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Multi-parametric evaluation of sit-to-stand and stand-to-sit transitions in elderly people
Medical Engineering & Physics 33 (2011) 1086–1093
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Multi-parametric evaluation of sit-to-stand and stand-to-sit transitions in elderly people R. Ganea a,∗ , A. Paraschiv-Ionescu a , C. Büla b , S. Rochat b , K. Aminian a a b
Ecole Polytechnique Fédérale de Lausanne (EPFL), Laboratory of Movement Analysis and Measurement, Lausanne, Switzerland Service of Geriatrics and Geriatric Rehabilitation, University of Lausanne Hospital Center, Lausanne, Switzerland
a r t i c l e
i n f o
Article history: Received 31 May 2010 Received in revised form 18 April 2011 Accepted 23 April 2011 Keywords: Ambulatory system Postural transitions Elderly Rehabilitation
a b s t r a c t The aim of this study was to extract multi-parametric measures characterizing different features of sit-to-stand (Si-St) and stand-to-sit (St-Si) transitions in older persons, using a single inertial sensor attached to the chest. Investigated parameters were transition’s duration, range of trunk tilt, smoothness of transition pattern assessed by its fractal dimension, and trunk movement’s dynamic described by local wavelet energy. A measurement protocol with a Si-St followed by a St-Si postural transition was performed by two groups of participants: the first group (N = 79) included Frail Elderly subjects admitted to a post-acute rehabilitation facility and the second group (N = 27) were healthy community-dwelling elderly persons. Subjects were also evaluated with Tinetti’s POMA scale. Compared to Healthy Elderly persons, frail group at baseline had significantly longer Si-St (3.85 ± 1.04 vs. 2.60 ± 0.32, p = 0.001) and St-Si (4.08 ± 1.21 vs. 2.81 ± 0.36, p = 0.001) transition’s duration. Frail older persons also had significantly decreased smoothness of Si-St transition pattern (1.36 ± 0.07 vs. 1.21 ± 0.05, p = 0.001) and dynamic of trunk movement. Measurements after three weeks of rehabilitation in frail older persons showed that smoothness of transition pattern had the highest improvement effect size (0.4) and discriminative performance. These results demonstrate the potential interest of such parameters to distinguish older subjects with different functional and health conditions. © 2011 IPEM. Published by Elsevier Ltd. All rights reserved.
1. Introduction As people get older, the ability to rise from a chair, usually labelled as the sit-to-stand (Si-St) postural transition, becomes a more demanding functional daily task. In those most vulnerable, postural transition is an important indicator of daily-life functional independence and mobility [1,2]. Si-St postural transition is commonly used as a functional test when assessing older persons to provide information about their lower extremities function and strength, as well as balance [1,3]. Si-St performance has been associated with age-related changes in muscular strength in leg extensors, vestibular disorders [3,4], as well as with changes in movement strategies [3,5]. Consequently, standardized assessment of Si-St and stand-to-sit (St-Si) postural transitions has been used for multiple purposes, including evaluation of postural control [6], fall risk [7,8], lower-extremity strength [9–11], impairment after
∗ Corresponding author at: EPFL-STI-LMAM, ELH 136/Station 11, CH-1015 Lausanne, Switzerland. Tel.: +41 216 935 971; fax: +41 216 936 915. E-mail addresses: raluca.ganea@epfl.ch (R. Ganea), anisoara.ionescu@epfl.ch (A. Paraschiv-Ionescu), [email protected] (C. Büla), [email protected] (S. Rochat), kamiar.aminian@epfl.ch (K. Aminian).
stroke [12–14], and proprioception [15], as well as a measure of overall disability [16,17]. Traditional clinical evaluation of Si-St transition is based on visual observation of joint angle motion to describe alterations in coordination and movement pattern. However, the validity of such assessment essentially depends on clinicians’ experience and training. Results might not have the precision needed to objectively assess the effect of rehabilitative intervention or the decline over time in Frail Elderly persons [18]. Kinematics of the trunk appears essential to maintain balance during Si-St transition [19,20]. This led to investigate the dynamic of trunk movements using body-worn inertial sensors [21,22]. These sensors can be used to collect unobtrusive, objective and valid measurements that capture specific postures and motion during normal daily life in subject’s personal environment [23]. One of the most straightforward parameter to characterize the subject’s performance in Si-St/St-Si postural transition is to measure its duration. Previous observations have shown that duration of Si-St and St-Si transitions can distinguish between older people at low and high risk for falls [20]. Despite these encouraging observations suggesting the usefulness of duration of Si-St/St-Si postural transition, other studies in patients suffering from Alzheimer’s or Parkinson’s disease also suggest that this parameter is not sufficient to fully describe the clinical performance in older persons [24,25].
1350-4533/$ – see front matter © 2011 IPEM. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.medengphy.2011.04.015
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Fig. 1. Kinematic features characterizing a Si-St postural transition: TD, the duration of the transition; TT, the range of the trunk tilt during posture transition.
Overall, these observations confirm that the Si-St transition is a complex movement and suggest that more detailed characterization than merely a measure of duration are needed to fully capture the various aspects of motor control and performance. To achieve this aim, additional parameters are needed that more specifically assess kinematic phases within the Si-St transition, such as the maximum trunk flexion [26–28], key events as temporal locations of the major peaks of vertical and sagittal acceleration [29]. For instance, the vertical power of Si-St and the force acting on trunk can be estimated from the acceleration measured by inertial sensors [30], making it possible to describe in more detail this phase of Si-St transition. Such detailed characterization of Si-St movement patterns opens the perspective for a broader range of clinical applications. For instance, elderly persons transitioning to frailty have increasing difficulties to perform Si-St transitions. Over time, these older persons may use different compensatory strategies to achieve successful transitions such as deviation from normal motion. This deviation is due to several types of motion irregularities, among which sway is the most frequently encountered. Sway consists in repetitive, quick changes in motion orientation due to a temporary loss of balance or to insufficient strength in lower limbs [31]. These results suggest that further investigation of postural transition with other additional parameters has the potential to provide important predictive information. The present study had several aims. The first aim was to determine whether trunk’s kinematics during Si-St and St-Si postural transitions can be used to define metrics characterizing functional performance. In particular, this paper presents how these metrics
can be extracted based on efficient signal processing of data recorded by a single inertial sensor that is fixed on the trunk and can be worn during daily conditions. The second aim was to investigate whether these new multi-parametric measures of postural transition could distinguish elderly persons with different health and functional status. Specifically, the hypotheses were that trunk movements during Si-St and St-Si transitions are ‘smooth’ and stable in Healthy Elderly persons and ‘rough’ as well as unsteady in Frail Elderly persons, such as those with mobility, balance, or strength impairments. A third aim was to compare the sensitivity to change of these new multi-parametric measures in a sample of older persons undergoing a rehabilitation program.
2. Methods 2.1. Subjects and measurement protocol Two groups of participants were enrolled in this study: the first group (Frail Elderly, N = 79) included Frail Elderly subjects admitted to a post-acute rehabilitation facility (fifty-three females and twenty-six males). Frailty is commonly defined as a state of increased vulnerability to stressors resulting from a cumulative decline in the physiological reserves of multiple systems. To be considered as frail subjects, subjects had to fulfil Fried’s criteria for frailty [32]. Such criteria comprise five core components: sarcopenia or weight loss, reduced muscular strength, slow walking speed, exhaustion, and low activity level. To be eligible, they had to be aged 65 years and older, to be able to stand more than 10 s
Fig. 2. (a) Mother wavelet db5; (b) Wavelet decomposition. Typical patterns of trunk acceleration norm during Si-St task at different DWT decomposition levels using a discrete “db5” wavelet as mother wavelet (c) Healthy Elderly subject; (d) Frail Elderly subject.
