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Open AccessStudy designs and methods

Implicit Cognitions, Use Expectancies and Gratification in Social-Networks-Use Disorder and Tobacco Use Disorder

A Study Protocol

Published Online:October 12, 2022https://doi.org/10.1024/0939-5911/a000782

Abstract

Abstract:Aims: The problematic use of social networks is discussed as a further specific type of Internet-use disorders. Our project aims to clarify whether social-networks-use disorder (SNUD) is marked by characteristics of addictive behaviors by tracking behavior and investigating the relevance of 1) implicit cognitions, 2) the experiences of gratification and compensation and 3) use expectancies in SNUD compared to tobacco-use disorder. Methodology: Four groups will be examined: individuals with 1) SNUD without tobacco use, 2) risky use patterns with regard to social networks without tobacco use, 3) tobacco use disorder and 4) healthy controls. All participants first complete a laboratory examination including the Implicit Association Test (IAT) and the Approach-Avoidance task (AAT). We will use smartphone-based data tracking for 14 days following laboratory testing to record smoking and social-networks-use patterns. During this period, we further measure use expectancies and the experience of gratification and compensation by means of a smartphone-based experience sampling method (ESM). Conclusions: This is the first study to examine relevant characteristics of addictive behaviors in individuals with SNUD compared to individuals with tobacco use, using a combination of experimental psychological methods and smartphone-based measurements. We expect that this investigative approach will contribute to a deeper understanding of the processes involved in SNUD.

Implizite Kognitionen, Nutzungserwartungen und Gratifikation bei Soziale-Netzwerke-Nutzungsstörung und Tabaknutzungsstörung: Ein Studienprotokoll

Zusammenfassung:Zielsetzung: Soziale Netzwerke wie Facebook und Instagram werden zahlreich genutzt. Mit der Aufnahme der Computerspielstörung in die ICD-11 als Störung durch süchtiges Verhalten wird die problematische Nutzung sozialer Netzwerke als eine weitere spezifische Form der Internetnutzungsstörung diskutiert. In unserem Projekt soll u. a. mittels einer Smartphone-gestützten Datenerhebung geklärt werden, ob die Soziale-Netzwerke-Nutzungsstörung (SNUD) durch Merkmale eines Suchtverhaltens gekennzeichnet ist, indem die Relevanz von 1) impliziten Kognitionen bei der Konfrontation mit suchtbezogenen Reizen, 2) das Erleben von Gratifikation und Kompensation nach dem Konsum und 3) die Nutzungserwartung bei der SNUD im Vergleich zur Tabaknutzungsstörung untersucht wird. Der Vergleich von Personen mit einem problematischen Gebrauch von sozialen Netzwerken und Personen mit einer Tabaknutzungsstörung basiert auf der Annahme, dass beide Störungsbilder ähnliche Charakteristika aufweisen (z. B. Suchtpotenzial, Craving im Verlauf des Tages etc.). Methoden: Auf der Grundlage eines Mixed-Methods Querschnittsdesigns werden insgesamt 204 Probanden rekrutiert, die in vier Untergruppen eingeteilt werden: Personen mit 1) SNUD ohne Tabaknutzung, 2) riskanten Nutzungsmustern sozialer Netzwerke ohne Tabaknutzung, 3) Tabaknutzungsstörung ohne SNUD oder riskante Nutzung sozialer Netzwerke und 4) Personen ohne SNUD oder riskanter Nutzung sozialer Netzwerke und ohne Tabaknutzung. Alle Teilnehmer und Teilnehmerinnen absolvieren zunächst eine umfassende Laboruntersuchung einschließlich des Impliziten Assoziationstests (IAT) und der Annäherungs-Vermeidungs-Aufgabe (Approach-Avoidance-Task; AAT). Um Fehler und Verzerrungen von Selbstberichtsdaten zu vermeiden, liegt ein methodischer Schwerpunkt unserer Arbeit in der möglichst objektiven Erfassung des Rauchens und des Nutzungsverhaltens in sozialen Netzwerken durch ein Smartphone-basiertes Datentracking, das im Anschluss an die Labortests für eine Dauer von 14 Tagen durchgeführt wird. Während dieses Zeitraums messen wir zusätzlich die Nutzungserwartungen und das Erleben von Gratifikation und Kompensation als Folge des Konsums mittels einer Smartphone-basierten Lösung im Stile der Experience Sampling Methode (ESM), um Daten mit hoher ökologischer Validität zu generieren. Schlussfolgerungen: Dies ist eine der ersten Studien, die relevante Merkmale des Suchtverhaltens bei Personen mit SNUD untersucht und diese mit Personen mit Tabaknutzungsstörung vergleicht, wobei eine Kombination aus experimentellen psychologischen Methoden und Smartphone-basierten Messungen verwendet wird. Wir erwarten, dass dieser Untersuchungsansatz zu einem tieferen Verständnis der an der SNUD beteiligten Prozesse führen wird.

