JMIRx med最新文献

{"title":"Predicting Waist Circumference From a Single Computed Tomography Image Using a Mobile App (Measure It): Development and Evaluation Study.","authors":"Abderrahmen Masmoudi, Amine Zouari, Ahmed Bouzid, Kais Fourati, Soulaimen Baklouti, Mohamed Ben Amar, Salah Boujelben","doi":"10.2196/38852","DOIUrl":"10.2196/38852","url":null,"abstract":"<p><strong>Background: </strong>Despite the existing evidence that waist circumference (WC) provides independent and additive information to BMI when predicting morbidity and mortality, this measurement is not routinely obtained in clinical practice. Using computed tomography (CT) scan images, mobile health (mHealth) has the potential to make this abdominal obesity parameter easily available even in retrospective studies.</p><p><strong>Objective: </strong>This study aimed to develop a mobile app as a tool for facilitating the measurement of WC based on a cross-sectional CT image.</p><p><strong>Methods: </strong>The development process included three stages: determination of the principles of WC measurement from CT images, app prototype design, and validation. We performed a preliminary validity study in which we compared WC measurements obtained both by the conventional method using a tape measurement in a standing position and by the mobile app using the last abdominal CT slice not showing the iliac bone. Pearson correlation, student t tests, and Q-Q and Bland-Altman plots were used for statistical analysis. Moreover, to perform a diagnostic test evaluation, we also analyzed the accuracy of the app in detecting abdominal obesity.</p><p><strong>Results: </strong>We developed a prototype of the app Measure It, which is capable of estimating WC from a single cross-sectional CT image. We used an estimation based on an ellipse formula adjusted to the gender of the patient. The validity study included 20 patients (10 men and 10 women). There was a good correlation between both measurements (Pearson R=0.906). The student t test showed no significant differences between the two measurements (P=.98). Both the Q-Q dispersion plot and Bland-Altman analysis graphs showed good overlap with some dispersion of extreme values. The diagnostic test evaluation showed an accuracy of 83% when using the mobile app to detect abdominal obesity.</p><p><strong>Conclusions: </strong>This app is a simple and accessible mHealth tool to routinely measure WC as a valuable obesity indicator in clinical and research practice. A usability and validity evaluation among medical teams will be the next step before its use in clinical trials and multicentric studies.</p>","PeriodicalId":73558,"journal":{"name":"JMIRx med","volume":"4 ","pages":"e38852"},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10958995/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139486678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Peer Review of “Predicting Waist Circumference From a Single Computed Tomography Image Using a Mobile App (Measure It): Development and Evaluation Study”","authors":"M. S. Arefin","doi":"10.2196/54012","DOIUrl":"https://doi.org/10.2196/54012","url":null,"abstract":"Waist Circumference From a Single Computed Tomography Image Using a Mobile App (Measure It): Development and Evaluation Study","PeriodicalId":73558,"journal":{"name":"JMIRx med","volume":"59 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139009604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Authors’ Response to Peer Reviews of “Predicting Waist Circumference From a Single Computed Tomography Image Using a Mobile App (Measure It): Development and Evaluation Study”","authors":"Abderrahmen Masmoudi, A. Zouari, Ahmed Bouzid, Kais Fourati, Soulaimen Baklouti, Mohamed Ben Amar, S. Boujelben","doi":"10.2196/53817","DOIUrl":"https://doi.org/10.2196/53817","url":null,"abstract":"","PeriodicalId":73558,"journal":{"name":"JMIRx med","volume":"47 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139006983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Peer Review of “Seroprevalence of SARS-CoV-2 in Niger State: Pilot Cross-Sectional Study”","authors":"Nadège Bourgeois-Nicolaos","doi":"10.2196/50391","DOIUrl":"https://doi.org/10.2196/50391","url":null,"abstract":"","PeriodicalId":73558,"journal":{"name":"JMIRx med","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135945114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seroprevalence of SARS-CoV-2 in Niger State: Pilot Cross-Sectional Study.","authors":"Hussaini Majiya,&nbsp;Mohammed Aliyu-Paiko,&nbsp;Vincent Tochukwu Balogu,&nbsp;Dickson Achimugu Musa,&nbsp;Ibrahim