Cough is a very common symptom and the most frequent reason for seeking medical advice. Optimized care goes inevitably through an adapted recording of this symptom and automatic processing. This study provides an updated exhaustive quantitative review of the field of cough sound acquisition, automatic detection in longer audio sequences and automatic classification of the nature or disease. Related studies were analyzed and metrics extracted and processed to create a quantitative characterization of the state-of-the-art and trends. A list of objective criteria was established to select a subset of the most complete detection studies in the perspective of deployment in clinical practice. One hundred and forty-four studies were short-listed, and a picture of the state-of-the-art technology is drawn. The trend shows an increasing number of classification studies, an increase of the dataset size, in part from crowdsourcing, a rapid increase of COVID-19 studies, the prevalence of smartphones and wearable sensors for the acquisition, and a rapid expansion of deep learning. Finally, a subset of 12 detection studies is identified as the most complete ones. An unequaled quantitative overview is presented. The field shows a remarkable dynamic, boosted by the research on COVID-19 diagnosis, and a perfect adaptation to mobile health.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Periodic demographic health surveillance and surveys are the main sources of health information in developing countries. Conducting survey in requires extensive use of paper-pen and manual work and lengthy processes to generate the required information. Despite the rise of popularity in using electronic data collection systems to alleviate the problems, sufficient evidence is not available to support the use of electronic data capture tools in interviewer administered data collection processes. Objective: The purpose of this study was to compare data quality parameters in the data collected using mobile electronic and standard paper-based data capture tools in one of health and demographic surveillance sites in northwest Ethiopia. Methods: A randomized controlled crossover Healthcare IT-Evaluation was conducted from May 10 to June 3rd, 2016 in demographic and surveillance site. Twelve interview administrator, as two individuals in six groups (one with a tablet computer and the other with paper-based questionnaire), were assigned in the six towns of the surveillance premises. Data collectors switch data collection method based on computer generated random order. Data was cleaned using Mysql program and transferred to SPSS and R statistical software for analysis. Descriptive and mixed ordinal logistic analyses employed. The qualitative interview audio record was transcribed, and the usability of this open data kit (ODK) based system was assessed using system usability scale (SUS) and interview mapping in the isometric dialogue principles for system usability. Results: From the submitted 1,251 complete records/questionnaires in each tools, 42 % (522) of the paper and pen data capture (PPDC) and 29 % (285) of the electronic data capture (EDC) tool questionnaires had one or more types of data quality errors. The overall error rates were 1.67% and 0.6% for PPDC and EDC respectively. The chances of more errors on PPDC tool were multiplied by 1.015 for each additional question in the interview compared to EDC. The SUS score of the data collectors was 85.6. In qualitative data response mapping, EDC had more positive suitability of task responses with few error tolerance characteristics. Conclusions: EDC possessed significantly better data quality and efficient compared with PPDC, explained in fewer errors, instant data submission, and easy handling. The EDC proved to be a usable data collection tool in the rural study setting. The consistent power source and decent internet connection, standby technical support, and security assurance for the device users suggested by the data collectors before full-fledged the implementation of the system.

