Frequent Measurements Research Articles

Long-term assessment and target achievement of LDL-C and systolic blood pressure in primary care after acute coronary syndrome

Abstract Background Hospital-based cardiac rehabilitation and risk factor control decreases the risk of recurrent events after acute coronary syndrome (ACS). Little is known on long-term assessment and target achievement after referral to primary care. Patients with diabetes, regardless of ACS, have yearly follow-up visits in primary care, whereas there is no structured follow-up after ACS for patients without diabetes. Purpose To describe long-term assessment and target achievement of LDL-C and systolic blood pressure (SBP) in ACS patients after referral to primary care. Further, to compare patients with and without diabetes at the time of index ACS. Methods A non-interventional population-based observational study of patients with first ever ACS in a Swedish region with 390,000 inhabitants. Data on clinical measurements after ACS were collected from the electronic medical records between 2012 and 2020. Patients were followed until new ACS event, death, moving out of the county, or end of study. Target levels studied were LDL-C <1.8 mmol/L and SBP <140 mmHg. The same assessments with the addition of target levels of HbA1c ≤70 mmol/mol were evaluated in patients with diabetes. Results Median follow-up was 2.9 years (inter quartile range [IQR]: 1.0–5.4) and 4,733 patients were detected. Median age was 72 years (IQR 63–80) and 34% were female. Follow-up data were available for 3,579 (76%) patients any time during the first, 2,891 (61%) during the second, and 2,308 (49%) during the third year after the index ACS event. The number of patients with diabetes was 1311 (28%), and of these 921, 723, and 562 could be followed for the first, second, and third year. During the first three years, 898 (19%) of all patients with available follow-up died. The proportion of patients with assessments of LDL-C and SBP declined for each year of follow-up and target achievement was low (Table). Among patients with diabetes a larger proportion had measurements for LDL-C and SBP, while the target achievement was similar to that of the whole study population. HbA1c was measured equally frequently as SBP and was at target in 81% of patients with diabetes during the three years of follow-up (Table). Conclusion The proportion of patients with follow-up assessment and measurements in primary care declined each year after the index ACS event. Patients with diabetes had more frequent LDL-C and SBP measurements but the same low target achievement after an ACS compared with patients without diabetes. This could potentially be due to an increased focus on glycaemic control at these visits. A structured long-term follow-up for ACS patients in primary care might improve both assessment and target achievement and prevent recurrent ACS events. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): AmgenSwedish Heart Lung Foundation

Morning Transition of the Boundary Layer over Dallas–Fort Worth

Abstract Temperature profiles of the lower atmosphere (<3 km) over complex urban areas are related to health risks, including heat stress and respiratory illness. This complexity leads to uncertainty in numerical simulations, and many studies call for more observations of the lower atmosphere over cities. Using 20 years of observations from the Aircraft Meteorological Data Relay (AMDAR) program over Dallas–Fort Worth, Texas, average profiles every 0.5 h are created from the 1.5 million individual soundings. Dallas–Fort Worth is ideal because it is a large urban area in the central Great Plains, has no major topographic or coastal influences, and has two major airports near the center of the urban heat island. With frequent and high-quality measurements over the city, we investigate the evolution of the lower atmosphere around sunrise to quantify the stability, boundary layer height, and duration of the morning transition when there are southerly winds, few clouds, and no precipitation so as to eliminate transient synoptic events. Characteristics of the lower atmosphere are separated by season and maximum wind speed because the the Great Plains low-level jet contributes to day-to-day variability. In all seasons, stronger wind over the city leads to a weaker nocturnal temperature inversion at sunrise and a shorter morning transition period, with the greatest difference during autumn and the smallest difference during summer. During summer, the boundary layer height at sunrise is higher on average, deepens the most as wind strengthens, and has the fewest days exhibiting a surface temperature inversion over the city. Significance Statement Cities impact health by creating an urban heat island caused by more heating at the surface, less evaporative cooling, and increased anthropogenic waste heat, and they can have high pollution. Cooling overnight stabilizes the lower atmosphere and traps pollutants near the surface until surface heating after sunrise mixes them away. Inadequate pollution observations make it difficult to study these issues. The greatest mixing occurs about 2 h after sunrise but can be modulated by wind speed. Observations from 1.5 million aircraft landing and taking off over Dallas–Fort Worth, Texas, reveal that strong low-level wind leads to morning transitions ending 0.84 h earlier on average than with light wind. Details from this vast dataset contribute to improved understanding of the lower atmosphere over cities and provide a baseline for simulations.