Maikudi Salihu,&nbsp;Abdullahi Abubakar Kawu,&nbsp;Ishaku Yakubu Bashir,&nbsp;Aishat Rabiu Sani,&nbsp;John Baba,&nbsp;Amina Tako Muhammad,&nbsp;Fatimah Ladidi Jibril,&nbsp;Ezekiel Bala,&nbsp;Nuhu George Obaje,&nbsp;Yahaya Badeggi Aliyu,&nbsp;Ramatu Gogo Muhammad,&nbsp;Hadiza Mohammed,&nbsp;Usman Naji Gimba,&nbsp;Abduljelili Uthman,&nbsp;Hadiza Muhammad Liman,&nbsp;Sule Alfa Alhaji,&nbsp;Joseph Kolo James,&nbsp;Muhammad Muhammad Makusidi,&nbsp;Mohammed Danasabe Isah,&nbsp;Ibrahim Abdullahi,&nbsp;Umar Ndagi,&nbsp;Bala Waziri,&nbsp;Chindo Ibrahim Bisallah,&nbsp;Naomi John Dadi-Mamud,&nbsp;Kolo Ibrahim,&nbsp;Abu Kasim Adamu","doi":"10.2196/29587","DOIUrl":"10.2196/29587","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic caused by SARS-CoV-2 is causing ongoing human and socioeconomic losses.</p><p><strong>Objective: </strong>To know how far the virus has spread in Niger State, Nigeria, a pilot study was carried out to determine the SARS-CoV-2 seroprevalence, patterns, dynamics, and risk factors in the state.</p><p><strong>Methods: </strong>A cross-sectional study design and clustered, stratified random sampling strategy were used to select 185 test participants across the state. SARS-CoV-2 IgG and IgM rapid test kits (colloidal gold immunochromatography lateral flow system) were used to determine the presence or absence of antibodies to the virus in the blood of sampled participants across Niger State from June 26 to 30, 2020. The test kits were validated using the blood samples of some of the Nigeria Center for Disease Control-confirmed positive and negative COVID-19 cases in the state. SARS-CoV-2 IgG and IgM test results were entered into the Epi Info questionnaire administered simultaneously with each test. Epi Info was then used to calculate the arithmetic mean and percentage, odds ratio, χ2 statistic, and regression at a 95% CI of the data generated.</p><p><strong>Results: </strong>The seroprevalence of SARS-CoV-2 in Niger State was found to be 25.4% (47/185) and 2.2% (4/185) for the positive IgG and IgM results, respectively. Seroprevalence among age groups, genders, and occupations varied widely. The COVID-19 asymptomatic rate in the state was found to be 46.8% (22/47). The risk analyses showed that the chances of infection are almost the same for both urban and rural dwellers in the state. However, health care workers, those who experienced flulike symptoms, and those who had contact with a person who traveled out of Nigeria in the last 6 months (February to June 2020) were at double the risk of being infected with the virus. More than half (101/185, 54.6%) of the participants in this study did not practice social distancing at any time since the pandemic started. Participants' knowledge, attitudes, and practices regarding COVID-19 are also discussed.</p><p><strong>Conclusions: </strong>The observed Niger State SARS-CoV-2 seroprevalence and infection patterns meansuggest that the virus has widely spread, far more SARS-CoV-2 infections have occurred than the reported cases, and there is a high asymptomatic COVID-19 rate across the state.</p>","PeriodicalId":73558,"journal":{"name":"JMIRx med","volume":"4 ","pages":"e29587"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10595504/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49685815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Peer Review of “Seroprevalence of SARS-CoV-2 in Niger State: Pilot Cross-Sectional Study”","authors":"Ari Samaranayaka","doi":"10.2196/49866","DOIUrl":"https://doi.org/10.2196/49866","url":null,"abstract":"","PeriodicalId":73558,"journal":{"name":"JMIRx med","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135945113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Authors’ Response to Peer Reviews of “Seroprevalence of SARS-CoV-2 in Niger State: Pilot Cross-Sectional Study”","authors":"Hussaini Majiya, Mohammed Aliyu-Paiko, Vincent Tochukwu Balogu, Dickson Achimugu Musa, Ibrahim Maikudi Salihu, Abdullahi Abubakar Kawu, Ishaku Yakubu Bashir, Aishat Rabiu Sani, John Baba, Amina Tako Muhammad, Fatimah Ladidi Jibril, Ezekiel Bala, Nuhu George Obaje, Yahaya Badeggi Aliyu, Ramatu Gogo Muhammad, Hadiza Mohammed, Usman Naji Gimba, Abduljelili Uthman, Hadiza Muhammad Liman, Sule Alfa Alhaji, Joseph Kolo James, Muhammad Muhammad Makusidi, Mohammed Danasabe Isah, Ibrahim Abdullahi, Umar Ndagi, Bala Waziri, Chindo Ibrahim Bisallah, Naomi John Dadi-Mamud, Kolo Ibrahim, Abu Kasim Adamu","doi":"10.2196/50515","DOIUrl":"https://doi.org/10.2196/50515","url":null,"abstract":"","PeriodicalId":73558,"journal":{"name":"JMIRx