Project Report_ Final Draft_ Central Group 1

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1 REPLICATION STUDY: REDUCING SOCIAL MEDIA USE Replication Study: Reducing Social Media Use Maraya Harn, Taylor LaNasa, Malcolm Haynes, Audrey Thomas and Courtney Jones SPCE 611 Section 802 December 10, 2023

2 REPLICATION STUDY: REDUCING SOCIAL MEDIA USE Abstract Social media use is increasing every year. Problematic social media use has been associated with interfering with sleep, relationships, work, and school (Stinson & Dallery, 2022). Stinson and Dallery (2022), tested a way to limit social media usage by an intervention package (contingency management, application limit, and selection of alternative activities). Their findings show that the intervention package was successful in decreasing social media usage. In this replication study, we conducted a similar intervention package using application limit, selecting three alternative activities, and using a point system for our contingency management. Our group consisted of 5 online graduate students living in the Midwest. The results show that the package intervention was effective in reducing social media usage for 3 out of 5 participants.

3 REPLICATION STUDY: REDUCING SOCIAL MEDIA USE Literature Review The reduction of time spent on social media is important to target due to our particularly busy schedules as full-time working students. Time management is the key to making time for reading materials, studying, homework and projects, not to mention other work and life obligations, for example, sleeping. Social media use can vary in duration from 30 s as we may wait in line at the store and as long as, if not longer, than 2 hr as we lay in bed at the end of the day. Time can slip away from us all. Social media use is increasing every year. Problematic social media use has been associated with interfering with sleep, relationships, work, and school (Stinson & Dallery, 2022). According to Stinson and Dallery (2022), 230 million Americans are active on social media. There have been many interventions that try to decrease social media usage. Kropp et al. (2017) argues that contingency management (CM) is a favorable intervention to help increase desired behavior. In a study, they found CM to be effective for increasing patients' behaviors in substance use disorder treatment. Early studies of CM have been effective for patients abstaining from substances and utilizing treatment. There are many versions of CM procedures such as receiving cash money each for desired behavior to doing a “fishbowl” method “in which performing the desired behavior results in the opportunity to draw a slip from a container that either has no value or indicates a prize in one of several value-tiers" (Kropp et al., 2017). In Kropp et al. (2017) study, they conducted a study to see if a low cost, simple CM (fishbowl method) would have the same effect as a high magnitude reinforcer such as cash. Participants would put their name in a raffle, the raffle will be drawn to see which patient would have the

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4 REPLICATION STUDY: REDUCING SOCIAL MEDIA USE chance to draw from the prize bowl. The results showed that the fishbowl method was effective for increasing patient group attendance in a drug rehab facility. In the study, Verbeji et al., (2021), adolescents tend to use multiple platforms to communicate resulting in time spent on social media occurring in rapid and fragmented ways. This makes it difficult for adolescents to gauge the amount of time spent on social media as a whole. The study’s focus had two hypotheses: more accuracy and higher validity or less accuracy and lower validity across time. Adults previously had overestimated their time spent on social media while adolescence had greatly overestimated their time spent on social media. Participants were instructed to participate in an online survey consisting of questions about demographic characteristics and adolescents’ average time spent on Instagram, WhatsApp and Snapchat. The participants then installed the experience sampling method (ESM) application Ethica on their phones. Ethica allowed researchers to track the social media apps used and when phones were turned off and on. Ethica also allowed researchers the opportunity to compare the traced activities on the participants’ phones to their self-reports. There is concern that accuracy and validity of self—reports may vary across various social media platforms. There is limitation regarding how to trace web-based applications or social media sites. The results showed that adolescents over- estimate their time spent on social media, their ESM estimates were more accurate for Instagram than Snapchat or WhatsApp, the ESM estimates decreased over time. A study conducted by Orzikulova et al., 2023 compared application limit and feature level to see which one decreased social media the most. The application limit is a set duration, usually per day, of how much time they can be on social media. When the time limit is up, you are alerted with a message saying you have reached your daily social media limit. This counts for all social media. On the other hand, feature level is an within-app feature. For example, you can set

