Is every-day walking in older adults more analogous to dual-task walking or to usual walking? Elucidating the gaps between gait performance in the lab and during 24/7 monitoring

Background

The traditional evaluation of gait in the laboratory during structured testing has provided important insights, but is limited by its “snapshot” character and observation in an unnatural environment. Wearables enable monitoring of gait in real-world environments over a week. Initial findings show that in-lab and real-world measures differ. As a step towards better understanding these gaps, we directly compared in-lab usual-walking (UW) and dual-task walking (DTW) to daily-living measures of gait.

Methods

In-lab gait features (e.g., gait speed, step regularity, and stride regularity) derived from UW and DTW were compared to the same gait features during daily-living in 150 elderly fallers (age: 76.5 ± 6.3 years, 37.6% men). In both settings, features were extracted from a lower-back accelerometer. In the real-world setting, subjects were asked to wear the device for 1 week and pre-processing detected 30-s daily-living walking bouts. A histogram of all walking bouts was determined for each walking feature for each subject and then each subject’s typical (percentile 50, median), worst (percentile 10) and the best (percentile 90) values over the week were determined for each feature. Statistics of reliability were assessed using Intra-Class correlations and Bland-Altman plots.

Results

As expected, in-lab gait speed, step regularity, and stride regularity were worse during DTW, compared to UW. In-lab gait speed, step regularity, and stride regularity during UW were significantly higher (i.e., better) than the typical daily-living values (p < 0.001) and different (p < 0.001) from the worst and best values. DTW values tended to be similar to typical daily-living values (p = 0.205, p = 0.053, p = 0.013 respectively). ICC assessment and Bland-Altman plots indicated that in-lab values do not reliably reflect the daily-walking values.

Conclusions

Gait values measured during relatively long (30-s) daily-living walking bouts are more similar to the corresponding values obtained in the lab during dual-task walking, as compared to usual walking. Still, gait performance during most daily-living walking bouts is worse than that measured during usual and dual-tasking in the lab. The values measured in the lab do not reliably reflect daily-living measures. That is, an older adult’s typical daily-living gait cannot be estimated by simply measuring walking in a structured, laboratory setting.

Analysis of Free-Living Gait in Older Adults With and Without Parkinson's Disease and With and Without a History of Falls: Identifying Generic and Disease-Specific Characteristics

BACKGROUND

Falls are associated with gait impairments in older adults (OA) and Parkinson's disease (PD). Current approaches for evaluating falls risk are based on self-report or one-time assessment and may be suboptimal. Wearable technology allows gait to be measured continuously in free-living conditions. The aim of this study was to explore generic and specific associations in free-living gait in fallers and nonfallers with and without PD.

METHODS

Two hundred and seventy-seven fallers (155 PD, 122 OA) who fell twice or more in the previous 6 months and 65 nonfallers (15 PD, 50 OA) were tested. Free-living gait was characterized as the volume, pattern, and variability of ambulatory bouts (Macro), and 14 discrete gait characteristics (Micro). Macro and Micro variables were quantified from free-living data collected using an accelerometer positioned on the low back for one week.

RESULTS

Macro variables showed that fallers walked with shorter and less variable ambulatory bouts than nonfallers, independent of pathology. Micro variables within ambulatory bouts showed fallers walked with slower, shorter and less variable steps than nonfallers. Significant interactions showed disease specific differences in variability with PD fallers demonstrating greater variability (step length) and OA fallers less variability (step velocity) than their nonfaller counterparts (p < 0.004).

CONCLUSIONS

Common and disease-specific changes in free-living Macro and Micro gait highlight generic and selective targets for intervention depending on type of faller (OA-PD). Our findings support free-living monitoring to enhance assessment. Future work is needed to confirm the optimal battery of measures, sensitivity to change and value for fall prediction.

The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control

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.