PSXIII-7 Field Testing of Lora-wan Sensors for Real-Time Tracking and Biosensing of Brangus and Raramuri Criollo Cattle Grazing a Small Pasture

Abstract The study’s objective was to investigate the use of Internet of Things (IoT) biosensors using Long Range Wide Area Network (LoRa-WAN) communication to compare the foraging behavior of two desert adapted cattle breeds. Two groups of mature Brangus (BA) and Raramuri Criollo (RC) cows were introduced into a small irrigated pasture (38ha) at the New Mexico State University’s Clayton Livestock Research Center during Dec-Jan, 2021 (41-days). The real-time position and activity count data (15- and 1-minute interval, respectively) were retrieved continuously from 4 BA and 5 RC cows collared with commercially available trackers (Abeeway Industrial-trackers). Response variables derived from GPS records included daily distance traveled (Dist) and daily area explored (Area). Response variables derived from accelerometers included daily percentage motion (%Act) and cumulative activity data (CumAct). All variables were grouped into six consecutive weekly periods and analyzed by a repeated measures mixed model that included the fixed effects of BREED, WEEK, and the BREED*WEEK interaction in a completely randomized design. A significant (P< 0.01) BREED*WEEK interaction was detected on all GPS-derived variables, with RC exhibiting a trend to explore larger areas than BA (P=0.06). For the accelerometer-derived variables, the BREED*WEEK interaction was only significant (P=0.01) for CumAct. Overall, BA spent a larger percentage of the day active (%Act; BA= 76.74 vs. RC= 73.16; P=0.02), while investing a significantly greater overall activity (CumAct; BA= 8816.63 vs. RC= 6311.31; P=0.03) compared to RC cows. The insensitivity of GPS-derived variables is hypothesized to be the result of aggregating errors associated with less frequent GPS location measurements coupled with position errors (~5m) and a low success rate of data packet transmission (~40%). The spatial scale of the study is hypothesized to have influenced the foraging behavior differences that were observed between the breeds, and the better performance of the accelerometer vs. GPS-derived variables.

GOCI-II based sea surface salinity estimation using machine learning for the first-year summer

ABSTRACT Estimation of sea surface salinity (SSS) using the Geostationary Ocean Color Imager-II (GOCI-II) measurements in the East China Sea (ECS) was conducted from July to September of 2021, when the first-year observations are available after GOCI-II launch in 2020. The SSS in the ECS is mainly affected by the Changjiang River plume, which varies from under 20–35 psu, and the discharged freshwater disperses from the river mouth towards Jeju Island, Korea. For the SSS estimation, a multi-layer perceptron neural network (MPNN) was employed to train the nonlinear processes of GOCI-II spectral measurements as inputs and the SSS of Soil Moisture Active Passive (SMAP) as the target. Because GOCI-II has four additional spectral bands (380, 510, 620 and 709 nm) compared to the bands in the first generation of GOCI (413, 443, 490, 555, 660 and 680 nm), we developed a new MPNN algorithm and analysed (1) how much these new spectral measurements increase SSS accuracy, and (2) how the enhanced spatial and temporal resolution of GOCI-II make SSS features different from GOCI. The first results showed that the root mean square error (RMSE) and coefficient of determination (R 2) were 0.68 psu and 0.92, respectively. Furthermore, R 2, when compared with in-situ measurements at the Ieodo Ocean Research Station (I-ORS), increased as much as 0.23 (0.20–0.43) for the 10-band model, which performed much better than previous 6-band model. The second result suggests how the improved spatial features at more frequent SSS measurements can be utilized to avoid low salinity intrusion into the aquafarming sites off the coast of Korea. More importantly, we elucidated why the MPNN algorithm performs better than conventional SSS estimation methods by comparing various optical properties with SSS variation; thus, the newly developed model can provide SSS not only in ECS but also low salinity water near southwest Korean coasts at hourly spatial resolution of 250 m.

