Behavioural Treatment: Compulsive Youtube Usage

Introduction

The internet and its continually progressive nature have shaped society in ways that have alleviated difficulties with communication (e.g. social media) and entertainment (e.g. streaming services). Amongst the advantages of the internet, a multitude of studies have argued the negative effects of sustained exposure to social media. Although addiction to social media has not been recognised in the DSM 5, an abundance of work has shown that social media is as addictive as any form of drug or alcoholic substance. Furthermore, O'reilly et al. (2018) asserted that adolescents are at risk for the deterioration of their physical and mental well-being, as they are regarded as avid users of social media.

Although social media platforms, such as ‘YouTube', has been classified as an aid for providing information and entertainment to university students, the platform has been termed as a deficit due to the compulsive behaviour you may develop from utilising it. Klobas, Mcgill, Moghavvemi, & Paramanathan (2018) defined this compulsive behaviour as the inability to limit the excessive frequency of use on the platform. The compulsive behaviour of excessive YouTube usage must be modified for the reason that failure to control the use of YouTube leads to distraction from completing school-related work, thus resulting in stress-related mental states. The past two decades consist of a plethora of treatment studies aimed to treat the targeted behaviour of limiting the use of YouTube.

Young's (2007) study consisted of 114 clients, ranging in age, who underwent CBT (Cognitive-Behavioural Therapy) treatments aimed to eliminate underlying factors contributing to online use. Each participant attended 12 counselling sessions in total and were provided surveys to be completed after sessions 3, 8 and 12 in order to examine the effectiveness of the treatment. A six-month follow-up was undertaken as part of the intervention for relapse prevention. A strength of this study was the large sample size as it provided more accurate values and significant outliers within the distribution of the survey data. A limitation of the data was the self-report measure utilised in the questionnaire, as subjective effects increased the likelihood of obtaining inaccurate results. The overall outcome of Young's (2007) study was the decrease in thoughts and behaviours associated with compulsive internet use. Effectiveness of the CBT treatment was reported long-term as clients were able to maintain management of symptoms in the six-month relapse prevention follow-up.

Du, Jiang, & Vance's (2010) study also utilised a CBT treatment aimed to reduce the frequency of excessive internet use. The study included fifty-six participants within the age range of 12-17 years old. The monitoring method consisted of no specific time frame; however, surveys were conducted before and after each CBT session and after the six-month follow-up. The intervention involved two conditions: one non-CBT intervention-based group and one group undergoing eight CBT sessions (teaching therapeutic learning principles for managing time and internet use). Although both groups reduced the frequency of internet usage and level of anxiety, the condition consisting of the CBT interventions revealed an improvement in self-management and positive emotional states. Generalisability of results was reported as a limitation due to its disregard of pre-existing conditions the participants may be diagnosed with. Du et al. (2010) stated that learning and psychiatric disorders, and deficits in speech and language development can affect measurements of the extent of treatment effectiveness. However, the intervention was effective on a long-term basis due to the six-month follow up.

A study conducted by Khazaei, Khazaei, & Ghanbari-H. (2017) entailed forty-eight participants, ranging in age, who underwent a series of behavioural treatments. The time frame of monitoring the behaviour of excessive internet use was not detailed, however, surveys were conducted before and after the entire intervention. The intervention consisted of 10 sessions of positive psychology, which entailed fostering positive thoughts, behaviour and emotions to sequentially improve overall well-being and reduce negative emotions. The intervention required fostering positive thoughts through community participation and reinforcement of positive emotions towards other people. A strength in this study was the random allocation of participants as this method limited participant variable from biases. Khazaei et. al (2017) conducted the intervention on students with internet addictions in one socioeconomic class. The restriction of diversity in the sample was reported a limitation as it restricted the possibility of evaluating situational differences between the participant and their treatment. The outcome of the study was the alleviation of intensity and rate of internet usage in students identified with internet addiction. The results did not include the duration of the effects of the treatment as the study excluded with no follow-up period.

The aim of this report was to produce an intervention that reduces the frequency of excessive YouTube usage from the participant's behavioural analysis and the three behavioural treatments from Yong (2007), Du et al. (2010) and Khazaei et. al (2017) to eliminate the targeted behaviour of excessive YouTube usage. The behavioural intervention formed in this report was hypothesised to maintain levels of physical and mental well-being by reducing the use of YouTube, thus reducing stress-related emotional states. This case study entailed monitoring the targeted behaviour of Participant B (N =1).

Method

Participant

The monitored participant, ‘B', was identified as a part-time university student and part-time employed 20-year-old male. The behaviour monitored was excessive ‘YouTube usage'.

Operational Definition

‘YouTube usage' was defined as the concurrent or non-concurrent watching and listening of videos on the video sharing service for more than five times a day. An instance of the behaviour was initiated as soon as the participant clicked on a video on the YouTube website/ application. The behaviour concluded when the participant exited the entire YouTube website/ application. A questionable instance that was scored was when he watched videos from any social media website and were sourced (included a link) from YouTube. A questionable instance that was not scored was when videos (sourced from YouTube) were associated with his lecture material and/ or career training. The behaviour was regarded as a concern for the reason that the occurrence of the behaviour was greater than five times daily. A self-monitored event frequency method was utilised to record the frequency of the behaviour daily during a 10-day period.