Postural Dynamics Are Associated With Cognitive Decline in Parkinson's Disease

Early features of Parkinson's disease (PD) include both motor and cognitive changes, suggesting shared common pathways. A common motor dysfunction is postural instability, a known predictor of falls, which have a major impact on quality of life. Understanding mechanisms of postural dynamics in PD and specifically how they relate to cognitive changes is essential for developing effective interventions. The aims of this study were to examine the changes that occur in postural metrics over time and explore the relationship between postural and cognitive dysfunction. The study group consisted of 35 people (66 ± 8years, 12 female, UPDRS III: 22.5 ± 9.6) diagnosed with PD who were recruited as part of the Incidence of Cognitive Impairment in Cohorts with Longitudinal Evaluation—PD Gait (ICICLE-GAIT) study. Postural and cognitive assessments were performed at 18, 36, and 54 months after enrolment. Participants stood still for 120 s, eyes open and arms by their side. Postural dynamics were measured using metrics derived from a single tri-axial accelerometer (Axivity AX3, York, UK) on the lower back. Accelerometry metrics included jerk (derivative of acceleration), root mean square, frequency, and ellipsis (acceleration area). Cognition was evaluated by neuropsychological tests including the Montreal Cognitive Assessment (MoCA) and digit span. There was a significant decrease in accelerometry parameters, greater in the anteroposterior direction, and a decline in cognitive function over time. Accelerometry metrics were positively correlated with lower cognitive function and increased geriatric depression score and negatively associated with a qualitative measure of balance confidence. In conclusion, people with PD showed reduced postural dynamics that may represent a postural safety strategy. Associations with cognitive function and depression, both symptoms that may pre-empt motor symptoms, suggest shared neural pathways. Further studies, involving neuroimaging, may determine how these postural parameters relate to underlying neural and clinical correlates.

Towards a toolkit for the assessment and monitoring of musculoskeletal ageing

The complexities and heterogeneity of the ageing process have slowed the development of consensus on appropriate biomarkers of healthy ageing. The MRC-Arthritis Research UK Centre for Integrated research into Musculoskeletal Ageing (CIMA) is a collaboration between researchers and clinicians at the Universities of Liverpool, Sheffield and Newcastle. One of CIMA’s objectives is to ‘Identify and share optimal techniques and approaches to monitor age-related changes in all musculoskeletal tissues, and to provide an integrated assessment of musculoskeletal function’, i.e. to develop a toolkit for assessing musculoskeletal ageing. This toolkit is envisaged as an instrument that can be used to characterise and quantify musculoskeletal function during ‘normal’ ageing, lend itself to use in large-scale, internationally important cohorts, and provide a set of biomarker outcome measures for epidemiological and intervention studies designed to enhance healthy musculoskeletal ageing. Such potential biomarkers include: biochemical measurements in biofluids or tissue samples, in vivo measurements of body composition, imaging of structural and physical properties, and functional tests. The CIMA Toolkit Working Group assessed candidate biomarkers of musculoskeletal ageing under these four headings, detailed their biological bases, strengths and limitations, and made practical recommendations for their use. In addition, the CIMA Toolkit Working Group identified gaps in the evidence base and suggested priorities for further research on biomarkers of musculoskeletal ageing.

Developing a toolkit for the assessment and monitoring of musculoskeletal ageing

The complexities and heterogeneity of the ageing process have slowed the development of consensus on appropriate biomarkers of healthy ageing. The Medical Research Council–Arthritis Research UK Centre for Integrated research into Musculoskeletal Ageing (CIMA) is a collaboration between researchers and clinicians at the Universities of Liverpool, Sheffield and Newcastle. One of CIMA’s objectives is to ‘Identify and share optimal techniques and approaches to monitor age-related changes in all musculoskeletal tissues, and to provide an integrated assessment of musculoskeletal function’—in other words to develop a toolkit for assessing musculoskeletal ageing. This toolkit is envisaged as an instrument that can be used to characterise and quantify musculoskeletal function during ‘normal’ ageing, lend itself to use in large-scale, internationally important cohorts, and provide a set of biomarker outcome measures for epidemiological and intervention studies designed to enhance healthy musculoskeletal ageing. Such potential biomarkers include: biochemical measurements in biofluids or tissue samples, in vivo measurements of body composition, imaging of structural and physical properties, and functional tests. This review assesses candidate biomarkers of musculoskeletal ageing under these four headings, details their biological bases, strengths and limitations, and makes practical recommendations for their use. In addition, we identify gaps in the evidence base and priorities for further research on biomarkers of musculoskeletal ageing.

