Heart failure (HF) remains a major cause of morbidity and mortality worldwide. Major advancements in optimal guideline-directed medical therapy, including novel pharmacological agents, are now available for the treatment of chronic HF including HF with reduced ejection fraction and HF with preserved ejection fraction. Despite these efforts, there are several limitations of medical therapy including but not limited to: delays in implementation and/or initiation; inability to achieve target dosing; tolerability; adherence; and recurrent and chronic costs of care. A significant proportion of patients remain symptomatic with poor HF-related outcomes including rehospitalization, progression of disease, and mortality. Driven by these unmet clinical needs, there has been a significant growth of innovative device-based interventions across all HF phenotypes over the past several decades. This state-of-the-art review will summarize the current landscape of guideline-directed medical therapy for chronic HF, discuss its limitations including barriers to implementation, and review device-based therapies which have established efficacy or demonstrated promise in the management of chronic HF.

BACKGROUND: Wearable physiological monitoring devices are promising tools for remote monitoring and early detection of potential health changes of interest. The widespread adoption of such an approach across communities and over long periods of time will require an automated data platform for collecting, processing, and analyzing relevant health information.
OBJECTIVE: In this study, we explore prospective monitoring of individual health through an automated data collection, metrics extraction, and health anomaly analysis pipeline in free-living conditions over a continuous monitoring period of several months with a focus on viral respiratory infections, such as influenza or COVID-19.
METHODS: A total of 59 participants provided smartwatch data and health symptom and illness reports daily over an 8-month window. Physiological and activity data from photoplethysmography sensors, including high-resolution interbeat interval (IBI) and step counts, were uploaded directly from Garmin Fenix 6 smartwatches and processed automatically in the cloud using a stand-alone, open-source analytical engine. Health risk scores were computed based on a deviation in heart rate and heart rate variability metrics from each individual\'s activity-matched baseline values, and scores exceeding a predefined threshold were checked for corresponding symptoms or illness reports. Conversely, reports of viral respiratory illnesses in health survey responses were also checked for corresponding changes in health risk scores to qualitatively assess the risk score as an indicator of acute respiratory health anomalies.
RESULTS: The median average percentage of sensor data provided per day indicating smartwatch wear compliance was 70%, and survey responses indicating health reporting compliance was 46%. A total of 29 elevated health risk scores were detected, of which 12 (41%) had concurrent survey data and indicated a health symptom or illness. A total of 21 influenza or COVID-19 illnesses were reported by study participants; 9 (43%) of these reports had concurrent smartwatch data, of which 6 (67%) had an increase in health risk score.
CONCLUSIONS: We demonstrate a protocol for data collection, extraction of heart rate and heart rate variability metrics, and prospective analysis that is compatible with near real-time health assessment using wearable sensors for continuous monitoring. The modular platform for data collection and analysis allows for a choice of different wearable sensors and algorithms. Here, we demonstrate its implementation in the collection of high-fidelity IBI data from Garmin Fenix 6 smartwatches worn by individuals in free-living conditions, and the prospective, near real-time analysis of the data, culminating in the calculation of health risk scores. To our knowledge, this study demonstrates for the first time the feasibility of measuring high-resolution heart IBI and step count using smartwatches in near real time for respiratory illness detection over a long-term monitoring period in free-living conditions.

BACKGROUND: The key to reducing the immense morbidity and mortality burdens of cardiovascular diseases is to help people keep their blood pressure (BP) at safe levels. This requires that more people with hypertension be identified, diagnosed, and given tools to lower their BP. BP monitors are critical to hypertension diagnosis and management. However, there are characteristics of conventional BP monitors (oscillometric cuff sphygmomanometers) that hinder rapid and effective hypertension diagnosis and management. Calibration-free, software-only BP monitors that operate on ubiquitous mobile devices can enable on-demand BP monitoring, overcoming the hardware barriers of conventional BP monitors.
OBJECTIVE: This study aims to investigate the accuracy of a contactless BP monitor software app for classifying the full range of clinically relevant BPs as hypertensive or nonhypertensive and to evaluate its accuracy for measuring the pulse rate (PR) and BP of people with BPs relevant to stage-1 hypertension.
METHODS: The software app, known commercially as Lifelight, was investigated following the data collection and data analysis methodology outlined in International Organization for Standardization (ISO) 81060-2:2018/AMD 1:2020 \"Non-invasive Sphygmomanometers-Part 2: Clinical investigation of automated measurement type.\" This validation study was conducted by the independent laboratory Element Materials Technology Boulder (formerly Clinimark). The study generated data from 85 people aged 18-85 years with a wide-ranging distribution of BPs specified in ISO 81060-2:2018/AMD 1:2020. At least 20% were required to have Fitzpatrick scale skin tones of 5 or 6 (ie, dark skin tones). The accuracy of the app\'s BP measurements was assessed by comparing its BP measurements with measurements made by dual-observer manual auscultation using the same-arm sequential method specified in ISO 81060-2:2018/AMD 1:2020. The accuracy of the app\'s PR measurements was assessed by comparing its measurements with concurrent electroencephalography-derived heart rate values.
RESULTS: The app measured PR with an accuracy root-mean-square of 1.3 beats per minute and mean absolute error of 1.1 (SD 0.8) beats per minute. The sensitivity and specificity with which it determined that BPs exceeded the in-clinic systolic threshold for hypertension diagnosis were 70.1% and 71.7%, respectively. These rates are consistent with those reported for conventional BP monitors in a literature review by The National Institute for Health and Care Excellence. The app\'s mean error for measuring BP in the range of normotension and stage-1 hypertension (ie, 65/85, 76% of participants) was 6.5 (SD 12.9) mm Hg for systolic BP and 0.4 (SD 10.6) mm Hg for diastolic BP. Mean absolute error was 11.3 (SD 10.0) mm Hg and 8.6 (SD 6.8) mm Hg, respectively.
CONCLUSIONS: A calibration-free, software-only medical device was independently tested against ISO 81060-2:2018/AMD 1:2020. The safety and performance demonstrated in this study suggest that this technique could be a potential solution for rapid and scalable screening and management of hypertension.

