I need a one page paper summery of the article and a presantion

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I need a one page paper summery of the article that I have attached also I need a presantion that comes with it, So you need FIRST to read the article then write a one page. It should be professional summery page. No plagiarism at all. Down blow is the requirements that the Prof asked for, make sure to read it and understand it carefully and feel free to ask me any question, thank you.


"CHI 2016 article review:

Each student will individually be expected to read a research paper from the CHI 2016 conference, and present a 5 to 7-minute summary of the paper during a class period. A one page written summary, of why the paper is important, and what the student learned, will also be required. Note that only full papers (10 pages) from CHI 2016, NOT short 4-page papers, can be read and analyzed for credit."

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Social Media and Health #chi4good, CHI 2016, San Jose, CA, USA Sleep Debt in Student Life: Online Attention Focus, Facebook, and Mood 1 1 2 2 Gloria Mark , Yiran Wang , Melissa Niiya , Stephanie Reich 1 2 Department of Informatics School of Education University of California, Irvine University of California, Irvine {gmark,yiranw2@uci.edu} {mniiya,smreich@uci.edu} This reflection on sleep by the poet Keats was written almost 200 years ago. Yet in our current digital age, the topic of sleep (or lack thereof) is as important as ever, widely discussed in the academic community as well as in the popular press. One dominant theme has been how people rarely get enough sleep--the term "sleep debt" is actively discussed in the public sphere. Sleep deprivation can have serious consequences, leading to errors in the workplace [23], accidents [2], and even to falling asleep involuntarily at critical times, such as while driving [2]. However, how sleep deprivation might affect how people use information technology (IT) has yet to be explored. ABSTRACT The amount of sleep college students receive has become a pressing societal concern. While studies show that information technology (IT) use affects sleep, here we examine the converse: how sleep duration might affect IT use. We conducted an in situ study, and logged computer and phone use and collected sleep diaries and daily surveys of 76 college students for seven days, all waking hours. We examined effects of sleep duration and sleep debt. Our results show that with less sleep, people report higher perceived work pressure and productivity. Also, computer focus duration is significantly shorter suggesting higher multitasking. The more sleep debt, the more Facebook use and the higher the negative mood. With less sleep, people may seek out activities requiring less attentional resources such as social media use. Our results have theoretical implications for multitasking: physiological and cognitive reasons could explain more computer activity switches: related to less sleep. Considering the current tendency of information workers and students to be online and accessible for interruptions much of the day (and night), the relationship of IT use and sleep is an important issue. Over the last decade, there has been discussion of how constant connectivity is associated with multitasking [32], interruptions [12], and checking email [32], during what might normally be sleeping hours, disrupting or shortening sleep. Author Keywords Multitasking; sleep; productivity; computer logging; in situ study; sensors; social media; Facebook; mobile phone; mood In human-computer interaction (HCI), attention has been given to prototypes and apps that provide novel ways to measure sleep [3, 11, 24], including sensing social data [38]. In other fields, although studies suggest that IT use might interfere with sleep, cf [1], there have not yet been studies showing the converse: how sleep duration might affect IT use. Why is it important to consider how sleep might affect IT use? First, if a connection between sleep duration and IT use is indeed found, then this would point to the notion of sleep as a variable that should be considered in HCI research, particularly as it relates to user performance with digital media. Second, this research could identify specific computer behaviors that are present when people are lacking sleep, which could prevent human error. Third, such research could inform the design of technological interventions that might lead people to improve their sleep habits, e.g. by recognizing and informing about digital media behaviors that might be impacted by sleep deprivation. ACM Classification Keywords H.5.3 [Information Interfaces and Presentation]: Group and Organization Interfaces; K.4.m [Computers and Society]: Miscellaneous. INTRODUCTION "O magic sleep! O comfortable That broodest o’er the troubled sea of the mind...." bird, John Keats, in Endymion, 1818 Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. CHI'16, May 07 - 12, 2016, San Jose, CA, USA Copyright is held by the owner/author(s). Publication rights licensed to ACM. ACM 978-1-4503-3362-7/16/05 $15.00 DOI: http://dx.doi.org/10.1145/2858036.2858437 The purpose of this study is to examine how sleep might affect IT use. Rather than rely on laboratory settings, we conducted an in situ study to capture real world contingencies that could play a role in the relationship between sleep and IT use. Sleep deprivation in young 5517 Social Media and Health #chi4good, CHI 2016, San Jose, CA, USA people, especially college students, has