Parkinson Disease Recognition Using a Gamified Website: Machine Learning Development and Usability Study

BACKGROUND

Parkinson disease (PD) affects millions globally, causing motor function impairments. Early detection is vital, and diverse data sources aid diagnosis. We focus on lower arm movements during keyboard and trackpad or touchscreen interactions, which serve as reliable indicators of PD. Previous works explore keyboard tapping and unstructured device monitoring; we attempt to further these works with structured tests taking into account 2D hand movement in addition to finger tapping. Our feasibility study uses keystroke and mouse movement data from a remotely conducted, structured, web-based test combined with self-reported PD status to create a predictive model for detecting the presence of PD.

OBJECTIVE

Analysis of finger tapping speed and accuracy through keyboard input and analysis of 2D hand movement through mouse input allowed differentiation between participants with and without PD. This comparative analysis enables us to establish clear distinctions between the two groups and explore the feasibility of using motor behavior to predict the presence of the disease.

METHODS

Participants were recruited via email by the Hawaii Parkinson Association (HPA) and directed to a web application for the tests. The 2023 HPA symposium was also used as a forum to recruit participants and spread information about our study. The application recorded participant demographics, including age, gender, and race, as well as PD status. We conducted a series of tests to assess finger tapping, using on-screen prompts to request key presses of constant and random keys. Response times, accuracy, and unintended movements resulting in accidental presses were recorded. Participants performed a hand movement test consisting of tracing straight and curved on-screen ribbons using a trackpad or mouse, allowing us to evaluate stability and precision of 2D hand movement. From this tracing, the test collected and stored insights concerning lower arm motor movement.

RESULTS

Our formative study included 31 participants, 18 without PD and 13 with PD, and analyzed their lower limb movement data collected from keyboards and computer mice. From the data set, we extracted 28 features and evaluated their significances using an extra tree classifier predictor. A random forest model was trained using the 6 most important features identified by the predictor. These selected features provided insights into precision and movement speed derived from keyboard tapping and mouse tracing tests. This final model achieved an average F1-score of 0.7311 (SD 0.1663) and an average accuracy of 0.7429 (SD 0.1400) over 20 runs for predicting the presence of PD.

CONCLUSIONS

This preliminary feasibility study suggests the possibility of using technology-based limb movement data to predict the presence of PD, demonstrating the practicality of implementing this approach in a cost-effective and accessible manner. In addition, this study demonstrates that structured mouse movement tests can be used in combination with finger tapping to detect PD.

Keystroke Biometrics as a Tool for the Early Diagnosis and Clinical Assessment of Parkinson's Disease

(1) Background: Parkinson's disease (PD) is the second most common neurodegenerative disease. Early diagnosis and reliable clinical assessments are essential for appropriate therapy and improving patients' quality of life. Keystroke biometrics, which capture unique typing behavior, have shown potential for early PD diagnosis. This study aimed to evaluate keystroke biometric parameters from two datasets to identify indicators that can effectively distinguish de novo PD patients from healthy controls. (2) Methods: Data from natural typing tasks in Physionet were analyzed to estimate keystroke biometric parameters. The parameters investigated included alternating-finger tapping (afTap) and standard deviations of interkey latencies (ILSD) and release latencies (RLSD). Sensitivity rates were calculated to assess the discriminatory ability of these parameters. (3) Results: Significant differences were observed in three parameters, namely afTap, ILSD, and RLSD, between de novo PD patients and healthy controls. The sensitivity rates were high, with values of 83%, 88%, and 96% for afTap, ILSD, and RLSD, respectively. Correlation analysis revealed a significantly negative correlation between typing speed and number of words typed with the standard motor assessment for PD, UPDRS-III, in patients with early PD. (4) Conclusions: Simple algorithms utilizing keystroke biometric parameters can serve as effective screening tests in distinguishing de novo PD patients from healthy controls. Moreover, typing speed and number of words typed were identified as reliable tools for assessing clinical statuses in PD patients. These findings underscore the potential of keystroke biometrics for early PD diagnosis and clinical severity assessment.

PMDedu: Assessing the educational needs of startups and academic investigators focused on pediatric medical device development

Background

The pediatric medical device development (PMDD) process is highly complex, beset by a variety of financial, technical, medical, and regulatory barriers. Startup company innovators and academic investigators often struggle with accessing specialized knowledge relating to regulatory requirements, product development, research, and marketing strategies.

Objectives

The West Coast Consortium for Technology & Innovation in Pediatrics (CTIP) conducted an educational needs assessment to understand knowledge gaps and inform our educational strategy.

Methods

We surveyed a total of 49 medical device startups and 52 academic investigators. Electronic surveys were developed for each group on Qualtrics and focused on manufacturing, regulatory, research, commercialization, and funding. Descriptive statistics were used.

Results

A larger proportion of academic investigator respondents had a clinical background compared to the startup respondents (45% vs. 22%). The biggest barriers for academic investigators were understanding regulatory and safety requirements testing (52%) and finding and obtaining non-dilutive funding was the most difficult (54%). Among startups, understanding clinical research methods and requirements was the biggest barrier (79%).

Conclusion

Startup companies and academic investigators have similar, but not identical, educational needs to better understand the PMD development process. Investigators need more support in identifying funding sources, while startup companies identified an increased need for education on research regulatory topics. These findings can help guide curriculum development as well as opportunities for partnerships between academia and startups.

Digital Application of Clinical Staging to Support Stratification in Youth Mental Health Services: Validity and Reliability Study

BACKGROUND

As the demand for youth mental health care continues to rise, managing wait times and reducing treatment delays are key challenges to delivering timely and quality care. Clinical staging is a heuristic model for youth mental health that can stratify care allocation according to individuals' risk of illness progression. The application of staging has been traditionally limited to trained clinicians yet leveraging digital technologies to apply clinical staging could increase the scalability and usability of this model in services.

OBJECTIVE

The aim of this study was to validate a digital algorithm to accurately differentiate young people at lower and higher risk of developing mental disorders.

METHODS

We conducted a study with a cohort comprising 131 young people, aged between 16 and 25 years, who presented to youth mental health services in Australia between November 2018 and March 2021. Expert psychiatrists independently assigned clinical stages (either stage 1a or stage 1b+), which were then compared to the digital algorithm's allocation based on a multidimensional self-report questionnaire.

RESULTS

Of the 131 participants, the mean age was 20.3 (SD 2.4) years, and 72% (94/131) of them were female. Ninety-one percent of clinical stage ratings were concordant between the digital algorithm and the experts' ratings, with a substantial interrater agreement (κ=0.67; P<.001). The algorithm demonstrated an accuracy of 91% (95% CI 86%-95%; P=.03), a sensitivity of 80%, a specificity of 93%, and an F1-score of 73%. Of the concordant ratings, 16 young people were allocated to stage 1a, while 103 were assigned to stage 1b+. Among the 12 discordant cases, the digital algorithm allocated a lower stage (stage 1a) to 8 participants compared to the experts. These individuals had significantly milder symptoms of depression (P<.001) and anxiety (P<.001) compared to those with concordant stage 1b+ ratings.

CONCLUSIONS

This novel digital algorithm is sufficiently robust to be used as an adjunctive decision support tool to stratify care and assist with demand management in youth mental health services. This work could transform care pathways and expedite care allocation for those in the early stages of common anxiety and depressive disorders. Between 11% and 27% of young people seeking care may benefit from low-intensity, self-directed, or brief interventions. Findings from this study suggest the possibility of redirecting clinical capacity to focus on individuals in stage 1b+ for further assessment and intervention.

Evaluation of a Passive Wearable Device for Post-Stroke Shoulder Abduction Support

Post-stroke upper extremity function can be improved by devices that support shoulder abduction. However, many of these devices provide limited assistance in activities of daily living due to their complexity and encumbrance. We developed and evaluated a passive, lightweight (0.6 kg) wearable device consisting of an aluminum frame and elastic bands attached to a posture vest to aid in shoulder abduction. The number and thickness of bands can be adjusted to provide supportive forces to the affected arm. We measured reachable workspace area and Wolf Motor Function Test (WMFT) performance in people with a history of stroke (n = 11) with and without the wearable. The device increased workspace area in 6 participants and improved average WMFT functional and timing scores in 7 and 12 tasks, respectively, out of 16 total tasks. On average, participants increased their arm motion within 20 cm of shoulder level by 22.4% and decreased their hand's average distance from trunk by 15.2%, both improvements in the device case.

Consumer Wearable Sleep Trackers: Are They Ready for Clinical Use?

As the importance of good sleep continues to gain public recognition, the market for sleep-monitoring devices continues to grow. Modern technology has shifted from simple sleep tracking to a more granular sleep health assessment. We examine the available functionalities of consumer wearable sleep trackers (CWSTs) and how they perform in healthy individuals and disease states. Additionally, the continuum of sleep technology from consumer-grade to medical-grade is detailed. As this trend invariably grows, we urge professional societies to develop guidelines encompassing the practical clinical use of CWSTs and how best to incorporate them into patient care plans.

