Management of Hypertension in the Digital Era: Small Wearable Monitoring Devices for Remote Blood Pressure Monitoring

Blood Pressure Time in Target Range and its Impact on Clinical Outcomes

PURPOSE OF REVIEW

To examine the concept of time in target range for blood pressure (BP) management, exploring its calculation methods, implications for patient outcomes, and potential use in patient care.

RECENT FINDINGS

Recent post-hoc analyses of clinical trials and observational studies highlight the importance of BP time in target range in predicting cardiovascular outcomes. Higher time in target range correlates with reduced risks of major adverse cardiovascular events including heart failure, stroke, myocardial infarction and all-cause mortality. Additionally, longer time in target range decreases the risk of incident atrial fibrillation and risk of developing dementia. BP time in target range is a novel metric offering valuable insights into BP control and its impact on clinical outcomes. Higher time in target range is consistently associated with better cardiovascular outcomes across various patient populations. However, the clinical application of BP time in target range requires further investigation through prospective clinical trials and real-world studies. Integrating wearable devices for continuous BP monitoring could enhance the practical utility of BP time in target range in hypertension management.

Recent Advancements in Acrylic Fabric Applications: A Comprehensive Review and Future Trends

Acrylic fibres, as synthetic polymers, have been used extensively in the textile industry to create a wide variety of products, ranging from apparel and home furnishings to car rooftops and carbon fibres. Their widespread application is attributed to a combination of desirable properties, including a soft, wool-like texture, chemical stability, and robust mechanical characteristics. Furthermore, the chemical structure of acrylic fibres can be modified to imbue them with additional features, such as antimicrobial properties, fire resistance, conductivity, water repellency, and ultraviolet protection. This review explores the technological methods employed to functionalise acrylic fibres and discusses future trends in their development.

HGCTNet: Handcrafted Feature-Guided CNN and Transformer Network for Wearable Cuffless Blood Pressure Measurement

Biosignals collected by wearable devices, such as electrocardiogram and photoplethysmogram, exhibit redundancy and global temporal dependencies, posing a challenge in extracting discriminative features for blood pressure (BP) estimation. To address this challenge, we propose HGCTNet, a handcrafted feature-guided CNN and transformer network for cuffless BP measurement based on wearable devices. By leveraging convolutional operations and self-attention mechanisms, we design a CNN-Transformer hybrid architecture to learn features from biosignals that capture both local information and global temporal dependencies. Then, we introduce a handcrafted feature-guided attention module that utilizes handcrafted features extracted from biosignals as query vectors to eliminate redundant information within the learned features. Finally, we design a feature fusion module that integrates the learned features, handcrafted features, and demographics to enhance model performance. We validate our approach using two large wearable BP datasets: the CAS-BP dataset and the Aurora-BP dataset. Experimental results demonstrate that HGCTNet achieves an estimation error of 0.9 ± 6.5 mmHg for diastolic BP (DBP) and 0.7 ± 8.3 mmHg for systolic BP (SBP) on the CAS-BP dataset. On the Aurora-BP dataset, the corresponding errors are -0.4 ± 7.0 mmHg for DBP and -0.4 ± 8.6 mmHg for SBP. Compared to the current state-of-the-art approaches, HGCTNet reduces the mean absolute error of SBP estimation by 10.68% on the CAS-BP dataset and 9.84% on the Aurora-BP dataset. These results highlight the potential of HGCTNet in improving the performance of wearable cuffless BP measurements.

Association of Remote Patient Monitoring with Mortality and Healthcare Utilization in Hypertensive Patients: a Medicare Claims-Based Study

BACKGROUND

Hypertension management is complex in older adults. Recent advances in remote patient monitoring (RPM) have warranted evaluation of RPM use and patient outcomes.

OBJECTIVE

To study associations of RPM use with mortality and healthcare utilization measures of hospitalizations, emergency department (ED) utilization, and outpatient visits.

DESIGN

A retrospective cohort study.

PATIENTS

Medicare beneficiaries aged ≥65 years with an outpatient hypertension diagnosis between July 2018 and September 2020. The first date of RPM use with a corresponding hypertension diagnosis was recorded (index date). RPM non-users were documented from those with an outpatient hypertension diagnosis; a random visit was selected as the index date. Six months prior continuous enrollment was required.

MAIN MEASURES

Outcomes studied within 180 days of index date included (i) all-cause mortality, (ii) any hospitalization, (iii) cardiovascular-related hospitalization, (iv) non-cardiovascular-related hospitalization, (v) any ED, (vi) cardiovascular-related ED, (vii) non-cardiovascular-related ED, (viii) any outpatient, (ix) cardiovascular-related outpatient, and (x) non-cardiovascular-related outpatient. Patient demographics and clinical variables were collected from baseline and index date. Propensity score matching (1:4) and Cox regression were performed. Hazard ratios (HR) and 95% confidence intervals (CI) are reported.

KEY RESULTS

The matched sample had 16,339 and 63,333 users and non-users, respectively. Cumulative incidences of mortality outcome were 2.9% (RPM) and 4.3% (non-RPM), with a HR (95% CI) of 0.66 (0.60-0.74). RPM users had lower hazards of any [0.78 (0.75-0.82)], cardiovascular-related [0.79 (0.73-0.87)], and non-cardiovascular-related [0.79 (0.75-0.83)] hospitalizations. No significant association was observed between RPM use and the three ED measures. RPM users had higher hazards of any [1.10 (1.08-1.11)] and cardiovascular-related outpatient visits [2.17 (2.13-2.19)], while a slightly lower hazard of non-cardiovascular-related outpatient visits [0.94 (0.93-0.96)].

CONCLUSIONS

RPM use was associated with substantial reductions in hazards of mortality and hospitalization outcomes with an increase in cardiovascular-related outpatient visits.

Augmenting K-Means Clustering With Qualitative Data to Discover the Engagement Patterns of Older Adults With Multimorbidity When Using Digital Health Technologies: Proof-of-Concept Trial

BACKGROUND

Multiple chronic conditions (multimorbidity) are becoming more prevalent among aging populations. Digital health technologies have the potential to assist in the self-management of multimorbidity, improving the awareness and monitoring of health and well-being, supporting a better understanding of the disease, and encouraging behavior change.

OBJECTIVE

The aim of this study was to analyze how 60 older adults (mean age 74, SD 6.4; range 65-92 years) with multimorbidity engaged with digital symptom and well-being monitoring when using a digital health platform over a period of approximately 12 months.

METHODS

Principal component analysis and clustering analysis were used to group participants based on their levels of engagement, and the data analysis focused on characteristics (eg, age, sex, and chronic health conditions), engagement outcomes, and symptom outcomes of the different clusters that were discovered.

