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 1000m2, 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.