Estimation of spatio-temporal parameters of gait from magneto-inertial measurement units: multicenter validation among Parkinson, mildly cognitively impaired and healthy older adults

Background

The use of miniaturized magneto-inertial measurement units (MIMUs) allows for an objective evaluation of gait and a quantitative assessment of clinical outcomes. Spatial and temporal parameters are generally recognized as key metrics for characterizing gait. Although several methods for their estimate have been proposed, a thorough error analysis across different pathologies, multiple clinical centers and on large sample size is still missing. The aim of this study was to apply a previously presented method for the estimate of spatio-temporal parameters, named Trusted Events and Acceleration Direct and Reverse Integration along the direction of Progression (TEADRIP), on a large cohort (236 patients) including Parkinson, mildly cognitively impaired and healthy older adults collected in four clinical centers. Data were collected during straight-line gait, at normal and fast walking speed, by attaching two MIMUs just above the ankles. The parameters stride, step, stance and swing durations, as well as stride length and gait velocity, were estimated for each gait cycle. The TEADRIP performance was validated against data from an instrumented mat.

Results

Limits of agreements computed between the TEADRIP estimates and the reference values from the instrumented mat were − 27 to 27 ms for Stride Time, − 68 to 44 ms for Stance Time, − 31 to 31 ms for Step Time and − 67 to 52 mm for Stride Length. For each clinical center, the mean absolute errors averaged across subjects for the estimation of temporal parameters ranged between 1 and 4%, being on average less than 3% (< 30 ms). Stride length mean absolute errors were on average 2% (≈ 25 mm). Error comparisons across centers did not show any significant difference. Significant error differences were found exclusively for stride and step durations between healthy elderly and Parkinsonian subjects, and for the stride length between walking speeds.

Conclusions

The TEADRIP method was effectively validated on a large number of healthy and pathological subjects recorded in four different clinical centers. Results showed that the spatio-temporal parameters estimation errors were consistent with those previously found on smaller population samples in a single center. The combination of robustness and range of applicability suggests the use of the TEADRIP as a suitable MIMU-based method for gait spatio-temporal parameter estimate in the routine clinical use. The present paper was awarded the “SIAMOC Best Methodological Paper 2017”.

Longitudinal changes over thirty-six months in postural control dynamics and cognitive function in people with Parkinson's disease

BACKGROUND

Postural instability is a common motor feature in people with Parkinson's disease (PD) together with non-motor features such as cognitive dysfunction. Management of postural instability is challenging as it is often resistant to dopaminergic therapy. Greater knowledge of postural control is essential to understand postural instability in PD.

RESEARCH QUESTION

This study aimed to answer how postural control differs in people with PD compared to healthy older adults (HOA). Additionally, postural control changes over a 36 month period and its relationship to cognitive impairment and motor scores were investigated.

METHODS

The study group consisted of 50 people diagnosed with PD and 59 HOAs, recruited as part of an incident cohort study (ICICLE-GAIT). Participants stood still for 2 min, eyes open and arms by their side. A single tri-axial accelerometer (Axivity AX3, York, UK) on the lower back recorded acceleration. Measurements were taken at 18, 36 and 54 months after recruitment. Sample entropy (SampEn), which measures signal predictability, was determined for the accelerometry data. Cognitive tests included the Montreal Cognitive Assessment and the Unified Parkinson's Disease Rating Scale (UPDRS III) quantified motor function. Linear mixed models, regression analysis and correlation analysis were applied to the data.

RESULTS

indicated that SampEn was greater for the PD group at all three time-points and along all three axes. However, there was no increase of SampEn with disease progression. Higher SampEn values were associated with greater cognitive impairment and lower UPDRS III, although correlations were weak. There was a difference between axial directions and cognitive and motor scores.

