Remote Screening Research Articles

Teleophthalmology Using Remote Retinal Imaging During the COVID-19 Pandemic.

Introduction: Lower insurance reimbursements have limited the financial sustainability of remote eye screening programs. Greater utilization and insurance coverage for teleophthalmology screening during the coronavirus disease 2019 (COVID-19) pandemic in 2020 may enhance awareness and expand remote retinal imaging services. This retrospective cross-sectional study evaluates utilization and insurance coverage for remote retinal imaging in the United States in 2020. Methods: We analyzed teleretinal imaging utilization and insurance payments from January 1 to December 31, 2020, using the Optum Labs Data Warehouse, a comprehensive national database of deidentified administrative claims for commercial and Medicare Advantage enrollees in the United States. We evaluated frequency of claims and insurance payment for services using the Current Procedural Terminology codes 92227 and 92228 for remote eye imaging by any provider, and 92250 for fundus photography by non-eye care providers. Results: The use of remote retinal imaging in the United States declined rapidly during the initial COVID-19 lockdown from 3,627 claims in February 2020 to 1,414 claims in April 2020, but returned to 3,133 claims by December 2020, similar to mean prepandemic levels in 2019 (2,841 ± 174.8 claims). The proportion of insurance payments for remote imaging increased temporarily from 47.4% in February to 56.7% in April, and then returned to 45.9% in December of 2020. Discussion: Utilization of remote retinal imaging declined steeply, while the insurance coverage increased during the initial COVID-19 lockdown in 2020, but returned to prepandemic levels by end of the year. Changes in utilization and relaxed restrictions on insurance reimbursements for teleophthalmology during the COVID-19 pandemic were not sustained.

The impact of a structured polygraphy screening incorporated in a novel remote mobile health pathway on sleep apnoea prevalence in patients with atrial fibrillation

Abstract Funding Acknowledgements Type of funding sources: None. Background Untreated obstructive sleep apnoea (OSA) contributes to progression of atrial fibrillation (AF) and reduces the success rate of heart rhythm control strategies. OSA remains one of the most frequently underdiagnosed modifiable risk factors in AF patients due to a lack of standardized screening methods and low awareness. Purpose To assess the impact of implementation of a structured remote OSA screening and management pathway on the prevalence of OSA in AF patients scheduled for AF ablation procedures. Methods In October 2020, a novel remote OSA screening and management pathway (VIRTUAL-SAFARI) was introduced in two AF outpatient clinics in the Netherlands. Consecutive patients scheduled for AF ablation were offered OSA screening consisting of sending a portable home sleep test to patients’ homes to perform a remote sleep recording for one night, analysis by a sleep physician, discussion of results with the patient, and initiation of treatment (when applicable). The impact of this structural screening strategy was assessed by comparing the prevalence of concomitant OSA (defined as apnoea-hypopnoea index ≥5) for patients scheduled for AF ablation in the year before and after introduction of the OSA management pathway. Results A total of 733 patients was studied, 308 in the year before (Oct ’19 - Sep ’20) and 425 in the year after (Oct ’20 - Sep ’21) introduction of the VIRTUAL-SAFARI pathway. Median age was 65 [58-71], 64% was male and median body mass index (BMI) was 27 [25-30] kg/m2. Baseline characteristics were comparable for the groups before and after introduction of the pathway (Table 1). In the cohort before pathway introduction, OSA had been diagnosed in 26 patients (8%, Figure 1) and was treated with positive airway pressure in 10 cases (3%). In the cohort after pathway introduction, OSA had previously been diagnosed in 53 patients (12%). Eighty-eight percent of patients without previous OSA screening was referred via the remote pathway. Results of the sleep recordings were available for 213 (59%) at the time of this analysis. Previously unknown OSA was diagnosed in 184 patients (86% of available recordings), increasing the prevalence of confirmed OSA to 237 (55%). For 22% of patients, results of sleep recordings are pending. Absence of OSA was confirmed in 9%, and 13% of patients had not been screened (e.g. because of patient preference or logistical reasons). After pathway introduction, 82 patients (19%) were treated or received advice to start treatment with positive airway pressure. Treatment decisions are pending for 9% of the cohort. Conclusion After the implementation of structured remote OSA screening in a well characterized cohort of consecutive patients scheduled for AF ablation, the prevalence of diagnosed OSA increased from 8% to 55%. Whether appropriate risk factor management, including treatment of OSA identified by structured screening, will improve AF outcomes needs to be tested in future studies.