The effects of therapeutic virtual reality experience to promote mental well-being in older people living with physical disabilities in long-term care facilities

BACKGROUND

Mental well-being is poor in long-term care facilities (LTCF) residents. Physical disabilities, impaired social engagement, and environmental stress are also common in LTCF which exacerbate the decline of the mental well-being of older people living in LTCF. Protective elements, including nature-based, reminiscence, outdoor, and group activities, are known to be effective to promote the mental well-being of older people living in LTCF. However, limited by their physical disabilities and poor social support, older people living in LTCF are not likely to benefit from these effective measures. Virtual reality has been proven to be feasible to be environmentally unrestricted to providing LTCF residents with all protective elements promoting mental well-being. However, its effects on the mental well-being of LTCF residents living with physical disabilities are unclear.

METHODS

This study employs a single-blinded, two-parallel-group (intervention-to-control group ratio = 1:1), non-inferiority, randomized controlled trial. Eligible participants are aged 60 years or above, LTCF residents, and living with physical disabilities. The study will be conducted in LTCF. In the intervention group, participants will receive a 6-week VR experience program. In the control group, participants will receive the usual care provided by the LTCF. The primary outcome is mental well-being, as measured by World Health Organization Five Well-being Index at the time point of baseline (i.e., week 0) and after completion of the intervention (i.e., week 7). This study aims to recruit a total of 216 participants. Generalized estimating equations (GEE) will be used to examine the effects of the intervention.

TRIAL REGISTRATION

The trial has been registered at ClinicalTrials.gov (Identifier: NCT05818579 ), Registered on April 5, 2023. The latest version of the protocol was published online on 19 April 2023. All items come from the World Health Organization Trial Registration Data Set. This study has been approved by the Research Ethics Committee of Tung Wah College, Hong Kong (reference number: REC2023158). The findings will be disseminated in peer-reviewed journals, presented at international and local conferences with related themes, and shared in local media.

Wearable and Noninvasive Device for Integral Congestive Heart Failure Management in the IoMT Paradigm

Noninvasive remote monitoring of hemodynamic variables is essential in optimizing treatment opportunities and predicting rehospitalization in patients with congestive heart failure. The objective of this study is to develop a wearable bioimpedance-based device, which can provide continuous measurement of cardiac output and stroke volume, as well as other physiological parameters for a greater prognosis and prevention of congestive heart failure. The bioimpedance system, which is based on a robust and cost-effective measuring principle, was implemented in a CMOS application specific integrated circuit, and operates as the analog front-end of the device, which has been provided with a radio-frequency section for wireless communication. The operating parameters of the proposed wearable device are remotely configured through a graphical user interface to measure the magnitude and the phase of complex impedances over a bandwidth of 1 kHz to 1 MHz. As a result of this study, a cardiac activity monitor was implemented, and its accuracy was evaluated in 33 patients with different heart diseases, ages, and genders. The proposed device was compared with a well-established technique such as Doppler echocardiography, and the results showed that the two instruments are clinically equivalent.

Antifouling Behavior of Copper-Modified Titania Nanotube Surfaces

Titanium and its alloys are commonly used to fabricate orthopedic implants due to their excellent mechanical properties, corrosion resistance, and biocompatibility. In recent years, orthopedic implant surgeries have considerably increased. This has also resulted in an increase in infection-associated revision surgeries for these implants. To combat this, various approaches are being investigated in the literature. One of the approaches is modifying the surface topography of implants and creating surfaces that are not only antifouling but also encourage osteointegration. Titania nanotube surfaces have demonstrated a moderate decrease in bacterial adhesion while encouraging mesenchymal stem cell adhesion, proliferation, and differentiation, and hence were used in this study. In this work, titania nanotube surfaces were fabricated using a simple anodization technique. These surfaces were further modified with copper using a physical vapor deposition technique, since copper is known to be potent against bacteria once in contact. In this study, scanning electron microscopy was used to evaluate surface topography; energy-dispersive X-ray spectroscopy and X-ray photoelectron spectroscopy were used to evaluate surface chemistry; contact angle goniometry was used to evaluate surface wettability; and X-ray diffraction was used to evaluate surface crystallinity. Antifouling behavior against a gram-positive and a gram-negative bacterium was also investigated. The results indicate that copper-modified titania nanotube surfaces display enhanced antifouling behavior when compared to other surfaces, and this may be a potential way to prevent infection in orthopedic implants.

Accuracy and Reliability of a Suite of Digital Measures of Walking Generated Using a Wrist-Worn Sensor in Healthy Individuals: Performance Characterization Study

BACKGROUND

Mobility is a meaningful aspect of an individual's health whose quantification can provide clinical insights. Wearable sensor technology can quantify walking behaviors (a key aspect of mobility) through continuous passive monitoring.

OBJECTIVE

Our objective was to characterize the analytical performance (accuracy and reliability) of a suite of digital measures of walking behaviors as critical aspects in the practical implementation of digital measures into clinical studies.

METHODS

We collected data from a wrist-worn device (the Verily Study Watch) worn for multiple days by a cohort of volunteer participants without a history of gait or walking impairment in a real-world setting. On the basis of step measurements computed in 10-second epochs from sensor data, we generated individual daily aggregates (participant-days) to derive a suite of measures of walking: step count, walking bout duration, number of total walking bouts, number of long walking bouts, number of short walking bouts, peak 30-minute walking cadence, and peak 30-minute walking pace. To characterize the accuracy of the measures, we examined agreement with truth labels generated by a concurrent, ankle-worn, reference device (Modus StepWatch 4) with known low error, calculating the following metrics: intraclass correlation coefficient (ICC), Pearson r coefficient, mean error, and mean absolute error. To characterize the reliability, we developed a novel approach to identify the time to reach a reliable readout (time to reliability) for each measure. This was accomplished by computing mean values over aggregation scopes ranging from 1 to 30 days and analyzing test-retest reliability based on ICCs between adjacent (nonoverlapping) time windows for each measure.

RESULTS

In the accuracy characterization, we collected data for a total of 162 participant-days from a testing cohort (n=35 participants; median observation time 5 days). Agreement with the reference device-based readouts in the testing subcohort (n=35) for the 8 measurements under evaluation, as reflected by ICCs, ranged between 0.7 and 0.9; Pearson r values were all greater than 0.75, and all reached statistical significance (P<.001). For the time-to-reliability characterization, we collected data for a total of 15,120 participant-days (overall cohort N=234; median observation time 119 days). All digital measures achieved an ICC between adjacent readouts of >0.75 by 16 days of wear time.

CONCLUSIONS

We characterized the accuracy and reliability of a suite of digital measures that provides comprehensive information about walking behaviors in real-world settings. These results, which report the level of agreement with high-accuracy reference labels and the time duration required to establish reliable measure readouts, can guide the practical implementation of these measures into clinical studies. Well-characterized tools to quantify walking behaviors in research contexts can provide valuable clinical information about general population cohorts and patients with specific conditions.

Collaborative Use of Lung Mechanics Simulation for Testing and Iterative Design for Three Emergency Use Ventilation Device Projects

SUMMARY STATEMENT

We describe our collaboration with engineering, clinical, and simulation colleagues to use a lung simulator (IngMar Medical ASL 5000) to aid in the development of 3 open-source ventilation devices for patients with COVID-19.Twenty-nine test conditions were created by programming software lung models of varying disease severity in the ASL 5000 to test basic functionality, safety features, and compliance with regulatory requirements for emergency use authorization for the 3 projects' prototypes. More than 200 simulations were performed, with the design team present to enable rapid troubleshooting and design iteration in real time.Working with 3 separate simultaneous ventilation device projects allowed us to rapidly learn from each, improving our ability to successfully collaborate with the different design/build teams.This project illustrates the role of simulation in facilitating collaborative innovation in health care, both in emergency and everyday settings that extend beyond the COVID-19 pandemic.

Portable digital devices for paediatric height and length measurement: A scoping review and target product profile matching analysis

BACKGROUND

Routine anthropometry of children, including length/height measurement, is an essential component of paediatric clinical assessments. UNICEF has called for the accelerated development of novel, digital height/length measurement devices to improve child nutrition and growth surveillance programs. This scoping review aimed to identify all digital, portable height/length measurement devices in the literature or otherwise available internationally. We also assessed identified devices against the UNICEF Target Product Profile (TPP) to identify those of highest potential for clinical and public health use.

METHOD

We searched four databases (Medline, Embase, CINAHL and Global Health) and the grey literature between 1st January 1992 and 2nd February 2023. We looked for studies or reports on portable, digital devices for height or length measurement in children up to 18 years old. Citations were screened independently by two reviewers, with data extraction and quality assessment performed in duplicate and disagreements resolved. Devices were evaluated and scored against the 34 criteria of the UNICEF TPP.

