Wearable Sensors Technology as a Tool for Discriminating Frailty Levels During Instrumented Gait Analysis

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There is growing scientific evidence that gait assessment using wearable sensors could be used for prefrailty and frailty screening. The purpose of this study was to examine the ability of a wearable sensor-based assessment of gait to discriminate between frailty levels (robust, prefrail, and frail). Methods: 133 participants (≥ 60 years) were recruited and frailty was assessed using the Fried criteria. Gait was assessed using wireless inertial sensors attached by straps on the thighs, shins, and feet. Between-group differences in frailty were assessed using analysis of variance. Associations between frailty and gait parameters was assessed using multinomial logistic models with frailty as the dependent variable. We used receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC) to estimate the predictive validity of each parameter. The cut-off values were calculated based on the Youden index. Results: Frailty was identified in 37 (28%) participants, prefrailty in 66 (50%), and no Fried criteria were found in 30 (23%) participants. Gait speed, stance phase time, swing phase time, stride time, double support time, and cadence were able to discriminate frailty from robust, and prefrail from robust. Stride time (AUC = 0.915), stance phase (AUC = 0.923), and cadence (AUC = 0.930) were the most sensitive parameters to separate frail or prefrail from robust. Other gait parameters, such as double support, had poor sensitivity. We determined the value of stride time (1.19s), stance phase time (0.68s), and cadence (101 steps/min) to identify individuals with prefrailty or frailty with sufficient sensitivity and specificity. Conclusions: The results of our study show that gait analysis using wearable sensors could discriminate between frailty levels. We were able to identify several gait indicators apart from gait speed that distinguish frail or prefrail from robust with sufficient sensitivity and specificity. If improved and adapted for everyday use, gait assessment technologies could contribute to frailty screening and monitoring. Geriatrics & Gerontology Frailty wearable sensors gait parameters accelerometer Figures Figure 1 Background According to the 2015 EU Ageing Report, the percentage of citizens aged over 65 years is predicted to rise from 18–28% by 2060. One of the greatest challenges facing the healthcare of the aging population is frailty. In Europe, 15% of older people aged 65 and over are considered as frail ( 1 ). Frailty is defined as a syndrome of decreased reserve and resistance to stressors, resulting from cumulative declines across multiple physiologic systems and causing vulnerability to adverse outcomes, such as worsening mobility, disability, incident falls, hospitalizations, and mortality ( 2 ). There is much potential for frailty to be reversed, particularly in its early stages ( 3 – 5 ). For that reason, the early identification and management of frailty is an important priority for both healthcare providers and healthcare policy makers ( 6 – 8 ). One of the most accepted definitions of frailty is the classification proposed by Fried et al. using five criteria: weight loss, exhaustion, inactivity, slow gait speed, and weakness ( 2 ). However, its use may have limited feasibility and reliability in a routine care setting ( 9 , 10 ). The criteria of weight loss, exhaustion, slowness, and low energy expenditure are usually self-reported measures and may be prone to bias ( 9 , 11 , 12 ). An objective frailty screening tool may be more appropriate for routine assessment. Slow gait speed has been reported as the most easily identifiable feature of physical frailty ( 2 , 13 ). Moreover, slow gait speed is a good predictor of future physical disability, falls and fractures, requirement for a caregiver, hospitalizations, and death ( 13 ). Gait speed evaluated by the 4-meter walking test could be used for frailty screening ( 14 ). Regarding recent studies, other temporal-spatial gait characteristics may have a strong association with frailty ( 15 , 16 ). A systematic review reported that frailty is associated with some temporal-spatial gait parameters beyond speed, including high stride time variability, reduced step length, and increased double support time ( 15 ). However, most studies are conducted in the laboratory environment using camera systems ( 17 , 18 ), force platforms ( 18 ), or computerized walkways ( 19 ) that, due to the high costs related to acquisition and application in everyday clinical practice, are not available in every healthcare institution. Therefore, more accessible alternatives could be used, such as wearable sensors, which are easier to use, more practical, and require fewer resources. There is growing scientific evidence that gait assessment using wearable sensors could be used for prefrailty and frailty screening ( 20 – 23 ). Simple and easy-to-use sensors (such as those incorporated into a smartphone) could be used to investigate gait at home and offer advantages in frailty evaluations where the full application or interpretation of Fried criteria is impracticable. There is currently a lack of data regarding which gait indicators are sensitive and specific for determining frailty and the appropriate thresholds for distinguishing between prefrailty and frailty. The purpose of this study was to examine the ability of wearable sensor-based assessments of gait to discriminate between frailty levels (robust, prefrail, and frail). Firstly, we sought to determine which sensor-derived gait parameters are able to discriminate between the three frailty levels. Secondly, we aimed to determine the cut-offs of the most sensitive gait parameters that separated the frailty levels. We hypothesized that we could separate the frailty groups (robust, prefrail, and frail) using a sensor-based assessment of gait. We also hypothesized that frailty is associated not only with slow gait speed, but also with longer stride time, stance phase, and double support time. Methods Study population Participants in this cross-sectional study were recruited from two secondary health care institutions and from the population who lived independently in community. A convenience sample of older adults was utilized for this study. All participants were community-dwelling adults and were eligible to participate if they were aged 60 and older and reported being able to ambulate 12 meters without an assistive device. Exclusion criteria were a Lithuanian version of the Mini-Mental State Examination (MMSE) ( 24 ) score < 21, Parkinson’s disease, recent stroke, terminal illness, or unwillingness to participate. Eligible subjects provided signed written informed consent based on the principles expressed in the Declaration of Helsinki ( 25 ). Ethical approval was obtained from the Vilnius Regional Ethics Board for Biomedical Research. Data collection occurred between May 2019 and February 2020. Initially, 165 subjects were invited, of whom 7 met the exclusion criteria (Parkinson’s disease = 2, recent stroke = 5), and 25 were rejected due to incomplete measurement. Data from 133 participants (86 women and 46 man) with an average age 75.1 ± 8 years were analysed. Data collection The research team conducted face-to-face interviews using structured questionnaires to record the required characteristics, such as age, sex, years of formal education, self-reported chronic diseases, number of prescribed and over-the-counter medications, self-reported history of falls in the previous 12 months, and use of an assistive device (yes/no). Height was obtained using a tape measure, weight was measured using a bathroom scale (Clatronic PW 3368, Clatronic®, Kempen, Germany), and BMI was calculated based on height and weight. Interviewer-administered questionnaires included MMSE and the Falls Efficacy Scale-International (FES-I) ( 26 ). Assessment of frailty criteria Frailty was assessed using the five components proposed by Fried et al. ( 2 ). Weight loss was determined by self-reported unintentional weight loss of > 4.54 kg over the past year. Weakness was evaluated by a grip strength measurement using a hydraulic hand dynamometer (Jamar®, Sammons PrestonRolyan, Bolingbrook, IL, USA). Three measures were performed, and the arithmetic mean was used to identify this criterion. Weakness was defined according to sex and the BMI cut-offs used by Fried et al. Exhaustion was evaluated by two statements of the Centre for Epidemiologic Studies Depression Scale(CES-D) questionnaire ( 27 ): ‘I felt everything I did was an effort’ and ‘I could not get going’. The frequency of ‘occasionally’ or ‘most of the time’ as a reply to either of these statements was considered as an indication of exhaustion. Slowness was defined by a walking speed of 4 m distance at the usual pace measured by a stopwatch and stratified by gender and height using the cut-offs defined by Fried et al. Low physical activity was determined using the Physical Activity Scale for Elderly (PASE) ( 28 ). PASE scores less than 64 for men and less than 52 for woman were used to indicate a positive response of low physical activity. Participants were scored one point for each criterion found, totalling a score that could range from 0 to 5. Frailty level was categorized following Fried et al. ( 2 ): robust = no criteria; prefrail = one or two criteria, and frail = three or more criteria. Physical performance tests The timed up and go test (TUG) ( 29 ) is widely used for the identification of older adults at a high risk of falling. Savva et al. (2013) ( 30 ) proposed that the TUG test is a sensitive and specific measure of frailty. In our study, participants were told to sit on a chair (seat height, 46 cm). Participants were then instructed to stand, to walk at their normal pace for a distance of 3 meters, to turn at the endpoint, to walk back the same distance, and sit on a chair. Total time starting from standing up to full sitting down was recorded. The time of one trial was taken as the TUG test score. The participants were then evaluated using the dynamic gait index (DGI) ( 31 ) for the