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Combining proximal remote sensing and machine learning (ML) has become a common approach to monitoring leaf chlorophyll content (LCC) for crop stress, productivity assessment, and nutrient management. However, the robustness of ML models is constrained by the limited numbers of in-situ training samples due to time-consuming and labour-intensive workflow in sample analysis. To cope with the issue of limited in-situ samples in monitoring potato LCC, this study used hybrid datasets that integrated limited in-situ measured samples and different-size PROSAIL model simulated samples to calibrate the ML models. Subsequently, the calibrated ML models were evaluated using independently field-measured data. During LCC sampling, canopy reflectance data (400–950 nm) were collected using a passive bi-directional spectrometer and an unmanned aerial vehicle carrying a hyperspectral sensor. Five types of ML models, including the partial least squares regression (PLSR), Gaussian process regression (GPR), random forest (RF), gradient boosting machines (GBM), and blending, were trained for LCC prediction. The scalability of the best ML models was evaluated using hyperspectral data extracted from unmanned aerial vehicle images. The results indicated that the ML models trained using the hybrid dataset outperformed those trained using the single limited in-situ measured dataset or the single PROSAIL simulated dataset when predicting the LCC of different potato cultivars. Nevertheless, when the number of measured in-situ samples was limited, the size of the simulated samples in the hybrid dataset influenced the prediction accuracy and robustness of the ML model. The RF model had the strongest generalization regardless of the handheld passive spectrometer data (R2 = 0.67, RPD = 1.55 and RMSE = 0.08 g m−2) and the aerial vehicle image data (R2 = 0.88, RPD = 1.97 and RMSE = 0.06 g m−2). Our results imply the potential of integrating limited in-situ samples with simulated data to achieve accurate and robust estimations for potato LCC. This study offers a key solution for crop chlorophyll monitoring in scenarios with restricted data availability. [ABSTRACT FROM AUTHOR]

Introduction: Digital health technologies have vastly improved monitoring, diagnosis, and care during pregnancy. As expectant mothers increasingly engage with social media, online platforms, and mobile applications, these innovations present valuable opportunities to enhance the quality of maternal healthcare services. Objective: This review aims to assess the applicability, outcomes, and recent advancement of digital health modalities in antenatal care. Method: We conducted a scoping review by searching four electronic databases (Scopus, Web of Science, PubMed, EBSCOhost), performing manual searches of Google Scholar, and examining the references of relevant studies. Eligible studies included original research published in English between 2010 and 2024 involving the use of digital health technologies for antenatal care, complying with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping review guidelines. Results: One hundred twenty-six eligible articles were identified, with the majority (61.11%) conducted in high-income countries, including the United States, United Kingdom, and Australia. Digital health studies have increased over time, driven by telehealth adoption in affluent nations. Interventions predominantly focused on patient-provider consultations, remote monitoring, and health education, complementing in-person visits or as a substitute when necessary. High levels of acceptance and satisfaction were reported among users. These interventions primarily targeted general maternal care (28.57%), gestational diabetes mellitus (15.07%), and mental health (13.49%) while also addressing gestational weight management, hypertensive disorders, high-risk pregnancies and maternal education. The findings demonstrated positive outcomes in managing clinical conditions, enhancing knowledge, promoting birth preparedness, and improving antenatal care access and utilisation. Additionally, the findings revealed the cost-effectiveness of these approaches in alleviating financial burdens for patients and healthcare systems. Conclusion: Digital health is emerging as a pivotal tool in maternal and child care, fostering positive outcomes and high acceptance among patients and healthcare providers. Its integration into antenatal care ensures the maintenance of standard care quality, with no adverse effects reported despite limited discussions on safety and privacy concerns. As these technologies continue to evolve, they are set to redefine antenatal care by offering more accessible, efficient, and patient-centred solutions, ultimately shaping the future of maternal healthcare delivery. [ABSTRACT FROM AUTHOR]

Objectives: This study examined whether community-dwelling older adults can independently capture wearable sensor data that can be used to classify frailty status. Methods: Fifty-one older adults (age 77.5 ± 8.4 years, height 163.6 77.5 ± 8.4, weight 72.0 ± 13.5 kg, female 76%) took part in this investigation. Participants independently captured physical activity and physical function data at home using a smartwatch and a research-grade inertial sensor system for 48-hours. Machine learning classifiers were used to determine whether the data obtained can discriminate between frailty levels. Results: Models incorporating variables from both the smartwatch and inertial sensor system were successful in the prediction of frailty status. Discussion: This study has demonstrated the ability of older adults to collect data which can be used to indicate their frailty risk. This may enable earlier intervention and lessen the impact of frailty on the individual and society as a whole. [ABSTRACT FROM AUTHOR]

