Analyse verbreiteter Anwendungen zum Lesen von elektronischen Büchern - Benenson, Zinaida; Berger, Frederik; Cherepantsev, Anatoliy; Datsevich, Sergey; Do, Long; Eckert, Moritz; Elsberger, Tassilo; Freiling, Felix; Frinken, Marius; Glameyer, Hendrik; Hafner, Steffen; Hagenhoff, Svenja; et al. (2019)
Der Marktanteil elektronischer Bucher (E-Books) am Buchmarkt wächst beständig. Um E-Books zu rezipieren, benötigt man spezielle Leseumgebungen, die als Software (im Browser oder als eigene Anwendung) oder als Spezialgerät (E-Reader) realisiert sein können. Diese Leseumgebungen sind geeignet, Daten über das Leseverhalten zu sammeln. Im Rahmen einer universitären Lehrveranstaltung wurden die Software-Leseumgebungen der beiden deutschen Marktführer Kindle und Tolino untersucht. Der vorliegende Bericht fasst die Ergebnisse dieser Analysen zusammen. Das Ergebnis ist eine umfassende Bestandsaufnahme der digitalen Spuren, die durch die Benutzung der Programme entstehen. Betrachtet wurden die zum Untersuchungszeitpunkt aktuellen Versionen der jeweiligen Webanwendungen und Android-Apps sowie des Kindle-Windows-Clients. Die Ergebnisse entstanden im Rahmen einer Übung zur Vorlesung "Fortgeschrittene forensische Informatik II" im Wintersemester 2018/19 an der Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), die gemeinsam durchgeführt wurde vom Lehrstuhl fur Informatik 1 und dem Institut für Buchwissenschaft an der FAU.

Concept of a Simple Reaction Test for eHealth-Based Opioid Response Assessment in Palliative Care - Fidorra, Jacob; Nissen, Michael; Abdullahi Yari, Imrana; Ostgathe, Christoph; Steigleder, Tobias; Eskofier, Björn (12th World Research Congress of the European Association for Palliative Care, 2022)
Background/aims: Opioids are often used to reduce symptom burden in palliative care. To allow for maximum therapeutic effect and minimal adverse effects (AE), AE have to be monitored. One side effect of opioids is a reduction in cognitive function. Various aspects of cognitive function can be assessed by tests and questionnaires, such as the confusion assessment method (CAM) or Nine Hole Peg Test. All tests are time-consuming and need to be conducted by health care professionals. We aim to develop a simple smartphone-based reaction test as eHealth application in palliative care. Here, we present initial results of healthy participants which serve as baseline for future work. Methods: We implemented a simple reaction test as smartphone application: Users are tasked with pressing the screen when the color of the display changes. The reaction time (RT) between the color change and the press of the display is recorded. A field study with 15 healthy young participants was conducted. One RT test consists of four color changes. Each participant performed ten tests. Additional metrics were assessed by a questionnaire (sex, fatigue, work activity, previous experience with the application). Results: Average reaction time was 391.81 ± 49.26 ms across all individuals. Fatigue did not have an impact on reaction time. Average intraindividual deviation was significantly different between first time (35.91 ± 10.53 ms) and experienced (18.75 ± 3.45 ms) users. Conclusions: We developed a fast, simple and digital reaction test. The reaction test average stayed constant for all groups. Intraindividual reaction time deviation may serve as a boundary to determine an unusual decrease in cognitive processing time (CPT). We aim to assess the influence of factors like medication on a more heterogeneous as well as patient population in the future. This allows us to determine whether our test pipeline is able to reliably detect CPT decrease.