A Machine Learning Approach for Detecting Digital Behavioral Patterns of Depression Using Nonintrusive Smartphone Data (Complementary Path to Patient Health Questionnaire-9 Assessment): Prospective Observational Study.

BackgroundDepression is a major global cause of morbidity, an economic burden, and the greatest health challenge leading to chronic disability. Mobile monitoring of mental conditions has long been a sought-after metric to overcome the problems associated with the screening, diagnosis, and monitoring of depression and its heterogeneous presentation. The widespread availability of smartphones has made it possible to use their data to generate digital behavioral models that can be used for both clinical and remote screening and monitoring purposes. This study is novel as it adds to the field by conducting a trial using private and nonintrusive sensors that can help detect and monitor depression in a continuous, passive manner.ObjectiveThis study demonstrates a novel mental behavioral profiling metric (the Mental Health Similarity Score), derived from analyzing passively monitored, private, and nonintrusive smartphone use data, to identify and track depressive behavior and its progression.MethodsSmartphone data sets and self-reported Patient Health Questionnaire-9 (PHQ-9) depression assessments were collected from 558 smartphone users on the Android operating system in an observational study over an average of 10.7 (SD 23.7) days. We quantified 37 digital behavioral markers from the passive smartphone data set and explored the relationship between the digital behavioral markers and depression using correlation coefficients and random forest models. We leveraged 4 supervised machine learning classification algorithms to predict depression and its severity using PHQ-9 scores as the ground truth. We also quantified an additional 3 digital markers from gyroscope sensors and explored their feasibility in improving the model’s accuracy in detecting depression.ResultsThe PHQ-9 2-class model (none vs severe) achieved the following metrics: precision of 85% to 89%, recall of 85% to 89%, F1 of 87%, and accuracy of 87%. The PHQ-9 3-class model (none vs mild vs severe) achieved the following metrics: precision of 74% to 86%, recall of 76% to 83%, F1 of 75% to 84%, and accuracy of 78%. A significant positive Pearson correlation was found between PHQ-9 questions 2, 6, and 9 within the severely depressed users and the mental behavioral profiling metric (r=0.73). The PHQ-9 question-specific model achieved the following metrics: precision of 76% to 80%, recall of 75% to 81%, F1 of 78% to 89%, and accuracy of 78%. When a gyroscope sensor was added as a feature, the Pearson correlation among questions 2, 6, and 9 decreased from 0.73 to 0.46. The PHQ-9 2-class model+gyro features achieved the following metrics: precision of 74% to 78%, recall of 67% to 83%, F1 of 72% to 78%, and accuracy of 76%.ConclusionsOur results demonstrate that the Mental Health Similarity Score can be used to identify and track depressive behavior and its progression with high accuracy.

Toward Linguistic Recognition of Generalized Anxiety Disorder.

BackgroundGeneralized anxiety disorder (GAD) refers to extreme, uncontrollable, and persistent worry and anxiety. The disorder is known to affect the social functioning and well-being of millions of people, but despite its prevalence and burden to society, it has proven difficult to identify unique behavioral markers. Interestingly, the worrying behavior observed in GAD is argued to stem from a verbal linguistic process. Therefore, the aim of the present study was to investigate if GAD can be predicted from the language people use to put their anxious worries into words. Given the importance of avoidance sensitivity (a higher likelihood to respond anxiously to novel or unexpected triggers) in GAD, this study also explored if prediction accuracy increases when individual differences in behavioral avoidance and approach sensitivity are taken into account.MethodAn expressive writing exercise was used to explore whether GAD can be predicted from linguistic characteristics of written narratives. Specifically, 144 undergraduate student participants were asked to recall an anxious experience during their university life, and describe this experience in written form. Clinically validated behavioral measures for GAD and self-reported sensitivity in behavioral avoidance/inhibition (BIS) and behavioral approach (BAS), were collected. A set of classification experiments was performed to evaluate GAD predictability based on linguistic features, BIS/BAS scores, and a concatenation of the two.ResultsThe classification results show that GAD can, indeed, be successfully predicted from anxiety-focused written narratives. Prediction accuracy increased when differences in BIS and BAS were included, which suggests that, under those conditions, negatively valenced emotion words and words relating to social processes could be sufficient for recognition of GAD.ConclusionsUndergraduate students with a high GAD score can be identified based on their written recollection of an anxious experience during university life. This insight is an important first step toward development of text-based digital health applications and technologies aimed at remote screening for GAD. Future work should investigate the extent to which these results uniquely apply to university campus populations or generalize to other demographics.