To examine the association between post-stroke depression (PSD) and macrostructural and microstructural brain measures, and to explore whether changes in accelerometer-measured physical activity (PA) are associated with PSD, we conducted an exploratory study in UK Biobank with dementia-free participants diagnosed with at least one prior stroke. Eligible participants (n = 1186) completed an MRI scan. Depression was classified based on positive depression screening scores (PHQ-2 ≥ 3). Multivariate linear regression models assessed the relationships between depression and structural and diffusion measures generated from brain MRI scans. Logistic regression models were used to examine the relationship between accelerometer-measured daily PA and future depression (n = 367). Depression was positively associated with total white matter hyperintensities (WMHs) volume (standardized β [95% CI]—0.1339 [0.012, 0.256]; FDR-adjusted p-value—0.039), periventricular WMHs volume (standardized β [95% CI]—0.1351 [0.020, 0.250]; FDR-adjusted p-value—0.027), and reduced MD for commissural fibers (standardized β [95% CI]—−0.139 [−0.255, −0.024]; adjusted p-value—0.045). The odds of depression decreased by 0.3% for each daily minute spent in objectively measured light PA, while each minute spent in sleep from midnight to 6:00 AM was associated with a 0.9% decrease in the odds of depression. This early-stage analysis using a population cohort offers a scientific rationale for researchers using multimodal data sources to investigate the heterogenous nature of PSD and, potentially, identify stroke patients at risk of poor outcomes. [ABSTRACT FROM AUTHOR]

Patients at risk for rheumatoid arthritis (RA) describe fluctuating and nonspecific symptoms, making it difficult to quantify symptom burden and recognize RA progression. This study aimed to assess feasibility and diagnostic value of a multimodal digital self-monitoring program in preclinical RA. This prospective cohort study included individuals at-risk for RA, who first watched self-produced educational videos about (preclinical) RA and joint self-examination techniques and then started the REMOTRA symptom monitoring. Key outcomes measured included patient acceptance (Net Promoter Score: NPS), monitoring program usability (System usability scale: SUS), monitoring adherence, diagnostic accuracy, and reported symptom burden. A total of 43 participants (65.9% female, mean age 50.1 years) were enrolled. The educational and self-examination videos received NPS ratings of 54.4 and 31.6, respectively. The monitoring software received usability scores of 88.1/100 (SD: 5.5) at three months and 85.4/100 (SD: 16.0) at 6 months. 24/41 (58.5%) completed all questionnaires, and the average app usage was 4.8 months (SD: 1.8). None of the patients with a REMOTRA score below 10 developed RA, yielding a negative predictive value and sensitivity of 100%. However, the positive predictive value was 12%, and the specificity was 42.1%. Analgesic and cortisone usage was reported by 58.5% and 29.3% of participants, respectively. The strong patient acceptance, ease of use, and high adherence rates, combined with encouraging diagnostic outcomes, underscore the potential of this personalized digital monitoring and education approach. These findings suggest that further validation through multicenter studies is warranted. [ABSTRACT FROM AUTHOR]

Improving patient outcomes in critical care settings is significantly connected to effective ventilation control. This research introduces a new method for improving ventilation methods in critical healthcare utilizing a long short-term memory (LSTM) network hosted in the cloud. Ventilators, pulse oximeters, and capnography are just a few examples of medical equipment that input data into the system, which then uploads the data to the cloud for analysis. The LSTM network can learn from data patterns and correlations, drawing on respiratory parameters' time dynamics, to provide real-time suggestions and predictions for ventilation settings. The system aims to improve clinical results and reduce the risk of ventilator-induced lung damage by tailoring ventilation techniques according to each patient's requirements and by forecasting potential issues. Due to remote monitoring technology, medical professionals can quickly analyze their patient's conditions and act accordingly. The system allows for continuous improvement using iterative learning of more data and feedback. With the ability to optimize breathing and enhance patient care in critical healthcare situations, a hopeful development in airway management is needed. [ABSTRACT FROM AUTHOR]

Healthcare providers face a significant challenge in the treatment of chronic pain, requiring creative responses to enhance patient outcomes and streamline healthcare delivery. It suggests using cloud-based remote management with machine learning (ML) to alleviate chronic pain. Wearable device data, electronic health record (EHR) data, and patient-reported outcomes are all inputs into the suggested system's data analysis pipeline, which combines support vector machines (SVM) with recurrent neural networks (RNN). SVM's powerful classification skills make it possible to classify patients' risks and predict how they will react to therapy. RNNs are very good at processing sequential data, which means they may identify trends in patient symptoms and drug adherence over time. By integrating these algorithms, healthcare professionals may create individualized treatment programs that consider each patient's preferences and specific requirements. Early intervention and proactive treatment of pain symptoms are made possible by the system's ability to monitor patients in real-time remotely. The system is further improved by using predictive analytics to identify patients who could benefit from extra support services and to forecast when they will have acute pain episodes. The proposed approach can change the game regarding managing chronic pain. It provides data-driven, individualized treatment that improves patient outcomes while cutting healthcare expenses. [ABSTRACT FROM AUTHOR]

