Fatigue Conditions Research Articles

Muscle fatigue detection based on sEMG signal using autocorrelation function and neural networks

Abstract Feature extraction from an recorded surface electromyography (sEMG) signal plays an important role in identifying and quantifying the characteristics of muscle activities. These features can be used for various applications like muscle function assessment, muscle fatigue detection, etc. Common features extracted from sEMG signal are time-domain or frequency-domain features. However, features which are sensitive to uncertainties in the signal like noise, movement artifacts, and outliers should be avoided. Autocorrelation function (ACF), which is a measure of similarity between a signal and its time delayed version, is considered in this work as a feature to overcome the impact of noise, artifacts, and outliers. An artificial neural network (ANN) is developed to differentiate between fatigue and non-fatigue conditions using the calculated ACF from sEMG segments. The performance of an ANN model that can be adapted by means of various regularization methods was investigated. The proposed ANN model achieved an accuracy of about 97.62 %, a precision of about 95.50 % and a sensitivity of about 100 % in the classification of fatigue and non-fatigue sEMG segments, outperforming k-means and linear support vector machine approaches that served as references.

Impact of specialized fatigue and backhand smash on the ankle biomechanics of female badminton players

During fatigued conditions, badminton players may experience adverse effects on their ankle joints during smash landings. In addition, the risk of ankle injury may vary with different landing strategies. This study aimed to investigate the influence of sport-specific fatigue factors and two backhand smash actions on ankle biomechanical indices. Thirteen female badminton players (age: 21.2 ± 1.9 years; height: 167.1 ± 4.1 cm; weight: 57.3 ± 5.1 kg; BMI: 20.54 ± 1.57 kg/m2) participated in this study. An 8-camera Vicon motion capture system and three Kistler force platforms were used to collect kinematic and kinetic data before and after fatigue for backhand rear-court jump smash (BRJS) and backhand lateral jump smash (BLJS). A 2 × 2 repeated measures analysis of variance was employed to analyze the effects of these smash landing actions and fatigue factors on ankle biomechanical parameters. Fatigue significantly affected the ankle-joint plantarflexion and inversion angles at the initial contact (IC) phase (p < 0.05), with both angles increasing substantially post-fatigue. From a kinetic perspective, fatigue considerably influenced the peak plantarflexion and peak inversion moments at the ankle joint, which resulted in a decrease the former and an increase in the latter after fatigue. The two smash landing actions demonstrated different landing strategies, and significant main effects were observed on the ankle plantarflexion angle, inversion angle, peak dorsiflexion/plantarflexion moment, peak inversion/eversion moment, and peak internal rotation moment (p < 0.05). The BLJS landing had a much greater landing inversion angle, peak inversion moment, and peak internal rotation moment compared with BRJS landing. The interaction effects of fatigue and smash actions significantly affected the muscle force of the peroneus longus (PL), with a more pronounced decrease in the force of the PL muscle post-fatigue in the BLJS action(post-hoc < 0.05). This study demonstrated that fatigue and smash actions, specifically BRJS and BLJS, significantly affect ankle biomechanical parameters. After fatigue, both actions showed a notable increase in IC plantarflexion and inversion angles and peak inversion moments, which may elevate the risk of lateral ankle sprains. Compared with BRJS, BLJS poses a higher risk of lateral ankle sprains after fatigue.

A Multimodal Feature Fusion Brain Fatigue Recognition System Based on Bayes-gcForest.

Modern society increasingly recognizes brain fatigue as a critical factor affecting human health and productivity. This study introduces a novel, portable, cost-effective, and user-friendly system for real-time collection, monitoring, and analysis of physiological signals aimed at enhancing the precision and efficiency of brain fatigue recognition and broadening its application scope. Utilizing raw physiological data, this study constructed a compact dataset that incorporated EEG and ECG data from 20 subjects to index fatigue characteristics. By employing a Bayesian-optimized multi-granularity cascade forest (Bayes-gcForest) for fatigue state recognition, this study achieved recognition rates of 95.71% and 96.13% on the DROZY public dataset and constructed dataset, respectively. These results highlight the effectiveness of the multi-modal feature fusion model in brain fatigue recognition, providing a viable solution for cost-effective and efficient fatigue monitoring. Furthermore, this approach offers theoretical support for designing rest systems for researchers.

