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Book ; Thesis: Biomechanische Charakterisierung des anterolateralen Ligaments

Zens, Martin

2015

Author's details	vorgelegt von Dipl. -Wi.-Ing. Martin Zens
Language	German
Size	XII, 80 Seiten, 10 Spalten, Illustrationen, Diagramme
Publishing place	Freiburg i. Br
Publishing country	Germany
Document type	Book ; Thesis
Thesis / German Habilitation thesis	Dissertation, Albert-Ludwigs-Universität Freiburg i. Br., 2015
HBZ-ID	HT018848999
Database	Catalogue ZB MED Medicine, Health

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Article ; Online: App-Based Tracking of Self-Reported COVID-19 Symptoms: Analysis of Questionnaire Data.

Zens, Martin / Brammertz, Arne / Herpich, Juliane / Südkamp, Norbert / Hinterseer, Martin

Journal of medical Internet research

2020 Volume 22, Issue 9, Page(s) e21956

Abstract: Background: COVID-19 is an infectious disease characterized by various clinical presentations. Knowledge of possible symptoms and their distribution allows for the early identification of infected patients.: Objective: To determine the distribution ... ...

Abstract	Background: COVID-19 is an infectious disease characterized by various clinical presentations. Knowledge of possible symptoms and their distribution allows for the early identification of infected patients. Objective: To determine the distribution pattern of COVID-19 symptoms as well as possible unreported symptoms, we created an app-based self-reporting tool. Methods: The COVID-19 Symptom Tracker is an app-based daily self-reporting tool. Between April 8 and May 15, 2020, a total of 22,327 individuals installed this app on their mobile device. An initial questionnaire asked for demographic information (age, gender, postal code) and past medical history comprising relevant chronic diseases. The participants were reminded daily to report whether they were experiencing any symptoms and if they had been tested for SARS-CoV-2 infection. Participants who sought health care services were asked additional questions regarding diagnostics and treatment. Participation was open to all adults (≥18 years). The study was completely anonymous. Results: In total, 11,829 (52.98%) participants completed the symptom questionnaire at least once. Of these, 291 (2.46%) participants stated that they had undergone an RT-PCR (reverse transcription-polymerase chain reaction) test for SARS-CoV-2; 65 (0.55%) reported a positive test result and 226 (1.91%) a negative one. The mean number of reported symptoms among untested participants was 0.81 (SD 1.85). Participants with a positive test result had, on average, 5.63 symptoms (SD 2.82). The most significant risk factors were diabetes (odds ratio [OR] 8.95, 95% CI 3.30-22.37) and chronic heart disease (OR 2.85, 95% CI 1.43-5.69). We identified chills, fever, loss of smell, nausea and vomiting, and shortness of breath as the top five strongest predictors for a COVID-19 infection. The odds ratio for loss of smell was 3.13 (95% CI 1.76-5.58). Nausea and vomiting (OR 2.84, 95% CI 1.61-5.00) had been reported as an uncommon symptom previously; however, our data suggest a significant predictive value. Conclusions: Self-reported symptom tracking helps to identify novel symptoms of COVID-19 and to estimate the predictive value of certain symptoms. This aids in the development of reliable screening tools. Clinical screening with a high pretest probability allows for the rapid identification of infections and the cost-effective use of testing resources. Based on our results, we suggest that loss of smell and taste be considered cardinal symptoms; we also stress that diabetes is a risk factor for a highly symptomatic course of COVID-19 infection.
MeSH term(s)	Adult ; Betacoronavirus ; COVID-19 ; Coronavirus Infections/diagnosis ; Diabetes Mellitus ; Early Diagnosis ; Female ; Humans ; Male ; Mass Screening/methods ; Middle Aged ; Mobile Applications ; Pandemics ; Pneumonia, Viral/diagnosis ; Risk Factors ; SARS-CoV-2 ; Self Report
Keywords	covid19
Language	English
Publishing date	2020-09-09
Publishing country	Canada
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2028830-X
ISSN	1438-8871 ; 1439-4456
ISSN (online)	1438-8871
ISSN	1439-4456
DOI	10.2196/21956
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: App-Based Tracking of Self-Reported COVID-19 Symptoms: Analysis of Questionnaire Data

Zens, Martin / Brammertz, Arne / Herpich, Juliane / Südkamp, Norbert / Hinterseer, Martin

J Med Internet Res

Abstract: BACKGROUND: COVID-19 is an infectious disease characterized by various clinical presentations. Knowledge of possible symptoms and their distribution allows for the early identification of infected patients. OBJECTIVE: To determine the distribution ... ...

