The Effects of Unpleasant Thermal and Auditory Stimulus on Forearm Muscle Activity During Discrete and Continuous Wrist Movements

The Effects of Unpleasant Thermal and Auditory Stimulus on Forearm Muscle Activity During Discrete and Continuous Wrist Movements | 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 Research Article The Effects of Unpleasant Thermal and Auditory Stimulus on Forearm Muscle Activity During Discrete and Continuous Wrist Movements Gabriel COHEN-AKNINE, Raphaël PIONNIER, Denis MOTTET, Arnaud François DUPEYRON This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6606053/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Oct, 2025 Read the published version in Experimental Brain Research → Version 1 posted You are reading this latest preprint version Abstract Experimental models of pain have demonstrated alterations in motor control in healthy subjects. However, pain involves different dimensions (cognitive-evaluative and affective-motivational, and sensory-discriminative) that have not been studied separately. Indeed, an aversive auditory stimulus is known to produce an unpleasant experience similar to a conventional thermal pain stimulus and may mobilize the cognitive-evaluative and affective-motivational dimensions of pain. The aim of this study was to evaluate forearm muscle activity using surface electromyography (sEMG) during discrete and continuous wrist movements in the presence of thermal and auditory stimuli compared to a control condition. Sixteen healthy subjects were recruited. The conditions were administered to each participant in a randomized order. Participants were instructed to perform a full range of wrist movements in both a discrete (single movement) and continuous (repetitive movements) modality. The auditory and control conditions did not alter motor activity, whereas the thermal stimulus increased wrist extensor activity only during the continuous movement modality. Other types of sEMG analysis (timing and frequency) were not affected by stimulus type. These results suggest that the cognitive-evaluative and affective-motivational dimensions of pain may not affect muscle activity, whereas the sensory-discriminative dimensions may be more susceptible to altering motor function. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Pain is a defined as un unpleasant sensory and emotional experience (Raja et al., 2020 ), involving multidimensional and highly personal perceptions that emphasize its unpleasant nature (Murray & Sessle, 2024 ). It has been shown that both clinical (Bazett-Jones et al., 2023 ; Smith et al., 2022 ) and experimental (Cabral et al., 2024 ) pain affect motor activity. However, some inconsistencies may hinder comprehension of this effect and the overall interpretation of the relationship between pain and movement particularly with regard to how pain is defined and movement is analyzed. Firstly, pain encompasses sensory-discriminative, cognitive-evaluative and affective-motivational dimensions (Murray & Sessle, 2024 ). However, it is difficult to distinguish these dimensions with conventional experimental pain stimuli, whether the stimulus is phasic (short duration with intense sensation) or tonic (long duration with moderate sensation) (Reddy et al., 2012 ), leading to heterogeneity in the interpretation of results. Some authors have proposed other unpleasant stimuli, such as aversive auditory stimuli (Mirz et al., 2000 ), which have been shown to be as unpleasant in intensity as experimental pain models (Chang et al., 2002 ; Valentini et al., 2022 ). Although aversive auditory stimuli may be more related to motivational-affective and cognitive-evaluative components of pain (Stankewitz et al., 2023 ), their effects on motor activity have not been compared with those of common experimental painful stimuli. Secondly, motor activity is often assessed in experimental pain designs using surface electromyography (sEMG) (Bank et al., 2013 ; Medved et al., 2020 ). However, previous research has shown heterogeneous results regarding the effect of noxious stimuli on motor activity (Bank et al., 2013 ; Devecchi et al., 2023 ) particularly in the type of task used in sEMG. For example, movement can be performed in different ways. These are known as discrete (single joint range of motion) and continuous (full or rhythmic joint range of motion) modalities (Angel & Park, 2025 ; Habas & Cabanis, 2008 ; Hatsopoulos et al., 2004 ; Huys et al., 2008 ). Previous reviews have highlighted the heterogeneity of results, particularly in relation to the movement patterns assessed during unpleasant experience (Bank et al., 2013 ; Burns et al., 2016 ; Izadi et al., 2022 ). For example, the meta-analysis by Ippersiel P. et al (Ippersiel et al., 2022 ) involved studies using either discrete tasks, such as lumbar flexion or extension, or continuous tasks, such as lumbar flexion/extension or gait, and suggested contrasting results regarding the trunk muscle activity. Thirdly, in terms of analysis type, sEMG can be performed using different methods, including global muscle activity analysis (e.g. root mean square, RMS), time series analysis (e.g. time to peak activity), and frequency analysis (Campanini et al., 2020 ; Clancy et al., 2023 ; McManus et al., 2020 ; Zawawi et al., 2018 ). Previous systematic reviews have suggested that unpleasant stimuli may alter muscle activity in terms of amplitude or contraction timing (Barton et al., 2013 ; Heales et al., 2016 ; Kinsella & Pizzari, 2017 ), such as reducing muscle activity using RMS analysis (Ciubotariu et al., 2007 ; Farina et al., 2005 ; Svensson et al., 1996 ), shorten time to peak (Dupuis et al., 2021 ), or changing mean power frequency with a shift to higher frequencies (Madeleine et al., 1999 ). Most systematic reviews have reported delays in muscle activation (Barton et al., 2013 ; Chester et al., 2008 ; Dupuis et al., 2021 ). However, for other measures, such as task performance, dexterity or stability, the literature suggests that unpleasant stimuli does not significantly affect the redistribution of muscle activity (Bank et al., 2013 ). In addition, a meta-analysis showed conflicting evidence regarding the effect of unpleasant stimuli on corticospinal excitability, with some studies reporting a decrease, others an increase, and some even showing no effects (Rohel et al., 2021 ). According to a systematic review (Talbot et al., 2019 ) that pointed out the challenge of manipulating the pain dimension using cognitive interventions, these inconsistencies in research findings on the effects of unpleasant stimuli on motor activity may have been influenced by the effects of the pain dimension, as mentioned above. The present study aimed to compare the effects of thermal and aversive auditory pain stimuli with those of a control condition in healthy participants, by mixing the types of tasks and analyses used. We hypothesized that thermal and auditory stimuli would delay muscle activation, reduce amplitude, and shift power frequency to higher frequencies, regardless of discrete or continuous movement modalities. Material and methods This work is part of a larger, experimental single center prospective design study conducted at the Research Unit of Euromov Digital Health & Motion (Montpellier, France) (the other part of this study was preprinted on medRxiv: doi: https://doi.org/10.1101/2025.03.06.25323546 ). The study was approved by the local ethics committee (Comité d'Éthique de la Recherche de l'Université de Montpellier: n° UM 2023-031) according to the declaration of Helsinski revised in 2013. Participants