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Fig. 3. Kinematical signals (norm of the acceleration and angular velocity) recorded during Si-St postural transition and the estimated fractal dimension, dF (based on box counting method) for (a) Healthy Elderly subject, (b) Frail Elderly subject.
and to walk 20 m without assistance. The second group (Healthy Elderly, N = 27) were healthy community dwelling elderly persons (sixteen females, eleven males) recruited from the community, who had to meet the following eligibility criteria: to be aged 65 years and older, to rate their health as excellent, very good, or good (based on Self-rated Health Status [33]), to be independent in all basic activities of daily living, and to be able to walk more than 1 km. The measurement protocol performed by the two groups included a Si-St followed by a St-Si postural transition using a standard chair with arm rest. In the first group of Frail Elderly persons admitted to rehabilitation, measurements were performed within the first three days post admission and repeated three weeks later. During these three weeks, participants underwent a rehabilitation program that consisted in daily supervised training of progressive intensity that was individualised according to patients’ initial performance. Training included flexibility exercises, gait and static/dynamic balance training, combined with strengthening exercises targeting muscular groups involved in standing and walking (lower limbs). Program also included transfer training. At baseline and follow-up, a clinical evaluation of gait and balance was also performed in these subjects, using Tinetti’s Performance Oriented Mobility Assessment (POMA) scale [34]. This scale was developed to assess fall risk in older persons. Scores range from 0 to 28, where lower values are associated with higher fall risk [34]. Measures of postural transitions were performed using a monitoring system composed of a small inertial sensor (25 mm × 25 mm × 15 mm) and a light portable data-logger (Physilog® , BioAGM, CH) carried on the waist. The sensor was directly attached on the skin to the sternum. In order to prevent the artefact signals related to accidental movements of the sensors, we used medical patches (Coloplast Systems, Denmark) [35]. The inertial sensor was composed of one gyroscope (ADXRS300, ±400◦ /s, Analog Devices) measuring the trunk angular velocity in sagittal plane (tilt velocity) and three accelerometers (ADXL202, ±2 g, Analog Devices) measuring vertical, frontal, and lateral accelerations, respectively. Sensor signals were amplified, low-pass filtered (cutoff frequency 17 Hz) to remove electronic noise, digitized at 40 Hz and recorded on a memory card. After recording, data were transferred to a computer for analysis.
The protocol of the study was approved by the local ethical committee and written consent was obtained from all subjects. 2.2. Multi-parametric characterization of sit-to-stand/stand-to-sit movement 2.2.1. Postural transition’s duration Postural transition was detected based on the algorithm that estimates changes in the trunk tilt from the time integral of the gyroscope signal [36]. Transition’s duration (TD = T2 − T1 ) was defined as the time interval between the two positive peaks T1 and T2 , before and after the maximum negative peak of trunk tilt [20] (Fig. 1). 2.2.2. Range of trunk tilt Angular velocity of the trunk was integrated during TD and its range, corresponding to trunk tilt (TT), was calculated [36]. 2.2.3. Local energy of the trunk dynamics (TEm ) The forces acting at the trunk level, FT can be estimated by multiplying the norm of acceleration, |a|, by the trunk mass, MT :
FT = | a| · MT
(1)
where MT was estimated using the method presented in Pavol et al. [37]. Since FT is a transient signal, the peak value and the energy of the signal can be used for quantifying the dynamics of body movement during postural transition. We choose the energy to characterize the signal, as it includes both the amplitude of the signal and the duration of the oscillation. Based on the Parseval’s theorem, the energy of FT summed across time is equal to the energy of the signal summed across all of its frequency components. Considering the non-stationary nature of FT , discrete wavelet transform (DWT) was used to estimate frequency content changes over the time [38]. DWT is a batch method that analyses a finite-length time-domain signal at different frequency bands and with different resolutions by successive decomposition into coarse “approximation” (A) and “detail” (D) information. ‘Approximations’ represent the slowly changing (low-frequency) features of a signal while ‘details’ represent the rapidly changing (high-frequency) features of the signal. DWT was largely applied on the patterns of postural transitions [39–41]. Wavelets come in many shapes called “mother wavelets”
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Table 1 Characteristics of the two groups of subjects. Parameter
Healthy Elderly subjects (H) (n = 27)
Frail Elderly subjects (B) (n = 79)
Gender Age (years) Height (m) Mass (kg) BMI (kg/m2 )
11 males, 16 females 73.0 ± 5.03 1.67 ± 0.09 71.5 ± 13.5 25.6 ± 4.2
26 males, 53 females 80.0 ± 7.1 1.62 ± 0.51 67.2 ± 15.4 25.3 ± 5.3
p-Value (H, B) *
p NS NS NS
NS = not significant. * p-Value < 0.05. ** p-Value < 0.01. *** p-Value < 0.001.
and the idea is to use a shape that best fits the signal. For the present work, in agreement with another study [42], Daubechies wavelet db5 was chosen using the Matlab 7.7 (Mathworks) Wavelet Toolbox [43,44]. Fig. 2 shows this decomposition into 4 levels of frequency range applied to a typical postural transition and the reconstructed “detail” signals (on the right). Under the assumption that the normal transition is performed predominantly in the sagittal plane, the detail signals, Dm (i.e., D1 to D4 ) of the force acting at the trunk level FT during the ‘normal’ transition movement appears as a signal with increased amplitude over the 4 levels and a more regular shape close to db5 mother wavelet at D4 ; conversely, for an ‘abnormal’ movement pattern Dm appears with lower amplitude and an irregular shape, as illustrated in Fig. 2. Then, we quantified the energy of the signal characterising the dynamic of the trunk movement by using the local wavelet energy [43] defined as: TEm =
Dm (k)2 k
(2)
where k is the total number of samples in the mth level. More information on the estimation of Dm can be found in [20]. According to Bussmann et al. [23], the frequency of trunk movements during daily activities occurs in bandwidth [0.6–4 Hz]. However, some authors consider rather 0.8 Hz as the minimum value [44]. Therefore, we considered the detail level of order m = 3, 4 to include the main information (1.25–5 Hz) about postural transition. 2.2.4. Postural transition smoothness In order to characterize the pattern and irregularities (roughness vs. smoothness) of trunk movement during Si-St and St-Si transitions, we plotted first the norm of acceleration signals |a| vs. trunk angular velocity measured around medio-lateral axis (ω). This phase plot-like presentation uses all available kinematical information and further shows whether the velocity controls the acceleration [45]. Fig. 3 illustrates such a plot of the kinematical signals recorded during a Si-St postural transition in a healthy community-dwelling elderly subject and in a frail rehab elderly subject. Given this representation, the next step was to quantify the differences between these types of plots, based on the assumption that normal, regular movement pattern is performed predominantly in the frontal direction and appears as a straight line in (|a|, ω) plane, while abnormal movement patterns expand towards an irregular surface in (|a|, ω) plane. We used the concept of fractals introduced by Mandelbrot [46] to describe objects with irregular shapes and complex structures. A shape with a higher fractal dimension is more complicated and irregular that one with a lower fractal dimension. Fractal dimension has been previously applied to characterize various 1D or 2D biomedical signals [47–50] and particularly in biomechanical signals by assessing the smoothness of gait acceleration [51] or instability of postural sway [39]. In order to quantify the sway-type irregularities of signals/shapes, we calculated the
fractal dimension using the box-counting method [52]. The implementation of the box-counting method is as follows: the surface of the plot is covered with a box (with length lb ), and then the box is divided in four quadrants. We then count the number of occupied cells and divide each subsequent quadrant into four sub-quadrants. This process continues until the minimum box size is equal to the resolution of the data. The fractal dimension (dF ) is given by: dF = −lim l→0
log10 Nb (lb ) log10 lb
(3)
where Nb (lb ) is the number of boxes needed to completely cover the pattern. (dF ) corresponds to the slope of the plot log10 Nb (lb ) vs. log10 lb . The larger dF is, the “rougher” the (|a|, ω) pattern is. 2.3. Statistics Above parameters were estimated for each subject, and their mean and standard deviation were calculated within each group. Differences between the Frail Elderly and Healthy Elderly groups were assessed using unpaired Student’s t-test. In order to assess the difference in Frail Elderly group before and after rehabilitation, a paired Student’s t-test was used. The significance level was set at p < 0.05 for all comparisons. Spearman rank correlations () were computed between POMA score and postural transition parameters. Each parameter’s sensitivity to change was calculated using effect size statistics. The standardized effect size was calculated by considering a sensitive statistics, Cohen’s distance (CD), [53]: CD =
1 − 2 ((n1 − 1) ∗ s12
+ (n2 − 1) ∗ s22 )/(n1 + n2 )
(4)
where n1 and n2 is the number of subjects in each group and s1 and s2 is standard deviation for each group. Small (CD < 0.3), medium (0.3 < CD < 0.5) and large (CD > 0.5) Cohen’s distance values indicate the degree of subject’s improvement [54]. To classify subjects into two groups (Healthy Elderly and Frail Elderly subjects) based on developed parameters, receiveroperating characteristic (ROC) curves were computed to determine the cut-off point. Optimal cut-off point was the value that maximized the sum of sensitivity and specificity. Sensitivity (true positive ratio) and specificity (true negative ratio) were calculated: sensitivity =
TP TP + FN
(5)
specificity =
TN TN + FP
(6)
(TP = true positive; TN = true negative; FP = false positive; FN = false negative). The ROC curves display plots of true positive rates or sensitivity (i.e., positively labelled test data classified as positive) vs. false positive rates (i.e., negatively labelled test data classified as positive). The area under the ROC curve (AUC) provides a measure of the overall performance of the discriminative parameter i.e., the larger the area underneath the curve indicates a better performance.
