An instrumented timed up and go: the added value of an accelerometer for identifying fall risk in idiopathic fallers

Remote timed up and go evaluation from activities of daily living reveals changing mobility after surgery

BACKGROUND

Mobility impairment is common in older adults and negatively influences the quality of life. Mobility level may change rapidly following surgery or hospitalization in the elderly. The timed up and go (TUG) is a simple, frequently used clinical test for functional mobility; however, TUG requires supervision from a trained clinician, resulting in infrequent assessments. Additionally, assessment by TUG in clinic settings may not be completely representative of the individual's mobility in their home environment.

OBJECTIVE

In this paper, we introduce a method to estimate TUG from activities detected in free-living, enabling continuous remote mobility monitoring without expert supervision. The method is used to monitor changes in mobility following total hip arthroplasty (THA).

METHODS

Community-living elderly (n  =  239, 65-91 years) performed a standardized TUG in a laboratory and wore a wearable pendant device that recorded accelerometer and barometric sensor data for at least three days. Activities of daily living (ADLs), including walks and sit-to-stand transitions, and their related mobility features were extracted and used to develop a regularized linear model for remote TUG test estimation. Changes in the remote TUG were evaluated in orthopaedic patients (n  =  15, 55-75 years), during 12-weeks period following THA.

MAIN RESULTS

In leave-one-out-cross-validation (LOOCV), a strong correlation (ρ  =  0.70) was observed between the new remote TUG and standardized TUG times. Test-retest reliability of 3-days estimates was high (ICC  =  0.94). Compared to week 2 post-THA, remote TUG was significantly improved at week 6 (11.7  ±  3.9 s versus 8.0  ±  1.8 s, p   <  0.001), with no further change at 12-weeks (8.1  ±  3.9 s, p   =  0.37).

SIGNIFICANCE

Remote TUG can be estimated in older adults using 3-days of ADLs data recorded using a wearable pendant. Remote TUG has discriminatory potential for identifying frail elderly and may provide a convenient way to monitor changes in mobility in unsupervised settings.

Wearable Inertial Sensors for Fall Risk Assessment and Prediction in Older Adults: A Systematic Review and Meta-Analysis

Wearable inertial sensors have been widely investigated for fall risk assessment and prediction in older adults. However, heterogeneity in published studies in terms of sensor location, task assessed and features extracted is high, making challenging evidence-based design of new studies and/or real-life applications. We conducted a systematic review and meta-analysis to appraise the best available evidence in the field. Namely, we applied established statistical methods for the analysis of categorical data to identify optimal combinations of sensor locations, tasks, and feature categories. We also conducted a meta-analysis on sensor-based features to identify a set of significant features and their pivot values. The results demonstrated that with a walking test, the most effective feature to assess the risk of falling was the velocity with the sensor placed on the shins. Conversely, during quite standing, linear acceleration measured at the lower back was the most effective combination of feature-placement. Similarly, during the sit-to-stand and/or the stand-to-sit tests, linear acceleration measured at the lower back seems to be the most effective feature-placement combination. The meta-analysis demonstrated that four features resulted significantly higher in fallers: the root-mean-square acceleration in the mediolateral direction during quiet standing with eyes closed [Mean Difference (MD): 0.01 g; 95% Confidence Interval (CI95%): 0.006 to 0.014]; the number of steps (MD: 1.638 steps; CI95%: 0.384 to 2.892) and total time (MD: 2.274 seconds; CI95%: 0.531 to 4.017) to complete the timed up and go test; and the step time (MD: 0.053; CI95%: 0.012 to 0.095; p = 0.01) during walking.

Sit-to-walk performance in Parkinson's disease: A comparison between faller and non-faller patients

BACKGROUND

Falls are one of the main concerns in people with Parkinson's disease, leading to poor quality of life and increased mortality. The sit-to-walk movement is the most frequent postural transition task during daily life and is highly demanding in terms of balance maintenance and muscular strength.

METHODS

With the aim of identifying biomechanical variables of high risk of falling, we investigated the sit-to-walk task performed by 9 Parkinson's disease patients with at least one fall episode in the six months preceding this study, 15 Parkinson's disease patients without previous falls, and 20 healthy controls. Motor performance was evaluated with an optoelectronic system and two dynamometric force plates after overnight suspension of all dopaminergic drugs and one hour after consumption of a standard dose of levodopa/benserazide.

FINDINGS

Poor trunk movements critically influenced the execution of the sit-to-walk movement in patients with a history of falling. The peak velocity of the trunk in the anterior-posterior direction discriminated faller from non-faller patients, with high specificity and sensitivity in both the medication-off and -on state.

INTERPRETATION

Our results confirm the difficulties in merging consecutive motor tasks in patients with Parkinson's disease. Trunk movements during the sit-to-walk can provide valuable measurements to monitor and possibly predict the risk of falling.

Mobility improves after high intensity aerobic exercise in individuals with Parkinson's disease

Emerging literature indicates aerobic exercise improves the motor symptoms associated with Parkinson's disease (PD). However, the impact of aerobic exercise on functional locomotor performance has not been evaluated systematically. The aim of this project was to determine the impact of an 8-week high intensity aerobic exercise intervention on Timed Up and Go (TUG) performance in PD. Fifty-nine participants with idiopathic PD completed 24 aerobic exercise sessions over 8 weeks. Two modes of exercise were utilized: forced (FE) and voluntary (VE). A mobile application was used to gather biomechanical data for the characterization of the TUG subtasks: Sit-Stand, Gait, Turning, and Stand-Sit. Participants were assessed in an off medication state at: 1) baseline, prior to any exercise intervention, and 2) after completion of exercise treatment. At baseline, the VE group completed the TUG in 9.41 s, while the FE group completed the TUG significantly faster in 8.0 s. Following the exercise intervention, the VE group decreased TUG time to 8.9 s (p < .01). Both exercise groups demonstrated significant improvements in Turning Velocity, time of Gait phase and Stand-Sit duration. Overall mobility in participants with PD was significantly improved after high intensity aerobic exercise training. Improvements in turning and gait speed, and in Stand-Sit times indicate exercise is effective in improving functional aspects of mobility that are often associated with falls and quality of life measures. These results support the use of high intensity aerobic exercise for improvements in functional lower extremity performance in a PD population.

Comparison of Standard Clinical and Instrumented Physical Performance Tests in Discriminating Functional Status of High-Functioning People Aged 61⁻70 Years Old

Assessment of physical performance by standard clinical tests such as the 30-sec Chair Stand (30CST) and the Timed Up and Go (TUG) may allow early detection of functional decline, even in high-functioning populations, and facilitate preventive interventions. Inertial sensors are emerging to obtain instrumented measures that can provide subtle details regarding the quality of the movement while performing such tests. We compared standard clinical with instrumented measures of physical performance in their ability to distinguish between high and very high functional status, stratified by the Late-Life Function and Disability Instrument (LLFDI). We assessed 160 participants from the PreventIT study (66.3 ± 2.4 years, 87 females, median LLFDI 72.31, range: 44.33⁻100) performing the 30CST and TUG while a smartphone was attached to their lower back. The number of 30CST repetitions and the stopwatch-based TUG duration were recorded. Instrumented features were computed from the smartphone embedded inertial sensors. Four logistic regression models were fitted and the Areas Under the Receiver Operating Curve (AUC) were calculated and compared using the DeLong test. Standard clinical and instrumented measures of 30CST both showed equal moderate discriminative ability of 0.68 (95%CI 0.60⁻0.76), p = 0.97. Similarly, for TUG: AUC was 0.68 (95%CI 0.60⁻0.77) and 0.65 (95%CI 0.56⁻0.73), respectively, p = 0.26. In conclusion, both clinical and instrumented measures, recorded through a smartphone, can discriminate early functional decline in healthy adults aged 61⁻70 years.

