Kimberley S. van Schooten

Ambulatory Fall-Risk Assessment: Amount and Quality of Daily-Life Gait Predict Falls in Older Adults

BACKGROUND Ambulatory measurements of trunk accelerations can provide valuable information on the amount and quality of daily-life activities and contribute to the identification of individuals at risk of falls. We compared associations between retrospective and prospective falls with potential risk factors as measured by daily-life accelerometry. In addition, we investigated predictive value of these parameters for 6-month prospective falls. METHODS One week of trunk accelerometry (DynaPort MoveMonitor) was obtained in 169 older adults (mean age 75). The amount of daily activity and quality of gait were determined and validated questionnaires on fall-risk factors, grip strength, and trail making test were obtained. Six-month fall incidence was obtained retrospectively by recall and prospectively by fall diaries and monthly telephone contact. RESULTS Among all participants, 35.5% had a history of ≥1 falls and 34.9% experienced ≥1 falls during 6-month follow-up. Logistic regressions showed that questionnaires, grip strength, and trail making test, as well as the amount and quality of gait, were significantly associated with falls. Significant associations differed between retrospective and prospective analyses although odds ratios indicated similar patterns. Predictive ability based on questionnaires, grip strength, and trail making test (area under the curve .68) improved substantially by accelerometry-derived parameters of the amount of gait (number of strides), gait quality (complexity, intensity, and smoothness), and their interactions (area under the curve .82). CONCLUSIONS Daily-life accelerometry contributes substantially to the identification of individuals at risk of falls, and can predict falls in 6 months with good accuracy.

Assessing gait stability: the influence of state space reconstruction on inter- and intra-day reliability of local dynamic stability during over-ground walking.

Estimating local dynamic stability is considered a powerful approach to identify persons with balance impairments. Its validity has been studied extensively, and provides evidence that short-term local dynamic stability is related to balance impairments and the risk of falling. Thus far, however, this relation has only been proven on group level. For clinical use, differences on the individual level should also be detectable, requiring reliability to be high. In the current study, reliability of short-term local dynamic stability was investigated within and between days. Participants walked 500 m back and forth on a straight outdoor footpath, on 2 non-consecutive days, and 3D linear accelerations were measured using an accelerometer (DynaPort MiniMod). The state space was reconstructed using 4 common approaches, all based on delay embedding. Within-session intra-class correlation coefficients were good (≥0.70), however between-session intra-class correlation coefficients were poor to moderate (≤0.63) and influenced by the reconstruction method. The same holds for the smallest detectable difference, which ranged from 17% to 46% depending on the state space reconstruction method. The best within- and between-session intra-class correlation coefficients and smallest detectable differences were achieved with a state space reconstruction with a fixed time delay and number of embedding dimensions. Overall, due to the influence of biological variation and measurement error, the short-term local dynamic stability can only be used to detect substantial differences on the individual level.

Sensitivity of trunk variability and stability measures to balance impairments induced by galvanic vestibular stimulation during gait

For targeted prevention of falls, it is necessary to identify individuals with balance impairments. To test the sensitivity of measures of variability, local stability and orbital stability of trunk kinematics to balance impairments during gait, we used galvanic vestibular stimulation (GVS) to impair balance in 12 young adults while walking on a treadmill at different speeds. Inertial sensors were used to measure trunk accelerations, from which variability in the medio-lateral direction and local and orbital stability were calculated. The short-term Lyapunov exponent and variability reflected the destabilizing effect of GVS, while the long-term Lyapunov exponent and Floquet multipliers suggested increased stability. Therefore, we concluded that only short-term Lyapunov exponents and variability can be used to asses stability of gait. In addition, to investigate the feasibility of using these measures in screening for fall risk, the presence or absence of GVS was predicted with variability and the short-term Lyapunov exponent. Predictions were good at all walking speeds, but best at preferred walking speed, with a correct classification in 83.3% of the cases.

Identification of fall risk predictors in daily life measurements: gait characteristics' reliability and association with self-reported fall history

Background. Gait characteristics extracted from trunk accelerations during daily life locomotion are complementary to questionnaire- or laboratory-based gait and balance assessments and may help to improve fall risk prediction. Objective. The aim of this study was to identify gait characteristics that are associated with self-reported fall history and that can be reliably assessed based on ambulatory data collected during a single week. Methods. We analyzed 2 weeks of trunk acceleration data (DynaPort MoveMonitor, McRoberts) collected among 113 older adults (age range, 65-97 years). During episodes of locomotion, various gait characteristics were determined, including local dynamic stability, interstride variability, and several spectral features. For each characteristic, we performed a negative binomial regression analysis with the participants’ self-reported number of falls in the preceding year as outcome. Reliability of gait characteristics was assessed in terms of intraclass correlations between both measurement weeks. Results. The percentages of spectral power below 0.7 Hz along the vertical and anteroposterior axes and below 10 Hz along the mediolateral axis, as well as local dynamic stability, local dynamic stability per stride, gait smoothness, and the amplitude and slope of the dominant frequency along the vertical axis, were associated with the number of falls in the preceding year and could be reliably assessed (all P < .05, intraclass correlation > 0.75). Conclusions. Daily life gait characteristics are associated with fall history in older adults and can be reliably estimated from a week of ambulatory trunk acceleration measurements.

