“Stress-Proof” with Smartphones: The Influence of Guilty-Pleasure Television and Second-Screens on Recovery Anoushé Dastmaltschi Student Number: 12317349 University of Amsterdam Master’s Thesis
Graduate School of Communication
Master’s Program Communication Science: Entertainment Communications Under Supervision of Dr. Jessica T. Piotrowski
28. June 2019
Author Note
Thank you to Marthe Möller for her help and feedback while piloting the experimental study, to professor Jessica Piotrowski for all her time and efforts throughout
Abstract
The goal of this study was to extend current research on entertaining media use and psychological well-being by exploring further ways how watching television may support recovery in depleted and mentally fatigued individuals. Studies thus far have found that entertaining media, specifically low-cognitive load want media, may indeed support recovery when feelings of guilt are not experienced. This study thus aimed to investigate the way this perception of guilt could be mitigated in order to support recovery — namely, via a
contemporary behavior that is often observed alongside television use: second-screening. This was evaluated using both self- and physiological reports of recovery in a between-subjects lab experiment (two conditions: second-screen use versus no second-screen) with a convenience sample of N = 116 fatigued Dutch Communications Science Bachelor Students between 18–25. In concurrence with previous work, the results of the multiple regression analyses indicate that increased feelings of procrastination while viewing guilty-pleasure television did lead to increased feelings of guilt, which led to lower levels of recovery. However, for individuals who engaged in second-screening while viewing, their feelings of guilt were noticeably less, and as a result they experienced comparably more self-reported recovery from their guilty-pleasure television time. This partial support for the hypothesized route to recovery thus bridges the gap between second-screens and entertainment media as a contemporary solution for recovery, which in turn could offer practical implications for media makers as well. This study lastly discusses limitations, such as the choice of an experimental design, the assumption of depletion and the measurement of productivity, and in addition recommends future research ideas focusing on understanding the various
dimensions of second-screen behavior.
Keywords: smartphone, second-screen, entertaining media, psychological recovery, stress, guilty-pleasure television, depletion
“Stress-Proof” with Smartphones: The Influence of Guilty-Pleasure Television and Second-Screens on Recovery
With a society thriving on high performance and success, stress and exhaustion are prevalent concerns in modern life (Collins, Cox, Wilcock, & Sethu-Jones, in press; Sharma & Rush, 2014). Methods for stress management that fulfill the need for finding a balance
between emotional welfare and the mounting levels of pressure are thus issues of increasing interest among researchers today. The overall trend in stress awareness can also be observed in the booming numbers of app-based mindfulness aids helping to "unplug by plugging in” (Collins et al., in press; Perkey, 2015; Pickert, 2014; Schroeder, 2017). However this further assertion of smartphone use may only intensify pressures to engage with this high
information load medium and thus even spike burnouts and social anxiety — potentially evoking the opposite effect to that intended (Hinsliff, 2016; Kneidinger-Müller, 2017; Reinecke et al., 2017). There are also more and more alternative methods that are being developed and offered, but the endless new options themselves can seem overwhelming at times. Yet, some simple and frequently consumed alternatives have already been shown to help improve state recovery, for example, engaging with entertaining media.
Research has demonstrated that watching entertaining television or playing video games can positively impact state recovery in fatigued individuals by serving as a form of escapism from a busy lifestyle and thus enabling relaxation (Reinecke, Klatt, & Krämer, 2011; Reinecke, Hartmann, & Eden, 2014; Rieger, Frischlich, Wulf, Bente, & Kneer, 2014; Rieger, Reinecke, Frischlich, & Bente, 2014; Rieger, Reinecke, & Bente, 2017). However, this is only true when this activity is not perceived as giving in to an indulgent temptation which in-turn evokes feelings of guilt (Eden, Johnson, & Hartmann, 2018; Reinecke & Hofmann, 2016; Schnauber-Stockmann, Meier, & Reinecke, 2018). In many cases, since simply “telling” exhausted people to embrace their media choices and not feel guilty about
them will not likely offset any negative appraisals associated with watching their guilty pleasures, this study aims to find a contemporary solution for this challenge by connecting media-induced-recovery with a notable phenomenon of the 21st century: second-screening.
Second-screening, most commonly defined as the use of a second-screen (e.g.,
smartphone) while watching easily consumable television or Netflix (de Feijter, Khan, & van Gisbergen, 2016; Segijn, 2016), occurs with nearly half of all smartphone and tablet owners (Nielsen, 2018). Second-screen smartphone use has been found to be motivated by wanting to escape from academic stress (Duke & Montag, 2017); by purely seeking more enjoyment (Segijn, Voorveld, Vandeberg, & Smit, 2017); or out of a habit triggered by cues such as the act of switching on a show (Schnauber-Stockmann et al., 2018). That said, Wang and
Tchernev (2012) found that while these affective motivations matter, cognitive need fulfillment is still a primary reason for second-screening. In the context of entertainment media and recovery, this raises the question as to whether people might turn to their phones while watching television in order to engage themselves cognitively (i.e., “not waste time”), and thus reduce associated feelings of so-called guilty-pleasure media. Thus far, research has yet to establish a connection between recovery via entertaining media consumption and the use of a second-screen while doing so as a means to assist in this recovery process. The aim of this research is to find out if this could be a viable contemporary solution to offset feelings of guilt, capitalize on the recovery potential of television, and enable an efficient recovery process. If we can demonstrate this, then we will have an actionable recommendation for how best to consume media to ensure optimum recovery results.