UNASSIGNED: Better interoperability is essential to derive maximum benefit from connected wireless diabetes devices. The need for this feature of diabetes devices is supported by trends in designing better performance for three types of devices: nonmedical devices, medical devices, and diabetes devices.
UNASSIGNED: First, interoperability is a standard attribute for the performance of nonmedical devices contained in smart systems that can sense and actuate. Second, interoperability is now mandated by the US Department of Health and Human Services as carried out by the Office of the National Coordinator for Health Information technology (ONC) and the US Food and Drug Administration (FDA) to improve the performance of all medical devices. Third, new guidance from the FDA and nongovernmental professional organizations are intended to promote interoperability because this feature will improve the performance of all diabetes devices.
UNASSIGNED: Wireless devices perform best when they are interoperable, and this is particularly true for diabetes devices.

BACKGROUND: Determining maximum oxygen uptake (VO2max) is essential for evaluating cardiorespiratory fitness. While laboratory-based testing is considered the gold standard, sports watches or fitness trackers offer a convenient alternative. However, despite the high number of wrist-worn devices, there is a lack of scientific validation for VO2max estimation outside the laboratory setting.
OBJECTIVE: This study aims to compare the Apple Watch Series 7\'s performance against the gold standard in VO2max estimation and Apple\'s validation findings.
METHODS: A total of 19 participants (7 female and 12 male), aged 18 to 63 (mean 28.42, SD 11.43) years were included in the validation study. VO2max for all participants was determined in a controlled laboratory environment using a metabolic gas analyzer. Thereby, they completed a graded exercise test on a cycle ergometer until reaching subjective exhaustion. This value was then compared with the estimated VO2max value from the Apple Watch, which was calculated after wearing the watch for at least 2 consecutive days and measured directly after an outdoor running test.
RESULTS: The measured VO2max (mean 45.88, SD 9.42 mL/kg/minute) in the laboratory setting was significantly higher than the predicted VO2max (mean 41.37, SD 6.5 mL/kg/minute) from the Apple Watch (t18=2.51; P=.01) with a medium effect size (Hedges g=0.53). The Bland-Altman analysis revealed a good overall agreement between both measurements. However, the intraclass correlation coefficient ICC(2,1)=0.47 (95% CI 0.06-0.75) indicated poor reliability. The mean absolute percentage error between the predicted and the actual VO2max was 15.79%, while the root mean square error was 8.85 mL/kg/minute. The analysis further revealed higher accuracy when focusing on participants with good fitness levels (mean absolute percentage error=14.59%; root-mean-square error=7.22 ml/kg/minute; ICC(2,1)=0.60 95% CI 0.09-0.87).
CONCLUSIONS: Similar to other smartwatches, the Apple Watch also overestimates or underestimates the VO2max in individuals with poor or excellent fitness levels, respectively. Assessing the accuracy and reliability of the Apple Watch\'s VO2max estimation is crucial for determining its suitability as an alternative to laboratory testing. The findings of this study will apprise researchers, physical training professionals, and end users of wearable technology, thereby enhancing the knowledge base and practical application of such devices in assessing cardiorespiratory fitness parameters.

