Large-Scale Sleep Condition Analysis Using Selfies from Social Media

Sleep condition is closely related to an individual's health. Poor sleep conditions such as sleep disorder and sleep deprivation affect one's daily performance, and may also cause many chronic diseases. Many efforts have been devoted to monitoring people's sleep conditions. However, traditional methodologies require sophisticated equipment and consume a significant amount of time. In this paper, we attempt to develop a novel way to predict individual's sleep condition via scrutinizing facial cues as doctors would. Rather than measuring the sleep condition directly, we measure the sleep-deprived fatigue which indirectly reflects the sleep condition. Our method can predict a sleep-deprived fatigue rate based on a selfie provided by a subject. This rate is used to indicate the sleep condition. To gain deeper insights of human sleep conditions, we collected around 100,000 faces from selfies posted on Twitter and Instagram, and identified their age, gender, and race using automatic algorithms. Next, we investigated the sleep condition distributions with respect to age, gender, and race. Our study suggests among the age groups, fatigue percentage of the 0-20 youth and adolescent group is the highest, implying that poor sleep condition is more prevalent in this age group. For gender, the fatigue percentage of females is higher than that of males, implying that more females are suffering from sleep issues than males. Among ethnic groups, the fatigue percentage in Caucasian is the highest followed by Asian and African American.Comment: 2017 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS'17

The effect of sleep deprivation on objective and subjective measures of facial appearance

This study was funded by the Swedish Research Council, FORTE (Swedish Research Council for Health, Working Life and Welfare), and The Swedish Foundation for Humanities and Social Sciences.The faces of people who are sleep deprived are perceived by others as looking paler, less healthy and less attractive compared to when well rested. However, there is little research using objective measures to investigate sleep‐loss‐related changes in facial appearance. We aimed to assess the effects of sleep deprivation on skin colour, eye openness, mouth curvature and periorbital darkness using objective measures, as well as to replicate previous findings for subjective ratings. We also investigated the extent to which these facial features predicted ratings of fatigue by others and could be used to classify the sleep condition of the person. Subjects (n = 181) were randomised to one night of total sleep deprivation or a night of normal sleep (8–9 hr in bed). The following day facial photographs were taken and, in a subset (n = 141), skin colour was measured using spectrophotometry. A separate set of participants (n = 63) later rated the photographs in terms of health, paleness and fatigue. The photographs were also digitally analysed with respect to eye openness, mouth curvature and periorbital darkness. The results showed that neither sleep deprivation nor the subjects’ sleepiness was related to differences in any facial variable. Similarly, there was no difference in subjective ratings between the groups. Decreased skin yellowness, less eye openness, downward mouth curvature and periorbital darkness all predicted increased fatigue ratings by others. However, the combination of appearance variables could not be accurately used to classify sleep condition. These findings have implications for both face‐to‐face and computerised visual assessment of sleep loss and fatigue.PostprintPeer reviewe

Social Media Awareness: The Impact of Social Media on Mental Health

Communication technology, especially smartphones and the social media apps on them, has become a very large part of the modern world. People of all ages spend hours every day on social media either posting about their experiences or viewing other posts. Though social media can be fun and sometimes useful, it can also have negative effects on mental health, especially in adolescence. Researchers have done studies on these effects and developed scales to measure impacts like social media addiction. These studies show correlation between social media addiction and conduct disorders, depression, and deteriorating social skills. There are resources available to help people become aware of their social media habits and improve them. This program is going to give students the information about social media addiction, emotional regulation, and skills to help them become more mindful of social media use

Voices of USU: An Anthology of Student Writing, 2015

This collection of student writing represents the voices of over 2,000 students who enroll each academic year in Utah State University’s second-year composition course, Intermediate Writing: Research Writing in a Persuasive Mode. Voices of USU celebrates excellence in writing by providing undergraduate students of diverse backgrounds and disciplines the opportunity to have their work published.https://digitalcommons.usu.edu/voicesofusu/1006/thumbnail.jp

Unraveling the Effects of Mobile Application Usage on Users’ Health Status: Insights from Conservation of Resources Theory

Numerous studies have documented adverse consequences arising from increased technology usage and advocated for a reduction in such usage as a plausible remedy. However, such recommendations are often infeasible and oversimplistic given mounting evidence attesting to users’ growing reliance on technology in both their personal and professional lives. Building on conservation of resources (COR) theory, we construct a research model to explain how mobile application usage, as delineated by its breadth and depth, affects users’ nomophobia and sleep deprivation, which can have negative impacts on users’ health status. We also consider the moderating influence of physical activity in mitigating the effects of mobile application usage on users’ health. We validated our hypotheses via data collected by surveying 5,842 respondents. Empirical findings reveal that (1) nomophobia is positively influenced by mobile application usage breadth but negatively influenced by mobile application usage depth, (2) sleep deprivation is negatively influenced by mobile application usage breadth but positively influenced by mobile application usage depth, and (3) sleep deprivation and nomophobia negatively impact users’ health status, whereas (4) physical activity attenuates the impact of mobile application usage on sleep deprivation but not nomophobia. The findings from this study not only enrich the extant literature on the health outcomes of mobile application usage by unveiling the impact of mobile application usage patterns and physical activity on users’ health but they also inform practitioners on how calibrating usage breadth and depth, along with encouraging physical activity, can promote healthy habits among users

Self-esteem and Social Media Dependency: A Comparative Analysis of Welsh- and English-Medium Pupils’ Perceptions

Despite not being officially recognized as an addiction, studies suggest social media dependency [SMD] retains similar traits as substance-based addictions and that adolescents are a group particularly at risk. Studies have shown significant positive correlations between SMD and depression, loneliness, and social anxiety. SMD has also shown a significant negative association with self-esteem. Research has yet to explore these relationships within a minority versus majority language comparative context, which is the objective of the thesis. The thesis used cross-sectional and longitudinal analyses (three equally-spaced timepoints over nine-months) incorporating quantitative and qualitative designs. There were 1,709 participants (Welsh/Bilingual-medium schools = 844; English-medium schools = 865) aged 12- to 15-years with a mean age of 13.61 years (standard deviation ±.933). All schools were State-maintained and located within Wales. At timepoint one, five Welsh/Bilingual- and four English-medium schools took part. Two Welsh/Bilingual-medium schools dropped out after timepoint one. SMD analysis (Chapter Four) showed a difference between school types but no difference between Welsh/Bilingual-medium attending first language Welsh- [FLWs] and English-speakers [FLEs]. The suggested reason for the difference between the school types was a marginalization of Welsh/Bilingual-medium FLWs’ and FLEs’ first languages within the social media and school environments, respectively. A difference in self-esteem (Chapter Five), depression, loneliness, and social anxiety (Chapter Six) scores was shown for FLWs and FLEs, also, with FLEs showing the poorer scores. The suggested reason was FLWs benefiting in terms of social identification processes and close affiliation to the Welsh language, culture, and community. Structural equation modeling [SEM] (Chapter Seven) indicated that first language mattered whenever SMD predicted self-esteem, depression, loneliness, and social anxiety. Longitudinal analyses (Chapter Eight) showed no difference in FLWs’ and FLEs’ SMD representation at low, medium and high levels over time, but a greater number of FLEs were represented at low self-esteem levels over time. Qualitative analysis (Chapter Nine) suggested FLWs identified a greater array of technical barriers to using Welsh on social media. In conclusion, the suggestion is an individual’s first language matters regarding self-esteem, depression, loneliness, and social anxiety, but not SMD. However, whenever SMD acts as a predictor variable, an individual’s first language appears to play a pivotal role