Age-associated motor and cognitive deficits increase the risk of falls, a major cause of morbidity and mortality. Because of the significant ramifications of falls, many interventions have been proposed, but few have aimed to prevent falls via an integrated approach targeting both motor and cognitive function. We aimed to test the hypothesis that an intervention combining treadmill training with non-immersive virtual reality (VR) to target both cognitive aspects of safe ambulation and mobility would lead to fewer falls than would treadmill training alone. We carried out this randomised controlled trial at five clinical centres across five countries (Belgium, Israel, Italy, the Netherlands, and the UK). Adults aged 60-90 years with a high risk of falls based on a history of two or more falls in the 6 months before the study and with varied motor and cognitive deficits were randomly assigned by use of computer-based allocation to receive 6 weeks of either treadmill training plus VR or treadmill training alone. Randomisation was stratified by subgroups of patients (those with a history of idiopathic falls, those with mild cognitive impairment, and those with Parkinson's disease) and sex, with stratification per clinical site. Group allocation was done by a third party not involved in onsite study procedures. Both groups aimed to train three times per week for 6 weeks, with each session lasting about 45 min and structured training progression individualised to the participant's level of performance. The VR system consisted of a motion-capture camera and a computer-generated simulation projected on to a large screen, which was specifically designed to reduce fall risk in older adults by including real-life challenges such as obstacles, multiple pathways, and distracters that required continual adjustment of steps. The primary outcome was the incident rate of falls during the 6 months after the end of training, which was assessed in a modified intention-to-treat population. Safety was assessed in all patients who were assigned a treatment. This study is registered with ClinicalTrials.gov, NCT01732653. Between Jan 6, 2013, and April 3, 2015, 302 adults were randomly assigned to either the treadmill training plus VR group (n=154) or treadmill training alone group (n=148). Data from 282 (93%) participants were included in the prespecified, modified intention-to-treat analysis. Before training, the incident rate of falls was similar in both groups (10·7 [SD 35·6] falls per 6 months for treadmill training alone vs 11·9 [39·5] falls per 6 months for treadmill training plus VR). In the 6 months after training, the incident rate was significantly lower in the treadmill training plus VR group than it had been before training (6·00 [95% CI 4·36-8·25] falls per 6 months; p<0·0001 vs before training), whereas the incident rate did not decrease significantly in the treadmill training alone group (8·27 [5·55-12·31] falls per 6 months; p=0·49). 6 months after the end of training, the incident rate of falls was also significantly lower in the treadmill training plus VR group than in the treadmill training group (incident rate ratio 0·58, 95% CI 0·36-0·96; p=0·033). No serious training-related adverse events occurred. In a diverse group of older adults at high risk for falls, treadmill training plus VR led to reduced fall rates compared with treadmill training alone. European Commission.

Quantifying gait and postural control adds valuable information that aids in understanding neurological conditions where motor symptoms predominate and cause considerable functional impairment. Disease-specific clinical scales exist; however, they are often susceptible to subjectivity, and can lack sensitivity when identifying subtle gait and postural impairments in prodromal cohorts and longitudinally to document disease progression. Numerous devices are available to objectively quantify a range of measurement outcomes pertaining to gait and postural control; however, efforts are required to standardise and harmonise approaches that are specific to the neurological condition and clinical assessment. Tools are urgently needed that address a number of unmet needs in neurological practice. Namely, these include timely and accurate diagnosis; disease stratification; risk prediction; tracking disease progression; and decision making for intervention optimisation and maximising therapeutic response (such as medication selection, disease staging, and targeted support). Using some recent examples of research across a range of relevant neurological conditions—including Parkinson’s disease, ataxia, and dementia—we will illustrate evidence that supports progress against these unmet clinical needs. We summarise the novel ‘big data’ approaches that utilise data mining and machine learning techniques to improve disease classification and risk prediction, and conclude with recommendations for future direction.