OBJECTIVE: Neuromodulation is a viable option for patients with drug-resistant epilepsies. We reviewed the management of patients with two deep brain neurostimulators. In addition, patients implanted with a device targeting the centromedian-parafascicular (CM-Pf) nuclear complex supplements this report to provide an illustrative case to implantation and programming a patient with three active devices.
METHODS: A narrative review using PubMed and Embase identified patients with drug-resistant epilepsy implanted with more than one neurostimulator was performed. Combinations of vagus nerve stimulation (VNS), deep brain stimulation (DBS), and responsive neurostimulation (RNS) were identified. We provide a background of a newly reported case of an adult with a triple implant eventually responding to CM-Pf DBS as the third implant following suboptimal benefit from VNS and RNS.
RESULTS: In review of the literature, dual-device therapy is increasing in reports of use with combinations of VNS, RNS, and DBS to treat patients with drug-resistant epilepsy. We review dual-device implants with thalamic DBS device combinations, functional neural networks, and programming patients with dual devices. CM-Pf is a new target for DBS and has shown a variable response in focal epilepsy. We report the unique case of 28-year-old male with drug-resistant focal epilepsy who experienced a 75% seizure reduction with CM-Pf DBS as his third device after suboptimal responses to VNS and RNS. After 9 months, he also experienced seizure freedom from recurrent focal to bilateral tonic-clonic seizures. No medical or surgical complications or safety issues were encountered.
CONCLUSIONS: We demonstrate safety and feasibility in an adult combining active VNS, RNS, and CM-Pf DBS. Patients with dual-device therapy who experience a suboptimal response to initial device use at optimized settings should not be considered a neuromodulation \"failure.\" Strategies to combine devices require a working knowledge of brain networks.

Tungsten disulfide (WS2) is a promising material with excellent electrical, magnetic, optical, and mechanical properties. It is regarded as a key candidate for the development of optoelectronic devices due to its high carrier mobility, high absorption coefficient, large exciton binding energy, polarized light emission, high surface-to-volume ratio, and tunable band gap. These properties contribute to its excellent photoluminescence and high anisotropy. These characteristics render WS2 an advantageous material for applications in light-emitting devices, memristors, and numerous other devices. This article primarily reviews the most recent advancements in the field of optoelectronic devices based on two-dimensional (2D) nano-WS2. A variety of advanced devices have been considered, including light-emitting diodes (LEDs), sensors, field-effect transistors (FETs), photodetectors, field emission devices, and non-volatile memory. This review provides a guide for improving the application of 2D WS2 through improved methods, such as introducing defects and doping processes. Moreover, it is of great significance for the development of transition-metal oxides in optoelectronic applications.

Sepsis continues to be challenging to diagnose due to its non-specific clinical signs and symptoms, emphasizing the importance of early detection. Our study aimed to enhance the accuracy of sepsis diagnosis by integrating multimodal monitoring technologies with conventional diagnostic methods. The research included a total of 121 newborns, with 39 cases of late-onset sepsis, 35 cases of early-onset sepsis, and 47 control subjects. Continuous monitoring of biosignals, including pulse oximetry (PO), near-infrared spectroscopy (NIRS), and skin temperature (ST), was conducted. An algorithm was then developed in Python to identify early signs of sepsis. The model demonstrated the capability to detect sepsis 6 to 48 h in advance with an accuracy rate of 87.67 ± 7.42%. Sensitivity and specificity were recorded at 76% and 90%, respectively, with NIRS and ST having the most significant impact on predictive accuracy. Despite the promising results, limitations such as sample size, data variability, and potential biases were noted. These findings highlight the critical role of non-invasive biosensing methods in conjunction with conventional biomarkers and cultures, offering a strong foundation for early sepsis detection and improved neonatal care. Further research should be conducted to validate these results across different clinical settings.

Heart failure (HF) is a complex and progressive disease marked by substantial morbidity and mortality rates, frequent episodes of decompensation, and a reduced quality of life (QoL), with severe financial burden on healthcare systems. In recent years, several large-scale randomized clinical trials (RCTs) have widely expanded the therapeutic armamentarium, underlining additional benefits and the feasibility of rapid titration regimens. This notwithstanding, mortality is not declining, and hospitalizations are constantly increasing. It is widely acknowledged that even with guideline-directed medical therapy (GDMT) on board, HF patients have a prohibitive residual risk, which highlights the need for innovative treatment options. In this scenario, groundbreaking devices targeting valvular, structural, and autonomic abnormalities have become crucial tools in HF management. This has led to a full-fledged translational boost with several novel devices in development. Thus, the aim of this review is to provide an update on both approved and investigated devices.

Sweat evaporation is critical to human thermoregulation, but current understanding of the process on 20 μm to 2 cm scale is limited. To this end, we introduce a wind-tunnel-shaped ventilated capsule with an infrared window for simultaneous infrared sweat imaging and evaporation rate measurement. Implementing the capsule in pilot human subject tests suggests that the common assumption of sweat being an isothermal film is only valid when the evaporation rate is low and sweat forms puddles on the skin. Before transitioning to this filmwise mode, sweating occurs in cyclic dropwise mode, displaying a 3x higher mass transfer coefficient in the same conditions. Imaging highlighted distinct phenomena occurring during and between these modes including out-of-duct evaporation, pulsating droplets, temporary and eventually lasting crevice filling, and individual drop-to-film spreading. In all, sweat evaporation is an impactful area that our results show is ripe for exploration, which can be achieved quantitatively using the introduced platform.