Fall Risks Assessment and Fall Prediction among Community Dwelling Elderly Using Wearable Wireless Sensors

Transcription

1 Fall Risks Assessment and Fall Prediction among Community Dwelling Elderly Using Wearable Wireless Sensors This study examines the predictive ability of fall risks among community dwelling elderly using wearable wireless sensors. Forty-eight community-dwelling elderly (17 non-fallers and 31 fallers) participated in the study. Timed up and go test, sit-to-stand test and ten-meter walking test were carried out. Activitiesspecific Balance Confidence (ABC) Scale was also obtained. The results showed fairly good predictive ability of fall risks among older adults, with stance time, walking velocity and timed up and go time being promising of indicating fall risks. Further investigation is warranted to better understand the signals from the wearable sensors and justify the model. Larger sample size is also warranted to validate the model. INTRODUCTION According to the US Census Bureau, there are more than 40 million older adults aged 65 and older in the US (US Census Bureau, 2011). In this population, approximately 35% to 40% of the community dwelling, generally healthy elderly persons fall annually (Rubenstein, 2006). Falls are among the most common and serious problems facing older adults and are associated with considerable mortality, morbidity, reduced functioning, and premature nursing home admissions (WHO, 2012). As such, early identification of older people at risk of falling is of great importance for timely initiation of fall prevention programs (Gillespie, 2004). A number of impairments and limitations in the elderly have been identified as fall risk factors (Kerr et al., 2003; Kerr et al., 2006; Podsidalo and Richardson, 1991). These risk factors have also been used to develop fall assessment tools to prevent falls by identifying fall prone individuals. It is believed that these tools represent an effective quantitative method to gather information on fall risks for older adults. However, most of these tools are based on laboratory settings (Magnan et al., 1996) or oversimplified in clinical practice (Prosser and Canby, 1997) (i.e. using subjective Likert scales and stop watches), thus lack of applicability in clinical settings or cannot provide important insights of human mobility. Therefore, in this study, we investigated the capability of using wearable wireless sensors in fall risks assessments and fall predictions. Clinical tools such as the Berg Balance Scale (BBS), Tinetti Balance subscale, Activities-specific and Balance Confidence (ABC) Scale, Timed Up and Go test (TUG), and functional reach have the ability to predict falls in the elderly (Lajoie and Gallagher, 2004). Older adults who score low on those scales or requiring longer time to complete the TUG task have been identified as faller. Fallers have also been associated with poor performance on functional tasks such as walking speed (Luukinen et al., 1995), balance (Gehlsen and Whaley, 1990), and sit-to-stand performance (Cheng et al., 1998). However, no single tool/measure is entirely representative of fall risks due to its lack of predictability during completing more complex tasks or activities that are concerned with steady-state postural control as well as anticipatory postural adjustments (Shumway-Cook and Woollacott, 2001). As such, a more concrete and portable fall risk assessment tool is warranted. The purpose of this study was to use wearable wireless sensors to evaluate gait characteristics during 10-meter walk, the ABC scale, TUG and sit to stand (STS) in community-dwelling elderly and to build an effective model with the underlying purpose of predicting falls in the elderly. In addition, cut-off values would be provided to offer a valuable tool for clinical assessment of fall risks. Participants METHOD Forty-eight community-dwelling older adults living in the Northern Virginia area were enrolled in the study after giving informed consent, which was approved by the Internal Review Borad of Virginia Polytechnic Institute and State University. The inclusion criteria were: (1) age 65years or older, (2) ability to understand the nature of the study and provide informed consent, and (3) ability to walk 10 m without gait aids. Individuals with any medical condition or disability that could prevent them from participating in routine clinical balance tests (e.g., TUG) were excluded. After informed consent was obtained, subjects completed questionnaires containing information on age, residential status, medical history, and fall history during the past twelve months. This information was used to characterize the demographics and health status of subjects participating in the study. Participants weight was measured to the nearest 0.1 kg on a Salter 200 scale. Height was measured to the nearest m with a wall-mounted stadiometer. Participants demographic information was shown in Table 1. Among all the participants, there are thirty-one fallers and seventeen nonfallers. Table 1 Participants demographic information Parameter Non-Fallers Fallers Age 74.9± ±6.9 Weight (lb) 164.8± ±32.8 Height (cm) ± ±8.69 Gender 10F/7M* 20F/11M Data are in the format of mean±sd F for female and M for male Equipment

