Wearable sensors: can they benefit patients with chronic kidney disease?

Objectively measured daily steps as an outcome in a clinical trial of chronic kidney disease: a systematic review

BACKGROUND

Physical inactivity is prevalent among individuals with chronic kidney disease (CKD) and is linked to unfavorable outcomes. In recent years, daily steps have emerged as a prominent target for interventions in clinical trials. The present study endeavors to scrutinize the effectiveness and/or efficacy of various interventions on daily steps in patients with full-spectrum CKD.

METHODS

In December 2022, a systematic search was conducted across three databases, namely PubMed, Embase, and Web of Science, and subsequently updated in June 2023. The inclusion criteria included randomized controlled studies, quasi-experimental studies, and single-arm trials that assessed an intervention's impact on objectively measured daily steps in patients with chronic kidney disease. The Risk Of Bias In Non-randomized Studies-of Interventions (ROBINS-I) tool was used to assess the risk of bias in non-randomized controlled trials (RCT), while the Cochrane revised tool (ROB-2) was utilized for RCTs.

RESULTS

Seventeen studies were deemed eligible for inclusion in this review, with a focus on examining the efficacy and/or effectiveness of exercise training-based interventions (n = 10), daily step goal-oriented interventions (n = 4), mobile health (mHealth) interventions (n = 1), different dialysis modalities (n = 1), and a "Sit Less, Interact, Move More" intervention (n = 1). The studies exhibit variability in their characteristics and assessment tools, reflecting the findings' heterogeneity. The results indicate that increasing physical activity levels remain challenging, as only a limited number of studies demonstrated significant improvements in participants' daily step counts from baseline to endpoint.

CONCLUSION

Clinical trials with daily steps as an outcome are still lacking in the CKD population. Well-designed clinical trials that objectively assess the physical activity of CKD patients are needed.

Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup

Acute kidney injury (AKI), which is a common complication of acute illnesses, affects the health of individuals in community, acute care and post-acute care settings. Although the recognition, prevention and management of AKI has advanced over the past decades, its incidence and related morbidity, mortality and health care burden remain overwhelming. The rapid growth of digital technologies has provided a new platform to improve patient care, and reports show demonstrable benefits in care processes and, in some instances, in patient outcomes. However, despite great progress, the potential benefits of using digital technology to manage AKI has not yet been fully explored or implemented in clinical practice. Digital health studies in AKI have shown variable evidence of benefits, and the digital divide means that access to digital technologies is not equitable. Upstream research and development costs, limited stakeholder participation and acceptance, and poor scalability of digital health solutions have hindered their widespread implementation and use. Here, we provide recommendations from the Acute Disease Quality Initiative consensus meeting, which involved experts in adult and paediatric nephrology, critical care, pharmacy and data science, at which the use of digital health for risk prediction, prevention, identification and management of AKI and its consequences was discussed.

Wearables in Nephrology: Fanciful Gadgetry or Prêt-à-Porter?

Telemedicine and digitalised healthcare have recently seen exponential growth, led, in part, by increasing efforts to improve patient flexibility and autonomy, as well as drivers from financial austerity and concerns over climate change. Nephrology is no exception, and daily innovations are underway to provide digitalised alternatives to current models of healthcare provision. Wearable technology already exists commercially, and advances in nanotechnology and miniaturisation mean interest is also garnering clinically. Here, we outline the current existing wearable technology pertaining to the diagnosis and monitoring of patients with a spectrum of kidney disease, give an overview of wearable dialysis technology, and explore wearables that do not yet exist but would be of great interest. Finally, we discuss challenges and potential pitfalls with utilising wearable technology and the factors associated with successful implementation.

Comparing Remote Speckle Plethysmography and Finger-Clip Photoplethysmography with Non-Invasive Finger Arterial Pressure Pulse Waves, Regarding Morphology and Arrival Time

OBJECTIVE

The goal was to compare Speckle plethysmography (SPG) and Photoplethysmography (PPG) with non-invasive finger Arterial Pressure (fiAP) regarding Pulse Wave Morphology (PWM) and Pulse Arrival Time (PAT).

