Discrepancies in Walking Speed Measurements Post-Bed-Rest: A Comparative Analysis of Real-World vs. Laboratory Assessments

Discrepancies in Walking Speed Measurements Post-Bed-Rest: A Comparative Analysis of Real-World vs. Laboratory Assessments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Discrepancies in Walking Speed Measurements Post-Bed-Rest: A Comparative Analysis of Real-World vs. Laboratory Assessments Marcello Grassi, Ramona Ritzmann, Fiona Von Der Straten, Jonas Böcker, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3960673/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Understanding differences between real-world walking speed (RWS) and laboratory-measured walking speed (LWS) is crucial for comprehensive mobility assessments, especially in context of prolonged immobilization. This study aimed to investigate disparities in walking speed following a 60-day bed-rest period. In eleven male subjects, RWS was continuously monitored using a tri-axial accelerometer worn on the waist, while LWS was assessed via a 10-meter walk test at preferred speed, on three different study days after immobilization. Statistical analyses included Bland-Altman and Pearson's correlation to evaluate agreement between RWS and LWS, alongside paired-sample t-tests and univariate linear regression models to assess significance of differences and temporal effects on gait speed. Results of Bland-Altman analysis showed no agreement between RWS and LWS (mean difference 0.77 m/s) and nonsignificant correlation (r = 0.19, p-value = 0.3). Paired-sample t-tests indicated significantly lower RWS compared to LWS for all study days (p-value < 0.001). Univariate linear regression models demonstrated a significant effect of test day on RWS (p-value < 0.001) but not on LWS (p-value = 0.23). These findings emphasize the importance of integrating both assessments to capture comprehensive mobility changes following prolonged periods of inactivity. Particularly significant is that RWS is constantly lower than LWS, with the former being more representative as it reflects what normally participants would do when not under observation. Lastly, understanding discrepancies between RWS and LWS would allow for more appropriate rehabilitation programs to speed up recovery while simultaneously keeping the rehabilitation safe and tailored. Health sciences/Anatomy/Musculoskeletal system Health sciences/Biomarkers Health sciences/Health care Health sciences/Signs and symptoms Health sciences/Medical research/Biomarkers Health sciences/Medical research/Outcomes research Figures Figure 1 Figure 2 Figure 3 Introduction Gait speed is an important well-recognized parameter that reflects mobility and the overall well-being of a subject, as well as being an indicator of physical functioning, cognitive impairment [ 1 ], disability, falls and mortality [ 2 – 5 ]. Slow self-selected walking speed is associated with a lower quality of life [ 6 ], symptoms of depression [ 7 ], higher healthcare utilization [ 8 ] and higher mortality rate [ 9 ]. The role of gait speed is also linked to the assessment of the recovery process after surgery [ 10 ] and after experimental bed-rest [ 11 – 13 ]. Due to its versatility in predicting different outcomes, as well as the relatively low cost and ease to administer, it has been named “ the sixth vital sign ” by Middleton [ 4 ]. However, in recent years it became evident that self-selected walking speed in a controlled environment (e.g., laboratory gait tests) can differ from self-selected walking speed in real-world conditions [ 14 – 16 ]. Even the representativity of laboratory gait tests for real-world behavior has been recently questioned [ 17 ] as laboratory-based assessments, often conducted over short distances in controlled settings, may not capture the complexity of real-world ambulation. It was shown that real-world gait assessments using wearables are better at predicting fall risk in older people than clinical gait assessments [ 18 ] and undoubtedly offers a more ecologically valid representation of daily mobility patterns [ 19 , 20 ]. Aforementioned distinctions are paramount when it comes to pathological conditions. Especially in the context of extended bed-rest, it is particularly important to represent gait capacity in realistic scenarios to support a conclusive clinical statement. Experimental bed-rest studies are widely accepted to simulate the effects of microgravity in space on different physiological systems and therefore, they provide valuable insight in the physiological impact of neuromuscular and coordinative deconditioning. Obviously, that is also relevant to clinical cases of bed-rest. Immobilization by experimental bed-rest leads to a rapid degradation process encompassing most bodily structures, systems, and organs and, hence, a decline in the overall fitness and health status [ 21 – 23 ]. Particularly, gait course analyses after bed-rest of differing lengths showed that preferred walking speed, moderate running speed and spatio-temporal parameters decrease with the duration of bed-rest and results in delayed recovery curves after bed-rest [ 11 – 13 ]. However, clear evidence on the diagnostic differences and specificities of wearable solutions in everyday life compared to laboratory tests is still unclear. The focus of this work is to study the differences between self-selected laboratory walking speed (LWS) and real-world walking speed (RWS) after 60 days of immobilization by experimental bed-rest. For that purpose, we assessed, in a prospective longitudinal study, gait speed at three equidistant time intervals after the end of bed-rest, named R0 , R7 and R13 , where the R before the number indicates the recovery phase and the number after the R indicates the day of the recovery phase in which the test was performed (e.g., R7 indicates the seventh day after bed-rest). We hypothesized that LWS and RWS would demonstrate major differences. Furthermore, we investigated whether the effect of time after bed-rest is reflected in LWS and RWS data. Results Wearing time Wearing time of the three-axial accelerometer throughout the three days of measurements was as follows (mean ± sd hours/day): R0 : 14.6 ± 2.9, R7 : 14.3 ± 4.2 and R13 : 15.7 ± 3.5. Levels of agreement Bland-Altman showed little to no agreement between the two measurements with a mean difference of 0.77 m/s and the 95% confidence intervals for the limit of agreements were 0.23 m/s to 1.31 m/s, and no significant correlation (t 30 = 1.05, p-value = 0.3, r = 0.19). Level of agreement by study day showed also no agreement between RWS and LWS, with mean difference and 95% confidence intervals for the limit of agreements (expressed in m/s) of 0.83 [0.29 to 1.38], 0.59 [0.18 to 1] and 0.89 [0.38 to 1.4] for R0 , R7 and R13 , respectively. Also, the Pearson’s product-moment correlation showed little to no correlation for all three study days ( R0 : t 9 = 1.71, p-value = 0.12; r = 0.49; R7 : t 9 = 1.27, p-value = 0.23, r = 0.39; R13 : t 8 = -0.64, p-value = 0.54, r = -0.22). Figure 1 and Table 1 present and summarize the results reported in this section. Table 1 Statistics values of the Bland-Altman and Pearson’s product-moment correlation by study day and with all study days combined. Study day Method Statistics Combined R0 R7 R13 Bland-Altman Mean Bias 0.77 0.83 0.59 0.89 Upper Limit of Agreement 1.31 1.38 1 1.4 Lower Limit of Agreement 0.23 0.29 0.18 0.38 Pearson's product-moment correlation Pearson’s correlation coefficient 0.19 0.49 0.39 -0.22 Degree of Freedom 30 9 9 8 t-value 1.05 1.71 1.27 -0.64 p-value 0.3 0.12 0.23 0.54 Moreover, in Fig. 1 , panel a, it can be seen that there is a positive correlation (t 30 = 2.73, p-value = 0.01; r = 0.45) between the difference of measurements (y-axis) and the mean of measurements (x-axis) suggesting an unequal variance between the two compared measurements. As values of RWS are not normally distributed (W = 0.91, p-value = 0.012), the Fligner-Killeen Test of Homogeneity of Variances was carried out to compare both variances. The test showed no significant difference between the two variances (RWS variance = 0.03, LWS variance = 0.07, \({x}^{2}\) = 1.86, p-value = 0.173). Speed difference between RWS and LWS The paired-sample t-test showed that RWS (mean = 0.78 m/s, sd = 0.16 m/s) was significantly lower than LWS (mean = 1.55 m/s, sd = 0.26 m/s), t 31 = -15.78, p-value < 0.001. Furthermore, paired-sample t-test executed on the data grouped by study day showed that for all the three study days RWS was significantly lower than LWS (R0: t 10 = -10.02, p-value < 0.001; R7: t 10 = -9.32, p-value < 0.001; R13: t 9 = -10.88, p-value < 0.001 – see Table 2 and Fig. 2 for the t-test results). All the p-values were adjusted using the Bonferroni correction for multiple comparisons. Table 2 Results of the paired-sample t-test. * p-value adjusted with Bonferroni correction for multiple comparisons. All samples R0 R7 R13 Mean ± sd (LWS) 1.55 ± 0.26 m/s 1.45 ± 0.3 m/s 1.55 ± 0.23 m/s 1.65 ± 0.22 m/s Mean ± sd (RWS) 0.78 ± 0.16 m/s 0.62 ± 0.05 m/s 0.96 ± 0.07 m/s 