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.

Examining the potential of VR program Tilt Brush in reducing anxiety

Recent advancement in technology has made virtual reality (VR) more accessible and immersive than ever before, resulting in its increasing utility in various industries. Despite this, VR has remained an underutilised tool within clinical psychology. This study aimed to explore the potential of using VR for therapeutic benefits through examining the level of flow and anxiety-reducing effects of freeform drawing in real life (on paper) versus drawing in VR (using Tilt Brush) via a randomised-controlled trial with 40 participants. State and trait anxiety was measured using the State-Trait Anxiety Inventory, level of flow was measured using the Long Flow State Scale, and level of presence was measured using the iGroup Presence Questionnaire. Overall level of flow was not significantly different between both groups, implying drawing in VR induces as much flow as drawing in real life. Level of flow was positively correlated to level of presence experienced in the VR group (p < .01). Although there was no significant interaction effect, both groups experienced an overall decrease in state anxiety, with the VR group experiencing a significant reduction of state anxiety from pre- to post-test (p < .01).

MARGINAL: An Automatic Classification of Variants in BRCA1 and BRCA2 Genes Using a Machine Learning Model

Implementation of next-generation sequencing (NGS) for the genetic analysis of hereditary diseases has resulted in a vast number of genetic variants identified daily, leading to inadequate variant interpretation and, consequently, a lack of useful clinical information for treatment decisions. Herein, we present MARGINAL 1.0.0, a machine learning (ML)-based software for the interpretation of rare BRCA1 and BRCA2 germline variants. MARGINAL software classifies variants into three categories, namely, (likely) pathogenic, of uncertain significance and (likely) benign, implementing the criteria established by the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG-AMP). We first annotated BRCA1 and BRCA2 variants using various sources. Then, we automatically implemented the ACMG-AMP criteria, and we finally constructed the ML model for variant classification. To maximize accuracy, we compared the performance of eight different ML algorithms in a classification scheme based on a serial combination of two classifiers. The model showed high predictive abilities with maximum accuracy of 92% and 98%, recall of 92% and 98% and specificity of 90% and 98% for the first and second classifiers, respectively. Our results indicate that using a gene and disease-specific ML automated software for clinical variant evaluation can minimize conflicting interpretations.

Genetics in parkinson's disease: From better disease understanding to machine learning based precision medicine

Parkinson's Disease (PD) is a neurodegenerative disorder with highly heterogeneous phenotypes. Accordingly, it has been challenging to robustly identify genetic factors associated with disease risk, prognosis and therapy response via genome-wide association studies (GWAS). In this review we first provide an overview of existing statistical methods to detect associations between genetic variants and the disease phenotypes in existing PD GWAS. Secondly, we discuss the potential of machine learning approaches to better quantify disease phenotypes and to move beyond disease understanding towards a better-personalized treatment of the disease.

Clinical decision support in the neonatal ICU

Clinical Decision Support (CDS) tools help the healthcare team diagnose, monitor, and treat patients more efficiently and consistently by executing clinical practice guidelines and recommendations. As a result, CDS has a direct impact on the delivery and healthcare outcomes. This review covers the fundamental concepts, as well as the infrastructure needed to create a CDS tool and examples of its use in the neonatal setting. This article also serves as a primer on what to think about when proposing the development of a new CDS tool, or when upgrading an existing one. We also highlight important elements that influence CDS development, such as informatics methodologies, data and device interoperability, and regulation.

Derivation of Bioimpedance Model Data Utilizing a Compact Analyzer and Two Capacitive Electrodes: A Forearm Example

The paper investigates the impacts of the selected electrical equivalent circuit model, measurement setup, and surrounding environment on the trustworthiness of electrical bioimpedance measurement and obtained model data in the human body. The influence of these constitutive components of the system on finding the model parameters is analyzed and illustrated with examples. The results based on experimental measurements on a forearm near the wrist are provided by employing the model, measurement setup, and novel 16-bit compact wireless impedance analyzer (CIA) according to the outcome of the analysis. The area near the wrist is of interest because of attempts to get cardiac-activity-related impedance changes. It is concluded that a two-electrode system with voltage excitation suits better for determining bioimpedance model parameters in the β dispersion area. The results obtained with the CIA and two capacitive bracelet electrodes on a left forearm were used for the fitting model parameters. Despite the small dimensions of 60 × 60 × 25 mm of the CIA reducing stray capacitance to 8 pF, it provides relative impedance magnitude measurement error below 0.3% and phase error below 0.2 ° in the 10 MHz range. Analysis of the model parameters allowed separation of the electrodes, skin, and internal tissue spectra and revealed the relative significance of model components at different frequencies.

UltraButton: A Minimalist Touchless Multimodal Haptic Button

We present UltraButton a minimalist touchless button including haptic, audio and visual feedback costing only $200. While current mid-air haptic devices can be too bulky and expensive (around $2 k) to be integrated into simple mid-air interfaces such as point and select, we show how a clever arrangement of 83 ultrasound transducers and a new modulation algorithm can produce compelling mid-air haptic feedback and parametric audio at a minimal cost. To validate our prototype, we compared its haptic output to a commercially-available mid-air haptic device through force balance measurements and user perceived strength ratings and found no significant differences. With the addition of 20 RGB LEDs, a proximity sensor and other off-the-shelf electronics, we then propose a complete solution for a simple multimodal touchless button interface. We tested this interface in a second experiment that investigated user gestures and their dependence on system parameters such as the haptic and visual activation times and heights above the device. Finally, we discuss new interactions and applications scenarios for UltraButtons.

Development of a Smartphone-Based mHealth Platform for Telerehabilitation

Telerehabilitation is becoming increasingly valuable as a method for expanding medical services. The smartphone-based mHealth platform (SMPT) has been developed to provide high-quality remote rehabilitation through a smartphone and inertial measurement units. The SMPT uses smartphone as a main platform with connection to medical backend server to provide telerehabilitation. Patients would be referred to therapists to receive a tutorial of exercise technique prior to conducting their home exercise. Once patients begin their home exercises, they can report any problems instantly through the SMPT. The medical staff can adjust the exercise program according to patient feedback and the data collected by the SMPT. After completing the exercise program, patients visit their clinician for re-evaluation. A Service User Technology Acceptability Questionnaire from both medical professional and public perspective revealed a high level of agreement on enhanced care, increased accessibility, and satisfaction and a moderate level of agreement on the use of this platform as a substitute for traditional rehabilitation. Concerns about privacy and discomfort were low in the medical professional and public groups. Concerns about care personnel were also significantly different between the two groups. The SMPT is a promising system for providing telerehabilitation as an adjunct to traditional rehabilitation, which may result in improved outcomes compared with those achieved when using traditional rehabilitation alone.