METHODS

Healthy volunteers (n = 8) were connected to a Non-Invasive Blood Pressure (NIBP) monitor providing fiAP pulse wave and PPG from a clinical transmission-mode SpO₂ finger clip. Biopac recorded 3-lead ECG. A camera placed at a 25 cm distance recorded a video stream (100 fps) of a finger illuminated by a laser diode at 639 nm. A chest belt (Polar) monitored respiration. All signals were recorded simultaneously during episodes of spontaneous breathing and paced breathing.

ANALYSIS

Post-processing was performed in Matlab to obtain SPG and analyze the SPG, PPG and fiAP mean absolute deviations (MADs) on PWM, plus PAT modulation.

RESULTS

Across 2599 beats, the average fiAP MAD with PPG was 0.17 (0-1) and with SPG 0.09 (0-1). PAT derived from ECG-fiAP correlated as follows: 0.65 for ECG-SPG and 0.67 for ECG-PPG.

CONCLUSION

Compared to the clinical NIBP monitor fiAP reference, PWM from an experimental camera-derived non-contact reflective-mode SPG setup resembled fiAP significantly better than PPG from a simultaneously recorded clinical transmission-mode finger clip. For PAT values, no significant difference was found between ECG-SPG and ECG-PPG compared to ECG-fiAP.

Arrhythmia and Heart Rate Variability during Long Interdialytic Periods in Patients on Maintenance Hemodialysis: Prospective Observational Cohort Study

Sudden cardiac death among hemodialysis patients is related to the hemodialysis schedule. Mortality is highest within 12 h before and after the first hemodialysis sessions of a week. We investigated the association of arrhythmia occurrence and heart rate variability (HRV) using an electrocardiogram (ECG) monitoring patch during the long interdialytic interval in hemodialysis patients. This was a prospective observational study with 55 participants on maintenance hemodialysis for at least six months. A patch-type ECG monitoring device was applied to record arrhythmia events and HRV during 72 h of a long interdialytic period. Forty-nine participants with sufficient ECG data out of 55 participants were suitable for the analysis. The incidence of supraventricular tachycardia and ventricular tachycardia did not significantly change over time. The square root of the mean squared differences of successive NN intervals (RMSSD), the proportion of adjacent NN intervals differing by >50 ms (pNN50), and high-frequency (HF) increased during the long interdialytic interval. The gap in RMSSD, pNN50, HF, and the low-frequency/high-frequency (LF/HF) ratio between patients with and without significant arrhythmias increased significantly over time during the long interdialytic interval. The daily changes in RMSSD, pNN50, HF, and the LF/HF ratio were more prominent in patients without significant arrhythmias than in those with significant arrhythmias. The electrolyte fluctuation between post-hemodialysis and subsequent pre-hemodialysis was not considered in this study. The study results suggest that the decreased autonomic response during interdialytic periods in dialysis patients is associated with poor cardiac arrhythmia events.

Time-specific associations of wearable sensor-based cardiovascular and behavioral readouts with disease phenotypes in the outpatient setting of the Chronic Renal Insufficiency Cohort

Patients with chronic kidney disease are at risk of developing cardiovascular disease. To facilitate out-of-clinic evaluation, we piloted wearable device-based analysis of heart rate variability and behavioral readouts in patients with chronic kidney disease from the Chronic Renal Insufficiency Cohort and controls (n  =  49). Time-specific partitioning of heart rate variability readouts confirm higher parasympathetic nervous activity during the night (mean RR at night 14.4  ±  1.9 ms vs. 12.8  ±  2.1 ms during active hours; n  =  47, analysis of variance (ANOVA) q  =  0.001). The α2 long-term fluctuations in the detrended fluctuation analysis, a parameter predictive of cardiovascular mortality, significantly differentiated between diabetic and nondiabetic patients (prominent at night with 0.58  ±  0.2 vs. 0.45  ±  0.12, respectively, adj. p  =  0.004). Both diabetic and nondiabetic chronic kidney disease patients showed loss of rhythmic organization compared to controls, with diabetic chronic kidney disease patients exhibiting deconsolidation of peak phases between their activity and standard deviation of interbeat intervals rhythms (mean phase difference chronic kidney disease 8.3 h, chronic kidney disease/type 2 diabetes mellitus 4 h, controls 6.8 h). This work provides a roadmap toward deriving actionable clinical insights from the data collected by wearable devices outside of highly controlled clinical environments.