The kinetics of isotonic and hypertonic resuscitation fluids is dependent on the sizes of the body fluid volumes.

The extracellular and intracellular fluid volumes (ECV and ICV) vary not only with age, gender, and body weight but also with the habitual intake of water. The present study examines whether the baseline variations in the ECV and ICV change the distribution and elimination of subsequently given infusion fluids. Twenty healthy male volunteers underwent 50 infusion experiments with crystalloid fluid for which the fluid volume kinetics was calculated based on frequent measurements of the hemodilution using mixed-effects modeling software. The results were compared with the ECV and ICV measured with multifrequency bioimpedance analysis before each infusion started. The fluids were given over 30 minutes and comprised 25 mL/kg Ringer's acetate (N = 20), Ringer's lactate, 5 mL/kg 7.5% saline, and 3 mL/kg 7.5% saline in 6% dextran 70 (these fluids, N = 10). A large ICV was associated with a small extravascular accumulation of infused fluid, which increased the plasma volume expansion and the urinary excretion. With hypertonic fluid, a large ECV greatly accelerated urinary excretion. The body weight did not serve as a covariate in the kinetic models. Albumin was recruited to the plasma during infusion of both types of fluid. The hypertonic fluids served as diuretics. The infused excess sodium and osmolality were distributed over a 35% larger space than the sum of the ECV and ICV. A large ICV reduced the rate of distribution of Ringer's solution, whereas a large ECV accelerated the excretion of hypertonic saline.

Quantum Zeno repeaters

Quantum repeaters pave the way for long-distance quantum communications and quantum Internet, and the idea of quantum repeaters is based on entanglement swapping which requires the implementation of controlled quantum gates. Frequently measuring a quantum system affects its dynamics which is known as the quantum Zeno effect (QZE). Beyond slowing down its evolution, QZE can be used to control the dynamics of a quantum system by introducing a carefully designed set of operations between measurements. Here, we propose an entanglement swapping protocol based on QZE, which achieves almost unit fidelity. Implementation of our protocol requires only simple frequent threshold measurements and single particle rotations. We extend the proposed entanglement swapping protocol to a series of repeater stations for constructing quantum Zeno repeaters which also achieve almost unit fidelity regardless of the number of repeaters. Requiring no controlled gates, our proposal reduces the quantum circuit complexity of quantum repeaters. Our work has potential to contribute to long distance quantum communications and quantum computing via quantum Zeno effect.

Evaluating the Typical Day-to-Day Variability of WHOOP-Derived Heart Rate Variability in Olympic Water Polo Athletes.

Heart rate (HR) and HR variability (HRV) can be used to infer readiness to perform exercise in athletic populations. Advancements in the photoplethysmography technology of wearable devices such as WHOOP allow for the frequent and convenient measurement of HR and HRV, and therefore enhanced application in athletes. However, it is important that the reliability of such technology is acceptable prior to its application in practical settings. Eleven elite male water polo players (age 28.8 ± 5.3 years [mean ± standard deviation]; height 190.3 ± 3.8 cm; body mass 95.0 ± 6.9 kg; international matches 117.9 ± 92.1) collected their HR and HRV daily via a WHOOP strap (WHOOP 3.0, CB Rank, Boston, MA, USA) over 16 weeks ahead of the 2021 Tokyo Olympic Games. The WHOOP strap quantified HR and HRV via wrist-based photoplethysmography during overnight sleep periods. The weekly (i.e., 7-day) coefficient of variation in lnRMSSD (lnRMSSDCV) and HR (HRCV) was calculated as a measure of day-to-day variability in lnRMSSD and HR, and presented as a mean of the entire recording period. The mean weekly lnRMSSDCV and HRCV over the 16-week period was 5.4 ± 0.7% (mean ± 95% confidence intervals) and 7.6 ± 1.3%, respectively. The day-to-day variability in WHOOP-derived lnRMSSD and HR is within or below the range of day-to-day variability in alternative lnRMSSD (~3–13%) and HR (~10–11%) assessment protocols, indicating that the assessment of HR and HRV by WHOOP does not introduce any more variability than that which is naturally present in these variables.