This systematic review aims to (i) evaluate functional near infrared spectroscopy (fNIRS) walking study design in young adults, older adults and people with Parkinson's disease (PD); (ii) examine signal processing techniques to reduce artefacts and physiological noise in fNIRS data; and (iii) provide evidence-based recommendations for fNIRS walking study design and signal analysis techniques. An electronic search was undertaken. The search request detailed the measurement technique, cohort and walking task. Thirty-one of an initial yield of 73 studies satisfied the criteria. Protocols and methods for removing artefacts and noise varied. Differences in fNIRS signals between studies were found in rest vs. walking, speed of walking, usual vs. complex walking and easy vs. difficult tasks. In conclusion, there are considerable technical and methodological challenges in conducting fNIRS studies during walking which can introduce inconsistencies in study findings. We provide recommendations for the construction of robust methodologies and suggest signal processing techniques implementing a theoretical framework accounting for the physiology of haemodynamic responses.

Background: Gait disturbance is an early, cardinal feature of Parkinson’s disease (PD) associated with falls and reduced physical activity. Progression of gait impairment in Parkinson’s disease is not well characterized and a better understanding is imperative to mitigate impairment. Subtle gait impairments progress in early disease despite optimal dopaminergic medication. Evaluating gait disturbances over longer periods, accounting for typical aging and dopaminergic medication changes, will enable a better understanding of gait changes and inform targeted therapies for early disease. This study aimed to describe gait progression over the first 6 years of PD by delineating changes associated with aging, medication, and pathology. Methods: One-hundred and nine newly diagnosed PD participants and 130 controls completed at least two gait assessments. Gait was assessed at 18-month intervals for up to 6 years using an instrumented walkway to measure sixteen spatiotemporal gait characteristics. Linear mixed-effects models assessed progression. Results: Ten gait characteristics significantly progressed in PD, with changes in four of these characteristics attributable to disease progression. Age-related changes also contributed to gait progression; changes in another two characteristics reflected both aging and disease progression. Gait impairment progressed irrespective of dopaminergic medication change for all characteristics except step width variability. Conclusions: Discrete gait impairments continue to progress in PD over 6 years, reflecting a combination of, and potential interaction between, disease-specific progression and age-related change. Gait changes were mostly unrelated to dopaminergic medication adjustments, highlighting limitations of current dopaminergic therapy and the need to improve interventions targeting gait decline.

Background: The development of optimal strategies to treat impaired mobility related to ageing and chronic disease requires better ways to detect and measure it. Digital health technology, including body worn sensors, has the potential to directly and accurately capture real-world mobility. Mobilise-D consists of 34 partners from 13 countries who are working together to jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes have the potential to provide a better, safer, and quicker way to assess, monitor, and predict the efficacy of new interventions on impaired mobility. The overarching objective of the study is to establish the clinical validity of digital outcomes in patient populations impacted by mobility challenges, and to support engagement with regulatory and health technology agencies towards acceptance of digital mobility assessment in regulatory and health technology assessment decisions. Methods/design: The Mobilise-D clinical validation study is a longitudinal observational cohort study that will recruit 2400 participants from four clinical cohorts. The populations of the Innovative Medicine Initiative-Joint Undertaking represent neurodegenerative conditions (Parkinson’s Disease), respiratory disease (Chronic Obstructive Pulmonary Disease), neuro-inflammatory disorder (Multiple Sclerosis), fall-related injuries, osteoporosis, sarcopenia, and frailty (Proximal Femoral Fracture). In total, 17 clinical sites in ten countries will recruit participants who will be evaluated every six months over a period of two years. A wide range of core and cohort specific outcome measures will be collected, spanning patient-reported, observer-reported, and clinician-reported outcomes as well as performance-based outcomes (physical measures and cognitive/mental measures). Daily-living mobility and physical capacity will be assessed directly using a wearable device. These four clinical cohorts were chosen to obtain generalizable clinical findings, including diverse clinical, cultural, geographical, and age representation. The disease cohorts include a broad and heterogeneous range of subject characteristics with varying chronic care needs, and represent different trajectories of mobility disability. Discussion: The results of Mobilise-D will provide longitudinal data on the use of digital mobility outcomes to identify, stratify, and monitor disability. This will support the development of widespread, cost-effective access to optimal clinical mobility management through personalised healthcare. Further, Mobilise-D will provide evidence-based, direct measures which can be endorsed by regulatory agencies and health technology assessment bodies to quantify the impact of disease-modifying interventions on mobility. Trial registration: ISRCTN12051706.