The transition between turning and sitting in patients with Parkinson's disease: A wearable device detects an unexpected sequence of events

BACKGROUND

When older adults turn to sit, about 80% of the subjects complete the turn before starting to sit i.e., a distinct-strategy, while in about 20%, part of the turning and sitting take place concurrently, i.e., an overlapping-strategy. A prolonged duration of the separation between tasks in the distinct-strategy (D-interval) and a prolonged duration of the overlap interval in overlapping-strategy (O-interval) are related to worse motor symptoms and poorer cognition. In the present study, we evaluated what strategy is employed by patients with Parkinson's disease (PD) when they transition from turning to sitting.

METHODS

96 participants with PD performed turn to sit as part of the Timed Up and Go test, both with and without medications, while wearing a body-fixed sensor. We quantified the turn-to-sit transition and determined which strategy (distinct or overlapping) was employed. We then stratified the cases and used regression models adjusted for age, gender, height, and weight to examine the associations of the D-interval or O-interval with parkinsonian features and cognition.

RESULTS

Most patients (66%) employed the overlapping-strategy, both off and on anti-parkinsonian medications. Longer O-intervals were associated with longer duration of PD, more severe PD motor symptoms, a higher postural-instability-gait-disturbance (PIGD) score, and worse freezing of gait. Longer D-intervals were not associated with disease duration or PD motor symptoms. Neither the D- nor O-intervals were related to cognitive function. Individuals who employed the overlapping-strategy had more severe postural instability (i.e., higher PIGD scores), as compared to those who used the distinct-strategy.

SIGNIFICANCE

In contrast to older adults without PD, most patients with PD utilize the overlapping strategy. Poorer postural and gait control are associated with the strategy choice and with the duration of concurrent performance of turning and sitting. Additional work is needed to further explicate the mechanisms underlying these strategies and their clinical implications.

Combining Russian stimulation with isometric exercise improves strength, balance, and mobility in older people with falls syndrome

One of the main causes of falls in older people is muscle strength loss associated with aging. Russian stimulation can improve muscle strength in healthy individuals, but the effect has never been tested in older individuals with falls syndrome. The aim of this study was to evaluate the usefulness of Russian stimulation plus isometric exercise to improve muscular strength, balance, and mobility in older people with falls syndrome. The recruited participants (older than 60 years, at least one fall in the past year) were evaluated by a physiatrist, who collected clinical data and performed baseline and final evaluations (muscle strength, Berg balance scale, Tinetti mobility test, get up and go test, and 6-min walk test). A physical therapist applied the 10/50/10 protocol for Russian stimulation, stimulating the quadriceps and tibialis anterior muscles separately; simultaneously, the participants performed isometric exercise at a frequency of three sessions per week for 12 weeks. Descriptive statistics, the paired-sample t-test, and the χ-test were performed. The study included 25 participants (96% women, mean age 65.2±5.5 years). After the intervention, there was a significant improvement in the strength of the quadriceps (~30%) and tibialis anterior (~40%) muscles as well as the results of the balance (Tinetti 22%, Berg 10%) and mobility (get up and go 25%, 6-min distance 20%) tests. On the basis of the improvements in the Tinetti and Berg scores, significantly fewer participants were classified as being at increased risk for falls. The muscle strength correlated with several clinical evaluation results, but not with the Tinetti test score. Russian stimulation plus isometric exercise improves strength, balance, and mobility, which may decrease the fall risk.

Thigh-Derived Inertial Sensor Metrics to Assess the Sit-to-Stand and Stand-to-Sit Transitions in the Timed Up and Go (TUG) Task for Quantifying Mobility Impairment in Multiple Sclerosis

Introduction: Inertial sensors generate objective and sensitive metrics of movement disability that may indicate fall risk in many clinical conditions including multiple sclerosis (MS). The Timed-Up-And-Go (TUG) task is used to assess patient mobility because it incorporates clinically-relevant submovements during standing. Most sensor-based TUG research has focused on the placement of sensors at the spine, hip or ankles; an examination of thigh activity in TUG in multiple sclerosis is wanting. Methods: We used validated sensors (x-IMU by x-io) to derive transparent metrics for the sit-to-stand (SI-ST) transition and the stand-to-sit (ST-SI) transition of TUG, and compared effect sizes for metrics from inertial sensors on the thighs to effect sizes for metrics from a sensor placed at the L3 level of the lumbar spine. Twenty-three healthy volunteers were compared to 17 ambulatory persons with MS (PwMS, HAI ≤ 2). Results: During the SI-ST transition, the metric with the largest effect size comparing healthy volunteers to PwMS was the Area Under the Curve of the thigh angular velocity in the pitch direction-representing both thigh and knee extension; the peak of the spine pitch angular velocity during SI-ST also had a large effect size, as did some temporal measures of duration of SI-ST, although less so. During the ST-SI transition the metric with the largest effect size in PwMS was the peak of the spine angular velocity curve in the roll direction. A regression was performed. Discussion: We propose for PwMS that the diminished peak angular velocity during SI-ST directly represents extensor weakness, while the increased roll during ST-SI represents diminished postural control. Conclusions: During the SI-ST transition of TUG, angular velocities can discriminate between healthy volunteers and ambulatory PwMS better than temporal features. Sensor placement on the thighs provides additional discrimination compared to sensor placement at the lumbar spine.

Evaluation of the Tinetti score and fall risk assessment via accelerometry-based movement analysis

Gait and balance disorders are among the main predisposing factors of falls in elderly. Clinical scales are widely employed to assess the risk of falling, but they require trained personnel. We investigate the use of objective measures obtained from a wearable accelerometer to evaluate the fall risk, determined by the Tinetti clinical scale. Seventy-nine patients and eleven volunteers were enrolled in two rehabilitation centers and underwent a full Tinetti test, while wearing a triaxial accelerometer at the chest. Tinetti scores were assessed by expert physicians and those subjects with a score ≤18 were considered at high risk. First, we analyzed 21 accelerometer features by means of statistical tests and correlation analysis. Second, one regression and one classification problem were designed and solved using a linear model (LM) and an artificial neural network (ANN) to predict the Tinetti outcome. Pearson's correlation between the Tinetti score and a subset of 9 features (mainly related with standing and walking) was 0.71. The misclassification error of high risk patient was 0.21 and 0.11, for LM and ANN, respectively. The work might foster the development of a new generation of applications meant to monitor the time evolution of the fall risk using low cost devices at home.

Quantifying turning behavior and gait in Parkinson's disease using mobile technology

•

Improvements in mobility were detected from meds using a mobile device IMU in PD.

•

Algorithms using mobile device IMU data can segment the TUG into subtasks.

•

The Cleveland Clinic Mobility App can provide an objective assessment of mobility.

Gait and balance impairments associated with Parkinson’s disease (PD) are often refractory to traditional treatments. Objective, quantitative analysis of gait patterns is crucial in successful management of these symptoms. This project aimed to 1) determine if biomechanical metrics from a mobile device inertial measurement unit were sensitive enough to characterize the effects of anti-parkinsonian medication during the Timed Up and Go (TUG) Test, and 2) develop the Cleveland Clinic Mobility and Balance application (CC-MB) to provide clinicians with objective report following completion of the TUG. The CC-MB captured 3-dimensional acceleration and rotational data from people with PD (pwPD) to characterize center of mass movement while performing the TUG. Trials were segmented into four components: Sit-to-Walk, Gait, Turning, and Stand-to-Sit. Thirty pwPD were tested On and Off (12 h) anti-PD medication. Significant improvements (p < 0.05) between On versus Off conditions included: reduction in MDS-UPDRS III motor scores (10.7%), faster trial times (9.3%), more dynamic walking as evident by increased normalized jerk scores (vertical: 17.3%, medial-lateral: 12.3%), shorter turn durations (10.4%), and faster turn velocities (8%). Measures in Sit-to-Walk and Stand-to-Sit did not show significant changes. Trial time and turn velocity showed excellent test-retest reliability (ICC range: 0.83-0.96) across both medication states. A mobile device platform provided quantitative measures of gait and turning during the TUG that detected significant improvements from anti-parkinsonian medications. This platform is a low-cost, easy-to-use tool that can provide objective reports immediately following the clinical assessments, making it ideal for use in and outside the clinical setting.

