The ability to pick up objects off the floor can degrade over time with elderly individuals, leading to a reduced quality of life and an increase in the risk of falling. Healthcare professionals have expressed an interest in monitoring the decline in pickup ability of a subject over extended periods of time and intervening when it becomes hazardous to the subject\'s health. The current means of evaluating pickup ability involving in-clinic patient visits is both time and financially expensive. There is a clear need for a cost-effective, remote means of pickup evaluation to ease the burden on both patients and physicians. To address these challenges, we introduce a Time-of-Pickup (ToP) solution, called ToPick, designed for the automatic assessment of pickup ability over time. The practical performance of ToPick is evident, demonstrated by a minimal median error of approximately 100 milliseconds in evaluating 20 pickup events among 10 elderly individuals. Furthermore, ToPick exhibits a high level of reliability, achieving perfect accuracy, precision, and recall scores for pickup event detection. We actualize our research findings by designing an application intended for adoption by both healthcare practitioners and elderly individuals. The app aims to reduce both time and financial costs while enabling mobile treatment for users.

Cardiovascular diseases (CVDs) are a predominant global health concern, accounting for over 17.9 million deaths in 2019, representing approximately 32% of all global fatalities. In North America and Europe, over a million adults undergo cardiac surgeries annually. Despite the benefits, such surgeries pose risks and require precise postsurgery monitoring. However, during the postdischarge period, where monitoring infrastructures are limited, continuous monitoring of vital signals is hindered. In this area, the introduction of implantable electronics is altering medical practices by enabling real-time and out-of-hospital monitoring of physiological signals and biological information postsurgery. The multimodal implantable bioelectronic platforms have the capability of continuous heart sensing and stimulation, in both postsurgery and out-of-hospital settings. Furthermore, with the emergence of machine learning algorithms into healthcare devices, next-generation implantables will benefit artificial intelligence (AI) and connectivity with skin-interfaced electronics to provide more precise and user-specific results. This Review outlines recent advancements in implantable bioelectronics and their utilization in cardiovascular health monitoring, highlighting their transformative deployment in sensing and stimulation to the heart toward reaching truly personalized healthcare platforms compatible with the Sustainable Development Goal 3.4 of the WHO 2030 observatory roadmap. This Review also discusses the challenges and future prospects of these devices.

UNASSIGNED: Research quality can be improved with reliable and reproducible experimental results when animal experiments are conducted using laboratory animals with guaranteed microbiological and genetic quality through health monitoring. Therefore, health monitoring requires the rapid and accurate diagnosis of infectious diseases in laboratory animals.
UNASSIGNED: This study presents a performance evaluation of a commercially available multiplex real-time PCR (mRT-PCR) assay for the rapid detection of 12 infectious pathogens (Set 1: Sendai virus [SeV, formally murine respirovirus], Mycoplasma spp., Rodentibacter pneumotropicus, and Rodentibacter heylii; Set 2: Helicobacter spp., Murine norovirus [MNV], Murine hepatitis virus [MHV], and Salmonella spp.; Set 3: Staphylococcus aureus, Streptobacillus moniliformis, Corynebacterium kutscheri, and Pseudomonas aeruginosa). To evaluate the efficacy of the mRT-PCR assay, 102 clinical samples encompassing fecal and cecal specimens were analyzed. The resulting data were then compared with the findings from sequence analysis for validation.
UNASSIGNED: The assay\'s detection limit ranged from 1 to 100 copies per reaction. Specificity testing involving various viruses and bacteria indicated no cross-reactivity between strains. Additionally, the assay exhibited good reproducibility, with mean coefficients of variation for inter- and intra assay variation below 3%. The overall positive rate was 52.9% (n = 54), with the mRT-PCR assay findings matching sequence analysis results (κ = 1). MHV (n = 29, 28.4%) was the most prevalent pathogen, followed by Helicobacter spp. (n = 28, 27.5%), R. heylii (n = 18, 17.6%), Mycoplasma spp. (n = 14, 13.7%), MNV (n = 12, 11.8%), S. aureus (n = 9, 8.8%), P. aeruginosa (n = 4, 3.9%), and R. pneumotropicus (n = 1, 0.9%).
UNASSIGNED: This assay offers a rapid turnaround time of 100 min, including 30 min for DNA preparation and 70 min for target DNA/RNA amplification. It ensures accuracy, minimizing false positives or negatives, making it a convenient tool for the simultaneous detection of infectious diseases in many samples. Overall, the propose‑d assay holds promise for the effective detection of the most important pathogens in laboratory animal health monitoring.