Digital Maternity Records: Motivation, Acceptance, Requirements, Usability and Prototype Evaluation of an Interface for Physicians and Midwives - Dörflinger, Luisa; Nissen, Michael; Jäger, Katharina; Wirth, Markus; Titzmann, Adriana; Pontones, Constanza; Fasching, Peter; Beckmann, Matthias; Gradl, Stefan; Eskofier, Björn (Mensch und Computer 2021, 2021)
Maternity records (”Mutterpass”) are a central tool in Germany’s antenatal care. Its digitalisation offers advantages such as improved readability, data standardisation, analysability, digital assistance and the avoidance of media breaks. Despite the examined necessity and technical possibility, no system has been implemented yet. In this work, we address this gap by assessing physicians’ and midwives’ perception, motivation, acceptance and requirements of a digital maternity record and its user interface in particular. Nine medical professionals participated in semi-structured interviews. Based on the results, an interface prototype was subsequently developed. The prototype laid a particular focus on two different gravidogram implementations. Thirteen medical professionals participated in the interactive user interface study, which aimed to assess general acceptance, completion time, error rate and implemented assistance functions compared to the analogue counterpart. Results show that the implementation of the digital maternity logbook is of value and that its advantages are both accepted and demanded by medical professionals. Eight key requirements were identified. A tabular gravidogram prototype, inspired by the analogue maternity record, was favoured over a more novel list prototype. Users presented a clear learning curve of the digital system. Nevertheless, the digital average completion time is 30% higher than its analogue counterpart. The digital prototype lead to a lower average number of errors per entry (0.45±0.3 vs. 0.58±0.4). With respect to digital maternity records, this work underlines user acceptance, summarizes key user requirements and presents a first step on the path to a digital maternity record interface for medical professionals. On a broader scale, it delivers insights based on several metrics with respect to digitalisation in an area that is still highly dominated by long established paper-based tools.

Evaluating the Effectiveness of Mobile Health in Breast Cancer Care: A Systematic Review - Flaucher, Madeleine; Zakreuskaya, Anastasiya; Nissen, Michael; Mocker, Alexander; Fasching, Peter; Beckmann, Matthias; Eskofier, Björn; Leutheuser, Heike (Oncologist, 2023)

Feasibility and Acceptance of Self-Guided Mobile Ultrasound among Pregnant Women in Routine Prenatal Care - Pontones, Constanza; Titzmann, Adriana; Hübner, Hanna; Danzberger, Nina; Rübner, Matthias; Häberle, Lothar; Eskofier, Björn; Nissen, Michael; Kehl, Sven; Faschingbauer, Florian; Beckmann, Matthias; Fasching, Peter; et al. (Journal of Clinical Medicine, 2023)
Background and objectives: Mobile and remote ultrasound devices are becoming increasingly available. The benefits and possible risks of self-guided ultrasound examinations conducted by pregnant women at home have not yet been well explored. This study investigated aspects of feasibility and acceptance, as well as the success rates of such examinations. Methods: In this prospective, single-center, interventional study, forty-six women with singleton pregnancies between 17 + 0 and 29 + 6 weeks of gestation were included in two cohorts, using two different mobile ultrasound systems. The participants examined the fetal heartbeat, fetal profile and amniotic fluid. Aspects of feasibility and acceptance were evaluated using a questionnaire. Success rates in relation to image and video quality were evaluated by healthcare professionals. Results: Two thirds of the women were able to imagine performing the self-guided examination at home, but 87.0% would prefer live support by a professional. Concerns about their own safety and that of the child were expressed by 23.9% of the women. Success rates for locating the target structure were 52.2% for videos of the fetal heartbeat, 52.2% for videos of the amniotic fluid in all four quadrants and 17.9% for videos of the fetal profile. Conclusion: These results show wide acceptance of self-examination using mobile systems for fetal ultrasonography during pregnancy. Image quality was adequate for assessing the amniotic fluid and fetal heartbeat in most participants. Further studies are needed to determine whether ultrasound self-examinations can be implemented in prenatal care and how this would affect the fetomaternal outcome.
CRIS-Team Scopus Importer:2023-07-28
Keywords: acceptance; feasibility; mobile ultrasound; pregnancy; prenatal care; self-guided ultrasound