Estimation of Respiratory Frequency in Women and Men by Kubios HRV Software Using the Polar H10 or Movesense Medical ECG Sensor during an Exercise Ramp

Monitoring of the physiologic metric, respiratory frequency (RF), has been shown to be of value in health, disease, and exercise science. Both heart rate (HR) and variability (HRV), as represented by variation in RR interval timing, as well as analysis of ECG waveform variability, have shown potential in its measurement. Validation of RF accuracy using newer consumer hardware and software applications have been sparse. The intent of this report is to assess the precision of the RF derived using Kubios HRV Premium software version 3.5 with the Movesense Medical sensor single-channel ECG (MS ECG) and the Polar H10 (H10) HR monitor. Gas exchange data (GE), RR intervals (H10), and continuous ECG (MS ECG) were recorded from 21 participants performing an incremental cycling ramp to failure. Results showed high correlations between the reference GE and both the H10 (r = 0.85, SEE = 4.2) and MS ECG (r = 0.95, SEE = 2.6). Although median values were statistically different via Wilcoxon testing, adjusted median differences were clinically small for the H10 (RF about 1 breaths/min) and trivial for the MS ECG (RF about 0.1 breaths/min). ECG based measurement with the MS ECG showed reduced bias, limits of agreement (maximal bias, -2.0 breaths/min, maximal LoA, 6.1 to -10.0 breaths/min) compared to the H10 (maximal bias, -3.9 breaths/min, maximal LoA, 8.2 to -16.0 breaths/min). In conclusion, RF derived from the combination of the MS ECG sensor with Kubios HRV Premium software, tracked closely to the reference device through an exercise ramp, illustrates the potential for this system to be of practical usage during endurance exercise.

Processing Photoplethysmograms Recorded by Smartwatches to Improve the Quality of Derived Pulse Rate Variability

Cardiac monitoring based on wearable photoplethysmography (PPG) is widespread because of its usability and low cost. Unfortunately, PPG is negatively affected by various types of disruptions, which could introduce errors to the algorithm that extracts pulse rate variability (PRV). This study aims to identify the nature of such artifacts caused by various types of factors under the conditions of precisely planned experiments. We also propose methods for their reduction based solely on the PPG signal while preserving the frequency content of PRV. The accuracy of PRV derived from PPG was compared to heart rate variability (HRV) derived from the accompanying ECG. The results indicate that filtering PPG signals using the discrete wavelet transform and its inverse (DWT/IDWT) is suitable for removing slow components and high-frequency noise. Moreover, the main benefit of amplitude demodulation is better preparation of the PPG to determine the duration of pulse cycles and reduce the impact of some other artifacts. Post-processing applied to HRV and PRV indicates that the correction of outliers based on local statistical measures of signals and the autoregressive (AR) model is only important when the PPG is of low quality and has no effect under good signal quality. The main conclusion is that the DWT/IDWT, followed by amplitude demodulation, enables the proper preparation of the PPG signal for the subsequent use of PRV extraction algorithms, particularly at rest. However, post-processing in the proposed form should be applied more in the situations of observed strong artifacts than in motionless laboratory experiments.

Wearables in Cardiovascular Disease

Wearable devices stand to revolutionize the way healthcare is delivered. From consumer devices that provide general health information and screen for medical conditions to medical-grade devices that allow collection of larger datasets that include multiple modalities, wearables have a myriad of potential uses, especially in cardiovascular disorders. In this review, we summarize the underlying technologies employed in these devices and discuss the regulatory and economic aspects of such devices as well as the future implications of their use.

The Apple Watch for Monitoring Mental Health-Related Physiological Symptoms: Literature Review

BACKGROUND

An anticipated surge in mental health service demand related to COVID-19 has motivated the use of novel methods of care to meet demand, given workforce limitations. Digital health technologies in the form of self-tracking technology have been identified as a potential avenue, provided sufficient evidence exists to support their effectiveness in mental health contexts.

OBJECTIVE

This literature review aims to identify current and potential physiological or physiologically related monitoring capabilities of the Apple Watch relevant to mental health monitoring and examine the accuracy and validation status of these measures and their implications for mental health treatment.

METHODS

A literature review was conducted from June 2021 to July 2021 of both published and gray literature pertaining to the Apple Watch, mental health, and physiology. The literature review identified studies validating the sensor capabilities of the Apple Watch.

RESULTS

A total of 5583 paper titles were identified, with 115 (2.06%) reviewed in full. Of these 115 papers, 19 (16.5%) were related to Apple Watch validation or comparison studies. Most studies showed that the Apple Watch could measure heart rate acceptably with increased errors in case of movement. Accurate energy expenditure measurements are difficult for most wearables, with the Apple Watch generally providing the best results compared with peers, despite overestimation. Heart rate variability measurements were found to have gaps in data but were able to detect mild mental stress. Activity monitoring with step counting showed good agreement, although wheelchair use was found to be prone to overestimation and poor performance on overground tasks. Atrial fibrillation detection showed mixed results, in part because of a high inconclusive result rate, but may be useful for ongoing monitoring. No studies recorded validation of the Sleep app feature; however, accelerometer-based sleep monitoring showed high accuracy and sensitivity in detecting sleep.

CONCLUSIONS

The results are encouraging regarding the application of the Apple Watch in mental health, particularly as heart rate variability is a key indicator of changes in both physical and emotional states. Particular benefits may be derived through avoidance of recall bias and collection of supporting ecological context data. However, a lack of methodologically robust and replicated evidence of user benefit, a supportive health economic analysis, and concerns about personal health information remain key factors that must be addressed to enable broader uptake.

Scaling up a decentralized offline patient ID generation and matching algorithm to accelerate universal health coverage: Insights from a literature review and health facility survey in Nigeria

Background

Quality of health service delivery data remains sub-optimal in many Low and middle-income countries (LMICs) despite over a decade of progress in digitization and Health Management Information Systems (HMIS) improvements. Identifying everyone residing in a country utilizing universal civil registration and/or national unique identification number systems especially for vulnerable patients seeking care within the care continuum is an essential part of pursuing universal health coverage (UHC). Many different strategies or candidate digital technologies exist for uniquely identifying and tracking patients within a health system, and the different strategies also have their advantages and trade-offs. The recent approval of Decentralized identifier (DID) core specification by World Wide Web Consortium (W3C) heralds the search for consensus on standard interoperable DID methods.