0.28 p **
– – – – 23.16 ± 2.49 22.45 ± 2.5 – NS = not significant. * p-Value < 0.05. ** p-Value < 0.01. *** p-Value < 0.001.
POMA score
0.28 0.21 0.03 0.27 0.40 p p NS NS ** p *
*
0.75 0.17 0.68 0.43 1.51 p p ** p NS *** p *
***
1.21 0.06 0.61 0.93 2.16 p NS * p ** p *** p
***
1.40 17.29 0.97 4.44 0.08 ± ± ± ± ± 3.72 26.4 1.15 3.06 1.34 1.21 15.37 1.15 2.56 0.07 ± ± ± ± ± 0.36 8.31 2.01 4.69 0.05 2.81 29.02 2.03 4.97 1.23 TD (s) TT (◦ ) TE3 (N) TE4 (N) dF StSi
± ± ± ± ±
4.08 29.87 1.18 2.08 1.37
NS NS NS NS * p 1.00 0.01 1.48 1.69 2.00 p NS *** p *** p *** p
***
1.38 0.19 1.70 1.63 2.31 p NS *** p *** p *** p
***
1.27 13.72 0.98 2.30 0.07 ± ± ± ± ± 3.70 22.06 1.05 2.87 1.34 1.04 12.18 0.79 2.62 0.07 ± ± ± ± ± 3.85 24.32 0.90 2.87 1.36 0.32 7.78 3.87 7.05 0.05 ± ± ± ± ± 2.60 22.22 4.15 9.64 1.21 TD (s) TT (◦ ) TE3 (N) TE4 (N) dF SiSt
p-Value (B, F) Effect size (CD) (H, F) Frail Elderly subjects baseline (B) (n = 79)
Frail Elderly subjects follow-up (F) (n = 79)
p-Value (H, B)
Effect size (CD) (H, B)
p-Value (H, F) Fig. 5. ROC curve analysis quantifying the probability of the TD and dF to accurately classify Healthy Elderly subjects and Frail Elderly subjects.
Healthy Elderly subjects (H) (n = 27)
Characteristics of the participants as well as baseline and followup POMA score in Frail Elderly subjects are reported in Table 1. As expected, Healthy Elderly subjects were younger and weighted more, although this last difference was not significant. The dynamics of body movement during postural transition (Si-St and St-Si) were characterized by the following parameters: transition duration TD, range of the sagittal trunk tilt TT, local energy of the trunk dynamics TE3 , TE4 , and smoothness of transition dF . In order to address the second aim of this study and determine whether these measures could adequately quantify subjects’ functional status, we compared Si-St/St-Si parameters in Healthy Elderly subjects and Frail Elderly subjects, as well as between baseline and follow-up in this last group. Table 2 shows that for Si-St transition, all parameters except trunk tilt were significantly different in Healthy Elderly subjects compared to Frail Elderly subjects
Parameter
3. Results
Table 2 Multi-parametric characterization of the Si-St/St-Si movement (mean ± SD) in Healthy Elderly subjects and Frail Elderly subjects; CD – Cohen’s distance.
Fig. 4. Relationships between dF (higher value indicate decreased smoothness in the movement) and POMA score at Baseline (range 0–28), higher score indicate better gait and balance.
0.13 0.18 0.17 0.00 0.29
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Effect size (CD) (B, F)
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R. Ganea et al. / Medical Engineering & Physics 33 (2011) 1086–1093 Table 3 Cut-off points, sensitivity, specificity and AUC of the parameters used to accurately classify Healthy Elderly subjects and Frail Elderly subjects based on the ROC curves interpretation. Parameter
Threshold
Sensitivity
Specificity
AUC
Si-St
TD (s) TT (◦ ) TE3 (N) TE4 (N) dF
2.92 25.85 1.47 4.59 1.27
0.92 0.55 0.74 0.70 0.92
0.83 0.35 0.83 0.82 0.88
0.90 0.50 0.80 0.81 0.96
St-Si
TD (s) TT (◦ ) TE3 (N) TE4 (N) dF
3.15 26.08 0.90 1.89 1.30
0.79 0.60 0.55 0.80 0.70
0.85 0.62 0.66 0.66 0.77
0.86 0.63 0.63 0.84 0.82
at baseline (p < 0.001, CD > 0.78). Similarly, for St-Si transition, transition duration TD, smoothness dF and local energy TE3 and TE4 all differed significantly between Healthy and Frail Elderly subjects at baseline. These differences in Si-St/St-Si parameters essentially remained when comparing values in Healthy Elderly subjects to those measured at follow-up in Frail Elderly subjects (Table 2). To address the third aim, we analyzed also whether Si-St and St-Si parameters could adequately quantify changes in functional performance in Frail Elderly subjects after they completed their rehabilitation program. Table 2 shows that for Si-St transition, smoothness dF of transition pattern was the only parameter that significantly improved after the rehabilitation. In addition, smoothness dF also showed a modest but significant negative correlation with POMA score at Baseline (Spearman = −0.3, p = 0.008) (Fig. 4). For St-Si transition, transition duration TD, trunk tilt TT and dF decreased significantly after completion of the rehabilitation program. Although local energy TE4 increased after rehabilitation, this difference did not reach statistical significance. According to Cohen’s d effect size, small to medium sensitivity to change were observed for most parameters. Highest sensitivity to change was observed for dF in both Si-St and St-Si transitions, and in transition duration TD as well as trunk tilt TT only in St-Si transition. Interestingly, improvement observed in Frail Elderly subjects (i.e., decrease in TD and dF ) translated into values becoming closer to those observed in Healthy Elderly subjects. A small but statistically significant improvement (p < 0.001) in POMA score was observed after rehabilitation. Optimal cut-off values for TD, TT, TE3 , TE4 and dF were 2.92 s, 25.85◦ , 1.47 N, 4.59 N, 1.27 and 3.15 s, 26.08◦ , 0.9 N, 1.89 N, 1.3 for SiSt and St-Si, respectively. The best sensitivity and specificity (0.92, 0.88) were obtained for dF , corresponding to the most discriminative parameter (Table 3). Based on the ROC curve interpretation (Fig. 5), the specificity of dF was higher as compared to TD (0.88 vs. 0.83). In a similar way, area under the curve (AUC) increased for TD comparing to TD (0.96 vs. 0.9).
