Appl Clin Inform 2010; 01(04): 377-393
DOI: 10.4338/ACI-2010-05-RA-0033
Research Article
Schattauer GmbH

Development of a decision support model for screening attention-deficit hyperactivity disorder with actigraph-based measurements of classroom activity

H.J. Kam
1  Department of Biomedical Informatics, Ajou University School of Medicine, Wonchon-dong, Yeongtong-gu, Suwon, South Korea
,
Y.M. Shin
2  Department of Psychiatry and Behavioral Science, Ajou University School of Medicine, Wonchon-dong, Yeongtong-gu, Suwon, South Korea
,
S.M. Cho
2  Department of Psychiatry and Behavioral Science, Ajou University School of Medicine, Wonchon-dong, Yeongtong-gu, Suwon, South Korea
,
S.Y. Kim
2  Department of Psychiatry and Behavioral Science, Ajou University School of Medicine, Wonchon-dong, Yeongtong-gu, Suwon, South Korea
,
K.W. Kim
3  Neuropsychiatry and Stroke Center, Seoul National University Bundang Hospital, Seongnam, South Korea
,
R.W. Park
1  Department of Biomedical Informatics, Ajou University School of Medicine, Wonchon-dong, Yeongtong-gu, Suwon, South Korea
› Author Affiliations
Further Information

Publication History

received: 21 May 2010

accepted: 10 October 2010

Publication Date:
16 December 2017 (online)

Summary

Objective: Questionnaire-based ADHD screening tests may not always be objective or accurate, owing to both subjectivity and prejudice. Despite attempts to develop objective measures to characterize ADHD, no widely applicable index currently exists. The principal aim of this study was to develop a decision support model for ADHD screening by monitoring children’s school activities using a 3-axial actigraph.

Methods: Actigraphs were placed on the non-dominant wrists of 153 children for 3 hours, while they were at school. Children who scored high on the questionnaires were clinically examined by child psychiatrists, who then confirmed ADHD. Mean, variance, and ratios of low-level (0.5-1.0G) and high-level (1.6-3.2G) activity were extracted as activity features from 142 children (10 ADHD, 132 non-ADHD). Two decision-tree models were constructed using the C5.0 algorithm: [A] from whole hours (class + playtime) and [B] during classes. Accuracy, sensitivity, and specificity were evaluated. PPV, NPV, likelihood ratio, and AUC were also calculated for evaluation.

Results: [Model A] One child without ADHD was misclassified, resulting in an accuracy score of 99.30%. Sensitivity and NPV were 1.0000. Specificity and PPV were 0.992 and 0.803-0.909, respectively. [Model B] Two children without ADHD were misclassified, resulting in an accuracy score of 98.59%. Specificity and PPV were scored at 0.985 and 0.671-0.832, respectively.

Conclusion: The selected features were consistent with the findings of previous studies. Objective screening of latent patients with ADHD can be accomplished with a simple watch-like sensor, which is worn for just a few hours while the child attends school. The model proposed herein can be applied to a great many children without heavy cost in time and manpower cost, and would generate valuable results from a public health perspective.