Research Work
I have a personal drive and commitment to contribute in a positive manner to society. I wish to become a part of a movement that will strengthen local and global communities and assist in implementing new strategies and ways of thinking that will change the whole world for the better. I am so passionate about research and evidence-based research and enjoy undertaking investigation and enquiry work to further my research and understanding of several sociological and psychological topics and fields. Having written a sociology thesis based on quantitative evidence-based methods, I am well practiced in seeking, prioritising and synthesising quality research to generate new insights and implementations. For real change to be supported and enacted, it is vital that the evidence-based research is accessible, systemic and transparent. As a passionate researcher and also a Mother of four, I am extremely mindful of the benefits of critical thinking and research to influence positive and proactive change to improve not only our communities, our country, but also the world.
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Can Online Smartphone Addiction caused by Operant Conditioning be Reversed?
A Literature Review
Online technologies, such as the smartphone, are pervasive across our society and are growing at a rate that exceeds our understanding of their implications. Much of the technology relies on methods of operant conditioning (based on a system of rewards and punishments for prior actions) to keep the user engaged through pleasurable and rewarding experiences, or even as a means of relief from negative moods (Wang, 2020). Over 82% of Australians own a smartphone (Winskel et al., 2019), therefore as rates of smartphone usage increase there can be subsequent behavioural addiction implications. Research is sparse, but some studies have reported how smartphone apps generate self-awareness of usage through Ecological Momentary Assessment/Intervention (EMA/EMI), decreasing addictive habits and behaviours (Runyan et al., 2013). Mindfulness behaviour implemented through apps may be more effective in reversing operant conditioning smartphone technology than traditional Cognitive Behavioural Theory (CBT) treatments which are not implemented through the technology itself (Brewer, 2019).
Andreassen and Pallesen (2014) posit that the motivation to use online technologies can be so strong that it impairs other social activities, studies/job, interpersonal relationships, and/or psychological health and well-being, causing addictive behaviours. The World Health Organisation (WHO) has now recognised the importance of online addictive treatment tools, defining mobile health (mHealth) as representing the promotion of mental health, wellbeing, and treatment through the mobile phone (Lam and Lam, 2016). Research has shown that smartphone addictive behaviours can be treated through software tools, using the principles of operant conditioning. Ko et al.(2015) implemented an incentivized goal setting and rewards group based software tool called NUGU (when No Use is Good Use) on participant’s smartphones. This software elicited significant positive results in addressing over-use and addictive tendencies by making participant’s usage habits socially accountable and also through reinforcing positive usage behaviour. This generated self-awareness and mitigated uncontrolled and impulsive usage.
Research into the assessment and measurement of smartphone usage has found real-time EMA/EMI app-generated data, rather than self-reported questionnaires (which often are subject to self-reflexive bias) more effective in providing accurate usage data (Wu et al., 2020). A study conducted by Runyan et al. (2013) provided smartphone users with real-time insight into their usage behaviour through the app iHabit. This elicited positive behavioural change through self-awareness, with 80.49% of participants reporting that the app made them more aware of how they spent their time, and 44% of participants agreed they had changed their behaviour because of the app. Recent research conducted by Schmuck (2020) found that 41.7 percent of young adults now use digital detox apps. EMA/EMI and mHealth apps elicit a greater understanding of smartphone usage and draw self-awareness to user habits, therefore decreasing addictive behaviours.
Studies have found that it can be difficult to ascertain if addictive smartphone usage is separate from the experience of actual pathological symptoms of the smartphone user (Kuss & Griffiths, 2017). There is even strong conjecture regarding the definition of smartphone addiction due to its eclectic nature (Wu et al., 2020). The current inability to identify smartphone addiction within a nosological capacity is one of the reasons why traditional CBT methods in clinical treatment settings are preferred. In addressing maladaptive cognitions exacerbated through external issues, CBT treatments address smartphone addiction through therapy sessions in support groups, setting goals and selective abstinence (Sharma and Palanichamy, 2018). Brewer (2019) however, argues that the pervasiveness of online technologies makes CBT methods such as abstinence, outdated and ineffective. Instead, he focuses upon the need to treat smartphone addiction through the technology itself as supported by the meta-analysis of neuroimaging data that display how the value of positively reinforcing behaviours encoded in the orbitofrontal cortex (OFC) of the brain can be harnessed by mindfulness technology.
There are various limitations within the studies, exacerbated by the research field being so young. As outlined by Kuss & Griffiths (2017), differing diagnostic criteria, and addiction scales based on varying theoretical frameworks and self-awareness assessment methods, continue to question the validity and correlational veracity of the research. App glitches with EMA/EMI technology and small socio-demographic sample sizes (Runyan et al., 2013) may not translate in different target populations. Gower and Moreno (2018) labeled EMA apps as burdensome to participants and sought to study new methodologies. Furthermore, research would benefit from in-depth analysis of longitudinal studies (Ko et al., 2015). Caution must also be used when ascertaining the effectiveness of apps and software as some researchers are the developers and have vested interest in their success.
