Social Media Addiction: Effective Intervention through ... › 2016 › ... · For the purpose of...

Running Head: REDUCING SOCIAL MEDIA USE

Social Media Addiction: Effective Intervention through Differential Reinforcement of Other

Behaviour

Name: Samantha McVicar

Student Number: n8547394

Tutor: Michelle Livock

Tutorial: 11am – 12pm Thursday

Due Date: 5th

September, 2016

Word Count: 2,493

REDUCING SOCIAL MEDIA USE 2

Abstract

The purpose of this study was to develop and implement an intervention for internet addiction

behaviour through the operant conditioning technique differential reinforcement of other

behaviour (DRO). It was hypothesised that the intervention would decrease the frequency of

social media use through the positive reinforcement of other behaviour. The participant was

23 year old university student S.M. who feels her excessive social media use is leading to a

dependency and addiction to the online social networking site Facebook. Self-monitoring and

functional analysis methods were utilised in order to understand the contingencies of the

target behaviour and to assist the development of the intervention. The findings supported the

research hypothesis and showed that DRO intervention through positive self-reinforcement of

on-task study behaviour, and high-value activity reinforcement, were effective techniques for

reducing social media use. However, due to the limitations and restricted parameters of this

study, further research is needed in order to provide a greater understanding of the

proficiency of operant conditioning techniques, like DRO, for the treatment of internet

addiction.


Social Media Addiction: Effective Intervention through Differential Reinforcement of Other

Behaviour

Due to the growing accessibility and integration of the internet in daily life, internet

addiction is becoming an emerging global concern (Carlisle et al., 2016; Telef, 2016).

Internet use is now an integral part of work, academics, communication, as well as

entertainment (Carlisle et al., 2016). Social networking sites in particular are increasing in

popularity, resulting in a growing number of users developing maladaptive behaviours

(Przepiorka & Blachnio, 2016). Facebook addiction is defined as excessive or compulsive use

of Facebook causing disruption in daily life (Przepiorka & Blachnio, 2016). More

specifically, Facebook can be utilised as an avoidance strategy, escaping real world problems

and stressors (Przepiorka & Blachnio, 2016).

Internet use is often perceived as pleasurable and provides satisfaction to the user; this

alteration of an individual’s mood can lead to dependence due to reinforcement from the

brain’s reward system (Carlisle et al., 2016; Telef, 2016; Yuan, Qin, Liu & Tian, 2011).

Internet addiction can be defined through compulsive-like behaviours, displaying symptoms

such as: cravings, urges, and disruption of social and occupational functioning (Carlisle et al.,

2016). Research shows high rates of comorbidity between internet addiction and: depressive

symptoms, anger problems, anxiety disorders, as well as social isolation (Przepiorka &

Blachnio, 2016; Ruiz, 2012; Yuan et al., 2011). Growing dependence on the internet can

result in maladaptive behaviours and negative consequences such as low psychological well-

being, and decreased academic and work performance (Carlisle et al., 2016; Telef, 2016;

Yuan et al., 2011). Despite this, research into effective interventions for internet addictions

remains somewhat limited.

Operant conditioning is an effective learning paradigm and behaviour modification

technique in which consequences are used to control and manipulate the frequency of


behaviour (Murphy & Lupfer, 2014). Consequences can be positive or negative and are

defined as either reinforces, used to increase the frequency of behaviour, or punishers, used to

decrease the frequency of behaviour (Murphy & Lupfer, 2014). Differential reinforcement of

other behaviour (DRO) is an effective operant conditioning method for the treatment of

addictive and problem behaviours; delivering reinforcement contingent on the presence of

other behaviours and absence of the target behaviour (Jessel, Borrero & Becraft, 2005;

Karsten & Carr, 2009; Watts et al., 2013).

Watts et al. (2013) explain that DRO is a commonly used and successful technique for

behaviour reduction interventions where the target behaviour occurs in excess. The DRO

process works through reinforcing the occurrence of a more appropriate behaviour that is

unrelated to the target behaviour (Vance, Gresham & Dart, 2012). As such, a reinforcer is

awarded in the presence of the other behaviour and absence of the target problem behaviour

(Jessel et al., 2005).