Blood pressure monitoring techniques in the natural state of multi-scenes: A review.

Blood pressure is one of the basic physiological parameters of human physiology. Frequent and repeated measurement of blood pressure along with recording of environmental or other physiological parameters when measuring blood pressure may reveal important cardiovascular risk factors that can predict occurrence of cardiovascular events. Currently, wearable non-invasive blood pressure measurement technology has attracted much research attention. Several different technical routes have been proposed to solve the challenge between portability or continuity of measurement methods and medical level accuracy of measurement results. The accuracy of blood pressure measurement technology based on auscultation and oscillography has been clinically verified, while majority of other technical routes are being explored at laboratory or multi-center clinical demonstration stage. Normally, Blood pressure measurement based on oscillographic method outside the hospital can only be measured at intervals. There is a need to develop techniques for frequent and high-precision blood pressure measurement under natural conditions outside the hospital. In this paper, we discussed the current status of blood pressure measurement technology and development trends of blood pressure measurement technology in different scenarios. We focuses on the key technical challenges and the latest advances in the study of miniaturization devices based on oscillographic method at wrist and PTT related method at finger positions as well as technology processes. This study is of great significance to the application of high frequency blood pressure measurement technology.

Recent Advances in Non-Invasive Blood Pressure Monitoring and Prediction Using a Machine Learning Approach.

Blood pressure (BP) monitoring can be performed either invasively via arterial catheterization or non-invasively through a cuff sphygmomanometer. However, for conscious individuals, traditional cuff-based BP monitoring devices are often uncomfortable, intermittent, and impractical for frequent measurements. Continuous and non-invasive BP (NIBP) monitoring is currently gaining attention in the human health monitoring area due to its promising potentials in assessing the health status of an individual, enabled by machine learning (ML), for various purposes such as early prediction of disease and intervention treatment. This review presents the development of a non-invasive BP measuring tool called sphygmomanometer in brief, summarizes state-of-the-art NIBP sensors, and identifies extended works on continuous NIBP monitoring using commercial devices. Moreover, the NIBP predictive techniques including pulse arrival time, pulse transit time, pulse wave velocity, and ML are elaborated on the basis of bio-signals acquisition from these sensors. Additionally, the different BP values (systolic BP, diastolic BP, mean arterial pressure) of the various ML models adopted in several reported studies are compared in terms of the international validation standards developed by the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) for clinically-approved BP monitors. Finally, several challenges and possible solutions for the implementation and realization of continuous NIBP technology are addressed.

What drives forest carbon storage? The ramifications of source-sink decoupling.

This article is a Commentary on Martínez‐Sancho et al. (2022), 236: 58–70.

Potassium Concentrations in Transgender Women Using Spironolactone: A Retrospective Chart Review