5 REPLICATION STUDY: REDUCING SOCIAL MEDIA USE how much time you are looking at comments on YouTube or scrolling through content on Instagram, etc. The results showed that the feature level decrease social media the most, but they did state that the application limit also decreased social media. In the study by Stinson and Dallery, 2022 conducted a study to decrease social media use by using an application limit. When limitations are placed on resources or factors, it often is the result of the over expenditure or overuse of those resources. As these factors begin to become limited, individuals have to find ways to make up for those losses with substitutions. An example of such a restriction is putting limitations on social media use. In the article by Brevers and Turel (2019), self-regulating strategies were discussed to help alleviate problematic social media addiction symptoms. This study was broken down into two parts; the first focusing on categorizing individuals into eight common self-regulating strategies and the second one identifying which strategy was most common and the factors that may have led to that response. In study one from the article, proactive and reactive strategies were discovered to be the most common features of self-control strategies; while habit of SNS, frequency of self-control strategy, and difficulty of usage were all results from utilizing four multi-item scales for the second study (Brevers & Turel, 2019). Results of the study showed that individuals with higher self-control found it easier to manage and use the strategies because they were more adept at building upon good habits instead of unhealthy habits. In relation to the replication study, alternative behaviors chosen by the given individuals as well as app limits were utilized. Each individual's ability to utilize self- control was tracked on how well they adhered to the limitation and alternative behaviors. Brevers and Turel (2019) discussed limitations regarding how specific each classification was in the first study and specific processes and strategy types used as a whole.

6 REPLICATION STUDY: REDUCING SOCIAL MEDIA USE Stanley et. Al (2022) investigated social media usage among college students while implementing contingency management. If the students met the daily intervention contingencies, they would receive online vouchers that they could exchange for payment. The study showed a decrease of the usage of social media and smartphone use during the intervention stage but during the follow-up stage the data showed an increase of usage which suggest that the contingency of gaining money reward was shown to be effective. The study we replicated was Reducing problematic social media usage via a package intervention by Stinson and Dallery, 2022. In this study Stinson and Dallery tested a package intervention with 9 undergraduate students with a multiple baseline design. In the package intervention they used a contingency management (CM), automated notifications of application use, and alternative activities. The CM in the study was money. The automated notifications of application use is an application limit. Each participant was able to set their own daily goal for how much time they could be on social media. Alternative activities were activities each individual was able to pick that they could do in place of social media. The study showed that 8 out of 9 participants had a decrease in social media (Stinson & Dallery, 2022). Purpose The purpose of this replication study was to see if we could get similar results from Stinson and Dallery's study using a package intervention including CM, application limits, and three alternative activities to reduce daily social media usage. Stinson and Dallery (2022) implemented this study with young adults. Our group wants to evaluate if we will get the same results for online graduate students living in separate locations throughout the Midwest. We also assess if the intervention package had social validity to provide meaningful outcomes for individuals.

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7 REPLICATION STUDY: REDUCING SOCIAL MEDIA USE Methods Participants : 5 participants, 2 cohorts Table 1 : Demographic Questionnaire: Name, age, gender, ethnicity Participant Age (years) Gender Ethnicity Maraya Harn 26 Female White Taylor LaNasa 27 Female White Malcolm Haynes 29 Male Black Audrey Thomas 31 Female Black Courtney Jones 29 Female White Materials Needed : In the replication study the participants used a smartphone such as an iPhone or Samsung. For iPhone users they used the screen time application to track how much time they used social media. For Samsung users they used work examiner to track how much time they used social media. Each participant completed a demographic questionnaire that asked name, age, gender, and ethnicity. The study also included a contingency management (points). Each participant also made a list of alternative activities to do instead of being on social media. Setting : The setting was each participant’s home, workspace, and community. Behavior Definition : Defined as minutes per day spent on social networking sites. Websites or applications used for posting or sending messages/images/videos. Not including text, email, or FaceTime. Examples : The participants sending videos on snapchat. The participants scrolling through TikTok. Non-Examples : The participant facetiming a friend. The participant taking a work call.