Vance et al. (2012) explain that DRO, along with a self-monitoring method, can be

effectively used to increase an individual’s on-task behaviour (i.e. study), whilst decreasing

disruptive problem behaviours (i.e. internet addiction). Furthermore, fixed-interval positive

reinforcement schedules are highly effective at decreasing problem behaviour, in particular

when high-value incentives such as leisure activities are awarded as reinforces (Cerutti &

Staddon, 2003; Payne & Dozier, 2013).

A functional analysis methodology will be utilised in this study in order to determine

the contingencies surrounding the problem behaviour of internet addiction (Payne & Dozier,

2013). Functional analysis assists in the formation of measurable and operational definitions,

as well as identifies contextual factors, environmental relationships, and consequences that

help influence and maintain certain behaviours (O’Brien & Carhat, 2011). This technique is


considered highly effective for the treatment of problem behaviours, as it facilitates the

development of unambiguous and measurable interventions (Matson et al., 2011; Payne &

Dozier, 2013).

The purpose of this study was to further supplement research by developing and

implementing an intervention, utilising functional analysis and self-monitoring methods, to

modify internet addiction behaviour through the operant conditioning technique DRO. It was

hypothesised that the intervention would decrease the frequency of social media use through

the positive reinforcement of other behaviour.

Method

Design

This study implemented a single subject experimental design with a baseline phase of

seven days, and intervention (treatment) phase of seven days.

Participant

The participant was 23 year old Caucasian female, S.M., who feels her excessive

social media use is leading to a dependency and addiction to the online social networking site

Facebook. This participant was chosen as she was motivated to change this excess behaviour.

S.M. currently studies a full time university degree at Queensland University of Technology

and works flexible hours form home. S.M.’s maladaptive checking behaviour negatively

impacts daily productivity and motivation, often leading to the procrastination of academic

and work endeavours. Therefore, this type of behaviour has been known to result in negative

consequences for S.M., such as decreased psychological wellbeing, assessment failure, and

social isolation (Ruiz, 2012).


Self-Monitoring Method

S.M.’s target behaviour was social media use, which referred to the intentional act of

accessing and engaging in social networking sites through mobile devices such as

smartphones and tablets. For the purpose of this study, social media was specifically defined

as the social networking site Facebook. This is due to the fact that S.M.’s excessive behaviour

primarily involves Facebook checking, and other popular social media platforms such as

Twitter and Instagram are not used. The behaviour, social media use, was only scored if

S.M.’s engagement on the social media site lasted longer than 5 seconds. This was in order to

account for any ‘accidental’ accessing of the page, for example through already open

smartphone applications. Furthermore, the behaviour was not scored when S.M. accessed

Facebook Messenger, as this was used as a main form of communication and interaction

between S.M.’s friends, colleagues and academic associations.

Event record was the monitoring method utilised for this study. This method scored

each occurrence of the target behaviour, the number of daily social media checks, throughout

both the baseline and intervention phase. Duration recording was also considered as a

monitoring method for this study; however S.M. was not overly concerned with the amount

of time spent on social media, just the excessiveness of her checking behaviour.

A narrative record was kept for each day of the baseline and intervention phase, in

order to tally and record the circumstances surrounding the target behaviour (refer to

Appendix A and B). The running tally was recorded using the ‘Notes’ application on S.M.’s

smartphone and later transferred to the narrative record. This smartphone application was

chosen as a scoring method due to its accessibility and convenience. In this narrative record,


S.M.’s behaviour and experiences were documented in order to assist the development of the

functional analysis.

Functional Analysis of Behaviour

A functional analysis was conducted using the SORCK method (refer to Appendix C)

in order to provide the necessary foundations for developing the intervention. Historically,

S.M.’s behaviour has developed gradually over the duration of her university degree, as a

result of decreasing motivation and increased disinterest in her studies. Contextually, S.M.’s

social media was contingent during times that provoke boredom or disinterest, or times that

lacked social interaction; usually occurring during university lectures, whilst studying, or

when commuting on public transport. Social media use was contingent on immediate stimuli

such as the feeling of boredom (disinterest) or the need for avoidance (procrastination), as

well as the appearance of Facebook notifications. Therefore, boredom, disinterest, lack of

motivation, procrastination, notifications and low social interaction were the main

contingencies that moderated S.M.’s social media use. Immediate consequences of S.M.’s

behaviour included the instant alleviation of boredom and the associated satisfaction of

feeling preoccupied. Delayed consequences of S.M.’s behaviour included falling behind on

study and assignments; resulting in the feelings of stress and guilt that further facilitated

avoidant behaviour that lead to social media use. Therefore, S.M.’s maladaptive social media

use was maintained through a continual cycle of positive reinforcement.

