driving exposure (1) overall, (2) in risky driving environments, and (3) in relationship to QoL. Methods: Twenty-eight drivers with idiopathic PD were assessed using the Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and RAND 36-Item Short Form Health Survey (SF-36). Real-world driving was monitored for 1 month. Overall driving exposure (miles driven) and risky driving exposure (miles driven in higher risk driving environments) were assessed across PD symptom severity. High traffic, night, and interstate roads were considered risky environments. Results: 18,642 miles (30,001km) driven were collected. Drivers with PD with worse motor symptoms (MDS-UPDRS Part III) drove more overall (b=0.17, P<.001) but less in risky environments (night: b=-0.35, P<.001; interstate roads: b=-0.23, P<.001; high traffic: b=-0.14, P<.001). Worse non-motor daily activities symptoms (MDS-UPDRS Part I) did not affect overall driving exposure (b=-0.05, P=.43) but did affect risky driving exposure. Worse non-motor daily activities increased risk exposure to interstate (b=0.36, P<.001) and high traffic (b=0.09, P=.03) roads while reducing nighttime risk exposure (b=-0.15, P=.01). Daily activity impacts from motor symptoms (MDS-UPDRS Part II) did not affect distance driven. Reduced driving exposure (number of drives per day) was associated with worse physical health-related QoL (b=2.87, P=.04). Conclusions: Results provide pilot data revealing specific PD symptom impacts on driving risk exposure and QoL. Drivers with worse non-motor impairments may have greater risk exposure. In contrast, drivers with worse motor impairments may have reduced driver risk exposure. Reduced driving exposure may worsen physical health-related QoL. Results show promise for using driving to inform clinical care. [ABSTRACT FROM AUTHOR]

Abstract Background High gait variability is associated with neurodegeneration and cognitive impairments and is predictive of cognitive impairment and dementia. The objective of this study was to identify cortical or subcortical structures of the brain shared by gait variability measured using a body-worn tri-axial accelerometer (TAA) and cognitive function. Methods This study is a part of a larger population-based cohort study on cognitive aging and dementia. The study included 207 participants without dementia, with a mean age of 72.6, and 45.4% of them are females. We conducted standardized diagnostic interview including a detailed medical history, physical and neurological examinations, and laboratory tests for cognitive impairment. We obtained gait variability during walking using a body-worn TAA along and measured cortical thickness and subcortical volume from brain magnetic resonance (MR) images. We cross-sectionally investigated the cortical and subcortical neural structures associated with gait variability and the shared neural substrates of gait variability and cognitive function. Results Higher gait variability was associated with the lower cognitive function and thinner cortical gray matter but not smaller subcortical structures. Among the clusters exhibiting correlations with gait variability, one that included the inferior temporal, entorhinal, parahippocampal, fusiform, and lingual regions in the left hemisphere was also associated with global cognitive and verbal memory function. Mediation analysis results revealed that the cluster’s cortical thickness played a mediating role in the association between gait variability and cognitive function. Conclusion Gait variability and cognitive function may share neural substrates, specifically in regions related to memory and visuospatial navigation.

Abstract Background Joint acoustic emissions from knees have been evaluated as a convenient, non-invasive digital biomarker of inflammatory knee involvement in a small cohort of children with Juvenile Idiopathic Arthritis (JIA). The objective of the present study was to validate this in a larger cohort. Findings A total of 116 subjects (86 JIA and 30 healthy controls) participated in this study. Of the 86 subjects with JIA, 43 subjects had active knee involvement at the time of study. Joint acoustic emissions were bilaterally recorded, and corresponding signal features were used to train a machine learning algorithm (XGBoost) to classify JIA and healthy knees. All active JIA knees and 80% of the controls were used as training data set, while the remaining knees were used as testing data set. Leave-one-leg-out cross-validation was used for validation on the training data set. Validation on the training and testing set of the classifier resulted in an accuracy of 81.1% and 87.7% respectively. Sensitivity / specificity for the training and testing validation was 88.6% / 72.3% and 88.1% / 83.3%, respectively. The area under the curve of the receiver operating characteristic curve was 0.81 for the developed classifier. The distributions of the joint scores of the active and inactive knees were significantly different. Conclusion Joint acoustic emissions can serve as an inexpensive and easy-to-use digital biomarker to distinguish JIA from healthy controls. Utilizing serial joint acoustic emission recordings can potentially help monitor disease activity in JIA affected joints to enable timely changes in therapy.

Abstract Background Depression remains a global health problem, with its prevalence rising worldwide. Digital biomarkers are increasingly investigated to initiate and tailor scalable interventions targeting depression. Due to the steady influx of new cases, focusing on treatment alone will not suffice; academics and practitioners need to focus on the prevention of depression (i.e., addressing subclinical depression). Aim With our study, we aim to (i) develop digital biomarkers for subclinical symptoms of depression, (ii) develop digital biomarkers for severity of subclinical depression, and (iii) investigate the efficacy of a digital intervention in reducing symptoms and severity of subclinical depression. Method Participants will interact with the digital intervention BEDDA consisting of a scripted conversational agent, the slow-paced breathing training Breeze, and actionable advice for different symptoms. The intervention comprises 30 daily interactions to be completed in less than 45 days. We will collect self-reports regarding mood, agitation, anhedonia (proximal outcomes; first objective), self-reports regarding depression severity (primary distal outcome; second and third objective), anxiety severity (secondary distal outcome; second and third objective), stress (secondary distal outcome; second and third objective), voice, and breathing. A subsample of 25% of the participants will use smartwatches to record physiological data (e.g., heart-rate, heart-rate variability), which will be used in the analyses for all three objectives. Discussion Digital voice- and breathing-based biomarkers may improve diagnosis, prevention, and care by enabling an unobtrusive and either complementary or alternative assessment to self-reports. Furthermore, our results may advance our understanding of underlying psychophysiological changes in subclinical depression. Our study also provides further evidence regarding the efficacy of standalone digital health interventions to prevent depression. Trial registration Ethics approval was provided by the Ethics Commission of ETH Zurich (EK-2022-N-31) and the study was registered in the ISRCTN registry (Reference number: ISRCTN38841716, Submission date: 20/08/2022).