Introduction

Social-networks-use disorder

Social networking applications, such as Facebook, WhatsApp, Instagram, or Tiktok, are widespread and open up a wide range of social exchange opportunities for their users (Wegmann & Brand, 2016). Although the different applications contain different features and main focuses (Montag, Lachmann et al., 2019) drawing interest by different user groups (Marengo et al., 2020), all of these applications have in common that their main idea is to be connected to other users, to exchange information, and to be entertaining (Wegmann & Brand, 2019, 2020). Given the different platform design, it is also likely that social networking applications differ regarding their “addictive potential” (Rozgonjuk et al., 2021). While most people are able to use these applications in a functional and productive manner, some individuals report suffering from uncontrolled use of social networks with sometimes significant effects on their psychological constitution (e. g. Andreassen, 2015; Kuss & Griffiths, 2011; Sindermann et al., 2020). Actual findings suggest, that the excessive use of social networks is associated with mental health disorders, psychological distress and less well-being (Brailovskaia et al., 2021; Frost & Rickwood, 2017; Huang, 2022; Marino et al., 2018). Conversely, experimental findings show that a reduction in the use of social networks can lead to an improvement in psychological well-being as well as a reduction in depressiveness and loneliness (Brailovskaia et al., 2020; Hunt et al., 2018; Tromholt, 2016) and can even contribute to a reduction in smoking behavior (Brailovskaia et al., 2020).

A growing number of research suggests that such an uncontrolled use of social networks could constitute a disorder due to addictive behaviors (Brand et al., 2020), which could also be referred to as social-networks-use disorder (SNUD). Based on the characteristics of gaming disorder, which has already been included in the DSM-5 as a condition for further study (American Psychiatric Association, 2013; see also Petry & O’Brien, 2013) and in the ICD-11 as disorder due to addictive behavior (World Health Organization, 2019; see also Rumpf et al., 2018; Saunders et al., 2017), the characteristics of SNUD are assumed to be 1) impaired control over the use, 2) growing priority given to the use, and 3) continuation of use despite the experience of negative consequences overall leading to functional impairment and marked distress in everyday life (World Health Organization, 2019). Nevertheless, as empirical findings on the specific characteristics and mechanisms of SNUD are still limited, especially when compared to the state of research on other Internet-related addictive behaviors such as gaming disorder (Wegmann & Brand, 2020), this problematic behavior has not yet found a specific diagnostic counterpart in current classification systems and is still a matter of ongoing discussion. However, SNUD is considered as a specific Internet-use disorder and is discussed to be classified in the category “other specified disorders due to addictive behaviors” similar to online buying-shopping disorder and online pornography-use disorder (Brand et al., 2020; see also recent taxonomy of Internet-use disorders by Montag et al., 2021).

The project that is described in this study protocol is a subproject (RP8) within the transregional research unit “Affective and cognitive mechanisms of specific Internet-use disorders (ACSID)” (FOR 2974, funded by the Deutsche Forschungsgemeinschaft, DFG, German Research Foundation, see description in Brand et al., 2021), which aims to investigate the affective and cognitive processes and their interactions involved in the development and maintenance of specific Internet-use disorders. Our project specifically seeks to provide evidence by examining the occurrence of characteristic features of addictive behaviors in SNUD, thus contributing to the current debate regarding the understanding of this problematic behavior including specific affective and cognitive mechanisms as well as possible similarities and differences to other addictive behaviors. The project proposal has been reviewed, the study is pre-registered (https://osf.io/9j8g3/), and data collection has started.

