Smart Wearable Device (gaitQ): Walk Better Project

Stroke Clinical Trial

Official title: Smart Wearable Device (gaitQ) That Helps People With Long-term Conditions Affecting Movement: Walk Better

Status Recruiting

Clinical Trial Summary

This study will be underpinned by the new MRC guidelines for developing a complex intervention with a participatory design methodology that uses evidence-based research and behaviour change models alongside COSMIN methodology for validating a measure. Research question: To what extent does gaitQ's smart cueing system improve people with long-term conditions including people with Parkinson's (PwP's) gait? Is it effective in the everyday environment? What factors are associated with good mobility? What is the impact of cueing on healthy people? Aims and objectives: To finalise the product development and evaluation comprising (1) algorithm refinement and (2) monitoring system development. To evaluate the reliability, concurrent validity, and potential for efficacy, as determined by responsiveness in response to the gaitQ product using gait data collected in laboratory environments. To prepare for market entry and NHS adoption: early economic modelling, pricing, marketing strategies, and early adopter partnerships. Design: Participatory design with testing for validity, reliability and responsiveness Participants: This will involve healthy people and people with long-term conditions affecting their movement, including people with Parkinson's [PwP]. Additional patient groups will be investigated, including stroke, and people with hip/knee injuries. Methods The Researchers will collect movement data using the gaitQ system, which monitors and cues, to both collect data and cue in the lab environment and investigate the reliability of the measure, concurrent validity of the metric to gold standard gait capture, the responsiveness of measures to the cueing system and usability for participants and clinical teams. To determine reliability, 60 participants will be invited to repeat testing on a second visit. Researchers will describe participants' conditions using standard questionnaires and their mobility and functioning. This study will be underpinned by the new MRC guidelines for developing a complex intervention with a participatory design methodology that uses evidence-based research and behaviour change models to identify intrinsic and extrinsic factors that contribute to a given outcome in a specific population.