Intervention

The aim of this intervention was to decrease the excess target behaviour, social media

use. The target rate for the intervention phase was to decrease S.M.’s social media use to less

than 10 times a day. This target was chosen as it was the lowest frequency of social media

use during the baseline phase. The intervention aimed to reduce S.M.’s dependence on social


media use during times of boredom or disinterest and as a result a DRO positive enforcement

schedule was utilised (Cerutti & Staddon, 2003; Payne & Dozier, 2013). Since S.M.’s social

media use increased in frequency during designated times of study, successful and

undisturbed fixed-interval study was chosen as the reinforced other behaviour (Cerutti &

Staddon, 2003; Otero & Haut, 2016). Hayes, Munt and Korn (1986) explain that positive self-

reinforcement schedules can improve studying behaviour during short intervention periods.

Therefore, positive self-reinforcement was implemented contingent on every hour of on-task

studying behaviour; during designated study periods, and with the absence of social media

use. Delayed activity reinforcement was also awarded if social media use was below the

target rate at the end of each day (Vance et al., 2012). Therefore, if social media use was

present during the designated study period, or the frequency reached the target rate, then

neither self-reinforcement nor an activity reinforcer was awarded (Jessel et al., 2005; Otero &

Haut, 2016). Activity reinforcers were activities that offered a high-value incentive to S.M.,

such as watching Netflix ‘guilt-free’ (Cerutti & Staddon, 2003; Payne & Dozier, 2013).

Results

The results of the baseline phase and intervention phase are presented in Figure 1

below. In the baseline condition, there was a significantly larger number of instances of social

media use (M = 14, Range = 10 - 18) compared to the intervention condition (M = 7.86,

Range = 6 - 9) (refer to Appendix D). Baseline data showed an increase in instances

throughout the duration of the condition, with both relative and absolute change showing

deterioration (relative change = 1, absolute change = 8). Whereas the data in the intervention

condition remained relatively stable (relative change = 1, absolute change = 0). This is also

reflected in Figure 1 where the baseline phase shows a larger variability in social media use

compared to the intervention phase.


Structured visual analysis (SVA) is one of the most common data-analysis techniques

used for single subject research designs, due to its ability to provide a reliable estimate of

trend in variable data patterns (Gast & Spriggs, 2010). SVA allows data to be inspected

visually in order to easily determine the effectiveness of an intervention (Gast & Spriggs,

2010). The estimate trend in Figure 1, as demonstrated by the quarter-intersect and split-

middle lines, unexpectedly indicated acceleration (an increase in value over time) during the

intervention condition, as well as during the baseline condition (Gast & Spriggs, 2010). This

suggests a deterioration of the intervention over time, as social media use increased during

the condition. However, Gast and Spriggs (2010) explain that SVA trends are merely an

estimate and may not accurately represent the interventions effectiveness. The percentage of

non-overlapping data was 100%.

Figure 1. Structured visual analysis of baseline and intervention conditions

Key

Split Middle

Quarter Intersect

PND Line


Discussion

The purpose of this study was to assess the effectiveness of a DRO intervention for

reducing the excess behaviour associated with internet addiction. The findings of this study

support the hypothesis that the implementation of the DRO intervention, and positive

reinforcement, would decrease the frequency of social media use.

The findings showed that social media use during the intervention phase was

significantly lower and the frequency remained below the daily target rate; with the average

frequency of the intervention phase nearly half of that in the baseline. Furthermore, the

percentage of non-overlapping data indicated that the intervention had a high impact on the

target behaviour social media use (Gast & Spriggs, 2010). This suggests that positive self-

reinforcement of other behaviour, and the high-value activity reinforcer, was successful at

reducing the social media use over a short period. Watts et al. (2013), Cerutti and Staddon

(2003), and Payne and Dozier (2013), support these findings and explain that fixed-interval,

high-value, and positive reinforcement schedules are extremely effective at decreasing

problem behaviour.

The findings suggest that implementation and reinforcement of on-task study

behaviour was an effective ‘other behaviour’ as it helped reduce the frequency of social

media use. This is supported by Vance et al. (2012) who found that DRO, along with self-

monitoring, was able to increase on-task behaviour whilst decreasing disruptive problem

behaviour.