SPARC: a new approach to quantifying gait smoothness in patients with Parkinson's disease

BACKGROUND

Impairments in biomechanics and neural control can disrupt the timing and muscle pattern activation necessary for smooth gait. Gait is one of the most affected motor characteristics in Parkinson's disease (PD), but its smoothness has not been well-studied. This work applies the recently proposed spectral arc length measure (SPARC) to study, for the first time, gait in patients with PD. We hypothesized that the gait of patients with PD would be less smooth than that of healthy controls, as reflected in the SPARC measures.

METHODS

The gait of 101 PD patients and 39 healthy controls was assessed using an inertial sensor. Smoothness of gait was estimated with SPARC (respectively from acceleration and angular velocity signals, SPARC-Acc and SPARC-Gyro) and harmonic ratios. Correlations between SPARC, traditional gait measures and the motor part of the Unified Parkinson's Disease Rating Scale (UPDRS) were evaluated. Measurements and analysis were conducted with and without anti-PD medication.

RESULTS

SPARC measures were lower (less smooth) in PD than in controls (SPARC-Acc: PD: - 6.11 ± 0.74; CO: -5.17 ± 0.79; p <  0.001). When comparing PD to controls, SPARC-Acc differed more than other measures of gait (i.e., largest effect size, which was > 1). SPARC measures were correlated with UPDRS motor score (r = - 0.65), while they were independent of other measures of gait smoothness. PD gait in the on state was smoother than in the off state (p <  0.001).

CONCLUSIONS

SPARC calculated from trunk acceleration and angular velocity signals provide valid measures of walking smoothness in PD. SPARC is sensitive to Parkinson's disease and PD medications and can be used of as another, complementary measure of the motor control of walking in PD.

Criterion validity of the instrumented Timed Up and Go test: A partial least square regression study

The Timed Up and Go (TUG) test is a common mobility measure in rehabilitation. With the instrumental TUG test (ITUG; i.e. the TUG measured by inertial measurement units, IMUs), several movement measures are newly available. However, the clinical meaning of these new measures is not totally clear. Aim of the current work is to evaluate the validity of different ITUG parameters as a measure of balance. Neurological patients (n = 122; 52 females; 89 older than 65 years) completed the TUG test with IMUs secured to their back. IMUs signals were used to split the TUG test in five phases (sit-to-stand, walk1, turn1, walk2 and turn-and-sit) and twelve movement parameters were obtained. Experienced clinicians administered the Mini-BESTest (MB) scale, a sound balance measure. The partial least square regression (PLSR) was used to explore the association between the ITUG variables and the MB measure. A PLSR model with twelve ITUG variables had satisfactory fit parameters (RMSEP: 11%; R²: 0.41, 95% CI: 0.28-0.54; regression line: 1, 95% CI: 0.78-1.22). Three ITUG variables (i.e. turn1 vertical angular velocity, turn1 duration and turn2 vertical angular velocity) were found to be the most important predictors of the MB measure. A PLSR model with the turning variables only had fit parameters comparable to that of the twelve variables model. Turning parameters from the TUG test are good predictors of the MB scale. The mean angular velocity during turning and the duration of the turning phase are thus proposed as a valid, ratio-level measures of balance in neurological patients.

Novel sensing technology in fall risk assessment in older adults: a systematic review

Background

Falls are a major health problem for older adults with significant physical and psychological consequences. A first step of successful fall prevention is to identify those at risk of falling. Recent advancement in sensing technology offers the possibility of objective, low-cost and easy-to-implement fall risk assessment. The objective of this systematic review is to assess the current state of sensing technology on providing objective fall risk assessment in older adults.

Methods

A systematic review was conducted in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement (PRISMA).

Results

Twenty-two studies out of 855 articles were systematically identified and included in this review. Pertinent methodological features (sensing technique, assessment activities, outcome variables, and fall discrimination/prediction models) were extracted from each article. Four major sensing technologies (inertial sensors, video/depth camera, pressure sensing platform and laser sensing) were reported to provide accurate fall risk diagnostic in older adults. Steady state walking, static/dynamic balance, and functional mobility were used as the assessment activity. A diverse range of diagnostic accuracy across studies (47.9% - 100%) were reported, due to variation in measured kinematic/kinetic parameters and modelling techniques.

Conclusions

A wide range of sensor technologies have been utilized in fall risk assessment in older adults. Overall, these devices have the potential to provide an accurate, inexpensive, and easy-to-implement fall risk assessment. However, the variation in measured parameters, assessment tools, sensor sites, movement tasks, and modelling techniques, precludes a firm conclusion on their ability to predict future falls. Future work is needed to determine a clinical meaningful and easy to interpret fall risk diagnosis utilizing sensing technology. Additionally, the gap between functional evaluation and user experience to technology should be addressed.

Gait

Gait is one of the keys to functional independence. For a long-time, walking was considered an automatic process involving minimal higher-level cognitive input. Indeed, walking does not take place without muscles that move the limbs and the "lower-level" control that regulates the timely activation of the muscles. However, a growing body of literature suggests that walking can be viewed as a cognitive process that requires "higher-level" cognitive control, especially during challenging walking conditions that require executive function and attention. Two main locomotor pathways have been identified involving multiple brain areas for the control of posture and gait: the dorsal pathway of cognitive locomotor control and the ventral pathway for emotional locomotor control. These pathways may be distinctly affected in different pathologies that have important implications for rehabilitation and therapy. The clinical assessment of gait should be a focused, simple, and cost-effective process that provides both quantifiable and qualitative information on performance. In the last two decades, gait analysis has gradually shifted from analysis of a few steps in a restricted space to long-term monitoring of gait using body fixed sensors, capturing real-life and routine behavior in the home and community environment. The chapter also describes this evolution and its implications.

Transition Between the Timed up and Go Turn to Sit Subtasks: Is Timing Everything?

OBJECTIVE

The Timed Up and Go (TUG), one of the most widely used tests of mobility, has been validated and associated with adverse outcomes in the community, acute care, and nursing home setting. It is composed of several distinct subtasks; however, the temporal relationship when transitioning between subtasks has not been well-studied. We tested the hypothesis that longer transition durations between the final turn to the sitting subtasks are associated with worse motor and cognitive performance in older adults.

METHODS

A total of 1055 participants (80.33 ± 7.57 years, 76.96% female) performed the TUG while wearing a 3-dimensional inertial sensor on their lower back. We employed a series of linear regressions to examine the association of the duration between the turn and sitting subtasks with clinical characteristics including motor and cognitive functions.

RESULTS

Participants employed 2 different strategies when they transitioned from turning to sitting. (1) Distinct transition strategy: 816 participants (77.34%) first completed the turn before starting to sit. The average duration between these distinct subtasks (D-interval) was 715 ± 980 ms. (2) Overlapping transition strategy: 239 participants (22.65%) started to sit before completing the turn. The average overlap duration between these tasks (O-interval) was 237 ± 269 ms. Participants who employed the distinct transition strategy were slightly younger than those who employed the overlapping transition strategy (P ≤ .013). Higher D-intervals and O-intervals were associated with worse TUG performance (P ≤ .02), with poorer motor and cognitive function, [ie, worse parkinsonian gait (P ≤ .001), lower level of perceptual speed (P ≤ .03), and with worse mobility disability (P ≤ .001)]. A longer D-interval was associated with worse gait speed and bradykinesia (P ≤ .001), whereas a longer O-interval was associated with increased rigidity (P = .004).

