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Validity and Reliability of Accelerometer-Based Gait Assessment in Patients with Diabetes on Challenging Surfaces

Validity and Reliability of Accelerometer-Based Gait Assessment in Patients with Diabetes on... Hindawi Publishing Corporation Journal of Aging Research Volume 2012, Article ID 954378, 9 pages doi:10.1155/2012/954378 Research Article Validity and Reliability of Accelerometer-Based Gait Assessment in Patients with Diabetes on Challenging Surfaces 1 1 1 2 Eling D. de Bruin, Michele ` Hubli, Pamela Hofer, Peter Wolf, 1 3, 4 Kurt Murer, and Wiebren Zijlstra Institute of Human Movement Sciences and Sport, ETH, 8093 Zurich, Switzerland Sensory-Motor Systems Lab, ETH, Zurich, Switzerland Center for Human Movement Sciences, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands Institut fur ¨ Bewegungs- und Sportgerontologie, Deutsche Sporthochschule Koln, ¨ Koln, ¨ Germany Correspondence should be addressed to Eling D. de Bruin, eling.debruin@hest.ethz.ch Received 25 April 2012; Revised 17 June 2012; Accepted 25 June 2012 Academic Editor: Bijan Najafi Copyright © 2012 Eling D. de Bruin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Walking on irregular terrain influences gait of diabetic patients. We investigate the test-retest reliability and construct validity of gait measured with the DynaPort MiniMod under single and dual task conditions in diabetic patients walking on irregular terrain to identify the measurement error (precision) and minimal clinical detectable change. 29 patients with Type 2 diabetes were measured once, and 25 repeated the measurement within 7 days. Patients walked on a therapy garden walkway. Differences between three groups of diabetics with various levels of lower extremity neuropathy were analyzed with planned contrasts. ICC was excellent for intervisit measurements with ICC’s > 0.824. Bland and Altman Plots, SEM, and SDD showed precise values, distributed around zero for both test conditions. A significant effect of grouping on step length performance hints at possible construct validity of the device. Good reliability of DynaPort MiniMod measurements on a therapy garden walkway and an indication for discriminatory capability suggests that DynaPort MiniMod could facilitate the study of gait in diabetic patients in conditions close to real-life situations. Good reliability, small measurement error, and values of minimal clinical detectable change recommend the further utilization of DynaPort MiniMod for the evaluation of gait parameters in diabetic patients. 1. Introduction In this context gait analysis is usually performed in specialized kinesiology laboratories. Cameras, force plat- The World Health Organization has described type 2 diabetes forms, and magnetic and ultrasound systems are thereby as an international epidemic. Current estimates suggest that often used technologies for the gait analysis [7, 8]. However, the number of persons with diabetes will reach 300 million by time expenditure and financial constraints limit their use in 2025 [1]. Fifty percent of patients who have diabetes for more clinical practice [9]. Moreover, gait analyses are traditionally than 20 years develop peripheral neuropathy (PN), which performed indoors, on a predefined, clean, and flat specific affects nerve function from the periphery to more proximal pathway. Such conditions enable precise recording but are regions [2, 3]. Because the increasing prevalence of diabetes not representative of the real-life context. Activities of daily is accompanied by gait problems and a heightened risk of life require us to move about in challenging environments falling, there is an increased need for understanding the and to walk on varied surfaces. Irregular terrain has been possible gait pattern changes diabetic patients are confronted shown to influence gait parameters such as speed, especially with [4]. It has, furthermore, been demonstrated that in a population at risk for falling [10], for example, patients with diabetes may also improve their gait due to patients with Diabetes [11, 12]. Furthermore, the fact that specific exercise programs [5, 6]. falling mainly occurs in a complex environment [13]under 2 Journal of Aging Research attention demanding conditions emphasizes the need for information and were requested to sign an informed consent clinicians to objectively record gait data in a real-life context statement. [14] under dual task conditions [15]. The recent use of body-fixed sensors suggests that they 2.1. Subjects. A convenience sample of 31 patients with could serve as a tool for analyzing the gait of patients diabetes Type 2 (with and without neuropathy) was recruited in more challenging walking environments [16–18]. In from the patients consulting the Division of Endocrinology, comparison with other motion measurement devices, body- Diabetes and Clinical Nutrition, University Hospital of fixed sensors have the advantage of being lightweight and Zurich (Table 1). Patients were included if they were medi- portable, which enables subjects to move relatively freely. cally diagnosed with diabetes Type 2, were between 50 and They permit data collection in a challenging environment; 70 years of age, and had the ability to walk without assistive they are easy to use, provide a good ratio in terms of cost devices. Patients were excluded if they had concomitant and amount of information retrieved, and can capture data foot ulcer, orthopaedic or surgical problems influencing from many gait cycles. Thus they seem ideal for extending gait parameters, a nondiabetic neuropathy (due to Charcot- our understanding of gait changes in specific populations Marie-Tooth disease, alcohol, or thyroid dysfunction), or by performing measures in real-life conditions, for example, neurological pathology influencing gait parameters. in diabetic patients [19]. An objective evaluation in real- Before gait analysis started, patients were assigned to one life conditions might help understand the causes of diabetic of three groups: “DIABETIC,” “MILD NEUROPATHY,” and gait problems and ultimately facilitate the choice or the “SEVERE NEUROPATHY” based on three tests. A Neurom- development of appropriate physical treatment. Therefore, eter CPT electrodiagnostic device was used for sensory nerve the potential of body-fixed sensor approaches should be conduction threshold (sNCT) evaluations at the great toe by investigated in the diabetic population in order to ensure determining current perception threshold (CPT) levels. CPT the validity and the reliability of data recorded during gait permits diagnosis of neuropathy due to its ability to diagnose analysis under single and dual task conditions on changing and quantify hyperaesthesia [26]. The used Rapid Screening types of surfaces. CPT (R-CPT) resulted in a value between 1 