BACKGROUND: The number of individuals using digital health devices has grown in recent years. A higher rate of use in patients suggests that primary care providers (PCPs) may be able to leverage these tools to effectively guide and monitor physical activity (PA) for their patients. Despite evidence that remote patient monitoring (RPM) may enhance obesity interventions, few primary care practices have implemented programs that use commercial digital health tools to promote health or reduce complications of the disease.
OBJECTIVE: This formative study aimed to assess the perceptions, needs, and challenges of implementation of an electronic health record (EHR)-integrated RPM program using wearable devices to promote patient PA at a large urban primary care practice to prepare for future intervention.
METHODS: Our team identified existing workflows to upload wearable data to the EHR (Epic Systems), which included direct Fitbit (Google) integration that allowed for patient PA data to be uploaded to the EHR. We identified pictorial job aids describing the clinical workflow to PCPs. We then performed semistructured interviews with PCPs (n=10) and patients with obesity (n=8) at a large urban primary care clinic regarding their preferences and barriers to the program. We presented previously developed pictorial aids with instructions for (1) providers to complete an order set, set step-count goals, and receive feedback and (2) patients to set up their wearable devices and connect them to their patient portal account. We used rapid qualitative analysis during and after the interviews to code and develop key themes for both patients and providers that addressed our research objective.
RESULTS: In total, 3 themes were identified from provider interviews: (1) providers\' knowledge of PA prescription is focused on general guidelines with limited knowledge on how to tailor guidance to patients, (2) providers were open to receiving PA data but were worried about being overburdened by additional patient data, and (3) providers were concerned about patients being able to equitably access and participate in digital health interventions. In addition, 3 themes were also identified from patient interviews: (1) patients received limited or nonspecific guidance regarding PA from providers and other resources, (2) patients want to share exercise metrics with the health care team and receive tailored PA guidance at regular intervals, and (3) patients need written resources to support setting up an RPM program with access to live assistance on an as-needed basis.
CONCLUSIONS: Implementation of an EHR-based RPM program and associated workflow is acceptable to PCPs and patients but will require attention to provider concerns of added burdensome patient data and patient concerns of receiving tailored PA guidance. Our ongoing work will pilot the RPM program and evaluate feasibility and acceptability within a primary care setting.

BACKGROUND: Technological advances based on mobile health (mHealth), the field of digital health (eHealth) aimed at managing health services and care and their concomitant transformations, have become increasingly important in the 21st century. In this context, care for individuals diagnosed with Chronic Non-Communicable Diseases (CNCDs) deserves to be highlighted. The aim of this study is to present the creation, development and implementation of the Telehealth Center of the Federal University of Viçosa (NUTELES - UFV), for the monitoring and management of health care for individuals with Arterial Hypertension (AH) and/or Diabetes Mellitus (DM).
METHODS: This study, carried out in 2022 and 2023, involved 374 patients diagnosed with AH and/or DM in the health micro-region of Viçosa/MG, Brazil, comprising nine municipalities. The research aims to describe the creation, development and implementation of a software (NutelesApp) for the collection, storage, management and analysis of epidemiological research in public health, focusing specifically, on the monitoring and management of health care for individuals with AH and/or DM monitored by Primary Health Care (PHC). The parameters collected and analyzed were obtained through a questionnaire applied to patients, consisting of 70 questions, subdivided into 12 classes of questions. The epidemiological survey data was collected using mobile devices and analyzed using computer techniques based on statistical analysis. Once the field teams had completed their work, the files were transferred to servers for general analysis processing, using estimates of means, prevalence and respective standard errors, calculated using the Statistical Package for Social Science (SPSS) program, which takes into account the planning variables and includes the basic weights resulting from the sampling process. This study was approved by the Human Research Ethics Committee and registered, prior to recruitment, by the Brazilian Registry of Clinical Trials (ReBEC), ID: RBR-45hqzmf (Last approval date: 11/30/2022).
RESULTS: The information obtained through data collection with subsequent exploratory analysis of epidemiological data using the NutelesApp software suggests that mobile applications intended for the purpose of monitoring and managing healthcare for people with AH and/or DM should address the variables necessary to support a process of understanding the health conditions and/or disease of the individual as a whole and provide short- and long-term learning. Regarding the results of the survey using the software, the variables of 374 people were analyzed. Majority are female (73.2%) and white (43.5%). Most patients are elderly (average 64 years), with blood pressure levels within the normal range for this population, BMI indicates overweight, AC increased risk for cardiovascular events and CC within the recommendation value. All biochemical parameters analyzed were above normal limits.
CONCLUSIONS: The description of the creation and development of the software includes practical examples of its implementation, the results collected and its applicability in real scenarios, presenting determining criteria that can provide assertive and timely interventions for monitoring and managing the health and/or disease parameters of patients with HA and/or DM. In addition, the beneficial consequences of using this application will extend to health units and their respective management, improving the services provided by PHC and enhancing strategies and actions for health promotion and disease prevention.