In conclusion, research into varying aspects of smartphone addiction and its different fields of specificity is sparse and complex. Traditional CBT treatments are challenged by smartphone apps that use EMA/EMI to obtain real-time data and treat addictive behaviours through self-awareness, mindfulness, and operant conditioning principles to elicit positive change in behaviour and habits. These apps therefore, may reverse addiction through greater self-awareness generated by operant conditioning processes. As smartphones are now an integral part of life, it is imperative that further smartphone addiction studies are conducted to ascertain the implications this technology has on individual behaviour and society as a whole.
The direction of future research needs to include the creation of a comprehensive taxonomy of online addiction to raise clinical significance, targeted treatment, and validity of measurement tools (Wu et al., 2020). Further research requires the inclusion of psychopathological influences on addictive smartphone behaviour, ideally with CBT conceptual understandings coupled with mindfulness apps, to draw attention to addictive smartphone usage (Lam and Lam, 2016). The use of the smartphone has proliferated through most demographic groups, and for many people of lower socio-economic status, the smartphone is their only access to technology (Lucas-Thompson et al., 2019). Therefore, EMA/EMI apps and their associated operant conditioning treatment interventions, implemented through smartphone technology, are the most accessible and relevant way in which to assess, intervene and treat all manner of addictions. mHealth requires continual research and refinement to effectively utilise such a pervasive and easily accessible tool that has the ability to generate the best outcomes for society.
References
Andreassen, C.S. & Pallesen, S. (2014). Social Network Site Addiction – an overview. Current pharmaceutical design, 20(25), 4053–4061. https://doi.org/10.2174/13816128113199990616
Brewer, J. (2019). Mindfulness training for addictions: has neuroscience revealed a brain hack by which awareness subverts the addictive process? Current Opinion in Psychology, 28 (1), 198-203. https://doi.org/10.1016/j.copsyc.2019.01.014.
Gower, A. D., & Moreno, M. A. (2018). A Novel Approach to Evaluating Mobile Smartphone Screen Time for iPhones: Feasibility and Preliminary Findings. JMIR mHealth and uHealth, 6(11), e11012. https://doi.org/10.2196/11012
Ko, M., Yang, S., Lee, J., Heizmann, C., Jeong, J., Lee, U,. Shin, D., Yatani, K., Song, J., Chung, K. (2015). NUGU: A group-based intervention app for improving self-regulation of limiting smartphone use. CSCW ’15: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing February 2015, 1235–1245. https://doi.org/10.1145/2675133.2675244
Kuss, D.J., and Griffiths, M.D. (2017). Social Networking Sites and Addiction: Ten Lessons Learned. Int J Environ Res Public Health, 14(3), 311. https://doi.org/10.3390/ijerph14030311
Lam, L.T. and Lam, M.K. (2016). eHealth Intervention for Problematic Internet Use (PIU). Current psychiatry reports, 18(12), 107. https://doi.org/10.1007/s11920-016-0747-5
Lucas-Thompson, R.G., Broderick, P.C., Coatsworth, J.D., & Smyth, J.M. (2019). New Avenues for Promoting Mindfulness in Adolescence using mHealth. Journal of Child and Family Studies, 28(1): 131-139. https://doi.org/10.1007/s10826-018-1256-4
Runyan, J. D., Steenbergh, T. A., Bainbridge, C., Daugherty, D. A., Oke, L., & Fry, B. N. (2013). A smartphone ecological momentary assessment/intervention “app” for collecting real-time data and promoting self-awareness. PLOS ONE, 8(8), Article e71325. https://doi.org/10.1371/journal.pone.0071325
Schmuck D. (2020). Does Digital Detox Work? Exploring the Role of Digital Detox Applications for Problematic Smartphone Use and Well-Being of Young Adults Using Multigroup Analysis. Cyberpsychology, behavior and social networking, 23(8), 526–532. https://doi.org/10.1089/cyber.2019.0578
Sharma, M.K. & Palanichamy, T.S. (2018). Psychosocial interventions for technological addictions. Indian Journal of Psychiatry (60). S541-S545. https://doi.org/10.4103/psychiatry.IndianJPsychiatry_40_18
Wang, X. (2020). Mobile SNS Addiction as A Learned Behavior: A Perspective from Learning Theory. Media Psychology 23. (4). 461-492. https://doi.org/10.1080/15213269.2019.1605912
Winskel, H., Kim, T-H., Kardash, L., Belic, I. (2019). Smartphone use and study behaviour: A Korean and Australian Comparison. Heliyon 5. https://doi.org/10.1016/j.heliyon.2019.e02158
Wu, Y-L., Lin, S-H., Lin, Y-H. (2020). Two-dimensional taxonomy of internet addiction and assessment of smartphone addiction with diagnostic criteria and mobile apps. Journal of Behavioral Addictions. https://doi.org/10.1556/2006.2020.00074
Beth Herbert Copyright 2022.
