Prevalence Of Mobile Phones Research Articles

"Lala and Lulu" stories: developing android-based social media for autistic children to learn social interaction

Autistic children encounter a distinct difficulty, namely in social contact. As a result, children with autism may face difficulties when participating in social interactions in their society. Thus, it is crucial to implement strategies that might improve the social skills of children with autism. A practical approach entails the application of social storytelling. Furthermore, there is a noticeable rise in the complexity of technological innovations, exemplified by the prevalence of mobile phones. Cell phones, especially those operating on the Android platform, provide many benefits when combined with internet access and various supplementary features. This study aims to develop an Android-based social media platform that enhances social interaction skills for children diagnosed with autism. The study aims to evaluate the feasibility and effectiveness of this platform. This study entails conducting research and engaging in development activities using the 4D development strategy, which encompasses design stages, definition, development, and dissemination. Questionnaires are utilized as a means of gathering data. The assessment components of material consist of material attributes, language attributes, and implementation attributes. The assessment aspect of media involves both the technical aspects of software engineering and the visual communication parts. The material expert's evaluation of social media platforms based on the Android operating system resulted in a satisfactory classification, with only minimal adjustments needed to ensure precise and reliable information. The media expert's assessment was accurately classified, with ample content requiring minimal modifications. Based on expert assessments, it is recommended to conduct limited testing on students until the shortcomings of Android-based social media have been resolved. The article will comprehensively discuss the analytical method and media development.

Leveraging Collaborative-Filtering for Personalized Behavior Modeling

The prevalence of mobile phones and wearable devices enables the passive capturing and modeling of human behavior at an unprecedented resolution and scale. Past research has demonstrated the capability of mobile sensing to model aspects of physical health, mental health, education, and work performance, etc. However, most of the algorithms and models proposed in previous work follow a one-size-fits-all (i.e., population modeling) approach that looks for common behaviors amongst all users, disregarding the fact that individuals can behave very differently, resulting in reduced model performance. Further, black-box models are often used that do not allow for interpretability and human behavior understanding. We present a new method to address the problems of personalized behavior classification and interpretability, and apply it to depression detection among college students. Inspired by the idea of collaborative-filtering, our method is a type of memory-based learning algorithm. It leverages the relevance of mobile-sensed behavior features among individuals to calculate personalized relevance weights, which are used to impute missing data and select features according to a specific modeling goal (e.g., whether the student has depressive symptoms) in different time epochs, i.e., times of the day and days of the week. It then compiles features from epochs using majority voting to obtain the final prediction. We apply our algorithm on a depression detection dataset collected from first-year college students with low data-missing rates and show that our method outperforms the state-of-the-art machine learning model by 5.1% in accuracy and 5.5% in F1 score. We further verify the pipeline-level generalizability of our approach by achieving similar results on a second dataset, with an average improvement of 3.4% across performance metrics. Beyond achieving better classification performance, our novel approach is further able to generate personalized interpretations of the models for each individual. These interpretations are supported by existing depression-related literature and can potentially inspire automated and personalized depression intervention design in the future.

Home Videos as a Cost-Effective Tool for the Diagnosis of Paroxysmal Events in Infants: Prospective Study.

BackgroundThe diagnosis of paroxysmal events in infants is often challenging. Reasons include the child’s inability to express discomfort and the inability to record video electroencephalography at home. The prevalence of mobile phones, which can record videos, may be beneficial to these patients. In China, this advantage may be even more significant given the vast population and the uneven distribution of medical resources.ObjectiveThe aim of this study is to investigate the value of mobile phone videos in increasing the diagnostic accuracy and cost savings of paroxysmal events in infants.MethodsClinical data, including descriptions and home videos of episodes, from 12 patients with paroxysmal events were collected. The investigation was conducted in six centers during pediatric academic conferences. All 452 practitioners present were asked to make their diagnoses by just the descriptions of the events, and then remake their diagnoses after watching the corresponding home videos of the episodes. The doctor’s information, including educational background, profession, working years, and working hospital level, was also recorded. The cost savings from accurate diagnoses were measured on the basis of using online consultation, which can also be done easily by mobile phone. All data were recorded in the form of questionnaires designed for this study.ResultsWe collected 452 questionnaires, 301 of which met the criteria (66.6%) and were analyzed. The mean correct diagnoses with and without videos was 8.4 (SD 1.7) of 12 and 7.5 (SD 1.7) of 12, respectively. For epileptic seizures, mobile phone videos increased the mean accurate diagnoses by 3.9%; for nonepileptic events, it was 11.5% and both were statistically different (P=.006 for epileptic events; P<.001 for nonepileptic events). Pediatric neurologists with longer working years had higher diagnostic accuracy; whereas, their working hospital level and educational background made no difference. For patients with paroxysmal events, at least US $673.90 per capita and US $128 million nationwide could be saved annually, which is 12.02% of the total cost for correct diagnosis.ConclusionsHome videos made on mobile phones are a cost-effective tool for the diagnosis of paroxysmal events in infants. They can facilitate the diagnosis of paroxysmal events in infants and thereby save costs. The best choice for infants with paroxysmal events on their initial visit is to record their events first and then show the video to a neurologist with longer working years through online consultation.