Theoretical Framework
Understanding Depletion, Procrastination, Guilt and Recovery
Depletion occurs when an individual has to engage in effortful and exhausting self-regulation tasks often encountered in daily life such as making decisions, adjusting
to social norms or avoiding mistakes (Meier, Reinecke, & Meltzer, 2016; Reinecke et al., 2014; Schnauber-Stockmann et al., 2018). Recovery from this type of fatigue is not simply achieved by removing any of the stressors — it is a multi-dimensional concept wherein by recovering, the individual is able to return their depleted resources to baseline levels; this is a crucial pre-condition for health and well-being (Meier et al., 2016; Sonnentag & Zijlstra, 2006). Sonnentag and Zijlstra (2006) describe physiological detachment, relaxation, control, and mastery as four necessary facets that lead to recovery. Engaging in low-effort activities, such as watching television, or more engaging, interactive activities, such as using a
smartphone have been found to satisfy these four needs and thus help experience state recovery (Rieger, Reinecke, Kneer, Frischlich, & Bente, 2013; Rieger et al., 2014).
There are two competing, yet related, theories that describe this salience to pursue recovery through media use: Mood Management and Uses and Gratifications Theory. According to Mood Management Theory (Zillmann, 1988), users seek entertainment media in order to regulate their moods and achieve homeostasis. They do so by actively selectively exposing themselves to media that either matches or augments their current moods. Similarly, Uses and Gratifications Theory (Katz, Blumler, & Gurevitch, 1973) assumes that individuals are even more aware of their specific needs, which lead them to consume particular media types in order for these needs to be fulfilled and satisfied. When their chosen media meets the expected need, gratifications are obtained and remembered for future similar circumstances.
In line with these theories studies have shown depleted and drained individuals with limited self-control capacities are particularly drawn to cognitively undemanding and hedonically rewarding activities as they can primarily be enjoyed as pleasant and joyful experiences for recreation, restoration, and recovery (Eden et al., 2018; Reinecke et al., 2014; Reinecke & Hofmann, 2016). However, recovery using hedonic entertainment can be limited when this media is negatively appraised or when its use is irrationally perceived as
impulsively succumbing to temptation for short-term mood optimization (Meier et al., 2016; Reinecke & Hofmann, 2016). In depleted individuals especially, research has demonstrated how the impression of needlessly procrastinating may trigger associations with negative appraisals, more specifically, substantial feelings of regret, guilt or
anxiety (Reinecke et al., 2014; Reinecke & Hofmann, 2016; Schnauber-Stockmann et al., 2018). This perception, in turn, negatively affects the recovery experience by
transforming this media use into a resource-consuming activity, which increases each time the media is selected, thus making it feel even more habitually impulsive
(Schnauber-Stockmann et al., 2018). Ironically, consumers often describe this type of content as guilty-pleasure television.
Despite these appraisals, entertainment media users still watch their guilty pleasures for almost three hours per day (Mediatijd, 2018; Reinecke et al., 2014); however, the research on recovery would suggest that their experienced guilt is hindering the opportunity for
recovery (Reinecke et al., 2014). So how can these feelings of guilt effectively be reduced so that the opportunity for recovery is enhanced? One method could be simply to tell people to not feel guilty about their television choices and to embrace them as a form of self-care (O'Keeffe, 2016). For example, researchers have found that when individuals are aware of how much control is required to engage with a medium and how much self-regulatory resources they still have available, they can begin to mindfully engage with media content and understand its recreational benefits (Collins et al., in press; Eden et al., 2018; Perks, 2019; Reinecke & Hofmann, 2016). This strategic and goal-oriented media use then allows individuals to properly enjoy the activity and benefit appropriately from the positive potential of hedonic content (Eden et al., 2018; Nabi, Prestin, & So, 2013). Nevertheless, given how feelings of procrastination are irrationally perceived in depleted individuals, simply telling them to not feel guilty might not counteract these
negative self-evaluations. Which other mechanism might then encourage overall less guilt? Could the contemporary inclusion of a smartphone via second-screening, and the cognitive feelings associated with its use, intercept the appraisal route to better support recovery?
Second-Screening
The contemporary multi-screening habit second-screening includes using any media with internet access simultaneously together with content on television (Segijn, 2016; Segijn, Voorveld, Vandeberg, Pennekamp, & Smit, 2017). The most frequently used second-screen is the smartphone. Popular activities that individuals engage in while watching television include scrolling through social media or browsing the web, both of which perceived as not distracting and easy to carry out (de Feijter et al., 2016).
Second-screeners might take to their phones for various immediate emotional
gratifications. They might be seeking entertainment and stress relief; want to engage in social interaction; or grab their phones out of habit (Segijn, 2016; Wang, J., Wang, Gaskin, & Wang, 2015; Wang, Z. & Tchernev, 2012). In fact, even when emotional needs are satisfied by second-screening, Wang and Tchernev (2012) found second-screen use to be primarily motivated by cognitive needs (as opposed to more affective recovery). Indeed, Segijn (2016) found that second-screeners are more likely to switch their attention to alternative media when the current task is no longer rewarding and they need a mental break. Then,
second-screeners might use their phones out of interest for more information, to reply to emails and notifications or to check their calendars (Wang, J. et al., 2015; Wang, Z. &
Tchernev, 2012; Wang, Z., Irwin, Cooper, & Srivastava, 2015). From the perspective of guilt and recovery, this is quite interesting since cognitive need fulfillment can also be interpreted as productive (Cacioppo, Petty, Feinstein, & Jarvis, 1996; Wang, Z. & Tchernev, 2012). As such, if a user feels that they are being more productive by second-screening with their
smartphone during guilty-pleasure television, their experienced guilt would be expected to be less, and their recovery predicted to be greater. In such a situation, second-screening could be an ideal way to help users benefit from guilty-pleasure media.