Cognitive mechanisms

According to dual-process theories of addiction, implicit cognitions such as implicit associations and approach tendencies play an important role in addiction-related decision-making situations (Bechara, 2005; Wiers & Stacy, 2006). Implicit associations are linked to the automatic processing of a dominant impulsive system which overrides the controlled system by initiating goal-directed drug-seeking behavior in the form of an implicit approach tendency that is further assumed to elicit drug-consumption (Robinson & Berridge, 1993; Wiers & Stacy, 2006). Following the I-PACE (Interaction of Person-Affect-Cognition-Execution) model by Brand et al. (2019), these implicit cognitions interact with use expectancies and the experience of gratification and compensation in the development and maintenance of behavioral addictions (see Figure 1). The authors outline that use expectancies refer to subjective thoughts regarding concrete effects linked to a specific addictive behavior. These expectancies may be explicit or implicit and are based on specific cognitive processes (e. g., past experiences with the behavior and attentional processes). After experiencing positive outcomes such as pleasure or enjoyment, this could result in positive (implicit) associations, which in turn function as reinforcers for future behaviors (Brand et al., 2019). Going a step further, the authors also assume that individuals have specific usage motives and specific needs, which are considered as relatively stable. Individuals can formulate these motives and needs associated with specific use expectancies and later on with the experience of gratification and additionally compensation. Based on learning processes and (positive) reinforcement mechanisms, the experience of the respective gratification could then result in the modification of the individual’s coping mechanisms, reinforcing the specific use expectancies and thus increasing the likelihood that the specific behavior will be seen as the best possibility to experience this gratification and later on compensation (Everitt & Robbins, 2016). Based on the I-PACE model, implicit cognitions, use expectancies, and the experience of gratification and compensation as a result of the usage behavior should also be detectable and interact with each other in individuals with higher propensity to experience SNUD symptoms.

Figure 1 The revised I-PACE model for addictive behaviors. A) shows early stages of the development of addictive behaviors. B) illustrates later stages of the process and factors contributing to the maintenance of addictive behaviors. Bolder arrows indicate stronger connections/accelerated mechanisms. Reprinted from Neuroscience & Biobehavioral Reviews, 104, September 2019, Matthias Brand, Elisa Wegmann, Rudolf Stark, Astrid Müller, Klaus Wölfling, Trevor W. Robbins, Marc N. Potenza, The Interaction of Person-Affect-Cognition-Execution (I-PACE) model for addictive behaviors: Update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors, Figure 2, page 3, Reprinted (2022) with permission from Elsevier.

Several studies have already demonstrated the relevance of implicit cognitions for substance-related disorders (e. g., tobacco use disorder; Rooke et al., 2008) and some Internet-use disorders such as Internet gaming disorder (Lorenz et al., 2013; van Holst et al., 2012) or pornography-use disorder (Schiebener et al., 2015; Snagowski et al., 2015; Snagowski & Brand, 2015). Furthermore, initial findings suggest a relevance of approach tendencies for problematic use of social networks (Juergensen & Leckfor, 2019). In addition, some studies using the Implicit Association Test have already identified a positive association between implicit associations when confronted with social media stimuli and the addictive use of social networks (Brailovskaia & Teichert, 2020; Turel & Serenko, 2020). Regarding the experience of gratification and compensation, new evidence from brain imaging studies provides support for the rewarding aspect of use of social networks (Meshi et al., 2013; Montag & Becker, 2019; Sherman et al., 2016; Wegmann et al., 2018). In addition to that, use expectancies have already been identified as a relevant factor in tobacco use disorder (Abrams et al., 2011) and SNUD (Wegmann et al., 2015). Nevertheless, there is a lack of research that integrates these constructs and their potential interactions in a comprehensive approach to clarify whether and to what extent SNUD is shaped by the mechanisms of addiction.

