Setup time

The final aim of the study was to determine the cost, in terms of time involved in setting up the FES Rehab Tool, and the training required, in order to effectively administer upper limb FES in a clinical setting. The literature review in section 2.8 of the thesis identified 3 studies that examined setup as part of the process (Pedrocchi et al, 2013; Fitzgerald et al, 2008; Dijkers et al, 1991), and only two of these (Pedrocchi et al, 2013; Dijkers et al, 1991) actually reported setup time, although in Dijkers et al, (1991), this was only estimated setup time. In the current study only the ‘sweeping coins’ and ‘pushing up from a chair’ tasks had sufficient initial setup time data to be compared with the lab based setup times. The average setup time for the clinical based testing ‘sweeping coins’ task was 34.28 mins (range 28.39-47.48) as opposed to 22.7 mins (range 14.50-29.33) for the lab based testing. Similarly for the ‘pushing up from chair’ task the mean setup time for the clinic based testing was 41.53 mins (range 26.21-56.56) as opposed to 31.4 mins (range 23.90-38.93). For both tasks setup time in the clinical setting took significantly longer than for the lab- based testing. This demonstrates the influence of training, regular use and familiarity with the technology on setup times. The therapists’ advisory group reported that setup time should not take more than 30 minutes. By contract, Pedrocchi et al, (2013) reported setup times of between 6-65 minutes depending on the complexity of the configuration. Considering the prototype nature of the UL FES Rehab Tool, with its’ multitude of wires and sensors, setup times for the system compare favourably when compared with technology of a similar level of complexity. Given the pressure on therapists’ time to deliver rehabilitation, and therefore the importance of setup time for new technologies, a method of allowing therapists to predict setup time in advance of commencing FES assisted treatment could be extremely helpful.

Chapter six of the thesis proposed a model that allowed prediction of setup times based on the patients’ level of impairment and the complexity of the task. The intention was to pool the setup time data collected from the clinical setting with that lab based data in order to strengthen the predictive model. However, when comparing setup times from the lab-based setting with the clinical testing, it was clear from Table 7.11 that factors other than impairment and task complexity were having a significant effect on setup time in the clinical setting. This difference in setup times

183 between the two studies was not surprising given that all therapists were inexperienced in using the system in comparison to the therapist that setup the system for the lab- based testing, who had the opportunity to use the system with 6 patients over a period of the approximately 6 months. A factor highlighted both in the literature (McHugh et al., 2013), and in the early study advisory group meetings, is the small amount of time available for rehabilitation of the upper limb. Continuing to build on this early work of a model to predict setup time would allow therapist to make informed choices regarding which task to select for the time available, thereby avoiding the situation where the therapist runs out of time to effectively complete an FES assisted treatment session.

Due to delays with patient recruitment, especially at centre B, training had occurred approximately 6 months prior to the start of the study. The importance of training and therapist confidence in using technology for therapists cannot be over stated. One way of addressing this current gap would be to introduce training on relevant rehabilitation technologies in the undergraduate curriculum for both physiotherapists and occupational therapists. It was notable in this study, that in spite of an invitation to be involved in the clinical study, the OT’s at both sites declined the invitation. In some universities in the UK, the undergraduate curriculum for OT’s is less likely to include rehabilitation technologies and there is less time dedicated to Anatomy in comparison to Physiotherapy. Both of these factors could be barriers to rehabilitation technology acceptance and use.

To date, rehabilitation technologies have not been found to be more effective at promoting upper limb recovery than intensive conventional therapies (Farmer et al., 2014; Burridge & Hughes, 2010). However, they might provide an opportunity for delivery of intensive training of the kind needed to promote upper limb recovery (MacLellan, 2011; Kwakkel, 2006; Boyd & Winstein, 2006) and free up valuable therapist time, allowing more patients to be treated. In 12 out of 13 sessions, two therapists were still required in order to effectively administer an FES-assisted practice session (one to setup the system and one to assist the patient) (Table 7.9). This was primarily due to patients requiring additional support in order to overcome the weight of the arm against gravity during a reaching movement. In these cases, FES alone could not generate sufficient proximal muscle recruitment i.e. around the

184 shoulder, requiring additional support from a therapist or rehabilitation assistant. In such cases, a de-weighting system such as the SaeboMAS might prove to be advantageous, thereby removing the need for the support from a second person. However, in the future this technology has the potential to offer other advantages over traditional therapies such as biofeedback, use of instrumented objects to aid incorporation of real life objects, and the ability to provide metrics to measure patient outcomes. The follow-on project from the NEAT LO30 project intends to incorporate these technological advances.