Yet, conversely, second-screening might actually have the reverse effect. First, consider a cognitive resource argument. Research has shown that pursuing unrelated goals (e.g., watching television while second-screening) results in a battle for cognitive resources and may actually lead to greater cognitive depletion, which would subsequently lead to less recovery (Lang, 2006; Salvucci & Taatgen, 2011; Segijn et al., 2017; Van Cauwenberge, Schaap, & van Roy, 2014). Furthermore, by having to switch attention between media types, the depth to which information is being processed is also diminished, as most cognitive resources are allocated to the second-screen (Rubenking, 2017; Segijn et al., 2017; Wang, Z. et al., 2015). Research has found that second-screening while watching hedonic content can overload an already enjoyable activity because it no longer enables consumers to fully immerse themselves in the TV experience (Oviedo, Tornquist, Cameron, & Chiappe, 2015; Park, Xu, Rourke, & Bellur, 2019).
At the same time, guilt appraisals might also be at work. Indeed, not all second-screening may be interpreted as productive — social media use, for example, is perceived by users to reflect more procrastination than productivity (Meier et al., 2016; Przybylski,
Murayama, DeHaan, & Gladwell, 2013; Reinecke et al., 2017). Furthermore, even an inability to promptly respond to messages can further lead to feelings of guilt (Duke & Montag, 2017), in which case the device itself causes a form of techno-stress (Lee, Chang, Lin, & Cheng, 2014). In such situations, the perceived guilt of watching guilty-pleasure television in combination with procrastination-based or guilt-inducing second-screening may even lead to an increase in perceived guilt (“double dose”), and thus even less recovery. Under this premise, it is quite unclear whether and how second-screening may augment the
effects of guilty-pleasure media on recovery amongst depleted users. Logically, it could decrease feelings of guilt and support recovery, or alternatively, either increase feelings of guilt or demand too many cognitive resources — both of which would hinder recovery. To that end, this study is designed to address the following research question (see the conceptual model in Figure 1):
RQ: Will second-screening augment (productivity route) or suppress (cognitive stress route) recovery experienced from watching guilty-pleasure television?
Method Design
A between-subjects lab experiment with two conditions was conducted to answer the leading research question. One group experienced the manipulation of freely using their smartphones; the other was the control group without a smartphone, which is common practice in media-multitasking studies (Segijn et al., 2017). Based on a power analysis assuming a medium effect size with 80% power (Erdfelder, Buchner, Faul, & Brandt, 2004), a minimum of 100 participants was needed.
Participants
A convenience sample of N = 116 Communication Science Bachelor Students between 18–25 from the University of Amsterdam (93 female, 22 male, Mage = 21.25) was recruited between April 25th 2019 to May 21st 2019 via the University’s lab recruitment tool (see lab.uva.nl). In addition to the lab’s website, recruitment was also conducted via a call-to-action during lectures and also by word-of-mouth. University students are an ideal sample group as younger people with, on average, a higher education are more likely to engage in media-multitasking behavior (Segijn et al., 2017). Furthermore, it also means an increase in the likelihood of fatigued participants — as most students have busy study schedules and deadlines during this time period.
To evaluate whether randomization worked as intended, the control and treatment group participants were compared on a number of demographic and descriptive variables. Analyses showed no significant differences by condition in gender (control: 56, 12 men 44 women; treatment: 59, 10 men, 49 women), age (control: M = 21.09, SD = 2.785; treatment: M = 21.40, SD = 2.800), preference for hedonic (control: M = 3.57, SD = .573; treatment: M = 3.58, SD = .578) or eudemonic content (control: M = 3.35, SD = .562; treatment: M = 3.34, SD = .565), and whether they considered television to be a procrastination-based activity (control: M = 3.52, SD = 1.033; treatment: M = 3.572, SD = 1.095; see Table 1 for all statistics).
Stimuli Selection and Treatment Operationalization
Low cognitive load television, aka “guilty pleasures”. In order to determine which guilty-pleasure programs would be included in the study, a stimuli-selection survey was conducted before starting the actual study. This survey and stimuli-selection process was modeled on that of Eden et al. (2018). Specifically, this pilot study included 79 shows selected from IMDb’s list of most popular shows, whereby the top humor, comedy, sitcoms and reality TV shows were chosen (IMDb, 2019). These shows were rated by 108
participants (84 women, 24 men, Mage = 28.12) in order to derive a list that reflects “want”1 content with low cognitive challenge2. Eden et al. (2018) define low cognitively challenging media as those that display a simple and familiar story. They do not require too many self-regulatory resources to control cognitive processes such as maintaining attentional focus or engaging working memory. Hedonic media content, such as light comedy, would fall in to this category. The authors additionally speak of “should” versus “want” media. Want media
1 According to Eden et al. (2018), a strong "want" show is one that someone would choose to watch for the pure enjoyment of it. The want score is intended to reflect the extent to which someone's decision to watch this show would be indulgent or pleasure based. An example of a want show would be a reality TV show that everyone is currently also watching.
2
According to Eden et al. (2018), a show with high intellectual challenge is one that challenges the viewer on a cognitive level, by introducing new concepts or ideas, or by presenting complex plot points and challenging situations. A film with no intellectual challenge does not present the viewer with a challenge in terms of comprehending the subject matter or plot points. An example of a low intellectual challenge would be a funny and light-hearted sitcom revolving around the lives of five friends.
are the most desirable options providing immediate gratifications. They are the guilty pleasures that individuals engage in, such as reality TV (Eden et al., 2018).
Following Eden et al.’s (2018) procedure, each participant was randomly presented with approximately 30 shows — specifically, a photo of its promotional poster and a short IMDb description. Then, each participant was asked to rate how intellectually challenging they found it (challenge rating (CR), 1 = easy, 2 = neither easy nor challenging,
3 = challenging, 4 = unsure) and how likely they would want to watch it at the end of a busy and long day (want rating (WR), 7-point scale, 1 = extremely likely and 7 = extremely
unlikely). They were also asked whether or not they had previously watched the show
(1 = yes, 2 = no, 3 = no, but want to). The shows with the lowest intellectual challenge ratings (below MCR = 1.5) and the highest want ratings (below MWR = 3.5) were then selected,
resulting in the following stimuli choices: Brooklyn Nine-Nine, New Girl, Friends, How I Met Your Mother, Modern Family, The Office and The Fresh Prince of Bel-Air3. All seven shows are sitcoms averaging around twenty minutes in length. As the first five are available on Netflix in the Netherlands, these programs were selected as the experimental stimuli.