Fetal Re-Identification in Multiple Pregnancy Ultrasound Images Using Deep Learning - Gabler, Elisabeth; Nissen, Michael; Altstidl, Thomas Robert; Titzmann, Adriana; Packhäuser, Kai; Maier, Andreas; Fasching, Peter; Eskofier, Björn; Leutheuser, Heike (2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023)
Ultrasound examinations during pregnancy can detect abnormal fetal development, which is a leading cause of perinatal mortality. In multiple pregnancies, the position of the fetuses may change between examinations. The individual fetus cannot be clearly identified. Fetal re-identification may improve diagnostic capabilities by tracing individual fetal changes. This work evaluates the feasibility of fetal re-identification on FETAL-PLANES-DB, a publicly available dataset of singleton pregnancy ultrasound images. Five dataset subsets with 6,491 images from 1,088 pregnant women and two re-identification frameworks (Torchreid, FastReID) are evaluated. FastReID achieves a mean average precision of 68.77% (68.42%) and mean precision at rank 10 score of 89.60% (95.55%) when trained on images showing the fetal brain (abdomen). Visualization with gradient-weighted class activation mapping shows that the classifiers appear to rely on anatomical features. We conclude that fetal re-identification in ultrasound images may be feasible. However, more work on additional datasets, including images from multiple pregnancies and several subsequent examinations, is required to ensure and investigate performance stability and explainability.Clinical relevance - To date, fetuses in multiple pregnancies cannot be distinguished between ultrasound examinations. This work provides the first evidence for feasibility of fetal re-identification in pregnancy ultrasound images. This may improve diagnostic capabilities in clinical practice in the future, such as longitudinal analysis of fetal changes or abnormalities.
Created from Fastlane, Scopus look-up
Keywords: Pregnancy; Deep learning; Visualization; Ultrasonic imaging; Fetus; Stability analysis; Biology

Heart Rate Measurement Accuracy of Fitbit Charge 4 and Samsung Galaxy Watch Active2: Device Evaluation Study - Nissen, Michael; Slim, Syrine; Jäger, Katharina; Flaucher, Madeleine; Hübner, Hanna; Danzberger, Nina; Fasching, Peter; Beckmann, Matthias; Gradl, Stefan; Eskofier, Björn (JMIR Formative Research, 2022)
Background: Fitness trackers and smart watches are frequently used to collect data in longitudinal medical studies. They allow continuous recording in real-life settings, potentially revealing previously uncaptured variabilities of biophysiological parameters and diseases. Adequate device accuracy is a prerequisite for meaningful research. Objective: This study aims to assess the heart rate recording accuracy in two previously unvalidated devices: Fitbit Charge 4 and Samsung Galaxy Watch Active2. Methods: Participants performed a study protocol comprising 5 resting and sedentary, 2 low-intensity, and 3 high-intensity exercise phases, lasting an average of 19 minutes 27 seconds. Participants wore two wearables simultaneously during all activities: Fitbit Charge 4 and Samsung Galaxy Watch Active2. Reference heart rate data were recorded using a medically certified Holter electrocardiogram. The data of the reference and evaluated devices were synchronized and compared at 1-second intervals. The mean, mean absolute error, mean absolute percentage error, Lin concordance correlation coefficient, Pearson correlation coefficient, and Bland-Altman plots were analyzed. Results: A total of 23 healthy adults (mean age 24.2, SD 4.6 years) participated in our study. Overall, and across all activities, the Fitbit Charge 4 slightly underestimated the heart rate, whereas the Samsung Galaxy Watch Active2 overestimated it (−1.66 beats per minute [bpm]/3.84 bpm). The Fitbit Charge 4 achieved a lower mean absolute error during resting and sedentary activities (seated rest: 7.8 vs 9.4; typing: 8.1 vs 11.6; laying down [left]: 7.2 vs 9.4; laying down [back]: 6.0 vs 8.6; and walking slowly: 6.8 vs 7.7 bpm), whereas the Samsung Galaxy Watch Active2 performed better during and after low- and high-intensity activities (standing up: 12.3 vs 9.0; walking fast: 6.1 vs 5.8; stairs: 8.8 vs 6.9; squats: 15.7 vs 6.1; resting: 9.6 vs 5.6 bpm). Conclusions: Device accuracy varied with activity. Overall, both devices achieved a mean absolute percentage error of just <10%. Thus, they were considered to produce valid results based on the limits established by previous work in the field. Neither device reached sufficient accuracy during seated rest or keyboard typing. Thus, both devices may be eligible for use in respective studies; however, researchers should consider their individual study requirement
Keywords: wearable validation; heart rate validation; Fitbit Charge 4; Samsung Galaxy Watch Active2; heart rate accuracy; fitness tracker accuracy; wearable accuracy; wearable (21); Fitbit (10); heart rate (14); fitness tracker (3); fitness (2); cardiovascular (8)