Validation of a novel mobile phone application for type 2 diabetes screening following gestational diabetes mellitus.

We sought to determine if using fasting blood glucose (FBG) through text-based care is an effective screening tool for type 2 diabetes in the postpartum period compared to in-person, 2-hour oral glucose tolerance testing (2hr OGTT). This was a single-center interventional study that included individuals diagnosed with gestational diabetes. Patients were enrolled in standard, office-based 2hr OGTT in combination with text-based remote diabetes screening. Study participants were instructed to record FBG for 3 consecutive days using a mobile application. We assessed agreement with 2hr OGTT using sensitivity, specificity, positive and negative predictive value with exact binomial 95% confidence intervals. A total of 446 individuals diagnosed with gestational diabetes met inclusion criteria, 239 of which were enrolled in standard office-based screening and 207 were enrolled in dual screening using standard 2hr OGTT testing combined with text-based remote FBG screening. A FBG value less than 100 mg/dL had 100% sensitivity (86-100%), 86% specificity (77-93%) with a 100% (94-100%) negative predictive value and 71% (54-85%) positive predictive value. Follow-up was significantly higher among individuals enrolled in remote text-based screening compared to standard in-office screening (48% vs. 25%, respectively; P<0.001). Text-based screening may be a feasible alternative to in-office screening. A mobile-based system using FBG successfully screened all patients with type 2 diabetes in the postpartum period with 100% sensitivity and negative predictive value. Remote telehealth screening significantly increased follow-up with type 2 diabetes screening.

Noninvasive Voice Biomarker Is Associated With Incident Coronary Artery Disease Events at Follow-up

ObjectiveTo evaluate the association between a preidentified voice biomarker and incident coronary artery disease (CAD) events. MethodsPatients referred for clinically indicated coronary angiography underwent a total of three 30-second voice recordings using the Vocalis Health smartphone application between January 1, 2015, and February 28, 2017. A pre-established voice biomarker was derived from each individual recording, and the mean biomarker value was calculated for each patient. Individuals were clinically observed through December 31, 2019. The prespecified primary outcome was a composite of presenting to the emergency department with chest pain, being admitted to the hospital with chest pain, or having an acute coronary syndrome; the prespecified secondary outcome was a composite of a positive stress test result at follow-up or the presence of CAD at follow-up coronary angiography. ResultsIn the final analysis, 108 patients were included (mean age, 59.47±11.44 years; male, 59 [54.6%]). The median follow-up time was 24 months (range, 1 to 60 months). In multivariable Cox proportional hazards models adjusting for CAD grade on baseline angiography, a high baseline mean voice biomarker was significantly associated with both the primary (hazard ratio, 2.61; 95% CI, 1.42 to 4.80; P=.002) and secondary (hazard ratio, 3.13; 95% CI, 1.13 to 8.68; P=.03) composite outcomes. ConclusionThis study found a significant association between a noninvasive voice biomarker and incident CAD events at follow-up. These results may have important clinical implications for the remote and noninvasive screening of patients to identify those at risk of coronary disease and its complications.

Temporal Speech Parameters Indicate Early Cognitive Decline in Elderly Patients With Type 2 Diabetes Mellitus.

