Social Networking Site Use: Implications for Health and Wellness Robert R

https://doi.org/10.24839/2325-7342.JN26.2.165

Social Networking Site Use: Implications for Health and Wellness Robert R. Wright1*, Austin Evans1, Chad Schaeffer1, Rhett Mullins1, and Laure Cast2 1Department of Psychology, Brigham Young University–Idaho 2Joya Communications Inc.

ABSTRACT. Technological advances such as smartphones and tablets have made social media and social networking sites (SNS; e.g., Facebook, Snapchat) increasingly accessible and popular. The literature, however, contains mixed results as to the effects associated with the increased use of SNS, with some studies suggesting benefits and others pointing to detriments to users’ overall wellness (e.g., social, mental, physical). As such, the current investigation examined the wellness of different SNS users among internet users solicited through Amazon’s MTurk (N = 2,083). Participants completed an online questionnaire that assessed their daily use of several SNS and constructs related to social, mental, and physical health. Results suggest that users of image-based SNS (e.g., Snapchat) show the most significant p( < .05) and substantial (d > .20) deficits and users of video-based (e.g., Marco Polo, WhatsApp) and professional (e.g., LinkedIn) SNS manifested the best wellness profiles. However, regardless of SNS type, increased total daily use of SNS was significantly p( < .05) related to worse health and wellness. Thus, differential health and wellness associated with SNS use may be at least partially explained by the type of SNS used such that using certain platforms may be more detrimental or beneficial than others. Keywords: social media, social networking, well-being, health, technology

ecent technological advances such as Wright et al., 2020). Given these mixed findings, smartphones and tablets alongside the there is an imperative need to investigate these R proliferation of social media (e.g., social relationships, particularly the relationships between networking sites) such as Facebook, Twitter, specific SNS use (e.g., Facebook, Instagram) and Snapchat, and Instagram have fueled increased health-related outcomes (e.g., physical, mental, communication around the world. Social media or social). As such, the purpose of the current study social networking sites (SNS) include a variety of was to address this research gap by examining platform types ranging from content broadcasted a range of health and wellness variables (e.g., with no particular audience (e.g., Facebook, depressive symptoms, loneliness, Body Mass Index) Twitter) to messaging services that send content among specific SNS users (e.g., Facebook, Twitter) directly to a recipient or group (e.g., Marco Polo, within a diverse sample of participants. WhatsApp). Moreover, SNS can be used virtually anywhere, anytime, and by anyone, with an estimate Social Networking Sites and Wellness that more than 70% of Americans use social It is well-documented that SNS use is tied to nega- media regularly (Lenhart, 2018). Although the tive health and wellness variables (Song et al., 2014; consequences of increased ability to communicate Tromholt, 2016). According to Huang’s (2010) dis- with people can provide dramatic social benefits placement hypothesis, with SNS so readily available (e.g., Clark et al., 2017; Waytz & Gray, 2018), and accessible, time spent on SNS may directly take SUMMER 2021 recent research has highlighted negative effects on time away from face-to-face interpersonal interac- individuals’ health and behavior (e.g., Andreassen tions. Moreover, social interactions through SNS PSI CHI JOURNAL OF et al., 2017; Huang, 2010; Twenge et al., 2017; could be more attractive initially but less satisfying PSYCHOLOGICAL RESEARCH

*Faculty mentor COPYRIGHT 2021 BY PSI CHI, THE INTERNATIONAL HONOR SOCIETY IN PSYCHOLOGY (VOL. 26, NO. 2/ISSN 2325-7342) 165 Social Networking Site Use and Health | Wright, Evans, Schaeffer, Mullins, and Cast

and unable to fulfill personal needs, leading to defi- Instagram and Snapchat use, largely image-based cits in well-being. For instance, numerous studies SNS, Pittman and Reich (2016) observed that text- have identified a positive association between SNS based platforms offer a forum with little intimacy use and perceived loneliness (e.g., Nowland et al., compared to image-based SNSs. Image-based SNS 2018; Twenge et al., 2017), suggesting that SNS use use, they argued, produces less loneliness and may provide a more convenient medium for social improved well-being, consistent with Yang’s (2016) interaction, though a less fulfilling or meaningful finding that Instagram browsing was related to way to engage with others. Moreover, other studies better outcomes than Instagram posting and social have found that limiting SNS use to 30 minutes per comparisons. However, in a longitudinal study, day can decrease the positive relationships SNS Frison and Eggermont (2017) found evidence to use has with depression and loneliness (e.g., Hunt support a causal effect of Instagram browsing pro- et al., 2018) and that passive use of Facebook, in ducing future negative outcomes (e.g., depressed particular, can undermine well-being by enhancing mood). Although little research has examined social envy (e.g., Verduyn et al., 2015), further sug- Snapchat, its similarities with Instagram suggests gesting a complicated relationship. comparable relationships. Although other factors like motivation for Even less research has been conducted on use and number of platforms used are clearly the well-being of users of video-based (e.g., Skype, influential, central to the links between SNS and Marco Polo) or other “mixed-type” SNS (e.g., wellness seems to be the daily amount of time WhatsApp, Linkedn). Most of the research to-date spent using them. According to the most recent has focused on their use as effective vehicles for Pew Research Center statistics on social media use healthcare consultations (Boulos et al., 2016; Cutler, in the USA (Lenhart, 2018), the most common 2015), improving healthcare access, and presum- SNS (in order of adults who use social media) ably, health outcomes. One study found a strong are Facebook (68%), Instagram (35%), Snapchat relationship between daily use of WhatsApp and (27%), LinkedIn (25%), Twitter (24%), and Twitter with poor quality of sleep (Asiri et al., 2018), WhatsApp (22%). Moreover, investigating wellness suggesting that users struggle with sleep hygiene. outcomes across a broad range of SNS platforms Another study found a reduction in depressive that differ in terms of modality (e.g., text, image, symptomology among those who used video chat video), popularity (e.g., use), and audience (e.g., SNS (e.g., Skype, Marco Polo) compared to email, one-to-one communication, general broadcast) other social media, and instant messaging two years should provide a contrast that would enable later (Teo et al., 2019). The researchers argued identification of unique relationships. As such, that this more “real-time” video interaction on SNS the present investigation focused on Facebook, fosters a greater sense of connection with others. Twitter, Instagram, Snapchat, Marco Polo, Skype, Finally, in a recent study among 630 under- WhatsApp, and LinkedIn. graduate college students, Wright and colleagues In the context of health and wellness, an (2020) examined student well-being across SNS appropriate distinction between SNS may be the users of Facebook, Snapchat, Instagram, Marco primary mode of communication (i.e., text-based, Polo, and LinkedIn. Users of image-based SNS (i.e., image-based, video-based). Facebook, the most Snapchat) showed the most deficits in well-being heavily researched of all SNS, is primarily text-based including greater perceived loneliness, negative and has been consistently linked to many negative affect, depressive symptoms, anxiety, and lower outcomes including loneliness (Song et al., 2014), levels of life satisfaction compared to those who low subjective well-being (Tromholt, 2016), poor did not use Snapchat. On the other hand, users health behaviors (Dibb, 2019), and high perceived of video-based SNS (i.e., Marco Polo) had better stress (Vanman et al., 2018). Twitter, another text- well-being including lower loneliness and greater based SNS, has not been researched as in-depth, perceived peer support and users of professional but some findings suggest similar relationships, SNS (i.e., LinkedIn) had greater positive affect particularly in terms of loneliness (Petrocchi et and subjective social status than those who did not al., 2015) and life satisfaction (Yang & Srinivasan, use these SNS, respectively. These results provide SUMMER 2021 2016). Some have argued that use of text-based further empirical support for a difference in health sites, such as Facebook and Twitter, have unique and well-being among users of different SNSs, PSI CHI outcomes compared to image-based sites such as although this study was conducted among college JOURNAL OF PSYCHOLOGICAL Instagram and Snapchat. For example, in a study on students, which may limit generalizability. RESEARCH