To elucidate whether the mechanisms of SNUD converge to those of an addiction-like behavior, a direct comparison with substance-related disorders, particularly those in which similar patterns of use can be assumed, may prove fruitful. In the area of substance-use disorders, tobacco use is characterized by having a high addiction potential (Niaura, 2010). In addition, individuals with moderate or severe tobacco use disorder consume cigarettes several times and frequently experience withdrawal or craving throughout the day (Niaura, 2010). The experienced gratification and compensation is very easy to achieve and can be done immediately after craving reactions which is why the usage patterns are considered comparable to those of social networks as the habitual use of smartphones or social networks applications can also occur very easily in most situations throughout the day, enabling interaction with other users, staying connected, and getting information many times per day (Niaura, 2010). We therefore propose that tobacco use serves as an appropriate control group behavior when investigating processes that might lead to risky use of social networks or SNUD.

Digital phenotyping and experience sampling

Based on these assumptions, the question arises as to the most appropriate method of measuring the constructs. While the implicit associations and approach-tendencies upon confrontation with addiction-related visual stimulus material can ideally be examined in a laboratory setting using standardized computer-based tests (see below), the most objective possible assessment of social networks usage-patterns as well as the assessment of gratifying and compensatory effects of use can hardly be guaranteed by a one-time self-report in a laboratory setting due to several problems. It is well known that self-report measures of behavior in artificial settings are susceptible to various cognitive and social biases (Jobe, 2003; Schwarz & Oyserman, 2001). Especially retrospective self-reports of behaviors that occur with high frequency and are highly integrated into subjects’ daily lives are often subjected to strong limitations due to the performance of autobiographical memory (Parry et al., 2021). These problems could be particularly severe when recording patterns of social networks use, as several studies show that humans often experience time distortions when using technological devices, which implies that they often overestimate or underestimate their own use of internet platforms (social networks in particular), depending on how they are asked in a survey (Lin et al., 2015; Montag & Rumpf, 2021; Parry et al., 2021; Turel et al., 2018). Several authors argue that a solution to this problem could be to predict psychological states/traits directly from the analysis of digital footprints that occur when using the Internet (Miller, 2012; Montag & Rumpf, 2021), which is referred to as digital phenotyping. In terms of social networks, the first approach could be to record usage time and frequency directly on the smartphone (Turel et al., 2018). In addition to that, smartphone-based recording of target behavior offers the potential to be combined with capturing other psychological mechanisms related to the use of social networks, such as use expectancies immediately prior to use and gratifying and compensatory effects of use, in the style of the Experience Sampling Method (ESM). The term ESM subsumes research methods that record psychological characteristics directly in the everyday life of the subjects and in direct temporal relation to the target behavior (Schüz et al., 2015). ESM provides actual insights into experiences (e. g. gratifying/compensatory effects) right at the moment and by that also minimizes the aforementioned biases of retrospective self-reports (Conner & Barrett, 2012; Schüz et al., 2015). Typically, ESM is based on multiple measurements within a time period, which allows to sample dynamic processes and provides a better understanding of the natural history of target processes (Schüz et al., 2015). Following these assumptions, smartphone-based survey methods in combination with digital phenotyping and the ESM could hold great potential for exploring psychological mechanisms of SNUD.

Aims and objectives

Our project aims at investigating the relevance of 1) implicit cognitions (i. e., implicit associations and approach-avoidance tendencies) upon confrontation with addiction-related stimuli, 2) the experiences of gratification and compensation, in terms of increasing positive affect through gratification or decreasing negative affect through compensation when using online social networks or tobacco, and 3) positive as well as negative use expectancies in SNUD and tobacco use disorder. Furthermore, as proposed in the I-PACE model (Brand et al., 2016, 2019), the effect of these constructs will not be investigated in isolation, as it can be assumed that the experiences of gratification and compensation due to the use of social networks or tobacco interact with specific use expectancies as well as with implicit cognitions, leading to positive implicit associations and approach biases.