Abstract	BACKGROUND: COVID-19 is an infectious disease characterized by various clinical presentations. Knowledge of possible symptoms and their distribution allows for the early identification of infected patients. OBJECTIVE: To determine the distribution pattern of COVID-19 symptoms as well as possible unreported symptoms, we created an app-based self-reporting tool. METHODS: The COVID-19 Symptom Tracker is an app-based daily self-reporting tool. Between April 8 and May 15, 2020, a total of 22,327 individuals installed this app on their mobile device. An initial questionnaire asked for demographic information (age, gender, postal code) and past medical history comprising relevant chronic diseases. The participants were reminded daily to report whether they were experiencing any symptoms and if they had been tested for SARS-CoV-2 infection. Participants who sought health care services were asked additional questions regarding diagnostics and treatment. Participation was open to all adults (≥18 years). The study was completely anonymous. RESULTS: In total, 11,829 (52.98%) participants completed the symptom questionnaire at least once. Of these, 291 (2.46%) participants stated that they had undergone an RT-PCR (reverse transcription-polymerase chain reaction) test for SARS-CoV-2; 65 (0.55%) reported a positive test result and 226 (1.91%) a negative one. The mean number of reported symptoms among untested participants was 0.81 (SD 1.85). Participants with a positive test result had, on average, 5.63 symptoms (SD 2.82). The most significant risk factors were diabetes (odds ratio [OR] 8.95, 95% CI 3.30-22.37) and chronic heart disease (OR 2.85, 95% CI 1.43-5.69). We identified chills, fever, loss of smell, nausea and vomiting, and shortness of breath as the top five strongest predictors for a COVID-19 infection. The odds ratio for loss of smell was 3.13 (95% CI 1.76-5.58). Nausea and vomiting (OR 2.84, 95% CI 1.61-5.00) had been reported as an uncommon symptom previously; however, our data suggest a significant predictive value. CONCLUSIONS: Self-reported symptom tracking helps to identify novel symptoms of COVID-19 and to estimate the predictive value of certain symptoms. This aids in the development of reliable screening tools. Clinical screening with a high pretest probability allows for the rapid identification of infections and the cost-effective use of testing resources. Based on our results, we suggest that loss of smell and taste be considered cardinal symptoms; we also stress that diabetes is a risk factor for a highly symptomatic course of COVID-19 infection.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #714499
Database	COVID19

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Article ; Online: App-based tracking of self-reported COVID-19 symptoms

Zens, Martin / Brammertz, Arne / Herpich, Juliane / Südkamp, Norbert / Hinterseer, Martin

Journal of medical internet research. - 22,

analysis of questionnaire data

2020 Volume 9, Issue , e21956, ISSN: 1438-8871

Abstract	Background: COVID-19 is an infectious disease characterized by various clinical presentations. Knowledge of possible symptoms and their distribution allows for the early identification of infected patients. Objective: To determine the distribution pattern of COVID-19 symptoms as well as possible unreported symptoms, we created an app-based self-reporting tool. Methods: The COVID-19 Symptom Tracker is an app-based daily self-reporting tool. Between April 8 and May 15, 2020, a total of 22,327 individuals installed this app on their mobile device. An initial questionnaire asked for demographic information (age, gender, postal code) and past medical history comprising relevant chronic diseases. The participants were reminded daily to report whether they were experiencing any symptoms and if they had been tested for SARS-CoV-2 infection. Participants who sought health care services were asked additional questions regarding diagnostics and treatment. Participation was open to all adults (≥18 years). The study was completely anonymous. Results: In total, 11,829 (52.98%) participants completed the symptom questionnaire at least once. Of these, 291 (2.46%) participants stated that they had undergone an RT-PCR (reverse transcription-polymerase chain reaction) test for SARS-CoV-2; 65 (0.55%) reported a positive test result and 226 (1.91%) a negative one. The mean number of reported symptoms among untested participants was 0.81 (SD 1.85). Participants with a positive test result had, on average, 5.63 symptoms (SD 2.82). The most significant risk factors were diabetes (odds ratio [OR] 8.95, 95% CI 3.30-22.37) and chronic heart disease (OR 2.85, 95% CI 1.43-5.69). We identified chills, fever, loss of smell, nausea and vomiting, and shortness of breath as the top five strongest predictors for a COVID-19 infection. The odds ratio for loss of smell was 3.13 (95% CI 1.76-5.58). Nausea and vomiting (OR 2.84, 95% CI 1.61-5.00) had been reported as an uncommon symptom previously; however, our data suggest a significant predictive value. Conclusions: Self-reported symptom tracking helps to identify novel symptoms of COVID-19 and to estimate the predictive value of certain symptoms. This aids in the development of reliable screening tools. Clinical screening with a high pretest probability allows for the rapid identification of infections and the cost-effective use of testing resources. Based on our results, we suggest that loss of smell and taste be considered cardinal symptoms; we also stress that diabetes is a risk factor for a highly symptomatic course of COVID-19 infection.
Keywords	COVID-19 ; Symptom ; Verfolgung ; App (Programm) ; Überwachung ; Verteilung ; Screening ; covid19
Subject code	150
Language	English
Publishing country	de
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: App-Based Tracking of Self-Reported COVID-19 Symptoms