assessment of the gait in response to changing tasks, such as turning the head while walking, stepping over the obstacle, climbing the stairs, and others. This index consists of eight tasks. Each task is scored from 0 to 3 points, with 0 being the worst and 3 being the best performance. The maximum score is 24 points. A result less than 19 points indicates impaired gait and a risk of falling. Sensor-Based assessment of gait We used a total of six wireless inertial sensors (Shimmer Research, Dublin, Ireland) attached by straps on the thighs, shins, and feet (Fig. 1 ). Each sensor includes triaxial accelerometer, gyroscope, and magnetometer and is able to measure linear acceleration, angular velocity, and magnetic heading in three dimensions. The data from sensors was acquired via a Bluetooth wireless connection at a sampling frequency of 256 Hz. Participant walked a distance of 4 m (13 feet) at the self-selected usual pace. Data from three trials was used. From all the data obtained from the inertial sensors, we selected shank angular velocity and foot linear acceleration to determine heel-strike and toe-off characteristic points. This data was filtered using a Butterworth second order low pass filter with an 8 Hz cut-off frequency and an additional least square method 25th order filter with a 10 Hz cut-off frequency for composite foot acceleration data. A gait event detection algorithm was made by picking toe-off points from the angular velocity data ( 32 ) and heel strike points from composite foot acceleration data ( 33 ). Gait parameters were calculated based on these gait events. The following quantitative gait parameters were calculated: stance phase time, swing phase time, stride time, on right and left leg accordingly, double support time, and cadence (steps/min). Statistical analysis Demographic and clinical characteristics were compared between frailty groups using one-way analysis of variance (ANOVA) for continuous variables and Pearson’s chi-squared test for categorical variables. One-way ANOVA was used to compare the frailty group scores on physical performance tests and gait parameters derived from sensor data. The presence of overall statistically significant results in the ANOVA was followed with post-hoc Tukey analysis to identify significant pairwise associations. Effect sizes were calculated as Cohen’s d. The guidelines ( 34 ) for interpreting this value are: 0.02 = small effect, 0.5 = moderate effect, and 0.8 = large effect. Multinomial logistic regression ( 35 ), with the robust group as the reference, was then used to investigate the gait parameters that discriminate the three frailty levels. The dependent variable was frailty, modelled as two indicator variables of prefrail and frail referenced to the category of robust. The independent variables were TUG time, DGI score, and eight different sensor-based gait parameters. The independent variables assessed had Cohen’s d effect sizes ≥ 0.8 for both prefrail versus robust and frail versus robust. Each of the independent variables was fitted in a separate univariate logistic regression model for a total of ten models. Each model estimated the odds ratios for prefrail relative to robust and frail relative to robust. Linear regression diagnostics were performed to evaluate multicollinearity and normality. There were no major deviations from normality and multicollinearity. We used receiver operating characteristic (ROC) curves to calculate the area under curve (AUC) to estimate the predictive validity of each parameter. The cut-off values were calculated based on the Youden index. Sensitivity and specificity were calculated based on the cut-off values. All analysis was performed using IBM SPSS for Windows software, version 20.0. Statistical significance was set at a p value less than 0.05. Results Frailty was identified in 37 (28%) participants, prefrailty in 66 (50%), and no criteria were found in 30 (23%) participants. Demographic and clinical characteristics are shown in Table 1 . Table 1 Demographic and clinical characteristics. Characteristic Total sample (n = 133) Robust (n = 30) Prefrail (n = 66) Frail (n = 37) p value Age, mean ± SD 75.1 ± 8 73 ± 6.3 73.9 ± 8.5 78.9 ± 7.3 0.002 Women, n (%) 86 (67.7) 20 (83.3) 41 (62.1) 25 (67.6) 0.163 BMI, mean ± SD 27.6 ± 5.8 28.9 ± 5.6 27.7 ± 5.7 26.5 ± 6.1 0.244 Right calf circumference, cm, mean ± SD 35.1 ± 69.2 38.3 ± 38.4 34.3 ± 84.5 33.7 ± 47.4 0.012 Number of comorbidities, mean ± SD 3.1 ± 1.7 2.9 ± 2 3 ± 1.6 3.5 ± 1.8 0.210 Number of medications, mean ± SD 3.4 ± 2.4 3.7 ± 2.1 3 ± 2.2 4.1 ± 2.8 0.078 MMSE score, mean ± SD 27.5 ± 2.2 28.1 ± 1.9 27.6 ± 1.8 26.6 ± 2.6 0.068 Physical activity scale for elderly score, mean ± SD 97.9 ± 52.7 109.9 ± 31.3 106.6 ± 61 72.9 ± 42.3 0.002 History of falls in previous 12 months, n (%) 60 (47.6) 5 (20.8) 33 (50.8) 22 (59.5) 0.010 History of falls in previous 3 months, n (%) 28 (22.2) 3 (12.5) 13 (20.0) 12 (32.4) 0.155 FES-I score, mean ± SD 22 ± 7.4 17.6 ± 3.3 23.3 ± 7.3 23.5 ± 8.7 < 0.001 Reported fear of falling, n (%) 73 (56.2) 9 (9.0) 43 (66.2) 21 (60.0) 0.004 Frailty criteria, n (%) Slow gait velocity Low physical activity Low hand grip Weight loss Exhaustion 89 (70.1) 39 (30.7) 26 (20.5) 25 (19.7) 52 (40.9) 0 0 0 0 0 54 (81.8) 22 (33.3) 4 (6.1) 4 (6.1) 19 (28.8) 35 (94.6) 17 (45.9) 22 (59.5) 21 (56.8) 33 (89.2) < 0.001 0.001 < 0.001 < 0.001 < 0.001 Values are presented as means ± SD or n (%). SD = standard deviation. BMI = body mass index. MMSE = Mini-Mental State Examination. FES-I = Falls Efficacy Scale-International. Statistically significant values are highlighted. We did not find any significant differences in gender, BMI, comorbidities, or number of medications used between these three groups. Frail participants were significant older, they had a smaller calf circumference, had a higher proportion of fallers, and higher FES-I scores, while prefrail subjects had a higher fear of falling prevalence than in other groups. Compared to prefrail individuals, frail individuals had a significantly higher prevalence of weak hand grip strength and weight loss criteria. The comparison of scores of physical performance tests and sensor-derived gait parameters between three frailty groups is presented in Table 2 . Table 2 Results of the gait assessment stratified by frailty status. Variable Total sample (n = 33) Robust (n = 30) Prefrail (n = 66) Frail (n = 37) p value a Effect size b R vs. P P vs. F R vs. F R vs. P P vs. F R vs. F TUG, s 12.34 ± 5.03 7.68 ± 1.86 12.71 ± 4.56 15.56 ± 4.87 < 0.001 0.004 < 0.001 - 1.28 -0.61 - 2.07 Dynamic gait index, score 16.25 ± 4.04 18.90 ± 3.63 16.12 ± 3.52 14.32 ± 4.15 0.003 0.053 < 0.001 0.78 0.48 1.16 Gait speed, m/s 0.68 ± 0.22 0.98 ± 0.17 0.63 ± 0.13 0.52 ± 0.13 < 0.001 < 0.001 < 0.001 2.40 0.86 3.14 Stride time, s 1.31 ± 0.24 1.06 ± 0.16 1.35 ± 0.16 1.46 ± 0.25 < 0.001 0.010 < 0.001 -1.79 -0.59 -1.89 Swing phase time, s 0.49 ± 0,08 0.44 ± 0.06 0.51 ± 0.08 0.50 ± 0.09 < 0.001 0.952 0.003 -0.92 0.06 -0.84 Stance phase time, s 0.83 ± 0.19 0.63 ± 0.11 0.84 ± 0.13 0.96 ± 0.21 < 0.001 0.001 < 0.001 -1.77 -0.74 -1.96 Swing phase, % 37.58 ± 4.97 41.29 ± 3.35 37.59 ± 4.40 34.53 ± 5.05 0.001 0.003 < 0.001 0.90 0.66 1.55 Stance phase, % 62.42 ± 4.97 58.71 ± 3.35 62.40 ± 4.40 65.47 ± 5.05 0.001 0.003 < 0.001 -0.90 - 0.66 -1.55 Double support time, s 0.15 ± 0.08 0.08 ± 0.04 0.15 ± 0.07 0.20 ± 0.09 < 0.001 < 0.001 < 0.001 -1.12 -0.74 -1.83 Cadence, steps per min 95.38 ± 17.29 117.17 ± 13.18 91.35 ± 11.92 84.89 ± 12.71 < 0.001 0.033 < 0.001 2.09 0.53 2.50 Physical performance tests scores and gait parameters (mean ± SD) derived from sensor data for different frailty status groups are presented. R = robust, P = prefrail, F = frail. TUG = timed up and go test. Statistically significant values are highlighted. a : One-way ANOVA, followed with post-hoc Tukey analysis to identify significant pairwise associations. b : Effect sizes have been calculated as Cohen’s d. All the comparisons revealed statistically significant differences, except the DGI score and swing phase time between prefrail and frail subjects. The discriminatory power of all variables was higher for robust versus prefrail and robust versus frail subjects (d = 0.78–3.14) compared to prefrail versus frail subjects (d = 0.06–0.86). Gait velocity best discriminated robust versus frail participants (d = 3.14), whereas the second best discriminator was cadence (d = 2.50). When comparing the means of stride time, stance phase and swing phase time on the left and right leg, we obtained identical results, so only the parameters from the right leg were used for this and further analyses. The results of multinomial logistic regression are presented in Table 3 . Table 3 The parameters for discriminating three frailty levels (robust, prefrail, and frail). Variable Prefrail vs. robust Frail vs. robust OR 95% CI p value OR 95% CI p value TUG time, s 2.36 1.68–3.31 < 0.001 2.67 1.89–3.78 < 0.001 Dynamic gait index, score 0.80 0.70–0.92 0.001 0.71 0.60–0.83 < 0.001 Gait speed, cm/s 0.93 0.90–0.95 < 0.001 0.92 0.89–0.95 < 0.001 Stride time, ms 1.006 1.003–1.009 < 0.001 1.006 1.003–1.009 < 0.001 Swing phase time, ms 1.007 1.001–1.013 0.028 1.008 1.001–1.015 0.024 Stance phase time, ms 1.009 1.005–1.013 < 0.001 1.008 1.004–1.012 < 0.001 Swing phase,% 0.80 0.71–0.91 0.001 0.69 0.60–0.90 < 0.001 Stance phase,% 1.24 1.10–1.41 0.001 1.44 1.25–1.67 < 0.001 Double support time, ms 1.02 1.01–1.03 < 0.001 1.01 1.01–1.02 0.002 Cadence, steps per min 0.87 0.83–0.92 < 0.001 0.83 0.78–0.89 < 0.001 Multinomial logistic regression with the robust group as reference. Statistically significant values highlighted. OR = odds ratio, CI = confidence interval. TUG - timed up and go test. All the evaluated variables were able to discriminate between prefrail and robust and frail and robust levels with statistically significant differences. TUG time, stride, stance time in milliseconds, percentage of gait cycle, and double support time increased the odds of being prefrail versus robust and frail versus robust, whereas a higher DGI score, gait velocity, and cadence decreased the odds. Table 4 shows the sensitivity and specificity of different gait parameters when used to identify