been given much attention, particularly on how sleep is affected by technology use (cf 1, for a review). As a first step to studying this converse relationship, we examine how technology use is affected by sleep duration with college students. Our results show that with less sleep, students perceive higher work pressure and productivity, spend shorter durations of time focusing their attention online, increase their time on Facebook, and experience more negative mood. These results contribute to the growing body of research examining the relationship of sleep and IT use. evening with negative effects on sleep as well as on academic performance [9]. Thus, while there is mounting evidence that IT use consequently affects sleep, no one has looked at the opposite relationship, which we explore in this study. Sleep and Social Media Use Increasingly, research has documented how social media might contribute to sleep deficiencies, largely from people staying awake to stream videos, peruse profiles, and chat online [39, 50]. However, studies are lacking on how sleep duration might affect social media and computer usage. Such relationships are quite plausible. Social media is often viewed as a lightweight activity [52] with socializing, escape, and distractions being common reasons people report for using social media [1, 47]. There is evidence of an association between more time on social networking sites and less effortful thinking [55]. Thus, sleep deprived individuals who are online may likely engage in less cognitively demanding and more distracting activities such as social media use. SLEEP AND INFORMATION TECHNOLOGY USE Sleep Duration and Performance Lack of sleep is linked to numerous negative cognitive and health outcomes [23]. Lack of sleep can lead to reduced memory and cognitive functioning [23, 30], poor academic and work performance [23, 43], and safety risks such as falling asleep while operating vehicles [2]. Sleep deprivation and its effects remain prevalent among the US adult population, with 37% of adults aged 20-39 years reporting less sleep than the recommended 7-9 hours per night [11]. Those people who stay up late or who are categorized as “evening types” report attentional and emotional problems [35]. Sleep and Multitasking A number of studies have examined multitasking, or activity switching, while working with digital media, [e.g. 12, 19]. Multitasking can occur by switching attention not only among computer screens but also between devices [22]. These studies have often pointed to the role of interruptions (from external sources or oneself) [19], email [32], or habitual behavior [19] in influencing multitasking. Although not as thoroughly explored, sleep may also affect technology use and multitasking. For instance, one study found that those who ended their daily activity and went to sleep earlier tended to multitask less the next day [33]. This suggests that perhaps sleep patterns might be associated with attention shifting. Sleep debt Sleep debt is the cumulative loss of, and consequent pressure for sleep, that results from an inadequate amount of physiologically normal sleep [51]. Sleep debt can accrue in a variety of ways including going to bed later but keeping wake time the same, having untreated sleep disorders, or experiencing external sleep disturbances. Since people vary in the amount of sleep they need, sleep debt is based on the “cumulative hours of sleep loss with respect to the subject-specific daily need for sleep" [51]. Numerous studies have found sleep debt to impact a host of outcomes such as mood [18], weight gain [4], performance [42], and health [29]. Studies of sleep deprivation and attention have found insufficient sleep to result in decreased visual and auditory attention [20]. Less sleep could thus contribute to less cognitively taxing behaviors while on the computer such as having less persistent attention with more switching of focus between windows and applications. This alteration in attention also applies to task switching [17]. It is quite plausible that similar low-attention behaviors, due to less sleep, could occur while on the computer as well. Thus, less sleep could contribute to higher online multitasking in the following ways. It could lead to more frequent switching of attention to different computer screens. This switching could be due to more self-interruptions, which comprise almost half of all interruptions in the workplace and can lead to task switches [12, 19]. Self-interruptions could be due to higher distractibility, and lack of sleep could make people more prone to distraction and thus more likely to switch focus from their current task at-hand. College Students and Sleep Duration Young adults may be particularly at risk for sleep disturbances and deprivation [6]. Lack of sleep is prevalent among college students, especially women [8]. Poor sleeping habits have negative impacts on undergraduate physical and mental health as well as academic performance such as lower test scores and grade point averages [43, 49]. Poor sleep quantity and quality, which include late nights, earlier wakeup times [49], and irregular sleep-wake patterns [28] are associated with lower academic performance, suggesting that routines around sleep, in addition to sleep amount, affect college students. Constant availability via mobile phones and the Internet may lead to sleep disturbances. For example, among a sample of young people, smartphones were often used before bed and caused frequent sleep interferences [25]. Among adolescents, IT