Background: The workload in health care is increasing and hence, mental health issues are on the rise among health care professionals (HCPs). The digitization of patient care could be related to the increase in stress levels. It remains unclear whether the health information system or systems and digital health technologies (DHTs) being used in health care relieve the professionals or whether they represent a further burden. The mental construct that best describes this burden of technologies is mental workload (MWL). The measurement methods of MWL are particularly relevant in this sensitive setting. Objective: This review aimed to address 2 different but related objectives: identifying the factors that contribute to the MWL of HCPs when using DHT and examining and exploring the applied assessments for the measurement of MWL with a special focus on eye tracking. Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 statement, we conducted a systematic review and processed a literature search in the following databases: MEDLINE (PubMed), Web of Science, Academic Search Premier and CINAHL (EBSCO), and PsycINFO. Studies were eligible if they assessed the MWL of HCPs related to DHT. The review was conducted as per the following steps: literature search, article selection, data extraction, quality assessment (using the Standard Quality Assessment Criteria for Evaluation Primary Research Papers From a Variety of Fields [QualSyst]), data analysis, and data synthesis (narrative and tabular). The process was performed by 2 reviewers (in cases of disagreement, a third reviewer was involved). Results: The literature search process resulted in 25 studies that fit the inclusion criteria and examined the MWL of health care workers resulting from the use of DHT in health care settings. Most studies had sample sizes of 10-50 participants, were conducted in the laboratory, and had quasi-experimental or cross-sectional designs. The main results can be grouped into two categories: assessment methods and factors related to DHT that contribute to MWL. Most studies applied subjective methods for the assessment of MWL. Eye tracking did not play a major role in the selected studies. The factors contributing to a higher MWL were clustered into organizational and systemic factors. Conclusions: Our review of 25 papers shows a diverse assessment approach toward the MWL of HCPs related to DHT as well as 2 groups of relevant contributing factors to MWL. Our results are limited in terms of interpretability and causality due to methodological weaknesses of the included studies and may be limited by some shortcomings in the search process. Future research should concentrate on adequate assessments of the MWL of HCPs dependent on the setting, the evaluation of quality criteria, and further assessment of the contributing factors to MWL. Trial Registration: PROSPERO (International Prospective Register of Systematic Reviews) CRD42021233271; https://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42021233271

Background: A population-level survey (PLS) is an essential and standard method used in public health research that supports the quantification of sociodemographic events, public health policy development, and intervention designs. Data collection mechanisms in PLS seem to be a significant determinant in avoiding mistakes. Using electronic devices such as smartphones and tablet computers improves the quality and cost-effectiveness of public health surveys. However, there is a lack of systematic evidence to show the potential impact of electronic data collection tools on data quality and cost reduction in interviewer-administered surveys compared with the standard paper-based data collection system. Objective: This systematic review aims to evaluate the impact of the interviewer-administered electronic data collection methods on data quality and cost reduction in PLS compared with traditional methods. Methods: We conducted a systematic search of MEDLINE, CINAHL, PsycINFO, the Web of Science, EconLit, Cochrane CENTRAL, and CDSR to identify relevant studies from 2008 to 2018. We included randomized and nonrandomized studies that examined data quality and cost reduction outcomes, as well as usability, user experience, and usage parameters. In total, 2 independent authors screened the title and abstract, and extracted data from selected papers. A third author mediated any disagreements. The review authors used EndNote for deduplication and Rayyan for screening. Results: Our search produced 3817 papers. After deduplication, we screened 2533 papers, and 14 fulfilled the inclusion criteria. None of the studies were randomized controlled trials; most had a quasi-experimental design, for example, comparative experimental evaluation studies nested on other ongoing cross-sectional surveys. A total of 4 comparative evaluations, 2 pre-post intervention comparative evaluations, 2 retrospective comparative evaluations, and 4 one-arm noncomparative studies were included. Meta-analysis was not possible because of the heterogeneity in study designs, types, study settings, and level of outcome measurements. Individual paper synthesis showed that electronic data collection systems provided good quality data and delivered faster compared with paper-based data collection systems. Only 2 studies linked cost and data quality outcomes to describe the cost-effectiveness of electronic data collection systems. Field data