0.76 ± 0.09 m/s Absolute Difference (|RWS – LWS|) 0.77 m/s 0.83 m/s 0.59 m/s 0.89 m/s Degree of Freedom 31 10 10 9 t-statistics -15.78 -10.02 -9.32 -10.88 p-value* < 0.001 < 0.001 < 0.001 < 0.001 Effect of test days The univariate linear regression model fitted on the RWS data showed a significant effect of the test days on RWS (F 2 = 63.01, p-value < 0.001). On the other hand, the univariate linear regression model fitted on the LWS data did not show any significant effect of the test days (F 2 = 1.548, p-value = 0.23) (see Table 3 ). Subsequent post-hoc testing on the results of the linear regression model performed on the RWS data showed that all the differences tested in the pairwise comparisons (R13-R0, R7-R0 and R7-R13) were significant (p-values = R13-R0: <0.001; R7-R0: <0.001; R7-R13: <0.001 – see Table 4 for a summary of the results of the post-hoc testing, and Fig. 3 for a graphical representation of the pairwise differences). Table 3 details about the two univariate linear regression models used to determine effect of test days on LWS and RWS respectively. LWS - univariate linear regression model Coefficients Model Name Estimate Std. Error t-value p-value F-statistics adjusted R 2 p-value Intercept 1.454 0.076 19.164 < 0.001 1.548 0.034 0.23 R7 0.096 0.107 0.898 0.377 R13 0.193 0.11 1.759 0.089 RWS - univariate linear regression model Coefficients Model Name Estimate Std. Error t-value p-value F-statistics adjusted R 2 p-value Intercept 0.62 0.022 28.8 < 0.001 63.01 0.8 < 0.001 R7 0.34 0.03 11.16 < 0.001 R13 0.138 0.031 4.42 < 0.001 Table 4 Summary of the TUKEY HSD post-hoc test for the pairwise comparisons of the effects of test days on LWS and RWS respectively. Tukey Honest Significant Difference test LWS RWS Comparison Difference (95% CI) adjusted p-value Difference (95% CI) adjusted p-value R7-R0 0.096 (-0.169:0.361) 0.646 0.34 (0.265:0.415) < 0.001 R13-R0 0.193 (-0.078:0.465) 0.201 0.138 (0.061:0.215) < 0.001 R13-R7 0.097 (-0.175:0.369) 0.655 -0.202 (-0.279:-0.125) < 0.001 Discussion This study provides an important insight into gait assessment within the context of physical impairments, delineating between two distinct methods of assessing gait speed. Results from the Bland-Altman analysis indicates a lack of agreement between the two measurements, with LWS being, on average, greater than RWS by approximately 0.77 m/s. Notably, as depicted in Fig. 1 , panel b, this discrepancy persisted across all subjects and study days. The Pearson’s correlation analysis confirmed the lack of agreement, as little to no correlation between RWS and LWS was found. Combined, it becomes evident that walking in a controlled laboratory settings differs significantly from everyday ambulation. Many factors can be attributed to the observed difference. Research by Hillel et al. [ 25 ] suggests that typical walking in natural environments more closely resembles dual-task walking in a controlled environment. Hillel et al. [ 25 ] investigated five commonly used spatial-temporal features of gait quality, including gait speed, in three different settings: in-lab usual walking gait speed, in-lab dual-tasking gait speed and daily-living gait speed. Comparing the gait measurements for the three different settings for a cohort of 150 people revealed that in-lab usual walking gait speed does not agree with measures obtained during daily-living, being significantly faster than daily-living walking speed. Even in-lab dual-tasking gait speed measures, which are overall closer to daily-living gait speed, do not mirror gait speed during daily-living. Consequently, Hillel et al. concluded that, generally-speaking, in-lab measures of gait cannot accurately reflect daily-living gait measures. The findings are in line with our results, indicating that in-lab measurements (LWS) of gait speed before and after physical impairment cannot be equated with the daily walking behavior regarding RWS. Hence, LWS and RWS both contain valuable information about a person’s gait characteristics, however, cannot be treated as equal measurements detached from their environmental contexts. Furthermore, in another study presented by Kawai [ 26 ], the authors explored the relationship between daily living walking speed (DWS) and laboratory-measured walking speed (LWS) in a cohort of 90 elderly individuals. They intended to find out whether DWS serves as reliable indicator of physical function and frailty. Participants were asked to carry a smartphone equipped with a global positioning system (GPS) application for measuring their DWS for one month. During regular checkups, participants performed gait tests in a laboratory to measure LWS at normal and maximum pace. Kawai et al. showed that DWS and LWS (both average and maximum measurements) differ from each other with a mean difference of 0.14 m/s and 0.1 m/s, respectively. While these differences are smaller in magnitude compared to those observed in our study (see Table 2 for gait speed differences overview), they may still hold clinical significance, as suggested by previous studies [ 27 , 28 ]. Indeed, the study of Kawai showed that DWS measures can be associated with physical performance measurements, hence, Kawai et al. conclude that DWS likely reflects the participants’ physical function. Both the study by Kawai et al. as well as the results of our data analysis underscore that RWS and LWS contain different information about a person’s physical functioning. Kawai et al. also highlight the potential of DWS to assess adverse health outcomes in the future as it can be measured over a long period of time and in different situations compared to LWS. In another study that focused on the robustness of in-laboratory and daily-life gait speed measures [ 29 ], the interrelation between laboratory and daily life gait measures was assessed. Gait measures of 189 elderly people in daily life were collected over the course of one year, as well as regular and frequent intervals in a laboratory environment. Calculating the Pearson’s correlations for in-laboratory and daily-life gait speed revealed negligible to low correlations for all investigated time points. Even though overall correlations increased with higher percentiles of daily-life gait speed, the authors only identified a consistent dissonant relationship between in-laboratory and daily-life gait measures. The authors concluded that both types of gait speed measures represent distinct personal features of a population of elderly people. As both RWS as well as LWS are clinically relevant measures, investigating both measures potentially yield more meaningful insights into actual daily-life physical behavior and improve predicting health outcomes. The present study revealed that only RWS, and not LWS, was affected by the bed-rest. In contrast previous studies have reported a significant decline and duration-dependent recovery in laboratory gait speed associated with physiological degradation [ 11 , 12 , 24 , 30 , 31 ]. As such changes were absent in the present, well-controlled study, we conclude that the transferability and significance of laboratory measurements for the real movement patterns in the clinical context must be judged very critically and interpreted thoroughly. The findings presented herein support the notion that walking speed assessed in uncontrolled environments is more sensitive to changes compared to that measured in controlled settings. This is supported by Fig. 1 , panel b, where differences in walking speed are discernible along the y-axis (RWS) but not along the x-axis (LWS). However, this observed dissimilarity should not be solely attributed to differences in measurement sensitivity; it may also stem from distinct underlying mechanisms involved in task execution, as proposed by Takayanagi [ 15 ] and Hillel [ 25 ]. Lastly, the high wearing time of the tri-axial accelerometer throughout the study period underscores the feasibility and practicality of continuous monitoring of RWS in real-world setting, enhancing the ecological validity of mobility assessment. However, despite the extensive wear time, the limited number of samples makes it difficult to know to which extend what observed by the data is generalizable. Furthermore, subjects were restricted to move in the DLR wards on 1000m 2 , so the ‘real-world’ walking bouts they could make were limited to the different stations/areas they had to go for e.g., testing/monitoring, etc. Moreover, it cannot be excluded that participants’ RWS may have been influenced by either DLR personnel while walking together to testing stations or DLR’s ward areas, or by others study participants. While it is reasonable to assume that adjustments to walking speed would be made by the DLR personnel to align with participants’ capabilities, mitigating the risk of injury or discomfort, this may not always have been the case when participants from different recovery days (e.g., R3 and R11 ) walked together for the DLR’s ward. In such instances, it is conceivable that one participant may have had to adapt its RWS to match the other participant’s RWS, potentially