Associating Internet Usage with Depressive Behavior Among College Students

Katikalapudi, R.; Chellappan, Sriram; Montgomery, Frances H.; Wunsch, Donald C.; Lutzen, K.

doi:10.1109/mts.2012.2225462

Cited by 76 publications

(43 citation statements)

References 21 publications

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“…56 Much of this seemed to stem from one of the applications that the researchers envisioned for their results: "[p]roactively discovering depressive symptoms from passive and unobtrusive Internet usage monitoring." 57 The study is noteworthy for our purposes for having taken a number of steps to ensure the anonymity and privacy of its research subjects while simultaneously -if unintentionally -demonstrating the limits of those very same protections for anyone who might be subject to the resulting model. The point is not to pick on these or other academic researchers; rather, it is to show that anonymity is not an escape from the ethical debates that researchers should be having about their obligations not only to their data subjects, but also to others who might be affected by their studies for precisely the reasons they have chosen to anonymize their data subjects.…”

Section: However Much These Protect Volunteers Such Techniques May Lmentioning

confidence: 99%

Privacy, Big Data, and the Public Good

Lane¹,

Stodden²,

Bender³

et al. 2014

110

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Massive amounts of data on human beings can now be analyzed. Pragmatic purposes abound, including selling goods and services, winning political campaigns, and identifying possible terrorists. Yet 'big data' can also be harnessed to serve the public good: scientists can use big data to do research that improves the lives of human beings, improves government services, and reduces taxpayer costs. In order to achieve this goal, researchers must have access to this data - raising important privacy questions. What are the ethical and legal requirements? What are the rules of engagement? What are the best ways to provide access while also protecting confidentiality? Are there reasonable mechanisms to compensate citizens for privacy loss? The goal of this book is to answer some of these questions. The book's authors paint an intellectual landscape that includes legal, economic, and statistical frameworks. The authors also identify new practical approaches that simultaneously maximize the utility of data access while minimizing information risk.

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Section: However Much These Protect Volunteers Such Techniques May Lmentioning

confidence: 99%

Privacy, Big Data, and the Public Good

Lane¹,

Stodden²,

Bender³

et al. 2014

110

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show abstract

“…In addition, Foley et al (2013) found that more Internet use in the last 90 min before sleep predicted a worse night's sleep among American children while other research suggested that more computer use among 10- and 11-year-old children predicted more psychological difficulties even after adjusting for activity levels (Page et al, 2010). Finally, studies with college students have shown that more Internet use was related to more depression (Cristakis, Moreno, Jelenchick, Myaing, & Zhou, 2011; Rosen, Whaling, Rab, Carrier, & Cheever, 2013) and one study by Kotikalapudi, Chellappan, Montgomery, Wunsch, and Lutzen (2012) even demonstrated that specific types of online activity–assessed from computer records alone–could predict depression levels in American college students.…”

Section: Introductionmentioning

confidence: 99%

Media and technology use predicts ill-being among children, preteens and teenagers independent of the negative health impacts of exercise and eating habits

Rosen

Lim

Felt

et al. 2014

Computers in Human Behavior

247

155

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The American Academy of Pediatrics recommends no screen time for children under the age of 2 and limited screen time for all children. However, no such guidelines have been proposed for preteens and teenagers. Further, research shows that children, preteens, and teenagers are using massive amounts of media and those with more screen time have been shown to have increased obesity, reduced physical activity, and decreased health. This study examined the impact of technology on four areas of ill-being–psychological issues, behavior problems, attention problems and physical health–among children (aged 4–8), preteens (9–12), and teenagers (13–18) by having 1030 parents complete an online, anonymous survey about their own and their child's behaviors. Measures included daily technology use, daily food consumption, daily exercise, and health. Hypothesis 1, which posited that unhealthy eating would predict impaired ill-being, was partially supported, particularly for children and preteens. Hypothesis 2, which posited that reduced physical activity would predict diminished health levels, was partially supported for preteens and supported for teenagers. Hypothesis 3, that increased daily technology use would predict ill-being after factoring out eating habits and physical activity, was supported. For children and preteens, total media consumption predicted illbeing while for preteens specific technology uses, including video gaming and electronic communication, predicted ill-being. For teenagers, nearly every type of technological activity predicted poor health. Practical implications were discussed in terms of setting limits and boundaries on technology use and encouraging healthy eating and physical activity at home and at school.

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“…Kotikalapudi et al [19] analyzed the patterns in the Web-browsing activities of college students that could signal depressive symptoms. Moreno et al [20] demonstrated that status updates on Facebook could reveal symptoms of major depressive episodes.…”

Section: Introductionmentioning

confidence: 99%

Activities on Facebook Reveal the Depressive State of Users

Park¹,

Lee²,

Kwak³

et al. 2013

J Med Internet Res

116

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BackgroundAs online social media have become prominent, much effort has been spent on identifying users with depressive symptoms in order to aim at early diagnosis, treatment, and even prevention by using various online social media. In this paper, we focused on Facebook to discern any correlations between the platform’s features and users’ depressive symptoms. This work may be helpful in trying to reach and detect large numbers of depressed individuals more easily.ObjectiveOur goal was to develop a Web application and identify depressive symptom–related features from users of Facebook, a popular social networking platform.Methods55 Facebook users (male=40, female=15, mean age 24.43, SD 3.90) were recruited through advertisement fliers distributed to students in a large university in Korea. Using EmotionDiary, the Facebook application we developed, we evaluated depressive symptoms using the Center for Epidemiological Studies-Depression (CES-D) scale. We also provided tips and facts about depression to participants and measured their responses using EmotionDiary. To identify the Facebook features related to depression, correlation analyses were performed between CES-D and participants’ responses to tips and facts or Facebook social features. Last, we interviewed depressed participants (CES-D≥25) to assess their depressive symptoms by a psychiatrist.ResultsFacebook activities had predictive power in distinguishing depressed and nondepressed individuals. Participants’ response to tips and facts, which can be explained by the number of app tips viewed and app points, had a positive correlation (P=.04 for both cases), whereas the number of friends and location tags had a negative correlation with the CES-D scale (P=.08 and P=.045 respectively). Furthermore, in finding group differences in Facebook social activities, app tips viewed and app points resulted in significant differences (P=.01 and P=.03 respectively) between probably depressed and nondepressed individuals.ConclusionsOur results using EmotionDiary demonstrated that the more depressed one is, the more one will read tips and facts about depression. We also confirmed depressed individuals had significantly fewer interactions with others (eg, decreased number of friends and location tagging). Our app, EmotionDiary, can successfully evaluate depressive symptoms as well as provide useful tips and facts to users. These results open the door for examining Facebook activities to identify depressed individuals. We aim to conduct the experiment in multiple cultures as well.

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Associating Internet Usage with Depressive Behavior Among College Students

Cited by 76 publications

References 21 publications

Privacy, Big Data, and the Public Good

Privacy, Big Data, and the Public Good

Media and technology use predicts ill-being among children, preteens and teenagers independent of the negative health impacts of exercise and eating habits

Activities on Facebook Reveal the Depressive State of Users

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