Diagnosing Attention Deficit Hyperactive Disorder (ADHD) in early childhood continues to prove problematic for pediatric psychological service providers. Traditional diagnostic methods have relied on children being able to engage with higher-order processes in tests which simply aren’t feasible for young children.
This inability to test is further compounded by difficulties inherent in observation-based diagnosis processes. Gilberg (2010) explains that for children 6 years old and younger it is often very difficult to differentiate between disorders and that it is common for multiple conditions to be present simultaneously (Gillberg, 2010). To resolve this problem, Gilberg helped to develop a diagnostic paradigm, ESSENCE, designed to improve the accuracy of ADHD diagnosis in young children (Gillberg, 2010).
A more recent study conducted by Alexandre, Lange, Bilenberg, Gorrissen, Søbye & Lambek (2018) has attempted to correct for noise and other problems inherent early childhood diagnosis of ADHD (Alexandre, et al., 2018). Specifically, this study performed a series of tests assessing symptom frequency in kindergarten age children. The study was run by observing a group in two settings. One observation was performed at home and the other was performed in kindergarten class rooms. Key findings from this study were the observation of regular differences between home and school-based observation on a symptom-specific basis (Alexandre, et al., 2018). The exact causes of ADHD are not known.
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In a recent study, Morgan, Staff, Hillemeier, Farkas & Maczuga (2013) showed significant difference in ADHD diagnosis rates for adolescents based on race and ethnicity; however, their conclusion was not able to attribute a specific cause. None of the confound variables tested for were proven to be significant (Morgan, et al., 2013). In another study Burke, Loeber & Lahey (2001) established positive correlations between household tobacco usage and the magnitude of expression for specific ADHD symptoms; however, they were not able to determine if household tobacco usage increases the chances of an ADHD diagnosis (Burke, Loeber & Lahey, 2001).
A study that was able to find a causal correlation between ADHD diagnosis rates and a predictive variable found that the number of hours children spend watching TV in early childhood correlates to higher diagnosis rates (Christakis, Zimmerman, DiGiuseooe & McCarty, 2004). In their work, Christakis & colleagues found that as little as 3 hours of TV per day could contribute to as much as a 28% increase in diagnosis probability by age 7 (Christakis at al., 2004).
Another more recent study has shown positive correlations between smartphone usage and the severity of ADHD symptom expression in adolescents (Kushley, Proulx & Dunn, 2016). More broadly, studies have also shown and inverse correlation between attention ps and cellphone usage for non-ADHD persons as well as increased cellphone usage rates for adolescents and young children (Kushley, Proulx & Dunn, 2016; Rosin, 2013).
Prior to this study, I completed work on XYZM in response to research which indicated potential links between technology use and reduced attention ps / focus levels in adults (Taslim, 2018). The combination of work performed by Gillberg and Alexandre’s research group allowed me (2018) to pursue development of the XYZ Measure questionnaire (XYZM) (Taslim, 2018). XYZM is a sophisticated collection of Likert scale response questions which can be provided to parents and/or caregivers in order to assess technology use and ADHD symptoms.
What makes XYZM so unique is that its use of control and decoy questions allows its scoring to more-accurately predict the age at which an ADHD diagnosis can be confirmed. XYZM also provides minimally biased electronics use report data using a series of adjustments/corrections designed to compensate for caregiver bias. In my clinical trials I, Taslim, (2018) confirmed XYZM’s ability to perform as intended (Taslim, 2018). Specifically, it has been established that XYZM consistently produces accurate technology use measurements data and adjusted age of diagnosis data under test conditions.
Taslim’s work is important because XYZM is one of the first-ever standardized and clinically proven tools for observational diagnosis of early childhood ADHD and it is also an effective tool for observational reporting of technology use. In my study, I plan to build on my, Taslim’s, previous work by exploring correlations between technology use and diagnosis age for ADHD persons. Specifically, I will use a modified version of XYZM to perform a retrospective survey of parents of children diagnosed with ADHD. I will deploy my survey by targeting support groups for parents of ADHD children and leveraging their membership bases for responses.
