#Sleep_as_Android: Feasibility of Using Sleep Logs on Twitter for Sleep Studies (original) (raw)
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Sleep health, 2018
The use of mobile device-based electronic social media (ESM) in bed is rapidly becoming commonplace, with potentially adverse impacts on sleep and daytime functioning. The purpose of this study was to determine the extent to which in-bed ESM use is associated with insomnia, daytime sleepiness, mood, and sleep duration in adults. This was a cross-sectional observational study conducted among 855 hospital employees and university students (mean age, 43.6years; 85% female) via an online questionnaire. Nearly 70% of participants indulged in in-bed ESM use, with nearly 15% spending an hour or more a night doing so. The degree of in-bed ESM use did not vary by gender, but higher levels of in-bed ESM use were seen in younger and middle-aged than elderly participants. Compared with participants with no in-bed ESM use and controlling for age, gender, and ethnicity, participants with high in-bed ESM use were more likely to have insomnia, anxiety, and short sleep duration on weeknights, but no...
Large-Scale Sleep Condition Analysis Using Selfies from Social Media
Social, Cultural, and Behavioral Modeling, 2017
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.
Already up? using mobile phones to track & share sleep behavior
International Journal of Human-Computer Studies, 2013
Users share a lot of personal information with friends, family members, and colleagues via social networks. Surprisingly, some users choose to share their sleeping patterns, perhaps both for awareness as well as a sense of connection to others. Indeed, sharing basic sleep data, whether a person has gone to bed or waking up, informs others about not just one's sleeping routines but also indicates physical state, and reflects a sense of wellness. We present Somnometer, a social alarm clock for mobile phones that helps users to capture and share their sleep patterns. While the sleep rating is obtained from explicit user input, the sleep duration is estimated based on monitoring a user's interactions with the app. Observing that many individuals currently utilize their mobile phone as an alarm clock revealed behavioral patterns that we were able to leverage when designing the app. We assess whether it is possible to reliably monitor one's sleep duration using such apps. We further investigate whether providing users with the ability to track their sleep behavior over a long time period can empower them to engage in healthier sleep habits. We hypothesize that sharing sleep information with social networks impacts awareness and connectedness among friends. The result from a controlled study reveals that it is feasible to monitor a user's sleep duration based just on her interactions with an alarm clock app on the mobile phone. The results from both an in-the-wild study and a controlled experiment suggest that providing a way for users to track their sleep behaviors increased user awareness of sleep patterns and induced healthier habits. However, we also found that, given the current broadcast nature of existing social networks, users were concerned with sharing their sleep patterns indiscriminately.
The relationship between social media use and sleep quality among undergraduate students
Information, Communication & Society, 2016
Insufficient sleep is a growing health problem among University students, especially for freshmen during their first quarter/semester of college. Little research has studied how social media technologies impact sleep quality among college students. This study aims to determine the relationship between social media use and sleep quality among freshman undergraduates during their first quarter in college. Specifically, we explored whether variations in Twitter use across the time of day and day of the week would be associated with self-reported sleep quality. We conducted a study of Freshman Twitter-using students (N = 197) over their first quarter of college, between October to December of 2015. We collected students' tweets, labeled the content of the tweets according to different emotional states, and gave theme weekly surveys on sleep quality. Tweeting more frequently on weekday late nights was associated with lower sleep quality (β = −0.937, SE = 0.352); tweeting more frequently on weekday evenings was associated with better quality sleep (β = 0.189, SE = 0.097). Tweets during the weekday that were labeled related to the emotion of fear were associated with lower sleep quality (β = −0.302, SE = 0.131). Results suggest that social media use is associated with sleep quality among students. Results provide can be used to inform future interventions to improve sleep quality among college students.
Real-world longitudinal data collected from the SleepHealth mobile app study
Scientific Data, 2020
Conducting biomedical research using smartphones is a novel approach to studying health and disease that is only beginning to be meaningfully explored. Gathering large-scale, real-world data to track disease manifestation and long-term trajectory in this manner is quite practical and largely untapped. Researchers can assess large study cohorts using surveys and sensor-based activities that can be interspersed with participants’ daily routines. In addition, this approach offers a medium for researchers to collect contextual and environmental data via device-based sensors, data aggregator frameworks, and connected wearable devices. The main aim of the SleepHealth Mobile App Study (SHMAS) was to gain a better understanding of the relationship between sleep habits and daytime functioning utilizing a novel digital health approach. Secondary goals included assessing the feasibility of a fully-remote approach to obtaining clinical characteristics of participants, evaluating data validity, ...