Lumbar spinal canal stenosis (LSS) is characterized by gait abnormalities, and objective quantitative gait analysis is useful for diagnosis and treatment. This review aimed to provide a review of objective quantitative gait analysis in LSS and note the current status and potential of smart shoes in diagnosing and treating LSS. The characteristics of gait deterioration in LSS include decreased gait velocity and asymmetry due to neuropathy (muscle weakness and pain) in the lower extremities. Previous laboratory objective and quantitative gait analyses mainly comprised marker-based three-dimensional motion analysis and ground reaction force. However, workforce, time, and costs pose some challenges. Recent developments in wearable sensor technology and markerless motion analysis systems have made gait analysis faster, easier, and less expensive outside the laboratory. Smart shoes can provide more accurate gait information than other wearable sensors. As only a few reports exist on gait disorders in patients with LSS, future studies should focus on the accuracy and cost-effectiveness of gait analysis using smart shoes.

Background: High gait variability is associated with neurodegeneration and cognitive impairments and is predictive of cognitive impairment and dementia. The objective of this study was to identify cortical or subcortical structures of the brain shared by gait variability measured using a body-worn tri-axial accelerometer (TAA) and cognitive function. Methods: This study is a part of a larger population-based cohort study on cognitive aging and dementia. The study included 207 participants without dementia, with a mean age of 72.6, and 45.4% of them are females. We conducted standardized diagnostic interview including a detailed medical history, physical and neurological examinations, and laboratory tests for cognitive impairment. We obtained gait variability during walking using a body-worn TAA along and measured cortical thickness and subcortical volume from brain magnetic resonance (MR) images. We cross-sectionally investigated the cortical and subcortical neural structures associated with gait variability and the shared neural substrates of gait variability and cognitive function. Results: Higher gait variability was associated with the lower cognitive function and thinner cortical gray matter but not smaller subcortical structures. Among the clusters exhibiting correlations with gait variability, one that included the inferior temporal, entorhinal, parahippocampal, fusiform, and lingual regions in the left hemisphere was also associated with global cognitive and verbal memory function. Mediation analysis results revealed that the cluster's cortical thickness played a mediating role in the association between gait variability and cognitive function. Conclusion: Gait variability and cognitive function may share neural substrates, specifically in regions related to memory and visuospatial navigation. [ABSTRACT FROM AUTHOR]

• A novel smartphone sensor-based assessment suite was developed for people with spinal muscular atrophy (SMA). • Tests were reliable, engaging, and easy for most people with SMA to complete. • Tests showed convergent validity with established SMA clinical assessments. • Our study demonstrates potential for digital monitoring of disease in SMA. Spinal muscular atrophy (SMA) is characterized by progressive muscle weakness and paralysis. Motor function is monitored in the clinical setting using assessments including the 32-item Motor Function Measure (MFM-32), but changes in disease severity between clinical visits may be missed. Digital health technologies may assist evaluation of disease severity by bridging gaps between clinical visits. We developed a smartphone sensor-based assessment suite, comprising nine tasks, to assess motor and muscle function in people with SMA. We used data from the risdiplam phase 2 JEWELFISH trial to assess the test–retest reliability and convergent validity of each task. In the first 6 weeks, 116 eligible participants completed assessments on a median of 6.3 days per week. Eight of the nine tasks demonstrated good or excellent test–retest reliability (intraclass correlation coefficients >0.75 and >0.9, respectively). Seven tasks showed a significant association (P < 0.05) with related clinical measures of motor function (individual items from the MFM-32 or Revised Upper Limb Module scales) and seven showed significant association (P < 0.05) with disease severity measured using the MFM-32 total score. This cross-sectional study supports the feasibility, reliability, and validity of using smartphone-based digital assessments to measure function in people living with SMA. [ABSTRACT FROM AUTHOR]

Background: This study relates to emerging concepts of appropriate trial designs to evaluate effects of intervention on the accumulation of irreversible disability in multiple sclerosis (MS). Major starting points of our study are the known limitations of current definitions of disability progression by rater-based clinical assessment and the high relevance of gait and balance dysfunctions in MS. The study aims to explore a novel definition of disease progression using repeated instrumental assessment of relevant motor functions performed by patients in their home setting. Methods: The study is a prospective single-center observational cohort study with the primary outcome acquired by participants themselves, a home-based assessment of motor functions based on an RGB-Depth (RGB-D) camera, a camera that provides both depth (D) and color (RGB) data. Participants are instructed to perform and record a set of simple motor tasks twice a day over a one-week period every 6 months. Assessments are complemented by a set of questionnaires. Annual research grade assessments are acquired at dedicated study visits and include clinical ratings as well as structural imaging (MRI and optical coherence tomography). In addition, clinical data from routine visits is provided semiannually by treating neurologists. The observation period is 24 months for the primary endpoint with an additional clinical assessment at 27 month to confirm progression defined by the Expanded Disability Status Scale (EDSS). Secondary analyses aim to explore the time course of changes in motor parameters and performance of the novel definition against different alternative definitions of progression in MS. The study was registered at Deutsches Register für Klinische Studien (DRKS00027042). Discussion: The study design presented here investigates disease progression defined by marker-less home-based assessment of motor functions against 3-month confirmed disease progression (3 m-CDP) defined by the EDSS. The technical approach was chosen due to previous experience in lab-based settings. The observation time per participant of 24, respectively, 27 months is commonly conceived as the lower limit needed to study disability progression. Defining a valid digital motor outcome for disease progression in MS may help to reduce observation times in clinical trials and add confidence to the detection of progression events in MS. [ABSTRACT FROM AUTHOR]