the frail and prefrail populations and their cut-offs. Table 4 The most sensitive parameters for discriminating frail from prefrail or robust and prefrail or frail from robust. Frail vs. prefrail or robust Prefrail or frail vs. robust Variables AUC Cut-off value a Sensitivity (%) Specificity (%) AUC Cut-off value a Sensitivity (%) Specificity (%) TUG test time, s 0.790 11.60 86.1 65.6 0.929 9.27 89.2 86.7 Dynamic gait index, score 0.675 15.00 54.1 75.0 0.735 19.0 73.8 56.7 Ch. gait speed, m/s 0.801 0.59 83.8 68.8 0.969 0.74 91.3 90.0 S. gait speed, m/s 0.810 0.60 78.4 75.0 0.958 0.82 94.2 86.7 Stride time, s 0.740 1.27 91.9 51.0 0.915 1.19 90.3 86.7 Stance phase time, s 0.773 0.80 83.8 62.5 0.923 0.68 96.1 73.3 Swing phase time, s 0.569 0.48 59.5 57.3 0.759 0.48 58.3 86.7 Stance phase,% 0.749 63.15 75.7 68.8 0.788 63.27 53.4 96.7 Swing phase,% 0.749 36.85 75.7 68.8 0.790 36.73 53.4 96.7 Double support time, s 0.778 0.16 70.3 76.0 0.858 0.14 62.1 96.7 Cadence, steps per min 0.724 99.54 94.6 44.8 0.930 101.22 84.4 90.0 AUC = area under curve. TUG = timed up and go test. Ch. gait speed measured by a chronometer. S. gait speed derived from sensor data. a : Cut-off value based on the Youden Index. All the variables had higher validity to separate prefrail or frail from robust (AUC = 0.735–0.969) than frail from prefrail or robust (AUC = 0.675–0.810). Among gait parameters, other than gait speed, stride time (AUC = 0.915), stance phase time (AUC = 0.923), and cadence (AUC = 0.930) were the best discriminators to separate frail or prefrail from robust. Other gait parameters, such as stance phase, normalized in percent of gait cycle, and double support time had sufficient validity to separate prefrail or frail from robust, although both had poor sensitivity (53.4% and 62.1%). Among the physical performance tests, TUG test time (AUC = 0.929) emerged as a sensitive parameter to discriminate between prefrail or frail and robust. The predictive validity of the dynamic gait index (AUC = 0.735) was inferior when compared to the other variables. Discussion This study examined the associations between many gait parameters, derived from wearable sensors, and frailty levels and identified which parameters can differentiate between different levels of frailty. Gait speed was the most sensitive parameter for the identification of frailty. It should be noted that the Fried frailty criteria includes slow gait speed ( 2 ), which explains the high discriminative power of this variable. In the present study, the discriminative ability of gait speed can be used as a reference for comparison with other parameters analysed. We found that the stride time was significantly longer in frail and prefrail compared to robust. This finding is in accordance with the results reported by Montero-Odasso et al. ( 16 ), which revealed the mean ± SD stride time in seconds: 1.2 ± 0.1 in frail, 1.1 ± 0.1 in prefrail, and 1.0 ± 0.1 in non-frail (robust). We suggest that stride time is sensitive for discriminating prefrail or frail from robust and determined a value of stride time for identifying individuals with prefrailty or frailty of 1.19 s, with sufficient sensitivity and specificity. We also found that stride time had a good sensitivity to separate frail from prefrail or robust; however, the specificity and area under the ROC curve were quite modest, thus potentially limiting its clinical usefulness. Other gait parameters that significantly differed between frailty levels were stance phase and swing phase. In comparisons of the average duration of the stance and swing phase time, the mean percentage of the gait cycle spent in the stance and swing phases was difficult to interpret due to the lack of sufficient data reported in the literature. We found a sufficiently sensitive and specific stance time estimate to separate prefrail or frail from robust, i.e. 0.68 s. The values for the sensitivity, specificity, and AUC of swing phase and stance phase percentage were quite modest. We found that prefrail and frail subjects had increased double support time. Schwenk et al. ( 20 ) identified double support time as one of the most sensitive discriminators between these three frailty levels. However, in our study, double support time had modest validity to discriminate prefrail or frail from robust (AUC = 0.858), poor sensitivity (62.1%), and high specificity (96.7%). This study revealed that cadence was reduced in frail and prefrail compared to robust; this result is consistent with the results of Montero-Odasso et al. ( 16 ), showing variations in mean ± SD cadence (steps/min) in frail (101 ± 21), prefrail (106 ± 9), and non-frail (robust) (118 ± 6). Our study showed that cadence is a sensitive and specific gait parameter that discriminates prefrail or frail from robust. The estimated cut-off was 101 steps per minute. Our study has limitations. The sample size was relatively small. The proposed gait parameters derived from our analysis must be validated in a larger sample to evaluate their true predictive potential. Other limitation is related to the gait assessment technique. During the 4-meter walk test, additional sections for the acceleration and deceleration phases were not marked. During these phases, the walking speed was usually slower, which affected the measurement of the 4-meter walking speed and led to errors. Our study is one of the first to provide thresholds for sensor-derived gait indicators to determine frailty and prefrailty. The hypotheses of the study were confirmed. The sensor technology we uses is quite complex in terms of the sophisticated data processing require due to larger number of sensors we used as compared to a study where a single wearable sensor was able to extract similar gait parameters ( 21 ). However, the application of multiple sensors allowed us determine not only temporal but also spatial biomechanical parameters like joint amplitudes during gait, which in turn may provide more quantitative parameters that may be sensitive to distinguishing frailty levels. This information could be useful for clinicians, so it would be useful to conduct further studies with these parameters. Further studies should also include longitudinal follow-up to determine which changes in gait parameters predict frailty over the time. Conclusions The results of our study show that gait analysis using wearable sensors can discriminate between frailty levels. We were able to identify several gait indicators that distinguish frail or prefrail from robust with sufficient sensitivity and specificity. We found that frailty is associated not only with slow walking speed, but also with longer stride time, stance phase, double support time, and reduced cadence. If improved and adapted for everyday use, gait assessment technologies could contribute to frailty screening and monitoring. Abbreviations ROC Receiver operating characteristic AUC Area under curve EU European Union MMSE Mini-Mental State Examination BMI Body mass index FES-I Falls Efficacy Scale-International CES-D Centre for Epidemiologic Studies depression scale TUG Timed up and go DGI Dynamic gait index SD Standard deviation ANOVA Analysis of variance Declarations Availability of data and materials The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Ethics approval and consent to participate The study was approved by the Vilnius Regional Ethics Board for Biomedical Research. Before participation, written informed consent based on the principles described in the Declaration of Helsinki was obtained from all participants. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding The authors received no funding for this work. Author contributions AA, VA, MT, KD, and JG were involved in the conception and design of the study. AA drafted the manuscript, with support from VS and DV. MT, AM, KD, VA, and JG offered insights on manuscript revisions. AM, LP, and JG contributed to data interpretation. VS, DV, and LP contributed to data collection and acquisition. All authors read and critically revised the manuscript and approved the final manuscript. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. Corresponding author Correspondence to Andrius Apsega. Acknowledgements Not applicable. References O’Caoimh R, Galluzzo L, Rodríguez-Laso Á, Van der Heyden J, Ranhoff AH, Lamprini-Koula M, et al. 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Variation in thresholds for reporting mobility disability between national population subgroups and studies. J Gerontol Ser A. 2004;59(12):1295-303. Tudor-Locke CE, Myers AM. Challenges and opportunities for measuring physical activity in sedentary adults. Sports Med Auckl NZ. 2001;31(2):91-100. Rothman MD, Leo‐Summers L, Gill TM. Prognostic significance of potential frailty criteria. J Am Geriatr Soc. 2008;56(12):2211-6. Lee L, Patel T, Costa A, Bryce E, Hillier LM, Slonim K, et al. Screening for frailty in primary care: Accuracy of gait speed and hand-grip strength. Can Fam Physician. 2017;63(1):e51-7. Schwenk M, Howe C, Saleh A, Mohler J, Grewal G, Armstrong D, et al. Frailty and technology: a systematic review of gait analysis in those with frailty. Gerontology. 2014;60(1):79-89. Montero-Odasso M, Muir SW, Hall M, Doherty TJ, Kloseck M, Beauchet O, et al. Gait variability is associated with frailty in community-dwelling older adults. J Gerontol Ser A. 2011;66A(5):568-76. Beauchet O, Dubost V, Herrmann F, Rabilloud M, Gonthier R, Kressig RW. Relationship between dual-task related gait changes and intrinsic risk factors for falls among transitional frail older adults. Aging Clin Exp Res. 2005;17(4):270-5. Kressig RW, Gregor RJ, Oliver A, Waddell D, Smith W, O’Grady M, et al. Temporal and spatial features of gait in older adults transitioning to frailty. Gait Posture. 2004;20(1):30-5. Verghese J, Holtzer R, Lipton RB, Wang C. Mobility stress test approach to predicting frailty, disability, and mortality in high-functioning older adults. J Am Geriatr Soc. 2012;60(10):1901-5. Schwenk M, Mohler J, Wendel C, D’Huyvetter K, Fain M, Taylor-Piliae R, et al. Wearable sensor-based in-home assessment of gait, balance, and physical activity for discrimination of frailty status: baseline results of the Arizona Frailty Cohort Study. Gerontology. 