introducing bias to RWS values either upwards or downward. Lastly, participants were instructed to not overdo physical activity on the first days of ambulation after bed-rest as they would become extremely sore from muscle soreness. In conclusion, the observed differences between RWS and LWS highlight the complexities of mobility assessment paradigms and the need for comprehensive evaluation methodologies that encompass both laboratory-based and real-world assessments. These findings have significant implications for clinical practice and research, emphasizing the importance of considering contextual factors and temporal variations in mobility measurements. Methods Study design A prospective longitudinal cohort study was performed at the : envihab research laboratory of the Institute of Aerospace Medicine at the German Aerospace Center - Deutsches Zentrum für Luft- und Raumfahrt (DLR) in Cologne, Germany. The bed-rest study took place from February to April 2016 and lasted for 60 consecutive days plus an in-house period of two weeks before and after the bed-rest. Thus, each subject had a two-week Baseline Data Collection (BDC) phase before immobilization, a 60-day 6° head down tilt bed-rest, and another two-week recovery (R) phase. This extended period of 3 months was the second campaign of a bigger study named RSL (Reactive Jumps in a Sledge Jump System as a Countermeasure during Long-Term Bed Rest 2015–2016) (see [ 32 ] for more details about the RSL study). During the two two-week phases before and after the immobilization phase, subjects were confined to the DLR ward to undergo various measurement procedures (including gait tests). As a countermeasure against the effect of immobilization by experimental bed rest, seven subjects were assigned to undergo 48 training sessions during the bed-rest phase on a Sledge Jump System (SJS) that allows mimicking reactive jumps in a horizonal position at different gravity loads (see [ 32 ] for more details on the training sessions and [ 33 ] for more information about the training device). On the morning of the first bed-rest day, subjects were randomly assigned to either a passive control group that did not perform any training sessions, or to an exercise group, which participated in the scheduled horizontal training sessions. Subjects Eleven healthy male subjects were randomly assigned into two groups, the control group (CTRL group, n = 4; age 31 ± 7 years; height 179 ± 2.1 cm; weight 73 ± 8.7 kg) and the exercise group (JUMP group, n = 7; age 29.5 ± 6 years; height 184 ± 4.7 cm; weight 81.5 ± 4.1 kg). Before being selected as study participants, volunteers had to go through an information session, an interview, two psychological tests and an elaborate medical screening to be sure they were physically and mentally fit for extended period of immobilization (see [ 32 ] for further details). Moreover, prior to participating in the study, each participant provided written informed consent for the experimental procedures, which received approval from both the ethics committee of the Northern Rhine Medical Association (Ärztekammer Nordrhein) in Duesseldorf, Germany, and the Federal Office for Radiation Protection (Bundesamt für Strahlenschutz). All experiments and methods described herein were conducted in compliance with the guidelines and regulations of the ethic committees that approved the study. Gait speed measurements Laboratory gait speed LGS was assessed on a 10m track [ 12 ] at the subjects’ preferred gait speed. To assess spatiotemporal characteristics of the locomotor pattern it was used Optogait (Optogait; Microgate, Bolzano, Italy) and 2D kinematics (Panasoni, Simi Motion 2D, Simi Reality Motion Systems GmbH, Unterschleissheim, Deutschland). The data was extracted at sampling frequencies of 1000 Hz and 200 Hz. Trials were repeated twice and averaged. Participants were explicitly asked to perform the course at a normal or self-selected pace. Real-world gait speed RWS was assessed via a tri-axial accelerometer (actibelt®, Trium Analysis Online GmbH, Munich, Germany) placed on the frontal region below the umbilicus. The device can record acceleration of the body center of mass over the three spatial axes with a sample frequency of 100 Hz. Participants were asked to wear the actibelt® as much as possible during the day to capture all possible walking segments. They were instructed to remove it while taking showers or when sleeping. Wearing time was assessed electronically by the device with a switch that can determine whether the belt buckle is closed. Statistical analysis Statistical analysis was performed using R Studio v. 4.0.3 (RStudio: Integrated Development Environment for R, RStudio, Inc., Boston, MA). Data distribution of RWS and LWS was assessed with the Shapiro-Wilk test of normality [ 34 ] using the function stats::shapiro.test() and homogeneity of variance between RWS and LWS was tested using the Fligner-Killeen Test of Homogeneity of Variance [ 35 ] using the R function stats::fligner.test() . Bland-Altman [ 36 ] and Pearson’s product-moment correlation analysis [ 37 ] were performed to assess the levels of agreement between the RWS and LWS. Pearson’s product-moment correlation analysis was performed using the function stats::cor.test() , while the Bland-Altman plot was built using custom R functions. A paired-sample t-test was performed to assess whether LWS is significantly greater than RWS using the function stats::t.test() among all the samples and by study day. The p-values were adjusted for multiple comparisons using the Bonferroni correction [ 38 , 39 ]. Significance of the effect of test days ( R0, R7 and R13 ) on RWS and LWS was assessed by fitting a univariate linear regression model to the data using the variable of interest (RWS or LWS) as dependent variable and study phase ( R0, R7 and R13 ) as independent variable. The model was fitted by calling the stats::lm() R function. Subsequently, a Tukey Honest Significant Difference test [ 40 ] was used as post-hoc test to determine differences between study days. All plots were made using the R library ggplot2 , version 3.3.6. Group information (training or control) was not factored in any of the analyses due to the relatively small number of participants in each group, which precludes a reliable assessment of the outcome. Data are given in mean ± standard deviations. Declarations Data Availability The datasets used for producing the current work are available in the Open Science Framework repositories rsl2016/rawdata under the following link https://osf.io/uwfk8/?view_only=772e65b5370b4873abfe471e265d1c0c. Code Availability The code used for producing the current work is available in the Open Science Framework repository rsl2016 under the following link https://osf.io/uwfk8/?view_only=772e65b5370b4873abfe471e265d1c0c. Acknowledgement The authors want to thank all participants and personnel from DLR involved in the study that made it possible. Authors Contribution M.G., F.V.D.S. analyzed the data and drafted the manuscript. M.G. contributed to the data collection and implemented the statistical analysis. R.R. contributed to the data collection, data analysis and drafted the manuscript. J.B. reviewed the manuscript. U.M. contributed to study preparation and data management. E.M. supervised the study and revised the manuscript. J.R. participated in study design, study implementation, study preparation and supervised the data analysis. M.D. participated in the study design and supervised the data analysis. All authors reviewed the manuscript. Competing Interests The authors declare the following competing interests: MD is employed by Trium Analysis Online GmbH. MD serves as Scientific Director for Sylvia Lawry Centre for Multiple Sclerosis Research e.V. and, together with Trium Analysis Online GmbH has ownership of trademarks/design/patent applications linked to actibelt® technology. MG has a competing non/financial interest as Sylvia Lawry Center for Multiple Sclerosis Research e.V. is providing access to the actibelt® data and related algorithms to pursue a doctoral title. The remaining authors declare no competing interests. References Zhou, H. et al. Digital Biomarkers of Cognitive Frailty: The Value of Detailed Gait Assessment Beyond Gait Speed. Gerontology . 68(2), 224–233 (2022). 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Meaningful change and responsiveness in common physical performance measures in older adults. J. Am. Geriatr. Soc. 54, 743–749 (2006). Kwon, S. et al. What is a meaningful change in physical performance? findings from a clinical trial in older adults (the LIFE-P study). J. Nutr. Health Aging 13, 538–544 (2009). Rojer, A.G.M. et al. Robustness of In-Laboratory and Daily-Life Gait Speed Measures over One Year in High Functioning 61- to 70-Year-Old Adults. Gerontology . 67(6), 650–659 (2021). Floreani, M. et al. Effects of 14 days of bed rest and following physical training on metabolic cost, mechanical work, and efficiency during walking in older and young healthy males. PLoS One. 13(3), e0194291; 10.1371/journal.pone.0194291 (2018). Aarden, J.J. et al. Hospital-ADL study group. Longitudinal Changes in Muscle Mass, Muscle Strength, and Physical Performance in Acutely Hospitalized Older Adults. J Am Med Dir Assoc . 