Introduction:The earliest signs of cognitive decline include deficits in temporal (time-based) speech characteristics. Type 2 diabetes mellitus (T2DM) patients are more prone to mild cognitive impairment (MCI). The aim of this study was to compare the temporal speech characteristics of elderly (above 50 y) T2DM patients with age-matched nondiabetic subjects.Materials and Methods:A total of 160 individuals were screened, 100 of whom were eligible (T2DM: n=51; nondiabetic: n=49). Participants were classified either as having healthy cognition (HC) or showing signs of MCI. Speech recordings were collected through a phone call. Based on automatic speech recognition, 15 temporal parameters were calculated.Results:The HC with T2DM group showed significantly shorter utterance length, higher duration rate of silent pause and total pause, and higher average duration of silent pause and total pause compared with the HC without T2DM group. Regarding the MCI participants, parameters were similar between the T2DM and the nondiabetic subgroups.Conclusions:Temporal speech characteristics of T2DM patients showed early signs of altered cognitive functioning, whereas neuropsychological tests did not detect deterioration. This method is useful for identifying the T2DM patients most at risk for manifest MCI, and could serve as a remote cognitive screening tool.

What is the accuracy of conventional oral examination, mouth self-examination, vital rinsing and remote screening for the early detection of oral cavity cancer?

What is the accuracy of conventional oral examination, mouth self-examination, vital rinsing and remote screening for the early detection of oral cavity cancer?

NON-INVASIVE VOICE BIOMARKER IS ASSOCIATED WITH INCIDENT CORONARY ARTERY DISEASE EVENTS AT FOLLOW-UP

Background: Voice biomarkers have been shown to be associated with coronary artery disease (CAD) diagnosed by angiography, hospitalization in patients with heart failure, and with hemodynamic indices of pulmonary hypertension. In the current study we evaluate the association between a pre-identified voice biomarker and incident CAD events at follow-up. Methods and Results: Patients referred for clinically indicated coronary angiography underwent a total of three 30-second voice recordings using the Vocalis Health smartphone application between January 1, 2015, and February 28, 2017: R1—reading aloud a pre-specified text; R2—describing a positive emotional experience; and R3—describing a negative emotional experience. A pre-established voice biomarker was derived from each individual recording, and the mean biomarker value was calculated for each patient. Individuals were clinically followed through December 31st 2019. The pre-specified primary outcome was a composite of presenting to the emergency department with chest pain and/or being admitted to hospital with chest pain and/or having unstable angina, non-ST-segment elevation myocardial infarction (NSTEMI) or STEMI; the pre-specified secondary outcome was a composite of a positive stress test at follow-up and/or the presence of CAD at follow-up coronary angiography. The association between the baseline voice biomarker and follow-up events was estimated using Cox proportional hazards models. One hundred and eight patients were included in the final analysis (mean age: 59.47 ± 11.44 years, males: 54.6%). The median follow-up time was 24 months (range 1 – 60 months). Overall, 43 (39.8%) patients had the composite primary outcome, and 15 (13.8%) had the composite secondary outcome. Patients were separated into three tertiles, according to the distribution of the mean voice biomarker (T1 lowest tertile, T2 middle tertile, T3 highest tertile), and were divided into two groups based on having a high (T3) versus low (T1 and T2) mean baseline voice biomarker value. Patients with a high mean voice biomarker value at baseline had a significantly higher frequency of the primary composite end-point compared to those with a low mean voice biomarker value at baseline (21 (58.3%) vs. 22 (30.6%), p=0.0056). The frequency of the secondary composite end-point was similar between groups (8 (22.2%) vs. 7 (9.7%), p=0.0848). In multivariable Cox proportional hazards models adjusting for CAD grade on baseline angiography, a high baseline mean voice biomarker was significantly associated with both the primary and secondary composite outcomes (HR 2.611, 95% CI 1.421 – 4.798, p=0.002; and HR 3.132, 95% CI 1.130 – 8.679, p=0.028). Conclusion The current study demonstrates a significant association between a noninvasive voice biomarker and incident CAD events at follow-up. These results may have important clinical implications for telemedicine, and the remote and non-invasive screening of patients to identify those at risk of coronary disease and its complications. Funding: This study was in part supported by Vocalis Health, Tel-Aviv Israel Declaration of Interest: All authors have no conflicts of interest to disclose Ethical Approval: protocol was approved by the institutional review board at Mayo Clinic, and all patients provided informed consent for participation.