Improving Deep Learning-based Cardiac Abnormality Detection in 12-Lead ECG with Data Augmentation - Qiu, Jingna; Oppelt, Maximilian; Nissen, Michael; Anneken, Lars; Breininger, Katharina; Eskofier, Björn (: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022)
Automated Electrocardiogram (ECG) classification using deep neural networks requires large datasets annotated by medical professionals, which is time-consuming and expensive. This work examines ECG augmentation as a method for enriching existing datasets at low cost. First, we introduce three novel augmentations: Limb Electrode Move and Chest Electrode Move both simulate a minor electrode mislocation during signal measurement, and Heart Vector Transform generates an ECG by modeling a rotated main heart axis. These techniques are then combined with nine time series signal augmentations from literature. Evaluation was performed on ICBEB, PTB-XL Diagnostic, PTB-XL Rhythm, and PTB-XL Form datasets. Compared to models trained without data augmentation, area under the receiver operating characteristic curve (AUC) was increased by 3.5%, 1.7%, 1.4% and 3.5%, respectively. Our experiments demonstrated that data augmentation can improve deep learning performance in ECG classification. Analyses of the individual augmenion effects established the efficacy of the three proposed augmentations.<br

Machine Learning-based Detection of In-Utero Fetal Presentation from Non-Invasive Fetal ECG - Jäger, Katharina; Nissen, Michael; Richer, Robert; Rahm, Simone; Titzmann, Adriana; Fasching, Peter; Eskofier, Björn; Leutheuser, Heike (2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2022)
Preterm births account for more than 10 % of all newborns. An adverse fetal presentation is a risk factor for intrapartum and neonatal mortality. To date, no technology enables a longitudinal, ubiquitous, and unobtrusive monitoring of fetal presentation. This study presents a first approach to fetal orientation detection based on non-invasive fetal electrocardiography (NI-fECG) using the non-invasive multi-modal foetal ECG-Doppler data set for antenatal cardiology research. The data set contains 60 recordings from 39 pregnant women (21–27 weeks), including NI-fECG and ultrasound position ground truth. We evaluated both handcrafted and generic features for five different classifiers (k-Nearest-Neighbor, Decision Tree Classifier, Support Vector Classification, AdaBoost Classifier, and Multilayer Perceptron) using cross-validation on subject splits on a cleaned subset. Best results for the distinction between vertex (head down) and breech (head up) were achieved using an AdaBoost classifier with a balanced accuracy of 86.5 ± 15.0 %. With this work, we take a first step towards longitudinal fetal presentation monitoring, which contributes to a better understanding of reduced fetal movements and extends the potential applications of NI-fECG in prenatal care. In future work, we will expand our classification system to detect more detailed fetal presentations using a newly created data set.<br