4. Discussion and conclusions This study is contributing to the field of activity monitoring from both an engineering and a clinical perspective. From an engineering perspective, this study is the first, to our knowledge, to describe a method to extract multi-parametric measures characterizing in more details different aspects of Si-St and St-Si transitions, using a single inertial sensor attached to the chest. Recorded data (i.e., accelerometer and angular velocity) was used to estimate time-domain features, movement’s smoothness, and local energy features.
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From a clinical point of view, this study shows that these new parameters have significant discriminating properties to distinguish between elderly subjects with different health and functional status. These results confirm and extend previous studies of SiSt/St-Si postural transitions that used the time-domain kinematics features of the trunk during movement task. The average transition duration in Healthy Elderly subjects was 2.60 ± 0.32 s, a duration similar to the rising phase of the sit-stand-sit cycle reported by Kerr et al. [29] in elderly females. This value was longer than those found in Najafi et al. [20] in subjects with low (2.95 s) and high (3.88 s) fall-risk. However, this study had only very small groups (N < 6), and values reported were averaged from Si-St and St-Si transitions. Although the approach based on the time-domain kinematics features was able to discriminate patients with movement disorders [55] or Frail Elderly subjects [20], the methodology developed in the current study extends current knowledge in kinematics analysis in providing additional information on the global pattern (i.e., smoothness) of transitions. In addition, results from comparisons between the two groups of elderly subjects as performed in the current study further indicate potential applications to health assessment outside the specific field of fall risk assessment, such as extended monitoring in elderly persons to detect early decline in functional independence. One important contribution from our study is also to demonstrate that time-domain parameters like the duration of transition did not detect some minor improvements resulting from rehabilitation. New parameters developed in the current study (i.e., dF ) are likely to be able to capture more subtle functional changes e.g. more dynamic, smooth and stable transition pattern that might help for prediction of adverse events occurring on the disability pathway. However, this needs to be further investigated in more extensive protocol settings. This study reveals that other parameters such as the features based on the local energy and the irregularity (or smoothness) of the kinematical patterns described by dF are relevant metrics to distinguish significantly between Healthy Elderly and elderly with medium level of fall risk (POMA score of 19–24). Such multi-parametric approach, that considers postural transition as a non-linear and non-stationary movement, allows detecting subtle changes in physical performance and may guide therapeutic interventions towards more customized and efficient schemes. The proposed approach was also successful in assessing changes in postural transition resulting from a rehabilitation program. The smoothness of the movement dF improved in both tasks (Si-St and St-Si) and showed modest but significant correlation with the POMA score. This could be interpreted as reflecting a more stable postural transition after rehabilitation program. The trunk tilt TT and transition’s duration TD decreased after rehabilitation in StSi, likely reflecting improvement associated with the rehabilitation process. After rehabilitation program, during St-Si, the lower frequency component of local energy TE4 increased. Even though this difference did not reach statistical significance, this observation needs to be further investigated to determine whether it could reflect the clinical improvement frequently observed in Frail Elderly persons who become able to lean back smoothly instead of “falling” in the chair when sitting down once their rehabilitation program is completed. Finally, another important contribution of this work is to demonstrate that the proposed measures could be more sensitive than a semi-objective measure of gait and balance such as POMA to detect improvements in functional performance. Improvements in almost all parameters were consistent with subject improvement in POMA score at follow-up. However, dF showed a higher sensitivity to change resulting from rehabilitation than POMA score. This result confirms the potential of these
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measures when assessing elderly persons for fall risk estimation but also suggests new additional applications for monitoring of improvement related to rehabilitation [56]. Although the proposed metrics were obtained using sensors placed at the sternum site, other locations on trunk should provide similar results. For instance, in a recent study, Zijlstra et al. [30] showed a positive relationship between the vertical force estimated from the data recorded by the sensor placed at different trunk location and from the force plate data. Nevertheless, we propose to keep the sternum location since it has been shown to be more practical in a number of previous studies [20,36,57], particularly when measuring trunk tilt and transition duration. In this study postural transitions were performed under controlled conditions. Many determinants may affect the results of a Si-St protocol, such as the initial position of the foot, the subject’s anthropometry or the height of the chair [22,58]. In this regard, a limitation of this study could be that the height of the chair was not adjusted to participants’ height. However, we used a chair with standard height of 45 cm that has the tremendous advantage to reflect real life situation for elderly subjects. Another criticism that could be made to the protocol used is that daily activities may produce different patterns of transition. Recordings performed in the current study might therefore not capture the complete picture of functional limitation. Nevertheless, by considering other methods [59,60] which use some sensor configuration and detect automatically the postural transitions, it is possible to extend the method proposed in this study to signal recorded over long period of daily activity. Therefore, this study demonstrates the feasibility of using the proposed metrics to characterize movement patterns recorded over daily-life conditions or to quantify long-term daily-life movement pattern changes that results from therapeutic interventions. Acknowledgements The work was supported by “Fonds National Suisse de la Recherche Scientifique”, Grant No. 510538/510867. The authors would express their gratitude to Mr. Pascal Morel and Mr. Jean Gramiger for their helps and assistances as well as all the subjects who kindly participated in this study. Conflict of interest None. References [1] Schenkman M, Hughes M, Samsa G, Studenski S. The relative importance of strength and balance in chair rise by functionally impaired older individuals. Journal of the American Geriatrics Society 1996;44:1441–6. [2] Dall PM, Kerr A. Frequency of the sit to stand task: an observational study of free-living adults. Applied Ergonomics 2010;41(1):58–61. [3] Kaya BK, Krebs DE, Riley PO. Dynamic stability in elders: momentum control in locomotor ADL. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 1998;53A(2):M126–34. [4] Ploutz-Snyder L, Manini T, Ploutz-Snyder R, Wolf D. Functionally relevant thresholds of quadriceps femoris strength. Journals of Gerontology Series A Biological Sciences and Medical Sciences 2002;57:B144–152. [5] Rogers MW, Pai Y-C. Dynamic transitions in stance support accompanying leg flexion movements in man. Experimental Brain Research 1990;81(2):398–402. [6] Lord SR, Murray SM, Chapman K, Munro B, Tiedemann A. Sit-to-stand performance depends on sensation, speed, balance, and psychological status in addition to strength in older people. Journal of Gerontology: Medical Science 2002;57(8):M539–543. [7] Nevitt M, Cummings S, Kidd S, Black D. Risk factors for recurrent nonsyncopal falls. A prospective study. The Journal of the American Medical Association 1989;261:2663–8. [8] Campbell AJ, Borrie MJ, Spears GF. Risk factors for falls in a communitybased prospective study of people 70 years and older. Journal of Gerontology 1989;44:p112. [9] Csuka M, McCarty D. Simple method for measurement of lower-extremity muscle strength. American Journal of Medicine 1985;78:77–81.