Simple Summary: This study developed an interpretable scoring system using artificial intelligence and bladder tissue images. It identified two distinct risk groups with different outcomes in high-grade bladder cancer. The scoring system was associated with various molecular features and gene mutations. This system can save shared clinical decision making and cost by identifying patients who need further molecular testing. Muscle-invasive bladder cancer (MIBC) is a highly heterogeneous and costly disease with significant morbidity and mortality. Understanding tumor histopathology leads to tailored therapies and improved outcomes. In this study, we employed a weakly supervised learning and neural architecture search to develop a data-driven scoring system. This system aimed to capture prognostic histopathological patterns observed in H&E-stained whole-slide images. We constructed and externally validated our scoring system using multi-institutional datasets with 653 whole-slide images. Additionally, we explored the association between our scoring system, seven histopathological features, and 126 molecular signatures. Through our analysis, we identified two distinct risk groups with varying prognoses, reflecting inherent differences in histopathological and molecular subtypes. The adjusted hazard ratio for overall mortality was 1.46 (95% CI 1.05–2.02; z: 2.23; p = 0.03), thus identifying two prognostic subgroups in high-grade MIBC. Furthermore, we observed an association between our novel digital biomarker and the squamous phenotype, subtypes of miRNA, mRNA, long non-coding RNA, DNA hypomethylation, and several gene mutations, including FGFR3 in MIBC. Our findings underscore the risk of confounding bias when reducing the complex biological and clinical behavior of tumors to a single mutation. Histopathological changes can only be fully captured through comprehensive multi-omics profiles. The introduction of our scoring system has the potential to enhance daily clinical decision making for MIBC. It facilitates shared decision making by offering comprehensive and precise risk stratification, treatment planning, and cost-effective preselection for expensive molecular characterization. [ABSTRACT FROM AUTHOR]

A growing body of evidence supports the link between eye movement anomalies and brain health. Indeed, the oculomotor system is composed of a diverse network of cortical and subcortical structures and circuits that are susceptible to a variety of degenerative processes. Here we show preliminary findings from the baseline measurements of an ongoing longitudinal cohort study in MS participants, designed to determine if disease and cognitive status can be estimated and tracked with high accuracy based on eye movement parameters alone. Using a novel gaze-tracking technology that can reliably and accurately track eye movements with good precision without the need for infrared cameras, using only an iPad Pro embedded camera, we show in this cross-sectional study that several eye movement parameters significantly correlated with clinical outcome measures of interest. Eye movement parameters were extracted from fixation, pro-saccade, anti-saccade, and smooth pursuit visual tasks, whereas the clinical outcome measures were the scores of several disease assessment tools and standard cognitive tests such as the Expanded Disability Status Scale (EDSS), Brief International Cognitive Assessment for MS (BICAMS), the Multiple Sclerosis Functional Composite (MSFC) and the Symbol Digit Modalities Test (SDMT). Furthermore, partial least squares regression analyses show that a small set of oculomotor parameters can explain up to 84% of the variance of the clinical outcome measures. Taken together, these findings not only replicate previously known associations between eye movement parameters and clinical scores, this time using a novel mobile-based technology, but also the notion that interrogating the oculomotor system with a novel eye-tracking technology can inform us of disease severity, as well as the cognitive status of MS participants. [ABSTRACT FROM AUTHOR]

A growing body of evidence supports the link between eye movement anomalies and brain health. Indeed, the oculomotor system is composed of a diverse network of cortical and subcortical structures and circuits that are susceptible to a variety of degenerative processes. Here we show preliminary findings from the baseline measurements of an ongoing longitudinal cohort study in MS participants, designed to determine if disease and cognitive status can be estimated and tracked with high accuracy based on eye movement parameters alone. Using a novel gaze-tracking technology that can reliably and accurately track eye movements with good precision without the need for infrared cameras, using only an iPad Pro embedded camera, we show in this cross-sectional study that several eye movement parameters significantly correlated with clinical outcome measures of interest. Eye movement parameters were extracted from fixation, pro-saccade, anti-saccade, and smooth pursuit visual tasks, whereas the clinical outcome measures were the scores of several disease assessment tools and standard cognitive tests such as the Expanded Disability Status Scale (EDSS), Brief International Cognitive Assessment for MS (BICAMS), the Multiple Sclerosis Functional Composite (MSFC) and the Symbol Digit Modalities Test (SDMT). Furthermore, partial least squares regression analyses show that a small set of oculomotor parameters can explain up to 84% of the variance of the clinical outcome measures. Taken together, these findings not only replicate previously known associations between eye movement parameters and clinical scores, this time using a novel mobile-based technology, but also the notion that interrogating the oculomotor system with a novel eye-tracking technology can inform us of disease severity, as well as the cognitive status of MS participants.

