In Chapter 1, we presented information about how the Internet is associated with our physical and psychological selves, including changes to our sleep cycle, sleep habits, our sense of loneliness and social connectivity, etc. For youth in particular, more time online means less time in bed getting adequate sleep (Ferreira et al., 2017; Nose et al., 2017). We further reviewed articles that associated lack of sleep to a host of psychological problems, including depression, anxiety, substance abuse, and suicidal ideation (Lemola, Perkinson-Gloor, Brand, Dewald-Kaufmann, & Grob, 2015).
But these studies are correlational only. What is less clear is whether the choice to use Internet technologies and other forms of today’s media before or instead of going to sleep is a function of something else that was not measured (also known as the third variable problem) or what the direction of the relationship may be. It is this lack of clarity that may also drive the inconsistencies demonstrated in the research about the impact of technology on our lives. For example, Amichai-Hamburger and Hayat (2011) found using the Internet was associated with a greater sense of connection to others, while other authors have uncovered associations between greater Internet use and loneliness (Stepanikova, Nie, & He, 2010). In determining the extent to which Internet usage is contributing to challenges in couple and family life, part of the conversation has to be whether Internet usage is a symptom of a larger problem or whether Internet usage is in and of itself the primary issue. Healthcare workers including therapists, physicians, nurse practitioners, counselors, social workers, and psychologists are left with the challenges of attempting to determine which came first: the proverbial chicken or the egg.
Perceived etiology of a presenting problem is critical in determining the scope of the problem and course of treatment. Case conceptualizations direct the treatment processes (Ridley, Jeffrey, & Roberson, 2017). The model by which we incorporate information into a case conceptualization is highly complex and is reliant on many things, including (but not limited to): the information obtained in the history/assessment phases of treatment, cultural/contextual considerations, and the theory from which the clinician selects to operate (Ridley et al., 2017). Of course, the clinician frequently has specific ways in which they gather information, which might also lead to a slant in how the information is conceptualized and packaged for treatment. Other factors that affect a clinician’s judgment include how easily they can recall information, how similar the information is to other cases they have seen, their personal beliefs about a given topic (self-of-the-therapist factors), and being overly confident in what they see as the issue. Misspecifications in these levels may lead to misunderstanding about etiology, thus impairing treatment (Falvey, 2001).
Another element that also may influence treatment is the determination as to whether the presenting problem has a biological basis. Evidence of one’s issues being rooted in biology has proven to reduce prison sentences, thus leading to the belief that the evidence of behavior that is tied to one’s biology reduces blame and furthers empathy (Aspinwall, Brown, & Tabery, 2012). When applied to psychological or clinical settings, however, the reverse seems to be true: when clinicians focus primarily on the biology contributing to the problem, they are less likely to feel empathy for the clinical participant (Haslam & Kvaale, 2015). Clinicians retain some level of empathy if they keep in mind the confluence of factors contributing to one’s symptoms, acknowledging the biological contribution and recognizing how that contribution interacts with the family/relational system dynamics, environment, etc. (Lebowitz & Ahn, 2014).
One’s case conceptualization may also be affected by extra therapeutic factors. There are inconsistent findings around whether issues such as profession, demographic variables of the therapist, work setting, or education have an impact (Falvey, 2001). There is, however, good evidence to suggest that the intersectionality of factors such as gender, sexual orientation, religion, and/or racial/ethnic identity of the clinical participants and clinicians affects treatment retention, therapeutic alliance, and clinical outcomes (Blumer, Ansara, & Watson, 2013; Green, Murphy, & Blumer, 2010; Green, Murphy, Blumer, & Palmanteer, 2009; Staczan et al., 2017; Wintersteen, Mensinger, & Diamond, 2005).