med","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136037650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Peer Review of “Seroprevalence of SARS-CoV-2 in Niger State: Pilot Cross-Sectional Study”","authors":"­ Anonymous","doi":"10.2196/50501","DOIUrl":"https://doi.org/10.2196/50501","url":null,"abstract":"","PeriodicalId":73558,"journal":{"name":"JMIRx med","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135945115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Peer Review “The Impact of SARS-CoV-2 Lineages (Variants) and COVID-19 Vaccination on the COVID-19 Epidemic in South Africa: Regression Study”","authors":"Rephaim Mpofu","doi":"10.2196/47143","DOIUrl":"https://doi.org/10.2196/47143","url":null,"abstract":"<jats:p />","PeriodicalId":73558,"journal":{"name":"JMIRx med","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43751956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Impact of SARS-CoV-2 Lineages (Variants) and COVID-19 Vaccination on the COVID-19 Epidemic in South Africa: Regression Study.","authors":"Thabo Mabuka, Natalie Naidoo, Nesisa Ncube, Thabo Yiga, Michael Ross, Kuzivakwashe Kurehwa, Mothabisi Nare Nyathi, Andrea Silaji, Tinashe Ndemera, Tlaleng Lemeke, Ridwan Taiwo, Willie Macharia, Mthokozisi Sithole","doi":"10.2196/34598","DOIUrl":"10.2196/34598","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Emerging SARS-CoV-2 variants have been attributed to the occurrence of secondary, tertiary, quaternary, and quinary COVID-19 epidemic waves threatening vaccine efforts owing to their immune invasiveness. Since the importation of SARS-CoV-2 in South Africa, with the first reported COVID-19 case on March 5, 2020, South Africa has observed 5 consecutive COVID-19 epidemic waves. The evolution of SARS-CoV-2 has played a major role in the resurgence of COVID-19 epidemic waves in South Africa and across the globe.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We aimed to conduct descriptive and inferential statistical analysis on South African COVID-19 epidemiological data to investigate the impact of SARS-CoV-2 lineages and COVID-19 vaccinations in South African COVID-19 epidemiology.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The general methodology involved the collation and stratification, covariance, regression analysis, normalization, and comparative inferential statistical analysis through null hypothesis testing (paired 2-tailed &lt;i&gt;t&lt;/i&gt; tests) of South African COVID-19 epidemiological data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The mean daily positive COVID-19 tests in South Africa's first, second, third, fourth, and fifth COVID-19 epidemic wave periods were 11.5% (SD 8.58%), 11.5% (SD 8.45%), 13.3% (SD 9.72%), 13.1% (SD 9.91%), and 14.3% (SD 8.49%), respectively. The COVID-19 transmission rate in the first and second COVID-19 epidemic waves in South Africa was similar, while the COVID-19 transmission rate was higher in the third, fourth, and fifth COVID-19 epidemic waves than in the aforementioned waves. Most COVID-19 hospitalized cases in South Africa were in the general ward (60%-79.1%). Patients with COVID-19 on oxygen were the second-largest admission status (11.2%-16.8%), followed by patients with COVID-19 in the intensive care unit (8.07%-16.7%). Most patients hospitalized owing to COVID-19 in South Africa's first, second, third, and fourth COVID-19 epidemic waves were aged between 40 and 49 years (16.8%-20.4%) and 50 and 59 years (19.8%-25.3%). Patients admitted to the hospital owing to COVID-19 in the age groups of 0 to 19 years were relatively low (1.98%-4.59%). In general, COVID-19 hospital admissions in South Africa for the age groups between 0 and 29 years increased after each consecutive COVID-19 epidemic wave, while for age groups between 30 and 79 years, hospital admissions decreased. Most COVID-19 hospitalization deaths in South Africa in the first, second, third, fourth, and fifth COVID-19 epidemic waves were in the ages of 50 to 59 years (15.8%-24.8%), 60 to 69 years (15.9%-29.5%), and 70 to 79 years (16.6%-20.7%).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The relaxation of COVID-19 nonpharmaceutical intervention health policies in South Africa and the evolution of SARS-CoV-2 were associated with increased COVID-19 transmission and severity in the South African population. COVID-19 vaccination in South Africa was strongly associ","PeriodicalId":73558,"journal":{"name":"JMIRx med","volume":"4 ","pages":"e34598"},"PeriodicalIF":0.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9839303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}