Identifying risk factors for wheelchair damage, part failure, and adverse consequences to the user.

No tools or technologies exist to inform data-driven inspection schedules for wheelchairs. To develop such a schedule, this study identifies risk factors linked with manual wheelchair damage, part failures, and consequences and evaluates preferences for a new wheelchair servicing technology. A mixed methods study was performed with manual wheelchair users at The Ohio State University Martha Morehouse Clinic. Demographic data, wheelchair information, failure counts, and consequences suffered by the user were collected using surveys. Wheelchair usage was collected for a month using a sensor. A servicing smartphone app that connects with the sensor was demonstrated as a new servicing technology, and participant preferences were recorded. Thirty participants completed the survey testing procedures. Twenty-three collected usage data and eighteen collected it for over a week. At least 215 wheelchair part failures with an average of 13.4 ± 14.8 self-reported part failures and 4.7 ± 4.8 high-risk failures occurred in 12 months before the first study visit. Two weeks of collected data from 18 participants showed that normalised road shocks, age, and weight were associated with the condition of wheels and frames, as well as self-reported caster failures. Participants responded with a favourable preference for the new wheelchair servicing technology, with more than half of them interested in buying and using it. Risk factors like road shocks and user's age and weight are associated with part damage towards failures and self-reported failures that risk injury. These factors can be modelled to develop and test the efficacy of wheelchair inspection schedules.