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We employ the Google and Apple mobility data to identify, quantify and classify different degrees of social distancing and characterise their imprint on the first wave of the COVID-19 pandemic in Europe and in the United States. We identify the period of enacted social distancing via Google and Apple data, independently from the political decisions. Our analysis allows us to classify different shades of social distancing measures for the first wave of the pandemic. We observe a strong decrease in the infection rate occurring two to five weeks after the onset of mobility reduction. A universal time scale emerges, after which social distancing shows its impact. We further provide an actual measure of the impact of social distancing for each region, showing that the effect amounts to a reduction by 20–40% in the infection rate in Europe and 30–70% in the US.

BackgroundPedestrians are commonly involved in vehicle collisions that result in injuries and fatalities. Pedestrian distraction has become an emerging safety issue as more pedestrians use their mobile phones while walking and crossing the street.ObjectivesThe purpose of this research synthesis and meta-analysis is to determine the extent to which cell phone conversation, text messaging or browsing, and listening to music affect a number of common pedestrian behavioural measures.MethodsA keyword search was developed with a subject librarian that used MeSH terms from selected databases including PsycINFO, SPORTDiscus, Medline and TRID. Supplemental searches were also conducted with Google Scholar and Mendeley.Effect size codingThirty-three studies met inclusion criteria and were subjected to data extraction. Statistical information (ie, M, SD, SE, 95% CI, OR, F, t) was extracted to generate standardised mean difference effect sizes (ie, Cohen’s d) and r effect sizes.ResultsFourteen experimental studies were ultimately included in an N-weighted meta-analysis (k=81 effect sizes), and eight observational studies were included in a qualitative overview. Both mobile phone conversation and text messaging increased rates of hits and close calls. Texting decreased rates of looking left and right prior to and/or during street crossing. As might be expected, text messaging was generally found to have the most detrimental effect on multiple behavioural measures.LimitationsA variety of study quality issues limit the interpretation and generalisation of the results, which are described, as are future study measurement and methods improvements.

The purpose of this study was to carry out a review of observational studies that consider links between mobile phone use and mental health from a psychological or behavioral perspective. Systematic literature searches in PubMed and PsycINFO for articles published until 2017 were done. Exclusion criteria included: papers that considered radiofrequency fields, attention, safety, relational consequences, sexual behavior, cyberbullying, and reviews, qualitative, and case or experimental studies. A total of 4738 papers were screened by title and abstract, 404 were retrieved in full text, and 290 were included. Only 5% had any longitudinal design. Self-reporting was the dominating method of measurement. One third of the studies included children or youth. A majority of adult populations consisted of university students and/or self-selected participants. The main research results included associations between frequent mobile phone use and mental health outcomes, such as depressive symptoms and sleep problems. Mobile phone use at bedtime was associated with, e.g., shorter sleep duration and lower sleep quality. “Problematic use” (dependency) was associated with several negative outcomes. In conclusion, associations between mobile phone use and adverse mental health outcomes are found in studies that take a psychological or behavioral perspective on the exposure. However, more studies of high quality are needed in order to draw valid conclusions about the mechanisms and causal directions of associations.

To determine the longitudinal associations of long-time mobile phone use (LTMPU) with sleep disturbances and mental distress in a prospective cohort of technical college students. A total of 4333 (response rate: 91.5%) and 3396 (response rate: 78.4%) participants were recruited at baseline and 8-month follow-up, respectively. Data were collected by a set of questionnaires including socio-demographics, lifestyle practice, duration of mobile phone use per day, sleep patterns on weekdays and weekends, as well as Insomnia Severity Index, Epworth Sleepiness Scale, reduced Morningness-Eveningness Questionnaire, Beck Depression Inventory, and Zung Self-Rating Anxiety Scale. LTMPU was defined as using mobile phone ≥4 hours/day. At baseline, 23.5% (n = 1020) of the participants reported using mobile phone ≥ 4 hours/day. LTMPU at baseline was positively associated with the new incidences (range, adjusted odds ratio 1.31-1.53) of a series of the sleep disturbances and mental distress at follow-up. The discontinuation of LTMPU was associated with an amelioration of the risks of most of these problems. Cross-lagged analyses revealed bidirectional associations of the duration of mobile phone use with poor sleep and mental health outcomes. LTMPU predicts the new incidences of most sleep disturbances and mental distress, while discontinuation of LTMPU is associated with amelioration of these problems. Moreover, there are bidirectional associations between the duration of mobile phone use and various sleep and mental outcomes. These findings highlight the critical role of prevention and early recognition of excessive mobile phone use and their accompanied mental and sleep problems.

While large scale mobility data has become a popular tool to monitor the mobility patterns during the COVID-19 pandemic, the impacts of non-compulsory measures in Tokyo, Japan on human mobility patterns has been under-studied. Here, we analyze the temporal changes in human mobility behavior, social contact rates, and their correlations with the transmissibility of COVID-19, using mobility data collected from more than 200K anonymized mobile phone users in Tokyo. The analysis concludes that by April 15th (1 week into state of emergency), human mobility behavior decreased by around 50%, resulting in a 70% reduction of social contacts in Tokyo, showing the strong relationships with non-compulsory measures. Furthermore, the reduction in data-driven human mobility metrics showed correlation with the decrease in estimated effective reproduction number of COVID-19 in Tokyo. Such empirical insights could inform policy makers on deciding sufficient levels of mobility reduction to contain the disease.