CONCLUSIONS

Older adults apparently employ 2 different strategies when transitioning from turning to sitting. The instrumented TUG can characterize additional gait and balance aspects that cannot be derived from traditional TUG assessments. These new measures offer novel targets for intervention to decrease the burden of late-life gait impairment.

Linear-Sigmoidal modelling of accelerometer features and Tinetti score for automatic fall risk assessment

Falling in elderly is a worldwide major problem and it can lead to severe injuries or death. Despite the effort made to ensure home environments safe and foster healthy lifestyles, it is still necessary to provide methodologies that can be used at home for detect risk factors associated with falls. In this study, we proposed a new simple non-linear model, i.e., Linear-Sigmoidal model (LS), easy to fit and simple to interpret, used to model accelerometer features and outcome of the clinical scale Tinetti (clinical scale for fall risk prediction). Also, subjects with a score ≤ 18 were considered as high risk of falling. One-hundred-twelve subjects underwent to a Tinetti test while wearing a 3D axis accelerometer at the chest, and the Tinetti score used as gold standard. Ninety subjects were used as training set and twenty-two ones were employed to test the model. The same sets were used to assess the performance of the standard linear regression (LR). Seven accelerometer features and the body mass index were used in the model regression. LS resulted better than LR in terms of model agreement (R²: 0.76 vs 0.72) and classification accuracy (0.91 vs 0.86) on the test set.

Better than counting seconds: Identifying fallers among healthy elderly using fusion of accelerometer features and dual-task Timed Up and Go

Devices and sensors for identification of fallers can be used to implement actions to prevent falls and to allow the elderly to live an independent life while reducing the long-term care costs. In this study we aimed to investigate the accuracy of Timed Up and Go test, for fallers’ identification, using fusion of features extracted from accelerometer data. Single and dual tasks TUG (manual and cognitive) were performed by a final sample (94% power) of 36 community dwelling healthy older persons (18 fallers paired with 18 non-fallers) while they wear a single triaxial accelerometer at waist with sampling rate of 200Hz. The segmentation of the TUG different trials and its comparative analysis allows to better discriminate fallers from non-fallers, while conventional functional tests fail to do so. In addition, we show that the fusion of features improve the discrimination power, achieving AUC of 0.84 (Sensitivity = Specificity = 0.83, 95% CI 0.62-0.91), and demonstrating the clinical relevance of the study. We concluded that features extracted from segmented TUG trials acquired with dual tasks has potential to improve performance when identifying fallers via accelerometer sensors, which can improve TUG accuracy for clinical and epidemiological applications.

Convergent Validity of a Wearable Sensor System for Measuring Sub-Task Performance during the Timed Up-and-Go Test

Background: The timed-up-and-go test (TUG) is one of the most commonly used tests of physical function in clinical practice and for research outcomes. Inertial sensors have been used to parse the TUG test into its composite phases (rising, walking, turning, etc.), but have not validated this approach against an optoelectronic gold-standard, and to our knowledge no studies have published the minimal detectable change of these measurements. Methods: Eleven adults performed the TUG three times each under normal and slow walking conditions, and 3 m and 5 m walking distances, in a 12-camera motion analysis laboratory. An inertial measurement unit (IMU) with tri-axial accelerometers and gyroscopes was worn on the upper-torso. Motion analysis marker data and IMU signals were analyzed separately to identify the six main TUG phases: sit-to-stand, 1st walk, 1st turn, 2nd walk, 2nd turn, and stand-to-sit, and the absolute agreement between two systems analyzed using intra-class correlation (ICC, model 2) analysis. The minimal detectable change (MDC) within subjects was also calculated for each TUG phase. Results: The overall difference between TUG sub-tasks determined using 3D motion capture data and the IMU sensor data was <0.5 s. For all TUG distances and speeds, the absolute agreement was high for total TUG time and walk times (ICC > 0.90), but less for chair activity (ICC range 0.5–0.9) and typically poor for the turn time (ICC < 0.4). MDC values for total TUG time ranged between 2–4 s or 12–22% of the TUG time measurement. MDC of the sub-task times were higher proportionally, being 20–60% of the sub-task duration. Conclusions: We conclude that a commercial IMU can be used for quantifying the TUG phases with accuracy sufficient for clinical applications; however, the MDC when using inertial sensors is not necessarily improved over less sophisticated measurement tools.

Accelerometry-based assessment and detection of early signs of balance deficits

Falls are the cause for more than half of the injury-related hospitalizations among older people. Accurate assessment of individuals' fall risk could enable targeted interventions to reduce the risk. This paper presents a novel method for using wearable accelerometers to detect early signs of deficits in balance from gait. Gait acceleration data were analyzed from 35 healthy female participants (73.86±5.40 years). The data were collected with waist-mounted accelerometer and the participants performed three supervised balance tests: Berg Balance Scale (BBS), Timed-Up-and-Go (TUG) and 4m walk. The follow-up tests with the same protocol were performed after one year. Altogether 43 features were extracted from the accelerometer signals. Sequential forward floating selection and ten-fold cross-validation were applied to determine models for 1) estimating the outcomes of BBS, TUG and 4m walk tests and 2) predicting decline in balance during one-year follow-up indicated as decline in BBS total score and one leg stance. Normalized root-mean-square errors (RMSE) of the assessment scale result estimates were 0.28 for BBS score, 0.18 for TUG time, and 0.22 for 4m walk test. Area under curve (AUC) was 0.78 for predicting decline in BBS total score and 0.82 for one leg stance, respectively. The results suggest that the gait features can be used to estimate the result of a clinical balance assessment scale and predict decline in balance. A simple walk test with wearable monitoring could be applicable as an initial screening tool to identify people with early signs of balance deficits.

Postural Transitions during Activities of Daily Living Could Identify Frailty Status: Application of Wearable Technology to Identify Frailty during Unsupervised Condition

BACKGROUND

Impairment of physical function is a major indicator of frailty. Functional performance tests have been shown to be useful for identification of frailty in older adults. However, these tests are often not translatable into unsupervised and remote monitoring of frailty status at home and/or community settings.

OBJECTIVE

In this study, we explored daily postural transition quantified using a chest-worn wearable technology to identify frailty in community-dwelling older adults.

METHODS

Spontaneous daily physical activity was monitored over 24 h in 120 community-dwelling elderly (age: 78 ± 8 years) using an unobtrusive wearable sensor (PAMSys™, BioSensics LLC, Watertown, MA, USA). Participants were classified as non-frail and pre-frail/frail using Fried's criteria. A validated software package was used to identify body postures and postural transition between each independent postural activity such as sit-to-stand, stand-to-sit, stand-to-walk, and walk-to-stand. The transition from walking to sitting was further classified as quick sitting and cautious sitting based on presence/absence of a standing posture pause between sitting and walking. A general linear model univariate test was used for between-group comparison. Pearson's correlation was used to determine the association between sensor-derived parameters and age. Logistic regression model was used to identify independent predictors of frailty.

RESULTS

According to Fried's criteria, 63% of participants were pre-frail/frail. The total number of postural transitions, stand-to-walk, and walk-to-stand were, respectively, 25.2, 30.2, and 30.6% lower in the pre-frail/frail group when compared to the non-frail group (p < 0.05, Cohen's d = 0.73-0.79). Furthermore, the ratio of cautious sitting was significantly higher by 6.2% in pre-frail/frail compared to non-frail (p = 0.025, Cohen's d = 0.22). Total number of postural transitions and the ratio of cautious sitting also showed significant negative and positive correlations with age, respectively (r = -0.51 and 0.29, p < 0.05). After applying a logistic regression model, among tested parameters, walk-to-stand (odds ratio [OR] = 0.997 p = 0.013), quick sitting (OR = 1.036, p = 0.05), and age (OR = 1.073, p = 0.016) were recognized as independent variables to identify frailty status.