Zens, Martin / Brammertz, Arne / Herpich, Juliane / Südkamp, Norbert / Hinterseer, Martin

Journal of Medical Internet Research, Vol 22, Iss 9, p e

Analysis of Questionnaire Data

2020 Volume 21956

Abstract: BackgroundCOVID-19 is an infectious disease characterized by various clinical presentations. Knowledge of possible symptoms and their distribution allows for the early identification of infected patients. ObjectiveTo determine the distribution pattern of ...

Abstract	BackgroundCOVID-19 is an infectious disease characterized by various clinical presentations. Knowledge of possible symptoms and their distribution allows for the early identification of infected patients. ObjectiveTo determine the distribution pattern of COVID-19 symptoms as well as possible unreported symptoms, we created an app-based self-reporting tool. MethodsThe COVID-19 Symptom Tracker is an app-based daily self-reporting tool. Between April 8 and May 15, 2020, a total of 22,327 individuals installed this app on their mobile device. An initial questionnaire asked for demographic information (age, gender, postal code) and past medical history comprising relevant chronic diseases. The participants were reminded daily to report whether they were experiencing any symptoms and if they had been tested for SARS-CoV-2 infection. Participants who sought health care services were asked additional questions regarding diagnostics and treatment. Participation was open to all adults (≥18 years). The study was completely anonymous. ResultsIn total, 11,829 (52.98%) participants completed the symptom questionnaire at least once. Of these, 291 (2.46%) participants stated that they had undergone an RT-PCR (reverse transcription-polymerase chain reaction) test for SARS-CoV-2; 65 (0.55%) reported a positive test result and 226 (1.91%) a negative one. The mean number of reported symptoms among untested participants was 0.81 (SD 1.85). Participants with a positive test result had, on average, 5.63 symptoms (SD 2.82). The most significant risk factors were diabetes (odds ratio [OR] 8.95, 95% CI 3.30-22.37) and chronic heart disease (OR 2.85, 95% CI 1.43-5.69). We identified chills, fever, loss of smell, nausea and vomiting, and shortness of breath as the top five strongest predictors for a COVID-19 infection. The odds ratio for loss of smell was 3.13 (95% CI 1.76-5.58). Nausea and vomiting (OR 2.84, 95% CI 1.61-5.00) had been reported as an uncommon symptom previously; however, our data suggest a significant predictive value. ...
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7 ; Public aspects of medicine ; RA1-1270
Subject code	150
Language	English
Publishing date	2020-09-01T00:00:00Z
Publisher	JMIR Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: "Back on Track": A Mobile App Observational Study Using Apple's ResearchKit Framework.

Zens, Martin / Woias, Peter / Suedkamp, Norbert P / Niemeyer, Philipp

JMIR mHealth and uHealth

2017 Volume 5, Issue 2, Page(s) e23

Abstract: Background: In March 2015, Apple Inc announced ResearchKit, a novel open-source framework intended to help medical researchers to easily create apps for medical studies. With the announcement of this framework, Apple presented 5 apps built in a beta ... ...