2 Three custom-made wireless inertial measurement units (IMUs) (TEMPO, Figure 1) were attached on participants sternum and both ankles (lateral sides) to obtain the temporal and kinematic data. TEMPO: Technology- Enabled Medical Precision Observation was manufactured in collaboration with the inertia team from University of Virginia. The TEMPO system consists of MMA7261QT triaxial accelerometers and IDG-300 (x and y plane) and ADXRS300A (z plane) uniaxial gyroscopes. These sensors capture three axes of both linear acceleration and angular rate at 128 Hz, providing six degrees of freedom motion capture in the form factor of a wristwatch (Barth et al., 2009). The data acquisition was carried out using a Bluetooth adapter and laptop via a custom-made LabView VI (Barth et al., 2009). Stopwatch was also used to time the 10-meter walking trials as well as the timed up & go trials. A standard armchair (seat height of 43cm, arm height of 65 cm) was used for participants to perform the timed up & go and sit to stand tasks. Figure 1 TEMPO (left: the TEMPO setup, right: X, Y and Z axis for accelerometer and gyroscope) Protocol Wearing their own footwear, participants started with sitting posture and their back against the chair, arms resting on the armrest, thighs paralleled with their feet. They were instructed that, on the word go they should get up and walk at a comfortable and safe pace to a line on the floor 3 meters away from the chair, turn, and return to the chair and sit down again. The participants had a chance to walk through the test once before being timed in order to become familiar with the test. The two consequent trials were timed and recorded for the experiment. The STS test was then performed. For this test, participants were asked to rise from the standard height chair with the assist of armrests. Participant s knee angle was set to degree measured using goniometer. Two trials were recorded and timed. During the 10-meter walking task, participants were asked to walk at self-selected pace for two trials. This was performed on a walking course marked by blue tape on the floor to indicate 10-meter distance. Total walking time was recorded. Then participants were asked to fill out another questionnaire consisted of the Activitiesspecific Balance Confidence (ABC) scale. This item consisted of a 16-item subscale where participants were asked to rate their confidence levels when asked to complete a number of various activities (Lajoie and Gallagher, 2004). The sequence of the trials for each participant was randomized. Data Analysis The mean of the two timed trials for each mobility task (STS, TUG, 10-meter walking) was used to represent the temporal and kinematic characteristics of each participant during the task. Transitional phase time and the peak velocity and acceleration during the transitional phase were calculated using MATLAB 2012a (the Mathworks, Inc., MA, USA) from the IMU data. Ensemble Empirical Mode Decomposition (EEMD) (Huang et al., 1999)-Golay was first used to denoise the IMU data. The chosen number of ensembles was 100 with the ratio of the standard deviation of the added noise to that of the signal as 0.2. The calculated parameters for TUG task included anterior posterior peak acceleration (AP acc), difference between the peak flexion angular velocity and the peak extension angular velocity (FE AV dif), peak flexion angular velocity (PFAV), peak extension angular velocity (PEAV), initial flexion angular acceleration (IFAA), before seat-off flexion angular deceleration (BS F Adec), after seatoff extension angular acceleration (AS E Aacc), trunk forward jerk (TFJ), time from start to peak flexion angular velocity (T1), time from start to seat-off (T2), time from start to peak extension angular velocity (T3), time from start to the first toe off (T4), time from start to first heel contact (T5), time from start to second toe off (T6), time from start to the second heel contact (T7), time for the first gait cycle (T8), time for single stance phase in the gait cycle (T9), ½ double support time (T10), time in between peak flexion and extension momentums (T11), time in between peak flexion angular velocity and seat off (T12), time in between seat off and peak extension angular velocity (T13) and time of timed up and go task completion (14). Regarding the STS, calculated parameters included difference between anterior peak acceleration and posterior peak acceleration (AP acc dif), difference between peak flexion angular velocity and peak extension angular velocity (FE AV dif*), peak flexion angular velocity (PFAV*), peak extension angular velocity (PEAV*), initial flexion angular acceleration (IFAA*), before seat-off flexion angular deceleration (BS F Adec*), after seat-off extension angular acceleration (AS E Aacc*), time from start to peak flexion angular velocity (T1*), time from start to seatoff (T2*), time from start to peak extension angular velocity (T3*), and time of timed up and go task completion (T4*). For the 10-meter walking trials, walking velocity (WV), gait cycle time (GCT), double support time (DST), stance time (ST), swing time (SwT), swing angle (SA), step length (SL) and cadence were calculated from the IMUs. One-way between-subject ANOVA was first performed to determine the statistical difference between fallers and non-fallers using SAS 9.0, with significance level of <0.05. All-possible-regression procedure was then used to select the most appropriate logistic regression model for this dataset, with fall history as the response variable. Chi-square score was used as an evaluation criterion for model selection. The chi-square score is an approximation to the log-likelihood difference between two models. The highest score for models with various predictor variables (varies from one to 43 variables) were compared to select the best fitted model for the dataset. Lowest AICp value was used to help determine the best fitted models. After selection of a model, Hosmer and

3 Lemeshow goodness of fit test was performed to determine any lack of fit of the model to the dataset. Then Pearson Chi- Square statistics, deviance statistics, and Cook s distances were computed to determine leverage and influential points. If any leverage or influence points were identified, new model would be fitted with those points temporarily eliminated from the dataset to check any significant difference from the model built with the full dataset. Finally, if the new model is not appreciably differ from the original model, the model fitted with full dataset would be used; otherwise, the new model would be used to determine the cutoff values. RESULTS Table 2-5 presented mean and standard deviation of TUG, STS, 10-meter walking and ABC tests results, respectively. The one-way ANOVA indicated that during the TUG tasks, fallers stood up with less anterior posterior acceleration (F(1,10)=5.0086, p=0.0492), but similar peak flexion and extension angular velocity, similar initial flexion angular acceleration, before seat-off flexion angular acceleration, after seat-off extension angular acceleration, trunk forward jerk. All participants spent relatively similar time in reaching the point of peak flexion angular velocity, peak flexion angular velocity, first toe off, first heel contact, second toe off, second heel contact from the initial movements, but fallers spent significant longer time to complete the TUG tasks (F=5.0931, p=0.0290). Additionally, fallers and non-fallers spent similar time in single support and double support phases. Furthermore, it took similar time for both groups of participants from peak flexion momentums to peak extension momentums, from peak flexion angular velocity to seat off, and from seat off to peak extension. The one-way ANOVA also indicated that the participants from the two groups performed the sit-to-stand task similarly. During the 10-meter walking, fallers walked with significant longer stance time than non-fallers (F=4.2828, p=0.0444). Fallers also had significant lower ABC score compared with nonfallers (F=6.7879, p=0.0125). Regarding the logistic regression, model with three predictor variables had the lowest AICp value (36.005) among all the models. The Hosmer and Lemeshow goodness of fit test did not indicate any lack of fit of the model to the dataset (X 2 =1.4182, p=0.9850). One data point was identified as an influential point; therefore, a new model was fitted without the data point. However, the new model do not appreciably differ from the one obtained from the full dataset, and thus the data point was retained. Three variables entered the model, which were TUG (Wald Chi-square 2 =7.1821, p=0.0074), WV (Wald Chi-square=5.1279, p=0.0235) and