Geospatial Clustering of Mobile Phone Use and Tuberculosis Health Outcomes Among African Health Systems.

BACKGROUND: While multiple studies have documented the impacts of mobile phone use on TB health outcomes for varied settings, it is not immediately clear what the spatial patterns of TB treatment completion rates among African countries are. This paper used Exploratory Spatial Data Analysis (ESDA) techniques to explore the clustering spatial patterns of TB treatment completion rates in 53 African countries and also their relationships with mobile phone use. Using an ESDA approach to identify countries with low TB treatment completion rates and reduced mobile phone use is the first step toward addressing issues related to poor TB outcomes. METHODS: TB notifications and treatment data from 2000 through 2015 that were obtained from the World Bank database were used to illustrate a descriptive epidemiology of TB treatment completion rates among African health systems. Spatial clustering patterns of TB treatment completion rates were assessed using differential local Moran's I techniques, and local spatial analytics was performed using local Moran's I tests. Relationships between TB treatment completion rates and mobile phone use were evaluated using ESDA approach. RESULT: Spatial autocorrelation patterns generated were consistent with Low-Low and High-Low cluster patterns, and they were significant at different p-values. Algeria and Senegal had significant clusters across the study periods, while Democratic Republic of Congo, Niger, South Africa, and Cameroon had significant clusters in at least two time-periods. ESDA identified statistically significant associations between TB treatment completion rates and mobile phone use. Countries with higher rates of mobile phone use showed higher TB treatment completion rates overall, indicating enhanced program uptake (p < 0.05). CONCLUSION: Study findings provide systematic evidence to inform policy regarding investments in the use of mHealth to optimize TB health outcomes. African governments should identify turnaround strategies to strengthen mHealth technologies and improve outcomes.

Problematic Mobile Phone Use and Life Satisfaction Among University Students During the COVID-19 Pandemic in Shanghai, China.

Objective: This study examined problematic mobile phone use (PMPU) and its relationship with life satisfaction in Chinese university students during the pandemic. Methods: An anonymous online survey was conducted in a university in China. The Mobile Phone Addiction Index (MPAI) and the Satisfaction with Life Scale (SWLS) were used to assess the severity of problematic mobile phone use and life satisfaction, respectively. Data on demographic and health-related factors were also collected. Results: A total of 1,491 undergraduate students (73.3% were male) completed the survey. On average, students in the survey reported spending 7.4 ± 4.3 h/day on phone use. Their MPAI score was 38.1 ± 13.3 and SWLS score was 24.9 ± 6.8, respectively. After controlling for confounding factors, the MPAI score was significantly associated with lower life satisfaction. Multiple linear regression revealed that higher monthly allowances, frequent insomnia, longer phone use duration were significantly associated with PMPU. Conclusion: University students in China spend nearly half of their waking hours on mobile phone use, significantly longer than before the COVID-19 pandemic. PMPU is associated with insomnia, lower life satisfaction and higher allowances. If the trend continues after the pandemic, interventions may be needed. Increase in-person interactions, limiting online social and gaming time, awareness campaign may be effective in reducing the impact of PMPU and improve life satisfaction.

Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions.

Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19, which is a challenging task, especially at high spatial resolutions. In this study, we develop a Spatiotemporal autoregressive model to predict county-level new cases of COVID-19 in the coterminous US using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features. We capture human interactions through 1) Facebook- and 2) cell phone-derived measures of connectivity and human mobility, and use them in two separate models for predicting county-level new cases of COVID-19. We evaluate the model on 14 forecast dates between 2020/10/25 and 2021/01/24 over one- to four-week prediction horizons. Comparing our predictions with a Baseline model developed by the COVID-19 Forecast Hub indicates an average 6.46% improvement in prediction Mean Absolute Errors (MAE) over the two-week prediction horizon up to 20.22% improvement in the four-week prediction horizon, pointing to the strong predictive power of our model in the longer prediction horizons.

An mCARE study on patterns of risk and resilience for children with ASD in Bangladesh.