Early detection of recurrent cervical cancer is important to improve survival rates. The aim of this study was to explore the clinical performance of DNA methylation markers and high‐risk human papillomavirus (HPV) in cervicovaginal self‐samples and urine for the detection of recurrent cervical cancer. Cervical cancer patients without recurrence (n = 47) collected cervicovaginal self‐samples and urine pre‐ and posttreatment. Additionally, 20 patients with recurrent cervical cancer collected cervicovaginal self‐samples and urine at time of recurrence. All samples were self‐collected at home and tested for DNA methylation and high‐risk HPV DNA by PCR. In patients without recurrent cervical cancer, DNA methylation levels decreased 2‐years posttreatment compared to pretreatment in cervicovaginal self‐samples (p <.0001) and urine (p <.0001). DNA methylation positivity in cervicovaginal self‐samples was more frequently observed in patients with recurrence (77.8%) than in patients without recurrence 2‐years posttreatment (25.5%; p =.0004). Also in urine, DNA methylation positivity was more frequently observed in patients with recurrence (65%) compared to those without recurrence (35.6%; p =.038). Similarly, high‐risk HPV positivity in both cervicovaginal self‐samples and urine was more frequent (52.6% and 55%, respectively) in patients with recurrence compared to patients without recurrence (14.9% and 8.5%, respectively) (p =.004 and p =.0001). In conclusion, this study shows the potential of posttreatment monitoring of cervical cancer patients for recurrence by DNA methylation and high‐risk HPV testing in cervicovaginal and urine samples collected at home. The highest recurrence detection rate was achieved by DNA methylation testing in cervicovaginal self‐samples, detecting 77.8% of all recurrences and, specifically, 100% of the local recurrences. [ABSTRACT FROM AUTHOR]

Background: There is growing interest in developing sensing solutions for remote health monitoring to support the safety and independence of older adults. To ensure these technologies are practical and relevant, people-centred design is essential. This study aims to explore the involvement of various stakeholders across different developmental stages to inform the design and assess the capabilities of unobtrusive sensing solutions being developed as part of the Advanced Care Research Centre (ACRC), Edinburgh, UK. Methods: This study was conducted in two phases. In Phase I (Ideation), discussions were held with stakeholders (n = 19), including senior geriatricians (n = 2), healthcare and care home professionals (n = 4), PPI experts (n = 2), researchers (n = 4) and public members aged 65 and above from the ACRC Patient and Public Involvement (PPI) Network (n = 7). The goal was to identify clinically significant health parameters and design preferences. Based on this, prototypes of unobtrusive sensors for monitoring movement, hydration, and respiration were developed. In Phase II (Development and Co-Design), an in-person PPI workshop was conducted with PPI experts (n = 2), researchers (n = 4) and PPI members (n = 8). The developed prototypes were demonstrated, and qualitative feedback was collected through focus group discussions on themes such as acceptability, usability, privacy, data sharing, and functionality enhancement. Results: Stakeholder input from Phase I emphasized the importance of non-contact sensing technologies that maintain privacy. Movement, hydration, and respiration were identified as critical health parameters. In Phase II, PPI members were optimistic about the prototypes, valuing their unobtrusive design and privacy-preserving features. Key themes identified included (1) the need for user-customized alarms, (2) clear data-sharing protocols, and (3) the importance of embedding sensors into familiar household objects. Suggestions for refining the prototypes included adding functionality for detecting deviations in daily routines and integrating feedback mechanisms for caregivers. Conclusions: Involving diverse stakeholders from the early stages of technology development enhanced the relevance and acceptability of unobtrusive sensing solutions. This study highlights the importance of integrating public perspectives into the design process. For successful implementation, developers of healthcare technologies should prioritize privacy, usability, and clear communication with end-users and caregivers. [ABSTRACT FROM AUTHOR]

