Brief Report: Mobile Technology to Support Parents in Reducing Stereotypy

ProVIA-Kids - outcomes of an uncontrolled study on smartphone-based behaviour analysis for challenging behaviour in children with intellectual and developmental disabilities or autism spectrum disorder

Introduction

Challenging behaviour (CB) is a common issue among children with autism spectrum disorder or intellectual and developmental disability. Mental health applications are low-threshold cost-effective tools to address the lack of resources for caregivers. This pre-post study evaluated the feasibility and preliminary effectiveness of the smartphone app ProVIA-Kids using algorithm-based behaviour analysis to identify causes of CB and provide individualized practical guidance to manage and prevent CB.

Methods

A total of 18 caregivers (M = 38.9 ± 5.0) of children with a diagnosis of autism spectrum disorder (44%), intellectual and developmental disabilities (33%) or both (22%) aged 4-11 years (M = 7.6 ± 1.8) were included. Assessments were performed before and after an 8-week intervention period. The primary outcome was the change in parental stress. Caregiver stress experience due to CB was also rated daily via ecological momentary assessments within the app. Secondary outcomes included the intensity of the child's CB, dysfunctional parenting, feelings of parental competency as well as caregivers' mood (rated daily in the app) and feedback on the app collected via the Mobile Application Rating Scale.

Results

We observed increases in parental stress in terms of conscious feelings of incompetence. However, we also saw improvements in parental stress experience due to CB and overreactive parenting, and descriptive improvements in CB intensity and caregiver mood.

Discussion

ProVIA-Kids pioneers behaviour analysis in a digital and automated format, with participants reporting high acceptance. Pilot results highlight the potential of the ProVIA-Kids app to positively influence child behaviour and caregiver mental health over a longer intervention period.

Registration

The study was registered at https://www.drks.de (ID = DRKS00029039) on May 31, 2022.

Immersive robotic telepresence system to support a person with intellectual and motor disabilities perform a daily task: a case study

Purpose

In this proof-of-concept study, the authors assessed the feasibility of using a humanoid robot controlled remotely via an immersive telepresence system to support a person with intellectual and motor disabilities performing a daily task (i.e. setting a table for lunch).

Design/methodology/approach

The system involved a head-mounted display and two joysticks. A teleoperator was able to see through the video cameras of the robot and deliver the instructions verbally to the participant located in a different room. To assess the system, a baseline phase (A) was followed by an intervention (i.e. tele-operated support) phase (B) and a return to a baseline phase (A).

Findings

Data showed a marked increase in the average frequency of task steps correctly performed from baseline (M = 15%) to intervention (M = 93%). Accuracy reached 100% in the return to baseline.

Originality/value

These preliminary findings, along with qualitative feedback from users, suggest that an immersive telepresence system may be used to provide remote support to people with intellectual and motor disabilities.

Toward automatic motivator selection for autism behavior intervention therapy

Children with autism spectrum disorder (ASD) usually show little interest in academic activities and may display disruptive behavior when presented with assignments. Research indicates that incorporating motivational variables during interventions results in improvements in behavior and academic performance. However, the impact of such motivational variables varies between children. In this paper, we aim to address the problem of selecting the right motivator for children with ASD using reinforcement learning by adapting to the most influential factors impacting the effectiveness of the contingent motivator used. We model the task of selecting a motivator as a Markov decision process problem. The states, actions and rewards design consider the factors that impact the effectiveness of a motivator based on applied behavior analysis as well as learners' individual preferences. We use a Q-learning algorithm to solve the modeled problem. Our proposed solution is then implemented as a mobile application developed for special education plans coordination. To evaluate the motivator selection feature, we conduct a study involving a group of teachers and therapists and assess how the added feature aids the participants in their decision-making process of selecting a motivator. Preliminary results indicated that the motivator selection feature improved the usability of the mobile app. Analysis of the algorithm performance showed promising results and indicated improvement of the recommendations over time.