RESULTS

Three clusters were identified: the typical user group, the least engaged user group, and the highly engaged user group. Our findings show that age, sex, and the types of chronic health conditions do not influence engagement. The 3 primary factors influencing engagement were whether the same device was used to submit different health and well-being parameters, the number of manual operations required to take a reading, and the daily routine of the participants. The findings also indicate that higher levels of engagement may improve the participants' outcomes (eg, reduce symptom exacerbation and increase physical activity).

CONCLUSIONS

The findings indicate potential factors that influence older adult engagement with digital health technologies for home-based multimorbidity self-management. The least engaged user groups showed decreased health and well-being outcomes related to multimorbidity self-management. Addressing the factors highlighted in this study in the design and implementation of home-based digital health technologies may improve symptom management and physical activity outcomes for older adults self-managing multimorbidity.

Recent developments in machine learning modeling methods for hypertension treatment

Hypertension is the leading cause of cardiovascular complications. This review focuses on the advancements in medical artificial intelligence (AI) models aimed at individualized treatment for hypertension, with particular emphasis on the approach to time-series big data on blood pressure and the development of interpretable medical AI models. The digitalization of daily blood pressure records and the downsizing of measurement devices enable the accumulation and utilization of time-series data. As mainstream blood pressure data shift from snapshots to time series, the clinical significance of blood pressure variability will be clarified. The time-series blood pressure prediction model demonstrated the capability to forecast blood pressure variabilities with a reasonable degree of accuracy for up to four weeks in advance. In recent years, various explainable AI techniques have been proposed for different purposes of model interpretation. It is essential to select the appropriate technique based on the clinical aspects; for example, actionable path-planning techniques can present individualized intervention plans to efficiently improve outcomes such as hypertension. Despite considerable progress in this field, challenges remain, such as the need for the prospective validation of AI-driven interventions and the development of comprehensive systems that integrate multiple AI methods. Future research should focus on addressing these challenges and refining the AI models to ensure their practical applicability in real-world clinical settings. Furthermore, the implementation of interdisciplinary collaborations among AI experts, clinicians, and healthcare providers are crucial to further optimizing and validate AI-driven solutions for hypertension management.

Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review

Cardiovascular diseases (CVDs) are significant health issues that result in high death rates globally. Early detection of cardiovascular events may lower the occurrence of acute myocardial infarction and reduce death rates in people with CVDs. Traditional data analysis is inadequate for managing multidimensional data related to the risk prediction of CVDs, heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis due to the complex pathological mechanisms and multiple factors involved. Artificial intelligence (AI) is a technology that utilizes advanced computer algorithms to extract information from large databases, and it has been integrated into the medical industry. AI methods have shown the ability to speed up the advancement of diagnosing and treating CVDs such as heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease, and more. In clinical settings, AI has shown usefulness in diagnosing cardiovascular illness, improving the efficiency of supporting tools, stratifying and categorizing diseases, and predicting outcomes. Advanced AI algorithms have been intricately designed to analyze intricate relationships within extensive healthcare data, enabling them to tackle more intricate jobs compared to conventional approaches.

Scheduling of Remote Monitoring for Peritoneal Dialysis Patients

Peritoneal dialysis (PD) is performed as a home-based treatment and in this context, telemedicine has been proven helpful for improving clinicians' surveillance and maintaining PD patients in their home setting. The new e-health devices make remote patient monitoring (RPM) for automated peritoneal dialysis (APD) treatment possible, evaluating the data at the end of every treatment and adapting the prescription at distance if necessary. This paper aims to share a method for improving clinical surveillance and enabling PD patients to receive their treatment at home. In the present case series, we delineate the clinical protocol of the Vicenza PD Center regarding patient characteristics, timing, and the purpose of the APD-RPM. We present the Vicenza PD Center's experience, illustrating its application through three case reports as exemplars. Telemedicine helps to carefully allocate healthcare resources while removing the barriers to accessing care. However, there is a risk of data overload, as some data might not be analyzed because of an increased workload for healthcare professionals. A proactive physician's attitude towards the e-health system has to be supported by clinical instructions and legislative rules. International and national guidelines may suggest which patients should be candidates for RPM, which parameters should be monitored, and with what timing. According to our experience, we suggest that the care team should define a workflow that helps in formulating a correct approach to RPM, adequately utilizing resources. The workflow has to consider the different needs of patients, in order to assure frequent remote control for incident or unstable patients, while prevalent and stable patients can perform their home treatment more independently, helped by periodic and deferred clinical supervision.

CURATE.AI-assisted dose titration for anti-hypertensive personalized therapy: study protocol for a multi-arm, randomized, pilot feasibility trial using CURATE.AI (CURATE.AI ADAPT trial)

Aims

Artificial intelligence-driven small data platforms such as CURATE.AI hold potential for personalized hypertension care by assisting physicians in identifying personalized anti-hypertensive doses for titration. This trial aims to assess the feasibility of a larger randomized controlled trial (RCT), evaluating the efficacy of CURATE.AI-assisted dose titration intervention. We will also collect preliminary efficacy and safety data and explore stakeholder feedback in the early design process.

Methods and results

In this open-label, randomized, pilot feasibility trial, we aim to recruit 45 participants with primary hypertension. Participants will be randomized in 1:1:1 ratio into control (no intervention), home blood pressure monitoring (active control; HBPM), or CURATE.AI arms (intervention; HBPM and CURATE.AI-assisted dose titration). The home treatments include 1 month of two-drug anti-hypertensive regimens. Primary endpoints assess the logistical (e.g. dose adherence) and scientific (e.g. percentage of participants for which CURATE.AI profiles can be generated) feasibility, and define the progression criteria for the RCT in a 'traffic light system'. Secondary endpoints assess preliminary efficacy [e.g. mean change in office blood pressures (BPs)] and safety (e.g. hospitalization events) associated with each treatment protocol. Participants with both baseline and post-treatment BP measurements will form the intent-to-treat analysis. Following their involvement with the CURATE.AI intervention, feedback from CURATE.AI participants and healthcare providers will be collected via exit survey and interviews.

Conclusion

Findings from this study will inform about potential refinements of the current treatment protocols before proceeding with a larger RCT, or potential expansion to collect additional information. Positive results may suggest the potential efficacy of CURATE.AI to improve BP control.

Trial registration number

NCT05376683.

Recent Progress of Wearable Triboelectric Nanogenerator-Based Sensor for Pulse Wave Monitoring

Today, cardiovascular diseases threaten human health worldwide. In clinical practice, it has been concluded that analyzing the pulse waveform can provide clinically valuable information for the diagnosis of cardiovascular diseases. Accordingly, continuous and accurate monitoring of the pulse wave is essential for the prevention and detection of cardiovascular diseases. Wearable triboelectric nanogenerators (TENGs) are emerging as a pulse wave monitoring biotechnology due to their compelling characteristics, including being self-powered, light-weight, and wear-resistant, as well as featuring user-friendliness and superior sensitivity. Herein, a comprehensive review is conducted on the progress of wearable TENGs for pulse wave monitoring. Firstly, the four modes of operation of TENG are briefly described. Secondly, TENGs for pulse wave monitoring are classified into two categories, namely wearable flexible film-based TENG sensors and textile-based TENG sensors. Next, the materials, fabrication methods, working mechanisms, and experimental performance of various TENG-based sensors are summarized. It concludes by comparing the characteristics of the two types of TENGs and discussing the potential development and challenges of TENG-based sensors in the diagnosis of cardiovascular diseases and personalized healthcare.