SIGNIFICANCE

People with PD exhibit decreased regularity of trunk dynamics when standing compared to HOAs. Nonlinear accelerometer metrics along all three axes are therefore a potential biomarker of PD. The relationship between trunk dynamics and cognitive function indicates common neural pathways.

Gait in Mild Alzheimer's Disease: Feasibility of Multi-Center Measurement in the Clinic and Home with Body-Worn Sensors: A Pilot Study

Gait is emerging as a potential diagnostic tool for cognitive decline. The 'Deep and Frequent Phenotyping for Experimental Medicine in Dementia Study' (D&FP) is a multicenter feasibility study embedded in the United Kingdom Dementia Platform designed to determine participant acceptability and feasibility of extensive and repeated phenotyping to determine the optimal combination of biomarkers to detect disease progression and identify early risk of Alzheimer's disease (AD). Gait is included as a clinical biomarker. The tools to quantify gait in the clinic and home, and suitability for multi-center application have not been examined. Six centers from the National Institute for Health Research Translational Research Collaboration in Dementia initiative recruited 20 individuals with early onset AD. Participants wore a single wearable (tri-axial accelerometer) and completed both clinic-based and free-living gait assessment. A series of macro (behavioral) and micro (spatiotemporal) characteristics were derived from the resultant data using previously validated algorithms. Results indicate good participant acceptability, and potential for use of body-worn sensors in both the clinic and the home. Recommendations for future studies have been provided. Gait has been demonstrated to be a feasible and suitable measure, and future research should examine its suitability as a biomarker in AD.

Comprehensive measurement of stroke gait characteristics with a single accelerometer in the laboratory and community: a feasibility, validity and reliability study

BACKGROUND

Application of objective measurement of stroke gait with accelerometer-based wearable technology and associated algorithms is increasing, despite reports questioning the accuracy of this technique in quantifying specific stroke-related gait impairments. The aim of this study is to determine the feasibility, validity and reliability of a low-cost open-source system incorporating algorithms and a single tri-axial accelerometer-based wearable to quantify gait characteristics in the laboratory and community post-stroke.

METHODS

Twenty-five participants with stroke wore the wearable (AX3, Axivity) on the lower back during a laboratory 2 minute continuous walk (preferred pace) on two occasions a week apart and continuously in the community for two consecutive 7 day periods. Video, instrumented walkway (GaitRite) and an OPAL accelerometer-based wearable were used as laboratory references.

RESULTS

Feasibility of the proposed system was good. The system was valid for measuring step count (ICC 0.899). Inherent differences in gait quantification between algorithm and GaitRite resulted in difficulties comparing agreement between the different systems. Agreement was moderate-excellent (ICC 0.503-0.936) for mean and variability gait characteristics vs. OPAL. Agreement was moderate-poor between the system and OPAL for asymmetry characteristics. Moderate-excellent reliability (ICC 0.534-0.857) was demonstrated for 11/14 laboratory measured gait characteristics. Community test-retest reliability was good-excellent (ICC 0.867-0.983) for all except one (ICC 0.699) of the 19 gait characteristics.

CONCLUSION

The proposed system is a low-cost, reliable tool for quantifying gait post-stroke with multiple potential applications. Further refinement to optimise gait quantification algorithms for certain gait characteristics including gait asymmetry is required.

Comprehensive measurement of stroke gait characteristics with a single accelerometer in the laboratory and community: a feasibility, validity and reliability study

Background

Application of objective measurement of stroke gait with accelerometer-based wearable technology and associated algorithms is increasing, despite reports questioning the accuracy of this technique in quantifying specific stroke-related gait impairments. The aim of this study is to determine the feasibility, validity and reliability of a low-cost open-source system incorporating algorithms and a single tri-axial accelerometer-based wearable to quantify gait characteristics in the laboratory and community post-stroke.