and 25, where To be clinically useful, an assessment procedure must the higher numbers indicate worse nerve conduction. The have a small measurement error to detect a real change value was used to grade neuropathy: no neuropathy = 6– and must be able to distinguish between subpopulations for 13, moderate neuropathy = 14–19, severe neuropathy = 20– example, diabetic patients with and without various stages 25. The Rydel-Seiffer tuning fork test was used to assess the of peripheral neuropathy. A test-retest difference in a patient with a value smaller than the standard error of the measure- vibratory threshold perception at the base of the great toe as a good predictor for impairment of the vibratory senses ment (SEM) is likely to be the result of “measurement noise” and is unlikely to be detected reliably in practice; a difference and, therefore, also usable to diagnose neuropathy [27–29]. greater than the smallest real difference is highly likely The Rydel-Seiffer tuning fork test acquires values between (with 95% confidence) to be a real difference [20]. Another 0 and 8, where the higher values indicate better vibratory example of these statistics is the smallest detectable difference senses. Patients were grouped by the test in one of the three (SDD) [21]. The DynaPort MiniMod body-fixed sensor has categories with application of an age-related correction [30]. previously been shown to be reliable, valid, and valuable The third test used was the Semmes-Weinstein monofilament in elderly for the analysis of gait performed on challenging test, a good test to diagnose but not to quantify neuropathy surfaces [22–25]. To date, little is known about the variability [31]. If the subjects did not notice five of seven stimuli, a in gait measures within the diabetic population and the neuropathy was diagnosed. reliable use of accelerometers in these patients. With this Testing and group assignment was performed by an MD in mind, we conducted this study to (a) investigate the unfamiliar with the study design and the patients. Based on validity and reliability of gait parameters measured with the results of all three tests the MD categorized the patients DynaPort MiniMod in diabetic patients walking under in one of three categories. The MD principally considered the single and dual task conditions on a challenging walking results from the Neurometer CPT/C tests where three values course, (b) identify the measurement error (precision), for every frequency were obtained for the right and the left and (c) identify the smallest clinical detectable difference. great toe. If at least two frequencies of the worse foot had We hypothesized [1] that walking quality in patients with a value over 14, the subject was allocated to the “moderate diabetes can be reliably measured with accelerometers [2], neuropathy’’ group. If at least two frequencies of the worse that the walking quality is different in patient subgroups (we foot had a value over 19, the subject was allocated to the expect diabetic neuropathy to change gait quality compared “severe neuropathy” group. If there were any uncertainties in to the group with no neuropathy), and [3] we believe that the group allocation according to R-CPT values, the Rydel- severe neuropathy effects walking quality more than mild Seiffer tuning fork test was the next criteria considered. The neuropathy. loading of the group arrangement’s criteria was Neurometer CPT/C > Rydel-Seiffer tuning fork > Semmes-Weinstein 2. Methods monofilament test. After the analysis of nerve conduction the gait analysis The study was approved by the ethics committee in Canton Zurich. All participants received written and oral started. Journal of Aging Research 3 Table 1: Demographic description of the consecutively recruited subjects at baseline. 2 ∗ Subject (no.) Sex (m/f) Age (years) Weight (kg) Height (m) BMI (kg/m)Group 01 m 60 82 1.76 26.5 2 02 m 70 88 1.74 29.1 2 03 f 64 92 1.73 30.7 3 04 m 63 94 1.76 30.4 3 05 m 69 84 1.76 27.1 2 06 m 57 102 1.78 32.2 2 07 f 63 67 1.55 27.9 2 08 m 70 95 1.76 30.7 3 09 m 69 73 1.79 22.8 2 10 m 57 98 1.84 29 3 11 m 61 103 1.79 32.2 1 12 m 69 66 1.72 22.3 2 13 m 53 90 1.71 30.8 3 14 m 70 102 1.85 29.8 3 15 f 67 78 1.64 29 1 16 m 65 94 1.74 31.1 1 17 m 62 81 1.75 26.5 1 18 m 62 92 1.70 31.8 1 19 m 56 108 1.82 32.6 1 20 m 59 67 1.67 24 1 21 m 60 76 1.73 25.4 1 22 m 56 93 1.70 32.2 1 23 f 64 54 1.60 21.1 2 24 m 60 92 1.78 29.0 1 25 f 50 60 1.63 22.6 2 26 m 57 85 1.80 26.2 2 27 f 60 75 1.60 29.3 1 28 m 55 85 1.78 26.8 1 29 m 65 81 1.87 23.2 2 BMI: body mass index; 1: “diabetic,” 2: “mild neuropathy,” and 3: “severe neuropathy”; m/f: male/female. 2.2. Apparatus. A triaxial accelerometer (DynaPort Mini- (S), and, at the end, the measurement was stopped (S + M5; Mod, McRoberts BV, The Hague, The Netherlands) was Figure 1(b)). used to measure pelvic accelerations. The accelerometer was (1) Test run with subject’s preferred walking speed: the placed at the lower back of the subject with the center of the subject received the most important information: no device at the level of the second sacral vertebrae. speaking, hold arms out of the pocket, and try not to stop walking during the measurement. 2.3. Test Procedures. Each subject was assessed during usual (2) First trial with preferred walking speed: the subject walking at preferred velocity under two different conditions was briefed to “walk like you would bring a letter to over an outdoor gait therapy walkway with different surfaces: the mailbox” (single task). (1) silent walking on the walkway and (2) walking on the walkway with a counting task. The walkway con- (3) Second trial with preferred walking speed and an tained a paved trajectory, cobble stones, and gravel rocks additional cognitive task (count backwards aloud in (Figure 1(a)). The complete walkway was 31 meter long steps of three): the subject had to walk and count and 1.5 meter wide. To measure steady state- walking, the aloud in steps of three. The subject was briefed to 16.6 meters (with the three different surfaces) of the walking “try to walk and count at the same time. Do not course was used as the test distance. The remaining parts of favour one task over the other but try to perform these the walkway were used for acceleration and deceleration. At concurrently” (dual task). the end of the first 31 meters the subjects had to stop for two seconds, then turn around, and walk the walkway back to the The dual task was subtracting repeatedly the number starting point. At the beginning the measurement was started three starting from 200 down and was practiced before gait 4 Journal of Aging Research Turning point Start 7.9 m 7.4 m 4.6 m 4.6 m 6.5 m Cobble Paved Gravel- stone trajectory rocks S/S + M5 M1/M4 M2/M3 S/S: Start/stop M: Marker (a) (b) Figure 1: (a) The gait garden walkway with paved trajectory, cobble stones, and gravel