ObjectiveTo assess the incidence of hyperkalemia in transgender women using spironolactone. MethodsThis was a retrospective chart review of transgender women who received gender-affirming hormone therapy that included spironolactone between January 2000 and September 2018. Forty-four participants who had paired potassium concentrations documented and were on spironolactone were included and analyzed. Study outcomes included the incidence of hyperkalemia (serum potassium concentrations > 5.0 mmol/L), the relationship between the duration of treatment and degree of hyperkalemia, and difference between serum potassium concentrations at the beginning of spironolactone treatment versus last serum potassium concentrations. ResultsThe median age of the participants was 36.5 years. The cohort was predominantly non-Hispanic White (32/44). No serum potassium concentration was >5.5 mmol/L, and all participants had serum creatinine level of <2 mg/dL. Median duration of treatment was 25 months (range 2-92 months) and 140 potassium measurements were available. The mean potassium concentration (3.87 mmol/L) before the initiation of spironolactone was lower than the mean potassium concentration (4.03 mmol/L) while on spironolactone (mean difference, 0.16 mmol/L, P = .013). The regression β, that is, the average change in potassium concentration per 1 additional month of treatment duration, was −.001 (95% CI [−.004, .001]; P = .255) signifying no relation between treatment duration and spironolactone use. ConclusionNo participant had laboratory evidence of significant hyperkalemia (K > 5.5 mmol/L) after initiation of spironolactone. Frequent measurement of potassium concentrations might be unnecessary in transgender women taking spironolactone in patients with serum creatinine levels of <2 mg/dL.

Design and use of an online drilling fluid pipe viscometer

The accurate and frequent measurement of the drilling fluid's rheological properties is essential for drilling engineering. To measure drilling fluid's rheological properties automatically and frequently, we design an online pipe viscometer. First, we introduce the measurement principle, calculation flow chart, and design scheme. The pulsation damper and back pressure valve are used to stabilize flowrate and pressure which can improve the accuracy of the pipe viscometer. Then the errors under various static hole sizes are measured, and the static hole with the smallest error is selected as the measurement pipe of the pipe viscometer. Finally, various drilling fluids are tested. Compared with the standard six-speed viscometer, the shear stress error of the six standard shear rates (1022, 511, 340, 170, 10, 5s−1) is within 1Pa. The designed pipe viscometer can frequently measure drilling fluid's rheological properties and meets the needs of on-site online testing.

Improved thickness measurement on rough surfaces by using guided wave cut-off frequency

Substantial variability is seen when thickness measurements using conventional ultrasonic time of flight measurements are carried out on rough surfaces; this makes it difficult to estimate corrosion rates when the corrosion mechanism leads to surface roughness. It has been shown that thickness measurements using guided wave cut-off frequencies (through thickness resonance frequencies) can be done at much lower frequencies than conventional time of flight thickness gauging for the same minimum thickness resolution. The lower frequency measurements are less susceptible to variations caused by surface roughness and so will give more consistent and reliable results in cases where corrosion leads to increased surface roughness. Measurements were carried out using guided wave cut-off frequencies on four plates with different surface roughness. On a plate with root mean square (rms) surface roughness of 0.3 mm, the thickness estimates followed the trend predicted from the plate geometry and probe footprint, whereas higher frequency measurements reported in the literature deviated from this trend at an rms roughness of 0.1 mm. The guided wave cut-offcut-off frequency measurements can be done using the same transduction system as that used for large area guided wave monitoring so it is possible to combine large and small area monitoring in a single unit. Frequent measurements enable the wall thickness obtained with guided wave cut-off measurements to be tracked with time, and the low susceptibility of the measurements to surface roughness means that accurate corrosion rates will be obtained.

Experience with Implementing a Beta-lactam Therapeutic Drug Monitoring Service in a Burn Intensive Care Unit: A Retrospective Chart Review.

Thermal injuries alter pharmacokinetics, complicating the prediction of standard antibiotic dose effectiveness. Therapeutic drug monitoring (TDM) has been proposed to prevent subtherapeutic dosing of antibiotic therapy, but remains scarcely studied in the burn patient population. A retrospective chart review of burn patients receiving beta-lactam TDM from 2016 to 2019 was conducted. Adult patients with thermal injury receiving cefepime, piperacillin/tazobactam, or meropenem for ≥48 hours were included. Between February 2016 and July 2017, we utilized selective TDM based on clinical judgement to guide treatment. From October 2018 until July 2019, TDM was expanded to all burn patients on beta-lactams. The primary endpoint was achievement of therapeutic concentration, and the secondary endpoints were clinical cure, culture clearance, new resistance, length of stay, and mortality. The selective (control) group included 19 patients and the universal (study) group reviewed 23 patients. In both groups, skin and lungs were the most common primary infection sources, with Pseudomonas aeruginosa as the most common species. In the universal cohort, patients were older with higher risk factors, but more frequently achieved the target drug concentration, required less days to start TDM (p < .0001), and had more frequent measurements and beta-lactam dose adjustments. Positive clinical outcome was reported in 77%, and microbial eradication in 82% of all patients. All clinical outcomes were similar between the groups. The implementation of beta-lactam TDM protocol shortened the time, increased the probability of appropriate target attainment, and individualized beta-lactam therapy in burn patients.