Consistency of gait characteristics as determined from acceleration data collected at different trunk locations.

Estimates of gait characteristics may suffer from errors due to discrepancies in accelerometer location. This is particularly problematic for gait measurements in daily life settings, where consistent sensor positioning is difficult to achieve. To address this problem, we equipped 21 healthy adults with tri-axial accelerometers (DynaPort MiniMod, McRoberts) at the mid and lower lumbar spine and anterior superior iliac spine (L2, L5 and ASIS) while continuously walking outdoors back and forth (20 times) over a distance of 20 m, including turns. We compared 35 gait characteristics between sensor locations by absolute agreement intra-class correlations (2, 1; ICC). We repeated these analyses after applying a new method for off-line sensor realignment providing a unique definition of the vertical and, by symmetry optimization, the two horizontal axes. Agreement between L2 and L5 after realignment was excellent (ICC>0.9) for stride time and frequency, speed and their corresponding variability and good (ICC>0.7) for stride regularity, movement intensity, gait symmetry and smoothness and for local dynamic stability. ICC values benefited from sensor realignment. Agreement between ASIS and the lumbar locations was less strong, in particular for gait characteristics like symmetry, smoothness, and local dynamic stability (ICC generally<0.7). Unfortunately, this lumbar-ASIS agreement did not benefit consistently from sensor realignment. Our findings show that gait characteristics are robust against limited repositioning error of sensors at the lumbar spine, in particular if our off-line realignment is applied. However, larger positioning differences (from lumbar positions to ASIS) yield less consistent estimates and should hence be avoided.

Daily-life gait quality as predictor of falls in older people: a 1-year prospective cohort study

Falls can have devastating consequences for older people. We determined the relationship between the likelihood of fall incidents and daily-life behavior. We used wearable sensors to assess habitual physical activity and daily-life gait quality (in terms of e.g. stability, variability, smoothness and symmetry), and determined their predictive ability for time-to-first-and-second-falls. 319 older people wore a trunk accelerometer (Dynaport MoveMonitor, McRoberts) during one week. Participants further completed questionnaires and performed grip strength and trail making tests to identify risk factors for falls. Their prospective fall incidence was followed up for six to twelve months. We determined interrelations between commonly used gait characteristics to gain insight in their interpretation and determined their association with time-to-falls. For all data -including questionnaires and tests- we determined the corresponding principal components and studied their predictive ability for falls. We showed that gait characteristics of walking speed, stride length, stride frequency, intensity, variability, smoothness, symmetry and complexity were often moderately to highly correlated (r > 0.4). We further showed that these characteristics were predictive of falls. Principal components dominated by history of falls, alcohol consumption, gait quality and muscle strength proved predictive for time-to-fall. The cross-validated prediction models had adequate to high accuracy (time dependent AUC of 0.66–0.72 for time-to-first-fall and 0.69–0.76 for -second-fall). Daily-life gait quality obtained from a single accelerometer on the trunk is predictive for falls. These findings confirm that ambulant measurements of daily behavior contribute substantially to the identification of elderly at (high) risk of falling.

Do Extreme Values of Daily-Life Gait Characteristics Provide More Information About Fall Risk Than Median Values?

Background Gait characteristics estimated from daily-life trunk accelerations reflect gait quality and are associated with fall incidence in older adults. While associations are based on median values of these gait characteristics, their extreme values may reflect either high-risk situations or steady-state gait and may thus be more informative in relation to fall risk. Objective The objective of this study was to improve fall-risk prediction models by examining whether the use of extreme values strengthens the associations with falls. Methods Trunk acceleration data (Dynaport MoveMonitor) were collected from 202 older adults over a full week. From all walking episodes, we estimated the median and, as reliable estimates of the extremes, the 10th and 90th percentiles of gait characteristics, all over 10-second epochs. In addition, the amount of daily activities was derived from the acceleration data, and participants completed fall-risk questionnaires. Participants were classified as fallers based on one or more falls during 6 months of follow-up. Univariate analyses were performed to investigate whether associations with falls were stronger for the extremes than for the medians. Subsequently, three fall-risk models were compared: (1) using questionnaire data only, (2) adding the amount of activities and medians of gait characteristics, and (3) using extreme values instead of medians in the case of stronger univariate associations of the extremes. Results Stronger associations were found for the extreme characteristics reflecting high regularity, low frequency variability, and low local instability in anterior-posterior direction, for high symmetry in all directions and for low entropy in anterior-posterior and vertical directions. The questionnaire-only model improved significantly by adding activities and gait characteristics’ medians. Replacing medians by extremes with stronger associations did improve the fall prediction model, but not significantly. Conclusions Associations were stronger for extreme values, indicating “high gait quality” situations (ie, 10th and 90th percentiles in case of positive and negative associations, respectively) and not for “low gait quality” situations. This suggests that gait characteristics during optimal performance gait provide more information about the risk of falling than high-risk situations. However, their added value over medians in prediction is limited.