received and signed a written informed consent form. Participants Eighteen healthy right-handed participants, aged 18–50 years, with no history of neurological or psychiatric disorders, no chronic pain and no pain in the upper limbs during the experiment were included. Participants were recruited from the students of the University of Montpellier. Recruitment was carried out by email and posters around the university. Participants were excluded from the study if they were unable to perform the procedure described below and/or because of a dysfunction of any equipment used in the protocol. Age, sex, weight, height, and body mass index (BMI) were recorded. Participants' handedness was assessed using the Edinburgh Handedness Inventory - Short Form (EHI-SF) (Veale, 2014 ), a questionnaire that scores handedness based on 10 daily activities, with a ratio and a cut-off of 40 points or more indicating right-handedness. Levels of physical activity and inactivity were measured using the International Physical Activity Questionnaire-Short Form (IPAQ-SF) (Meh et al., 2021 ). This self-report questionnaire uses an algorithm to calculate the number of minutes of physical activity (PA) per week, categorized into three levels (low, moderate and vigorous), and a score for inactivity (corresponding to the number of minutes spent sitting). The total score is expressed in MET (Metabolic Equivalent of Task) minutes/week. This is a unit used in the international literature to measure PA intensity in absolute terms. It is defined as the ratio of the energy expended during the activity in question to the amount of energy expended at rest. For example, 1 MET corresponds to resting metabolic rate or basal metabolic rate (Bull et al., 2020 ). Procedure Participants were seated in a chair in front of a computer in a relaxed position with their forearms resting on the armrests and their elbows naturally flexed in pronation, so that the wrists were fully flexed and relaxed. The procedure consisted of 2 parts: a discrete modality (consisting of single imagined and executed wrist movements) and a continuous modality (consisting of repeated imagined and executed wrist movements). In the discrete modality, two tasks were performed: "resting state" and "motor execution" and only "motor execution" for the continuous assessment. During the resting state, a central cross was displayed on the screen with instructions to stare at the cross and remain relaxed. For the “motor execution” task, participants were instructed to perform the movement described below, in parts 1 and 2. The instructions were displayed on a screen using PsychoPy software (v 2023.1.2) (Peirce et al., 2019 , 2022 ). To familiarize participants with the movement, a training session was conducted prior to the experiment in which participants were asked to perform the movement "in their own way" with respect to the previous instructions, without additional guidance. Part 1: discrete With the wrist fully flexed as described above, participants were instructed to perform a full range of motion wrist movement in the sagittal plane (from fully flexed to fully extended position), known as discrete modality, and return to the starting position, within a time window of 4 seconds. This procedure was repeated 20 times. Part 2: continuous In the same position as part 1, participants were instructed to perform a repetitive/continuous full range of motion wrist movement in the sagittal plane (from fully flexed to fully extended position) for 25 seconds. This procedure was repeated 3 times. The two painful conditions and control condition were tested for both parts. Conditions Three blocks of conditions were created: ‘normal’ (= control), ‘auditory’ (aversive auditory stimulus) and ‘heat’ (thermal painful stimulus). The order of the conditions was pseudo-randomized using six designs determined by dice. In the 'normal' condition, movement was performed and imagined without any unpleasant stimulus. The aversive ‘auditory’ stimulus was created using Audacity® (v 3.3.3) with a duration of 4 seconds, a start and end fade of 0.2 seconds, a frequency of 5000 Hz (sawtooth waveform) and a baseline volume of 75 dB. This stimulus was inspired by a previous publication by Valentini et al (Valentini et al., 2022 ). The thermal 'heat' stimulus was applied using hot water in a 14/19 L thermostatic bath (CORIO C-BT19, Julabo®, Seelbach, Germany) with a baseline temperature of 45°C (Granot et al., 2008 ). To standardize variability between participants, each participant selected their level of discomfort for each stimulus on a 100-point visual analogue scale (VAS) (0 = 'no discomfort' to 100 = 'unbearable discomfort'). Participants were instructed to choose the water temperature and the auditory sound within a range between 60 and 75 on the VAS (Fig. 1 ). The levels were adjusted in increments of 1°C for temperature and 1dB for sound volume until the chosen level of discomfort was reached. For the thermal stimulus, the right hand was immersed in water up to the wrist joint, and for the auditory stimulus, the sound was played for periods of 25 seconds through loudspeakers. The temperature level was monitored using the CORIO C-BT19 thermometer and the aversive auditory level was measured using the "Decibel X - Pro Sonomètre smartphone application" (SkyPaw Co.®, Ltd) (Murphy & King, 2016 ). A thermal heat stimulus was chosen because it results in a shorter duration of pain, a more constant quantitative description and a steeper slope for the intensity of the sensation compared to cold pain stimulus (Morin & Bushnell, 1998 ). The instructions and the design of the protocol are shown in Fig. 2 and Fig. 3 respectively. Outcomes The motor activity of the wrist extensor muscles was recorded during a concentric to eccentric contraction (full wrist flexion/extension range of motion). Primary outcome measures The time series was calculated using a Time to Peak approach corresponding to the time taken for a signal to reach its maximum amplitude or peak value during the epoch window. Secondary Outcomes • Amplitude analysis: Root Mean Square (RMS) The RMS is a measure of the magnitude of the electric muscle activity during the epoch window. • Amplitude analysis: Mean Absolute Value (MAV) The MAV is the average of the absolute values of the sEMG signal over the epoch window. • Frequency analysis: Power Spectrum Analysis (PSD) The PSD is a method used to decompose the raw amplitude signal into its constituent frequencies and analyze the distribution of power (or variance) of a signal across different frequencies during the epoch window. Signal processing Electrode placement Prior to electrode placement, the skin was prepared according to the SENIAM guidelines ( http://seniam.org/sensor_location.htm ) by shaving, light abrading and cleaning with alcohol. Bipolar electrodes were placed on the midline and midpoint of the muscles of interest, maintaining an inter-electrode distance (IED) of approximately 20 mm (ranging from 6 mm to 40 mm) (McManus et al., 2020 ). 