Call to Action: Investigating Interaction Delay in Smartphone Notifications

Notifications are an essential part of the user experience on smart mobile devices. While some apps have to notify users immediately after an event occurs, others can schedule notifications strategically to notify them only on opportune moments. This tailoring allows apps to shorten the users' interaction delay. In this paper, we present the results of a comprehensive study that identified the factors that influence users' interaction delay to their smartphone notifications. We analyzed almost 10 million notifications collected in-the-wild from 922 users and computed their response times with regard to their demographics, their Big Five personality trait scores and the device's charging state. Depending on the app category, the following tendencies can be identified over the course of the day: Most notifications were logged in late morning and late afternoon. This number decreases in the evening, between 8 p.m. and 11 p.m., and at the same time exhibits the lowest average interaction delays at daytime. We also found that the user's sex and age is significantly associated with the response time. Based on the results of our study, we encourage developers to incorporate more information on the user and the executing device in their notification strategy to notify users more effectively.

Sentiments about Mental Health on Twitter-Before and during the COVID-19 Pandemic

During the COVID-19 pandemic, the novel coronavirus had an impact not only on public health but also on the mental health of the population. Public sentiment on mental health and depression is often captured only in small, survey-based studies, while work based on Twitter data often only looks at the period during the pandemic and does not make comparisons with the pre-pandemic situation. We collected tweets that included the hashtags #MentalHealth and #Depression from before and during the pandemic (8.5 months each). We used LDA (Latent Dirichlet Allocation) for topic modeling and LIWC, VADER, and NRC for sentiment analysis. We used three machine-learning classifiers to seek evidence regarding an automatically detectable change in tweets before vs. during the pandemic: (1) based on TF-IDF values, (2) based on the values from the sentiment libraries, (3) based on tweet content (deep-learning BERT classifier). Topic modeling revealed that Twitter users who explicitly used the hashtags #Depression and especially #MentalHealth did so to raise awareness. We observed an overall positive sentiment, and in tough times such as during the COVID-19 pandemic, tweets with #MentalHealth were often associated with gratitude. Among the three classification approaches, the BERT classifier showed the best performance, with an accuracy of 81% for #MentalHealth and 79% for #Depression. Although the data may have come from users familiar with mental health, these findings can help gauge public sentiment on the topic. The combination of (1) sentiment analysis, (2) topic modeling, and (3) tweet classification with machine learning proved useful in gaining comprehensive insight into public sentiment and could be applied to other data sources and topics.

Self-Assessment of Having COVID-19 With the Corona Check Mhealth App

At the beginning of the COVID-19 pandemic, with a lack of knowledge about the novel virus and a lack of widely available tests, getting first feedback about being infected was not easy. To support all citizens in this respect, we developed the mobile health app Corona Check. Based on a self-reported questionnaire about symptoms and contact history, users get first feedback about a possible corona infection and advice on what to do. We developed Corona Check based on our existing software framework and released the app on Google Play and the Apple App Store on April 4, 2020. Until October 30, 2021, we collected 51,323 assessments from 35,118 users with explicit agreement of the users that their anonymized data may be used for research purposes. For 70.6% of the assessments, the users additionally shared their coarse geolocation with us. To the best of our knowledge, we are the first to report about such a large-scale study in this context of COVID-19 mHealth systems. Although users from some countries reported more symptoms on average than users from other countries, we did not find any statistically significant differences between symptom distributions (regarding country, age, and sex). Overall, the Corona Check app provided easily accessible information on corona symptoms and showed the potential to help overburdened corona telephone hotlines, especially during the beginning of the pandemic. Corona Check thus was able to support fighting the spread of the novel coronavirus. mHealth apps further prove to be valuable tools for longitudinal health data collection.

Corona Health-A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic

Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures.

Myosin light chain efflux after heart transplantation in infants and children and its correlation with ischemic preservation time

Serum levels of cardiac myosin light chain 1 after heart transplantation were studied in 24 infants and children who underwent heart transplantation between June 1990 and April 1991. The ages of the patients ranged from 4 days to 6 years 7 months (mean, 9.9 months), and their body weights ranged from 2.2 to 20 kg (mean, 5.6 kg). The ages of the donors ranged from 2 days to 8 years, 7 months (mean, 26.6 months), and their body weights ranged from 2.5 to 26 kg (mean, 11.4 kg). The donor heart ischemic time ranged from 90 minutes to 482 minutes (mean, 279 minutes). Peak myosin levels after heart transplantation showed significant correlation with the duration of graft ischemia (p < 0.01) and with diastolic cardiac function in the first posttransplant week (p < 0.05). Peak myosin levels did not correlate with systolic cardiac function, age of the donor, or age of the recipient. Myosin levels of the 15 patients with graft ischemic times exceeding 4 hours averaged 6.30 +/- 3.50 ng/ml. These levels were significantly higher than those of patients with graft ischemia lasting less than 4 hours (2.60 +/- 1.20 ng/ml; p < 0.01). Both of the values are higher than previously reported values of normal controls but lower than previously reported values of patients with myocardial infarction. Preservation techniques used for this series of transplant operations provided good clinical protection of the donor heart for up to 8 hours, although release of the cardiac myosin light chain fragment correlated with duration of graft ischemia. Cardiac myosin levels appeared to be a good indicator of heart graft damage during ischemic preservation. It remains to be determined at what level of myosin release (and, hence, at what duration of graft ischemia) irreversible myocardial damage, which might result in permanent functional compromise, occurs.

Intravitreal antibiotics: accuracy of dilution by pharmacists, ophthalmologists, and ophthalmic assistants, using three protocols

We compared the accuracy with which general ophthalmologists, pharmacy personnel, and ophthalmic assistants diluted intravitreal antibiotics, using a method of their own devising, a new protocol using an insulin syringe, and Duane's protocol. The insulin syringe method proved less accurate and more complex than Duane's protocol, but was better than not using a standardized protocol at all. With all methods, the pharmacy personnel produced the most accurate dilutions, with the least variability. The ophthalmologists' dilutions were much more variable both without a protocol and with the insulin syringe method, but they were acceptable with the Duane's protocol. The ophthalmic assistants produced unacceptable results with all methods. Since the decision to inject antibiotics is usually made under emergency conditions, when errors of dilution are more likely, whenever possible, pharmacy personnel should be entrusted with providing the dosage of medications to be injected intraocularly. If pharmacists are unavailable, ophthalmologists may reconstitute medications using a reliable and simple protocol such as Duane's.