Summary
Objective
Responsive neurostimulation decreases the frequency of disabling seizures when used as an adjunctive therapy in patients with medically refractory partial‐onset seizures. The effect ...of long‐term responsive neurostimulation on neuropsychological performance has not yet been established.
Methods
Neuropsychological data were collected from subjects participating in the open‐label arm of a randomized controlled trial of responsive neurostimulation with the RNS® System. Primary cognitive outcomes were the Boston Naming Test (BNT) and Rey Auditory Verbal Learning (AVLT) test. Neuropsychological performance was evaluated at baseline and again following 1 and 2 years of RNS System treatment. Follow‐up analyses were conducted in patients with seizure onset restricted to either the mesial temporal lobe or neocortex.
Results
No significant cognitive declines were observed for any neuropsychological measure through 2 years. When examined as a function of seizure onset region, a double dissociation was found, with significant improvement in naming across all patients (p < 0.0001), and for patients with neocortical seizure onsets (p < 0.0001) but not in patients with mesial temporal lobe (MTL) seizure onsets (p = 0.679). In contrast, a significant improvement in verbal learning was observed across all patients (p = 0.03), and for patients with MTL seizure onsets (p = 0.005) but not for patients with neocortical onsets (p = 0.403).
Significance
Treatment with the RNS System is not associated with cognitive decline when tested through 2 years. In fact, there were small but significant beneficial treatment effects on naming in patients with neocortical onsets and modest improvements in verbal learning for patients with seizure onsets in MTL structures. These results suggest that there are modest cognitive improvements in some domains that vary as a function of the region from which seizures arise.
Continuous, objective monitoring of motor signs and symptoms may help improve tracking of disease progression and treatment response in Parkinson's disease (PD). This study assessed the analytical ...and clinical validity of multi-sensor smartwatch measurements in hospitalized and home-based settings (96 patients with PD; mean wear time 19 h/day) using a twice-daily virtual motor examination (VME) at times representing medication OFF/ON states. Digital measurement performance was better during inpatient clinical assessments for composite V-scores than single-sensor-derived features for bradykinesia (Spearman |r|= 0.63, reliability = 0.72), tremor (|r|= 0.41, reliability = 0.65), and overall motor features (|r|= 0.70, reliability = 0.67). Composite levodopa effect sizes during hospitalization were 0.51-1.44 for clinical assessments and 0.56-1.37 for VMEs. Reliability of digital measurements during home-based VMEs was 0.62-0.80 for scores derived from weekly averages and 0.24-0.66 for daily measurements. These results show that unsupervised digital measurements of motor features with wrist-worn sensors are sensitive to medication state and are reliable in naturalistic settings.Trial Registration: Japan Pharmaceutical Information Center Clinical Trials Information (JAPIC-CTI): JapicCTI-194825; Registered June 25, 2019.
Sensor-based remote monitoring could help better track Parkinson's disease (PD) progression, and measure patients' response to putative disease-modifying therapeutic interventions. To be useful, the ...remotely-collected measurements should be valid, reliable, and sensitive to change, and people with PD must engage with the technology. We developed a smartwatch-based active assessment that enables unsupervised measurement of motor signs of PD. Participants with early-stage PD (N = 388, 64% men, average age 63) wore a smartwatch for a median of 390 days. Participants performed unsupervised motor tasks both in-clinic (once) and remotely (twice weekly for one year). Dropout rate was 5.4%. Median wear-time was 21.1 h/day, and 59% of per-protocol remote assessments were completed. Analytical validation was established for in-clinic measurements, which showed moderate-to-strong correlations with consensus MDS-UPDRS Part III ratings for rest tremor (⍴ = 0.70), bradykinesia (⍴ = -0.62), and gait (⍴ = -0.46). Test-retest reliability of remote measurements, aggregated monthly, was good-to-excellent (ICC = 0.75-0.96). Remote measurements were sensitive to the known effects of dopaminergic medication (on vs off Cohen's d = 0.19-0.54). Of note, in-clinic assessments often did not reflect the patients' typical status at home. This demonstrates the feasibility of smartwatch-based unsupervised active tests, and establishes the analytical validity of associated digital measurements. Weekly measurements provide a real-life distribution of disease severity, as it fluctuates longitudinally. Sensitivity to medication-induced change and improved reliability imply that these methods could help reduce sample sizes needed to demonstrate a response to therapeutic interventions or disease progression.
