Real-world walking behaviors are associated with early-stage heart failure: a Project Baseline Health Study.

BACKGROUND: Data collected via wearables may complement in-clinic assessments to monitor subclinical heart failure (HF). OBJECTIVES: Evaluate the association of sensor-based digital walking measures with HF stage and characterize their correlation with in-clinic measures of physical performance, cardiac function and participant reported outcomes (PROs) in individuals with early HF. METHODS: The analyzable cohort included participants from the Project Baseline Health Study (PBHS) with HF stage 0, A, or B, or adaptive remodeling phenotype (without risk factors but with mild echocardiographic change, termed RF-/ECHO+) (based on available first-visit in-clinic test and echocardiogram results) and with sufficient sensor data. We computed daily values per participant for 18 digital walking measures, comparing HF subgroups vs stage 0 using multinomial logistic regression and characterizing associations with in-clinic measures and PROs with Spearman's correlation coefficients, adjusting all analyses for confounders. RESULTS: In the analyzable cohort (N=1265; 50.6% of the PBHS cohort), one standard deviation decreases in 17/18 walking measures were associated with greater likelihood for stage-B HF (multivariable-adjusted odds ratios [ORs] vs stage 0 ranging from 1.18-2.10), or A (ORs vs stage 0, 1.07-1.45), and lower likelihood for RF-/ECHO+ (ORs vs stage 0, 0.80-0.93). Peak 30-minute pace demonstrated the strongest associations with stage B (OR vs stage 0=2.10; 95% CI:1.74-2.53) and A (OR vs stage 0=1.43; 95% CI:1.23-1.66). Decreases in 13/18 measures were associated with greater likelihood for stage-B HF vs stage A. Strength of correlation with physical performance tests, echocardiographic cardiac-remodeling and dysfunction indices and PROs was greatest in stage B, then A, and lowest for 0. CONCLUSIONS: Digital measures of walking captured by wearable sensors could complement clinic-based testing to identify and monitor pre-symptomatic HF.

Brain-responsive neurostimulation in patients with medically intractable seizures arising from eloquent and other neocortical areas.

OBJECTIVE: Evaluate the seizure-reduction response and safety of brain-responsive stimulation in adults with medically intractable partial-onset seizures of neocortical origin. METHODS: Patients with partial seizures of neocortical origin were identified from prospective clinical trials of a brain-responsive neurostimulator (RNS System, NeuroPace). The seizure reduction over years 2-6 postimplantation was calculated by assessing the seizure frequency compared to a preimplantation baseline. Safety was assessed based on reported adverse events. Additional analyses considered safety and seizure reduction according to lobe and functional area (e.g., eloquent cortex) of seizure onset. RESULTS: There were 126 patients with seizures of neocortical onset. The average follow-up was 6.1 implant years. The median percent seizure reduction was 70% in patients with frontal and parietal seizure onsets, 58% in those with temporal neocortical onsets, and 51% in those with multilobar onsets (last observation carried forward [LOCF] analysis). Twenty-six percent of patients experienced at least one seizure-free period of 6 months or longer and 14% experienced at least one seizure-free period of 1 year or longer. Patients with lesions on magnetic resonance imaging (MRI; 77% reduction, LOCF) and those with normal MRI findings (45% reduction, LOCF) benefitted, although the treatment response was more robust in patients with an MRI lesion (p = 0.02, generalized estimating equation [GEE]). There were no differences in the seizure reduction in patients with and without prior epilepsy surgery or vagus nerve stimulation. Stimulation parameters used for treatment did not cause acute or chronic neurologic deficits, even in eloquent cortical areas. The rates of infection (0.017 per patient implant year) and perioperative hemorrhage (0.8%) were not greater than with other neurostimulation devices. SIGNIFICANCE: Brain-responsive stimulation represents a safe and effective treatment option for patients with medically intractable epilepsy, including adults with seizures of neocortical onset, and those with onsets from eloquent cortex.