2015;61(3):258-67. Pradeep Kumar D, Toosizadeh N, Mohler J, Ehsani H, Mannier C, Laksari K. Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment. BMC Geriatr. 2020;20(1):164. Dasenbrock L, Heinks A, Schwenk M, Bauer JM. Technology-based measurements for screening, monitoring and preventing frailty. Z Für Gerontol Geriatr. 2016;49(7):581-95. Thiede R, Toosizadeh N, Mills JL, Zaky M, Mohler J, Najafi B. Gait and balance assessments as early indicators of frailty in patients with known peripheral artery disease. Clin Biomech. 2016;32:1-7. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189-98. General Assembly of the World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. J Am Coll Dent. 2014;81(3):14-8. Kempen GIJM, Yardley L, Van Haastregt JCM, Zijlstra GAR, Beyer N, Hauer K, et al. The Short FES-I: a shortened version of the falls efficacy scale-international to assess fear of falling. Age Ageing. 2008;37(1):45-50. Orme JG, Reis J, Herz EJ. Factorial and discriminant validity of the center for epidemiological studies depression (CES-D) scale. J Clin Psychol. 1986;42(1):28-33. Washburn RA, Smith KW, Jette AM, Janney CA. The physical activity scale for the elderly (PASE): Development and evaluation. J Clin Epidemiol. 1993;46(2):153-62. Podsiadlo D, Richardson S. The timed “up & go”: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. 1991;39(2):142-8. Savva GM, Donoghue OA, Horgan F, O’Regan C, Cronin H, Kenny RA. Using timed up-and-go to identify frail members of the older population. J Gerontol Ser A. 2013;68(4):441-6. Linden V, W D. Shumway-Cook A, Wollacott MH. Motor Control: Theory and Practical Applications. Baltimore, MD: Williams and Wilkins Inc.; 1995. Hardcover, 475 pp. 43. pre. 1996;20(1):64-65. Greene BR, McGrath D, O’Neill R, O’Donovan KJ, Burns A, Caulfield B. An adaptive gyroscope-based algorithm for temporal gait analysis. Med Biol Eng Comput. 2010;48(12):1251-60. Rueterbories J, Spaich EG, Andersen OK. Gait event detection for use in FES rehabilitation by radial and tangential foot accelerations. Med Eng Phys. 2014;36(4):502-8. Cohen J. Statistical Power Analysis for the Behavioral Sciences. Academic Press; 2013. 459 p. Böhning D. Multinomial logistic regression algorithm. Ann Inst Stat Math. 1992;44(1):197-200. Cite Share Download PDF Status: Published Journal Publication published 26 Nov, 2020 Read the published version in Applied Sciences → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-33024","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":661253,"identity":"b3660de1-09c5-4b2c-9459-82422344f9ce","order_by":0,"name":"Andrius Apsega","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYHACNhAhByIOPCBFizFYSwIpWhIbQCRRWvhntz978OGXXfr8sMMPgbbYyek2ENAiceeMueHMvuTcjbfTDIBako3NDhCy5kYOmzRvD3PuxtkJIC0HErcR0iJ/I/2Z9N+e+nTD2ekfiNNicCPBTJrhx+EEeekcIm0xvHPGTLK34bjhBumcggMJBkT4Re52+zOJH3+q5eVnp2/+8KHCTo6w9yWAmLEN6EKwSgNCymFaGP4Aw6GBGNWjYBSMglEwIgEA1RhJUPl3LdAAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9109-2956","institution":"Vilniaus Universitetas","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Andrius","middleName":"","lastName":"Apsega","suffix":""},{"id":661254,"identity":"eb986e97-8a24-4f11-b218-5d63f807f50c","order_by":1,"name":"Liudvikas Petrauskas","email":"","orcid":"","institution":"Vilniaus Gedimino Technikos 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Universitetas","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Viktorija","middleName":"","lastName":"Sevcenko","suffix":""},{"id":661258,"identity":"8c803bbf-4db1-47e5-a8fb-e96303349656","order_by":5,"name":"Asta Mastaviciute","email":"","orcid":"","institution":"Vilniaus Universitetas","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Asta","middleName":"","lastName":"Mastaviciute","suffix":""},{"id":661259,"identity":"2680c137-bb18-4b7c-97b1-5b2dc01073c9","order_by":6,"name":"Dovydas Vitkus","email":"","orcid":"","institution":"Vilniaus Universitetas","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dovydas","middleName":"","lastName":"Vitkus","suffix":""},{"id":661260,"identity":"9a6f0b9c-dae1-4278-9039-0d72740e6b42","order_by":7,"name":"Marija Tamulaitiene","email":"","orcid":"","institution":"Vilniaus Universitetas","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marija","middleName":"","lastName":"Tamulaitiene","suffix":""},{"id":661261,"identity":"69145876-b452-4a9b-bfa7-a6c46115acd0","order_by":8,"name":"Julius Griskevicius","email":"","orcid":"","institution":"Vilniaus Gedimino Technikos Universitetas","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Julius","middleName":"","lastName":"Griskevicius","suffix":""}],"badges":[],"createdAt":"2020-06-02 03:43:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-33024/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-33024/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.3390/app10238451","type":"published","date":"2020-11-26T19:05:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":1328794,"identity":"f17b1046-8a30-4493-b407-2dad830ab6b9","added_by":"auto","created_at":"2020-06-15 14:51:43","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22191,"visible":true,"origin":"","legend":"Wireless sensors fixed on the lower limb segments","description":"","filename":"Fig1.JPG","url":"https://assets-eu.researchsquare.com/files/rs-33024/v1/Fig1.JPG"},{"id":13541683,"identity":"01130855-db0c-40c2-add8-0b7d4e921267","added_by":"auto","created_at":"2021-09-17 01:52:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":470736,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-33024/v1/ddaa153f-b8ab-4002-af21-bdd6b61508f0.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eWearable Sensors Technology as a Tool for Discriminating Frailty Levels During Instrumented Gait Analysis\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eAccording to the 2015 EU Ageing Report, the percentage of citizens aged over 65\u0026nbsp;years is predicted to rise from 18\u0026ndash;28% by 2060. One of the greatest challenges facing the healthcare of the aging population is frailty. In Europe, 15% of older people aged 65 and over are considered as frail (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Frailty is defined as a syndrome of decreased reserve and resistance to stressors, resulting from cumulative declines across multiple physiologic systems and causing vulnerability to adverse outcomes, such as worsening mobility, disability, incident falls, hospitalizations, and mortality (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). There is much potential for frailty to be reversed, particularly in its early stages (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). For that reason, the early identification and management of frailty is an important priority for both healthcare providers and healthcare policy makers (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the most accepted definitions of frailty is the classification proposed by Fried et al. using five criteria: weight loss, exhaustion, inactivity, slow gait speed, and weakness (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). However, its use may have limited feasibility and reliability in a routine care setting (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The criteria of weight loss, exhaustion, slowness, and low energy expenditure are usually self-reported measures and may be prone to bias (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). An objective frailty screening tool may be more appropriate for routine assessment.\u003c/p\u003e \u003cp\u003eSlow gait speed has been reported as the most easily identifiable feature of physical frailty (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Moreover, slow gait speed is a good predictor of future physical disability, falls and fractures, requirement for a caregiver, hospitalizations, and death (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Gait speed evaluated by the 4-meter walking test could be used for frailty screening (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Regarding recent studies, other temporal-spatial gait characteristics may have a strong association with frailty (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). A systematic review reported that frailty is associated with some temporal-spatial gait parameters beyond speed, including high stride time variability, reduced step length, and increased double support time (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). However, most studies are conducted in the laboratory environment using camera systems (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), force platforms (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), or computerized walkways (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) that, due to the high costs related to acquisition and application in everyday clinical practice, are not available in every healthcare institution. Therefore, more accessible alternatives could be used, such as wearable sensors, which are easier to use, more practical, and require fewer resources. There is growing scientific evidence that gait assessment using wearable sensors could be used for prefrailty and frailty screening (\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Simple and easy-to-use sensors (such as those incorporated into a smartphone) could be used to investigate gait at home and offer advantages in frailty evaluations where the full application or interpretation of Fried criteria is impracticable. There is currently a lack of data regarding which gait indicators are sensitive and specific for determining frailty and the appropriate thresholds for distinguishing between prefrailty and frailty.\u003c/p\u003e \u003cp\u003eThe purpose of this study was to examine the ability of wearable sensor-based assessments of gait to discriminate between frailty levels (robust, prefrail, and frail). Firstly, we sought to determine which sensor-derived gait parameters are able to discriminate between the three frailty levels. Secondly, we aimed to determine the cut-offs of the most sensitive gait parameters that separated the frailty levels. We hypothesized that we could separate the frailty groups (robust, prefrail, and frail) using a sensor-based assessment of gait. We also hypothesized that frailty is associated not only with slow gait speed, but also with longer stride time, stance phase, and double support time.