Several limitations were identified throughout this study. Firstly, the short-time frame

for both the baseline and intervention phase may not provide an accurate representation of the

participant’s behaviour, impacting the reliability of the results. External validity is also

considered a limitation to this study, due to the sample consisting of only one participant;


limiting generalizability of the findings (Hayes et al., 1986; Otero & Haut, 2016). The

Hawthorne effect may have also impacted reliability of results as the participant was made

aware of their behaviour during the self-monitoring process; this may have cause a decrease

in the frequency of problem behaviour during the experimental conditions (Sedgwick &

Greenwood, 2015). Although the participant’s behaviour was excessive and exhibited certain

addictive traits, an addiction may not yet have been present, influencing the perceived

effectiveness of the DRO intervention for internet addictions. Furthermore, this study is

limited to one social media platform, Facebook, and does not take into account other social

media networks that may have been utilised during the experiment.

Future research is necessary to further examine the effectiveness of DRO and positive

reinforcement as an intervention (Otero & Haut, 2016). Research should aim to alleviate the

limitations of this study, in order to provide a reliable and generalizable intervention for the

modification of problem behaviours like internet addiction.

The findings of this study suggest that DRO and positive reinforcement are effective

techniques for reducing social media use. However, due to the limitations and restricted

parameters of this study, further research is needed in order to provide a greater

understanding of the proficiency of operant conditioning techniques, like DRO, for the

treatment of internet addiction.


References

Carlisle, K. L., Carlisle, R. M., Polychronopoulos, G. B., Goodman-Scott, E., & Kirk-

Jenkins, A. (2016). Exploring internet addiction as a process addiction. Journal of

Mental Health Counseling, 38(2), 170-182. doi:10.17744/mehc.38.2.07

Cerutti, D.T., & Staddon, J.E.R. (2003). Operant conditioning. Annual Review of

Psychology,54, pp.115-144. doi: 10.1146/annurev.psych.54.101601.145124

Gast, D. L., & Spriggs, A. D. (2010). Chapter 9: Visual Analysis of Graphical Data. In Gast,

David L. and Ledford, Jennifer, Single subject research methodology in behavioral

sciences, (pp.199 - 233). New York: Routledge.

Hayes, S. C., Munt, E. D., & Korn, Z. (1986). The effect of feedback and self-reinforcement

instructions on studying performance. Psychological Record, 3627-37.

Jessel, J., Borrero, J., & Becraft, J. (2015). Differential reinforcement of other behaviour

increases untargeted behaviour. Journal of Applied Behaviour Analysis, 48(2), 402-

416. doi: 10.1002/jaba.204

Karsten, A., & Carr, J. (2009). The effects of differential reinforcement of unprompted

responding on the skill acquisition of children with autism. Journal of Applied

Behaviour Analysis, 42(2), 327-334. doi: 10.1901/jaba.2009.42-327

Matson, J. L., Shoemaker, M. E., Sipes, M., Horovitz, M., Worley, J. A., & Kozlowski, A. M.

(2011). Replacement behaviors for identified functions of challenging behaviors.

Research in Developmental Disabilities, 32(2), 681-684.

doi:10.1016/j.ridd.2010.11.014

Murphy, E. S., & Lupfer, G. J. (2014). Basic principles of operant conditioning. In F. K.

McSweeney, E. S. Murphy, F. K. McSweeney, E. S. Murphy (Eds.), The Wiley

https://qutvirtual3.qut.edu.au/qv/cmd_common_p.show_item?p_item_id=210541

https://qutvirtual3.qut.edu.au/qv/cmd_common_p.show_item?p_item_id=210541


Blackwell handbook of operant and classical conditioning (pp. 167-194). Wiley-

Blackwell. doi:10.1002/9781118468135.ch8

O’Brien, W., & Carhat, V. (2011). Functional analysis in behavioural medicine. European

Journal of Psychological Assessment, 27(1), 4-16. doi:

http://dx.doi.org.ezp01.library.qut.edu.au/10.1027/1015-5759/a000052

Otero, T. L., & Haut, J. M. (2016). Differential effects of reinforcement on the self-

monitoring of on-task behavior. School Psychology Quarterly, 31(1), 91-103.

doi:10.1037/spq0000113

Payne, S. W., & Dozier, C. L. (2013). Positive reinforcement as treatment for problem

behavior maintained by negative reinforcement. Journal of Applied Behavior

Analysis, 46(3), 699-703. doi:10.1002/jaba.54

Przepiorka, A., & Blachnio, A. (2016). Time perspective in internet and Facebook addiction.