CSF-GTNet: A Novel Multi-Dimensional Feature Fusion Network Based on Convnext-GeLU- BiLSTM for EEG-Signals-Enabled Fatigue Driving Detection.

Electroencephalography (EEG) signal has been recognized as an effective fatigue detection method, which can intuitively reflect the drivers' mental state. However, the research on multi-dimensional features in existing work could be much better. The instability and complexity of EEG signals will increase the difficulty of extracting data features. More importantly, most current work only treats deep learning models as classifiers. They ignored the features of different subjects learned by the model. Aiming at the above problems, this paper proposes a novel multi-dimensional feature fusion network, CSF-GTNet, based on time and space-frequency domains for fatigue detection. Specifically, it comprises Gaussian Time Domain Network (GTNet) and Pure Convolutional Spatial Frequency Domain Network (CSFNet). The experimental results show that the proposed method effectively distinguishes between alert and fatigue states. The accuracy rates are 85.16% and 81.48% on the self-made and SEED-VIG datasets, respectively, which are higher than the state-of-the-art methods. Moreover, we analyze the contribution of each brain region for fatigue detection through the brain topology map. In addition, we explore the changing trend of each frequency band and the significance between different subjects in the alert state and fatigue state through the heat map. Our research can provide new ideas in brain fatigue research and play a specific role in promoting the development of this field.

Ultrahigh surface hardness and excellent hardness gradient induced by ultrafine dual-carbides in a novel stainless bearing steel

A novel alloy composition design (Fe–14Cr–13Co–5Mo–2Ni–1W–1V-0.16C-0.12Nb-0.074Si-0.027Mn, wt. %) was proposed for modification on that of the conventional CSS-42L stainless steel. The present homogeneous structure without carburizing displays an excellent synergy of yield strength and fracture toughness, which should improve the performance of the corresponding case-carburized steels under the contact/bending fatigue conditions. An ultrahigh surface hardness (71 HRC) and an excellent hardness gradient along the depth are achieved in the present case-carburized gradient structure. Higher surface hardness at a temperature range of 200–400 °C is also observed in the present case-carburized gradient structure, as compared to the other case-carburized advanced bearing/gear steels, such as the M50 steel, the M50NiL steel and the conventional CSS-42L steel. Besides the previously reported M6C carbides in the case-carburized conventional CSS-42L steel, M7C3 carbides are newly discovered in the present case-carburized gradient structure due to the novel chemical design. The ultrahigh surface hardness at both room and high temperatures and the excellent hardness gradient distribution in the present case-carburized gradient structure should be attributed to the increased amount of dual-carbides, as well as the ultrafine dual-carbides embedded in the ultrafine martensite matrix. The superior mechanical properties of the present case-carburized gradient structure should be desirable for real bearing/gear applications.

