Affiliations: [a]
Department of Occupational Therapy. Faculty of Rehabilitation Medicine. University of Alberta. 2-64 Corbett Hall, Edmonton, AB. Canada T6 G 2G4 | [b]
Glenrose Rehabilitation Research, Innovation & Technology (GRRIT). Glenrose Rehabilitation Hospital, Edmonton, Canada
| [c]
School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
Correspondence:
[*]
Address for correspondence: Associate Professor. Department of Occupational Therapy. Faculty of Rehabilitation Medicine. University of Alberta. 2-64 Corbett Hall, Edmonton, AB. Canada T6G 2G4. Tel.: +1 780 492 5108. E-mail[email protected].
Abstract: BACKGROUND:Functional Capacity Evaluation (FCE) is a crucial component within return-to-work decision making. However, clinician-based physical FCE interpretation may introduce variability and biases. The rise of technological applications such as machine learning and artificial intelligence, could ensure consistent and precise results. OBJECTIVE:This review investigates the application of information and communication technologies (ICT) in physical FCEs specific for return-to-work assessments. METHODS:Adhering to the PRISMA guidelines, a search was conducted across five databases, extracting study specifics, populations, and technological tools employed, through dual independent reviews. RESULTS:Nine studies were identified that used ICT in FCEs. These technologies included electromyography, heart rate monitors, cameras, motion detectors, and specific software. Notably, although some devices are commercially available, these technologies were at a technology readiness level of 5–6 within the field of FCE. A prevailing trend was the combined use of diverse technologies rather than a single, unified solution. Moreover, the primary emphasis was on the application of technology within study protocols, rather than a direct evaluation of the technology usability and feasibility. CONCLUSION:The literature underscores limited ICT integration in FCEs. The current landscape of FCEs, marked by a high dependence on clinician observations, presents challenges regarding consistency and cost-effectiveness. There is an evident need for a standardized technological approach that introduces objective metrics to streamline the FCE process and potentially enhance its outcomes.
Keywords: Information technology, return to work, machine learning, rehabilitation, artificial intelligence, work, bio-mechanics
DOI: 10.3233/WOR-230560
Journal: Work, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
