It’s “Facebook-official.” The Internet and new technologies are in a relationship—with us. As heavy consumers of technology, we are tied to devices with an Internet connectivity nearly continuously. We check our emails in the morning, before bed, and an average of 15 times per day and ten times that if you are a millennial (Social Media Week, 2016). We set up alerts to notify us of any activities or incoming information so that we may be advised of any activities at a moment’s notice. We can connect to others an ocean away or gain information about a topic that we could not possibly have accessed a decade ago. We rely on technology to help us more effectively move through the world, increase our social capital, gain information, socialize, and be entertained (Filipovic, 2013).
With these advances come some unintended (or unacknowledged) consequences. The ability we possess via these technologies to access others also provides a mechanism through which we, too, can be accessed. For example, we may be accessed at home and off-hours by those with whom we only have connections via work and career. Former partners, peers, and friends can make a connection to us or simply obtain updates on our daily life through quickly typing names into search engines. It is a way of staying connected without connecting. Further, the unintended consequences may have a substantial impact on others around us. The advances in technology are too fast and too numerous for anyone to keep up with all of them and their effects. By the time we identify how relationships are affected by these technologies, the technology we have figured out has been replaced by a new trend with its own set of implications and consequences.
Couples and families/relational systems who are living in today’s world with technology as a prominent part in their organization of tasks and method of communication need to roll back the lens on what specific devices, software, and applications have contributed to the function (or dysfunction) in their relationships. Instead, we should evaluate how factors such as access, affordability, accommodation, and acceptability advance or interfere with a relationship’s basic structure and functions. How, for example, does being accessible to anyone 24 hours a day and seven days a week affect how we communicate with those in our own home? How does the affordability and acceptability of having phones play into a parent’s decision to gift a phone (and accessibility) to a child? How do we set rules to maintain the benefits of having these technologies in our lives, but limit what we may view as potential negatives?
With data from fields such as information technologies, education, sociology, psychology, biology, family studies, and psychotherapy, we are developing a rich library from which to develop appropriate adaptive responses to these questions. Family studies scholars are piecing together the circumstances in which the Internet can be used to augment relationships, as well as where the Internet may be contributing to problematic outcomes. In some cases, this distinction is easy to make. For example, cases of pathological Internet use, “addiction,” or even Internet infidelity are commonly lumped into the negative outcomes category. It may be more difficult in other cases to quantify the net impact on our relationships without consideration of each couple’s or family’s unique characteristics and how they interact with technology and new media. For example, talking to an individual of a differing or similar gender via Face-book messaging may be perfectly permissible in one couple’s relationship, but constitute a serious betrayal in another.
Technology is infused in the everyday experience for nearly half of the world’s population. Of 7.4 billion people in the world, 50% (3.7 billion) indicated they are Internet users and 66% (or 4.92 billion) are identified as unique mobile phone users. This number represents a growth of 10% in Internet users and an increase of 21% in social media users over the last year alone (Hootsuites, 2017). Another 2.5 billion identify themselves as users of social media. Estimates of technological saturation in the population range anywhere from 30 to 99%, depending on where you are on the globe. For example, 88% of the population in North America are Internet users; this is in stark contrast to Africa or South Asia, where 29% and 33% of the population, respectively, are Internet users (Hootsuites, 2017). The United Arab Emirates has the highest penetration of Internet users at 99% and North Korea the lowest at 0.1% (Hootsuites, 2017). The difference in Internet usage between 11 developed countries (such as the United States, Canada, European countries, and developed Asian countries) and emerging countries is 33% (Greenwood, Perrin, & Duggan, 2016). Of those who use the Internet in developed countries, 75% report accessing the Internet on a daily basis (Poushter, Bishop, & Chwe, 2018). Most frequently, global Internet users are using cellphones to text message, take photos and videos, access social media sites, get information, and make payments (Poushter et al., 2018).
Internet use is influenced by diverse intersectional elements like one’s cohort or socioeconomic status (SES). For instance, vast majorities (98%) of Internet users are college-educated, make more than $75,000 annually, and are younger (between the ages of 18 and 29, or 18 and 34, depending on the study). Those who are accessing the Internet more than once a day are also more likely to fit these characteristics (Greenwood et al., 2016). For example, sub-Saharan Africa, which is classified as economically underdeveloped, has one of the lowest rates of adults who use the Internet (25%) (Poushter et al., 2018). In fact, Internet usage is highly correlated with a country’s per capita income but has plateaued over the last few years in developed countries (Poushter et al., 2018).
Technology is immersed in our lives, with computers and tablets being a primary mechanism for usage. Somewhere on the order of 450 million hardware devices (e.g., computers, laptops, and tablets) are shipped annually. Specifically, people are opting for smaller and smaller devices, as the purchasing of desktops has decreased in favor of laptops and tablets (Anderson, 2015). One study found that the introduction of a smart-phone reduced the use of laptops and, at least in some cases, increased media consumption in other parts of the day (Ley et al., 2013), in part because they may be viewed as more flexible devices and more convenient to accomplish a required task (Ley et al., 2013).
As prevalent as the Internet is in our lives, there still exists a small proportion of the population that is new to the Internet. Those who did not consider themselves Internet users, once given tablets, were still not all inclined to surf online: they also stated the reason they did not have access to technology in the first place was that they did not need the technology (Perrin & Bertoni, 2017). For the 61% who did some online exploration, top activities included looking up news and using applications (apps). In addition, new users reported struggles in using technology, including struggles with the password and login processes, using touch screens, and keeping the device charged (Perrin & Bertoni, 2017).
Over time, our ability to be connected has grown even smaller. In the US, 95% of adults own a cellphone—which is a considerable jump from 2005, when 65% of American adults had a cellphone (Rainie, 2015), with no differences in ownership based on racial or ethnic identification. Of US adults who own devices, there has been a steady rise from about 65% to approximately 95% in the number of cellphone owners between 2004 and 2016 and a sharp rise of smartphone owners from nearly 35% to around 80% between 2011 and 2016, with a notable increase globally since 2013 (Greenwood et al., 2016), particularly in developed countries. Between 2013 and 2015, global ownership of smartphones increased from 45% to 54%. Countries such as Turkey, Brazil, Chile, and Malaysia experienced an increase in smartphone ownership of at least 25% (and, in the case of Turkey, up to 42%) (Greenwood et al., 2016). Specifically, there is greater smartphone usage in the US and Europe, and less usage in South Asia and Africa (Poushter et al., 2018). Mobile devices are in use globally, even if the phone is not technically a smartphone.
According to a study conducted by Hootsuites (2017) comparing global smartphone connections to total global mobile connections, the total of global connections across devices was 8.05 billion, the total of connections made on smartphones was 4.42 billion, and the total of connections used on feature-phone devices was 3.38 billion. They then compared the share of smartphone devices and share of feature-phone connections with the total connections. What they found was that the share of smartphone connections and the share of feature-phone connections were 55% and 45% respectively compared to total connections. Not surprisingly, Internet and smartphone penetration is related to a country’s overall wealth (Poushter et al., 2018).
Another key advantage to new devices and applications is the flexibility in making connections to others (Barkhaus & Polichar, 2011). In a study examining why people use particular mediums, participants noted there was a great deal of choice and they chose a communication method based on the goal they were trying to meet. For example, if they were interested in making contact with someone due to an emergency, text messaging was preferred over other modalities. Other considerations included the sender’s context and response time. For example, if one is going to be near their phone and that is the most convenient way to send a message, they may choose to use the phone. Flexibility was described as “paramount” in making such choices (Barkhaus & Polichar, 2011, p. 632).
Today, 40% of adults report having a game console such as an Xbox or PlayStation. Only one-third of households, however, that earn $30,000 or less per year will own a game console compared to the 54% of adults in households that earn $75,000 or more per year. Young adults are particularly likely to play video games, as well as to identify as “gamers.” About half of US adults (49%) “ever play video games on a computer, TV, game console or portable device like a cellphone,” and 10% consider themselves to be “gamers” (Duggan, 2015). In fact, 77% of men aged 18 to 29 play video games, which is more than any other demographic group, in comparison with 57% of young women who play. Younger US males are the most likely to play video games (Brown, 2017). Of men and women, 24% of men said they play video games often, 23% reported sometimes with a net of 47% confirming video game use as opposed to women, where 19% said they played often, 21% sometimes with a net of 39%. Approximately 40% of people over 65 report playing video games online (Brown, 2017). People of Hispanic identities/backgrounds in the US tend to have the highest net of players at 48%, but persons of Black identities/experience have the highest percentage reporting that they play often at 24%. The highest percentage of gamers who claim to play were between the ages of 18 and 29 with a 60% net consisting of 29% of players saying they game often and 31% saying they game sometimes. Gamers aged 30 to 49 were the second most likely to participate in gaming (Brown, 2017). When looked at in terms of education level, those who had completed some college as opposed to earning a degree or a high school diploma netted the most reported gamers at 50% with 25% saying they play often and 25% saying they play sometimes. Those who had earned a high school diploma or less came in second with a net of 42%.
Social media is defined by Kaplan and Haenlein (2010, p. 61) as “a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0 and that allow the creation and exchange of User Generated Content.” Social media tools allow two-way information exchange, videos, free applications, and other services (Fusi & Feeney, 2016). In terms of messaging capabilities, over half of those using messaging apps between the ages of 18 and 29 use apps that auto-delete messages, significantly more than any other age group (Greenwood et al., 2016). Facebook boasts over one billion users, coming second in terms of Internet traffic only to Google. In fact, Facebook is increasing in its adoptions, whereas other social media sites are remaining steady in that regard (Greenwood et al., 2016). In contrast, 79% of the US population have at least one Facebook account; 32% use Instagram, followed by Pinterest at 31%, LinkedIn at 29%, and Twitter at 24% (Greenwood et al., 2016). The percentage of Facebook users now also represents a 7% increase from 2015 (Greenwood et al., 2016). Further, women are using social media more than men (83% versus 75% respectively), which is interesting given that men tend to use the Internet more than women (Greenwood et al., 2016; Rainie, 2015).