\u003c/p\u003e "},{"header":"Methods","content":" \u003cp\u003eStudy population\u003c/p\u003e \u003cp\u003eParticipants in this cross-sectional study were recruited from two secondary health care institutions and from the population who lived independently in community. A convenience sample of older adults was utilized for this study. All participants were community-dwelling adults and were eligible to participate if they were aged 60 and older and reported being able to ambulate 12 meters without an assistive device. Exclusion criteria were a Lithuanian version of the Mini-Mental State Examination (MMSE) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) score\u0026thinsp;\u0026lt;\u0026thinsp;21, Parkinson\u0026rsquo;s disease, recent stroke, terminal illness, or unwillingness to participate. Eligible subjects provided signed written informed consent based on the principles expressed in the Declaration of Helsinki (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Ethical approval was obtained from the Vilnius Regional Ethics Board for Biomedical Research. Data collection occurred between May 2019 and February 2020. Initially, 165 subjects were invited, of whom 7 met the exclusion criteria (Parkinson\u0026rsquo;s disease\u0026thinsp;=\u0026thinsp;2, recent stroke\u0026thinsp;=\u0026thinsp;5), and 25 were rejected due to incomplete measurement. Data from 133 participants (86 women and 46 man) with an average age 75.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u0026nbsp;years were analysed.\u003c/p\u003e \u003cp\u003eData collection\u003c/p\u003e \u003cp\u003eThe research team conducted face-to-face interviews using structured questionnaires to record the required characteristics, such as age, sex, years of formal education, self-reported chronic diseases, number of prescribed and over-the-counter medications, self-reported history of falls in the previous 12 months, and use of an assistive device (yes/no). Height was obtained using a tape measure, weight was measured using a bathroom scale (Clatronic PW 3368, Clatronic\u0026reg;, Kempen, Germany), and BMI was calculated based on height and weight. Interviewer-administered questionnaires included MMSE and the Falls Efficacy Scale-International (FES-I) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAssessment of frailty criteria\u003c/p\u003e \u003cp\u003eFrailty was assessed using the five components proposed by Fried et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Weight loss was determined by self-reported unintentional weight loss of \u0026gt;\u0026thinsp;4.54\u0026nbsp;kg over the past year. Weakness was evaluated by a grip strength measurement using a hydraulic hand dynamometer (Jamar\u0026reg;, Sammons PrestonRolyan, Bolingbrook, IL, USA). Three measures were performed, and the arithmetic mean was used to identify this criterion. Weakness was defined according to sex and the BMI cut-offs used by Fried et al. Exhaustion was evaluated by two statements of the Centre for Epidemiologic Studies Depression Scale(CES-D) questionnaire (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e): \u0026lsquo;I felt everything I did was an effort\u0026rsquo; and \u0026lsquo;I could not get going\u0026rsquo;. The frequency of \u0026lsquo;occasionally\u0026rsquo; or \u0026lsquo;most of the time\u0026rsquo; as a reply to either of these statements was considered as an indication of exhaustion. Slowness was defined by a walking speed of 4\u0026nbsp;m distance at the usual pace measured by a stopwatch and stratified by gender and height using the cut-offs defined by Fried et al. Low physical activity was determined using the Physical Activity Scale for Elderly (PASE) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). PASE scores less than 64 for men and less than 52 for woman were used to indicate a positive response of low physical activity.\u003c/p\u003e \u003cp\u003eParticipants were scored one point for each criterion found, totalling a score that could range from 0 to 5. Frailty level was categorized following Fried et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e): robust\u0026thinsp;=\u0026thinsp;no criteria; prefrail\u0026thinsp;=\u0026thinsp;one or two criteria, and frail\u0026thinsp;=\u0026thinsp;three or more criteria.\u003c/p\u003e \u003cp\u003ePhysical performance tests\u003c/p\u003e \u003cp\u003eThe timed up and go test (TUG) (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) is widely used for the identification of older adults at a high risk of falling. Savva et al. (2013) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) proposed that the TUG test is a sensitive and specific measure of frailty. In our study, participants were told to sit on a chair (seat height, 46\u0026nbsp;cm). Participants were then instructed to stand, to walk at their normal pace for a distance of 3 meters, to turn at the endpoint, to walk back the same distance, and sit on a chair. Total time starting from standing up to full sitting down was recorded. The time of one trial was taken as the TUG test score.\u003c/p\u003e \u003cp\u003eThe participants were then evaluated using the dynamic gait index (DGI) (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) for the assessment of the gait in response to changing tasks, such as turning the head while walking, stepping over the obstacle, climbing the stairs, and others. This index consists of eight tasks. Each task is scored from 0 to 3 points, with 0 being the worst and 3 being the best performance. The maximum score is 24 points. A result less than 19 points indicates impaired gait and a risk of falling.\u003c/p\u003e \u003cp\u003eSensor-Based assessment of gait\u003c/p\u003e \u003cp\u003eWe used a total of six wireless inertial sensors (Shimmer Research, Dublin, Ireland) attached by straps on the thighs, shins, and feet (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEach sensor includes triaxial accelerometer, gyroscope, and magnetometer and is able to measure linear acceleration, angular velocity, and magnetic heading in three dimensions. The data from sensors was acquired via a Bluetooth wireless connection at a sampling frequency of 256\u0026nbsp;Hz. Participant walked a distance of 4\u0026nbsp;m (13 feet) at the self-selected usual pace. Data from three trials was used. From all the data obtained from the inertial sensors, we selected shank angular velocity and foot linear acceleration to determine heel-strike and toe-off characteristic points. This data was filtered using a Butterworth second order low pass filter with an 8\u0026nbsp;Hz cut-off frequency and an additional least square method 25th order filter with a 10\u0026nbsp;Hz cut-off frequency for composite foot acceleration data. A gait event detection algorithm was made by picking toe-off points from the angular velocity data (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) and heel strike points from composite foot acceleration data (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Gait parameters were calculated based on these gait events. The following quantitative gait parameters were calculated: stance phase time, swing phase time, stride time, on right and left leg accordingly, double support time, and cadence (steps/min).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDemographic and clinical characteristics were compared between frailty groups using one-way analysis of variance (ANOVA) for continuous variables and Pearson\u0026rsquo;s chi-squared test for categorical variables. One-way ANOVA was used to compare the frailty group scores on physical performance tests and gait parameters derived from sensor data. The presence of overall statistically significant results in the ANOVA was followed with post-hoc Tukey analysis to identify significant pairwise associations. Effect sizes were calculated as Cohen\u0026rsquo;s d. The guidelines (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) for interpreting this value are: 0.02\u0026thinsp;=\u0026thinsp;small effect, 0.5\u0026thinsp;=\u0026thinsp;moderate effect, and 0.8\u0026thinsp;=\u0026thinsp;large effect.\u003c/p\u003e \u003cp\u003eMultinomial logistic regression (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), with the robust group as the reference, was then used to investigate the gait parameters that discriminate the three frailty levels. The dependent variable was frailty, modelled as two indicator variables of prefrail and frail referenced to the category of robust. The independent variables were TUG time, DGI score, and eight different sensor-based gait parameters. The independent variables assessed had Cohen\u0026rsquo;s d effect sizes\u0026thinsp;\u0026ge;\u0026thinsp;0.8 for both prefrail versus robust and frail versus robust. Each of the independent variables was fitted in a separate univariate logistic regression model for a total of ten models. Each model estimated the odds ratios for prefrail relative to robust and frail relative to robust. Linear regression diagnostics were performed to evaluate multicollinearity and normality. There were no major deviations from normality and multicollinearity.\u003c/p\u003e \u003cp\u003eWe used receiver operating characteristic (ROC) curves to calculate the area under curve (AUC) to estimate the predictive validity of each parameter. The cut-off values were calculated based on the Youden index. Sensitivity and specificity were calculated based on the cut-off values. All analysis was performed using IBM SPSS for Windows software, version 20.0. Statistical significance was set at a \u003cem\u003ep\u003c/em\u003e value less than 0.05.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cp\u003eFrailty was identified in 37 (28%) participants, prefrailty in 66 (50%), and no criteria were found in 30 (23%) participants. Demographic and clinical characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal sample (n\u0026thinsp;=\u0026thinsp;133)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobust\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrefrail\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFrail\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (67.