Perceived Threat and Internet Use Predict Intentions to Get Bowel Cancer Screening (Colonoscopy): A Longitudinal Questionnaire Study

Becker, Daniela; Grapendorf, Johannes; Greving, Hannah; Sassenberg, Kai

doi:10.2196/preprints.9144

Cited by 2 publications

(2 citation statements)

References 19 publications

(22 reference statements)

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“…4 Still, they are invasive, complex procedures involving bowel preparation, along with possible severe complications such as hemorrhage, colonic perforation, and cardiorespiratory problems. 5 Metabolic variations in the blood can bring us some clues for the detection of CRC. The carcinoembryonic antigen (CEA) has been identified as a biomarker for CRC.…”

Section: Introductionmentioning

confidence: 99%

Colorectal Cancer Detected by Machine Learning Models Using Conventional Laboratory Test Data

Lin

Xiao

et al. 2021

Technol Cancer Res Treat

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Background: Current diagnostic methods for colorectal cancer (CRC) are colonoscopy and sigmoidoscopy, which are invasive and complex procedures with possible complications. This study aimed to determine models for CRC identification that involve minimally invasive, affordable, portable, and accurate screening variables. Methods: This was a retrospective study that used data from electronic medical records of patients with CRC and healthy individuals between July 2017 and June 2018. Laboratory data, including liver enzymes, lipid profiles, complete blood counts, and tumor biomarkers, were extracted from the electronic medical records. Five machine learning models (logistic regression, random forest, k-nearest neighbors, support vector machine [SVM], and naïve Bayes) were used to identify CRC. The performances were evaluated using the areas under the curve (AUCs), sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV). Results: A total of 1164 electronic medical records (CRC patients: 582; healthy controls: 582) were included. The logistic regression model achieved the highest performance in identifying CRC (AUC: 0.865, sensitivity: 89.5%, specificity: 83.5%, PPV: 84.4%, NPV: 88.9%). The first four weighted features in the model were carcinoembryonic antigen (CEA), hemoglobin (HGB), lipoprotein (a) (Lp(a)), and high-density lipoprotein (HDL). A diagnostic model for CRC was established based on the four indicators, with an AUC of 0.849 (0.840-0.860) for identifying all CRC patients, and it performed best in discriminating patients with late colon cancer from healthy individuals with an AUC of 0.905 (0.889-0.929). Conclusions: The logistic regression model based on CEA, HGB, Lp(a), and HDL might be a powerful, noninvasive, and cost-effective method to identify CRC.

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Section: Introductionmentioning

confidence: 99%

Colorectal Cancer Detected by Machine Learning Models Using Conventional Laboratory Test Data

Lin

Xiao

et al. 2021

Technol Cancer Res Treat

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“…Some government departments, medical and health institutions, and non-pro t organizations communicate more and more health information on the Internet. The Internet has gradually become an important resource for the public to obtain health information and seek medical and health care activities [12].…”

Section: Introductionmentioning

confidence: 99%

A reliability and validity study of the electronic health literacy scale among stroke patients in China

Guo

Zauszniewski

et al. 2021

Topics in Stroke Rehabilitation

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Background: It is of great signi cance for brain and heart health managers to assess the electronic health literacy of people at high risk of stroke for improving the current situation of stroke in China. Although various measuring instruments have been developed, there is still a lack of suitable tools to match the development of network.Aim: To examine the reliability and validity of the electronic Health Literacy Scale e-HLS among people at high risk of stroke in China, so as to provide appropriate measurement tools for brain heart health managers.Methods: A demographic questionnaire, the electronic Health Literacy Scale e-HLS and the eHealth Literacy Scale (eHEALS) were administered to a sample of 648 people at high risk of stroke recruited from November to December 2020 in a tertiary hospital.Results: The Cronbach'α coe cient on the e-HLS-CHI was 0.91. Three factors were extracted by Exploratory Factor Analysis (EFA), accounting for 90.84% of the total variance. Con rmatory Factory Analysis (CFA) revealed that three factors of e-HLS-CHI t well ( NFI = 0.979, RFI = 0.955, IFI = 0.987, TLI = 0.972, CFI=0.987, RMSEA = 0.070, CMIN/DF= 2.586). Good simultaneous validity was suggested by the positive correlation of 0.94 between the e-HLS-CHI and eHEALS. And when using eHEALS as the standard, the area under the ROC curve of e-HLS-CHI was 0.896 (95% CI: 0.831-0.960, P = 0.000). After calculation, the sensitivity and speci city were 97.8% and 70.4% respectively, indicating that it has nice predictive validity.Conclusions: The e-HLS can be used to evaluate electronic health literacy of people at high risk of stroke in China. This may be helpful for brain and heart health managers to assess the current situation of electronic health literacy in people at high risk of stroke and provide reference for future health promotion programs.

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Perceived Threat and Internet Use Predict Intentions to Get Bowel Cancer Screening (Colonoscopy): A Longitudinal Questionnaire Study

Abstract: The following full text is a publisher's version.For additional information about this publication click this link. https://hdl.handle.net/2066/219759Please be advised that this information was generated on 2024-05-30 and may be subject to change.

Cited by 2 publications

References 19 publications

Colorectal Cancer Detected by Machine Learning Models Using Conventional Laboratory Test Data

Colorectal Cancer Detected by Machine Learning Models Using Conventional Laboratory Test Data

A reliability and validity study of the electronic health literacy scale among stroke patients in China

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