Social Media App Usage in Relation with PHQ-9 Depression Scores during the COVID-19 Pandemic

With about 300 million affected people, major depressive disorder (MDD) is one of the most common diseases worldwide. During the COVID-19 pandemic, the number of cases increased even further, by 28%. Many factors may be correlated with MDD, including the excessive use of social media apps. In this paper, we investigated the relationship between the use of social media and communication apps and depressive symptoms during the COVID-19 pandemic. The pandemic and social distancing like lockdowns probably changed smartphone usage times and usage patterns. While previous studies have shown an association between depression and social media usage, we report about the situation during these special circumstances. We employed a log-linear regression to examine the association of social media and communication app usage and depression. To quantify the usage, we applied the total usage time in hours of social media apps (e.g., WhatsApp, Facebook) as well as communication apps (Phone and Messaging) within one week. To measure depressive symptoms, we used the PHQ-9 score. We discovered a significant association between the usage time and the PHQ-9 score (beta=0.0084, p-value=0.010). We conclude that social media usage is a robust marker for depression severity and future research should focus on a better understanding of the underlying causality and potential counter-measures. © 2022 ACM.

Changes in the Usage Pattern of Public Library Collections during the COVID Pandemic

The aim of the research was to examine changes in the usage patterns of public library collections during the COVID pandemic. Usage data for e-loans (e-books and e-audio books) and physical loans from two library networks in Sydney (Australia) for 2019–2021 was analyzed. While the use of physical collections dropped to less than half, the use of electronic collections increased by almost as much. E-loans peaked, and physical loans plummeted during lockdowns. However, the increase of e-loans was very small in terms of absolute volume (about a 44 k increase) compared to the decline in physical loans (about half a million decrease), and the use of physical collections did not reach pre-pandemic levels during the period studied. The analysis of subjects and genres of loaned items indicates that users were generally keen to continue reading the sorts of material they had been reading prior to the pandemic, even if this meant switching format, although there were some changes in genre and subject preferences during the lockdowns, which might have been influenced by the circumstances of the individual users. Readers appeared to show particular interest in various nonfiction topics in the initial lockdown (early 2020), with more practical considerations holding sway at this stage of the pandemic, whereas, by the time of the second lockdown (mid-2021), these considerations had been resolved to some extent, and been overtaken by a more "escapist,” or at least a more recreational, attitude on the part of the average user. [ FROM AUTHOR] Copyright of Collection Management is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

User Segmentation Based on Travel Regularity in E-Scooter Sharing Service

The dockless e-scooter sharing service is rapidly spreading, replacing existing transportation, and improving last-mile accessibility. User segmentation with travel regularity and segment-level behavior analysis, which are already conducted in public transit, also benefits e-scooter sharing service to enhance service quality and increase usage. In this work, we group e-scooter users according to their travel regularity and identify each group's usage characteristics. Through the dockless e-scooter usage data, as operated in six cities in South Korea, travel regularity measured by users' repetitive departure time and destination is discovered and spatiotemporal usage patterns are identified. We divide e-scooter users into three groups by type of travel regularity: irregular user, spatially regular user, and regular user. Regular users more frequently use e-scooters, travel shorter distances, and walk longer distances to find an e-scooter than other groups. It is also revealed that the use in morning peak hours only occurs in the regular user group. By decomposing the temporal patterns of spatially regular and regular users, we discover that spatially regular users are composed of daytime, evening peak, and nighttime users. In contrast, regular users are composed of morning peak, evening peak, and lockdown (restriction in response to COVID-19 pandemic) peak users. This research suggests user segmentation based on travel regularity in e-scooter sharing services, enabling multiple strategies to be drawn to retain users with high regularity and convert users with low regularity to regular users.

YouTube as a Source of Educational Content in Teenagers' Learning Practices

YouTube is one of the most popular online social spaces nowadays combining features of both a huge repository of information and a social networking service. Millions of people use this video-sharing platform daily. Entertainment (sports, comedy, music, movie trailers), information seeking (missed news, product reviews, research on a specific topic), and educational purposes (how-to videos, learning math, or tactics for video games) were discussed as main motivational aspects for watching YouTube videos (Lagger et al. 2017). Usage of YouTube for educational purposes became particularly relevant for teenagers as a support for their home-schooling. Our goal is to find out what strategies teenagers use to find relevant educational content on the service and how important this content was for their everyday learning practices before and during the COVID pandemic. We analyzed online behavior of 34, 14 to 15-year old teenagers (47% male) who took part in a long-term adventure trip with digital media left aside. We gathered quantitative data seven months before the trip (March 2019), just before the trip (October 2019), on the last day of the trip (April 2020), and five months after the trip (September 2020). We also conducted in-depth interviews with nine teenagers, who named YouTube as their favourite online service. Our intention is now to conduct nine additional interviews with the same teenagers to see whether their everyday learning practices changed within the last year. Implications drawn from this study, further research perspectives, and limitations will be presented and discussed. © The Authors, (2022). All Rights Reserved. No reproduction, copy or transmission may be made without written permission from the individual authors.