Objective The transition from the neonatal intensive care unit (NICU) to the home is complex and multifaceted for families and infants, particularly those with ongoing medical needs. Our hospital utilizes a remote monitoring program called Growing @ Home (G@H) to support discharge from the NICU with continued nasogastric tube (NGT) feeds. We aim to describe the experience of the transition from NICU to home for families enrolled in G@H. Study Design Using a semistructured interviewing technique, parents of infants discharged on G@H were interviewed at NICU discharge, at 1 month, and at 6 months after NICU discharge. Interviews were recorded and transcribed into data analysis software. Conventional content analysis was used to analyze qualitative data. Codes were assigned to describe key elements of the interviews and used to identify major themes. Results Parents (n = 17) identified three major themes when discussing the effect of G@H on the transition to home. The program provided a means of escape from the NICU, allowing families to stop living split lives between their homes and the NICU. It acted as a middle ground between the restrictive yet supportive NICU environment, and the normal yet isolated home environment. G@H served as a safety net for families, providing a continued connection to the NICU for their still-fragile infants. Conclusion G@H utilizes telehealth to positively support the complex transition from NICU to home for families and infants discharged with NGT feeds. Key Points G@H program supported parents in their transition from NICU to home. G@H program provided a means of escape from the NICU. G@H program was a middle ground between the NICU and home. G@H program created a safety net after discharge. Follow-up with a consistent provider was essential to a positive parent experience. [ABSTRACT FROM AUTHOR]

Background: Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality in the United States. Frequent exacerbations result in higher use of emergency services and hospitalizations, leading to poor patient outcomes and high costs. The objective of this study is to demonstrate the feasibility of a multimodal, community-based intervention in treating acute COPD exacerbations. Results: Over 18 months, 1,333 patients were approached and 100 (7.5%) were enrolled (mean age 66, 52% female). Ninety-six participants (96%) remained in the study for the full enrollment period. Fifty-five (55%) participated in tele-pulmonary-rehabilitation. Participants wore the smartwatch for a median of 114 days (IQR 30–210) and 18.9 h/day (IQR16-20) resulting in a median of 1034 min/day (IQR 939–1133). The rate at which participants completed scheduled survey instruments ranged from 78–93%. Nearly all participants (85%) performed COPD ecological momentary assessment at least once with a median of 4.85 recordings during study participation. On average, a 2.48-point improvement (p = 0.03) in COPD Assessment Test Score was observed from baseline to study completion. The adherence and symptom improvement metrics were not associated with baseline patient activation measures. Conclusions: A multimodal intervention combining preventative care, symptom and biometric monitoring, and MIH services was feasible in adults living with COPD. Participants demonstrated high protocol fidelity and engagement and reported improved quality of life. Trial Registration: The study is registered at Clinicaltrials.gov NCT06000696 (Registered on 08/14/2023). [ABSTRACT FROM AUTHOR]

Hepatic encephalopathy (HE) is a critical neuropsychiatric complication of liver cirrhosis with a significant impact on patient quality of life and survival. The global prevalence of cirrhosis and associated HE necessitates a comprehensive understanding of the condition and effective systems of care to optimize outcomes. This review addresses the epidemiology, classification, diagnosis, and management of HE, with an emphasis on systems of care that improve outcomes for people with HE. Current diagnostic challenges include differentiating cognitive deficits attributable to HE from those caused by other etiologies, highlighting the need for accurate diagnostic methods. Traditional psychometric tests, while valuable for diagnosing covert HE (CHE), are limited in their ability to predict overt HE (OHE) due to various confounding factors. As a result, non-psychometric tools have been developed to provide outcome-based predictions aligned with the clinical course of HE. The management of HE includes addressing precipitating factors, pharmacologic interventions to reduce ammonia levels, and supportive care, with lactulose and rifaximin playing a central role. Preventive strategies with the use of remote monitoring in the outpatient management of HE, integrating technology for real-time tracking of therapy compliance and symptom evolution, could contribute to reducing hospital readmissions and improving patient care. [ABSTRACT FROM AUTHOR]

Human hands have over 20 degrees of freedom, enabled by a complex system of bones, muscles, and joints. Hand differences can significantly impair dexterity and independence in daily activities. Accurate assessment of hand function, particularly digit movement, is vital for effective intervention and rehabilitation. However, current clinical methods rely on subjective observations and limited tests. Smart gloves with inertial measurement unit (IMU) sensors have emerged as tools for capturing digit movements, yet their sensor accuracy remains underexplored. This study developed and validated an IMU-based smart glove system for measuring finger joint movements in individuals with hand differences. The glove measured 3D digit rotations and was evaluated against an industrial robotic arm. Tests included rotations around three axes at 1°, 10°, and 90°, simulating extension/flexion, supination/pronation, and abduction/adduction. The IMU sensors demonstrated high accuracy and reliability, with minimal systematic bias and strong positive correlations (p > 0.95 across all tests). Agreement matrices revealed high agreement (<1°) in 24 trials, moderate (1–10°) in 12 trials, and low (>10°) in only 4 trials. The Root Mean Square Error (RMSE) ranged from 1.357 to 5.262 for the 90° tests, 0.094 to 0.538 for the 10° tests, and 0.129 to 0.36 for the 1° tests. Likewise, mean absolute error (MAE) ranged from 0.967 to 4.679 for the 90° tests, 0.073 to 0.386 for the 10° tests, and 0.102 to 0.309 for the 1° tests. The sensor provided precise measurements of digit angles across 0–90° in multiple directions, enabling reliable clinical assessment, remote monitoring, and improved diagnosis, treatment, and rehabilitation for individuals with hand differences. [ABSTRACT FROM AUTHOR]

