Ratings of perceived fatigue predict fatigue induced declines in muscle strength during tasks with different distributions of effort and recovery

Whittaker, Rachel L.; Sonne, Mette P.; Potvin, Jim R.

doi:10.1016/j.jelekin.2019.05.012

Cited by 32 publications

(20 citation statements)

References 20 publications

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“…While the relationship between perception of fatigue and quantitative measures of fatigue has not been clarified, there are reports of linearity between the Borg Category‐Ratio scale and the intensity of isometric contractions involving the flexor pollicis longus (Whittaker, Sonne, Sonne, & Potvin, 2019), quadriceps (Pincivero, Coelho, Coelho, & Erikson, 2000), and elbow flexors (Kavanagh, McFarland, McFarland, & Taylor, 2019). Moreover, sex‐differences in perceived exertion may be most evident for isometric contractions, as perceptions of effort during dynamic contractions do not typically differ between men and women (Hunter, 2009; Springer & Pincivero, 2010).…”

Section: Discussionmentioning

confidence: 99%

Sex differences in muscle activity emerge during sustained low‐intensity contractions but not during intermittent low‐intensity contractions

2020

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Sex differences in motor performance may arise depending on the mode of contraction being performed. In particular, contractions that are held for long durations, rather than contractions that are interspersed with rest periods, may induce greater levels of fatigue in men compared to women. The purpose of this study was to examine fatigue responses in a cohort of healthy men (n = 7, age [mean] = 21.6 ± [SD]1.1 year) and women (n = 7, age: 22.0 ± 2.0 year) during sustained isometric and intermittent isometric contractions. Two contraction protocols were matched for intensity (20% MVC) and total contraction time (600-s). Biceps brachii EMG and elbow flexion torque steadiness were examined throughout each protocol, and motor nerve stimulation was used to quantify central and peripheral fatigue. Overall, there were few sex-related differences in the fatigue responses during intermittent contractions. However, men exhibited progressively lower maximal torque generation (39% versus 27% decrease), progressively greater muscle activity (220% versus 144% increase), progressively greater declines in elbow flexion steadiness (354% versus 285% decrease), and progressively greater self-perception of fatigue (Borg scale:8.8 ± 1.2 versus 6.3 ± 1.1) throughout the sustained contractions. The mechanism underlying fatigue responses had a muscle component, as voluntary activation of the biceps brachii did not differ between sexes, but the amplitude of resting twitches decreased throughout the sustained contractions (m: 32%, w: 10% decrease). As generating large sustained forces causes a progressive increase in intramuscular pressure and mechanical occlusion-which has the effect of enhancing metabolite accumulation and peripheral fatigue-it is likely that the greater maximal strength of men contributed to their exacerbated levels of fatigue.

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Section: Discussionmentioning

confidence: 99%

Sex differences in muscle activity emerge during sustained low‐intensity contractions but not during intermittent low‐intensity contractions

2020

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“…The troughs indicate that the volunteer has reached the release position, while peaks indicate that they have reached the contraction position, as demonstrated in Section 2.1. A recent work on quantifying muscle fatigue has selected an RPE value of 16 as the threshold of true fatigue to estimate the declines in muscle strength during tasks [23]. Therefore, we extract and label each repetition manually according to the RPE values reported for the set, where we label repetitions with reported RPE values larger than 16 as fatigue.…”

Section: Data Processing and Feature Extractionmentioning

confidence: 99%

“…For this reason, we focus on studying the biceps concentration curls exercise, which involves flexing one of the most active skeletal muscles at the elbow joint countless times to pick, lift, and pull objects [12,13]. We collect a dataset for biceps concentration curls from 20 volunteers aged between [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. Then, we analyze the data patterns that occurred in repetitions during the presence of fatigue and extract all the non-fatigue repetitions into a subset.…”