20 mm circular Ag/AgCl bipolar electrodes were placed on the extensor wrist muscles of the forearm, aligned longitudinally with the muscle fibres, on the muscle belly, proximally to the lateral epicondyle (Hermens et al., 2000 ), at approximately 89% of the distance between the ulnar styloid and the lateral epicondyle, as recommended by Ghapanchizadeh et al (Ghapanchizadeh et al., 2015 ). The reference electrode was placed on either the mastoid process or the contralateral wrist, depending on the pre-test signal quality assessment. Signal analyzing The sEMG amplifier had an impedance greater than 100 MΩ and a common mode rejection ratio (CMRR) greater than 90 dB (REFA, TMSI ®). The sEMG signal was sampled at a frequency of 1024 Hz. Signals were synchronized with PsychoPy triggers using LSL (LabStreamingLayer) (Kothe et al., 2024 ) in XDF format. The signal processing pipeline followed the recommended guidelines (Zawawi et al., 2018 ) and included the following steps: (1) detrending, (2) low-pass filtering, (3) high-pass filtering, (4) notch filtering (and harmonics), and (5) squaring. The signal was detrended and filtered using a notch filter at 50 Hz (with additional notch filters at its harmonics: 100, 150 and 200 Hz) and a Butterworth filter with a bandpass range of 20–500 Hz (Tankisi et al., 2020 ). The signal was then segmented into epochs ranging from − 0.5 to 4 seconds relative to each trigger. Signal processing was performed using MATLAB® software (v. R2021b). Analysis was performed according to the methods described by Clancy et al (Clancy et al., 2023 ) and Muceli S & Merletti R (Muceli & Merletti, 2024 ). Means of muscle activity for all trials (20 movements for the discrete modality and 3 movements for the continuous modality) were calculated for each participant and condition. Time to peak, defined as the interval between 1 s before the start of the epoch window and the maximum root mean square (RMS) value, was calculated for each participant across all tasks and conditions for both flexor and extensor wrist muscles. The MATLAB RMS function was used to calculate the root mean square (RMS). The mean RMS was calculated for each participant across all tasks and conditions for both flexor and extensor wrist muscles in both discrete and continuous motion modalities. The Mean Absolute Value (MAV), defined as the mean of the RMS for each task signal during the epoch window, was calculated for each participant across tasks and conditions for both flexor and extensor wrist muscles in both discrete and continuous motion modalities. A Fast Fourier Transform (FFT) power spectrum analysis (PSD) using Welch's method (Stefanou et al., 2022 ) was performed for each participant across tasks and conditions for both flexor and extensor wrist muscles, in both discrete and continuous motion modalities. This analysis was performed in the frequency range of 20 to 300 Hz (Han et al., 2021 ). Statistical analysis Statistical analysis was performed using RStudio® software (v. 2024.04.2). Some authors have recommended non-parametric approaches for small sample sizes (Harwell, 1988 ; Whitley & Ball, 2002 ). Demographic and sEMG analyses were performed using frequentist statistical methods, and results are presented as median [interquartile range (IQR)]. Comparison between the three conditions was analyzed using the Friedman test, followed by post hoc pairwise comparisons using the Wilcoxon signed-rank test with Bonferroni-Holm correction for multiple comparisons. Effect sizes were calculated using Rosenthal's r for the paired-sample Wilcoxon signed-rank test, and significance levels were classified as small (0.20), moderate (0.50), or large (0.80) (Maher et al., 2013 ). Statistical results are reported with a 95% confidence interval (CI) and an alpha level of 0.05. Results Demographic analysis Sixteen participants (6 female, 10 male) were initially included in the study. However, two participants were subsequently excluded from the analysis due to a high presence of artefacts in the sEMG signals, making their data uninterpretable. The characteristics of the remaining participants were as follows: median age of 23.5 (IQR = 4) years, median body mass index (BMI) of 22.6 (IQR = 4.02) kg/m², median Edinburgh Handedness Inventory (EHI) score of 80 (IQR = 23.88), and median physical activity level of 4914 (IQR = 2887) MET minutes/week. Median selected thermal pain temperatures were 46°C (IQR = 1.62) and auditory volume was 81. dB (IQR = 8.25), consistent with the design of Valentini et al. (Valentini et al., 2022 ). Primary outcome: Time series analysis Discrete motion modality There was no statistically significant difference between conditions in the time to peak wrist extensor muscle activity (χ²(2) = 4.133, p = 0.127). Continuous motion modality Due to the repetitive nature of the movements, time series analysis was not appropriate for the continuous motion modality. Secondary outcomes Amplitude analysis: Root Mean Square (RMS) Discrete motion modality There was a statistically significant difference between conditions for RMS amplitude (χ²(2) = 6.533, p = 0.038). However, post-hoc analysis using the Bonferroni correction revealed no significant differences between conditions, although there was a tendency for the Heat condition to increase RMS amplitude compared to the Normal condition (W = 21, p = 0.077, effect size r = 0.735). Continuous motion modality There was a statistically significant difference between conditions for the RMS amplitude (χ²(2) = 6.4, p = 0.041). Post-hoc analysis showed that the Heat pain condition increased RMS amplitude compared to the aversive Auditory condition (W = 108, p = 0.013, effect size r = 0.704) (Fig. 4 ). However, there was no significant difference between the Heat pain condition and the Normal condition (W = 26, p = 0.166, effect size r = 0.499). Amplitude analysis: Mean Absolute Values (MAV) Discrete motion modality There was no statistically significant difference between conditions for MAV amplitude (χ²(2) = 4.933, p = 0.085). Continuous motion modality There was a statistically significant difference between conditions for MAV amplitude (χ²(2) = 6.53, p = 0.038). However, post-hoc analysis using the Wilcoxon test with Bonferroni correction showed no significant differences between conditions, although there was a tendency for the Heat condition to increase MAV amplitude compared to the Auditory condition (W = 98, p = 0.091, effect size r = 0.557). Frequency analysis: Power Spectrum analysis (PSD) Discrete motion modality Power Spectrum Analysis (PSD) was performed on the discrete motion modality to examine the distribution of signal power across frequencies. There was no statistically significant difference in mean frequency between conditions (χ²(2) = 3.6, p = 0.165). This indicates that the frequency components of muscle activity did not change significantly across experimental conditions. Continuous motion modality Similarly, PSD analysis revealed no statistically significant difference in mean frequency between conditions (χ²(2) = 1.6, p = 0.449). This suggests that the overall frequency characteristics of muscle activity remained consistent across experimental conditions. The complete statistical analysis is provided in the Supplementary Information (SI) in HTML format. Discussion Our results did not support our hypothesis that muscle activation would be delayed, amplitude reduced, and power spectral density shifted to higher frequencies in response to pain. Rather, these results indicated that unpleasant stimuli did not affect the timing, amplitude, or mean frequency of muscle contraction. In addition, our results showed that only thermal heat unpleasant stimuli increased muscle activity compared to aversive auditory stimuli only during continuous wrist movement. First, our results were not consistent with previous studies showing delayed muscle activation in the presence of experimental pain during discrete movement modalities (Devecchi et al., 2023 ; Dupuis et al., 2021 ). However, these studies analyzed noxious exposure stimuli at proximal body sites (back and shoulder). In contrast, a recent study using thermal