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48 Educational Differences in Digital Clock Drawing for the Command Condition: A Bayesian Network Analysis

Published online by Cambridge University Press:  21 December 2023

Emily F Matusz*
Affiliation:
University of Florida, Gainesville, FL, USA.
Brandon E Frank
Affiliation:
Boston University, Boston, MA, USA.
Catherine Dion
Affiliation:
University of Florida, Gainesville, FL, USA.
Udell Holmes III
Affiliation:
University of Florida, Gainesville, FL, USA.
Yonah Joffe
Affiliation:
University of Florida, Gainesville, FL, USA.
Sabyasachi Bandyopadhyay
Affiliation:
University of Florida, Gainesville, FL, USA.
Parisa Rashidi
Affiliation:
University of Florida, Gainesville, FL, USA.
Patrick Tighe
Affiliation:
University of Florida, Gainesville, FL, USA.
David J Libon
Affiliation:
Rowan University, Stratford, NJ, USA
Catherine C Price
Affiliation:
University of Florida, Gainesville, FL, USA.
*
Correspondence: Emily F. Matusz University of Florida, Gainesville, FL [email protected]
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Abstract

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Objective:

Research shows that highly educated individuals have at least 20 graphomotor features associated with clock drawing with hands set for '10 after 11' (Davoudi et al., 2021). Research has yet to understand clock drawing features in individuals with fewer years of education. In the current study, we compared older adults with < 8 years of education to those with > 9 years of education on number and pattern of graphomotor feature relationships in the clock drawing command condition.

Participants and Methods:

Participants age 65+ from the University of Florida (UF) and UF Health (N= 10,491) completed both command and copy conditions of the digital Clock Drawing Test (dCDT) as a part of a federally-funded investigation. Participants were categorized into two education groups: < 8 years of education (n= 304) and > 9 years of education (n= 10,187). Propensity score matching was then used to match participants from each subgroup (n= 266 for each subgroup) on the following demographic characteristics: age, sex, race, and ethnicity (n= 532, age= 74.99±6.21, education= 10.41±4.45, female= 42.7%, non-white= 32.0%). Network models were derived using Bayesian Structure Learning (BSL) with the hill-climbing algorithm to obtain optimal directed acyclic graphs (DAGs) from all possible solutions in each subgroup for the dCDT command condition.

Results:

Both education groups retained 13 of 91 possible edges (14.29%). For the < 8 years of education group (education= 6.65±1.74, ASA= 3.08±0.35), the network included 3 clock face (CF), 7 digit, and 3 hour hand (HH) and minute hand (MH) independent, or “parent,” features connected to the retained edges (BIC= -7395.24). In contrast, the > 9 years of education group (education= 14.17±2.88, ASA= 2.90±0.46) network retained 1 CF, 6 digit, 5 HH and MH, and 1 additional parent features representing the total number of pen strokes (BIC= -6689.92). Both groups showed that greater distance from the HH to the center of the clock also had greater distance from the MH to the center of the clock [ßz(< 8 years)= 0.73, ßz(> 9 years)= 0.76]. Groups were similar in the size of the digit height relative to the distance of the digits to the CF [ßz(< 8 years)= 0.27, ßz(> 9 years)= 0.56]. Larger HH angle was associated with larger MH angle across groups [ßz(< 8 years)= 0.28, ßz(> 9 years)= 0.23].

Conclusions:

Education groups differed in the ratio of dCDT parent feature types. Specifically, copy clock production in older adults with < 8 years of education relied more heavily on CF parent features. In contrast, older adults with > 9 years of education relied more heavily on HH and MH parent features. Individuals with < 8 years of education may more infrequently present the concept of time in the clock drawing command condition. This study highlights the importance of considering education level in interpreting dCDT scores and features.

Type
Poster Session 08: Assessment | Psychometrics | Noncredible Presentations | Forensic
Copyright
Copyright © INS. Published by Cambridge University Press, 2023