Potential Long-Term Health Problems Associated with Ultra-Endurance Running: A Narrative Review

It is well established that physical activity reduces all-cause mortality and can prolong life. Ultra-endurance running (UER) is an extreme sport that is becoming increasingly popular, and comprises running races above marathon distance, exceeding 6 h, and/or running fixed distances on multiple days. Serious acute adverse events are rare, but there is mounting evidence that UER may lead to long-term health problems. The purpose of this review is to present the current state of knowledge regarding the potential long-term health problems derived from UER, specifically potential maladaptation in key organ systems, including cardiovascular, respiratory, musculoskeletal, renal, immunological, gastrointestinal, neurological, and integumentary systems. Special consideration is given to youth, masters, and female athletes, all of whom may be more susceptible to certain long-term health issues. We present directions for future research into the pathophysiological mechanisms that underpin athlete susceptibility to long-term issues. Although all body systems can be affected by UER, one of the clearest effects of endurance exercise is on the cardiovascular system, including right ventricular dysfunction and potential increased risk of arrhythmias and hypertension. There is also evidence that rare cases of acute renal injury in UER could lead to progressive renal scarring and chronic kidney disease. There are limited data specific to female athletes, who may be at greater risk of certain UER-related health issues due to interactions between energy availability and sex-hormone concentrations. Indeed, failure to consider sex differences in the design of female-specific UER training programs may have a negative impact on athlete longevity. It is hoped that this review will inform risk stratification and stimulate further research about UER and the implications for long-term health.

Evaluating the Validity and Utility of Wearable Technology for Continuously Monitoring Patients in a Hospital Setting: Systematic Review

BACKGROUND

The term posthospital syndrome has been used to describe the condition in which older patients are transiently frail after hospitalization and have a high chance of readmission. Since low activity and poor sleep during hospital stay may contribute to posthospital syndrome, the continuous monitoring of such parameters by using affordable wearables may help to reduce the prevalence of this syndrome. Although there have been systematic reviews of wearables for physical activity monitoring in hospital settings, there are limited data on the use of wearables for measuring other health variables in hospitalized patients.

OBJECTIVE

This systematic review aimed to evaluate the validity and utility of wearable devices for monitoring hospitalized patients.

METHODS

This review involved a comprehensive search of 7 databases and included articles that met the following criteria: inpatients must be aged >18 years, the wearable devices studied in the articles must be used to continuously monitor patients, and wearables should monitor biomarkers other than solely physical activity (ie, heart rate, respiratory rate, blood pressure, etc). Only English-language studies were included. From each study, we extracted basic demographic information along with the characteristics of the intervention. We assessed the risk of bias for studies that validated their wearable readings by using a modification of the Consensus-Based Standards for the Selection of Health Status Measurement Instruments.

RESULTS

Of the 2012 articles that were screened, 14 studies met the selection criteria. All included articles were observational in design. In total, 9 different commercial wearables for various body locations were examined in this review. The devices collectively measured 7 different health parameters across all studies (heart rate, sleep duration, respiratory rate, oxygen saturation, skin temperature, blood pressure, and fall risk). Only 6 studies validated their results against a reference device or standard. There was a considerable risk of bias in these studies due to the low number of patients in most of the studies (4/6, 67%). Many studies that validated their results found that certain variables were inaccurate and had wide limits of agreement. Heart rate and sleep were the parameters with the most evidence for being valid for in-hospital monitoring. Overall, the mean patient completion rate across all 14 studies was >90%.

CONCLUSIONS

The included studies suggested that wearable devices show promise for monitoring the heart rate and sleep of patients in hospitals. Many devices were not validated in inpatient settings, and the readings from most of the devices that were validated in such settings had wide limits of agreement when compared to gold standards. Even some medical-grade devices were found to perform poorly in inpatient settings. Further research is needed to determine the accuracy of hospitalized patients' digital biomarker readings and eventually determine whether these wearable devices improve health outcomes.