RESULTS

Twenty studies describing twelve height/length measurement devices were identified, most of which used prospective validation designs. Additional devices were found in the grey literature, but these did not report key performance data so were not included. Across the twelve devices, only 10 of 34 UNICEF criteria on average could be fully assessed. Six met UNICEF's ideal accuracy standard and one device met the minimum accuracy standard. The Leica DistoD2 device scored highest (41%), followed by Autoanthro in a controlled environment (33%) and GLM30 (32%). These devices may be high potential for further assessment and development, though further research is required.

CONCLUSION

While 12 portable, digital devices exist for child height/length measurement, insufficient data are available to fully assess whether they meet the industry's needs. Although some devices show promise, further research is needed to test the validity of these devices in varying contexts, and continued development and commercialization will be important to improve reliability and precision of these devices for widespread use.

Heart Rate Variability Code: Does It Exist and Can We Hack It?

A code is generally defined as a system of signals or symbols for communication. Experimental evidence is synthesized for the presence and utility of such communication in heart rate variability (HRV) with particular attention to fetal HRV: HRV contains signatures of information flow between the organs and of response to physiological or pathophysiological stimuli as signatures of states (or syndromes). HRV exhibits features of time structure, phase space structure, specificity with respect to (organ) target and pathophysiological syndromes, and universality with respect to species independence. Together, these features form a spatiotemporal structure, a phase space, that can be conceived of as a manifold of a yet-to-be-fully understood dynamic complexity. The objective of this article is to synthesize physiological evidence supporting the existence of HRV code: hereby, the process-specific subsets of HRV measures indirectly map the phase space traversal reflecting the specific information contained in the code required for the body to regulate the physiological responses to those processes. The following physiological examples of HRV code are reviewed, which are reflected in specific changes to HRV properties across the signal-analytical domains and across physiological states and conditions: the fetal systemic inflammatory response, organ-specific inflammatory responses (brain and gut), chronic hypoxia and intrinsic (heart) HRV (iHRV), allostatic load (physiological stress due to surgery), and vagotomy (bilateral cervical denervation). Future studies are proposed to test these observations in more depth, and the author refers the interested reader to the referenced publications for a detailed study of the HRV measures involved. While being exemplified mostly in the studies of fetal HRV, the presented framework promises more specific fetal, postnatal, and adult HRV biomarkers of health and disease, which can be obtained non-invasively and continuously.

Monitoring of Cardiorespiratory Parameters during Sleep Using a Special Holder for the Accelerometer Sensor

Sleep is extremely important for physical and mental health. Although polysomnography is an established approach in sleep analysis, it is quite intrusive and expensive. Consequently, developing a non-invasive and non-intrusive home sleep monitoring system with minimal influence on patients, that can reliably and accurately measure cardiorespiratory parameters, is of great interest. The aim of this study is to validate a non-invasive and unobtrusive cardiorespiratory parameter monitoring system based on an accelerometer sensor. This system includes a special holder to install the system under the bed mattress. The additional aim is to determine the optimum relative system position (in relation to the subject) at which the most accurate and precise values of measured parameters could be achieved. The data were collected from 23 subjects (13 males and 10 females). The obtained ballistocardiogram signal was sequentially processed using a sixth-order Butterworth bandpass filter and a moving average filter. As a result, an average error (compared to reference values) of 2.24 beats per minute for heart rate and 1.52 breaths per minute for respiratory rate was achieved, regardless of the subject's sleep position. For males and females, the errors were 2.28 bpm and 2.19 bpm for heart rate and 1.41 rpm and 1.30 rpm for respiratory rate. We determined that placing the sensor and system at chest level is the preferred configuration for cardiorespiratory measurement. Further studies of the system's performance in larger groups of subjects are required, despite the promising results of the current tests in healthy subjects.

The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity

BACKGROUND

Alterations in heart rate (HR) may provide new information about physiological signatures of depression severity. This 2-year study in individuals with a history of recurrent major depressive disorder (MDD) explored the intra-individual variations in HR parameters and their relationship with depression severity.

METHODS

Data from 510 participants (Number of observations of the HR parameters = 6666) were collected from three centres in the Netherlands, Spain, and the UK, as a part of the remote assessment of disease and relapse-MDD study. We analysed the relationship between depression severity, assessed every 2 weeks with the Patient Health Questionnaire-8, with HR parameters in the week before the assessment, such as HR features during all day, resting periods during the day and at night, and activity periods during the day evaluated with a wrist-worn Fitbit device. Linear mixed models were used with random intercepts for participants and countries. Covariates included in the models were age, sex, BMI, smoking and alcohol consumption, antidepressant use and co-morbidities with other medical health conditions.

RESULTS

Decreases in HR variation during resting periods during the day were related with an increased severity of depression both in univariate and multivariate analyses. Mean HR during resting at night was higher in participants with more severe depressive symptoms.

CONCLUSIONS

Our findings demonstrate that alterations in resting HR during all day and night are associated with depression severity. These findings may provide an early warning of worsening depression symptoms which could allow clinicians to take responsive treatment measures promptly.

Virtual Reality and Cardiac Diseases: A Systematic Review of Applications and Effects

Introduction

Cardiac diseases have grown significantly in recent years, causing many deaths globally. Cardiac diseases can impose a significant economic burden on societies. The development of virtual reality technology has attracted the attention of many researchers in recent years. This study aimed to investigate the applications and effects of virtual reality (VR) technology on cardiac diseases.

Methods

A comprehensive search was carried out in four databases, including Scopus, Medline (through PubMed), Web of Science, and IEEE Xplore to identify related articles published until May 25, 2022. Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) guideline for systematic reviews was followed. All randomized trials that investigated the effects of virtual reality on cardiac diseases were included in this systematic review.

Results

Twenty-six studies were included in this systematic review. The results illustrated that virtual reality applications in cardiac diseases can be classified in three categories of physical rehabilitation, psychological rehabilitation, and education/training. This study revealed that the use of virtual reality in psychological and physical rehabilitation can reduce stress, emotional tension, Hospital Anxiety and Depression Scale (HADS) total score, anxiety, depression, pain, systolic blood pressure, and length of hospitalization. Finally, the use of virtual reality in education/training can enhance technical performance, increase the speed of procedures, and improve the user's skills, level of knowledge, and self-confidence as well as facilitate learning. Also, the most limitations mentioned in the studies included small sample size and lack of or short duration of follow-up.

Conclusions

The results showed that the positive effects of using virtual reality in cardiac diseases are much more than its negative effects. Considering that the most limitations mentioned in the studies were the small sample size and short duration of follow-up, it is necessary to conduct studies with adequate methodological quality to report their effects in the short term and long term.

At-home wireless sleep monitoring patches for the clinical assessment of sleep quality and sleep apnea

Although many people suffer from sleep disorders, most are undiagnosed, leading to impairments in health. The existing polysomnography method is not easily accessible; it's costly, burdensome to patients, and requires specialized facilities and personnel. Here, we report an at-home portable system that includes wireless sleep sensors and wearable electronics with embedded machine learning. We also show its application for assessing sleep quality and detecting sleep apnea with multiple patients. Unlike the conventional system using numerous bulky sensors, the soft, all-integrated wearable platform offers natural sleep wherever the user prefers. In a clinical study, the face-mounted patches that detect brain, eye, and muscle signals show comparable performance with polysomnography. When comparing healthy controls to sleep apnea patients, the wearable system can detect obstructive sleep apnea with an accuracy of 88.5%. Furthermore, deep learning offers automated sleep scoring, demonstrating portability, and point-of-care usability. At-home wearable electronics could ensure a promising future supporting portable sleep monitoring and home healthcare.

Ferroelectret nanogenerators for the development of bioengineering systems

Bioengineering devices and systems will become a practical and versatile technology in society when sustainability issues, primarily pertaining to their efficiency, sustainability, and human-machine interaction, are fully addressed. It has become evident that technological paths should not rely on a single operation mechanism but instead on holistic methodologies that integrate different phenomena and approaches with complementary advantages. As an intriguing invention, the ferroelectret nanogenerator (FENG) has emerged with promising potential in various fields of bioengineering. Utilizing the changes in the engineered macro-scale electric dipoles to create displacement current (and vice versa), FENGs have been demonstrated to be a compelling strategy for bidirectional conversion of energy between the electrical and mechanical domains. Here we provide a comprehensive overview of the latest advancements in integrating FENGs in bioengineering systems, focusing on the applications with the most potential and the underlying current constraints.

The digital future of nursing: making sense of taxonomies and key concepts

Digital technology is becoming increasingly common in routine nursing practice. The adoption of digital technologies such as video calling, and other digital communication, has been hastened by the recent COVID-19 pandemic. Use of these technologies has the potential to revolutionise nursing practice, leading to potentially more accurate patient assessment, monitoring processes and improved safety in clinical areas. This article outlines key concepts related to the digitalisation of health care and the implications for nursing practice. The aim of this article is to encourage nurses to consider the implications, opportunities and challenges associated with the move towards digitalisation and advances in technology. Specifically, this means understanding key digital developments and innovations associated with healthcare provision and appreciating the implications of digitalisation for the future of nursing practice.