Power-MF: Robust fetal QRS detection from non-invasive fetal electrocardiogram recordings - Jäger, Katharina; Nissen, Michael; Rahm, Simone; Titzmann, Adriana; Fasching, Peter; Beilner, Janina; Eskofier, Björn; Leutheuser, Heike (Physiological Measurement, 2024)
Objective. Perinatal asphyxia poses a significant risk to neonatal health, necessitating accurate fetal heart rate monitoring for effective detection and management. The current gold standard, cardiotocography, has inherent limitations, highlighting the need for alternative approaches. The emerging technology of non-invasive fetal electrocardiography shows promise as a new sensing technology for fetal cardiac activity, offering potential advancements in the detection and management of perinatal asphyxia. Although algorithms for fetal QRS detection have been developed in the past, only a few of them demonstrate accurate performance in the presence of noise and artifacts. Approach. In this work, we propose Power-MF, a new algorithm for fetal QRS detection combining power spectral density and matched filter techniques. We benchmark Power-MF against three open-source algorithms on two recently published datasets (Abdominal and Direct Fetal ECG Database: ADFECG, subsets B1 Pregnancy and B2 Labour; Non-invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research: NInFEA). Main results. Our results show that Power-MF outperforms state-of-the-art algorithms on ADFECG (B1 Pregnancy: 99.5 % ± 0.5 % F1-score, B2 Labour: 98.0 % ± 3.0 % F1-score) and on NInFEA in three of six electrode configurations by being more robust against noise. Significance. Through this work, we contribute to improving the accuracy and reliability of fetal cardiac monitoring, an essential step toward early detection of perinatal asphyxia with the long-term goal of reducing costs and making prenatal care more accessible.<br

Prevalence and course of pregnancy symptoms using self-reported pregnancy app symptom tracker data - Nissen, Michael; Barrios Campo, Nuria; Flaucher, Madeleine; Jäger, Katharina; Titzmann, Adriana; Blunck, Dominik; Fasching, Peter; Engelhardt, Victoria; Eskofier, Björn; Leutheuser, Heike (npj Digital Medicine, 2023)
During pregnancy, almost all women experience pregnancy-related symptoms. The relationship between symptoms and their association with pregnancy outcomes is not well understood. Many pregnancy apps allow pregnant women to track their symptoms. To date, the resulting data are primarily used from a commercial rather than a scientific perspective. In this work, we aim to examine symptom occurrence, course, and their correlation throughout pregnancy. Self-reported app data of a pregnancy symptom tracker is used. In this context, we present methods to handle noisy real-world app data from commercial applications to understand the trajectory of user and patient-reported data. We report real-world evidence from patient-reported outcomes that exceeds previous works: 1,549,186 tracked symptoms from 183,732 users of a smartphone pregnancy app symptom tracker are analyzed. The majority of users track symptoms on a single day. These data are generalizable to those users who use the tracker for at least 5 months. Week-by-week symptom report data are presented for each symptom. There are few or conflicting reports in the literature on the course of diarrhea, fatigue, headache, heartburn, and sleep problems. A peak in fatigue in the first trimester, a peak in headache reports around gestation week 15, and a steady increase in the reports of sleeping difficulty throughout pregnancy are found. Our work highlights the potential of secondary use of industry data. It reveals and clarifies several previously unknown or disputed symptom trajectories and relationships. Collaboration between academia and industry can help generate new scientific knowledge.
CRIS-Team Scopus Importer:2023-10-20

Smartphone-Based Colorimetric Analysis of Urine Test Strips for At-Home Prenatal Care - Flaucher, Madeleine; Nissen, Michael; Jäger, Katharina; Titzmann, Adriana; Pontones, Constanza; Hübner, Hanna; Fasching, Peter; Beckmann, Matthias; Gradl, Stefan; Eskofier, Björn (IEEE Journal of Translational Engineering in Health and Medicine, 2022)
Objective: Clinical urine tests are a key component of prenatal care. As of now, urine test strips are evaluated through a time consuming, often error-prone and operator-dependent visual color comparison of test strips and reference cards by medical staff. Methods and procedures: This work presents an automated pipeline for urinalysis with urine test strips using smartphone camera images in home environments, combining several image processing and color combination techniques. Our approach is applicable to off-the-shelf test strips in home conditions with no additional hardware required. For development and evaluation of our pipeline we collected image data from two sources: i) A user study (26 participants, 150 images) and ii) a lab study (135 images). Results: We trained a region-based convolutional neural network that is able to detect the urine test strip location and orientation in images with a wide variety of light conditions, backgrounds and perspectives with an accuracy of 85.5%. The reference card can be robustly detected through a feature matching approach in 98.6% of the images. Color comparison by Hue channel (0.81 F1-Score), Matching factor (0.80 F1-Score) and Euclidean distance (0.70 F1-Score) were evaluated to determine the urinalysis results. Conclusion: We show that an automated smartphone-based colorimetric analysis of urine test strips in a home environment is feasible. It facilitates examinations and provides the possibility to shift care into an at-home environment. Clinical impact: The findings demonstrate that routine urine examinations can be transferred into the home environment using a smartphone. Simultaneously, human error is avoided, accuracy is increased and medical staff is relieve