noxious stimuli (laser stimulation) on the hand showed no differences in EMG peak or early activity (Ogalo et al., 2024 ). Another study using capsaicin and hypersaline injection on the biceps brachialis also found no differences in time to peak amplitude (Qerama et al., 2005 ). The localization of the stimuli, whether proximal or distal, may affect muscle activity and may explain the apparent inconsistencies in our findings. Second, our results showed that the thermal unpleasant stimulus increased the sEMG amplitude only during continuous modality. Conversely, the aversive auditory unpleasantness stimulus did not change the sEMG amplitude. The aversive auditory stimuli were used to simulate the cognitive-evaluative and affective-motivational dimension of pain, as suggested by previous studies. These studies highlighted the presence of tinnitus-like sensations (Mirz et al., 2000 ), similar arousal or unpleasantness scales (Chang et al., 2002 ; Valentini et al., 2022 ), and activation of brain areas associated with negative affect (Čeko et al., 2022 ). Our results suggest that the sensory-discriminative dimension of pain may have a greater influence on motor activity than the cognitive-evaluative and affective-motivational dimensions. Our results are not consistent with some studies that have shown a decrease in sEMG amplitude in the presence of unpleasant stimuli (Baad-Hansen et al., 2009 ; Bank et al., 2013 ; Ervilha et al., 2005 ; Farina et al., 2005 ). Conversely, another study using hypersaline injection in the quadriceps showed an increase in EMG amplitude during a 10% maximal voluntary contraction in a discrete motion modality (Poortvliet et al., 2019 ). As suggested by the systematic review by Bank et al (Bank et al., 2013 ), the level of muscle activity during a task may influence the consequence of muscle activity, highlighting the principle of neuromuscular adaptation with non-stereotypic behavior (Devecchi et al., 2023 ). These observations are consistent with the consensual theory of pain and movement known as the protective response theory (Hodges & Tucker, 2011 ; Merkle et al., 2020 ). This theory posits that body movements are highly adaptive to external perturbations in order to maintain function. Third, there was no change in mean frequency using power spectral density analysis during the unpleasant stimulus. This is in contrast to a previous study that demonstrated an EMG shift to higher frequencies during a saline injection in the shoulder muscle during a 30 Newton cutting task (Madeleine et al., 1999 ). However, other authors found no change in frequency analysis during saline injection in the tibialis anterior muscle during a task requiring 30% of maximal voluntary contraction (Farina et al., 2005 ). According to some authors (Bank et al., 2013 ; Falla & Gallina, 2020 ), these heterogeneous results suggest muscle adaptation and likely activity redistribution depending on the task intensity analyzed during experimental pain. Another explanation for our inconsistencies could be the influence of personal factors such as age, sex, or genotype (Fillingim, 2017 ). In addition, sampling bias has been found in individuals participating in experimental pain studies, specifically with regard to their cognitive-evaluative and affective-motivational dimension of pain, such as pain catastrophizing or fear of pain (Karos et al., 2018 ). Finally, discrete and continuous movements are produced by different temporal behaviors, with discrete movements requiring precise timing to achieve point-to-point movement (Huys et al., 2008 ). Some authors have shown that experimental pain reduces corticomuscular coherence during voluntary discrete movements compared to postural movements. This difference has been attributed to increased cognitive load in cortical networks, such as attention (Poortvliet et al., 2019 ). However, experimental pain has been shown to moderately impair alerting attention (Gong et al., 2019 ). Our study have shown that the type of movement modality such as discrete and continuous wrist movements may not influence muscle behavior. However, these types of pain stimuli do not influence the modality of movement, confirming the human ability to maintain motor function in the presence of pain experience. Limitations and opportunities This study included young participants (median age 23.5 years), and previous studies have shown that age influences pain intensity, with lower levels initially in older adults (Daguet et al., 2020 ), as well as muscle activity (Jensen & Fuglsang-Frederiksen, 1994 ). In addition, the participants were active, with a median physical activity level of 4914 MET minutes per week. Previous studies have shown that an active lifestyle reduces spinal nociception as assessed by the nociceptive withdrawal reflex (Dhondt et al., 2021 ). Another point of interest is the relative imprecision of sEMG when using a single electrode. On the one hand, our study analyzed only the wrist extensors, and the activity of a single muscle may not sufficiently capture the complexity of muscle redistribution during experimental pain, as suggested by some authors (Hug et al., 2014 ). On the other hand, sEMG captures the sum of motor unit action potentials (MUAP) (Campanini et al., 2020 ; Clancy et al., 2023 ). High-density EMG offers the possibility to better capture the complexity of the spatial and temporal redistribution of muscle activity during experimental pain (Abboud et al., 2018 ; Falla & Gallina, 2020 ). These analyses provide interesting opportunities to better understand behavioral strategies, particularly for individuals suffering from pain disorders (Varrecchia et al., 2023 ). Conclusion The results of this experimental study suggest that the cognitive-evaluative and affective-motivational dimensions of pain may not influence muscle activity. In contrast, the sensory-discriminative dimension of pain may increase muscle activity during continuous motion modalities. The literature shows heterogeneous results, reporting increases, decreases, or no effect of unpleasant experiences on muscle activity, emphasizing the suggested non-stereotypical motor behavior during pain. Declarations Author Contribution Conceptualization, Methodology, Formal Analysis, Investigation, Writing-Original Draft: [Cohen-Aknine, Gabriel], Methodology, Data Curation, Writing-Review & Editing, Visualization: [Pionnier Raphaël], Methodology, Validation, Data Curation, Writing-Review & Editing, Visualization, Supervision: [Mottet, Denis], Project Administration, Methodology, Validation, Writing-Review & Editing, Visualization, Supervision: [Dupeyron, Arnaud] Data Availability On request, the corresponding author can provide the raw data and analysis code. 