Skeletal muscle stiffness is altered by age and fatigue at the cellular level

Advanced age is associated with increased skeletal muscle stiffness at the cellular and whole-tissue levels. In older adults, fatigue increases falls risk. Our lab has previously observed fatigue-induced reductions in skeletal muscle cellular stiffness in younger adults, prompting the question of whether the same occurs in older adults. If so, it is possible that fatigue-induced reduction in cellular stiffness of older adults would reduce the rate of tendon loading and/or increase tissue compliance, ultimately contributing to increased falls risk. Therefore, the OBJECTIVE of the present study is to measure cellular passive and active stiffness in younger and older adults across fatigue conditions to test the hypothesis that fatigue induces tissue compliance that predisposes older adults to falls. Methods: 11 untrained males and females (7 younger, 20±1 yrs.; 4 older, 74±4 yrs.) performed unilateral knee extensions to fatigue, followed by bilateral biopsy of the Vastus Lateralis providing fatigued (F) and non-fatigued (NF) samples. Single fibers (146 younger, 130 older) were passively stretched to 156% initial length and passive modulus was calculated as the slope of the resulting stress-strain curve. Separately, Residual Force Enhancement (RFE) was measured in a subset of fibers (9 younger, 40 older) to assess passive contributions (titin) to active (actin-myosin) stretch. Main effects (age/fatigue) on passive modulus and RFE were tested via linear mixed model and t-tests, respectively. Results: Passive modulus was increased with age (p=0.04) and reduced by fatigue in both the younger (p&lt;0.01) and older (p&lt;0.01) participants. In a subset of fibers, RFE was increased in older women compared to younger (p&lt;0.01) and older men (p&lt;0.01). No main effect of fatigue on RFE was detected (p=0.49). However, RFE trended towards reduction with fatigue in a younger male (p=0.13) and was significantly reduced by fatigue in myosin heavy chain (MHC) I fibers from an older male (p=0.02). CONCLUSION: In agreement with existing literature, cellular passive modulus was higher in older versus younger adults. Additionally, fatigue reduced passive modulus in skeletal muscle fibers from both older and younger adults. Though no main effect of fatigue on RFE was detected, these results suggest that cellular RFE measures are age- and fiber-type specific. This work was supported by funds from the Wu Tsai Human Performance Alliance. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Driver fatigue detection method based on facial features using deep learning

Fatigue driving can result in delayed driver reactions and lack of concentration, increasing the risk of traffic accidents. Therefore, finding effective fatigue detection methods is crucial for accident prevention and ensuring public safety. Traditional driver fatigue detection methods based on facial features are limited by their reliance on manually designed features and classifiers, leading to low accuracy, slow detection speed, and inefficient performance. Hence, methods based on deep learning have gradually become a trend in fatigue-driving detection research. This paper focuses on driver fatigue detection methods based on facial features using deep learning. In this study, we analyze numerous research outcomes on how to accurately extract, analyze, and integrate various facial features, such as eye features, mouth features, facial expressions, and head postures, to precisely determine the driver's fatigue state. Then, we compare the latest deep learning-based technologies. This paper provides an information-rich repository to help researchers select more promising designs, that can drive the widespread development of fatigue-driving detection technologies.

Effects of Biofeedback Training on HRV, Mood State and Shooting Performance of Shooters

This study examines how biofeedback training affects athletes' HRV, emotional states, and shooting performance. Findings reveal substantial improvements in HRV indices (p &lt; 0.05) and reductions in negative emotions such as tension and anger (p &lt; 0.05). There were increases in vitality and positivity, although changes in fatigue and self-emotional state were not statistically significant (p &gt; 0.05). Shooting performance showed significant enhancement following the training (p &lt; 0.01). These results indicate that biofeedback training positively influences autonomic nervous system function, emotional stability, and shooting performance. By enhancing HRV, improving mood states, biofeedback training markedly improves shooting performance.

Crack initiation mechanisms of micro-textured Ti60 alloys with different dwell sensitivities subjected to fatigue and dwell fatigue loading

In this work, Ti60 alloys with different micro-texture intensity were designed to elucidate the effect of micro-texture on fatigue life and dwell sensitivity and the difference in crack initiation mechanism for low-cycle fatigue (LCF) and dwell fatigue (DF). It was found that micro-texture region (MTR) significantly reduced LCF and DF lifetimes and increased dwell sensitivity compared to no-MTR samples. However, micro-texture does not change the characteristics of the crack initiation grains, and the fracture initiation region facet of all samples matches the (0001) basal plane and is related to basal slip. Therefore, the [0001] orientation domains for crack initiation in both fatigue states are discussed, and it is found that LCF tends to nucleate along the basal slip bands or basal twist grain boundaries (BTGBs) of the high SF grains, whereas DF nucleation is independent of the basal SF value, and the mechanism of initiation of hard oriented grain cracking is also included. In situ further demonstrated that hard grains can activate basal slip as well as cracking, and the possibility of soft/hard grain pairs generating dislocation pile-up stresses leading to cracking of hard grains was determined by theoretical calculations, providing experimental support and evidence for the soft/hard grain model. Based on these findings, criteria for the angle (θ) of the c-axis with respect to the loading axis and the basal SF range are proposed to determine the differences between LCF and DF with respect to the mechanisms of hard grain cracking and BTGB crack initiation.