Personalized Machine Learning-Coupled Nanopillar Triboelectric Pulse Sensor for Cuffless Blood Pressure Continuous Monitoring

A wearable system that can continuously track the fluctuation of blood pressure (BP) based on pulse signals is highly desirable for the treatments of cardiovascular diseases, yet the sensitivity, reliability, and accuracy remain challenging. Since the correlations of pulse waveforms to BP are highly individualized due to the diversity of the patients' physiological characteristics, wearable sensors based on universal designs and algorithms often fail to derive BP accurately when applied on individual patients. Herein, a wearable triboelectric pulse sensor based on a biomimetic nanopillar layer was developed and coupled with Personalized Machine Learning (ML) to provide accurate and continuous monitoring of BP. Flexible conductive nanopillars as the triboelectric layer were fabricated through soft lithography replication of a cicada wing, which could effectively enhance the sensor's output performance to detect weak signal characteristics of pulse waveform for BP derivation. The sensors were coupled with a personalized Partial Least-Squares Regression (PLSR) ML to derive unknown BP based on individual pulse characteristics with reasonable accuracy, avoiding the issue of individual variability that was encountered by General PLSR ML or formula algorithms. The cuffless and intelligent design endow this ML-sensor as a highly promising platform for the care and treatments of hypertensive patients.

Integrated Analysis of miRNA and mRNA Regulation Network in Hypertension

Hypertension is the most common chronic disease. Early diagnosis is helpful for early medical intervention. The miRNAs and the messenger RNAs (mRNAs) network may be valuable disease diagnosis markers. We aimed to explore the diagnostic value of the miRNA-mRNA network for hypertension patients. Data of miRNAs and mRNAs expression were obtained from the Gene Expression Omnibus database. The weighted gene co-expression network analysis was performed to screen hypertension-related gene modules, and these genes undergone functional enrichment analysis using "clusterProfiler" package. Differential expression analysis was applied on miRNAs expression profiles using "limma" package. TargetScanHuman and miRDB databases were used to select target mRNAs. Cytoscape software was used to visualize the miRNA-mRNA regulation network. P value < 0.05 was considered statistically significant after t test. There were 123 screened mRNAs which were enriched in 161 Gene ontology (GO) terms and 14 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Thirty-five differentially expressed miRNAs (DEMs) are found in the GSE75670. Totally 36 miRNA-mRNA pairs were obtained after the integrated analysis, and three mRNAs and the hsa-miRNA-5589-5p were identified as key joints. Hub genes, KIAA0513, ARID3A and LRPAP1, and key hsa-miRNA-5589-5p are potential diagnostic biomarkers for hypertension. Our findings are promising in the clinical application, conducive to early detection and prompt intervention of hypertension.

Noncontact remote sensing of abnormal blood pressure using a deep neural network: a novel approach for hypertension screening

Background

As the global burden of hypertension continues to increase, early diagnosis and treatment play an increasingly important role in improving the prognosis of patients. In this study, we developed and evaluated a method for predicting abnormally high blood pressure (HBP) from infrared (upper body) remote thermograms using a deep learning (DL) model.

Methods

The data used in this cross-sectional study were drawn from a coronavirus disease 2019 (COVID-19) pilot cohort study comprising data from 252 volunteers recruited from 22 July to 4 September 2020. Original video files were cropped at 5 frame intervals to 3,800 frames per slice. Blood pressure (BP) information was measured using a Welch Allyn 71WT monitor prior to infrared imaging, and an abnormal increase in BP was defined as a systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg. The PanycNet DL model was developed using a deep neural network to predict abnormal BP based on infrared thermograms.

Results

A total of 252 participants were included, of which 62.70% were male and 37.30% were female. The rate of abnormally high HBP was 29.20% of the total number. In the validation group (upper body), precision, recall, and area under the receiver operating characteristic curve (AUC) values were 0.930, 0.930, and 0.983 [95% confidence interval (CI): 0.904-1.000], respectively, and the head showed the strongest predictive ability with an AUC of 0.868 (95% CI: 0.603-0.994).

Conclusions

This is the first technique that can perform screening for hypertension without contact using existing equipment and data. It is anticipated that this technique will be suitable for mass screening of the population for abnormal BP in public places and home BP monitoring.

Home blood pressure-centered approach - from digital health to medical practice: HOPE Asia Network consensus statement 2023

Recent innovations in digital technology have enabled the simultaneous accumulation, and the linking and analysis of time-series big data relating to several factors that influence blood pressure (BP), including biological indicators, physical activity, and environmental information. Various approaches can be used to monitor BP: in the office/clinic; at home; 24-h ambulatory recording; or with wearable and cuffless devices. Of these, home BP monitoring is a reliable and convenient method, and is recommended for hypertension management by current national and international guidelines. This recommendation is based on evidence showing that home BP is an important predictor of cardiovascular, cerebrovascular and kidney disease in patients with hypertension. In addition, lifetime personalized health record (PHR)-based home BP with telemonitoring combined with co-interventions has been shown to lower BP more effectively than the traditional approach based on office BP. Thus, home BP represents a key metric for personalized anticipation medicine, from digital healthcare to digital medicine. This paper summarizes the latest evidence on home BP monitoring and proposes a Hypertension Cardiovascular Outcome Prevention and Evidence in Asia (HOPE Asia) Network consensus on a home BP-centered approach to the management of hypertension.

Digital hypertension towards to the anticipation medicine

"Digital Hypertension" is a new research field proposed by the Japanese Society of Hypertension that integrates digital technology into hypertension management and proactively promotes research activities. This novel approach includes the development of new technologies for better BP management, such as sensors for detecting environmental factors that affect BP, information processing, and machine learning. To facilitate "Digital Hypertension," a more sophisticated BP monitoring system capable of measuring an individual's BP more frequently in various situations would be required. With the use of these technologies, hypertension management could shift from the current "dots" management based on office BP readings during clinic visits to a "line" management system based on seamless home BP or individual BP data taken by a wearable BP monitoring device. DTx is the innovation to change hypertension management from "dots" to "line", completely achieved by wearable BP.

