The Experts below are selected from a list of 195 Experts worldwide ranked by ideXlab platform
Constance H C Drossaert - One of the best experts on this subject based on the ideXlab platform.
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development of the digital health literacy instrument measuring a broad spectrum of health 1 0 and health 2 0 skills
Journal of Medical Internet Research, 2017Co-Authors: Rosalie Van Der Vaart, Constance H C DrossaertAbstract:Background: With the digitization of health care and the wide availability of Web-based applications, a broad set of skills is essential to properly use such facilities; these skills are called digital health literacy or eHealth literacy. Current instruments to measure digital health literacy focus only on information gathering (Health 1.0 skills) and do not pay attention to interactivity on the Web (Health 2.0). To measure the complete spectrum of Health 1.0 and Health 2.0 skills, including actual competencies, we developed a new instrument. The Digital Health Literacy Instrument (DHLI) measures operational skills, navigation skills, information searching, evaluating reliability, determining relevance, adding self-generated content, and protecting privacy. Objective: Our objective was to study the Distributional properties, reliability, content validity, and construct validity of the DHLI’s self-report scale (21 items) and to explore the feasibility of an additional set of performance-based items (7 items). Methods: We used a paper-and-pencil survey among a sample of the general Dutch population, stratified by age, sex, and educational level (T1; N=200). The survey consisted of the DHLI, sociodemographics, Internet use, health status, health literacy and the eHealth Literacy Scale (eHEALS). After 2 weeks, we asked participants to complete the DHLI again (T2; n=67). Cronbach alpha and intraclass correlation analysis between T1 and T2 were used to investigate reliability. Principal component analysis was performed to determine content validity. Correlation analyses were used to determine the construct validity. Results: Respondents (107 female and 93 male) ranged in age from 18 to 84 years (mean 46.4, SD 19.0); 23.0% (46/200) had a lower educational level. Internal consistencies of the total scale (alpha=.87) and the subscales (alpha range .70-.89) were satisfactory, except for protecting privacy (alpha=.57). Distributional properties showed an Approximately Normal Distribution. Test-retest analysis was satisfactory overall (total scale intraclass correlation coefficient=.77; subscale intraclass correlation coefficient range .49-.81). The performance-based items did not together form a single construct (alpha=.47) and should be interpreted individually. Results showed that more complex skills were reflected in a lower number of correct responses. Principal component analysis confirmed the theoretical structure of the self-report scale (76% explained variance). Correlations were as expected, showing significant relations with age (ρ=–.41, P<.001), education (ρ=.14, P=.047), Internet use (ρ=.39, P<.001), health-related Internet use (ρ=.27, P<.001), health status (ρ range .17-.27, P<.001), health literacy (ρ=.31, P<.001), and the eHEALS (ρ=.51, P<.001). Conclusions: This instrument can be accepted as a new self-report measure to assess digital health literacy, using multiple subscales. Its performance-based items provide an indication of actual skills but should be studied and adapted further. Future research should examine the acceptability of this instrument in other languages and among different populations.
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development of the digital health literacy instrument measuring a broad spectrum of health 1 0 and health 2 0 skills
Journal of Medical Internet Research, 2017Co-Authors: Rosalie Van Der Vaart, Constance H C DrossaertAbstract:Background: With the digitization of health care and the wide availability of Web-based applications, a broad set of skills is essential to properly use such facilities; these skills are called digital health literacy or eHealth literacy. Current instruments to measure digital health literacy focus only on information gathering (Health 1.0 skills) and do not pay attention to interactivity on the Web (Health 2.0). To measure the complete spectrum of Health 1.0 and Health 2.0 skills, including actual competencies, we developed a new instrument. The Digital Health Literacy Instrument (DHLI) measures operational skills, navigation skills, information searching, evaluating reliability, determining relevance, adding self-generated content, and protecting privacy. Objective: Our objective was to study the Distributional properties, reliability, content validity, and construct validity of the DHLI’s self-report scale (21 items) and to explore the feasibility of an additional set of performance-based items (7 items). Methods: We used a paper-and-pencil survey among a sample of the general Dutch population, stratified by age, sex, and educational level (T1; N=200). The survey consisted of the DHLI, sociodemographics, Internet use, health status, health literacy and the eHealth Literacy Scale (eHEALS). After 2 weeks, we asked participants to complete the DHLI again (T2; n=67). Cronbach alpha and intraclass correlation analysis between T1 and