Community-wide lockdowns in response to COVID-19 influenced many families, but the developmental cascade for children with autism spectrum disorder (ASD) may be especially detrimental. Our objective was to evaluate behavioral patterns of risk and resilience for children with ASD across parent-report assessments before (from November 2019 to February 2020), during (March 2020 to May 2020), and after (June 2020 to November 2020) an extended COVID-19 lockdown. In 2020, our study Mobile-based care for children with ASD using remote experience sampling method (mCARE) was inactive data collection before COVID-19 emerged as a health crisis in Bangladesh. Here we deployed "Cohort Studies", where we had in total 300 children with ASD (150 test group and 150 control group) to collect behavioral data. Our data collection continued through an extended COVID-19 lockdown and captured parent reports of 30 different behavioral parameters (e.g., self-injurious behaviors, aggression, sleep problems, daily living skills, and communication) across 150 children with ASD (test group). Based on the children's condition, 4-6 behavioral parameters were assessed through the study. A total of 56,290 behavioral data points was collected (an average of 152.19 per week) from parent cell phones using the mCARE platform. Children and their families were exposed to an extended COVID-19 lockdown. The main outcomes used for this study were generated from parent reports child behaviors within the mCARE platform. Behaviors included of child social skills, communication use, problematic behaviors, sensory sensitivities, daily living, and play. COVID-19 lockdowns for children with autism and their families are not universally negative but supports in the areas of "Problematic Behavior" could serve to mitigate future risk.

Driving through a pandemic: A study of speeding and phone use while driving during COVID-19 restrictions.

OBJECTIVE: In response to the COVID-19 pandemic, restrictions were implemented, however, data indicates road crash rates have not decreased proportionately to the decline in traffic density. This study explored how speeding and phone use while driving (road behaviors associated with a high crash risk) changed as a result of COVID-19 restrictions in Queensland. METHODS: Two cross-sectional studies were conducted in Queensland, Australia to examine self-reported changes in speeding and phone use while driving before, during and after the easing of restrictions (while also considering exposure to the road and driving location). Study 1 (n = 351) was conducted between 12 May and 12 June 2020, when the majority of COVID-19 restrictions were enforced. Study 2 (n = 427) was conducted between 24 June and 12 August 2020, when restrictions were easing. RESULTS: The findings indicated, overall, self-reported speeding and phone use significantly decreased during restrictions (likely due to reduced travel), but not for those who continued to drive regularly. There was an additional, significant self-reported decrease in phone use while driving after restrictions were eased when compared to engagement during restrictions, which may (in part) be due to the simultaneous introduction of roadside mobile phone detection cameras. CONCLUSION: These findings highlight the importance of visible deterrence and provide a glimpse of possible consequences if there is a more sustained reduction in policing presence on roads.

Cell-phone traces reveal infection-associated behavioral change.

Epidemic preparedness depends on our ability to predict the trajectory of an epidemic and the human behavior that drives spread in the event of an outbreak. Changes to behavior during an outbreak limit the reliability of syndromic surveillance using large-scale data sources, such as online social media or search behavior, which could otherwise supplement healthcare-based outbreak-prediction methods. Here, we measure behavior change reflected in mobile-phone call-detail records (CDRs), a source of passively collected real-time behavioral information, using an anonymously linked dataset of cell-phone users and their date of influenza-like illness diagnosis during the 2009 H1N1v pandemic. We demonstrate that mobile-phone use during illness differs measurably from routine behavior: Diagnosed individuals exhibit less movement than normal (1.1 to 1.4 fewer unique tower locations; [Formula: see text]), on average, in the 2 to 4 d around diagnosis and place fewer calls (2.3 to 3.3 fewer calls; [Formula: see text]) while spending longer on the phone (41- to 66-s average increase; [Formula: see text]) than usual on the day following diagnosis. The results suggest that anonymously linked CDRs and health data may be sufficiently granular to augment epidemic surveillance efforts and that infectious disease-modeling efforts lacking explicit behavior-change mechanisms need to be revisited.

Canadian Adolescents' Internalized Symptoms in Pandemic Times: Association with Sociodemographic Characteristics, Confinement Habits, and Support.