An Update on the Use of Wearable Devices in Men's Health

Men's health represents an often-overlooked aspect of public health. Men have higher mortality rates worldwide and are more negatively affected by chronic conditions such as obesity and heart disease, as well as addiction to alcohol and tobacco. Men also have health issues such as prostate cancer and male sexual dysfunction which only affect them. Because of the skewed burden of morbidity and mortality on men, it is imperative from a public health perspective to make a concerted effort to specifically improve men's health. The use of wearable devices in medical practice presents a novel avenue to invest in men's health in a safe, easily scalable, and economic fashion. Wearable devices are now ubiquitous in society, and their use in the healthcare setting is only increasing with time. There are commercially available devices such as smart watches which are available to lay people and healthcare professionals alike to improve overall health and wellness, and there are also purpose-built wearable devices which are used to track or treat a specific disease. In our review of the literature, we found that while research in the field of wearable devices is still in its early stages, there is ample evidence that wearable devices can greatly improve men's health in the long-term.

The Promise and Illusion of Continuous, Cuffless Blood Pressure Monitoring

PURPOSE OF REVIEW

Blood pressure (BP) fluctuations outside of clinic are increasingly recognized for their role in the development of cardiovascular disease, syncope, and premature death and as a promising target for tailored hypertension treatment. However, current cuff-based BP devices, including home and ambulatory devices, are unable to capture the breadth of BP variability across human activities, experiences, and contexts.

RECENT FINDINGS

Cuffless, wearable BP devices offer the promise of beat-to-beat, continuous, noninvasive measurement of BP during both awake and sleep periods with minimal patient inconvenience. Importantly, cuffless BP devices can characterize BP variability, allowing for the identification of patient-specific triggers of BP surges in the home environment. Unfortunately, the pace of evidence, regulation, and validation testing has lagged behind the pace of innovation and direct consumer marketing. We provide an overview of the available technologies and devices for cuffless BP monitoring, considerations for the calibration and validation of these devices, and the promise and pitfalls of the cuffless BP paradigm.

Peak home blood pressure as an earlier and strong novel risk factor for stroke: the practitioner-based nationwide J-HOP study extended

While home blood pressure (BP) measurement is recommended for hypertension management, the clinical implications of peak home BP values have not been well studied. This study investigated the association between pathological threshold or frequency of peak home BP and cardiovascular events in patients with ≥1 cardiovascular risk factor. The Japan Morning Surge-Home Blood Pressure (J-HOP) study enrolled participants from 2005-2012 with extended follow-up from December 2017 to May 2018, which generated the dataset for this analysis. Average peak home systolic BP (SBP) was defined as average of the highest three BP values on 14-day measurement period. Patients were divided into quintiles of peak home BP, and the risk of stroke, coronary artery disease (CAD) and atherosclerotic cardiovascular disease (ASCVD; stroke+CAD) was determined. In 4231 patients (mean 65 years) followed for 6.2 years there were 94 strokes and 124 CAD events. The adjusted hazard ratio (HR) (95% CI) for the risk of stroke and ASCVD in patients with average peak home SBP in the highest versus lowest quintile was 4.39 (1.85-10.43) and 2.04 (1.24-3.36), respectively. Risk was greatest for stroke in the first 5 years: HR 22.66 (2.98-172.1). The pathological threshold of average peak home SBP for 5-year stroke risk was 176 mmHg. There was a linear association between the number of times peak home SBP > 175 mmHg and stroke risk. Peak home BP was a strong risk factor for stroke, especially within the first 5 years. We propose exaggerated peak home SBP > 175 mmHg as an early and strong novel risk factor for stroke.

Telemedicine and Digital Medicine in the Clinical Management of Hypertension and Hypertension-Related Cardiovascular Diseases: A Position Paper of the Italian Society of Arterial Hypertension (SIIA)

High blood pressure is the leading cause of death and disability globally and an important treatable risk factor for cardiovascular, cerebrovascular and chronic kidney diseases. Digital technology, including mobile health solutions and digital therapy, is expanding rapidly in clinical medicine and has the potential to improve the quality of care and effectiveness of drug treatment by making medical interventions timely, tailored to hypertensive patients' needs and by improving treatment adherence. Thus, the systematic application of digital technologies could support diagnosis and awareness of hypertension and its complications, ultimately leading to improved BP control at the population level. The progressive implementation of digital medicine in the national health systems must be accompanied by the supervision and guidance of health authorities and scientific societies to ensure the correct use of these new technologies with consequent maximization of the potential benefits. The role of scientific societies in relation to the rapid adoption of digital technologies, therefore, should encompass the entire spectrum of activities pertaining to their institutional role: information, training, promotion of research, scientific collaboration and advice, evaluation and validation of technological tools, and collaboration with regulatory and health authorities.

Monitoring blood pressure and cardiac function without positioning via a deep learning-assisted strain sensor array

Continuous and reliable monitoring of blood pressure and cardiac function is of great importance for diagnosing and preventing cardiovascular diseases. However, existing cardiovascular monitoring approaches are bulky and costly, limiting their wide applications for early diagnosis. Here, we developed an intelligent blood pressure and cardiac function monitoring system based on a conformal and flexible strain sensor array and deep learning neural networks. The sensor has a variety of advantages, including high sensitivity, high linearity, fast response and recovery, and high isotropy. Experiments and simulation synergistically verified that the sensor array can acquire high-precise and feature-rich pulse waves from the wrist without precise positioning. By combining high-quality pulse waves with a well-trained deep learning model, we can monitor blood pressure and cardiac function parameters. As a proof of concept, we further constructed an intelligent wearable system for real-time and long-term monitoring of blood pressure and cardiac function, which may contribute to personalized health management, precise and early diagnosis, and remote treatment.

Consumer Wearable Health and Fitness Technology in Cardiovascular Medicine: JACC State-of-the-Art Review

The use of consumer wearable devices (CWDs) to track health and fitness has rapidly expanded over recent years because of advances in technology. The general population now has the capability to continuously track vital signs, exercise output, and advanced health metrics. Although understanding of basic health metrics may be intuitive (eg, peak heart rate), more complex metrics are derived from proprietary algorithms, differ among device manufacturers, and may not historically be common in clinical practice (eg, peak V˙O2, exercise recovery scores). With the massive expansion of data collected at an individual patient level, careful interpretation is imperative. In this review, we critically analyze common health metrics provided by CWDs, describe common pitfalls in CWD interpretation, provide recommendations for the interpretation of abnormal results, present the utility of CWDs in exercise prescription, examine health disparities and inequities in CWD use and development, and present future directions for research and development.