Facial expressions play a crucial role in the diagnosis of mental illnesses characterized by mood changes. The Facial Action Coding System (FACS) is a comprehensive framework that systematically categorizes and captures even subtle changes in facial appearance, enabling the examination of emotional expressions. In this study, we investigated the association between facial expressions and depressive symptoms in a sample of 59 older adults without cognitive impairment. Utilizing the FACS and the Korean version of the Beck Depression Inventory-II, we analyzed both "posed" and "spontaneous" facial expressions across six basic emotions: happiness, sadness, fear, anger, surprise, and disgust. Through principal component analysis, we summarized 17 action units across these emotion conditions. Subsequently, multiple regression analyses were performed to identify specific facial expression features that explain depressive symptoms. Our findings revealed several distinct features of posed and spontaneous facial expressions. Specifically, among older adults with higher depressive symptoms, a posed face exhibited a downward and inward pull at the corner of the mouth, indicative of sadness. In contrast, a spontaneous face displayed raised and narrowed inner brows, which was associated with more severe depressive symptoms in older adults. These findings suggest that facial expressions can provide valuable insights into assessing depressive symptoms in older adults. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Joint acoustic emissions from knees have been evaluated as a convenient, non-invasive digital biomarker of inflammatory knee involvement in a small cohort of children with Juvenile Idiopathic Arthritis (JIA). The objective of the present study was to validate this in a larger cohort. Findings: A total of 116 subjects (86 JIA and 30 healthy controls) participated in this study. Of the 86 subjects with JIA, 43 subjects had active knee involvement at the time of study. Joint acoustic emissions were bilaterally recorded, and corresponding signal features were used to train a machine learning algorithm (XGBoost) to classify JIA and healthy knees. All active JIA knees and 80% of the controls were used as training data set, while the remaining knees were used as testing data set. Leave-one-leg-out cross-validation was used for validation on the training data set. Validation on the training and testing set of the classifier resulted in an accuracy of 81.1% and 87.7% respectively. Sensitivity / specificity for the training and testing validation was 88.6% / 72.3% and 88.1% / 83.3%, respectively. The area under the curve of the receiver operating characteristic curve was 0.81 for the developed classifier. The distributions of the joint scores of the active and inactive knees were significantly different. Conclusion: Joint acoustic emissions can serve as an inexpensive and easy-to-use digital biomarker to distinguish JIA from healthy controls. Utilizing serial joint acoustic emission recordings can potentially help monitor disease activity in JIA affected joints to enable timely changes in therapy. [ABSTRACT FROM AUTHOR]

Background: Depression remains a global health problem, with its prevalence rising worldwide. Digital biomarkers are increasingly investigated to initiate and tailor scalable interventions targeting depression. Due to the steady influx of new cases, focusing on treatment alone will not suffice; academics and practitioners need to focus on the prevention of depression (i.e., addressing subclinical depression). Aim: With our study, we aim to (i) develop digital biomarkers for subclinical symptoms of depression, (ii) develop digital biomarkers for severity of subclinical depression, and (iii) investigate the efficacy of a digital intervention in reducing symptoms and severity of subclinical depression. Method: Participants will interact with the digital intervention BEDDA consisting of a scripted conversational agent, the slow-paced breathing training Breeze, and actionable advice for different symptoms. The intervention comprises 30 daily interactions to be completed in less than 45 days. We will collect self-reports regarding mood, agitation, anhedonia (proximal outcomes; first objective), self-reports regarding depression severity (primary distal outcome; second and third objective), anxiety severity (secondary distal outcome; second and third objective), stress (secondary distal outcome; second and third objective), voice, and breathing. A subsample of 25% of the participants will use smartwatches to record physiological data (e.g., heart-rate, heart-rate variability), which will be used in the analyses for all three objectives. Discussion: Digital voice- and breathing-based biomarkers may improve diagnosis, prevention, and care by enabling an unobtrusive and either complementary or alternative assessment to self-reports. Furthermore, our results may advance our understanding of underlying psychophysiological changes in subclinical depression. Our study also provides further evidence regarding the efficacy of standalone digital health interventions to prevent depression. Trial registration Ethics approval was provided by the Ethics Commission of ETH Zurich (EK-2022-N-31) and the study was registered in the ISRCTN registry (Reference number: ISRCTN38841716, Submission date: 20/08/2022). [ABSTRACT FROM AUTHOR]

We propose a digital biomarker related to muscle strength and muscle endurance (DB/MS and DB/ME) for the diagnosis of muscle disorders based on a multi-layer perceptron (MLP) using stimulated muscle contraction. When muscle mass is reduced in patients with muscle-related diseases or disorders, measurement of DBs that are related to muscle strength and endurance is needed to suitably recover damaged muscles through rehabilitation training. Furthermore, it is difficult to measure DBs using traditional methods at home without an expert; moreover, the measuring equipment is expensive. Additionally, because traditional measurements depend on the subjectb’s volition, we propose a DB measurement technique that is unaffected by the subjectb’s volition. To achieve this, we employed an impact response signal (IRS) based on multi-frequency electrical stimulation (MFES) using an electromyography sensor. The feature vector was then extracted using the signal. Because the IRS is obtained from stimulated muscle contraction, which is caused by electrical stimulation, it provides biomedical information about the muscle. Finally, to estimate the strength and endurance of the muscle, the feature vector was passed through the DB estimation model learned through the MLP. To evaluate the performance of the DB measurement algorithm, we collected the MFES-based IRS database for 50 subjects and tested the model with quantitative evaluation methods using the reference for the DB. The reference was measured using torque equipment. The results were compared with the reference, indicating that it is possible to check for muscle disorders which cause decreased physical performance using the proposed algorithm.