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (67.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight calf circumference, cm, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.1\u0026thinsp;\u0026plusmn;\u0026thinsp;69.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.3\u0026thinsp;\u0026plusmn;\u0026thinsp;38.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.3\u0026thinsp;\u0026plusmn;\u0026thinsp;84.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.7\u0026thinsp;\u0026plusmn;\u0026thinsp;47.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of comorbidities, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of medications, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE score, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity scale for elderly score, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.9\u0026thinsp;\u0026plusmn;\u0026thinsp;52.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109.9\u0026thinsp;\u0026plusmn;\u0026thinsp;31.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106.6\u0026thinsp;\u0026plusmn;\u0026thinsp;61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.9\u0026thinsp;\u0026plusmn;\u0026thinsp;42.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of falls in previous 12 months, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of falls in previous 3 months, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFES-I score, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReported fear of falling, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (66.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrailty criteria, n (%)\u003c/p\u003e \u003cp\u003eSlow gait velocity\u003c/p\u003e \u003cp\u003eLow physical activity\u003c/p\u003e \u003cp\u003eLow hand grip\u003c/p\u003e \u003cp\u003eWeight loss\u003c/p\u003e \u003cp\u003eExhaustion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (70.1)\u003c/p\u003e \u003cp\u003e39 (30.7)\u003c/p\u003e \u003cp\u003e26 (20.5)\u003c/p\u003e \u003cp\u003e25 (19.7)\u003c/p\u003e \u003cp\u003e52 (40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (81.8)\u003c/p\u003e \u003cp\u003e22 (33.3)\u003c/p\u003e \u003cp\u003e4 (6.1)\u003c/p\u003e \u003cp\u003e4 (6.1)\u003c/p\u003e \u003cp\u003e19 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (94.6)\u003c/p\u003e \u003cp\u003e17 (45.9)\u003c/p\u003e \u003cp\u003e22 (59.5)\u003c/p\u003e \u003cp\u003e21 (56.8)\u003c/p\u003e \u003cp\u003e33 (89.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eValues are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n (%). SD\u0026thinsp;=\u0026thinsp;standard deviation. BMI\u0026thinsp;=\u0026thinsp;body mass index. MMSE\u0026thinsp;=\u0026thinsp;Mini-Mental State Examination. FES-I\u0026thinsp;=\u0026thinsp;Falls Efficacy Scale-International. Statistically significant values are highlighted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eWe did not find any significant differences in gender, BMI, comorbidities, or number of medications used between these three groups. Frail participants were significant older, they had a smaller calf circumference, had a higher proportion of fallers, and higher FES-I scores, while prefrail subjects had a higher fear of falling prevalence than in other groups. Compared to prefrail individuals, frail individuals had a significantly higher prevalence of weak hand grip strength and weight loss criteria.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe comparison of scores of physical performance tests and sensor-derived gait parameters between three frailty groups is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the gait assessment stratified by frailty status.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal sample (n\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobust (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrefrail (n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFrail\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eEffect size \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR vs. P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP vs. F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR vs. F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eR vs. P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP vs. F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR vs. F\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTUG, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e12.34\u0026thinsp;\u0026plusmn;\u0026thinsp;5.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.71 \u0026plusmn;\u003c/p\u003e \u003cp\u003e4.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.56 \u0026plusmn;\u003c/p\u003e \u003cp\u003e4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003cb\u003e1.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003cb\u003e2.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDynamic gait index, score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16.25 \u0026plusmn;\u003c/p\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e18.90 \u0026plusmn;\u003c/p\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.12 \u0026plusmn;\u003c/p\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.32 \u0026plusmn;\u003c/p\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGait speed, m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.68 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.98 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.52 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e2.40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e3.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride time, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.31 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.06 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.46 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-1.79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-1.89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwing phase time, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.49 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0,08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.44 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-0.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-0.84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStance phase time, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.83 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.63 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-1.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-1.96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwing phase, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e37.58 \u0026plusmn;\u003c/p\u003e \u003cp\u003e4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e41.29 \u0026plusmn;\u003c/p\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.59 \u0026plusmn;\u003c/p\u003e \u003cp\u003e4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.53\u0026thinsp;\u0026plusmn;\u0026thinsp;5.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStance phase, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e62.42 \u0026plusmn;\u003c/p\u003e \u003cp\u003e4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e58.71 \u0026plusmn;\u003c/p\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.40 \u0026plusmn;\u003c/p\u003e \u003cp\u003e4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.47\u0026thinsp;\u0026plusmn;\u0026thinsp;5.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-0.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-1.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouble support time, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.15 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.08 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20 \u0026plusmn;\u003c/p\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-1.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-1.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCadence, steps per min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e95.38 \u0026plusmn;\u003c/p\u003e \u003cp\u003e17.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e117.17 \u0026plusmn;\u003c/p\u003e \u003cp\u003e13.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.35 \u0026plusmn;\u003c/p\u003e \u003cp\u003e11.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.89 \u0026plusmn;\u003c/p\u003e \u003cp\u003e12.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e2.09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e2.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003ePhysical performance tests scores and gait parameters (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) derived from sensor data for different frailty status groups are presented. R\u0026thinsp;=\u0026thinsp;robust, P\u0026thinsp;=\u0026thinsp;prefrail, F\u0026thinsp;=\u0026thinsp;frail. TUG\u0026thinsp;=\u0026thinsp;timed up and go test. Statistically significant values are highlighted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003ea\u003c/sup\u003e: One-way ANOVA, followed with post-hoc Tukey analysis to identify significant pairwise associations.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003eb\u003c/sup\u003e: Effect sizes have been calculated as Cohen\u0026rsquo;s d.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eAll the comparisons revealed statistically significant differences, except the DGI score and swing phase time between prefrail and frail subjects. The discriminatory power of all variables was higher for robust versus prefrail and robust versus frail subjects (d\u0026thinsp;=\u0026thinsp;0.78\u0026ndash;3.14) compared to prefrail versus frail subjects (d\u0026thinsp;=\u0026thinsp;0.06\u0026ndash;0.86). Gait velocity best discriminated robust versus frail participants (d\u0026thinsp;=\u0026thinsp;3.14), whereas the second best discriminator was cadence (d\u0026thinsp;=\u0026thinsp;2.50). When comparing the means of stride time, stance phase and swing phase time on the left and right leg, we obtained identical results, so only the parameters from the right leg were used for this and further analyses.