A Feedback Control Strategy for a Fed-Batch Monoclonal Antibody Production Process Utilising Infrequent and Irregular Sampled Measurements

The ability to take non-invasive Raman measurements presents a unique opportunity to use one Raman probe across multiple vessels in parallel, reducing costs but making measurements infrequent. Under these conditions, infrequent and irregular feedback signals can result in poor closed-loop control performance. This study addressed the issue of infrequent and irregular Raman measurements using a linear dynamic model developed from interpolated data to predict more frequent measurements of the controlled variable. The simulated monoclonal antibody production was sampled hourly with white noise added to the simulated glucose concentration to replicate real Raman measurements. The hourly samples were interpolated into 15 min intervals and a linear dynamic model was developed to predict the glucose concentration at 15 min intervals. These predicted values were then used in a feedback control loop by using model predictive control or a conventional proportional and integral controller to control the glucose concentration at 15 min sampling intervals. For setpoint tracking, the model predictive control reduced the integral of absolute errors to 14,600 from 15,900 (with a 1 h sampling time) or 8.2% reduction. With adaptive model predictive control, the integral of absolute errors was reduced from 14,500 (1 h sampling time) to 14,200 for setpoint tracking and from 13,500 (1 h sampling time) to 13,300 for disturbance rejection. A final comparison demonstrated that the proposed method can also cope with random variations in the sampling time.

Implementing intermittent current interruption into Li-ion cell modelling for improved battery diagnostics

A novel electroanalytical method, the intermittent current interruption (ICI) technique, has recently been promoted as a versatile tool for battery analysis and diagnostics. The technique enables frequent and continuous measurement of battery resistance, which then undergoes statistical analysis. Here, this method is implemented for commercial Li-ion cylindrical cells, and combined with a physics-based finite element model (FEM) of the battery to better interpret the measured resistances. Ageing phenomena such as solid electrolyte interphase (SEI) formation and metallic Li plating on the surface of the negative graphite particles are considered in the model. After validation, a long-term cycling simulation is conducted to mimic the ageing scenario of commercial cylindrical 21700 cells. The large number of internal resistance measurements obtained are subsequently visualized by creating a ‘resistance map’ as a function of both capacity and cycle numbers, providing a straight-forward image of their continuous evolution. By correlating the observed ageing scenarios with specific physical processes, the origins of ageing are investigated. The result shows that a decrease of the electrolyte volume fraction contributes significantly to the increase of internal resistance and affect the electrolyte diffusivity properties. Additionally, effects of porosity and particle radius of the different electrodes are investigated, providing valuable suggestions for battery design.

Application of a Deep Learning Neural Network for Voiding Dysfunction Diagnosis Using a Vibration Sensor

In a clinical context, there are increasing numbers of people with voiding dysfunction. To date, the methods of monitoring the voiding status of patients have included voiding diary records at home or urodynamic examinations at hospitals. The former is less objective and often contains missing data, while the latter lacks frequent measurements and is an invasive procedure. In light of these shortcomings, this study developed an innovative and contact-free technique that assists in clinical voiding dysfunction monitoring and diagnosis. Vibration signals during urination were first detected using an accelerometer and then converted into the mel-frequency cepstrum coefficient (MFCC). Lastly, an artificial intelligence model combined with uniform manifold approximation and projection (UMAP) dimensionality reduction was used to analyze and predict six common patterns of uroflowmetry to assist in diagnosing voiding dysfunction. The model was applied to the voiding database, which included data from 76 males aged 30 to 80 who required uroflowmetry for voiding symptoms. The resulting system accuracy (precision, recall, and f1-score) was around 98% for both the weighted average and macro average. This low-cost system is suitable for at-home urinary monitoring and facilitates the long-term uroflow monitoring of patients outside hospital checkups. From a disease treatment and monitoring perspective, this article also reviews other studies and applications of artificial intelligence-based methods for voiding dysfunction monitoring, thus providing helpful diagnostic information for physicians.