Importantly, two lines of research have pro- were recruited using MTurk to follow a link to an vided some general theoretical framework for online survey (Qualtrics) wherein all participants these preliminary findings in the literature. First, provided informed consent and received $0.75 for SNS platforms that use images and video may be survey completion. We selected the MTurk website perceived as more credible than text-based displays. because it provides an opportunity to recruit many Sundar’s (2008) model regarding credibility of internet users with diverse backgrounds. Moreover, different technology characterizes visual stimuli it provides the benefit of being a reasonably inex- such as images and video to be more trustworthy pensive method of obtaining a large sample size, than text due to a “realism heuristic.” This heuristic which enables the detection of unique and complex builds on the assumption that a visual image is more relationships in the health and wellness of the users real and accurate than a word description. This, in of specific SNS. A total of 2,023 responses were turn, may lead to a more authentic social experi- gathered and recorded during January 2019. Given ence of the same information (Pittman & Reich, concerns regarding the initial low number of Marco 2016; Teo et al., 2019). Second, SNS use motivations Polo users (n = 69), we solicited additional par- may be influential. In fact, social comparison orien- ticipants through a random sample of Marco Polo tation theory posits that individuals identify others app users who had accessed the app recently for who are perceived as similar to themselves as an an additional 116 respondents, or a total of 2,139 appropriate heuristic to evaluate their own accom- respondents. Participants voluntarily completed plishments, situations, and experiences (Buunk & the survey in English and received compensation Gibbons, 2006). When these social comparisons through MTurk. Those solicited outside of MTurk portray the individual as being deficient (e.g., not were entered in a random drawing for a gift card. as successful), negative outcomes (e.g., negative Participants were required to be at least 18 mood, lower life satisfaction) often emerge, even years old and reside in the contiguous western in the use of social media (Ilakkuvan et al., 2019; United States (i.e., AZ, CA, CO, ID, MT, NM, OR, Yang, 2016). Hence, SNS platforms that are primar- UT, WA, WY). Five responses were omitted due to ily image- and video-based and those that primarily a failed attention check response along with 51 advocate professional use (e.g., LinkedIn), should responses because the survey completion time was be associated with improved health and wellness. below 3 minutes, which was observed as a natural Despite the importance of potential health break in the data and deemed insufficient time differences among users of different SNS and the for accurate survey completion (finaln = 2,083). differential impact of SNS use on health, these rela- Participant sample characteristics are reported tionships have yet to be examined within a diverse in Table 1, and participant characteristics by each and large sample. Thus, the current study explored SNS are reported in Table 2. Median completion health and wellness in physical, social, and mental time of the survey was 12.15 minutes (M = 16.92; domains among those who used SNS platforms SD = 41.45), and the average age of the sample was guided by two specific research questions. First, do 35.93 years (SD = 11.93) with a range from 18 to 83 health and wellness vary among specific SNS users years. A slight majority of the sample self-identified according to their daily use or time spent (dose or as women (52.1%), White (64%), and full-time level of exposure) on each SNS (specific platform employed (51%), and the sample was diverse in comparisons)? Second, do health and wellness vary other respects (see Table 1). between users of these separate platforms and nonusers (exposure versus no exposure comparisons)? Measures To do so, we conducted a study among a diverse Participants responded to a series of questions sample of SNS users via Amazon’s Mechanical Turk assessing wellness, social media use, and psycho- (MTurk) website. social variables. Daily time spent on social media during the past month was assessed using a single Method item for combined time spent on all social media Participants and Procedure during the past month. Participants identified SNS Following institutional review board approval (on they used (i.e., Facebook, Instagram, Snapchat, October 10, 2018) from Brigham Young University- Marco Polo, LinkedIn, Twitter, Skype, WhatsApp) SUMMER 2021 Idaho, we proceeded to conduct a correlational and how much time they spent daily on each study, where we employed a single-sample cross- platform. Participants responded to six questions PSI CHI JOURNAL OF sectional design in our data collection. Participants about their attitudes toward social media (Wright, PSYCHOLOGICAL RESEARCH