Introduction/Aims: Fluctuating symptoms and fatigue are common issues in myasthenia gravis (MG), but it is unclear if these symptoms are related to physical activity or sleep patterns. This study sought to determine the day‐to‐day relationship between patient‐reported symptoms and physical activity and sleep over 12 weeks. Methods: Sixteen participants with generalized MG wore a wrist‐mounted accelerometer continuously for the study duration and reported their symptoms and fatigue each evening. Cumulative link mixed models were used to analyze whether clinical and demographic characteristics, physical activity, and sleep were related to symptom severity and fatigue over the study period. Three types of models were constructed: a cohort model, a model in which data was scaled to each participant, and individual models. Results: The cohort model indicated that higher disease severity, female sex, more comorbidities, less physical activity, more inactive time, and lower quantity of sleep were significantly associated with increased symptom severity and fatigue (p <.05). However, in the within‐participant scaled model, there were almost no significant associations with physical activity or sleep. In the individual models, some participants showed similar results to the cohort model, but others showed no associations or the opposite response in some variables. Discussion: While physical activity and sleep were associated with self‐reported symptoms and fatigue within this population, this was not necessarily applicable to individuals. This demonstrates the importance of an individualized analysis for determining how physical activity and sleep may impact outcomes in MG, with implications for clinical and self‐management. [ABSTRACT FROM AUTHOR]

Background: Emerging evidence suggests that virtual patient observation (VPO) may help promote patient safety. Purpose: The purpose of this study was to examine and describe the demographic and clinical characteristics of patients who incurred VPO. Methods: A retrospective analysis was conducted. Differences in total VPO hours between groups were examined, followed by a hierarchical regression to investigate the effect of predictor variables on VPO utilization variance. Results: A total of 286 patient charts were reviewed. Mean VPO hours were higher in patients with an intensive care unit admission history. Adjusted for gender and history of dementia/Alzheimer's/memory impairment, the prediction of total VPO hours increased with the patient's intensive care unit admission history and overall hospital length of stay. Conclusions: Examining the prevalence and variability in the pattern of VPO utilization by specific patient characteristics is essential for identifying what patients could best benefit from this technology. [ABSTRACT FROM AUTHOR]

Purpose of Review: The purpose of this review is to describe the development and key features of the Prospera™ Spinal Cord Stimulation (SCS) System, as well as the clinical evidence supporting its use. Prospera delivers therapy using a proprietary multiphase stimulation paradigm and is the first SCS system to offer proactive care through automatic, objective, daily, remote device monitoring and remote programming capabilities. Recent Findings: Results from the recently published BENEFIT-02 trial support the short-term safety and efficacy of multiphase stimulation in patients with chronic pain. BENEFIT-03 is an ongoing, multicenter, single-arm study with 24-month follow-up; interim analyses suggest that multiphase therapy is safe and effective and that patients and clinicians have positive experiences with remote device management. Summary: Preliminary evidence suggests that the Prospera SCS System represents an opportunity to improve patient care by combining an effective multiphase stimulation paradigm with an efficient proactive care model. [ABSTRACT FROM AUTHOR]

Objectives Mobile health (mHealth) regimens can improve health through the continuous monitoring of biometric parameters paired with appropriate interventions. However, adherence to monitoring tends to decay over time. Our randomized controlled trial sought to determine: (1) if a mobile app with gamification and financial incentives significantly increases adherence to mHealth monitoring in a population of heart failure patients; and (2) if activity data correlate with disease-specific symptoms. Materials and Methods We recruited individuals with heart failure into a prospective 180-day monitoring study with 3 arms. All 3 arms included monitoring with a connected weight scale and an activity tracker. The second arm included an additional mobile app with gamification, and the third arm included the mobile app and a financial incentive awarded based on adherence to mobile monitoring. Results We recruited 111 heart failure patients into the study. We found that the arm including the financial incentive led to significantly higher adherence to activity tracker (95% vs 72.2%, P  = .01) and weight (87.5% vs 69.4%, P  = .002) monitoring compared to the arm that included the monitoring devices alone. Furthermore, we found a significant correlation between daily steps and daily symptom severity. Discussion and Conclusion Our findings indicate that mobile apps with added engagement features can be useful tools for improving adherence over time and may thus increase the impact of mHealth-driven interventions. Additionally, activity tracker data can provide passive monitoring of disease burden that may be used to predict future events. [ABSTRACT FROM AUTHOR]