Long-term changes in blood metal ion levels in patients with hip resurfacing implants: implications for patient surveillance after 10 years follow-up.

Studies investigating changes in blood metal ion levels during the second decade of the implant lifetime in MoM hip resurfacing patients are scarce. Patients implanted with either Birmingham Hip Resurfacing (BHR) or Articular Surface Replacement (ASR) hip resurfacings with >10 years follow-up and repeated blood metal ion measurements were identified at 2 large specialist European arthroplasty centres. After excluding patients with initial metal ion levels >7 ppb, the proportion of patients with an increase in blood metal ion levels above previously validated implant-specific thresholds (cobalt 2.15 ppb for unilateral implants, cobalt or chromium 5.5 ppb for bilateral) was assessed. We included 2743 blood metal ion measurements from 457 BHR patients (555 hips) and 216 ASR patients (263 hips). Of patients with initial metal ion levels below implant specific thresholds, increases in cobalt or chromium level, respectively, to above these thresholds during the second decade were seen as follows: unilateral BHR (cobalt = 15.6%), unilateral ASR (cobalt = 13.8%), bilateral BHR (cobalt = 8.2%, chromium = 11.8%), bilateral ASR (cobalt = 8.5%, chromium = 4.3%). Measurement-to-measurement changes exceeding +2.15 ppb or +5.5 ppb were, however, uncommon during the second decade. Subgroup results with small diameter (<50 mm) implants were similar. We recommend less frequent blood metal ion measurements are needed (every 3 to 5 years) for hip resurfacing patients if initial values were below 7ppb.

Reliability and validity of the Roche PD Mobile Application for remote monitoring of early Parkinson’s disease

Digital health technologies enable remote and therefore frequent measurement of motor signs, potentially providing reliable and valid estimates of motor sign severity and progression in Parkinson’s disease (PD). The Roche PD Mobile Application v2 was developed to measure bradykinesia, bradyphrenia and speech, tremor, gait and balance. It comprises 10 smartphone active tests (with ½ tests administered daily), as well as daily passive monitoring via a smartphone and smartwatch. It was studied in 316 early-stage PD participants who performed daily active tests at home then carried a smartphone and wore a smartwatch throughout the day for passive monitoring (study NCT03100149). Here, we report baseline data. Adherence was excellent (96.29%). All pre-specified sensor features exhibited good-to-excellent test–retest reliability (median intraclass correlation coefficient = 0.9), and correlated with corresponding Movement Disorder Society–Unified Parkinson's Disease Rating Scale items (rho: 0.12–0.71). These findings demonstrate the preliminary reliability and validity of remote at-home quantification of motor sign severity with the Roche PD Mobile Application v2 in individuals with early PD.

Quantum Zeno and Anti-Zeno Probes of Noise Correlations in Photon Polarization.

We experimentally demonstrate, for the first time, noise diagnostics by repeated quantum measurements, establishing the ability of a single photon subjected to random polarization noise to diagnose non-Markovian temporal correlations of such a noise process. Both the noise spectrum and temporal correlations are diagnosed by probing the photon with frequent (partially) selective polarization measurements. We show that noise with positive temporal correlations corresponds to our single photon undergoing a dynamical regime enabled by the quantum Zeno effect (QZE), whereas noise characterized by negative (anti) correlations corresponds to regimes associated with the anti-Zeno effect (AZE). This is the first step toward a novel noise spectroscopy based on QZE and AZE in single-photon state probing able to extract information on the noise while protecting the probe state, a conceptual paradigm shift with respect to traditional interferometric measurements.