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 naturemore »
Tedious Versus Taxing: The Nature of Work in a Behavioral Health Context
The goal of this study was to examine the work practices of behavioral health professionals with a view towards designing interactive systems to support their work. We conducted a qualitative workplace study, including in situ observations and semi-structured interviews, in a multidisciplinary clinic treating pediatric feeding disorders. This paper contributes a detailed characterization of clinicians' work practices and conducts a comparative analysis of three types of work: treatment, record management, and preparation work. We found that clinicians have a preference for taxing over tedious work. For example, they experience real-time data collection as more taxing but less tedious than retroactive data entry. Design efforts should balance the tension between addressing the taxing (data collection during meals) versus the tedious (manually entering data into spreadsheets). Although addressing the taxing improves within-routine efficiency, addressing the tedious improves overall morale. Further, we hypothesize that there is a rewarding or unrewarding quality to work that is dictated in part by its social, temporal, and clinical characteristics. We discuss conceptual and design implications for supporting clinical work, and highlight considerations unique to behavioral health.
- Award ID(s):
- 1816319
- Publication Date:
- NSF-PAR ID:
- 10332286
- Journal Name:
- Proceedings of the ACM on Human-Computer Interaction
- Volume:
- 5
- Issue:
- CSCW2
- Page Range or eLocation-ID:
- 1 to 24
- ISSN:
- 2573-0142
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Background The classic Marshmallow Test, where children were offered a choice between one small but immediate reward (eg, one marshmallow) or a larger reward (eg, two marshmallows) if they waited for a period of time, instigated a wealth of research on the relationships among impulsive responding, self-regulation, and clinical and life outcomes. Impulsivity is a hallmark feature of self-regulation failures that lead to poor health decisions and outcomes, making understanding and treating impulsivity one of the most important constructs to tackle in building a culture of health. Despite a large literature base, impulsivity measurement remains difficult due to the multidimensional nature of the construct and limited methods of assessment in daily life. Mobile devices and the rise of mobile health (mHealth) have changed our ability to assess and intervene with individuals remotely, providing an avenue for ambulatory diagnostic testing and interventions. Longitudinal studies with mobile devices can further help to understand impulsive behaviors and variation in state impulsivity in daily life. Objective The aim of this study was to develop and validate an impulsivity mHealth diagnostics and monitoring app called Digital Marshmallow Test (DMT) using both the Apple and Android platforms for widespread dissemination to researchers, clinicians, and the generalmore »
-
Although combination antiretroviral therapy (ART) with three or more drugs is highly effective in suppressing viral load for people with HIV, many ART agents may exacerbate mental health-related adverse effects including depression. Therefore, understanding the effects of combination ART on mental health can help clinicians personalize medicine with less adverse effects to avoid undesirable health outcomes. The emergence of electronic health records offers researchers unprecedented access to HIV data including individuals' mental health records, drug prescriptions, and clinical information over time. However, modeling such data is challenging due to high-dimensionality of the drug combination space, the individual heterogeneity, and sparseness of the observed drug combinations. To address these challenges, we develop a Bayesian nonparametric approach to learn drug combination effect on mental health in people with HIV adjusting for socio-demographic, behavioral, and clinical factors. The proposed method is built upon the subset-tree kernel that represents drug combinations in a way that synthesizes known regimen structure into a single mathematical representation. It also utilizes a distance-dependent Chinese restaurant process to cluster heterogeneous populations while considering individuals' treatment histories. We evaluate the proposed approach through simulation studies, and apply the method to a dataset from the Women's Interagency HIV Study, showing themore »
-
Background Shift work sleep disorders (SWSDs) are associated with the high turnover rates of nurses, and are considered a major medical safety issue. However, initial management can be hampered by insufficient awareness. In recent years, it has become possible to visualize, collect, and analyze the work-life balance of health care workers with irregular sleeping and working habits using wearable sensors that can continuously monitor biometric data under real-life settings. In addition, internet-based cognitive behavioral therapy for psychiatric disorders has been shown to be effective. Application of wearable sensors and machine learning may potentially enhance the beneficial effects of internet-based cognitive behavioral therapy. Objective In this study, we aim to develop and evaluate the effect of a new internet-based cognitive behavioral therapy for SWSD (iCBTS). This system includes current methods such as medical sleep advice, as well as machine learning well-being prediction to improve the sleep durations of shift workers and prevent declines in their well-being. Methods This study consists of two phases: (1) preliminary data collection and machine learning for well-being prediction; (2) intervention and evaluation of iCBTS for SWSD. Shift workers in the intensive care unit at Mie University Hospital will wear a wearable sensor that collects biometric datamore »
-
Abstract Taking an action research approach, we engaged in fieldwork with school-based behavioral health care teams to: observe record keeping practices, design and deploy a prototype system addressing key challenges, and reflect on its use. We describe the challenges of capturing behavioral data using both paper and electronic records. Creating records of behaviors requires direct observation, and as a result the record keeping responsibility is challenging to distribute across a care team. Behavioral data on paper must be transferred and prepared for reporting, both inside the organization and to stakeholders outside of the organization. In prototyping a computerized working record, we targeted user needs for capturing details of a behavioral incident in the moment. Challenges persisted through the transition from paper to our prototype, and based on these empirical findings over two years of fieldwork, we present five tensions in representing behavioral data in an electronic health record. These tensions reflect the differences between entering behavioral data into the record for intraorganizational use versus interorganizational use.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1145/3476043