T2 were used to investigate reliability. Principal component analysis was performed to determine content validity. Correlation analyses were used to determine the construct validity. Results: Respondents (107 female and 93 male) ranged in age from 18 to 84 years (mean 46.4, SD 19.0); 23.0% (46/200) had a lower educational level. Internal consistencies of the total scale (alpha=.87) and the subscales (alpha range .70-.89) were satisfactory, except for protecting privacy (alpha=.57). Distributional properties showed an Approximately Normal Distribution. Test-retest analysis was satisfactory overall (total scale intraclass correlation coefficient=.77; subscale intraclass correlation coefficient range .49-.81). The performance-based items did not together form a single construct (alpha=.47) and should be interpreted individually. Results showed that more complex skills were reflected in a lower number of correct responses. Principal component analysis confirmed the theoretical structure of the self-report scale (76% explained variance). Correlations were as expected, showing significant relations with age (ρ=–.41, P<.001), education (ρ=.14, P=.047), Internet use (ρ=.39, P<.001), health-related Internet use (ρ=.27, P<.001), health status (ρ range .17-.27, P<.001), health literacy (ρ=.31, P<.001), and the eHEALS (ρ=.51, P<.001). Conclusions: This instrument can be accepted as a new self-report measure to assess digital health literacy, using multiple subscales. Its performance-based items provide an indication of actual skills but should be studied and adapted further. Future research should examine the acceptability of this instrument in other languages and among different populations. [J Med Internet Res 2017;19(1):e27]
David Newman - One of the best experts on this subject based on the ideXlab platform.
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Validation of a noninvasive measure of local myocardial repolarization in a conscious human model: adaptation of repolarization to changes in rate.
Journal of Cardiovascular Electrophysiology, 1999Co-Authors: Paul Dorian, Edward Davies, Catherine Dunne, Miney Paquette, Michael Geist, Aiala Barr, David NewmanAbstract:Rate Adaptation of Myocardial Repolarization. Introduction: A commercial pacemaker sensor measure of the unipolar endocardial stimulus to T wave interval may accurately reflect changes in the monophasic action potential duration at 90% repolarization (APD90). This sensor system was used to study the kinetics of adaptation of repolarization duration to changes in heart rate in humans. Methods and Results: Patients were studied using an external pacemaker capable of displaying all stimulus to T wave intervals for each paced beat. Right ventricular stimulation was delivered via the pacemaker and compared simultaneously to APD90. Steady-state pacing was simulated by 60 seconds of pacing at cycle lengths (CLs) 350 to 700 msec. Adaptation to a new ventricular rate was analyzed with a sudden 200-msec decrease in CL. The relation between repolarization measure and steady-state CL (n = 16) was linear with a slope of 0.16 and 0.19 for APD90 and stimulus to T wave interval, respectively (P = NS). The adaptation of both repolarization measures to a sudden change in rate were best modeled by a biexponential function. Stimulus to T wave interval exhibited a parallel course to APD90, and an analysis of Normalized differences between APD90 and stimulus to T wave interval followed an Approximately Normal Distribution, with 93.5% of the paired differences within 2 SD of the mean. Conclusion: A pacemaker sensor measure of stimulus to T wave interval accurately parallels APD90, during both steady-state and sudden changes in rate. Repolarization in human endocardium follows a linear relation to steady-state CL and adapts to a new rate with a biexponential function. This model represents a novel method for studying human cardiac repolarization.
Hassan Y. Aboul-enein - One of the best experts on this subject based on the ideXlab platform.
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New approach application of data transformation in mean centering of ratio spectra method.
Spectrochimica acta. Part A Molecular and biomolecular spectroscopy, 2015Co-Authors: Mahmoud Mohamed Issa, R’afat Mahmoud Nejem, Raluca Ioana Stefan Van Staden, Hassan Y. Aboul-eneinAbstract:Most of mean centering (MCR) methods are designed to be used with data sets whose values have a Normal or nearly Normal Distribution. The errors associated with the values are also assumed to be independent and random. If the data are skewed, the results obtained may be doubtful. Most of the time, it was assumed a Normal Distribution and if a confidence interval includes a negative value, it was cut off at zero. However, it is possible to transform the data so that at least an Approximately Normal Distribution is attained. Taking the logarithm of each data point is one transformation frequently used. As a result, the geometric mean is deliberated a better measure of central tendency than the arithmetic mean. The developed MCR method using the geometric mean has been successfully applied to the analysis of a ternary mixture of aspirin (ASP), atorvastatin (ATOR) and clopidogrel (CLOP) as a model. The results obtained were statistically compared with reported HPLC method.
