Currently submitted to: JMIR Human Factors
Date Submitted: May 15, 2026
Open Peer Review Period: May 15, 2026 - Jul 10, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Formative usability evaluation of an AI-based rehabilitation system for congenital muscular torticollis: Focus Group Interviews and SUS
ABSTRACT
Background:
Home-based exercise programs for congenital muscular torticollis (CMT) depend heavily on caregivers, yet adherence is often hindered by cognitive and emotional burdens.
Objective:
This study conducted a formative usability evaluation of an AI-based CMT rehabilitation system comprising a clinician web interface and a caregiver mobile application to identify user requirements and design improvements during early development.
Methods:
A formative evaluation was performed involving eight clinical experts (rehabilitation physicians, nurses, and physical therapists). Participants engaged in focus group interviews (FGIs) following a product demonstration and completed the System Usability Scale (SUS). Qualitative data were analyzed thematically using QualCoder, while quantitative usability was assessed through SUS scoring.
Results:
The thematic analysis identified two core themes: "Usability Barriers of the CMT Rehabilitation System" and "Improvement Requirements," encompassing 10 subthemes and 21 concepts. Key findings highlighted the necessity of enhancing AI feedback reliability and simplifying data visualization to reduce the cognitive workload of caregivers. The mean SUS score was 73.44 (SD = 18.01), indicating "Good" and "Acceptable" (Grade B-) usability.
Conclusions:
While the AI-based CMT rehabilitation system demonstrated acceptable usability, the experts emphasized that it must function as a therapeutic bridge between clinicians and caregivers. The findings of this study provide immediate design specifications for the manufacturing of AI-based rehabilitation devices and offer actionable operational strategies for enhancing service delivery in home-based clinical environments. Future iterations should incorporate the identified improvements and undergo summative evaluation in actual home environments to verify clinical effectiveness.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.