DynamicSleepNet: a multi-exit neural network with adaptive inference time for sleep stage classification

Sleep is an essential human physiological behavior, and the quality of sleep directly affects a person's physical and mental state. In clinical medicine, sleep stage is an important basis for doctors to diagnose and treat sleep disorders. The traditional method of classifying sleep stages requires sleep experts to classify them manually, and the whole process is time-consuming and laborious. In recent years, with the help of deep learning, automatic sleep stage classification has made great progress, especially networks using multi-modal electrophysiological signals, which have greatly improved in terms of accuracy. However, we found that the existing multimodal networks have a large number of redundant calculations in the process of using multiple electrophysiological signals, and the networks become heavier due to the use of multiple signals, and difficult to be used in small devices. To solve these two problems, this paper proposes DynamicSleepNet, a network that can maximize the use of multiple electrophysiological signals and can dynamically adjust between accuracy and efficiency. DynamicSleepNet consists of three effective feature extraction modules (EFEMs) and three classifier modules, each EFEM is connected to a classifier. Each EFEM is able to extract signal features while making the effective features more prominent and the invalid features are suppressed. The samples processed by the EFEM are given to the corresponding classifier for classification, and if the classifier considers the uncertainty of the sample to be below the threshold we set, the sample can be output early without going through the whole network. We validated our model on four datasets. The results show that the highest accuracy of our model outperforms all baselines. With accuracy close to baselines, our model is faster than the baselines by a factor of several to several tens, and the number of parameters of the model is lower or close. The implementation code is available at: https://github.com/Quinella7291/A-Multi-exit-Neural-Network-with-Adaptive-Inference-Time-for-Sleep-Stage-Classification/.

A Systematic Review of Post-Work Core Temperature Cooling Rates Conferred by Passive Rest

Physical work increases energy expenditure, requiring a considerable elevation of metabolic rate, which causes body heat production that can cause heat stress, heat strain, and hyperthermia in the absence of adequate cooling. Given that passive rest is often used for cooling, a systematic search of literature databases was conducted to identify studies that reported post-work core temperature cooling rates conferred by passive rest, across a range of environmental conditions. Data regarding cooling rates and environmental conditions were extracted, and the validity of key measures was assessed for each study. Forty-four eligible studies were included, providing 50 datasets. Eight datasets indicated a stable or rising core temperature in participants (range 0.000 to +0.028 °C min^-1), and forty-two datasets reported reducing core temperature (-0.002 to -0.070 °C min^-1) during passive rest, across a range of Wet-Bulb Globe Temperatures (WBGT). For 13 datasets where occupational or similarly insulative clothing was worn, passive rest resulted in a mean core temperature decrease of -0.004 °C min^-1 (-0.032 to +0.013 °C min^-1). These findings indicate passive rest does not reverse the elevated core temperatures of heat-exposed workers in a timely manner. Climate projections of higher WBGT are anticipated to further marginalise the passive rest cooling rates of heat-exposed workers, particularly when undertaken in occupational attire.

Validating Force Sensitive Resistor Strip Sensors for Cardiorespiratory Measurement during Sleep: A Preliminary Study

Sleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, and expensive nature of using the standard approaches such as polysomnography, it is of great interest to develop a noninvasive and unobtrusive in-home sleep monitoring system that can reliably and accurately measure cardiorespiratory parameters while causing minimal discomfort to the user's sleep. We developed a low-cost Out of Center Sleep Testing (OCST) system with low complexity to measure cardiorespiratory parameters. We tested and validated two force-sensitive resistor strip sensors under the bed mattress covering the thoracic and abdominal regions. Twenty subjects were recruited, including 12 males and 8 females. The ballistocardiogram signal was processed using the 4th smooth level of the discrete wavelet transform and the 2nd order of the Butterworth bandpass filter to measure the heart rate and respiration rate, respectively. We reached a total error (concerning the reference sensors) of 3.24 beats per minute and 2.32 rates for heart rate and respiration rate, respectively. For males and females, heart rate errors were 3.47 and 2.68, and respiration rate errors were 2.32 and 2.33, respectively. We developed and verified the reliability and applicability of the system. It showed a minor dependency on sleeping positions, one of the major cumbersome sleep measurements. We identified the sensor under the thoracic region as the optimal configuration for cardiorespiratory measurement. Although testing the system with healthy subjects and regular patterns of cardiorespiratory parameters showed promising results, further investigation is required with the bandwidth frequency and validation of the system with larger groups of subjects, including patients.

Supporting Tremor Rehabilitation Using Optical See-Through Augmented Reality Technology

Tremor is a movement disorder that significantly impacts an individual's physical stability and quality of life, and conventional medication or surgery often falls short in providing a cure. Rehabilitation training is, therefore, used as an auxiliary method to mitigate the exacerbation of individual tremors. Video-based rehabilitation training is a form of therapy that allows patients to exercise at home, reducing pressure on rehabilitation institutions' resources. However, it has limitations in directly guiding and monitoring patients' rehabilitation, leading to an ineffective training effect. This study proposes a low-cost rehabilitation training system that utilizes optical see-through augmented reality (AR) technology to enable tremor patients to conduct rehabilitation training at home. The system provides one-on-one demonstration, posture guidance, and training progress monitoring to achieve an optimal training effect. To assess the system's effectiveness, we conducted experiments comparing the movement magnitudes of individuals with tremors in the proposed AR environment and video environment, while also comparing them with standard demonstrators. Participants wore a tremor simulation device during uncontrollable limb tremors, with tremor frequency and amplitude calibrated to typical tremor standards. The results showed that participants' limb movement magnitudes in the AR environment were significantly higher than those in the video environment, approaching the movement magnitudes of the standard demonstrators. Hence, it can be inferred that individuals receiving tremor rehabilitation in the AR environment experience better movement quality than those in the video environment. Furthermore, participant experience surveys revealed that the AR environment not only provided a sense of comfort, relaxation, and enjoyment but also effectively guided them throughout the rehabilitation process.

Screening for Major Depressive Disorder Using a Wearable Ultra-Short-Term HRV Monitor and Signal Quality Indices

To encourage potential major depressive disorder (MDD) patients to attend diagnostic sessions, we developed a novel MDD screening system based on sleep-induced autonomic nervous responses. The proposed method only requires a wristwatch device to be worn for 24 h. We evaluated heart rate variability (HRV) via wrist photoplethysmography (PPG). However, previous studies have indicated that HRV measurements obtained using wearable devices are susceptible to motion artifacts. We propose a novel method to improve screening accuracy by removing unreliable HRV data (identified on the basis of signal quality indices (SQIs) obtained by PPG sensors). The proposed algorithm enables real-time calculation of signal quality indices in the frequency domain (SQI-FD). A clinical study conducted at Maynds Tower Mental Clinic enrolled 40 MDD patients (mean age, 37.5 ± 8.8 years) diagnosed on the basis of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and 29 healthy volunteers (mean age, 31.9 ± 13.0 years). Acceleration data were used to identify sleep states, and a linear classification model was trained and tested using HRV and pulse rate data. Ten-fold cross-validation showed a sensitivity of 87.3% (80.3% without SQI-FD data) and specificity of 84.0% (73.3% without SQI-FD data). Thus, SQI-FD drastically improved sensitivity and specificity.

Artificial Intelligence in Musculoskeletal Conditions

Artificial intelligence (AI) refers to computer capabilities that resemble human intelligence. AI implies the ability to learn and perform tasks that have not been specifically programmed. Moreover, it is an iterative process involving the ability of computerized systems to capture information, transform it into knowledge, and process it to produce adaptive changes in the environment. A large labeled database is needed to train the AI system and generate a robust algorithm. Otherwise, the algorithm cannot be applied in a generalized way. AI can facilitate the interpretation and acquisition of radiological images. In addition, it can facilitate the detection of trauma injuries and assist in orthopedic and rehabilitative processes. The applications of AI in musculoskeletal conditions are promising and are likely to have a significant impact on the future management of these patients.

Wrist Accelerometer Estimates of Physical Activity Intensity During Walking in Older Adults and People Living With Complex Health Conditions: Retrospective Observational Data Analysis Study

BACKGROUND

Accurate measurement of daily physical activity (PA) is important as PA is linked to health outcomes in older adults and people living with complex health conditions. Wrist-worn accelerometers are widely used to estimate PA intensity, including walking, which composes much of daily PA. However, there is concern that wrist-derived PA data in these cohorts is unreliable due to slow gait speed, mobility aid use, disease-related symptoms that impact arm movement, and transient activities of daily living. Despite the potential for error in wrist-derived PA intensity estimates, their use has become ubiquitous in research and clinical application.