Thus far, one study has examined the process of treatment decisions in cases of couple therapy with the Internet as an element in the presenting problem. Hertlein and Piercy (2008) examined the effect of client gender, therapist gender, and the association of the Internet in the assessment and treatment of infidelity cases. In this study, 504 licensed marriage and family therapists (LMFTs) were provided a script of a couple seeking treatment. The scripts were delivered via stratified random sample. One-quarter of the clinicians (all female) received a script of a female cheating on her male partner; a second quarter (also all female) received a script of a male cheating on his female partner. Another quarter (all male) received the script of a female cheating on her male partner. The final quarter (all male) received a script of a male cheating on his female partner. In addition, there were three different clinical scenarios, also distributed evenly to the respondents. One scenario described a case where one partner met and corresponded in a flirtatious manner with another individual and the email exchange was discovered in the trash by their partner. The second scenario was where an individual met someone online and ended up meeting this individual offline. The third scenario was where one person was watching pornography online. When the client who was involved with someone else or using pornography online was a man, the therapist tended to conceptualize the case as having a higher degree of sex “addiction” than when the identified client was a woman, and they were consequently more likely to assign individual treatment. Female therapists were more likely to connect the treatment to larger relational problems than male therapists (Hertlein & Piercy, 2008).
What is most surprising about this finding is despite the couple therapists clearly indicating they believed the problem was a couple problem (stemming from either a deficit in the relationship or other impaired processes), when the problem between the couple had some connection to a computer or the Internet, the participant therapists were more inclined to approve environmental changes (moving the computer to another room) than to employ a change that better reflected their original theory about why the couple was having the problems they were experiencing in the first place (Hertlein & Piercy, 2008).
So why the incongruence? Something almost sinister happens when a computer gets involved. When the Internet is introduced into the clinical setting, it leads clinicians to inappropriate conclusions about how to navigate treatment, potentially ascribing the Internet as the cause of the problem. Part of it may be due to the familiarity that the clinician themselves has with online technology or even their use of such technologies, their own degree of technophobia, and, as Falvey (2001) stated, their personal beliefs—in this case, about technology. For example, couple and family therapists (CFTs) are generally not comfortable with using the Internet as a service-delivery platform (Hertlein, Blumer, & Smith, 2014). In a survey study, CFTs reported they were uncomfortable with the Internet as the sole mechanism for delivery of therapeutic services (Hertlein et al., 2014). While this discomfort was experienced for treating individuals online (42% noted they were very uncomfortable with this modality for individuals in treatment), that proportion increased to 50% of CFTs expressing they were very uncomfortable with couples treatment delivered online and increased again for families (54.4% reported being very uncomfortable with this service delivery) (Hertlein et al., 2014).
CFTs, in particular, cling to the misguided notion that the therapeutic relationship, a key part of treatment, is significantly compromised when therapy is conducted in an online format, despite the empirical evidence to the contrary (Germain, Marchand, Bouchard, Guay, & Drouin, 2010; Glueck, 2013a, b; Hertlein & Earl, in press; Morgan, Patrick, & Magaletta, 2008; Twist & Hertlein, 2016). Why? It makes us, as relational therapists, feel better to think that in-person interactions cannot replicate ones facilitated by a heartless, soulless machine. As many as 61% of therapists surveyed reported that their perception was that the therapeutic alliance was weaker in therapeutic relationships developed online as compared to those developed offline (Hennigan & Goss, 2016). This perception persists despite evidence that most of the time, joining is not impaired by online therapy (Glueck, 2013b), regardless of presenting problem (Jenkins-Guarnieri, Pruitt, Luxton, & Johnson, 2015). In fact, in at least one study the therapeutic relationship was stronger online than offline (Knaevelsrud & Maercker, 2006).
When joining is not necessarily impaired, there may be a level of comfort around face-to-face therapy for clinical participants. In a study with young adults, a significant proportion reported a preference toward face-to-face therapy (Rogers, Griffin, Wykle, & Fitzpatrick, 2009). These results, however, are tempered by the fact that the authors did not use people in treatment. Moreover, individuals were screened out if they were not between the ages of 21 and 30 only. Rather, this sample of convenience included people recruited from Facebook not specifically in therapy and focused on their perception of being in different modalities of therapy. Further, those who indicated they preferred face-to-face might have responded to subsequent questions in a way that would endorse their previously espoused view.