Wearable Smart Fabric Based on Hybrid E-Fiber Sensor for Real-Time Finger Motion Detection

Wearable electronic sensors have attracted considerable interest in hand motion monitoring because of their small size, flexibility, and biocompatibility. However, the range of motion and sensitivity of many sensors are inadequate for complex and precise finger motion capture. Here, organic and inorganic materials were incorporated to fabricate a hybrid electronic sensor and optimized and woven into fabric for hand motion detection. The sensor was made from flexible porous polydimethylsiloxane (PDMS) filled with multiwalled carbon nanotubes (MWCNTs). The weight ratios of MWCNTs and geometric characteristics were optimized to improve the hybrid electronic sensor, which showed a high elongation at the breaking point (i.e., more than 100%) and a good sensitivity of 1.44. The strain-related deformation of the PDMS/MWCNT composite network resulted in a variation in the sensor resistance; thus, the strain level that corresponds to different finger motions is be calculated. Finally, the fabricated and optimized electronic sensor in filiform structure with a 6% MWCNT ratio was integrated with smart fabric to create a finger sleeve for real-time motion capture. In conclusion, a novel hybrid E-fiber sensor based on PDMS and MWCNTs was successfully fabricated in the current study with an optimal M/P ratio and structure, and textile techniques were adopted as new packaging approaches for such soft electronic sensors to create smart fabric for wearable and precise detection with highly enhanced sensing performance. The successful results in the current study demonstrate the great potential of such hybrid soft sensors in smart wearable healthcare management, including motion detection.

Headache clinicians' perspectives on the remote monitoring of patients' electronic diary data: A qualitative study

OBJECTIVE

We assessed headache clinicians' viewpoints on potential remote access to patients' digital headache diary data and the practicalities of data utilization.

BACKGROUND

With the ubiquitous nature of electronic medical records and the existence of remote monitoring (RM) for many medical conditions, there is now the potential for remote symptom monitoring for patients with headache disorders. While patients are asked to utilize headache diaries, clinicians may or may not have access to the data before patient visits, and their perspectives regarding this emerging technology are currently unknown.

METHODS

After recruiting participants from the National Institutes of Health Pain Consortium Network, the American Headache Society Special Interest Section listservs, and Twitter and Facebook social media platforms, we conducted 20 semi-structured qualitative interviews of headache providers across the United States from various types of institutions and asked them their perspectives on remote access to patient headache diary data. We transcribed the interviews, which were then coded by two independent coders. Themes and sub-themes were developed using inductive content analysis.

RESULTS

All clinicians felt the RM data needed to be integrated into the electronic medical record. Six themes emerged from the interviews: (i) Clinician perspectives on how RM could be beneficial but at other times could create obstacles/challenges, (ii) operationally, data integration could benefit headache care, (iii) there should be initial logistical considerations for bringing RM into clinical care, (iv) education may need to be provided to both patients and clinicians, (v) there are likely research benefits associated with RM, and (vi) additional suggestions for considering potential integration of RM into practice.

CONCLUSIONS

While headache clinicians had mixed opinions on the benefits/challenges that RM presents to patient care, patient satisfaction, and visit time, new ideas emerged that may help advance the field.

Continuous Monitoring of Health and Mobility Indicators in Patients with Cardiovascular Disease: A Review of Recent Technologies

Cardiovascular diseases kill 18 million people each year. Currently, a patient's health is assessed only during clinical visits, which are often infrequent and provide little information on the person's health during daily life. Advances in mobile health technologies have allowed for the continuous monitoring of indicators of health and mobility during daily life by wearable and other devices. The ability to obtain such longitudinal, clinically relevant measurements could enhance the prevention, detection and treatment of cardiovascular diseases. This review discusses the advantages and disadvantages of various methods for monitoring patients with cardiovascular disease during daily life using wearable devices. We specifically discuss three distinct monitoring domains: physical activity monitoring, indoor home monitoring and physiological parameter monitoring.

Physics-informed neural networks for modeling physiological time series for cuffless blood pressure estimation

The bold vision of AI-driven pervasive physiological monitoring, through the proliferation of off-the-shelf wearables that began a decade ago, has created immense opportunities to extract actionable information for precision medicine. These AI algorithms model input-output relationships of a system that, in many cases, exhibits complex nature and personalization requirements. A particular example is cuffless blood pressure estimation using wearable bioimpedance. However, these algorithms need training over significant amount of ground truth data. In the context of biomedical applications, collecting ground truth data, particularly at the personalized level is challenging, burdensome, and in some cases infeasible. Our objective is to establish physics-informed neural network (PINN) models for physiological time series data that would use minimal ground truth information to extract complex cardiovascular information. We achieve this by building Taylor's approximation for gradually changing known cardiovascular relationships between input and output (e.g., sensor measurements to blood pressure) and incorporating this approximation into our proposed neural network training. The effectiveness of the framework is demonstrated through a case study: continuous cuffless BP estimation from time series bioimpedance data. We show that by using PINNs over the state-of-the-art time series models tested on the same datasets, we retain high correlations (systolic: 0.90, diastolic: 0.89) and low error (systolic: 1.3 ± 7.6 mmHg, diastolic: 0.6 ± 6.4 mmHg) while reducing the amount of ground truth training data on average by a factor of 15. This could be helpful in developing future AI algorithms to help interpret pervasive physiologic data using minimal amount of training data.

Health Research in the Wake of Disasters: Challenges and Opportunities for Sensor Science

BACKGROUND

Disaster events adversely affect the health of millions of individuals each year. They create exposure to physical, chemical, biological, and psychosocial hazards while simultaneously exploiting community and individual-level vulnerabilities that allow such exposures to exert harm. Since 2013, the National Institute of Environmental Health Sciences (NIEHS) has led the development of the Disaster Research Response (DR2) program and infrastructure; however, research exploring the nature and effects of disasters on human health is lacking. One reason for this research gap is the challenge of developing and deploying cost-effective sensors for exposure assessment during disaster events.

OBJECTIVES

The objective of this commentary is to synergize the consensus findings and recommendations from a panel of experts on sensor science in support of DR2.

METHODS

The NIEHS convened the workshop, "Getting Smart about Sensors for Disaster Response Research" on 28 and 29 July 2021 to discuss current gaps and recommendations for moving the field forward. The workshop invited full discussion from multiple viewpoints, with the goal of identifying recommendations and opportunities for further development of this area of research. The panel of experts included leaders in engineering, epidemiology, social and physical sciences, and community engagement, many of whom had firsthand experience with DR2.

DISCUSSION

The primary finding of this workshop is that exposure science in support of DR2 is severely lacking. We highlight unique barriers to DR2, such as the need for time-sensitive exposure data, the chaos and logistical challenges that ensue from a disaster event, and the lack of a robust market for sensor technologies in support of environmental health science. We highlight a need for sensor technologies that are more scalable, reliable, and versatile than those currently available to the research community. We also recommend that the environmental health community renew efforts in support of DR2 facilitation, collaboration, and preparedness. https://doi.org/10.1289/EHP12270.