Abstract	Background: In March 2015, Apple Inc announced ResearchKit, a novel open-source framework intended to help medical researchers to easily create apps for medical studies. With the announcement of this framework, Apple presented 5 apps built in a beta phase based on this framework. Objective: The objective of this study was to better understand decision making in patients with acute anterior cruciate ligament (ACL) ruptures. Here, we describe the development of a ResearchKit app for this study. Methods: A multilanguage observatory study was conducted. At first a suitable research topic, target groups, participating territories, and programming method were carefully identified. The ResearchKit framework was used to program the app. A secure server connection was realized via Secure Sockets Layer. A data storage and security concept separating personal information and study data was proposed. Furthermore, an efficient method to allow multilanguage support and distribute the app in many territories was presented. Ethical implications were considered and taken into account regarding privacy policies. Results: An app study based on ResearchKit was developed without comprehensive iPhone Operating System (iOS) development experience. The Apple App Store is a major distribution channel causing significant download rates (>1.200/y) without active recruitment. Preliminary data analysis showed moderate dropout rates and a good quality of data. A total of 180 participants were currently enrolled with 107 actively participating and producing 424 completed surveys in 9 out of 24 months. Conclusions: ResearchKit is an easy-to-use framework and powerful tool to create medical studies. Advantages are the modular built, the extensive reach of iOS devices, and the convenient programming environment.
Language	English
Publishing date	2017-02-28
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2719220-9
ISSN	2291-5222
ISSN	2291-5222
DOI	10.2196/mhealth.6259
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Acute myocardial infarction due to coronary stent thrombosis in a symptomatic COVID-19 patient.

Hinterseer, Martin / Zens, Martin / Wimmer, Roland Jean / Delladio, Simon / Lederle, Susanne / Kupatt, Christian / Hartmann, Bernd

Clinical research in cardiology : official journal of the German Cardiac Society

2020 Volume 110, Issue 2, Page(s) 302–306

MeSH term(s)	Aged ; COVID-19/physiopathology ; Coronary Disease/therapy ; Coronary Thrombosis/complications ; Humans ; Male ; Myocardial Infarction/etiology ; Stents
Keywords	covid19
Language	English
Publishing date	2020-05-19
Publishing country	Germany
Document type	Case Reports ; Letter ; Research Support, Non-U.S. Gov't
ZDB-ID	2213295-8
ISSN	1861-0692 ; 1861-0684
ISSN (online)	1861-0692
ISSN	1861-0684
DOI	10.1007/s00392-020-01663-4
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Development of a Modular Research Platform to Create Medical Observational Studies for Mobile Devices.

Zens, Martin / Grotejohann, Birgit / Tassoni, Adrian / Duttenhoefer, Fabian / Südkamp, Norbert P / Niemeyer, Philipp

JMIR research protocols

2017 Volume 6, Issue 5, Page(s) e99

Abstract: Background: Observational studies have proven to be a valuable resource in medical research, especially when performed on a large scale. Recently, mobile device-based observational studies have been discovered by an increasing number of researchers as a ...

Abstract	Background: Observational studies have proven to be a valuable resource in medical research, especially when performed on a large scale. Recently, mobile device-based observational studies have been discovered by an increasing number of researchers as a promising new source of information. However, the development and deployment of app-based studies is not trivial and requires profound programming skills. Objective: The aim of this project was to develop a modular online research platform that allows researchers to create medical studies for mobile devices without extensive programming skills. Methods: The platform approach for a modular research platform consists of three major components. A Web-based platform forms the researchers' main workplace. This platform communicates via a shared database with a platform independent mobile app. Furthermore, a separate Web-based login platform for physicians and other health care professionals is outlined and completes the concept. Results: A prototype of the research platform has been developed and is currently in beta testing. Simple questionnaire studies can be created within minutes and published for testing purposes. Screenshots of an example study are provided, and the general working principle is displayed. Conclusions: In this project, we have created a basis for a novel research platform. The necessity and implications of a modular approach were displayed and an outline for future development given. International researchers are invited and encouraged to participate in this ongoing project.
Language	English
Publishing date	2017-05-23
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2719222-2
ISSN	1929-0748
ISSN	1929-0748
DOI	10.2196/resprot.7705
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Smartphone-based screening for atrial fibrillation: a pragmatic randomized clinical trial.

Rizas, Konstantinos D / Freyer, Luisa / Sappler, Nikolay / von Stülpnagel, Lukas / Spielbichler, Peter / Krasniqi, Aresa / Schreinlechner, Michael / Wenner, Felix N / Theurl, Fabian / Behroz, Amira / Eiffener, Elodie / Klemm, Mathias P / Schneidewind, Annika / Zens, Martin / Dolejsi, Theresa / Mansmann, Ulrich / Massberg, Steffen / Bauer, Axel

Nature medicine

2022 Volume 28, Issue 9, Page(s) 1823–1830

Abstract: Digital smart devices have the capability of detecting atrial fibrillation (AF), but the efficacy of this type of digital screening has not been directly compared to usual care for detection of treatment-relevant AF. In the eBRAVE-AF trial ( NCT04250220 ) ...