For this purpose, we combine experimental-psychological measurements in a laboratory setting with a smartphone-based ambulatory assessment in style of digital phenotyping and ESM. Based on our assumption that the psychological mechanisms as well as the use patterns of SNUD and tobacco use disorder are similar, we include smartphone applications that allow the recording of smoking behavior and social network use as well as use expectancies and experienced gratification and compensation of use/consumption in order to be able to draw direct comparisons regarding these variables between both groups.

Based on the aforementioned theoretical considerations and preliminary findings, we expect the following effects:

  • •
    Positive implicit associations and approach tendencies can be detected in SNUD and are related to symptom severity, whereas individuals with tobacco use disorder show stronger positive implicit associations and approach tendencies only for smoking-related content.
  • •
    Positive and negative use expectancies are detectable in SNUD and are related to symptom severity.
  • •
    The experience of gratification as well as the experience of compensation are detectable in SNUD and are related to symptom severity.
  • •
    The relationship of the performance in tasks assessing implicit associations and approach-avoidance tendencies in the specific addiction-related conditions with symptom severity in SNUD and tobacco use disorder is moderated by the experience of gratification and the experience of compensation after using social networks or tobacco.
  • •
    The relationship between the use expectancies and symptom severity in SNUD and tobacco-use disorder is moderated by the experience of gratification and the experience of compensation after using social networks or tobacco.

We will additionally aim to explore possible group differences or similarities in usage patterns of addictive behaviors and the experiences of gratification and compensation between individuals with SNUD and tobacco-use disorder by using data tracking and the ambulatory assessment (see below).

Methods

Study design and sample collection

This project is based on a mixed-methods, cross-sectional, between-subjects design with four groups of participants. Three groups will include individuals who have developed a pathological or problematic behavior: individuals with 1) SNUD without tobacco use, 2) risky use of social networks, but without tobacco use, and 3) tobacco use disorder without SNUD or risky social networks use. In addition, a group of 4) control subjects who use social networks non-problematically and who do not use any form of tobacco or nicotine (e. g., cigarettes, e-cigarettes) will be recruited and matched with the other three groups regarding age, gender, and education.

Participants will be recruited at the study sites of the Universities of Lübeck and Duisburg-Essen through advertising measures such as flyers, notices, or newspaper advertisements. In a second recruitment wave, potential participants will be recruited directly at vocational schools. Depending on demand, online recruitment strategies via social media will be used if necessary. All participants are first asked to provide information about their use of social networks and smoking behavior on a pre-screening website. In the case of potential eligibility, these criteria and the presence of further inclusion and exclusion criteria are verified by a subsequent telephone screening. If all relevant criteria (see below) are met, participants receive an invitation to the main investigation, which consists of two parts: the laboratory assessment (T1) and the smartphone-app-based ambulatory assessment (T2, see Figure 1). The project has been pre-registered on OSF (https://osf.io/9j8g3/).

Inclusion criteria for all groups are age ≥ 16 and ≤ 65 years and sufficient German language skills. Exclusion criteria for all groups are current diagnosis of learning or developmental disorders, psychosis, mania, current substance-use disorder, acute suicidal ideation, and any psychoactive substances known to interfere with performance in the cognitive tasks. In addition, participation in our study is only possible for users of smartphones with Android operating systems, as the apps we have developed for the ambulatory assessment can only be used on these devices.

Sample size

The sample sizes of all subprojects of our research unit were calculated centrally and refer to the comparison of pathological and non-pathological users of the Internet in the context of the overall project. In the context of our explorative study we plan to examine a total sample of N=204 subjects, equally divided (n=51 each) between individuals with social-networks-use disorder, individuals with risky use, control subjects and indidivuals with tobbacco use disorder.