OBJECTIVE

The goals of this work were to (1) determine the accuracy of wrist-based estimates of PA intensity during known walking periods in older adults and people living with cerebrovascular disease (CVD) or neurodegenerative disease (NDD) and (2) explore factors that influence wrist-derived intensity estimates.

METHODS

A total of 35 older adults (n=23 with CVD or NDD) wore an accelerometer on the dominant wrist and ankle for 7 to 10 days of continuous monitoring. Stepping was detected using the ankle accelerometer. Analyses were restricted to gait bouts ≥60 seconds long with a cadence ≥80 steps per minute (LONG walks) to identify periods of purposeful, continuous walking likely to reflect moderate-intensity activity. Wrist accelerometer data were analyzed within LONG walks using 15-second epochs, and published intensity thresholds were applied to classify epochs as sedentary, light, or moderate-to-vigorous physical activity (MVPA). Participants were stratified into quartiles based on the percent of walking epochs classified as sedentary, and the data were examined for differences in behavioral or demographic traits between the top and bottom quartiles. A case series was performed to illustrate factors and behaviors that can affect wrist-derived intensity estimates during walking.

RESULTS

Participants averaged 107.7 (SD 55.8) LONG walks with a median cadence of 107.3 (SD 10.8) steps per minute. Across participants, wrist-derived intensity classification was 22.9% (SD 15.8) sedentary, 27.7% (SD 14.6) light, and 49.3% (SD 25.5) MVPA during LONG walks. All participants measured a statistically lower proportion of wrist-derived activity during LONG walks than expected (all P<.001), and 80% (n=28) of participants had at least 20 minutes of LONG walking time misclassified as sedentary based on wrist-derived intensity estimates. Participants in the highest quartile of wrist-derived sedentary classification during LONG walks were significantly older (t₁₆=4.24, P<.001) and had more variable wrist movement (t₁₆=2.13, P=.049) compared to those in the lowest quartile.

CONCLUSIONS

The current best practice wrist accelerometer method is prone to misclassifying activity intensity during walking in older adults and people living with complex health conditions. A multidevice approach may be warranted to advance methods for accurately assessing PA in these groups.

Digitally Diagnosing Multiple Developmental Delays using Crowdsourcing fused with Machine Learning: A Research Protocol

Background

Roughly 17% percent of minors in the United States aged 3 through 17 years have a diagnosis of one or more developmental or psychiatric conditions, with the true prevalence likely being higher due to underdiagnosis in rural areas and for minority populations. Unfortunately, timely diagnostic services are inaccessible to a large portion of the United States and global population due to cost, distance, and clinician availability. Digital phenotyping tools have the potential to shorten the time-to-diagnosis and to bring diagnostic services to more people by enabling accessible evaluations. While automated machine learning (ML) approaches for detection of pediatric psychiatry conditions have garnered increased research attention in recent years, existing approaches use a limited set of social features for the prediction task and focus on a single binary prediction.

Objective

I propose the development of a gamified web system for data collection followed by a fusion of novel crowdsourcing algorithms with machine learning behavioral feature extraction approaches to simultaneously predict diagnoses of Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) in a precise and specific manner.

Methods

The proposed pipeline will consist of: (1) a gamified web applications to curate videos of social interactions adaptively based on needs of the diagnostic system, (2) behavioral feature extraction techniques consisting of automated ML methods and novel crowdsourcing algorithms, and (3) development of ML models which classify several conditions simultaneously and which adaptively request additional information based on uncertainties about the data.

Conclusions

The prospective for high reward stems from the possibility of creating the first AI-powered tool which can identify complex social behaviors well enough to distinguish conditions with nuanced differentiators such as ASD and ADHD.

Development of a Virtual Reality Tool for the Treatment of Pediatric Patients in the ICU

Prolonged stays in the intensive care unit (ICU) cause difficulties in rehabilitation and other disorders for patients. This problem is exacerbated in the case of pediatric patients. The use of virtual reality can help with the lack of external stimuli and contribute as potential nonpharmacological therapies in some patient rehabilitation processes. To this end, we have developed a virtual reality application for use in the pediatric ICU as a tool for the treatment and rehabilitation of delirium. The tool consists of two applications: an immersive environment for a virtual reality headset used by the patient, and a web application managed by a therapist with which they can customize, control, adapt, and analyze in real time everything that happens in the patient's virtual world. Our application has been designed jointly with a university center and a hospital, and initial evaluations indicate the results to be promising.

Diversity, Equity, and Inclusion in Clinical Research: A Path Toward Precision Health for Everyone

Healthcare disparities are a persistent societal problem. One of the contributing factors to this status quo is the lack of diversity and representativeness of research efforts, which result in nongeneralizable evidence that, in turn, provides suboptimal means to enable the best possible outcomes at the individual level. There are several strategies that research teams can adopt to improve the diversity, equity, and inclusion (DEI) of their efforts; these strategies span the totality of the research path, from initial design to the shepherding of clinical data through a potential regulatory process. These strategies include more intentionality and DEI-based goal-setting, more diverse research and leadership teams, better community engagement to set study goals and approaches, better tailored outreach interventions, decentralization of study procedures and incorporation of innovative technology for more flexible data collection, and self-surveillance to identify and prevent biases. Within their remit of overlooking research efforts, regulatory authorities, as stakeholders, also have the potential for a positive effect on the DEI of emerging clinical evidence. All these are implementable tools and mechanisms that can make study participation more approachable to diverse communities, and ultimately generate evidence that is more generalizable and a conduit for better outcomes. The research community has an imperative to make DEI principles key foundational aspects in study conduct in order to pursue better personalized medicine for diverse patient populations.

Has machine learning over-promised in healthcare? A critical analysis and a proposal for improved evaluation, with evidence from Parkinson’s disease

Adoption of artificial intelligence (AI) by the medical community has long been anticipated, endorsed by a stream of machine learning literature showcasing AI systems that yield extraordinary performance. However, many of these systems are likely over-promising and will under-deliver in practice. One key reason is the community's failure to acknowledge and address the presence of inflationary effects in the data. These simultaneously inflate evaluation performance and prevent a model from learning the underlying task, thus severely misrepresenting how that model would perform in the real world. This paper investigated the impact of these inflationary effects on healthcare tasks, as well as how these effects can be addressed. Specifically, we defined three inflationary effects that occur in medical data sets and allow models to easily reach small training losses and prevent skillful learning. We investigated two data sets of sustained vowel phonation from participants with and without Parkinson's disease, and revealed that published models which have achieved high classification performances on these were artificially enhanced due to the inflationary effects. Our experiments showed that removing each inflationary effect corresponded with a decrease in classification accuracy, and that removing all inflationary effects reduced the evaluated performance by up to 30%. Additionally, the performance on a more realistic test set increased, suggesting that the removal of these inflationary effects enabled the model to better learn the underlying task and generalize. Source code is available at https://github.com/Wenbo-G/pd-phonation-analysis under the MIT license.

Are Activity Wrist-Worn Devices Accurate for Determining Heart Rate during Intense Exercise?

The market for wrist-worn devices is growing at previously unheard-of speeds. A consequence of their fast commercialization is a lack of adequate studies testing their accuracy on varied populations and pursuits. To provide an understanding of wearable sensors for sports medicine, the present study examined heart rate (HR) measurements of four popular wrist-worn devices, the (Fitbit Charge (FB), Apple Watch (AW), Tomtom runner Cardio (TT), and Samsung G2 (G2)), and compared them with gold standard measurements derived by continuous electrocardiogram examination (ECG). Eight athletes participated in a comparative study undergoing maximal stress testing on a cycle ergometer or a treadmill. We analyzed 1,286 simultaneous HR data pairs between the tested devices and the ECG. The four devices were reasonably accurate at the lowest activity level. However, at higher levels of exercise intensity the FB and G2 tended to underestimate HR values during intense physical effort, while the TT and AW devices were fairly reliable. Our results suggest that HR estimations should be considered cautiously at specific intensities. Indeed, an effective intervention is required to register accurate HR readings at high-intensity levels (above 150 bpm). It is important to consider that even though none of these devices are certified or sold as medical or safety devices, researchers must nonetheless evaluate wrist-worn wearable technology in order to fully understand how HR affects psychological and physical health, especially under conditions of more intense exercise.

Recent trends in carbon nanotube (CNT)-based biosensors for the fast and sensitive detection of human viruses: a critical review

The current COVID-19 pandemic, with its numerous variants including Omicron which is 50-70% more transmissible than the previously dominant Delta variant, demands a fast, robust, cheap, and easily deployed identification strategy to reduce the chain of transmission, for which biosensors have been shown as a feasible solution at the laboratory scale. The use of nanomaterials has significantly enhanced the performance of biosensors, and the addition of CNTs has increased detection capabilities to an unrivaled level. Among the various CNT-based detection systems, CNT-based field-effect transistors possess ultra-sensitivity and low-noise detection capacity, allowing for immediate analyte determination even in the presence of limited analyte concentrations, which would be typical of early infection stages. Recently, CNT field-effect transistor-type biosensors have been successfully used in the fast diagnosis of COVID-19, which has increased research and commercial interest in exploiting current developments of CNT field-effect transistors. Recent progress in the design and deployment of CNT-based biosensors for viral monitoring are covered in this paper, as are the remaining obstacles and prospects. This work also highlights the enormous potential for synergistic effects of CNTs used in combination with other nanomaterials for viral detection.