Blood Pressure Variability in Clinical Practice: Past, Present and the Future

Recent advances in wearable technology through convenient and cuffless systems will enable continuous, noninvasive monitoring of blood pressure (BP), heart rate, and heart rhythm on both longitudinal 24-hour measurement scales and high-frequency beat-to-beat BP variability and synchronous heart rate variability and changes in underlying heart rhythm. Clinically, BP variability is classified into 4 main types on the basis of the duration of monitoring time: very-short-term (beat to beat), short-term (within 24 hours), medium-term (within days), and long-term (over months and years). BP variability is a strong risk factor for cardiovascular diseases, chronic kidney disease, cognitive decline, and mental illness. The diagnostic and therapeutic value of measuring and controlling BP variability may offer critical targets in addition to lowering mean BP in hypertensive populations.

Quantifying Blood Pressure Visit-to-Visit Variability in the Real-World Setting: A Retrospective Cohort Study

BACKGROUND

Visit-to-visit variability (VVV) in blood pressure values has been reported in clinical studies. However, little is known about VVV in clinical practice and whether it is associated with patient characteristics in real-world setting.

METHODS

We conducted a retrospective cohort study to quantify VVV in systolic blood pressure (SBP) values in a real-world setting. We included adults (age ≥18 years) with at least 2 outpatient visits between January 1, 2014 and October 31, 2018 from Yale New Haven Health System. Patient-level measures of VVV included SD and coefficient of variation of a given patient's SBP across visits. We calculated patient-level VVV overall and by patient subgroups. We further developed a multilevel regression model to assess the extent to which VVV in SBP was explained by patient characteristics.

RESULTS

The study population included 537 218 adults, with a total of 7 721 864 SBP measurements. The mean age was 53.4 (SD 19.0) years, 60.4% were women, 69.4% were non-Hispanic White, and 18.1% were on antihypertensive medications. Patients had a mean body mass index of 28.4 (5.9) kg/m2 and 22.6%, 8.0%, 9.7%, and 5.6% had a history of hypertension, diabetes, hyperlipidemia, and coronary artery disease, respectively. The mean number of visits per patient was 13.3, over an average period of 2.4 years. The mean (SD) intraindividual SD and coefficient of variation of SBP across visits were 10.6 (5.1) mm Hg and 0.08 (0.04). These measures of blood pressure variation were consistent across patient subgroups defined by demographic characteristics and medical history. In the multivariable linear regression model, only 4% of the variance in absolute standardized difference was attributable to patient characteristics.

CONCLUSIONS

The VVV in real-world practice poses challenges for management of patients with hypertension based on blood pressure readings in outpatient settings and suggest the need to go beyond episodic clinic evaluation.

Monitoring with wearable devices will clarify the association between indoor temperature and blood pressure

The association of blood pressure and temperature is well known in seasonal observation, and low temperature in the winter season is often considered a cause of high blood pressure. The current evidence for short-term studies of temperature and blood pressure is based on the daily observation, however continuous monitoring with wearable devices will allow us to evaluate the rapid effect of cold temperature exposure on blood pressure. In a Japanese, prospective intervention study from 2014 to 2019 (the Smart Wellness Housing survey), approximately 90% of Japanese lived in cold houses (indoor temperature less than 18 °C). Importantly, the indoor temperature was associated with the increase of morning systolic blood pressure. We recently addressed the sympathetic nervous activation of individuals in both their houses and a highly insulated and airtight model house in the winter season using portable electrocardiography equipment. A few subjects showed a morning surge in sympathetic activity, which was more intense at their cold houses, which suggests the importance of the indoor environment in the management of early morning hypertension. In near future, real-time monitoring with wearable devices will provide important information for a better life-environment, leading to risk reduction of morning surge and cardiovascular events.

Feasibility and measurement stability of smartwatch-based cuffless blood pressure monitoring: A real-world prospective observational study

Cuffless wearable devices are currently being developed for long-term monitoring of blood pressure (BP) in patients with hypertension and in apparently healthy people. This study evaluated the feasibility and measurement stability of smartwatch-based cuffless BP monitoring in real-world conditions. Users of the first smartwatch-based cuffless BP monitor approved in Korea (Samsung Galaxy Watch) were invited to upload their data from using the device for 4 weeks post calibration. A total of 760 participants (mean age 43.7 ± 11.9, 80.3% men) provided 35,797 BP readings (average monitoring 22 ± 4 days [SD]; average readings 47 ± 42 per participant [median 36]). Each participant obtained 1.5 ± 1.3 readings/day and 19.7% of the participants obtained measurements every day. BP showed considerable variability, mainly depending on the day and time of the measurement. There was a trend towards higher BP levels on Mondays than on other days of the week and on workdays than in weekends. BP readings taken between 00:00 and 04:00 tended to be the lowest, whereas those between 12:00 and 16:00 the highest. The average pre-post calibration error for systolic BP (difference in 7-day BP before and after calibration), was 6.8 ± 5.6 mmHg, and was increased with higher systolic BP levels before calibration. Smartwatch-based cuffless BP monitoring is feasible for out-of-office monitoring in the real-world setting. The stability of BP measurement post calibration and the standardization and optimal time interval for recalibration need further investigation.

Short- to long-term blood pressure variability: Current evidence and new evaluations

Increased blood pressure (BP) variability and the BP surge have been reported to be associated with increased cardiovascular risk independently of BP levels and can also be a trigger of cardiovascular events. There are multiple types of BP variation: beat-to-beat variations related to breathing and the autonomic nervous system, diurnal BP variation and nocturnal dipping related to sleep and physical activity over a 24-hr period, day-to-day BP variability with anomalous readings within a several-day period, visit-to-visit BP variability between outpatient visits, and seasonal variations. BP variability is also associated with the progression to hypertension from prehypertension and the progression of chronic kidney disease and cognitive impairments. Our research group proposed the "resonance hypothesis of blood pressure surge" as a new etiological hypothesis of BP variability and surges; i.e., the concept that when the time phases of surges and hypertension-inducing environmental influences coincide, resonance occurs and is amplified into a larger "dynamic surge" that triggers the onset of cardiovascular disease. New devices to assess BP variability as well as new therapeutic interventions to reduce BP variability are being developed. Although there are still issues to be addressed (including measurement accuracy), cuffless devices and information and communication technology (ICT)-based BP monitoring devices have been developed and validated. These new devices will be useful for the individualized optimal management of BP. However, evidence regarding the usefulness of therapeutic interventions to control BP variability is still lacking.

Tests for the identification of reflex syncope mechanism

INTRODUCTION

Treatment efficacy of reflex syncope is mainly related to the mechanism underlying syncope rather than its etiology or clinical presentation. The predominant mechanism underlying reflex syncope can be assigned to hypotensive or to bradycardic phenotypes.