CONCLUSIONS

This study demonstrated that daily number of specific postural transitions such as walk-to-stand and quick sitting could be used for monitoring frailty status by unsupervised monitoring of daily physical activity. Further study is warranted to explore whether tracking the daily number of specific postural transitions is also sensitive to track change in the status of frailty over time.

The validity of an accelerometer-based method for estimating fluidity in the sit-to-walk task in a community setting

[Purpose] Fluidity in the sit-to-walk task has been quantitatively measured with three-dimensional motion analysis system. The purpose of this study was to determine the validity of an accelerometer-based method for estimating fluidity in community-dwelling elderly individuals. [Subjects and Methods] Seventeen community-dwelling elderly females performed a sit-to-walk task. The motion was recorded by an accelerometer, a three-dimensional motion analysis system and a foot pressure sensor simultaneously. The timings of events determined from the acceleration waveform were compared to the timings determined from the three-dimensional motion analysis data (task onset, maximum trunk inclination) or foot pressure sensor data (first heel strike). Regression analysis was used to estimate the fluidity index from the duration between events. [Results] The characteristics of the acceleration waveform were similar to those previously reported in younger adults. Comparisons of event timings from accelerometer and motion analysis system data indicated no systematic bias. Regression analysis showed that the duration from maximum trunk inclination to the first heel strike was the best predictor of fluidity index. [Conclusion] An accelerometer-based method using the duration between characteristic events may be used to precisely and conveniently assess fluidity in a sit-to-walk task in a community setting.

Disentangling the health benefits of walking from increased exposure to falls in older people using remote gait monitoring and multi-dimensional analysis

Falls and physical deconditioning are two major health problems for older people. Recent advances in remote physiological monitoring provide new opportunities to investigate why walking exercise, with its many health benefits, can both increase and decrease fall rates in older people. In this paper we combine remote wearable device monitoring of daily gait with non-linear multi-dimensional pattern recognition analysis; to disentangle the complex associations between walking, health and fall rates. One week of activities of daily living (ADL) were recorded with a wearable device in 96 independent living older people prior to completing 6 months of exergaming interventions. Using the wearable device data; the quantity, intensity, variability and distribution of daily walking patterns were assessed. At baseline, clinical assessments of health, falls, sensorimotor and physiological fall risks were completed. At 6 months, fall rates, sensorimotor and physiological fall risks were re-assessed. A non-linear multi-dimensional analysis was conducted to identify risk-groups according to their daily walking patterns. Four distinct risk-groups were identified: The Impaired (93% fallers), Restrained (8% fallers), Active (50% fallers) and Athletic (4% fallers). Walking was strongly associated with multiple health benefits and protective of falls for the top performing Athletic risk-group. However, in the middle of the spectrum, the Active risk-group, who were more active, younger and healthier were 6.25 times more likely to be fallers than their Restrained counterparts. Remote monitoring of daily walking patterns may provide a new way to distinguish Impaired people at risk of falling because of frailty from Active people at risk of falling from greater exposure to situations were falls could occur, but further validation is required. Wearable device risk-profiling could help in developing more personalised interventions for older people seeking the health benefits of walking without increasing their risk of falls.

Intermittent Hypoxia and Locomotor Training Enhances Dynamic but Not Standing Balance in Patients With Incomplete Spinal Cord Injury

OBJECTIVE

To test the effect of combined intermittent hypoxia (IH) and body weight-supported treadmill training (BWSTT) on standing and dynamic balance in persons with incomplete spinal cord injury (iSCI).

DESIGN

Randomized, triple-blind, placebo-controlled study.

SETTING

Rehabilitation medical centers.

PARTICIPANTS

Study participants (N=35) with chronic iSCI with American Spinal Injury Association grades C and D (>1y postinjury) were randomly assigned to either IH plus BWSTT (n=18) or continued normoxia (placebo) plus BWSTT protocol (n=17).

INTERVENTIONS

Participants received either IH (alternating 1.5min 9% inspired O₂ with 1.5min 21% inspired O₂, 15 cycles per day) or continued normoxia (21% O₂) combined with 45 minutes of BWSTT for 5 consecutive days, followed by 3 times per week IH or normoxia plus BWSTT, for 3 additional weeks.

MAIN OUTCOME MEASURES

Standing balance (normalized jerk and root-mean-square [RMS]) and dynamic balance (turning duration, cadence in a turn, and turn-to-sit duration) were assessed before and after IH and normoxia protocol by means of instrumented sway and instrumented timed Up and Go test.

RESULTS

There was no significant difference in standing balance between interventions for both normalized jerk and RMS instrumented sway components (both P>.05). There was a significantly faster cadence (P<.001), turning duration (P<.001), and turn-to-sit duration (P=.001) in subjects receiving IH plus BWSTT, compared with placebo.

CONCLUSIONS

A 4-week protocol of IH combined with locomotor training improves dynamic, but not standing, balance in persons with iSCI.

Timed Up-and-Go phase segmentation in Parkinson's disease patients using unobtrusive inertial sensors

A widely accepted functional motor test for measuring basic mobility capabilities is the `Timed Up-and-Go' (TUG) test. Although several basic mobility tasks are included, only the total time is used as outcome parameter. It has been shown that timings of sub-phases can be used as relevant clinical parameters for the assessment of Parkinson's disease patients. A variety of systems and methods have been proposed for instrumenting the TUG test, but only limited information has been published regarding phase classification. In this paper an automated TUG phase classification methodology is proposed and validated in a study with 16 Parkinson's disease patients. Statistical, signal energy, chronological and gait features were extracted from acceleration and orientation signals of shoe mounted inertial measurement units. The phases `sit to walk', `walking', `first turn', `second turn' and `turn to sit' were segmented in a two stage classifier approach. Strides were used for a separation of the walking phase and classifiers like NaiveBayes, k-Nearest-Neighbor, Support Vector Machine (SVM) and Random Forest for the final phase segmentation. SVM performed best with a mean sensitivity of 81.80% over all phases. Additionally, the impact of UPDRS and Hoehn & Yahr ratings on the phase times was assessed. The proposed methodology could be used to analyze gait parameters of sub-phases like stride length, stride time, foot clearance, heel-strike or toe-off angle for an improved assessment of Parkinson's disease patients.

Multivariate Analyses and Classification of Inertial Sensor Data to Identify Aging Effects on the Timed-Up-and-Go Test

Many tests can crudely quantify age-related mobility decrease but instrumented versions of mobility tests could increase their specificity and sensitivity. The Timed-up-and-Go (TUG) test includes several elements that people use in daily life. The test has different transition phases: rise from a chair, walk, 180° turn, walk back, turn, and sit-down on a chair. For this reason the TUG is an often used test to evaluate in a standardized way possible decline in balance and walking ability due to age and or pathology. Using inertial sensors, qualitative information about the performance of the sub-phases can provide more specific information about a decline in balance and walking ability. The first aim of our study was to identify variables extracted from the instrumented timed-up-and-go (iTUG) that most effectively distinguished performance differences across age (age 18-75). Second, we determined the discriminative ability of those identified variables to classify a younger (age 18-45) and older age group (age 46-75). From healthy adults (n = 59), trunk accelerations and angular velocities were recorded during iTUG performance. iTUG phases were detected with wavelet-analysis. Using a Partial Least Square (PLS) model, from the 72-iTUG variables calculated across phases, those that explained most of the covariance between variables and age were extracted. Subsequently, a PLS-discriminant analysis (DA) assessed classification power of the identified iTUG variables to discriminate the age groups. 27 variables, related to turning, walking and the stand-to-sit movement explained 71% of the variation in age. The PLS-DA with these 27 variables showed a sensitivity and specificity of 90% and 85%. Based on this model, the iTUG can accurately distinguish young and older adults. Such data can serve as a reference for pathological aging with respect to a widely used mobility test. Mobility tests like the TUG supplemented with smart technology could be used in clinical practice.