During the experiment, participants could select any full-length episode from any of the seasons of these five shows that appealed to them most at that moment4. Although providing full-length episodes increases the length of the entire study, it also adds to the external validity of the experiment (Rieger et al., 2014). As such, it was deemed appropriate to allow full-length episode viewing. While all programs were selected during the study, the series Friends was the most popular choice.
Treatment operationalization. While participants in the control group did not have access to their phone during the study, participants in the treatment group were asked to use their (second-screen) phone in any way they wanted to while watching Netflix. Such an
3 See Appendix A, Table A1 for stimuli scores.
approach is said to increase the ecological validity of the manipulation as their choice will seem less forced and closer to their intrinsically motivated actual behavior (Segijn, Xiong, & Duff, 2018). To facilitate potential ad hoc analyses, participants in the treatment group were asked to describe their phone behavior while watching Netflix in order to further understand how individuals engage with their second-screens. They were given multiple options to select from such as social media, web browsing, texting, or playing video games, as well as an open-answer option. The majority of the participants in this study used their second-screen for social media (43.1%) or texting (41.4%), which is similar to the activities found in previous research (Segijn, 2016; Wang, J. et al., 2015; Wang, Z. & Tchernev, 2012). Measures
Descriptive statistics for all measures can be found in Table 1. For all correlations, see Table 2.
State depletion. Level of fatigue was assessed with 24 items from the full length State Self-Control Capacity Scale (Ciarocco, Twenge, Muraven, & Tice, 2007). All of the original 25 items were included except for one, statement 24: “I am having a hard time controlling my urges”, which did not fit in the scope of the lab. The items were all adjusted to include more state phrases such as “right now” or “at this moment”, in order to highlight current exhaustion. Example items rated on a 7-point scale (1 = not true to 7 = very true) include “Right now, I feel like my willpower is gone” and “Currently, I feel drained”. Due to the nature of the questions, the items 4, 7, 11, 15, 17, 20 and 22 were recoded and reversed. The 24-item scale proved to be reliable as indicated by a Cronbach’s Alpha of .943. State Depletion (min. = 1.46, max. = 6.21, M = 3.5, SD = 1.076) was formed by calculating the mean of the 24 items. Participants with lower scores on this measure had lower depletion and those with higher scores a higher depletion.
State perceived procrastination. State-based feelings of procrastination associated with media use during the experiment were assessed using three items from the 8-item Irrational Procrastination Scale (Steel, 2010). The other five items did not fit the scope of the experiment and were thus excluded from this study. The wording of the items was adapted to fit state feelings of procrastination by including phrases such as “right now”. Item 3, “Right now, I feel like I could have spent this time better”, item 5, “My life would be better, if I had completed some tasks before participating in this experiment today” and item 6, “Right now, I feel I spent my time wisely today”, were rated on a 5-point scale (1 = does not apply at all to 5 = fully applies). Due to the nature of the question, item 6 was recoded and reversed.
An Exploratory Factor Analysis with Oblimin rotation indicated that the scale was unidimensional for all items explaining 57.758% of the variance. The 3-item scale for state procrastination proved to be reliable as indicated by a Cronbach’s Alpha of .633. The three items formed the measure State Perceived Procrastination (min. = 1, max. = 5, M = 2.61, SD = .864) by calculating their mean. Participants with lower scores thus had lower feelings of perceived procrastination and those with higher scores a higher perception.
State guilt. Feelings of guilt associated with media use were measured with all ten items of the state guilt subscale from the Guilt Inventory (Jones, Schratter, & Kugler, 2000). The items are rated on a 5-point scale (1 = strongly disagree to 5 = strongly agree). Although they already reflect state-based feelings, this was further enhanced by including words such as “right now” or “currently” to reflect the experimental setting. Example items include “Right now, I feel good about myself and what I have done today” and “Right now, there is at least one thing in the past hour that I would like to change”. Four positively-formulated items were reversed and recoded. The full ten-item
scale proved to be reliable as indicated by a Cronbach’s Alpha of .849. State Guilt (min. = 1.10, max. = 4.80, M = 2.21, SD = .704) was computed by calculating the mean of the ten items. Participants with lower scores on this variable had lower feelings of guilt and those with higher scores higher.
State perceived productivity. Feelings of state perceived productivity were assessed in three different ways: First, two adjusted items based on a study by Haapakangas, Hallman, Mathiassen and Jahncke (2018) were rated on a 7-point scale (1 = not at all productive to 7 = maximally productive). They included “What score would you give to your overall productivity while generally watching television?” and “What score would you give to your overall productivity in the last hour?”. Furthermore, an item rated on a 5-point scale (1 = not at all to 5 = a great deal) assessing task-productivity of generally
engaging with a second-screen asked: “How likely are you to agree with the following statement: Using my smartphone while watching Netflix is a productive activity” (Torkzadeh & Doll, 1999). Finally, an open-answer question asking why the participant experienced the past hour as productive or unproductive assessed whether merely attending the study was considered to be the productive activity.
An Exploratory Factor Analysis with Oblimin rotation indicated that the scale was unidimensional for all items explaining 54.52% of the variance. The three-item scale proved to be almost reliable as indicated by a Cronbach’s Alpha of .573. This score improved to .597 when removing the last item. Thus, State Perceived Productivity (min. = 1.00, max. = 6.00, M = 3.11, SD = 1.289) was computed by calculating the mean of the first two items. Participants with lower scores on this measure had a lower perception of productivity and those with higher scores a higher perception. The open answers were interpreted qualitatively.