The practice of auscultation, interpreting body sounds to assess organ health, has greatly benefited from technological advancements in sensing and electronics. The advent of portable and wearable acoustic sensing devices marks a significant milestone in telemedicine, home health, and clinical diagnostics. This review summarises the contemporary advancements in acoustic sensing devices, categorized based on varied sensing principles, including capacitive, piezoelectric, and triboelectric mechanisms. Some representative acoustic sensing devices are introduced from the perspective of portability and wearability. Additionally, the characteristics of sound signals from different human organs and practical applications of acoustic sensing devices are exemplified. Challenges and prospective trends in portable and wearable acoustic sensors are also discussed, providing insights into future research directions.

Battery power is crucial for wearable devices as it ensures continuous operation, which is critical for real-time health monitoring and emergency alerts. One solution for long-lasting monitoring is energy harvesting systems. Ensuring a consistent energy supply from variable sources for reliable device performance is a major challenge. Additionally, integrating energy harvesting components without compromising the wearability, comfort, and esthetic design of healthcare devices presents a significant bottleneck. Here, we show that with a meticulous design using small and highly efficient photovoltaic (PV) panels, compact thermoelectric (TEG) modules, and two ultra-low-power BQ25504 DC-DC boost converters, the battery life can increase from 9.31 h to over 18 h. The parallel connection of boost converters at two points of the output allows both energy sources to individually achieve maximum power point tracking (MPPT) during battery charging. We found that under specific conditions such as facing the sun for more than two hours, the device became self-powered. Our results demonstrate the long-term and stable performance of the sensor node with an efficiency of 96%. Given the high-power density of solar cells outdoors, a combination of PV and TEG energy can harvest energy quickly and sufficiently from sunlight and body heat. The small form factor of the harvesting system and the environmental conditions of particular occupations such as the oil and gas industry make it suitable for health monitoring wearables worn on the head, face, or wrist region, targeting outdoor workers.

Wearable sensors are important components, converting mechanical vibration energy into electrical signals or other forms of output, which are widely used in healthcare, disaster warning, and transportation. However, the reliance on batteries limits the portability of wearable sensors and hinders their application in the field of Internet of Things. To solve this problem, we designed a miniaturized high-performance hybrid nanogenerator (MHP-HNG), which combined the functions of triboelectric sensing and electromagnetic power generation as well as the advantages of miniaturization. By optimizing the design of TENG and EMG, the wearable sensor achieved a voltage output of 14.14 V and a power output of 49 mW. Based on the wireless optical communication and wireless communication technologies, the wearable sensor achieved the integration of sensing, communication, and self-powered function, which is expected to realize health monitoring, emergency warning, and rehabilitation assistance, and further extend the potential application value in the medical field.

Preterm birth (PTB) remains a global health concern, impacting neonatal mortality and lifelong health consequences. Traditional methods for estimating PTB rely on electronic health records or biomedical signals, limited to short-term assessments in clinical settings. Recent studies have leveraged wearable technologies for in-home maternal health monitoring, offering continuous assessment of maternal autonomic nervous system (ANS) activity and facilitating the exploration of PTB risk. In this paper, we conduct a longitudinal study to assess the risk of PTB by examining maternal ANS activity through heart rate (HR) and heart rate variability (HRV). To achieve this, we collect long-term raw photoplethysmogram (PPG) signals from 58 pregnant women (including seven preterm cases) from gestational weeks 12-15 to three months post-delivery using smartwatches in daily life settings. We employ a PPG processing pipeline to accurately extract HR and HRV, and an autoencoder machine learning model with SHAP analysis to generate explainable abnormality scores indicative of PTB risk. Our results reveal distinctive patterns in PTB abnormality scores during the second pregnancy trimester, indicating the potential for early PTB risk estimation. Moreover, we find that HR, average of interbeat intervals (AVNN), SD1SD2 ratio, and standard deviation of interbeat intervals (SDNN) emerge as significant PTB indicators.

Thanks to its structural characteristics and signal patterns similar to those of human brain synapses, memristors are widely believed to be applicable for neuromorphic computing. However, to our knowledge, memristors have not been effectively applied in the biomedical field, especially in disease diagnosis and health monitoring. In this work, a blood-based biomemristor was prepared for in vitro detection of hyperglycemia and hyperlipidemia. It was found that the device exhibits excellent resistance switching (RS) behavior at lower voltage biases. Through mechanism analysis, it has been confirmed that the RS behavior is driven by Ohmic conduction and ion rearrangement. Furthermore, the hyperglycemia and hyperlipidemia detection devices were constructed for the first time based on memristor logic circuits, and circuit simulations were conducted. These results confirm the feasibility of blood-based biomemristors in detecting hyperglycemia and hyperlipidemia, providing new prospects for the important application of memristors in the biomedical field.