In an attempt to reduce the infection rate of the COrona VIrus Disease-19 (Covid-19) countries around the world have echoed the exigency for an economical, accessible, point-of-need diagnostic test to identify Covid-19 carriers so that they (individuals who test positive) can be advised to self isolate rather than the entire community. Availability of a quick turnaround time diagnostic test would essentially mean that life, in general, can return to normality-at-large. In this regards, studies concurrent in time with ours have investigated different respiratory sounds, including cough, to recognise potential Covid-19 carriers. However, these studies lack clinical control and rely on Internet users confirming their test results in a web questionnaire (crowdsourcing) thus rendering their analysis inadequate. We seek to evaluate the detection performance of a primary screening tool of Covid-19 solely based on the cough sound from 8,380 clinically validated samples with laboratory molecular-test (2,339 Covid-19 positive and 6,041 Covid-19 negative) under quantitative RT-PCR (qRT-PCR) from certified laboratories. All collected samples were clinically labelled, i.e. Covid-19 positive or negative, according to the results in addition to the disease severity based on the qRT-PCR threshold cycle (Ct) and lymphocytes count from the patients. Our proposed generic method is an algorithm based on Empirical Mode Decomposition (EMD) for cough sound detection with subsequent classification based on a tensor of audio sonographs and deep artificial neural network classifier with convolutional layers called 'DeepCough'. Two different versions of DeepCough based on the number of tensor dimensions, i.e. DeepCough2D and DeepCough3D, have been investigated. These methods have been deployed in a multi-platform prototype web-app 'CoughDetect'.Covid-19 recognition results rates achieved a promising AUC (Area Under Curve) of 98.80% ± 0.83%, sensitivity of 96.43% ± 1.85%, and specificity of 96.20% ± 1.74% and average AUC of 81.08% ± 5.05% for the recognition of three severity levels. Our proposed web tool as a pointof-need primary diagnostic test for Covid-19 facilitates the rapid detection of the infection. We believe it has the potential to significantly hamper the Covid-19 pandemic across the world.

It is necessary to develop self-reported instruments that evaluate the process of living with chronic heart failure (HF) holistically. The Living with Chronic Illness Scale—HF (LW-CI-HF) is the only available tool to evaluate how patients are living with HF. The aim is to analyse the psychometric properties of the LW-CI scale in the HF population. An international, cross-sectional validation study was carried out in 603 patients living with HF from Spain and Colombia. The variables measured were living with HF, perceived social support, satisfaction with life, quality of life and global impression of severity. The LW-CI-HF scale presented good data quality and acceptability. All domains showed high internal consistency with Cronbach’s alpha coefficient ≥ 0.7. The intraclass correlation coefficient for the total score was satisfactory (0.9) in test–retest reliability. The LW-CI-HF correlated 0.7 with social support and quality of life measures. Standard error of measurement was 6.5 for total scale. The LW-CI-HF scale is feasible, reliable and valid. However, results should be taken with caution in order to be used in clinical practice to evaluate the complex process of living with HF. Further research is proposed.

We seek to evaluate the detection performance of a rapid primary screening tool of Covid-19 solely based on the cough sound from 8,380 clinically validated samples with laboratory molecular-test (2,339 Covid-19 positive and 6,041 Covid-19 negative). Samples were clinically labelled according to the results and severity based on quantitative RT-PCR (qRT-PCR) analysis, cycle threshold and lymphocytes count from the patients. Our proposed generic method is a algorithm based on Empirical Mode Decomposition (EMD) with subsequent classification based on a tensor of audio features and deep artificial neural network classifier with convolutional layers called DeepCough'. Two different versions of DeepCough based on the number of tensor dimensions, i.e. DeepCough2D and DeepCough3D, have been investigated. These methods have been deployed in a multi-platform proof-of-concept Web App CoughDetect to administer this test anonymously. Covid-19 recognition results rates achieved a promising AUC (Area Under Curve) of 98.800.83%, sensitivity of 96.431.85%, and specificity of 96.201.74%, and 81.08%5.05% AUC for the recognition of three severity levels. Our proposed web tool and underpinning algorithm for the robust, fast, point-of-need identification of Covid-19 facilitates the rapid detection of the infection. We believe that it has the potential to significantly hamper the Covid-19 pandemic across the world.

Background: Worldwide, type 2 diabetes mellitus (T2DM) is one of the most prevalent chronic diseases and one of those producing greatest impact on patients’ day-to-day quality of life. Our study aim is to validate the “Living with Chronic Illness Scale” for a Spanish-speaking T2DM population.Methods: In this observational, international, cross-sectional study, 582 persons with T2DM were recruited in primary care and outpatient hospital consultations, in Spain and Colombia, during the period from May 2018 to June 2019. The properties analysed were feasibility/acceptability, internal consistency, reliability, precision and (structural) content-construct validity including confirmatory factor analysis (CFA). The COSMIN checklist was used to assess the methodological/psychometric quality of the instrument.Results: The scale had an adequate internal consistency and test retest reliability (Cronbach’s alpha = 0.90; intraclass correlation coefficient = 0.96, respectively). In addition, the instrument is precise (standard error of measurement = 3.34, with values <½SD = 8.52) and correlates positively with social support (DUFSS) (rs = 0.56), quality of life (WHOQOL) (rs = 0.51-0.30) and satisfaction (SLS-6) (rs = 0.50-0.38). The CFA supported the 5-domains structure, but a 23-item version showed better fit: CMIN/df= 3.11; goodness of fit index= 0.91; comparative fit index= 0.91 and root mean square error of approximation = 0.06 (90% confidence interval, 0.06-0.07). The COSMIN checklist is favourable for all the properties analysed, although weaknesses are detected for content validity.Conclusions: “Living with T2DM” (LW-T2DM) is a valid, reliable and accurate instrument for use in clinical practice to determine how a person’s life is affected by the presence of diabetes. This instrument correlates well with the associated constructs of social support, quality of life and satisfaction. Additional research is needed to determine how well the questionnaire structure performs when robust factor analysis methods are applied.