The retrospective survey will provide comparative data for technology use as compared to specifically touch screen use while also comparing XYZM-predicted diagnosis ages to actual historic diagnosis ages. The purpose of providing this comparative data is to lay the foundation for future investigations into technology use and ADHD diagnosis linkages.
My project is important because establishing this foundation for future research may eventually help to prescriptive understandings of causal links between specific technology uses and attention-based disorder occurrences, particularly ADHD, in young children. Such understandings, should they be achieved, would prove invaluable in the development of new ADHD diagnosis, treatment, and prevention methodologies.
Based on the research discussed, I expect to find positive correlations between types of technology used and ADHD diagnosis rates. A higher diagnosis rate is taken to mean an earlier age of initial diagnosis. It is hypothesized that (H1) age of diagnosis will be inversely correlated with increased XYZM technology use scores; that (H2) XYZM adjusted age of diagnosis will be more consistently and significantly correlated with increased XYZM technology use scores; and that (H3) significant covariance will exist between different XYZM technology use measures as they relate to outcome measures.
If H1 is supported, it will provide evidence supporting correlation between early technology use ADHD diagnosis. If H2 is supported, it will provide evidence that adjusting for confound variables using a sophisticated 3rd party observational technique is an effective means of evaluating ADHD diagnoses in young children. If H3 is supported, it would provide evidence that specific elements of technology use, Ex: touch screen exposure, have unique impacts upon ADHD symptoms and related diagnoses. Taken together, if all 3 hypotheses are confirmed then my study model will have been proven an effective platform for further investigations of ADHD diagnosis and technology use relationships.
I will be leveraging the XYZM to conduct a proof of concept study investigating links between specific technology usage scores and ADHD diagnosis rates for young children. To serve as a robust foundation for future research, my survey must engage a wide and normally distributed sample from the general population of ADHD diagnosed persons. The study will require considerable time to accrue enough response data. Compensation may be required to induce participation. If I am resource constrained, study participation can be scaled up/down modularly by increasing/decreasing the number of support groups with which we develop relationships.
Target participants are persons who were formally children diagnosed with ADHD between the ages of 1 and 7 years old. Response data will be collected using retrospective analysis provided by the subjects’ primary caregivers. Subjects for which response data is received must be widely varied in their race, gender, and socioeconomic status; ideally, I would hope to achieve normal distribution across as many demographic variables as possible.
Primary caregivers will be indirectly. I will target support groups which offer services to caregivers of ADHD diagnosed persons whom I will then use to reach current primary caregivers. It is possible compensation will be required to access support group memberships. If compensation is required, we will attempt to offer as little as is required to induce enough responses. It is also possible compensation will be required to induce primary caregivers to complete the time-consuming XYZM.
In both cases, compensation expenses are not expected to exceed $10 (USD) per respondent and not more than $5,000 (USD) in total. Note, among current primary caregivers, emphasis will be placed on caregivers for children currently aged 2-8 at the time of the study who were diagnosed within the last 12 months. If possible, limiting responses to current providers will help to minimize bias introduced by the retrospective nature of my proposed data collection. Note, it will be important to observe all applicable medical privacy laws re: ADHD diagnoses and to encrypt/secure/anonymize participant data as needed.
XYZM is an adaptive and sophisticated measure of questions which is administered remotely via a web browser interface. Participants will be able to login and participate via session-specific direct links emailed to them. Note, XYZM is not mobile compatible at this time and must be administered using a desktop computer with an up-to-date web browser. Each XYZM session will take participants between 30 and 60 minutes to complete. The significant completion time variance is the result of XYZM’s adaptive nature.
For a given participant, their experience will be as follows: First, they will be contacted by a representative from their support group. Second, they will review the prepared survey marketing materials (email/flyer). Third, if they are interested, they will follow a general link provided to setup an online account and generate a session code.
