Phone Users Research Articles

PHOTO CONTENT OF COMMUNICATION PLATFORMS AS A MECHANISM OF OPPOSING RUSSIAN PROPAGANDA (ON THE EXAMPLE OF THE UKRAINIAN WAR OF LIBERATION)

The purpose. To outline the photo content of communication platforms as one of the most powerful levers of opposition to Russian propaganda in the media environment. The scientific novelty lies in the latest approaches to the visualization of the content of communication platforms as a way of countering the propaganda that the russian federation is waging against Ukraine during the information war. This issue is studied very deeply. Every day the aggravation takes place not only on the geographical, but also on the informational front of the liberation war. Communication platforms having distanced themselves from social networks, Internet trading platforms and network channels have turned into an autonomous and unique plane. Photos play a key role. Conclusions. The article offers vision of mechanisms for countering communication platforms as components of the communication process, capable of resisting russian propaganda. Photography is considered as key factor in the visualization of content and the possibility of building resistance mechanisms in the minds of the audience. At the time when Ukraine is suffering from missile attacks and has interruptions with light and communication, photography remains the leading element of creating a visual component of communication. It weighs much less than streams and audio files that may not be uploaded to the user's phone due to a lack of traffic. The photo content of modern communication platforms and channels are the most accessible to the Ukrainian audience due to their efficiency and interactivity has been analyzed in this work. Among them: “This is Kryvyi Rih, baby”, “Ukraine online”, “Ukraine in shock”, “Crimean Tatarian resource center”.

Prevalence of Phantom Vibrations and Ringing Syndrome, and its Association with Smartphone Addiction among Medical Students of Teaching Hospital, South India

Introduction Phantom vibration syndrome (PVS) and phantom ringing syndrome (PRS) is a false sensation of phone vibration and ringing when it is not. This study aimed to estimate the prevalence of PVS/PRS among medical students and to determine factors including level of smartphone usage associated with the development of phantom sensations. Methods Using cross-sectional analytical design, sociodemographic details, information on phantom sensations and phone usage among medical students in our institution in Puducherry, India, was obtained. Stratified random sampling strategy was incorporated to achieve a sample size of 369. Also, overuse of phone was assessed using Smartphone Addiction scale – short version (SAS-SV). Prevalence of PVS and PRS was summarized as a proportion with a 95% confidence interval (CI). Prevalence ratio for the factors associated with the phantom syndrome (PVS/PRS) was estimated using log-binomial regression. Results Using data from 383 students, the prevalence of phantom syndrome (PVS/PRS) was 44.9% (39.9–50.0%). PVS was more prevalent than PRS (27.9% vs 21.2%). There was a significant difference in SAS-SV scores between those with the phantom syndrome and those without. Students who stay in hostel (aPR = 1.88; 95% CI: 1.46 to 2.43), self-reported overuse of phone (aPR = 1.96; 95% CI: 1.56 to 2.45) and higher smartphone addiction score (aPR = 1.26; 95% CI: 1.04 to 1.53) were found to be significant determinants to have PVS/PRS. Conclusion Overall, one in two medical students had either PVS or PRS in our institution, Puducherry. Higher smartphone usage was a significant predictor of the presence of PVS/PRS.

Overestimating or underestimating communication findings? Comparing self-reported with log mobile data by data donation method

Mobile phone usage is typically measured via self-reporting. However, scholars have questioned the validity of self-reported data, which may lead to Type I or Type II errors. Using an online survey ( n = 777), this study compared self-reported and log mobile phone usage data using a simplified version of the mobile data donation method. The results showed that people generally underreported their mobile phone usage in terms of time duration, the number of notifications, and apps used. Moreover, self-reported data may either have no additional effect on or overestimate the communication findings depending on the outcome variables. This challenges the Type II error explanation and suggests that the effect sizes of self-reported data might not be underestimated after all. Instead, past research examining mobile use and pertinent outcomes may have false-positive findings and Type I errors. Given the potential inaccuracies of self-reported data, future research on mobile media and communications should go beyond self-reported data to enhance the validity of findings.