Computers in Human Behavior, 60,13-18. doi:10.1016/j.chb.2016.02.045

Ruiz, C. (2012). Internet addiction. Neuropsychiatrie de l’Enfance et de l’Adolescence, 60(5),

118. doi: 10.1016/j.neurenf.2012.05.503

Sedgwick, P., & Greenwood, N. (2015). Understanding the Hawthorne effect. British

Medical Journal (Clinical Research Ed.), 351. doi:10.1136/bmj.h4672

Telef, B. b. (2016). Investigating the relationship among internet addiction, positive and

negative affects, and life satisfaction in Turkish adolescents. International Journal of

Progressive Education, 12(1), 128-135. Retrieved from

http://web.a.ebscohost.com.ezp01.library.qut.edu.au/ehost/pdfviewer/pdfviewer?sid=c

62b7e93-f4d6-4783-abbe-bb2e151f5ecf%40sessionmgr4009&vid=5&hid=4106


Vance, M. m., Gresham, F. M., & Dart, E. H. (2012). Relative effectiveness of DRO and self-

monitoring in a general education classroom. Journal of Applied School Psychology,

28(1), 89-109. doi:10.1080/15377903.2012.643758

Watts, A. C., Wilder, D. A., Gregory, M. K., Leon, Y., & Ditzian, K. (2013). The effect of

rules on differential reinforcement of other behavior. Journal of Applied Behavior

Analysis, 46(3), 680-684. doi:10.1002/jaba.53

Yuan, K., Qin, W., Liu, Y., & Tian, J. (2011). Internet addiction neuroimaging findings.

Communicative and Integrative Biology, 4(6), 637-639. doi: 10.4161/cib.17871


Appendix A - Baseline Narrative Record

Day Date Amount of

Target

Behaviour

Thoughts, Feelings, Ideas for Intervention

1.

12/8/16 10 Day Off – I used social media when I was bored

and to procrastinate doing things I didn’t want to

do, like assignments and study. This made me feel

guilty as I had set aside time specifically to study

and didn’t end up utilising it. I don’t like that I feel

the need to constantly check Facebook, it makes

me feel like I’m dependent on it. Recording my

social media use made me more aware of my

behaviour.

2. 13/8/16 12 Weekend – I was checking social media less when

I was busy enjoying myself and socialising. During

these times Facebook notifications prompt me to

check my newsfeed. Tended to check social media

more during the morning and at night when I was

alone.

3. 14/8/16 16 Weekend – Designated some time to do study but

found myself using my phone to check social

media. I used social media to avoid the fact that I

was meant to study. Felt guilty at the end of the

day because I hadn’t gotten as much done as I had

planned. Seem to use social media more when I’m

meant to study. I would like to decrease my social

media use and increase the amount of study I get

down.

4. 15/8/16 12 Work Day – Again I had aimed to do more study

during my spare time but used Facebook instead as

a distraction. This caused me to feel stressed at the

end of the day. I would feel a lot better if I was


more productive during my spare time.

5. 16/8/16 17 Uni Day – I found myself being easily distracted

and disinterested during lectures. I didn’t get

nearly as much study done during breaks as I had

planned. Used social media to keep myself busy on

public transport. Allowed myself to become easily

distracted by Facebook notifications.

6. 17/8/16 13 Uni Day – Thought that being at Uni would mean

I had less time to check social media, however my

self-control was lacking and I easily gave in to the

behaviour. This was mostly prompted by boredom,

notifications and attempts to procrastinate.

7. 18/8/16 18 Uni Day – Again I used Facebook to keep busy

between lectures/classes, on public transport and

during breaks. I would really like to decrease my

dependence on social media so that I can become

more productive in other aspects such as study.

Thinking that DRO, replacing the behaviour with

study, would be the most beneficial form of

intervention for me.


Appendix B – Intervention Narrative Record

Target Rate: 10 times a day

Day Date Amount of Target

Behaviour

Thoughts, Feelings, Ideas for Intervention

1. 22/8/16 8 Day off – Found myself feeling motivated to be

productive during my designated study time.

Found it easier to decrease the behaviour when I

busied myself with assignment work. Really

focused on proactively using my time. Felt better

at the end of the day for achieving my study goals.