Smartphone pregnancy apps: systematic analysis of features, scientific guidance, commercialization, and user perception - Nissen, Michael; Huang, Shih-Yuan; Jäger, Katharina; Flaucher, Madeleine; Titzmann, Adriana; Bleher, Hannah; Pontones, Constanza; Hübner, Hanna; Danzberger, Nina; Fasching, Peter; Eskofier, Björn; Leutheuser, Heike (BMC Pregnancy and Childbirth, 2024)
Background Over 50% of pregnant women use pregnancy applications (apps). Some app s lack credibility, information accuracy, and evidence-based clinical advice, containing potentially harmful functionality. Previous studies have only conducted a limited analysis of pregnancy app functionalities, expert involvement/evidence-based content, used commercialization techniques, and user perception. Methods We used the keyword “pregnancy” to scrape (automatically extract) apps and app information from Apple App Store and Google Play. Unique functionalities were derived from app descriptions and user reviews. App descriptions were screened for evidence-based content and expert involvement, and apps were subsequently analyzed in detail. Apps were opened and searched for used commercialization techniques, such as advertisements or affiliate marketing. Automated text analysis (natural language processing) was used on app reviews to assess users’ perception of evidence-based content/expert involvement and commercialization techniques. Results In total, 495 apps were scraped. 226 remained after applying exclusion criteria. Out of these, 36 represented 97%/88% of the total market share (Apple App Store/Google Play), and were thus considered for review. Overall, 49 distinct functionalities were identified, out of which 6 were previously unreported. Functionalities for fetal kick movement counting were found. All apps are commercial. Only 15 apps mention the involvement of medical experts. 10.3% of two-stars user reviews include commercial topics, and 0.6% of one-/two-/three-/five stars user reviews include references to scientific content accuracy. Conclusion Problematic features and inadequate advice continue to be present in pregnancy apps. App developers should adopt an evidence-based development approach and avoid implementing as many features as possible, potentially at the expense of their quality or over-complication (“feature creep”). Financial incentives, such as grant programs, could support adequate content quality. Caregivers play a key role in pregnant individuals’ decision-making, should be aware of potential dangers, and could guide them to trustworthy app