State self-reported recovery. Recovery was measured both with items from the Recovery Experience Questionnaire (Sonnentag & Fritz, 2007), as well as physiologically in the lab via heart rate monitoring (see section State physiological recovery). While the original questionnaire includes four different facets of recovery, this study only focused on two subscales as they best reflected the type of recovery included in the over-arching research question: physiological detachment, the mental shift away from life stressors and hassles; and relaxation, usually encountered when an individual is experiencing low cognitive and physical demands to unwind and optimize their mood. The eight questionnaire items were rated on a 5-point scale (1 = strongly disagree to 5 = strongly agree) and adjusted to reflect state-based feelings of recovery by including statements such as “right now” and “currently”. Examples include “While watching Netflix right now, I relaxed”.
An Exploratory Factor Analysis with Oblimin rotation indicated that the
subscales loaded on to one factor explaining 64.05% of the variance, unlike the original scale (Sonnentag & Fritz, 2007). When forcing the items to load onto two factors, the scale continued to clearly load onto one, indicating they formed one construct of recovery. This was further supported by a reliable Cronbach’s Alpha of .912. Relaxation and psychological detachment were thus taken together to form
Self-Reported State Recovery (min. = 1.5, max. = 5.00, M = 3.87 SD = .819) by calculating the mean of all items. Participants with lower scores on this variable had lower feelings of state recovery and those with higher scores higher feelings.
State physiological recovery. Participant’s tonic heart rate (HR), heart rate
variability (HRV) and skin conductance levels (SCL) were measured throughout the entire experimental session as physiological indicators of exhaustion and recovery. This enabled assessments to be made about the tendencies of stress-recovery, as HRV, the fluctuation of length of the HR-intervals, best represents the heart’s ability to respond to a variety of
physiological and environmental stimuli (Kim, Cheon, Bai, Lee, & Koo, 2018). In studies of HRV, a low HRV has been associated with impaired automatic nervous system functions in otherwise healthy hearts which generally reduces the body’s ability to cope with internal and external stressors. This means that the body is already stressed and fatigued enough as is. Stressors can by physical but also mental, such as job/university-related stress, making complex decisions, and performing tests (Kim et al., 2018). A higher HRV would thus indicate a more relaxed state or a better recovery status (Walker, 2017). State Physiological Recovery (min. = – 94.32, max. = 69.89, M = –10.21 SD = 19.618) was created by subtracting the last HRV value of the time spent watching Netflix from the last HRV value of completing the Letter E Task (see section Procedure). A positive number would indicate a significant increase in HRV between the two measurement points.
Procedure
This study received full ethical approval before beginning with data collection. Once the participant arrived in the lab, they were greeted and asked to read the consent form. They were then connected to the ECG and the skin conductance electrodes. HRV, HR and SCL were recorded throughout the study using VSRRP98 software. While measuring their baseline values, participants were asked to begin the ten-minute Letter E Task (Arber et al., 2017; Baumeister, Bratslavsky, Muraven, & Tice, 1998; Myers et al., 2018). The Letter E Task is a classic task designed to fatigue participants by first establishing a habit and then forcing them to overrule this habit with each following task increasing in difficulty. It thus served as a suitable task to ensure that participants were sufficiently depleted before data collection began (see Appendix B for a copy of the task and instructions). A marker was set at the beginning and at the end of the ten minutes in order to measure HRV; a decrease would indicate sympathetic system elevation and cognitive strain. On average, participants experienced low levels of cognitive strain
(beginning of task HRV: M = 40.85, SD = 26.308 / end of task HRV: M = 39.74 SD = 25.191).
After ten minutes, participants were instructed to continue with the Qualtrics survey on the computer in front of them — starting with the Self-Control Capacity Scale (Ciarocco et al., 2007, see Appendix C which contains the survey and questions to the
participants). The next instructions on Qualtrics asked participants to switch to the open Netflix tab in the web browser, and to select one episode from any season of any of the five shows listed. Furthermore, they were also reminded to imagine they were at home, on the couch, while watching (Eden et al., 2018). The five shows chosen in the stimuli-selection survey were presented under the “My List” function that Netflix provides. In the second-screen group, participants were also asked to engage with their smartphones in whatever way seemed appropriate to them at that moment. Markers were again set at the beginning and at the end of the episode; an increase in HRV during this period would indicate a parasympathetic system dominance or relaxation. On average, participants did experience recovery (beginning of Netflix HRV: M = 44.79, SD = 26.348 / end of Netflix HRV: M = 53.50 SD = 28.109).
Once the episode was finished, the participant returned to the Qualtrics survey and answered the remainder of the questions. First, they were exposed to the
manipulation check, asking how long they had been on their phones. Then, they had to select the show they watched and state whether or not they considered it to be a guilty pleasure. The questionnaire continued by assessing their preference for hedonic versus eudemonic content as possible controls. This was followed by the items to measure feelings of perceived procrastination, guilt, perceived productivity and recovery. Finally, they answered demographic questions such as age and gender, and whether or not they usually second-screened while watching television. The entire experiment took
between forty-five minutes to an hour. Communications Science Bachelor Students were compensated with two research credits for their participation.
Results Procedural Check
Assessing depletion. Levels of state depletion within the sample were measured in two ways. First, a question was included at the end of the questionnaire to assess how busy the participants’ week was (1 = not at all busy to 5 = very busy, M = 4.16, SD = .978). This score indicates that the sample experienced a (subjectively) busy week. To further ensure depletion, participants completed a ten-minute Letter E Task designed to increase their fatigue. This was assessed via HRV and self-reports (state depletion, M = 3.5, SD = 1.076). A Paired-Samples t-Test comparing HRV at the beginning (M = 40.85, SD = 26.308) and the end of the task (M = 39.74 SD = 25.191) showed that low levels of fatigue were achieved, albeit insignificant t (115) = .651, p = .516. Taken together, there is sufficient evidence to suggest some fatigue was present — although not particularly extreme.