Prediction of Angina Pectoris Events in Middle-Aged and Elderly People Using RR Interval Time Series in the Resting State: A Cohort Study Based on SHHS

Angina pectoris is associated with adverse cardiovascular events. In this study, a Bi-directional Long Short-Term Memory (Bi-LSTM) prediction model with the Attention layer was established to explore the predictive value of the resting-state RR interval time series on the occurrence of angina pectoris. The data of this cohort study were from the Sleep Heart Health Study database, 2,977 people were included with the follow-up of 15 years. We used the RR interval time series of electrocardiogram signals in the resting state. The outcome variables were any angina events during the follow-up. We randomly divided 2,977 participants into training (n = 2680) and testing sets (n = 297) with a partition ratio of 9:1. The prediction model of Bi-LSTM with Attention layer was developed and the predictive performance was assessed. 1,236 had angina pectoris and 1,741 patients did not have angina pectoris during the follow-up period. The predictive performance of the Bi-LSTM model was great with the value of accuracy = 0.913, area under the curve (AUC) = 0.922, precision = 0.913 in the testing set. RR intervals may be the potential predictors of angina events. It is more and more convenient to obtain heart rate with the development of wearable devices; the Bi-LSTM prediction model established in this study is expected to provide support for the intelligent prediction of angina pectoris events.

vexptoolbox: A software toolbox for human behavior studies using the Vizard virtual reality platform

Virtual reality (VR) is a powerful tool for researchers due to its potential to study dynamic human behavior in highly naturalistic environments while retaining full control over the presented stimuli. Due to advancements in consumer hardware, VR devices are now very affordable and have also started to include technologies such as eye tracking, further extending potential research applications. Rendering engines such as Unity, Unreal, or Vizard now enable researchers to easily create complex VR environments. However, implementing the experimental design can still pose a challenge, and these packages do not provide out-of-the-box support for trial-based behavioral experiments. Here, we present a Python toolbox, designed to facilitate common tasks when developing experiments using the Vizard VR platform. It includes functionality for common tasks like creating, randomizing, and presenting trial-based experimental designs or saving results to standardized file formats. Moreover, the toolbox greatly simplifies continuous recording of eye and body movements using any hardware supported in Vizard. We further implement and describe a simple goal-directed reaching task in VR and show sample data recorded from five volunteers. The toolbox, example code, and data are all available on GitHub under an open-source license. We hope that our toolbox can simplify VR experiment development, reduce code duplication, and aid reproducibility and open-science efforts.

Feature Extraction From Single-Channel EEG Using Tsfresh and Stacked Ensemble Approach for Sleep Stage Classification

The smart world under Industry 4.0 is witnessing a notable spurt in sleep disorders and sleep-related issues in patients. Artificial intelligence and IoT are taking a giant leap in connecting sleep patients remotely with healthcare providers. The contemporary single-channel-based monitoring devices play a tremendous role in predicting sleep quality and related issues. Handcrafted feature extraction is a time-consuming job in machine learning-based automatic sleep classification. The proposed single-channel work uses Tsfresh to extract features from both the EEG channels (Pz-oz and Fpz-Cz) of the SEDFEx database individually to realise a single-channel EEG. The adopted mRMR feature selection approach selected 55 features from the extracted 787 features. A stacking ensemble classifier achieved 95%, 94%, 91%, and 88% accuracy using stratified 5-fold validation in 2, 3, 4, and 5 class classification employing healthy subjects data. The outcome of the experiments indicates that Tsfresh is an excellent tool to extract standard features from EEG signals.

Virtual reality use and patient outcomes in palliative care: A scoping review

Objective

Virtual reality is increasingly used in healthcare settings. Potentially, it's use in palliative carecould have a positive impact; however, there is limited evidence on the scope, purpose and patient outcomes relating to virtual reality use in this context. The objective of this scoping review is to chart the literature on virtual reality use in palliative care, identifying any evidence relating to biopsychosocial patient outcomes which could support its use in practice.

Methods

A scoping review of the literature, involving . a systematic search across 10 electronic bibliographic databases in December 2021, . Eligibility criteria were primary research studies, of any research designwithin a 10-year timeframe, which reported on virtual reality use and patient outcomes in palliative care. A total of 993 papers were identified, andcomprehensive screening resulted in 10 papers for inclusion.

Results

This scoping review identified 10 papers addressing virtual reality in palliative care, published within a three-year timeframe 2019-2021. Research methodologies included mixed methods, quantitative and qualitative. The evidence highlightsvirtual reality use with patients receiving palliative care in a variety of settings, and data around useability, feasibility and acceptability is positive. However, the evidence regarding biopsychosocial patient outcomes linked to virtual reality use is limited.

Conclusion

Virtual reality is gathering momentum in palliative care and is potentially a helpful intervention; however more research is needed to underpin the evidence base supporting its application, particularly in understanding the impact on biopsychosocial patient outcomes and ascertaining the best approach for measuring intervention effectiveness.

Device-based and subjective measurements of sleep in children with cerebral palsy: a comparison of sleep diary, actigraphy, and bed sensor data

STUDY OBJECTIVES

To investigate how subjective assessments and device-based measurements of sleep relate to each other in children with cerebral palsy (CP).

METHODS

Sleep of children with CP, classified at Gross Motor Function Classification System levels I-III, was measured during 7 consecutive nights using 1 subjective (ie, sleep diary) and 2 device-based (ie, actigraphy and bed sensor) instruments. The agreement between the instruments was assessed for all nights and separately for school- and weekend nights, using intraclass correlation coefficients (ICC) and Bland-Altman plots.

RESULTS

A total of 227 nights from 38 children with CP (53% male; median age [range] 6 [2-12] years), were included in the analyses. Sleep parameters showed poor agreement between the 3 instruments, except for total time in bed, which showed satisfactory agreement between (1) actigraphy and sleep diary (ICC > 0.86), (2) actigraphy and bed sensor (ICC > 0.84), and (3) sleep diary and bed sensor (ICC > 0.83). Furthermore, agreement between sleep diary and bed sensor was also satisfactory for total sleep time (ICC > 0.70) and wakefulness after sleep onset (ICC = 0.55; only during weekend nights).

CONCLUSIONS

Researchers and clinicians need to be aware of the discrepancies between instruments for sleep monitoring in children with CP. We recommend combining both subjective and device-based measures to provide information on the perception as well as an unbiased estimate of sleep. Further research needs to be conducted on the use of a bed sensor for sleep monitoring in children with CP.

CITATION

van Rijssen IM, Hulst RY, Gorter JW, et al. Device-based and subjective measurements of sleep in children with cerebral palsy: a comparison of sleep diary, actigraphy, and bed sensor data. J Clin Sleep Med. 2023;19(1):35-43.

A Novel In-Home Sleep Monitoring System Based on Fully Integrated Multichannel Front-End Chip and Its Multilevel Analyses

OBJECTIVE

A novel in-home sleep monitoring system with an 8-channel biopotential acquisition front-end chip is presented and validated via multilevel data analyses and comparision with advanced polysomnography.

METHODS AND PROCEDURES

The chip includes a cascaded low-noise programmable gain amplifier (PGA) and 24-bit [Formula: see text]-[Formula: see text] analog-to-digital converter (ADC). The PGA is based on three op-amp structure while the ADC adopts cascade of integrator feedforward and feedback (CIFF-B) architecture. An innovative chopper-modulated input-scaling-down technique enhances the dynamic range. The proposed system and commercial polysomnography were used for in-home sleep monitoring of 20 healthy participants. The consistency and significance of the two groups' data were analyzed.

RESULTS

Fabricated in 180 nm BCD technology, the input-referred noise, input impedance, common-mode rejection ratio, and dynamic range of the acquisition front-end chip were [Formula: see text]Vpp, 1.25 GN), 113.9 dB, and 119.8 dB. The kappa coefficients between the sleep stage labels of the three scorers were 0.80, 0.76, and 0.79. The consistency of the slowing index, multiscale entropy, and percentile features between the two devices reached 0.958, 0.885, and 0.834. The macro sleep architecture characteristics of the two devices were not significantly different (all p [Formula: see text] 0.05).

CONCLUSION

The proposed chip was applied to develop an in-home sleep monitoring system with significantly reduced size, power, and cost. Multilevel analyses demonstrated that this system collects stable and accurate in-home sleep data.

CLINICAL IMPACT

The proposed system can be applied for long-term in-home sleep monitoring outside of laboratory environments and sleep disorders screening that with low cost.