Systematic Comparison of ECG Delineation Algorithm Performance on Smartwatch Data - Jäger, Katharina; Nissen, Michael; Flaucher, Madeleine; Graf, Luisa; Joanidopoulos, Joana; Anneken, Lars; Hübner, Hanna; Goossens, Chloe; Titzmann, Adriana; Pontones, Constanza; Fasching, Peter; Beckmann, Matthias; et al. (IEEE Access, 2024)
Cardiovascular diseases are the leading cause of global mortality, necessitating early detection and continuous monitoring for timely interventions. Smartwatches with electrocardiogram (ECG) recording capabilities enable real-time, at-home cardiac monitoring. Specific ECG characteristics can provide insights into cardiovascular diseases. The delineation of ECGs, which is the identification of fiducial points (such as onsets, offsets, and peaks), is a time-consuming task. Automated ECG delineation can enhance this process, but existing research comparing available algorithms is limited. Furthermore, to the best of our knowledge, none have addressed single-lead ECGs from smartwatches, which can be noisy and unfiltered. Thus, this study evaluates the best-performing open-source algorithm for single-lead and smartwatch ECG data. We used two public datasets (Lobachevsky University Database, QT Database) and two smartwatch datasets (SmartHeartWatch Dataset, SMART Start Dataset) including two devices (AppleWatch, Withings ScanWatch). Algorithms from three toolkits (NeuroKit, ECGKit, ECGdeli) were assessed based on the time deviation between algorithm outputs and reference annotations, sensitivity, true positives, and false negatives. Results were further evaluated against the Common Standards for Quantitative Electrocardiography (CSE) recommendations. ECGdeli outperformed the other algorithms. For QRS on- and offset, ECGkit shows comparable sensitivity, but otherwise lower scores. NeuroKit consistently shows lower sensitivity across all four data sets, however, the temporal deviation between detected point and reference was higher. Overall, sensitivity scores were higher for Apple Watch data compared to Withings ScanWatch data. This study demonstrates that segmentation algorithms are applicable to single-lead smartwatch ECG data, with ECGdeli being the most stable overall, and NeuroKit recommended for scenarios prioritizing the temporal accuracy of detected points.
CRIS-Team Scopus Importer:2024-11-15
Keywords: Cardiovascular Diseases; ECG Segmentation; Mobile Health; Wearable Devices

The Effectiveness of Patient Training in Inflammatory Bowel Disease Knowledge via Instagram: Randomized Controlled Trial - Blunck, Dominik; Kastner, Lena; Nissen, Michael; Winkler, Jacqueline (Journal of Medical Internet Research, 2022)
Background: Patients’ knowledge was found to be a key contributor to the success of therapy. Many efforts have been made to educate patients in their disease. However, research found that many patients still lack knowledge regarding their disease. Integrating patient education into social media platforms can bring materials closer to recipients. Objective: The aim of this study is to test the effectiveness of patient education via Instagram. Methods: A randomized controlled trial was conducted to test the effectiveness of patient education via Instagram among patients with inflammatory bowel disease. Participants were recruited online from the open Instagram page of a patient organization. The intervention group was educated via Instagram for 5 weeks by the research team; the control group did not receive any educational intervention. The knowledge about their disease was measured pre- and postintervention using the Inflammatory Bowel Disease Knowledge questionnaire. Data were analyzed by comparing mean knowledge scores and by regression analysis. The trial was purely web based. Results: In total, 49 participants filled out both questionnaires. The intervention group included 25 participants, and the control group included 24 participants. The preintervention knowledge level of the intervention group was reflected as a score of 18.67 out of 24 points; this improved by 3 points to 21.67 postintervention. The postintervention difference between the control and intervention groups was 3.59 points and was statistically significant (t32.88=–4.56, 95% CI 1.98-5.19; P<.001). Results of the regression analysis, accounting for preintervention knowledge and group heterogeneity, indicated an increase of 3.33 points that was explained by the intervention (P<.001). Conclusions: Patient education via Instagram is an effective way to increase disease-related knowledge. Future studies are needed to assess the effects in other conditions and to compare different means of patient educatio
Keywords: social media; Instagram; patient training; patient education; disease-related knowledge; RCT; randomized controlled trial; Germany; inflammatory bowel disease; IBD-KNOW

