Surface Topography Has Less Influence on Peri-Implantitis than Patient Factors: A Comparative Clinical Study of Two Dental Implant Systems

OBJECTIVES

This study aims to assess the risk of peri-implantitis (PI) onset among different implant systems and evaluate the severity of the disease from a population of patients treated in a university clinic. Furthermore, this study intends to thoroughly examine the surface properties of the implant systems that have been identified and investigated.

MATERIAL AND METHODS

Data from a total of six hundred and 14 patients were extracted from the Institute of Clinical Dentistry, Dental Faculty, University of Oslo. Subject- and implant-based variables were collected, including the type of implant, date of implant installation, medical records, recall appointments up to 2022, periodontal measurements, information on diabetes, smoking status, sex, and age. The outcome of interest was the diagnosis of PI, defined as the occurrence of bleeding on probing (BoP), peri-implant probing depth (PD) ≥ 5 mm, and bone loss (BL). Data were analyzed using multivariate linear and logistic regression. Scanning electron microscopy, light laser profilometer, and X-ray photoelectron spectroscopy were utilized for surface and chemical analyses.

RESULTS

Among the patients evaluated, 6.8% were diagnosed with PI. A comparison was made between two different implant systems: Dentsply Sirona, OsseospeedTM and Straumann SLActive, with mean follow-up times of 3.84 years (SE: 0.15) and 3.34 years (SE: 0.15), respectively. The surfaces have different topographies and surface chemistry. However, no significant association was found between PI and implant surface/system, including no difference in the onset or severity of the disease. Nonetheless, plaque control was associated with an increased risk of developing PI, along with the gender of the patient. Furthermore, patients suffering from PI exhibited increased BL in the anterior region.

CONCLUSION

No differences were observed among the evaluated implant systems, although the surfaces have different topography and chemistry. Factors that affected the risk of developing PI were plaque index and male gender. The severity of BL in patients with PI was more pronounced in the anterior region. Consequently, our findings show that success in implantology is less contingent on selecting implant systems and more on a better understanding of patient-specific risk factors, as well as on implementing biomaterials that can more effectively debride dental implants.

Identifying oncology caregivers' pretreatment educational and emotional needs to inform future virtual reality educational interventions

PURPOSE

To identify oncology caregivers' unmet educational needs for the development of a virtual reality experience.

METHODS

A qualitative descriptive methodology was used; data were collected via online surveys.

RESULTS

Eighteen participants said their educational experiences were overwhelming and emotionally exhausting. They suggested a need to deliver educational information through different modalities and provide more clinician-based resources and support.

CONCLUSION

This study identified opportunities to complement traditional pretreatment education tailored to the caregivers' needs and experiences, such as specific procedural information and emotional management while being a caregiver. Creating virtual reality experiences exclusively for oncology caregivers is a novel nurse-led approach that is currently not in existence.

Real-time video telerehabilitation shows comparable satisfaction and similar or better attendance and adherence compared with in-person physiotherapy: a systematic review

QUESTION

How does physiotherapy delivered by real-time, video-based telerehabilitation compare with in-person delivery for the outcomes of attendance, adherence and satisfaction?

DESIGN

Systematic review of randomised control trials indexed in PubMed, CINAHL, Embase, Cochrane and PEDro on 12 March 2024.

PARTICIPANTS

Adults aged > 18 years.

INTERVENTION

Physiotherapy delivered via real-time video telerehabilitation.

OUTCOME MEASURES

Attendance, adherence and satisfaction.

RESULTS

Eight studies were included for attendance (n = 1,110), nine studies for adherence (n = 1,190) and 12 studies for satisfaction (n = 1,247). Telerehabilitation resulted in attendance at treatment sessions that was 8% higher (95% CI -1 to 18) and adherence to exercise programs that was 9% higher (95% CI 2 to 16) when compared with in-person physiotherapy. Satisfaction was similar with both modes of delivery (SMD 0.03 in favour of telerehabilitation, 95% CI -0.23 to 0.28). The level of certainty assessed by GRADE ranged from very low to low, primarily due to inconsistency and high risk of bias.

DISCUSSION

Attendance at appointments among participants assigned to telerehabilitation was somewhere between similar to and considerably higher than among control participants. Adherence to self-management with telerehabilitation was better than with in-person delivery, although with some uncertainty about the magnitude of the effect. Reported satisfaction levels were similar between the two modes of treatment delivery. Given the significance of attendance, adherence and satisfaction for successful outcomes, telerehabilitation offers a valuable alternative mode for physiotherapy delivery.

CONCLUSION

Real-time telerehabilitation has potentially favourable effects on attendance at treatment appointments and adherence to exercise programs, with similar satisfaction when compared with traditional in-person physiotherapy.

REGISTRATION

PROSPERO CRD42022329906.

Assessing the usability of an immersive virtual reality grocery store in healthy controls

BACKGROUND

Immersive virtual reality (IVR) as a research platform to study human behaviors is an emerging field and may be useful for studying self-care management, especially in the gap between formal healthcare recommendations and day-to-day living. Self-care activities, such as grocery shopping, can be challenging for people with chronic illness. We developed an IVR environment that simulates a real-life grocery store and conducted a usability study to demonstrate the safety and acceptability of IVR as an experimental environment.

METHODS

This study was a three-arm randomized control trial involving 24 participants, conducted as a usability study to evaluate aspects of the experimental condition including the effectiveness of a training exposure, the occurrence of undesirable effects associated with IVR, and participants' experiences of realism, immersion, and spatial presence. The experiment, using a head mounted device and handheld controllers, included a 10-minute training exposure, followed by one of three unique 30-minute experimental conditions which exposed participants to different combinations of tasks and stimuli, and a post-experience interview. We measured controller errors, undesirable symptoms associated with IVR, and the perception of realism, immersion, and spatial presence.

RESULTS

Participants used controllers effectively to interact within the IVR environment. Hand controller use errors were fewer during the experimental conditions compared to the training exposure. Minimal undesirable IVR symptoms were reported. Presence was rated in the middle range with no significant differences based on experimental condition. Overall, user experience feedback was positive.

CONCLUSIONS

We demonstrated that participants could engage in our IVR environment without excessive error or experiencing undesirable effects and confirmed that the virtual experience attained a level of presence necessary to effectively engage in the study. These findings give us confidence that this IVR intervention designed to explore instrumental activities of daily living is safe, effective and provides a credible, controlled simulated community-like setting.

On the Use of Nanoparticles in Dental Implants

Results obtained in physics, chemistry and materials science on nanoparticles have drawn significant interest in the use of nanostructures on dental implants. The main focus concerns nanoscale surface modifications of titanium-based dental implants in order to increase the surface roughness and provide a better bone-implant interfacial area. Surface coatings via the sol-gel process ensure the deposition of a homogeneous layer of nanoparticles or mixtures of nanoparticles on the titanium substrate. Nanotubular structures created on the titanium surface by anodic oxidation yield an interesting nanotopography for drug release. Carbon-based nanomaterials hold great promise in the field of dentistry on account of their outstanding mechanical properties and their structural characteristics. Carbon nanomaterials that include carbon nanotubes, graphene and its derivatives (graphene oxide and graphene quantum dots) can be used as coatings of the implant surface. Their antibacterial properties as well as their ability to be functionalized with adequate chemical groups make them particularly useful for improving biocompatibility and promoting osseointegration. Nevertheless, an evaluation of their possible toxicity is required before being exploited in clinical trials.

A hybrid human recognition framework using machine learning and deep neural networks

Faces are a crucial environmental trigger. They communicate information about several key features, including identity. However, the 2019 coronavirus pandemic (COVID-19) significantly affected how we process faces. To prevent viral spread, many governments ordered citizens to wear masks in public. In this research, we focus on identifying individuals from images or videos by comparing facial features, identifying a person's biometrics, and reducing the weaknesses of person recognition technology, for example when a person does not look directly at the camera, the lighting is poor, or the person has effectively covered their face. Consequently, we propose a hybrid approach of detecting either a person with or without a mask, a person who covers large parts of their face, and a person based on their gait via deep and machine learning algorithms. The experimental results are excellent compared to the current face and gait detectors. We achieved success of between 97% and 100% in the detection of face and gait based on F1 score, precision, and recall. Compared to the baseline CNN system, our approach achieves extremely high recognition accuracy.

Sleep-wake variations of electrodermal activity in bipolar disorder

BACKGROUND

Affective states influence the sympathetic nervous system, inducing variations in electrodermal activity (EDA), however, EDA association with bipolar disorder (BD) remains uncertain in real-world settings due to confounders like physical activity and temperature. We analysed EDA separately during sleep and wakefulness due to varying confounders and potential differences in mood state discrimination capacities.

METHODS

We monitored EDA from 102 participants with BD including 35 manic, 29 depressive, 38 euthymic patients, and 38 healthy controls (HC), for 48 h. Fifteen EDA features were inferred by mixed-effect models for repeated measures considering sleep state, group and covariates.