Objective

This paper aims to: (1) assess how candidate Patient Identification Systems fit the digital Patient ID desirable attributes framework in literature; and (2) use insights from Nigeria to propose the scale-up of an offline, interoperable decentralized Patient ID generation and a matching model for addressing network reliability challenges of centralized electronic registries in LMICs.

Methods

We combined: (i) systematic review of the literature to identify the characteristics of leading candidates for Patient ID systems, with (ii) review of policies and (iii) quantitative survey of 14 general hospitals in Nigeria's Federal Capital Territory to understand the model(s) of patient ID strategies currently implemented by public hospitals.

Results

Evidence from the literature review and quantitative survey showed that no current Patient ID strategy in Nigeria simultaneously meets the six attributes of uniqueness, unchanging, uncontroversial, inexpensive, ubiquitous, and uncomplicated required for ensuring the reliability of unique patient identification systems and of the HMIS more generally.

Conclusions

The findings are used to propose a model of algorithms for universal-offline Patient ID generation and matching models that is cost effective and can be easily scaled-up throughout Nigeria. The prototype has promise for generating and validating a universally unique Patient ID given a set of patient characteristics without a central rigid authority. The model can also help to fast-track the implementation of a Master Patient Index (MPI) and interoperability of existing digital health platforms in LMICs.

Using rhythm for rehabilitation: the acceptability of a novel haptic cueing device in extended stroke rehabilitation

Purpose

Restoration of walking ability is a key goal to both stroke survivors and their therapists. However, the intensity and duration of rehabilitation available after stroke can be limited by service constraints, despite the potential for improvement which could reduce health service demands in the long run. The purpose of this paper is to present qualitative findings from a study that explored the acceptability of a haptic device aimed at improving walking as part of an extended intervention in stroke rehabilitation.

Design/methodology/approach

Pre-trial focus groups and post-trial interviews to assess the acceptability of Haptic Bracelets were undertaken with seven stroke survivors.

Findings

Five themes were identified as impacting on the acceptability of the Haptic Bracelet: potential for improving quality of life; relationships with technology; important features; concerns; response to trial and concentration. Participants were interested in the haptic bracelet and hoped it would provide them with more confidence making them: feel safer when walking; have greater ability to take bigger strides rather than little steps; a way to combat mistakes participants reported making due to tiredness and reduced pain in knees and hips.

Originality/value

Haptic Bracelets are an innovative development in the field of rhythmic cueing and stroke rehabilitation. The haptic bracelets also overcome problems encountered with established audio-based cueing, as their use is not affected by external environmental noise.

Peer review

The peer review history for this article is available at: https://publons.com/publon 10.1108/JET-01-2021-0003

The phase of sensorimotor mu and beta oscillations has the opposite effect on corticospinal excitability

BACKGROUND

Neural oscillations in the primary motor cortex (M1) shape corticospinal excitability. Power and phase of ongoing mu (8-13 Hz) and beta (14-30 Hz) activity may mediate motor cortical output. However, the functional dynamics of both mu and beta phase and power relationships and their interaction, are largely unknown.

OBJECTIVE

Here, we employ recently developed real-time targeting of the mu and beta rhythm, to apply phase-specific brain stimulation and probe motor corticospinal excitability non-invasively. For this, we used instantaneous read-out and analysis of ongoing oscillations, targeting four different phases (0°, 90°, 180°, and 270°) of mu and beta rhythms with suprathreshold single-pulse transcranial magnetic stimulation (TMS) to M1. Ensuing motor evoked potentials (MEPs) in the right first dorsal interossei muscle were recorded. Twenty healthy adults took part in this double-blind randomized crossover study.

RESULTS

Mixed model regression analyses showed significant phase-dependent modulation of corticospinal output by both mu and beta rhythm. Strikingly, these modulations exhibit a double dissociation. MEPs are larger at the mu trough and rising phase and smaller at the peak and falling phase. For the beta rhythm we found the opposite behavior. Also, mu power, but not beta power, was positively correlated with corticospinal output. Power and phase effects did not interact for either rhythm, suggesting independence between these aspects of oscillations.

CONCLUSION

Our results provide insights into real-time motor cortical oscillation dynamics, which offers the opportunity to improve the effectiveness of TMS by specifically targeting different frequency bands.

Novel wearable and contactless heart rate, respiratory rate, and oxygen saturation monitoring devices: a systematic review and meta-analysis

We performed a systematic review and meta-analysis to identify, classify and evaluate the body of evidence on novel wearable and contactless devices that measure heart rate, respiratory rate and oxygen saturations in the clinical setting. We included any studies of hospital inpatients, including sleep study clinics. Eighty-four studies were included in the final review. There were 56 studies of wearable devices and 29 of contactless devices. One study assessed both types of device. A high risk of patient selection and rater bias was present in proportionally more studies assessing contactless devices compared with studies assessing wearable devices (p = 0.023 and p < 0.0001, respectively). There was high but equivalent likelihood of blinding bias between the two types of studies (p = 0.076). Wearable device studies were commercially available devices validated in acute clinical settings by clinical staff and had more real-time data analysis (p = 0.04). Contactless devices were more experimental, and data were analysed post-hoc. Pooled estimates of mean (95%CI) heart rate and respiratory rate bias in wearable devices were 1.25 (-0.31-2.82) beats.min^-1 (pooled 95% limits of agreement -9.36-10.08) and 0.68 (0.05-1.32) breaths.min^-1 (pooled 95% limits of agreement -5.65-6.85). The pooled estimate for mean (95%CI) heart rate and respiratory rate bias in contactless devices was 2.18 (3.31-7.66) beats.min^-1 (pooled limits of agreement -6.71-10.88) and 0.30 (-0.26-0.87) breaths.min^-1 (pooled 95% limits of agreement -3.94-4.29). Only two studies of wearable devices measured S_p O₂ ; these reported mean measurement biases of 3.54% (limits of agreement -5.65-11.45%) and 2.9% (-7.4-1.7%). Heterogeneity was observed across studies, but absent when devices were grouped by measurement modality and reference standard. We conclude that, while studies of wearable devices were of slightly better quality than contactless devices, in general all studies of novel devices were of low quality, with small (< 100) patient datasets, typically not blinded and often using inappropriate statistical techniques. Both types of devices were statistically equivalent in accuracy and precision, but wearable devices demonstrated less measurement bias and more precision at extreme vital signs. The statistical variability in precision and accuracy between studies is partially explained by differences in reference standards.

