Walking indoors, outdoors, and on a treadmill: Gait differences in healthy young and older adults

Schmitt, Abigail C.; Baudendistel, Sidney T.; Lipat, Ania L.; White, Tatiana; Raffegeau, Tiphanie E.; Hass, Chris J.

doi:10.1016/j.gaitpost.2021.09.197

Cited by 21 publications

(18 citation statements)

References 36 publications

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“…Second, the difference in set-up where walking on a treadmill and walking on a street pose different requirements on cognitive and motion abilities so that the resulting gait and movement features may not directly be comparable. A previous study reported a slower gait with shorter, less variable strides during treadmill walking compared with walking outdoors on the sideway in young and older adults ( Schmitt et al, 2021 ). Thus, walking on a motorized treadmill may reduce the variability of gait characteristics compared with walking outdoors, thereby also reducing disorientation-induced changes in gait characteristics.…”

Section: Discussionmentioning

confidence: 97%

Prediction of Disorientation by Accelerometric and Gait Features in Young and Older Adults Navigating in a Virtually Enriched Environment

Teipel

Amaefule²,

Lüdtke³

et al. 2022

Front. Psychol.

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ObjectiveTo determine whether gait and accelerometric features can predict disorientation events in young and older adults.MethodsCognitively healthy younger (18–40 years, n = 25) and older (60–85 years, n = 28) participants navigated on a treadmill through a virtual representation of the city of Rostock featured within the Gait Real-Time Analysis Interactive Lab (GRAIL) system. We conducted Bayesian Poisson regression to determine the association of navigation performance with domain-specific cognitive functions. We determined associations of gait and accelerometric features with disorientation events in real-time data using Bayesian generalized mixed effect models. The accuracy of gait and accelerometric features to predict disorientation events was determined using cross-validated support vector machines (SVM) and Hidden Markov models (HMM).ResultsBayesian analysis revealed strong evidence for the effect of gait and accelerometric features on disorientation. The evidence supported a relationship between executive functions but not visuospatial abilities and perspective taking with navigation performance. Despite these effects, the cross-validated percentage of correctly assigned instances of disorientation was only 72% in the SVM and 63% in the HMM analysis using gait and accelerometric features as predictors.ConclusionDisorientation is reflected in spatiotemporal gait features and the accelerometric signal as a potentially more easily accessible surrogate for gait features. At the same time, such measurements probably need to be enriched with other parameters to be sufficiently accurate for individual prediction of disorientation events.

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Section: Discussionmentioning

confidence: 97%

Prediction of Disorientation by Accelerometric and Gait Features in Young and Older Adults Navigating in a Virtually Enriched Environment

Teipel

Amaefule²,

Lüdtke³

et al. 2022

Front. Psychol.

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“…This aspect is important for senior citizens to maintain independence in situations of daily living when faster walking speeds, e.g., crossing a street, is required [ 45 ]. In addition, walking on a treadmill differs from ground walking and changes the gait parameters [ 46 , 47 ]. This effect might be stronger depending on previous treadmill experiences.…”

Section: Discussionmentioning

confidence: 99%

Relationship between Muscle Strength and Gait Parameters in Healthy Older Women and Men

Stotz

Hamacher

Zech

2023

IJERPH

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Maintaining sufficient muscle strength is fundamental to prevent a decline in basic physical functions such as gait, and is therefore a prerequisite for a healthy independent life in older people. However, the relationship between gait parameters and the strength of single muscle groups is reported with inconclusive results. The objective of this study was to analyze the relationship of strength of nine single muscle groups of lower and upper leg muscles as well as handgrip strength for gait parameters in older adults. Sixty-nine independently living older adults participated in the study. Maximum ankle plantar- and dorsiflexion, knee flexion and extension, as well as hip abduction, adduction, flexion, and extension strength, were measured using an isokinetic dynamometer. Additionally, hand grip strength measured via a hand dynamometer was obtained. Walking gait parameters were recorded with a 3D motion capture system on an instrumented treadmill. The relationships between multiple strength and gait variables were analyzed by Pearson’s correlation coefficient. Linear regression analyses were performed to identify the predictive ability of muscle strength (normalized to body weight) for gait speed, stride time, stance time, stride length and step width. Multiple significant weak to moderate positive ([r = 0.343, p = 0.047]–[r = 0.538, p = 0.002]) and negative ([r = −0.340, p = 0.046]–[r = 0.593, p = 0.001]) correlations that were unequally distributed between both sexes were detected. Significant regression models explained ([r2 = 16.6%, p = 0.015]–[r2 = 44.3 %, p = 0.003]) and ([r2 = 21.8%, p = 0.022]–[r2 = 36.1%, p = 0.044]) of the gait parameter variations for men and women, respectively. The results suggest a sex-specific relevance of single muscle groups for all gait parameters. This may be attributed to anatomical differences and it is important to prevent strength-related changes in gait parameters.

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“…Second, all experiments were conducted indoors because the MoCap results were used as the gold standard. However, according to previous studies, different gait kinematic results are obtained depending on the test environment, including whether the subjects are barefoot or in shoes [ 42 ], indoors or outdoors [ 43 ], male or female [ 44 ], and young or old [ 45 ]. Thus, in future studies, a larger group of participants with differences in demographic features, such as sex, age, height, and weight, needs to be recruited, and experiments should be conducted in various environments.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Lower Extremity Multi-Joint Angles during Overground Walking by Using a Single IMU with a Low Frequency Based on an LSTM Recurrent Neural Network

Sung

Han

Park

et al. 2021

Sensors

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The joint angle during gait is an important indicator, such as injury risk index, rehabilitation status evaluation, etc. To analyze gait, inertial measurement unit (IMU) sensors have been used in studies and continuously developed; however, they are difficult to utilize in daily life because of the inconvenience of having to attach multiple sensors together and the difficulty of long-term use due to the battery consumption required for high data sampling rates. To overcome these problems, this study propose a multi-joint angle estimation method based on a long short-term memory (LSTM) recurrent neural network with a single low-frequency (23 Hz) IMU sensor. IMU sensor data attached to the lateral shank were measured during overground walking at a self-selected speed for 30 healthy young persons. The results show a comparatively good accuracy level, similar to previous studies using high-frequency IMU sensors. Compared to the reference results obtained from the motion capture system, the estimated angle coefficient of determination (R2) is greater than 0.74, and the root mean square error and normalized root mean square error (NRMSE) are less than 7° and 9.87%, respectively. The knee joint showed the best estimation performance in terms of the NRMSE and R2 among the hip, knee, and ankle joints.

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Walking indoors, outdoors, and on a treadmill: Gait differences in healthy young and older adults

Cited by 21 publications

References 36 publications

Prediction of Disorientation by Accelerometric and Gait Features in Young and Older Adults Navigating in a Virtually Enriched Environment

Prediction of Disorientation by Accelerometric and Gait Features in Young and Older Adults Navigating in a Virtually Enriched Environment

Relationship between Muscle Strength and Gait Parameters in Healthy Older Women and Men

Prediction of Lower Extremity Multi-Joint Angles during Overground Walking by Using a Single IMU with a Low Frequency Based on an LSTM Recurrent Neural Network

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