Attitudes and perspectives of nurses and physicians in South Korea towards the clinical use of person-generated health data

This study aimed to explore the adoption of person-generated health data in clinical settings and discern the factors influencing clinicians' willingness to use it. A web-based survey containing 48 questions was developed based on prior research and the Unified Theory of Acceptance and Use of Technology 2 model. The survey was administered to a convenience sample of 486 nurses and physicians in South Korea recruited through an online community and snowball sampling. Of these, 70.7% were physicians. While 65% had used mobile health apps and devices, only 12.8% were familiar with person-generated health data. Still, a promising 73.3% expressed interest in incorporating person-generated health data into patient care, particularly data on blood glucose and vital signs. The findings of the study also indicated that clinicians specializing in internal medicine (OR: 1.9, CI: 1.16-3.19), familiar with person-generated health data (OR: 2.6, CI: 1.58-4.29), with a positive view of information and communication technology adoption (OR: 2.6, CI: 1.65-4.13), and who see the value in person-generated health data (OR: 3.9, CI: 2.55-6.09) showed higher inclination to utilize it. However, those in outpatient settings (OR: 0.4, CI: 0.19-0.73) showed less enthusiasm. The findings of this study suggest that despite the willingness of clinicians to use person-generated health data, various barriers must be addressed first, including a lack of knowledge regarding its use, concerns about data reliability and quality, and a lack of provider incentives. Overcoming these challenges demands concerted organizational or policy support. This research underscores person-generated health data's untapped potential in healthcare and the pressing need for strategies that facilitate its clinical integration.

COVID-19 Lessons Learned: Response to the Anticipated Ventilator Shortage

Early in the COVID-19 pandemic predictions of a worldwide ventilator shortage prompted a worldwide search for solutions. The impetus for the scramble for ventilators was spurred on by inaccurate and often unrealistic predictions of ventilator requirements. Initial efforts looked simply at acquiring as many ventilators as possible from national and international sources. Ventilators from the Strategic National Stockpile were distributed to early hotspots in the Northeast and Northwest United States. In a triumph of emotion over logic, well-intended experts from other industries turned their time, talent, and treasure toward making a ventilator for the first time. Interest in shared ventilation (more than one patient per ventilator) was ignited by an ill-advised video on social media that ignored the principles of gas delivery in deference to social media notoriety. With shared ventilation, a number of groups mistook a physiologic problem for a plumbing problem. The United States government invoked the Defense Production Act to push automotive manufacturers to partner with existing ventilator manufacturers to speed production. The FDA granted emergency use authorization for "splitters" to allow shared ventilation as well as for ventilators and ancillary equipment. Rationing of ventilators was discussed in the lay press and medical literature but was never necessary in the US. Finally, planners realized that staff with expertise in providing mechanical ventilation were the most important shortage. Over 200,000 ventilators were purchased by the United States government, states, cities, health systems, and individuals. Most had little value in caring for patients with COVID-19 ARDS. This paper attempts to look at where miscalculations were made, with an eye toward what we can do better in the future.

A mobile health intervention for improving the technique of inhaled medications among children with asthma: A pilot study

Objective

BreatheSuite MDI is a Bluetooth-enabled inhaler attachment and mobile application which aims to improve asthma control. The objective was to compare pressurized metered dose inhaler (pMDI) technique and asthma control test (ACT) scores pre- and post-use of the device and mobile application. Secondary objectives were to assess user satisfaction and therapy adherence.

Methods

Patients between the ages of 8 and 18 were recruited from several pediatric asthma clinics. Technique and ACT scores were assessed at baseline. Users were given no prompts on technique during the first month of device use. For the subsequent three months, users were given technique scores through the mobile application after each inhaler use and provided weekly performance summaries. At the end of the study, technique and ACT scores were analyzed and an exit survey was completed. Conditional logistic regression was used to examine the association between well-controlled asthma (ACT score > 19) and the intervention.

Results

24 patients completed the study. Technique scores improved following the use of Breathesuite (44.19 vs. 62.54; P = 0.01). Well-controlled asthma did not significantly improve (OR = 1.20 [0.4-3.9], P = 0.76). 87% of study subjects agreed or strongly agreed that their asthma control improved while using BreatheSuite; 79% were satisfied with the device and mobile application; and 91% preferred using the device compared to a standard logbook to track inhaler usage.

Conclusions

In this pilot study, the use of BreatheSuite device was associated with improved technique scores. These results need to be confirmed by a randomized controlled trial. There was high user satisfaction with the BreatheSuite device.

Comparison of a palm-based biometric solution with a name-based identification system in rural Bangladesh

Background

Unique identifiers are not universal in low- and middle-income countries. Biometric solutions have the potential to augment existing name-based searches used for identification in these settings. This paper describes a comparison of the searching accuracy of a palm-based biometric solution with a name-based database.

Objective

To compare the identification of individuals between a palm-based biometric solution to a name-based District Health Information Software 2 (DHIS2) Android application, in a low-resource setting.

Methods

The study was conducted in Chandpur district, Bangladesh. Trained data collectors enrolled 150 women of reproductive age into two android applications – i) a name-based DHIS2 application, and ii) a palm-based biometric solution – both run on tablets. One week after enrollment, a different research team member attempted to re-identify each enrolled woman using both systems. A single image or text-based name was used for searching at the time of re-identification. We interviewed data collectors at the end of the study.

Results

Significantly more women were successfully identified on the first attempt with a palm-based biometric application (84%) compared with the name-based DHIS2 application (61%). The proportion of identifications that required three or more attempts was similar between name-based (7%, CI 3.7–12.3) and palm-based biometric system (5%, CI: 1.9–9.4). However, the total number of attempts needed was significantly lower with the palm-based solution (mean 1.2 vs. 1.5, p < 0.001). In a group discussion, data collectors reported that the palm-based biometric identification system was both accurate and easy to use.

Conclusion

A palm-based biometric identification system on mobile devices was found to be an easy-to-use and accurate technology for the unique identification of individuals compared to an existing name-based application. Our findings imply that palm-based biometrics on mobile devices may be the next step in establishing unique identifiers in remote and rural settings where they are currently absent.

Bioimpedance-Derived Membrane Capacitance: Clinically Relevant Sources of Variability, Precision, and Reliability

Membrane capacitance (CM), a bioimpedance-derived measure of cell membrane health, has been suggested as an indicator of health status. However, there are few published data to support its use in clinical settings. Hence, this study evaluated clinically relevant sources of variation, precision, and reliability of CM measurements. This longitudinal study included 60 premenopausal women. Sources of variability (e.g., demographics, body composition, serum measures, diet) were identified by stepwise regression. Precision and reliability were assessed by the coefficient of variation (CV), intraclass correlation coefficients (ICC), and technical error of the measurement (TEM) for intra-day (30 min apart) and inter-day measurements (7-14 days apart). Body composition, temperature, and metabolic activity were identified as sources of variability. CM measurements had high precision (CV = 0.42%) and high reliability for intra-day (ICC = 0.996) and inter-day (ICC = 0.959) measurements, independent of menstrual cycle and obesity status. Our results showed that CM measurements are sensitive to clinical factors and have high precision and reliability. The results of this study suggest that CM is sufficiently reliable for health status monitoring in conditions with variation in body composition, metabolic activity, or body temperature among premenopausal women.

Correlated-Weighted Statistically Modeled Contourlet and Curvelet Coefficient Image-Based Breast Tumor Classification Using Deep Learning

Deep learning-based automatic classification of breast tumors using parametric imaging techniques from ultrasound (US) B-mode images is still an exciting research area. The Rician inverse Gaussian (RiIG) distribution is currently emerging as an appropriate example of statistical modeling. This study presents a new approach of correlated-weighted contourlet-transformed RiIG (CWCtr-RiIG) and curvelet-transformed RiIG (CWCrv-RiIG) image-based deep convolutional neural network (CNN) architecture for breast tumor classification from B-mode ultrasound images. A comparative study with other statistical models, such as Nakagami and normal inverse Gaussian (NIG) distributions, is also experienced here. The weighted entitled here is for weighting the contourlet and curvelet sub-band coefficient images by correlation with their corresponding RiIG statistically modeled images. By taking into account three freely accessible datasets (Mendeley, UDIAT, and BUSI), it is demonstrated that the proposed approach can provide more than 98 percent accuracy, sensitivity, specificity, NPV, and PPV values using the CWCtr-RiIG images. On the same datasets, the suggested method offers superior classification performance to several other existing strategies.

Use of a Telerehabilitation Platform in a Stroke Continuum: A Qualitative Study of Patient and Therapist Acceptability

The purpose of this study was to describe the acceptability of a stroke telerehabilitation platform from the perspective of both patients and therapists. Two public rehabilitation centers participated in a pilot telerehabilitation trial. A theoretical framework was used to conceptualize acceptability. Semi-structured individual interviews with patients and focus groups of therapists were conducted. Most participants and therapists were satisfied with the intervention. Participants emphasized the advantages of staying at home to get their treatments. Therapists were more skeptical at first about their self-efficacy to deliver therapy remotely. There was a consensus among therapists about the need for a combination of telerehabilitation and in-person visits to optimize treatments. While we found overall good acceptability, effectiveness of this technology could be improved via an accessible user interface, complementary rehabilitation material, and ongoing training and technical just-in-time support with therapists.