use tends to stretch late into the RESEARCH QUESTIONS In contrast to prior studies, which focused on how IT use affects sleep, we look at the converse: how sleep duration 5518 Social Media and Health #chi4good, CHI 2016, San Jose, CA, USA might affect how people use IT. While numerous studies point to the detrimental effects of having less sleep [23, 30, 43, 49], amount of sleep could also affect IT use. Further, while we expect sleep debt to be related to sleep duration, there are differences. One night of short sleep may affect some performance measures the next day and not others. However, sleep debt represents an accumulation of the effects of loss of sleep. In other words, consistently sleeping shorter than one's ideal amount could accumulate and produce effects on different variables than sleep duration. We thus examine sleep duration the night before and sleep debt with different variables. The following are our research questions and hypotheses. lightweight, requiring little effort and often serving as a quick break (see [52]). As such, it is not surprising that youth report using social media for such things as distraction, a way to fill time, and as a mechanism for socializing [47]. Further, college students who use social media, especially social networking sites like Facebook, report feeling a need to check these sites regularly to stay connected to others [48]. This is consistent with other research that suggests that Facebook use is habitual [13]. We would not expect social media habits like Facebook use to change so easily after one night of sleep loss but rather would expect to see changes over longer term sleep loss accumulation (i.e. sleep debt). For this research question we examine sleep debt, as we are interested in whether we find a relationship of accumulated sleep loss with the tendency to increase lightweight activities. RQ1: What are the impacts of sleep on college students’ perceptions of productivity and work pressure? Before examining sleep effects on IT use, we begin by looking at related experiences to sleep duration. Decades of research have shown that sleep and health are related and that deficits in sleep can lead to personal and fiscal costs [23]. Studies of worker productivity and sleep patterns have found that decreased sleep leads to reduced memory, cognitive functioning, and work performance [30]; and for college students, sleep deficits result in lower academic performance [43, 49]. Conversely, when students are well rested, they tend to perform better academically and exercise more [5]. Thus, we explore whether less sleep duration the night before is associated with higher subjective work pressure and lower perceived productivity. We hypothesize that: When students are more sleep deprived and using technology, they might seek out activities involving fewer cognitive resources [15]. When working online, they may also be more likely to be distracted from work, and social media is reported as a common form of distraction [47]. A study of Facebook use and student engagement, using the National Survey of Student Engagement (NSSE) showed a negative relationship of Facebook use and engagement in educational activities [21]. It has also been observed in the workplace that when people are engaged in computer work Facebook affords a light break from work [31]. The most common social media site among adults ages 18-29 is Facebook, with 87% reporting using it in 2014 [14]. Others have also reported Facebook as the most popular social media site among college students [21]. We therefore focus on Facebook use and thus expect that when students have higher sleep debt, when online they would be more likely to spend more time doing Facebook as a lightweight activity and also as a distraction. H1: Students with shorter sleep duration the night before will have higher perceived work pressure the next day. H2: Students with shorter sleep duration the night before will rate their productivity to be lower the next day. RQ2: Is sleep related to college students’ multitasking on the computer? Surveys of college students have found high association between online computer use and sleep deficits for that evening [37]. Other research shows that longer duration of focus on the computer is related to less stress [32]. Coupling these patterns with evidence that decreased sleep reduces attention and focus [26], computer behavior when people had less sleep might involve more switching and less focused attention. A higher frequency of switching on the computer has been considered as a measure of higher multitasking [32]. In other words, from a fine-grained perspective it is an indicator of switching attention between different activities as screens and content change. Thus, we expect that less sleep should be associated with higher multitasking, as reflected by shorter attention duration on any computer screen. Thus, we expect that: H4: Students with more sleep debt will engage in more Facebook use. RQ4. How is sleep debt related to mood? A meta-analysis of laboratory sleep studies found that sleep deprivation significantly negatively impacts mood [42]. However, in contrast, a study of college students who were sleep deprived for 24 hours showed no significant changes in mood, such as negative mood states of anger and anxiety [43]. The authors explain this to the notion that 24 hours of sleep deprivation is long enough to affect fatigue but not long enough to impact mood. A large cross-sectional survey study of college students though did find a relationship between self-reported