Methods

Twenty-five participants with stroke wore the wearable (AX3, Axivity) on the lower back during a laboratory 2 minute continuous walk (preferred pace) on two occasions a week apart and continuously in the community for two consecutive 7 day periods. Video, instrumented walkway (GaitRite) and an OPAL accelerometer-based wearable were used as laboratory references.

Results

Feasibility of the proposed system was good. The system was valid for measuring step count (ICC 0.899). Inherent differences in gait quantification between algorithm and GaitRite resulted in difficulties comparing agreement between the different systems. Agreement was moderate-excellent (ICC 0.503–0.936) for mean and variability gait characteristics vs. OPAL. Agreement was moderate-poor between the system and OPAL for asymmetry characteristics. Moderate-excellent reliability (ICC 0.534–0.857) was demonstrated for 11/14 laboratory measured gait characteristics. Community test-retest reliability was good-excellent (ICC 0.867–0.983) for all except one (ICC 0.699) of the 19 gait characteristics.

Conclusion

The proposed system is a low-cost, reliable tool for quantifying gait post-stroke with multiple potential applications. Further refinement to optimise gait quantification algorithms for certain gait characteristics including gait asymmetry is required.

Validation of a Step Detection Algorithm during Straight Walking and Turning in Patients with Parkinson's Disease and Older Adults Using an Inertial Measurement Unit at the Lower Back

INTRODUCTION

Inertial measurement units (IMUs) positioned on various body locations allow detailed gait analysis even under unconstrained conditions. From a medical perspective, the assessment of vulnerable populations is of particular relevance, especially in the daily-life environment. Gait analysis algorithms need thorough validation, as many chronic diseases show specific and even unique gait patterns. The aim of this study was therefore to validate an acceleration-based step detection algorithm for patients with Parkinson's disease (PD) and older adults in both a lab-based and home-like environment.

METHODS

In this prospective observational study, data were captured from a single 6-degrees of freedom IMU (APDM) (3DOF accelerometer and 3DOF gyroscope) worn on the lower back. Detection of heel strike (HS) and toe off (TO) on a treadmill was validated against an optoelectronic system (Vicon) (11 PD patients and 12 older adults). A second independent validation study in the home-like environment was performed against video observation (20 PD patients and 12 older adults) and included step counting during turning and non-turning, defined with a previously published algorithm.

RESULTS

A continuous wavelet transform (cwt)-based algorithm was developed for step detection with very high agreement with the optoelectronic system. HS detection in PD patients/older adults, respectively, reached 99/99% accuracy. Similar results were obtained for TO (99/100%). In HS detection, Bland-Altman plots showed a mean difference of 0.002 s [95% confidence interval (CI) -0.09 to 0.10] between the algorithm and the optoelectronic system. The Bland-Altman plot for TO detection showed mean differences of 0.00 s (95% CI -0.12 to 0.12). In the home-like assessment, the algorithm for detection of occurrence of steps during turning reached 90% (PD patients)/90% (older adults) sensitivity, 83/88% specificity, and 88/89% accuracy. The detection of steps during non-turning phases reached 91/91% sensitivity, 90/90% specificity, and 91/91% accuracy.

CONCLUSION

This cwt-based algorithm for step detection measured at the lower back is in high agreement with the optoelectronic system in both PD patients and older adults. This approach and algorithm thus could provide a valuable tool for future research on home-based gait analysis in these vulnerable cohorts.

Towards holistic free-living assessment in Parkinson's disease: Unification of gait and fall algorithms with a single accelerometer

Technological developments have seen the miniaturization of sensors, small enough to be embedded in wearable devices facilitating unobtrusive and longitudinal monitoring in free-living environments. Concurrently, the advances in algorithms have been ad-hoc and fragmented. To advance the mainstream use of wearable technology and improved functionality of algorithms all methodologies must be unified and robustly tested within controlled and free-living conditions. Here we present and unify a (i) gait segmentation and analysis algorithm and (ii) a fall detection algorithm. We tested the unified algorithms on a cohort of young healthy adults within a laboratory. We then deployed the algorithms on longitudinal (7 day) accelerometer-based data from an older adult with Parkinson's disease (PD) to quantify real world gait and falls. We compared instrumented falls to a self-reported falls diary to test algorithm efficiency and discuss the use of unified algorithms to impact free-living assessment in PD where accurate recognition of gait may reduce the number of automated detected falls (38/week). This informs ongoing work to use gait and related outcomes as pragmatic clinical markers.