Diagnostic Accuracy of the Telephone Interview for Cognitive Status for the Detection of Dementia in Primary Care.

Cognitive diagnostic work-up in primary care is not always physically feasible, owing to chronic disabilities and/or travel restrictions. The identification of dementia might be facilitated with diagnostic instruments that are time efficient and easy to perform, as well as useful in the remote setting. We assessed whether the Telephone Interview for Cognitive Status (TICS) might be a simple and accurate alternative for remote diagnostic cognitive screening in primary care. We administered the TICS (range, 0-41) for 810 of 1,473 older people aged 84.5 (SD, 2.4) years. We scrutinized electronic health records for participants with TICS scores ≤30 and for a random sample of participants with TICS scores >30 for a dementia diagnosis using all data from the Prevention of Dementia by Intensive Vascular Care (preDIVA) trial for 8-12 years of follow-up. We used multiple imputation to correct for verification bias. Of the 810 participants, 155 (19.1%) had a TICS score ≤30, and 655 (80.9%) had a TICS score >30. Electronic health records yielded 8.4% (13/154) dementia diagnoses for participants with TICS ≤30 vs none with TICS >30. Multiple imputation for TICS >30 yielded a median of 7/655 (1.1%; interquartile range, 5-8) estimated dementia cases. After multiple imputation, the optimal cutoff score was ≤29, with mean sensitivity 65.4%, specificity 87.8%, positive predictive value 11.9%, negative predictive value 99.0%, and area under the curve 77.4% (95% CI, 56.3%-90.0%). In the present older population, the TICS performed well as a diagnostic screening instrument for excluding dementia and might be particularly useful when face-to-face diagnostic screening is not feasible in family practice or research settings. The potential reach to large numbers of people at low cost could contribute to more efficient medical management in primary care.

Quality Assessment of Screen Content Images in Wavelet Domain

Objective image quality assessment (IQA) aims to predict human judgment on an image’s visual quality. This computational model is applicable in various scenarios, such as monitoring, image compression and image restoration. However, the research in this area began with natural scenes while image contents has become much more diverse. Remote computing and screen sharing applications led to the prevalence of screen content images (SCIs), which are full of texts, flat areas, and also natural scenes. Due to their different characteristics, traditional methods for IQA are insufficient for SCIs. In this paper, we show that capturing the distortions in the horizontal and vertical structures of an SCI is effective for predicting its quality. We also take into account the human visual system’s trend to analyze a scene in a multi-scale fashion. Based on these observations, we propose wavelet analysis as a means to accomplish the mentioned strategies. Experimental results on three SCI quality datasets show that plausible predictions can be made by the proposed full-reference method, while having an acceptable time complexity.

Pre-Employment Health Screening for Nurse During COVID-19 Pandemic from Occupational Health Perspective: a Document Review

&lt;p&gt;&lt;em&gt;The COVID-19 pandemic presents a major challenge to the pre-employment health screening system for nurses looking for jobs. For this reason, a pre-employment screening system is needed that sort it easier for all parties (nurses as prospective employees, companies, and company owners) in the health screening system. To offer a health screening system in the form of Remote Pre-employment Health Screening for nurses from the occupational health nursing (OHN) perspectives. The study used Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) Analysis with qualitative descriptive methods. The documents were extracted from Google Scholar, ResearchGate, and PubMed. The indicators included respondents (nurses), research methods (mixture of quantitative, document review, case study, opinion, and report), years (2016-2021), results (focus on pre-employment health screening), and language (English). Document search used the PICOT (Population, Intervention, Comparison, Outcome, and Time) model. The search keywords were pre-employment health screening, OHN, and Covid-19. After analyzing the documents in the database (n=702), the records were eligible (n=10), and met the requirements for review (n=7). The findings of reviewed records were that the pandemic caused a disastrous impact on the nursing profession including the need to review the pre-employment health screening system. To prepare a remote pre-employment health screening system, the study suggests organization to a three in one formula: to make a memorandum of understanding, to set health examination standards, and to create an electronic application. &lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt; &lt;strong&gt;K&lt;/strong&gt;&lt;strong&gt;eywords&lt;/strong&gt;&lt;strong&gt;: &lt;/strong&gt;Pre-employment, health screening, OHN, occupational healthCovid-19.&lt;/em&gt;&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; &lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt; &lt;/strong&gt;&lt;/p&gt;

Remote Screening for Atrial Fibrillation by a Federal Cardiac Monitoring System in Primary Care Patients in Russia: Results from the Prospective Interventional Multicenter FECAS-AFS Study.