22(4), 839–845 (2021). Kramer, A. et al. High-Intensity Jump Training Is Tolerated during 60 Days of Bed Rest and Is Very Effective in Preserving Leg Power and Lean Body Mass: An Overview of the Cologne RSL Study. PLoS One . 12(1), e0169793; 10.1371/journal.pone.0169793 (2017). Kramer, A., Ritzmann, R., Gollhofer, A., Gehring, D., Gruber, M. A new sledge jump system that allows almost natural reactive jumps. J Biomech . 43(14), 2672–2677 (2010). Shapiro, S.S., Wilk, M.B. An analysis of variance test for normality (complete samples). Biometrika. 52(3–4), 591–611 (1965). Conover, W.J., Johnson, M.E., Johnson, M.M. A Comparative Study of Tests for Homogeneity of Variances, with Applications to the Outer Continental Shelf Bidding Data. Technometrics , 23(4), 351–361 (1981). Altman, D.G., Bland, J.M. Measurement in Medicine: The Analysis of Method Comparison Studies. Journal of the Royal Statistical Society . Series D (The Statistician) . 32(3), 307–317 (1983). Freedman, D., Pisani, R., Purves, R. Statistics (international student edition). (4th Ed. WW Norton & Company, New York, 2007). Abdi, H. The Bonferroni and Sidak corrections for multiple comparisons in Encyclopedia of measurement and statistics . (Sage, Thousand Oaks, 2007). Bonferroni, C.E. Teoria statistica delle classi e calcolo delle probabilità. 3–62 (Pubblicazioni dell’Istituto Superiore di Scienze Economiche e Commerciali di Firenze, 1936). Keselman, H.J., Rogan, J.C. The Tukey multiple comparison test: 1953–1976. Psychological Bulletin . 84(5), 1050–1056 (1977). Additional Declarations Competing interest reported. M.D. is employed by Trium Analysis Online GmbH. M.D. serves as Scientific Director for Sylvia Lawry Centre for Multiple Sclerosis Research e.V. and, together with Trium Analysis Online GmbH has ownership of trademarks/design/patent applications linked to actibelt® technology. M.G. has a competing non/financial interest as Sylvia Lawry Center for Multiple Sclerosis Research e.V. is providing access to the actibelt® data and related algorithms to pursue a doctoral title. The remaining authors declare no competing interests. Cite Share Download PDF Status: Posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3960673","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":275540122,"identity":"840f2317-c301-42bc-9b57-10a798aacafd","order_by":0,"name":"Marcello Grassi","email":"","orcid":"","institution":"Sylvia Lawry Centre for Multiple Sclerosis Research","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marcello","middleName":"","lastName":"Grassi","suffix":""},{"id":275540123,"identity":"1738ab88-665d-4a50-8571-65b2f1f5ccc3","order_by":1,"name":"Ramona Ritzmann","email":"","orcid":"","institution":"University of Freiburg","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ramona","middleName":"","lastName":"Ritzmann","suffix":""},{"id":275540124,"identity":"134219f0-dbda-42ec-9d55-54283af8ec7c","order_by":2,"name":"Fiona Von Der Straten","email":"","orcid":"","institution":"Technical University of Munich","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fiona","middleName":"Von Der","lastName":"Straten","suffix":""},{"id":275540125,"identity":"3940e1f8-56e4-4264-b082-c767c8478f8e","order_by":3,"name":"Jonas Böcker","email":"","orcid":"","institution":"German Aerospace Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jonas","middleName":"","lastName":"Böcker","suffix":""},{"id":275540126,"identity":"ebc105a9-3055-4abc-89a9-2fb93420528f","order_by":4,"name":"Uwe Mittag","email":"","orcid":"","institution":"German Aerospace Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Uwe","middleName":"","lastName":"Mittag","suffix":""},{"id":275540127,"identity":"ee8e6879-e73f-4b85-95a5-a0365257a7b6","order_by":5,"name":"Edwin Mulder","email":"","orcid":"","institution":"German Aerospace Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Edwin","middleName":"","lastName":"Mulder","suffix":""},{"id":275540128,"identity":"1e9d5a50-bb6c-418b-974c-ff0c42c5ab97","order_by":6,"name":"Martin Daumer","email":"","orcid":"","institution":"Sylvia Lawry Centre for Multiple Sclerosis Research","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Daumer","suffix":""},{"id":275540129,"identity":"81e4889c-9f17-488f-aec4-171c7c92d484","order_by":7,"name":"Jörn Rittweger","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYBACxgYIzQMTkGM4jszFp4WHDcIwZjhMQAscwLQkNhDSwtzefOxzAcM9GXv5HsOHXyrupfcd5n3A8KYCj8N6jiXPnsFQDHQYj7GxzJni3JmH2Q0Y55zBo2VGjjEzD0MCUAvvNmnJtoTcDYfZGJh52/Bpyf8M07L9t+S/hHQDsJZ/eG1hhtvC+LEhIQGipQGvX4AOMwBqOZb/WZrhWILhTKCWg3OO4dZi2N78mJmnIsGevflY4scfNQnyfMfbGB+8qcGjBewCAwiHGRYdB3BrYGCQR3HlD3xKR8EoGAWjYMQCAGRLRg/4Iu7hAAAAAElFTkSuQmCC","orcid":"","institution":"German Aerospace Center","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jörn","middleName":"","lastName":"Rittweger","suffix":""}],"badges":[],"createdAt":"2024-02-16 08:03:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3960673/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3960673/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51971093,"identity":"60663c35-8fd9-44c7-b731-f1255e4d66ec","added_by":"auto","created_at":"2024-03-04 18:50:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":100529,"visible":true,"origin":"","legend":"\u003cp\u003eBland-Altman plot and scatter plot comparing RWS (Real-world Walking Speed) and LWS (Laboratory-measured Walking Speed).\u003cbr\u003e\nPanel a: Bland-Altman plot illustrating the pairwise agreement between two measurements, RWS and LWS, across three study days. The x-axis represents the average of the two measurements, while the y-axis depicts the difference between them. A dashed horizontal line denotes the mean difference between the two measurements. Additionally, two dotted lines indicate the lower and upper limits of agreement. The plot is color-coded with three different shades of grey to differentiate the data points and regression lines corresponding to each of the three study days.\u003cbr\u003e\nPanel b: Scatter plot showing the relationship between LWS and RWS, both expressed in meters per second (m/s). The x-axis represents LWS, while the y-axis represents RWS. Each data point in the plot corresponds to a measurement obtained from the study. The plot is distinguished by three different shades of grey, each representing data collected on a different study day, together with the respective regression lines.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3960673/v1/940cbf28b2dcce6d05da3d3a.png"},{"id":51971094,"identity":"374c379f-9cba-4d49-87df-90d7b3cd02b7","added_by":"auto","created_at":"2024-03-04 18:50:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70253,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot illustrating the distribution of LWS (Laboratory-measured Walking Speed) and RWS (Real-world Walking Speed), both expressed in meters per second (m/s).\u003cbr\u003e\nPanel a: Boxplot of the distribution of LWS and RWS for all data points collected.\u003cbr\u003e\nPanel b: Boxplot of the distribution of LWS and RWS faceted by study day (R0, R7 and R13).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3960673/v1/ee158f69719a14c832d76f65.png"},{"id":51971095,"identity":"745acf1e-306c-4e48-a156-0432a7719864","added_by":"auto","created_at":"2024-03-04 18:50:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26320,"visible":true,"origin":"","legend":"\u003cp\u003eFamily-wise confidence level plot illustrating differences in mean levels of test day, expressed in meters per second (m/s) for LWS (Laboratory-measured Walking Speed) and RWS (Real-world Walking Speed).\u003cbr\u003e\nThe x-axis displays the differences in mean values between pairs of test days, while the y-axis represents the pairwise comparisons. The mean differences are accompanied by 95% confidence intervals, providing a measure of uncertainty around the estimated means. This plot aids in visualizing the significance of differences between test days while considering multiple comparisons simultaneously.\u003cbr\u003e\nPanel a: Family-wise confidence level plot for LWS.\u003cbr\u003e\nPanel b: Family-wise confidence level plot for RWS.