Accessing social media tends to be a regular occurrence in daily life. Some estimates indicate that access to social networking sites (SNSs) occurs approximately every 4 minutes. Data on social media use that looked at monthly active users reported by the most active social media platforms in each country found that there are 2.789 billion active social media users with 2.549 billion users accessing social media via mobile devices. This means that 37% of the total population is accessing social media, while 34% of the total population is accessing their social media through a mobile device (Hootsuites, 2017). Interestingly, countries with the lower proportion of Internet users have higher levels of social media usage, such as the Middle East, Africa, and Latin America. For example, while those in developing countries have less smartphone penetration than developed countries (Hootsuites, 2017), they are more likely to use social media when online (Greenwood et al., 2016). The 76% of those who use Facebook in the US report accessing the site daily (an increase from the 70% reporting such activity in 2015), with 55% reporting accessing sites multiple times per day (Greenwood et al., 2016).
Social media is also becoming the place where a majority of millennials get their news (Rainie, 2015)—via extelligence. Extelligence is described as knowledge gained externally; that is, it is the knowledge each of us contribute to as a collective entity, as well as the withdrawals we make from this collective (Stewart & Cohen, 1997). It describes the process of knowledge-creation in academia where one researcher takes the information provided by another and builds on it, both taking from the collective and contributing more, once they have concluded their studies (Friedman, Whitworth, & Brownstein, 2010). According to Brown, Campbell, and Ling (2011): “The Internet is an important means of acquiring and maintaining social capital” (p. 145), and is a primary reason why many millennials use the Internet. Wikipedia, for example, is one domain where multiple authors contribute to the knowledge presented to others. Sixty percent of those aged 51 to 68 obtain information and news about politics and the government via the television; 68% of millennials (aged 18 to 34), however, get their news about government and media through Facebook (Rainie, 2015). Younger individuals (aged 18 to 39) tend to use Instagram significantly more than older people. Specifically, 58% of those 18 to 29 used Instagram as compared to 33% of those 30 to 49 and 8% of those 65 and older (Greenwood et al., 2016). In fact, in a Green-wood et al. (2016) study, half of the respondents noted that they received information about the 2016 US presidential election from social media. As stated by one undergraduate student at my university: “I’m not big on research. I prefer social media because it’s more current and explains better concepts.” While access to Facebook may be universal, the way in which it is used is not. In a study on cultural differences in Facebook usage, Hong and Na (2018) found that Americans tend to perceive Face-book activities as individual, whereas Koreans see them as interdependent. These perceptions translate into activities on Facebook, as Koreans engaged in more interdependent activities on Facebook.
The effects of technology can be either psychological or physical, or contain elements of both. We are human beings who live in context. That context has changed dramatically over the last 50 years. At present, 1% of US citizens own over one-third of the wealth (Schwartz, 2011). We are also living in an era with the highest proportion of families living in poverty, at 10.1% the same proportion that live without health insurance, a figure that is disproportionate to other geographic regions (U.S. Census Bureau, 2017a). In 2009, 43 million Americans were living in poverty—representing 14.3% of the population and the highest number since 1959 (Schwartz, 2011). We have grown from 1940 with 34,949 households to over 126,000 households in 2017. The number of married couples, however, has dropped. In 1940, 76% of the total households were married couples; in 2017, 47.6% were composed of married individuals (U. S. Census, 2017c). There have also been changes to the family structure. In the 1950s, the trend was for adult children to grow up and move away from the family home. As a consequence of unemployment, poverty, and other negative concerns this trend is reversing: as many as 8% of adults lived with extended family in 2009 and 3.9% of US citizens live in multigenerational households, with a higher proportion in California and the southern US than in other areas of the country (U.S. Census Bureau, 2017a). Since 1940, the number of single person households has steadily been increasing (U.S. Census Bureau, 2017b). On average, the number of persons per household has decreased from an average of 3.33 persons per household in 1960 to 2.54 in 2017.
Like any activity, technology usage intersects with our physiology. For example, certain tasks on Facebook such as hitting the like button are associated with less cardiac involvement, suggesting it is a more automatic task than other directed Facebook activities (Alhabash, Almutairi, Lou, & Kim, 2018). Research has demonstrated that there are physical implications for young people who frequently use cellphones. The most common symptom is headaches, suffered by half of the respondents, followed extremely closely by irritability. Another symptom is impaired concentration (Acharya, Acharya, & Waghrey, 2013). For youth, consequences of using the Internet more than four hours per day include hypersomnia, skipping meals, and sleeping later (Kim et al., 2010).
Zheng, Wei, Li, Zhu, and Ning (2016) conducted probably the most comprehensive study on the effects of the Internet on our physical well-being. In a sample of 513 individuals, several negative physical impacts directly related to Internet use were observed. Over half of the sample suffered from dry eyes and declining eyesight (73.7% and 64.1% respectively). Nearly half of the sample reported cervical problems (48.1%). Worsening skin was also a problem associated with use, particularly for women (37.8% total, but reported by 26% of the men and 51% of the women). One-third of the sample reported headaches; another third reported lumbar pain and yet another third decreased sleep (34.1%, 31.8%, and 30.0% respectively). A related effect was decreased anti-fatigue for 27%, and nearly the same amount reported greasy hair (26.9%). Approximately 20% of the sample (19.9%) reported weight gain. This was followed by another 12.9% reporting finger numbness, 10.7% reporting wrist pain, 9.9% reporting hair loss, and 8.8% indicating they lose their appetite. Finally, the relationship was not tied to the relationship one developed with technology, but rather the frequency of use (i.e., number of hours online). In other words, the more you use the Internet, the more likely you will be to suffer these effects (Zheng et al., 2016).
There are also specific effects related to the use of mobile phone technologies. For example, 4 hours or more of exposure to the radiofrequency radiation in phones (at least 1800 MHz) was associated with cells with abnormal nuclei and cell death (Meena, Verma, Kohli, & Ingle, 2016). This is particularly true with heavy users and those under 20 years of age (Wojcik, 2016). Finally, in a review of 45 articles, Coenen, Howie, and Straker (2017) found evidence of significant musculoskeletal symptoms from using our touchscreen devices, including:
On the other hand, such technologies have allowed people to have a more active and participatory role in their healthcare (Banos et al., 2014). For example, instead of waiting for a visit to a doctor to gain information about one’s internal state, a host of devices can monitor our bodies and provide real-time information on blood sugar, heart rate, skin temperature, oxygen saturation, hand tremors, etc. (García-Magariño, Medrano, Plaza, & Oliván, 2016). The information provided online has also not interfered with the patient-physician relationship. In a study of approximately 3,200 participants, Murray et al. (2003) found one-third had looked for health information on the Internet in the past 12 months, 16% found health information relevant to themselves, and half of those took the information they found relevant and presented it to their physician. Of those who brought the information to their physician, they were more likely to ask the physician their opinion rather than demanding that the physician follow a particular intervention.
The same positive findings have occurred in promoting health behaviors in youth (Militello, Kelly, & Melnyk, 2012). The ability to text message others seems to increase frequency in blood glucose monitoring (Eng & Lee, 2013), liver transplant success (Miloh et al., 2009), and other healthy lifestyle reminders. Hill-Kayser, Vachani, Hampshire, Di Lullo, and Metz (2012) demonstrated the positive use of an Internet-based tool to create a plan after cancer, a tool that positively improved communication with the healthcare team and adherence to the developed plan. There also might be some data indicating that video games can improve health outcomes, though the quality of the studies is generally poor (Primack et al., 2012). For example, social media is being target as a way to increase awareness of melanoma in young people (Falzone et al., 2017).
Alternatively, the media may also produce misinformation regarding certain health conditions, which can compromise physical well-being. “Fake news” was probably one of the most infamous terms in 2017. Sadly, such “fake news” can have deleterious effects on our well-being, particularly for those who have the greatest level of exposure (Balmas, 2014). The more vast the Internet becomes, the greater the potential for misinformation. Part of the misinformation can be the presentation of symptoms linking to a certain condition that, upon reading, one might become concerned they have. As misinformation about conditions is present online, access to this information may create a condition called a nocebo effect— meaning that one may experience a detriment to their health because of what they have learned to expect based on the misinformation the Internet provides (Crichton & Petrie, 2015). The key to counteracting the nocebo effect is to provide positively framed messages where a different (and positive) expectation can be provided.
The debate around whether the Internet and new technologies contribute to a sedentary lifestyle is difficult to resolve. Sedentary lifestyles, defined as those that expend less than or equal to 1.5 metabolic equivalents (Prince, Reed, McFetridge, Tremblay, & Reid, 2017), are on an upward trend. Sedentary behaviors include activities such as watching television or using computers. Those who engage in more sedentary behaviors are at a higher risk for cardiac problems, cancers, and overall increased mortality (Wilmont et al., 2012). At this point, we are sitting for an average of 7.5 hours per day, with men sitting more than women and differences among cultural groups (Carson, Staiano, & Katzmarzyk, 2015). Researchers seem to agree that using a computer increases sitting time and, subsequently, increases risk for negative health outcomes (Fotheringham, Wonnacott, & Owen, 2000). This is also true in couples, as one partner’s sedentary behavior affects the other partner’s (Wood, Jago, Sebire, Zahra, & Thompson, 2015). The associations, however, between sitting time, socioeconomic status, type of setting in which one resides, and one’s educational level may dictate how much time one sits. For example, looking at metabolic syndrome, there were no statistically significant differences between rural and urban individuals in how much time they sat during each day. When the dependent variable became measuring obesity, however, there was a difference in that rural individuals spent more than 4 hours a day on the computer.
On the flipside, when one excluded using a computer for work, individuals who lived in urban areas, had a higher level of education, and had higher incomes sat for longer periods of time (Prince et al., 2017). The other challenge is mitigating the negative health consequences attached to living a sedentary life. In one study, researchers discovered those who forego engagement in physical activity in favor of their cellphone have more negative outcomes, specifically those who self-identified as high-frequency cellphone users. In these cases, the more one used a cellphone the less likely one was engaged in cardiovascular activities. This finding was still true when controlled for gender, self-efficacy, and percentage of fat of participants. For example, the high-frequency cellphone users were also more likely to participate in sedentary behaviors more related to the phone. Finally, those who identified themselves as low-frequency users tended to use the cellphone to connect with others in social situations, including setting times to be physically active and engage in physical activities (Lepp, Barkley, Sanders, Rebold, & Gates, 2013). In another study, however, the effect of cellphone use was more tied to sedentary behavior, but not physical activity (Barkley, Lepp, & Salehi-Esfahani, 2016).