Application of Smartphone Technologies in Disease Monitoring: A Systematic Review

Technologies play an essential role in monitoring, managing, and self-management of chronic diseases. Since chronic patients rely on life-long healthcare systems and the current COVID-19 pandemic has placed limits on hospital care, there is a need to explore disease monitoring and management technologies and examine their acceptance by chronic patients. We systematically examined the use of smartphone applications (apps) in chronic disease monitoring and management in databases, namely, Medline, Web of Science, Embase, and Proquest, published from 2010 to 2020. Results showed that app-based weight management programs had a significant effect on healthy eating and physical activity (p = 0.002), eating behaviours (p < 0.001) and dietary intake pattern (p < 0.001), decreased mean body weight (p = 0.008), mean Body Mass Index (BMI) (p = 0.002) and mean waist circumference (p < 0.001). App intervention assisted in decreasing the stress levels (paired t-test = 3.18; p < 0.05). Among cancer patients, we observed a high acceptance of technology (76%) and a moderately positive correlation between non-invasive electronic monitoring data and questionnaire (r = 0.6, p < 0.0001). We found a significant relationship between app use and standard clinical evaluation and high acceptance of the use of apps to monitor the disease. Our findings provide insights into critical issues, including technology acceptance along with regulatory guidelines to be considered when designing, developing, and deploying smartphone solutions targeted for chronic patients.

A Call for an Ethics and Governance Action Plan to Harness the Power of Artificial Intelligence and Digitalization in Nephrology

Digitalization in nephrology has progressed in a manner that is disparate and siloed, even though learning (under a broader Learning Health System initiative) has been manifested in all the main areas of clinical application. Most applications based on artificial intelligence/machine learning (AI/ML) are still in the initial developmental stages and are yet to be adequately validated and shown to contribute to positive patient outcomes. There is also no consistent or comprehensive digitalization plan, and insufficient data are a limiting factor across all of these areas. In this article, we first consider how digitalization along nephrology care pathways relates to the Learning Health System initiative. We then consider the current state of AI/ML-based software and devices in nephrology and the ethical and regulatory challenges in scaling them up toward broader clinical application. We conclude with our proposal to establish a dedicated ethics and governance framework that is centered around health care providers in nephrology and the AI/ML-based software to which their work relates. This framework should help to integrate ethical and regulatory values and considerations, involve a wide range of stakeholders, and apply across normative domains that are conventionally demarcated as clinical, research, and public health.

Solution Blowing Spinning Technology towards Green Development of Urea Sensor Nanofibers Immobilized with Hydrazone Probe

Cellulose has been one of the most widespread materials due to its renewability, excellent mechanical properties, biodegradability, high absorption ability, biocompatibility and cheapness. Novel, simple and green colorimetric fibrous film sensor was developed by immobilization of urease enzyme (U) and tricyanofuran hydrazone (TCFH) molecular probe onto cellulose nanofibers (CNF). Cellulose acetate nanofibers (CANF) were firstly prepared from cellulose acetate using the simple, green and low cost solution blowing spinning technology. The produced CANF was exposed to deacetylation to introduce CNF, which was then treated with a mixture of TCFH and urease enzyme to introduce CNF-TCFH-U nanofibrous biosensor. CNF were reinforced with tricyanofuran hyrazone molecular probe and urease enzyme was encapsulated into calcium alginate biopolymer to establish a biocomposite film. This CNF-TCFH-U naked-eye sensor can be applied as a disposable urea detector. CNF demonstrated a large surface area and was utilized as a carrier for TCFH, which is the spectroscopic probe and urease is a catalyst. The biochromic CNF-TCFH-U nanofibrous biosensor responds to an aqueous medium of urea via a visible color signal changing from off-white to dark pink. The morphology of the generated CNF and CNF-TCFH-U nanofibrous films were characterized by different analytical tools, including energy-dispersive X-ray patterns (EDX), polarizing optical microscope (POM), Fourier-transform infrared spectroscopy (FT-IR) and scanning electron microscope (SEM). SEM images of CNF-TCFH-U nanofibers demonstrated diameters between 800 nm and 2.5 μm forming a nonwoven fabric with a homogeneous distribution of TCFH/urease-containing calcium alginate nanoparticles on the surface of CNF. The morphology of the cross-linked calcium alginate nanoparticles was also explored using transmission electron microscopy (TEM) to indicate an average diameter of 56-66 nm. The photophysical performance of the prepared CNF-TCFH-U was also studied by CIE Lab coloration parameters. The nanofibrous film biosensor displayed a relatively rapid response time (5-10 min) and a limit of detection as low as 200 ppm and as high as 1400 ppm. Tricyanofuran hydrazone is a pH-responsive disperse dye comprising a hydrazone detection group. Determination of urea occurs through a proton transfer from the hydrazone group to the generated ammonia from the reaction of urea with urease.