Adolescence is a time of increased risk for developing symptoms of anxiety and depression, especially for girls. The stress and social isolation experienced during confinement add new threats to already vulnerable adolescents' daily lives. This study is aimed at determining which sociodemographic characteristics (age, family composition, achievement), confinement habits (schedule, new hobby, sleep duration, cellphone and computer use, sports, schoolwork), and sources of support (parents and teachers) are associated with more or less internalized symptoms in Canadian adolescents. Differences between boys and girls are also investigated. Between April 8 and 30 (2020) and through an online survey, 895 Canadian adolescents (74% girls) aged between 12 and 17 years (M = 14.7) were recruited. Path analysis was performed to identify significant associations between sociodemographic characteristics, confinement habits, support variables, and internalized symptoms. Independent samples t-tests and invariance tests were conducted to compare boys and girls. Results suggest that certain confinement habits (time spent using cellphones, doing sports and schoolwork, finding a new hobby) and support variables (parents working outside the home) were significantly and negatively associated with internalized symptoms. Regarding the sex differences, girls used their cellphones more and invariance test results showed that all associations between predictors and symptoms were statistically similar for boys and girls. This study's results help understand better adolescents' experience in confinement. It sheds light on the habits likely to characterize those who are less at risk of experiencing distress, making it possible to better support adolescents during this challenging period.

Limited Technology Access Among Residents of Affordable Senior Housing During the COVID-19 Pandemic.

While U.S. adults living in affordable senior housing represent a vulnerable population during the COVID-19 pandemic, affordable housing may provide a foundation for interventions designed to improve technology access to support health. To better understand technology access among residents of affordable senior housing, we surveyed members of a national association of resident service coordinators to assess their experiences working with residents during the pandemic (n = 1,440). While nearly all service coordinators report that most or all residents have reliable phone access, under a quarter report that most or all have reliable internet access; they also report limited access to technology for video calls. Lack of internet access and technology literacy are perceived as barriers to medical visits and food procurement for low-income older adult residents of affordable housing. Policies to expand internet access as well as training and support to enable use of online services are required to overcome these barriers.

Mental Health and Behavior of College Students During the COVID-19 Pandemic: Longitudinal Mobile Smartphone and Ecological Momentary Assessment Study, Part II.

BACKGROUND: Since late 2019, the lives of people across the globe have been disrupted by COVID-19. Millions of people have become infected with the disease, while billions of people have been continually asked or required by local and national governments to change their behavioral patterns. Previous research on the COVID-19 pandemic suggests that it is associated with large-scale behavioral and mental health changes; however, few studies have been able to track these changes with frequent, near real-time sampling or compare these changes to previous years of data for the same individuals. OBJECTIVE: By combining mobile phone sensing and self-reported mental health data in a cohort of college-aged students enrolled in a longitudinal study, we seek to understand the behavioral and mental health impacts associated with the COVID-19 pandemic, measured by interest across the United States in the search terms coronavirus and COVID fatigue. METHODS: Behaviors such as the number of locations visited, distance traveled, duration of phone use, number of phone unlocks, sleep duration, and sedentary time were measured using the StudentLife mobile smartphone sensing app. Depression and anxiety were assessed using weekly self-reported ecological momentary assessments, including the Patient Health Questionnaire-4. The participants were 217 undergraduate students. Differences in behaviors and self-reported mental health collected during the Spring 2020 term, as compared to previous terms in the same cohort, were modeled using mixed linear models. RESULTS: Linear mixed models demonstrated differences in phone use, sleep, sedentary time and number of locations visited associated with the COVID-19 pandemic. In further models, these behaviors were strongly associated with increased interest in COVID fatigue. When mental health metrics (eg, depression and anxiety) were added to the previous measures (week of term, number of locations visited, phone use, sedentary time), both anxiety and depression (P<.001) were significantly associated with interest in COVID fatigue. Notably, these behavioral and mental health changes are consistent with those observed around the initial implementation of COVID-19 lockdowns in the spring of 2020. CONCLUSIONS: In the initial lockdown phase of the COVID-19 pandemic, people spent more time on their phones, were more sedentary, visited fewer locations, and exhibited increased symptoms of anxiety and depression. As the pandemic persisted through the spring, people continued to exhibit very similar changes in both mental health and behaviors. Although these large-scale shifts in mental health and behaviors are unsurprising, understanding them is critical in disrupting the negative consequences to mental health during the ongoing pandemic.