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Muscle Nerve 31(1):25–33. https://doi.org/10.1002/mus.20182 Raja SN, Carr DB, Cohen M, Finnerup NB, Flor H, Gibson S, Keefe F, Mogil JS, Ringkamp M, Sluka KA, Song X-J, Stevens B, Sullivan M, Tutelman P, Ushida T, Vader K (2020) The Revised IASP definition of pain : Concepts, challenges, and compromises. Pain 161(9):1976–1982. https://doi.org/10.1097/j.pain.0000000000001939 Reddy KS, kumar, Naidu MUR, Rani PU, Rao TRK (2012) Human experimental pain models : A review of standardized methods in drug development. J Res Med Sci : Official J Isfahan Univ Med Sci 17(6):587–595 Rohel A, Bouffard J, Patricio P, Mavromatis N, Billot M, Roy J-S, Bouyer L, Mercier C, Masse-Alarie H (2021) The effect of experimental pain on the excitability of the corticospinal tract in humans : A systematic review and meta-analysis. Eur J Pain 25(6):1209–1226. https://doi.org/10.1002/ejp.1746 Smith JA, Stabbert H, Bagwell JJ, Teng H-L, Wade V, Lee S-P (2022) Do people with low back pain walk differently? A systematic review and meta-analysis. J Sport Health Sci 11(4):450–465. https://doi.org/10.1016/j.jshs.2022.02.001 Stankewitz A, Mayr A, Irving S, Witkovsky V, Schulz E (2023) Pain and the emotional brain : Pain-related cortical processes are better reflected by affective evaluation than by cognitive evaluation. Sci Rep 13(1):8273. https://doi.org/10.1038/s41598-023-35294-2 Stefanou T, Guiraud D, Fattal C, Azevedo-Coste C, Fonseca L (2022) Frequency-Domain sEMG Classification Using a Single Sensor. Sensors (Basel, Switzerland) , 22 (5), 1939. https://doi.org/10.3390/s22051939 Svensson P, Arendt-Nielsen L, Houe L (1996) Sensory-motor interactions of human experimental unilateral jaw muscle pain : A quantitative analysis. Pain 64(2):241. https://doi.org/10.1016/0304-3959(95)00133-6 Talbot K, Madden VJ, Jones SL, Moseley GL (2019) The sensory and affective components of pain : Are they differentially modifiable dimensions or inseparable aspects of a unitary experience? A systematic review. Br J Anaesth 123(2):e263–e272. https://doi.org/10.1016/j.bja.2019.03.033 Tankisi H, Burke D, Cui L, de Carvalho M, Kuwabara S, Nandedkar SD, Rutkove S, Stålberg E, van Putten MJAM, Fuglsang-Frederiksen A (2020) Standards of instrumentation of EMG. Clin Neurophysiol 131(1):243–258. https://doi.org/10.1016/j.clinph.2019.07.025 Valentini E, Halder S, McInnerney D, Cooke J, Gyimes IL, Romei V (2022) Assessing the specificity of the relationship between brain alpha oscillations and tonic pain. NeuroImage 255:119143. https://doi.org/10.1016/j.neuroimage.2022.119143 Varrecchia T, Ranavolo A, Chini G, De Nunzio AM, Draicchio F, Martinez-Valdes E, Falla D, Conforto S (2023) High-density surface electromyography allows to identify risk conditions and people with and without low back pain during fatiguing frequency-dependent lifting activities. J Electromyogr Kinesiology: Official J Int Soc Electrophysiological Kinesiol 73:102839. https://doi.org/10.1016/j.jelekin.2023.102839 Veale JF (2014) Edinburgh Handedness Inventory - Short Form : A revised version based on confirmatory factor analysis. Laterality 19(2):164–177. https://doi.org/10.1080/1357650X.2013.783045 Whitley E, Ball J (2002) Statistics review 6 : Nonparametric methods. Crit Care 6(6):509–513 Zawawi TNST, Abdullah AR, Jopri MH, Sutikno T, Saad NM, Sudirman R (2018) A Review of Electromyography Signal Analysis Techniques for Musculoskeletal Disorders. Indonesian J Electr Eng Comput Sci 11(3). Article 3. https://doi.org/10.11591/ijeecs.v11.i3.pp1136-1146 Supplementary Information The Supplementary Information file is not available with this version. Additional Declarations No competing interests reported. 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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-6606053","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":455655996,"identity":"dd940492-4481-42ec-b434-f67fce1ee680","order_by":0,"name":"Gabriel COHEN-AKNINE","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABPklEQVRIie3RMUvDQBjG8TcE4uDFrieR9itcOWgtuvs1EgJxia3QJUPBQCFdjF2z+RWUQnVUDjKldhaXFleRilAalOIldUiTuDvcf3oh/fGEFEAk+pchAB0ON0dyKgDSTHL5LafPq38QvEVkkiG0lECW8BScEigntZNJuJg5GCoD//H1vGe197R+6MT3DJo7u8Z8BaSTI/WobQZ6hAFHTyYNQrurHITWsx8xaPXVEUVAWm6OuDYFw8NAsN3QkOLw2268SB67uGHqWAP4JvmV4Rsn65Q0v9A6IZ1llxMgTL2L+YvlSQ0nK+5mRVY9O1lR5F8y5t+kQAh+N0EPMcJRRDX1yuLEovu+d5qQkYZIcWV4xqRV77haGVzWP9HSNK4Dc76IvSMg08ntx8oprjxk/p2y8oCvuMVfiUQikWi7H/ZOZ9KOKDl1AAAAAElFTkSuQmCC","orcid":"","institution":"EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, CHU Nimes ,Nimes, France","correspondingAuthor":true,"prefix":"","firstName":"Gabriel","middleName":"","lastName":"COHEN-AKNINE","suffix":""},{"id":455655997,"identity":"c1bae618-2d50-467e-b31c-2c216776b20b","order_by":1,"name":"Raphaël PIONNIER","email":"","orcid":"","institution":"Department of Physical 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19:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6606053/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6606053/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00221-025-07165-x","type":"published","date":"2025-10-09T15:58:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82851175,"identity":"175e800a-fc14-4744-b202-47a0e176f28f","added_by":"auto","created_at":"2025-05-16 03:21:54","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":105504,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalogue visual scale for unpleasant stimulus levels\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6606053/v1/574001155a4632d670fe5b3b.jpeg"},{"id":82851183,"identity":"f97e6c64-e9e2-4da8-8039-a7b0712bc17d","added_by":"auto","created_at":"2025-05-16 03:21:54","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":209782,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInstructions design\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6606053/v1/c8ac2c28e5583844a393f1f0.jpeg"},{"id":82851176,"identity":"8b369a81-11b2-4a39-a62c-2d7c146000e2","added_by":"auto","created_at":"2025-05-16 03:21:54","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":138282,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtocol design\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6606053/v1/41f8e0ce8e0849fdef3aebaf.jpeg"},{"id":82851179,"identity":"93e27edf-8f43-4373-84c5-e49964cc0471","added_by":"auto","created_at":"2025-05-16 03:21:54","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":134848,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBoxplot of Root Mean Square (RMS in Millivolts) Means for Wrist Extensor Muscle During Continuous Flexion/Extension Wrist Motions Under Normal, Heat, and Auditory Conditions\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6606053/v1/a18bb520ba1ad399bda91c80.jpeg"},{"id":93419784,"identity":"49fd4581-ecf6-4889-9776-f6dce74b218a","added_by":"auto","created_at":"2025-10-13 16:07:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1529218,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6606053/v1/dae66d88-6b89-4b1d-aed3-0b15f52bbb79.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Effects of Unpleasant Thermal and Auditory Stimulus on Forearm Muscle Activity During Discrete and Continuous Wrist Movements","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePain is a defined as un unpleasant sensory and emotional experience (Raja et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), involving multidimensional and highly personal perceptions that emphasize its unpleasant nature (Murray \u0026amp; Sessle, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It has been shown that both clinical (Bazett-Jones et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Smith et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and experimental (Cabral et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) pain affect motor activity. However, some inconsistencies may hinder comprehension of this effect and the overall interpretation of the relationship between pain and movement particularly with regard to how pain is defined and movement is analyzed.