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3960673/v1/d0d3a9d518ee12fefc89648d.png"},{"id":55264377,"identity":"f3b3d6b8-811f-4bb9-b416-3f51f2d6f915","added_by":"auto","created_at":"2024-04-25 01:41:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":910996,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3960673/v1/12f5fa0c-d29a-4bcc-8864-32b80e040a43.pdf"}],"financialInterests":"Competing interest reported. M.D. is employed by Trium Analysis Online GmbH. M.D. serves as Scientific Director for Sylvia Lawry Centre for Multiple Sclerosis Research e.V. and, together with Trium Analysis Online GmbH has ownership of trademarks/design/patent applications linked to actibelt® technology. M.G. has a competing non/financial interest as Sylvia Lawry Center for Multiple Sclerosis Research e.V. is providing access to the actibelt® data and related algorithms to pursue a doctoral title. \nThe remaining authors declare no competing interests.","formattedTitle":"Discrepancies in Walking Speed Measurements Post-Bed-Rest: A Comparative Analysis of Real-World vs. Laboratory Assessments","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGait speed is an important well-recognized parameter that reflects mobility and the overall well-being of a subject, as well as being an indicator of physical functioning, cognitive impairment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], disability, falls and mortality [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Slow self-selected walking speed is associated with a lower quality of life [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], symptoms of depression [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], higher healthcare utilization [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and higher mortality rate [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The role of gait speed is also linked to the assessment of the recovery process after surgery [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and after experimental bed-rest [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Due to its versatility in predicting different outcomes, as well as the relatively low cost and ease to administer, it has been named \u0026ldquo;\u003cem\u003ethe sixth vital sign\u003c/em\u003e\u0026rdquo; by Middleton [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, in recent years it became evident that self-selected walking speed in a controlled environment (e.g., laboratory gait tests) can differ from self-selected walking speed in real-world conditions [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Even the representativity of laboratory gait tests for real-world behavior has been recently questioned [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] as laboratory-based assessments, often conducted over short distances in controlled settings, may not capture the complexity of real-world ambulation. It was shown that real-world gait assessments using wearables are better at predicting fall risk in older people than clinical gait assessments [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and undoubtedly offers a more ecologically valid representation of daily mobility patterns [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Aforementioned distinctions are paramount when it comes to pathological conditions. Especially in the context of extended bed-rest, it is particularly important to represent gait capacity in realistic scenarios to support a conclusive clinical statement.\u003c/p\u003e \u003cp\u003eExperimental bed-rest studies are widely accepted to simulate the effects of microgravity in space on different physiological systems and therefore, they provide valuable insight in the physiological impact of neuromuscular and coordinative deconditioning. Obviously, that is also relevant to clinical cases of bed-rest. Immobilization by experimental bed-rest leads to a rapid degradation process encompassing most bodily structures, systems, and organs and, hence, a decline in the overall fitness and health status [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Particularly, gait course analyses after bed-rest of differing lengths showed that preferred walking speed, moderate running speed and spatio-temporal parameters decrease with the duration of bed-rest and results in delayed recovery curves after bed-rest [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, clear evidence on the diagnostic differences and specificities of wearable solutions in everyday life compared to laboratory tests is still unclear.\u003c/p\u003e \u003cp\u003eThe focus of this work is to study the differences between self-selected laboratory walking speed (LWS) and real-world walking speed (RWS) after 60 days of immobilization by experimental bed-rest. For that purpose, we assessed, in a prospective longitudinal study, gait speed at three equidistant time intervals after the end of bed-rest, named \u003cem\u003eR0\u003c/em\u003e, \u003cem\u003eR7\u003c/em\u003e and \u003cem\u003eR13\u003c/em\u003e, where the \u003cem\u003eR\u003c/em\u003e before the number indicates the recovery phase and the number after the \u003cem\u003eR\u003c/em\u003e indicates the day of the recovery phase in which the test was performed (e.g., \u003cem\u003eR7\u003c/em\u003e indicates the seventh day after bed-rest).\u003c/p\u003e \u003cp\u003eWe hypothesized that LWS and RWS would demonstrate major differences. Furthermore, we investigated whether the effect of time after bed-rest is reflected in LWS and RWS data.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eWearing time\u003c/h2\u003e \u003cp\u003eWearing time of the three-axial accelerometer throughout the three days of measurements was as follows (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd hours/day): \u003cem\u003eR0\u003c/em\u003e: 14.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9, \u003cem\u003eR7\u003c/em\u003e: 14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2 and \u003cem\u003eR13\u003c/em\u003e: 15.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eLevels of agreement\u003c/h2\u003e \u003cp\u003eBland-Altman showed little to no agreement between the two measurements with a mean difference of 0.77 m/s and the 95% confidence intervals for the limit of agreements were 0.23 m/s to 1.31 m/s, and no significant correlation (t\u003csub\u003e30\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.05, p-value\u0026thinsp;=\u0026thinsp;0.3, r\u0026thinsp;=\u0026thinsp;0.19).\u003c/p\u003e \u003cp\u003eLevel of agreement by study day showed also no agreement between RWS and LWS, with mean difference and 95% confidence intervals for the limit of agreements (expressed in m/s) of 0.83 [0.29 to 1.38], 0.59 [0.18 to 1] and 0.89 [0.38 to 1.4] for \u003cem\u003eR0\u003c/em\u003e, \u003cem\u003eR7\u003c/em\u003e and \u003cem\u003eR13\u003c/em\u003e, respectively. Also, the Pearson\u0026rsquo;s product-moment correlation showed little to no correlation for all three study days (\u003cem\u003eR0\u003c/em\u003e: t\u003csub\u003e9\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.71, p-value\u0026thinsp;=\u0026thinsp;0.12; r\u0026thinsp;=\u0026thinsp;0.49; \u003cem\u003eR7\u003c/em\u003e: t\u003csub\u003e9\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.27, p-value\u0026thinsp;=\u0026thinsp;0.23, r\u0026thinsp;=\u0026thinsp;0.39; \u003cem\u003eR13\u003c/em\u003e: t\u003csub\u003e8\u003c/sub\u003e = -0.64, p-value\u0026thinsp;=\u0026thinsp;0.54, r = -0.22). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e present and summarize the results reported in this section.\u003c/p\u003e \u003cp\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\u003eStatistics values of the Bland-Altman and Pearson\u0026rsquo;s product-moment correlation by study day and with all study days combined.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eStudy day\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eStatistics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCombined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBland-Altman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Bias\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpper Limit of Agreement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower Limit of Agreement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePearson's product-moment correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson\u0026rsquo;s correlation coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDegree of Freedom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMoreover, in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, panel a, it can be seen that there is a positive correlation (t\u003csub\u003e30\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.73, p-value\u0026thinsp;=\u0026thinsp;0.01; r\u0026thinsp;=\u0026thinsp;0.45) between the difference of measurements (y-axis) and the mean of measurements (x-axis) suggesting an unequal variance between the two compared measurements. As values of RWS are not normally distributed (W\u0026thinsp;=\u0026thinsp;0.91, p-value\u0026thinsp;=\u0026thinsp;0.012), the Fligner-Killeen Test of Homogeneity of Variances was carried out to compare both variances. The test showed no significant difference between the two variances (RWS variance\u0026thinsp;=\u0026thinsp;0.03, LWS variance\u0026thinsp;=\u0026thinsp;0.07, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}^{2}\\)\u003c/span\u003e\u003c/span\u003e= 1.86, p-value\u0026thinsp;=\u0026thinsp;0.173).