One peripherally related study examining transgenerational transmission of depression found depression-like symptoms in a sample of Siberian hamsters whose parents were exposed to nighttime illumination (i.e., much like the light we may be exposed to on computers) prior to mating (Cissé, Russart, & Nelson, 2017). The explanation for this finding is that early-life environments predispose certain individuals to particular conditions, and that stressful environments create toxins, which have an effect on how genes express themselves. Obviously, humans are not hamsters. But such findings beg the question as to whether there is similar transmission of stressful events and, consequently, how these events manifest in our genetic makeup. We already have evidence that some transmission of stressful events from parent to child occurs (Wolstenholme et al., 2012).
Technology has also affected our sleep style—or rather whether we sleep at all. For children and adolescents, the results are clear: there is a detrimental effect to the amount of time spent online and their sleep schedule (Nose et al., 2017). Fifty-six percent of teens reported texting, tweeting, or messaging others while in bed, and 20% indicated they woke up to texts (Polos et al., 2015). The issues with sleep in young people are consistently present when one’s Internet use is problematic, typically defined as excessive time spent online (Ferreira et al., 2017). Ferreira et al. (2017) asked seventh and eighth graders to complete a self-report inventory about their Internet use, their sleep habits, and their sleepiness during the day. Youth who fell into the Internet dependence category represented 19% of the sample, and were correlated with excessive daytime sleepiness, difficulty falling asleep, difficulty staying asleep, and using social media. In another study of 11 to 13 year olds, those who frequently used their mobile phones, played video games, engaged in social networking, and listened to music via devices had more difficulty falling asleep than their non-connected counterparts (Arora, Broglia, Thomas, & Taheri, 2014). In fact, those who listened to music had the most nightmares; those who watched TV were more likely to sleepwalk (Arora et al., 2014). Other problematic consequences include academic underperformance (Polos et al., 2015) and disruption to the next day’s activities (Nose et al., 2017). More often than not, adolescents and young adults are using phones before bed, thus negatively impacting their sleep quantity and quality (Levenson, Shensa, Sidani, Colditz, & Primack, 2016).
For adults, the data is conflicting. An overwhelming majority of Americans (90%) report using some form of screen or technology prior to going to bed—with TV being the media used for 60%. At the same time, one study examined the amount of time one spends online prior to going to bed, with most (60%) noting TV was the media being used. Further, interactive technologies used within an hour of bedtime are associated with more problems in both falling asleep and obtaining restful sleep (Gradisar et al., 2013). While there is some evidence of the times at which adults’ sleep schedules are shifting for those online more frequently (i.e., going to bed later and waking later), technologies such as TV or tablets in their bedroom do not seem to negatively impact sleep hygiene (Custers & Van den Bulck, 2012). In fact, in one study with a small sample (11 young adults), the amount they engaged in social exposure via media was affected by the sleep they got the night before (Butt, Ouarda, Quan, Pentland, & Khayal, 2015).
Finally, there are many ways in which technology is designed to augment sleep and deep relaxation. Countless apps for promoting deep sleep and relaxation are available, including websites and YouTube channels, and there are nearly 700 apps dedicated to mindfulness (Mani, Kavanagh, Hides, & Stoyanov, 2015), despite that their effectiveness is difficult to assess. The sheer number of apps advertised can be overwhelming and limit an individual’s ability to sort through them all to find one that best fits or is evidence-based (Coulon, Monroe, & West, 2016). Handel (2011) outlined several apps that were characterized by good functionality, ease of use, workable interface, quality, ratings, and information provided, but apps change so constantly that this list may already be out of date. A branch of therapeutic intervention known as eco-wellness focuses on exposure and connection to nature settings as a way to promote relaxation. YouTube promotes methods by which one may tap into nature in sensory ways through watching a relaxing nature video and hearing natural sounds, such as ocean, waterfall, birds, streams, insects, rain, and thunder (Reese et al., 2016).
We are human beings who live in context. That context has changed dramatically over the last 50 years. Authors such as Stephen Ilardi (2009) point out that our lifestyle change from agricultural and rural beginnings to the life that many live now has been so drastic that we cannot help but have emotional, physical, and intellectual consequences. Younger age cohorts of the US are now five times more likely to exceed psychopathology cut off scores on the Minnesota Multiphasic Personality Inventory (MMPI), including in the areas of paranoia, hypomania (which 40% fit), psychopathic deviation, and depression (Twenge et al., 2010). Depression rates (and correlationally, suicide rates) have increased over the last 50 years, a fact in the US, and one that is not true in less-developed and more communal regions and cultures around the world. High school students report more subtle symptoms consistent with depression now than in cohorts before (Twenge, 2014), but fewer overt symptoms (i.e., less suicidal ideation and less likely to perceive themselves as being depressed). This increase in the US has prompted researchers to explore more how the environment may be a correlational factor in the increase. For example, teens who are exposed to chronic peer stress for three years are more likely to be diagnosed with depression later, a finding particularly true for girls (Hankin et al., 2015). One study of 816 teens explored the impact of social comparison and feedback seeking (a key part of social media usage, particularly for teens), and found adolescents were more likely to experience depressive symptoms if they engaged in higher levels of social comparison and feedback seeking. Further, adolescent boys with more depressive symptoms were more likely to continue to engage in social comparison and feedback seeking activities, thus potentially establishing a cycle of negative reinforcement (Nesi, Miller, & Prinstein, 2017).
The role of technology as a factor in contributing to depression continues to be explored. For instance, nationally representative annual survey research on 1.1 million 8th, 10th, and 12th graders between the years of 1991 and 2016 revealed a sudden and dramatic decrease in the psychological well-being of this adolescent population beginning in 2012 (Twenge, Martin, & Campbell, 2018). The researchers found that cyclical economic factors like unemployment and home loss due to foreclosure (as was occurring during the recession at the time of the study) were not significantly correlated with the well-being in this adolescent population, and were not the cause of the decreased well-being (Twenge et al., 2018). Instead, this decrease is believed to be related to the population’s rapidly acquired smartphones and their related increased time spent communicating through and with technologies (e.g., social media, the Internet, texting, gaming) and less time spent engaging in non-technology-based activities (e.g., in-person social exchanges, sport/exercise, homework, attending cultural events) (Twenge et al., 2018). It is important to note that the adolescents in this population who were the happiest in terms of psychological well-being were those who spent time engaging in non-technology-based activities, but did also engage in technology-based activities—just for relatively shorter periods of time (Twenge et al., 2018)
We are not suggesting that cellphones and technology make people depressed and cause death. On the contrary, technologies have been and are used to intervene for someone having a difficult time or contemplating suicide, or for those who find benefit in keeping connections with peers and family, and enable them with a larger, more supportive, and immediate social network. In fact, Harwood, Dooley, Scott, and Joiner (2014) found no relationship between smartphone use and depression, and a negative relationship between depression and phone calls.
Integrating the research, the trend seems to be that while there are mixed reviews about the impact of technology on the psychology of adults, there appears to be more scholarly certainty of the negative impacts of technology on children and youth. For example, in a study of over 3500 children aged 10 and 11, those who used social networking sites and participated in online gaming fared worse psychologically than those who did not engage in such activities or did so to a lesser degree (Devine & Lloyd, 2012). So, what does the lack of sleep associated with the Internet do to our individual psychological selves? First, shorter sleep duration (related to excessive Internet use) increases the likelihood of some pretty significant negative outcomes, including depression, suicidal ideation, and obesity for young people (Demirci et al., 2015; Lemola, Perkinson-Gloor, Brand, Dewald-Kaufmann, & Grob, 2015) and behavior problems (Soni et al., 2017). These effects were both a direct result of Internet use, as well as indirectly the result of getting less sleep in favor of more time online.
The increased presence (or reliance) on social media is not unilaterally positive. Researcher Dr. Brian Primack suggests the net effect of the… er… “net” is probably leaning toward the negative, citing specifically the association of using social media and increased depression and feelings of isolation. People experience anxiety being away from their phones and seem to struggle with not living the ideal life that seems to be displayed on so many others’ Facebook pages. He and his team also discovered that the more social media sites you use, the worse off you are in terms of mental health outcomes. In a meta-analysis of 23 studies including over 13,000 participants, a negative correlation was found between Facebook usage and well-being, as well as a positive correlation between Face-book usage and psychological distress, particularly for older individuals (Marino, Gini, Vieno, & Spada, 2018). On the other hand, from examining people’s participation in dating apps (which are different than social media), Stanford sociologist Dr. Michael Rosenfeld suggests the Internet is indifferent—it is not positive or negative, but rather a tool people use to accomplish a task (Rosenfeld, 2018).
Video games also seem to have a detrimental impact on psychological well-being. For instance, for children, Gameboy usage was associated with lower levels of social acceptance (whereas high Internet usage was associated with lower academic competence) in a sample of 825 Norwegian school children aged 10 to 12 (Heim, Brandtzæg, Hertzberg Kaare, Endestad, & Torgersen, 2007). Impaired psychological well-being for youth has been found to be associated with greater video game usage, as well as using the Internet for communicative purposes, particularly for Black youth (Jackson et al., 2008). Similar findings were discovered for adults, where the amount of time participating in massively multiplayer online role-playing games (MMORPGs) is associated with poorer psychological outcomes (Kirby, Jones, & Copello, 2014).