Predicting Patient Wait Times by Using Highly Deidentified Data in Mental Health Care: Enhanced Machine Learning Approach

BACKGROUND

Wait times impact patient satisfaction, treatment effectiveness, and the efficiency of care that the patients receive. Wait time prediction in mental health is a complex task and is affected by the difficulty in predicting the required number of treatment sessions for outpatients, high no-show rates, and the possibility of using group treatment sessions. The task of wait time analysis becomes even more challenging if the input data has low utility, which happens when the data is highly deidentified by removing both direct and quasi identifiers.

OBJECTIVE

The first aim of this study was to develop machine learning models to predict the wait time from referral to the first appointment for psychiatric outpatients by using real-time data. The second aim was to enhance the performance of these predictive models by utilizing the system's knowledge while the input data were highly deidentified. The third aim was to identify the factors that drove long wait times, and the fourth aim was to build these models such that they were practical and easy-to-implement (and therefore, attractive to care providers).

METHODS

We analyzed retrospective highly deidentified administrative data from 8 outpatient clinics at Ontario Shores Centre for Mental Health Sciences in Canada by using 6 machine learning methods to predict the first appointment wait time for new outpatients. We used the system's knowledge to mitigate the low utility of our data. The data included 4187 patients who received care through 30,342 appointments.

RESULTS

The average wait time varied widely between different types of mental health clinics. For more than half of the clinics, the average wait time was longer than 3 months. The number of scheduled appointments and the rate of no-shows varied widely among clinics. Despite these variations, the random forest method provided the minimum root mean square error values for 4 of the 8 clinics, and the second minimum root mean square error for the other 4 clinics. Utilizing the system's knowledge increased the utility of our highly deidentified data and improved the predictive power of the models.

CONCLUSIONS

The random forest method, enhanced with the system's knowledge, provided reliable wait time predictions for new outpatients, regardless of low utility of the highly deidentified input data and the high variation in wait times across different clinics and patient types. The priority system was identified as a factor that contributed to long wait times, and a fast-track system was suggested as a potential solution.

The economic imperatives for technology enabled wellness centered healthcare

A 2020 World Health Organization report underscored the impact of rising healthcare spending globally and questioned the long-term economic sustainability of current funding models. Increases in costs associated with care of late-stage irreversible diseases and the increasing prevalence of debilitating neurodegenerative disorders, coupled with increases in life expectancy are likely to overload the healthcare systems in many nations within the next decade if not addressed. One option for sustainability of the healthcare system is a change in emphasis from illness to wellness centered care. An attractive model is the P4 (Predictive, Preventative, Personalized and Participatory) medicine approach. Recent advances in connected health technology can help accelerate this transition; they offer prediction, diagnosis, and monitoring of health-related parameters. We explain how to integrate such technologies with conventional approaches and guide public health policy toward wellness-based care models and strategies to relieve the escalating economic burdens of managed care.

A novel maturity index for assessing medical device startups

Background

Startup companies in the healthcare sector often fail because they lack sufficient entrepreneurial, regulatory, and business development expertise. Maturity models provide useful frameworks to assess the state of business elements more systematically than heuristic assessments. However, previous models were developed primarily to characterize the business state of larger nonmedical companies. A maturity index designed specifically for startup companies in the medical product sector could help to identify areas in which targeted interventions could assist business development.

Methods

A novel MedTech Startup Maturity Index (SMI) was developed by a collaborative team of academic and industry experts and refined through feedback from external stakeholders. Pediatric medical device startups associated with the West Coast Consortium for Technology & Innovation in Pediatrics (CTIP) were scored and ranked according to the SMI following semi-structured interviews. The CTIP executive team independently ranked the maturity of each company based on their extensive experiences with the same companies.

Results

SMI scores for 16 companies ranged from 1.2 to 3.8 out of 4. These scores were well aligned with heuristic CTIP rankings for 14 out of 16 companies, reflected by strong correlations between the two datasets (Spearman's rho = 0.721, P = 0.002, and Kendall's tau-b = 0.526, P = 0.006).

Conclusions

The SMI yields maturity scores that correlate well with expert rankings but can be assessed without prior company knowledge and can identify specific areas of concern more systematically. Further research is required to generalize and validate the SMI as a pre-/post-evaluation tool.

Prospect of Internet of Medical Things: A Review on Security Requirements and Solutions

With the widespread and increasing use of Internet-of-Things (IoT) devices in all aspects of daily life, a hopeful future for people, data, and processes is emerging. Extensive spans allow for an integrated life cycle to be created from home to enterprise. The Internet of Medical things (IoMT) forms a flourishing surface that incorporates the sensitive information of human life being sent to doctors or hospitals. These open an enormous space for hackers to utilize flaws of the IoMT network to make a profit. This creates a demand for standardizing regulations and a secure system. Though many authorities are making standards, there are some lacking in the system which makes the product vulnerable. Although many established mechanisms are present for the IoT network, there are a number of obstacles preventing its general implementation in the IoMT network. One of the adoption challenges is the IoMT devices itself, because many IoMT networks consist of battery-powered devices with constrained processing capability. A general overview of the different security integrations with IoT applications has been presented in several papers. Therefore, this paper aims to provide an overview of the IoMT ecosystem, regulations, challenges of standards, security mechanisms using cryptographic solutions, physical unclonable functions (PUF)-based solutions, blockchain, and named data networking (NDN) as well, with pros and cons.

Virtual Reality for Shoulder Rehabilitation: Accuracy Evaluation of Oculus Quest 2

Virtual reality (VR) systems are becoming increasingly attractive as joint kinematics monitoring systems during rehabilitation. This study aimed to evaluate the accuracy of the Oculus Quest 2 in measuring translational and rotational displacements. As the Oculus Quest 2 was chosen for future applications in shoulder rehabilitation, the translation range (minimum: ~200 mm, maximum: ~700 mm) corresponded to the forearm length of the 5th percentile female and the upper limb length of the 95th percentile male. The controller was moved on two structures designed to allow different translational displacements and rotations in the range 0-180°, to cover the range of motion of the upper limb. The controller measures were compared with those of a Qualisys optical capture system. The results showed a mean absolute error of 13.52 ± 6.57 mm at a distance of 500 mm from the head-mounted display along the x-direction. The maximum mean absolute error for rotational displacements was found to be 1.11 ± 0.37° for a rotation of 40° around the z-axis. Oculus Quest 2 is a promising VR tool for monitoring shoulder kinematics during rehabilitation. The inside-out movement tracking makes Oculus Quest 2 a viable alternative to traditional motion analysis systems.