\u003c/p\u003e \u003cp\u003eFirstly, pain encompasses sensory-discriminative, cognitive-evaluative and affective-motivational dimensions (Murray \u0026amp; Sessle, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, it is difficult to distinguish these dimensions with conventional experimental pain stimuli, whether the stimulus is phasic (short duration with intense sensation) or tonic (long duration with moderate sensation) (Reddy et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), leading to heterogeneity in the interpretation of results. Some authors have proposed other unpleasant stimuli, such as aversive auditory stimuli (Mirz et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), which have been shown to be as unpleasant in intensity as experimental pain models (Chang et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Valentini et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although aversive auditory stimuli may be more related to motivational-affective and cognitive-evaluative components of pain (Stankewitz et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), their effects on motor activity have not been compared with those of common experimental painful stimuli.\u003c/p\u003e \u003cp\u003eSecondly, motor activity is often assessed in experimental pain designs using surface electromyography (sEMG) (Bank et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Medved et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, previous research has shown heterogeneous results regarding the effect of noxious stimuli on motor activity (Bank et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Devecchi et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) particularly in the type of task used in sEMG. For example, movement can be performed in different ways. These are known as discrete (single joint range of motion) and continuous (full or rhythmic joint range of motion) modalities (Angel \u0026amp; Park, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Habas \u0026amp; Cabanis, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Hatsopoulos et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Huys et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Previous reviews have highlighted the heterogeneity of results, particularly in relation to the movement patterns assessed during unpleasant experience (Bank et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Burns et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Izadi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For example, the meta-analysis by Ippersiel P. et al (Ippersiel et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) involved studies using either discrete tasks, such as lumbar flexion or extension, or continuous tasks, such as lumbar flexion/extension or gait, and suggested contrasting results regarding the trunk muscle activity.\u003c/p\u003e \u003cp\u003eThirdly, in terms of analysis type, sEMG can be performed using different methods, including global muscle activity analysis (e.g. root mean square, RMS), time series analysis (e.g. time to peak activity), and frequency analysis (Campanini et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Clancy et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; McManus et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zawawi et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Previous systematic reviews have suggested that unpleasant stimuli may alter muscle activity in terms of amplitude or contraction timing (Barton et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Heales et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kinsella \u0026amp; Pizzari, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), such as reducing muscle activity using RMS analysis (Ciubotariu et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Farina et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Svensson et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), shorten time to peak (Dupuis et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), or changing mean power frequency with a shift to higher frequencies (Madeleine et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Most systematic reviews have reported delays in muscle activation (Barton et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Chester et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Dupuis et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, for other measures, such as task performance, dexterity or stability, the literature suggests that unpleasant stimuli does not significantly affect the redistribution of muscle activity (Bank et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In addition, a meta-analysis showed conflicting evidence regarding the effect of unpleasant stimuli on corticospinal excitability, with some studies reporting a decrease, others an increase, and some even showing no effects (Rohel et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to a systematic review (Talbot et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) that pointed out the challenge of manipulating the pain dimension using cognitive interventions, these inconsistencies in research findings on the effects of unpleasant stimuli on motor activity may have been influenced by the effects of the pain dimension, as mentioned above.\u003c/p\u003e \u003cp\u003e The present study aimed to compare the effects of thermal and aversive auditory pain stimuli with those of a control condition in healthy participants, by mixing the types of tasks and analyses used. We hypothesized that thermal and auditory stimuli would delay muscle activation, reduce amplitude, and shift power frequency to higher frequencies, regardless of discrete or continuous movement modalities.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eThis work is part of a larger, experimental single center prospective design study conducted at the Research Unit of Euromov Digital Health \u0026amp; Motion (Montpellier, France) (the other part of this study was preprinted on medRxiv: doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2025.03.06.25323546\u003c/span\u003e\u003cspan address=\"10.1101/2025.03.06.25323546\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The study was approved by the local ethics committee (Comit\u0026eacute; d'\u0026Eacute;thique de la Recherche de l'Universit\u0026eacute; de Montpellier: n\u0026deg; UM 2023-031) according to the declaration of Helsinski revised in 2013. Participants received and signed a written informed consent form.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003e Eighteen healthy right-handed participants, aged 18\u0026ndash;50 years, with no history of neurological or psychiatric disorders, no chronic pain and no pain in the upper limbs during the experiment were included. Participants were recruited from the students of the University of Montpellier. Recruitment was carried out by email and posters around the university. Participants were excluded from the study if they were unable to perform the procedure described below and/or because of a dysfunction of any equipment used in the protocol.\u003c/p\u003e \u003cp\u003eAge, sex, weight, height, and body mass index (BMI) were recorded. Participants' handedness was assessed using the Edinburgh Handedness Inventory - Short Form (EHI-SF) (Veale, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), a questionnaire that scores handedness based on 10 daily activities, with a ratio and a cut-off of 40 points or more indicating right-handedness. Levels of physical activity and inactivity were measured using the International Physical Activity Questionnaire-Short Form (IPAQ-SF) (Meh et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This self-report questionnaire uses an algorithm to calculate the number of minutes of physical activity (PA) per week, categorized into three levels (low, moderate and vigorous), and a score for inactivity (corresponding to the number of minutes spent sitting). The total score is expressed in MET (Metabolic Equivalent of Task) minutes/week. This is a unit used in the international literature to measure PA intensity in absolute terms. It is defined as the ratio of the energy expended during the activity in question to the amount of energy expended at rest. For example, 1 MET corresponds to resting metabolic rate or basal metabolic rate (Bull et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eParticipants were seated in a chair in front of a computer in a relaxed position with their forearms resting on the armrests and their elbows naturally flexed in pronation, so that the wrists were fully flexed and relaxed.