8 REPLICATION STUDY: REDUCING SOCIAL MEDIA USE Data Collection : Each participant records their daily social media use each morning for the prior day on an excel document shared by all participants. Reports will be turned in between 6:30 AM and 8:00 AM Central time. Baseline procedure : Social media reports prior to application limits and contingency management (CM). Intervention : Application limits and contingency management (CM). Each participant will set their own application limit in the beginning of the intervention. The contingency management is a point system. Each participant will be awarded 1 point each day if they did not exceed their application limit and recorded on the shared excel sheet during the designated time frame (6:30 AM – 8:00 AM) each day. One point will also be awarded for each screenshot sent when doing IOA. At the end of the intervention whoever has the most points win. Also, each participant will pick 3 alternative behaviors at the beginning of the intervention. Participant daily social media goal: Maraya Harn: 90 min Taylor LaNasa: 60 min Malcolm Haynes: 90 min Audrey Thomas: 45 min Courtney Jones: 90 min Alternative behavior(s) : Maraya Harn: watch TV, spend time with my dog, and reading/studying Taylor LaNasa: yoga, spending time with my dog, and listening to music Malcolm Haynes: watch tv, study, listen to music, exercise Audrey Thomas: watch television, read a book, and play with my dogs

9 REPLICATION STUDY: REDUCING SOCIAL MEDIA USE Courtney Jones: workout/take a walk, read a book, or bake sweets Contingency Management : Data collection on treatment integrity randomly collection throughout intervention phase. A point is awarded for every point on the checklist met, zero is awarded for a point not earned, a dash is input when data was not collected for that session. Table 2 : Contingency Management Session Number Daily Goal IOA Screenshots 11 0 1 12 1 - 13 1 - 14 1 - 15 1 - 16 1 1 17 1 - 18 1 - 19 1 1 20 1 1 21 1 - 22 1 1 23 1 1 24 1 - 25 1 - IOA: Screens shots of social media reports (30%). Each participant will have a designated IOA partner. For 30% of the session’s participants will share their screen shot with their partner, vis versa, and compare what their partner has documented on the shared excel sheet. The IOA will be calculated as follow: smaller number divided by the larger number time 100. Equation: SM/L x 100 = blank% Results of IOA: Each participant received 100% on IOA.

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10 REPLICATION STUDY: REDUCING SOCIAL MEDIA USE Table 3.1 Table 3.1 shows the average and range scored for the baseline and intervention phases for each participant. To calculate the scores, we used the participant data sheet that recorded their daily duration of social media usage. Table 3.1-3.5 are samples of those data sheets for participants. For each participant, we added all the scores in baseline condition and then divided the number by how many days they were in baseline. For example, in Table 3.1, Audrey’s baseline data shows that she had a mean of 52.8. We did a similar calculation for Table 3.2 to obtain Malcolm’s baseline mean of 128. We used this similar calculation for the intervention phase. To calculate the range, we looked at information like the data charts in Table 3.1 and 3.2 and ranked the lowest and highest duration of daily media use in both baseline and intervention phase. A higher mean was seen in the baseline condition indicating that the participants indulge in social media for longer period of time before the intervention was implemented.

11 REPLICATION STUDY: REDUCING SOCIAL MEDIA USE Figure 1: Cohort 1 Figure 1: Daily duration of social media use via smartphone. App limit = application limit used each day (90 min for Maraya & Malcolm and 60 min for Taylor), CM = contingency management. The dotted horizontal line indicates the goal duration of social media use per day. TI = Treatment Integrity check. (*) indicates checked with IOA.

12 REPLICATION STUDY: REDUCING SOCIAL MEDIA USE Figure 2: Cohort 2 Figure 2: Daily duration of social media use via smartphone. App limit = application limit used each day (45 min for Audrey and 90 min for Courtney), CM = contingency management. The dotted horizontal line indicates the goal duration of social media use per day. TI = Treatment Integrity check. (*) indicates checked with IOA.

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13 REPLICATION STUDY: REDUCING SOCIAL MEDIA USE Validity (TI) Table 3.2: This checklist was used to keep track of the validity of each participant’s data. Validity was collected the same days that IOA data was collected. The mark 1 was given if the section was met and a 0 when the section was not met. Equation for TF: Number of interventions steps performed correctly divided by the total intervention steps times 100. Total number of points are discussed in the results. Results In both Figure 1 and Figure 2, a multiple baseline graph of data for social media use was taken from Cohort 1 (Maraya, Taylor, and Malcolm) and Cohort 2 (Audrey and Courtney). From the data collected, we were able to identify if a functional relationship occurred for any of the participants while under the intervention stage. Of the five participants, the three participants from Cohort 1 displayed a functional relationship in the reduction of social media use after the intervention was implemented. With each of these participants we can see variability in the baseline data as well as an overall increasing trend in data. Once intervention took place, the data became stable, consistent, and had more of a slightly decreasing trend. In Cohort 2, the participants were not able to show a functional relationship due to several factors. Courtney’s data displayed an overlap between baseline and intervention phases. This overlap makes the