Kinematic changes during running-induced fatigue and relations with core endurance in novice runners

OBJECTIVES This study aimed to investigate kinematic changes experienced during running-induced fatigue. Further, the study examined relations between kinematic changes and core endurance. DESIGN Repeated measures and correlation. METHODS Seventeen novice runners participated in a running-induced fatigue protocol and underwent core endurance assessment. Participants ran at a steady state corresponding to an intensity of 13 on the Borg scale and continued until 2min after a Borg score of 17 or 90% of maximum heart rate was reached. Kinematic data were analyzed for the lower extremities and trunk throughout a running protocol and, on separate days, core endurance measures were recorded. Changes in pre- and post-fatigue running kinematics and their relations with core endurance measures were analyzed. RESULTS Analysis of peak joint angles revealed significant increases in trunk flexion (4°), decreases in trunk extension (3°), and increases in non-dominant ankle eversion (1.6°) as a result of running-induced fatigue. Post-fatigue increased trunk flexion changes displayed a strong to moderate positive relation with trunk extensor core endurance measures, in contrast to expected negative relations. CONCLUSIONS Novice runners displayed an overall increase in trunk inclination and increased ankle eversion peak angles when fatigued utilizing a running-induced fatigue protocol. As most pronounced changes were found for the trunk, trunk kinematics appear to be significantly affected during fatigued running and should not be overlooked. Core endurance measures displayed unexpected relations with running kinematics and require further investigation to determine the significance of these relations.

Improved prediction of falls in community-dwelling older adults through phase-dependent entropy of daily-life walking

Age and age-related diseases have been suggested to decrease entropy of human gait kinematics, which is thought to make older adults more susceptible to falls. In this study we introduce a new entropy measure, called phase-dependent generalized multiscale entropy (PGME), and test whether this measure improves fall-risk prediction in community-dwelling older adults. PGME can assess phase-dependent changes in the stability of gait dynamics that result from kinematic changes in events such as heel strike and toe-off. PGME was assessed for trunk acceleration of 30 s walking epochs in a re-analysis of 1 week of daily-life activity data from the FARAO study, originally described by van Schooten et al. (2016). The re-analyzed data set contained inertial sensor data from 52 single- and 46 multiple-time prospective fallers in a 6 months follow-up period, and an equal number of non-falling controls matched by age, weight, height, gender, and the use of walking aids. The predictive ability of PGME for falls was assessed using a partial least squares regression. PGME had a superior predictive ability of falls among single-time prospective fallers when compared to the other gait features. The single-time fallers had a higher PGME (p < 0.0001) of their trunk acceleration at 60% of their step cycle when compared with non-fallers. No significant differences were found between PGME of multiple-time fallers and non-fallers, but PGME was found to improve the prediction model of multiple-time fallers when combined with other gait features. These findings suggest that taking into account phase-dependent changes in the stability of the gait dynamics has additional value for predicting falls in older people, especially for single-time prospective fallers.

Fractional Stability of Trunk Acceleration Dynamics of Daily-Life Walking: Toward a Unified Concept of Gait Stability

Over the last decades, various measures have been introduced to assess stability during walking. All of these measures assume that gait stability may be equated with exponential stability, where dynamic stability is quantified by a Floquet multiplier or Lyapunov exponent. These specific constructs of dynamic stability assume that the gait dynamics are time independent and without phase transitions. In this case the temporal change in distance, d(t), between neighboring trajectories in state space is assumed to be an exponential function of time. However, results from walking models and empirical studies show that the assumptions of exponential stability break down in the vicinity of phase transitions that are present in each step cycle. Here we apply a general non-exponential construct of gait stability, called fractional stability, which can define dynamic stability in the presence of phase transitions. Fractional stability employs the fractional indices, α and β, of differential operator which allow modeling of singularities in d(t) that cannot be captured by exponential stability. The fractional stability provided an improved fit of d(t) compared to exponential stability when applied to trunk accelerations during daily-life walking in community-dwelling older adults. Moreover, using multivariate empirical mode decomposition surrogates, we found that the singularities in d(t), which were well modeled by fractional stability, are created by phase-dependent modulation of gait. The new construct of fractional stability may represent a physiologically more valid concept of stability in vicinity of phase transitions and may thus pave the way for a more unified concept of gait stability.