Rural small scale farmers' smart mobile phone usage acceptance prognosticators for agricultural marketing information access.

The online version contains supplementary material available at 10.1007/s43545-022-00562-x.

The relationship between mobile broadband usage and user mobility with lockdown restrictions in Spain

ABSTRACT This paper studies how COVID-19 lockdown restrictions encouraged and allowed people to form habits of increased mobile phone usage in Spain, even after the most restrictive measures were lifted. We have used data from the mobile network of a national telecommunications operator to study the influence of 15 different mobility restrictions on citizens’ use of mobile phones from 15 March 2020 to 15 April 2021 in a big Spanish city. In order to approach this issue, a quasi-experimental technique (before and after methodology) was implemented. We found that the use of smartphones presented an initial reduction with the first and most drastic mobility restrictions, but showed a growing use after their removal. However, user mobility was negatively influenced by all the mobility restrictions.

Hybridized model selection with Gifi system for categorical data using the genetic algorithm and information complexity

In the cross-disciplinary fields of social and behavioral sciences, biology, e-commerce, econometrics, medical data mining, and in engineering applications the available data are mostly composed of many categorical, continuous, and mixed data types with both categorical and continuous variables. Modeling such data structures creates many challenges and difficulties in terms of the underlying probability distributional assumptions to model. This paper proposes a novel categorical regression (CATREG) model using optimal scaling technique in Gifi system to resolve the current existing problem by transforming the categorical data to a continuous data and then performing the analysis of the data in the new transformed Gifi space. Such transformation preserves the scaling properties of the original variables without loss of any information and mapping is one-to-one and onto, unlike the kernel mapping in feature space in machine learning. We introduce a hybridized model selection via the information complexity (ICOMP) criterion along with the genetic algorithm (GA) in CATREG model and provide interpretable results. Two real numerical examples are provided to study the effects of the cell phone usage on the sleep patterns of individuals, and a second example is based on building a predictive model of e-commerce for new car market. In both of these numerical examples subset selection of the best predictor variables are determined to build an optimal predictive model. Our results show the efficiency and the versatility of the proposed new approach.

Detecting the usage of a mobile phone during an online test using AI technology

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informujące swoich czytelników o najnowszych osiągnięciach naukowych i nowoczesnych rozwiązaniach technicznych w Polsce i na świecie, popularyzuje problemy techniczne oraz poszerza wiedzę i kulturę techniczną.

Mobile phone access and preferences among medical inpatients at an urban Canadian hospital for post-discharge planning: A pre-COVID-19 cross-sectional survey.

Digital health interventions are increasingly used for patient care, yet little data is available on the phone access type and usage preferences amongst medical ward inpatients to inform the most appropriate digital interventions post-discharge. To identify mobile phone ownership, internet access, and cellular use preferences among medical inpatients, we conducted a researcher-administered survey of patients admitted to five internal medicine units at Vancouver General Hospital (VGH) in January 2020. The survey was administered over 2 days separated by a 2-week period. A total of 81 inpatients completed the questionnaire. Survey found that 85.2% of survey respondents had mobile phone access where 63.0% owned their own mobile phone, and 22.2% had access to a mobile phone via a proxy (or an authorized third-party) such as a family member. All participants with mobile phone access had cellular plans (i.e., phone and text); however, a quarter of respondents did not have data plans with internet access. Survey showed that 71.1% of males owned a mobile phone compared to only 52.8% of females. All participants at a "high" risk of readmission had access to a mobile phone, either as phone-owners or proxy-dependent users. Access to mobile phones among medical ward inpatients, 85.2%, was comparable to smartphone penetration rates amongst Canadians in 2019, 85.1%. More patients had cellular than data plans (i.e., internet and applications). Understanding patient-specific access is key to informing potential uptake of digital health interventions aimed at using patients' mobile phones (mHealth) from an effectiveness and equity lens.

Road Safety Regulations: How Compliant are Commercial Motorcyclists in Semi-Urban Towns in Western Nigeria?