Objective characterization of daily living transitions in patients with Parkinson's disease using a single body-fixed sensor

Body-fixed sensors (BFS), e.g., accelerometers worn for several days, can be used to augment the traditional clinical assessment. Long-term recordings obtained with BFS have been applied to study tremor, postural control, freezing of gait, turning abilities, motor response fluctuations and fall risk among older adults and patients with Parkinson's disease (PD). We aimed to test whether BFS-derived measures of transitions differ between patients with PD and healthy controls, and to evaluate whether there are differences among patients with mild PD, compared to more severe patients, and to controls. We also explored the added value of the metrics extracted from the sensor as compared to traditional testing in the lab. Ninety-nine patients with PD and 38 healthy older adults (HOA) participated in this study and wore a body-fixed sensor for 3 days. Walk-to-sit (n = 3286) and Sit-to-walk (n = 2858) transitions were analyzed and a machine learning algorithm was applied to distinguish between the groups. Significant differences in transitions were observed between PD patients and HOA, between mild and severe PD, and between mild PD and HOA, both in temporal and distribution features. The machine learning algorithm discriminated patients from HOA (accuracy = 92.3 %), mild from severe patients (accuracy = 89.8 %), and mild patients from HOA (accuracy = 85.9 %). These initial results suggest that body-fixed sensor-derived metrics of everyday transitions can characterize disease severity and differentiate mild PD patients from healthy older adults. Perhaps this approach can help with the integration of BFS into clinical care and the tracking of disease progression and the response to therapy.

The validity of assessing temporal events, sub-phases and trunk kinematics of the sit-to-walk movement in older adults using a single inertial sensor

The aim of this study was to develop a method to identify temporal events, sub-phases and trunk kinematics of the sit-to-walk (STW) using a single inertial sensor (IS) worn at the lower back and to determine the validity of this method. Twenty-seven healthy older adults performed a STW movement, which started from sitting in a chair and included walking 3m. Participants׳ movements were recorded with the IS, a reference measurement system consisting of an optical motion capture system (3 markers on the IS and one on each foot) and on-off switches located in the seat of the chair. Using the data from the IS and the reference measurement systems, the following signals and variables were calculated and compared: 3D IS motion (accelerations, velocities, displacements and angles), temporal events (start of trunk movement, seat-off, end of trunk flexion phase, end of trunk rising phase and gait initiation) and trunk kinematics (flexion range, maximum flexion velocity, maximum forward velocity and forward velocity during seat-off and at first heel-strike and maximum vertical velocity and vertical velocity at first heel-strike). For most variables acceptable differences (RMSE<10%) were found between IS and reference measurement systems, except for sideways displacements and non-sagittal plane rotations. Furthermore, good results were found for temporal event detection, with ICC values for all variables being 0.988 or higher. With exception of the vertical velocity at heel-strike agreement for trunk kinematics was high, with ICC values being 0.867 or higher.

Effects of Balance Control Through Trunk Movement During Square and Semicircular Turns on Gait Velocity, Center of Mass Acceleration, and Energy Expenditure in Older Adults

BACKGROUND

Turning during ambulation is a common movement in everyday life, but complex and challenging for older adults. Balance control through trunk movement provides a stable platform during walking, thus it is an essential component of safe and efficient turning during walking in elderly individuals.

OBJECTIVES

To investigate the effects of balance control during square turning (ST) and semicircular turning (SCT) on gait velocity, center of mass (COM) acceleration, and energy expenditure in elderly women.

DESIGN

Cross-sectional design.

SETTING

Village community center.

PARTICIPANTS

Twenty community-dwelling elderly women capable of independent walking were enrolled in the study.

METHODS

Participants walked at a self-selected speed along a marked path that included 2 types of turns (the path was divided into 3 segments: straight, turning, and straight return), while fitted with an accelerometer attached over the L3 spinous process.

MAIN OUTCOME MEASUREMENTS

Differences in gait velocity, normalized COM acceleration, and energy expenditure were analyzed using paired t-tests for comparisons between ST and SCT tasks and using a one-way repeated-measures analysis of variance for within tasks.

RESULTS

During the ST task, which was characterized by the use of a less-stable balance maintenance strategy, gait velocity and vertical COM acceleration were lower (P < .05), whereas greater medial-lateral COM acceleration (P < .05) and energy expenditure (P < .001) were observed during turning and return straight stages compared with the SCT task. For both tasks, velocity during turning stage was the slowest, among the 3 stages, the straight stage was the fastest (P < .05). For the SCT task, the anterior-posterior COM acceleration during the straight stage was significantly higher than during the turning stage, and the vertical COM acceleration during the straight stage was significantly lower than during the return-straight stage (P < .05). In both tasks, the energy expenditure of the turning stage was significantly higher than in the straight and return straight stage (P ≤ .001), and in the return straight stage was higher than the straight stage-only ST task (P < .05).

CONCLUSIONS

We suggest that elderly individuals participate in balance and gait training using a variety of turns, including turns requiring medial-lateral and vertical COM balance control, to prevent falls and to improve energy efficiency of walking.

LEVEL OF EVIDENCE

IV.

Fall Risk Assessment Through Automatic Combination of Clinical Fall Risk Factors and Body-Worn Sensor Data

Falls are the leading global cause of accidental death and disability in older adults and are the most common cause of injury and hospitalization. Accurate, early identification of patients at risk of falling, could lead to timely intervention and a reduction in the incidence of fall-related injury and associated costs. We report a statistical method for fall risk assessment using standard clinical fall risk factors (N = 748). We also report a means of improving this method by automatically combining it, with a fall risk assessment algorithm based on inertial sensor data and the timed-up-and-go test. Furthermore, we provide validation data on the sensor-based fall risk assessment method using a statistically independent dataset. Results obtained using cross-validation on a sample of 292 community dwelling older adults suggest that a combined clinical and sensor-based approach yields a classification accuracy of 76.0%, compared to either 73.6% for sensor-based assessment alone, or 68.8% for clinical risk factors alone. Increasing the cohort size by adding an additional 130 subjects from a separate recruitment wave (N = 422), and applying the same model building and validation method, resulted in a decrease in classification performance (68.5% for combined classifier, 66.8% for sensor data alone, and 58.5% for clinical data alone). This suggests that heterogeneity between cohorts may be a major challenge when attempting to develop fall risk assessment algorithms which generalize well. Independent validation of the sensor-based fall risk assessment algorithm on an independent cohort of 22 community dwelling older adults yielded a classification accuracy of 72.7%. Results suggest that the present method compares well to previously reported sensor-based fall risk assessment methods in assessing falls risk. Implementation of objective fall risk assessment methods on a large scale has the potential to improve quality of care and lead to a reduction in associated hospital costs, due to fewer admissions and reduced injuries due to falling.

Aging-related decrements during specific phases of the dual-task Timed Up-and-Go test

BACKGROUND

It is unclear how young and older adults modulate dual-task mobility under changing postural challenges.

AIM

To examine age-related changes in dual-task processing during specific phases of dual-task Timed Up-and-Go (TUGdual-task).

METHOD

Healthy young and older adults performed the Timed Up-and-Go (TUG) with the following dual-task conditions: (1) serial-three subtractions, (2) carrying cup of water, (3) combined subtraction and carrying water, and (4) dialing cell phone. The primary outcome was the dual-task cost on performance of TUG (percent change from single- to dual-task) based on duration and peak trunk velocity of each phase: (a) straight-walk, (b) sit-to-stand, (c) turn, (d) turn-to-sit. Mixed-design univariate analysis of variance was performed for each type of task.

RESULTS

Older adults had more pronounced mobility decrements than young adults during straight-ahead walking and turns when the secondary task engaged both cognitive and manual modalities. Simple cognitive or manual tasks during TUGdual-task did not differentiate young from older participants. Subtraction performance during simple and complex cognitive conditions differed by phase of the TUG. Manual task performance of carrying water did not vary by phase or age.