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Medical Engineering & Physics journal homepage: www.elsevier.com/locate/medengphy
Multi-parametric evaluation of sit-to-stand and stand-to-sit transitions in elderly people R. Ganea a,∗ , A. Paraschiv-Ionescu a , C. Büla b , S. Rochat b , K. Aminian a a b
Ecole Polytechnique Fédérale de Lausanne (EPFL), Laboratory of Movement Analysis and Measurement, Lausanne, Switzerland Service of Geriatrics and Geriatric Rehabilitation, University of Lausanne Hospital Center, Lausanne, Switzerland
a r t i c l e
i n f o
Article history: Received 31 May 2010 Received in revised form 18 April 2011 Accepted 23 April 2011 Keywords: Ambulatory system Postural transitions Elderly Rehabilitation
a b s t r a c t The aim of this study was to extract multi-parametric measures characterizing different features of sit-to-stand (Si-St) and stand-to-sit (St-Si) transitions in older persons, using a single inertial sensor attached to the chest. Investigated parameters were transition’s duration, range of trunk tilt, smoothness of transition pattern assessed by its fractal dimension, and trunk movement’s dynamic described by local wavelet energy. A measurement protocol with a Si-St followed by a St-Si postural transition was performed by two groups of participants: the first group (N = 79) included Frail Elderly subjects admitted to a post-acute rehabilitation facility and the second group (N = 27) were healthy community-dwelling elderly persons. Subjects were also evaluated with Tinetti’s POMA scale. Compared to Healthy Elderly persons, frail group at baseline had significantly longer Si-St (3.85 ± 1.04 vs. 2.60 ± 0.32, p = 0.001) and St-Si (4.08 ± 1.21 vs. 2.81 ± 0.36, p = 0.001) transition’s duration. Frail older persons also had significantly decreased smoothness of Si-St transition pattern (1.36 ± 0.07 vs. 1.21 ± 0.05, p = 0.001) and dynamic of trunk movement. Measurements after three weeks of rehabilitation in frail older persons showed that smoothness of transition pattern had the highest improvement effect size (0.4) and discriminative performance. These results demonstrate the potential interest of such parameters to distinguish older subjects with different functional and health conditions. © 2011 IPEM. Published by Elsevier Ltd. All rights reserved.
1. Introduction As people get older, the ability to rise from a chair, usually labelled as the sit-to-stand (Si-St) postural transition, becomes a more demanding functional daily task. In those most vulnerable, postural transition is an important indicator of daily-life functional independence and mobility [1,2]. Si-St postural transition is commonly used as a functional test when assessing older persons to provide information about their lower extremities function and strength, as well as balance [1,3]. Si-St performance has been associated with age-related changes in muscular strength in leg extensors, vestibular disorders [3,4], as well as with changes in movement strategies [3,5]. Consequently, standardized assessment of Si-St and stand-to-sit (St-Si) postural transitions has been used for multiple purposes, including evaluation of postural control [6], fall risk [7,8], lower-extremity strength [9–11], impairment after
∗ Corresponding author at: EPFL-STI-LMAM, ELH 136/Station 11, CH-1015 Lausanne, Switzerland. Tel.: +41 216 935 971; fax: +41 216 936 915. E-mail addresses: raluca.ganea@epfl.ch (R. Ganea), anisoara.ionescu@epfl.ch (A. Paraschiv-Ionescu), [email protected] (C. Büla), [email protected] (S. Rochat), kamiar.aminian@epfl.ch (K. Aminian).
stroke [12–14], and proprioception [15], as well as a measure of overall disability [16,17]. Traditional clinical evaluation of Si-St transition is based on visual observation of joint angle motion to describe alterations in coordination and movement pattern. However, the validity of such assessment essentially depends on clinicians’ experience and training. Results might not have the precision needed to objectively assess the effect of rehabilitative intervention or the decline over time in Frail Elderly persons [18]. Kinematics of the trunk appears essential to maintain balance during Si-St transition [19,20]. This led to investigate the dynamic of trunk movements using body-worn inertial sensors [21,22]. These sensors can be used to collect unobtrusive, objective and valid measurements that capture specific postures and motion during normal daily life in subject’s personal environment [23]. One of the most straightforward parameter to characterize the subject’s performance in Si-St/St-Si postural transition is to measure its duration. Previous observations have shown that duration of Si-St and St-Si transitions can distinguish between older people at low and high risk for falls [20]. Despite these encouraging observations suggesting the usefulness of duration of Si-St/St-Si postural transition, other studies in patients suffering from Alzheimer’s or Parkinson’s disease also suggest that this parameter is not sufficient to fully describe the clinical performance in older persons [24,25].
1350-4533/$ – see front matter © 2011 IPEM. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.medengphy.2011.04.015
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Fig. 1. Kinematic features characterizing a Si-St postural transition: TD, the duration of the transition; TT, the range of the trunk tilt during posture transition.
Overall, these observations confirm that the Si-St transition is a complex movement and suggest that more detailed characterization than merely a measure of duration are needed to fully capture the various aspects of motor control and performance. To achieve this aim, additional parameters are needed that more specifically assess kinematic phases within the Si-St transition, such as the maximum trunk flexion [26–28], key events as temporal locations of the major peaks of vertical and sagittal acceleration [29]. For instance, the vertical power of Si-St and the force acting on trunk can be estimated from the acceleration measured by inertial sensors [30], making it possible to describe in more detail this phase of Si-St transition. Such detailed characterization of Si-St movement patterns opens the perspective for a broader range of clinical applications. For instance, elderly persons transitioning to frailty have increasing difficulties to perform Si-St transitions. Over time, these older persons may use different compensatory strategies to achieve successful transitions such as deviation from normal motion. This deviation is due to several types of motion irregularities, among which sway is the most frequently encountered. Sway consists in repetitive, quick changes in motion orientation due to a temporary loss of balance or to insufficient strength in lower limbs [31]. These results suggest that further investigation of postural transition with other additional parameters has the potential to provide important predictive information. The present study had several aims. The first aim was to determine whether trunk’s kinematics during Si-St and St-Si postural transitions can be used to define metrics characterizing functional performance. In particular, this paper presents how these metrics
can be extracted based on efficient signal processing of data recorded by a single inertial sensor that is fixed on the trunk and can be worn during daily conditions. The second aim was to investigate whether these new multi-parametric measures of postural transition could distinguish elderly persons with different health and functional status. Specifically, the hypotheses were that trunk movements during Si-St and St-Si transitions are ‘smooth’ and stable in Healthy Elderly persons and ‘rough’ as well as unsteady in Frail Elderly persons, such as those with mobility, balance, or strength impairments. A third aim was to compare the sensitivity to change of these new multi-parametric measures in a sample of older persons undergoing a rehabilitation program.
2. Methods 2.1. Subjects and measurement protocol Two groups of participants were enrolled in this study: the first group (Frail Elderly, N = 79) included Frail Elderly subjects admitted to a post-acute rehabilitation facility (fifty-three females and twenty-six males). Frailty is commonly defined as a state of increased vulnerability to stressors resulting from a cumulative decline in the physiological reserves of multiple systems. To be considered as frail subjects, subjects had to fulfil Fried’s criteria for frailty [32]. Such criteria comprise five core components: sarcopenia or weight loss, reduced muscular strength, slow walking speed, exhaustion, and low activity level. To be eligible, they had to be aged 65 years and older, to be able to stand more than 10 s
Fig. 2. (a) Mother wavelet db5; (b) Wavelet decomposition. Typical patterns of trunk acceleration norm during Si-St task at different DWT decomposition levels using a discrete “db5” wavelet as mother wavelet (c) Healthy Elderly subject; (d) Frail Elderly subject.
