Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Please note that all times are shown in the time zone of the conference. The current conference time is: 19th Aug 2022, 04:23:29pm CEST

Presentations including 'Andone'

ST-D2-1: Short Talks Day 2 - C
Time: 16/Mar/2021: 10:30am-11:30am · Location: Short C

Good morning Smartphone! Assessing bed-time and wake-up patterns of large cohorts with smartphone data

Clara Sophie Vetter1,2, Ionut Andone1,3, Konrad Blaszkiewicz1,3, Mengtong Li1,3, Qais Kasem1, Alex Markowetz1,4

1Murmuras; 2University of Amsterdam, The Netherlands; 3University of Bonn, Germany; 4University of Marburg, Germany

As increasingly ubiquitous companions, smartphones offer insights into our everyday life. They accompany us to bed, and we use them first thing after waking up. Smartphone usage data has been shown to correlate with the user's lifestyle. Thus, it promises a way of assessing sleep while overcoming the downsides of traditional sleep-assessment methods such as self-assessment (e.g., questionnaires) and methods requiring medical assistance (e.g., polysomnogram). Further, smartphone-logged data shows our app engagement patterns. Previous research suggests that social media usage is related to sleep quality. Real-time smartphone-logging may be an objective means to analyze how app behavior relates to our sleep and health.

Our objective was to explore how sleeping patterns can be estimated using smartphone usage data alone. We analyzed real-time smartphone data of 860 participants from Germany from July until October 2020. We used socio-demographic information to analyze differences between age groups, sex, and different employment states.

Results suggest that smartphone data is a promising means of approximating bed-time and wake-up patterns on individual and population levels. For example, we found that in 20% of nights, employed people in Germany have less than six hours of smartphone-free time at a stretch. Another finding is that, on average, students, compared to workers, wake up an hour later in the morning. Social media apps are among the top last-used apps at night, and the first-used apps the next morning.

While our analysis was only exploratory, it shows the potential of smartphone data for sleep research and beyond.

ST-D2-1: Short Talks Day 2 - C
Time: 16/Mar/2021: 10:30am-11:30am · Location: Short C

Logging application content and active user interaction on Android smartphones

Ionut Andone1,2, Konrad Blaszkiewicz1,2, Qais Kasem1, Alexander Markowetz1,3

1Murmuras; 2University of Bonn; 3Philipps University of Marburg

Smartphones are an integral part of our daily life. They are the trusty companion that helps us communicate with our peers, catch up with the latest news, do the shopping and pay for it, navigate the city, and many more daily tasks. This behavior can be observed up to some level of accuracy. Until now, researchers could log what apps are being used but not for which purpose. This information could be retrieved from the user through ESM/EMA at the time or after usage. However, these methods are prone to bias and the collected data can be subjective. Automatic recording of the user behavior inside apps brings objective assessment into the world. Researchers can observe if the user is actively interacting in an app by writing comments and liking posts, or if he is just consuming content passively by scrolling aimlessly. The interests of the users can be better mapped when using portal type apps which offer a wide variety of content. The type will describe his actions better than just observing the usage of a specific app. Certain game theory scenarios could be tested in real life situations without influencing the user in any way. Choosing a mean of transport is influenced by a multitude of factors with price playing a big role. Analyzing application content and the associated user interaction could lead to a new wave of research and experiments that was not possible before.

S08.1-LT: Human-Computer Interaction
Time: 16/Mar/2021: 1:45pm-2:45pm · Location: Azure Area

1:57pm - 2:09pm

Smartphone behavior during the Coronavirus (COVID-19) pandemic

Konrad Błaszkiewicz1,2, Qais Kasem1, Clara Sophie Vetter1,3, Ionut Andone1,2, Alexander Markowetz1,4

1Murmuras, Germany; 2University of Bonn, Germany; 3University of Amsterdam, Netherlands; 4University of Marburg, Germany

Smartphone behavior gives us a chance to better understand how the corona pandemic has impacted people’s lives. Studying app usage can provide a deep insight into digital as well as real world activities in the times of the pandemic. To this end we analyze data of over 1000 participants from Germany, who registered for our Murmuras study over a period of the whole year 2020.

In particular, we observe strong changes in early spring months, when COVID-19 spread rapidly in Germany, and the government introduced strict lock-down and social distancing measures. With limited in-person communication, social media and digital messengers can provide important means of coping with isolation. Our data supports such hypothesis. We see an increase in usage of apps in social and communication categories in March (> 20%) and April (> 15 %) compared to February 2020. Interestingly, we observe reverse effect for Media & Video category - usage dropped by 18% in March, while total phone usage increased by 15 min in the same period. Moreover, strong changes in usage of other apps like Google Maps provide insights into non-digital behavior and indicate how mobility was reduced.

Finally, we show how these trends continue and are impacted by the easing and later increasing restrictions over summer, autumn and winter months.

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