RESULTS

Thirteen EDA feature models were significantly influenced by sleep state, notably including phasic peaks (p < 0.001). During wakefulness, phasic peaks showed different values for mania (M [SD] = 6.49 [5.74, 7.23]), euthymia (5.89 [4.83, 6.94]), HC (3.04 [1.65, 4.42]), and depression (3.00 [2.07, 3.92]). Four phasic features during wakefulness better discriminated between HC and mania or euthymia, and between depression and euthymia or mania, compared to sleep. Mixed symptoms, average skin temperature, and anticholinergic medication affected the models, while sex and age did not.

CONCLUSION

EDA measured from awake recordings better distinguished between BD states than sleep recordings, when controlled by confounders.

Agreement between Vital Signs Measured Using Mat-Type Noncontact Sensors and Those from Conventional Clinical Assessment

Vital signs are crucial for assessing the condition of a patient and detecting early symptom deterioration. Noncontact sensor technology has been developed to take vital measurements with minimal burden. This study evaluated the accuracy of a mat-type noncontact sensor in measuring respiratory and pulse rates in patients with cardiovascular diseases compared to conventional methods. Forty-eight hospitalized patients were included; a mat-type sensor was used to measure their respiratory and pulse rates during bed rest. Differences between mat-type sensors and conventional methods were assessed using the Bland-Altman analysis. The mean difference in respiratory rate was 1.9 breaths/min (limits of agreement (LOA): -4.5 to 8.3 breaths/min), and proportional bias existed with significance (r = 0.63, p < 0.05). For pulse rate, the mean difference was -2.0 beats/min (LOA: -23.0 to 19.0 beats/min) when compared to blood pressure devices and 0.01 beats/min (LOA: -11.4 to 11.4 beats/min) when compared to 24-h Holter electrocardiography. The proportional bias was significant for both comparisons (r = 0.49, p < 0.05; r = 0.52, p < 0.05). These were considered clinically acceptable because there was no tendency to misjudge abnormal values as normal. The mat-type noncontact sensor demonstrated sufficient accuracy to serve as an alternative to conventional assessments, providing long-term monitoring of vital signs in clinical settings.

Acceptability and feasibility of virtual reality to promote health literacy in primary care from the health professional's view: A qualitative study

OBJECTIVE

The development of health literacy is important in the management of chronic pain and virtual reality may be an effective medium for its development. This study aims to understand the usability and acceptability of a virtual reality-based pain education system for the facilitation of health literacy.

METHODS

Semi-structured interviews were conducted with health professionals who had used a VR-based pain education system within their clinical practice, to explore perceptions of feasibility. Data collection and analyses were informed by the Unified Theory of Acceptance and Use of Technology and the Integrated Model of Health Literacy.

RESULTS

From 10 participants, the VR-based system was considered feasible in providing immersive experiential learning which addressed patient understanding and health-related communication.

CONCLUSION

VR appears to be perceived as an acceptable and feasible technology to support the development of health literacy in people with chronic pain. Its largest perceived benefit was its capacity to provide an immersive and entertaining alternative to conventional methods of pain education.

PRACTICE IMPLICATIONS

Virtual reality is considered as a feasible method of facilitating patient understanding and health-related communication related to chronic pain. Feasibility of such a tool relies clinically on time available, social expectations of VR, and the role of immersive and experiential learning within the management of chronic pain.

Mechanotransducive surfaces for enhanced cell osteogenesis, a review

Novel strategies employing mechano-transducing materials eliciting biological outcomes have recently emerged for controlling cellular behaviour. Targeted cellular responses are achieved by manipulating physical, chemical, or biochemical modification of material properties. Advances in techniques such as nanopatterning, chemical modification, biochemical molecule embedding, force-tuneable materials, and artificial extracellular matrices are helping understand cellular mechanotransduction. Collectively, these strategies manipulate cellular sensing and regulate signalling cascades including focal adhesions, YAP-TAZ transcription factors, and multiple osteogenic pathways. In this minireview, we are providing a summary of the influence that these materials, particularly titanium-based orthopaedic materials, have on cells. We also highlight recent complementary methodological developments including, but not limited to, the use of metabolomics for identification of active biomolecules that drive cellular differentiation.

Association between heart rate variability metrics from a smartwatch and self-reported depression and anxiety symptoms: a four-week longitudinal study

Background

Elucidating the association between heart rate variability (HRV) metrics obtained through non-invasive methods and mental health symptoms could provide an accessible approach to mental health monitoring. This study explores the correlation between HRV, estimated using photoplethysmography (PPG) signals, and self-reported symptoms of depression and anxiety.

Methods

A 4-week longitudinal study was conducted among 47 participants. Time-domain and frequency-domain HRV metrics were derived from PPG signals collected via smartwatches. Mental health symptoms were evaluated using the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) at baseline, week 2, and week 4.

Results

Among the investigated HRV metrics, RMSSD, SDNN, SDSD, LF, and the LF/HF ratio were significantly associated with the PHQ-9 score, although the number of significant correlations was relatively small. Furthermore, only SDNN, SDSD and LF showed significant correlations with the GAD-7 score. All HRV metrics showed negative correlations with self-reported clinical symptoms.

Conclusions

Our findings indicate the potential of PPG-derived HRV metrics in monitoring mental health, thereby providing a foundation for further research. Notably, parasympathetically biased HRV metrics showed weaker correlations with depression and anxiety scores. Future studies should validate these findings in clinically diagnosed patients.

Assessing the feasibility of digital keypress statistics to detect seizures and capture cognitive impairment in patients with epilepsy: A pilot study

BACKGROUND

Efficient, non-invasive monitoring may provide a more accurate and comprehensive understanding of seizure frequency and the development of some comorbidities in people with epilepsy. Novel keyboard technology measuring digital keypress statistics has demonstrated its practical value for neurodegenerative diseases including Parkinson's Disease and Dementia. Smartphones integrated into daily life may serve as a low-burden longitudinal monitoring system for patients with epilepsy.

OBJECTIVE

This study aimed to assess the feasibility of keyboard statistics as an objective measure of seizure frequency for patients with epilepsy, in addition to tracking differences between cognitively normal and cognitively impaired patients.

METHODS

Six adult patients admitted to the Epilepsy Monitoring Unit (EMU) at Mayo Clinic in Rochester, Minnesota were studied. The keyboard was installed on the patient's smartphone. In the EMU, typing statistics were correlated to electroencephalogram (EEG) confirmed seizures. After discharge, participants continued using their keyboards and kept a seizure log. We also analyzed the key press/release times and usage of participants' keyboards for adherence.

RESULTS

Keyboard sessions during and after seizures assessed for key press/release differences versus baseline showed no statistically significant difference (p = 0.44). Using one-way ANOVA, cognitive impairment's potential impact on keyboard statistics was explored in patients who had neuropsychological testing (N = 3). Significant differences were found between patients with and without cognitive impairment (p < 0.001). No significant difference was noted between patients with mild intellectual disability and normal cognitive function (p = 0.55).

Opportunities and Challenges for Augmented Reality in Family Caregiving: Qualitative Video Elicitation Study

BACKGROUND

Although family caregivers play a critical role in care delivery, research has shown that they face significant physical, emotional, and informational challenges. One promising avenue to address some of caregivers' unmet needs is via the design of digital technologies that support caregivers' complex portfolio of responsibilities. Augmented reality (AR) applications, specifically, offer new affordances to aid caregivers as they perform care tasks in the home.

OBJECTIVE

This study explored how AR might assist family caregivers with the delivery of home-based cancer care. The specific objectives were to shed light on challenges caregivers face where AR might help, investigate opportunities for AR to support caregivers, and understand the risks of AR exacerbating caregiver burdens.

METHODS

We conducted a qualitative video elicitation study with clinicians and caregivers. We created 3 video elicitations that offer ways in which AR might support caregivers as they perform often high-stakes, unfamiliar, and anxiety-inducing tasks in postsurgical cancer care: wound care, drain care, and rehabilitative exercise. The elicitations show functional AR applications built using Unity Technologies software and Microsoft Hololens2. Using elicitations enabled us to avoid rediscovering known usability issues with current AR technologies, allowing us to focus on high-level, substantive feedback on potential future roles for AR in caregiving. Moreover, it enabled nonintrusive exploration of the inherently sensitive in-home cancer care context.

RESULTS

We recruited 22 participants for our study: 15 clinicians (eg, oncologists and nurses) and 7 family caregivers. Our findings shed light on clinicians' and caregivers' perceptions of current information and communication challenges caregivers face as they perform important physical care tasks as part of cancer treatment plans. Most significant was the need to provide better and ongoing support for execution of caregiving tasks in situ, when and where the tasks need to be performed. Such support needs to be tailored to the specific needs of the patient, to the stress-impaired capacities of the caregiver, and to the time-constrained communication availability of clinicians. We uncover opportunities for AR technologies to potentially increase caregiver confidence and reduce anxiety by supporting the capture and review of images and videos and by improving communication with clinicians. However, our findings also suggest ways in which, if not deployed carefully, AR technologies might exacerbate caregivers' already significant burdens.