Opportunities in the cloud or pie in the sky? Current status and future perspectives of telemedicine in nephrology

The use of telehealth to support, enhance or substitute traditional methods of delivering healthcare is becoming increasingly common in many specialties, such as stroke care, radiology and oncology. There is reason to believe that this approach remains underutilized within nephrology, which is somewhat surprising given the fact that nephrologists have always driven technological change in developing dialysis technology. Despite the obvious benefits that telehealth may provide, robust evidence remains lacking and many of the studies are anecdotal, limited to small numbers or without conclusive proof of benefit. More worryingly, quite a few studies report unexpected obstacles, pitfalls or patient dissatisfaction. However, with increasing global threats such as climate change and infectious disease, a change in approach to delivery of healthcare is needed. The current pandemic with coronavirus disease 2019 (COVID-19) has prompted the renal community to embrace telehealth to an unprecedented extent and at speed. In that sense the pandemic has already served as a disruptor, changed clinical practice and shown immense transformative potential. Here, we provide an update on current evidence and use of telehealth within various areas of nephrology globally, including the fields of dialysis, inpatient care, virtual consultation and patient empowerment. We also provide a brief primer on the use of artificial intelligence in this context and speculate about future implications. We also highlight legal aspects and pitfalls and discuss the 'digital divide' as a key concept that healthcare providers need to be mindful of when providing telemedicine-based approaches. Finally, we briefly discuss the immediate use of telenephrology at the onset of the COVID-19 pandemic. We hope to provide clinical nephrologists with an overview of what is currently available, as well as a glimpse into what may be expected in the future.

Artificial intelligence enabled applications in kidney disease

Artificial intelligence (AI) is considered as the next natural progression of traditional statistical techniques. Advances in analytical methods and infrastructure enable AI to be applied in health care. While AI applications are relatively common in fields like ophthalmology and cardiology, its use is scarcely reported in nephrology. We present the current status of AI in research toward kidney disease and discuss future pathways for AI. The clinical applications of AI in progression to end-stage kidney disease and dialysis can be broadly subdivided into three main topics: (a) predicting events in the future such as mortality and hospitalization; (b) providing treatment and decision aids such as automating drug prescription; and (c) identifying patterns such as phenotypical clusters and arteriovenous fistula aneurysm. At present, the use of prediction models in treating patients with kidney disease is still in its infancy and further evidence is needed to identify its relative value. Policies and regulations need to be addressed before implementing AI solutions at the point of care in clinics. AI is not anticipated to replace the nephrologists' medical decision-making, but instead assist them in providing optimal personalized care for their patients.

Volume Balance in Chronic Kidney Disease: Evaluation Methodologies and Innovation Opportunities

BACKGROUND

Patients affected by chronic kidney disease are at a risk of cardiovascular morbidity and mortality. Body fluids unbalance is one of the main characteristics of this condition, as fluid overload is highly prevalent in patients affected by the cardiorenal syndrome.

SUMMARY

We describe the state of the art and new insights into body volume evaluation. The mechanisms behind fluid balance are often complex, mainly because of the interplay of multiple regulatory systems. Consequently, its management may be challenging in clinical practice and even more so out-of-hospital. Availability of novel technologies offer new opportunities to improve the quality of care and patients' outcome. Development and validation of new technologies could provide new tools to reduce costs for the healthcare system, promote personalized medicine, and boost home care. Due to the current COVID-19 pandemic, a proper monitoring of chronic patients suffering from fluid unbalances is extremely relevant. Key Message: We discuss the main mechanisms responsible for fluid overload in different clinical contexts, including hemodialysis, peritoneal dialysis, and heart failure, emphasizing the potential impact provided by the implementation of the new technologies.