Andrew J Vickers - One of the best experts on this subject based on the ideXlab platform.
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empirical estimates of the lead time Distribution for prostate cancer based on two independent representative cohorts of men not subject to prostate specific antigen screening
Cancer Epidemiology Biomarkers & Prevention, 2010Co-Authors: Caroline Savage, Hans Lilja, Angel M Cronin, David Ulmert, Andrew J VickersAbstract:Background: Lead time, the estimated time by which screening advances the date of diagnosis, is used to calculate the risk of overdiagnosis. We sought to describe empirically the Distribution of lead times between an elevated prostate-specific antigen (PSA) and subsequent prostate cancer diagnosis. Methods: We linked the Swedish cancer registry to two independent cohorts: 60-year-olds sampled in 1981-1982 and 51- to 56-year-olds sampled in 1982-1985. We used univariate kernel density estimation to characterize the lead time Distribution. Linear regression was used to model the lead time as a function of baseline PSA and logistic regression was used to test for an association between lead time and either stage or grade at diagnosis. Results: Of 1,167 older men, 132 were diagnosed with prostate cancer, of which 57 had PSA ≥3 ng/mL at baseline; 495 of 4,260 younger men were diagnosed with prostate cancer, of which 116 had PSA ≥3 ng/mL at baseline. The median lead time was slightly longer in the younger men (12.8 versus 11.8 years). In both cohorts, wide variation in lead times followed an Approximately Normal Distribution. Longer lead times were significantly associated with a lower risk of high-grade disease in older and younger men [odds ratio, 0.82 ( P = 0.023) and 0.77 ( P < 0.001)]. Conclusion: Our findings suggest that early changes in the natural history of the disease are associated with high-grade cancer at diagnosis. Impact: The distinct differences between the observed Distribution of lead times and those used in modeling studies illustrate the need to model overdiagnosis rates using empirical data. Cancer Epidemiol Biomarkers Prev; 19(5); 1201–7. ©2010 AACR.
Paul Dorian - One of the best experts on this subject based on the ideXlab platform.
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Validation of a noninvasive measure of local myocardial repolarization in a conscious human model: adaptation of repolarization to changes in rate.
Journal of Cardiovascular Electrophysiology, 1999Co-Authors: Paul Dorian, Edward Davies, Catherine Dunne, Miney Paquette, Michael Geist, Aiala Barr, David NewmanAbstract:Rate Adaptation of Myocardial Repolarization. Introduction: A commercial pacemaker sensor measure of the unipolar endocardial stimulus to T wave interval may accurately reflect changes in the monophasic action potential duration at 90% repolarization (APD90). This sensor system was used to study the kinetics of adaptation of repolarization duration to changes in heart rate in humans. Methods and Results: Patients were studied using an external pacemaker capable of displaying all stimulus to T wave intervals for each paced beat. Right ventricular stimulation was delivered via the pacemaker and compared simultaneously to APD90. Steady-state pacing was simulated by 60 seconds of pacing at cycle lengths (CLs) 350 to 700 msec. Adaptation to a new ventricular rate was analyzed with a sudden 200-msec decrease in CL. The relation between repolarization measure and steady-state CL (n = 16) was linear with a slope of 0.16 and 0.19 for APD90 and stimulus to T wave interval, respectively (P = NS). The adaptation of both repolarization measures to a sudden change in rate were best modeled by a biexponential function. Stimulus to T wave interval exhibited a parallel course to APD90, and an analysis of Normalized differences between APD90 and stimulus to T wave interval followed an Approximately Normal Distribution, with 93.5% of the paired differences within 2 SD of the mean. Conclusion: A pacemaker sensor measure of stimulus to T wave interval accurately parallels APD90, during both steady-state and sudden changes in rate. Repolarization in human endocardium follows a linear relation to steady-state CL and adapts to a new rate with a biexponential function. This model represents a novel method for studying human cardiac repolarization.