Hardy, et al., 2018; α = .91) on a 7-point agreement (BMI) calculations. Aerobic exercise per week for scale from 1 (strongly disagree) to 7 (strongly agree) the past month was examined using the 5-item so that greater values indicated a stronger positive Stanford Patient Education Research Center mea- attitude toward social media. sure (Lorig et al., 1996; α = .63). Physical health We assessed physical health with a variety of symptoms were measured using Spector and Jex’s measures. First, overall subjective physical health (1998) 18-item Physical Symptom Inventory (e.g., was evaluated using a single item (Kind et al., headache, fatigue) during the past 30 days. Sleep 2005), so participants rated their own health on quantity was reported in hours per night, and sleep a scale from 0 (worst physical health) to 100 (best quality was assessed by a single item on a 5-point physical health). Participants provided estimates of Likert-type scale from 1 (very poor) to 5 (very good) their current weight (in pounds) and height (in during the past month. Fruit and vegetable con- feet and inches) for standard Body Mass Index sumption over the past month was assessed using one item for each on a 10-point serving frequency TABLE 1 scale (Wright, Hardy, et al., 2018), where serving Participant Characteristics sizes were specified. Representing an unhealthy diet, frequency of consumption of sugary snacks Sample Size 2,083 (e.g., cakes, cookies, donuts) and sugary drinks Age M = 35.93 (SD = 11.93) (e.g., soda, sport drinks) were queried on the same Gender Women = 1,085 (52.1%) 10-point scale. (in order of prevalence) Men = 869 (41.7%) Missing = 129 (6.2%) Regarding social health, loneliness during the past month was assessed using the 3-item Ethnicity White = 1,334 (64.0%) (in order of prevalence) Asian = 231 (11.1%) Short Loneliness Scale (Hughes et al., 2004) on a Hispanic/Latino(a) = 203 (9.7%) 5-point frequency scale from 1 (never) to 5 (all of Black/African American = 69 (3.3%) the time; α = .90). Social integration (i.e., in-person More than one race = 61 (2.9%) American Indian/Alaska Native = 28 (1.3%) social interactions) was examined using eight Other = 17 (0.8%) items (Twenge et al., 2017) on a daily frequency Native Hawaiian/Pacific Islander = 11 (0.5%) scale (α = .85). Interpersonal conflict was assessed Relationship status Married = 777 (37.3%) using six items (Wright et al., 2017) on a 5-point (in order of prevalence) Single = 624 (30.0%) frequency scale from 1 (never) to 5 (very often; α Committed relationship = 334 (16.0%) Divorced/separated = 134 (6.4%) = .90). Perceived peer social support (Wood et Engaged to be married = 68 (3.3%) al., 2004; 8 items; α = .80) was also assessed on a Other = 17 (0.8%) 7-point Likert-type agreement scale from 1 (strongly Religious affiliation Agnosticism/Atheism/Secularism = 739 (35.5%) disagree) to 7 (strongly agree). (in order of prevalence) Catholicism = 310 (15.9%) Nondenominational Christianity = 254 (13.0%) For mental health, affect was captured using an Other = 176 (8.4%) 8-item measure of mood on a 5-point scale from 1 Church of Jesus Christ of Latter-day Saints = 162 (8.3%) (not at all) to 5 (extremely) regarding how much a Protestant = 128 (6.6%) Baptist = 59 (2.8%) mood adjective described their mood over the past Buddhism = 50 (2.4%) month along positive (i.e., happy, alert, enthusiastic, Judaism = 38 (1.8%) relaxed; α = .78) and negative (i.e., sad, irritable, Muslim = 23 (1.1%) Hinduism = 15 (0.7%) bored, nervous; α = .77) dimensions (Wright et Education Bachelor’s degree = 771 (37%) al., 2017). Acute depressive symptoms during the (in order of prevalence) Some college = 471 (25.4%) past week were assessed using a 5-item measure Associate degree = 246 (11.8%) (Bohannon et al., 2003) on a 4-point scale from 1 Master’s degree = 220 (10.6%) High school diploma = 169 (9.1%) (rarely or none of the time) to 4 (most or all of the time; Professional/vocational = 34 (1.6%) α = .81). Perceived stress was examined using seven Doctoral degree = 26 (1.2%) items from the Perceived Stress Scale (Cohen et al., Some high school = 17 (0.8%) 1983) on a 5-point frequency scale from 1 (never) Family income <$25,000 = 343 (16.5%) to 5 (very often; α = .88). Anxiety over the past 3 $25,000–$50,000 = 602 (28.9%) months was assessed using a 4-item measure on $50,000–$75,000 = 403 (19.3%) a 5-point frequency scale from 1 (never) to 5 (very SUMMER 2021 $75,000–$100,000 = 271 (13%) $100,000–$150,000 = 199 (9.6%) often; α = .85; Butz & Yogeeswaran, 2011). Using a PSI CHI Don’t know/not sure/decline = 57 (2.8%) 7-point agreement scale from 1 (strongly disagree) to JOURNAL OF >$150,000 = 79 (3.8%) PSYCHOLOGICAL 7 (strongly agree), satisfaction with life (Diener et al., RESEARCH

1985; 5 items; α = .92) was assessed. Self-regulation TABLE 2 was also captured using the 10-item Self-Regulation Scale (Diehl et al., 2010) on a 4-point scale from 1 Participant Characteristics by Specific SNS Platforms (Users) (not at all) to 4 (completely true; α = .82). FB TW IG SC MP SK WA LI Ethnicity White 69% 63% 65% 56% 89% 69% 55% 67% Data Analysis Asian 12% 12% 13% 15% 1% 14% 21% 18% We conducted correlational analyses to identify Hispanic/Latino(a) 10% 13% 12% 19% 3% 6% 10% 6% relationships between specific SNS use and health variables. We also used independent-samples t tests Black/African American 4% 6% 4% 4% 3% 4% 7% 5% to examine mean differences between users of SNS More than One Race 3% 4% 4% 3% 1% 3% 4% 2% platforms. Although every potential confounding American Indian/Alaskan Native 2% 1% 1% 2% 1% 1% 2% 1% variable could not be included, we examined well- Other 1% 0% 1% 1% 1% 2% 2% 1% ness variable differences in SNS users for age and Native Hawaiian/Alaskan Native 1% 1% 0% 0% 0% 1% 0% 1% number of SNS platforms as well as gender differ- Relationship status Married 42% 31% 39% 27% 70% 45% 47% 41% ences. Closely following the analytical approach of a prior study using a student sample (Wright Single 30% 41% 32% 41% 15% 31% 32% 31% et al., 2020), our t tests were of two types. First, we Committed relationship 17% 19% 20% 25% 6% 14% 15% 17% examined differences between users of each SNS Divorced/separated 7% 6% 5% 4% 5% 6% 4% 6% platform (e.g., Facebook vs. Instagram). Next, Engaged 4% 3% 4% 3% 4% 4% 2% 4% because users of one SNS could also be a user of Other 1% 1% 0% 0% 1% 0% 0% 1% another SNS, we conducted another set of t tests Religious affiliation Agnostic/Atheist/Secular 35% 40% 38% 40% 12% 33% 28% 41% that examined differences between users of a specific SNS platform to those who do not use that Catholic 18% 18% 17% 20% 6% 21% 26% 16% platform. Hence, by doing both sets of analyses, we Nondenominational Christian 14% 13% 14% 12% 5% 12% 11% 12% aimed to answer our research questions more fully. Other 8% 9% 8% 8% 4% 8% 8% 6% Because statistical significance can be misleading Church of Jesus Christ of LDS 9% 4% 9% 6% 64% 8% 7% 7% for these analyses and due to the high number of Protestant 6% 8% 6% 5% 3% 6% 6% 7% t tests conducted, we report those relationships Baptist 3% 2% 2% 2% 3% 4% 3% 4% that are significant at thep < .05 level and have a Cohen’s d effect size > .20 (at least a small effect). Buddhism 3% 3% 3% 3% 2% 2% 3% 2% Judaism 2% 2% 2% 3% 1% 3% 3% 5% Results Muslim 1% 1% 1% 1% 0% 2% 4% 1% Participants reported daily average time spent on all Hinduism 1% 1% 0% 0% 0% 1% 3% 1% social media of 2.33 hours (SD = 2.02) and an aver- Education Bachelor's degree 41% 43% 41% 37% 48% 44% 49% 50% age of 4.15 (SD = 2.21) social media platforms (of Some college 24% 25% 23% 27% 17% 22% 17% 19% those surveyed). In order of popularity, Facebook was used most (n = 1,523, 77.5%), followed by Associate's degree 13% 9% 12% 14% 13% 10% 8% 7% Instagram (n = 1,062, 54%), Twitter (n = 690, Master's degree 11% 11% 12% 8% 9% 16% 19% 17% 35.1%), Snapchat (n = 452, 23%), LinkedIn (n = High school diploma 8% 8% 8% 10% 9% 4% 4% 3% 420, 21.4%), Skype (n = 346, 17.6%), WhatsApp Professional/vocational 2% 1% 2% 2% 2% 1% 1% 1% (n = 338, 17.2%), and Marco Polo (n = 150, 7.6%). Doctoral degree 1% 2% 1% 2% 1% 2% 2% 2% Most respondents (n = 1,751, 89.1%) used more Home high school 1% 1% 1% 1% 1% 0% 0% 0% than one social media app (none reported using Family income <$25,000 17% 20% 15% 15% 11% 14% 13% 13% no SNSs), women reported spending significantly more time on social media daily (M = 2.45, SD = $25,000–$50,000 31% 30% 30% 35% 29% 29% 28% 25% 2.07) than men (M = 2.19, SD = 1.96; p = .003), and $50,000–$75,000 22% 22% 23% 22% 19% 21% 24% 21% women spent more time than men on each specific $75,000–$100,000 14% 15% 14% 14% 22% 15% 16% 20% SNS except Facebook. Finally, the gender distribu- $100,000–$150,000 10% 7% 10% 8% 13% 12% 12% 11% tion within each SNS platform varied between the >$150,000 4% 4% 5% 3% 3% 5% 3% 9% low of 25.5% (Marco Polo) and the high of 51.3% Don't know/Not sure/decline 2% 3% 2% 3% 3% 3% 4% 3% (WhatsApp) for men, and except for WhatsApp Note. FB = Facebook, TW = Twitter, IG = Instagram, SC = Snapchat, MP = Marco Polo, SK = Skype, WA = (46.4%) and Skype (49.6%), women comprised WhatsApp, LI = LinkedIn. most users within each SNS.