Research on Fatigue Driving Detection Technology Based on CA-ACGAN.

Driver fatigue represents a significant peril to global traffic safety, necessitating the advancement of potent fatigue monitoring methodologies to bolster road safety. This research introduces a conditional generative adversarial network with a classification head that integrates convolutional and attention mechanisms (CA-ACGAN) designed for the precise identification of fatigue driving states through the analysis of electroencephalography (EEG) signals. First, this study constructed a 4D feature data model capable of mirroring drivers' fatigue state, meticulously analyzing the EEG signals' frequency, spatial, and temporal dimensions. Following this, we present the CA-ACGAN framework, a novel integration of attention schemes, the bottleneck residual block, and the Transformer element. This integration was designed to refine the processing of EEG signals significantly. In utilizing a conditional generative adversarial network equipped with a classification header, the framework aims to distinguish fatigue states effectively. Moreover, it addresses the scarcity of authentic data through the generation of superior-quality synthetic data. Empirical outcomes illustrate that the CA-ACGAN model surpasses various extant methods in the fatigue detection endeavor on the SEED-VIG public dataset. Moreover, juxtaposed with leading-edge GAN models, our model exhibits an efficacy in in producing high-quality data that is clearly superior. This investigation confirms the CA-ACGAN model's utility in fatigue driving identification and suggests fresh perspectives for deep learning applications in time series data generation and processing.

Monitoring fatigue state with heart rate-based and subjective methods during intensified training in recreational runners.

The purpose of this study was firstly to examine the sensitivity of heart rate (HR)-based and subjective monitoring markers to intensified endurance training; and secondly, to investigate the validity of these markers to distinguish individuals in different fatigue states. A total of 24 recreational runners performed a 3-week baseline period, a 2-week overload period, and a 1-week recovery period. Performance was assessed before and after each period with a 3000m running test. Recovery was monitored with daily orthostatic tests, nocturnal HR recordings, questionnaires, and exercise data. The participants were divided into subgroups (overreached/OR, n=8; responders/RESP, n=12) based on the changes in performance and subjective recovery. The responses to the second week of the overload period were compared between the subgroups. RESP improved their baseline 3000m time (p<0.001) after the overload period (-2.5±1.0%), and the change differed (p<0.001) from OR (0.6±1.2%). The changes in nocturnal HR (OR 3.2±3.1%; RESP -2.8±3.7%, p=0.002) and HR variability (OR -0.7±1.8%; RESP 2.1±1.6%, p=0.011) differed between the subgroups. In addition, the decrease in subjective readiness to train (p=0.009) and increase in soreness of the legs (p=0.04) were greater in OR compared to RESP. Nocturnal HR, readiness to train, and exercise-derived HR-running power index had ≥85% positive and negative predictive values in the discrimination between OR and RESP individuals. In conclusion, exercise tolerance can vary substantially in recreational runners. The results supported the usefulness of nocturnal HR and subjective recovery assessments in recognizing fatigue states.

Prevalence of work-related burnout and associated factors among police officers in central Gondar zone, Northwest Ethiopia, 2023.