Materials and Procedure

The method entailed the observation of the time of day, specific individual/s in participant B's proximity, the geographical location, and the thoughts, feelings, and behaviour before and after the targeted behaviour's occurrence. B utilised the self-monitoring method by recording each instance into an electronic Excel spreadsheet immediately after each occurrence. A strength to the self-monitoring event frequency method was B's ability to observe and record his own interpersonal state, such as his physical and mental status associated to the targeted behaviour. Nevertheless, this method entailed a limitation to the replication of results and inferences to a population.

Results

Monitoring of the behaviour focused on the measurement of the targeted behaviours occurrence throughout the 10-day period. As shown in Figure 1, the first six days of the monitoring period consisted of the greatest frequencies within the entire monitoring period. The highest number of instances of the behaviour was revealed to be on the second, fifth and sixth day consisting of a value of six instances (Mean = 4.90, SD = 0.99). On the other hand, the least number of instances were present on the ninth day of the monitoring period with the value of three instances. A downward trend within the second half of the monitoring period showed a slight decline of instances, thus revealing a positively skewed distribution with more instances presented at the beginning of the monitoring period and fewer instances at the end. Overall the 10-day period showed variation between the number of instances, thus revealing inconsistency in the rate of frequency.

Continencies

Historical

• Advanced courses in university and a new work resulted in increase of tension and panic.
• B follows popular YouTubers for 2 years and has been keeping up to date on their status of uploads.

Contextual

• Occurred the most with no one in the vicinity of B.
• Events in the middle of a study session or before and after work.
• When on the bus surrounded by strangers.
• Activities such as watching a uninteresting movie triggered occurrence of behaviour.

Immediate Stimuli

• B would watch in the morning to check notifications of any recent uploads therefore voluntarily checking himself.
• B went on YouTube to check recipes for food.
• Various biological factors related between the stimulus and response were feelings elicited from isolation at work and the physical state of exhaustion from the day before, or after an intense activity, such as work or studying sessions. Therefore, B relied on YouTube usage to overcome feelings of not having interacted and reenergising himself from fatigue.
• Feelings of boredom and disinterest predisposed seeking of entertainment, thus relying on YouTube to find emotionally uplifting content.
• Distraction was recorded to precede targeted behaviour so B could reduce anxiety from having people stare at him or reduce the thought of starvation when waiting for food to be brought out from the restaurant.
• The behaviour being monitored is excessive ‘YouTube usage’. The behaviour is described as the watching and/ or listening of videos on the YouTube website or application abundantly throughout the day. An instance of the behaviour will initiate as soon as the participant has clicked on a video on the YouTube website/ application. The behaviour concludes when the participant exits a video and the entire YouTube website/ application.

Immediate

• Feelings of unhappiness due to spending a lot of time procrastinating
• B no longer felt bored and more enthusiastic to study.
• B felt more energetic
• Feelings of happiness and pride from completing tasks like school work
• Not having school work completed before the due date thus building stress

Long Term

• Eye inflammations and /or loss of vision from excessive blue light exposure
• Chronic migraines from watching for a long period of time
• Obesity
• Not fulfilling goals (i.e. not completing university/ not acquiring a full-time job) from constant procrastination

Behavioural Formulation

Participant B was a 20-year-old male identified with an internet addiction, specifically the behaviour of excessive YouTube usage. For the past two years, B has described an increase in levels of tension- and panic-related feelings due to entering advanced university work and beginning a new job. Furthermore, he has described himself a follower of well-known users on YouTube for over two years. In context, B’s excessive usage of YouTube is present with no one in his vicinity, and in areas surrounded by unknown individuals (i.e. public commuters). The behaviour occurs directly during events that disinterest B, such as watching an uninteresting movie or when feelings of boredom arise during study sessions. B has reported to routinely check his phone every morning when a new video is uploaded and is in interest to him. B reported that he used YouTube as a distraction to reduce his anxiety. B stated that when he used YouTube, he feels a relief from stress and feelings of happiness. However, B has reported to feel unhappy when watching YouTube for a long duration as he could not complete his work on time. The procrastination led to long term consequences of thinking B would not perform well in the future. The outcome of using YouTube relieved B from stress and increased feelings of happiness, hence why he continued to enact on this behaviour.