The Male Domain-Digital Game-Based Learning for Human Papillomavirus Vaccination Among Young Males

Background: Human papillomavirus (HPV) is the most common sexually transmitted disease, with the highest infection rates among those sexually active under 25. Although vaccination can reduce HPV cancers among men, public health interventions have primarily targeted females. Increased gaming rates among men provide innovative opportunities to motivate behavior change. This study sought to explore which game development and design strategies are most effective in a game for sexual health, specifically focused on HPV. We also sought to capture information relevant to preferred gaming platforms and game mechanics (health messages, avatars, and visual imagery). Materials and Methods: Twenty-two (n = 22) qualitative interviews were conducted with experts. Using grounded theory, interview data was coded, and emergent themes were identified. Results: Game mechanics most mentioned included simulation/role-playing, social interaction, narrative, and rewards. Experts felt it was important to keep the game in the context of the target audience and integrate the game into an existing game/game scenario or application. Experts also felt the game should link to external resources and enlist partnerships or collaborations with external health agencies. Moreover, while there are benefits to each gaming platform, games on mobile phones and tablets are most appropriate. Conclusion: Digital games are a nonconfrontational approach to discussing HPV and can increase knowledge/awareness and positively influence behavior change toward vaccine uptake. Digital games present a safe environment for role-playing through simulated activities without real-world consequences.

Hand Rehabilitation Devices: A Comprehensive Systematic Review

A cerebrovascular accident, or a stroke, can cause significant neurological damage, inflicting the patient with loss of motor function in their hands. Standard rehabilitation therapy for the hand increases demands on clinics, creating an avenue for powered hand rehabilitation devices. Hand rehabilitation devices (HRDs) are devices designed to provide the hand with passive, active, and active-assisted rehabilitation therapy; however, HRDs do not have any standards in terms of development or design. Although the categorization of an injury’s severity can guide a patient into seeking proper assistance, rehabilitation devices do not have a set standard to provide a solution from the beginning to the end stages of recovery. In this paper, HRDs are defined and compared by their mechanical designs, actuation mechanisms, control systems, and therapeutic strategies. Furthermore, devices with conducted clinical trials are used to determine the future development of HRDs. After evaluating the abilities of 35 devices, it is inferred that standard characteristics for HRDs should include an exoskeleton design, the incorporation of challenge-based and coaching therapeutic strategies, and the implementation of surface electromyogram signals (sEMG) based control.

Wearables: An R Package With Accompanying Shiny Application for Signal Analysis of a Wearable Device Targeted at Clinicians and Researchers

Physiological signals (e.g., heart rate, skin conductance) that were traditionally studied in neuroscientific laboratory research are currently being used in numerous real-life studies using wearable technology. Physiological signals obtained with wearables seem to offer great potential for continuous monitoring and providing biofeedback in clinical practice and healthcare research. The physiological data obtained from these signals has utility for both clinicians and researchers. Clinicians are typically interested in the day-to-day and moment-to-moment physiological reactivity of patients to real-life stressors, events, and situations or interested in the physiological reactivity to stimuli in therapy. Researchers typically apply signal analysis methods to the data by pre-processing the physiological signals, detecting artifacts, and extracting features, which can be a challenge considering the amount of data that needs to be processed. This paper describes the creation of a “Wearables” R package and a Shiny “E4 dashboard” application for an often-studied wearable, the Empatica E4. The package and Shiny application can be used to visualize the relationship between physiological signals and real-life stressors or stimuli, but can also be used to pre-process physiological data, detect artifacts, and extract relevant features for further analysis. In addition, the application has a batch process option to analyze large amounts of physiological data into ready-to-use data files. The software accommodates users with a downloadable report that provides opportunities for a careful investigation of physiological reactions in daily life. The application is freely available, thought to be easy to use, and thought to be easily extendible to other wearable devices. Future research should focus on the usability of the application and the validation of the algorithms.

Dynamic Phase Extraction: Applications in Pulse Rate Variability

Pulse rate variability is a physiological parameter that has been extensively studied and correlated with many physical ailments. However, the phase relationship between inter-beat interval, IBI, and breathing has very rarely been studied. Develop a technique by which the phase relationship between IBI and breathing can be accurately and efficiently extracted from photoplethysmography (PPG) data. A program based on Lock-in Amplifier technology was written in Python to implement a novel technique, Dynamic Phase Extraction. It was tested using a breath pacer and a PPG sensor on 6 subjects who followed a breath pacer at varied breathing rates. The data were then analyzed using both traditional methods and the novel technique (Dynamic Phase Extraction) utilizing a breath pacer. Pulse data was extracted using a PPG sensor. Dynamic Phase Extraction (DPE) gave the magnitudes of the variation in IBI associated with breathing [Formula: see text] measured with photoplethysmography during paced breathing (with premature ventricular contractions, abnormal arrhythmias, and other artifacts edited out). [Formula: see text] correlated well with two standard measures of pulse rate variability: the Standard Deviation of the inter-beat interval (SDNN) (ρ = 0.911) and with the integrated value of the Power Spectral Density between 0.04 and 0.15 Hz (Low Frequency Power or LF Power) (ρ = 0.885). These correlations were comparable to the correlation between the SDNN and the LF Power (ρ = 0.877). In addition to the magnitude [Formula: see text], Dynamic Phase Extraction also gave the phase between the breath pacer and the changes in the inter-beat interval (IBI) due to respiratory sinus arrythmia (RSA), and correlated well with the phase extracted using a Fourier transform (ρ = 0.857). Dynamic Phase Extraction can extract both the phase between the breath pacer and the changes in IBI due to the respiratory sinus arrhythmia component of pulse rate variability ([Formula: see text], but is limited by needing a breath pacer.

Physical therapists' perceptions of and satisfaction with delivering telerehabilitation sessions to patients with knee osteoarthritis during the Covid-19 pandemic: Preliminary study

OBJECTIVE

This preliminary study was conducted to explore physical therapists' (PT) perceptions of and satisfaction with delivering telerehabilitation sessions to patients with knee osteoarthritis during the Covid-19 pandemic.

STUDY DESIGN

An exploratory preliminary study using an internet-based survey followed by focus group sessions.

METHODS

A programme of sessions was administered by 12 PTs from the Physical Therapy Department at Prince Sultan Military Medical City. An internet-based survey containing 17 statements was completed by the PTs.