Work-related burnout is a state of physical and psychological fatigue and exhaustion resulting from chronic workplace stress related to work. The police workforce is vulnerable to this psychosocial hazard, which affects service delivery by police workers. However, there is little evidence about the prevalence of work-related burnout and associated factors among police officers in Ethiopia. Therefore, this research investigated the prevalence and predictor variables of work-related burnout among police officers in central Gondar zone, Northwest Ethiopia, 2023. An institution-based cross-sectional study was carried out from April 12 to May 12, 2023. A sample of 633 police officers was recruited through multistage random sampling techniques. To measure work-related burnout, a standardized, self-administered Copenhagen Burnout Inventory was used. To enter the collected data, EpiData V 4.6 and to analyze SPSS V 26 were used. To examine the association between work-related burnout and its predictor variables, both bivariable (p < 0.2) and multivariable (<0.05) logistic regression analyses were performed, and statistical significance was established via multivariable logistic regression. The response rate in this study was 96.05% (n = 608). The majority, 452 (74.3%) of the police officers, were male; the median (IQR) age of participants was 28. In the past six months, 45.7% of the police officers had experienced work-related burnout (n = 278). Being female, having a high job demand, having a high level of organizational police stress, having a moderate level of operational police stress, having a high level of operational police stress, experiencing job dissatisfaction, and sleeping troubles were risk factors significantly related to the occurrence of work-related burnout among police officers. According to this study, a significant number of police officers suffer from burnout due to their work. Police officers' work-related burnout was found to be influenced by factors such as sex, job demands, job satisfaction, workplace stress, organizational police stress, and sleeping troubles. To address this problem, improving the handling of work pressure, developing a stress management program, finding joy at work by improving interpersonal relationships and working conditions, offering support and inspiration to female police officers, and ensuring sufficient and restful sleep are advised.

The Effects of Fatigue on Landing Performance in Young Female Soccer Players

Objectives. Jumping and landing constitute two movements that are used with a very high frequency in all sports and are two skills that have received a lot of attention in the literature because of their relationship with injury risk. The aim of this study is to analyze the following aspects: (a) to determine whether fatigue negatively affects landing technique after a jump, and (b) to test whether different metabolic and neuromuscular exercises have an effect on the landing technique. Materials and methods. The sample is composed of young female soccer players Under 14 (n = 29, 13.06 ± 1.27 years, 47.8 ± 2.6 kg; 143.2 ± 3.4 cm). Before and after two training protocols aimed at determining the state of fatigue (functional agility short-term fatigue protocol, and RSA protocol), landing technique was assessed using the Landing ErrorScoring System (LESS). Results. The LESS assessment for the pre-fatigue and post-functional agility short-term fatigue protocol conditions showed a value of 6.78 ± 0.81 and 8.74 ± 0.77 respectively (p &gt; 0.001, ES: 1.39). The LESS assessment for the post-RSA protocol condition was 8.52 ± 0.87; this score was statistically significantly higher than the pre-fatigue condition (p &gt; 0.001, ES: 1.24). Conclusions. Intensive exercise, both neuromuscular and metabolic, appears to have an effect on the landing technique of young female soccer players.

The non-visual effects of correlated color temperature on the alertness, cognition, and mood of fatigued individuals during the afternoon

Railway dispatchers are prone to mental fatigue due to prolonged periods of intense concentration. As the main or even the only light source in the enclosed control centers, artificial lighting has non-visual effects on human alertness, cognition, and mood. To investigate whether external environmental cues could counteract performance decline induced by endogenous cardiac exhaustion, the current study modulated four correlated color temperature (CCT) levels and developed one visual task and six cognitive tasks representing basic and advanced cognitive functions, including sustained attention, response inhibition, working memory, risk decision-making, executive control, and emotion perception. A within-subjects design was employed to collect subjective and behavioral responses under both normal and fatigued mental antecedent states. Electroencephalogram (EEG) with alpha and theta bands as well as four fatigue-related algorithms including θ/β, α/β, (θ+α)/β, and (θ+α)/(α+β) were analyzed to explore the underlying neural mechanisms behind these responses. The results indicated a complex interactive pattern of CCT and mental antecedent state on alertness, cognition, and mood. Higher CCT had a supportive effect on subjective alertness and positive mood, independent of mental antecedent state. However, it exhibited a slight performance-impairing effect on sustained attention tasks. Conversely, higher CCT demonstrated a significant enhancement in advanced cognitive functions for fatigued individuals. EEG data revealed alpha oscillations increased, but theta activity and (θ + α)/β suppressed at 12,000 K, suggesting higher CCT partially alleviated the decline in alertness and cognition induced by mental fatigue. To develop an appropriate photobiological lighting scheme for fatigue intervention, larger and longer-term studies are needed in the future to measure a range of confounding factors and individual differences.

