GeniAuti: Toward Data-Driven Interventions to Challenging Behaviors of Autistic Children through Caregivers' Tracking

Author:

Jo Eunkyung1,Park Seora2,Bang Hyeonseok3,Hong Youngeun4,Kim Yeni5,Choi Jungwon6,Kim Bung Nyun7,Epstein Daniel A.1,Hong Hwajung8

Affiliation:

1. University of California, Irvine, Irvine, CA, USA

2. Seoul National University, Seoul, Republic of Korea

3. Yonsei University, Seoul, Republic of Korea

4. National Center for Mental Health, Gwangjin-gu, Republic of Korea

5. Dongguk International Hospital, Goyang, Republic of Korea

6. National Center for Mental Health, Seoul, Republic of Korea

7. Seoul National University Hospital, Seoul, Republic of Korea

8. KAIST, Daejeon, Republic of Korea

Abstract

Challenging behaviors significantly impact learning and socialization of autistic children and can stress and burden their caregivers. Documentation of challenging behaviors is fundamental for identifying what environmental factors influence them, such as how others respond to a child's such behaviors. Caregiver-tracked data on their child's challenging behaviors can help clinical experts make informed recommendations about how to manage such behaviors. To support caregivers in recording their children's challenging behaviors, we developed GeniAuti, a mobile-based data-collection tool built upon a clinical data collection form to document challenging behaviors and other clinically relevant contextual information such as place, duration, intensity, and what triggers such behaviors. Through an open-ended deployment with 19 parent-child pairs and three expert collaborators, caregivers found GeniAuti valuable for (1) becoming more attentive and reflective to behavioral contexts, including their own response strategies, (2) discovering positive aspects of their children's behaviors, and (3) promoting collaboration with clinical experts around the caregiver-tracked data to develop tailored intervention strategies for their children. However, participant experiences surface challenges of logging behaviors in social circumstances, conflicting views between caregivers and clinical experts around the structured recording process, and emotional struggles resulting from recording and reflecting on intensely negative experiences. Considering the complex nature of caregiver-based health tracking and caregiver--clinician collaboration, we suggest design opportunities for facilitating negotiations between caregivers and clinicians and accounting for caregivers' emotional needs.

Funder

Ministry of Health and Welfare, Republic of Korea

National Science Foundation, United States

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Human-Computer Interaction,Social Sciences (miscellaneous)

Reference90 articles.

1. Fahd Albinali , Matthew S. Goodwin , and Stephen S . Intille . 2009 . Recognizing stereotypical motor movements in the laboratory and classroom. (2009), 71. https://doi.org/10.1145/1620545.1620555 10.1145/1620545.1620555 Fahd Albinali, Matthew S. Goodwin, and Stephen S. Intille. 2009. Recognizing stereotypical motor movements in the laboratory and classroom. (2009), 71. https://doi.org/10.1145/1620545.1620555

2. Parents' Use of Physical Interventions in the Management of Their Children's Severe Challenging Behaviour

3. “You Get Reminded You’re a Sick Person”: Personal Data Tracking and Patients With Multiple Chronic Conditions

4. Quantifying the Body and Caring for the Mind

5. Flexible and Mindful Self-Tracking

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