Loneliness and problematic mobile phone use among adolescents during the COVID-19 pandemic: The roles of escape motivation and self-control.

In this cross-sectional study, we explored the relationship between loneliness and problematic mobile phone use among Chinese adolescents during the COVID-19 pandemic, considering the effects of escape motivation and self-control. We recruited 1034 adolescents (mean age 15.76 ± 1.20 years) from China. The results showed that loneliness was positively associated with escape motivation and adolescent problematic mobile phone use. Furthermore, when controlling for gender, escape motivation mediated the relationship between loneliness and problematic mobile phone use, and self-control moderated the relationship between escape motivation and problematic mobile phone use. Specifically, as self-control increased, escape motivation was less likely to induce problematic mobile phone use. Thus, loneliness and escape motivation may be factors that increase the risk of problematic mobile phone use, and self-control should be considered in prevention and intervention strategies aimed at attenuating adolescent problematic mobile phone use.

Smartphone addiction across the lifetime during Italian lockdown for COVID-19.

BACKGROUND: Smartphone misuse, also known as Nomophobia is the fear of not being able to consult your own mobile phone, of not being connected or traceable. During the Italian lockdown caused by COVID-19, while the use of technology was the fundamental basis of adaptation for smart working, school and professional training, leading to a change in the population's lifestyle, smartphone dependency caused impaired social relationships. To date, the impact of smartphone dependency in men and women is unclear. We conducted this study with the hypothesis that a period of lockdown fosters the growth of a pathological use of the cell phone different in women and men. OBJECTIVE: The purpose of this work is to investigate gender differences in the level of smartphone dependency in teens and adults during the COVID-19 lockdown period. MATERIAL AND METHODS: The NoMobilePhobia-Questionnaire (NMP-Q) was presented online to 1264 participants between the ages of 15 and 67. RESULTS: The results show no significant main effects for the two factors taken into account (Gender and Age of participants). However, the significant interaction shows that female participants reported on average higher scores on NMP-Q than males, [F(4,1253) =7.06 and p<.001, observed power close to 1 (0.99) and effect size = 0.03 (ETA partial squared)] for the younger age group (15-44), while for those over the age of 44, the average highest scores were for male participants. CONCLUSIONS: One of the "positive" aspects of the COVID-19 pandemic is the use of the Internet and smartphones, and our analysis aimed to document the frequency of use in the Italian context with the NMP-Q. However, we can also conclude that this research is relevant because it can give us a glimpse of the relationship between dependency and mental issues. The results reveal the risk in some of the Italian population of developing forms of smartphone dependency, especially in circumstances that prohibit direct social interactions.

Mining Google and Apple mobility data: temporal anatomy for COVID-19 social distancing.

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.

Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic.

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.

Human mobility trends during the early stage of the COVID-19 pandemic in the United States.

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.

Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic.

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.

Is Covid-19 lockdown related to an increase of accesses for seizures in the emergency department? An observational analysis of a paediatric cohort in the Southern Italy.

BACKGROUND: The World Health Organization (WHO) declared a global pandemic of Covid-19 on 11 March 2020. The lockdown caused a lifestyle changes: an increase in the use of mobile media devices (MMDs), sleep and psychiatric disorders, incorrect habits regarding food and physical activities. We investigate prevalence of admission for seizures at our emergency department (ED), during Italian lockdown, comparing with that of the same period of the previous year (2019), and the relationship with some lifestyle changes. METHODS: In this observational study, patients (4-14 years) with seizures that accessed at our ED, during Italian lockdown, were eligible. Non-epileptic events and febrile seizures were excluded. We describe two groups: patients with new-onset seizures and not. Moreover, a questionnaire concerning use of MMDs and sleep habits was administered. RESULTS: Fifty-seven patients were included; median age 8.03 years. Considering only paediatric medical emergencies, the prevalence of accesses for seizures was 2.6% (CI 95% 0.020-0.034), while the incidence was 0.94% (CI 95% 0.006-0.0149). There was a statistically significant difference with prevalence of previous years, χ2 102.21 (p = 0.0001). We also reported a difference in daily screen time (DST) (p = 0.001) and total sleep time (TST) (p = 0.045), in all population, between period pre- and during lockdown. A negative correlation between DST and seizures latency (Spearman's ρ -0.426, p = 0.038) was found. In the two groups, the results were partially overlapping. CONCLUSIONS: During lockdown period, we assisted to an increase of accesses for seizures. It is conceivable that a sleep time change and/or higher MMD use could act as triggers for seizures.