Introduction: Progressive cognitive decline is the cardinal behavioral symptom in most dementia-causing diseases such as Alzheimer’s disease. While most well-established measures for cognition might not fit tomorrow’s decentralized remote clinical trials, digital cognitive assessments will gain importance. We present the evaluation of a novel digital speech biomarker for cognition (SB-C) following the Digital Medicine Society’s V3 framework: verification, analytical validation, and clinical validation. Methods: Evaluation was done in two independent clinical samples: the Dutch DeepSpA (N = 69 subjective cognitive impairment [SCI], N = 52 mild cognitive impairment [MCI], and N = 13 dementia) and the Scottish SPeAk datasets (N = 25, healthy controls). For validation, two anchor scores were used: the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating (CDR) scale. Results: Verification: The SB-C could be reliably extracted for both languages using an automatic speech processing pipeline. Analytical Validation: In both languages, the SB-C was strongly correlated with MMSE scores. Clinical Validation: The SB-C significantly differed between clinical groups (including MCI and dementia), was strongly correlated with the CDR, and could track the clinically meaningful decline. Conclusion: Our results suggest that the ki:e SB-C is an objective, scalable, and reliable indicator of cognitive decline, fit for purpose as a remote assessment in clinical early dementia trials.

INTRODUCTION: Deep brain stimulation (DBS) is widely used to alleviate symptoms of movement disorders. During intraoperative stimulation the influence of active or passive movements on the neuronal activity is often evaluated but the evaluation remains mostly subjective. The objective of this paper is to investigate the potential of a previously developed Weighted-frequency Fourier Linear combiner and Kalman filter-based recursive algorithm to identify tremor phases and types. METHODS: Ten accelerometer recordings from eight patients were acquired during DBS from which 186 phases were manually annotated into: rest, postural and kinetic phase without tremor, and rest, postural and kinetic phase with tremor. The method first estimates the instantaneous tremor frequency and then decomposes the motion signal into voluntary and tremorous parts. The tremorous part is used to quantify tremor and the voluntary part to differentiate rest, postural and kinetic phases. RESULTS: Instantaneous tremor frequency and amplitude are successfully tracked online. The overall accuracy for tremorous phases only is 89.1% and 76.3% when also non-tremorous phases are considered. Two main misclassification cases are identified and further discussed. CONCLUSION: The results demonstrate the potential of the developed algorithm as an online tremorous movement classifier. It would benefit from a more advanced tremor detector but nevertheless the obtained digital biomarkers offer an evidence-based analysis and could optimize the efficacy of DBS treatment.

A considerable number of inpatients, including patients who have undergone spinal surgery, live in bed for a certain period of time, which exposes them to various complications caused by muscle weakness. To mitigate this risk, medical staff often educate patients about the importance of exercise and posture changes. However, it is challenging to objectively determine whether patients are performing exercises correctly. In this work, we design and implement a deep learning model that recognizes bedside lower extremity exercises and postures using a single digital biomarker. Data from 20 healthy adults were collected to evaluate the performance and utility of the model in various environments. The model achieved a recognition accuracy of 95.42% for 8 postures and 14 exercises. The findings of this study can contribute to faster recovery, improved exercise effects, and prevention of complications by assisting medical staff in monitoring the rehabilitation exercises and postures of bedside patients. [ABSTRACT FROM AUTHOR]

Background: Objective measures for screening, prioritizing, and planning care for frail individuals are essential for appropriate aged care provision. This study evaluates metrics derived from actigraphy measures (captured by wrist accelerometer) as a digital biomarker to identify frail individuals at risk of adverse outcomes, including death, hospitalization, and cognitive decline. Methods: This was a secondary study using data from a randomized controlled trial assessing the effectiveness of an ongoing pharmacist service in residential aged care facilities. Three metrics are studied and compared: the Frailty Index, the daily time spent in light time activity, and the temporal correlation of the actigraphy signal, measured by detrended fluctuation analysis. The association between actigraphy‐derived metrics at baseline and adverse events within 12 months (death, cognitive decline, and hospitalizations) was assessed using logistic regression. Results: Actigraphy records were available for 213 participants living in aged‐care, median age of 85 years. Individuals with higher temporal correlation (activity is less random) were at lower risk of death (Standardized OR: 0.49; 95% CI 0.34, 0.7, p < 0.001) and hospitalization (Standardized OR: 0.57; 95% CI 0.42, 0.77, p < 0.001) in 12 months, but there was no difference in cognitive decline (Standardized OR: 1; 95% CI 0.74, 1.35, p = 0.98). The predictive model that included temporal correlation had an area under the curve of 0.70 (CI 0.60–0.80) for death and 0.64 (CI 0.54–0.72) for hospitalization. Conclusion: Temporal correlation of the actigraphy signal from aged care residents was strongly associated with death and hospitalization, but not cognitive decline. Digital biomarkers may have a place as an objective, accurate, and low‐cost patient metric to support risk stratification and clinical planning. [ABSTRACT FROM AUTHOR]