collectors reported that an electronic data collection system was a feasible, acceptable, and preferable tool for their work. Onsite data error prevention, fast data submission, and easy-to-handle devices were the comparative advantages offered by electronic data collection systems. Challenges during implementation included technical difficulties, accidental data loss, device theft, security concerns, power surges, and internet connection problems. Conclusions: Although evidence exists of the comparative advantages of electronic data collection compared with paper-based methods, the included studies were not methodologically rigorous enough to combine. More rigorous studies are needed to compare paper and electronic data collection systems in public health surveys considering data quality, work efficiency, and cost reduction. International Registered Report Identifier (IRRID): RR2-10.2196/10678

Background: Although surgical suturing is one of the most important basic skills, many medical school graduates do not acquire sufficient knowledge of it due to its lack of integration into the curriculum or a shortage of tutors. E-learning approaches attempt to address this issue but still rely on the involvement of tutors. Furthermore, the learning experience and visual-spatial ability appear to play a critical role in surgical skill acquisition. Virtual reality head-mounted displays (HMDs) could address this, but the benefits of immersive and stereoscopic learning of surgical suturing techniques are still unclear. Material and methods: In this multi-arm randomized controlled trial, 150 novices participated. Three teaching modalities were compared: an e-learning course (monoscopic), an HMD-based course (stereoscopic, immersive), both self-directed and a tutor-led course with feedback. Suturing performance was recorded by video camera both before and after course participation (>26 h of video material) and assessed in a blinded fashion using the Objective Structured Assessment of Technical Skills (OSATS) Global Rating Score (GRS). Furthermore, the optical flow of the videos was determined using an algorithm. The number of sutures performed was counted, the visual-spatial ability was measured with the Mental Rotation Test (MRT), and courses were assessed with questionnaires. Results: Students’ self-assessment in the HMD-based course was comparable to that of the tutor-led course and significantly better than in the e-learning course (P=0.003). Course suitability was rated best for the tutor-led course (x̄=4.8), followed by the HMD-based (x̄=3.6) and e-learning (x̄=2.5) courses. The median ΔGRS between courses was comparable (P=0.15) at 12.4 (95% CI 10.0–12.7) for the e-learning course, 14.1 (95% CI 13.0–15.0) for the HMD-based course, and 12.7 (95% CI 10.3–14.2) for the tutor-led course. However, the ΔGRS was significantly correlated with the number of sutures performed during the training session (P=0.002), but not with visual-spatial ability (P=0.615). Optical flow (R ²=0.15, P<0.001) and the number of sutures performed (R ²=0.73, P<0.001) can be used as additional measures to GRS. Conclusion: The use of HMDs with stereoscopic and immersive video provides advantages in the learning experience and should be preferred over a traditional web application for e-learning. Contrary to expectations, feedback is not necessary for novices to achieve a sufficient level in suturing; only the number of surgical sutures performed during training is a good determinant of competence improvement. Nevertheless, feedback still enhances the learning experience. Therefore, automated assessment as an alternative feedback approach could further improve self-directed learning modalities. As a next step, the data from this study could be used to develop such automated AI-based assessments.

Background: In severe cases, SARS-CoV-2 infection leads to acute respiratory distress syndrome (ARDS), often treated by extracorporeal membrane oxygenation (ECMO). During ECMO therapy, anticoagulation is crucial to prevent device-associated thrombosis and device failure, however, it is associated with bleeding complications. In COVID-19, additional pathologies, such as endotheliitis, may further increase the risk of bleeding complications. To assess the frequency of bleeding events, we analyzed data from the German COVID-19 autopsy registry (DeRegCOVID). Methods: The electronic registry uses a web-based electronic case report form. In November 2021, the registry included N = 1129 confirmed COVID-19 autopsy cases, with data on 63 ECMO autopsy cases and 1066 non-ECMO autopsy cases, contributed from 29 German sites. Findings: The registry data showed that ECMO was used in younger male patients and bleeding events occurred much more frequently in ECMO cases compared to non-ECMO cases (56% and 9%, respectively). Similarly, intracranial bleeding (ICB) was documented in 21% of ECMO cases and 3% of non-ECMO cases and was classified as the immediate or underlying cause of death in 78% of ECMO cases and 37% of non-ECMO cases. In ECMO cases, the three most common immediate causes of death were multi-organ failure, ARDS and ICB, and in non-ECMO cases ARDS, multi-organ failure and pulmonary bacterial ± fungal superinfection, ordered by descending frequency. Interpretation: Our study suggests the potential value of autopsies and a joint interdisciplinary multicenter (national) approach in addressing fatal complications in COVID-19.