Foreign Language Assessment: Instructional Considerations for Students with Specific Learning Disabilities

Students with specific learning disabilities (SLDs) continue to be more likely to experience anxiety about learning a foreign language, and possibly less likely to enroll in foreign language courses at all. As schools become more inclusive and more aware of diverse learning needs, it is important to be cognizant of how the individual needs of students with SLDs may relate to classroom instruction and assessment practices. What follows is a discussion of assessment practices, with an emphasis on diagnostic and formative assessment in the context of teaching students with SLDs. Given the prevalence of mobile phones and other devices in contemporary classrooms, a strong emphasis on the role of online polling resources plays a prominent role in this discussion. Specific examples of instructional activities which align with assessments are also provided, as part of a selective literature review intended to connect larger topics in language instruction to the typical needs of students with specific learning disabilities. The perspective of the discussion pertains primarily to students with SLDs in the United States, where the vast majority of foreign language instruction occurs in the middle and secondary grades.

Learning in Nonstationary Environments: A Survey

The prevalence of mobile phones, the internet-of-things technology, and networks of sensors has led to an enormous and ever increasing amount of data that are now more commonly available in a streaming fashion [1]-[5]. Often, it is assumed - either implicitly or explicitly - that the process generating such a stream of data is stationary, that is, the data are drawn from a fixed, albeit unknown probability distribution. In many real-world scenarios, however, such an assumption is simply not true, and the underlying process generating the data stream is characterized by an intrinsic nonstationary (or evolving or drifting) phenomenon. The nonstationarity can be due, for example, to seasonality or periodicity effects, changes in the users' habits or preferences, hardware or software faults affecting a cyber-physical system, thermal drifts or aging effects in sensors. In such nonstationary environments, where the probabilistic properties of the data change over time, a non-adaptive model trained under the false stationarity assumption is bound to become obsolete in time, and perform sub-optimally at best, or fail catastrophically at worst.

Anonymizing mobility data using semantic cloaking

The prevalence of mobile phones has led to an explosion in the amounts of human mobility data stored in the cloud. It has been shown that seemingly anonymized location datasets are highly susceptible for re-identification, and may not provide enough privacy protection. In this paper we quantitatively show how semantic cloaking, the application of semantic labeling to achieve anonymization, can improve the privacy of a mobility dataset for use cases where location coordinates can be replaced by semantic categories. We develop a semantic labeling framework, apply it and evaluate it using the dataset uniqueness (ε) measure. Our experiments show an improvement in uniqueness ranging between two- and twenty two-fold in comparison to the original, naively anonymized, dataset.

Prevalence of mobile phones and factors influencing usage by caregivers of young children in daily life and for health care in rural China: a mixed methods study.

IntroductionTo capitalise on mHealth, we need to understand the use of mobile phones both in daily life and for health care.ObjectiveTo assess the prevalence and factors that influence usage of mobile phones by caregivers of young children.Materials and MethodsA mixed methods approach was used, whereby a survey (N=1854) and semi-structured interviews (N=17) were conducted concurrently. The quantitative and qualitative data obtained were compared and integrated. Participants were caregivers of young children in Zhao County, Hebei Province, China.ResultsFour main themes were found: (i) trends in mobile phone ownership; (ii) usage of mobile phone functions; (iii) factors influencing replying to text messages; and (iv) uses of mobile phones for health care. The majority of 1,854 survey participants (1,620; 87.4%) used mobile phones, but usage was much higher among mothers (1,433; 92.6%) and fathers (41; 100.0%) compared to grandparents (142; 54.6%). Parents were able to send text messages, grandparents often not. Factors influencing the decision to reply to text messages in daily life were checking the mobile phone, trusting the sender, emotion or feeling when receiving a text message, the importance of replying and ease of use of text messages. Of 1,620 survey participants who used a mobile phone, about one in four (432; 26.7%) had used it for health care in the past three months and most (1,110; 93.5%) of 1,187 who had not wished to use their phone to receive health information.ConclusionWe found that usage of mobile phones is high, several factors influencing usage and an interest of caregivers to use phones for health care in Zhao County, rural China, which can be used to inform studies in settings with similar characteristics. Future work needs to assess factors influencing mobile phone usage in-depth to optimize experiences of users for specific mHealth-based interventions.