There is some evidence that suggests that romantic relationships develop online more quickly than offline relationships and are characterized by more commitment and greater levels of intimacy because of the increased amount of self-disclosure that occurs in online relationships (Farci, Rossi, Boccia Artieri, & Giglietto, 2017; Hertlein & Blumer, 2013). Self-disclosure on the client’s end is a key part of the therapeutic process and has been demonstrated to lead to powerful changes in personal relationships and life changes (Han & O’Brian, 2014).
The research indicating that effectiveness toward intention to change behavior in conversation is more likely to convince therapists that online therapy may not be ideal (Hammick & Lee, 2014). Finally, younger therapists saw the technology as less of a problem than therapists of a relatively older age in the Hertlein and Piercy (2008) study. While again not causal, it may be that the generational effect and comfort with using technology contribute to one considering the role of technology in treatment as less pathological when compared to clinicians of an older age, as they are generally less comfortable (Schreurs, Quan-Haase, & Martin, 2017). This is in part because comfort is based to some degree on increased digital literacy, which happens through more experience (Murray & Pérez, 2014). In the case of older individuals, if they are reluctant to adopt the technology, they will not have the experience of working toward greater degrees of literacy (Morris, 2007). It may be that therapists are operating from a different community, one described by Christopher Lasch (1979):
Those who dig bomb shelters hope to survive by surrounding themselves with the latest products of modern technology. Communards in the country adhere to the opposite plan: to free themselves on the dependence of technology and thus to outlive its destruction and collapse.
(p. 4)
For all of the aforementioned reasons, it is important to conduct a thorough assessment as to what might be facing a couple or family system when the Internet is involved. It is not as easy as assuming the Internet is the problem, but at the same time, the Internet could be contributing in substantial ways to the difficulties. There are many survey tools designed to assess both the perception of Internet use as well as the extent it may remain problematic in one’s life.
Problematic Internet use is not a well-defined construct. For example, 5% of women in a Swedish sample self-reported their Internet use as problematic, whereas 13% of men in the sample reported the same. This proportion is consistent with another study by the American Psychiatric Association (APA, 2013), which put the rate of problematic Internet use at between 6% and 14%. Because the estimate of those reporting some level of out-of-control behaviors related to the Internet ranges from 0.3% to near 60% (Parsons, Severino, Grov, Bimbi, & Morgenstern, 2007), it is difficult to determine what proportion of them may actually be experiencing some kind of behavioral “addiction”; potentially 2% of the women and 5% of the men characterized their problematic use as serious (Hertlein & Cravens, 2014). The challenge in identifying problematic behavior is further confused by multiple terms—such as Internet dependence, addiction, and pathological Internet use (Cravens, Hertlein, & Blumer, 2013; Hertlein & Cravens, 2014).
Rather than working with strict definitions only, scholars in the field may focus instead on a cluster of behaviors. Bulut Serin (2011) found that problematic Internet use was tied loosely to gender (for instance, men seemed to have more of a problem than women). Those with higher levels of neuroticism were more likely to use the Internet for entertaining (as opposed to extroverts, who use it for connections with others); in addition, those with higher levels of neuroticism, psychoticism, and who practice more deceptive behaviors have higher levels of out-of-control technology-related behaviors. They may also be the same people who are dissatisfied with life—and in fact, score lower on the Life Satisfaction Scale (Diener, Emmons, Larsen, & Griffin, 1985).
Further, Jones and Hertlein (2012) outlined key differences between various types of problematic Internet behavior across four dimensions. One dimension is the parties that are involved. In Internet infidelity, the involved party is typically an identifiable third person. In so-called “Internet sex addiction,” there may be interaction with others online, but not necessarily an identifiable third person. In “Internet addiction,” there are no involved parties per se—instead, the issue is website surfing and usage. A second dimension is the view of the problem—whether the issue is time spent online (as is the case in “Internet addiction”) or how that time is spent online (content is related to sexual behavior or content is about benign topics). A third dimension is whether physical symptoms are present, as having an “addiction” would imply co-occurrence of withdrawal and tolerance. A final dimension is exploring the extent to which factors such as speed, potency of information, and the importance of being connected to another individual are playing out.