Differences Between Specific SNS Users Analyses peer support, subjective overall health, fruit and Means, standard deviations, and comparisons vegetable consumption, positive mood), though they between users of the different social media plat- also expressed some poor health and wellness asso- forms using independent-samples t tests and effect ciations (e.g., Body Mass Index, physical symptoms, sizes are presented in Table 3. Snapchat users, as a sugary snack consumption) compared to many other group, were significantly younger, used more social SNS users. Compared to other SNS users, WhatsApp media platforms, and reported more health and users demonstrated many healthy relationships (e.g., wellness deficits (e.g., loneliness, negative mood, social integration, aerobic exercise, sleep quantity, depressive symptoms) relative to many of the other sleep quality) and LinkedIn users reported having social media platform users. Marco Polo users, on greater self-regulation. Thus, Snapchat users, on average, reported many healthy relationships (e.g., average, were more likely to have poorer wellness,

TABLE 3 Means, Standard Deviations, and Comparisons Between Social Media Platform Users Variable Facebook Twitter Instagram Snapchat Marco Polo Skype WhatsApp LinkedIn Diff between platforms † Cohen's d † † M(SD) M(SD) M(SD) M(SD) M(SD) M(SD) M(SD) M(SD) Daily time (hr) 2.19 (3.14) 1.90 (3.50) 2.10 (3.49) 2.10 (3.49) 1.81 (3.49) 1.46 (3.37) 2.24 (3.74)* 0.98 (2.48) WA more than LI, SK 0.40 to 0.22 Age 35.98 (11.85) 33.55 (11.71) 33.16 (10.09) 28.96 (8.80)* 34.20 (10.74) 36.59 (11.75) 33.36 (9.83) 36.83 (12.64) SC younger than all others 0.72 to 0.44 SM attitude 4.05 (1.52) 4.25 (1.49)* 4.17 (1.46) 4.24 (1.41) 4.04 (1.44) 4.03 (1.59) 4.09 (1.55) 3.88 (1.59) TW higher than LI, SK, FB 0.24 to 0.13 Number of SNS 4.61 (2.16) 5.37 (2.30) 5.24 (2.08) 6.00 (2.16)* 5.87 (2.33) 5.95 (2.45) 5.82 (2.44) 5.93 (2.46) SC higher than FB, IG 0.64 to 0.36 Physical health Subjective health 74.20 (18.21) 72.76 (18.82) 75.03 (17.66) 74.10 (17.61) 78.16 (17.84) 74.74 (19.14) 77.75 (17.34) 75.26 (17.48) MP higher than TW, SC, FB 0.29 to 0.22 Body mass index 26.41 (7.96) 26.54 (8.50) 25.79 (8.03) 25.72 (7.65) 26.82 (8.25)* 26.20 (7.13) 24.72 (9.58) 26.51 (7.59) MP higher than WA 0.23 Aerobic activity 33.42 (32.26) 34.50 (33.71) 34.67 (33.84) 34.23 (33.93) 30.85 (30.23) 37.46 (34.01) 42.89 (39.33)* 34.73 (30.06) WA higher than MP, FB, SC, IG, TW, LI 0.34 to 0.23 Physical symp 5.50 (4.02) 5.51 (4.16) 5.57 (3.94) 5.89 (4.06) 6.21 (4.09) 5.54 (4.15) 5.64 (4.60) 5.49 (3.79) Sleep quantity 6.80 (1.21) 6.78 (1.21) 6.78 (1.17) 6.76 (1.27) 6.64 (1.15) 6.79 (1.21) 6.91 (1.34)* 6.72 (1.14) WA higher than MP 0.22 Sleep quality 3.74 (0.76) 3.75 (0.79) 3.73 (0.77) 3.71 (0.75) 3.73 (0.73) 3.76 (0.82) 3.92 (0.75)* 3.73 (0.77) WA higher than MP, SC, IG, FB 0.26 to 0.24 Fruit 1.02 (1.00) 0.91 (0.95) 1.03 (1.03) 0.99 (1.00) 1.35 (1.19)* 1.21 (1.08) 1.25 (1.18) 0.99 (0.95) MP higher than TW, LI, SC, FB, IG 0.41 to 0.29 Vegetable 1.32 (1.13) 1.25 (1.10) 1.33 (1.15) 1.17 (1.07) 1.50 (1.29)* 1.50 (1.25) 1.44 (1.25) 1.25 (1.07) MP higher than SC, TW, Li 0.28 to 0.21 Sugary snack 0.79 (0.94) 0.80 (0.99) 0.77 (0.94) 0.78 (0.97) 0.97 (1.08)* 0.81 (0.97) 0.87 (1.02) 0.79 (1.00) MP higher than IG, FB 0.20 to 0.18 Sugary drink 0.75 (1.09) 0.74 (1.09) 0.67 (0.99) 0.82 (1.09) 0.63 (1.01) 0.75 (1.12) 0.81 (1.07) 0.70 (1.08) Social health Loneliness 2.53 (1.10) 2.60 (1.14) 2.55 (1.10) 2.72 (1.08)* 2.44 (0.99) 2.54 (1.15) 2.58 (1.12) 2.55 (1.09) SC higher than MP, FB, IG, LI, SK 0.46 to 0.16 Inter. conflict 2.24 (0.90) 2.22 (0.89) 2.25 (0.90) 2.41 (0.91)* 2.23 (0.87) 2.26 (0.95) 2.36 (0.97) 2.27 (0.88) SC higher than TW, MP, FB, IG, SK, LI 0.21 to 0.16 Social integration 0.12 (0.18) 0.12 (0.19) 0.13 (0.19) 0.15 (0.21) 0.15 (0.24) 0.14 (0.21) 0.19 (0.28)* 0.12 (0.18) WA higher than FB, TW, LI, IG 0.30 to 0.25 Peer support 4.65 (1.05) 4.61 (1.08) 4.70 (1.06) 4.78 (1.00) 4.94 (1.04)* 4.75 (1.01) 4.71 (1.07) 4.68 (1.03) MP higher than TW, FB, LI, IG, WA 0.31 to 0.22 Mental health Positive mood 3.18 (0.84) 3.10 (0.85) 3.20 (0.83) 3.10 (0.85) 3.39 (0.75)* 3.29 (0.85) 3.34 (0.83) 3.27 (0.83) MP higher than TW, SC, FB, IG 0.36 to 0.24 Negative mood 2.33 (0.89) 2.33 (0.92) 2.40 (0.89) 2.52 (0.91)* 2.51 (0.84) 2.27 (0.92) 2.32 (0.93) 2.29 (0.88) SC higher than SK, LI, WA, TW, FB, IG 0.27 to 0.13 Depressive symp 8.56 (3.36) 8.69 (3.42) 8.65 (3.35) 8.86 (3.50) 8.75 (3.37) 8.51 (3.45) 8.34 (3.31) 8.53 (3.16) Perceived stress 2.64 (0.82) 2.66 (0.82) 2.64 (0.80) 2.73 (0.77) 2.57 (0.80) 2.56 (0.84) 2.60 (0.74) 2.50 (0.80) SC higher than LI, SK, MP, WA 0.29 to 0.17 Anxiety 2.75 (0.90) 2.77 (0.90) 2.77 (0.89) 2.87 (0.86)* 2.78 (0.87) 2.65 (0.91) 2.63 (0.86) 2.67 (0.88) SC higher WA, SK, LI, FB, IG 0.28 to 0.11 Life satisfaction 4.37 (1.54) 4.12 (1.54) 4.44 (1.51) 4.36 (1.43) 5.19 (1.32)* 4.41 (1.55) 4.68 (1.48) 4.40 (1.49) MP higher than all others 0.75 to 0.36 Self-regulation 2.80 (0.56) 2.78 (0.59) 2.78 (0.56) 2.74 (0.54) 2.77 (0.49) 2.86 (0.59) 2.76 (0.51) 2.88 (0.55)* LI higher than SC, WA, MP, IG 0.26 to 0.18 Note. † Only those that are statistically significant are presented in this column – if blank, no statistical difference was observed;†† Effect size (Cohen’s d) is interpreted as such: d > .20 is small, d > .50 is medium, d > .80 is large; A range of Cohen’s d values are presented when multiple comparisons are evaluated; SNS = Social media and social networking sites, FB = Facebook, TW = Twitter, IG = Instagram, SC = Snapchat, MP = Marco Polo, SK = Skype, WA = WhatsApp, LI = LinkedIn. *p < .05.

whereas Marco Polo and WhatsApp users had better negative mood, depressive symptoms, conflict), but wellness, compared to other SNS users. also some positive outcomes (e.g., peer support, As the next step in examining differences social integration, life satisfaction), suggesting among users and nonusers of SNS, Table 4 displays intricate relationships, likely depending on situ- the correlations between daily time spent on spe- ational characteristics. cific SNS, number of SNS platforms, and the study Finally, the use of a specific SNS was associated variables. These results suggest more total daily with unique patterns. For instance, Marco Polo time spent on social media, regardless of platform users demonstrated both beneficial (e.g., social used, is associated with poorer health and wellness integration, aerobic activity, positive mood) and (e.g., loneliness, physical symptoms, depressive detrimental wellness associations (e.g., loneliness, symptoms). However, the number of SNS used was conflict, physical symptoms, sugary snack and drink associated with several negative outcomes (e.g., consumption, depressive symptoms) related to

TABLE 4 Correlations Between Daily Time Spent on Each Social Media Platform and Psychosocial Variables Variable Entire Sample Facebook Twitter Instagram Snapchat Marco Polo Skype WhatsApp Linkdein N = 2,074 n = 1,557 n = 707 n = 1,085 n = 462 n = 149 n = 355 n = 345 n = 431 Daily SNS time # SNS Daily time Daily time Daily time Daily time Daily time Daily time Daily time Daily time M(SD) 2.32 (2.02) 4.15 (2.21) 2.19 (3.41) 1.90 (3.50) 2.10 (3.49) 1.89 (3.41) 1.81 (3.49) 1.46 (3.37) 2.24 (3.74) 0.98 (2.48) Age −.25** −.22** −.07* −.08* −.01 .08 −.06 −.07 −.01 .00 Social media attitude .44** .35** .23** .20** −.17** .17** .25** .15** .27** .16** Number of SNS .32** – .04 .09* .08** .13** .23** .11* −.07 .24** Physical health Subjective physical health −.06* .05* −.04 −.04 −.01 −.06* −.07 .02 .04 −.12* Body mass index .04 .02 −.04 −.04 −.10** −.14** −.04 −.06 .04 −.06 Aerobic activity .07** .06** .19** .22** .24** .23** .53** .29** .42** .21** Physical symptoms .23** .13** .23** .27** .24** .23** .43** .31** .34** .29** Sleep quantity −.04 .00 .02 .06 .04 .04 −.15 −.03 .10 −.09 Sleep quality −.03 .01 .05 .12* .08* .07 .18* .14* .21** .03 Fruit consumption .01 .03 .10** .13** .12** .12* .23** .12* .19** .09 Vegetable consumption −.05* .00 .03 .04 .02 .01 .08 .01 .16** .03 Sugary snack consumption .12** .07** .20** .17** .18* .17** .40** .13* .22** .25** Sugary drink consumption .15** .03 .17** .18** .20** .16** .39** .21** .20** .14** Social health Loneliness .16** .06** .14** .13** .14** .12* .28** .18** .22** .09 Social integration .23** .14** .40** .47** .46** .42** .65** .50** .54** .50** Interpersonal conflict .22** .10** .25** .27** .28** .23** .41** .30** .37** .23** Peer support .09** .18** −.01 .06 −.02 −.07 .11 .11* .09 .04 Mental health Positive mood .00 .05* .07** .08* .08* .05 .17* .07 .19** .08 Negative mood .20** .12** .15** .13** .13** .10* .44** .20** .21** .16** Depressive symptoms .15** .05* .15** .18** .16** .11* .34** .17** .28** .16** Perceived stress .14** .01 .08** .04 .07* .08 .13 .05 .05 .08 Anxiety .11** .06** .03 −.01 .00 .01 .09 −.02 −.06 .03 Life satisfaction .00 .08** .09** .12** .08** .05 .08 .12* .23** .07 SUMMER 2021 Self-regulation −.15** −.05* −.14** −.10** −.13** −.13** −.14 −.10 −.21** −.11* Note. SNS = Social media and social networking sites. PSI CHI *p < .05. **p < .01. JOURNAL OF PSYCHOLOGICAL RESEARCH