Potential Use of Wearable Sensors to Assess Cumulative Kidney Trauma in Endurance Off-Road Running

(1) Background: This study aimed to explore wearable sensors' potential use to assess cumulative mechanical kidney trauma during endurance off-road running. (2) Methods: 18 participants (38.78 ± 10.38 years, 73.24 ± 12.6 kg, 172.17 ± 9.48 cm) ran 36 k off-road race wearing a Magnetic, Angular Rate and Gravity (MARG) sensor attached to their lower back. Impacts in g forces were recorded throughout the race using the MARG sensor. Two blood samples were collected immediately pre- and post-race: serum creatinine (sCr) and albumin (sALB). (3) Results: Sixteen impact variables were grouped using principal component analysis in four different principal components (PC) that explained 90% of the total variance. The 4th PC predicted 24% of the percentage of change (∆%) of sCr and the 3rd PC predicted the ∆% of sALB by 23%. There were pre- and post-race large changes in sCr and sALB (p ≤ 0.01) and 33% of participants met acute kidney injury diagnosis criteria. (4) Conclusions: The data related to impacts could better explain the cumulative mechanical kidney trauma during mountain running, opening a new range of possibilities using technology to better understand how the number and magnitude of the g-forces involved in off-road running could potentially affect kidney function.

Wearable health devices and personal area networks: can they improve outcomes in haemodialysis patients?

Digitization of healthcare will be a major innovation driver in the coming decade. Also, enabled by technological advancements and electronics miniaturization, wearable health device (WHD) applications are expected to grow exponentially. This, in turn, may make 4P medicine (predictive, precise, preventive and personalized) a more attainable goal within dialysis patient care. This article discusses different use cases where WHD could be of relevance for dialysis patient care, i.e. measurement of heart rate, arrhythmia detection, blood pressure, hyperkalaemia, fluid overload and physical activity. After adequate validation of the different WHD in this specific population, data obtained from WHD could form part of a body area network (BAN), which could serve different purposes such as feedback on actionable parameters like physical inactivity, fluid overload, danger signalling or event prediction. For a BAN to become clinical reality, not only must technical issues, cybersecurity and data privacy be addressed, but also adequate models based on artificial intelligence and mathematical analysis need to be developed for signal optimization, data representation, data reliability labelling and interpretation. Moreover, the potential of WHD and BAN can only be fulfilled if they are part of a transformative healthcare system with a shared responsibility between patients, healthcare providers and the payors, using a step-up approach that may include digital assistants and dedicated ‘digital clinics’. The coming decade will be critical in observing how these developments will impact and transform dialysis patient care and will undoubtedly ask for an increased ‘digital literacy’ for all those implicated in their care.

Daily physical activity in patients on chronic haemodialysis and its relation with fatigue and depressive symptoms

OBJECTIVE

Fatigue and depressed mood are considered main impediments to physical activity in haemodialysis (HD) patients. A better understanding of their interrelationships is crucial to develop effective therapies. Moreover, measurement of daily physical activity (DPA) in HD patients is tricky, as it is usually assessed by subjective self-report questionnaires. Therefore, we aimed to objectively measure sponteanous DPA with motion sensors and to explore its relation with fatigue and depressive symptoms.

METHODS

DPA was assessed for seven consecutive days in 37 HD patients based on their daily step count measured with the SenseWear™ Armband. The Fatigue Severity Scale (FSS) and Beck Depression Inventory-II (BDI-II) were administered to evaluate fatigue and depressed mood.

RESULTS

Median DPA was 2424 steps/day, (IQR:892-4545). In 81% of subjects, DPA felt within a sedentary lifestyle classification, as they made < 5.000 steps/day. DPA did not correlate with fatigue (r_s = 0.04, p = 0.832), and did not significantly differ between patients categorized as clinically fatigued (n = 23, FSS ≥ 4) or not (n = 14, FSS < 4) (p = 0.654, d = 0.20). Although low-depressed subjects (n = 19, BDI-II ≤ 13) made on average 1.7 times more steps/day than high-depressed subjects (n = 18, BDI-II > 13) (p = 0.111, d = 0.60), depressive mood did also not correlate significantly with DPA (r_s = - 0.23, p = 0.175).

CONCLUSION

Objective assessment of DPA with motion sensors is feasible in HD patients and allows identifying a sedentary lifestyle. Our results suggest that spontanous DPA is determined by age rather than by fatigue or mood.

Recent advances of biosensors for hypertension and nephrology

PURPOSE OF REVIEW

Hypertension (HTN) and chronic kidney disease (CKD) are significant problems. With recent advances in technologies, biosensors have shown a great potential to provide better home monitoring in hypertension (HTN), medication compliance, diagnostic device for kidney disease, CKD/end-stage renal disease (ESRD) care, and post kidney transplant management.