Examining Stress and Residual Symptoms in Remitted and Partially Remitted Depression Using a Wearable Electrodermal Activity Device: A Pilot Study

Consistent evidence suggests residual symptoms and stress are the most reliable predictors of relapse in remitted depression. Prevailing methodologies often do not enable continuous real-time sampling of stress. Thus, little is known about day-to-day interactions between residual symptoms and stress in remitted depression. In preparation for a full-scale trial, this study aimed to pilot a wrist-worn wearable electrodermal activity monitor: ADI (Analog Devices, Inc.) Study Watch for assessing interactions between physiological stress and residual depressive symptoms following depression remission. 13 individuals remitted from major depression completed baseline, daily diary, and post-daily diary assessments. Self-reported stress and residual symptoms were measured at baseline and post-daily diary. Diary assessments required participants to wear ADI's Study Watch during waking hours and complete self-report questionnaires every evening over one week. Sleep problems, fatigue, energy loss, and agitation were the most frequently reported residual symptoms. Average skin conductance responses (SCRs) were 16.09 per-hour, with an average of 11.30 hours of wear time per-day. Increased residual symptoms were associated with enhanced self-reported stress on the same day. Increased SCRs on one day predicted increased residual symptoms on the next day. This study showed a wearable electrodermal activity device can be recommended for examining stress as a predictor of remitted depression. This study also provides preliminary work on relationships between residual symptoms and stress in remitted depression. Importantly, significant findings from the small sample of this pilot are preliminary with an aim to follow up with a 3-week full-scale study to draw conclusions about psychological processes explored. Clinical and Translational Impact Statemen-ADI's wearable electrodermal activity device enables a continuous measure of physiological stress for identifying its interactions with residual depressive symptoms following remission. This novel procedure is promising for future studies.

Older Adults' Experiences and Perceptions of Immersive Virtual Reality: Systematic Review and Thematic Synthesis

BACKGROUND

Immersive virtual reality (IVR) can be defined as a fully computer-generated environment shown on a head-mounted display. Existing research suggests that key features of IVR can assist older adults in their everyday lives, providing opportunities for health promotion and tackling social isolation and loneliness. There has been a surge in qualitative studies exploring older adults' experiences and perceptions of IVR. However, there has been no systematic synthesis of these studies to inform the design of new, more accessible IVR technologies.

OBJECTIVE

This study aimed to systematically review and synthesize qualitative studies exploring older adults' experiences and perceptions of IVR.

METHODS

A systematic review and thematic synthesis were conducted following the ENTREQ (Enhancing Transparency in Reporting the Synthesis of Qualitative Research) guidelines. In total, 2 reviewers completed title and abstract screening, full-text screening, data extraction, and quality appraisal. Thematic synthesis is derived from the qualitative method, thematic analysis. It involves 3 key steps: initial coding and grouping of these codes, the formation of descriptive themes from these codes, and going beyond these data to form analytical themes. Confidence in the evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation-Confidence in the Evidence from Reviews of Qualitative Research approach.

RESULTS

Overall, 13 studies were included in the final synthesis, including 224 participants across 9 countries and 5 continents. Confidence in the evidence ranged from high to moderate. Three descriptive themes were generated: practical aspects of IVR use, experiencing unique features of IVR, and perceptions of IVR. The findings from the descriptive themes suggested that there are several improvements that need to be made to existing IVR devices to facilitate older adults' use of this technology. However, older adults' responses to IVR were generally positive. Three analytical themes were generated: tolerating the bad to experience the good, buying in to IVR (don't judge a book by its cover), and "it proves to me I can do it." The analytical themes illustrated that older adults were willing to tolerate discomforts that accompany existing IVR technologies to experience features such as immersive social networking. There was a discrepancy between older adults' perceptions of IVR before use-which were generally negative-and after use-which were generally positive-and IVR provided a platform for older adults to access certain activities and environments more easily than in the real world because of limitations caused by aging.

CONCLUSIONS

This review offers insights into older adults' experiences and perceptions of IVR and suggests how a few improvements to its existing hardware and software as well as how it is first presented could offer new opportunities for older adults to take part in meaningful activities tailored to their needs and preferences.

TRIAL REGISTRATION

PROSPERO CRD42020200774; https://tinyurl.com/8f48w2vt.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1177/16094069211009682.

If AFP is elevated, where is cancer? The case report on hereditary persistence of Alpha-fetoprotein

Alpha-fetoprotein (AFP) is expressed by tumors with a high mitotic index such as hepatocellular carcinoma and germ cell tumors, therefore it is used as a tumor biomarker. Interestingly, although there is no underlying cause, elevated AFP has been reported in some genetically predisposed individuals. This is a very rare and benign condition called "hereditary persistence of AFP (HPAFP)" and an inherited in an autosomal dominant manner. To our knowledge, only 28 families have been reported to date. Some of the reported cases received inappropriate treatments such as chemotherapy and surgery. The possibility of HPAFP should be kept in mind in patients with high AFP in the absence of radiological evidence of hepatocellular carcinoma or germ cell tumor to avoid harmful procedures. It can be easily confirmed by analyzing AFP levels in other family members. We report a case of HPAFP with surprisingly higher AFP levels than previously reported cases and this is the first case reported from Turkey.

Laser-Induced µ-Rooms for Osteocytes on Implant Surface: An In Vivo Study

Laser processing of dental implant surfaces is becoming a more widespread replacement for classical techniques due to its undeniable advantages, including control of oxide formation and structure and surface relief at the microscale. Thus, using a laser, we created several biomimetic topographies of various shapes on the surface of titanium screw-shaped implants to research their success and survival rates. A distinctive feature of the topographies is the presence of "µ-rooms", which are special spaces created by the depressions and elevations and are analogous to the µ-sized room in which the osteocyte will potentially live. We conducted the comparable in vivo study using dental implants with continuous (G-topography with µ-canals), discrete (S-topography with μ-cavities), and irregular (I-topography) laser-induced topographies. A histological analysis performed with the statistical method (with p-value less than 0.05) was conducted, which showed that G-topography had the highest BIC parameter and contained the highest number of mature osteocytes, indicating the best secondary stability and osseointegration.

Use of the digital assistant “Vigo” at home environment for stroke recovery: focus group discussion with specialists working in neurorehabilitation (Preprint)

BACKGROUND

There is a lack of resources for the provision of adequate rehabilitation after a stroke, thus creating a challenge to provide the necessary high-quality, patient-centered, and cost-efficient rehabilitation services at a time when they are needed the most. Tablet-based therapeutic programs present an alternative way to access rehabilitation services and show a new paradigm for providing therapeutic interventions following a stroke anytime and anywhere. The digital assistant Vigo is an artificial intelligence-based app that provides an opportunity for a new, more integrative way of carrying out a home-based rehabilitation program. Considering the complexity of the stroke recovery process, factors such as a suitable population, appropriate timing, setting, and the necessary patient-specialist support structure need to be thoroughly researched. There is a lack of qualitative research exploring the perspectives of professionals working in neurorehabilitation of the content and usability of the digital tool for the recovery of patients after a stroke.

OBJECTIVE

The aim of this study is to identify the requirements for a tablet-based home rehabilitation program for stroke recovery from the perspective of a specialist working in stroke rehabilitation.

METHODS

The focus group study method was chosen to explore specialists' attitudes, experience, and expectations related to the use of the digital assistant Vigo as a home-based rehabilitation program for stroke recovery in domains of the app's functionality, compliance, usability, and content.

RESULTS

In total, 3 focus groups were conducted with a participant count of 5-6 per group and the duration of the discussion ranging from 70 to 80 minutes. In total, 17 health care professionals participated in the focus group discussions. The participants represented physiotherapists (n=7, 41.2%), occupational therapists (n=7, 41.2%), speech and language therapists (n=2, 11.8%), and physical medicine and rehabilitation physicians (n=1, 5.9%). Audio and video recordings of each discussion were created for further transcription and analysis. In total, 4 themes were identified: (1) the clinician's views on using Vigo as a home-based rehabilitation system, (2) patient-related circumstances facilitating and limiting the use of Vigo; (3) Vigo's functionality and use process (program creation, individual use, remote support); and (4) complementary and alternative Vigo use perspectives. The last 3 themes were divided further into 10 subthemes, and 2 subthemes had 2 sub-subthemes each.

CONCLUSIONS

Health care professionals expressed a positive attitude toward the usability of the Vigo app. It is important that the content and use of the app be coherent with the aim to avoid (1) misunderstanding its practical use and the need for integration in practice and (2) misusing the app. In all focus groups, the importance of close involvement of rehabilitation specialists in the process of app development and research was highlighted.