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSpeed difference between RWS and LWS\u003c/h2\u003e \u003cp\u003eThe paired-sample t-test showed that RWS (mean\u0026thinsp;=\u0026thinsp;0.78 m/s, sd\u0026thinsp;=\u0026thinsp;0.16 m/s) was significantly lower than LWS (mean\u0026thinsp;=\u0026thinsp;1.55 m/s, sd\u0026thinsp;=\u0026thinsp;0.26 m/s), t\u003csub\u003e31\u003c/sub\u003e = -15.78, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003eFurthermore, paired-sample t-test executed on the data grouped by study day showed that for all the three study days RWS was significantly lower than LWS (R0: t\u003csub\u003e10\u003c/sub\u003e = -10.02, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001; R7: t\u003csub\u003e10\u003c/sub\u003e = -9.32, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001; R13: t\u003csub\u003e9\u003c/sub\u003e = -10.88, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 \u0026ndash; see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for the t-test results). All the p-values were adjusted using the Bonferroni correction for multiple comparisons.\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 paired-sample t-test. * p-value adjusted with Bonferroni correction for multiple comparisons.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR13\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd (LWS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26 m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23 m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22 m/s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd (RWS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16 m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 m/s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute Difference (|RWS \u0026ndash; LWS|)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77 m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83 m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59 m/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89 m/s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDegree of Freedom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et-statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-15.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-value*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEffect of test days\u003c/h2\u003e \u003cp\u003eThe univariate linear regression model fitted on the RWS data showed a significant effect of the test days on RWS (F\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;63.01, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). On the other hand, the univariate linear regression model fitted on the LWS data did not show any significant effect of the test days (F\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.548, p-value\u0026thinsp;=\u0026thinsp;0.23) (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Subsequent post-hoc testing on the results of the linear regression model performed on the RWS data showed that all the differences tested in the pairwise comparisons (R13-R0, R7-R0 and R7-R13) were significant (p-values\u0026thinsp;=\u0026thinsp;R13-R0: \u0026lt;0.001; R7-R0: \u0026lt;0.001; R7-R13: \u0026lt;0.001 \u0026ndash; see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for a summary of the results of the post-hoc testing, and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for a graphical representation of the pairwise differences).\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\u003edetails about the two univariate linear regression models used to determine effect of test days on LWS and RWS respectively.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eLWS - univariate linear regression model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEstimate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eStd. Error\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003et-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eF-statistics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eadjusted R\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eRWS - univariate linear regression model\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEstimate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eStd. Error\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003et-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eF-statistics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eadjusted R\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e63.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eSummary of the TUKEY HSD post-hoc test for the pairwise comparisons of the effects of test days on LWS and RWS respectively.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTukey Honest Significant Difference test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLWS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRWS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDifference (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eadjusted p-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eDifference (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eadjusted p-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR7-R0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.096 (-0.169:0.361)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34 (0.265:0.415)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR13-R0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.193 (-0.078:0.465)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.138 (0.061:0.215)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR13-R7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.097 (-0.175:0.369)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.202 (-0.279:-0.125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides an important insight into gait assessment within the context of physical impairments, delineating between two distinct methods of assessing gait speed. Results from the Bland-Altman analysis indicates a lack of agreement between the two measurements, with LWS being, on average, greater than RWS by approximately 0.77 m/s. Notably, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, panel b, this discrepancy persisted across all subjects and study days. The Pearson\u0026rsquo;s correlation analysis confirmed the lack of agreement, as little to no correlation between RWS and LWS was found. Combined, it becomes evident that walking in a controlled laboratory settings differs significantly from everyday ambulation. Many factors can be attributed to the observed difference. Research by Hillel et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] suggests that typical walking in natural environments more closely resembles dual-task walking in a controlled environment. Hillel et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] investigated five commonly used spatial-temporal features of gait quality, including gait speed, in three different settings: in-lab usual walking gait speed, in-lab dual-tasking gait speed and daily-living gait speed. Comparing the gait measurements for the three different settings for a cohort of 150 people revealed that in-lab usual walking gait speed does not agree with measures obtained during daily-living, being significantly faster than daily-living walking speed. Even in-lab dual-tasking gait speed measures, which are overall closer to daily-living gait speed, do not mirror gait speed during daily-living. Consequently, Hillel et al. concluded that, generally-speaking, in-lab measures of gait cannot accurately reflect daily-living gait measures. The findings are in line with our results, indicating that in-lab measurements (LWS) of gait speed before and after physical impairment cannot be equated with the daily walking behavior regarding RWS. Hence, LWS and RWS both contain valuable information about a person\u0026rsquo;s gait characteristics, however, cannot be treated as equal measurements detached from their environmental contexts.\u003c/p\u003e \u003cp\u003eFurthermore, in another study presented by Kawai [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], the authors explored the relationship between daily living walking speed (DWS) and laboratory-measured walking speed (LWS) in a cohort of 90 elderly individuals. They intended to find out whether DWS serves as reliable indicator of physical function and frailty. Participants were asked to carry a smartphone equipped with a global positioning system (GPS) application for measuring their DWS for one month. During regular checkups, participants performed gait tests in a laboratory to measure LWS at normal and maximum pace. Kawai et al. showed that DWS and LWS (both average and maximum measurements) differ from each other with a mean difference of 0.14 m/s and 0.1 m/s, respectively. While these differences are smaller in magnitude compared to those observed in our study (see Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for gait speed differences overview), they may still hold clinical significance, as suggested by previous studies [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Indeed, the study of Kawai showed that DWS measures can be associated with physical performance measurements, hence, Kawai et al. conclude that DWS likely reflects the participants\u0026rsquo; physical function. Both the study by Kawai et al. as well as the results of our data analysis underscore that RWS and LWS contain different information about a person\u0026rsquo;s physical functioning. Kawai et al. also highlight the potential of DWS to assess adverse health outcomes in the future as it can be measured over a long period of time and in different situations compared to LWS.