CONCLUSIONS

These findings can inform both the design of future AR devices, software, and applications and the design of caregiver support interventions based on already available technology and processes. Our study suggests that AR technologies and the affordances they provide (eg, tailored support, enhanced monitoring and task accuracy, and improved communications) should be considered as a part of an integrated care journey involving multiple stakeholders, changing information needs, and different communication channels that blend in-person and internet-based synchronous and asynchronous care, illness, and recovery.

Online dissemination of Cochrane reviews on digital health technologies: a cross-sectional study

BACKGROUND

This cross-sectional study investigated the online dissemination of Cochrane reviews on digital health technologies.

METHODS

We searched the Cochrane Database of Systematic Reviews from inception up to May 2023. Cochrane reviews with any population (P), intervention or concept supported by any digital technology (I), any or no comparison (C), and any health outcome (O) were included. Data on review characteristics (bibliographic information, PICO, and evidence quality) and dissemination strategies were extracted and processed. Dissemination was assessed using review information on the Cochrane website and Altmetric data that trace the mentions of academic publications in nonacademic online channels. Data were analysed using descriptive statistics and binary logistic regression analysis.

RESULTS

Out of 170 records identified in the search, 100 Cochrane reviews, published between 2005 and 2023, were included. The reviews focused on consumers (e.g. patients, n = 86), people of any age (n = 44), and clinical populations (n = 68). All reviews addressed interventions or concepts supported by digital technologies with any devices (n = 73), mobile devices (n = 17), or computers (n = 10). The outcomes focused on disease treatment (n = 56), health promotion and disease prevention (n = 27), or management of care delivery (n = 17). All reviews included 1-132 studies, and half included 1-10 studies. Meta-analysis was performed in 69 reviews, and certainty of evidence was rated as high or moderate for at least one outcome in 46 reviews. In agreement with the Cochrane guidelines, all reviews had a plain language summary (PLS) that was available in 3-14 languages. The reviews were disseminated (i.e. mentioned online) predominantly via X/Twitter (n = 99) and Facebook (n = 69). Overall, 51 reviews were mentioned in up to 25% and 49 reviews in 5% of all research outputs traced by Altmetric data. Dissemination (i.e. higher Altmetric scores) was associated with bibliographic review characteristics (i.e. earlier publication year and PLS available in more languages), but not with evidence quality (i.e. certainty of evidence rating, number of studies, or meta-analysis performed in review).

CONCLUSIONS

Online attention towards Cochrane reviews on digital health technologies is high. Dissemination is higher for older reviews and reviews with more PLS translations. Measures are required to improve dissemination of Cochrane reviews based on evidence quality.

SYSTEMATIC REVIEW REGISTRATION

The study was prospectively registered at the Open Science Framework ( https://osf.io/mpw8u/ ).

Enhancing Pediatric Dental Care: The Influence of Virtual Reality

OBJECTIVE

 The purpose of this study was to assess the effects of virtual reality (VR) in reducing pain and anxiety levels in children. The study also compared active and passive distraction methods using VR during the delivery of inferior alveolar nerve blocks (IANBs) in dental procedures in children.

MATERIAL AND METHODS

 The study comprised 45 preschool patients, aged between 4 and 6 years, with no prior dental anesthetic experience. The participants were randomly assigned to three groups based on the sort of management style: Group A used the tell-show-do technique, Group B engaged in passive distraction by watching cartoons using a VR headset, and Group C participated in active distraction by playing games using a controller with the VR headset. Pain and anxiety were evaluated using physiological measurements, namely by analyzing the variations in blood pressure, heart rate, and oxygen saturation before and after the administration of IANB. Psychological assessments were conducted using the Wong-Baker faces scale, Modified Dental Anxiety scale questionnaires, and Revised Face, Legs, Activity, Cry and Consolability scale after administering IANB.

RESULTS

 The physiological outcomes revealed no statistically significant differences in blood pressure and oxygen saturation. However, there was a statistically significant increase in the heart rate in group A compared with groups B and C. In terms of psychological measurements, groups B and C exhibited a significant improvement in pain experience and a decrease in anxiety.

CONCLUSION

 This study concluded that VR reduced pain and anxiety levels in its passive and active forms.

Validation of the Sleepiz One + as a radar-based sensor for contactless diagnosis of sleep apnea

PURPOSE

The cardiorespiratory polysomnography (PSG) is an expensive and limited resource. The Sleepiz One + is a novel radar-based contactless monitoring device that can be used e.g. for longitudinal detection of nocturnal respiratory events. The present study aimed to compare the performance of the Sleepiz One + device to the PSG regarding the accuracy of apnea-hypopnea index (AHI).

METHODS

From January to December 2021, a total of 141 adult volunteers who were either suspected of having sleep apnea or who were healthy sleepers took part in a sleep study. This examination served to validate the Sleepiz One + device in the presence and absence of additional SpO2 information. The AHI determined by the Sleepiz One + monitor was estimated automatically and compared with the AHI derived from manual PSG scoring.

RESULTS

The correlation between the Sleepiz-AHI and the PSG-AHI with and without additional SpO2 measurement was rp = 0.94 and rp = 0,87, respectively. In general, the Bland-Altman plots showed good agreement between the two methods of AHI measurement, though their deviations became larger with increasing sleep-disordered breathing. Sensitivity and specificity for recordings without additional SpO2 was 85% and 88%, respectively. Adding a SpO2 sensor increased the sensitivity to 88% and the specificity to 98%.

CONCLUSION

The Sleepiz One + device is a valid diagnostic tool for patients with moderate to severe OSA. It can also be easily used in the home environment and is therefore beneficial for e.g. immobile and infectious patients. TRIAL REGISTRATION NUMBER AND DATE OF REGISTRATION FOR PROSPECTIVELY REGISTERED TRIALS: This study was registered on clinicaltrials.gov (NCT04670848) on 2020-12-09.

Measuring Physical Functioning Using Wearable Sensors in Parkinson Disease and Chronic Obstructive Pulmonary Disease (the Accuracy of Digital Assessment of Performance Trial Study): Protocol for a Prospective Observational Study

BACKGROUND

Physical capacity and physical activity are important aspects of physical functioning and quality of life in people with a chronic disease such as Parkinson disease (PD) or chronic obstructive pulmonary disease (COPD). Both physical capacity and physical activity are currently measured in the clinic using standardized questionnaires and tests, such as the 6-minute walk test (6MWT) and the Timed Up and Go test (TUG). However, relying only on in-clinic tests is suboptimal since they offer limited information on how a person functions in daily life and how functioning fluctuates throughout the day. Wearable sensor technology may offer a solution that enables us to better understand true physical functioning in daily life.

OBJECTIVE

We aim to study whether device-assisted versions of 6MWT and TUG, such that the tests can be performed independently at home using a smartwatch, is a valid and reliable way to measure the performance compared to a supervised, in-clinic test.

METHODS

This is a decentralized, prospective, observational study including 100 people with PD and 100 with COPD. The inclusion criteria are broad: age ≥18 years, able to walk independently, and no co-occurrence of PD and COPD. Participants are followed for 15 weeks with 4 in-clinic visits, once every 5 weeks. Outcomes include several walking tests, cognitive tests, and disease-specific questionnaires accompanied by data collection using wearable devices (the Verily Study Watch and Modus StepWatch). Additionally, during the last 10 weeks of this study, participants will follow an aerobic exercise training program aiming to increase physical capacity, creating the opportunity to study the responsiveness of the remote 6MWT.

RESULTS

In total, 89 people with PD and 65 people with COPD were included in this study. Data analysis will start in April 2024.

CONCLUSIONS

The results of this study will provide information on the measurement properties of the device-assisted 6MWT and TUG in the clinic and at home. When reliable and valid, this can contribute to a better understanding of a person's physical capacity in real life, which makes it possible to personalize treatment options.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05756075; https://clinicaltrials.gov/study/NCT05756075.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/55452.

Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review

Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The diagnosis, clinical staging, and treatment of HF become complex and subjective, impacting patient prognosis and mortality. Technological advancements, like artificial intelligence (AI), have been significant roleplays in medicine and are increasingly used in cardiovascular medicine to transform drug discovery, clinical care, risk prediction, diagnosis, and treatment. Medical and surgical interventions specific to HF patients rely significantly on early identification of HF. Hospitalization and treatment costs for HF are high, with readmissions increasing the burden. AI can help improve diagnostic accuracy by recognizing patterns and using them in multiple areas of HF management. AI has shown promise in offering early detection and precise diagnoses with the help of ECG analysis, advanced cardiac imaging, leveraging biomarkers, and cardiopulmonary stress testing. However, its challenges include data access, model interpretability, ethical concerns, and generalizability across diverse populations. Despite these ongoing efforts to refine AI models, it suggests a promising future for HF diagnosis. After applying exclusion and inclusion criteria, we searched for data available on PubMed, Google Scholar, and the Cochrane Library and found 150 relevant papers. This review focuses on AI's significant contribution to HF diagnosis in recent years, drastically altering HF treatment and outcomes.