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eThe results of multinomial logistic regression are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe parameters for discriminating three frailty levels (robust, prefrail, and frail).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePrefrail vs. robust\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eFrail vs. robust\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTUG time, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68\u0026ndash;3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.89\u0026ndash;3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDynamic gait index, score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70\u0026ndash;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60\u0026ndash;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGait speed, cm/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90\u0026ndash;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89\u0026ndash;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride time, ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.003\u0026ndash;1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.003\u0026ndash;1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwing phase time, ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.001\u0026ndash;1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.001\u0026ndash;1.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStance phase time, ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.005\u0026ndash;1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.004\u0026ndash;1.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwing phase,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71\u0026ndash;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60\u0026ndash;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStance phase,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10\u0026ndash;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.25\u0026ndash;1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouble support time, ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCadence, steps per min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83\u0026ndash;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78\u0026ndash;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eMultinomial logistic regression with the robust group as reference. Statistically significant values highlighted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eOR\u0026thinsp;=\u0026thinsp;odds ratio, CI\u0026thinsp;=\u0026thinsp;confidence interval. TUG - timed up and go test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAll the evaluated variables were able to discriminate between prefrail and robust and frail and robust levels with statistically significant differences. TUG time, stride, stance time in milliseconds, percentage of gait cycle, and double support time increased the odds of being prefrail versus robust and frail versus robust, whereas a higher DGI score, gait velocity, and cadence decreased the odds.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the sensitivity and specificity of different gait parameters when used to identify the frail and prefrail populations and their cut-offs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe most sensitive parameters for discriminating frail from prefrail or robust and prefrail or frail from robust.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eFrail vs. prefrail or robust\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003ePrefrail or frail vs. robust\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCut-off value \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCut-off value \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTUG test time, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e89.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e86.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDynamic gait index, score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e56.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCh. gait speed, m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e91.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. gait speed, m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e94.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e86.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride time, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e86.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStance phase time, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e73.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwing phase time, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e86.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStance phase,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e53.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e96.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwing phase,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e53.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e96.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouble support time, s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e96.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCadence, steps per min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e101.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e84.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eAUC\u0026thinsp;=\u0026thinsp;area under curve. TUG\u0026thinsp;=\u0026thinsp;timed up and go test. Ch. gait speed measured by a chronometer. S. gait speed derived from sensor data. \u003csup\u003ea\u003c/sup\u003e: Cut-off value based on the Youden Index.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eAll the variables had higher validity to separate prefrail or frail from robust (AUC\u0026thinsp;=\u0026thinsp;0.735\u0026ndash;0.969) than frail from prefrail or robust (AUC\u0026thinsp;=\u0026thinsp;0.675\u0026ndash;0.810). Among gait parameters, other than gait speed, stride time (AUC\u0026thinsp;=\u0026thinsp;0.915), stance phase time (AUC\u0026thinsp;=\u0026thinsp;0.923), and cadence (AUC\u0026thinsp;=\u0026thinsp;0.930) were the best discriminators to separate frail or prefrail from robust. Other gait parameters, such as stance phase, normalized in percent of gait cycle, and double support time had sufficient validity to separate prefrail or frail from robust, although both had poor sensitivity (53.4% and 62.1%). Among the physical performance tests, TUG test time (AUC\u0026thinsp;=\u0026thinsp;0.929) emerged as a sensitive parameter to discriminate between prefrail or frail and robust. The predictive validity of the dynamic gait index (AUC\u0026thinsp;=\u0026thinsp;0.735) was inferior when compared to the other variables.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"Discussion","content":" \u003cp\u003eThis study examined the associations between many gait parameters, derived from wearable sensors, and frailty levels and identified which parameters can differentiate between different levels of frailty.\u003c/p\u003e \u003cp\u003eGait speed was the most sensitive parameter for the identification of frailty. It should be noted that the Fried frailty criteria includes slow gait speed (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), which explains the high discriminative power of this variable. In the present study, the discriminative ability of gait speed can be used as a reference for comparison with other parameters analysed.\u003c/p\u003e \u003cp\u003eWe found that the stride time was significantly longer in frail and prefrail compared to robust. This finding is in accordance with the results reported by Montero-Odasso et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), which revealed the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD stride time in seconds: 1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 in frail, 1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 in prefrail, and 1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 in non-frail (robust). We suggest that stride time is sensitive for discriminating prefrail or frail from robust and determined a value of stride time for identifying individuals with prefrailty or frailty of 1.19\u0026nbsp;s, with sufficient sensitivity and specificity. We also found that stride time had a good sensitivity to separate frail from prefrail or robust; however, the specificity and area under the ROC curve were quite modest, thus potentially limiting its clinical usefulness.\u003c/p\u003e \u003cp\u003eOther gait parameters that significantly differed between frailty levels were stance phase and swing phase. In comparisons of the average duration of the stance and swing phase time, the mean percentage of the gait cycle spent in the stance and swing phases was difficult to interpret due to the lack of sufficient data reported in the literature. We found a sufficiently sensitive and specific stance time estimate to separate prefrail or frail from robust, i.e. 0.68\u0026nbsp;s. The values for the sensitivity, specificity, and AUC of swing phase and stance phase percentage were quite modest.\u003c/p\u003e \u003cp\u003eWe found that prefrail and frail subjects had increased double support time. Schwenk et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) identified double support time as one of the most sensitive discriminators between these three frailty levels. However, in our study, double support time had modest validity to discriminate prefrail or frail from robust (AUC\u0026thinsp;=\u0026thinsp;0.858), poor sensitivity (62.1%), and high specificity (96.7%).\u003c/p\u003e \u003cp\u003eThis study revealed that cadence was reduced in frail and prefrail compared to robust; this result is consistent with the results of Montero-Odasso et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), showing variations in mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD cadence (steps/min) in frail (101\u0026thinsp;\u0026plusmn;\u0026thinsp;21), prefrail (106\u0026thinsp;\u0026plusmn;\u0026thinsp;9), and non-frail (robust) (118\u0026thinsp;\u0026plusmn;\u0026thinsp;6). Our study showed that cadence is a sensitive and specific gait parameter that discriminates prefrail or frail from robust. The estimated cut-off was 101 steps per minute.