Myotonic dystrophy type 1 (DM1) is a dominantly inherited neuromuscular disease caused by the abnormal expansion of CTG-repeats in the 30- untranslated region of the Dystrophia Myotonica Protein Kinase (DMPK) gene, characterized by multisystemic symptoms including muscle weakness, myotonia, cardio-respiratory problems, hypersomnia, cognitive dysfunction and behavioral abnormalities. Sleep-related disturbances are among the most reported symptoms that negatively affect the quality of life of patients and that are present in early and adult-onset forms of the disease. DMSXL mice carry a mutated human DMPK transgene containing >1,000 CTGrepeats, modeling an early onset, severe form of DM1. They exhibit a pathologic neuromuscular phenotype and also synaptic dysfunction resulting in neurological and behavioral deficits similar to those observed in patients. Additionally, they are underweight with a very high mortality within the first month after birth presenting several welfare issues. To specifically explore sleep/rest-related behaviors of this frail DM1 mouse model we used an automated home cage-based system that allows 24/7 monitoring of their activity non-invasively. We tested male and female DMSXL mice and their wild-type (WT) littermates in Digital Ventilated Cages (DVCR) assessing activity and rest parameters on day and night for 5 weeks. We demonstrated that DMSXL mice show reduced activity and regularity disruption index (RDI), higher percentage of zero activity per each hour and longer periods of rest during the active phase compared to WT. This novel rest-related phenotype in DMSXL mice, assessed unobtrusively, could be valuable to further explore mechanisms and potential therapeutic interventions to alleviate the very common symptom of excessive daytime sleepiness in DM1 patients. [ABSTRACT FROM AUTHOR]

Abstract Background The currently available immunotherapies already changed the strategy how many cancers are treated from first to last line. Understanding even the most complex heterogeneity in tumor tissue and mapping the spatial cartography of the tumor immunity allows the best and optimized selection of immune modulating agents to (re‐)activate the patient's immune system and direct it against the individual cancer in the most effective way. Recent Findings Primary cancer and metastases maintain a high degree of plasticity to escape any immune surveillance and continue to evolve depending on many intrinsic and extrinsic factors In the field of immune‐oncology (IO) immune modulating agents are recognized as practice changing therapeutic modalities. Recent studies have shown that an optimal and lasting efficacy of IO therapeutics depends on the understanding of the spatial communication network and functional context of immune and cancer cells within the tumor microenvironment. Artificial intelligence (AI) provides an insight into the immune‐cancer‐network through the visualization of very complex tumor and immune interactions in cancer tissue specimens and allows the computer‐assisted development and clinical validation of such digital biomarker. Conclusions The successful implementation of AI‐supported digital biomarker solutions guides the clinical selection of effective immune therapeutics based on the retrieval and visualization of spatial and contextual information from cancer tissue images and standardized data. As such, computational pathology (CP) turns into “precision pathology” delivering individual therapy response prediction. Precision Pathology does not only include digital and computational solutions but also high levels of standardized processes in the routine histopathology workflow and the use of mathematical tools to support clinical and diagnostic decisions as the basic principle of a “precision oncology”.

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.

Digital biomarkers such as wearables are of increasing interest in monitoring rheumatic diseases, but they usually lack disease specificity. In this study, we apply convolutional neural networks (CNN) to real-world hand photographs in order to automatically detect, extract, and analyse dorsal finger fold lines as a correlate of proximal interphalangeal (PIP) joint swelling in patients with rheumatoid arthritis (RA). Hand photographs of RA patients were taken by a smartphone camera in a standardized manner. Overall, 190 PIP joints were categorized as either swollen or not swollen based on clinical judgement and ultrasound. Images were automatically preprocessed by cropping PIP joints and extracting dorsal finger folds. Subsequently, metrical analysis of dorsal finger folds was performed, and a CNN was trained to classify the dorsal finger lines into swollen versus non-swollen joints. Representative horizontal finger folds were also quantified in a subset of patients before and after resolution of PIP swelling and in patients with disease flares. In swollen joints, the number of automatically extracted deep skinfold imprints was significantly reduced compared to non-swollen joints (1.3, SD 0.8 vs. 3.3, SD 0.49, p < 0.01). The joint diameter/deep skinfold length ratio was significantly higher in swollen (4.1, SD 1.4) versus non-swollen joints (2.1, SD 0.6, p < 0.01). The CNN model successfully differentiated swollen from non-swollen joints based on finger fold patterns with a validation accuracy of 0.84, a sensitivity of 88%, and a specificity of 75%. A heatmap of the original images obtained by an extraction algorithm confirmed finger folds as the region of interest for correct classification. After significant response to disease-modifying antirheumatic drug ± corticosteroid therapy, longitudinal metrical analysis of eight representative deep finger folds showed a decrease in the mean diameter/finger fold length (finger fold index, FFI) from 3.03 (SD 0.68) to 2.08 (SD 0.57). Conversely, the FFI increased in patients with disease flares. In conclusion, automated preprocessing and the application of CNN algorithms in combination with longitudinal metrical analysis of dorsal finger fold patterns extracted from real-world hand photos might serve as a digital biomarker in RA.

Myotonic dystrophy type 1 (DM1) is a dominantly inherited neuromuscular disease caused by the abnormal expansion of CTG-repeats in the 3′-untranslated region of the Dystrophia Myotonica Protein Kinase (DMPK) gene, characterized by multisystemic symptoms including muscle weakness, myotonia, cardio-respiratory problems, hypersomnia, cognitive dysfunction and behavioral abnormalities. Sleep-related disturbances are among the most reported symptoms that negatively affect the quality of life of patients and that are present in early and adult-onset forms of the disease. DMSXL mice carry a mutated human DMPK transgene containing >1,000 CTGrepeats, modeling an early onset, severe form of DM1. They exhibit a pathologic neuromuscular phenotype and also synaptic dysfunction resulting in neurological and behavioral deficits similar to those observed in patients. Additionally, they are underweight with a very high mortality within the first month after birth presenting several welfare issues. To specifically explore sleep/rest-related behaviors of this frail DM1 mouse model we used an automated home cage-based system that allows 24/7 monitoring of their activity non-invasively. We tested male and female DMSXL mice and their wild-type (WT) littermates in Digital Ventilated Cages (DVCR) assessing activity and rest parameters on day and night for 5 weeks. We demonstrated that DMSXL mice show reduced activity and regularity disruption index (RDI), higher percentage of zero activity per each hour and longer periods of rest during the active phase compared to WT. This novel rest-related phenotype in DMSXL mice, assessed unobtrusively, could be valuable to further explore mechanisms and potential therapeutic interventions to alleviate the very common symptom of excessive daytime sleepiness in DM1 patients.