Abstract	Digital smart devices have the capability of detecting atrial fibrillation (AF), but the efficacy of this type of digital screening has not been directly compared to usual care for detection of treatment-relevant AF. In the eBRAVE-AF trial ( NCT04250220 ), we randomly assigned 5,551 policyholders of a German health insurance company who were free of AF at baseline (age 65 years (median; interquartile range (11) years, 31% females)) to digital screening (n = 2,860) or usual care (n = 2,691). In this siteless trial, for digital screening, participants used a certified app on their own smartphones to screen for irregularities in their pulse waves. Abnormal findings were evaluated by 14-day external electrocardiogram (ECG) loop recorders. The primary endpoint was newly diagnosed AF within 6 months treated with oral anti-coagulation by an independent physician not involved in the study. After 6 months, participants were invited to cross-over for a second study phase with reverse assignment for secondary analyses. The primary endpoint of the trial was met, as digital screening more than doubled the detection rate of treatment-relevant AF in both phases of the trial, with odds ratios of 2.12 (95% confidence interval (CI), 1.19-3.76; P = 0.010) and 2.75 (95% CI, 1.42-5.34; P = 0.003) in the first and second phases, respectively. This digital screening technology provides substantial benefits in detecting AF compared to usual care and has the potential for broad applicability due to its wide availability on ordinary smartphones. Future studies are needed to test whether digital screening for AF leads to better treatment outcomes.
MeSH term(s)	Atrial Fibrillation/diagnosis ; Atrial Fibrillation/drug therapy ; Child ; Delivery of Health Care ; Electrocardiography ; Female ; Humans ; Male ; Mass Screening ; Smartphone
Language	English
Publishing date	2022-08-28
Publishing country	United States
Document type	Journal Article ; Randomized Controlled Trial
ZDB-ID	1220066-9
ISSN	1546-170X ; 1078-8956
ISSN (online)	1546-170X
ISSN	1078-8956
DOI	10.1038/s41591-022-01979-w
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Article ; Online: Rationale and design of a digital trial using smartphones to detect subclinical atrial fibrillation in a population at risk: The eHealth-based bavarian alternative detection of Atrial Fibrillation (eBRAVE-AF) trial.

Freyer, Luisa / von Stülpnagel, Lukas / Spielbichler, Peter / Sappler, Nikolay / Wenner, Felix / Schreinlechner, Michael / Krasniqi, Aresa / Behroz, Amira / Eiffener, Elodie / Zens, Martin / Dolejsi, Theresa / Massberg, Steffen / Rizas, Konstantinos D / Bauer, Axel

American heart journal

2021 Volume 241, Page(s) 26–34

Abstract: Current guidelines recommend opportunistic screening for subclinical atrial fibrillation (AF) taking advantage of e-health-based technologies. However, the efficacy of a fully scalable e-health-based strategy for AF detection in a head-to-head comparison ...

Abstract	Current guidelines recommend opportunistic screening for subclinical atrial fibrillation (AF) taking advantage of e-health-based technologies. However, the efficacy of a fully scalable e-health-based strategy for AF detection in a head-to-head comparison with routine symptom-based screening is unknown. eBRAVE-AF is an investigator-initiated, digital, prospective, randomized, siteless, open-label, cross-over study to evaluate an e-health-based strategy for detection of AF in a real-world setting. 67,488 policyholders of a large German health insurance company (Versicherungskammer Bayern, Germany) selected by age ≥ 50 years and a CHA
MeSH term(s)	Asymptomatic Diseases/epidemiology ; Atrial Fibrillation/complications ; Atrial Fibrillation/diagnosis ; Atrial Fibrillation/drug therapy ; Atrial Fibrillation/epidemiology ; Cross-Over Studies ; Female ; Germany/epidemiology ; Humans ; Insurance, Health/statistics & numerical data ; Male ; Middle Aged ; Mobile Applications ; Monitoring, Ambulatory/instrumentation ; Monitoring, Ambulatory/methods ; Randomized Controlled Trials as Topic/methods ; Smartphone ; Telemedicine/instrumentation ; Telemedicine/methods
Language	English
Publishing date	2021-07-09
Publishing country	United States
Document type	Clinical Trial Protocol ; Journal Article
ZDB-ID	80026-0
ISSN	1097-6744 ; 0002-8703
ISSN (online)	1097-6744
ISSN	0002-8703
DOI	10.1016/j.ahj.2021.06.008
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