\u003c/p\u003e \u003cp\u003eOur study has limitations. The sample size was relatively small. The proposed gait parameters derived from our analysis must be validated in a larger sample to evaluate their true predictive potential. Other limitation is related to the gait assessment technique. During the 4-meter walk test, additional sections for the acceleration and deceleration phases were not marked. During these phases, the walking speed was usually slower, which affected the measurement of the 4-meter walking speed and led to errors.\u003c/p\u003e \u003cp\u003eOur study is one of the first to provide thresholds for sensor-derived gait indicators to determine frailty and prefrailty. The hypotheses of the study were confirmed. The sensor technology we uses is quite complex in terms of the sophisticated data processing require due to larger number of sensors we used as compared to a study where a single wearable sensor was able to extract similar gait parameters (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). However, the application of multiple sensors allowed us determine not only temporal but also spatial biomechanical parameters like joint amplitudes during gait, which in turn may provide more quantitative parameters that may be sensitive to distinguishing frailty levels. This information could be useful for clinicians, so it would be useful to conduct further studies with these parameters. Further studies should also include longitudinal follow-up to determine which changes in gait parameters predict frailty over the time.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eThe results of our study show that gait analysis using wearable sensors can discriminate between frailty levels. We were able to identify several gait indicators that distinguish frail or prefrail from robust with sufficient sensitivity and specificity. We found that frailty is associated not only with slow walking speed, but also with longer stride time, stance phase, double support time, and reduced cadence. If improved and adapted for everyday use, gait assessment technologies could contribute to frailty screening and monitoring.\u003c/p\u003e "},{"header":"Abbreviations","content":" \u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEuropean Union\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMini-Mental State Examination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFES-I\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalls Efficacy Scale-International\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCES-D\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCentre for Epidemiologic Studies depression scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTUG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTimed up and go\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDGI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDynamic gait index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eEthics approval and consent to participate\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Vilnius Regional Ethics Board for Biomedical Research. Before participation, written informed consent based on the principles described in the Declaration of Helsinki was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConsent for publication\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCompeting interests\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no funding for this work.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAuthor contributions\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAA, VA, MT, KD, and JG were involved in the conception and design of the study. AA drafted the manuscript, with support from VS and DV. MT, AM, KD, VA, and JG\u0026nbsp;offered insights on manuscript revisions. AM, LP, and JG contributed to data interpretation. VS, DV, and LP contributed to data collection and acquisition. All authors read and critically revised the manuscript and approved the final manuscript. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCorresponding author\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Andrius Apsega.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAcknowledgements\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eO\u0026rsquo;Caoimh R, Galluzzo L, Rodr\u0026iacute;guez-Laso \u0026Aacute;, Van der Heyden J, Ranhoff AH, Lamprini-Koula M, et al. Prevalence of frailty at population level in European ADVANTAGE Joint Action Member States: a systematic review and meta-analysis. Ann Ist Super Sanita. 2018;54(3):226-38.\u003c/li\u003e\n\u003cli\u003eFried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. 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Age Ageing. 2008;37(1):45-50.\u003c/li\u003e\n\u003cli\u003eOrme JG, Reis J, Herz EJ. Factorial and discriminant validity of the center for epidemiological studies depression (CES-D) scale. J Clin Psychol. 1986;42(1):28-33.\u003c/li\u003e\n\u003cli\u003eWashburn RA, Smith KW, Jette AM, Janney CA. The physical activity scale for the elderly (PASE): Development and evaluation. J Clin Epidemiol. 1993;46(2):153-62.\u003c/li\u003e\n\u003cli\u003ePodsiadlo D, Richardson S. The timed \u0026ldquo;up \u0026amp; go\u0026rdquo;: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. 1991;39(2):142-8.\u003c/li\u003e\n\u003cli\u003eSavva GM, Donoghue OA, Horgan F, O\u0026rsquo;Regan C, Cronin H, Kenny RA. Using timed up-and-go to identify frail members of the older population. J Gerontol Ser A. 2013;68(4):441-6.\u003c/li\u003e\n\u003cli\u003eLinden V, W D. Shumway-Cook A, Wollacott MH. Motor Control: Theory and Practical Applications. Baltimore, MD: Williams and Wilkins Inc.; 1995. Hardcover, 475 pp. 43. pre. 1996;20(1):64-65.\u003c/li\u003e\n\u003cli\u003eGreene BR, McGrath D, O\u0026rsquo;Neill R, O\u0026rsquo;Donovan KJ, Burns A, Caulfield B. An adaptive gyroscope-based algorithm for temporal gait analysis. Med Biol Eng Comput. 2010;48(12):1251-60.\u003c/li\u003e\n\u003cli\u003eRueterbories J, Spaich EG, Andersen OK. Gait event detection for use in FES rehabilitation by radial and tangential foot accelerations. Med Eng Phys. 2014;36(4):502-8.\u003c/li\u003e\n\u003cli\u003eCohen J. Statistical Power Analysis for the Behavioral Sciences. Academic Press; 2013. 459 p.\u003c/li\u003e\n\u003cli\u003eB\u0026ouml;hning D. Multinomial logistic regression algorithm. Ann Inst Stat Math. 1992;44(1):197-200.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Frailty, wearable sensors, gait parameters, accelerometer","lastPublishedDoi":"10.21203/rs.3.rs-33024/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-33024/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: One of the greatest challenges facing the healthcare of the aging population is frailty. There is growing scientific evidence that gait assessment using wearable sensors could be used for prefrailty and frailty screening. The purpose of this study was to examine the ability of a wearable sensor-based assessment of gait to discriminate between frailty levels (robust, prefrail, and frail).\u003c/p\u003e\u003cp\u003eMethods: 133 participants (≥\u0026nbsp;60\u0026nbsp;years) were recruited and frailty was assessed using the Fried criteria. Gait was assessed using wireless inertial sensors attached by straps on the thighs, shins, and feet. Between-group differences in frailty were assessed using analysis of variance. Associations between frailty and gait parameters was assessed using multinomial logistic models with frailty as the dependent variable. We used receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC) to estimate the predictive validity of each parameter. The cut-off values were calculated based on the Youden index.\u003c/p\u003e\u003cp\u003eResults: Frailty was identified in 37 (28%) participants, prefrailty in 66 (50%), and no Fried criteria were found in 30 (23%) participants. Gait speed, stance phase time, swing phase time, stride time, double support time, and cadence were able to discriminate frailty from robust, and prefrail from robust. Stride time (AUC = 0.915), stance phase (AUC = 0.923), and cadence (AUC = 0.930) were the most sensitive parameters to separate frail or prefrail from robust. Other gait parameters, such as double support, had poor sensitivity. We determined the value of stride time (1.19s), stance phase time (0.68s), and cadence (101 steps/min) to identify individuals with prefrailty or frailty with sufficient sensitivity and specificity.\u003c/p\u003e\u003cp\u003eConclusions: The results of our study show that gait analysis using wearable sensors could discriminate between frailty levels. We were able to identify several gait indicators apart from gait speed that distinguish frail or prefrail from robust with sufficient sensitivity and specificity. If improved and adapted for everyday use, gait assessment technologies could contribute to frailty screening and monitoring.\u003c/p\u003e","manuscriptTitle":"Wearable Sensors Technology as a Tool for Discriminating Frailty Levels During Instrumented Gait Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-06-15 14:51:42","doi":"10.21203/rs.3.rs-33024/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2a47c943-eaf9-4f70-a311-1f2159ba3c84","owner":[],"postedDate":"June 15th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":118697,"name":"Geriatrics \u0026 Gerontology"}],"tags":[],"updatedAt":"2021-07-22T19:05:59+00:00","versionOfRecord":{"articleIdentity":"rs-33024","link":"https://doi.org/10.3390/app10238451","journal":{"identity":"applied-sciences","isVorOnly":true,"title":"Applied Sciences"},"publishedOn":"2020-11-26 19:05:59","publishedOnDateReadable":"November 26th, 2020"},"versionCreatedAt":"2020-06-15 14:51:42","video":"","vorDoi":"10.3390/app10238451","vorDoiUrl":"https://doi.org/10.3390/app10238451","workflowStages":[]},"version":"v1","identity":"rs-33024","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-33024","identity":"rs-33024","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}