Assessment and data collection

Pre-Screening and Screening

For screening purposes, use of social networks will be assessed using the checklist for the Assessment of Internet and Computer game Addiction (AICA-C:9; Müller et al., 2017) an instrument based on the DSM-5 criteria for gaming disorder modified for SNUD. This screening information is verified in the context of laboratory testing by a structured clinical interview (AICA-SKI:IBS; Müller et al., 2017). Throughout all diagnostic steps in this project, the presence of a SNUD will be assumed from at least five of nine modified DSM-5 criteria met. Risky use is assumed from at least one fulfilled criterion. The Fagerström Test for Nicotine Dependence (FTND, Heatherton et al., 1991) will be used to measure tobacco-use disorder. Based on a latent class analysis, a score of less than four is considered as non-addictive use (Agrawal et al., 2011). In addition, the Assessment of Criteria for Specific Internet-use disorders modified for SNUD will be used to measure symptom severity based on the ICD-11 criteria for gaming disorder (Müller et al., 2022).

Laboratory Assessment (T1)

The laboratory examination has a duration of approx. 4–5 hours in the premises of the respective examination location and mainly consists of the core battery which is an accumulation of several psychological tests and questionnaires and is carried out in a standardized manner in all subprojects of FOR 2974. The core battery includes (among other tasks and questionnaires) the Implicit Association Test and the Approach-Avoidance Task (AAT). A detailed overview of all procedures performed in the laboratory and their chronological order can be found in our public pre-registration documents (link to OSF project page: https://osf.io/9j8g3/)

Assessment of Implicit Cognitions: IAT & AAT

A modified version of the IAT (Greenwald et al., 1998) will be used to assess implicit associations towards addiction-related pictures. The current version of the IAT will be modified with social-networks-related pictures and smoking-related pictures. Experimental and control conditions in the presentation of the stimulus material for each of the groups can be found in Figure 1. As suggested by Greenwald et al. (2003) as dependent variable the D2SD score will be calculated from the test statistics, with higher D2SD scores indicating a stronger positive association towards addiction related stimuli.

The AAT (Rinck & Becker, 2007) will be used to assess approach and avoidance tendencies toward addiction-related stimuli (e. g., Cousijn et al., 2011; Wiers et al., 2013). We will use the task as implemented by Snagowski and Brand (2015) to assess approach-avoidance tendencies in addictive behaviors. Experimental and control conditions regarding the presentation of visual stimuli can be found in Figure 1. The reaction time (RT) of each trial and the median RT scores will be analyzed and transformed into a compatibility effect score for the addiction-related and for the control category. The compatibility effect scores represent the strength of approach and avoidance tendencies; therefore, positive effect scores indicate an approach tendency and negative effect scores indicate an avoidance tendency. Additionally, an overall RT score will be calculated by subtracting the median RT for non-addiction-related cues from the median RT for the addiction-related pictures. A positive score indicates slower RTs to addiction-related cues and a negative value indicates faster RTs to addiction-related cues.

Details on both test procedures, such as trial number, trial length as well as examples of stimulus material can be found in our public pre-registration documents (link to OSF project page: https://osf.io/9j8g3/)

Ambulatory Assessment (T2)

The ambulatory assessment immediately follows the laboratory assessment for a period of consecutive 14 days and is based on a tracking of the use patterns of social networks or tobacco consumption as well as a multiple daily query of use expectancies and the experience of gratification and compensation following the use of social networks or tobacco. The data collection is carried out by means of two applications for Android smartphones developed or modified for this project. All subjects who have agreed to participate in the ambulatory assessment will install the applications on their smartphones under the instructions of the test administration following the laboratory examination.

Tracking of social-networks-use

The use patterns of social networks are tracked with the Insights-App (Montag, Baumeister, et al., 2019) which is a professional tool for data collection in scientific studies. Among others, this application can record screen-unlocks and screen-on events and is able to link these behaviors to social networks use (as specific app activation behavior can be recorded). “Screen-on events” are defined by merely “flicking-on” the phone to check the time (as an example), whereas a “screen-unlock event” reflects more engagement with one’s smartphone, since unlocking the screen enables the person to use other applications (such as social networking apps). The Insights-App will be used to track user sessions (screen on/off events, screen unlock, session duration, elapsed time since last session), app sessions (app title, app package name, duration of use; app use frequency can be extracted from the number of app use instances) as well as app usage statistics (daily, weekly, or monthly aggregated data, app title, app package name, total duration of usage) of the target social network apps which are defined for this project (see below).