rocks. (b) Schematic representation of the walkway and test procedure used. M signifies markers that are set in the signal to recognize the gait data for analyses. testing while sitting on a chair. Subjects were told to try and the mean of the difference and SD the standard deviation diff perform both tasks at the same time without prioritizing of the difference. The measurement error (standard error either the walking or counting. A small receiver was mounted of the mean difference (SEM)) was reported, and the on the accelerometer and the researcher placed a marker in smallest detectable difference (SDD) for each parameter was the data through triggering by the use of a remote control calculated as described by de Vet et al. [35]. SEM was derived when the subjects passed distance lines (Figure 1(b)). The by σ (1 − ICC) in which σ represents the total variance researcher walked alongside the subjects to ensure their [36]. The smallest detectable change was calculated with the safety. At the end of the last trial the SD card was removed formula 1.96 × SEM × 2. from the accelerometer, and the measurements were checked To identify differences between groups we used an for completeness on a laptop. The subject was asked to come analysis with planned contrasts [37]. All statistical analyses again for the retest one week later at the same time and to were performed using SPSS 17 for Windows (SPSS Inc.). wear the same shoes as during the first trial. Per trial, all measured data between the two trigger 4. Results signals (M1-M2/M3-M4) were used for analysis. Walking speed (V), cadence, mean values (X)ofstepduration(SDu) Of the 31 patients screened for eligibility all met the inclusion and step length (SL), and corresponding standard deviations criteria. Data from two individuals were, however, not (SD) were calculated for each subject and each trial. available. One person presented with hypersensibility of the feet and could not be measured. Technical problems prevented data acquisition for the second person. This 3. Statistical Analyses resulted in complete data for 29 patients (21 male and Normality of the data was tested with the Kolmogorov- 8 female) at baseline, mean age: 61.9 (±5.5) years; body Smirnov test. Descriptive statistics were used to define the mass index: 28.2 (±3.5)) kg/m ; leg length 0.84 (±0.06) m study population and to calculate gait characteristics. (Table 1). We used the intraclass correlation (ICC ) with 95% Twelve patients were categorized as “DIABETIC,” eleven (2,1) confidence intervals to calculate intervisit reliability between as “MILD NEUROPATHY,” and six as “SEVERE NEUROPA- visit 1 and visit 2. ICC was used because individual THY.” Post hoc ANOVA revealed that the groups did not (2,1) ratings constitute the unit of analysis, and raters and subjects differ in Age F(2, 26) = 0.949, P = .40; height F(2, 26) = were conceived as being a random selection. There was one 1.26, P = .302; SDu F(2, 26) = 1.99, P = .157; V F(2, 26) = week between visit 1 and 2. To interpret ICC values 3.01, P = .067; cadence F(2, 26) = 1.98, P = .159 and showed (2,1) we used benchmarks suggested by Shrout and Fleiss [32] to be different for weight F(2, 26) = 4.729, P = .018; BMI (>0.75 excellent reliability, 0.4–0.75 fair to good reliability, F(2, 26) = 4.28, P = .025; SL F(2, 26) = 3.14, P = .048. and <0.4 poor reliability). To evaluate precision the 95% Four patients were unable or refused to perform the limits of agreement statistics (Bland and Altman) were used. retest due to time limitations or lack of motivation. For It expresses the degree of error proportional to the mean, the reliability testing we had twenty-five patients performing and was calculated as d ± 2SD [33, 34], where d is retesting (17maleand 8female);meanage:61(±5.7) diff Journal of Aging Research 5 Table 2: Results of repeated measurements (N = 25). mild and severe PN did not significantly alter step length compared to diabetic patients presenting without PN, t(26) Test visit 1 Test visit 2 P value = −1.318, P = .101, and having severe PN significantly mean ± SD mean ± SD influenced step length compared to mild PN, t(26) = −2.469, Single task P = .046 (one tailed). SDu (s) 0.54 (0.051) 0.55 (0.045) 0.771 SL (m) 0.69 (0.095) 0.69 (0.111) 0.884 5. Discussion V (m/s) 1.28 (0.213) 1.28 (0.243) 0.942 This study has shown that the reliability of walking speed, Cad (step/min) 0.652 111.7 (10.234) 111.0 (9.031) cadence, step duration and step length on different surfaces Dual task and under dual task conditions was high with excellent ICCs, SDu (s) 0.046 0.58 (0.079) 0.56 (0.056) small SEMs and RLOAs in older adults with diabetes using SL (m) 0.325 0.67 (0.109) 0.68 (0.107) the DynaPort MiniMod system. Results from discriminant V (m/s) 0.154 1.17 (0.273) 1.23 (0.233) validity were essentially non conclusive, with the exception Cad (step/min) 0.046 104.51 (12.626) 107.7 (10.170) of step length. There are, therefore, only indications that the system might also be able to distinguish between subpopu- SD: standard deviation; SDu: step duration; SL: step length; V : velocity; Cad: cadence. lations within the population of patients with diabetes based on step length. The disease status of the elderly participants in our study varied from having diabetes without PN and years; Body Mass Index: 28.7 (±3.5) kg/m , leg length 0.83 having diabetes with mild or severe polyneuropathy. We (±0.06) m. Eleven patients were “DIABETIC,” eight “MILD thus expected our subjects to represent a heterogeneous NEUROPATHY” and six “SEVERE NEUROPATHY”. group with regard to walking abilities. From previous studies we know that disease severity negatively influences walking velocity [4] especially in challenging environments where 4.1. Differences between the Walking Conditions. Table 2 patients with neuropathy walk slower when compared to presents means and SDs of both tests. Significant differences patients without neuropathy [12]. We think that the negative between the two test conditions at baseline, single versus dual findings in our cross-sectional sample are very likely related task walking, were identified for all gait parameters (walking to the limited statistical power of this analysis and might speed: t(28) = 3.616, P = .001, cadence: t(28) = 3.221, be attributed to a possible Type I error. A post hoc power P = .003, step duration: t(28) = −3.112, P = .004, and step analysis revealed that Power (1-β err prob) = 0.19. Our data length: t(28) = 2.308, P = .029. Walking speed, step length, allow for an a priori sample size calculation for a future trial and cadence were significantly decreased under dual tasking, with a fixed effects one-way ANOVA design and under the and step duration was significantly increased compared to assumption of a moderate effect size of 0.25. To avoid a type normal walking. IorIIerror in thisfuturetrial,weneed