AREAS COVERED

Methodology and diagnostic criteria of the most useful tests for the identification of hypotensive and bradycardic phenotypes are discussed. Diagnostic tests for the hypotensive phenotype include office blood pressure measurement with active standing test, home, and wearable blood pressure monitoring, 24-h ambulatory blood pressure monitoring and tilt table test. Diagnostic tests for the bradycardic phenotype include carotid sinus massage, tilt table test and prolonged ECG monitoring.

EXPERT OPINION

In reflex syncope, the documentation of bradycardia/asystole during a syncopal episode does not rule out the possibility that a preceding or parallel hypotensive reflex plays an important role. Similarly, even when a hypotensive mechanism is established, the possibility of an associated cardioinhibitory reflex should be investigated. Investigating the mechanism of reflex syncope is mandatory in patients with severe recurrent episodes, with the final aim to develop a personalized treatment strategy. Recent trials have demonstrated the benefits of personalized mechanism-based therapy, thus highlighting the importance of a comprehensive assessment of the mechanisms underlying syncope.

Is remote blood pressure monitoring and management a better approach for patients with hypertension? A narrative review

Hypertension is a global public health problem which affects the physical and mental health of individuals. The management of hypertension is a long-term process that requires the cooperation of both doctors and patients, and the blood pressure variability is closely related to the clinical prognosis. In recent years, the development of telemedicine has promoted better blood pressure monitoring and management for patients, as well as better medical intervention and health education for patients by medical staff. This article provides a review of remote blood pressure monitoring and management.

Validating cuffless continuous blood pressure monitoring devices

Cuff-based home blood pressure (BP) devices, which have been the standard for BP monitoring for decades, are limited by physical discomfort, convenience, and their ability to capture BP variability and patterns between intermittent readings. In recent years, cuffless BP devices, which do not require cuff inflation around a limb, have entered the market, offering the promise of continuous beat-to-beat measurement of BP. These devices take advantage of a variety of principles to determine BP, including (1) pulse arrival time, (2) pulse transit time, (3) pulse wave analysis, (4) volume clamping, and (5) applanation tonometry. Because BP is calculated indirectly, these devices require calibration with cuff-based devices at regular intervals. Unfortunately, the pace of regulation of these devices has failed to match the speed of innovation and direct availability to patient consumers. There is an urgent need to develop a consensus on standards by which cuffless BP devices can be tested for accuracy. In this narrative review, we describe the landscape of cuffless BP devices, summarize the current status of validation protocols, and provide recommendations for an ideal validation process for these devices.

An integrated group decision-making method for the evaluation of hypertension follow-up systems using interval-valued q-rung orthopair fuzzy sets

It is imperative to comprehensively evaluate the function, cost, performance and other indices when purchasing a hypertension follow-up (HFU) system for community hospitals. To select the best software product from multiple alternatives, in this paper, we develop a novel integrated group decision-making (GDM) method for the quality evaluation of the system under the interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs). The design of our evaluation indices is based on the characteristics of the HFU system, which in turn represents the evaluation requirements of typical software applications and reflects the particularity of the system. A similarity is extended to measure the IVq-ROFNs, and a new score function is devised for distinguishing IVq-ROFNs to figure out the best IVq-ROFN. The weighted fairly aggregation (WFA) operator is then extended to the interval-valued q-rung orthopair WFA weighted average operator (IVq-ROFWFAWA) for aggregating information. The attribute weights are derived using the LINMAP model based on the similarity of IVq-ROFNs. We design a new expert weight deriving strategy, which makes each alternative have its own expert weight, and use the ARAS method to select the best alternative based on these weights. With these actions, a GDM algorithm that integrates the similarity, score function, IVq-ROFWFAWA operator, attribute weights, expert weights and ARAS is proposed. The applicability of the proposed method is demonstrated through a case study. Its effectiveness and feasibility are verified by comparing it to other state-of-the-art methods and operators.

Physics-Informed Neural Networks for Modeling Physiological Time Series: A Case Study with Continuous Blood Pressure

The bold vision of AI-driven pervasive physiological monitoring, through the proliferation of off-the-shelf wearables that began a decade ago, has created immense opportunities to extract actionable information for precision medicine. These AI algorithms model the input-output relationships of a system that, in many cases, exhibits complex nature and personalization requirements. A particular example is cuffless blood pressure estimation using wearable bioimpedance. However, these algorithms need to be trained with a significant amount of ground truth data. In the context of biomedical applications, collecting ground truth data, particularly at the personalized level is challenging, burdensome, and in some cases infeasible. Our objective is to establish physics-informed neural network (PINN) models for physiological time series data that would reduce reliance on ground truth information. We achieve this by building Taylor's approximation for the gradually changing known cardiovascular relationships between input and output (e.g., sensor measurements to blood pressure) and incorporating this approximation into our proposed neural network training. The effectiveness of the framework is demonstrated through a case study: continuous cuffless BP estimation from time series bioimpedance data. We show that by using PINNs over the state-of-the-art time series regression models tested on the same datasets, we retain a high correlation (systolic: 0.90, diastolic: 0.89) and low error (systolic: 1.3 ± 7.6 mmHg, diastolic: 0.6 ± 6.4 mmHg) while reducing the amount of ground truth training data on average by a factor of 15. This could be helpful in developing future AI algorithms to help interpret pervasive physiologic data using minimal amount of training data.

Blood-Pressure-Monitoring Smartphone Applications: Ushering in a New Era of Wearable Cardiac Devices? Comment on Vischer et al. Comparability of a Blood-Pressure-Monitoring Smartphone Application with Conventional Measurements-A Pilot Study. Diagnostics 2022, 12, 749

Systemic arterial hypertension in adults is generally defined as a systolic blood pressure (SBP) of >140 mmHg and/or a diastolic blood pressure (DBP) of >90 mmHg [...]

Long-term stability of over-the-counter cuffless blood pressure monitors: a proposal

Blood pressure is an important cardiovascular parameter. Currently, the cuff-based sphygmomanometer is a popular, reliable, measurement method, but blood pressure monitors without cuffs have become popular and are now available without a prescription. Blood pressure monitors must be approved by regulatory authorities. Current cuffless blood pressure (CL-BP) monitors are not suitable for at-home management and prevention of hypertension. This paper proposes simple criteria for over-the-counter CL-BP monitoring. First, the history of the sphygmomanometer and current standard blood pressure protocol are reviewed. The main components of CL-BP monitoring are accuracy during the resting condition, accuracy during dynamic blood pressure changes, and long-term stability. In this proposal we recommend intermittent measurement to ensure that active measurement accuracy mirrors resting condition accuracy. A new experimental protocol is proposed to maintain long-term stability. A medically approved automated sphygmomanometer was used as the standard device in this study. The long-term accuracy of the test device is based on the definition of propagation error, i.e., for an oscillometric automated sphygmomanometer (5 ± 8 mmHg) ± the error for the test device static accuracy (-0.12 ± 5.49 mmHg for systolic blood pressure and - 1.17 ± 5.06 mmHg for diastolic blood pressure). Thus, the long-term stabilities were - 3.38 ± 7.1 mmHg and - 1.38 ± 5.4 mmHg, which satisfied propagation error. Further research and discussion are necessary to create standards for use by manufacturers; such standards should be readily evaluated and ensure high-quality evidence.