Website analytics for government user behavior during COVID-19 pandemic

Purpose: The present study aims to clarify the following two research objectives: (1) the user behavior of government websites during the coronavirus disease (COVID-19) period and (2) how the government improved government's website design during the COVID-19 period. Design/methodology/approach: The authors used website analytics to examine usage patterns and behaviors of the government website via personal computer (PC) and mobile devices during the COVID-19 pandemic. In-depth interviews were conducted to understand the user experience of government website users and to gather users' opinions about how government websites should be redesigned. Findings: With the rising of the COIVID-19 pandemic, most studies expect that the use of government websites through a mobile device will grow astonishingly. The authors uncovered that the COVID-19 pandemic did not increase the use of government websites. Instead, severe declines in website usage were observed for all device users with the declines being more pronounced in mobile device users than in PC users. This is an admonitory caveat that reveals public health and pandemic prevention information announced on government websites cannot be effectively transmitted to the general public through official online platforms. Originality/value: The study highlights the gap in information behavior and usage patterns between PC and mobile device users when visiting government websites. Although mobile devices brought many new visitors, mobile devices are ineffective in retaining visitors and continuous long-term use. The results of localize experience is helpful in the improvement of government website evaluation worldwide. © 2022, Emerald Publishing Limited.

Durations of Dockless E-Scooter Trips Before and During the COVID-19 Pandemic in Austin, TX: An Analysis Using Hazard-Based Duration Models

The pandemic arising from the 2019 coronavirus disease has significantly affected all facets of human life across the world, including economies and transportation systems, thereby changing people's travel behaviors. This research was aimed at exploring the relationship between socio-economic factors and e-scooter trip durations before and during the pandemic. We developed a hazard-based duration approach and estimated multiple spatial and non-spatial models on the basis of 2019 and 2020 dockless e-scooter data collected from the City of Austin's Open Data Portal. The results indicated an overall increase in e-scooter trip durations after the pandemic. Moreover, analysis of variables revealed potential changes in users' behavior before and during the pandemic. In particular, whereas e-scooter trip durations were found to be positively associated with aggregate travel time to work before the pandemic, this trend was reversed during the pandemic. In addition, during the pandemic, e-scooter travel time was positively correlated with the ratio of individuals with bachelor's degrees or greater to those with associate degrees or lower. However, no specific pattern was observed before the pandemic. Lastly, the results showed the presence of disparities within the study area;therefore, it is vital to extend e-scooter service areas to cover underserved communities.

Assessing Californians' awareness of their daily electricity use patterns

The timing of electricity consumption is increasingly important for grid operations. In response, households are being encouraged to alter their daily usage patterns through demand response and time-varying pricing, although it is unknown if they are aware of these patterns. Here we introduce an energy literacy concept, 'load shape awareness', and apply it to a sample of California residents (n = 186) who provided their household's hourly electricity data and completed an energy use questionnaire. Choosing from four prominent load shape designations, half of respondents (51%) correctly identified their dominant load shape before COVID-19 shelter-in-place (SIP) orders while only one-third (31%) did so during SIP orders. Those aware of their load shape were more likely to have chosen evening peak, the most frequent dominant shape in the electricity data. Our work provides proof of principle for the load shape awareness concept, which could prove useful in designing energy conservation interventions and helping consumers adapt to an evolving energy system. © 2022, The Author(s), under exclusive licence to Springer Nature Limited.

Characterization of a Wearable LiDAR Sensor for Social Distancing

LiDAR sensors are widely used in autonomous driving, mobile robotics, aerospace, manufacturing, and many other fields. The speed, reliability, and range of LiDAR sensors can be affected by environmental conditions and usage patterns. Each application requires a deep understanding of sensor limitations. This paper explores the operational parameters of one of the most miniature and least expensive LiDAR sensors packaged in a wearable case. The target application, in this case, is social distancing during pandemics. This study focuses on the performance of miniature LiDAR sensors under different levels of light intensity, temperature, distance to the object, object size, angle of view, and object color. The experimental data enables a match between the sensor capabilities and application scenarios and provides direction for future work in improving the wearable sensors of this class. © 2022 IEEE.

Understanding Multi-Device Usage Patterns: Physical Device Configurations and Fragmented Workflows

To better ground technical (systems) investigation and interaction design of cross-device experiences, we contribute an in-depth survey of existing multi-device practices, including fragmented workflows across devices and the way people physically organize and configure their workspaces to support such activity. Further, this survey documents a historically significant moment of transition to a new future of remote work, an existing trend dramatically accelerated by the abrupt switch to work-from-home (and having to contend with the demands of home-at-work) during the COVID-19 pandemic. We surveyed 97 participants, and collected photographs of home setups and open-ended answers to 50 questions categorized in 5 themes. We characterize the wide range of multi-device physical configurations and identify five usage patterns, including: partitioning tasks, integrating multi-device usage, cloning tasks to other devices, expanding tasks and inputs to multiple devices, and migrating between devices. Our analysis also sheds light on the benefits and challenges people face when their workflow is fragmented across multiple devices. These insights have implications for the design of multi-device experiences that support people's fragmented workflows. © 2022 Owner/Author.