Background: Frailty caused by deterioration in multiple physiological systems has led to a significant increase in adverse events such as falls, disability, and death in frail older people. Similar to frailty, sarcopenia, defined as loss of skeletal muscle mass and strength, is tightly related to mobility disorders, falls, and fractures. With population aging, co-occurrences of frailty and sarcopenia are increasingly common in the elderly, which are more deleterious for the health and independence of older adults. But the high similarity and overlap between the frailty and sarcopenia increase the difficulty of early recognition of frailty with sarcopenia. The purpose of this study is to use detailed gait assessment to determine the more convenient and sensitive digital biomarker of sarcopenia in the frail population. Methods: Ninety-five frail elderly people (age = 86 ± 7 years old, BMI, and body mass index = 23.21 ± 3.40 kg/m2) were screened out by the evaluation of Fried criteria. Then, 41 participants (46%) were identified with sarcopenia, and 51 participants (54%) were identified without sarcopenia. Using a validated wearable platform, participants' gait performance was evaluated under single-task and dual-task (DT). Participants walked back and forth on the 7-m-long trail for 2 min at a habitual speed. Gait parameters of interest include cadence, gait cycle duration, step duration, gait speed, variability of gait speed, stride length, turn duration, and steps in turn. Results: Our results showed that compared with the frail elderly without sarcopenia, the gait performance of the sarcopenic group in single-task and dual-task walking was worse. Overall, the parameters with high performance were the gait speed (DT) (OR 0.914; 95% CI 0.868-0.962) and turn duration (DT) (OR 7.907; 95% CI 2.401-26.039) under dual-task conditions, and the AUC in distinguishing between frail older adults with and without sarcopenia was 0.688 and 0.736, respectively. Turn duration in dual-task testing had larger observed effect than gait speed to identify sarcopenia in the frail population, this result remained significant even after controlling for potential confounds. When gait speed (DT) and turn duration (DT) were combined in the model, AUC increased from 0.688 to 0.763. Conclusion: This study shows that gait speed and turn duration under dual-task are good predictors of sarcopenia in frail elderly, and turn duration (DT) has a better predictive ability. The gait speed (DT) combined with turn duration (DT) is a potential gait digital Biomarker of sarcopenia in the frail elderly. Dual-task gait assessment and detailed gait indexes provide important value for identification of sarcopenia in frail elderly people. [ABSTRACT FROM AUTHOR]

As the population ages with advances in technology, health monitoring through early detection is increasing. There are several approaches to the analysis, monitoring and management of human activity in health care. Gaining proficiency and knowledge from complicated, multidimensional, and dissimilar biomedical data remains a key challenge for healthcare innovation. Different kinds of data have been appearing in modern biomedical research. Recent advances in deep learning technology provide a powerful new paradigm for deriving end to end learning models out of complicated data. Speech technology is anticipated to lead to changes in health care, transforming traditional treatment modalities. The present language interface is being transformed into a next generation medical companion who interacts among people and monitors their health, according to researchers. New genuine mechanisms are required to reinforce the medical prognosis of the diseases and lessen the hour spent in the diagnostic process. This allows speech to be used as a future digital biomarker for disease detection, with benefits such as improved access to diagnosis, symptom monitoring, cost savings and improved diagnostic accuracy. The motive of this paper is to conduct an extensive survey related to speech technology in healthcare. Because of its societal implications, research in this subject has a broad reach.

Background: Early differential diagnosis of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) is important for treatment and disease management, but it remains challenging. Although computer-based drawing analysis may help differentiate AD and DLB, it has not been studied.Objective: We aimed to identify the differences in features characterizing the drawing process between AD, DLB, and cognitively normal (CN) individuals, and to evaluate the validity of using these features to identify and differentiate AD and DLB.Methods: We collected drawing data with a digitizing tablet and pen from 123 community-dwelling older adults in three clinical diagnostic groups of mild cognitive impairment or dementia due to AD (n = 47) or Lewy body disease (LBD; n = 27), and CN (n = 49), matched for their age, sex, and years of education. We then investigated drawing features in terms of the drawing speed, pressure, and pauses.Results: Reduced speed and reduced smoothness in speed and pressure were observed particularly in the LBD group, while increased pauses and total durations were observed in both the AD and LBD groups. Machine-learning models using these features achieved an area under the receiver operating characteristic curve (AUC) of 0.80 for AD versus CN, 0.88 for LBD versus CN, and 0.77 for AD versus LBD.Conclusion: Our results indicate how different types of drawing features were particularly discriminative between the diagnostic groups, and how the combination of these features can facilitate the identification and differentiation of AD and DLB. [ABSTRACT FROM AUTHOR]

The study presents a novel approach to objectively assessing the upper-extremity motor symptoms in spinocerebellar ataxia (SCA) using data collected via a wearable sensor worn on the patient's wrist during upper-extremity tasks associated with the Assessment and Rating of Ataxia (SARA). First, we developed an algorithm for detecting/extracting the cycles of the finger-to-nose test (FNT). We extracted multiple features from the detected cycles and identified features and parameters correlated with the SARA scores. Additionally, we developed models to predict the severity of symptoms based on the FNT. The proposed technique was validated on a dataset comprising the seventeen (n = 17) participants' assessments. The cycle detection technique showed an accuracy of 97.6% in a Bland–Altman analysis and a 94% accuracy (F1-score of 0.93) in predicting the severity of the FNT. Furthermore, the dependency of the upper-extremity tests was investigated through statistical analysis, and the results confirm dependency and potential redundancies in the upper-extremity SARA assessments. Our findings pave the way to enhance the utility of objective measures of SCA assessments. The proposed wearable-based platform has the potential to eliminate subjectivity and inter-rater variabilities in assessing ataxia. [ABSTRACT FROM AUTHOR]