In March of this year, COVID-19 was declared a pandemic, and it continues to threaten public health. This global health crisis imposes limitations on daily movements, which have deteriorated every sector in our society. Understanding public reactions to the virus and the non-pharmaceutical interventions should be of great help to fight COVID-19 in a strategic way. We aim to provide tangible evidence of the human mobility trends by comparing the day-by-day variations across the U.S. from January 2020 to early April 2020. Large-scale public mobility at an aggregated level is observed by leveraging mobile device location data and the measures related to social distancing. Our study captures spatial and temporal heterogeneity as well as the sociodemographic variations and teleworking trends regarding the pandemic propagation and the non-pharmaceutical mobility interventions. All metrics adapted capture decreased public movements after the national emergency declaration. The population staying home has increased in all states before the stay-at-home mandates implemented and becomes more stable after the order with a smaller range of fluctuation. The public had been taking active responses, voluntarily staying home more, to the in-state confirmed cases while the stay-at-home orders stabilize the variations. As the estimated teleworking rates also continue to incline throughout the study period, the teleworking trend can be another driving factor for the growing stay-at-home population. We confirm that there exists overall mobility heterogeneity between the income or population density groups. The study suggests that public mobility trends are in line with the government message urging to stay home. We anticipate our data-driven analysis offers integrated perspectives and serves as evidence to raise public awareness and, consequently, reinforce the importance of social distancing while assisting policymakers.

This study aimed to compare the mean scores of mobile phone use, mobile phone addiction, and depressive symptoms at three-time points among Korean adolescents according to gender and to examine the differences in the long-term relationships among the three abovementioned variables between Korean boys and girls in a four-year period. Data for 1794 adolescents (897 boys and 897 girls) were obtained from three waves of the second panel of the Korean Children and Youth Panel Survey. Multigroup structural equation modeling was used for data analyses. The study findings showed that at each of the three-time points, Korean girls tended to use their mobile phones more frequently and were at a higher risk of mobile phone addiction and depressive symptoms than Korean boys. Significant changes were observed in the longitudinal relationships among phone use, mobile phone addiction, and depressive symptoms in Korean adolescents across time periods, but no gender differences were found in the strengths of these relationships. These findings contribute to expanding the knowledge base of mobile phone addiction and depressive symptoms among Korean adolescents.

Background Walking is a complex activity that requires high levels of perception and cognitive abilities. Healthy pedestrians who use mobile phones while walking will have their decision-making process affected to varying degrees and may be at greater risk of injury. Previous studies have shown that using mobile phones while walking is becoming increasingly common. Therefore, the objectives of this study are to solve the following problems: What factors influence the intention and behavior of teenagers to use mobile phones while walking? Do different factors play the same role in students of different genders, school types, mobile phone dependence, or mental health status? Based on the above results, what measures should we take to reduce the behaviors of teenagers using mobile phones while walking? Method This study used a cross-sectional online survey design. The study was conducted in six junior high schools and four senior high schools in Shanghai, China, from December 2019 to January 2020 ( N  = 4,082 students in Shanghai). The questionnaire was designed based on the theory of planned behavior and analyzed by structural equation model analysis and multi-group invariance analysis. Results Girls, junior middle school students, and students without mental health problems or mobile phone dependence can better understand the hazards of using mobile phones while walking. They are more willing to accept other people’s suggestions about not using mobile phones while walking. Additionally, they exhibit better self-control and lower levels of intention and behavior when using mobile phones while walking. Conclusions The key points to preventing teenagers from using mobile phones while walking are to instill a correct attitude toward dangerous behavior, build a stronger sense of norm, help them form good habits in mobile phone use, and improve their ability to control their behavior.

Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the COVID-19 pandemic. In this data descriptor, we introduce a regularly-updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users’ visits to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications. Measurement(s) mobility • Interaction Technology Type(s) GPS navigation system • machine learning Factor Type(s) geographic scale • temporal interval • geographic location • spatiotemporal region Sample Characteristic - Location United States of America Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13135085

Nonsuicidal self-injury (NSSI) is a critical public health concern among university students, often considered a gateway behavior to suicide. With the widespread use of mobile phones, understanding the association between specific mobile phone use behaviors (eg, presleep and postwake mobile phone use) and NSSI has become increasingly important for targeted prevention. This study aimed to explore the association between presleep and postwake mobile phone use and NSSI among Chinese university students, examining potential dose-response relationships and sex differences. A multistage random cluster sampling survey was conducted across 6 universities in Shaanxi province (northwest China) from October 2022 to November 2022. A total of 18,585 undergraduates were included in the final analysis. Binary logistic regression models were used to examine the association between presleep and postwake mobile phone use duration and past-month NSSI, whereas restricted cubic spline regression was applied to assess dose-response relationships. The prevalence of past-month NSSI among participants was 3.81% (709/18,585). Compared with individuals who reported lower presleep mobile phone use (0-30 minutes per day), those with higher presleep mobile phone use had substantially increased odds of NSSI, with odds ratios of 1.34 (95% CI 1.07-1.66) for the group with 61 to 120 minutes per day of use and 1.93 (95% CI 1.53-2.42) for the group with ≥120 minutes per day of use. For postwake mobile phone use, compared with the group with 0 to 1 minute per day of use, the participants in the group with >30 minutes per day of use showed a significant association with NSSI (odds ratio 1.27, 95% CI 1.02-1.58) in the fully adjusted model. Continuous variable analyses revealed that each 10-minute increase in presleep and postwake use was associated with a 3% and 2% higher NSSI risk, respectively. Restricted cubic spline analysis confirmed linear dose-response relationships for both presleep and postwake use (P>.05 for nonlinearity). No significant sex differences were observed in these associations. Prolonged presleep and postwake mobile phone use exhibited linear associations with NSSI among Chinese university students, with no significant sex disparities. These findings underscore the necessity of longitudinal studies to establish causality, elucidate underlying mechanisms, and inform targeted interventions.