stance time (Wald Chisqure=5.9467, p=0.0147). Our logistic regression model can be expressed as following: Logit(p)= *TUG *walking velocity *stance time (1) Table 2 Mean, standard deviation and ANOVA of TUG tasks AP acc (m/s 2 ) 0.09± ±0.12 N.S. FE AV dif (deg/s) ± ±93.46 N.S. PFAV (deg/s) 98.89± ±59.16 N.S. PEAV (deg/s) ± ±60.45 N.S. IFAA 3.09± ±1.94 N.S. BS F Adec -5.12± ±3.22 N.S. AS E Aacc -6.54± ±4.53 N.S. TFJ (m/s 3 ) -0.02± ±0.01 N.S. T1(s) 0.84± ±1.12 N.S. T2 (s) 0.68± ±0.54 N.S. T3 (s) 0.49± ±0.09 N.S. T4 (s) 0.92± ±1.13 N.S. T5 (s) 1.35± ±1.13 N.S. T6 (s) 1.42± ±1.22 N.S. T7 (s) 1.95± ±0.37 N.S. T8 (s) 1.04± ±0.07 N.S. T9 (s) 0.87± ±0.03 N.S. T10 (s) 0.06± ±0.04 N.S. T11 (s) 0.34± ±0.19 N.S. T12 (s) 0.22± ±0.15 N.S. T13 (s) 0.12± ±0.09 N.S. T14 (s) 8.87± ±3.47 * Table 3 Mean, standard deviation and ANOVA of STS tasks AP acc dif 1.12± ±0.26 N.S. (m/s 2 ) FE AV dif* ± ±73.11 N.S. (deg/s) PFAV* ± ±49.02 N.S. (deg/s) PEAV 62.15± ±27.37 N.S. *(deg/s) IFAA* 2.08± ±1.77 N.S. BS F Adec* 3.67± ±2.34 N.S. AS E Aacc 3.22± ±2.26 N.S. * T1*(s) 1.35± ±0.47 N.S. T2* (s) 1.62± ±0.70 N.S. T3 *(s) 2.65± ±1.02 N.S. T4 *(s) 4.70± ±2.09 N.S. Table 4 Mean, standard deviation and ANOVA of 10-meter walking tasks WV (m/s) 0.97± ±0.22 N.S. GCT (s) 0.84± ±0.19 N.S. DST(s) 0.20± ±0.22 N.S. ST (s) 0.46± ±0.15 * SwT (s) 0.48± ±0.12 N.S. SA(deg) 29.06± ±5.85 N.S. SL (m) 0.37± ±0.07 N.S. Cadence (step/min) 129.1± ±22.4 N.S. Note: * stands for significant, N.S. stands for not significant. Results are in the format of mean± standard deviation. Table 5 Mean, standard deviation and ANOVA of ABC scores ABC 81.41± ±20.42 * Note: * stands for significant, N.S. stands for not significant. Results are in the format of mean± standard deviation.

4 The cutoff point (0.67) was estimated from the dataset, given 31 fallers out of 48 total participants. It is determined that as shown in Figure 2, the sensitivity is 0.8 and the specificity is in prediction of new observations. These values are considered acceptable for this study. However, as can be seen the ROC curve (Figure 2) is relatively bumpy which may indicate the sample size is not big enough. It is also determined that the cutoff value for TUG, walking velocity and stance time are 9.12s, 0.87m/s and 0.49s, respectively. Figure 2 Receiver Operating Characteristic curve DISCUSSION The purpose of this study was to use wearable wireless sensors to evaluate gait characteristics during 10- meter walk, the ABC scale, TUG and sit to stand (STS) in community-dwelling elderly and to build an effective model with the underlying purpose of predicting falls in the elderly. Several tests were carried among forty-eight communitydwelling elderly to evaluate their fall risks. The tests included the Activities-specific Balance Confidence (ABC) scale, timed-up-and-go (TUG) test, sit-to-stand (STS) test and 10- meter walking test. Each test was performed twice and the mean value of the two trials was used to build a fall prediction model and subsequent cut-off values were also obtained. We applied the all-possible-model-selection method to select the best-fitted model for our dataset. We compared models with different predictor variables based on the lowest AICp value. Leverage and influential points were also checked using Pearson Chi-Square statistics, deviance statistics, and Cook s distances. Our results indicated that fallers took significantly longer time (8.87 seconds for non-fallers and for fallers) to complete the TUG task than non-fallers (Table 2). Timed up and go test is a clinically established test to differentiate faller and non-fallers (Schoene et al., 2013). This test was designed to assess the four main factors associated with the risk of falling among the elderly: 1) strength in lower extremities, 2) coordination, 3) balance, 4) gait (Gine-Garriga et al., 2009). Our TUG completion time was comparable and within the range of literature reported values (Schoene et al., 2013). The significant longer completion time among fallers was in agreement with other studies, which indicated that increased timed up and go time is associated with increased fall risks. Increased TUG time is associated with decreased functional mobility (Greene et al., 2010; Shumway-Cook et al., 2000). Decreased functional mobility is linked to poor muscle strength, poor balance, slow gait speed, fear of falling, physical inactivity, and impairments relating to basic and instrumental activities of daily living (Janssen et al., 2004; Podsidalo and Richardson, 1991; Takahashi et al., 2006). Each of these associated factors is also a known risk factor of falls in older people (American Geriatrics