\u003c/p\u003e \u003cp\u003eThe procedure consisted of 2 parts: a discrete modality (consisting of single imagined and executed wrist movements) and a continuous modality (consisting of repeated imagined and executed wrist movements).\u003c/p\u003e \u003cp\u003eIn the discrete modality, two tasks were performed: \"resting state\" and \"motor execution\" and only \"motor execution\" for the continuous assessment. During the resting state, a central cross was displayed on the screen with instructions to stare at the cross and remain relaxed. For the \u0026ldquo;motor execution\u0026rdquo; task, participants were instructed to perform the movement described below, in parts 1 and 2. The instructions were displayed on a screen using PsychoPy software (v 2023.1.2) (Peirce et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo familiarize participants with the movement, a training session was conducted prior to the experiment in which participants were asked to perform the movement \"in their own way\" with respect to the previous instructions, without additional guidance.\u003c/p\u003e\n\u003ch3\u003ePart 1: discrete\u003c/h3\u003e\n\u003cp\u003eWith the wrist fully flexed as described above, participants were instructed to perform a full range of motion wrist movement in the sagittal plane (from fully flexed to fully extended position), known as discrete modality, and return to the starting position, within a time window of 4 seconds. This procedure was repeated 20 times.\u003c/p\u003e\n\u003ch3\u003ePart 2: continuous\u003c/h3\u003e\n\u003cp\u003eIn the same position as part 1, participants were instructed to perform a repetitive/continuous full range of motion wrist movement in the sagittal plane (from fully flexed to fully extended position) for 25 seconds. This procedure was repeated 3 times. The two painful conditions and control condition were tested for both parts.\u003c/p\u003e\n\u003ch3\u003eConditions\u003c/h3\u003e\n\u003cp\u003eThree blocks of conditions were created: \u0026lsquo;normal\u0026rsquo; (=\u0026thinsp;control), \u0026lsquo;auditory\u0026rsquo; (aversive auditory stimulus) and \u0026lsquo;heat\u0026rsquo; (thermal painful stimulus). The order of the conditions was pseudo-randomized using six designs determined by dice. In the 'normal' condition, movement was performed and imagined without any unpleasant stimulus.\u003c/p\u003e \u003cp\u003eThe aversive \u0026lsquo;auditory\u0026rsquo; stimulus was created using Audacity\u0026reg; (v 3.3.3) with a duration of 4 seconds, a start and end fade of 0.2 seconds, a frequency of 5000 Hz (sawtooth waveform) and a baseline volume of 75 dB. This stimulus was inspired by a previous publication by Valentini et al (Valentini et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The thermal 'heat' stimulus was applied using hot water in a 14/19 L thermostatic bath (CORIO C-BT19, Julabo\u0026reg;, Seelbach, Germany) with a baseline temperature of 45\u0026deg;C (Granot et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo standardize variability between participants, each participant selected their level of discomfort for each stimulus on a 100-point visual analogue scale (VAS) (0 = 'no discomfort' to 100 = 'unbearable discomfort'). Participants were instructed to choose the water temperature and the auditory sound within a range between 60 and 75 on the VAS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The levels were adjusted in increments of 1\u0026deg;C for temperature and 1dB for sound volume until the chosen level of discomfort was reached.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the thermal stimulus, the right hand was immersed in water up to the wrist joint, and for the auditory stimulus, the sound was played for periods of 25 seconds through loudspeakers. The temperature level was monitored using the CORIO C-BT19 thermometer and the aversive auditory level was measured using the \"Decibel X - Pro Sonom\u0026egrave;tre smartphone application\" (SkyPaw Co.\u0026reg;, Ltd) (Murphy \u0026amp; King, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A thermal heat stimulus was chosen because it results in a shorter duration of pain, a more constant quantitative description and a steeper slope for the intensity of the sensation compared to cold pain stimulus (Morin \u0026amp; Bushnell, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The instructions and the design of the protocol are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e respectively.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eThe motor activity of the wrist extensor muscles was recorded during a concentric to eccentric contraction (full wrist flexion/extension range of motion).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrimary outcome measures\u003c/h3\u003e\n\u003cp\u003eThe time series was calculated using a Time to Peak approach corresponding to the time taken for a signal to reach its maximum amplitude or peak value during the epoch window.\u003c/p\u003e\n\u003ch3\u003eSecondary Outcomes\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; Amplitude analysis: Root Mean Square (RMS)\u003c/h2\u003e \u003cp\u003eThe RMS is a measure of the magnitude of the electric muscle activity during the epoch window.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; Amplitude analysis: Mean Absolute Value (MAV)\u003c/h2\u003e \u003cp\u003eThe MAV is the average of the absolute values of the sEMG signal over the epoch window.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e\u0026bull; Frequency analysis: Power Spectrum Analysis (PSD)\u003c/h2\u003e \u003cp\u003eThe PSD is a method used to decompose the raw amplitude signal into its constituent frequencies and analyze the distribution of power (or variance) of a signal across different frequencies during the epoch window.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSignal processing\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eElectrode placement\u003c/h2\u003e \u003cp\u003ePrior to electrode placement, the skin was prepared according to the SENIAM guidelines (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://seniam.org/sensor_location.htm\u003c/span\u003e\u003cspan address=\"http://seniam.org/sensor_location.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) by shaving, light abrading and cleaning with alcohol. Bipolar electrodes were placed on the midline and midpoint of the muscles of interest, maintaining an inter-electrode distance (IED) of approximately 20 mm (ranging from 6 mm to 40 mm) (McManus et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). 20 mm circular Ag/AgCl bipolar electrodes were placed on the extensor wrist muscles of the forearm, aligned longitudinally with the muscle fibres, on the muscle belly, proximally to the lateral epicondyle (Hermens et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), at approximately 89% of the distance between the ulnar styloid and the lateral epicondyle, as recommended by Ghapanchizadeh et al (Ghapanchizadeh et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The reference electrode was placed on either the mastoid process or the contralateral wrist, depending on the pre-test signal quality assessment.