The rise of the Internet and its insertion into our everyday life have created a near-dependence on the Internet to function in society. Businesses, social groups, and work settings function through a wide variety of software programs and systems with a heavy reliance on passwords, logins, and interactions through email addresses and text notifications. As a result, the discussion around Internet use and the problems associated with it are a mainstream topic in today’s society.
The term “Internet addiction” was actually introduced in 1996 (Goldberg, 1996) and was compared to having the same characteristics as other addictions at the time, including tolerance when playing, symptoms of withdrawal when not using the Internet, and even impairment in one’s academic, workplace, and relationship endeavors (Lortie & Guitton, 2013). The data on problematic usage, however, confirms that the individual’s physiology and psychological state after using the Internet match those using opiates and alcohol (Reed et al., 2017). But, as scholars have used the term “Internet addiction” the term has become more confusing. Jones and Hertlein (2012), for example, argue that the criteria for Young’s (2001) version of Internet addiction contains elements that do not involve the Internet. Young’s criteria led to the development of the Internet Addiction Test, but this inventory focuses on tolerance, withdrawal, ignoring other activities, and using the Internet nearly constantly, all of which are not supported empirically (Cho et al., 2014).
Since the creation of the term, many other inventories have been developed. These include but are not limited to:
(Ekizoglu & Ozcinar, 2011)
(Monacis, Sinatra, Griffiths, & Palo, 2018).
(Meerkerk, Van Den Eijnden, Vermulst, & Garretsen, 2009)
(Khazaalm et al., 2011)
(Yong, Inoue, & Kawakami, 2017)
(Carson, Gansner, & Khang, 2018)
(Hou et al., 2014)
(Caplan, 2002)
(Cho et al., 2014)
(Nichols & Nicki, 2004)
(Beranuy Fargues, Chamarro Lusar, Graner Jordania, & Carbonell S*ánchez, 2009)
(Kim, Lee, Lee, Karin Nam, & Chung, 2014)
(Zhang & Xin, 2013)
(Boubeta, 2015)
(Demetrovics, Szeredi, & Rózsa, 2008)
(Demetrovics et al., 2016)
(Davis, Flett, & Besser, 2002)
(Ceyhan, Ceyhan, & Gurcan, 2007)
(Mak et al., 2014)
(Lin et al., 2014)
Lortie and Guitton (2013) evaluated the criteria included in 14 assessments for problematic Internet behavior. They found seven dimensions present in their review: compulsive use, negative outcomes, withdrawal symptoms, salience, mood regulation, escapism, and social comfort. The “core” of the questionnaires, however, was composed of three of these: compulsive use, negative outcomes, and salience. In addition, the authors were able to find evidence for six of the seven dimensions of having a diagnosis of behavioral addiction, as classified by the APA (2000) in the Diagnostic Statistical Manual of Mental Disorders-IV-Text Revision (DSM-IV-TR), and five of the seven classifying criteria in the International Statistical Classification of Diseases and Related Health Problems-10 (ICD-10) (Lortie & Guitton, 2013). Some scholars have pared down problematic Internet usage into two factors—dependency and distraction (Jia & Jia, 2009). Still others have uncovered more nuanced factors in their operational definitions of problematic Internet use, such as issues in time commitments, emotional/psychological conflict, and difficulty managing one’s mood (Widyanto, Griffiths, & Brunsden, 2011).
In a revision of the GPIU Scale (GPIUS2), three different Internet users were identified in relation to how risky their behavior was in regard to leaving them with a problem with their computer usage (Pontes, Caplan, & Griffiths, 2016). The authors summarized the three groups as such:
Participants in the “low risk” class accounted for almost half of the sample (46.7%) and were characterized as experiencing very few PIU cognitions, behaviors, and/or negative outcomes. Participants with “medium risk” of PIU represented 40.7% of the total sample and tended to use the Internet more often as a way of enhancing their mood, as demonstrated by the markedly high scores on the mood regulation subscale of the GPIUS2. The third and final class comprised 12.6% of the total sample and featured participants showing “high risk” of PIU cognitions, behaviors, and negative outcomes due to Internet use.