Background: Pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH) are severe, progressive diseases characterized by key symptoms such as dyspnea and fatigue. These symptoms impair physical functioning, with patients struggling to perform their daily activities. One traditional measure of physical functioning and exercise capacity is the 6-minute walk test (6MWT). Actigraphy represents a promising tool to complement the 6MWT and provide a holistic picture of physical performance in patients with PAH or CTEPH. However, the current literature holds limited evidence on content validity of actigraphy in these populations, as reported by patients themselves. The primary objective of this study was to understand which physical functioning concepts are most meaningful to patients with PAH or CTEPH and identify relevant actigraphy variables and appropriate timeframes for their measurement. Methods: This was a cross-sectional, qualitative study in adults with a confirmed diagnosis of PAH or CTEPH. Participants from the UK and USA were interviewed one-on-one via a web-based platform, with interviewers using a semi-structured discussion guide that included concept elicitation and cognitive debriefing sections. Data within the anonymized interview transcripts were coded and thematically analyzed. Results: Concept elicitation identified the physical functioning concepts most meaningful to patients with PAH or CTEPH and generated a combined conceptual model of physical functioning, which strongly aligned with previous literature. During cognitive debriefing, of the four actigraphy variables debriefed in relation to these physical functioning concepts, study participants highly valued time spent in non-sedentary physical activity and time spent in moderate to vigorous activity, while step count and walking speed emerged as less relevant. Participants indicated four alternative variables as relevant: walking distance, walking up hills or inclines, duration of continuous walking bouts, and time spent walking. Regardless of the variable, participants suggested a timeframe of approximately 10 or 12 h/day over a minimum of 14 days for measuring physical functioning. Conclusions: By demonstrating the content validity of actigraphy measures of physical functioning, this qualitative study begins to address the evidence gaps identified by the regulatory requirements for using actigraphy endpoints in future PAH and CTEPH clinical trials. [ABSTRACT FROM AUTHOR]

Background/Objectives: This study assessed the feasibility and security of remote surgical wound monitoring using the RedScar© smartphone app, which employs automated diagnosis for early visual detection of infections without direct healthcare personnel involvement. Additionally, patient satisfaction with telematic care was evaluated as a secondary aim. Surgical site infection (SSI) is the second leading cause of healthcare-associated infections (HAIs), leading to prolonged hospital stays, heightened patient distress, and increased healthcare costs. Methods: The study employed a prospective paired-cohort and single-blinded design, with a sample size of 47 adult patients undergoing abdominal surgery. RedScar© was used for remote telematic monitoring, evaluating the feasibility and security of this approach. A satisfaction questionnaire assessed patient experience. The study protocol was registered at ClinicalTrials.gov under the identifier NCT05485233. Results: Out of 47 patients, 41 successfully completed both remote and in-person follow-ups. RedScar© demonstrated a sensitivity of 100% in detecting SSIs, with a specificity of 83.13%. The kappa coefficient of 0.8171 indicated substantial agreement between the application's results and human observers. Patient satisfaction with telemonitoring was high: 97.6% believed telemonitoring reduces costs, 90.47% perceived it prevents work/school absenteeism, and 80.9% found telemonitoring comfortable. Conclusions: This is the first study to evaluate an automatic smartphone application on real patients for diagnosing postoperative wound infections. It establishes the safety and feasibility of telematic follow-up using the RedScar© application for surgical wound assessment. The high sensitivity suggests its utility in identifying true cases of infection, highlighting its potential role in clinical practice. Future studies are needed to address limitations and validate the efficacy of RedScar© in diverse patient populations. [ABSTRACT FROM AUTHOR]

Rapid assessment of the results of forest fires during the fire season can be useful for solving various applied problems, including timely assessment of the efforts of forest fire services and planning the work for upcoming fire seasons. The paper presents the first results of assessing fire risk of forests in the 2023 fire season. The work discusses some meteorological features of the season and shows that the 2023 fire season began on average 11 days later than usual. It is noted that in terms of the total area covered by forest fires, the fire season of 2023 cannot be called "extreme"; it should rather be classified as a season of medium or low fire intensity. An attempt is made to use quantitative criteria to assess fire rates in different regions, which would make it possible to compare the situations in regions with different forest conditions. To do this, we analyze the relative fire intensity by region (the ratio of the area covered by fire to the area of forests), which is compared with average data for 11 years, as well as with data from the previous year 2022. A fairly simple criterion is used to classify the situation observed in a particular region, attributing a normal (close to the long-term average), low, or high fire rate to it. For this purpose, we use information on statistical norms obtained for various regions on the basis of information on fire rates for previous years and the observed standard deviations from them. According to the results of the analysis, fire rate was at the long-term average level in 63% of the country's territory, whereas it was higher only in 19% of the territory. [ABSTRACT FROM AUTHOR]