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Growth Mindset Program For First Year University Students Identifying as Dyslexic -
A Research Proposal
The following is a part of my Post Graduate Diploma of Psychology research proposal and ethics application regarding developing and implementing a Growth Mindset program for first year University Undergraduate students that identified as dyslexic.
Research suggests that dyslexia affects 4-12% of the entire population, but dyslexic students are greatly under-represented at University often due to unequitable educational access, generating lower results and retention levels in comparison to those students with no reported disabilities (MacCullagh et.al., 2016). Dyslexia is a learning difficulty characterised by low levels of reading fluency and problems developing effective word-decoding strategies (Warmington et.al., 2013). Studies have shown that dyslexia extends into adulthood and despite their reading difficulties, people with dyslexia display average or above average intellectual aptitude (Wisehart and Altmann, 2017). Previous studies have demonstrated that growth mindset interventions assist in improving dyslexic school-age student’s mindset through various interventions (de Carvalho and Skipper, 2019). However, no known studies have investigated the effect of a growth mindset intervention for dyslexics at University level, where the learning environment is vastly different. Dweck’s (1999) Growth Mindset theory describes how intelligence and other attributes are malleable and able to be changed with applicable learning strategies, combined with effort (Goegan et.al., 2021). In contrast, a fixed mindset is when the person believes that intelligence and other attributes are innate and unchanging, often reflective of students with learning disabilities (Yeager and Dweck, 2012). This study proposes that a growth mindset intervention for dyslexic University students in their first year of study will potentially enhance effort, generating effective strategies and equitable educational access in a new learning environment, resulting in future higher rates of University retention and a greater number of dyslexic students completing their Higher Education degrees.
The research aims to explore whether an online growth mindset intervention called Brainology (Snipes et al., 2012) will encourage a growth mindset for dyslexic students in a new and challenging University learning environment. This experimental design research will explore the impact of the intervention on randomly selected First Year dyslexic university students over a two-week period with their growth mindset scores as collated from the validated Dweck Mindset Instrument (DMI) (Dweck, 1999), compared to those First Year dyslexic University students that did not participate in the intervention.
It is hypothesized that the First Year dyslexic University students (experimental group) that participate in the growth mindset intervention will score higher on the Dweck Mindset Instrument (DMI) (Dweck, 1999) as administered after the intervention period, in comparison to the First Year dyslexic students (control group) that did not participate in the intervention.
Participants who are interested in participating in the study will be directed to the study website where they will find further information and be able to register their email address to express interest. The prospective participants will be emailed an Explanatory and Consent form, which are required to be read and signed before embarking on the study requirements. After this process, the participants will be emailed information regarding the requirements depending upon which group they have been randomly allocated to. All participants will access the materials from their Moodle dashboards. They must all fill in their short five-minute Demographic survey with information on their birth date, gender, and the university course that they are enrolled in. There are no targeted gender ratios for this study. This experimental study is a between-subjects independent measures design, with the independent variable having two levels – control and experimental (who complete a growth mindset intervention). The dependent variable is the growth mindset measurement (Dweck Mindset Instrument (DMI), 1999). The study hypothesises that the First Year University dyslexic students (experimental group) that participate in the growth mindset intervention will score higher on the DMI as administered after the intervention period, in comparison to the First Year University dyslexic students (control group) that did not participate in the intervention.
Participants in the experimental group will have two weeks to complete the online growth mindset intervention, Brainology (Snipes et al., 2012), completing tasks and activities in their own time, equating to around 50 minutes. These will be easily accessed from their Moodle dashboard. The program will involve a series of five, short 10-minute activities that incorporate self-reflection; emotions, stress and learning understanding; goal setting; growth mindset; and effective effort. Any participant that is not in the experimental group will be offered access to the Brainology program after completing the DMI questionnaire. The Brainology responses will not be recorded, the only data that is officially recorded is the de-identified demographic and DMI questionnaire responses.
After the growth mindset intervention has been completed, all participants from both the experimental and control groups will complete the DMI online and submit their results (approximate time 5-10 minutes). The DMI is a validated 6-point Likert mindset scale (ordinal) with the summed data set measured and analysed as interval data. Each question is scored on a response scale from 1 (Strongly Agree) to 6 (Strongly Disagree), with every even question reverse scored (6-1). The range of scores reflects your mindset, with a high score indicating a high growth mindset. The DMI is composed of questions such as “You have a certain amount of intelligence, and you really can’t do much to change it” and “You can change even your basic level of talent considerably”. Based on a power analysis using G*Power 3.1, for a two group independent samples t-test design, medium effect size (0.5), and power = .80, a minimum of 128 participants (with 64 in each group) will be required for this study. The sampling method is convenience.