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Fig. 3. Kinematical signals (norm of the acceleration and angular velocity) recorded during Si-St postural transition and the estimated fractal dimension, dF (based on box counting method) for (a) Healthy Elderly subject, (b) Frail Elderly subject.
and to walk 20 m without assistance. The second group (Healthy Elderly, N = 27) were healthy community dwelling elderly persons (sixteen females, eleven males) recruited from the community, who had to meet the following eligibility criteria: to be aged 65 years and older, to rate their health as excellent, very good, or good (based on Self-rated Health Status [33]), to be independent in all basic activities of daily living, and to be able to walk more than 1 km. The measurement protocol performed by the two groups included a Si-St followed by a St-Si postural transition using a standard chair with arm rest. In the first group of Frail Elderly persons admitted to rehabilitation, measurements were performed within the first three days post admission and repeated three weeks later. During these three weeks, participants underwent a rehabilitation program that consisted in daily supervised training of progressive intensity that was individualised according to patients’ initial performance. Training included flexibility exercises, gait and static/dynamic balance training, combined with strengthening exercises targeting muscular groups involved in standing and walking (lower limbs). Program also included transfer training. At baseline and follow-up, a clinical evaluation of gait and balance was also performed in these subjects, using Tinetti’s Performance Oriented Mobility Assessment (POMA) scale [34]. This scale was developed to assess fall risk in older persons. Scores range from 0 to 28, where lower values are associated with higher fall risk [34]. Measures of postural transitions were performed using a monitoring system composed of a small inertial sensor (25 mm × 25 mm × 15 mm) and a light portable data-logger (Physilog® , BioAGM, CH) carried on the waist. The sensor was directly attached on the skin to the sternum. In order to prevent the artefact signals related to accidental movements of the sensors, we used medical patches (Coloplast Systems, Denmark) [35]. The inertial sensor was composed of one gyroscope (ADXRS300, ±400◦ /s, Analog Devices) measuring the trunk angular velocity in sagittal plane (tilt velocity) and three accelerometers (ADXL202, ±2 g, Analog Devices) measuring vertical, frontal, and lateral accelerations, respectively. Sensor signals were amplified, low-pass filtered (cutoff frequency 17 Hz) to remove electronic noise, digitized at 40 Hz and recorded on a memory card. After recording, data were transferred to a computer for analysis.
The protocol of the study was approved by the local ethical committee and written consent was obtained from all subjects. 2.2. Multi-parametric characterization of sit-to-stand/stand-to-sit movement 2.2.1. Postural transition’s duration Postural transition was detected based on the algorithm that estimates changes in the trunk tilt from the time integral of the gyroscope signal [36]. Transition’s duration (TD = T2 − T1 ) was defined as the time interval between the two positive peaks T1 and T2 , before and after the maximum negative peak of trunk tilt [20] (Fig. 1). 2.2.2. Range of trunk tilt Angular velocity of the trunk was integrated during TD and its range, corresponding to trunk tilt (TT), was calculated [36]. 2.2.3. Local energy of the trunk dynamics (TEm ) The forces acting at the trunk level, FT can be estimated by multiplying the norm of acceleration, |a|, by the trunk mass, MT :
FT = | a| · MT
(1)
where MT was estimated using the method presented in Pavol et al. [37]. Since FT is a transient signal, the peak value and the energy of the signal can be used for quantifying the dynamics of body movement during postural transition. We choose the energy to characterize the signal, as it includes both the amplitude of the signal and the duration of the oscillation. Based on the Parseval’s theorem, the energy of FT summed across time is equal to the energy of the signal summed across all of its frequency components. Considering the non-stationary nature of FT , discrete wavelet transform (DWT) was used to estimate frequency content changes over the time [38]. DWT is a batch method that analyses a finite-length time-domain signal at different frequency bands and with different resolutions by successive decomposition into coarse “approximation” (A) and “detail” (D) information. ‘Approximations’ represent the slowly changing (low-frequency) features of a signal while ‘details’ represent the rapidly changing (high-frequency) features of the signal. DWT was largely applied on the patterns of postural transitions [39–41]. Wavelets come in many shapes called “mother wavelets”
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Table 1 Characteristics of the two groups of subjects. Parameter
Healthy Elderly subjects (H) (n = 27)
Frail Elderly subjects (B) (n = 79)
Gender Age (years) Height (m) Mass (kg) BMI (kg/m2 )
11 males, 16 females 73.0 ± 5.03 1.67 ± 0.09 71.5 ± 13.5 25.6 ± 4.2
26 males, 53 females 80.0 ± 7.1 1.62 ± 0.51 67.2 ± 15.4 25.3 ± 5.3
p-Value (H, B) *
p NS NS NS
NS = not significant. * p-Value < 0.05. ** p-Value < 0.01. *** p-Value < 0.001.
and the idea is to use a shape that best fits the signal. For the present work, in agreement with another study [42], Daubechies wavelet db5 was chosen using the Matlab 7.7 (Mathworks) Wavelet Toolbox [43,44]. Fig. 2 shows this decomposition into 4 levels of frequency range applied to a typical postural transition and the reconstructed “detail” signals (on the right). Under the assumption that the normal transition is performed predominantly in the sagittal plane, the detail signals, Dm (i.e., D1 to D4 ) of the force acting at the trunk level FT during the ‘normal’ transition movement appears as a signal with increased amplitude over the 4 levels and a more regular shape close to db5 mother wavelet at D4 ; conversely, for an ‘abnormal’ movement pattern Dm appears with lower amplitude and an irregular shape, as illustrated in Fig. 2. Then, we quantified the energy of the signal characterising the dynamic of the trunk movement by using the local wavelet energy [43] defined as: TEm =
Dm (k)2 k
(2)
where k is the total number of samples in the mth level. More information on the estimation of Dm can be found in [20]. According to Bussmann et al. [23], the frequency of trunk movements during daily activities occurs in bandwidth [0.6–4 Hz]. However, some authors consider rather 0.8 Hz as the minimum value [44]. Therefore, we considered the detail level of order m = 3, 4 to include the main information (1.25–5 Hz) about postural transition. 2.2.4. Postural transition smoothness In order to characterize the pattern and irregularities (roughness vs. smoothness) of trunk movement during Si-St and St-Si transitions, we plotted first the norm of acceleration signals |a| vs. trunk angular velocity measured around medio-lateral axis (ω). This phase plot-like presentation uses all available kinematical information and further shows whether the velocity controls the acceleration [45]. Fig. 3 illustrates such a plot of the kinematical signals recorded during a Si-St postural transition in a healthy community-dwelling elderly subject and in a frail rehab elderly subject. Given this representation, the next step was to quantify the differences between these types of plots, based on the assumption that normal, regular movement pattern is performed predominantly in the frontal direction and appears as a straight line in (|a|, ω) plane, while abnormal movement patterns expand towards an irregular surface in (|a|, ω) plane. We used the concept of fractals introduced by Mandelbrot [46] to describe objects with irregular shapes and complex structures. A shape with a higher fractal dimension is more complicated and irregular that one with a lower fractal dimension. Fractal dimension has been previously applied to characterize various 1D or 2D biomedical signals [47–50] and particularly in biomechanical signals by assessing the smoothness of gait acceleration [51] or instability of postural sway [39]. In order to quantify the sway-type irregularities of signals/shapes, we calculated the
fractal dimension using the box-counting method [52]. The implementation of the box-counting method is as follows: the surface of the plot is covered with a box (with length lb ), and then the box is divided in four quadrants. We then count the number of occupied cells and divide each subsequent quadrant into four sub-quadrants. This process continues until the minimum box size is equal to the resolution of the data. The fractal dimension (dF ) is given by: dF = −lim l→0
log10 Nb (lb ) log10 lb
(3)
where Nb (lb ) is the number of boxes needed to completely cover the pattern. (dF ) corresponds to the slope of the plot log10 Nb (lb ) vs. log10 lb . The larger dF is, the “rougher” the (|a|, ω) pattern is. 2.3. Statistics Above parameters were estimated for each subject, and their mean and standard deviation were calculated within each group. Differences between the Frail Elderly and Healthy Elderly groups were assessed using unpaired Student’s t-test. In order to assess the difference in Frail Elderly group before and after rehabilitation, a paired Student’s t-test was used. The significance level was set at p < 0.05 for all comparisons. Spearman rank correlations () were computed between POMA score and postural transition parameters. Each parameter’s sensitivity to change was calculated using effect size statistics. The standardized effect size was calculated by considering a sensitive statistics, Cohen’s distance (CD), [53]: CD =
1 − 2 ((n1 − 1) ∗ s12
+ (n2 − 1) ∗ s22 )/(n1 + n2 )
(4)
where n1 and n2 is the number of subjects in each group and s1 and s2 is standard deviation for each group. Small (CD < 0.3), medium (0.3 < CD < 0.5) and large (CD > 0.5) Cohen’s distance values indicate the degree of subject’s improvement [54]. To classify subjects into two groups (Healthy Elderly and Frail Elderly subjects) based on developed parameters, receiveroperating characteristic (ROC) curves were computed to determine the cut-off point. Optimal cut-off point was the value that maximized the sum of sensitivity and specificity. Sensitivity (true positive ratio) and specificity (true negative ratio) were calculated: sensitivity =
TP TP + FN
(5)
specificity =
TN TN + FP
(6)
(TP = true positive; TN = true negative; FP = false positive; FN = false negative). The ROC curves display plots of true positive rates or sensitivity (i.e., positively labelled test data classified as positive) vs. false positive rates (i.e., negatively labelled test data classified as positive). The area under the ROC curve (AUC) provides a measure of the overall performance of the discriminative parameter i.e., the larger the area underneath the curve indicates a better performance.