Manipulation check. To check phone use according to their group allocation an Independent Samples t-Test was performed, with the control and treatment groups (no phone vs. phone) as the independent variable and length of second-screen use as the dependent variable (1 = not at all to 5 = during the entire episode) was conducted. A significant effect of condition allocation was found on whether or not participants used their phones t (114) = –10.86, p < .000. Participants in the control group did not look at their phones (M = 1.25, SD = .580) while participants in the treatment group did for, on average, at least a short length of time (M = 2.85, SD = .971).
Serial Moderated Mediation
Two multiple regression analyses using Hayes’ PROCESS Model 83 (Hayes, 2017) were run with both self-reported and physiological recovery as the dependent variables. All
respective coefficients are displayed in Figure 2 and Figure 3. For results of the regression analyses, see Table 3 and Table 4.
Self-reported state recovery. The results for self-reported recovery lend some support to the theoretical expectations, but not always as anticipated (see Figure 2). There was no direct effect relationship between perceptions of procrastination and self-reported recovery, however, there was evidence for an indirect effect (index = .1359, standard error = .0678, 95% CI [.0078; .2771]). Thus, consistent with theoretical expectations, increased feelings of procrastination predicted increased feelings of guilt. Subsequently, increased feelings of guilt led to lower self-reported recovery. Productivity did not explain the route between guilt and recovery, as originally hypothesized. As anticipated, the pathway between procrastination and guilt was significantly moderated by second-screening. More specifically, this relationship was less robust for individuals that engaged in second-screening. In other words, individuals that engaged in second-screening experienced less guilt, and as a result, reported experiencing more recovery (indirect effect, treatment group with second-screen = –.1764, 95% CI [–.3173; –.0686]; control group = –.3123, 95% CI [–.4898; –.1462]).
State physiological recovery. The results for physiological recovery (via HRV) similarly lend some support to the theoretical expectations, but not always as anticipated (see Figure 3). There was no direct-effect relationship between perceptions of procrastination and physiological recovery, nor was there evidence for an indirect effect (index = –1.3037; standard error = 1.1166; 95% CI [–3.8163; .4743]). However, as found in the model for self-reported recovery, and consistent with theoretical expectations, increased feelings of
procrastination predicted increased feelings of guilt. And, as with self-reported recovery, this pathway was significantly moderated by second-screening such that second-screening
Discussion
The key goal of this study was to extend the current research on entertaining media use, stress, psychological well-being, and recovery by exploring supplementary methods for watching television to support physiological recovery. Studies thus far have found that entertaining media, specifically low-cognitive load want media, may indeed support recovery when feelings of guilt are not experienced (Eden et al., 2018; Reinecke et al., 2014). The reason for this, scholars believe, is that content perceived as so-called guilty-pleasure media evokes feelings of guilt, thus countering and decreasing the recovery potential. This study aimed to investigate the way this perception of guilt could be mitigated in order to support recovery — namely, via a contemporary behavior that is often observed alongside television use: second-screening (Segijn et al., 2017; Wang, Z. et al., 2015). It was observed that second-screening can be perceived as a productive activity, which might be reason enough to offset any negative appraisals associated with watching hedonic content and thus aid
recovery. The question is, however, does this experienced perception augment guilt and associated feelings of procrastination to sufficiently bolster recovery, or might it alternatively lead to techno-stress and overload — thereby hindering recovery? This question was tested in an experimental lab study using both self- and physiological reports of recovery.
The Route to Recovery
Ultimately, the results of this study suggest that the hypothesized route to recovery was only partially supported. Specifically in the predictions of self-reported recovery, the results demonstrate that increased feelings of procrastination while viewing guilty-pleasure television did lead to increased feelings of guilt, which led to less reported recovery. However, for individuals who engaged in second-screening while viewing, the feelings of guilt were noticeably less, and as a result they experienced comparably more self-reported recovery from their guilty-pleasure television time. While a similar pattern was also noted for
physiological recovery, the results were less robust: the data pointed more clearly to the role of second-screening in reducing guilt and less convincingly to influencing recovery.
While it is interesting to reflect on the extent to which self-reported and physiologically measured recovery converge and diverge (see, for example, Elfering, Grebner, & Semmer, 2003; Nabi, Prestin, & So, 2016), in the context of this study, the
findings for self-reported recovery are of particular interest. Recall that the model posited that perceptions of productivity could be a key route through which recovery would be supported — in particular, that second-screening would be perceived as a more productive experience and thereby influence recovery. The data, however, did not support this assumption. On the one hand, there may be some other process at work that this study did not capture. On the other hand, this finding could be an artifact of study design. Our measure of productivity suggested that nearly all participants felt somewhat productive during the study. This may be because all participants received university credits for participating in the study — thus the act of simply being in the lab could be viewed as “productive” (and indeed, their anecdotal comments bear support for this). Following on from this, replication in a non-university based setting would certainly be warranted.
Having said that, it is clear that second-screen use does indicate a reduction in guilt. This is an interesting finding that deserves further attention. In particular, this finding immediately lends itself to the question: what type of second-screening behavior is best at reducing guilt? In this study, and similar to previous research, the majority of the participants used their second-screen for social media (43.1%) or texting (41.4%) (Segijn, 2016; Wang, J. et al., 2015; Wang, Z. & Tchernev, 2012). However, the sample size and variance in second-screening behaviors were insufficient for potential ad hoc analyses with this data, and this strongly indicates that a key step in future research would be to identify what types of second-screening behaviors are best (and contrastingly, worse) when it comes to guilt (and
potential recovery) during entertainment media use.