an estimatedsample size of 159 (53 individuals per group). This would result in 4.2. Reliability. All data were normally distributed and 80% power at α-level 0.05 [39]. showed no heteroscedasticity. The results of the repeated The gait changes that we observed in dual task walking measurements for the different gait parameters SDu, SL, V, relative to single task walking are consistent with other stud- and Cadence are presented in Table 2.Exceptfor cadence ies that demonstrate that cognitive tasks have a destabilizing under dual task condition there were no differences in effect on gait [40–44]. This finding seems to indicate that walking between visit 1 and 2. it is important to consider additional cognitive tasks in All gait parameters on the walking trajectory under single gait assessment of diabetic patient populations in clinical and dual task walking with regard to test retest reliability practice. are illustrated in Figure 2 by Bland-Altman plots. The results There is scarce information available about the reliability of the test retest reliabilities are summarized in Table 3. of body fixed sensor approaches to assess gait parameters The reliability of single task walking speed, cadence, step in older adults with diabetes. The ICCs for walking speed duration, and step length was “excellent” [32] (ICCs between and cadence that we found were, however, similar to values 0.824–0.898 and SEMs between 0.03–5.2) and comparable reported by Allet et al. [19] who were using the Physilog to the reliability of dual task walking speed, cadence, step system in older, diabetic subjects. duration and step length (ICCs between 0.826–0.869 and The relative reliability is the degree to which individuals SEMs between 0.1–5.38). maintain their test results in a sample with repeated measure- ments and is affected by sample heterogeneity, that means 4.3. Validity. The mean values and standard deviations of the more heterogeneous a sample is, the higher the relative the gait parameters of 29 evaluated patients at baseline are reliability becomes. Therefore, a high correlation may still reported in Table 4 for their grouping. Planned contrasts mean unacceptable measurement error for some analytical showed that there was no significant effect on SDu, V, goals, for example, for individualised assessments [36], and and cadence and a significant effect of grouping on step data about absolute reliabilities of a test are desired for length performance. This latter parameter, however, showed clinical use. The determination of what constitutes an accept- alarge effect [38]. The planned contrasts revealed that having able RLOA depends on what size difference the researcher 6 Journal of Aging Research 0.2 +1.96 sd 0.05 0.15 +1.96 sd 0.1 Mean −0.001 0.05 −0.05 −1.96 sd −0.05 −1.96 sd −0.1 −0.1 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.45 0.5 0.55 0.6 0.65 Average of test 1 and test 2 Average of test 1 and test 2 (a) 0.2 0.3 +1.96 sd 0.2 0.1 +1.96 sd 0.1 Mean −0.002 Mean −0.015 −0.1 −1.96 sd −0.1 −1.96 sd −0.2 −0.2 0.4 0.5 0.6 0.7 0.8 0.9 0.4 0.5 0.6 0.7 0.8 0.9 Average of test 1 and test 2 Average of test 1 and test 2 (b) 0.6 0.5 +1.96 sd 0.4 +1.96 sd 0.25 0.2 Mean −0.056 Mean 0.003 −0.25 −0.2 −1.96 sd −1.96 sd −0.5 −0.4 0.75 1 1.25 1.5 1.75 0.75 1 1.25 1.5 1.75 Average of test 1 and test 2 Average of test 1 and test 2 (c) Figure 2: Continued. Mean 0.021 Test 1-test 2 Test 1-test 2 Test 1-test 2 Test 1-test 2 Test 1-test 2 Test 1-test 2 Journal of Aging Research 7 20 20 +1.96 sd +1.96 sd Mean −3.196 Mean 0.671 −10 −1.96 sd −20 −10 −1.96 sd −30 90 100 110 120 130 140 80 90 100 110 120 130 Average of test 1 and test 2 Average of test 1 and test 2 (d) Figure 2: Bland-Altman plots of (a) step duration, (b) step length, (c) walking velocity, and (d) cadence (top to bottom). Left side represents the single task condition, and the right side represents the dual task walking. Table 3: Reliability of different gait parameters at preferred speed (ICC: intraclass correlation coefficient, CI 95% confidence interval 95%, SEM standard error of measurement, SDD smallest detectable difference, and LALB limits of agreement lower boundary, LAUB limits of agreement upper boundary). ICC CI 95% SEM CI 95% SDD LALB LAUB Single task SDu (s) 0.848 0.652–0.933 0.03 ±0.06 0.09 −0.072 0.071 SL (m) 0.898 0.767–0.955 0.04 ±0.09 0.12 −0.128 0.124 V (m/s) 0.824 0.597–0.923 0.12 ±0.25 0.35 −0.349 0.354 Cad (step/min) 0.834 0.623–0.927 5.20 ±10.2 14.42 −13.754 15.096 Dual task SDu (s) 0.829 0.597–0.926 0.10 ±0.20 0.28 −0.077 0.119 SL (m) 0.869 0.706–0.942 0.17 ±0.34 0.48 −0.159 0.130 V (m/s) 0.829 0.616–0.924 0.13 ±0.26 0.37 −0.431 0.318 Cad (step/min) 0.826 0.672–0.940 5.38 ±10.54 14.90 −18.101 14.901 SDu: step duration; SL: step length; V : velocity; Cad: cadence. √ √ Calculations—SEM: Mean square error; CI 95% = ±1.96 × SEM; SDD = 1.96 × 2× SEM. Table 4: The mean and standard deviations of the gait parameters of 29 evaluated patients, grouped based on disease status, at baseline. Group Performance measure Diabetic (n = 12) Mild neuropathy (n = 11) Severe neuropathy (n = 6) Single task Step duration (SDu; s) 0.56 ± 0.03 0.53 ± 0.05 0.55 ± 0.05 Step length (SL; m) 0.72 ± 0.06 0.73 ± 0.10.62 ± 0.12 −1 Velocity (m·s)1.29 ± 0.14 1.39 ± 0.14 1.14 ± 0.37 Cadence (steps/min) 107 ± 5.8 115 ± 10.2 107.6 ± 15 Dual task Step duration (SDu; s) 0.6 ± 0.08 0.55 ± 0.05 0.6 ± 0.12 Step length (SL; m) 0.7 ± 0.07 0.7 ± 0.13 0.63 ± 0.1 −1 Velocity (m·s)1.18 ± 0.21 1.31 ± 0.28 1.09 ± 0.32 Cadence (steps/min) 101.4 ± 11 110 ± 9.3 103.4 ± 17.6 Test 1-test 2 Test 1-test 2 8 Journal of Aging Research or clinician wants to detect when comparing groups or support and for referring his patients to this study. The when assessing the effect of interventions [45]. Whether authors of this manuscript do not have a direct financial the absolute reliability reported here for the gait measures relation with the commercial identities mentioned in this is sufficiently high to identify gait impairments or small paper that might lead to a conflict of interests for any of the effects of an intervention program to improve walking in authors. populations suffering from diabetes should be part of future studies. In particular, the RLOAs for step length and gait References velocity might be indicative for rather large needed changes to be detected with the system. A study that investigated [1] K. Osei, “Global epidemic of type 2 diabetes: implications for gait recovery in a sample of patients with diabetes due to developing countries,” Ethnicity and Disease,vol. 13, no.2, specific exercises [46], with a mean age of 63 years and supplement 2, pp. S102–S106, 2003. that used the Physilog gait analysis system for evaluation [2] J. B. Dingwell and P. R. 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Validity and Reliability of Accelerometer-Based Gait Assessment in Patients with Diabetes on Challenging Surfaces