0.28 p **
– – – – 23.16 ± 2.49 22.45 ± 2.5 – NS = not significant. * p-Value < 0.05. ** p-Value < 0.01. *** p-Value < 0.001.
POMA score
0.28 0.21 0.03 0.27 0.40 p p NS NS ** p *
*
0.75 0.17 0.68 0.43 1.51 p p ** p NS *** p *
***
1.21 0.06 0.61 0.93 2.16 p NS * p ** p *** p
***
1.40 17.29 0.97 4.44 0.08 ± ± ± ± ± 3.72 26.4 1.15 3.06 1.34 1.21 15.37 1.15 2.56 0.07 ± ± ± ± ± 0.36 8.31 2.01 4.69 0.05 2.81 29.02 2.03 4.97 1.23 TD (s) TT (◦ ) TE3 (N) TE4 (N) dF StSi
± ± ± ± ±
4.08 29.87 1.18 2.08 1.37
NS NS NS NS * p 1.00 0.01 1.48 1.69 2.00 p NS *** p *** p *** p
***
1.38 0.19 1.70 1.63 2.31 p NS *** p *** p *** p
***
1.27 13.72 0.98 2.30 0.07 ± ± ± ± ± 3.70 22.06 1.05 2.87 1.34 1.04 12.18 0.79 2.62 0.07 ± ± ± ± ± 3.85 24.32 0.90 2.87 1.36 0.32 7.78 3.87 7.05 0.05 ± ± ± ± ± 2.60 22.22 4.15 9.64 1.21 TD (s) TT (◦ ) TE3 (N) TE4 (N) dF SiSt
p-Value (B, F) Effect size (CD) (H, F) Frail Elderly subjects baseline (B) (n = 79)
Frail Elderly subjects follow-up (F) (n = 79)
p-Value (H, B)
Effect size (CD) (H, B)
p-Value (H, F) Fig. 5. ROC curve analysis quantifying the probability of the TD and dF to accurately classify Healthy Elderly subjects and Frail Elderly subjects.
Healthy Elderly subjects (H) (n = 27)
Characteristics of the participants as well as baseline and followup POMA score in Frail Elderly subjects are reported in Table 1. As expected, Healthy Elderly subjects were younger and weighted more, although this last difference was not significant. The dynamics of body movement during postural transition (Si-St and St-Si) were characterized by the following parameters: transition duration TD, range of the sagittal trunk tilt TT, local energy of the trunk dynamics TE3 , TE4 , and smoothness of transition dF . In order to address the second aim of this study and determine whether these measures could adequately quantify subjects’ functional status, we compared Si-St/St-Si parameters in Healthy Elderly subjects and Frail Elderly subjects, as well as between baseline and follow-up in this last group. Table 2 shows that for Si-St transition, all parameters except trunk tilt were significantly different in Healthy Elderly subjects compared to Frail Elderly subjects
Parameter
3. Results
Table 2 Multi-parametric characterization of the Si-St/St-Si movement (mean ± SD) in Healthy Elderly subjects and Frail Elderly subjects; CD – Cohen’s distance.
Fig. 4. Relationships between dF (higher value indicate decreased smoothness in the movement) and POMA score at Baseline (range 0–28), higher score indicate better gait and balance.
0.13 0.18 0.17 0.00 0.29
R. Ganea et al. / Medical Engineering & Physics 33 (2011) 1086–1093
Effect size (CD) (B, F)
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R. Ganea et al. / Medical Engineering & Physics 33 (2011) 1086–1093 Table 3 Cut-off points, sensitivity, specificity and AUC of the parameters used to accurately classify Healthy Elderly subjects and Frail Elderly subjects based on the ROC curves interpretation. Parameter
Threshold
Sensitivity
Specificity
AUC
Si-St
TD (s) TT (◦ ) TE3 (N) TE4 (N) dF
2.92 25.85 1.47 4.59 1.27
0.92 0.55 0.74 0.70 0.92
0.83 0.35 0.83 0.82 0.88
0.90 0.50 0.80 0.81 0.96
St-Si
TD (s) TT (◦ ) TE3 (N) TE4 (N) dF
3.15 26.08 0.90 1.89 1.30
0.79 0.60 0.55 0.80 0.70
0.85 0.62 0.66 0.66 0.77
0.86 0.63 0.63 0.84 0.82
at baseline (p < 0.001, CD > 0.78). Similarly, for St-Si transition, transition duration TD, smoothness dF and local energy TE3 and TE4 all differed significantly between Healthy and Frail Elderly subjects at baseline. These differences in Si-St/St-Si parameters essentially remained when comparing values in Healthy Elderly subjects to those measured at follow-up in Frail Elderly subjects (Table 2). To address the third aim, we analyzed also whether Si-St and St-Si parameters could adequately quantify changes in functional performance in Frail Elderly subjects after they completed their rehabilitation program. Table 2 shows that for Si-St transition, smoothness dF of transition pattern was the only parameter that significantly improved after the rehabilitation. In addition, smoothness dF also showed a modest but significant negative correlation with POMA score at Baseline (Spearman = −0.3, p = 0.008) (Fig. 4). For St-Si transition, transition duration TD, trunk tilt TT and dF decreased significantly after completion of the rehabilitation program. Although local energy TE4 increased after rehabilitation, this difference did not reach statistical significance. According to Cohen’s d effect size, small to medium sensitivity to change were observed for most parameters. Highest sensitivity to change was observed for dF in both Si-St and St-Si transitions, and in transition duration TD as well as trunk tilt TT only in St-Si transition. Interestingly, improvement observed in Frail Elderly subjects (i.e., decrease in TD and dF ) translated into values becoming closer to those observed in Healthy Elderly subjects. A small but statistically significant improvement (p < 0.001) in POMA score was observed after rehabilitation. Optimal cut-off values for TD, TT, TE3 , TE4 and dF were 2.92 s, 25.85◦ , 1.47 N, 4.59 N, 1.27 and 3.15 s, 26.08◦ , 0.9 N, 1.89 N, 1.3 for SiSt and St-Si, respectively. The best sensitivity and specificity (0.92, 0.88) were obtained for dF , corresponding to the most discriminative parameter (Table 3). Based on the ROC curve interpretation (Fig. 5), the specificity of dF was higher as compared to TD (0.88 vs. 0.83). In a similar way, area under the curve (AUC) increased for TD comparing to TD (0.96 vs. 0.9).