(p. 830)
But, of course, this distinction makes no difference to people living with these types of users. In 2018, the World Health Organization tried for a classification of Internet gaming addiction (Revell, 2018).
As with many of the behavioral addictions, the determination as to whether a particular set of Internet behaviors is diagnosable is challenging. There are three commonly used diagnostic labels that are applied to out-of-control Internet usage: impulse control disorder, obsessive-compulsive disorder, and addictive disorders, with an “other” or “unspecified” condition (previously not-otherwise-specified in the DSM-IV-TR; APA, 2000; Juhnke & Hagedorn, 2006). Despite their similarities, behavioral addictions do not share all of the same properties as addictions related to substance abuse and differ from impulse control disorders. For example, both behavioral addictions and substance abuse may begin with an
urge or craving state prior to initiating the behavior, as do individuals with substance use disorders prior to substance use. Additionally, these behaviors often decrease anxiety and result in a positive mood state or “high,” similar to substance intoxication. Emotional dysregulation may contribute to cravings in both behavioral and substance use disorders.
(Grant, Potenza, Weinstein, & Gorelick, 2010, p. 234)
Such disorders differ from obsessive-compulsive disorder in that obsessive-compulsive behaviors generally start out as more distressing and/or unacceptable, while addictive behaviors typically become more unpleasant over time as their participation is frequently motivated by avoiding withdrawal or positive reinforcement for continuing the behavior (Grant et al., 2010. Still, the APA (2013) has made little mention of behavioral addictions altogether in the DSM-5. Gaming disorder was included, and Internet gaming disorder was considered, but ultimately not included (Hertlein & Cravens, 2014).
As with the “Internet addiction” inventories, there are also a host of inventories that attempt to measure so-called “sexual addiction” on the Internet, and most notably, the concept of cybersex. One of the areas that is assessed in cybersex is the intensity or severity (Hertlein & Cravens, 2014). One such inventory is the Preoccupied, Ashamed, Treatment, Hurt others, Out of control, Sad (PATHOS) scale (Carnes et al., 2012). Based on inventories that assess severity of alcoholism, PATHOS has six items that focus on how much time is spent pursuing sex online as compared to other activities; sadness, shame, or other negative feelings associated with this activity; one’s awareness of the impact on others of their sexual online behavior; and whether one can govern their online sexual behavior. Other well-known inventories include the Internet Sex Screening Test (ISST) and the Online Sexual Addiction Questionnaire (OSA-Q) (Delmonico, 1997; Putnam, 2013). Both of these latter inventories attempt to assess the manner in which the online sexual activities disrupt day-to-day life. The OSA-Q, in particular, attempts to assess the characteristics associated with addiction in general, including withdrawal, tolerance, and cravings/compulsions to engage in sexual behaviors online.
Another common way for online sexual behavior to be assessed is through a clinical interview (Hertlein & Cravens, 2014). Such an interview not only enables the clinician to ask the client about their online sexual behavior, but also provides an opportunity to ask their partners and family/relational system members about their online sexual behavior (e.g., symptoms and issues they might observe. This may include how the identified patient engages in online interactions with others, the presence of sexual fantasies and how those play out in the couple’s life, any secrecy in one’s activities online, and the presence of any guilt and shame as a consequence of one’s sexual activities online). For American Association of Sexuality Educators, Counselors, and Therapists (AASECT)–certified providers, however, a cautionary note is extended regarding the assessments and line of clinical interviewing one does with regard to exploring online sexual behaviors as it is considered unethical to use assessment tools and/or to frame one’s clinical interviewing in a way that denotes a clinical participant is experiencing a “sex addiction” or an “online sex addiction” (AASECT, 2016). Instead, what is recommended is to work with the clinical participant(s) using a framework like the one proposed by clinicians Douglas Braun-Harvey and Michael A. Vigorito in 2016, which views online sexual behaviors as potentially being out-of-control behaviors in the context of looking at how a person and their partners are experiencing their sexual health and well-being.