Care Workers Making Use of Robots: Results of a Three-Month Study on Human-Robot Interaction within a Care Home

Research on social robots in care has often focused on either the care recipients or the technology itself, neglecting the care workers who, in and through their collaborative and coordinative practices, will need to work with the robots. To better understand these interactions with a social robot (Pepper), we undertook a 3 month long-term study within a care home to gain empirical insights into the way the robot was used. We observed how care workers learned to use the device, applied it to their daily work life, and encountered obstacles. Our findings show that the care workers used the robot regularly (1:07 hours/day) mostly in one-to-one interactions with residents. While the robot had a limited effect on reducing the workload of care workers, it had other positive effects, demonstrating the potential to enhance the quality of care. © 2022 Owner/Author.

Estimation of Hourly Utility Usage Using Machine Learning

The COVID-19 pandemic has had a major impact on the usage of various utilities. To assess the impact, this research explores the (baseline) estimation of hourly utility usage if the pandemic did not happen. Using usage data from Harris SmartWorks, various machine learning algorithms are implemented to show that they are effective in modelling hourly usage patterns, calendar effects, as well as 'lingering' effects of the exogenous factors and produce accurate results. © 2022 IEEE.

Post-pandemic mobility: profiles and uses of micromobility in Barcelona

The emergence of micromobility as a sustainable and inclusive alternative to conventional motorized modes has gained particular importance as a result of the COVID-19 crisis. The aim of the article is to study the profile of the users of these means of transport and how the pandemic has modified their trips, contributing to the incipient research in this area. The study offers an approach to these new forms of mobility in Barcelona, using the first survey carried out in the city on users of electric scooters, shared conventional and electric bicycles, and shared electric motorcycle (n = 902). The presented analysis indicates that the most frequent users are young, employed men. The reasons for travel are diverse, although they vary depending on the vehicle. The results also indicate that almost a third of micromobility users changed their usual transport mode due to the pandemic, especially those who previously travelled by public transport or walking. The potential of micromobility to reduce emissions is questioned and social implications are pointed out, both due to the differentiated use based on sociodemographic characteristics, and to the impact on health and public space.

Educational Data Mining to Identify the Patterns of Use made by the University Professors of the Moodle Platform

Due to the events caused by the COVID-19 pandemic and social distancing measures, learning management systems have gained importance, preserving quality standards, they can be used to implement remote education or as support for face-to-face education. Consequently, it is important to know how teachers and students use them. In this work, clustering techniques are used to analyze the use, made by university professors, of the resources and activities of the Moodle platform. The CRISP-DM methodology was applied to implement a data mining process, based on the Simple K-Means algorithm;to identify associated groups of teachers it was necessary to categorize the data obtained from the platform. The Apriori algorithm was applied to identify associations in the use of resources and activities. Performance scales were established in the use of Moodle functionalities, the results show the use made by teachers was very low. Rules were generated to identify the associations between activities and resources. As a result the functionalities that need to be enhanced in the teacher training processes were identified. Having identified the patterns of use of the Moodle platform, it is concluded that it was necessary to use a Likert scale to transform the frequency of use of activities and resources and identify the rules of association that establish profiles of teachers and tools that should be promoted in future training actions © 2022,International Journal of Advanced Computer Science and Applications.All Rights Reserved

The influence of the COVID-19 pandemic on the demand for different shades of green.

COVID-19 has heightened the dependence of urban dwellers on cultural ecosystem services provided by urban green spaces (UGS), specifically in regard to the provision of recreational opportunities, and psychological and physical health benefits arising from their use.As different types and levels of cultural ecosystem services are provided by different types of UGS, people may seek out different UGS to satisfy personal needs over various phases of COVID-19 mobility restrictions imposed by cities. We report on a study that took advantage of the different phases of COVID-19 mobility restrictions to assess the demand for and perception of different types of UGS in Singapore.The study utilised four datasets to compare demand for and visitorship patterns of UGS before the pandemic (Pre-Circuit Breaker), the duration of the strictest mobility restrictions (Circuit Breaker), and after the measures were relaxed (Post-Circuit Breaker). We used Google Search trends as a proxy for UGS demand, Google mobility data for an overview of population visitorship trends, visitor counts for granular insights on actual visitorship trends, and qualitative data on perception of parks by park visitors after restrictions eased. Parks were categorised as manicured and less manicured UGS for analysis.Search interest for UGS overall fell by more than 50% from during Circuit Breaker but the post-Circuit Breaker levels exceeded pre-Circuit Breaker, with a 70.9% increase for less manicured UGS compared to 20.8% for manicured UGS. This corroborated with Google mobility and visitor counts, which showed a steep decrease in park use followed by a rapid increase in the same periods, and with increased visitorship in the less manicured UGS. The perception study also showed that more than 50% of respondents reported visiting parks that they have never visited before, and there was a greater appreciation and use of UGS after the pandemic and preference for less manicured and more naturalistic landscapes.The pandemic has heightened the demand for cultural ecosystem services provided by UGS. Our study showed that this demand is not uniform across different types of UGS, with an increase visitorship and preference for less manicured green spaces. Read the free Plain Language Summary for this article on the Journal blog.