Section: Introductionmentioning

confidence: 99%

On the Impact of Biceps Muscle Fatigue in Human Activity Recognition

Elshafei

Costa

Shihab

2021

Sensors

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Nowadays, Human Activity Recognition (HAR) systems, which use wearables and smart systems, are a part of our daily life. Despite the abundance of literature in the area, little is known about the impact of muscle fatigue on these systems’ performance. In this work, we use the biceps concentration curls exercise as an example of a HAR activity to observe the impact of fatigue impact on such systems. Our dataset consists of 3000 biceps concentration curls performed and collected from 20 volunteers aged between 20–35. Our findings indicate that fatigue often occurs in later sets of an exercise and extends the completion time of later sets by up to 31% and decreases muscular endurance by 4.1%. Another finding shows that changes in data patterns are often occurring during fatigue presence, causing seven features to become statistically insignificant. Further findings indicate that fatigue can cause a substantial decrease in performance in both subject-specific and cross-subject models. Finally, we observed that a Feedforward Neural Network (FNN) showed the best performance in both cross-subject and subject-specific models in all our evaluations.

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“…The notion of fatigue was added to the definition of Borg by Noble et al [29] and emphasized by the American College of Sports and Medicine [30]. The Borg scale has been reported to be a good estimator of muscle fatigue after exhaustive tasks [31,32]. Moreover, it was validated to be a reliable outcome in the fatigue assessment of MS patients [33].…”

Section: Perceived Fatiguementioning

confidence: 99%

Inertial sensor-based gait parameters reflect patient-reported fatigue in multiple sclerosis

Ibrahim

Küderle

Gaßner

et al. 2020

J NeuroEngineering Rehabil

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Background Multiple sclerosis (MS) is a disabling disease affecting the central nervous system and consequently the whole body’s functional systems resulting in different gait disorders. Fatigue is the most common symptom in MS with a prevalence of 80%. Previous research studied the relation between fatigue and gait impairment using stationary gait analysis systems and short gait tests (e.g. timed 25 ft walk). However, wearable inertial sensors providing gait data from longer and continuous gait bouts have not been used to assess the relation between fatigue and gait parameters in MS. Therefore, the aim of this study was to evaluate the association between fatigue and spatio-temporal gait parameters extracted from wearable foot-worn sensors and to predict the degree of fatigue. Methods Forty-nine patients with MS (32 women; 17 men; aged 41.6 years, EDSS 1.0–6.5) were included where each participant was equipped with a small Inertial Measurement Unit (IMU) on each foot. Spatio-temporal gait parameters were obtained from the 6-min walking test, and the Borg scale of perceived exertion was used to represent fatigue. Gait parameters were normalized by taking the difference of averaged gait parameters between the beginning and end of the test to eliminate inter-individual differences. Afterwards, normalized parameters were transformed to principle components that were used as input to a Random Forest regression model to formulate the relationship between gait parameters and fatigue. Results Six principal components were used as input to our model explaining more than 90% of variance within our dataset. Random Forest regression was used to predict fatigue. The model was validated using 10-fold cross validation and the mean absolute error was 1.38 points. Principal components consisting mainly of stride time, maximum toe clearance, heel strike angle, and stride length had large contributions (67%) to the predictions made by the Random Forest. Conclusions The level of fatigue can be predicted based on spatio-temporal gait parameters obtained from an IMU based system. The results can help therapists to monitor fatigue before and after treatment and in rehabilitation programs to evaluate their efficacy. Furthermore, this can be used in home monitoring scenarios where therapists can monitor fatigue using IMUs reducing time and effort of patients and therapists.

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Ratings of perceived fatigue predict fatigue induced declines in muscle strength during tasks with different distributions of effort and recovery

Cited by 32 publications

References 20 publications

Sex differences in muscle activity emerge during sustained low‐intensity contractions but not during intermittent low‐intensity contractions

Sex differences in muscle activity emerge during sustained low‐intensity contractions but not during intermittent low‐intensity contractions

On the Impact of Biceps Muscle Fatigue in Human Activity Recognition

Inertial sensor-based gait parameters reflect patient-reported fatigue in multiple sclerosis

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