Related Experiment Video
Updated: Jun 24, 2026
Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
Published on: June 20, 2020
Explaining the Use Behavior of Digital Technologies in Pediatric Rehabilitation: Structural Equation Modeling
Johanne Mensah Gourmel1,2,3,4, Sylvain Brochard1,2,3,4, Saranda Bekteshi5
1UMR1101, Université de Bretagne Occidentale, Brest, France.
Background:
Digital technologies for rehabilitation (DT4R), such as robotics and treadmill systems (RobTS), virtual reality and active video gaming (VR-AVG), and telehealth and apps (T&Apps), are promising tools for pediatric motor rehabilitation. Identifying acceptance factors is essential for effective clinical adoption.
Objective:
This study aimed to analyze the use of 3 different technologies for rehabilitation-RobTS, VR-AVG, and T&Apps-through a causal model based on the Unified Theory of Acceptance and Use of Technology (UTAUT).
Methods:
This study was part of RehaTech4child, a cross-sectional survey (2022) supported by the European Academy of Childhood-onset Disability, aimed at professionals working in pediatric motor rehabilitation across Europe. It assessed DT4R use, intention to use, and UTAUT concepts (performance expectancy, effort expectancy, social influence, and barriers). Structural equation modeling was performed to analyze the data and understand relationships between observed and latent variables.
Results:
A total of 1397 responses were received, and 635 fulfilled the eligibility criteria. The fitness indices suggested a satisfactory fit between the data and the model. The model explained 67% of the variance in the use of RobTS, 62% in VR-AVG, and 57% in T&Apps. Among all studied determinants, access had the strongest impact on use for all 3 categories of DT4R (RobTS: β=0.78, VR-AVG: β=0.73, and T&Apps: β=0.70; P<.001). Intention to use significantly impacted use behavior for all technologies; it was the second determinant after access for VR-AVG (β=0.18, P<.001) and T&Apps (β=0.21, P<.001), with a lower weight for RobTS (β=0.06, P=.007; P<.001). In the subgroup analysis of respondents reporting easy access, intention to use was the strongest determinant of use. The model explained 61% of the variance in intention to use for RobTS, 67% for VR-AVG, and 68% for T&Apps. Performance expectancy had the strongest effect on intention to use for the 3 technologies (RobTS: β=0.81, VR-AVG: β=0.84, and T&Apps: β=0.90; P<.001). For this concept, the items with the highest weights were significantly related to the effectiveness of DT4R on rehabilitation. Social influence and effort expectancy had a slight impact on intention to use.
Conclusions:
These results underscore the need to ensure easy access as a prerequisite for assessing relevant determinants of acceptance. Developing the evidence base for DT4R effectiveness and ensuring the availability of existing evidence may facilitate DT4R implementation. In our study, within the framework of the UTAUT model, no acceptance barrier was linked to the use of DT4R with children. Gathering families' views may be useful for the implementation of RobTS. T&Apps may be useful for involving parents in their child's rehabilitation. Further studies should focus on children's and families' points of view.
