Title:
Supporting in-home behavior specimen collection and sharing for diagnostic assessment of children with autism
Supporting in-home behavior specimen collection and sharing for diagnostic assessment of children with autism
| dc.contributor.advisor | Abowd, Gregory D. | |
| dc.contributor.advisor | Arriaga, Rosa I. | |
| dc.contributor.author | Nazneen, Fnu | |
| dc.contributor.committeeMember | DiSalvo, Betsy | |
| dc.contributor.committeeMember | Do, Ellen Yi-Luen | |
| dc.contributor.committeeMember | Rozga, Agata | |
| dc.contributor.committeeMember | Karahalios, Karrie G. | |
| dc.contributor.department | Computer Science | |
| dc.date.accessioned | 2015-06-08T18:20:00Z | |
| dc.date.available | 2015-06-09T05:30:06Z | |
| dc.date.created | 2014-05 | |
| dc.date.issued | 2014-04-11 | |
| dc.date.submitted | May 2014 | |
| dc.date.updated | 2015-06-08T18:20:00Z | |
| dc.description.abstract | Direct observation of the child remains an essential part of the gold standard clinical practice in the diagnosis, assessment, and treatment planning for children with Autism and related developmental conditions. Clinical professionals acknowledge that observing behavior in the natural environment is crucial to obtain an accurate and comprehensive assessment of child behavior. However, direct observation in the natural environment is not feasible for wide-scale implementation into clinical practice. Another key challenge with respect to autism diagnosis is that there is significant time lag (about 20-60 months) between age at which parents first get concerned and the age at which the child finally gets diagnosed. Delay in diagnosis can lead to delay in invaluable early interventions which could impact a child’s future developmental outcomes. A capture and access solution can play a role to fill these gaps. This thesis presents the design, development, and evaluation of a Clinician-directed Capture and Access System that can enable parents to easily collect in-home behavior specimens that have clinical utility and clinicians to use this data to complete a diagnostic assessment for autism. Analogous to a traditional lab specimen collection and assessment procedure, this approach involves three steps: 1) the clinician’s prescription of behavior specimens to be collected; 2) in-home collection and remote sharing of prescribed behavior specimens by parents; and 3) diagnostic assessment of behavior specimens by a remote and qualified clinician. The contributions of this thesis are as follows: a) identified potential opportunities and challenges specific to the design and adoption of a technology solution that enables in-home behavior specimen collection and diagnostic assessment for autism; b) identified key features and functionalities of a smart-phone based Capture System that can enable parents to easy collect in-home behavior specimens that have clinical utility; c) identified key features and functionalities of the web-based Access System that facilitate clinicians to complete remote diagnostic assessment based on in-home behavior specimens.; d) demonstrated through an end-to-end study with parents and clinicians that parents can easily capture behavior specimens through the Capture System and clinicians can complete remote autism diagnostic assessment based on in-home behavior specimens via the Access System. | |
| dc.description.degree | Ph.D. | |
| dc.embargo.terms | 2015-05-01 | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/53466 | |
| dc.language.iso | en_US | |
| dc.publisher | Georgia Institute of Technology | |
| dc.subject | Autism | |
| dc.subject | Capture and access | |
| dc.subject | Remote autism diagnosis | |
| dc.subject | In-home video collection | |
| dc.title | Supporting in-home behavior specimen collection and sharing for diagnostic assessment of children with autism | |
| dc.type | Text | |
| dc.type.genre | Dissertation | |
| dspace.entity.type | Publication | |
| local.contributor.advisor | Abowd, Gregory D. | |
| local.contributor.advisor | Arriaga, Rosa I. | |
| local.contributor.corporatename | College of Computing | |
| relation.isAdvisorOfPublication | a9e4f620-85d6-4fb9-8851-8b0c3a0e66b4 | |
| relation.isAdvisorOfPublication | c4946ddb-b81d-4f4e-9034-58c21c45acb7 | |
| relation.isOrgUnitOfPublication | c8892b3c-8db6-4b7b-a33a-1b67f7db2021 | |
| thesis.degree.level | Doctoral |