Data output from the study will be sent automatically to the University computer and stored in a de-identified and coded database. There is low risk limited disclosure involved in this study to minimise response bias and all participants will be fully debriefed via email when the study is completed. Participants are able to contact the Chief Investigator at any time and may also request a summary of the study and an email link to any published articles. Participants are able to withdraw at any time and the explanatory statement offers information for services that may be required for assistance or mental health.
Students that have registered as dyslexic with the university Disability Service and have opted-in to be contacted for any research studies will be contacted to see if they would like to register their interest. Facebook advertisements on the various University Facebook pages and Twitter accounts, flyers posted to notice boards around the University campuses, and information to register published in all of the Faculty newsletters. A website will be set up as a point of contact for registration to participate. Once participants have registered their interest and their email addresses and mobile phone numbers, they will be contacted with an explanatory statement and consent forms. After registering their interest to participate in the study and retuning their signed explanation and consent forms, half of the participants will be randomly allocated into the Experimental (growth mindset intervention) group. After registering their interest to participate in the study and retuning their signed explanation and consent forms, half of the participants will be randomly allocated into the Control group.
There are no anticipated risks in taking part in the study and questionnaire submissions will be completely de-identified. Participants may feel slightly uncomfortable in personal reflection activities, but this is not seen as a long-term risk. They may also feel inconvenienced at having to spend personal time on the activities, however tasks are short and can be completed in their own time. If not initially chosen in the growth mindset program group, disappointment may be averted through the explanatory statement expressing that those participants will be offered the program after the DMI questionnaire has been submitted. There is limited disclosure in the initial information and explanatory statement in regards to the study. This is deemed low risk. To minimise bias and maintain internal validity in the study so responses are not influenced by the knowledge that the program is a growth mindset intervention and that the questionnaire is measuring mindset, it is stated that the study is about First Year dyslexic University students wellbeing.
Limited disclosure of the true nature of the study will influence participants’ responses, therefore, “growth mindset” has been replaced by “wellbeing” in the explanatory statement. This is low risk. Participants will be debriefed through their contact email at the end of the study, so they are made fully aware of the study’s aims and also to outline the growth mindset program they had been administered. All participants will be made aware that the questionnaire was measuring mindset and not wellbeing. The debrief will also explain that it was necessary to disguise that the study was a growth mindset intervention to minimise response bias and maintain internal validity.
Participants can complete the program and also the questionnaire in their own time. A progress bar will be displayed on the Moodle to show how many activities they have completed, as to alleviate boredom. Participants will not receive their scores for the DMI, so therefore they cannot interpret them. They are also volunteering self-information, so it is not anything that they do not already understand about themselves. A thorough debrief, giving a comprehensive overview of growth mindset and the DMI measurement scale and the aims and study will be provided to each participant via email at the end of the study.
Growth Mindset Intervention – Brainology (Snipes et.al., 2012). Brainology is a growth mindset program that will require participants to complete short activities that incorporate self-reflection tasks, activity tasks on emotions, stress and learning understanding; goal setting; growth mindset; and effective effort. These activities encourage a growth mindset and are completed through the participants Moodle site. Please see the Procedures section for more information on this intervention.
The basic Demographic questionnaire, the growth mindset intervention (Brainology), and the validated DMI questionnaire will all be accessed through the participants Moodle. All participants will fill in the basic Demographic questionnaire, which requires the student’s age, gender, and university course that they are enrolled in. (Please see the Procedures section). The experimental group will access the growth mindset intervention activities from the Moodle (please see the Procedures section for the details of the growth mindset intervention). The short 10-minute Growth Mindset questionnaire (DMI) will be administered on both the Experimental and Control groups after the Experimental group has participated in the Brainology intervention over a two-week period. The questionnaire is the validated DMI comprising of 16 growth mindset questions on a 6-point Likert scale (please see the Procedures section). Once the participants have pressed “Submit”, their de-identified and coded demographic and DMI responses will be sent straight to the database, collated and recorded by the Chief Investigator.
Participants will be provided with a debriefing statement sent to their contact email at the end of the study. This will outline the aims of the study, and give information about the growth mindset program and the DMI questionnaire that they had completed. The debrief will explain that limited disclosure was necessary in order to minimise response bias and maintain internal validity. Participants will also have the opportunity to ask the Chief Investigator questions regarding the study after they have been debriefed.
https://www.techtello.com/fixed-mindset-vs-growth-mindset/
Beth Herbert Copyright 2022
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