\u003c/p\u003e \u003cp\u003eIn another study that focused on the robustness of in-laboratory and daily-life gait speed measures [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], the interrelation between laboratory and daily life gait measures was assessed. Gait measures of 189 elderly people in daily life were collected over the course of one year, as well as regular and frequent intervals in a laboratory environment. Calculating the Pearson\u0026rsquo;s correlations for in-laboratory and daily-life gait speed revealed negligible to low correlations for all investigated time points. Even though overall correlations increased with higher percentiles of daily-life gait speed, the authors only identified a consistent dissonant relationship between in-laboratory and daily-life gait measures. The authors concluded that both types of gait speed measures represent distinct personal features of a population of elderly people. As both RWS as well as LWS are clinically relevant measures, investigating both measures potentially yield more meaningful insights into actual daily-life physical behavior and improve predicting health outcomes.\u003c/p\u003e \u003cp\u003eThe present study revealed that only RWS, and not LWS, was affected by the bed-rest. In contrast previous studies have reported a significant decline and duration-dependent recovery in laboratory gait speed associated with physiological degradation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. As such changes were absent in the present, well-controlled study, we conclude that the transferability and significance of laboratory measurements for the real movement patterns in the clinical context must be judged very critically and interpreted thoroughly. The findings presented herein support the notion that walking speed assessed in uncontrolled environments is more sensitive to changes compared to that measured in controlled settings. This is supported by Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, panel b, where differences in walking speed are discernible along the y-axis (RWS) but not along the x-axis (LWS). However, this observed dissimilarity should not be solely attributed to differences in measurement sensitivity; it may also stem from distinct underlying mechanisms involved in task execution, as proposed by Takayanagi [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and Hillel [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLastly, the high wearing time of the tri-axial accelerometer throughout the study period underscores the feasibility and practicality of continuous monitoring of RWS in real-world setting, enhancing the ecological validity of mobility assessment. However, despite the extensive wear time, the limited number of samples makes it difficult to know to which extend what observed by the data is generalizable. Furthermore, subjects were restricted to move in the DLR wards on 1000m\u003csup\u003e2\u003c/sup\u003e, so the \u0026lsquo;real-world\u0026rsquo; walking bouts they could make were limited to the different stations/areas they had to go for e.g., testing/monitoring, etc. Moreover, it cannot be excluded that participants\u0026rsquo; RWS may have been influenced by either DLR personnel while walking together to testing stations or DLR\u0026rsquo;s ward areas, or by others study participants. While it is reasonable to assume that adjustments to walking speed would be made by the DLR personnel to align with participants\u0026rsquo; capabilities, mitigating the risk of injury or discomfort, this may not always have been the case when participants from different recovery days (e.g., \u003cem\u003eR3\u003c/em\u003e and \u003cem\u003eR11\u003c/em\u003e) walked together for the DLR\u0026rsquo;s ward. In such instances, it is conceivable that one participant may have had to adapt its RWS to match the other participant\u0026rsquo;s RWS, potentially introducing bias to RWS values either upwards or downward. Lastly, participants were instructed to not overdo physical activity on the first days of ambulation after bed-rest as they would become extremely sore from muscle soreness.\u003c/p\u003e \u003cp\u003eIn conclusion, the observed differences between RWS and LWS highlight the complexities of mobility assessment paradigms and the need for comprehensive evaluation methodologies that encompass both laboratory-based and real-world assessments. These findings have significant implications for clinical practice and research, emphasizing the importance of considering contextual factors and temporal variations in mobility measurements.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eA prospective longitudinal cohort study was performed at the :\u003cem\u003eenvihab\u003c/em\u003e research laboratory of the Institute of Aerospace Medicine at the German Aerospace Center - Deutsches Zentrum f\u0026uuml;r Luft- und Raumfahrt (DLR) in Cologne, Germany. The bed-rest study took place from February to April 2016 and lasted for 60 consecutive days plus an in-house period of two weeks before and after the bed-rest. Thus, each subject had a two-week Baseline Data Collection (BDC) phase before immobilization, a 60-day 6\u0026deg; head down tilt bed-rest, and another two-week recovery (R) phase. This extended period of 3 months was the second campaign of a bigger study named RSL (Reactive Jumps in a Sledge Jump System as a Countermeasure during Long-Term Bed Rest 2015\u0026ndash;2016) (see [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] for more details about the RSL study). During the two two-week phases before and after the immobilization phase, subjects were confined to the DLR ward to undergo various measurement procedures (including gait tests).\u003c/p\u003e \u003cp\u003eAs a countermeasure against the effect of immobilization by experimental bed rest, seven subjects were assigned to undergo 48 training sessions during the bed-rest phase on a Sledge Jump System (SJS) that allows mimicking reactive jumps in a horizonal position at different gravity loads (see [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] for more details on the training sessions and [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] for more information about the training device). On the morning of the first bed-rest day, subjects were randomly assigned to either a passive control group that did not perform any training sessions, or to an exercise group, which participated in the scheduled horizontal training sessions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003eEleven healthy male subjects were randomly assigned into two groups, the control group (CTRL group, n\u0026thinsp;=\u0026thinsp;4; age 31\u0026thinsp;\u0026plusmn;\u0026thinsp;7 years; height 179\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 cm; weight 73\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7 kg) and the exercise group (JUMP group, n\u0026thinsp;=\u0026thinsp;7; age 29.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6 years; height 184\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 cm; weight 81.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 kg). Before being selected as study participants, volunteers had to go through an information session, an interview, two psychological tests and an elaborate medical screening to be sure they were physically and mentally fit for extended period of immobilization (see [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] for further details). Moreover, prior to participating in the study, each participant provided written informed consent for the experimental procedures, which received approval from both the ethics committee of the Northern Rhine Medical Association (\u0026Auml;rztekammer Nordrhein) in Duesseldorf, Germany, and the Federal Office for Radiation Protection (Bundesamt f\u0026uuml;r Strahlenschutz). All experiments and methods described herein were conducted in compliance with the guidelines and regulations of the ethic committees that approved the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGait speed measurements\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eLaboratory gait speed\u003c/h2\u003e \u003cp\u003eLGS was assessed on a 10m track [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] at the subjects\u0026rsquo; preferred gait speed. To assess spatiotemporal characteristics of the locomotor pattern it was used Optogait (Optogait; Microgate, Bolzano, Italy) and 2D kinematics (Panasoni, Simi Motion 2D, Simi Reality Motion Systems GmbH, Unterschleissheim, Deutschland). The data was extracted at sampling frequencies of 1000 Hz and 200 Hz. Trials were repeated twice and averaged.\u003c/p\u003e \u003cp\u003eParticipants were explicitly asked to perform the course at a \u003cem\u003enormal\u003c/em\u003e or \u003cem\u003eself-selected\u003c/em\u003e pace.