It is important to work toward understanding the role of the Internet, coping skills, personality and temperament, health, and moderating factors in the contribution to out-of-control technology-related behaviors. At the same time, to merely give an assessment to determine whether one person is out of control with regard to their Internet usage may not be sufficient for family/relational system members and partners who are in pain as a consequence of one’s Internet usage. For example, if a therapist gives an individual inventory and comes to the conclusion that someone is “addicted,” then what? Do the family and partner somehow become empathetic to the partner’s plight? Does it matter what it is called if the behavior is the same? Further, the assessment of Internet “addiction” is not very easy (Pawlikowski, Nader, Burger, Stieger, & Brand, 2014).
Such work has inspired the development of relationally based Internet assessment tools. One example was developed by Campbell and Murray (2015). Known as the Technology and Intimate Relationship Assessment, it poses a series of questions about the respondent’s use of technology, their partner’s use of technology, whether this technology usage is a barrier to communication and intimacy, or whether the use of technology serves as a useful adjunct to the development and maintenance of intimacy in a romantic relationship. Scholars such as Carlisle, Carlisle, Polychronopoulos, Goodman-Scott, and Kirk-Jenkins (2016) consider Internet addiction a process addiction, and propose to measure it within that frame. Loosely defined, a process addiction is where “an individual compulsively engages in a particular activity despite suffering negative consequences after repeated attempts to stop…. Examples include addictions to activities such as gambling, shopping, nonparaphilic hypersexual activities, video games and Internet use” (Northrup, Lapierre, Kirk, & Rae, 2015, p. 342). One of the interesting things, however, from Northrup et al.’s (2015) research was that: (1) their Internet Addiction Test was not related to the gambling addiction test (which the authors surmise may have had something to do with the gambling test), and (2) cellphone usage was not correlated with any of the other technology constructs, which suggested perhaps misspecification in that construct as well.
Hertlein and Blumer (2013) provided an assessment of ecological factors related to the Internet, and we have revised it for this text. Rather than a tool to assess out-of-control technology-related behaviors this tool can evaluate where an individual’s vulnerabilities and strengths lie in the context of Internet usage. Such knowledge will help individuals and families/relational systems identify areas for improvement and change. It is recommended that this revised assessment be used in combination with other assessment tools designed to assess relationship process and structure. This type of assessment would help to establish a closer relationship between teens and parents, while also helping teens cope with their internalizing behaviors that may be contributing to out-of-control technology-related behaviors (Van den Eijnden, Meerkerk, Vermulst, Spijkerman, & Engels, 2008). This type of assessment would also be helpful in working with lesbian, gay, and bisexual (LGB)–identifying individuals and partnerships in making decisions around management of ecological elements like anonymity and accommodation, which manifest often for LGB folks in the form of electronic visibility management (Twist, Belous, Maier, & Bergdall, 2017; Twist, Bergdall, Belous, & Maier, 2017). This tool would also be beneficial to clinicians and their clinical participants in assessing common technology-based issues between couples like the interplay between online infidelity and electronic partner surveillance (Hertlein, Dulley, Cloud, Leon, & Chang, 2017). Honestly, it is our belief that this tool is the most comprehensive and fitting assessment for gaining information and effectively helping relational systems with virtually any technology-based concern with which they present.
The assessment we provide evaluates one’s activities each month across the dimensions described in an earlier section of this book—the ecological elements, as well as the process and the structure of relationships. In addition, we have provided some questions for reflection regarding how one’s family of origin and choice, and their technology usage, has impacted their own views and behaviors around sexuality. This tool can be used for developing understanding for couples and families/relational systems around why technology usage may persist in patterned ways across time and space in relational systems/families. See the Appendix for a full list of the assessment items.
In this chapter, we reviewed a variety of formal assessments and measurements of technology-related issues in the lives of individuals, couples, and families/relational systems. As researchers continue to explore these concepts, we expect this list of potential measures will grow exponentially. It is our hope that assessment will continue to focus not only on the specific Internet-usage behaviors, but also on the provided assessments related to the impact on one’s familial and romantic relationships.
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