sleep disturbances and negative mood [28]. Because sleep loss over 24 hours did not affect mood, we expect that longer accumulated sleep could affect mood. Therefore in this research question we examine sleep debt. Based on these studies we expect that the more sleep debt one has, the more it would negatively impact mood. Most studies of the effects of sleep deprivation and mood have been done in the laboratory or with surveys. We are not H3: Students with shorter sleep duration the night before will have shorter focus duration on computer windows the next day. RQ3: Is sleep debt associated with Facebook use? Social media consumption (as opposed to production) is often 5519 Social Media and Health #chi4good, CHI 2016, San Jose, CA, USA aware of studies that have examined the relationship of sleep deprivation and mood where people have been tracked in an in situ environment. Therefore, we hypothesize: user opened a new window or switched between already open windows. A window was an application or a web browser tab. Each log record included the start time and duration of the active window, the name of the application, a URL if the window was a web browser tab, and idle time. Timestamps were recorded to the second. Only time spent in the current window was measured. For example, if a webpage was open in the background when the user used Word in the foreground, Kidlogger only counted the time spent in Word. Computer idle time was not included. Phone logging over seven days was collected using the AWARE Framework application (www.awareframework.com). A log record was generated each time a user opened an application, web browser tab or switched between apps. The log captured activity to the millisecond. Data from computer and phone logging was partitioned into daily logs and numbered, generating at least seven days of computer/phone use per person. Due to scheduling issues, for some participants more than 7 days of data was collected. The distribution of full days of data collection was as follows: 58 students have 5 full days (7 days in total); 9 students have 6 full days (8 days in total); 1 student has 8 full days (10 days in total); 3 students have 7 full days (9 days in total); and 1 student has 8 full days (10 days in total). H5: Students with more sleep debt will experience more negative mood. METHODOLOGY We conducted an in situ observational study at a large public university on the U.S. west coast in Jan.-May of 2014. We used a mixed-methods approach with automatic computer and phone logging as the primary method of data collection. In order to capture the context of students’ life at school, their sleep schedule, and mood, we also used daily surveys and a one-time general survey about attitudes and demographic information. Computer and phone logging and other data collection occurred over seven days. Participants and Procedure Participants were recruited from undergraduate courses, student residences, and snowball sampling. In total, we collected data from 76 undergraduates: 34 males and 42 females. Due to the availability of monitoring software and its limitations, the study was restricted to students who used both Windows computers and Android phones. Students’ ages ranged from 18 to 23 years (mean=19.3) and the median college year was sophomore. Facebook (FB). Measures of FB usage from both the computer and phone logs were used to calculate frequency and duration of visits. The duration of time on FB from computer and phone logs were added together to produce a total daily time on Facebook. On Day 1 of the study, participants visited a campus laboratory where the computer and phone logging software were installed on their devices. Students who also had desktop computers were given software installation instructions. Participants were sent a link to an online general survey on Day 1 and instructed to complete it before the end of the study (by Day 7). Two daily surveys were sent each day: one at 7 a.m. (a sleep diary for the night before), asking participants to complete it once awake and another at 9 p.m. (an end-of-day survey), asking them to complete it before going to bed. On Day 7, semi-structured exit interviews (for about 30 minutes) were conducted. Participants were asked about their experiences during the study, their technology habits, and their beliefs about how technology could affect stress, productivity, and mood. Descriptive coding was used to identify key themes that emerged from these interviews [46]. Participants were paid $100 for their participation. Focus duration. Multitasking can be considered from different perspectives: at a broad level examining task switching, or at finer-granularity, examining attention shifts among different activities which could be within the same task. We adopt this fine-granularity perspective to investigate the relationship of sleep and its effects on how people shift their attention when working on the computer. We feel that measuring duration of focus on computer screens is a reasonable proxy for attention duration with computer work, and this has been used as a measure of attention in multitasking, cf [32]. By computing its inverse, average duration of focus can alternatively be converted into a measure of average switching among different computer windows per time unit. Focus duration is measured as the average duration spent on any active window, based on the logging software. Measures A total of 71 people were used in the analysis. Five people were excluded due to missing computer data (logging software problems, software