Background: Acute remote home monitoring of acutely ill patients with COVID-19 holds potential for early detection of deterioration and thus subsequentearly intervention that may prevent or mitigate progression to severe illness and need for respiratory support. Our aim was to describe common features of acute remote home monitoring programs for acutely ill patients with COVID-19 in the Netherlands. Methods: We performed literature searches (both grey and academic) between 1st March 2020 and 1st March 2023 to identify Dutch acute remote home monitoring initiatives, excluding studies on early hospital discharge. From the available protocols, we extracted relevant information on patient eligibility, organization of acute remote home monitoring and home management. Results: We identified and approached ten acute remote home monitoring initiatives for information regarding their used protocols. Seven out of ten protocols were retrieved and assessed. All initiatives focused on adult patients with COVID-19 who where at risk of developing severe COVID-19, and all initiatives provided close follow-up through remote home monitoring using medically certified pulse oximeters. Daily measurements included peripheral oxygen saturation (all initiatives, n = 7), body temperature (n = 6), heart frequency per minute (n = 4) and breathing rate per minute (n = 4). For follow-up and review of measured values, in most initiatives (n = 6) the physician (general practitioner or hospital physician) in charge was supported by a dedicated monitoring center. In 5 out of 7 initiatives, the general practitioner (GP) was responsible for supervising the patients and monitoring staff. Conclusion: The acute remote home monitoring initiatives that emerged in the Netherlands during the first wave of the COVID-19 pandemic were similarly organized. Common building blocks for home monitoring include daily check of peripheral oxygen saturation, monitoring through a dedicated remote monitoring center alongside healthcare personnel and a supervising physician. [ABSTRACT FROM AUTHOR]

Purpose Postpartum hypertension (PPHTN) poses increased risks, including of stroke. Timely assessment and management by clinicians is imperative but challenging. Team-based care involving pharmacists has shown promise in improving blood pressure control, yet its application in PPHTN management remains unexplored. The objective of this study was to determine the impact and feasibility of an interprofessional model for PPHTN management. Summary This initiative implemented a novel interprofessional model at a safety-net hospital to address previous workflow limitations. Ambulatory care pharmacists collaborated with an obstetric nurse (OBRN) and a maternal fetal medicine specialist to manage high-risk patients with PPHTN utilizing electronic consults (e-consults). Data collection and symptom assessment were completed by an OBRN via telemedicine appointments. Pharmacists employed a collaborative practice agreement based on a preestablished algorithm to initiate medications. Data on patient demographics, consult volume, prescriptions, and pharmacist comfort were collected during the first quarter of full integration. Pharmacists completed 55 e-consults and generated 54 prescriptions. The average time spent per chart review was 12.5 minutes, and the average time to completion of e-consults was 54 minutes. Forty-five unique patients received care, who were primarily non–English-speaking and non-Hispanic Black patients. Pharmacists reported moderate to high comfort levels in managing PPHTN based on the algorithm and provided feedback leading to workflow adjustments. Conclusion Integration of pharmacists into PPHTN care enables prompt medication initiation and titration. This innovative model, involving remote blood pressure monitoring, telemedicine visits with an OBRN, and e-consults completed by pharmacists, ensures delivery of timely and equitable care and improved access across a diverse population. [ABSTRACT FROM AUTHOR]

This systematic review critically evaluates the current state and future potential of real-time, end-to-end smart, and automated irrigation management systems, focusing on integrating the Internet of Things (IoTs) and machine learning technologies for enhanced agricultural water use efficiency and crop productivity. In this review, the automation of each component is examined in the irrigation management pipeline from data collection to application while analyzing its effectiveness, efficiency, and integration with various precision agriculture technologies. It also investigates the role of the interoperability, standardization, and cybersecurity of IoT-based automated solutions for irrigation applications. Furthermore, in this review, the existing gaps are identified and solutions are proposed for seamless integration across multiple sensor suites for automated systems, aiming to achieve fully autonomous and scalable irrigation management. The findings highlight the transformative potential of automated irrigation systems to address global food challenges by optimizing water use and maximizing crop yields. [ABSTRACT FROM AUTHOR]

Substance use disorders affect 17.3% of Americans. Digital health solutions that use machine learning to detect substance use from wearable biosignal data can eventually pave the way for real-time digital interventions. However, difficulties in addressing severe between-subject data heterogeneity have hampered the adaptation of machine learning approaches for substance use detection, necessitating more robust technological solutions. We tested the utility of personalized machine learning using participant-specific convolutional neural networks (CNNs) enhanced with self-supervised learning (SSL) to detect drug use. In a pilot feasibility study, we collected data from 9 participants using Fitbit Charge 5 devices, supplemented by ecological momentary assessments to collect real-time labels of substance use. We implemented a baseline 1D-CNN model with traditional supervised learning and an experimental SSL-enhanced model to improve individualized feature extraction under limited label conditions. Results: Among the 9 participants, we achieved an average area under the receiver operating characteristic curve score across participants of 0.695 for the supervised CNNs and 0.729 for the SSL models. Strategic selection of an optimal threshold enabled us to optimize either sensitivity or specificity while maintaining reasonable performance for the other metric. Conclusion: These findings suggest that Fitbit data have the potential to enhance substance use monitoring systems. However, the small sample size in this study limits its generalizability to diverse populations, so we call for future research that explores SSL-powered personalization at a larger scale. [ABSTRACT FROM AUTHOR]