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Copyright © 2012 Eling D. de Bruin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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10.1155/2012/954378
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Hindawi Publishing Corporation Journal of Aging Research Volume 2012, Article ID 954378, 9 pages doi:10.1155/2012/954378 Research Article Validity and Reliability of Accelerometer-Based Gait Assessment in Patients with Diabetes on Challenging Surfaces 1 1 1 2 Eling D. de Bruin, Michele ` Hubli, Pamela Hofer, Peter Wolf, 1 3, 4 Kurt Murer, and Wiebren Zijlstra Institute of Human Movement Sciences and Sport, ETH, 8093 Zurich, Switzerland Sensory-Motor Systems Lab, ETH, Zurich, Switzerland Center for Human Movement Sciences, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands Institut fur ¨ Bewegungs- und Sportgerontologie, Deutsche Sporthochschule Koln, ¨ Koln, ¨ Germany Correspondence should be addressed to Eling D. de Bruin, eling.debruin@hest.ethz.ch Received 25 April 2012; Revised 17 June 2012; Accepted 25 June 2012 Academic Editor: Bijan Najafi Copyright © 2012 Eling D. de Bruin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Walking on irregular terrain influences gait of diabetic patients. We investigate the test-retest reliability and construct validity of gait measured with the DynaPort MiniMod under single and dual task conditions in diabetic patients walking on irregular terrain to identify the measurement error (precision) and minimal clinical detectable change. 29 patients with Type 2 diabetes were measured once, and 25 repeated the measurement within 7 days. Patients walked on a therapy garden walkway. Differences between three groups of diabetics with various levels of lower extremity neuropathy were analyzed with planned contrasts. ICC was excellent for intervisit measurements with ICC’s > 0.824. Bland and Altman Plots, SEM, and SDD showed precise values, distributed around zero for both test conditions. A significant effect of grouping on step length performance hints at possible construct validity of the device. Good reliability of DynaPort MiniMod measurements on a therapy garden walkway and an indication for discriminatory capability suggests that DynaPort MiniMod could facilitate the study of gait in diabetic patients in conditions close to real-life situations. Good reliability, small measurement error, and values of minimal clinical detectable change recommend the further utilization of DynaPort MiniMod for the evaluation of gait parameters in diabetic patients. 1. Introduction In this context gait analysis is usually performed in specialized kinesiology laboratories. Cameras, force plat- The World Health Organization has described type 2 diabetes forms, and magnetic and ultrasound systems are thereby as an international epidemic. Current estimates suggest that often used technologies for the gait analysis [7, 8]. However, the number of persons with diabetes will reach 300 million by time expenditure and financial constraints limit their use in 2025 [1]. Fifty percent of patients who have diabetes for more clinical practice [9]. Moreover, gait analyses are traditionally than 20 years develop peripheral neuropathy (PN), which performed indoors, on a predefined, clean, and flat specific affects nerve function from the periphery to more proximal pathway. Such conditions enable precise recording but are regions [2, 3]. Because the increasing prevalence of diabetes not representative of the real-life context. Activities of daily is accompanied by gait problems and a heightened risk of life require us to move about in challenging environments falling, there is an increased need for understanding the and to walk on varied surfaces. Irregular terrain has been possible gait pattern changes diabetic patients are confronted shown to influence gait parameters such as speed, especially with [4]. It has, furthermore, been demonstrated that in a population at risk for falling [10], for example, patients with diabetes may also improve their gait due to patients with Diabetes [11, 12]. Furthermore, the fact that specific exercise programs [5, 6]. falling mainly occurs in a complex environment [13]under 2 Journal of Aging Research attention demanding conditions emphasizes the need for information and were requested to sign an informed consent clinicians to objectively record gait data in a real-life context statement. [14] under dual task conditions [15]. The recent use of body-fixed sensors suggests that they 2.1. Subjects. A convenience sample of 31 patients with could serve as a tool for analyzing the gait of patients diabetes Type 2 (with and without neuropathy) was recruited in more challenging walking environments [16–18]. In from the patients consulting the Division of Endocrinology, comparison with other motion measurement devices, body- Diabetes and Clinical Nutrition, University Hospital of fixed sensors have the advantage of being lightweight and Zurich (Table 1). Patients were included if they were medi- portable, which enables subjects to move relatively freely. cally diagnosed with diabetes Type 2, were between 50 and They permit data collection in a challenging environment; 70 years of age, and had the ability to walk without assistive they are easy to use, provide a good ratio in terms of cost devices. Patients were excluded if they had concomitant and amount of information retrieved, and can capture data foot ulcer, orthopaedic or surgical problems influencing from many gait cycles. Thus they seem ideal for extending gait parameters, a nondiabetic neuropathy (due to Charcot- our understanding of gait changes in specific populations Marie-Tooth disease, alcohol, or thyroid dysfunction), or by performing measures in real-life conditions, for example, neurological pathology influencing gait parameters. in diabetic patients [19]. An objective evaluation in real- Before gait analysis started, patients were assigned to one life conditions might help understand the causes of diabetic of three groups: “DIABETIC,” “MILD NEUROPATHY,” and gait problems and ultimately facilitate the choice or the “SEVERE NEUROPATHY” based on three tests. A Neurom- development of appropriate physical treatment. Therefore, eter CPT electrodiagnostic device was used for sensory nerve the potential of body-fixed sensor approaches should be conduction threshold (sNCT) evaluations at the great toe by investigated in the diabetic population in order to ensure determining current perception threshold (CPT) levels. CPT the validity and the reliability of data recorded during gait permits diagnosis of neuropathy due to its ability to diagnose analysis under single and dual task conditions on changing and quantify hyperaesthesia [26]. The used Rapid Screening types