The aim of the study was to determine compliance with road safety laws and factors that influence it among commercial motorcyclists in semi-urban towns in Western Nigeria. A descriptive cross-sectional study was done using a multi-stage sampling technique of 502 commercial motorcyclists. Data was collected using a pretested semi-structured questionnaire. Analysis was done using descriptive and inferential statistics with statistical significance determined at a 95% confidence interval and p-value of 0.05. The median age of respondents was 30 years; most were in the second and third phases of life. Compliance with road safety regulations was good in only 26% of respondents; mobile phone usage and alcohol drink riding was found in 31.9% and 28.1%, respectively. Approximately 96.4% indulged in carrying more than one pillion rider, 48% admitted to riding against traffic, and 83.3% wore reflective jackets at night. Use of stimulants was reported among 39.4% of respondents, while only 13.7% smoke cigarettes. The reported crash was found in 56.8% of respondents. Those with previous traffic violations and those that admitted fault for the crash were 2.75 times and 2.51 times more likely to comply with road safety regulations [AOR:2.748, 95%CI: 1.784- 4.332 and AOR: 2.512, 95% CI: 1.195-5-5.278] respectively. Compliance with road safety regulations was poor among commercial motorcyclists in the study population. Measures gingered towards increasing observance of road safety regulations such as increased safety education, effective policing to enforce compliance and punishment of erring riders, should be instituted. This will eventually translate to increased awareness and enhance road safety

The association between self-reported mobile phone usage with blood pressure and heart rate: evidence from a cross-sectional study

BackgroundWith the advancement of technology, the rate of access and use of mobile phones in different communities has increased significantly. Mobile phones emit electromagnetic waves and therefore excessive use of them may have harmful effects on physical and mental health and especially on the cardiovascular system. This study aimed to investigate the association between self-reported mobile phone use duration and blood pressure and heart rate (HR) using data from Ravansar non-communicable diseases (RaNCD) cohort study.MethodsThe present cross-sectional study was performed using the data of 8905 out of 10,065 participants in the RaNCD study in Iran. According to the mean self-reported duration of mobile phone usage (min/day) over the previous 12 months, all users were divided into four groups. The first and fourth groups had the least and most time using mobile phones respectively. The relationship between blood pressure and the duration of mobile phone use was determined using univariate and multiple linear regression.ResultsOf 8905 participants aged 35–65 years, 1515 (17.0%) of them didn't use mobile phones. The minimum, maximum, and mean duration of self-reported mobile phone use between users were 3.4, 50.4, and 19.5 min/day, respectively. A decrease in women's systolic and diastolic blood pressure (SBP and DBP) and HR was observed by increasing the duration of mobile phone use. With adjustment for effective confounding factors, there was a significant negative association between SBP [-2.52 (-4.11, -0.94)], DBP [-1.86 (-2.83, -0.89)], and duration of mobile use.ConclusionIn this study, a significant decreasing trend was found between SBP, DBP, and HR and higher mobile phone usage in women. Based on regression analysis, SBP, DBP, and duration of mobile phone use were associated negatively in those who used their phones for at least 8 h.

An Ensemble-Based Machine Learning Model for Emotion and Mental Health Detection

Recent studies have highlighted several mental health problems in India, caused by factors such as lack of trained counsellors and a stigma associated with discussing mental health. These challenges have raised an increasing need for alternate methods that can be used to detect a person’s emotion and monitor their mental health. Existing research in this field explores several approaches ranging from studying body language to analysing micro-expressions to detect a person’s emotions. However, these solutions often rely on techniques that invade people’s privacy and thus face challenges with mass adoption. The goal is to build a solution that can detect people’s emotions, in a non-invasive manner. This research proposes a journaling web application wherein the users enter their daily reflections. The application extracts the user’s typing patterns (keystroke data) and primary phone usage data. It uses this data to train an ensemble machine learning model, which can then detect the user’s emotions. The proposed solution has various applications in today’s world. People can use it to keep track of their emotions and study their emotional health. Also, any individual family can use this application to detect early signs of anxiety or depression amongst the members.