Economic evaluations of medical devices in paediatrics: a systematic review and a quality appraisal of the literature

BACKGROUND

Although economic evaluations (EEs) have been increasingly applied to medical devices, little discussion has been conducted on how the different health realities of specific populations may impact the application of methods and the ensuing results. This is particularly relevant for pediatric populations, as most EEs on devices are conducted in adults, with specific aspects related to the uniqueness of child health often being overlooked. This study provides a review of the published EEs on devices used in paediatrics, assessing the quality of reporting, and summarising methodological challenges.

METHODS

A systematic literature search was performed to identify peer-reviewed publications on the economic value of devices used in paediatrics in the form of full EEs (comparing both costs and consequences of two or more devices). After the removal of duplicates, article titles and abstracts were screened. The remaining full-text articles were retrieved and assessed for inclusion. In-vitro diagnostic devices were not considered in this review. Study descriptive and methodological characteristics were extracted using a structured template. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 checklist was used to assess the quality of reporting. A narrative synthesis of the results was conducted followed by a critical discussion on the main challenges found in the literature.

RESULTS

39 full EEs were eligible for review. Most studies were conducted in high-income countries (67%) and focused on high-risk therapeutic devices (72%). Studies comprised 25 cost-utility analyses, 13 cost-effectiveness analyses and 1 cost-benefit analysis. Most of the studies considered a lifetime horizon (41%) and a health system perspective (36%). Compliance with the CHEERS 2022 items varied among the studies.

CONCLUSIONS

Despite the scant body of evidence on EEs focusing on devices in paediatrics results highlight the need to improve the quality of reporting and advance methods that can explicitly incorporate the multiple impacts related to the use of devices with distinct characteristics, as well as consider specific child health realities. The design of innovative participatory approaches and instruments for measuring outcomes meaningful to children and their families should be sought in future research.

An Update on Implant-Associated Malignancies and Their Biocompatibility

Implanted medical devices are widely used across various medical specialties for numerous applications, ranging from cardiovascular supports to orthopedic prostheses and cosmetic enhancements. However, recent observations have raised concerns about the potential of these implants to induce malignancies in the tissues surrounding them. There have been several case reports documenting the occurrence of cancers adjacent to these devices, prompting a closer examination of their safety. This review delves into the epidemiology, clinical presentations, pathological findings, and hypothesized mechanisms of carcinogenesis related to implanted devices. It also explores how the surgical domain and the intrinsic properties and biocompatibility of the implants might influence the development of these rare but serious malignancies. Understanding these associations is crucial for assessing the risks associated with the use of medical implants, and for developing strategies to mitigate potential adverse outcomes.

A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health

Modern machine learning approaches have led to performant diagnostic models for a variety of health conditions. Several machine learning approaches, such as decision trees and deep neural networks, can, in principle, approximate any function. However, this power can be considered to be both a gift and a curse, as the propensity toward overfitting is magnified when the input data are heterogeneous and high dimensional and the output class is highly nonlinear. This issue can especially plague diagnostic systems that predict behavioral and psychiatric conditions that are diagnosed with subjective criteria. An emerging solution to this issue is crowdsourcing, where crowd workers are paid to annotate complex behavioral features in return for monetary compensation or a gamified experience. These labels can then be used to derive a diagnosis, either directly or by using the labels as inputs to a diagnostic machine learning model. This viewpoint describes existing work in this emerging field and discusses ongoing challenges and opportunities with crowd-powered diagnostic systems, a nascent field of study. With the correct considerations, the addition of crowdsourcing to human-in-the-loop machine learning workflows for the prediction of complex and nuanced health conditions can accelerate screening, diagnostics, and ultimately access to care.

Photonics-powered augmented reality skin electronics for proactive healthcare: multifaceted opportunities

Rapid technological advancements have created opportunities for new solutions in various industries, including healthcare. One exciting new direction in this field of innovation is the combination of skin-based technologies and augmented reality (AR). These dermatological devices allow for the continuous and non-invasive measurement of vital signs and biomarkers, enabling the real-time diagnosis of anomalies, which have applications in telemedicine, oncology, dermatology, and early diagnostics. Despite its many potential benefits, there is a substantial information vacuum regarding using flexible photonics in conjunction with augmented reality for medical purposes. This review explores the current state of dermal augmented reality and flexible optics in skin-conforming sensing platforms by examining the obstacles faced thus far, including technical hurdles, demanding clinical validation standards, and problems with user acceptance. Our main areas of interest are skills, chiroptical properties, and health platform applications, such as optogenetic pixels, spectroscopic imagers, and optical biosensors. My skin-enhanced spherical dichroism and powerful spherically polarized light enable thorough physical inspection with these augmented reality devices: diabetic tracking, skin cancer diagnosis, and cardiovascular illness: preventative medicine, namely blood pressure screening. We demonstrate how to accomplish early prevention using case studies and emergency detection. Finally, it addresses real-world obstacles that hinder fully realizing these materials' extraordinary potential in advancing proactive and preventative personalized medicine, including technical constraints, clinical validation gaps, and barriers to widespread adoption.

pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis

Objective.Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and being increasingly used for a variety of research and clinical applications to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and limited open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers.Approach.This work describes the creation of a standard Python toolbox, denotedpyPPG, for long-term continuous PPG time-series analysis and demonstrates the detection and computation of a high number of fiducial points and digital biomarkers using a standard fingerbased transmission pulse oximeter.Main results.The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2054 adult polysomnography recordings totaling over 91 million reference beats. The algorithm outperformed the open-source original Matlab implementation by ∼5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points.Significance.Based on these fiducial points,pyPPGengineered a set of 74 PPG biomarkers. Studying PPG time-series variability usingpyPPGcan enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models.pyPPGis available onhttps://physiozoo.com/.

Real-world walking behaviors are associated with early-stage heart failure: a Project Baseline Health Study

BACKGROUND

Data collected via wearables may complement in-clinic assessments to monitor subclinical heart failure (HF).

OBJECTIVES

Evaluate the association of sensor-based digital walking measures with HF stage and characterize their correlation with in-clinic measures of physical performance, cardiac function and participant reported outcomes (PROs) in individuals with early HF.

METHODS

The analyzable cohort included participants from the Project Baseline Health Study (PBHS) with HF stage 0, A, or B, or adaptive remodeling phenotype (without risk factors but with mild echocardiographic change, termed RF-/ECHO+) (based on available first-visit in-clinic test and echocardiogram results) and with sufficient sensor data. We computed daily values per participant for 18 digital walking measures, comparing HF subgroups vs stage 0 using multinomial logistic regression and characterizing associations with in-clinic measures and PROs with Spearman's correlation coefficients, adjusting all analyses for confounders.

RESULTS

In the analyzable cohort (N=1265; 50.6% of the PBHS cohort), one standard deviation decreases in 17/18 walking measures were associated with greater likelihood for stage-B HF (multivariable-adjusted odds ratios [ORs] vs stage 0 ranging from 1.18-2.10), or A (ORs vs stage 0, 1.07-1.45), and lower likelihood for RF-/ECHO+ (ORs vs stage 0, 0.80-0.93). Peak 30-minute pace demonstrated the strongest associations with stage B (OR vs stage 0=2.10; 95% CI:1.74-2.53) and A (OR vs stage 0=1.43; 95% CI:1.23-1.66). Decreases in 13/18 measures were associated with greater likelihood for stage-B HF vs stage A. Strength of correlation with physical performance tests, echocardiographic cardiac-remodeling and dysfunction indices and PROs was greatest in stage B, then A, and lowest for 0.

CONCLUSIONS

Digital measures of walking captured by wearable sensors could complement clinic-based testing to identify and monitor pre-symptomatic HF.

Perspectives and expectations of stroke survivors using egocentric cameras for monitoring hand function at home: a mixed methods study

PURPOSE

Most stroke survivors have remaining upper limb impairment six months after stroke and require additional rehabilitation and help from family members to enhance their performance of daily activities. First-person (egocentric) video has been proposed to capture the activities of daily living (ADLs) of stroke survivors in order to assess their hand function at home. This study explored the experiences and expectations of stroke survivors regarding the use of egocentric cameras in daily life for rehabilitation applications.

METHODS

Twenty-one chronic stroke survivors recruited for the study were asked to record three sessions of 1.5 h of video of their ADLs at home over two weeks. Their experiences and expectations after completing the recordings were discussed using a structured questionnaire and a semi-structured interview. The questionnaire and interview data were analysed using descriptive statistics and content analysis, respectively. The results were further integrated using a mixed methods analysis for mutual explanation and elaboration.