DISCUSSION

Our findings suggest that dual-task processing is dynamic across phases of TUGdual-task. Aging-related dual-task decrements are demonstrated during straight-ahead walking and turning, particularly when the secondary task is more complex.

CONCLUSION

Older adults are susceptible to reduced dual-task mobility during straight-ahead walking and turning particularly when attentional loading was increased.

An accelerometer-based method for estimating fluidity in the sit-to-walk task

[Purpose] The purpose of this study was to clarify the validity of accelerometer data for quantifying fluidity during the sit-to-walk task. [Subjects] The participants were 16 healthy young males. [Methods] The timing of events (task onset, maximum trunk inclination, and first heel strike) was determined from the acceleration waveform and compared to the timing determined from a three-dimensional motion analysis (task onset, maximum trunk inclination) or foot pressure sensor data (first heel strike). Regression analysis was used to estimate the fluidity index (FI) from the duration between events and the magnitude of the acceleration peak. The task was performed at two speeds (comfortable and maximum). [Results] A comparison of the timings from two different systems indicated no systematic bias. Specific events could be identified from acceleration data using regression analysis under both speed conditions. In addition, significant regression equations predictive of FI were constructed using the duration between events under both speed conditions. The duration from the maximum trunk inclination to the first heel strike was the best predictor of FI. [Conclusion] Accelerometer data may be used to precisely and conveniently evaluate fluidity. The clinical utility of these data should be tested in elderly individuals or patient populations.

Review: Are we stumbling in our quest to find the best predictor? Over-optimism in sensor-based models for predicting falls in older adults

The field of fall risk testing using wearable sensors is bustling with activity. In this Letter, the authors review publications which incorporated features extracted from sensor signals into statistical models intended to estimate fall risk or predict falls in older people. A review of these studies raises concerns that this body of literature is presenting over-optimistic results in light of small sample sizes, questionable modelling decisions and problematic validation methodologies (e.g. inherent problems with the overly-popular cross-validation technique, lack of external validation). There seem to be substantial issues in the feature selection process, whereby researchers select features before modelling begins based on their relation to the target, and either perform no validation or test the models on the same data used for their training. This, together with potential issues related to the large number of features and their correlations, inevitably leads to models with inflated accuracy that are unlikely to maintain their reported performance during everyday use in relevant populations. Indeed, the availability of rich sensor data and many analytical options provides intellectual and creative freedom for researchers, but should be treated with caution, and such pitfalls must be avoided if we desire to create generalisable prognostic tools of any clinical value.

Performing gait analysis within the timed up & go assessment test: comparison of aTUG to a marker-based tracking system

Results from a technical validation of the aTUG (ambient Timed Up & Go) system are presented. The approach's gait analysis capabilities were compared to a gold standard: SIMI Motion, a marker-based motion tracking system. Seven people participated and computation of step length and step duration happened with a median error of 3 cm (IQR 3 cm) respectively 0.08 s (IQR 0.07 s). These results show that aTUG has a measurement precision which is sufficient for use in clinical gait analysis and enables the use of the device without a gold standard, i.e. in hospitals outside laboratories or in the homes of patients. aTUG is an approach and system that utilizes only ambient sensor technologies to support the execution of geriatric mobility assessment tests and to perform a gait analysis simultaneously. Such capabilities are strongly demanded in order to support physicians in executing the geriatric assessment tests frequently and objectively in professional and domestic environments. The latter may enable more early prevention and more sustainable rehabilitation.

Evaluation of an inertial sensor system for analysis of timed-up-and-go under dual-task demands

Functional tests, such as the timed-up-and-go (TUG), are routinely used to screen for mobility issues and fall risk. While the TUG is easy to administer and evaluate, its single time-to-completion outcome may not discriminate between different mobility challenges. Wearable sensors provide an opportunity to collect a variety of additional variables during clinical tests. The purpose of this study was to assess a new wearable inertial sensor system (iTUG) by investigating the effects of cognitive tasks in a dual-task paradigm on spatiotemporal and kinematic variables during the TUG. No previous studies have looked at both spatiotemporal variables and kinematics during dual-task TUG tests. 20 healthy young participants (10 males) performed a total 15 TUG trials with two different cognitive tasks and a normal control condition. Total time, along with spatiotemporal gait parameters and kinematics for all TUG subtasks (sit-to-stand, walking, turn, turn-to-sit), were measured using the inertial sensors. Time-to-completion from iTUG was highly correlated with concurrent manual timing. Spatiotemporal variables during walking showed expected differences between control and cognitive dual-tasks while trunk kinematics appeared to show more sensitivity to dual-tasks than reported previously in straight line walking. Non-walking TUG subtasks showed only minor changes during dual-task conditions indicating a possible attentional shift away from the cognitive task. Stride length and some variability measures were significantly different between the two cognitive tasks suggesting an ability to discriminate between tasks. Overall, the use of the iTUG system allows the collection of both traditional and potentially more discriminatory variables with a protocol that is easily used in a clinical setting.

Instrumented assessment of test battery for physical capability using an accelerometer: a feasibility study

Recent work has identified subdomains (tests) of physical capability that are recommended for assessment of the healthy ageing phenotype (HAP). These include: postural control, locomotion, endurance, repeated sit-to-stand-to-sit and TUG. Current assessment methods lack sensitivity and are error prone due to their lack of consistency and heterogeneity of reported outcomes; instrumentation with body worn monitors provides a method to address these potential weaknesses. This work proposes the use of a single tri-axial accelerometer-based device with appropriate algorithms (referred to here as a body worn monitor, BWM) for the purposes of instrumented testing during physicality capability assessment. In this pilot study we present 14 BWM-based outcomes across the subdomains which include magnitude, frequency and spatio-temporal characteristics. Where possible, we compared BWM outcomes with manually recorded values and found no significant differences between locomotion and TUG tasks (p ≥ 0.319). Significant differences were found for the total distance walked during endurance (p = 0.037) and times for repeated sit-to-stand-to-sit transitions (p < 0.000). We identified reasons for differences and make recommendations for future testing. We were also able to quantify additional characteristics of postural control and gait which could be sensitive outcomes for future HAP assessment. Our findings demonstrate the feasibility of this method to enhance measurement of physical capacity. The methodology can also be applied to a wide variety of accelerometer-based monitors and is applicable to a range of intervention-based studies or pathological assessment.

Effect of rivastigmine on mobility of patients with higher-level gait disorder: a pilot exploratory study

Background

Higher-level gait disorder (HLGD) in older adults is characterized by postural instability, stepping dysrhythmicity, recurrent falls and progressive immobility. Cognitive impairments are frequently associated with HLGD.

Objectives

The aim of this study was to compare gait and cognitive performance before and after the use of rivastigmine in patients with HLGD, free from cognitive impairment or Parkinsonism.

Methods

Fifteen non-demented patients with HLGD (age 79.2 ± 5.9 years; 11 women; Mini-Mental State Examination [MMSE] 28.3 ± 1.4) received escalating doses of rivastigmine for 12 weeks in an open-label, pilot study. They were assessed before and after treatment (week 0 and week 12), and after a 4-week washout period (week 16). Assessments included the Mindstreams computerized neuropsychological battery, Activities-specific Balance Confidence Scale, State-Trait Anxiety Inventory, Geriatric Depression Scale, Timed Up and Go (TUG) test, gait speed and stride time variability. One-way multiple analysis of variance tests for repeated measures were used, and Pillai’s trace test was considered as robust to investigate significant differences.

Results

The mean dose of rivastigmine during the 8–12 week period was 5.1 ± 2.3 mg/day. A positive effect was observed on the Mindstreams memory subscale and anxiety scores [Pillai’s trace: F(6,724) = 0.508, p = 0.010; and F(7,792) = 0.545, p = 0.006, respectively, over the course of the study] as well as on mobility (TUG test) [Pillai’s trace: F(4,863) = 0.448; p = 0.028], whereas gait speed and stride time variability did not change.