RESULTS

With regard to telephone-delivered care, four statements related to patients' privacy, programme convenience, safe patients time and money achieved consensus agreement (≥75% agreed or strongly agreed), there was majority agreement (≥50% of respondents agreed or strongly agreed) with seven of the statements regarding the effectiveness, affordability and safety of the programme, but there was no consensus with regard to the remaining five statements. In addition, most of the participants (84.6%) believed that a telephone consultation should cost 25% or 50% less than a face-to-face session.

CONCLUSION

Despite the lack of physical contact with patients, the PTs agreed that telerehabilitation would offer patients an easy method of being prescribed a therapeutic programme, save time and money, and maintain patient privacy. Further, the PTs reported barriers and suggested adaptations for this method of service delivery.

Peak Detection and HRV Feature Evaluation on ECG and PPG Signals

Heart Rate Variability (HRV) evaluates the autonomic nervous system regulation and can be used as a monitoring tool in conditions such as cardiovascular diseases, neuropathies and sleep staging. It can be extracted from the electrocardiogram (ECG) and the photoplethysmogram (PPG) signals. Typically, the HRV is obtained from the ECG processing. Being the PPG sensor widely used in clinical setups for physiological parameters monitoring such as blood oxygenation and ventilatory rate, the question arises regarding the PPG adequacy for HRV extraction. There is not a consensus regarding the PPG being able to replace the ECG in the HRV estimation. This work aims to be a contribution to this research area by comparing the HRV estimation obtained from simultaneously acquired ECG and PPG signals from forty subjects. A peak detection method is herein introduced based on the Hilbert transform: Hilbert Double Envelope Method (HDEM). Two other peak detector methods were also evaluated: Pan-Tompkins and Wavelet-based. HRV parameters for time, frequency and the non-linear domain were calculated for each algorithm and the Pearson correlation, T-test and RMSE were evaluated. The HDEM algorithm showed the best overall results with a sensitivity of 99.07% and 99.45% for the ECG and the PPG signals, respectively. For this algorithm, a high correlation and no significant differences were found between HRV features and the gold standard, for the ECG and PPG signals. The results show that the PPG is a suitable alternative to the ECG for HRV feature extraction.

A U-Shaped Optical Fiber Temperature Sensor Coated with Electrospinning Polyvinyl Alcohol Nanofibers: Simulation and Experiment

This study describes the fabrication of an electrospun, U-shaped optical fiber sensor for temperature measurements. The sensor is based on single mode fibers and was fabricated into a U-shaped optical fiber sensor through flame heating. This study applied electrospinning to coat PVA, a polymer, onto the sensor layer to reduce its sensitivity to humidity. The sensor is used to measure temperature variations ranging from 30 °C to 100 °C. The objectives of this study were to analyze the sensitivity variation of the sensor with different sensor layer thicknesses resulting from different electrospinning durations, as well as to simulate the wavelength signals generated at different electrospinning durations using COMSOL. The results revealed that the maximum wavelength sensitivity, transmission loss sensitivity, and linearity of the sensor were 25 dBm/°C, 70 pm/°C, and 0.956, respectively. Longer electrospinning durations resulted in thicker sensor layers and higher sensor sensitivity, that wavelength sensitivity of the sensor increased by 42%.

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Recent years have seen significant advances in the sensing capabilities of smartphones, enabling them to collect rich contextual information such as location, device usage, and human activity at a given point in time. Combined with widespread user adoption and the ability to gather user data remotely, smartphone-based sensing has become an appealing choice for health research. Numerous studies over the years have demonstrated the promise of using smartphone-based sensing to monitor a range of health conditions, particularly mental health conditions. However, as research is progressing to develop the predictive capabilities of smartphones, it becomes even more crucial to fully understand the capabilities and limitations of using this technology, given its potential impact on human health. To this end, this paper presents a narrative review of smartphone-sensing literature from the past 5 years, to highlight the opportunities and challenges of this approach in healthcare. It provides an overview of the type of health conditions studied, the types of data collected, tools used, and the challenges encountered in using smartphones for healthcare studies, which aims to serve as a guide for researchers wishing to embark on similar research in the future. Our findings highlight the predominance of mental health studies, discuss the opportunities of using standardized sensing approaches and machine-learning advancements, and present the trends of smartphone sensing in healthcare over the years.

Potentials and Challenges of Pervasive Sensing in the Intensive Care Unit

Patients in critical care settings often require continuous and multifaceted monitoring. However, current clinical monitoring practices fail to capture important functional and behavioral indices such as mobility or agitation. Recent advances in non-invasive sensing technology, high throughput computing, and deep learning techniques are expected to transform the existing patient monitoring paradigm by enabling and streamlining granular and continuous monitoring of these crucial critical care measures. In this review, we highlight current approaches to pervasive sensing in critical care and identify limitations, future challenges, and opportunities in this emerging field.

Contactless remote monitoring of sleep: evaluating the feasibility of an under-mattress sensor mat in a real-life deployment

Sleep is so important, particularly for the elderly. The lack of sleep may increase the risk of cognitive decline. Similarly, it may also increase the risk of Alzheimer's disease. Nonetheless, many people underestimate the importance of getting enough rest and sleep. In-laboratory polysomnography is the gold-standard method for assessing the quality of sleep. This method is considered impractical in the clinical environment, seen as labour-intensive and expensive owing to its specialised equipment, leading to long waiting lists. Hence, user-friendly (remote and non-intrusive) devices are being developed to help patients monitor their sleep at home. In this paper, we first discuss commercially-available non-wearable devices that measure sleep, in which we highlight the features associated with each device, including sensor type, interface, outputs, dimensions, power supply, and connectivity. Second, we evaluate the feasibility of a non-wearable device in a free-living environment. The deployed device comprises a sensor mat with an integrated micro-bending multimode fibre. Raw sensor data were gathered from five senior participants living in a senior activity centre over a few to several weeks. We were able to analyse the participants' sleep quality using various sleep parameters deduced from the sensor mat. These parameters include the wake-up time, bedtime, the time in bed, nap time. Vital signs, namely heart rate, respiratory rate, and body movements, were also reported to detect abnormal sleep patterns. We have employed pre-and post-surveys reporting each volunteer's sleep hygiene to confirm the proposed system's outcomes for detecting the various sleep parameters. The results of the system were strongly correlated with the surveys for reporting each sleep parameter. Furthermore, the system proved to be highly effective in detecting irregular patterns that occurred during sleep.

