CuePD in the Home: Retraining Gait in Parkinson's Disease Via a Personalised App
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 40
- 试验地点
- 1
研究概览
简要总结
Introduction Parkinson's disease (PD) limits mobility by worsening gait/walking and increasing fall risk. Falls lead to injuries and reduce confidence in performing everyday tasks. That lowers a person's ability to participate in community activities such as going to the shops or visiting friends, which reduces their quality of life. Development of interventions for gait impairments and falls is a research priority for Parkinson's UK.
Understanding gait Traditionally, one approach a physiotherapist may use to try and improve/retrain a person's gait is with an electronic metronome which is a device that "beeps" nearly every second. The physiotherapist sets the metronome beeping, and the person tries to step to each beep. However, success depends on the physio's expertise/experience. Regardless, beeping sounds are described as boring.
Smartphone app An app may be the solution. Smartphones have many sensors, meaning they can accurately measure gait but also deliver retraining via music. That is possible by the creation of an "app" that can be downloaded and installed on anyone's smartphone.
Research proposal The investigators have developed and validated an app (CuePD) that uses music for gait retraining, to make it more enjoyable by having people listen to their preferred music. The aim for this study is to get people with PD (PwPD) using CuePD in their home and when out walking for 12-weeks to determine: (i) how PwPD use and value CuePD and (ii) CuePD's ability to improve gait to reduce fall risk.
详细描述
In the UK, falls cost the NHS >£2billion/year. Parkinson's disease (PD) is one of the most common and progressive neurological disorders, with prevalence projected to double in the next 30-years. PD increases fall risk through walking/gait disturbances, with gait variability related to an increased falls rate/risk. Approximately 60% of people with PD (PwPD) encounter many falls annually. To better understand underlying causes of falls, a gait assessment is undertaken to develop bespoke and targeted/personalised strategies to minimise fall risk.
Instrumenting fall risk Typically, a gait assessment is undertaken by visual inspection, where a physiotherapists tacit expertise/experience enables the identification of obvious and/or subtle gait disturbances. However, reliance on visual observation alone introduces inconsistency/discrepancies in designing fall reduction strategies. To overcome, digital technologies have been investigated e.g., instrumented walkways provide high-resolution gait data to inform retraining via auditory cueing (e.g., stepping to a metronome beat) but they are very costly and bulky to use in most PD services. Regardless, PwPD perceive metronome cueing as monotonous while musical alternatives lack personalisation. Accordingly, there is a need to develop and adopt affordable, pervasive and scalable approaches that are personalised and engaging.
Lab on a phone Technologies that are scalable and easily accessible could facilitate personalised fall reduction programs anywhere. Smartphones are ubiquitous to facilitate near real-time intervention. This study uses our novel app (CuePD), designed to provide cross-platform (i.e., iOS and Android) scalable and personalised auditory cueing for gait retraining to reduce fall risk. CuePD's gait and music algorithms are validated to assess and retrain gait in PwPD within a lab (87-99% accuracy). This project's aim is to deploy CuePD in the home/community, to determine its efficacy as an everyday gait retraining tool. The project is a feasibility randomized controlled trial (RCT) to inform a future grant involving a multicentre observer blind parallel group RCT.
Research questions and hypotheses
- Principal question: Is a personalised home/community gait retraining programme via CuePD feasible and more effective to reduce fall risk than advice about improving gait/walking (i.e., usual care) in people with Parkinson's disease (PwP)?
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Supportive Care
- 盲法
- Double (Investigator, Outcomes Assessor)
入排标准
- 年龄范围
- 50 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Able to walk unaided.
- •Diagnosis of idiopathic Parkinson's.
- •Score ≥21/30 on Montreal Cognitive Assessment (MoCA) which is used to classify non-demented Parkinson's (Parkinson's dementia is <21/30).
- •Uses a smartphone.
排除标准
- •Non-English speakers
- •Use of any mobility aids e.g., walking stick
- •History of stroke, traumatic brain injury or other neurological disorders (other than Parkinson's)
- •Unable to comply with the testing protocol or currently participating in another interfering research project.
- •Does not use a smartphone.
- •Body mass index ≥35 (i.e., severe to morbid obesity)