14 REPLICATION STUDY: REDUCING SOCIAL MEDIA USE effect of the independent variable, the intervention phase, less clear and cannot be acknowledged as the reason for potential change. Audrey’s data shows a decreasing trend in the use of social media while in the baseline phase. This would mean that if intervention had not started then the social media use would have continued to decrease. Treatment integrity and IOA are also both displayed on both Figure 1 and 2 for instructional purposes only. TI Percentage of Points: We decided to calculate percentage of points because each participant started intervention at different times. Equation: (points per submission of min on social media per day + points for submitting IOA) divided by (days in intervention + IOA days) times 100 = points% Maraya Harn: 100% Taylor LaNasa: 96% Malcolm Haynes: 93% Audrey Thomas: 96% Courtney Jones: 95% Limitations The system used to track social media usage, phone settings, was user friendly, but it did not come without its flaws. The social media tracker did not collect data on social media apps used like Pinterest and reddit. For Android phones, their social media tracking counted all internet searches composed, this is problematic due to Google searches being unrelated to what the study considered social media usage. While the participants communicated throughout the study, the social media tracker counted their communication through GroupMe as social media usage. Though the use of GroupMe does fall within the definition of social media usage, future

15 REPLICATION STUDY: REDUCING SOCIAL MEDIA USE replications of this study should consider excluding counting the participant's communication as social media usage. Conclusion In conclusion, the current study that we replicated assessed the effectiveness of a package intervention, utilizing alternative behaviors, contingency management, and app limitations for reducing the duration of daily social media use via cellular device. Our results show that this intervention was successful for 3 out 5 participants showing a functional relationship and effective in decreasing the duration of social media use. This finding was consistent with that of the findings from the Stinson and Dallery article (2022). Due to the rapidly growing increase in social media usage, this study shows social validity through the practical, meaningful, and acceptable outcomes of treatment. IOA for all participants was 100% and Treatment Integrity was found to be favorable with percentages above 90%. Future research is needed for the continued exploration and experimentation on the accessibility and cost effectiveness of treatment for the problematic use of social media. Table 4: Contribution Log

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16 REPLICATION STUDY: REDUCING SOCIAL MEDIA USE Table 4 is a summary of the contributions of each participant and their time working on and collaborating their data, graphs, presentation and report since data from each component contributed to the overall report.

17 REPLICATION STUDY: REDUCING SOCIAL MEDIA USE References Brevers, D., & Turel, O. (2019). Strategies for self-controlling social media use: Classification and role in preventing social media addiction symptoms. Journal of Behavioral Addictions, 8(3), 554–563. https://doi.org/10.1556/2006.8.2019.49 Fong, E. H., Ficklin, S., & Lee, H. Y. (2017). Increasing cultural understanding and diversity in applied behavior analysis. Behavior Analysis (Washington, D.C.), 17 (2), 103-113. https://doi.org/10.1037/bar0000076 Kropp, F., Lewis, D., & Winhusen, T. (2017). The effectiveness of ultra-low magnitude reinforcers: Findings from a “real-world” application of contingency management. Journal of Substance Abuse Treatment, 72, 111–116. https://doi.org/10.1016/j.jsat.2016.06.012 Orzikulova, A., Cho, H., Chung, H.-Y., Hong, H., Lee, U., & Lee, S.-J. (2023). FinerMe: Examining app-level and feature-level interventions to regulate mobile social media use. Proceedings of the ACM on Human-Computer Interaction , 7 (CSCW2), 1–30. https://doi.org/10.1145/3610065 Stinson, L., & Dallery, J. (2023). Reducing problematic social media use via a package intervention. Journal of Applied Behavior Analysis, 56 (2), 323-335. https://doi.org/10.1002/jaba.975 Verbeij, T., Pouwels, J. L., Beyens, I., & Valkenburg, P. M. (2021). The accuracy and validity of self-reported social media use measures among adolescents. Computers in Human Behavior Reports, 3, 100090. https://doi.org/10.1016/j.chbr.2021.100090

Project Report_ Final Draft_ Central Group 1

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