RESULTS

The themes generated were Camera Usability, Privacy Concerns Related to Home Recordings, Future Use of the Camera in Public, and Information Usefulness. The participants perceived that the camera was easy to use, the information obtained from the recordings was beneficial, and no major concerns about recording at home. A discreet camera and a solution to privacy issues were prerequisites to recording tasks in public.

CONCLUSIONS

There was high acceptance among stroke survivors regarding the use of wearable cameras for rehabilitation purposes in the future. Concerns to be managed include discomfort, self-consciousness, and the privacy of others.Implications for rehabilitationThe egocentric camera was easy for the stroke survivors to use at home. However, they expressed a preference for cameras to be less noticeable and lighter in the future to minimize self-consciousness and discomfort.Expectations for future use of an egocentric camera for upper limb rehabilitation at home from the perspectives of stroke survivors included receiving feedback on their hand function in daily life and guidance on how to improve function.Privacy concerns of stroke survivors regarding recording activities of daily living were mostly avoidable by planning in advance. However, some personal hygiene tasks and virtual meetings were recorded by accident. A checklist of common activities that may raise privacy issues can be provided along with the camera to serve as a reminder to avoid these issues.

Telerehabilitation: Exploring the Untapped Potential

Telerehabilitation is a burgeoning field that holds immense promise in revolutionizing the delivery of rehabilitation services. Defined as a branch of telecommunication utilizing technologies such as the internet, it facilitates remote interaction between healthcare providers and patients, transcending geographical barriers. This method proves invaluable in patient assessment, counseling, and treatment across various medical domains, including physical therapy, speech therapy, psychotherapy, and occupational therapy. Particularly beneficial for individuals with disabilities or those unable to access traditional healthcare facilities, telerehabilitation mitigates the constraints of time and cost associated with travel. This paper explores the evolution, types, uses, and research findings in telerehabilitation, shedding light on its transformative potential in health care.

Cloud-Integrated Smart Nanomembrane Wearables for Remote Wireless Continuous Health Monitoring of Postpartum Women

Noncommunicable diseases (NCD), such as obesity, diabetes, and cardiovascular disease, are defining healthcare challenges of the 21st century. Medical infrastructure, which for decades sought to reduce the incidence and severity of communicable diseases, has proven insufficient in meeting the intensive, long-term monitoring needs of many NCD disease patient groups. In addition, existing portable devices with rigid electronics are still limited in clinical use due to unreliable data, limited functionality, and lack of continuous measurement ability. Here, a wearable system for at-home cardiovascular monitoring of postpartum women-a group with urgently unmet NCD needs in the United States-using a cloud-integrated soft sternal device with conformal nanomembrane sensors is introduced. A supporting mobile application provides device data to a custom cloud architecture for real-time waveform analytics, including medical device-grade blood pressure prediction via deep learning, and shares the results with both patient and clinician to complete a robust and highly scalable remote monitoring ecosystem. Validated in a month-long clinical study with 20 postpartum Black women, the system demonstrates its ability to remotely monitor existing disease progression, stratify patient risk, and augment clinical decision-making by informing interventions for groups whose healthcare needs otherwise remain unmet in standard clinical practice.

Analyzing the Surface Topography of Hafnium Nitride Coating on Titanium Screws: An In Vitro Analysis

Background The use of surface coatings to enhance the properties lacking in titanium has attracted significant focus in recent times. Hafnium nitride (HfN) coatings could be explored as promising in the osteoinductive properties of titanium implants. HfN exhibits excellent mechanical attributes, such as hardness and wear resistance, and is often used as a coating on high-end equipment for protection. The findings from this research may carve a new path for the production and optimization of HfN coatings to enhance the longevity and augment properties of implant materials. Thus, the present study was orchestrated to elucidate the surface morphology of HfN coating, ultimately contributing to the advancement of dental implant biomaterials. Materials and methods A total of twenty samples of medical grade commercially pure titanium screws (2 mm diameter and 7 mm length) were procured from G. R. Bioure Surgical System Pvt. Ltd., Ravali, Uttar Pradesh, India, and ten samples were reacted with HfN (0.1 M) (Nano Research Elements, Kurukshetra, Haryana, India) in 100% ethanol and stirred continuously for about 48 hours. Then these screw samples were immersed in the prepared colloidal suspension and sintered for two hours at 400 degrees centigrade. The implant screws were affixed onto metal supports. The magnifications for photomicrographs at ×30, ×200, ×1,500, ×3,000, and ×5,000 were standardized. Elementary semi-quantitative analysis of both dental implants was conducted using energy-dispersive X-ray spectrometry (EDX) coupled with the field emission scanning electron microscope (FE-SEM) equipment (JEOL Ltd., Akishima, Tokyo, Japan). The software used for the analysis of the obtained images is SEM Center. Results The surface analysis using the scanning electron microscope (SEM) showed the coating of HfN over titanium screws. The difference in surface morphology of both the group of implant screws can be visualized under 40.0 and 10.0 mm working distance (WD) for both groups. The surface analysis using the EDX of uncoated titanium screws shows five elements in the spectrum: titanium (Ti), oxygen (O), aluminum (Al), carbon (C), and vanadium (V). The EDX of the HfN-coated screws has two additional metals dispersed in the spectrum, hafnium (Hf). The element characteristics are tabulated with their apparent concentration, k ratio, line type, weight percentage, standard label, and factory label for uncoated titanium screws and HfN-coated titanium screws. Conclusion The study evaluated HfN coating over medical grade commercially pure titanium. The surface topography of coated versus uncoated was visualized. The scanning electron microscope (SEM) images showed a homogenous coating over the titanium surfaces, and the EDX showed elemental dispersion of the coated implant. The study aims to provide a comprehensive understanding of the coating's surface morphology, which will aid in the development of more durable and biocompatible implants. This thereby provides a promising scope for further research of this novel metal coating for use in the biomedical sectors, specifically for dental implants.

Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things-Based Thermostat Data and Google Mobility Insights

BACKGROUND

The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google's GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored.

OBJECTIVE

This study investigates in-home mobility data from ecobee's smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google's residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies.

METHODS

Motion sensor data were acquired from the ecobee "Donate Your Data" initiative via Google's BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces-Ontario, Quebec, Alberta, and British Columbia-during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights.

RESULTS

The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google's data set. Examination of Google's daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events.

CONCLUSIONS

This study's findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google's out-of-house residential mobility data and ecobee's in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts.

Immersive virtual reality-based learning as a supplement for biomedical engineering labs: challenges faced and lessons learned

Introduction

Immersive virtual reality (VR) based laboratory demonstrations have been gaining traction in STEM education as they can provide virtual hands-on experience. VR can also facilitate experiential and visual learning and enhanced retention. However, several optimizations of the implementation, in-depth analyses of advantages and trade-offs of the technology, and assessment of receptivity of modern techniques in STEM education are required to ensure better utilization of VR-based labs.

Methods

In this study, we developed VR-based demonstrations for a biomolecular engineering laboratory and assessed their effectiveness using surveys containing free responses and 5-point Likert scale-based questions. Insta360 Pro2 camera and Meta Quest 2 headsets were used in combination with an in-person lab. A cohort of 53 students watched the experimental demonstration on VR headsets in the lab after a brief lab overview in person and then performed the experiments in the lab.

Results

Only 28.29% of students reported experiencing some form of discomfort after using the advanced VR equipment as opposed to 63.63% of students from the previous cohort. About 40% of the students reported that VR eliminated or reduced auditory and visual distractions from the environment, the length of the videos was appropriate, and they received enough information to understand the tasks.

Discussion

The traditional lab method was found to be more suitable for explaining background information and lab concepts while the VR was found to be suitable for demonstrating lab procedures and tasks. Analyzing open-ended questions revealed several factors and recommendations to overcome the potential challenges and pitfalls of integrating VR with traditional modes of learning. This study provides key insights to help optimize the implementation of immersive VR to effectively supplement in-person learning experiences.

Accelerating neurotechnology development using an Agile methodology

Novel bioelectronic medical devices that target neural control of visceral organs (e.g., liver, gut, spleen) or inflammatory reflex pathways are innovative class III medical devices like implantable cardiac pacemakers that are lifesaving and life-sustaining medical devices. Bringing innovative neurotechnologies early into the market and the hands of treatment providers would benefit a large population of patients inflicted with autonomic and chronic immune disorders. Medical device manufacturers and software developers widely use the Waterfall methodology to implement design controls through verification and validation. In the Waterfall methodology, after identifying user needs, a functional unit is fabricated following the verification loop (design, build, and verify) and then validated against user needs. Considerable time can lapse in building, verifying, and validating the product because this methodology has limitations for adjusting to unanticipated changes. The time lost in device development can cause significant delays in final production, increase costs, and may even result in the abandonment of the device development. Software developers have successfully implemented an Agile methodology that overcomes these limitations in developing medical software. However, Agile methodology is not routinely used to develop medical devices with implantable hardware because of the increased regulatory burden of the need to conduct animal and human studies. Here, we provide the pros and cons of the Waterfall methodology and make a case for adopting the Agile methodology in developing medical devices with physical components. We utilize a peripheral nerve interface as an example device to illustrate the use of the Agile approach to develop neurotechnologies.