Toward a low-cost gait analysis system for clinical and free-living assessment

Gait is an important clinical assessment tool since changes in gait may reflect changes in general health. Measurement of gait is a complex process which has been restricted to bespoke clinical facilities until recently. The use of inexpensive wearable technologies is an attractive alternative and offers the potential to assess gait in any environment. In this paper we present the development of a low cost analysis gait system built using entirely open source components. The system is used to capture spatio-temporal gait characteristics derived from an existing conceptual model, sensitive to ageing and neurodegenerative pathology (e.g. Parkinson's disease). We demonstrate the system is suitable for use in a clinical unit and will lead to pragmatic use in a free-living (home) environment. The system consists of a wearable (tri-axial accelerometer and gyroscope) with a Raspberry Pi module for data storage and analysis. This forms ongoing work to develop gait as a low cost diagnostic in modern healthcare.

A model of free-living gait: A factor analysis in Parkinson's disease

INTRODUCTION

Gait is a marker of global health, cognition and falls risk. Gait is complex, comprised of multiple characteristics sensitive to survival, age and pathology. Due to covariance amongst characteristics, conceptual gait models have been established to reduce redundancy and aid interpretation. Previous models have been derived from laboratory gait assessments which are costly in equipment and time. Body-worn monitors (BWM) allow for free-living, low-cost and continuous gait measurement and produce similar covariant gait characteristics. A BWM gait model from both controlled and free-living measurement has not yet been established, limiting utility.

METHODS

103 control and 67 PD participants completed a controlled laboratory assessment; walking for two minutes around a circuit wearing a BWM. 89 control and 58 PD participants were assessed in free-living, completing normal activities for 7 days wearing a BWM. Fourteen gait characteristics were derived from the BWM, selected according to a previous model. Principle component analysis derived factor loadings of gait characteristics.

RESULTS

Four gait domains were derived for both groups and conditions; pace, rhythm, variability and asymmetry. Domains totalled 84.84% and 88.43% of variance for controlled and 90.00% and 93.03% of variance in free-living environments for control and PD participants respectively. Gait characteristic loading was unambiguous for all characteristics apart from gait variability which demonstrated cross-loading for both groups and environments. The model was highly congruent with the original model.

CONCLUSIONS

The conceptual gait models remained stable using a BWM in controlled and free-living environments. The model became more discrete supporting utility of the gait model for free-living gait.

Detecting free-living steps and walking bouts: validating an algorithm for macro gait analysis

Research suggests wearables and not instrumented walkways are better suited to quantify gait outcomes in clinic and free-living environments, providing a more comprehensive overview of walking due to continuous monitoring. Numerous validation studies in controlled settings exist, but few have examined the validity of wearables and associated algorithms for identifying and quantifying step counts and walking bouts in uncontrolled (free-living) environments. Studies which have examined free-living step and bout count validity found limited agreement due to variations in walking speed, changing terrain or task. Here we present a gait segmentation algorithm to define free-living step count and walking bouts from an open-source, high-resolution, accelerometer-based wearable (AX3, Axivity). Ten healthy participants (20-33 years) wore two portable gait measurement systems; a wearable accelerometer on the lower-back and a wearable body-mounted camera (GoPro HERO) on the chest, for 1 h on two separate occasions (24 h apart) during free-living activities. Step count and walking bouts were derived for both measurement systems and compared. For all participants during a total of almost 20 h of uncontrolled and unscripted free-living activity data, excellent relative (rho  ⩾  0.941) and absolute (ICC_(2,1)  ⩾  0.975) agreement with no presence of bias were identified for step count compared to the camera (gold standard reference). Walking bout identification showed excellent relative (rho  ⩾  0.909) and absolute agreement (ICC_(2,1)  ⩾  0.941) but demonstrated significant bias. The algorithm employed for identifying and quantifying steps and bouts from a single wearable accelerometer worn on the lower-back has been demonstrated to be valid and could be used for pragmatic gait analysis in prolonged uncontrolled free-living environments.