Such follow-up research would ideally not simply manipulate whether or not a second-screen is present (as was done here), but rather also seek to look at the various expressions and dimensions of second-screening. For example, this could be based on the length of the second-screen use. Consider the manipulation check of this study finding most participants in the treatment group to be using their phones for at least a short while. Previous work aiming to optimize binge-watching behavior, for instance, was able to find an optimal number of episodes watched in one viewing experience (de Feijter et al., 2016). Similarly, future work might explore an optimal threshold for second-screen time as well, especially given how phone use while watching television increases with longer viewing times.
Further next steps could also be inspired by the definition of recovery itself (Sonnentag & Fritz, 2007). In this study, recall that recovery was defined as feelings of relaxation and psychological detachment. However, scholars interested in understanding smartphone use and interactive media have also found that control and mastery are
contributing aspects of recovery (Reinecke et al., 2011; Rieger et al., 2014; Rieger, Hefner, & Vorderer, 2017). Unlike lean-back media forms such as television, the smartphone allows users to interactively engage in ways entirely determined by themselves, which provides a sense of competence and control. Furthermore, various apps, games and even certain social interactions may allow for users to learn new skills or experience challenges, yielding the impression of mastery and further enhancing their mood while watching television (Collins & Cox, 2014; Reinecke, 2009). These facets were not captured in this study but would be an interesting addition to the field.
Specifically, by expanding the definition of recovery alongside an expanded, more nuanced conceptualization of second-screening, researchers might be able to better identify the conditions under which second-screening can support (some aspect of) recovery, and
which behaviors might actually hinder it (Park et al., 2019). Doing so would not only deepen our theoretical understanding of the relationship between entertainment media, second-screening, and recovery, but would simultaneously offer practical input both to media makers and audiences in general. Indeed, one can imagine media creators thinking about “healthy” second-screen engagement while users themselves may become more mindful of what they do and how long they do it for while watching their own guilty pleasures.
Limitations and Future Directions
While there are clear implications for future work both in terms of understanding the route to recovery and in paying more careful attention to the expressions of second-screening and their connection to guilt and potential recovery, the inherent limitations to this study also highlight opportunities for the future.
Perhaps most clearly, the decision to use an experimental design comes with clear limitations. While an experimental procedure increases internal validity and aims to
understand something that does indeed happen, the forced exposure and controlled elements are artificial and limit the extent to which the findings can be generalized to other second-screen using contexts (Segijn et al., 2018; Van Cauwenberge et al., 2014). In real-life situations, the combined activities might have distinct multitasking goals, motivations or strategies beyond simply engaging with one’s phone because they are instructed to do so. This is especially true for individuals who generally never second-screen. Furthermore, according to Rieger et al. (2014), testing mood repair under laboratory conditions always neglects Mood Management Theory’s underlying assumption of the selective exposure hypothesis. An effort was made to address this point by basing the stimuli choices on previous findings (Eden et al., 2018), asking about general preferences for hedonic or eudemonic content (Oliver & Raney, 2011) and allowing individuals to select their own guilty-pleasure program (Eden et al., 2018; Reinecke et al., 2014). Nonetheless this caveat
remains, and certainly demands an extension in a context with greater external validity. Beyond design, it is also important to recognize that the study assumed depletion — and indeed included a task to better ensure depletion along with recruitment during a busy week. Yet, both self-reported and physiological depletion were moderate at best. As Eden et al. (2018) note, these levels could indeed have impacted the findings, just as they may have affected other lab studies’ inability to replicate commonly cited effects of depletion (Arber et al., 2017; Dang, 2018; Hagger et al., 2016; Myers et al., 2018). As such, future work should thus expand on methods of ensuring varying levels of mental depletion not only by
evaluating in more depleting contexts (e.g., naturalistic studies), but also by considering other ways beyond the Letter E Task to deplete in lab studies.
Furthermore, it is important to mention the differences in gender distribution in both the stimuli-selection survey and the experiment. The checks for randomization showed no gender differences by condition, and gender was also not a between-subjects factor, however the found patterns could potentially still be different in both groups due to the
female-centricity. Even so, research has found that women are indeed stereotypically more attached to their phones, use it more often than men, and are more likely to engage in
media-multitasking behaviors (Lee et al., 2014; Segijn et al., 2017). Due to these tendencies, future research should aim to investigate gender differences in addition to exploring various
dimensions of second-screen use, by comparing variability and second-screen use by gender. Finally, measurement is also an important area for improvement. Recall that perceived productivity did not provide to be a significant predictor in final models. This concept was measured by a researcher-developed measurement that achieved only a low level of internal consistency (.597). While on the face of it there were relevant indicators that would seem to suggest productivity, the scale ultimately did not work as well as intended. Future work should aim to create a suitable scale that adequately measures this concept.
Concluding Thoughts
In an age where people are struggling to deal with the stress of a high-paced lifestyle, we should consider how best to utilize the technological tools that can at times also
significantly contribute to attenuating the stress in our lives to bring about relief and well-being. These tools are already integrated into our lives, so how can we use them to help “stress-proof” our lives? By combining different theoretical paradigms and bridging the gap between the two, this study was able to show that the use of a second-screen while viewing guilty-pleasure content can reduce guilt. The experienced result is greater perceived recovery from the perspective of the users. Although the physiological data does not entirely support this assertion and the hypothesized pathway was not fully proven, this study has still opened up avenues for further research into how entertainment media in combination with second-screening is a legitimate path towards recovery. Furthermore, in the interest of understanding and finding the ideal “sweet spot” between entertainment media and second-screening, further research could constructively be done by the entertainment industry in ways to facilitate maximum relaxation using this medium. While this research has opened up many areas showing that there is still much to be learned, it seems that one step we can all take is to allow ourselves some guilt-free time with our guilty-pleasure content — perhaps with a side of second-screening (and definitely some popcorn)!