Clinical Trial Description

Detailed Description: Mobility issues affect 1/3 of the adult population requiring rehabilitation. Current shortages in skilled rehabilitation professionals require novel approaches to address this unmet rehabilitation need. Rehabilitation typically sets out to monitor and support people to live life as they wish using appropriate therapies. Here the investigators propose to develop and test a gait device for people with mobility issues. The investigators will first focus on people with Parkinsons' (PwP) and then on people with other conditions affecting their movement, including stroke and arthritis. There are several practical challenges to bring cueing to the daily lives of PwP: PwP have difficulty multi-tasking. Basic rhythmic cueing methods do not adapt to changes in gait quality or the activities undertaken. PwP habituates to cues that are constantly in action. These challenges limit the effectiveness and adoption of current cueing products by PwP. gaitQ is developing a smart cueing device that: 1) directly addresses these challenges enabling an effective, practical solution for everyday use; 2) aims to improve quality of life, and mental well-being and reduce fall risks; 3) supports more effective and accurate disease management; and 4) does this in a way that preserves PwP's discretion and privacy. GaitQ: The gaitQ system comprises two wearable devices worn on the back of the user's legs. The devices provide vibrational cues with specific patterns tailored to the user's walking characteristics to help them overcome FOG and FSG, and improve their normal gait quality, such as stride length and step symmetry. As Parkinson's gait symptoms contribute to a two-fold risk of falling in PwP, by enabling a more fluid and stable gait while reducing the occurrence of FOG and FSG, the gaitQ system sets out to help reduce the risk of falling. By the time of the trial, gaitQ device will be certified with UKCA certificate. Rationale: Considering Parkinson's alone, an analysis has shown that 1 in every 37 people will be diagnosed with PD in their lifetime. FOG and FSG are severely debilitating aspects of the disease which greatly reduce the quality of life of PwP and contribute to the two-fold increased fall risk and related injuries. It is vital that investigators provide a solution to support PwP with a more fluid and safer gait, greater independence and better disease management and care. Cueing with visual, auditory, or somatosensory stimuli is a well-documented and clinically validated method to overcome FOG and FSG. Research studies have also shown that cueing modalities improve gait in PwP during both free and treadmill walking, whilst improving balance and reducing the need for stabilising support. Gait parameters, such as step frequency, stride length and gait symmetry, have been shown to be measurable with inertial motion sensors for gait quality assessment. Studies have shown that objective measurement of the disease can improve treatment outcomes in PD. There are basic visual and rhythmic cueing products available (such as laser shoes, a metronome app and a vibrational button), however, they are still based on simple continuous cues which have profound limitations on usability and effectiveness in the everyday environment and do not include gait analytics systems to facilitate better patient outcome and experience. The gaitQ system will be the first unique solution that uses artificial intelligence and smart adaptive cueing to help patients effectively overcome FOG and FSG in their daily environment while improving gait quality. STUDY DESIGN Clinical trials will be conducted in close collaboration with the Royal Devon University Healthcare NHS Foundation Trust to collect clinical evidence and usability data on the impact of the gaitQ product. The project will be underpinned by the new MRC guidelines for developing a complex intervention with a participatory design methodology that uses evidence-based research and behaviour change models to identify intrinsic and extrinsic factors that contribute to a given outcome in a specific population to collect clinical evidence and usability data on the impact of the gaitQ product. The project will be underpinned by the new MRC guidelines for developing a complex intervention (1) with a participatory design methodology that uses evidence-based research and behaviour change models to identify intrinsic and extrinsic factors that contribute to a given outcome in a specific population. Key outcomes: Improvement in gait metrics, reducing freezing & festination episodes comparing with and without the gaitQ device. Improvement in gait quality comparing with and without the gaitQ device, in terms of stride length, step frequency, step symmetry, walking speed. User feedback on usability and acceptability Data for developing/verifying gait metrics algorithms & for developing FOG detection algorithms Acceptability and safety will be recorded by engagement with the device and completion of >70% of planned sessions. Within the proposed NIHR i4i project, the researchers will investigate how the gaitQ product can be potentially integrated into the clinical practices for these conditions. STUDY SETTING This study will be based at the University of Exeter's VSimulator facility and testing site (https://vsimulators.co.uk), at Exeter Science Park, Clyst Honiton, Exeter, EX5 2FN. Or at the Oxford University Hospitals NHS Trust. Gait facility. People with conditions will be recruited from the Royal Devon University Healthcare NHS Foundation Trust, University of Oxford, University Hospitals Plymouth NHS Trust and Bristol and Weston NHS Foundation Trust and open recruitment through adverts and social media. Sampling technique Convenience sampling is used for this study. Participants need to be identified within the timeline and scope of the project. Obtaining volunteers that are easily available and willing is a sensible sampling strategy for the scope of the project. Convenience sampling is appropriate because the research is exploratory in nature and/or the conclusions to be drawn from the data will not be threatened by issues concerning selection bias, generalisability, sampling error, and/or statistical power. Recruitment Eligible participants will be approached by their treating clinician and asked if they'd like to hear more about the study from a member of the research team. If happy to hear more about the study, and have their details passed on to the study team, participants will be offered a participant information sheet and informed consent form and have a chance to discuss the study in more detail with the research team. Patient consent may be taken at this appointment, or the completed consent form can be returned online or via post. Data collection/data processing Motion capture and force plate data will be collected using the VSimulator force plate and motion capture systems (Optitrack). This data will then be recorded onto the system computer. Recordings will not contain any identifiable data of each participant, with participant numbers being used for file names. Video data will be collected on participants using a camera during activities to assess the FOG. The recorded video will be saved locally inside the camera memory SD card, and after each test, it will be transferred to a University encrypted laptop. All participants will be given a participant number with all data pseudonymised. A participant number document will be kept separate from all data, which has information on the identity of each participant should follow-up contact be needed. Questionnaire data: Data will be collected using an Excel spreadsheet with participant identity pseudonymised. Participant numbers will be used for researchers to identify the corresponding answers in the follow-up analysis. The filled Excel files will be kept stored on an online university drive with the name list stored on a separate SharePoint. Optitrack data will be recorded on the VSimulator system computer. Upon completion of recording the data will be transferred to a password protected university drive. Data file name will use participant number and will have no identifiable elements. Upon transfer of data to university drive the data will be deleted from the system computer. Motion capture data will be kept on the university onedrive. The written informed consent form will be scanned, uploaded directly to the encrypted University of Exeter servers as soon as it is signed and the hard copy immediately destroyed in the confidential waste, leaving no physically identifiable information at the VSimulator. The gaitQ devices contain 6-degrees-of-freedom motion sensors and high-performance micro-controllers to enable collection, analysis and storage of the user's motion data. This will enable the gaitQ product to deliver automated and adaptive/personalised smart cueing to the user. IMU data from the gaitQ devices will be stored in the gaitQ secured cloud server. This data will be used, in conjunction with the motion capture data from the VSimulator (non-identifiable), to develop the automatic, adaptive cueing algorithms. It will not contain any identifiable information of each participant with participant numbers used as labels. Following the testing procedure participants will be made comfortable and supervised in a separate room for as long as required, until they feel able to continue with normal activities. At this point they will remove the motion capture suit and their participation in the study will be completed. The investigators will monitor how long the return to normal activities takes. Analysis Primary analysis: Interview data will be transcribed verbatim and analysed using a thematic approach with nvivo. Lab data will be cleaned and processed using a standardised procedure . The system will be validated in the lab to determine point estimates and means for system measures for all parameters and all groups Criterion Validity of categorised groups will be based on agreement between classification/value with overall accuracy explored using Fisher's exact test [Categories of agree/disagree] or Bland-Altman plots or with ICC (3). Descriptive and frequency statistics will be reported for demographic, usability, acceptability and feasibility data and SUS questionnaire, as well as reporting of missing data. Absolute and relative reliability, relative reliability ICC (3) and absolute reliability by standard error of measurement (SEM) and minimal detectable change at the 95% confidence interval. Potential for effect will be determined with responsiveness of gait metrics, using the device Feasibility of measure use: this will focus on the proportion of participants who used the system successfully ( 1) identify design limitations when the system is used in the manner intended for future use within the home setting 2) determine safety during testing by monitoring adverse events 3) explore feasibility (usability/acceptability) of the technology to measure people A [Acceptability and safety will be recorded by recruitment rate >20%, engagement with the FA-IMAGINE and completion of >70% of sessions/measures over the 12 months and monitoring of any adverse events. Usability will be assessed through the successful establishment of a usability SUS target score >68 (https://www.usability.gov/how-to-and-tools/methods/system-usability-scale.html). Determine the potential for effect: to determine the amount of change in metrics: Participants will be classified as those changing/not changing ≥ minimal detectable change (MDC) [MDC = 1.96 x SEM x square root of 2, MDC95 based on a 95% confidence interval ] at testing and calculate the significance of the change using repeated measure ANOVA, Generalised Estimating Equation (GEE)/ General Linear Model (GLM)as appropriate. Peer review: The study has been peer-reviewed by the funder (NIHR). ;

Related Conditions & MeSH terms

• Arthritis
• Multiple Sclerosis
• Osteoporosis
• Parkinson Disease
• Stroke

• NCT number: NCT05913219
• Study type: Observational
• Source: University of Exeter
• Contact: Helen Dawes, PhD
• Phone: 01392
• Email: h.dawes@exeter.ac.uk
• Status: Recruiting
• Start date: September 15, 2023
• Completion date: July 30, 2025