The determination as to whether psychological effects are positive or negative may have something to do with the type of technology and platform used. For example, in the context of online gaming, those with higher levels of game-contingent self-worth are also more likely to develop Internet gaming disorders (Beard & Wickham, 2016). In the case for Facebook, for example, findings are mixed as to whether positives or negatives are dominant (Song et al., 2014). One study found that loneliness predicts Facebook use instead of the other way around (Song et al., 2014). In fact, those technologies where there is more richness in the media contribute to higher levels of satisfaction than media that are less rich (Goodman-Deane, Mieczakowski, Johnson, Goldhaber, & Clarkson, 2016). The function of why technology is used also may have an effect on the outcomes. In a study examining mental health and Internet usage, Panova and Lleras (2016) found a positive relationship between impaired mental health and problematic Internet usage, particularly when the Internet assists someone in avoiding negative feelings. The same was not true, however, when the Internet is used to manage boredom (Panova & Lleras, 2016). These same nuanced effects regarding playing MMORPGs—the impact of the game on one’s psychological well-being—had to do with the motivation for playing (Shen & Williams, 2011). In fact, psychological resilience mitigates negative effects of social media use (Hou et al., 2017). In addition, children with cellphones were more likely to be nervous, have a bad temper, and be mentally distracted than children without phones. These behaviors were worse if the child had a phone at an early age (Divan, Kheifets, Obel, & Olsen, 2012).
Further, the impact on one’s mental and emotional health has something to do with one’s involvement and relationship with their device rather than use of the device itself. In a sample of 274 ranging in age from 16 to 59, researchers administered: (1) mood inventories (a depression/anxiety/stress inventory), (2) a series of questions assessing the purpose of Internet usage (i.e., information seeking, entertainment, etc.), (3) a series of questions regarding mobile phone involvement, and (4) an inventory on Internet “addiction” (Harwood et al., 2014). Contrary to previous research, they did not find associations with smartphone use and depression, stress, or anxiety; they did, however, distinguish smartphone involvement, such as obsessive phone checking, an activity that is not reported as phone usage, but that signifies a relationship with the phone without these mental health outcomes (Harwood et al., 2014). This is particularly important when we consider evidence that young people check their phone, on average, over 150 times per day (Social Media Week, 2016). Further support of media usage comes from the systematic review conducted by Seabrook, Kern, and Rickard (2016) in which they examined 70 studies on both passive and directed social networking site interactions. The interactions that were most frequently associated with poorer outcomes (i.e., depression and anxiety) were those that were negative interactions with others, and those that involved social comparison (Lin & Utz, 2015; further discussed in Chapter 3 in the present text). Intersecting with this type of usage is one’s gender—girls, for example, who use Facebook passively are more likely to encounter harmful outcomes than girls who use it actively in either public or private settings; boys, on the other hand, have more negative outcomes when they use Facebook actively in a public setting only (Frison & Eggermont, 2015). These negative outcomes were exacerbated in cases where individuals had a tendency to ruminate about those negative interactions online. Finally, the more authentic a young person is online, the more social support they receive and, consequently, the more they are able to ward off depression (Xie et al., 2018).
Technology overload (defined by the number of hours using technology) is associated with a host of negative outcomes, including negative effects on self-esteem, the development of the inability to delay gratification, attention problems, problems with boundary settings, the emergence of personality disorders such as narcissism and antisocial behavior, and mood conditions such as depression and anxiety (Scott, Valley, & Simecka, 2016). In addition, young people (aged 30 or less) engage in the heaviest cellphone usage across groups. Taking measures including, but not limited to, the following: depending less on technologies, limiting time spent on technology, and finding ways technology is advantageous while mitigating the negative impacts might be worthwhile endeavors (Acharya et al., 2013). In fact, such technologies are developing to assist in monitoring mental health processes much in the same way that physical health may be monitored (Gaggioli et al., 2013). The effect of the benefits online may be tied to development. Less sleep in teens means a higher risk for suicidality (Lopes, Boronat, Wang, & Fu-I, 2016), particularly sleeping under 8 hours per night in Chinese youth and under 6 hours in Taiwanese youth (Yen, King, & Tang, 2010). In a rather disturbing study, Oshima et al. (2012) found a higher rate of mental health disturbance in both young and late adolescents associated with using a mobile phone after lights out bedtime. Specifically, those who used the phone after their bedtime were more likely to have suicidal thoughts and thoughts of self-harm, even after controlling for length of sleep.
The lack of sleep may have interpersonal and relational consequences (Gordon, Mendes, & Prather, 2017), in part because sleep is critical for the processing of emotions (Deliens, Gilson, & Peigneux, 2014). For example, in a study looking at husbands’ and wives’ sleep habits, heterosexual couples reported they were more satisfied if they had more hours of sleep (Maranges & McNulty, 2017). In fact, the more sleep husbands get, the more globally happy they are about the relationship (a finding not true for wives) (Maranges & McNulty, 2017). Lack of sleep in young people can have an impact in the development of depression, anxiety, and substance abuse (Lemola et al., 2015), which can in turn have indirect effects on relationship perception and satisfaction. For example, lack of sleep may have interpersonal consequences. In a study of 77 heterosexual couples, those who got less sleep and had lower quality sleep were more likely to report feeling hurt and rejected by their partner (Gilbert, Pond, Haak, DeWall, & Keller, 2015). Several studies found the reduction in sleep was associated with lower levels of empathy for one’s partner and, consequently, an impaired ability to successfully resolve conflict (Gordon & Chen, 2014), but that inflammatory response is reduced when one is able to express their own feelings (Wilson et al., 2017).
In addition to the disruption to sleep quantity and quality, nighttime is also a time when sexual activity is more frequent. A couple’s sex life, then, may be interrupted by a text message or email notification, as is now common (Wilmer & Chein, 2016). Finally, the information on life satisfaction and computer usage also may affect our relationships. Further, life satisfaction has an effect on relationship adjustment, and relationship adjustment can have an effect on overall life satisfaction (Stanley, Ragan, Rhoades, & Markman, 2012). And when that life satisfaction is mitigated by how we feel about ourselves, the conclusions we come to when we compare ourselves to others online, the constant checking and involvement in our cellphones, and the physical effects of screen times and small devices will ultimately have an impact on our relationships.
Life satisfaction may be either positive or negative depending on the study. One study indicates life satisfaction for society is at an increase as compared to a decade ago—with the Internet as a key player in that change (Lissitsa & Chachashvili-Bolotin, 2016). As mentioned earlier, increased time on the computer may result in leading a more sedentary lifestyle. This not only has the physical implications listed earlier, but can also have psychological implications. For example, one study found that leisure time spent engaging in physical activity is related to higher levels of overall life satisfaction, as well as a lowered perception of stress (Schnohr, Kristensen, Prescott, & Scharling, 2005). In another study, however, life satisfaction was negatively correlated with amount of time spent online (Stepanikova, Nie, & He, 2010). Çelik and Odacı (2013) found that the more problematic Internet use was negatively associated with life satisfaction. In one study in Taiwan, positive impacts of the Internet were observed across many different domains. Liang, Peng, and Yu (2012) conducted phone interviews with 3,563 respondents aged 15 and over in Taiwan. They found that people who have a home computer and those who have the Internet at home have higher life satisfaction in many areas, including SES, physical health, social competence, and psychological pressure. Internet use is positively related to the development of empathy for Black American adolescents. Controlling for age, gender, and previous level of empathy, Black teens who use the Internet for race-related purposes scored higher on an empathy index one year later; however, that effect did not occur when the teen was exposed to discrimination online (Lozada & Tynes, 2017).
Other benefits of time online include increased opportunities for social connection and interaction (Chan, 2015). Connection through online experience is positively associated with the development of social relations to family members, friends, those with similar political and religious interests, and those in a similar profession (Oh, Ozkaya, & LaRose, 2014). The ability to participate in support groups or receive supportive messages is demonstrated to be significant in obtaining more positive health outcomes for both young people through older adulthood (Sims, Reed, & Carr, 2017), though frequency of involvement in social media does not seem to enhance the perception of social support (Shensa, Sidani, Lin, Bowman, & Primack, 2016). Text messaging among various support groups has had positive outcomes for the management of diabetes (Turner et al., 2013), and was associated with significantly fewer rejections after liver transplants (Miloh et al., 2009). But more friends does not equal increased subjective well-being across the board; the relationship is actually curvilinear (Kim & Lee, 2011). Specifically, fewer friends means less time becoming involved in others’ lives, connected to a lower subjective well-being. As the number of Facebook friends increases, an investment in those friends likewise increases alongside one’s subjective well-being. The subjective well-being, however, drops if even more friends are added to the point where the ability to be involved in their lives decreases due to the sheer number of friends (Kim & Lee, 2011).
Rainie (2013) argues that the Internet has made us more connected to others instead of being lonelier. Rather than turning to a physically close immediate family or network, we have shifted to larger, more loosely defined groups for connection. Both older and younger individuals cite Facebook and social media as a key factor in the maintenance of connections and increased quality of life (Quinn, Chen, Mulvenna, & Bond, 2016). This is specifically true when we use Facebook to maintain relationships we already have rather than establishing new ones—in that case, psychological outcomes tend to be worse (Erae & Lonborg, 2015). Our networks have, therefore, become more diversified.
Technology use has also had a significant effect on how we conduct ourselves in the workplace (Vitak, Crouse, & LaRose, 2011). Over half (53%) of Generation Y individuals (more commonly known as millennials) reported using social media at work; 53% of those older than Generation Y also reported using social media at work—but were more likely to use it for personal reasons than work-related (Holland, Cooper, & Hecker, 2016). In education, students indicate work is more collaborative when they have access to the Internet at school, but findings show they are more disengaged (Heflin, Shewmaker, & Nguyen, 2017). Some of these findings could be explained by the developmental level of the student. For example, freshmen in college who spend more time online have fewer friends, potentially negatively affecting their academic skills, whereas seniors actually benefit from their Facebook interactions, use it for social connection, and can limit it from interfering with their academics (Kalpidou, Costin, & Morris, 2011). One of the primary areas of investigation has been the extent to which having social media present in our workplaces has affected employee productivity. Part of this area of investigation has focused on the perception of managers of employees’ social media use, and this research has shown that if managers perceive social media positively, they will regard others’ usage as positive (Fusi & Feeney, 2016). Managers also play an unknowing but integral part in social media use at work. Social media is a tool used to accomplish many things—one of which is to gain social support. If supervisors provide social support for their staff in general, the staff is less likely to use social media (Charoensukmongkol, 2014). Additionally, one study found that top-level managers actually use social media for personal reasons at work more than their staff do (Andreassen, Torsheim, & Pallesen, 2014).