This made the reward of ‘guilt free’ Netflix even

more enjoyable.

2. 23/8/16 8 Uni Day – Worked hard to keep myself focused

and more attemptive during lectures. Found it easy

to check social media less when I was busy doing

assignments. Self-monitoring has made it easier to

manage Facebook use. I am still tempted to use

social media during breaks or when I’m

commuting on public transport.

3. 24/8/16 8 Uni Day – Found the intervention to useful for

getting me motivated. Managed to get some good

assignment prep done instead of wasting time on

Facebook. This made me feel more accomplished

at the end of the day and I got to enjoy my activity

reward of ‘guilt-free’ Netflix for decreasing my

social media use.

4. 25/8/16 6 Day Off – It was easier to enforce my intervention

today as I was out at an event that involved social

interaction most of the day. This kept me

preoccupied and less tempted to use social media

as I was enjoying what I was doing. However,

because I was out all day I was not able to do any


study. Since I was out all day doing something I

enjoyed, and wasn’t able to study, I considered the

day-off as my reward.

5. 26/8/16 8 Day Off – Managed to get a good amount of study

done today and felt really proud of my efforts. My

focus on study made it easy to decrease social

media behaviour. This was made even easier with

the idea of watching ‘guilt free’ Netflix that night.

6. 27/8/16 9 Weekend – Today was the first day during the

intervention that I felt like I was lacking

motivation. I went back to using social media to

procrastinate. However social media use was still

less than the target rate so I still got the reward of

guilt free Netflix.

7. 28/8/16 8 Weekend – I found not checking Facebook easier

today compared to yesterday as I spent most of the

day writing assignments and preoccupying myself

with study.


Appendix C- SORCK analysis

S (Stimuli)

Historical S.M., being in her fifth year of what was meant to be a four year undergraduate

university degree, has gradually become less motivated and disinterested over

the semesters; often looking for more ways to procrastinate in order to avoid the

demands of studying. As well as this, S.M.’s work load has decreased

dramatically, going from part-time work to now working flexible contracted

hours, resulting in her having a larger amount of spare time. However, the

boundary between what should be leisure time and study time is often blurred

and results in S.M. using social media during times that should be designated for

academic activities.

Contextual S.M. would check social media during times where social interaction was

absent, or times that provoked boredom, such as during university lectures or

when commuting on public transport. S.M.’s checking behaviour appeared to

increase dramatically during times that had been set aside specifically for study

as well as during the process of S.M. studying. S.M. is also more likely to check

social media either first thing when she woke up or when she went to bed.

Immediate S.M. would use social media when she was bored or doing something that she

did not want to do or find enjoyable, particularly when it involved things to do

with university. Other stimuli that prompted S.M.’s social media use was the

presence of Facebook notifications, this would result in S.M. unlocking her

phone and checking the social media site. Therefore, S.M.’s behaviour was

either used to alleviate feelings of boredom or disinterest, or to satisfy a burning

curiosity in regards to the notifications.


O (Organismic)

Boredom, disinterest, lack of motivation and low social interaction were the

main variables that moderated S.M.’s social media use. S.M. used Facebook as a

tool to escape or avoid the situation at hand.

R (Response)

Social Media use.

C (Consequences)

Immediate The immediate consequence of S.M.’s social media behaviour was the instant

alleviation of boredom along with the satisfaction of being preoccupied.

Additionally to this, S.M. was able to keep up-to-date with current events as

well as feel ‘in the loop’. However, the feeling of boredom could also

immediately reoccur after social media use if there were no new posts on S.M.’s

Facebook newsfeed since the last check. Another immediate consequence of

S.M.’s behaviour is that it can cause social isolation during the times of social

media use.

Delayed S.M.’s social media use often meant that she was distracted from the important

tasks at hand, such as concentrating in lectures or making progress with study.

This would result in S.M. feeling stressed, guilty and under increased pressure

from falling behind. Consequently this would feed her avoidant behaviour that

sought out further distraction.

K (Contingencies)

S.M.’s social media use is contingent on her levels of interest (or boredom) at

the time. S.M. would use social media as a way to escape or procrastinate during

times of disinterest. This maladaptive behaviour is maintained through positive

reinforcement as social media use alleviates S.M.’s feelings of boredom.


Appendix D – Data Analysis Worksheet

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