Experience sampling of behavior, use expectancies, and experienced gratification and compensation

The Tracking-App has been developed for the present project and allows the measurement of gratification and compensation experiences as well as use expectancies when smoking and using social networks following the method of experience sampling (ESM). Figure 3 shows the measurement time points of the Tracking-App within a day and the parameters recorded in each case. Subjects are notified five times a day via push notification and asked to provide information within the app.

Figure 3 Daily procedure and measurement within the Tracking-App.

The experience of gratification or compensation following the use of social networks or tobacco is surveyed retrospectively for the past time period (see Figure 2), whereas the survey of use expectancies, differentiated into positive and negative expectations is conducted prospectively for the time period following the survey. Participants rate all items on visual analog scales (VAS) from 1 to 10. In addition, social networks users are asked at the beginning of each session which apps they have used in the past period. The participants can choose from ten target apps defined for this project: Facebook (including Facebook-Messenger), Instagram, Pinterest, Snapchat, Twitter, Tumblr, WhatsApp, Telegram, Signal and TikTok.

Figure 2 Study design: Sample groups and their main characteristics to be investigated at the two steps of main investigation.

For smokers, the Tracking-App additionally allows quantification of consumption over the course of the day (analogous to the measurement of social media use with the Insights-App). For this purpose, subjects are asked to press a button within the app each time they consume cigarettes and to indicate the extent of consumption (specifically: the number of cigarettes consumed).

Furthermore, the Tracking-App is used to conduct the ambulatory end of day standardized assessment, which is administered in all subprojects of the FOR 2974 (Brand et al., 2021). This includes a standardized assessment measuring mood, dominance of the first-choice application throughout the day, urge to use, intensity of usage, experiences of pleasure, and stress relief. Within our subproject, an “end of week” assessment is additionally prompted every seven days, which captures global positive and negative affects by using the Positive and Negative Affect Schedule (PANAS, Watson et al., 1988) for the respective previous week to be able to control consumption patterns as well as the experience sampling results for affective fluctuations.

Statistical analysis

Following the aforementioned between-subjects design, data on the implicit cognitions (measured with the AAT and IAT), use expectancies, and the experiences of gratification and the experiences of compensation will be analyzed using repeated measures analysis of variance with the within-subjects factor ‘stimulus category’ (addiction-related vs. non-addiction-related pictures) and the between-subjects factor ‘group’ (individuals with SNUD, risky users, healthy controls, and individuals with tobacco use disorder) to identify differences regarding these variables between the subgroups of the sample. Interactions between task performances in the AAT and IAT, use expectancies, and experienced gratification and compensation of social networks use or tobacco use will be analyzed with hierarchical moderated regression analyses. Regarding the exploratory approach, multiple regression analyses, analyses of variance with repeated measures and multilevel analyses will be used to examine use expectancies and experienced gratification and compensation over the course of a day but also the 14-day ambulatory assessment within as well as between groups. We will also compare the key variables of the data tracking in the four groups which means that the frequency of cigarette consumption (frequency of button pushed within the Tracking-App) will be compared with frequency of smartphone/social networks checks providing insights into interruptions due to both addictive behaviors.

Careless responder analyses will be done as recommended by Curran (2016) and Meade and Craig (2012). This will include inspection of awareness checks, response times for the questionnaire blocks; long-string analysis, Mahalanobis-distance and even-odd analysis. Individuals who do not meet inclusion criteria after the experimental data has been collected, will be excluded. If hypotheses cannot be tested between the subgroups with the existing data due to missing data, we plan to aggregate groups and use a dimensional approach.

Discussion

As with other specific Internet-use disorders, SNUD is associated with negative sequela that can be considered a cause for significant cost burden (Rumpf et al., 2022). For the purposes of prevention, treatment, and monitoring, the nature of SNUD as a potential addictive behavior is of special importance. Therefore, studies are needed that use a theoretical framework (Brand & Potenza, 2021) to elucidate processes and mechanisms underlying this condition. To the best of our knowledge, this is the first study that will compare individuals with SNUD, individuals with risky use of social networks, and individuals with non-problematic use of social networks against individuals with a substance-use disorder, namely tobacco-use disorder, with respect to their gratification and compensation experiences, use expectancies, and implicit cognitions as mechanisms for the development of problematic and addictive patterns of use.