increased daily use. WhatsApp users demonstrated TABLE 5 similar mixed results, relative to other platforms, Specific Platform User vs. Nonuser Difference t-Test Results with improved sleep quality, positive mood, and life satisfaction, but decreased self-regulation. Facebook Outcome User M(SD) Nonuser M(SD) Δ t(df) d Better? and Instagram users were the only platforms Facebook where increased use was positively correlated with Social integration 0.12 (0.18) 0.09 (0.13) +.03 4.23(2037)*** 0.20 Yes perceived stress, and only Skype users manifested Twitter greater peer support with increased daily use. Life satisfaction 4.12 (1.54) 4.44 (1.54) −.32 4.43(2060)*** 0.21 No Thus, each of the platforms demonstrated unique Instagram wellness associations, although Marco Polo and WhatsApp users had many of the strongest relation- Social integration 0.13 (0.19) 0.09 (0.14) +.04 4.75(2037)*** 0.24 Yes ships (beneficial and detrimental). Peer support 4.70 (1.06) 4.47 (1.14) +.23 4.76(2037)*** 0.21 Yes Negative affect 2.40 (0.89) 2.21 (0.89) +.19 4.68(2062)*** 0.21 No Users and Nonusers Difference Analyses Snapchat Next, we examined users of specific SNS compared Loneliness 2.72 (1.08) 2.47 (1.12) +.25 4.30(2037)*** 0.23 No to all those who did not use that SNS in a series of Social integration 0.15 (0.21) 0.10 (0.16) +.05 5.40(2037)*** 0.27 Yes independent-samples t tests (see Table 5). First, some platform users were significantly younger than Interpersonal conflict 2.41 (0.91) 2.15 (0.88) +.26 5.63(2037)*** 0.29 No those who did not use their respective platforms. *** Peer support 4.78 (1.00) 4.54 (1.12) +.24 4.43(2037) 0.23 Yes Specifically, Snapchat users were younger by more Negative affect 2.52 (0.91) 2.25 (0.88) +.27 5.73(2062)*** 0.30 No than 9 years (d = 0.86), Instagram users were Anxiety 2.87 (0.86) 2.69 (0.90) +.18 3.76(2060)*** 0.21 No younger by about 6 years (d = 0.52), and WhatsApp Marco Polo users were also younger by about 3 years (d = 0.28). Social integration 0.15 (0.24) 0.11 (0.16) +.04 2.37(2037)* 0.20 Yes Second, regarding social media attitudes, users for Peer support 4.94 (1.04) 4.56 (1.10) +.38 3.98(2037)*** 0.36 Yes every platform except LinkedIn reported significantly higher attitudes regarding the importance Subjective health 78.16 (17.84) 73.89 (18.35) +4.27 2.74(2066)** 0.24 Yes of social media in their lives, ranging from small *** Fruit consumption 1.35 (1.19) 0.99 (1.00) +.36 3.58(2066) 0.33 Yes (e.g., Skype users d = 0.20) to large effect sizes (e.g., Sugary snack consumption 0.97 (1.08) 0.74 (0.90) +.23 2.47(2066)* 0.23 No Facebook users d = 0.84). Whereas some specific Positive affect 3.39 (0.75) 3.15 (0.85) +.24 3.77(2062)*** 0.30 Yes SNS users demonstrated only one statistically Negative affect 2.51 (0.84) 2.29 (0.90) +.22 3.03(2062)** 0.25 No significant association compared to nonusers (i.e., Life satisfaction 5.19 (1.32) 4.26 (1.55) +.93 8.15(2060)*** 0.65 Yes Facebook, Twitter, LinkedIn), others demonstrated many (i.e., Snapchat, Marco Polo, WhatsApp). Skype Some SNS profiles had unilaterally healthy rela- ** Social integration 0.14 (0.21) 0.10 (0.16) +.04 2.95(2037) 0.21 Yes tionships (i.e., Facebook, LinkedIn, Skype) or Fruit consumption 1.21 (1.08) 0.98 (1.01) +.23 3.68(2066)*** 0.22 Yes unhealthy (i.e., Twitter), and others were much WhatsApp more complex (i.e., Instagram, Snapchat, Marco Social integration 0.19 (0.28) 0.09 (0.13) +.10 6.47(2037)*** 0.46 Yes Polo, WhatsApp). Despite substantial variation Interpersonal conflict 2.36 (0.97) 2.17 (0.88) +.19 3.26(2037)** 0.28 No in the results, users of several SNS had improved Subjective health 77.75 (17.34) 73.49 (18.46) +4.26 3.96(2066)*** 0.24 Yes health and wellness relative to those who did not use that specific platform. Snapchat users seemed Body mass index 24.72 (9.58) 26.54 (7.32) −1.82 3.72(1798)*** 0.21 Yes to have the most detrimental associations (with a *** Aerobic exercise 42.89 (39.33) 30.80 (29.74) −12.09 5.41(2066) 0.35 Yes few positive), and Marco Polo and WhatsApp users Sleep quality 3.92 (0.75) 3.70 (0.78) +.22 4.98(2066)*** 0.29 Yes seemed to have the most beneficial associations Fruit consumption 1.25 (1.18) 0.98 (0.98) +.27 4.03(2066)*** 0.25 Yes (both with some detrimental) compared to their Positive affect 3.34 (0.83) 3.13 (0.85) +.21 4.08(2062)*** 0.25 Yes respective nonuser counterparts.