Technological Requirements and Challenges in Wireless Body Area Networks for Health Monitoring: A Comprehensive Survey

With the rapid growth in healthcare demand, an emergent, novel technology called wireless body area networks (WBANs) have become promising and have been widely used in the field of human health monitoring. A WBAN can collect human physical parameters through the medical sensors in or around the patient’s body to realize real-time continuous remote monitoring. Compared to other wireless transmission technologies, a WBAN has more stringent technical requirements and challenges in terms of power efficiency, security and privacy, quality of service and other specifications. In this paper, we review the recent WBAN medical applications, existing requirements and challenges and their solutions. We conducted a comprehensive investigation of WBANs, from the sensor technology for the collection to the wireless transmission technology for the transmission process, such as frequency bands, channel models, medium access control (MAC) and networking protocols. Then we reviewed its unique safety and energy consumption issues. In particular, an application-specific integrated circuit (ASIC)-based WBAN scheme is presented to improve its security and privacy and achieve ultra-low energy consumption.

Establishing best practices in photoplethysmography signal acquisition and processing

Photoplethysmography is now widely utilised by clinical devices such as pulse oximeters, and wearable devices such as smartwatches. It holds great promise for health monitoring in daily life. This editorial considers whether it would be possible and beneficial to establish best practices for photoplethysmography signal acquisition and processing. It reports progress made towards this, balanced with the challenges of working with a diverse range of photoplethysmography device designs and intended applications, each of which could benefit from different approaches to signal acquisition and processing. It concludes that there are several potential benefits to establishing best practices. However, it is not yet clear whether it is possible to establish best practices which hold across the range of photoplethysmography device designs and applications.

Effect of Surface Tooling Techniques of Medical Titanium Implants on Bacterial Biofilm Formation In Vitro

The aim of this study was to assess the biofilm formation of Streptococcus mutans, Staphylococcus aureus, Enterococcus faecalis, and Escherichia coli on titanium implants with CAD-CAM tooling techniques. Twenty specimens of titanium were studied: Titanium grade 2 tooled with a Planmeca CAD-CAM milling device (TiGrade 2), Ti6Al4V grade 5 as it comes from CAD-DMLS device (computer aided design-direct metal laser sintering device) (TiGrade 5), Ti6Al4V grade 23 as it comes from a CAD-CAM milling device (TiGrade 23), and CAD-DMLS TiGrade 5 polished with an abrasive disc (TiGrade 5 polished). Bacterial adhesion on the implants was completed with and without saliva treatment to mimic both extraoral and intraoral surgical methods of implant placement. Five specimens/implant types were used in the bacterial adhesion experiments. Autoclaved implant specimens were placed in petri plates and immersed in saliva solution for 30 min at room temperature and then washed 3×with 1 ×PBS. Bacterial suspensions of each strain were made and added to the specimens after saliva treatment. Biofilm was allowed to form for 24 h at 37 °C and the adhered bacteria was calculated. Tooling techniques had an insignificant effect on the bacterial adhesion by all the bacterial strains studied. However, there was a significant difference in biofilm formation between the saliva-treated and non-saliva-treated implants. Saliva contamination enhanced S. mutans, S. aureus, and E. faecalis adhesion in all material types studied. S. aureus was found to be the most adherent strain in the saliva-treated group, whereas E. coli was the most adherent strain in the non-saliva-treated group. In conclusion, CAD-CAM tooling techniques have little effect on bacterial adhesion. Saliva coating enhances the biofilm formation; therefore, saliva contamination of the implant must be minimized during implant placement. Further extensive studies are needed to evaluate the effects of surface treatments of the titanium implant on soft tissue response and to prevent the factors causing implant infection and failure.

System Performance and User Feedback Regarding Wearable Bioimpedance System for Multi-Site Knee Tissue Monitoring: Free-Living Pilot Study With Healthy Adults

Knee-focused wearable devices have the potential to support personalized rehabilitation therapies by monitoring localized tissue alterations related to activities that reduce functional symptoms and pain. However, supporting these applications requires reported data to be reliable and accurate which can be challenging in the unsupervised free-living conditions that wearable devices are deployed. This pilot study has assessed a knee-focused wearable sensor system to quantify 1) system performance (operation, rates of data artifacts, environment impacts) to estimate realistic targets for reliable data with this system and 2) user experiences (comfort, fit, usability) to help inform future designs to increase usability and adoption of knee-focused wearables. Study data was collected from five healthy adult participants over 2 days, with 84.5 and 35.9% of artifact free data for longitudinal and transverse electrode configurations. Small to moderate positive correlations were also identified between changes in resistance, temperature, and humidity with respect to acceleration to highlight how this system can be used to explore relationships between knee tissues and environmental/activity context.

Smartphone-Enabled versus Conventional Otoscopy in Detecting Middle Ear Disease: A Meta-Analysis

Traditional otoscopy has some limitations, including poor visualization and inadequate time for evaluation in suboptimal environments. Smartphone-enabled otoscopy may improve examination quality and serve as a potential diagnostic tool for middle ear diseases using a telemedicine approach. The main objectives are to compare the correctness of smartphone-enabled otoscopy and traditional otoscopy and to evaluate the diagnostic confidence of the examiner via meta-analysis. From inception through 20 January 2022, the Cochrane Library, PubMed, EMBASE, Web of Science, and Scopus databases were searched. Studies comparing smartphone-enabled otoscopy with traditional otoscopy regarding the outcome of interest were eligible. The relative risk (RR) for the rate of correctness in diagnosing ear conditions and the standardized mean difference (SMD) in diagnostic confidence were extracted. Sensitivity analysis and trial sequential analyses (TSAs) were conducted to further examine the pooled results. Study quality was evaluated by using the revised Cochrane risk of bias tool 2. Consequently, a total of 1840 examinees were divided into the smartphone-enabled otoscopy group and the traditional otoscopy group. Overall, the pooled result showed that smartphone-enabled otoscopy was associated with higher correctness than traditional otoscopy (RR, 1.26; 95% CI, 1.06 to 1.51; p = 0.01; I² = 70.0%). Consistently significant associations were also observed in the analysis after excluding the simulation study (RR, 1.10; 95% CI, 1.00 to 1.21; p = 0.04; I² = 0%) and normal ear conditions (RR, 1.18; 95% CI, 1.01 to 1.40; p = 0.04; I² = 65.0%). For the confidence of examiners using both otoscopy methods, the pooled result was nonsignificant between the smartphone-enabled otoscopy and traditional otoscopy groups (SMD, 0.08; 95% CI, -0.24 to 0.40; p = 0.61; I² = 16.3%). In conclusion, smartphone-enabled otoscopy was associated with a higher rate of correctness in the detection of middle ear diseases, and in patients with otologic complaints, the use of smartphone-enabled otoscopy may be considered. More large-scale studies should be performed to consolidate the results.