Infection and Erosion Rates in Trials of a Cranially Implanted Neurostimulator Do Not Increase with Subsequent Neurostimulator Placements.

BACKGROUND/AIMS: The RNS® System utilizes a cranially implanted neurostimulator attached to leads placed at the seizure focus to provide brain responsive stimulation for the treatment of medically intractable partial onset epilepsy. Infection and erosion rates related to the cranial implant site were assessed overall and by neurostimulator procedure to determine whether rates increased with additional procedures. METHODS: Infection and erosion rates were calculated as (1) chance per neurostimulator procedure, (2) incidence per patient implant year, and (3) rates for initial and each subsequent neurostimulator implant (generalized estimating equation). RESULTS: In 256 patients followed for an average of 7 years, the infection rate was 3.7% per neurostimulator procedure (n = 31/840), and the rate of erosions was 0.8% per neurostimulator procedure (n = 7/840). Rates did not increase with subsequent neurostimulator procedures (p = 0.66, infection; p = 0.70, erosion). A prior infection or erosion at the implant site did not significantly increase the risk at a later procedure (p ≥ 0.05 for all combinations). CONCLUSION: These data indicate that the risk for infection compares favorably to other neurostimulation devices and suggest that rates of infection and erosion do not increase with subsequent neurostimulator replacements.

Differential neuropsychological outcomes following targeted responsive neurostimulation for partial-onset epilepsy.

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.

Quality of life and mood in patients with medically intractable epilepsy treated with targeted responsive neurostimulation.

PURPOSE: The primary efficacy and safety measures from a trial of responsive neurostimulation for focal epilepsy were previously published. In this report, the findings from the same study are presented for quality of life, which was a supportive analysis, and for mood, which was assessed as a secondary safety endpoint. METHODS: The study was a multicenter randomized controlled double-blinded trial of responsive neurostimulation in 191 patients with medically resistant focal epilepsy. During a 4-month postimplant blinded period, patients were randomized to receive responsive stimulation or sham stimulation, after which all patients received responsive neurostimulation in open label to complete 2years. Quality of life (QOL) and mood surveys were administered during the baseline period, at the end of the blinded period, and at year 1 and year 2 of the open label period. RESULTS: The treatment and sham groups did not differ at baseline. Compared with baseline, QOL improved in both groups at the end of the blinded period and also at 1year and 2years, when all patients were treated. At 2years, 44% of patients reported meaningful improvements in QOL, and 16% reported declines. There were no overall adverse changes in mood or in suicidality across the study. Findings were not related to changes in seizures and antiepileptic drugs, and patients with mesial temporal seizure onsets and those with neocortical seizure onsets both experienced improvements in QOL. CONCLUSIONS: Treatment with targeted responsive neurostimulation does not adversely affect QOL or mood and may be associated with improvements in QOL in patients, including those with seizures of either mesial temporal origin or neocortical origin.

Measuring Physical Functioning Using Wearable Sensors in Parkinson Disease and Chronic Obstructive Pulmonary Disease (the Accuracy of Digital Assessment of Performance Trial Study): Protocol for a Prospective Observational Study.

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: In total, 89 people with PD and 65 people with COPD were included in this study. Data analysis will start in April 2024. CONCLUSIONS: 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. TRIAL REGISTRATION: ClinicalTrials.gov NCT05756075; https://clinicaltrials.gov/study/NCT05756075. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/55452.

Accuracy and Reliability of a Suite of Digital Measures of Walking Generated Using a Wrist-Worn Sensor in Healthy Individuals: Performance Characterization Study.