4. Discussion and conclusions This study is contributing to the field of activity monitoring from both an engineering and a clinical perspective. From an engineering perspective, this study is the first, to our knowledge, to describe a method to extract multi-parametric measures characterizing in more details different aspects of Si-St and St-Si transitions, using a single inertial sensor attached to the chest. Recorded data (i.e., accelerometer and angular velocity) was used to estimate time-domain features, movement’s smoothness, and local energy features.
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From a clinical point of view, this study shows that these new parameters have significant discriminating properties to distinguish between elderly subjects with different health and functional status. These results confirm and extend previous studies of SiSt/St-Si postural transitions that used the time-domain kinematics features of the trunk during movement task. The average transition duration in Healthy Elderly subjects was 2.60 ± 0.32 s, a duration similar to the rising phase of the sit-stand-sit cycle reported by Kerr et al. [29] in elderly females. This value was longer than those found in Najafi et al. [20] in subjects with low (2.95 s) and high (3.88 s) fall-risk. However, this study had only very small groups (N < 6), and values reported were averaged from Si-St and St-Si transitions. Although the approach based on the time-domain kinematics features was able to discriminate patients with movement disorders [55] or Frail Elderly subjects [20], the methodology developed in the current study extends current knowledge in kinematics analysis in providing additional information on the global pattern (i.e., smoothness) of transitions. In addition, results from comparisons between the two groups of elderly subjects as performed in the current study further indicate potential applications to health assessment outside the specific field of fall risk assessment, such as extended monitoring in elderly persons to detect early decline in functional independence. One important contribution from our study is also to demonstrate that time-domain parameters like the duration of transition did not detect some minor improvements resulting from rehabilitation. New parameters developed in the current study (i.e., dF ) are likely to be able to capture more subtle functional changes e.g. more dynamic, smooth and stable transition pattern that might help for prediction of adverse events occurring on the disability pathway. However, this needs to be further investigated in more extensive protocol settings. This study reveals that other parameters such as the features based on the local energy and the irregularity (or smoothness) of the kinematical patterns described by dF are relevant metrics to distinguish significantly between Healthy Elderly and elderly with medium level of fall risk (POMA score of 19–24). Such multi-parametric approach, that considers postural transition as a non-linear and non-stationary movement, allows detecting subtle changes in physical performance and may guide therapeutic interventions towards more customized and efficient schemes. The proposed approach was also successful in assessing changes in postural transition resulting from a rehabilitation program. The smoothness of the movement dF improved in both tasks (Si-St and St-Si) and showed modest but significant correlation with the POMA score. This could be interpreted as reflecting a more stable postural transition after rehabilitation program. The trunk tilt TT and transition’s duration TD decreased after rehabilitation in StSi, likely reflecting improvement associated with the rehabilitation process. After rehabilitation program, during St-Si, the lower frequency component of local energy TE4 increased. Even though this difference did not reach statistical significance, this observation needs to be further investigated to determine whether it could reflect the clinical improvement frequently observed in Frail Elderly persons who become able to lean back smoothly instead of “falling” in the chair when sitting down once their rehabilitation program is completed. Finally, another important contribution of this work is to demonstrate that the proposed measures could be more sensitive than a semi-objective measure of gait and balance such as POMA to detect improvements in functional performance. Improvements in almost all parameters were consistent with subject improvement in POMA score at follow-up. However, dF showed a higher sensitivity to change resulting from rehabilitation than POMA score. This result confirms the potential of these
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measures when assessing elderly persons for fall risk estimation but also suggests new additional applications for monitoring of improvement related to rehabilitation [56]. Although the proposed metrics were obtained using sensors placed at the sternum site, other locations on trunk should provide similar results. For instance, in a recent study, Zijlstra et al. [30] showed a positive relationship between the vertical force estimated from the data recorded by the sensor placed at different trunk location and from the force plate data. Nevertheless, we propose to keep the sternum location since it has been shown to be more practical in a number of previous studies [20,36,57], particularly when measuring trunk tilt and transition duration. In this study postural transitions were performed under controlled conditions. Many determinants may affect the results of a Si-St protocol, such as the initial position of the foot, the subject’s anthropometry or the height of the chair [22,58]. In this regard, a limitation of this study could be that the height of the chair was not adjusted to participants’ height. However, we used a chair with standard height of 45 cm that has the tremendous advantage to reflect real life situation for elderly subjects. Another criticism that could be made to the protocol used is that daily activities may produce different patterns of transition. Recordings performed in the current study might therefore not capture the complete picture of functional limitation. Nevertheless, by considering other methods [59,60] which use some sensor configuration and detect automatically the postural transitions, it is possible to extend the method proposed in this study to signal recorded over long period of daily activity. Therefore, this study demonstrates the feasibility of using the proposed metrics to characterize movement patterns recorded over daily-life conditions or to quantify long-term daily-life movement pattern changes that results from therapeutic interventions. Acknowledgements The work was supported by “Fonds National Suisse de la Recherche Scientifique”, Grant No. 510538/510867. The authors would express their gratitude to Mr. Pascal Morel and Mr. Jean Gramiger for their helps and assistances as well as all the subjects who kindly participated in this study. Conflict of interest None. References [1] Schenkman M, Hughes M, Samsa G, Studenski S. The relative importance of strength and balance in chair rise by functionally impaired older individuals. Journal of the American Geriatrics Society 1996;44:1441–6. [2] Dall PM, Kerr A. Frequency of the sit to stand task: an observational study of free-living adults. Applied Ergonomics 2010;41(1):58–61. [3] Kaya BK, Krebs DE, Riley PO. Dynamic stability in elders: momentum control in locomotor ADL. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 1998;53A(2):M126–34. [4] Ploutz-Snyder L, Manini T, Ploutz-Snyder R, Wolf D. Functionally relevant thresholds of quadriceps femoris strength. Journals of Gerontology Series A Biological Sciences and Medical Sciences 2002;57:B144–152. [5] Rogers MW, Pai Y-C. Dynamic transitions in stance support accompanying leg flexion movements in man. Experimental Brain Research 1990;81(2):398–402. [6] Lord SR, Murray SM, Chapman K, Munro B, Tiedemann A. Sit-to-stand performance depends on sensation, speed, balance, and psychological status in addition to strength in older people. Journal of Gerontology: Medical Science 2002;57(8):M539–543. [7] Nevitt M, Cummings S, Kidd S, Black D. Risk factors for recurrent nonsyncopal falls. A prospective study. The Journal of the American Medical Association 1989;261:2663–8. [8] Campbell AJ, Borrie MJ, Spears GF. Risk factors for falls in a communitybased prospective study of people 70 years and older. Journal of Gerontology 1989;44:p112. [9] Csuka M, McCarty D. Simple method for measurement of lower-extremity muscle strength. American Journal of Medicine 1985;78:77–81.
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