RECENT FINDINGS

Multiple devices/biosensors have been developed related to HTN, kidney function including real-time glomerular filtration rate, CKD/end-stage renal disease, and transplant care. In recent advances in wearable biosensors, point of care monitoring system could provide more integrated care to the patients via telenephrology.

SUMMARY

This review focuses on the recent advances in biosensors which may be useful for HTN and nephrology. We will discuss future potential clinical implication of these biosensors.

Machine learning, the kidney, and genotype-phenotype analysis

With biomedical research transitioning into data-rich science, machine learning provides a powerful toolkit for extracting knowledge from large-scale biological data sets. The increasing availability of comprehensive kidney omics compendia (transcriptomics, proteomics, metabolomics, and genome sequencing), as well as other data modalities such as electronic health records, digital nephropathology repositories, and radiology renal images, makes machine learning approaches increasingly essential for analyzing human kidney data sets. Here, we discuss how machine learning approaches can be applied to the study of kidney disease, with a particular focus on how they can be used for understanding the relationship between genotype and phenotype.

Is Wearable Technology Becoming Part of Us? Developing and Validating a Measurement Scale for Wearable Technology Embodiment

BACKGROUND

To experience external objects in such a way that they are perceived as an integral part of one's own body is called embodiment. Wearable technology is a category of objects, which, due to its intrinsic properties (eg, close to the body, inviting frequent interaction, and access to personal information), is likely to be embodied. This phenomenon, which is referred to in this paper as wearable technology embodiment, has led to extensive conceptual considerations in various research fields. These considerations and further possibilities with regard to quantifying wearable technology embodiment are of particular value to the mobile health (mHealth) field. For example, the ability to predict the effectiveness of mHealth interventions and knowing the extent to which people embody the technology might be crucial for improving mHealth adherence. To facilitate examining wearable technology embodiment, we developed a measurement scale for this construct.

OBJECTIVE

This study aimed to conceptualize wearable technology embodiment, create an instrument to measure it, and test the predictive validity of the scale using well-known constructs related to technology adoption. The introduced instrument has 3 dimensions and includes 9 measurement items. The items are distributed evenly between the 3 dimensions, which include body extension, cognitive extension, and self-extension.

METHODS

Data were collected through a vignette-based survey (n=182). Each respondent was given 3 different vignettes, describing a hypothetical situation using a different type of wearable technology (a smart phone, a smart wristband, or a smart watch) with the purpose of tracking daily activities. Scale dimensions and item reliability were tested for their validity and Goodness of Fit Index (GFI).

RESULTS

Convergent validity of the 3 dimensions and their reliability were established as confirmatory factor analysis factor loadings (>0.70), average variance extracted values (>0.50), and minimum item to total correlations (>0.40) exceeded established threshold values. The reliability of the dimensions was also confirmed as Cronbach alpha and composite reliability exceeded 0.70. GFI testing confirmed that the 3 dimensions function as intercorrelated first-order factors. Predictive validity testing showed that these dimensions significantly add to multiple constructs associated with predicting the adoption of new technologies (ie, trust, perceived usefulness, involvement, attitude, and continuous intention).

CONCLUSIONS

The wearable technology embodiment measurement instrument has shown promise as a tool to measure the extension of an individual's body, cognition, and self, as well as predict certain aspects of technology adoption. This 3-dimensional instrument can be applied to mixed method research and used by wearable technology developers to improve future versions through such things as fit, improved accuracy of biofeedback data, and customizable features or fashion to connect to the users' personal identity. Further research is recommended to apply this measurement instrument to multiple scenarios and technologies, and more diverse user groups.