A strength of our project is the combination of experimental psychological methods, such as the IAT and AAT, with smartphone-supported ambulatory data assessment. An important issue is to overcome methodological shortcomings of previous research by the innovative approach of tracking usage patterns and measuring psychological mechanisms of consumption in terms of digital phenotyping and experience sampling. This is suggested to provide more objective insights into usage patterns and accompanying experiences which will lead to a better understanding of SNUD. It should be noted, however, that we do not consider retrospective self-reports to be obsolete. On the contrary, as things stand at present, it can be assumed that linking tracking data with self-report data for symptom burden provides the best picture of an individual’s tendency towards SNUD (Montag & Rumpf, 2021), which is why we consider both approaches important in our study. Furthermore, by using the smartphone-based application of ESM to record use expectancies as well as gratification and compensation directly in the everyday lives of smokers and users of social networks, we also expect to create data of significantly higher ecological validity. We see research approaches like ours as a necessary first step in a much broader development in which smartphone-based data collection in terms of digital phenotyping and experience sampling will be made usable for diagnostic investigations as well as for preventive and therapeutic services.

Regarding the tracking of usage patterns of social networks, it must be noted that our study approach only allows us to map the use of corresponding applications on the subjects’ smartphones. A summarized analysis of the use of social networks on multiple devices (e. g., additionally tablet, notebook, etc.) is currently not possible, as there is currently no comprehensive all-in-one solution for monitoring and merging the data (Montag & Rumpf, 2021) of all possible devices of use, which limits the generalizability of our findings to the use of the smartphone itself. With regard to the application of the IAT, it must be admitted that this procedure is not entirely uncontroversial from a methodological point of view (Teige-Mocigemba et al., 2010). For example, some authors argue that the processes underlying the IAT are still unclear and that it is not yet sufficiently understood how the measured constructs are translated into observed responses during the test (De Houwer et al., 2009). Some studies have already identified several factors to contribute additional but construct-unrelated variance to IAT results (Teige-Mocigemba et al., 2010) which leads to much ongoing debate regarding the validity of IAT measurements (Blanton et al., 2009; Stacy & Wiers, 2010). Although the IAT has been widely used in research on both substance- and non-substance-related addictions, and its application allows us to make a direct comparison to these preliminary studies, its results will have to be interpreted with caution in the face of this methodological controversy. Furthermore, it must be noted that, especially in the group of smokers, active reporting of smoking behavior via the smartphone app can lead to behavioral changes, in particular to a reduction in tobacco consumption, which is why such possible effects must be taken into account when analyzing and interpreting the data. In addition, smartphone-based data collection is always associated with methodological challenges, especially in the area of data protection and privacy (for reflections see Montag et al., 2020). Through careful preparation and in close cooperation with the ethics committees at the respective study sites (see below), we have established clear guidelines for data collection and processing within our project, thus ensuring that the privacy of our subjects is protected at all times during the study. Some more general methodological challenges of our project might include keeping sociodemographic characteristics equal across sample subgroups to meaningfully interpret the resulting intergroup comparisons and recruiting a sample of sufficient size during the ongoing COVID-19 pandemic.

All in all, we expect that our project will provide an enhanced understanding of the processes underlying the development and maintenance of SNUD and add knowledge to the existing literature. We are convinced that our research might further inspire how treatments for problematic use of social networks can be optimized by addressing more specifically implicit cognitions or rewarding effects of social networks use. A potential starting point could be interventions from the field of cognitive bias modification, in particular approach bias modification, as already used for substance-related addictions (Wiers et al., 2020), should such cognitive biases be identified in individuals with SNUD.

Parts of this manuscript have been taken from the proposal (RP8) of the Research Unit FOR 2974.

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