Introduction: An estimated 50% of patients with Lewy body dementias (LBD), including Parkinson's disease dementia (PDD) and Dementia with Lewy bodies (DLB), have co-occurring Alzheimer's disease (AD) that is associated with worse prognosis. This study tests an automated analysis of natural speech as an inexpensive, non-invasive screening tool for AD co-pathology in biologically-confirmed cohorts of LBD patients with AD co-pathology (SYN + AD) and without (SYN-AD).Methods: We analyzed lexical-semantic and acoustic features of picture descriptions using automated methods in 22 SYN + AD and 38 SYN-AD patients stratified using AD CSF biomarkers or autopsy diagnosis. Speech markers of AD co-pathology were identified using best subset regression, and their diagnostic discrimination was tested using receiver operating characteristic. ANCOVAs compared measures between groups covarying for demographic differences and cognitive disease severity. We tested relations with CSF tau levels, and compared speech measures between PDD and DLB clinical disorders in the same cohort.Results: Age of acquisition of nouns (p = 0.034, |d| = 0.77) and lexical density (p = 0.0064, |d| = 0.72) were reduced in SYN + AD, and together showed excellent discrimination for SYN + AD vs. SYN-AD (95% sensitivity, 66% specificity; AUC = 0.82). Lower lexical density was related to higher CSF t-Tau levels (R = -0.41, p = 0.0021). Clinically-diagnosed PDD vs. DLB did not differ on any speech features.Conclusion: AD co-pathology may result in a deviant natural speech profile in LBD characterized by specific lexical-semantic impairments, not detectable by clinical disorder diagnosis. Our study demonstrates the potential of automated digital speech analytics as a screening tool for underlying AD co-pathology in LBD. [ABSTRACT FROM AUTHOR]

Background: Conventional neuropsychological screening tools for mild cognitive impairment (MCI) face challenges in terms of accuracy and practicality. Digital health solutions, such as unobtrusively capturing smartphone interaction data, offer a promising alternative. However, the potential of digital biomarkers as a surrogate for MCI screening remains unclear, with few comparisons between smartphone interactions and existing screening tools., Objective: This study aimed to investigate the effectiveness of smartphone-derived keystroke dynamics, captured via the Neurokeys keyboard app, in distinguishing patients with MCI from healthy controls (HCs). This study also compared the discriminant performance of these digital biomarkers against the Korean version of the Montreal Cognitive Assessment (MoCA-K), which is widely used for MCI detection in clinical settings., Methods: A total of 64 HCs and 47 patients with MCI were recruited. Over a 1-month period, participants generated 3530 typing sessions, with 2740 (77.6%) analyzed for this study. Keystroke metrics, including hold time and flight time, were extracted. Receiver operating characteristics analysis was used to assess the sensitivity and specificity of keystroke dynamics in discriminating between HCs and patients with MCI. This study also explored the correlation between keystroke dynamics and MoCA-K scores., Results: Patients with MCI had significantly higher keystroke latency than HCs (P<.001). In particular, latency between key presses resulted in the highest sensitivity (97.9%) and specificity (96.9%). In addition, keystroke dynamics were significantly correlated with the MoCA-K (hold time: r=-.468; P<.001; flight time: r=-.497; P<.001), further supporting the validity of these digital biomarkers., Conclusions: These findings highlight the potential of smartphone-derived keystroke dynamics as an effective and ecologically valid tool for screening MCI. With higher sensitivity and specificity than the MoCA-K, particularly in measuring flight time, keystroke dynamics can serve as a noninvasive, scalable, and continuous method for early cognitive impairment detection. This novel approach could revolutionize MCI screening, offering a practical alternative to traditional tools in everyday settings., Trial Registration: Thai Clinical Trials Registry TCTR20220415002; https://www.thaiclinicaltrials.org/show/TCTR20220415002., (©Jin-Hyuck Park. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.10.2024.)

Speech features have been investigated as novel digital biomarkers for many psychiatric and neurocognitive diseases. Microphones are the most used devices for speech recording but inevitably suffering from several disadvantages such as privacy leakage and environmental noises, limiting their clinical applications particularly for long-term ambulatory monitoring. The aim of the present study is therefore to explore the feasibility of extracting speech features from the acceleration recorded on the sternum. Ten healthy subjects volunteered in our study. Two speech tasks, that is, repeating one sentence 20 times and reading 20 different sentences, were performed by each subject, with each task repeated eight times under different speech rate and loudness. Voice signals and speech-caused chest vibrations were simultaneously recorded by a microphone and an accelerometer placed on the sternum. Forty-two acoustic features and six time-related prosodic features were extracted from both signals using a standard toolbox, and then compared by a linear fit and correlation analysis. Good agreement between the acceleration features and microphone features is observed in all six time-related prosodic features for both tasks, but only in 19 and 17 acoustic features for task 1 and 2, respectively, with most of them loudness- or pitch-related. Our results suggest the sternum acceleration to track time-related speech prosody, loudness, and pitch very well, demonstrating the feasibility of deriving digital biomarkers from the acceleration signal for diseases strongly related to time-related prosodic and loudness features., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)