Information and Communication Technology (ICT) on Economic Growth in Asia: A Panel Data Analysis

Information and communication technology (ICT) plays an increasingly important role in driving economic growth and sustainability. Asia, being the most heterogenous region in terms of ICT development, consist of both most connected and least connected countries based on the global ICT Development Index 2017. Therefore, this study investigates the impact of ICT on economic growth for 20 Asian countries from year 2000 to 2020. Using the robust panel Fixed Effect model and Panel Feasible Generalized Least Squares method, findings revealed that fixed telephone subscriptions, mobile cellular subscriptions and Internet users are the ICT variables that have significant and positive effects on economic growth in Asia. Fixed telephone subscriptions however, emerged insignificant which could be due to its high cost and low practicality relative to other forms of ICT. Findings from this study benefit related parties such as firms and policymakers by encouraging adoption and usage of mobile phones and the Internet through provision of adequate infrastructure and affordable ICT services.

Smartphone-Based Recognition of Access Trip Phase to Public Transport Stops Via Machine Learning Models

Abstract The usage of mobile phones is nowadays reaching full penetration rate in most countries. Smartphones are a valuable source for urban planners to understand and investigate passengers’ behavior and recognize travel patterns more precisely. Different investigations tried to automatically extract transit mode from sensors embedded in the phones such as GPS, accelerometer, and gyroscope. This allows to reduce the resources used in travel diary surveys, which are time-consuming and costly. However, figuring out which mode of transportation individuals use is still challenging. The main limitations include GPS, and mobile sensor data collection, and data labeling errors. First, this paper aims at solving a transport mode classification problem including (still, walking, car, bus, and metro) and then as a first investigation, presents a new algorithm to compute waiting time and access time to public transport stops based on a random forest model. Several public transport trips with different users were saved in Rome to test our access trip phase recognition algorithm. We also used Convolutional Neural Network as a deep learning algorithm to automatically extract features from one sensor (linear accelerometer), obtaining a model that performs well in predicting five modes of transport with the highest accuracy of 0.81%.

A survey of the mutual effects of mobile phone usage on the citizenship culture in developing countries

Information and Communication Technologies have made great changes to the lifestyle of human beings. Mobile phones, with their rapid growth in different parts of the world, essential parts of our lives. Mobile phone has helped to overcome location barriers and have had great impacts on the people’s lives, especially in urban areas. In developing countries, mobile phone has grown rapidly in the recent decade and has had great impacts on communications and other aspects of social life. The purpose of this study is to investigate the effects of the mobile phone technology push on the culture of citizens, in developing countries. Running a survey-type research, a sample of people in Tehran, the capital city of Iran, have been asked to fill a questionnaire; then, after gathering and cleansing and analysing the data, some recommendations are added that could be used in urban policy making in the various developing countries

Mobile phones usage and awareness of potential health risks from radiation emitted by mobile phones among medical students at Benue State University (BSU), Makurdi

The global increase in mobile phone (MP) usage in proximity to the human-body and proliferation of base stations has created potential health concerns about exposure to radiation emitted from these devices. This study aims to identify students who are knowledgeable about radiation emitted from MPs, assess their degree of awareness of the potential health risks from MP usage, and suggest precautionary and safety measures that can reduce or eliminate the health hazards of MP radiation. We prospectively evaluated medical students' knowledge of MP-emitted radiation, potential health risks, precautionary and safety measures at Benue State University (BSU), Makurdi, between May 13th and July 12th, 2022. Data was obtained through a well-structured questionnaire, analyzed using the Statistical Package for Social Sciences (SPSS) version 23 with a p value<0.05. Results were presented in tables and figures. The study included 147 fourth-sixth year medical students, aged 20-38 years, male: female ratio 3:1, and mean age of 26.5±3.4. Knowledge of radiation emitted from MPs was high, 134(91.2%), especially among final year students. Similarly, 93 (63.3%) students were aware of potential health risks associated with MP usage, with some evidently experiencing the negative consequences. Many students ignored precautionary measures and continued making long phone-conversations 80(54.4%), putting MPs in their pockets 92(62.6%) and at their bed-head 77(52.4%), prompting crucial safety measures. Knowledge of radiation emitted by MPs was outstanding, with considerable awareness of potential health risks from MP usage. Important safety measures were proposed, even though the precautionary measures to minimize these risks were largely ignored.