The terms “cyberloafing” and “cyberslacking” describe the phenomenon by which we engage in social media instead of working (Vitak et al., 2011). Cyberloafing is defined as using the Internet during work to engage in personal (non-workplace) business (Liberman, Seidman, McK-enna, & Buffardi, 2011). Whether one engages in cyberloafing, however, depends on a complex interweave of factors. First, several studies have demonstrated that using the web during a task may be associated with several positive outcomes, including feeling less bored, more engaged, less stressed, and feeling a greater work-home balance (Andreassen et al., 2014; Malik, Saleem, & Ahmad, 2010). Second, those who tend to have a positive attitude toward using social media at work tend to be younger, more highly educated, and single (Andreassen et al., 2014). Cyberloafers more often have greater levels of autonomy in their position (also not surprising since that user would be able to get away with more activities that were not monitored (Andreassen et al., 2014). Other characteristics positively associated with cyberloafing include being younger, male, and in a minority group (Vitak et al., 2011). Finally, individuals who engage in cyberslacking may also rate more highly on the personality dimensions of extraversion and neuroticism scale of the Big 5 Personality test (Andreassen et al., 2014).
Using social media at work is also related to perceived job satisfaction (Koch, Gonzalez, & Leidner, 2012), potentially in part because policies that allow workers to use social media may increase morale (Bennett, Owers, Pitt, & Tucker, 2010), and consequently, productivity and performance (Nduhura & Prieler, 2017). Social media used for work, however, does not lend itself to more productivity (Leftheriotis & Giannakos, 2014). There is also age and generational differences, as one might expect with social media use. Holland et al. (2016) noted Generation Y-ers were more likely to voice concerns about work over social media if they were dissatisfied with their job. Looking at Facebook use specifically, those who more frequently use social media to connect are those who are part time workers or contract workers. In addition, the more some of these workers spent on Facebook, the more they perceived their job as a calling (Hanna, Kee, & Robertson, 2017. On the flipside, cyberloafing may also contribute to some negative outcomes as others may compare their status to what they see from others on Facebook (Andre-assen et al., 2014). Finally, we are now becoming multi-taskers, which may impact learning. For example, in a study by Rosen and colleagues, students unlocked their phones over 50 times a day. Of course, adults are also people who are unlocking their phones (Rosen, Whaling, Rab, Carrier, & Cheever, 2012). This level of multi-tasking may interfere with work and emotional processing, and connect with some potentially negative outcomes.
The latest technology trends in 2017 include both micro and macro changes. Micro changes include the presence of online harassment—a condition affecting 40% of overall Internet users, but 67% of younger users (Anderson, 2017). Social media is the venue by which most online harassment takes place (Rainie, 2017). The repeal of Net Neutrality is also a topic that will have impact on a micro level. In short, Net Neutrality protected consumers from Internet Service Providers’ ability to speed up or slow down one’s connection based on the information is sought. The country with the most protections to ensure a fair Internet service is India (Lyengar, 2018). With the repeal of Net Neutrality in the United States, Internet Service Providers will have more control over users’ access, thus changing access, searches, communication, and information seeking (“Loss of Net Neutrality Could Harm Research”, 2017). Another micro trend is the way in which we watch television—as we shift from cable and television networks to online streaming and a la carte viewing (Anderson, 2017). Macro impacts include the advent of driverless cars, the future of technology and automation in workplaces, and the integration of social media and technology in elections and political processes (Anderson, 2017).
In summarizing changes to society via new media, Rainie (2013) characterized the Internet and media as “portable, participatory, and pervasive” (Rainie, 2013). These precise characteristics demand more attention from couples and family therapy scholars. We are active participants in communication with others—our partners, our families— from anywhere. Vast majorities of us are making these connections to the Internet via a cellphone or tablet—both portable devices—and do so from virtually anywhere at any time. The opportunity to engage and interact with these technologies is everywhere you look and will continue to be everywhere (Rainie, 2015). Screens, for example, will be everywhere you look (Rainie, 2015). From a consumer perspective, there will be more encounters with media and more sharing of media. A second projection is that augmented reality will increasingly make its way into our offline lives. This will result in pointed information directed at us regarding our immediate location and advertising in local areas. A third trend will be the continued development and immersion in virtual reality, which may include more changes in one’s brain due to the use of these technologies. We will also receive more alerts and notifications through media, which means that we will be checking media with more frequency than we already are. A fifth trend is the effect of automation on jobs and services, where such technologies will be even more integrated (and depended on) to complete certain jobs or assist us in accomplishing tasks (Rainie, 2015).
A significant amount of literature early on in the field of Couple and Family Technology studies focused on the impact of the Internet on our personal lives and had a negative tinge. Part of this may be because the literature was written by therapists or others who viewed some of the problems with technology in daily life. For example, Hertlein and Webster (2008) concluded that scholars in the family therapy field default to resolve problems in our daily lives; therefore, the literature at the time largely reflected a pathological view of technology. Then again, this was around the time that the Internet was just becoming immersed in our lives, where much of the writing about it was negative.
Technology, however, is not inherently good or bad. Just like anything, technology can have negative implications based on how it is used. It can also be beneficial in our lives. We do not believe there is much value in pathologizing the Internet or social media, particularly because that pathology will get us nowhere. The Internet is here to stay, and we need to adopt a perspective on it that is helpful and adaptive rather than considering it unilaterally negative.
There are many reasons that one could consider the computer and Internet unilaterally negative on people and relationships. As we have discussed earlier, there are associations to lack of sleep, depression, behavioral issues, and a host of other issues when these technologies are in use. In short, there is a general tendency in the media to present the idea that the computer and technology are a bad thing. The media is replete with the endorsement of a pathological perspective on technology as evidenced by news articles warning us of the dangers of technology “addiction” and cyberbullying of youth, and advising on how to become “unwired” to our devices, etc. This view, in our opinion, is neither accurate nor helpful. To simply assume that we can disconnect at this point is unrealistic: to only acknowledge the power of how the Internet can be used for maladaptive gains is also inaccurate and ignores the power and ways in which we can use the technology presented to augment our relationships and our development in healthy ways.
There are many implications of increased usage of technology in relationships. For instance, it could change how families interact with each other compared to other generations, but it could also look the same, simply with different language involved surrounding technology. How do these changes to information, automation, and connectivity change the way we accomplish the same tasks we did relationally? In addition to our interactions, the way we accomplish tasks has changed. For example, cellphones increased the likelihood adult women sustain contact with their mothers (Treas & Gubernskaya, 2018).
The purpose of this text is to answer these questions and to pose new ones about how technology shapes our roles, rules, boundaries, and relational processes. We will explore how these changes to our psychology, environment, and presentation in online venues impact how we initiate, structure, and terminate relationships. We will identify where technology is advantageous in our relationships and how we can tap into the resources technology has to offer to access its benefits, individually and relationally. Technology’s role in our lives is not exclusively positive or negative. The ways in which it bridges us to others outside of our relationship may both enrich our relationships and create divisions and separations from each other. It may provide entertainment and greater opportunities to cultivate new ideas as much as it might promote isolation and foster a sense of disconnection. So what exactly is our relationship with technology? Let’s just say “it’s complicated.”
Acharya, I., Acharya, J. P., & Waghrey, D. (2013). A study on some psychological health effects of cell-phone usage amongst college going students. International Journal of Medical Research & Health Sciences, 2 (3), 388–394. doi:10.5958/j.2319-5886.2.3.068
Alhabash, S., Almutairi, N., Lou, C., & Kim, W. (2018). Pathways to virality: Psychophysiological responses preceding likes, shares, comments, and status updates on Facebook. Media Psychology, 1–21. doi:10.1080/15213269.2017. 1416296
Anderson, M. (2015). Technology device ownership: 2015. Pew Research Center. Retrieved December 29, 2017, from www.pewInternet.org/2015/10/29/technology-device-ownership-2015
Anderson, M. (2017). Key trends shaping technology in 2017. PEW Research Center. Retrieved December 29, 2017, from www.pewresearch.org/fact-tank/2017/12/28/key-trends-shaping-technology-in-2017/
Andreassen, C. S., Torsheim, T., & Pallesen, S. (2014). Predictors of use of social network sites at work—A specific type of cyberloafing. Journal of Computer-Mediated Communication, 19 (4), 906–921. doi:10.1111/jcc4.12085
Arora, T., Broglia, E., Thomas, G. N., & Taheri, S. (2014). Associations between specific technologies and adolescent sleep quantity, sleep quality, and parasomnias. Sleep Medicine, 15, 240–247. doi:10.1016/j.sleep.2013.08.799
Balmas, M. (2014). When fake news becomes real. Communication Research, 41 (3), 430–454. doi:10.1177/0093650212453600
Banos, O., Villalonga, C., Damas, M., Gloesekoetter, P., Pomares, H., & Rojas, I. (2014). PhysioDroid: Combining wearable health sensors and mobile devices for a ubiquitous, continuous, and personal monitoring. The Scientific World Journal, 2014, 11–22.