Addition of a non-immersive virtual reality component to treadmill training to reduce fall risk in older adults (V-TIME): a randomised controlled trial

BACKGROUND

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.

METHODS

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.

FINDINGS

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.

INTERPRETATION

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.

FUNDING

European Commission.

Free-living gait characteristics in ageing and Parkinson's disease: impact of environment and ambulatory bout length

Background

Gait is emerging as a powerful diagnostic and prognostic tool, and as a surrogate marker of disease progression for Parkinson’s disease (PD). Accelerometer-based body worn monitors (BWMs) facilitate the measurement of gait in clinical environments. Moreover they have the potential to provide a more accurate reflection of gait in the home during habitual behaviours. Emerging research suggests that measurement of gait using BWMs is feasible but this has not been investigated in depth. The aims of this study were to explore (i) the impact of environment and (ii) ambulatory bout (AB) length on gait characteristics for discriminating between people with PD and age-matched controls.

Methods

Fourteen clinically relevant gait characteristics organised in five domains (pace, variability, rhythm, asymmetry, postural control) were quantified using laboratory based and free-living data collected over 7 days using a BWM placed on the lower back in 47 PD participants and 50 controls.

Results

Free-living data showed that both groups walked with decreased pace and increased variability, rhythm and asymmetry compared to walking in the laboratory setting. Four of the 14 gait characteristics measured in free-living conditions were significantly different between controls and people with PD compared to two measured in the laboratory. Between group differences depended on bout length and were more apparent during longer ABs. ABs ≤ 10s did not discriminate between groups. Medium to long ABs highlighted between-group significant differences for pace, rhythm and asymmetry. Longer ABs should therefore be taken into account when evaluating gait characteristics in free-living conditions.

Conclusion

This study provides encouraging results to support the use of a single BWM for free-living gait evaluation in people with PD with potential for research and clinical application.

Within trial validation and reliability of a single tri-axial accelerometer for gait assessment

Gait is a sensitive biomarker of decline in both cognitive and physical function. Therefore, the collection of gait data is an important feature of clinical assessments. Accelerometer-based body worn sensors are quickly becoming the preferred tool for assessing gait because they are small, useable in a wide variety of settings, offer more continuous spatio-temporal analysis and are inexpensive when compared with traditional gait assessment methodologies. The purpose of this study was to determine the validity and within test reliability of a low cost body worn movement sensor with associated algorithms to assess gait in a large group of older and younger healthy adults. We collected gait data over intermittent walks on an instrumented walkway for a within trial validation and also used the same accelerometer derived gait data for a within test reliability analysis. ICCs for validation and reliability were >0.756 and >0.965, respectively.

Time-dependent changes in postural control in early Parkinson's disease: what are we missing?

Impaired postural control (PC) is an important feature of Parkinson's disease (PD), but optimal testing protocols are yet to be established. Accelerometer-based monitors provide objective measures of PC. We characterised time-dependent changes in PC in people with PD and controls during standing, and identified outcomes most sensitive to pathology. Thirty-one controls and 26 PD patients were recruited: PC was measured with an accelerometer on the lower back for 2 minutes (mins). Preliminary analysis (autocorrelation) that showed 2 seconds (s) was the shortest duration sensitive to changes in the signal; time series analysis of a range of PC outcomes was undertaken using consecutive 2-s windows over the test. Piecewise linear regression was used to fit the time series data during the first 30 s and the subsequent 90 s of the trial. PC outcomes changed over the 2 mins, with the greatest change observed during the first 30 s after which PC stabilised. Changes in PC were reduced in PD compared to controls, and Jerk was found to be discriminative of pathology. Previous studies focusing on average performance over the duration of a test may miss time-dependent differences. Evaluation of time-dependent change may provide useful insights into PC in PD and effectiveness of intervention.