Diabetes is a metabolic disorder often diagnosed late and requires continuous monitoring of blood glucose. We introduce GlucoBreath, a user-centric, cost-effective, and portable pre-diagnostic ...solution to address this global challenge. GlucoBreath addresses the urgent need for an accessible and non-intrusive diabetes detection device, offering affordability, mobility, and comfortable non-invasive diabetes testing, especially among economically weaker sections of society. GlucoBreath comprises (i) a non-intrusive multi-sensor Internet of Things device comprising multiple sensors detecting volatile organic compounds in breath, (ii) BreathProfiles dataset encompasses information from 492 patients, which includes demographic details, physiological measurements, and sensor readings derived by analysing breath samples with our device, (iii) an innovative Machine Learning-based diabetes prediction system trained on the BreathProfiles dataset, and (iv) a user-friendly web interface for seamless device interaction and viewing diabetes reports. Given a person's breath sample, demographic data, and body vitals as input, GlucoBreath predicts (a) whether the person has diabetes. (b) If the person has diabetes, then the blood glucose level (BGL) of the person is moderate or high. GlucoBreath's groundbreaking approach supersedes current methods, achieving an impressive mean accuracy of 98.4% using a Logistic Regression-AdaBoost stack-metamodel, marking a substantial 43.3% improvement over an existing method. Due to its portability, non-intrusiveness, and rapid response, GlucoBreath is a valuable pre-diagnostic tool that can facilitate the early detection of diabetes in many individuals. Furthermore, GlucoBreath's prediction of BGL can help alert people to control their sugar consumption in the case of moderate BGL or see a doctor for high BGL.
Diabetes mellitus is a widespread chronic metabolic disorder that requires regular blood glucose level surveillance. Current invasive techniques, such as finger-prick tests, often result in ...discomfort, leading to infrequent monitoring and potential health complications. The primary objective of this study was to design a novel, portable, non-invasive system for diabetes detection using breath samples, named DiabeticSense, an affordable digital health device for early detection, to encourage immediate intervention. The device employed electrochemical sensors to assess volatile organic compounds in breath samples, whose concentrations differed between diabetic and non-diabetic individuals. The system merged vital signs with sensor voltages obtained by processing breath sample data to predict diabetic conditions. Our research used clinical breath samples from 100 patients at a nationally recognized hospital to form the dataset. Data were then processed using a gradient boosting classifier model, and the performance was cross-validated. The proposed system attained a promising accuracy of 86.6%, indicating an improvement of 20.72% over an existing regression technique. The developed device introduces a non-invasive, cost-effective, and user-friendly solution for preliminary diabetes detection. This has the potential to increase patient adherence to regular monitoring.
Background
Measuring the amount of physical activity and its patterns using wearable sensor technology in real-world settings can provide critical insights into health status.
Objective
This study’s ...aim was to develop and evaluate the analytical validity and transdemographic generalizability of an algorithm that classifies binary ambulatory status (yes or no) on the accelerometer signal from wrist-worn biometric monitoring technology.
Methods
Biometric monitoring technology algorithm validation traditionally relies on large numbers of self-reported labels or on periods of high-resolution monitoring with reference devices. We used both methods on data collected from 2 distinct studies for algorithm training and testing, one with precise ground-truth labels from a reference device (n=75) and the second with participant-reported ground-truth labels from a more diverse, larger sample (n=1691); in total, we collected data from 16.7 million 10-second epochs. We trained a neural network on a combined data set and measured performance in multiple held-out testing data sets, overall and in demographically stratified subgroups.