Feasibility of virtual reality based training for optimising COVID-19 case handling in Uganda

BACKGROUND

Epidemics and pandemics are causing high morbidity and mortality on a still-evolving scale exemplified by the COVID-19 pandemic. Infection prevention and control (IPC) training for frontline health workers is thus essential. However, classroom or hospital ward-based training portends an infection risk due to the in-person interaction of participants. We explored the use of Virtual Reality (VR) simulations for frontline health worker training since it trains participants without exposing them to infections that would arise from in-person training. It does away with the requirement for expensive personal protective equipment (PPE) that has been in acute shortage and improves learning, retention, and recall. This represents the first attempt in deploying VR-based pedagogy in a Ugandan medical education context.

METHODS

We used animated VR-based simulations of bedside and ward-based training scenarios for frontline health workers. The training covered the donning and doffing of PPE, case management of COVID-19 infected individuals, and hand hygiene. It used VR headsets to actualize an immersive experience, via a hybrid of fully-interactive VR and 360° videos. The level of knowledge acquisition between individuals trained using this method was compared to similar cohorts previously trained in a classroom setting. That evaluation was supplemented by a qualitative assessment based on feedback from participants about their experience.

RESULTS

The effort resulted in a COVID-19 IPC curriculum adapted into VR, corresponding VR content, and a pioneer cohort of VR trained frontline health workers. The formalized comparison with classroom-trained cohorts showed relatively better outcomes by way of skills acquired, speed of learning, and rates of information retention (P-value = 4.0e-09). In the qualitative assessment, 90% of the participants rated the method as very good, 58.1% strongly agreed that the activities met the course objectives, and 97.7% strongly indicated willingness to refer the course to colleagues.

CONCLUSION

VR-based COVID-19 IPC training is feasible, effective and achieves enhanced learning while protecting participants from infections within a pandemic setting in Uganda. It is a delivery medium transferable to the contexts of other highly infectious diseases.

The Importance of T-Scan III Digital Electronic System in Fixed Restorations Occlusal Analysis – A Review

BACKGROUND: Occlusion is one of the most important aspects of dentistry. Dental occlusion differs among individuals. Subsequently, realizing the correct physiological occlusion is essential for the complex functioning of the stomatognathic system during the mouth prosthodontics rehabilitation. AIM: The purpose of this review paper is to provide an overview of the T-Scan system over its utility in fixed restorations occlusal analysis. MATERIALS AND METHODS: An electronic search was performed through PubMed, MEDLINE, Web of Science, and Google Scholar. The inclusion criteria for selection were articles reporting the occlusion analysis using articulating paper as a conventional method, and occlusion analysis using T-Scan III digital electronic system to detect contact points between the mandibular and maxillary teeth. RESULTS: The electronic search identified 70 titles. Authors ended up with eight studies which have been divided into two tables. The first table is described four studies which present a comparison between the T-Scan III electronic system and articulating paper. The second table is described 4 studies which present occlusal analysis using the T-Scan III electronic system. CONCLUSION: Based on the published literature, this review has highlighted some advantages of the T-Scan electronic system compared to articulating paper. The studies showed that the T-Scan computerized occlusal analysis which can provide quantifiable occlusal force and timing data that will make it possible to better protect brittle dental ceramic, and most importantly, to improve the quality of patient’s lives with improved bite comfort and chewing function.

An Exploratory EEG Analysis on the Effects of Virtual Reality in People with Neuropathic Pain Following Spinal Cord Injury

Neuropathic pain in people with spinal cord injury is thought to be due to altered central neuronal activity. A novel therapeutic intervention using virtual reality (VR) head-mounted devices was investigated in this study for pain relief. Given the potential links to neuronal activity, the aim of the current study was to determine whether use of VR was associated with corresponding changes in electroencephalography (EEG) patterns linked to the presence of neuropathic pain. Using a within-subject, randomised cross-over pilot trial, we compared EEG activity for three conditions: no task eyes open state, 2D screen task and 3D VR task. We found an increase in delta activity in frontal regions for 3D VR with a decrease in theta activity. There was also a consistent decrease in relative alpha band (8-12 Hz) and an increase in low gamma (30-45 Hz) power during 2D screen and 3D VR corresponding, with reduced self-reported pain. Using the nonlinear and non-oscillatory method of extracting fractal dimensions, we found increases in brain complexity during 2D screen and 3D VR. We successfully classified the 3D VR condition from 2D screen and eyes opened no task conditions with an overall accuracy of 80.3%. The findings in this study have implications for using VR applications as a therapeutic intervention for neuropathic pain in people with spinal cord injury.

Performance of the new SmartCardia wireless, wearable oximeter: a comparison with arterial SaO2 in healthy volunteers

BACKGROUND

Our goal was to evaluate the performance of a new wearable arm located pulse oximeter.

METHODS

Twelve volunteers were monitored with three pulse oximeters and underwent desaturation to 70% SaO2. We compared the accuracy of SpO2 reading from the SmartCardia system with SpO2 using two well established devices (Masimo and Nellcor) as reference. Oximetry was performed at different level of oxygen saturation varying from 70 to 100%. Bias, ARMS and precision were evaluated using Bland-Altman plots.

RESULTS

The mean (SD) differences between SaO2 compared to SpO2 and the devices were as follows: SaO2 versus Masimo 2,12 ± 1,01% (95% CI 1,45 to 2,79), SaO2 versus Nellcor 0,78 ± 0,58% (95% CI - 0,29 to 1,65) and SaO2 versus SmartCardia 0,42 ± 0,24% (95% CI - 0,64 to 1,46). The bias between SmartCardia, Masimo, Nellcor devices and SaO2 was 0.16 (95% CI 0.05 to 0.33) and LoA (level of agreements) 2.96 (95% CI - 2,68 to 2,89) for SmartCardia, 2,02 (95% CI 1,49 to 2,54) and LoA - 6 to 11 for Masimo, and 0,76 (95% CI 0,5 to - 1) and LoA - 3,5 to 5,0 for Nellcor. ARMS for the 70-100% SaO2 range was 1,4 for SmartCardia, 5,0 for Masimo and 2,31 for Nellcor.

CONCLUSIONS

The new wireless SmartCardia SpO2 measurement system demonstrated in-line results, bias, ARMS and precision in healthy volunteers, when compared with the gold standard SaO2 and with two well established systems, Masimo and Nellcor.

TRIAL REGISTRATION

The present trial was prospectively registered at UCSF record (registration number:10-00437), on March 8, 2021.