of surfaces. CPT (R-CPT) resulted in a value between 1 and 25, where To be clinically useful, an assessment procedure must the higher numbers indicate worse nerve conduction. The have a small measurement error to detect a real change value was used to grade neuropathy: no neuropathy = 6– and must be able to distinguish between subpopulations for 13, moderate neuropathy = 14–19, severe neuropathy = 20– example, diabetic patients with and without various stages 25. The Rydel-Seiffer tuning fork test was used to assess the of peripheral neuropathy. A test-retest difference in a patient with a value smaller than the standard error of the measure- vibratory threshold perception at the base of the great toe as a good predictor for impairment of the vibratory senses ment (SEM) is likely to be the result of “measurement noise” and is unlikely to be detected reliably in practice; a difference and, therefore, also usable to diagnose neuropathy [27–29]. greater than the smallest real difference is highly likely The Rydel-Seiffer tuning fork test acquires values between (with 95% confidence) to be a real difference [20]. Another 0 and 8, where the higher values indicate better vibratory example of these statistics is the smallest detectable difference senses. Patients were grouped by the test in one of the three (SDD) [21]. The DynaPort MiniMod body-fixed sensor has categories with application of an age-related correction [30]. previously been shown to be reliable, valid, and valuable The third test used was the Semmes-Weinstein monofilament in elderly for the analysis of gait performed on challenging test, a good test to diagnose but not to quantify neuropathy surfaces [22–25]. To date, little is known about the variability [31]. If the subjects did not notice five of seven stimuli, a in gait measures within the diabetic population and the neuropathy was diagnosed. reliable use of accelerometers in these patients. With this Testing and group assignment was performed by an MD in mind, we conducted this study to (a) investigate the unfamiliar with the study design and the patients. Based on validity and reliability of gait parameters measured with the results of all three tests the MD categorized the patients DynaPort MiniMod in diabetic patients walking under in one of three categories. The MD principally considered the single and dual task conditions on a challenging walking results from the Neurometer CPT/C tests where three values course, (b) identify the measurement error (precision), for every frequency were obtained for the right and the left and (c) identify the smallest clinical detectable difference. great toe. If at least two frequencies of the worse foot had We hypothesized [1] that walking quality in patients with a value over 14, the subject was allocated to the “moderate diabetes can be reliably measured with accelerometers [2], neuropathy’’ group. If at least two frequencies of the worse that the walking quality is different in patient subgroups (we foot had a value over 19, the subject was allocated to the expect diabetic neuropathy to change gait quality compared “severe neuropathy” group. If there were any uncertainties in to the group with no neuropathy), and [3] we believe that the group allocation according to R-CPT values, the Rydel- severe neuropathy effects walking quality more than mild Seiffer tuning fork test was the next criteria considered. The neuropathy. loading of the group arrangement’s criteria was Neurometer CPT/C > Rydel-Seiffer tuning fork > Semmes-Weinstein 2. Methods monofilament test. After the analysis of nerve conduction the gait analysis The study was approved by the ethics committee in Canton Zurich. All participants received written and oral started. Journal of Aging Research 3 Table 1: Demographic description of the consecutively recruited subjects at baseline. 2 ∗ Subject (no.) Sex (m/f) Age (years) Weight (kg) Height (m) BMI (kg/m)Group 01 m 60 82 1.76 26.5 2 02 m 70 88 1.74 29.1 2 03 f 64 92 1.73 30.7 3 04 m 63 94 1.76 30.4 3 05 m 69 84 1.76 27.1 2 06 m 57 102 1.78 32.2 2 07 f 63 67 1.55 27.9 2 08 m 70 95 1.76 30.7 3 09 m 69 73 1.79 22.8 2 10 m 57 98 1.84 29 3 11 m 61 103 1.79 32.2 1 12 m 69 66 1.72 22.3 2 13 m 53 90 1.71 30.8 3 14 m 70 102 1.85 29.8 3 15 f 67 78 1.64 29 1 16 m 65 94 1.74 31.1 1 17 m 62 81 1.75 26.5 1 18 m 62 92 1.70 31.8 1 19 m 56 108 1.82 32.6 1 20 m 59 67 1.67 24 1 21 m 60 76 1.73 25.4 1 22 m 56 93 1.70 32.2 1 23 f 64 54 1.60 21.1 2 24 m 60 92 1.78 29.0 1 25 f 50 60 1.63 22.6 2 26 m 57 85 1.80 26.2 2 27 f 60 75 1.60 29.3 1 28 m 55 85 1.78 26.8 1 29 m 65 81 1.87 23.2 2 BMI: body mass index; 1: “diabetic,” 2: “mild neuropathy,” and 3: “severe neuropathy”; m/f: male/female. 2.2. Apparatus. A triaxial accelerometer (DynaPort Mini- (S), and, at the end, the measurement was stopped (S + M5; Mod, McRoberts BV, The Hague, The Netherlands) was Figure 1(b)). used to measure pelvic accelerations. The accelerometer was (1) Test run with subject’s preferred walking speed: the placed at the lower back of the subject with the center of the subject received the most important information: no device at the level of the second sacral vertebrae. speaking, hold arms out of the pocket, and try not to stop walking during the measurement. 2.3. Test Procedures. Each subject was assessed during usual (2) First trial with preferred walking speed: the subject walking at preferred velocity under two different conditions was briefed to “walk like you would bring a letter to over an outdoor gait therapy walkway with different surfaces: the mailbox” (single task). (1) silent walking on the walkway and (2) walking on the walkway with a counting task. The walkway con- (3) Second trial with preferred walking speed and an tained a paved trajectory, cobble stones, and gravel rocks additional cognitive task (count backwards aloud in (Figure 1(a)). The complete walkway was 31 meter long steps of three): the subject had to walk and count and 1.5 meter wide. To measure steady state- walking, the aloud in steps of three. The subject was briefed to 16.6 meters (with the three different surfaces) of the walking “try to walk and count at the same time. Do not course was used as the test distance. The remaining parts of favour one task over the other but try to perform these the walkway were used for acceleration and deceleration. At concurrently” (dual task). the end of the first 31 meters the subjects had to stop for two seconds, then turn around, and walk the walkway back to the The dual task was subtracting repeatedly the number starting point. At the beginning the measurement was started three starting from 200 down and was practiced before gait 4 Journal of Aging Research Turning point Start 7.9 m 7.4 m 4.6 m 4.6 m 6.5 m Cobble Paved Gravel- stone trajectory rocks S/S + M5 M1/M4 M2/M3 S/S: Start/stop M: Marker (a) (b) Figure 1: (a) The gait garden