Barkhaus, L., & Polichar, V. E. (2011). Empowerment through seamfulness: Smart phones in everyday life. Personal and Ubiquitous Computing, 15 (6), 629–639. doi:10.1007/s00779-010-0342-4
Barkley, J. E., Lepp, A., & Salehi-Esfahani, S. (2016 [2015]). College students’ mobile telephone use is positively associated with sedentary behavior. American Journal of Lifestyle Medicine, 10 (6), 437–441. doi:10.1177/1559827615594338
Beard, C. L., & Wickham, R. E. (2016). Gaming-contingent self-worth, gaming motivation, and Internet gaming disorder. Computers in Human Behavior, 61, 507–515. doi:10.1016/j.chb.2016.03.046
Bennett, J., Owers, M., Pitt, M., & Tucker, M. (2010). Workplace impact of social networking. Property Management, 28 (3), 138–148. doi:10.1108/02637471 011051282
Brown, A. (2017). Younger men play video games, but so do a diverse group of other Americans. Retrieved December 8, 2017, from www.pewresearch.org/fact-tank/2017/09/11/younger-men-play-video-games-but-so-do-a-diverse-group-of-other-americans/
Brown, K., Campbell, S., & Ling, R. (2011). Mobile phones bridging the digital divide for teens in the US? Future Internet, 3 (2), 144–158. doi: 10.3390/fi3020144
Butt, M., Ouarda, T. B., Quan, S. F., Pentland, A., & Khayal, I. (2015). Technologically sensed social exposure related to slow-wave sleep in healthy adults. Sleep and Breathing, 19 (1), 255–261. doi:10.1007/s11325-014-1005-x
Carson, V., Staiano, A., & Katzmarzyk, P. (2015). Physical activity, screen time, and sitting among U.S. adolescents. Pediatric Exercise Science, 27 (1), 151–159. doi:10.1123/pes.2014-0022
Çelik, C. B., & Odacı, H. (2013). The relationship between problematic Internet use and interpersonal cognitive distortions and life satisfaction in university students. Children and Youth Services Review, 35 (3), 505–508. doi:10.1016/j. childyouth.2013.01.001
Chan, M. (2015). Mobile phones and the good life: Examining the relationships among mobile use, social capital and subjective well-being. New Media & Society, 17 (1), 96–113. doi:10.1177/1461444813516836
Charoensukmongkol, P. (2014). Effects of support and job demands on social media use and work outcomes. Computers in Human Behavior, 36, 340–349. doi:10.1016/j.chb.2014.03.061
Cissé, Y. M., Russart, K. L. G., & Nelson, R. J. (2017). Depressive-like behavior is elevated among offspring of parents exposed to dim light at night prior to mating. Psychoneuroendocrinology, 83, 182–186. doi:10.1016/j.psyneuen.2017.06.004
Coenen, P., Howie, E., & Straker, L. (2017). The associations of mobile touch screen device use with musculoskeletal symptoms and exposures: A systematic review. PLoS ONE, 12 (8), e0181220. doi:10.1371/journal.pone.0181220
Coulon, S. M., Monroe, C. M., & West, D. S. (2016). A systematic, multi-domain review of mobile smartphone apps for evidence-based stress management. American Journal of Preventive Medicine, 51 (1), 95–105. doi:10.1016/j.amepre. 2016.01.026
Crichton, F., & Petrie, K. J. (2015). Accentuate the positive: Counteracting psycho-genic responses to media health messages in the age of the Internet. Journal of Psychosomatic Research, 79 (3), 185–189. doi:10.1016/j.jpsychores.2015.04.014
Custers, K., & Van den Bulck, J. (2012). Television viewing, Internet use, and self-reported bedtime and rise time in adults: Implications for sleep hygiene recommendations from an exploratory cross-sectional study. Behavioral Sleep Medicine, 10 (2), 96–105. doi:10.1080/15402002.2011.596599
Deliens, G., Gilson, M., & Peigneux, P. (2014). Sleep and the processing of emotions. Experimental Brain Research, 232 (5), 1403–1414. doi:10.1007/s00221-014-3832-1
Demi̇rci̇, K., Akgönül, M., & Akpinar, A. (2015). Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. Journal of Behavioral Addictions, 4 (2), 85–92. doi:10.1556/2006.4.2015.010
Devine, P., & Lloyd, K. (2012). Internet use and psychological well-being among 10-year-old and 11-year-old children. Child Care in Practice, 18 (1), 5–22. doi: 10.1080/13575279.2011.621888
Divan, H. A., Kheifets, L., Obel, C., & Olsen, J. (2012). Cellphone use and behavioural problems in young children. Journal of Epidemiology & Community Health, 66 (6), 524–529. doi:10.1136/jech.2010.115402
Duggan, M. (2015, December). Gaming and gamers. Pew Research Center. Retrieved December 20, 2017, from www.pewInternet.org/2015/12/15/gaming-and-gamers/
Eng, D., & Lee, J. (2013). The promise and peril of mobile health applications for diabetes and endocrinology. Pediatric Diabetes, 14 (4), 231–238. doi: 10.1111/pedi.12034
Erae, J. R., & Lonborg, S. D. (2015). Do motivations for using Facebook moderate the association between Facebook use and psychological well-being? Frontiers in Psychology, 6, 771. doi:10.3389/fpsyg.2015.00771
Falzone, A. E., Brindis, C. D., Chren, M. M., Junn, A., Pagoto, S., Wehner, M., & Linos, E. (2017). Teens, Tweets, and Tanning Beds: Rethinking the use of social media for skin cancer prevention. American Journal of Preventive Medicine, 53 (3), 86–S94. doi:10.1016/j.amepre.2017.04.027
Ferreira, C., Ferreira, H., Vieira, M. J., Costeira, M., Branco, L., Dias, Â., & Macedo, L. (2017). Epidemiology of Internet use by an adolescent population and its relation with sleep habits. Acta Médica Portuguesa, 30 (7), 524–533. doi:10.20344/amp.8205
Filipovic, J. (2013). The attractiveness of different online formats motives and frequencies of use. Communications & Strategies, 89, 105–115.
Fotheringham, M. J., Wonnacott, R. L., & Owen, N. (2000). Computer use and physical inactivity in young adults: Public health perils and potentials of new information technologies. Annals of Behavioral Medicine, 22 (4), 269–275. doi:10.1007/BF02895662
Friedman, R., Whitworth, B., & Brownstein, M. (2010). Realizing the power of extelligence: A new business model for academic publishing. The International Journal of Technology, Knowledge and Society, 6, 105–118. doi:10.18848/1832-3669/cgp/v06i02/56079
Frison, E., & Eggermont, S. (2015). Exploring the relationships between different types of Facebook use, perceived online social support, and adolescents’ depressed mood. Social Science Computer Review, 34, 153–171. doi:10.1177/0894439314567449
Fusi, F., & Feeney, M. K. (2016). Social media in the workplace: Information exchange, productivity, or waste? The American Review of Public Administration, 48 (5), 395–412. doi:10.1177/0275074016675722
Gaggioli, A., Pioggia, G., Tartarisco, G., Baldus, G., Corda, D., Cipresso, P., & Riva, G. (2013). A mobile data collection platform for mental health research. Personal and Ubiquitous Computing, 17 (2), 241–251. doi:10.1007/s00779-011-0465-2
García-Magariño, I., Medrano, C., Plaza, I., & Oliván, B. (2016). A smartphone-based system for detecting hand tremors in unconstrained environments. Personal and Ubiquitous Computing, 20 (6), 959–971. doi:10.1007/s00779-016-0956-2
Gilbert, L., Pond, R., Haak, E., DeWall, C., & Keller, P. (2015). Sleep problems exacerbate the emotional consequences of interpersonal rejection. Journal of Social and Clinical Psychology, 34 (1), 50–63. doi:10.1521/jscp.2015.34.1.50
Goodman-Deane, J., Mieczakowski, A., Johnson, D., Goldhaber, T., & Clarkson, P. J. (2016). The impact of communication technologies on life and relationship satisfaction. Computers in Human Behavior, 57, 219–229. doi:10.1016/j. chb.2015.11.053
Gordon, A., & Chen, S. (2014). The role of sleep in interpersonal conflict. Social Psychological and Personality Science, 5 (2), 168–175. doi:10.1177/19485506 13488952
Gordon, A., Mendes, W., & Prather, A. (2017). The social side of sleep: Elucidating the links between sleep and social processes. Current Directions in Psychological Science, 26 (5), 470–475. doi:10.1177/0963721417712269
Gradisar, M., Wolfson, A. R., Harvey, A. G., Hale, L., Rosenberg, R., & Czeisler, C. A. (2013). The sleep and technology use of Americans: Findings from the National Sleep Foundation’s 2011 sleep in America poll. Journal of Clinical Sleep Medicine, 9 (12), 1291–1299. doi:10.5664/jcsm.3272
Greenwood, S., Perrin, A., & Duggan, M. (2016, November). Social media update 2016. PEW Research Center; Information & Technology. Retrieved September 29, 2018, from www.pewInternet.org/2016/11/11/social-media-update-2016/
Handel, M. J. (2011). mHealth (mobile health)—Using apps for health and wellness. Explore: The Journal of Science and Healing, 7 (4), 256–261. doi:10.1016/j. explore.2011.04.011
Hankin, B. L., Young, J. F., Abela, J. R. Z., Smolen, A., Jenness, J. L., Gulley, L. D.,… Oppenheimer, C. W. (2015). Depression from childhood into late adolescence: Influence of gender, development, genetic susceptibility, and peer stress. Journal of Abnormal Psychology, 124 (4), 803–816. doi:10.1037/abn0000089
Hanna, B., Kee, K. F., & Robertson, B. W. (2017). Positive impacts of social media at work: Job satisfaction, job calling, and Facebook use among co-workers. SHS Web of Conferences, 33, 12. doi:10.1051/shsconf/20173300012
Harwood, J., Dooley, J., Scott, A., & Joiner, R. (2014). Constantly connected—The effects of smart-devices on mental health. Computers in Human Behavior, 34, 267–272. doi:10.1016/j.chb.2014.02.006
Heflin, H., Shewmaker, J., & Nguyen, J. (2017). Impact of mobile technology on student attitudes, engagement, and learning. Computers & Education, 107, 91–99. doi:10.1016/j.compedu.2017.01.006
Heim, J., Brandtzæg, P. B., Hertzberg Kaare, B., Endestad, T., & Torgersen, L. (2007). Children’s usage of media technologies and psychosocial factors. New Media & Society, 9 (3), 425–454. doi:10.1177/1461444807076971
Hertlein, K. M., & Webster, M. (2008). Technology, relationships, and problems: A research synthesis. Journal of Marital and Family Therapy, 34, 445–460. doi:10.1111/j.1752-0606.2008.00087.x
Hill-Kayser, C., Vachani, C., Hampshire, M., Di Lullo, G. A., & Metz, J. M. (2012). Positive impact of Internet-based survivorship care plans on healthcare and lifestyle behaviors. International Journal of Radiation Oncology, Biology, Physics, 84 (3), 211–S212. doi:10.1016/j.ijrobp.2012.07.549
Holland, P., Cooper, B. K., & Hecker, R. (2016). Use of social media at work: A new form of employee voice? The International Journal of Human Resource Management, 27 (21), 2621–2634. doi:10.1080/09585192.2016.1227867
Hong, S., & Na, J. (2018). How Facebook is perceived and used by people across cultures: The implications of cultural differences in the use of Facebook. Social Psychological and Personality Science, 9 (4), 435–443. doi:10.1177/194855061 7711227
Hootsuites. (2017). Digital trends in 2017: 106 pages of Internet, mobile and social media data. INSIGHTS. Retrieved from https://thenextweb.com/insights/2017/01/24/digital-trends-2017-report-Internet/
Hou, X., Wang, H., Guo, C., Gaskin, J., Rost, D. H., & Wang, J. (2017). Psychological resilience can help combat the effect of stress on problematic social networking site usage. Personality and Individual Differences, 109, 61–66. doi:10.1016/j.paid.2016.12.048
Ilardi, S. (2009). The depression cure the 6-step program to beat depression without drugs (1st Da Capo Press ed.). Cambridge, MA: Da Capo Lifelong.