Transvenous lead failure associated with cardiac pacing and defibrillation remains an important clinical problem, with an estimated incidence between 1 to 2%. Oversensing of non-physiological signals usually precede lead impedance changes and may result in clinical compliations such as pacing inhibition and inappropriate shocks. Device based algorithms that identify non-physiological signals can be used in conjunction with remote monitoring to facilitate early diagnosis and management of lead failure and avoid serious adverse outcomes. This review highlights mechanisms of lead failure and proposes a diagnostic approach to suspected lead failure. [ABSTRACT FROM AUTHOR]

Gait is one of the most extensively studied motor tasks using motion capture systems, the gold standard for instrumental gait analysis. Various sensor-based solutions have been recently proposed to evaluate gait parameters, typically providing lower accuracy but greater flexibility. Validation procedures are crucial to assess the measurement accuracy of these solutions since residual errors may arise from environmental, methodological, or processing factors. This study aims to enhance validation by employing machine learning techniques to investigate the impact of such errors on the overall assessment of gait profiles. Two datasets of gait trials, collected from healthy and post-stroke subjects using a motion capture system and a 3D camera-based system, were considered. The estimated gait profiles include spatiotemporal, asymmetry, and body center of mass parameters to capture various normal and pathologic gait peculiarities. Machine learning models show the equivalence and the high level of agreement and concordance between the measurement systems in assessing gait profiles (accuracy: 98.7%). In addition, they demonstrate data interchangeability and integrability despite residual errors identified by traditional statistical metrics. These findings suggest that validation procedures can extend beyond strict measurement differences to comprehensively assess gait performance. [ABSTRACT FROM AUTHOR]

Purpose of Review: To survey recent progress in the application of implantable and wearable sensors to detection and management of cardiac arrhythmias. Recent Findings: Sensor-enabled strategies are critical for the detection, prediction and management of arrhythmias. In the last several years, great innovation has occurred in the types of devices (implanted and wearable) that are available and the data they collect. The integration of artificial intelligence solutions into sensor-enabled strategies has set the stage for a new generation of smart devices that augment the human clinician. Summary: Smart devices enhanced by new sensor technologies and Artificial Intelligence (AI) algorithms promise to reshape the care of cardiac arrhythmias. [ABSTRACT FROM AUTHOR]

The document explores the role of digital health technologies in improving medication adherence for cardiovascular disease (CVD) patients. It highlights the challenges of adherence to prescribed medications and lifestyle changes, emphasizing the importance of adherence for better health outcomes. Various factors affecting medication adherence in CVD, such as patient-related, disease-related, medication-related, healthcare system, and socioeconomic conditions, are discussed. The document also delves into the potential of digital health tools like wearable devices, apps, and telemedicine platforms in enhancing treatment adherence, providing real-time monitoring, and personalized support. Lastly, it addresses barriers to adopting digital health solutions and suggests future directions for improving CVD treatment adherence through collaboration, personalization, and data security measures. [Extracted from the article]

Purpose of Review: Neuromodulation techniques have emerged as promising strategies for managing chronic pain. These techniques encompass various modalities of nerve stimulation, including Spinal Cord Stimulation (SCS), Dorsal Root Ganglion Stimulation (DRG-S), and Peripheral Nerve Stimulation (PNS). Studies consistently demonstrate significant improvements in pain intensity, quality of life, and reduced opioid usage among patients treated with these modalities. However, neuromodulation presents challenges, such as the need for frequent in-person follow-up visits to ensure proper functionality of the implanted device. Our review explored factors impacting compliance in current neuromodulation users and examined how remote monitoring can mitigate some of these challenges. We also discuss outcomes of recent studies related to remote monitoring of neuromodulation. Recent Findings: While remote monitoring capabilities for neuromodulation devices is an emerging development, there are promising results supporting its role in improving outcomes for chronic pain patients. Higher patient satisfaction, improved pain control, and reduced caretaker burdens have been observed with the use of remote monitoring. Summary: This review discusses the current challenges with neuromodulation therapy and highlights the role of remote monitoring. As the field continues to evolve, understanding the importance of remote monitoring for neuromodulation is crucial for optimizing pain management outcomes. [ABSTRACT FROM AUTHOR]

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