Fourth, they will receive an email with a session code which they will then use to login to an XYZM session. Fifth, they will complete the XYZM session and receive a confirmation message containing a unique confirmation code upon completion. If compensation is given to participants, it will be done so via redemption of confirmation codes. Following these steps will ensure only unique identities are scored. The use of confirmation codes for compensation will also allow for optimizing compensation strategies for future studies.
XYZM contains many measures; however, the only measures our study is concerned with using are touch screen use, screen use, age of diagnosis, and adjusted age of diagnosis. (Note, the adjusted age of diagnosis is an algorithmic output of the XYZM. Values produced represent the age at which the child would have been diagnosed with all other factors being equal.
Age outputs range from 1 to 7 years old). The first two variables are independent variables being used to measure specific types of technology usage. XYZM aggregates a series of adaptive Likert scale question responses to produce an index score for each ranging from 1 to 10 with 1 being the lowest possible amount/degree of the specific technology usage and 10 being the highest possible amount/degree.
Age of diagnosis is a self-reported dependent variable, respondents are asked at what age their subject was diagnosed. Adjusted age of diagnosis derived from a series of corrective steps designed to minimize and normalize the impact of confound variables upon diagnosis age across racial, gender, ethnic, socioeconomic, and geographic conditions. Adjusted ages of diagnosis range from 1 to 7 years old. For a full and complete description of how XYZM’s measures are derived and scored, please reference Taslim’s report (Taslim, 2018). See Appendix A, Table 1 for predicted results and see Appendix B for related scatter plot visualizations.
Data Analysis Plan
The overall intent of this study is to establish preliminary correlations between several variables measured. Specifically, I plan to establish correlations between the independent variables of different types of technology use and dependent variables of diagnosis and adjusted diagnosis age. I will establish correlations by running correlation tests using the SPSS statistical software package. Correlation tests are appropriate for establishing correlations because no additional testing is required.
Specifically, correlation tests are expressly designed to establish correlations, there is no more-appropriate test. Assuming one or more theorized correlations exist, I also plan to compare their magnitudes to confirm H2 and conduct an analysis of variance (ANOVA) between the four variables to address H3. An ANOVA is appropriate in this case because it will help isolate variance between groups so that more-accurate comparisons can be made. Based on literature reviewed, I predict significant correlations will exist; however, it is unclear what the interrelationships will be between said correlations. Confirming H1 should be likely; however, I am less confident about confirming H2 and H3.
Because the overall purpose of this study is to establish preliminary findings as the basis for future work, and because it is unclear what baselines to expect, I plan to set my initial p-value requirement at .1 with a two-tailed t-test. Using this relatively low threshold for significance will allow me to cast a wide net in my analysis. Use of the two-tailed t-test is important because I am unable to predict in which direction my predictions may be incorrect. Note, higher degrees of confidence will be used where applicable; however, lower degrees of confidence will be considered as failing to reject their related null hypotheses.
I am measuring two related independent variables. The first (IV1) is touch screen technology usage as measured by XYZM. The second (IV2) is screen technology usage as measured by XYZM. It is theorized the interactive nature “touch” technologies will cause them to be more significant predictors of ADHD than their “non-touch” counterparts.
I am measuring one dependent variable in two different ways. The first (DV1) is a simple self-reported value of the age at which a given participant was diagnosed with ADHD. The second (DV2) is an adjusted age of diagnosis value derived from XYZM’s adaptive algorithm. It is theorized adjusted age values will more-closely match overall IV-DV correlation trends across varied demographic clusters.
For H1 I expect to find strong inverse correlations between age of diagnosis and technology use: The higher a participant’s technology use score, the lower their age of diagnosis. For H2 I expect to see stronger correlations for DV2 with IV1/IV2 than for DV1, I also expect an ANOVA to reveal reduced variance within DVD2 response groups as compared to variance within DV1 response groups. For H3 I expect to see a stronger and/or more significant inverse correlation between IV1 and DV1/2 than for IV2: Touch screen usage is a stronger predictor of ADHD diagnosis than screen usage. See Appendix A, Table 1 for predicted results and see Appendix B for related scatter plot visualizations.
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