walkway with paved trajectory, cobble stones, and gravel rocks. (b) Schematic representation of the walkway and test procedure used. M signifies markers that are set in the signal to recognize the gait data for analyses. testing while sitting on a chair. Subjects were told to try and the mean of the difference and SD the standard deviation diff perform both tasks at the same time without prioritizing of the difference. The measurement error (standard error either the walking or counting. A small receiver was mounted of the mean difference (SEM)) was reported, and the on the accelerometer and the researcher placed a marker in smallest detectable difference (SDD) for each parameter was the data through triggering by the use of a remote control calculated as described by de Vet et al. [35]. SEM was derived when the subjects passed distance lines (Figure 1(b)). The by σ (1 − ICC) in which σ represents the total variance researcher walked alongside the subjects to ensure their [36]. The smallest detectable change was calculated with the safety. At the end of the last trial the SD card was removed formula 1.96 × SEM × 2. from the accelerometer, and the measurements were checked To identify differences between groups we used an for completeness on a laptop. The subject was asked to come analysis with planned contrasts [37]. All statistical analyses again for the retest one week later at the same time and to were performed using SPSS 17 for Windows (SPSS Inc.). wear the same shoes as during the first trial. Per trial, all measured data between the two trigger 4. Results signals (M1-M2/M3-M4) were used for analysis. Walking speed (V), cadence, mean values (X)ofstepduration(SDu) Of the 31 patients screened for eligibility all met the inclusion and step length (SL), and corresponding standard deviations criteria. Data from two individuals were, however, not (SD) were calculated for each subject and each trial. available. One person presented with hypersensibility of the feet and could not be measured. Technical problems prevented data acquisition for the second person. This 3. Statistical Analyses resulted in complete data for 29 patients (21 male and Normality of the data was tested with the Kolmogorov- 8 female) at baseline, mean age: 61.9 (±5.5) years; body Smirnov test. Descriptive statistics were used to define the mass index: 28.2 (±3.5)) kg/m ; leg length 0.84 (±0.06) m study population and to calculate gait characteristics. (Table 1). We used the intraclass correlation (ICC ) with 95% Twelve patients were categorized as “DIABETIC,” eleven (2,1) confidence intervals to calculate intervisit reliability between as “MILD NEUROPATHY,” and six as “SEVERE NEUROPA- visit 1 and visit 2. ICC was used because individual THY.” Post hoc ANOVA revealed that the groups did not (2,1) ratings constitute the unit of analysis, and raters and subjects differ in Age F(2, 26) = 0.949, P = .40; height F(2, 26) = were conceived as being a random selection. There was one 1.26, P = .302; SDu F(2, 26) = 1.99, P = .157; V F(2, 26) = week between visit 1 and 2. To interpret ICC values 3.01, P = .067; cadence F(2, 26) = 1.98, P = .159 and showed (2,1) we used benchmarks suggested by Shrout and Fleiss [32] to be different for weight F(2, 26) = 4.729, P = .018; BMI (>0.75 excellent reliability, 0.4–0.75 fair to good reliability, F(2, 26) = 4.28, P = .025; SL F(2, 26) = 3.14, P = .048. and <0.4 poor reliability). To evaluate precision the 95% Four patients were unable or refused to perform the limits of agreement statistics (Bland and Altman) were used. retest due to time limitations or lack of motivation. For It expresses the degree of error proportional to the mean, the reliability testing we had twenty-five patients performing and was calculated as d ± 2SD [33, 34], where d is retesting (17maleand 8female);meanage:61(±5.7) diff Journal of Aging Research 5 Table 2: Results of repeated measurements (N = 25). mild and severe PN did not significantly alter step length compared to diabetic patients presenting without PN, t(26) Test visit 1 Test visit 2 P value = −1.318, P = .101, and having severe PN significantly mean ± SD mean ± SD influenced step length compared to mild PN, t(26) = −2.469, Single task P = .046 (one tailed). SDu (s) 0.54 (0.051) 0.55 (0.045) 0.771 SL (m) 0.69 (0.095) 0.69 (0.111) 0.884 5. Discussion V (m/s) 1.28 (0.213) 1.28 (0.243) 0.942 This study has shown that the reliability of walking speed, Cad (step/min) 0.652 111.7 (10.234) 111.0 (9.031) cadence, step duration and step length on different surfaces Dual task and under dual task conditions was high with excellent ICCs, SDu (s) 0.046 0.58 (0.079) 0.56 (0.056) small SEMs and RLOAs in older adults with diabetes using SL (m) 0.325 0.67 (0.109) 0.68 (0.107) the DynaPort MiniMod system. Results from discriminant V (m/s) 0.154 1.17 (0.273) 1.23 (0.233) validity were essentially non conclusive, with the exception Cad (step/min) 0.046 104.51 (12.626) 107.7 (10.170) of step length. There are, therefore, only indications that the system might also be able to distinguish between subpopu- SD: standard deviation; SDu: step duration; SL: step length; V : velocity; Cad: cadence. lations within the population of patients with diabetes based on step length. The disease status of the elderly participants in our study varied from having diabetes without PN and years; Body Mass Index: 28.7 (±3.5) kg/m , leg length 0.83 having diabetes with mild or severe polyneuropathy. We (±0.06) m. Eleven patients were “DIABETIC,” eight “MILD thus expected our subjects to represent a heterogeneous NEUROPATHY” and six “SEVERE NEUROPATHY”. group with regard to walking abilities. From previous studies we know that disease severity negatively influences walking velocity [4] especially in challenging environments where 4.1. Differences between the Walking Conditions. Table 2 patients with neuropathy walk slower when compared to presents means and SDs of both tests. Significant differences patients without neuropathy [12]. We think that the negative between the two test conditions at baseline, single versus dual findings in our cross-sectional sample are very likely related task walking, were identified for all gait parameters (walking to the limited statistical power of this analysis and might speed: t(28) = 3.616, P = .001, cadence: t(28) = 3.221, be attributed to a possible Type I error. A post hoc power P = .003, step duration: t(28) = −3.112, P = .004, and step analysis revealed that Power (1-β err prob) = 0.19. Our data length: t(28) = 2.308, P = .029. Walking speed, step length, allow for an a priori sample size calculation for a future trial and cadence were significantly decreased under dual tasking, with a fixed effects one-way ANOVA design and under the and step duration was significantly increased compared to assumption of a moderate effect size of 0.25. To avoid