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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., (Copyright © 2024 Verily Life Sciences LLC. Published by Elsevier Inc. All rights reserved.)

In the frame of a collaboration between the Italian National Research Council (CNR) and Mares s.r.l., a study, about the possibility of determining radon vertical distribution at different soil depths in order to trace light non-aqueous phase liquid (LNAPL) contaminations, was developed. The radon deficit technique, based on the preferential solubility of soil gas radon into non-polar fluids, such as refined hydrocarbons, has been investigated by various theoretical and applied research so far. According to international scientific literature, radon deficit can be used both for geochemical prospection of the spatial irregular NAPL dispersion and for monitoring of remediation activities. Even though it is well known that this type of pollutants can be distributed along the vertical soil profile-firstly due to their density in comparison to water density, and secondly due to fluctuations of shallow aquifers, soil pore size, aging of contamination, and so on-the vertical localization of the plume still represents a scientific challenge. In this article, a method to determine the radon vertical profile is tested and applied to assess the potential use of the radon deficit technique in the vertical detection of pollutant presence for the first time in a fuelling station. Two LNAPL-contaminated sites were selected for a pilot test. Experimental findings seem to support the use of vertical radon geochemical prospection to delimit the depth range of a LNAPL pollution directly. Systematic data collection and modeling may lead to a 3D reconstruction of the dispersion of contaminant in different soil levels., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

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., (©Nathan Kowahl, Sooyoon Shin, Poulami Barman, Erin Rainaldi, Sara Popham, Ritu Kapur. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 03.08.2023.)

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., (Copyright © 2023 Verily Life Sciences LLC. Published by Elsevier Ltd.. All rights reserved.)

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., (© 2023. The Author(s).)

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., (© 2022. The Author(s).)