Discussion

The aim of this report was to collate behavioural data from participant B over a 10-day period to formulate a four-phase intervention that eliminates the frequency of YouTube usage to zero number of instances. The results revealed that there was no consistency in the rate of usage as it fluctuated daily. The time of day was considered an immediate stimuli for the behaviour to occur as B would engage in the behaviour regularly in the morning when he woke up. After using YouTube in the morning, B felt energetic and for this reason B would continually use YouTube on a daily basis. However, a consequence of this routine it’s distractive nature to which built stress from uncompleted school-related work. A long-term consequence was the negative impact on B’s physical health such as migraines and eye inflammations. This may explain why frequency decreased over days two to four and seven to ten as B was aware of the physical consequences of the targeted behaviour. These consequences can then be related to the contingencies (reinforcers and punishers) as positive reinforcement, positive punishment and negative punishment, respectively. As mentioned above, the findings were inconsistent, but all three articles showed consistency as they all supported the hypothesis. The intervention formulated for B was not fully integrated from the three aforementioned as they had large numbers of participants and the costly for this study.

The outcome goal of this intervention is to eliminate participant B’s behaviour of excessive usage of YouTube under conditions not related to university work. With a combination of Skinner’s theory of Operant Conditioning and Young’s (2007) study, this intervention consists of four phases that combines punishment using different schedules of reinforcements and a CBT strategy to attain the specific desired behaviour for each phase. The first phase involves a negative punishment contingency in a variable ratio schedule. The desired outcome of phase one is to have the behaviour occur less than 5 times daily. The second phase entails a positive punishment contingency in a variable interval schedule. The desired outcome of phase two is to decrease the behaviour to occur once daily. The third phase involves a positive reinforcement contingency in a fixed ratio schedule. The desired outcome of phase three is to acquire a form of stress relief and entertainment through a learnt CBT strategy. Lastly, the fourth phase is extinction, where B has learnt not to use YouTube at all for unrelated schoolwork.

Phase one consists of removing B’s mobile phone immediately after enacting on the targeted behaviour for more than five instances a day. By confiscating B’s device, this eliminates the chances of the targeted behaviour occurring more than the defined amount of excessive usage. The consequence would stop at the end of the day by giving back the phone. Intensity of this consequence is considered quite severe compared to phases two and three due to the necessity of his mobile device in his daily life. Once B could maintain using YouTube less than five instances a day for six days, B can progress to phase two. Phase two involves administering an online add-on, ‘website block’, to B’s devices which blocks a user from entering any website. Administering the consequence entails applying the add-on after an unpredictable amount of times for the following two days, which is ideally resistant to extinction. Although the rate to respond to the change of behaviour is slower than phase one, the change is steadily occurring. After B has adapts to using YouTube once daily, B could progress to the next phase.

Phase three entails administering the CBT treatment. As B has reported to practice music, instrumental playing can eliminate the target behaviour whilst relieve the unpleasant emotions of relapse. Administering this strategy of learning a new skill is aimed to prevent relapsing and to cope with stress and anxiety whilst it increases levels of entertainment. The CBT strategy should be administered for the last two days and another 10 days to aid in decreasing the rate of behaviour occurring and diminish the occurrence of a relapse. In the last phase, punishments in both phase one and two will be detracted and B should not act on using YouTube. For this very reason, the behaviour should not be punished anymore as it is an automatic response to B to not think of using YouTube again. Once the intervention is applied, B can invest in more time to think about his future and to also perform well in his studies. Overall the intervention aimed to teach the desired behaviour using scheduling of reinforcement and punishment.

As the treatment was focused on the pattern of the targeted behaviour within a 10-day period, effectiveness of this intervention may be affected due to the inefficient time for the participant to adapt to the change of behaviour from the consequences. Additionally, a limitation to the method of self-monitoring is the idea of using YouTube for university- or work-related activities but getting side-tracked. This disregards the instance as the targeted behaviour. Another limitation to the study is replication of results and application to a population due to individual personality traits.

References

1. Du, Y.-s., Jiang, W., & Vance, A. (2010). Longer term effect of randomized, controlled group cognitive behavioural therapy for Internet addiction in adolescent students in Shanghai. Australian and New Zealand Journal of Psychiatry, 44(2), 129-134. doi:https://doi-org.ezproxy.library.uq.edu.au/10.3109%2F00048670903282725
2. Khazaei, F., Khazaei, O., & Ghanbari-H., B. (2017). Positive psychology interventions for internet addiction treatment. Computers in Human Behavior, 72, 304-311. doi:https://doi.org/10.1016/j.chb.2017.02.065
3. Klobas, J. E., Mcgill, T. J., Moghavvemi, S., & Paramanathan, T. (2018). Compulsive YouTube usage: A comparison of use motivation and personality effects. Computers in Human Behavior, 129-139. doi:https://doi.org/10.1016/j.chb.2018.05.038
4. O'reilly, M., Dogra, N., Whiteman, N., Hughes, J., Eruyar, S., & Reilly, P. (2018). Is social media bad for mental health and wellbeing? Exploring the perspectives of adolescents. Clinical Child Psychology and Psychiatry, 23(4), 601-613. doi:https://doi-org.ezproxy.library.uq.edu.au/10.1177%2F1359104518775154
5. Young, K. S. (2007). Cognitive behavior therapy with Internet addicts: treatment outcomes and implications. Cyberpsychology & behavior : the impact of the Internet, multimedia and virtual reality on behavior and society, 10(5), 671-679.