Results
The algorithm was accurate at classifying ambulatory status in 10-second epochs (area under the curve 0.938; 95% CI 0.921-0.958) and on daily aggregate metrics (daily mean absolute percentage error 18%; 95% CI 15%-20%) without significant performance differences across subgroups.
Conclusions
Our algorithm can accurately classify ambulatory status with a wrist-worn device in real-world settings with generalizability across demographic subgroups. The validated algorithm can effectively quantify users’ walking activity and help researchers gain insights on users’ health status.
Physical capacity and physical activity are important aspects of physical functioning and quality of life in people with a chronic disease such as Parkinson disease (PD) or chronic obstructive ...pulmonary disease (COPD). Both physical capacity and physical activity are currently measured in the clinic using standardized questionnaires and tests, such as the 6-minute walk test (6MWT) and the Timed Up and Go test (TUG). However, relying only on in-clinic tests is suboptimal since they offer limited information on how a person functions in daily life and how functioning fluctuates throughout the day. Wearable sensor technology may offer a solution that enables us to better understand true physical functioning in daily life.
We aim to study whether device-assisted versions of 6MWT and TUG, such that the tests can be performed independently at home using a smartwatch, is a valid and reliable way to measure the performance compared to a supervised, in-clinic test.
This is a decentralized, prospective, observational study including 100 people with PD and 100 with COPD. The inclusion criteria are broad: age ≥18 years, able to walk independently, and no co-occurrence of PD and COPD. Participants are followed for 15 weeks with 4 in-clinic visits, once every 5 weeks. Outcomes include several walking tests, cognitive tests, and disease-specific questionnaires accompanied by data collection using wearable devices (the Verily Study Watch and Modus StepWatch). Additionally, during the last 10 weeks of this study, participants will follow an aerobic exercise training program aiming to increase physical capacity, creating the opportunity to study the responsiveness of the remote 6MWT.
In total, 89 people with PD and 65 people with COPD were included in this study. Data analysis will start in April 2024.
The results of this study will provide information on the measurement properties of the device-assisted 6MWT and TUG in the clinic and at home. When reliable and valid, this can contribute to a better understanding of a person's physical capacity in real life, which makes it possible to personalize treatment options.
ClinicalTrials.gov NCT05756075; https://clinicaltrials.gov/study/NCT05756075.
DERR1-10.2196/55452.
Cyber-Physical Systems (CPSs) are systems in which software and hardware components interact with each other. Understanding the specific nature and root cause of CPS bugs would help to design better ...verification and validation (V&V) techniques for these systems such as domain-specific mutants.
We look at CPS bugs from an open-source perspective, trying to understand what kinds of bugs occur in a set of open-source CPSs belonging to different domains.
We analyze 1151 issues from 14 projects related to drones, automotive, robotics, and Arduino. We apply a hybrid card-sorting procedure to create a taxonomy of CPS bugs, by extending a previously proposed taxonomy specific to the automotive domain.
We provide a taxonomy featuring 22 root causes, grouped into eight high-level categories. Our qualitative and quantitative analyses suggest that 33.4% of the analyzed bugs occurring in CPSs are peculiar to those and, consequently, require specific care during verification and validation activities.
The taxonomy provides an overview of the root causes related to bugs found in open-source CPSs belonging to different domains. Such root causes are related to different components of a CPS, including hardware, interface, configuration, network, data, and application logic.
•We empirically derive a taxonomy of Cyber-Physical Systems (CPSs) Bugs.•The taxonomy extends a previous one from autonomous vehicles.•The taxonomy features 22 root causes for bugs.•Root causes concern hardware, interface, configuration, network, or logic.•33.4% of the analyzed bugs are CPS-specific.
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