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Tables Table 1
Descriptive Statistics For Age, Gender, General Perceived Procrastination, General Preference for Eudemonic Content, General Preference for Hedonic Content, State Depletion, State Perceived Procrastination, State Guilt, State Perceived Productivity,
Self-Reported State Recovery and State Physiological Recovery
Measures
Control
M (SD)
Treatment
M (SD)
Statistic min. max. Kurt. Skew.
Age 21.09 (2.785) 21.40 (2.800) t (113) = .001 17 33 3.21 1.58 Gendera χ2 (1) = .175 1.00 2.00 .357 –1.53 General Pr. 3.52 (1.033) 3.572 (1.095) t (114) = –.245 1.00 5.00 –.781 –.467 Eudemonic 3.56 (.637) 3.54 (.713) t (114) = .201 1.75 5.00 –.132 –.106 Hedonic 4.13 (.718) 4.04 (.774) t (114) = .620 2.00 5.00 –.462 –.745 State Depletion 3.55 (1.119) 3.56 (1.041) α = .943 1.46 6.21 –.349 .193 Perceived Pr. 2.51 (.838) 2.71 (.883) α = .633 1.00 5.00 –.045 .227 State Guilt 2.22 (.770) 2.21 (.643) α = .849 1.10 4.80 .476 .658 Productivity 3.17 (1.287) 3.06 (1.300) α = .597 1.00 6.00 –.974 .166 Self. Recovery 4.01 (.790) 3.74 (.830) α = .912 1.93 4.50 .292 –.725 Phys. Recovery –9.56 (20.776) –10.81 (18.630) –94.32 69.89 5.20 –.514
Notes. N = 116. General Pr. = General Perceived Procrastination. Eudemonic = General Preference for
Eudemonic Content. Hedonic = General Preference for Hedonic Content. Perceived Pr. = State Perceived Procrastination. Productivity = State Perceived Productivity. Self. Recovery = Self-Reported State Recovery. Phys. Recovery = State Physiological Recovery. Kurt. = Kurtosis. Skew. = Skewness.
a
Table 2
Correlations among and Descriptive Statistics for Second-Screen Use, State Depletion, State Perceived Procrastination, State Guilt, State Perceived Productivity, Self-Reported State Recovery and State Physiological Recovery
M (SD) SS Depl. Per. Pr. Guilt Prod. Re. Ph.
SS 2.08 (1.136) - –.020 .090 .020 –.045 –.292* .168 Depl. 3.50 (1.076) –.020 - .082 .265** –.031 –.060 .057 Per. Pr. 2.61 (.864) .090 .082 - .600** –.287** –.249** –.085 Guilt 2.22 (.704) .020 .265** .600** - –.291** –.406** .038 Prod. 3.11 (1.289) –.045 –.031 –.287** –.291** - .112 .106 Re. 3.87 (.819) –.292** –.060 –.249** –.406** .112 - –.019 Ph. –10.21 (19.618) .168 .057 –.085 .038 .106 –.019 -
Notes. N = 116. SS = Second-Screen Use in the treatment group. Depl. = State Depletion. Per. Pr. = State
Perceived Procrastination. Prod. = State Perceived Productivity. Re. = Self-Reported State Recovery. Ph. = State Physiological Recovery. All variables represent state-based measures.
Table 3
Results of the Multiple Regression Analysis predicting Self-Reported State Recovery using PROCESS model 83 Outcome b t p F df1 df2 R2 Guilt Overall Model .000* 23.92 3 112 .391 Per. Pr. .665 6.87 .000* SS .780 2.32 .022* Interaction –.290 –2.34 .021* Productivity Overall Model .001* 6.59 2 113 .105 Per. Pr. –.262 –1.58 .118 Guilt –.341 –1.67 .098 Recovery Overall Model .000* 7.39 3 112 .165 Per. Pr. –.008 –.081 .935 Guilt –.470 –3.69 .000* Productivity .005 –.086 .932
Notes. b = Unstandardized Regression Coefficient. Variables in bold are regression model
outcomes. State Guilt and State Perceived Productivity are mediators, Self-Reported State Recovery is the dependent variable. SS = Second-Screen use in the treatment group. Per. Pr. = State Perceived Procrastination. All variables represent state-based measures. * p < .05.
Table 4
Results of the Multiple Regression Analysis predicting State Physiological Recovery using
PROCESS model 83 Outcome b t p F df1 df2 R2 Guilt Overall Model .000* 17.78 4 111 .391 Per. Pr. .666 6.81 .000* SS .782 2.31 .022* Interaction –.290 –2.33 .022* Baseline HRV .0002 .070 .945 Productivity Overall Model 4.37 3 112 .105 Per. Pr. –.264 –1.58 .118 Guilt –.341 –1.66 .099 Baseline HRV –.0009 –.172 .864 Recovery Overall Model 1.62 4 111 .055 Per. Pr. –3.08 –1.16 .248 Guilt 4.50 1.38 .170 Productivity 1.71 1.15 .251 Baseline HRV .138 1.71 .091
Notes. b = Unstandardized Regression Coefficient. Variables in bold are regression model
outcomes. State Guilt and State Perceived Productivity are mediators, State Physiological Recovery is the dependent variable. SS = Second-Screen use in the treatment group. Per. Pr. = State Perceived Procrastination. All variables represent state-based measures. * p < .05.
Figures
Figure 1. Conceptual Model of the Serial Moderated Mediation. All variables represent state-based measures. Recovery represents both self-reported as well as physiological recovery. Second-Screen only used in treatment group.
Second-Screen Perceived Procrastination Guilt Perceived Productivity Recovery
Figure 2. Serial Moderated Mediation with Self-Reported State Recovery as the outcome variable based on Hayes’ Model 83. Scores in the figure represent unstandardized path coefficients. All variables represent state-based measures. Second-Screen only used in treatment group. * p < .05.
Perceived
Procrastination Self-Reported Recovery
Perceived Productivity Guilt Second-Screen –.341 .665* –.005 –.008 .780* –.262 –.470*