Electrical bioimpedance analysis and comparison in biological tissues through crystalloid solutions implementation

Electrical bioimpedance is a non-invasive and radiation-free technique that was proposed to be used in different clinical areas, however, its practical use is limited due to its low capacity to discriminate between tissues. In order to overcome this limitation, our research group proposes to incorporate the contrast media into the electrical bioimpedance procedure. The main objective of the present study was to assess the crystalloid solutions as a possible contrast media to discriminate between different tissue types in the bioimpedance technique. Two medical-grade crystalloid solutions (Hartmann and NaCl 0.9%) were injected into three biological ex vivo models: kidney, liver, and brain. BIOPAC system was used to acquire bioimpedance data before and after the injections. The data was adjusted to the Debye electrical model. The analysis of measured values showed substantial bioimpedance disparities in tissues subjected to isotonic solutions. The NaCl solution exhibited more pronounced changes in electrical parameters compared to the Hartmann solution. Similarly, NaCl solution displayed superior discriminatory capabilities among tissues, with variations of 465%, 157%, and 206%. Distinct spectral modifications were identified, with tissues demonstrating unique responses at each frequency of analysis relative to untreated tissue. Variations in bandwidth alterations were discernible among tissues, providing clear distinctions. In conclusion, the research showed that the crystalloid solution exhibited greater sensitivity and superior tissue contrast at specific frequencies. This study's findings underscore the feasibility of implementing crystalloid solutions to enhance tissue discrimination, similar to the effects of contrast agents.

Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study

BACKGROUND

The increasing use of social media platforms has given rise to an unprecedented surge in user-generated content, with millions of individuals publicly sharing their thoughts, experiences, and health-related information. Social media can serve as a useful means to study and understand public health. Twitter (subsequently rebranded as "X") is one such social media platform that has proven to be a valuable source of rich information for both the general public and health officials. We conducted the first study applying Twitter data mining to autism screening.

OBJECTIVE

This study used Twitter as the primary source of data to study the behavioral characteristics and real-time emotional projections of individuals identifying with autism spectrum disorder (ASD). We aimed to improve the rigor of ASD analytics research by using the digital footprint of an individual to study the linguistic patterns of individuals with ASD.

METHODS

We developed a machine learning model to distinguish individuals with autism from their neurotypical peers based on the textual patterns from their public communications on Twitter. We collected 6,515,470 tweets from users' self-identification with autism using "#ActuallyAutistic" and a separate control group to identify linguistic markers associated with ASD traits. To construct the data set, we targeted English-language tweets using the search query "#ActuallyAutistic" posted from January 1, 2014, to December 31, 2022. From these tweets, we identified unique users who used keywords such as "autism" OR "autistic" OR "neurodiverse" in their profile description and collected all the tweets from their timeline. To build the control group data set, we formulated a search query excluding the hashtag, "-#ActuallyAutistic," and collected 1000 tweets per day during the same time period. We trained a word2vec model and an attention-based, bidirectional long short-term memory model to validate the performance of per-tweet and per-profile classification models. We also illustrate the utility of the data set through common natural language processing tasks such as sentiment analysis and topic modeling.

RESULTS

Our tweet classifier reached a 73% accuracy, a 0.728 area under the receiver operating characteristic curve score, and an 0.71 F1-score using word2vec representations fed into a logistic regression model, while the user profile classifier achieved an 0.78 area under the receiver operating characteristic curve score and an F1-score of 0.805 using an attention-based, bidirectional long short-term memory model. This is a promising start, demonstrating the potential for effective digital phenotyping studies and large-scale intervention using text data mined from social media.

CONCLUSIONS

Textual differences in social media communications can help researchers and clinicians conduct symptomatology studies in natural settings.

Digitally Diagnosing Multiple Developmental Delays Using Crowdsourcing Fused With Machine Learning: Protocol for a Human-in-the-Loop Machine Learning Study

BACKGROUND

A considerable number of minors in the United States are diagnosed with developmental or psychiatric conditions, potentially influenced by underdiagnosis factors such as cost, distance, and clinician availability. Despite the potential of digital phenotyping tools with machine learning (ML) approaches to expedite diagnoses and enhance diagnostic services for pediatric psychiatric conditions, existing methods face limitations because they use a limited set of social features for prediction tasks and focus on a single binary prediction, resulting in uncertain accuracies.

OBJECTIVE

This study aims to propose the development of a gamified web system for data collection, followed by a fusion of novel crowdsourcing algorithms with ML behavioral feature extraction approaches to simultaneously predict diagnoses of autism spectrum disorder and attention-deficit/hyperactivity disorder in a precise and specific manner.

METHODS

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

RESULTS

A preliminary version of the web interface has been implemented, and a prior feature selection method has highlighted a core set of behavioral features that can be targeted through the proposed gamified approach.

CONCLUSIONS

The prospect for high reward stems from the possibility of creating the first artificial intelligence-powered tool that can identify complex social behaviors well enough to distinguish conditions with nuanced differentiators such as autism spectrum disorder and attention-deficit/hyperactivity disorder.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/52205.

Effects of virtual reality on chest tube removal pain management in patients undergoing coronary artery bypass grafting: a randomized clinical trial

The pain associated with chest tube removal (CTR) is one of the significant complications of cardiac surgery. The management of this pain is recognized as a vital component of nursing care. The application of distraction techniques using virtual reality (VR) is an effective and straightforward non-pharmacological approach to alleviate pain. This study aimed to determine the impact of VR technology on the management of pain caused by CTR following coronary artery bypass grafting (CABG). This randomized clinical trial was conducted on 70 patients undergoing CABG at Imam Reza and Qaem hospitals in Mashhad, Iran, in 2020. The patients were randomly divided into two groups of 35. For the intervention group, a 360-degree video was played using VR glasses 5 min before the CTR procedure. The pain intensity was measured before, immediately after, and 15 min after CTR, using the Visual Analogue Scale. Also, the Depression Anxiety and Stress Scale-21 (DASS-21), and the Rhoten Fatigue Scale (RFS) were used to evaluate intervention and control groups before the CTR procedure. The collected data was analyzed using statistical tests, such as Chi-square, independent t-test, and Mann-Whitney test. The patients were homogeneous in terms of stress, anxiety, and fatigue levels before CTR, and they did not show any significant differences (P > 0.05). The average pain intensity score of patients in the intervention group significantly decreased immediately and 15 min after CTR, compared to the control group (P < 0.001). Given the positive impact of VR distraction on the severity of pain associated with CTR in patients undergoing CABG, this technique can serve as an effective, accessible, and cost-efficient non-pharmacological approach for managing pain in these patients.Trial registration: This study was registered in the Iranian Registry of Clinical Trials (code: IRCT20190708044147N1; approval date, 08/26/2019).

An Optical Signal Simulator for the Characterization of Photoplethysmographic Devices

(1) Background: An optical simulator able to provide a repeatable signal with desired characteristics as an input to a photoplethysmographic (PPG) device is presented in order to compare the performance of different PPG devices and also to test the devices with PPG signals available in online databases. (2) Methods: The optical simulator consists of an electronic board containing a photodiode and LEDs at different wavelengths in order to simulate light reflected by the body; the PPG signal taken from the chosen database is reproduced by the electronic board, and the board is used to test a wearable PPG medical device in the form of earbuds. (3) Results: The PPG device response to different average and peak-to-peak signal amplitudes is shown in order to assess the device sensitivity, and the fidelity in tracking the actual heart rate is also investigated. (4) Conclusions: The developed optical simulator promises to be an affordable, flexible, and reliable solution to test PPG devices in the lab, allowing the testing of their actual performances thanks to the possibility of using PPG databases, thus gaining useful and significant information before on-the-field clinical trials.

Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review

Virtual Rehabilitation (VRehab) is a promising approach to improving the physical and mental functioning of patients living in the community. The use of VRehab technology results in the generation of multi-modal datasets collected through various devices. This presents opportunities for the development of Artificial Intelligence (AI) techniques in VRehab, namely the measurement, detection, and prediction of various patients' health outcomes. The objective of this scoping review was to explore the applications and effectiveness of incorporating AI into home-based VRehab programs. PubMed/MEDLINE, Embase, IEEE Xplore, Web of Science databases, and Google Scholar were searched from inception until June 2023 for studies that applied AI for the delivery of VRehab programs to the homes of adult patients. After screening 2172 unique titles and abstracts and 51 full-text studies, 13 studies were included in the review. A variety of AI algorithms were applied to analyze data collected from various sensors and make inferences about patients' health outcomes, most involving evaluating patients' exercise quality and providing feedback to patients. The AI algorithms used in the studies were mostly fuzzy rule-based methods, template matching, and deep neural networks. Despite the growing body of literature on the use of AI in VRehab, very few studies have examined its use in patients' homes. Current research suggests that integrating AI with home-based VRehab can lead to improved rehabilitation outcomes for patients. However, further research is required to fully assess the effectiveness of various forms of AI-driven home-based VRehab, taking into account its unique challenges and using standardized metrics.