Background:Screening for atrial fibrillation has the potential to significantly reduce cardiovascular morbidity and mortality. However, questions in regard to how to screen, on whom to screen, and the optimal setting of screening remain unanswered.Objective:To assess the applicability of a federal cardiac monitoring for atrial fibrillation (AF) screening and remote heart rhythm monitoring in patients at high cardiovascular risk in a mixed urban and rural population in Russia.Methods:This is a prospective multicenter cohort study including 3249 individuals with high cardiovascular risk (mean age 56 ± 12.8 years) from the larger Moscow region who were screened for AF using a smartphone-case based single-lead ECG monitor over a period of 18 month. The endpoints were considered as number of newly diagnosed AF; mean time to diagnosis; number of patients for the first time assigned to anticoagulation therapy; frequency of adverse events.Results:A trial fibrillation was diagnosed in 126 patients, 36 of them for the first time. The mean time to diagnosis was 3 ± 2 days. Of 36 patients, the CHA2DS2-VASc score was ≥1 in 34 cases, ≥2 in 29 cases. Anticoagulant therapy was first induced in 31 patients. One death in newly diagnosed group and two deaths in chronic group were registered. There were a total of eight hospitalizations: one in newly diagnosed and seven in chronic AF patients.Conclusion:Our results indicate that a Federal AF screening system in patients at high cardiovascular risk by using a smartphone-case based single lead ECG which is supported by centrally located ECG specialist and central data management is feasible and reliable when performed in a mixed urban and rural area. Further studies are needed to evaluate the full potential of this approach.

Effectiveness of remote screening for diabetic retinopathy among patients referred to Mozambican Diabetes Association (AMODIA): a retrospective observational study.

AimsDiabetes represents a growing public health problem in sub-Saharan Africa, where diabetic retinopathy (DR) is a major cause of permanent visual loss. We reported the results of a remote screening of DR among urbanized Mozambican people with diabetes.MethodsWe retrospectively collected retinal images and clinical characteristics from 536 patients screened for DR in Maputo (Mozambique), over a period of 2 years (2018–2019). Retinal photographs were captured, digitally stored, and scored locally and by an expert ophthalmologist in Italy remotely.ResultsThe overall prevalence of DR was 29% with sight-threatening forms accounting for 8.1% of that number. Inter-reader agreement between the local and the Italian ophthalmologists was poor (k < 0.2). Patients with DR were older, had a longer duration of disease, worse glycaemic control, and a higher prevalence of comorbidities. In the multivariate logistic regression analysis, HbA1c, diabetes duration, and coronary heart disease (CHD) were associated with DR.ConclusionPrevalence of DR among urbanized Mozambican patients was similar to that observed in Western countries. Telediagnosis might partially overcome the paucity of local ophthalmologists with experience in DR.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00592-021-01834-3.

Assessment of Patient Journey Metrics for Users of a Digital Obstructive Sleep Apnea Program: Single-Arm Feasibility Pilot Study.