*** Life satisfaction 4.68 (1.48) 4.26 (1.55) +.42 4.67(2060) 0.28 Yes Discussion LinkedIn The purpose of the current study was to address ** Perceived life stress 2.50 (0.80) 2.66 (0.84) −.16 3.46(2062) 0.20 Yes the current lack of information regarding how Note. Δ represents difference in user of platform relative to nonusers; Effect size (Cohen’s d) is interpreted as health and wellness may be linked to the use of such: d > .20 is small, d > .50 is medium, d > .80 is large. different social media and networking sites (SNS). * p < .05. ** p < .01. *** p < .001. Specifically, we explored eight contrasting SNS

among a diverse sample of internet users through health outcomes were similar. This suggests that Amazon’s MTurk site that represented an older time spent on any SNS, rather than the specific sample than previous similar studies among college type of SNS, was more important for physical health students (Wright et al., 2020). Indeed, our results outcomes. However, in terms of comparing users of highlighted complex and unique associations with different SNS, potentially, the use of videography the use of these specific SNS including physical, may promote wellness whereby users can feel social, and mental health extending and building on more connected with others (beyond text and still prior research among young adult college students. images) through a more authentic social experience First, increased daily use of social media, (Sundar, 2008). Furthermore, Marco Polo and regardless of the specific SNS used, was related to WhatsApp along with Skype all deliver messages to more detrimental outcomes, especially social and a specific recipient rather than a broadcast or post mental (e.g., loneliness, negative affect, anxiety, to a general audience, which may reduce unhealthy depressive symptoms), which corroborates many social comparisons. Similarly, the motivation to findings in the literature (Andreassen et al., 2017; engage with someone directly through video or, in Song et al., 2014; Twenge et al., 2017; Wright, Hardy, the case of LinkedIn and WhatsApp, using an SNS et al., 2018; Wright et al., 2020). Moreover, when for professional reasons (e.g., locating a job, speak- specific SNS are considered, daily use of each SNS ing with a colleague) may elicit improved outcomes individually often resulted in poorer self-reported (Ilakkuvan et al., 2019; Yang, 2016) because the health. Thus, it seems that those who spent more user is likely not on the SNS for entertainment or time on social media had poorer self-reported comparison purposes (other than for professional health, or those who had poorer self-reported development or improvement). health spent more time on social media. This may Also noteworthy were the relationships regard- be due to a variety of reasons. For instance, one who ing poorer health and wellness for those who used is physically unhealthy may be limited in activities image-based SNS (especially Snapchat) and, to a they can engage in, thus potentially leading to lesser extent, those who used text-based SNS (i.e., increased use of SNS. Moreover, those who feel Facebook, Twitter). However, these relationships more socially isolated or mentally unhealthy (e.g., were also very complex and not easily interpreted. depressed) may turn to SNS for social interac- Compared to users of other SNS, Snapchat users tion that may be considered more “safe” or less demonstrated stronger relationships with poor “risky” than direct personal interaction. However, social and mental health outcomes like loneliness, contrary to other studies (Hardy & Castonguay, negative mood, and anxiety, although the users of 2018; Primack et al., 2017), the number of SNS each SNS demonstrated at least a few associations used was related to some positive social health with poor social and mental health indicators. variables including social integration and perceived However, the results regarding Snapchat users are peer support. Among those who use the internet consistent with findings among college students extensively, an online “presence” by having multiple (Wright et al., 2020), but contrary to the findings SNS accounts may increase perceptions of peer of Pittman and Reich (2016) who found less depres- acceptance and provide topics of discussion with sive symptomology for image-based SNS users (i.e., peers during interactions. Thus, although increased Instagram). Two potential interpretations may time spent on social media seemed to be related account for these findings. First, consistent with to poorer health outcomes, social media, in this social comparison orientation (Buunk & Gibbons, case, may provide a social foundation whereupon 2006), image-based communications may enable relationships with peers can be built and nurtured. social comparisons more readily. Plus, given that Second, consistent with Wright and colleagues people generally post overly positive types of images (2020), video-based (i.e., Marco Polo, Skype) and (Cramer et al., 2016; Yang, 2016) and may possibly more professional-based (i.e., LinkedIn, WhatsApp) trust images more than text (Sundar, 2008), this SNS users demonstrated the strongest associations could activate negative emotions and cognitions. with greater wellness, although the relationships It is difficult to know, however, because Instagram, were very complex. For instance, although Marco another primarily image-based SNS, did not Polo, WhatsApp, and LinkedIn users manifested bet- manifest similar poorer relationships relative to SUMMER 2021 ter physical, social, and mental health compared to other SNS users. Second, rather than the use of a users of other SNS platforms, associations between specific SNS exerting influence over these health PSI CHI JOURNAL OF time spent on all eight SNS with physical and social outcomes, it may be that those who have certain PSYCHOLOGICAL RESEARCH