A method for selecting the optimal warping path of dynamic time warping in gait analysis

This study aims to demonstrate that when performing dynamic time warping (DTW) on gait data, multiple optimal warping paths (OWPs) with a minimum sum of local costs can occur and to propose an additional OWP selection method to address this problem. A 3-dimensional motion analysis experiment was conducted on 55 adult participants, including both males and females, to acquire gait data. This study analyzed 990 instances of DTW on gait data to examine the occurrence of multiple OWPs with the minimum sum of local costs. We subsequently applied an additional selection method to the multiple OWPs to determine the feasibility of identifying a single OWP. Multiple OWPs through DTW were observed 82 times, accounting for 8.28%. Notably, on the ankle joint of males, the rate was the highest at 11.11%. Cases with two multiple OWPs were the most prevalent at 56.10%, and cases with ten or more multiple OWPs accounted for 19.51%. The additional selection method proposed in this study was applied to the 82 instances in which multiple OWPs occurred. The results demonstrated the ability to identify a unique OWP in all cases. These results hold significance in identifying the shortcomings of conventional OWP selection methods previously employed and proposing solutions. It enhances the reliability, validity, and accuracy of studies utilizing DTW.

Visualization of health information within immersive virtual reality environments

The Advanced Visualization Branch of the National Institute of Nursing Research uses computer technologies to study information visualization in support of self-care management. Advanced technologies, such as immersive virtual reality (IVR), afford researchers the opportunity to study health information visualization where user-initiated information search in visually dense settings precedes acquisition, interpretation, and use. While IVR has broad applicability in healthcare, we chose to target lay people managing chronic disease because of the growing unmet need to translate clinical recommendations into everyday behaviors. To explore how lay people seek, acquire, and interpret health information in everyday settings, we developed an IVR grocery store. In this environment, a person can locate food products, read and compare nutrition labels, and use information to make food selections. The goal of this perspective is to introduce the opportunities afforded by IVR to both present and study health information visualization and to highlight critical design considerations.

Association between suicidal ideation and tandem repeats in contactins

Background

Death by suicide is one of the leading causes of death among adolescents. Genome-wide association studies (GWAS) have identified loci that associate with suicidal ideation and related behaviours. One such group of loci are the six contactin genes (CNTN1-6) that are critical to neurodevelopment through regulating neurite structure. Because single nucleotide polymorphisms (SNPs) detected by GWAS often map to non-coding intergenic regions, we investigated whether repetitive variants in CNTNs associated with suicidality in a young cohort aged 8 to 21. Understanding the genetic liability of suicidal thought and behavior in this age group will promote early intervention and treatment.

Methods

Genotypic and phenotypic data were obtained from the Philadelphia Neurodevelopment Cohort (PNC). Across six CNTNs, 232 short tandem repeats (STRs) were analyzed in up to 4,595 individuals of European ancestry who expressed current, previous, or no suicidal ideation. STRs were imputed into SNP arrays using a phased SNP-STR haplotype reference panel from the 1000 Genomes Project. We tested several additive and interactive models of locus-level burden (i.e., sum of STR alleles) with respect to suicidal ideation. Additive models included sex, birth year, developmental stage ("DevStage"), and the first 10 principal components of ancestry as covariates; interactive models assessed the effect of STR-by-DevStage considering all other covariates.

Results

CNTN1-[T]N interacted with DevStage to increase risk for current suicidal ideation (CNTN1-[T]N-by-DevStage; p = 0.00035). Compared to the youngest age group, the middle (OR = 1.80, p = 0.0514) and oldest (OR = 3.82, p = 0.0002) participant groups had significantly higher odds of suicidal ideation as their STR length expanded; this result was independent of polygenic scores for suicidal ideation.

Discussion

These findings highlight diversity in the genetic effects (i.e., SNP and STR) acting on suicidal thoughts and behavior and advance our understanding of suicidal ideation across childhood and adolescence.

Deep Brain Stimulation for Children with Generalized Epilepsy

Intracranial neuromodulation is an evolving therapy for patients with drug-resistant epilepsy (DRE). Deep brain stimulation (DBS) is now available as a therapy for patients with DRE and focal-onset seizures in select health care systems; however, there remains a substantial need of efficacy data before DBS can be more widely adopted into routine clinical practice. This review and commentary focuses on a particular shifting paradigm: DBS as a therapy for children with generalized-onset seizures.

Telehealth and Technology: New Directions in Cancer Care

ABSTRACT

Telehealth is a broad concept that refers to any delivery of health care in real time using technologies to connect people or information that are not in the same physical location. Until fairly recently, telehealth was more aspiration than reality. This situation changed radically due in part to the COVID-19 pandemic, which led to a near-overnight inability for patients to be seen for routine management of chronic health conditions, including those with cancer. The purpose of this brief narrative review is to outline some areas where emerging and future technology may allow for innovations with specific implications for people with a current or past diagnosis of cancer, including underserved and/or historically excluded populations. Specific topics of telehealth are broadly covered in other areas of the special issue.

Bioadhesive Technology Platforms

Bioadhesives have emerged as transformative and versatile tools in healthcare, offering the ability to attach tissues with ease and minimal damage. These materials present numerous opportunities for tissue repair and biomedical device integration, creating a broad landscape of applications that have captivated clinical and scientific interest alike. However, fully unlocking their potential requires multifaceted design strategies involving optimal adhesion, suitable biological interactions, and efficient signal communication. In this Review, we delve into these pivotal aspects of bioadhesive design, highlight the latest advances in their biomedical applications, and identify potential opportunities that lie ahead for bioadhesives as multifunctional technology platforms.

A Pilot Study Evaluating a Virtual Reality-Based Nontechnical Skills Training Application for Urology Trainees: Usability, Acceptability, and Impact

OBJECTIVE

This study aimed to develop and evaluate a virtual reality (VR)-based nontechnical skills (NTS) training application for urology trainees and assess its effectiveness in improving their skills and confidence.

DESIGN

A mixed-methods study was conducted to develop and evaluate a VR-based NTS training application for 32 urology trainees. The development process involved collaboration with 5 urology experts, 2 medical education specialists, and a human factors researcher. The study evaluated the application's usability, acceptability, and efficacy through 3 phases: scenario development with expert feedback integration, storyboarding and creation processes with facilitators and urology trainees, and a final evaluation by trainees.

SETTING

The data were collected during a 4-day urology boot camp in October 2022.

PARTICIPANTS

Thirty-two urology trainees participated in the study and completed 2 VR scenarios designed to enhance their NTS skills RESULTS: The System Usability Scale (SUS) showed a moderate usability score of 66. The Training Evaluation Inventory (TEI) and additional feedback demonstrated positive effects on trainees' learning and confidence in their NTS abilities. Most participants found the application easy to use, and effective and they expressed interest in using similar VR applications for other aspects of surgical training.

CONCLUSIONS

VR-based NTS training applications show potential for enhancing urology trainees' nontechnical skills. The integration of expert feedback and immersive technology offers a promising, accessible, and cost-effective solution to the challenges of delivering NTS training. Future research should explore the long-term impact of VR-based NTS training on trainees' performance and patient outcomes and consider incorporating advanced AI technologies for personalized and dynamic learning experiences.

Exploring the top-cited literature in telerehabilitation for joint replacement using the descriptive, diagnostic, predictive, and prescriptive analytics model: A thematic and bibliometric analysis

BACKGROUND

Telerehabilitation offers a novel approach supplementing or replacing traditional physical rehabilitation. While research on telerehabilitation for joint replacement (TJR) has expanded, no study has investigated the top 100 cited articles (T100TJR) using the descriptive, diagnostic, predictive, and prescriptive analytics (DDPP) model. This study aims to examine the features of T100TJR in TJR through the DDPP approaches.

METHODS

A comprehensive search of the Web of Science Core Collection was conducted to locate all pertinent English-language documents from the database's inception until August 2, 2023. The T100TJR articles were then identified based on citation counts. The DDPP analytics model, along with 7 visualization techniques, was used to analyze metadata elements such as countries, institutions, journals, authors, references, and keywords. An impact timeline view was employed to highlight 2 particularly noteworthy articles.

RESULTS

We analyzed 712 articles and observed a consistent upward trend in publications, culminating in a noticeable peak in 2022. The United States stood out as the primary contributor. A detailed examination of the top 100 articles (T100TJR) revealed the following leading contributors since 2010: the United States (by country), University of Sherbrooke, Canada (by institutions), 2017 (by publication year), and Dr Hawker from Canada (by authors). We delineated 4 major themes within these articles. The theme "replacement" dominated, featuring in 89% of them. There was a strong correlation between the citations an article garnered and its keyword prominence (F = 3030.37; P < .0001). Additionally, 2 particularly high-impact articles were underscored for recommendation.