Computers in Human Behavior, 2020
Emergent research suggests that "fear of missing out" (FoMO)-driven nocturnal use of social media may result in sleep disturbances and adversely influence quality of sleep. Previous research in this area primarily focused on adolescents. Therefore, knowledge of these occurrences in young adults is limited. This study addresses this knowledge gap by investigating the associations of FoMO, psychological well-being (anxiety, depression), compulsive social media use (CSMU), and sleep hygiene (habits that promote/inhibit sleep) with problematic sleep adults in both academic and employment settings. Cross-sectional surveys were conducted to collect data from two cohorts including (i) full-time students (N ¼ 1398), and (ii) full-time working professionals (N ¼ 472). Data were analyzed with structural equation modeling. The results indicated that psychological well-being influences CSMU, which in concurrence with sleep habits, influences the association between FoMO and problematic sleep. Significant differences existed in the strength of the association between CSMU and FoMO between the two cohorts. Interestingly, FoMO is more strongly associated with CSMU among working professionals. This study provides novel insights into the differential effects of CSMU and FoMO on sleep behaviors in young adult students versus working professionals.
Relationship Between the Intensity of Social Media Usage with Sleep Quality
2020
Background : Social media has become a part of human daily life, including students. The high intensity of social media usage can affect various aspects of life, one of which is the quality of sleep. The high intensity of social media usage is thought to be related with poor sleep quality. This study analyzes the relationship between the intensity of social media usage and sleep quality. Objective : To know the relationship between the intensity of social media usage with sleep quality in dental students. Met hod : This research was an observational analytic study with a cross-sectional design. The sample was students of the Dentistry Study Program at Faculty of Medicine, University of Diponegoro (n = 79). The intensity of social media usage was measured using the Social Network Time Use Scale and sleep quality was measured using the Pittsburgh Sleep Quality Index. Measurement of dependent and independent variables was done once at a time. Result : Among respondents, 34,2% were repo...
To assess the relationship between social media addiction and sleep quality among students
International Journal of Multidisciplinary Research and Growth Evaluation, 2024
This research investigates the intricate relationship between social media addiction and sleep quality among university students, with a particular focus on both male and female participants. Employing a correlational survey design, the study, conducted in the Bathinda region, garnered data from 100 students (55 males and 45 females) aged 18 to 29. The data collection process utilized Google Forms, incorporating questionnaires designed to assess social media addiction and sleep quality. The discerned findings underscore a noteworthy negative correlation between social media addiction and sleep quality among the student population. This correlation is consistently observed across male and female participants, with a marginally higher correlation coefficient identified in the female cohort. The study unequivocally rejects the null hypotheses, lending support to the alternate hypotheses, thereby positing that social media addiction adversely impacts sleep quality. The strengths of this research lie in its comprehensive exploration of social media usage, encompassing diverse platforms, rationales for usage, employed devices, and the temporal dynamics of usage preceding bedtime. Despite these merits, the study is not without limitations, notably the confinement of the sample to a single center, limiting the broader applicability of its findings. Additionally, the reliance on self-report measures introduces susceptibility to response bias. Future research endeavours stand to benefit from the integration of more robust data collection tools, such as the Polysomnography technique, and a broader, more diverse sample size, thereby enhancing the study's generalizability and relevance. In summary, this study furnishes valuable insights into the nuanced interplay between social media addiction and sleep quality among university students. As society grapples with the multifaceted implications of escalating social media usage, understanding its ramifications on sleep emerges as an imperative facet for promoting holistic well-being.
"I Can't Get No Sleep": Discussing #insomnia on Twitter
Emerging research has shown that social media services are being used as tools to disclose a range of personal health information. To explore the role of social media in the discussion of mental health issues, and with particular reference to insomnia and sleep disorders, a corpus of 18,901 messages - or Tweets - posted to the microblogging social media service Twitter were analysed using a mixed methods approach. We present a content analysis which revealed that Tweets that contained the word “insomnia” contained significantly more negative health information than a random sample, strongly suggesting that individuals were making disclosures about their sleep disorder. A subsequent thematic analysis then revealed two themes: coping with insomnia, and describing the experience of insomnia. We discuss these themes as well as the implications of our research for those in the interaction design community interested in integrating online social media systems in health interventions.
2021
Background Eighty-eight percent of people aged 18-25 engage in social media use. Facebook, Instagram, Twitter, YouTube, and Snapchat are the five most popular applications. The rise of social media use raises the question of how these applications may impact sleep quality. Studies have linked high social media use, phone addiction, and media device use to poor sleep quality, yet few studies have explored how specific application use and multiuse may influence sleep. Purpose The purpose of this study was to examine how different social media applications affect sleep quality in undergraduate nursing students. Methods A personalized questionnaire was created to assess social media use and confounding variables. The Pittsburg Sleep Quality Index (PSQI) was used to assess sleep quality. The sample for this study included 133 undergraduate nursing students. Following data collection descriptive statistics, Pearson's r correlation, and ANOVA with Scheffe post hoc procedure were used to analyze data. Findings and Implications Using PSQI scoring, the overall sleep score of this sample showed that most participants reported poor sleep quality. Results found that individuals that used Snapchat and Twitter reported poorer sleep quality. Individuals with nighttime multiuse of applications and use of sleep aid medications also reported poorer sleep. Participants reported spending up to five hours on one application during the day. Recommendations for future research include analyzing multiuse with a larger population.