Cell Phone Activity in Categories of Places and Associations With Growth in Cases of COVID-19 in the US.

Importance: It is unknown how well cell phone location data portray social distancing strategies or if they are associated with the incidence of coronavirus disease 2019 (COVID-19) cases in a particular geographical area. Objective: To determine if cell phone location data are associated with the rate of change in new COVID-19 cases by county across the US. Design, Setting, and Participants: This cohort study incorporated publicly available county-level daily COVID-19 case data from January 22, 2020, to May 11, 2020, and county-level daily cell phone location data made publicly available by Google. It examined the daily cases of COVID-19 per capita and daily estimates of cell phone activity compared with the baseline (where baseline was defined as the median value for that day of the week from a 5-week period between January 3 and February 6, 2020). All days and counties with available data after the initiation of stay-at-home orders for each state were included. Exposures: The primary exposure was cell phone activity compared with baseline for each day and each county in different categories of place. Main Outcomes and Measures: The primary outcome was the percentage change in COVID-19 cases 5 days from the exposure date. Results: Between 949 and 2740 US counties and between 22 124 and 83 745 daily observations were studied depending on the availability of cell phone data for that county and day. Marked changes in cell phone activity occurred around the time stay-at-home orders were issued by various states. Counties with higher per-capita cases (per 100 000 population) showed greater reductions in cell phone activity at the workplace (ß, -0.002; 95% CI, -0.003 to -0.001; P < 0.001), areas classified as retail (ß, -0.008; 95% CI, -0.011 to -0.005; P < 0.001) and grocery stores (ß, -0.006; 95% CI, -0.007 to -0.004; P < 0.001), and transit stations (ß, -0.003, 95% CI, -0.005 to -0.002; P < 0.001), and greater increase in activity at the place of residence (ß, 0.002; 95% CI, 0.001-0.002; P < 0.001). Adjusting for county-level and state-level characteristics, counties with the greatest decline in workplace activity, transit stations, and retail activity and the greatest increases in time spent at residential places had lower percentage growth in cases at 5, 10, and 15 days. For example, counties in the lowest quartile of retail activity had a 45.5% lower growth in cases at 15 days compared with the highest quartile (SD, 37.4%-53.5%; P < .001). Conclusions and Relevance: Our findings support the hypothesis that greater reductions in cell phone activity in the workplace and retail locations, and greater increases in activity at the residence, are associated with lesser growth in COVID-19 cases. These data provide support for the value of monitoring cell phone location data to anticipate future trends of the pandemic.

United but divided: Policy responses and people's perceptions in the EU during the COVID-19 outbreak.

To understand the public sentiment toward the measures used by policymakers for COVID-19 containment, a survey among representative samples of the population in seven European countries was carried out in the first two weeks of April 2020. The study addressed people's support for containment policies, worries about COVID-19 consequences, and trust in sources of information. Citizens were overall satisfied with their government's response to the pandemic; however, the extent of approval differed across countries and policy measures. A north-south divide in public opinion was noticeable across the European states. It was particularly pronounced for intrusive policy measures, such as mobile data use for movement tracking, economic concerns, and trust in the information from the national government. Considerable differences in people's attitudes were noticed within countries, especially across individual regions and age groups. The findings suggest that the epidemic acts as a stressor, causing health and economic anxieties even in households that were not directly affected by the virus. At the same time, the burden of stress was unequally distributed across regions and age groups. Based on the data collected, we draw lessons from the containment stage and identify several insights that can facilitate the design of lockdown exit strategies and future containment policies so that a high level of compliance can be expected.