Jackson, L. A., Fitzgerald, H. E., Zhao, Y., Kolenic, A., von Eye, A., & Harold, R. (2008). Information Technology (IT) use and children’s psychological well-being. CyberPsychology & Behavior, 11 (6), 755–757. doi:10.1089/cpb.2008.0035
Kalpidou, M., Costin, D., & Morris, J. (2011). The relationship between Facebook and the well-being of undergraduate college students. Cyberpsychology, Behavior, and Social Networking, 14 (4), 183–189. doi:10.1089/cyber.2010.0061
Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53 (1), 59–68. doi:10.1016/j.bushor.2009.09.003
Kim, J. H., Lau, C. H., Cheuk, K., Kan, P., Hui, H. L. C., & Griffiths, S. M. (2010). Brief report: Predictors of heavy Internet use and associations with health-promoting and health risk behaviors among Hong Kong university students. Journal of Adolescence, 33 (1), 215–220. doi:10.1016/j.adolescence.2009.03.012
Kim, J. H., & Lee, J. (2011). The Facebook paths to happiness: Effects of the number of Facebook friends and self-presentation on subjective well-being. Cyberpsychology, Behavior, and Social Networking, 14 (6), 359–364.
Kirby, A., Jones, C., & Copello, A. (2014). The impact of massively multiplayer online role playing games (MMORPGs) on psychological wellbeing and the role of play motivations and problematic use. International Journal of Mental Health and Addiction, 12 (1), 36–51. doi:10.1007/s11469-013-9467-9
Koch, H., Gonzalez, E., & Leidner, D. (2012). Bridging the work/social divide: The emotional response to organizational social networking sites. European Journal of Information Systems, 21 (6), 699–717. doi:10.1057/ejis.2012.18
Leftheriotis, I., & Giannakos, M. N. (2014). Using social media for work: Losing your time or improving your work? Computers in Human Behavior, 31, 134–142. doi.org/10.1016/j.chb.2013.10.016
Lemola, S., Perkinson-Gloor, N., Brand, S., Dewald-Kaufmann, J. F., & Grob, A. (2015). Adolescents’ electronic media use at night, sleep disturbance, and depressive symptoms in the smartphone age. Journal of Youth and Adolescence, 44 (2), 405–418. doi:10.1007/s10964-014-0176-x
Lepp, A., Barkley, J. E., Sanders, G. J., Rebold, M., & Gates, P. (2013). The relationship between cellphone use, physical and sedentary activity, and cardiorespiratory fitness in a sample of U.S. college students. The International Journal of Behavioral Nutrition and Physical Activity, 10 (1), 79–79. doi:10.1186/1479-5868-10-79
Levenson, J. C., Shensa, A., Sidani, J. E., Colditz, J. B., & Primack, B. (2016). The association between social media use and sleep disturbance among young adults. Preventive Medicine, 85, 36–41. doi:10.1016/j.ypmed.2016.01.001
Ley, B., Ogonowski, C., Hess, J., Reichling, T., Wan, L., & Wulf, V. (2014). Impacts of new technologies on media usage and social behaviour in domestic environments . Behaviour & Information Technology, 33 (8), 815–828. doi:10. 1080/0144929X.2013.832383
Liang, T., Peng, J., & Yu, C. (2012). A simpler quality of e-life indicator: Does the Internet have a positive impact on the quality of life in Taiwan. Quality & Quantity, 46 (4), 1025–1045. doi:10.1007/s11135-011-9446-9
Liberman, B., Seidman, G., McKenna, K. Y., & Buffardi, L. E. (2011). Employee job attitudes and organizational characteristics as predictors of cyberloafing. Computers in Human Behavior, 27 (6), 2192–2199. doi:10.1016/j.chb.2011.06.015
Lin, R., & Utz, S. (2015). The emotional responses of browsing Facebook: Happiness, envy, and the role of tie strength. Computers in Human Behavior, 52, 29–38. doi:10.1016/j.chb.2015.04.064
Lissitsa, S., & Chachashvili-Bolotin, S. (2016). Life satisfaction in the Internet age—changes in the past decade. Computers in Human Behavior, 54, 197–206. doi:10.1016/j.chb.2015.08.001
Lopes, M., Boronat, A. C., Wang, Y., & Fu-I, L. (2016). Sleep complaints as risk factor for suicidal behavior in severely depressed children and adolescents. CNS Neuroscience & Therapeutics, 22 (11), 915–920. doi:10.1111/cns. 12597
Loss of net neutrality could harm research. (2017). Nature, 552 (7684), 147.
Lozada, F. T., & Tynes, B. M. (2017). Longitudinal effects of online experiences on empathy among African American adolescents. Journal of Applied Developmental Psychology, 52, 181–190. doi:10.1016/j.appdev.2017.07.009
Lyengar, R. (2018). India now has the “world’s strongest” net neutrality rules. CNN Tech! Retrieved from https://money.cnn.com/2018/07/12/technology/india-net-neutrality-rules-telecom/index.html
Malik, M. I., Saleem, F., & Ahmad, M. (2010). Work-life balance and job satisfaction among doctors in Pakistan. South Asian Journal of Management, 17 (2), 112–123.
Mani, M., Kavanagh, D. J., Hides, L., & Stoyanov, S. R. (2015). Review and evaluation of mindfulness-based iPhone apps. JMIR mHealth and uHealth, 3 (3), e82. doi:10.2196/mhealth.4328
Maranges, H. M., & McNulty, J. K. (2017). The rested relationship: Sleep benefits marital evaluations. Journal of Family Psychology, 31 (1), 117–122. doi:10.1037/fam0000225
Marino, C., Gini, G., Vieno, A., & Spada, M. M. (2018). The associations between problematic Facebook use, psychological distress and well-being among adolescents and young adults: A systematic review and meta-analysis. Journal of Affective Disorders, 226, 274–281. doi:10.1016/j.jad.2017.10.007
Meena, J., Verma, A., Kohli, C., & Ingle, G. (2016). Mobile phone use and possible cancer risk: Current perspectives in India. Indian Journal of Occupational and Environmental Medicine, 20 (1), 5–9. doi:10.4103/0019-5278.183827
Militello, L. K., Kelly, S. A., & Melnyk, B. M. (2012). Systematic review of Text-Messaging interventions to promote healthy behaviors in pediatric and adoles cent populations: Implications for clinical practice and research. Worldviews on Evidence-Based Nursing, 9 (2), 66–77. doi:10.1111/j.1741-6787.2011.00239.x
Miloh, T., Annunziato, R., Arnon, R., Warshaw, J., Parkar, S., Suchy, F. J.,… Kerkar, N. (2009). Improved adherence and outcomes for pediatric liver transplant recipients by using text messaging. Pediatrics, 124 (5), e844–e850. doi:10.1542/peds.2009-0415
Murray, E., Lo, B., Pollack, L., Donelan, K., Catania, J., White, M.,… Turner, R. (2003). The impact of health information on the Internet on the Physician-Patient Relationship. Archives of Internal Medicine, 163 (14), 1727. doi:10.1001/archinte.163.14.1727
Nduhura, D., & Prieler, M. (2017). When I chat online, I feel relaxed and work better: Exploring the use of social media in the public sector workplace in Rwanda. Telecommunications Policy, 41 (7–8), 708–716. doi:10.1016/j.telpol.2017.05.008
Nesi, J., Miller, A. B., & Prinstein, M. J. (2017). Adolescents’ depressive symptoms and subsequent technology-based interpersonal behaviors: A multi-wave study. Journal of Applied Developmental Psychology, 51, 12–19. doi:10.1016/j. appdev.2017.02.002
Nose, Y., Fujinaga, R., Suzuki, M., Hayashi, I., Moritani, T., Kotani, K., & Nagai, N. (2017). Association of evening smartphone use with cardiac autonomic nervous activity after awakening in adolescents living in high school dormitories. Child’s Nervous System, 33 (4), 653–658. doi:10.1007/s00381-017-3388-z
Oh, H. J., Ozkaya, E., & Larose, R. (2014). How does online social networking enhance life satisfaction? The relationships among online supportive interaction, affect, perceived social support, sense of community, and life satisfaction. Computers in Human Behavior, 30 (C), 69–78.