Mobile phones usage and awareness of potential health risks from radiation emitted by mobile phones among medical students at Benue State University (BSU), Makurdi

Mobile phones usage and awareness of potential health risks from radiation emitted by mobile phones among medical students at Benue State University (BSU), Makurdi

Are We Online or in Class? Students’ Smartphone Usage Habits

Smartphones, which enable us to be online everywhere and every time, are also commonly used by students today. This study aims to reveal undergraduate students’ habits of using smartphones. With this purpose, the phone usage track application was installed on students’ smartphones, and their 7-day use was recorded with the application and was then analyzed. In addition to that, the students’ views on using smartphones during classes were also investigated through open-ended questions. As a result, it was found that they used their smartphones for 4 hours and 50 minutes on average and that they used the same or different applications approximately 380 times a day. The most frequently used smartphone applications were found to be social media and messaging applications. The students were found to use their smartphones the most intensely between 21.00 and 24.00. The majority of the students used their smartphones during classes and their reasons for using the phones for non-educational purposes were more than the reasons for using them for educational purposes. The students stated that they used their smartphones for non-educational purposes such as check the time, make calls, and texting and for educational purposes such as research, take the photo of the blackboard or slides. While the great majority of the students had negative views about using smartphones during classes, some of them also had positive views.

Cell phone usage detection in roadway images: from plate recognition to violation classification

Cell phone usage detection in roadway images: from plate recognition to violation classification

Mobile Cloud Computing and Sustainable Development

The number of smartphone users has increased from 3.6 billion in 2016 to 6.25 billion by 2021, which shows that mobile phone usage has increased dramatically over the past few years. This is due to the development of mobile computing applications like commerce, healthcare, e-learning, etc. The use of mobile devices has resulted in an exponential rise in the amount of data generated and as a result the amount of energy consumed has increased. This is where cloud computing plays a major role. Cloud computing has transformed traditional mobile computing. The new mobile cloud not only provides on-demand services but also data storage and increased energy efficiency. Through mobile computing based on cloud computing, mobile device functions can be virtualized, reducing power consumption. In this paper, the authors survey application and potential of mobile cloud computing and present the energy-efficient ways. Also, the paper discusses development opportunities of mobile cloud computing. The research also mentions some of the major challenges in current mobile computing technology.

Impact of Infodemic on Public Perception on Covid-19 Pandemic: Web-Based Cross-Sectional Survey

Infodemic’ (an overabundance of information) that makes it tough for people to find responsible sources and reliable guidance when they need it. This generates a need to know whether the public are able to distinguish fake news from true ones and the information sources used. The study aims to determine the impact of infodemic on public perception of health during the COVID- 19 pandemic and to assess its impact on the psychological well-being of the public. An online web based cross sectional study was conducted among people who were aged above 15 years. The data was collected through online mode by providing google links to fill the validated questionnaire form through various social media platforms. The questionnaire consists of demographic details, knowledge and practice related questions. The data was analyzed using Chi-square test. A total of 715 responses were considered for analysis in which the majority of the age group belongs to 18- 24 years and mostly living in urban (44%) and rural (41%). Information Sources mostly used include Internet (630), Mobile phone usage (630), and TV (530). Standard (or) Trusted sources according to people were Government websites (407). It was observed that most people have good knowledge about COVID-19 prevention and were practicing the safety measures. The study concludes that availability of abundance of information sources and lack of awareness on how to crosscheck made people to follow some activities in the view of protection without knowing the truth. This impact of infodemic can be controlled by health care professionals including pharmacists by giving updated and evidence-based scientific advice on reliable COVID- 19 information to their communities via flyers and multimedia.