BackgroundDespite the importance of diagnosis and treatment, obstructive sleep apnea (OSA) remains a vastly underdiagnosed condition; this is partially due to current OSA identification methods and a complex and fragmented diagnostic pathway.ObjectiveThis prospective, single-arm, multistate feasibility pilot study aimed to understand the journey in a nonreferred sample of participants through the fully remote OSA screening and diagnostic and treatment pathway, using the Primasun Sleep Apnea Program (formally, Verily Sleep Apnea Program).MethodsParticipants were recruited online from North Carolina and Texas to participate in the study entirely virtually. Eligible participants were invited to schedule a video telemedicine appointment with a board-certified sleep physician who could order a home sleep apnea test (HSAT) to be delivered to the participant's home. The results were interpreted by the sleep physician and communicated to the participant during a second video telemedicine appointment. The participants who were diagnosed with OSA during the study and prescribed a positive airway pressure (PAP) device were instructed to download an app that provides educational and support-related content and access to personalized coaching support during the study’s 90-day PAP usage period. Surveys were deployed throughout the study to assess baseline characteristics, prior knowledge of sleep apnea, and satisfaction with the program.ResultsFor the 157 individuals who were ordered an HSAT, it took a mean of 7.4 (SD 2.6) days and median 7.1 days (IQR 2.0) to receive their HSAT after they completed their first televisit appointment. For the 114 individuals who were diagnosed with OSA, it took a mean of 13.9 (SD 9.6) days and median 11.7 days (IQR 10.1) from receiving their HSAT to being diagnosed with OSA during their follow-up televisit appointment. Overall, the mean and median time from the first televisit appointment to receiving an OSA diagnosis was 21.4 (SD 9.6) days and 18.9 days (IQR 9.2), respectively. For those who were prescribed PAP therapy, it took a mean of 8.1 (SD 9.3) days and median 6.0 days (IQR 4.0) from OSA diagnosis to PAP therapy initiation.ConclusionsThese results demonstrate the possibility of a highly efficient, patient-centered pathway for OSA workup and treatment. Such findings support pathways that could increase access to care, reduce loss to follow-up, and reduce health burden and overall cost. The program’s ability to efficiently diagnose patients who otherwise may have not been diagnosed with OSA is important, especially during a pandemic, as the United States shifted to remote care models and may sustain this direction. The potential economic and clinical impact of the program’s short and efficient journey time and low attrition rate should be further examined in future analyses. Future research also should examine how a fast and positive diagnosis experience impacts success rates for PAP therapy initiation and adherence.Trial RegistrationClinicalTrials.gov NCT04599803; https://clinicaltrials.gov/ct2/show/NCT04599803

Tele-Audiology: Current State and Future Directions.

The importance of tele-audiology has been heightened by the current COVID-19 pandemic. The present article reviews the current state of tele-audiology practice while presenting its limitations and opportunities. Specifically, this review addresses: (1) barriers to hearing healthcare, (2) tele-audiology services, and (3) tele-audiology key issues, challenges, and future directions. Accumulating evidence suggests that tele-audiology is a viable service delivery model, as remote hearing screening, diagnostic testing, intervention, and rehabilitation can each be completed reliably and effectively. The benefits of tele-audiology include improved access to care, increased follow-up rates, and reduced travel time and costs. Still, significant logistical and technical challenges remain from ensuring a secure and robust internet connection to controlling ambient noise and meeting all state and federal licensure and reimbursement regulations. Future research and development, especially advancements in artificial intelligence, will continue to increase tele-audiology acceptance, expand remote care, and ultimately improve patient satisfaction.

Organization of Work on the Prevention of Common Colds and Infectious Diseases with the Involvement of ICT

The article discusses the features of the organization of work on the prevention of colds and infectious diseases with the involvement of ICT. The author notes that due to the recent massive spread of coronavirus infection, specialists considered it necessary to turn to the potential of information technologies for the purpose of prevention, as well as providing timely necessary assistance to patients. The Internet of Things (IoT) is one of the most effective paradigms in an intelligent world in which artificial intelligence (AI) technologies, such as cloud computing and big data analysis, play a vital role, for example, in preventing the spread of a virus, the COVID-19 pandemic. Artificial intelligence and 5G technologies are developing by leaps and bounds, further strengthening the intellectual capabilities and connectivity of IoT applications, and traditional IoT is gradually being upgraded to become more powerful AI + IoT (AIoT). Their use is optimal, for example, from the point of view of remote screening and diagnosis of patients with COVID-19. Artificial intelligence technologies based on machine learning and deep learning have recently significantly upgraded medical equipment and changed the workflow with minimal contact with patients, so medical professionals can make clinical decisions more effectively, providing the best protection not only for patients, but also for specialists themselves