Research on the fatigue level of underground mine truck drivers with integration of cardiovascular and brain features

The fatigue of miners is one of the primary factors influencing unsafe behaviors in mining. To accurately identify the fatigue status of miners, prevent coal mine accidents, and simulate the loading and unloading operations of underground mine truck drivers.Collecting electroencephalogram (EEG) and electrocardiogram (ECG) feature indicators of mine truck drivers during different work periods, using SPSS software to conduct correlation analysis on the collected fatigue feature indicators, and studying the changes in fatigue feature indicators before and after the operation of mine truck drivers. The results indicate that when the underground mine truck drivers' brainwave band entropy (β/ θ)exceeds the fatigue threshold of 60 or the HR_MEAN heart rate index is below 0.05uv, the drivers are in a state of severe fatigue. The fatigue levels of underground mine truck drivers vary during different work periods, with drivers at 13:00 experiencing the most severe fatigue. The study found that reducing the midday working hours for underground mine truck drivers can effectively decrease the occurrence of fatigue.

Effect of Testing Conditions on Low-Cycle Fatigue Durability of Pre-Strained S420M Steel Specimens.

S420M steel subjected to strain-controlled low-cycle fatigue does not exhibit a period of cyclic properties stabilization. The maximum stress on a cycle continuously drops until fracture. For this reason, it is difficult to plan experimental research for different types of control in such a way that their results can be considered comparable. The aim of this paper is to present and discuss the results of tests conducted in various conditions of low-cycle fatigue of S420M steel specimens, both undeformed and pre-strained. In both loading conditions, after initial deformation, a significant change in the cyclic properties of steel described by the parameters of the hysteresis loop was observed. Also, the fatigue life of the pre-strained specimens appeared to be different from unstrained specimens and was affected by the test loading conditions. The reduction in life under controlled stress conditions was attributed to the increase in the extent of plastic deformation and stress and the occurrence of creep.

Study of the Point Method for fretting fatigue life prediction

With the improvement and success of the Theory of Critical Distances (TCD) in notch fatigue problems, the TCD, especially the Point Method (PM), combined with multiaxial fatigue criteria, has been used over the last few years to predict the fretting fatigue life. However, little attention is paid to the effect of different damage mechanisms, especially stress distribution, between the two conditions on the application of the PM. This paper aims to select a suitable path for the PM, considering various stress distributions, for the fretting fatigue life prediction of complete contacts. A new method is developed to select the suitable path of the PM for fretting fatigue life prediction. To validate the analysis, corresponding tests were carried out using an improved proving ring-like rig in a Ti-6Al-4V alloy. Predicted lives on all paths were calculated using the PM combined with different multiaxial fatigue criteria, without incorporating wear effects, and compared with experimental lives using an error bar graph. Results indicate that when the fretting wear is not considered, the minimum predicted life path is more suitable for the PM than the perpendicular and maximum predicted life paths. This suitable path is compared to the path selection in the uniaxial symmetric V-notch fatigue condition. Analysis shows that the conventional path (the notch bisector) of the PM produces the maximum predicted life for the notch fatigue condition. The cause of this different path selection of the PM in the two fatigue conditions is assumed to be the effect of the asymmetric stress distribution and fretting wear damage in the fretting fatigue condition.