Oshima, N., Nishida, A., Shimodera, S., Tochigi, M., Ando, S., Yamasaki, S.,… Sasaki, T. (2012). The suicidal feelings, self-injury, and mobile phone use after lights out in adolescents. Journal of Pediatric Psychology, 37 (9), 1023–1030. doi:10.1093/jpepsy/jss072
Panova, T., & Lleras, A. (2016). Avoidance or boredom: Negative mental health outcomes associated with use of information and communication technologies depend on users’ motivations. Computers in Human Behavior, 58, 249–258. doi:10.1016/j.chb.2015.12.062
Perrin, A., & Bertoni, N. (2017, December 1). First-time Internet users: Who they are and what they do when they get online. Pew Research Center. Retrieved December 8, 2017, from www.pewresearch.org/fact-tank/2017/12/01/first-time-Internet-users-who-they-are-and-what-they-do-when-they-get-online/
Polos, P. G., Bhat, S., Gupta, D., O’Malley, R. J., DeBari, V. A., Upadhyay, H.,… Chokroverty, S. (2015). The impact of sleep time-related information and communication technology (STRICT) on sleep patterns and daytime functioning in american adolescents. Journal of Adolescence, 44, 232–244. doi:10.1016/j. adolescence.2015.08.002
Poushter, J., Bishop, C., & Chwe, H. (2018, June). Social media use continues to rise in developing countries, but plateaus across developed ones. Retrieved September 29, 2018, from www.pewglobal.org/2018/06/19/social-media-use-continues-to-rise-in-developing-countries-but-plateaus-across-developed-ones/
Primack, B. A., Carroll, M. V., Mcnamara, M., Klem, M. L., King, B., Rich, M.,… Nayak, S. (2012). Role of video games in improving health-related outcomes: A systematic review. American Journal of Preventive Medicine, 42 (6), 630–638. doi:10.1016/j.amepre.2012.02.023
Prince, S. A., Reed, J. L., McFetridge, C., Tremblay, M. S., & Reid, R. D. (2017). Correlates of sedentary behaviour in adults: A systematic review. Obesity Reviews, 18 (8), 915–935. doi:10.1111/obr.12529
Quinn, D., Chen, L., Mulvenna, M. D., & Bond, R. (2016). Exploring the relationship between online social network site usage and the impact on quality of life for older and younger users: An interaction analysis. Journal of Medical Internet Research, 18 (9), 163–178. doi:10.2196/jmir.5377
Rainie, L. (2013). Personal. portable. participatory. Pervasive the digital landscape in 2013 and its impact on communities. Retrieved from www.pewInternet.org/2013/07/18/personal-portable-participatory-pervasive-the-digital-landscape-in-2013-and-its-impact-on-communities/
Rainie, L. (2015). The changing digital landscape: Where things are headed. Retrieved from www.pewInternet.org/2015/11/20/the-changing-digital-landscape-where-things-are-heading/
Rainie, L. (2017). The reckoning for social media. Retrieved December 29, 2017, from www.pewInternet.org/2017/08/01/the-reckoning-for-social-media/
Reese, R. F., Seitz, C. M., Stroud, D., Lehman, B., Caldwell, S., Ecklund, E., & Winn, A. (2016). YouTube nature preferences: A content analysis study. The Journal of Humanistic Counseling, 55 (3), 183–199. doi:10.1002/johc.12033
Rosen, L. D., Whaling, K., Rab, S., Carrier, L.M., & Cheever, N. A. (2012). Is Facebook creating “iDisorders”? The link between clinical symptoms of psychiatric disorders and technology use, attitudes and anxiety. Computers in Human Behavior, 29 (3), 1243–1254.
Rosenfeld, M. (2018). Are Tinder and dating apps changing the dating and mating in the USA? In J. van Hook, S. McHale, & V. King (Eds.), Families and technology (pp. 103–120). New York: Springer.
Schnohr, P., Kristensen, T. S., Prescott, E., & Scharling, H. (2005). Stress and life dissatisfaction are inversely associated with jogging and other types of physical activity in leisure time—The Copenhagen City Heart Study. Scandinavian Journal of Medicine & Science in Sports, 15, 107–112. doi:10.1111/j.1600-0838.2004. 00394.x
Schwartz, S. A. (2011). Trends that will affect your future…. EXPLORE: The Journal of Science and Healing, 2 (5), 394–398. doi:10.1016/j.explore.2006. 06.007
Scott, D. A., Valley, B., & Simecka, B. A. (2016). Mental health concerns in the digital age. International Journal of Mental Health and Addiction, 15 (3), 604–613. doi:10.1007/s11469-016-9684-0
Seabrook, E. M., Kern, M. L., & Rickard, N. S. (2016). Social networking sites, depression, and anxiety: A systematic review. JMIR Mental Health, 3 (4), e50. doi:10.2196/mental.5842
Shen, C., & Williams, D. (2011). Unpacking time online: Connecting Internet and massively multiplayer online game use with psychosocial well-being. Communication Research, 38 (1), 123–149. doi:10.1177/0093650210377196
Shensa, A., Sidani, J. E., Lin, L. Y., Bowman, N. D., & Primack, B. A. (2016). Social media use and perceived emotional support among US young adults. Journal of Community Health, 41 (3), 541–549. doi:10.1007/s10900-015-0128-8
Sims, T., Reed, A. E., & Carr, D. C. (2017). Information and communication technology use is related to higher well-being among the oldest-old. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, gbw130. doi:10.1093/geronb/gbw130
Social Media Week. (2016). Millennials check their phones more than 157 times per day. Retrieved November 15, 2017, from https://socialmediaweek.org/newyork/2016/05/31/millennials-check-phones-157-times-per-day/
Song, H., Zmyslinski-Seelig, A., Kim, J., Drent, A., Victor, A., Omori, K., & Allen, M. (2014). Does Facebook make you lonely?: A meta analysis. Computers in Human Behavior, 36, 446–452. doi:10.1016/j.chb.2014.04.011
Soni, R., Upadhyay, R., & Mahendra, J. (2017). Prevalence of smartphone addiction, sleep quality and associated behavior problems in adolescents. International Journal of Research in Medical Sciences, 5 (2), 515–519. doi:10.18203/2320-6012.ijrms20170142
Stanley, S. M., Ragan, E. P., Rhoades, G. K., & Markman, H. J. (2012). Examining changes in relationship adjustment and life satisfaction in marriage. Journal of Family Psychology, 26 (1), 165–170. doi:10.1037/a0026759
Stepanikova, I., Nie, N. H., & He, X. (2010). Time on the Internet at home, loneliness, and life satisfaction: Evidence from panel time-diary data. Computers in Human Behavior, 26 (3), 329–338. doi: 10.1016/j.chb.2009.11.002
Stewart, I., & Cohen, J. (1997). Figments of reality: The evolution of the curious mind. Cambridge: Cambridge University Press.
Treas, J., & Gubernskaya, Z. (2018). Did mobile phones increase adult children’s material contact? In J. Van Hook, S. McHale, & V. King’s (Eds.), Families and technology (pp. 139–151). New York, NY: Springer.
Turner, J. W., Robinson, J. D., Tian, Y., Neustadtl, A., Angelus, P., Russell, M.,… Levine, B. (2013). Can messages make a difference? The association between E-Mail messages and health outcomes in diabetes patients. Human Communication Research, 39 (2), 252–268. doi:10.1111/j.1468-2958.2012.01437.x
Twenge, J. M. (2014). Time period and birth cohort differences in depressive symptoms in the U.S. 1982–2013. Social Indicators Research, 121 (2), 437–454. doi:10.1007/s11205-014-0647-1
Twenge, J. M., Gentile, B., DeWall, C. N., Ma, D., Lacefield, K., & Schurtz, D. R. (2010). Birth cohort increases in psychopathology among young Americans, 1938–2007: A cross-temporal meta-analysis of the MMPI. Clinical Psychology Review, 30 (4), 145–154. doi:10.1016/j.cpr.2009.10.005
Twenge, J. M., Martin, G. N., & Campbell, W. K. (2018). Decreases in psychological well-being among American adolescents after 2012 and links to screen time during the rise of smartphone technology. Emotion, 18 (6), 765–780. doi:10.1037/emo0000403
U.S. Census Bureau. (2017a). Fast facts. Retrieved December 29, 2017, from www.census.gov/quickfacts/fact/table/US/PST045217
U.S. Census Bureau. (2017b). More children live with just their fathers than a decade ago. Retrieved December 29, 2017, from www.census.gov/newsroom/press-releases/2017/living-arrangements.html
Vitak, J., Crouse, J., & LaRose, R. (2011). Personal Internet use at work: Understanding cyberslacking. Computers in Human Behavior, 27 (5), 1751–1759. doi:10.1016/j.chb.2011.03.002
Wilmer, H. H., & Chein, J. M. (2016). Mobile technology habits: Patterns of association among device usage, intertemporal preference, impulse control, and reward sensitivity. Psychonomic Bulletin & Review, 23 (5), 1607–1614. doi:10.3758/s13423-016-1011-z
Wilmont, E. G., Edwardson, C. L., Achana, F. A., Davis, M. J., Gorely, T., Gray, L. J.,… Biddle, S. J. H. (2012). Sedentary time in adults and the association with diabetes, cardiovascular disease and death: Systematic review and meta-analysis. Diabetologia, 55 (11), 2895–2905. doi:10.1007/s00125-012-2677-z
Wilson, S. J., Jaremka, L. M., Fagundes, C. P., Andridge, R., Peng, J., Malarkey, W. B.,… Kiecolt-Glaser, J. K. (2017). Shortened sleep fuels inflammatory responses to marital conflict: Emotion regulation matters. Psychoneuroendocrinology, 79, 74–83. doi:10.1016/j.psyneuen.2017.02.015
Wojcik, D. (2016). Primary brain tumors and mobile cellphone usage. Cancer Epidemiology, 44, 123–124. doi:10.1016/j.canep.2016.08.007
Wolstenholme, J. T., Edwards, M., Shetty, S. R., Gatewood, J. D., Taylor, J. A., Rissman, E. F., & Connelly, J. J. (2012). Gestational exposure to bisphenol a produces transgenerational changes in behaviors and gene expression. Endocrinology, 153 (8), 3828–3838. doi:10.1210/en.2012-1195
Wood, L., Jago, R., Sebire, S. J., Zahra, J., & Thompson, J. L. (2015). Sedentary time among spouses: A cross-sectional study exploring associations in sedentary time and behaviour in parents of 5 and 6 year old children. BMC Research Notes, 8, 787–795. doi:10.1186/s13104-015-1758-8
Xie, X., Wang, X., Zhao, F., Lei, L., Niu, G., & Wang, P. (2018). Online real-self presentation and depression among Chinese teens: Mediating role of social support and moderating role of dispositional optimism. Child Indicators Research, 11 (5), 1531–1544.
Yen, C. F., King, B. H., & Tang, T. C. (2010). The association between short and long nocturnal sleep durations and risky behaviours and the moderating factors in Taiwanese adolescents. Psychiatry Research, 179 (1), 69–74.
Zheng, Y., Wei, D., Li, J., Zhu, T., & Ning, H. (2016). Internet use and its impact on individual physical health. IEEE Access, 4, 5135–5142. doi:10.1109/access.2016.2602301