a type normal walking. IorIIerror in thisfuturetrial,weneed an estimatedsample size of 159 (53 individuals per group). This would result in 4.2. Reliability. All data were normally distributed and 80% power at α-level 0.05 [39]. showed no heteroscedasticity. The results of the repeated The gait changes that we observed in dual task walking measurements for the different gait parameters SDu, SL, V, relative to single task walking are consistent with other stud- and Cadence are presented in Table 2.Exceptfor cadence ies that demonstrate that cognitive tasks have a destabilizing under dual task condition there were no differences in effect on gait [40–44]. This finding seems to indicate that walking between visit 1 and 2. it is important to consider additional cognitive tasks in All gait parameters on the walking trajectory under single gait assessment of diabetic patient populations in clinical and dual task walking with regard to test retest reliability practice. are illustrated in Figure 2 by Bland-Altman plots. The results There is scarce information available about the reliability of the test retest reliabilities are summarized in Table 3. of body fixed sensor approaches to assess gait parameters The reliability of single task walking speed, cadence, step in older adults with diabetes. The ICCs for walking speed duration, and step length was “excellent” [32] (ICCs between and cadence that we found were, however, similar to values 0.824–0.898 and SEMs between 0.03–5.2) and comparable reported by Allet et al. [19] who were using the Physilog to the reliability of dual task walking speed, cadence, step system in older, diabetic subjects. duration and step length (ICCs between 0.826–0.869 and The relative reliability is the degree to which individuals SEMs between 0.1–5.38). maintain their test results in a sample with repeated measure- ments and is affected by sample heterogeneity, that means 4.3. Validity. The mean values and standard deviations of the more heterogeneous a sample is, the higher the relative the gait parameters of 29 evaluated patients at baseline are reliability becomes. Therefore, a high correlation may still reported in Table 4 for their grouping. Planned contrasts mean unacceptable measurement error for some analytical showed that there was no significant effect on SDu, V, goals, for example, for individualised assessments [36], and and cadence and a significant effect of grouping on step data about absolute reliabilities of a test are desired for length performance. This latter parameter, however, showed clinical use. The determination of what constitutes an accept- alarge effect [38]. The planned contrasts revealed that having able RLOA depends on what size difference the researcher 6 Journal of Aging Research 0.2 +1.96 sd 0.05 0.15 +1.96 sd 0.1 Mean −0.001 0.05 −0.05 −1.96 sd −0.05 −1.96 sd −0.1 −0.1 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.45 0.5 0.55 0.6 0.65 Average of test 1 and test 2 Average of test 1 and test 2 (a) 0.2 0.3 +1.96 sd 0.2 0.1 +1.96 sd 0.1 Mean −0.002 Mean −0.015 −0.1 −1.96 sd −0.1 −1.96 sd −0.2 −0.2 0.4 0.5 0.6 0.7 0.8 0.9 0.4 0.5 0.6 0.7 0.8 0.9 Average of test 1 and test 2 Average of test 1 and test 2 (b) 0.6 0.5 +1.96 sd 0.4 +1.96 sd 0.25 0.2 Mean −0.056 Mean 0.003 −0.25 −0.2 −1.96 sd −1.96 sd −0.5 −0.4 0.75 1 1.25 1.5 1.75 0.75 1 1.25 1.5 1.75 Average of test 1 and test 2 Average of test 1 and test 2 (c) Figure 2: Continued. Mean 0.021 Test 1-test 2 Test 1-test 2 Test 1-test 2 Test 1-test 2 Test 1-test 2 Test 1-test 2 Journal of Aging Research 7 20 20 +1.96 sd +1.96 sd Mean −3.196 Mean 0.671 −10 −1.96 sd −20 −10 −1.96 sd −30 90 100 110 120 130 140 80 90 100 110 120 130 Average of test 1 and test 2 Average of test 1 and test 2 (d) Figure 2: Bland-Altman plots of (a) step duration, (b) step length, (c) walking velocity, and (d) cadence (top to bottom). Left side represents the single task condition, and the right side represents the dual task walking. Table 3: Reliability of different gait parameters at preferred speed (ICC: intraclass correlation coefficient, CI 95% confidence interval 95%, SEM standard error of measurement, SDD smallest detectable difference, and LALB limits of agreement lower boundary, LAUB limits of agreement upper boundary). ICC CI 95% SEM CI 95% SDD LALB LAUB Single task SDu (s) 0.848 0.652–0.933 0.03 ±0.06 0.09 −0.072 0.071 SL (m) 0.898 0.767–0.955 0.04 ±0.09 0.12 −0.128 0.124 V (m/s) 0.824 0.597–0.923 0.12 ±0.25 0.35 −0.349 0.354 Cad (step/min) 0.834 0.623–0.927 5.20 ±10.2 14.42 −13.754 15.096 Dual task SDu (s) 0.829 0.597–0.926 0.10 ±0.20 0.28 −0.077 0.119 SL (m) 0.869 0.706–0.942 0.17 ±0.34 0.48 −0.159 0.130 V (m/s) 0.829 0.616–0.924 0.13 ±0.26 0.37 −0.431 0.318 Cad (step/min) 0.826 0.672–0.940 5.38 ±10.54 14.90 −18.101 14.901 SDu: step duration; SL: step length; V : velocity; Cad: cadence. √ √ Calculations—SEM: Mean square error; CI 95% = ±1.96 × SEM; SDD = 1.96 × 2× SEM. Table 4: The mean and standard deviations of the gait parameters of 29 evaluated patients, grouped based on disease status, at baseline. Group Performance measure Diabetic (n = 12) Mild neuropathy (n = 11) Severe neuropathy (n = 6) Single task Step duration (SDu; s) 0.56 ± 0.03 0.53 ± 0.05 0.55 ± 0.05 Step length (SL; m) 0.72 ± 0.06 0.73 ± 0.10.62 ± 0.12 −1 Velocity (m·s)1.29 ± 0.14 1.39 ± 0.14 1.14 ± 0.37 Cadence (steps/min) 107 ± 5.8 115 ± 10.2 107.6 ± 15 Dual task Step duration (SDu; s) 0.6 ± 0.08 0.55 ± 0.05 0.6 ± 0.12 Step length (SL; m) 0.7 ± 0.07 0.7 ± 0.13 0.63 ± 0.1 −1 Velocity (m·s)1.18 ± 0.21 1.31 ± 0.28 1.09 ± 0.32 Cadence (steps/min) 101.4 ± 11 110 ± 9.3 103.4 ± 17.6 Test 1-test 2 Test 1-test 2 8 Journal of Aging Research or clinician wants to detect when comparing groups or support and for referring his patients to this study. The when assessing the effect of interventions [45]. Whether authors of this manuscript do not have a direct financial the absolute reliability reported here for the gait measures relation with the commercial identities mentioned in this is sufficiently high to identify gait impairments or small paper that might lead to a conflict of interests for any of the effects of an intervention program to improve walking in authors. populations suffering from diabetes should be part of future studies. In particular, the RLOAs for step length and gait References velocity might be indicative for rather large needed changes to be detected with the system. A study that investigated [1] K. Osei, “Global epidemic of type 2 diabetes: implications for gait recovery in a sample of patients with diabetes due to developing countries,” Ethnicity and Disease,vol. 13, no.2, specific exercises [46], with a mean age of 63 years and supplement 2, pp. S102–S106, 2003. that used the Physilog gait analysis system for evaluation [2] J. B. Dingwell and P. R. 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