Usability and Perception of a Wearable-Integrated Digital Maternity Record App in Germany: User Study - Nissen, Michael; Perez, Carlos A.; Jäger, Katharina; Bleher, Hannah; Flaucher, Madeleine; Hübner, Hanna; Danzberger, Nina; Titzmann, Adriana; Pontones, Constanza; Fasching, Peter; Beckmann, Matthias; Eskofier, Björn; et al. (JMIR Pediatrics and Parenting, 2023)
Background: Although digital maternity records (DMRs) have been evaluated in the past, no previous work investigated usability or acceptance through an observational usability study. Objective: The primary objective was to assess the usability and perception of a DMR smartphone app for pregnant women. The secondary objective was to assess personal preferences and habits related to online information searching, wearable data presentation and interpretation, at-home examination, and sharing data for research purposes during pregnancy. Methods: A DMR smartphone app was developed. Key features such as wearable device integration, study functionalities (eg, questionnaires), and common pregnancy app functionalities (eg, mood tracker) were included. Women who had previously given birth were invited to participate. Participants completed 10 tasks while asked to think aloud. Sessions were conducted via Zoom. Video, audio, and the shared screen were recorded for analysis. Task completion times, task success, errors, and self-reported (free text) feedback were evaluated. Usability was measured through the System Usability Scale (SUS) and User Experience Questionnaire (UEQ). Semistructured interviews were conducted to explore the secondary objective. Results: A total of 11 participants (mean age 34.6, SD 2.2 years) were included in the study. A mean SUS score of 79.09 (SD 18.38) was achieved. The app was rated “above average” in 4 of 6 UEQ categories. Sixteen unique features were requested. We found that 5 of 11 participants would only use wearables during pregnancy if requested to by their physician, while 10 of 11 stated they would share their data for research purposes. Conclusions: Pregnant women rely on their medical caregivers for advice, including on the use of mobile and ubiquitous health technology. Clear benefits must be communicated if issuing wearable devices to pregnant women. Participants that experienced pregnancy complications in the past were overall more open toward the use of wearable devices in pregnancy. Pregnant women have different opinions regarding access to, interpretation of, and reactions to alerts based on wearable data. Future work should investigate personalized concepts covering these aspects.
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Keywords: app; application; applications; apps; attitude; attitudes; data sharing; develop; development; digital; electronic; electronic maternity record; experience; experiences; feature; features; health record; health records; information behavior; information behaviour; information seeking; log; logbook; maternal; maternity; maternity log; maternity logbook; maternity record; mHealth; mobile health; online search; opinion; opinions; paper hand-held record; perception; perceptions; personal health record; perspective; perspectives; PHR; pregnancy; pregnancy app; pregnancy record; pregnant; privacy; searching; smartphone; smartphones; usability; wearable; wearables

WebPPG: Feasibility and Usability of Self-Performed, Browser-Based Smartphone Photoplethysmography - Nissen, Michael; Flaucher, Madeleine; Jäger, Katharina; Hübner, Hanna; Danzberger, Nina; Titzmann, Adriana; Pontones, Constanza; Fasching, Peter; Eskofier, Björn; Leutheuser, Heike (2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023)
Smartphones enable and facilitate biomedical studies as they allow the recording of various biomedical signals, including photoplethysmograms (PPG). However, user engagement rates in mobile health studies are reduced when an application (app) needs to be installed. This could be alleviated by using installation-free web apps. We evaluate the feasibility of browser-based PPG recording, conducting the first usability study on smartphone-based PPG. We present an at-home study using a web app and library for PPG recording using the rear camera and flash. The underlying library is freely made available to researchers. 25 Android users participated, using their own smartphones. The study consisted of a demographic and anamnestic questionnaire, the signal recording itself (60 s), and a consecutive usability questionnaire. After filtering, heart rate was extracted (14/17 successful), signal-to-noise ratios assessed (0.64 ± 0.50 dB, mean ± standard deviation), and quality was visually inspected (12/17 usable for diagnosis). Recording was not supported in 9 cases. This was due to the browser's insufficient support for the flash light API. The app received a System Usability Scale score of 82 ± 9, which is above the 90th percentile. Overall, browser flash light support is the main limiting factor for broad device support. Thus, browser-based PPG is not yet widely applicable, although most participants feel comfortable with the recording itself. The utilization of the user-facing camera might represent a more promising approach. This study contributes to the development of low-barrier, user-friendly, installation-free smartphone signal acquisition. This enables profound, comprehensive data collection for research and clinical practice.Clinical relevance - WebPPG offers low-barrier remote diagnostic capabilities without the need for app installation
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Keywords: Heart rate; Limiting; Cameras; Photoplethysmography; Libraries; Recording; Usability