Experimental Study on Low-Cycle Fatigue Characteristics of Marine Structural Steel

This research focuses on the experimental investigation of the low-cycle fatigue characteristics of marine structural steel. The study aimed to explore the behavior of marine-grade steel under low-cycle fatigue conditions. The experimental parameters include the fatigue life of the material, crack propagation behavior, and a comprehensive analysis of mechanical properties associated with various loading conditions. Based on the experimental results, a low-cycle fatigue crack propagation rate model for marine structural steel plates was established using CTOD as a characterization parameter. The primary objective of this research is likely to enhance the durability and safety of maritime structures, providing valuable technical insights for the field of naval engineering.

Algorithm for drowsiness detection based on hybrid brain network parameter optimization

Drowsiness detection is a test designed to detect a person’s reaction ability and speed while in a state of fatigue. This type of drowsiness detection has many practical applications in areas like driver monitoring, transportation industry, and workplace safety. In this paper, a new machine learning drowsiness detection algorithm based on hybrid brain network feature extraction is proposed. First, the raw electroencephalogram signals are segmented into 30 s epochs, which are decomposed with wavelet packet transform into 7 layers to extract 30 s sleep characteristic subbands and the 7-th layer subbands. After that, 30 s subbands are divided into 5 s subbands with 50% overlap. At the same time, 30 s subbands are reconstructed by phase space reconstruction (PSR), whose parameters are optimized through energy valley optimizer. Then, based on mutual information and horizontal visual graph, a hybrid brain network is constructed with the 7-th layer subbands, 5 s subbands and PSR subbands, respectively. Finally, network features and time–frequency features are extracted and input into random forest classifier. The performance of the algorithm is evaluated through two tests: subject-independent test and subject-non-independent test. The results show that in Sleep-EDF dataset, accuracy, κ coefficient, recall, precision and F1-score obtained by the proposed algorithm can reach 94.19%, 88.39%, 95.37%, 92.53% and 93.93%, respectively. In Sleep-EDF Expanded dataset, accuracy, κ coefficient, recall, precision and F1-score can reach 93.65%, 87.65%, 94.53%, 92.11%, and 92.89%, respectively.

Evaluating the feasibility and acceptability of an adapted fencing intervention in breast cancer surgery post-operative care: the RIPOSTE pilot randomized trial.

Adapted physical activity programs have shown promising results in reducing the physical, social and psychological side effects associated with breast cancer, but the extent to which they can be effectively adopted, implemented and maintained is unclear. The aim of this study is to use the framework to guide the planning and evaluation of programs according to the 5 following keys: Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework to evaluate a fencing program under the French acronym RIPOSTE (Reconstruction, Image de soi, Posture, Oncologie, Santé, Thérapie, Escrime) literally in English (Reconstruction, Self-Image, Posture, Oncology, Health, Therapy, Fencing). This program is an innovative intervention focused on improving the quality of life (QoL) of breast cancer surgery patients through fencing. A convergent mixed methods pilot study was conducted to preliminary evaluate the different RE-AIM dimension of the pilot program. Twenty-four participants who have just undergone surgery for invasive breast cancer were randomly allocated in two groups: one group started immediately after their inclusion (Early RIPOSTE group) and the other started 3 months following their inclusion (Delayed RIPOSTE group). Participants answered a questionnaire at inclusion and at the end of the program on QoL, shoulder functional capacity, fatigue, anxiety-depression and physical activity. RIPOSTE program was able to reach mainly young and dynamic participants, attracted by the originality of fencing and keen to improve their physical condition. Regarding effectiveness, our results suggest a trend to the improvement of QoL, shoulder functional capacity, fatigue and anxiety-depression state, even without any significant differences between the Early RIPOSTE group and the Delayed RIPOSTE group. The cooperation, exchanges and cohesion within the group greatly facilitated the adoption of the program, whereas interruptions during school vacations were the main barriers. The intervention was moderately well implemented and adherence to the protocol was suitable. RIPOSTE is an acceptable and effective program for involving breast cancer survivors in physical activity, that needs to be tested at a larger scale to investigate its effectiveness, but has the potential to be transferred and scaled up worldwide.