Supplementary information

The online version contains supplementary material available at 10.1007/s12553-023-00726-6.

Advancement in the Cuffless and Noninvasive Measurement of Blood Pressure: A Review of the Literature and Open Challenges

Hypertension is a chronic condition that is one of the prominent reasons behind cardiovascular disease, brain stroke, and organ failure. Left unnoticed and untreated, the deterioration in a health condition could even result in mortality. If it can be detected early, with proper treatment, undesirable outcomes can be avoided. Until now, the gold standard is the invasive way of measuring blood pressure (BP) using a catheter. Additionally, the cuff-based and noninvasive methods are too cumbersome or inconvenient for frequent measurement of BP. With the advancement of sensor technology, signal processing techniques, and machine learning algorithms, researchers are trying to find the perfect relationships between biomedical signals and changes in BP. This paper is a literature review of the studies conducted on the cuffless noninvasive measurement of BP using biomedical signals. Relevant articles were selected using specific criteria, then traditional techniques for BP measurement were discussed along with a motivation for cuffless measurement use of biomedical signals and machine learning algorithms. The review focused on the progression of different noninvasive cuffless techniques rather than comparing performance among different studies. The literature survey concluded that the use of deep learning proved to be the most accurate among all the cuffless measurement techniques. On the other side, this accuracy has several disadvantages, such as lack of interpretability, computationally extensive, standard validation protocol, and lack of collaboration with health professionals. Additionally, the continuing work by researchers is progressing with a potential solution for these challenges. Finally, future research directions have been provided to encounter the challenges.

Variation in blood pressure and heart rate of radiological technologists in worktime tracked by a wearable device: A preliminary study

The aim of this preliminary study was to measure the systolic BP (SBP) and diastolic BP (DBP) and heart rate (HR) of radiological technologists by WD, and evaluate variation among individuals by worktime, day of the week, job, and workplace. Measurements were obtained using a wristwatch-type WD with optical measurement technology that can measure SBP and DBP every 10 minutes and HR every 30 minutes. SBP, DBP, and HR data obtained at baseline and during work time were combined with the hours of work, day of the week, job, and workplace recorded by the participants in 8 consecutive weeks. We calculated the mean, the ratio to baseline and coefficient of variation [CV(%)] for SBP, DBP, and HR. SBP, DBP, and HR values were significantly higher during work hours than at baseline (p<0.03). The ratio to baseline values ranged from 1.02 to 1.26 for SBP and from 1.07 to 1.30 for DBP. The ratio to baseline for SBP and DBP showed CV(%) of approximately 10% according to the day of the week and over the study period. For HR, ratio to baseline ranged from 0.95 to 1.29. The ratio of mean BP to baseline was >1.2 at the time of starting work, middle and after lunch, and at 14:00. The ratio to baseline of SBP were 1.2 or more for irradiation, equipment accuracy control, registration of patient data, dose verification and conference time, and were also working in CT examination room, treatment planning room, linac room, and the office. CV(%) of BP and HR were generally stable for all workplaces. WD measurements of SBP, DBP, and HR were higher during working hours than at baseline and varied by the individuals, work time, job, and workplace. This method may enable evaluation of unconscious workload in individuals.

Wearable blood pressure measurement devices and new approaches in hypertension management: the digital era

Out-of-office blood pressure (BP) measurement is considered an integral component of the diagnostic algorithm and management of hypertension. In the era of digitalization, a great deal of wearable BP measuring devices has been developed. These digital blood pressure monitors allow frequent BP measurements with minimal annoyance to the patient while they do promise radical changes regarding the diagnostic accuracy, as the importance of making an accurate diagnosis of hypertension has become evident. By increasing the number of BP measurements in different conditions, these monitors allow accurate identification of different clinical phenotypes, such as masked hypertension and pathological BP variability, that seem to have a negative impact on cardiovascular prognosis. Frequent measurements of BP and the incorporation of new features in BP variability, both enable well-rounded interpretation of BP data in the context of real-life settings. This article is a review of all different technologies and wearable BP monitoring devices.

Treatment Adherence in CKD and Support From Health care Providers: A Qualitative Study

Rationale & Objective

Adherence to recommended medical treatment is critical in chronic kidney disease (CKD) to prevent complications and progression to kidney failure. Overall adherence to treatment is low in CKD, and as few as 40% of patients with kidney failure receive any documented CKD-related care. The purpose of this study was to explore the experiences of patients with CKD and their adherence to CKD treatment plans, and the role their health care providers played in supporting their adherence.

Study Design

One-on-one interviews were conducted in 2019-2020 using a semi-structured interview guide. Participants described experiences with adherence to treatment plans and what they did when experiencing difficulty.

Setting & Participants

Participants were recruited from the Chronic Renal Insufficiency Cohort (CRIC) study. All CRIC participants were older than 21 years with CKD stages 2-4; this sample consisted of participants from the University of Pennsylvania CRIC site.

Analytical Approach

Interviews were recorded, transcribed, and coded using conventional content analysis. Data were organized into themes using NVivo 12.

Results

The sample (n = 32) had a mean age of 67 years, 53% were women, 59% were non-White, with a mean estimated glomerular filtration rate of 56.6 mL/min/1.73 m2. From analysis of factors relevant to treatment planning and adherence, following 4 major themes emerged: patient factors (multiple chronic conditions, motivation, outlook), provider factors (attentiveness, availability/accessibility, communication), treatment planning factors (lack of plan, proactive research, provider-focused treatment goals, and shared decision making), and treatment plan responses (disagreeing with treatment, perceived capability deficit, lack of information, and positive feedback).

Limitations

The sample was drawn from the CRIC study, which may not be representative of the general population with CKD.

Conclusions

These themes align with Behavioral Learning Theory, which includes concepts of internal antecedents (patient factors), external antecedents (provider factors), behavior (treatment planning factors), and consequences (treatment plan responses). In particular, the treatment plan responses point to innovative potential intervention approaches to support treatment adherence in CKD.

Translational gaps and opportunities for medical wearables in digital health

A confluence of advances in biosensor technologies, enhancements in health care delivery mechanisms, and improvements in machine learning, together with an increased awareness of remote patient monitoring, has accelerated the impact of digital health across nearly every medical discipline. Medical grade wearables-noninvasive, on-body sensors operating with clinical accuracy-will play an increasingly central role in medicine by providing continuous, cost-effective measurement and interpretation of physiological data relevant to patient status and disease trajectory, both inside and outside of established health care settings. Here, we review current digital health technologies and highlight critical gaps to clinical translation and adoption.