BACKGROUND: Mobility is a meaningful aspect of an individual's health whose quantification can provide clinical insights. Wearable sensor technology can quantify walking behaviors (a key aspect of mobility) through continuous passive monitoring. OBJECTIVE: Our objective was to characterize the analytical performance (accuracy and reliability) of a suite of digital measures of walking behaviors as critical aspects in the practical implementation of digital measures into clinical studies. METHODS: We collected data from a wrist-worn device (the Verily Study Watch) worn for multiple days by a cohort of volunteer participants without a history of gait or walking impairment in a real-world setting. On the basis of step measurements computed in 10-second epochs from sensor data, we generated individual daily aggregates (participant-days) to derive a suite of measures of walking: step count, walking bout duration, number of total walking bouts, number of long walking bouts, number of short walking bouts, peak 30-minute walking cadence, and peak 30-minute walking pace. To characterize the accuracy of the measures, we examined agreement with truth labels generated by a concurrent, ankle-worn, reference device (Modus StepWatch 4) with known low error, calculating the following metrics: intraclass correlation coefficient (ICC), Pearson r coefficient, mean error, and mean absolute error. To characterize the reliability, we developed a novel approach to identify the time to reach a reliable readout (time to reliability) for each measure. This was accomplished by computing mean values over aggregation scopes ranging from 1 to 30 days and analyzing test-retest reliability based on ICCs between adjacent (nonoverlapping) time windows for each measure. RESULTS: In the accuracy characterization, we collected data for a total of 162 participant-days from a testing cohort (n=35 participants; median observation time 5 days). Agreement with the reference device-based readouts in the testing subcohort (n=35) for the 8 measurements under evaluation, as reflected by ICCs, ranged between 0.7 and 0.9; Pearson r values were all greater than 0.75, and all reached statistical significance (P<.001). For the time-to-reliability characterization, we collected data for a total of 15,120 participant-days (overall cohort N=234; median observation time 119 days). All digital measures achieved an ICC between adjacent readouts of >0.75 by 16 days of wear time. CONCLUSIONS: We characterized the accuracy and reliability of a suite of digital measures that provides comprehensive information about walking behaviors in real-world settings. These results, which report the level of agreement with high-accuracy reference labels and the time duration required to establish reliable measure readouts, can guide the practical implementation of these measures into clinical studies. Well-characterized tools to quantify walking behaviors in research contexts can provide valuable clinical information about general population cohorts and patients with specific conditions.

An Algorithm to Classify Real-World Ambulatory Status From a Wearable Device Using Multimodal and Demographically Diverse Data: Validation Study.

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.

DiabeticSense: A Non-Invasive, Multi-Sensor, IoT-Based Pre-Diagnostic System for Diabetes Detection Using Breath.

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.

Wrist-worn sensor-based measurements for drug effect detection with small samples in people with Lewy Body Dementia.

INTRODUCTION: Few late-stage clinical trials in Parkinson's disease (PD) have produced evidence on the clinical validity of sensor-based digital measurements of daily life activities to detect responses to treatment. The objective of this study was to assess whether digital measures from patients with mild-to-moderate Lewy Body Dementia demonstrate treatment effects during a randomized Phase 2 trial. METHODS: Substudy within a 12-week trial of mevidalen (placebo vs 10, 30, or 75 mg), where 70/344 patients (comparable to the overall population) wore a wrist-worn multi-sensor device. RESULTS: Treatment effects were statistically significant by conventional clinical assessments (Movement Disorder Society-Unified Parkinson's Disease Rating Scale [MDS-UPDRS] sum of Parts I-III and Alzheimer's Disease Cooperative Study-Clinical Global Impression of Change [ADCS-CGIC] scores) in the full study cohort at Week 12, but not in the substudy. However, digital measurements detected significant effects in the substudy cohort at week 6, persisting to week 12. CONCLUSIONS: Digital measurements detected treatment effects in a smaller cohort over a shorter period than conventional clinical assessments. TRIAL REGISTRATION: clinicaltrials.gov, NCT03305809.

Analytical and clinical validity of wearable, multi-sensor technology for assessment of motor function in patients with Parkinson's disease in Japan.

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.

Virtual exam for Parkinson's disease enables frequent and reliable remote measurements of motor function.

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.