preexisting conditions are drawn to certain SNS. As be investigated such as older adults and children such, perhaps those who want more entertainment, to see if certain populations may be more at-risk have poor health, or desire social comparisons to for developing detrimental outcomes from SNS others are drawn to image-based platforms such use. For instance, children who are just beginning as Snapchat, and those drawn to video-based SNS to use social media may be at greater risk for may already be healthier than their counterparts. developing detrimental health outcomes faster This study has some potential limitations. First, due to earlier initiation. Additionally, the effects the cross-sectional nature of the data precludes any of social distancing or cultural/societal differences clear causal conclusions such that those with poor that mandate different social norms might have on wellness may spend more time on social media or social media use should be investigated for health those who spend more time on social media develop implications. Furthermore, future studies could poor wellness. Second, despite using a large sample, examine objectively measured health outcomes due to the subjective self-report and self-selection like BMI, blood pressure, or clinical psychological bias inherent in survey-based studies, some of these diagnoses, which can be discrepant from subjective results may not be generalizable or accurately repre- measures (Wright, Perkes, et al., 2018). sent relationships between wellness and social media In sum, although the results of this study were use. For instance, different cultural or religious complex, it seems that image-based social media practices may encourage or discourage the use of (i.e., Snapchat) use was generally associated with SNS or of the internet altogether. Third, the time poorer health and video-based (i.e., Marco Polo, spent on each respective SNS could have substantial WhatsApp), and professional social media (i.e., overlap with the use of other SNS use, as the users LinkedIn) use was related to more improved health could have used them simultaneously, inhibiting and wellness. However, the strongest link seems to our ability to tie specific outcomes to specific SNS more clearly be that increased daily use of social use. Many avid internet users may, for example, use media, regardless of the specific platform, had nega- multiple SNS at the same time in their browsing or tive impacts on the user’s health. Thus, our results posting. Fourth, we did not investigate motivations suggest that moderation of SNS use is likely the best for SNS use, which can impact wellness because behavior for overall health and wellness of the user. these motivations can be influenced by different extraneous factors (e.g., job hunting, pornography References viewing) that may come with a unique health profile Andreassen, C. S., Pallesen, S., & Griffiths, M. D. (2017). The relationship between (Barker, 2009; Yang, 2016). Furthermore, how their addictive use of social media, narcissism, and self-esteem: Findings from a time was spent when on SNS was not considered, large national survey. Addictive Behaviors, 64, 287–293. https://doi.org/10.1016/j.addbeh.2016.03.006 as directly interacting with others could produce Asiri, A. K., Almetrek, M. A., Alsamghan, A. S., Mustafa, O., & Alshehri, S. F. (2018). different effects than passive observation. Moreover, Impact of Twitter and WhatsApp on sleep quality among medical students SNS usage was determined via a single item asking in King Khalid University, Saudi Arabia. Sleep and Hypnosis, 20(4), 247–252. https://doi.org/10.5350/Sleep.Hypn.2018.20.0158 participants to estimate time spent on each SNS in Barker, V. (2009). Older adolescents’ motivations for social network site use: the past month, which may have introduced retro- The influence of gender, group identity, and collective self-esteem. spective self-report bias in this estimation. Finally, it CyberPsychology & Behavior, 12(2), 209–213. is possible that certain outstanding characteristics of https://doi.org/10.1089/cpb.2008.0228 Bohannon, R. W., Maljanian, R., & Goethe, J. (2003). Screening for depression in the Marco Polo sample (e.g., 64% Latter-day Saint, clinical practice: Reliability and validity of a five-item subset of the CES- 70% married, 89% White) may have influenced depression. Perceptual and Motor Skills, 97(3), 855–861. the observed associations with health and wellness, https://doi.org/10.2466/pms.97.7.855-861 Boulos, M. N. K., Guistini, D. M., & Wheeler, S. (2016). Instagram and WhatsApp in suggesting a potentially complex relationship. health and healthcare: An overview. Future Internet, 8(3), 37–51. The findings from this study invite future https://doi.org/10.3390/fi8030037 research to replicate and further clarify these rela- Butz, D. A., & Yogeeswaran, K. (2011). A new threat in the air: Macroeconomic threat increases prejudice against Asian Americans. Journal of tionships. Future research should employ carefully Experimental Social Psychology, 47(1), 22–27. constructed studies to investigate the directionality https://doi.org/10.1016/j.jesp.2010.07.014 of the relationship between health and SNS use Buunk, A. P., & Gibbons, F. X. (2006). Social comparison orientation: A new perspective on those who do and those who don’t compare with others. In by longitudinal, controlled, and experimental S. Guimond (Ed.), Social comparison and social psychology: Understanding means. One such avenue could be the exploration cognition, intergroup relations and culture (pp. 15–32). Cambridge of SNS withdrawal among those who may show University Press. SUMMER 2021 Clark, J. L., Algoe, S. B., & Green, M. C. (2017). Social network sites and well- behavioral dependencies by removing SNS access being: The role of social connection. Current Directions in Psychological PSI CHI to examine causal relationships between SNS use Science, 27(1), 32–37. https://doi.org/10.1177/0963721417730833 JOURNAL OF and health. Moreover, different populations should Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived PSYCHOLOGICAL RESEARCH

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Instagram use, loneliness, and social comparison orientation: Lorig, K., Stewart, A., Ritter, P., González, V., Laurent, D., & Lynch, J. (1996). Interact and browse on social media, but don’t compare. Cyberpsychology, Outcome Measures for Health Education and other Health Care Behavior, and Social Networking, 19(12), 703–708. Interventions (pp. 25, 37–38). Sage Publications. https://doi.org/10.1089/cyber.2016.0201 Nowland, R., Necka, E. A., & Cacioppo, J. T. (2018). Loneliness and social internet Yang, C., & Srinivasan, P. (2016). Life satisfaction and the pursuit of happiness on use: Pathways to reconnection in a digital world? Perspectives on Twitter. PLoS ONE, 11(3), e0150881. https://doi.org/10.1371/journal.pone.0150881 Psychological Science, 13(1), 70–87. https://doi.org/10.1177/1745691617713052 Petrocchi, N., Asnaani, A., Martinez, A. P., Nadkarni, A., & Hofmann, S. G. (2015). Author Note. Robert R. Wright https://orcid.org/0000-0002- Differences between people who use only Facebook and those who 4101-7840 use Facebook plus Twitter. International Journal of Human-Computer Austin Evans https://orcid.org/0000-0002-1486-4630 Interaction, 31(2), 157–165. https://doi.org/10.1080/10447318.2014.986640 Chad Schaeffer https://orcid.org/0000-0001-8633-843X Pittman, M., & Reich, B. (2016). Social media and loneliness: Why an Instagram Rhett Mullins https://orcid.org/0000-0003-2903-8216 picture may be worth more than a thousand Twitter words. Computers in Laure Cast https://orcid.org/0000-0001-7568-5727 Human Behavior, 62, 155–167. https://doi.org/10.1016/j.chb.2016.03.084 This research was supported by internal funding from Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, Brigham Young University–Idaho for student- and faculty- J. B., & James, A. E. (2017). Use of multiple social media platforms and directed research. symptoms of depression and anxiety: A nationally-representative study The authors declare a potential conflict of interest as among U.S. young adults. Computers in Human Behavior, 69, 1–9. this research investigation was, in part, also supported by https://doi.org/10.1016/j.chb.2016.11.013 funding from Joya Communications Inc (the organization Song, H., Zmyslinski-Seelig, A., Kim, J., Drent, A., Victor, A., Omori, K., & Allen, M. that manages the Marco Polo app), which also assisted in (2014). Does Facebook make you lonely? A meta analysis. Computers in reviewing data analysis and interpretation. Human Behavior, 36, 446–452. https://doi.org/10.1016/j.chb.2014.04.011 We would like to thank Amanda Butler for her assistance Spector, P. E. & Jex, S. M. (1998). Development of four self-report measures of job on an earlier draft of this paper. stressors and strain: Interpersonal conflict at work scale, organizational Correspondence concerning this article should be constraints scale, quantitative workload inventory, and physical symptoms addressed to Robert R. Wright, Department of Psychology, SUMMER 2021 inventory. Journal of Occupational Health Psychology, 3(4), 356–367. Brigham Young University–Idaho, 525 South Center St. https://doi.org/10.1037/1076-8998.3.4.356 Rexburg, ID 83460-2140. Telephone: 208-496-4085. PSI CHI Sundar, S. S. (2008). The MAIN model: A heuristic approach to understanding Email: [email protected] JOURNAL OF PSYCHOLOGICAL RESEARCH

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