getting uninstalled by antivirus software, and one participant uninstalling the software). For these 71 participants, we collected computer and phone logs, sleep duration, social media use and daily surveys, and single general surveys. Sleep duration. Daily sleep duration was measured via a self-report sleep diary, modified from [36]. Starting from Day 2 of their study, participants were asked to enter the time they went to bed the previous day (hour and minute) and the time they woke up (hour and minute). Thus, data were collected for at least six nights (sleep on Days 1-6). Five days is considered sufficient to produce reliable measures of sleep [45]. Sleep diaries have been found to be very accurate [7], are commonly used in sleep studies [5, 8, Computer and phone logging. Computer activity over seven days was captured with Kidlogger (kidlogger.net). This Windows freeware generated one log record each time a 5520 Social Media and Health #chi4good, CHI 2016, San Jose, CA, USA 9, 41] and have been used as a gold standard when testing sleep-monitoring devices, e.g. [34]. Self-reported sleep has also been validated with actigraphs [54]. Sleep diary reports were included in the end-of-day surveys described below. productive do you feel you were today? (using a Likert scale, 1= Not at all; 5=Extremely). Sleep debt. People can accumulate sleep debt on days when they work and they generally compensate for loss of sleep on free days. Sleep debt is operationalized as follows. The larger the difference between mid-sleep time on free days compared to mid-sleep time on work days, the larger is considered the sleep debt [44]. The mid-sleep time is calculated as the mid-point between sleep onset and awakening. The analysis by Roenneberg and colleagues [44] shows that most people compensate for sleep loss during the week (work days) by sleeping longer on weekends (free days). Therefore, the ideal mid-sleep time on free days is calculated from observed mid-sleep time on free days, corrected by the weekend and weekday sleep durations, by the following formula recommended by [44]: • Sleep Duration: (Sleep diary) the amount of sleep selfreported daily based on time to sleep the previous night and time one woke up the next morning. Greater values for sleep duration reflect a greater amount of sleep. • Sleep Debt: calculated by the formula above, based on self-reported daily sleep. Greater values represent a greater amount of sleep debt. • Total Computer Duration: the total daily active time on the computer, based on the logging software. • Focus Duration: the average duration spent on any active computer window, based on the logging software. One outlier was removed. • FB Duration on Computer/Phone: the total daily FB use on the computer and phone combined, based on the logging software. One outlier was removed. A square root transformation was done to improve normality. • Affect balance: the positive daily PANAS score minus the negative daily PANAS score (End-of-day survey) • Interviews: participant responses to semi-structured interviews of their technology experiences. MSFSC = MSF - .05*(SDF-(5*SDW + 2*SDF)/7) where MSF=mid-sleep time on free days, SDF=sleep duration on free days, and SDW is sleep duration on work days. (5*SDW + 2*SDF)/7 is the average weekly sleep need. Control variables: Based on students' schedules, we used weekday as a "workday" and weekends were "free days". We took the difference between the mid-sleep time of each workday and the ideal mid-sleep time, as the daily sleep debt. We took the absolute value, since the theory of sleep debt is that it is the absolute departure from the ideal mid-sleep time that is representative of sleep debt. It is the disruption from ideal sleep that matters. As sleep debt has a long tail, we used a log transformation to make the distribution normal. • Age, gender (General survey) • Workload: number of course credit units taken at the time of the study (General survey) • Deadlines: (From the end-of-day survey): How much did deadlines influence you today? (using a Likert scale, 1= Not at all; 5=Extremely) (End of day survey). RESULTS We collected more than 1,720 hours of computer logs from 71 participants, excluding computer idle time. Most participants reported in exit interviews that the week of the study was representative of a typical school week. Mood. Mood was measured using the PANAS scale at the end of every day. The PANAS [53] is a well-validated scale that measures two dimensions of mood: positive and negative affect. Following [27], we created a measure of Affect Balance, which refers to a balance between positive and negative affect, where the negative score is subtracted from the positive score. Overview of results Survey measures. Participants filled out daily end-of-day surveys where they answered questions on work pressure and perceived productivity. Other daily measures were also asked but are not covered in this paper. A general survey asked students for demographic information, technology use habits, course load, and class standing. We begin by reporting an overview of the results. Table 1 shows descriptive statistics of the measures of interest. The average sleep duration in our sample was 7.9 hours, with a wide range of 2.6 to 14.0 hours. Students showed a positive sleep debt, meaning that on average they accumulated sleep loss. Focus duration on any computer window averaged 1 minute, 38 seconds. Sleep duration and sleep debt are weakly negatively correlated: r= -.11, p
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