CONCLUSIONS

Telerehabilitation for TJR has seen rising interest, with the U.S. leading contributions. The study highlighted dominant themes, especially "replacement," in top-cited articles. The significant correlation between article citations and keyword importance indicates the criticality of keyword selection. The research underscores the importance of 2 pivotal articles, recommending them for deeper insights.

Exploring Variations in Sleep Perception: Comparative Study of Chatbot Sleep Logs and Fitbit Sleep Data

BACKGROUND

Patient-generated health data are important in the management of several diseases. Although there are limitations, information can be obtained using a wearable device and time-related information such as exercise time or sleep time can also be obtained. Fitbits can be used to acquire sleep onset, sleep offset, total sleep time (TST), and wakefulness after sleep onset (WASO) data, although there are limitations regarding the depth of sleep and satisfaction; therefore, the patient's subjective response is still important information that cannot be replaced by wearable devices.

OBJECTIVE

To effectively use patient-generated health data related to time such as sleep, it is first necessary to understand the characteristics of the time response recorded by the user. Therefore, the aim of this study was to analyze the characteristics of individuals' time perception in comparison with wearable data.

METHODS

Sleep data were acquired for 2 weeks using a Fitbit. Participants' sleep records were collected daily through chatbot conversations while wearing the Fitbit, and the two sets of data were statistically compared.

RESULTS

In total, 736 people aged 30-59 years were recruited for this study, and the sleep data of 543 people who wore a Fitbit and responded to the chatbot for more than 7 days on the same day were analyzed. Research participants tended to respond to sleep-related times on the hour or in 30-minute increments, and each participant responded within the range of 60-90 minutes from the value measured by the Fitbit. On average for all participants, the chat responses and the Fitbit data were similar within a difference of approximately 15 minutes. Regarding sleep onset, the participant response was 8 minutes and 39 seconds (SD 58 minutes) later than that of the Fitbit data, whereas with respect to sleep offset, the response was 5 minutes and 38 seconds (SD 57 minutes) earlier. The participants' actual sleep time (AST) indicated in the chat was similar to that obtained by subtracting the WASO from the TST measured by the Fitbit. The AST was 13 minutes and 39 seconds (SD 87 minutes) longer than the time WASO was subtracted from the Fitbit TST. On days when the participants reported good sleep, they responded 19 (SD 90) minutes longer on the AST than the Fitbit data. However, for each sleep event, the probability that the participant's AST was within ±30 and ±60 minutes of the Fitbit TST-WASO was 50.7% and 74.3%, respectively.

CONCLUSIONS

The chatbot sleep response and Fitbit measured time were similar on average and the study participants had a slight tendency to perceive a relatively long sleep time if the quality of sleep was self-reported as good. However, on a participant-by-participant basis, it was difficult to predict participants' sleep duration responses with Fitbit data. Individual variations in sleep time perception significantly affect patient responses related to sleep, revealing the limitations of objective measures obtained through wearable devices.

An Open-Source Graphical User Interface-Embedded Automated Electrocardiogram Quality Assessment: A Balanced Class Representation Approach

The rise in cardiovascular diseases necessitates accurate electrocardiogram (ECG) diagnostics, making high-quality ECG recordings essential. Our CNN-LSTM model, embedded in an open-access GUI and trained on balanced datasets collected in clinical settings, excels in automating ECG quality assessment. When tested across three datasets featuring varying ratios of acceptable to unacceptable ECG signals, it achieved an F1 score ranging from 95.87% to 98.40%. Training the model on real noise sources significantly enhances its applicability in real-life scenarios, compared to simulations. Integrated into a user-friendly toolbox, the model offers practical utility in clinical environments. Furthermore, our study underscores the importance of balanced class representation during training and testing phases. We observed a notable F1 score change from 98.09% to 95.87% when the class ratio shifted from 85:15 to 50:50 in the same testing dataset with equal representation. This finding is crucial for future ECG quality assessment research, highlighting the impact of class distribution on the reliability of model training outcomes.

Contralaterally controlled functional electrical stimulation video game therapy for hand rehabilitation after stroke: a randomized controlled trial

PURPOSE

To estimate the effect of integrating custom-designed hand therapy video games (HTVG) with contralaterally controlled functional electrical stimulation (CCFES) therapy.

METHODS

Fifty-two stroke survivors with chronic (>6 months) upper limb hemiplegia were randomized to 12 weeks of CCFES or CCFES + HTVG. Treatment involved self-administration of technology-mediated therapy at home plus therapist-administered CCFES-assisted task practice in the lab. Pre- and post-treatment assessments were made of hand dexterity, upper limb impairment and activity limitation, and cognitive function.

RESULTS

No significant between-group differences were found on any outcome measure, and the average magnitudes of improvement within both groups were small. The incidence of technical problems with study devices at home was greater for the CCFES + HTVG group. This negatively affected adherence and may partially explain the absence of effect of HTVG. At end-of-treatment, large majorities of both treatment groups had positive perceptions of treatment efficacy and expressed enthusiasm for the treatments.

CONCLUSION

This study makes an important contribution to the research literature on the importance of environmental factors, concomitant impairments, and technology simplification when designing technology-based therapies intended to be self-administered at home. This study failed to show any added benefit of HTVG to CCFES therapy.Clinicaltrials.gov (NCT03058796).

Sleep-wake monitoring of people with intellectual disability: Examining the agreement of EMFIT QS and actigraphy

BACKGROUND

Gaining insight into sleep-wake patterns of persons with intellectual disabilities is commonly done using wrist actigraphy. For some people, contactless alternatives are needed. This study compares a contactless bed sensor with wrist actigraphy to monitor sleep-wake patterns of people with moderate to profound intellectual disabilities.

METHOD

Data were collected with EMFIT QS (activity and presence) and MotionWatch 8/Actiwatch 2 (activity, ambient light, and event marker/sleep diary) for 14 nights in 13 adults with moderate-profound intellectual disabilities residing in intramural care.

RESULTS

In a care-as-usual setting, EMFIT QS and actigraphy assessment show little agreement on sleep-wake variables.

CONCLUSION

Currently, EMFIT QS should not be considered an alternative to wrist actigraphy for sleep-wake monitoring. Further research is needed into assessing sleep-wake variables using (contactless) technological devices and how the data should be interpreted within the care context to achieve reliable and valid information on sleep-wake patterns of people with intellectual disabilities.

Digital Marker for Early Screening of Mild Cognitive Impairment Through Hand and Eye Movement Analysis in Virtual Reality Using Machine Learning: First Validation Study

BACKGROUND

With the global rise in Alzheimer disease (AD), early screening for mild cognitive impairment (MCI), which is a preclinical stage of AD, is of paramount importance. Although biomarkers such as cerebrospinal fluid amyloid level and magnetic resonance imaging have been studied, they have limitations, such as high cost and invasiveness. Digital markers to assess cognitive impairment by analyzing behavioral data collected from digital devices in daily life can be a new alternative. In this context, we developed a "virtual kiosk test" for early screening of MCI by analyzing behavioral data collected when using a kiosk in a virtual environment.

OBJECTIVE

We aimed to investigate key behavioral features collected from a virtual kiosk test that could distinguish patients with MCI from healthy controls with high statistical significance. Also, we focused on developing a machine learning model capable of early screening of MCI based on these behavioral features.

METHODS

A total of 51 participants comprising 20 healthy controls and 31 patients with MCI were recruited by 2 neurologists from a university hospital. The participants performed a virtual kiosk test-developed by our group-where we recorded various behavioral data such as hand and eye movements. Based on these time series data, we computed the following 4 behavioral features: hand movement speed, proportion of fixation duration, time to completion, and the number of errors. To compare these behavioral features between healthy controls and patients with MCI, independent-samples 2-tailed t tests were used. Additionally, we used these behavioral features to train and validate a machine learning model for early screening of patients with MCI from healthy controls.

RESULTS

In the virtual kiosk test, all 4 behavioral features showed statistically significant differences between patients with MCI and healthy controls. Compared with healthy controls, patients with MCI had slower hand movement speed (t49=3.45; P=.004), lower proportion of fixation duration (t49=2.69; P=.04), longer time to completion (t49=-3.44; P=.004), and a greater number of errors (t49=-3.77; P=.001). All 4 features were then used to train a support vector machine to distinguish between healthy controls and patients with MCI. Our machine learning model achieved 93.3% accuracy, 100% sensitivity, 83.3% specificity, 90% precision, and 94.7% F1-score.

CONCLUSIONS

Our research preliminarily suggests that analyzing hand and eye movements in the virtual kiosk test holds potential as a digital marker for early screening of MCI. In contrast to conventional biomarkers, this digital marker in virtual reality is advantageous as it can collect ecologically valid data at an affordable cost and in a short period (5-15 minutes), making it a suitable means for early screening of MCI. We call for further studies to confirm the reliability and validity of this approach.