Using Technology to Inform and Deliver Precise Personalized Care to Patients With End-Stage Kidney Disease

Consistent with the increase of precision medicine, the care of patients with end-stage kidney disease (ESKD) requiring maintenance dialysis therapy should evolve to become more personalized. Precise and personalized care is nuanced and informed by a number of factors including an individual's needs and preferences, disease progression, and response to and tolerance of treatments. Technology can support the delivery of more precise and personalized care through multiple mechanisms, including more accurate and real-time assessments of key care elements, enhanced treatment monitoring, and remote monitoring of home dialysis therapies. Data from health care and non-health care sources and advanced analytical methods such as machine learning can be used to create novel insights, and large volumes of data can be integrated to support clinical decisions. Health care models continue to evolve and the opportunities and need for novel care approaches supported by technology and health informatics continue to expand as the delivery and organization of health care changes. Ultimately, precise personalized care for ESKD, including dialysis therapy, will become more feasible as the biological, social, and environmental determinants of health are more broadly understood and as advances in science, engineering, and information management create the means to provide truly precise care for ESKD.

Big science and big data in nephrology

There have been tremendous advances during the last decade in methods for large-scale, high-throughput data generation and in novel computational approaches to analyze these datasets. These advances have had a profound impact on biomedical research and clinical medicine. The field of genomics is rapidly developing toward single-cell analysis, and major advances in proteomics and metabolomics have been made in recent years. The developments on wearables and electronic health records are poised to change clinical trial design. This rise of 'big data' holds the promise to transform not only research progress, but also clinical decision making towards precision medicine. To have a true impact, it requires integrative and multi-disciplinary approaches that blend experimental, clinical and computational expertise across multiple institutions. Cancer research has been at the forefront of the progress in such large-scale initiatives, so-called 'big science,' with an emphasis on precision medicine, and various other areas are quickly catching up. Nephrology is arguably lagging behind, and hence these are exciting times to start (or redirect) a research career to leverage these developments in nephrology. In this review, we summarize advances in big data generation, computational analysis, and big science initiatives, with a special focus on applications to nephrology.

Conductive Thread-Based Textile Sensor for Continuous Perspiration Level Monitoring

Individual perspiration level indicates a person’s physical status as well as their comfort level. Therefore, continuous perspiration level measurement enables people to monitor these conditions for applications including fitness assessment, athlete physical status monitoring, and patient/elderly care. Prior work on perspiration (sweat) sensing required the user either to be static or to wear the adhesive sensor directly on the skin, which limits users’ mobility and comfort. In this paper, we present a novel conductive thread-based textile sensor that measures an individual’s on-cloth sweat quantity. The sensor consists of three conductive threads. Each conductive thread is surrounded by a braided cotton cover. An additional braided cotton cover is placed outside the three conductive threads, holding them in a position that is stable for measurement. the sensor can be embedded at various locations on a person’s clothing. When the person sweats, the cotton braids absorb the sweat and change the conductivity (resistance) between conductive threads. We used a voltage dividing circuit to measure this resistance as the sensor output (DC). We then conducted a sensor calibration to map this measured voltage to the quantity of electrolyte solution (with the same density as sweat) applied to the sensor. We used this sensor to measure individuals’ perspiration quantity and infer their perceived perspiration levels. The system is able to limit the average prediction error to 0.4 levels when compared to five pre-defined perceived perspiration levels.

Wearable Health Devices-Vital Sign Monitoring, Systems and Technologies

Wearable Health Devices (WHDs) are increasingly helping people to better monitor their health status both at an activity/fitness level for self-health tracking and at a medical level providing more data to clinicians with a potential for earlier diagnostic and guidance of treatment. The technology revolution in the miniaturization of electronic devices is enabling to design more reliable and adaptable wearables, contributing for a world-wide change in the health monitoring approach. In this paper we review important aspects in the WHDs area, listing the state-of-the-art of wearable vital signs sensing technologies plus their system architectures and specifications. A focus on vital signs acquired by WHDs is made: first a discussion about the most important vital signs for health assessment using WHDs is presented and then for each vital sign a description is made concerning its origin and effect on heath, monitoring needs, acquisition methods and WHDs and recent scientific developments on the area (electrocardiogram, heart rate, blood pressure, respiration rate, blood oxygen saturation, blood glucose, skin perspiration, capnography, body temperature, motion evaluation, cardiac implantable devices and ambient parameters). A general WHDs system architecture is presented based on the state-of-the-art. After a global review of WHDs, we zoom in into cardiovascular WHDs, analysing commercial devices and their applicability versus quality, extending this subject to smart t-shirts for medical purposes. Furthermore we present a resumed evolution of these devices based on the prototypes developed along the years. Finally we discuss likely market trends and future challenges for the emerging WHDs area.