Conclusions

The use of relatively low-dose rivastigmine did not affect gait speed and stride time variability; however, the general mobility and anxiety were improved. These preliminary results warrant a larger, randomized, placebo-controlled study.

iCap: Instrumented assessment of physical capability

•

Instrumented testing of five physical capability tasks with a single accelerometer.

•

Evaluated on a large cohort of older adults.

•

iCap provides robust quantitative data about physical capability.

•

iCap captures gait and postural control data known as sensitive to ageing/pathology.

•

Methodology may have practical utility in a wide range of surveys and studies.

Objectives

The aims of this study were to (i) investigate instrumented physical capability (iCap) as a valid method during a large study and (ii) determine whether iCap can provide important additional features of postural control and gait to categorise cohorts not previously possible with manual recordings.

Study design

Cross-sectional analysis involving instrumented testing on 74 adults who were recruited as part of a pilot intervention study; LiveWell. Participants wore a single accelerometer-based monitor (lower back) during standardised physical capability tests so that outcomes could be compared directly with manual recordings (stopwatch and measurement tape) made concurrently.

Main outcome measures

Time, distance, postural control and gait characteristics.

Results

Agreement between manual and iCap ranged from moderate to excellent (0.649–0.983) with mean differences between methods low and deemed acceptable. Additionally, iCap successfully quantified (i) postural control characteristics which showed sensitivity to distinguish between 5 variations of the standing balance test and (ii) 14 gait characteristics known to be sensitive to age/pathology.

Conclusions

Our findings show that iCap can provide robust quantitative data about physical capability during standardised tests while also providing sensitive (age/pathology) postural control and gait characteristics not previously quantifiable with manual recordings. The methodology which we propose may have practical utility in a wide range of clinical and public health surveys and studies, including intervention studies, where assessment could be undertaken within diverse settings. This will need to be tested in further validation studies in a wider range of settings.

Instrumenting gait with an accelerometer: a system and algorithm examination

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 the laboratory until relatively recently. The application of an inexpensive body worn sensor with appropriate gait algorithms (BWM) is an attractive alternative and offers the potential to assess gait in any setting. In this study we investigated the use of a low-cost BWM, compared to laboratory reference using a robust testing protocol in both younger and older adults. We observed that the BWM is a valid tool for estimating total step count and mean spatio-temporal gait characteristics however agreement for variability and asymmetry results was poor. We conducted a detailed investigation to explain the poor agreement between systems and determined it was due to inherent differences between the systems rather than inability of the sensor to measure the gait characteristics. The results highlight caution in the choice of reference system for validation studies. The BWM used in this study has the potential to gather longitudinal (real-world) spatio-temporal gait data that could be readily used in large lifestyle-based intervention studies, but further refinement of the algorithm(s) is required.

Role of body-worn movement monitor technology for balance and gait rehabilitation

This perspective article will discuss the potential role of body-worn movement monitors for balance and gait assessment and treatment in rehabilitation. Recent advances in inexpensive, wireless sensor technology and smart devices are resulting in an explosion of miniature, portable sensors that can quickly and accurately quantify body motion. Practical and useful movement monitoring systems are now becoming available. It is critical that therapists understand the potential advantages and limitations of such emerging technology. One important advantage of obtaining objective measures of balance and gait from body-worn sensors is impairment-level metrics characterizing how and why functional performance of balance and gait activities are impaired. Therapy can then be focused on the specific physiological reasons for difficulty in walking or balancing during specific tasks. A second advantage of using technology to measure balance and gait behavior is the increased sensitivity of the balance and gait measures to document mild disability and change with rehabilitation. A third advantage of measuring movement, such as postural sway and gait characteristics, with body-worn sensors is the opportunity for immediate biofeedback provided to patients that can focus attention and enhance performance. In the future, body-worn sensors may allow therapists to perform telerehabilitation to monitor compliance with home exercise programs and the quality of their natural mobility in the community. Therapists need technological systems that are quick to use and provide actionable information and useful reports for their patients and referring physicians. Therapists should look for systems that provide measures that have been validated with respect to gold standard accuracy and to clinically relevant outcomes such as fall risk and severity of disability.

Reliability and validity of a smartphone-based application for the quantification of the sit-to-stand movement in healthy seniors

INTRODUCTION

Recently, it has been demonstrated that biomechanical parameters measured with accelerometers during everyday activities might provide early information about mobility in seniors. The use of smartphones has been suggested for this purpose. The aim of this study was to develop and validate a smartphone-based application for the quantification of the sit-to-stand movement (STS) in healthy seniors.

METHODS

Sixteen healthy seniors performed the STS with a smartphone first attached to the lower back and then to the sternum. The subjects' vertical ground reaction forces (VGRF) and vertical acceleration (VAcc) were recorded simultaneously using two force plates (reference standard) and the smartphone, respectively. VAcc data were converted to force; power was then calculated based on both force-time curves. Subsequently, the total movement duration (Ttotal), peak force (Fmax), rate of force development (RFD) and peak power (Pmax) were determined. Relative (intraclass correlation coefficient [ICC]) and absolute (standard error of measurement [SEM]) reliability were also calculated. For the variables showing strong reliability (ICC≥0.75), Pearson correlation coefficients (r) between VGRF- and VAcc-derived data and one-sample T-tests for the mean differences were carried out.

RESULTS

In the sternal configuration, all variables showed strong reliability (range: 0.86-0.93), except the smartphone-derived parameter RFD (ICC=0.43). Fmax and Ttotal were found to be highly valid (r=0.86 and 0.98, respectively).

CONCLUSION

The results showed that the smartphone-based application can be used as a reliable and valid tool for the quantification of the STS in healthy seniors if the proposed protocol is followed.

Toward Automating Clinical Assessments: A Survey of the Timed Up and Go

Older adults often suffer from functional impairments that affect their ability to perform everyday tasks. To detect the onset and changes in abilities, healthcare professionals administer standardized assessments. Recently, technology has been utilized to complement these clinical assessments to gain a more objective and detailed view of functionality. In the clinic and at home, technology is able to provide more information about patient performance and reduce subjectivity in outcome measures. The timed up and go (TUG) test is one such assessment recently instrumented with technology in several studies, yielding promising results toward the future of automating clinical assessments. Potential benefits of technological TUG implementations include additional performance parameters, generated reports, and the ability to be self-administered in the home. In this paper, we provide an overview of the TUG test and technologies utilized for TUG instrumentation. We then critically review the technological advancements and follow up with an evaluation of the benefits and limitations of each approach. Finally, we analyze the gaps in the implementations and discuss challenges for future research toward automated self-administered assessment in the home.

Kinematic parameters to evaluate functional performance of sit-to-stand and stand-to-sit transitions using motion sensor devices: a systematic review

Clinicians commonly use questionnaires and tests based on daily life activities to evaluate physical function. However, the outcomes are usually more qualitative than quantitative and subtle differences are not detectable. In this review, we aim to assess the role of body motion sensors in physical performance evaluation, especially for the sit-to-stand and stand-to-sit transitions. In total, 53 full papers and conference abstracts on related topics were included and 16 different parameters related to transition performance were identified as potentially meaningful to explain certain disabilities and impairments. Transition duration is the most used to evaluate chair-related tests in real clinical settings. High-fall-risk fallers and frail subjects presented longer and more variable transition duration. Other kinematic parameters have also been highlighted in the literature as potential means to detect age-related impairments. In particular, vertical linear velocity and trunk tilt range were able to differentiate between different frailty levels. Frequency domain measures such as spectral edge frequency were also higher for elderly fallers. Lastly, approximate entropy values were larger for subjects with Parkinson's disease and were significantly reduced after treatment. This information could help clinicians in their evaluations as well as in prescribing a physical fitness program to correct a specific deficit.