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eReal-world gait speed\u003c/h2\u003e \u003cp\u003eRWS was assessed via a tri-axial accelerometer (actibelt\u0026reg;, Trium Analysis Online GmbH, Munich, Germany) placed on the frontal region below the umbilicus. The device can record acceleration of the body center of mass over the three spatial axes with a sample frequency of 100 Hz. Participants were asked to wear the actibelt\u0026reg; as much as possible during the day to capture all possible walking segments. They were instructed to remove it while taking showers or when sleeping. Wearing time was assessed electronically by the device with a switch that can determine whether the belt buckle is closed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using R Studio v. 4.0.3 (RStudio: Integrated Development Environment for R, RStudio, Inc., Boston, MA).\u003c/p\u003e \u003cp\u003eData distribution of RWS and LWS was assessed with the Shapiro-Wilk test of normality [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] using the function \u003cem\u003estats::shapiro.test()\u003c/em\u003e and homogeneity of variance between RWS and LWS was tested using the Fligner-Killeen Test of Homogeneity of Variance [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] using the R function \u003cem\u003estats::fligner.test()\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eBland-Altman [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and Pearson\u0026rsquo;s product-moment correlation analysis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] were performed to assess the levels of agreement between the RWS and LWS. Pearson\u0026rsquo;s product-moment correlation analysis was performed using the function \u003cem\u003estats::cor.test()\u003c/em\u003e, while the Bland-Altman plot was built using custom R functions.\u003c/p\u003e \u003cp\u003eA paired-sample t-test was performed to assess whether LWS is significantly greater than RWS using the function \u003cem\u003estats::t.test()\u003c/em\u003e among all the samples and by study day. The p-values were adjusted for multiple comparisons using the Bonferroni correction [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSignificance of the effect of test days (\u003cem\u003eR0, R7\u003c/em\u003e and \u003cem\u003eR13\u003c/em\u003e) on RWS and LWS was assessed by fitting a univariate linear regression model to the data using the variable of interest (RWS or LWS) as dependent variable and study phase (\u003cem\u003eR0, R7\u003c/em\u003e and \u003cem\u003eR13\u003c/em\u003e) as independent variable. The model was fitted by calling the \u003cem\u003estats::lm()\u003c/em\u003e R function. Subsequently, a Tukey Honest Significant Difference test [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] was used as post-hoc test to determine differences between study days. All plots were made using the R library \u003cem\u003eggplot2\u003c/em\u003e, version 3.3.6.\u003c/p\u003e \u003cp\u003eGroup information (training or control) was not factored in any of the analyses due to the relatively small number of participants in each group, which precludes a reliable assessment of the outcome.\u003c/p\u003e \u003cp\u003eData are given in mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe datasets used for producing the current work are available in the Open Science Framework repositories rsl2016/rawdata under the following link https://osf.io/uwfk8/?view_only=772e65b5370b4873abfe471e265d1c0c.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe code used for producing the current work is available in the Open Science Framework repository rsl2016 under the following link https://osf.io/uwfk8/?view_only=772e65b5370b4873abfe471e265d1c0c.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors want to thank all participants and personnel from DLR involved in the study that made it possible.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAuthors Contribution\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eM.G., F.V.D.S. analyzed the data and drafted the manuscript. M.G. contributed to the data collection and implemented the statistical analysis. R.R. contributed to the data collection, data analysis and drafted the manuscript. J.B. reviewed the manuscript. U.M. contributed to study preparation and data management. E.M. supervised the study and revised the manuscript. J.R. participated in study design, study implementation, study preparation and supervised the data analysis. M.D. participated in the study design and supervised the data analysis. All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors declare the following competing interests:\u003c/p\u003e\n\u003cp\u003eMD is employed by Trium Analysis Online GmbH. MD serves as Scientific Director for Sylvia Lawry Centre for Multiple Sclerosis Research e.V. and, together with Trium Analysis Online GmbH has ownership of trademarks/design/patent applications linked to actibelt\u0026reg; technology. MG has a competing non/financial interest as Sylvia Lawry Center for Multiple Sclerosis Research e.V. is providing access to the actibelt\u0026reg; data and related algorithms to pursue a doctoral title.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe remaining authors declare no competing interests.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhou, H. et al. Digital Biomarkers of Cognitive Frailty: The Value of Detailed Gait Assessment Beyond Gait Speed. \u003cem\u003eGerontology\u003c/em\u003e. 68(2), 224\u0026ndash;233 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerghese, J., Wang, C., \u0026amp; Holtzer, R. 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The Tukey multiple comparison test: 1953\u0026ndash;1976. \u003cem\u003ePsychological Bulletin\u003c/em\u003e. 84(5), 1050\u0026ndash;1056 (1977).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-3960673/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3960673/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding differences between real-world walking speed (RWS) and laboratory-measured walking speed (LWS) is crucial for comprehensive mobility assessments, especially in context of prolonged immobilization. This study aimed to investigate disparities in walking speed following a 60-day bed-rest period. In eleven male subjects, RWS was continuously monitored using a tri-axial accelerometer worn on the waist, while LWS was assessed via a 10-meter walk test at preferred speed, on three different study days after immobilization. Statistical analyses included Bland-Altman and Pearson's correlation to evaluate agreement between RWS and LWS, alongside paired-sample t-tests and univariate linear regression models to assess significance of differences and temporal effects on gait speed.\u003c/p\u003e \u003cp\u003eResults of Bland-Altman analysis showed no agreement between RWS and LWS (mean difference 0.77 m/s) and nonsignificant correlation (r\u0026thinsp;=\u0026thinsp;0.19, p-value\u0026thinsp;=\u0026thinsp;0.3). Paired-sample t-tests indicated significantly lower RWS compared to LWS for all study days (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Univariate linear regression models demonstrated a significant effect of test day on RWS (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but not on LWS (p-value\u0026thinsp;=\u0026thinsp;0.23).\u003c/p\u003e \u003cp\u003eThese findings emphasize the importance of integrating both assessments to capture comprehensive mobility changes following prolonged periods of inactivity. Particularly significant is that RWS is constantly lower than LWS, with the former being more representative as it reflects what normally participants would do when not under observation. Lastly, understanding discrepancies between RWS and LWS would allow for more appropriate rehabilitation programs to speed up recovery while simultaneously keeping the rehabilitation safe and tailored.\u003c/p\u003e","manuscriptTitle":"Discrepancies in Walking Speed Measurements Post-Bed-Rest: A Comparative Analysis of Real-World vs. Laboratory Assessments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-04 18:50:03","doi":"10.21203/rs.3.rs-3960673/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":"d3658a12-52e5-4c86-b756-bac545cb9a77","owner":[],"postedDate":"March 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29045518,"name":"Health sciences/Anatomy/Musculoskeletal system"},{"id":29045519,"name":"Health sciences/Biomarkers"},{"id":29045520,"name":"Health sciences/Health care"},{"id":29045521,"name":"Health sciences/Signs and symptoms"},{"id":29045522,"name":"Health sciences/Medical research/Biomarkers"},{"id":29045523,"name":"Health sciences/Medical research/Outcomes research"}],"tags":[],"updatedAt":"2024-04-22T05:11:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-04 18:50:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3960673","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3960673","identity":"rs-3960673","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}