Society, 2001; Tinetti and Kumar, 2010). Therefore, it is reasonable for fallers to have longer time to finish the TUG tasks. Additionally, ten-meter walking task was included in our study because falls among older adults often occur during walking, and gait dysfunction is included among the many risk factors for falls (Campbell et al., 1989; Hausdorfff et al., 2001; Rubenstein, et al., 1988). As such, marked differences in the gait patterns of fallers and non-fallers might be anticipated. Therefore, the significant difference in stance time between fallers and non-fallers seemed to be reasonable (Table 4), although few quantitative studies have reported this parameter. These alterations may be reflective of the influenced central pattern generator due to musculoskeletal and sensory system degradation, exhibiting as cautious-gait by increasing the stance time. Furthermore, our study indicated that fallers have a significantly lower ABC score when compared to their nonfaller counterparts (Table 5). ABC scores were linked with the psychological factor, more commonly referred to the fear of falling, which has significant implications on an elderly s independence level and results in loss of confidence in performing activities of daily living (Lajoie and Gallagher, 2004). As a result, individuals with a fall history are more influenced by the fear of falling complex and seem to be more restricted in their daily activities. Similar results were found in other studies (Lajoie and Gallagher, 2004; Powell and Myers, 1995). The ABC scores obtained from this study were within the range of literature values, with fallers scored slightly higher than other studies (Lajoie and Gallagher, 2004; Morris et al., 1987). Our results shown that several variables obtained from the measures of daily activity and walking tasks using IMUs can be used to predict fall risks. TUG time, stance time and walking velocity all entered the model. Surprisingly, ABC score was not in the model. One reasonable explanation could be that the altered gait patterns and daily activities among the fallers may reflect a cautious adaptation due to their fall history. As such, ABC score may not present in the model along with gait and daily activity parameters, as they may explain some similar amount of variability in fall history. Although no significant difference was found in walking velocity between fallers and non-fallers, WV was identified as one of the variables to predict falls. In fact, walking velocity has been associated with fall risks by several different groups of researchers (Lockhart et al., 2003; Luukinen et al., 1995; You et al., 2001). The slowed walking velocity among fallers may reflect degradations in musculoskeletal and sensory

5 systems and lack of confidence in walking safely (Luukinen et al., 1995). The predictive ability of using IMU on fall risks is promising based on the result from this study. However, some issues need to be further addressed before applying in clinical settings. First, in this study we did not have a balanced gender group and some of our participants were obese or severe obese, which may further influence their fall risks (Wu et al., 2012). Further study may be carried out with larger sample size with balanced gender and obesity may be considered as one of the risk factors. Second, the reliability of the IMU data is still under investigation. Third, it might be useful to incorporate a few more clinical balance tests (i.e. limits of stability (LOS) test). Fourth, cross-validation of the model is warranted before further applications. CONCLUSION This study examines the predictability of using IMUs on fall risks among community dwelling elderly. The results showed fairly good predictive ability of fall risks among older adults, with stance time, walking velocity and timed up and go time being promising of indicating fall risks. Further investigation is needed to further understand the signals from the IMUs and justify the model. REFERENCES American Geriatrics Society, Guideline for the prevention of falls in older persons. Journal of American Geriatric Society, 49: Barth, A.T., Hanson, M.A., Powell Jr., H.C., Lach, J., TEMPO 3.1: A body area sensor network platform for continuous movement assessment. Body Sensor Networks: Campbell, A.J., Borrie, M.J., Spears, G.F., Risk factors for falls in a community-based prospective study of people 70 years and older. Journal of Gerontology, 44: M112-M117. Cheng, P.T., Liaw, M.Y., Wong, M.K., Tang, F.T., Lee, M.Y., Lin, P.S., The sit-to-stand movement in stroke patients and its correlation with falling. 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