History of Alternative ☆ Energy Contest

The Alternative ☆ Energy Contest is a new contest on "energy utilization" mainly for university and technical college students, which started in 2008 as an event in which the technology and society departments of the JSME cooperate. The mark "☆" in the title of the contest indicates something that goes beyond the preconceived notions of alternative energy, and the essence of this contest that appeals for novel ideas appears there. From 2012, the year after the Fukushima nuclear accident caused by the Great East Japan Earthquake, a stationary event was held at the Nihon University College of Engineering located in Koriyama City, Fukushima Prefecture, as an event to watch over the reconstruction of Fukushima. In recognition of the efforts up to the 11th (September 7, 2018), we received the Japan Society of Mechanical Engineers Education Award under the title of "Promotion of Engineering Education on Sustainable Energy Utilization through the Alternative ☆ Energy Contest". The 12th meeting (October 19, 2019) had to be abandoned face-to-face due to the enormous flood damage caused by Typhoon No. 19. Since the application was accepted and the summary was printed, the summary was published as scheduled, and the poster was confirmed to have been announced. Despite the sudden change to remote screening based on poster data, the screening by the sponsoring companies and organizations responded promptly, and the award was given as usual. After the 13th (October 17, 2020), due to the influence of the spread of COVID-19 infection, we had to change to the WEB holding. We were able to reproduce a poster session using Zoom by making use of our experience of web lessons at the university. It was a memorable event to receive an application by a member of society for the first time in the contest during this period. The 15th meeting (October 15, 2022) is scheduled to be held on the website with the College of Engineering, Nihon University as the operating base. We are grateful for the opportunity to give an overview of this contest at this civic forum in this paper.

TSA-SCC: Text Semantic-Aware Screen Content Coding With Ultra Low Bitrate.

Due to the rapid growth of web conferences, remote screen sharing, and online games, screen content has become an important type of internet media information and over 90% of online media interactions are screen based. Meanwhile, as the main component in the screen content, textual information averagely takes up over 40% of the whole image on various commonly used screen content datasets. However, it is difficult to compress the textual information by using the traditional coding schemes as HEVC, which assumes strong spatial and temporal correlations within the image/video. State-of-the-art screen content coding (SCC) standard as HEVC-SCC still adopts a block-based coding framework and does not consider the text semantics for compression, thus inevitably blurring texts at a lower bitrate. In this paper, we propose a general text semantic-aware screen content coding scheme (TSA-SCC) for ultra low bitrate setting. This method detects the abrupt picture in a screen content video (or image), recognizes textual information (including word, position, font type, font size and font color) in the abrupt picture based on neural networks, and encodes texts with text coding tools. The other pictures as well as the background image after removing texts from the abrupt picture via inpainting, are encoded with HEVC-SCC. Compared with HEVC-SCC, the proposed method TSA-SCC reduces bitrate by up to 3× at a similar compression quality. Moreover, TSA-SCC achieves much better visual quality with less bitrate consumption when encoding the screen content video/image at ultra low bitrates.

User Perceptions &amp; Subjective Memory Concerns are Associated With Brief, Remote Cognitive Assessment Performance

Online cognitive tests offer a cost-effective, accessible means of cognitive screening and may prove especially important for individuals with memory complaints, a risk factor for cognitive impairment (Kaup et al., 2015). Although older adults’ perceptions of everyday technologies impact their uptake and adoption, there is limited understanding about how perceptions of online cognitive screening tests impact test performance. The purpose of the current study was to examine relationships between performance on a brief, self-administered, web-based cognitive assessment tool (SMART) and user perceptions (e.g., ratings of challenge and length), technology confidence, brain health activities, and memory complaints. Participants were 1336 adults without a diagnosis of cognitive impairment (Mage=60.48 years, SD=15.18; 65.8% female; 81.8% White; 21.2% with subjective memory complaints). Most participants (97%) were willing to complete the SMART again, with over half (53.5%) willing to complete the SMART on at least a weekly basis. After adjusting for age and education, better SMART performance (i.e., faster completion time) was associated with user ratings of greater ease of completion, higher technology confidence, and greater participation in brain health activities (p<.05). In a subsample aged 60+, individuals with memory complaints took longer to complete certain SMART tasks (Trail Making Test B, Total SMART) than those without memory complaints (p<.05). Results suggest that the SMART is a well-accepted tool for frequent remote cognitive screening and highlight the importance of user perceptions, technology confidence, and memory complaints on online cognitive test performance.