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSignal analyzing\u003c/h2\u003e \u003cp\u003eThe sEMG amplifier had an impedance greater than 100 MΩ and a common mode rejection ratio (CMRR) greater than 90 dB (REFA, TMSI \u0026reg;). The sEMG signal was sampled at a frequency of 1024 Hz. Signals were synchronized with PsychoPy triggers using LSL (LabStreamingLayer) (Kothe et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) in XDF format.\u003c/p\u003e \u003cp\u003eThe signal processing pipeline followed the recommended guidelines (Zawawi et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and included the following steps: (1) detrending, (2) low-pass filtering, (3) high-pass filtering, (4) notch filtering (and harmonics), and (5) squaring. The signal was detrended and filtered using a notch filter at 50 Hz (with additional notch filters at its harmonics: 100, 150 and 200 Hz) and a Butterworth filter with a bandpass range of 20\u0026ndash;500 Hz (Tankisi et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The signal was then segmented into epochs ranging from \u0026minus;\u0026thinsp;0.5 to 4 seconds relative to each trigger. Signal processing was performed using MATLAB\u0026reg; software (v. R2021b).\u003c/p\u003e \u003cp\u003eAnalysis was performed according to the methods described by Clancy et al (Clancy et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Muceli S \u0026amp; Merletti R (Muceli \u0026amp; Merletti, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Means of muscle activity for all trials (20 movements for the discrete modality and 3 movements for the continuous modality) were calculated for each participant and condition.\u003c/p\u003e \u003cp\u003eTime to peak, defined as the interval between 1 s before the start of the epoch window and the maximum root mean square (RMS) value, was calculated for each participant across all tasks and conditions for both flexor and extensor wrist muscles.\u003c/p\u003e \u003cp\u003eThe MATLAB RMS function was used to calculate the root mean square (RMS). The mean RMS was calculated for each participant across all tasks and conditions for both flexor and extensor wrist muscles in both discrete and continuous motion modalities.\u003c/p\u003e \u003cp\u003eThe Mean Absolute Value (MAV), defined as the mean of the RMS for each task signal during the epoch window, was calculated for each participant across tasks and conditions for both flexor and extensor wrist muscles in both discrete and continuous motion modalities.\u003c/p\u003e \u003cp\u003eA Fast Fourier Transform (FFT) power spectrum analysis (PSD) using Welch's method (Stefanou et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) was performed for each participant across tasks and conditions for both flexor and extensor wrist muscles, in both discrete and continuous motion modalities. This analysis was performed in the frequency range of 20 to 300 Hz (Han et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using RStudio\u0026reg; software (v. 2024.04.2). Some authors have recommended non-parametric approaches for small sample sizes (Harwell, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Whitley \u0026amp; Ball, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Demographic and sEMG analyses were performed using frequentist statistical methods, and results are presented as median [interquartile range (IQR)]. Comparison between the three conditions was analyzed using the Friedman test, followed by post hoc pairwise comparisons using the Wilcoxon signed-rank test with Bonferroni-Holm correction for multiple comparisons. Effect sizes were calculated using Rosenthal's r for the paired-sample Wilcoxon signed-rank test, and significance levels were classified as small (0.20), moderate (0.50), or large (0.80) (Maher et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Statistical results are reported with a 95% confidence interval (CI) and an alpha level of 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eDemographic analysis\u003c/h2\u003e \u003cp\u003eSixteen participants (6 female, 10 male) were initially included in the study. However, two participants were subsequently excluded from the analysis due to a high presence of artefacts in the sEMG signals, making their data uninterpretable. The characteristics of the remaining participants were as follows: median age of 23.5 (IQR\u0026thinsp;=\u0026thinsp;4) years, median body mass index (BMI) of 22.6 (IQR\u0026thinsp;=\u0026thinsp;4.02) kg/m\u0026sup2;, median Edinburgh Handedness Inventory (EHI) score of 80 (IQR\u0026thinsp;=\u0026thinsp;23.88), and median physical activity level of 4914 (IQR\u0026thinsp;=\u0026thinsp;2887) MET minutes/week. Median selected thermal pain temperatures were 46\u0026deg;C (IQR\u0026thinsp;=\u0026thinsp;1.62) and auditory volume was 81. dB (IQR\u0026thinsp;=\u0026thinsp;8.25), consistent with the design of Valentini et al. (Valentini et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePrimary outcome: Time series analysis\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003eDiscrete motion modality\u003c/h2\u003e \u003cp\u003eThere was no statistically significant difference between conditions in the time to peak wrist extensor muscle activity (χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;4.133, p\u0026thinsp;=\u0026thinsp;0.127).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eContinuous motion modality\u003c/h2\u003e \u003cp\u003eDue to the repetitive nature of the movements, time series analysis was not appropriate for the continuous motion modality.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eSecondary outcomes\u003c/h2\u003e \u003cdiv id=\"Sec24\" class=\"Section4\"\u003e \u003ch2\u003eAmplitude analysis: Root Mean Square (RMS)\u003c/h2\u003e \u003cp\u003e \u003cb\u003eDiscrete motion modality\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThere was a statistically significant difference between conditions for RMS amplitude (χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;6.533, p\u0026thinsp;=\u0026thinsp;0.038). However, post-hoc analysis using the Bonferroni correction revealed no significant differences between conditions, although there was a tendency for the Heat condition to increase RMS amplitude compared to the Normal condition (W\u0026thinsp;=\u0026thinsp;21, p\u0026thinsp;=\u0026thinsp;0.077, effect size r\u0026thinsp;=\u0026thinsp;0.735).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eContinuous motion modality\u003c/h2\u003e \u003cp\u003eThere was a statistically significant difference between conditions for the RMS amplitude (χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;6.4, p\u0026thinsp;=\u0026thinsp;0.041). Post-hoc analysis showed that the Heat pain condition increased RMS amplitude compared to the aversive Auditory condition (W\u0026thinsp;=\u0026thinsp;108, p\u0026thinsp;=\u0026thinsp;0.013, effect size r\u0026thinsp;=\u0026thinsp;0.704) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, there was no significant difference between the Heat pain condition and the Normal condition (W\u0026thinsp;=\u0026thinsp;26, p\u0026thinsp;=\u0026thinsp;0.166, effect size r\u0026thinsp;=\u0026thinsp;0.499).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eAmplitude analysis: Mean Absolute Values (MAV)\u003c/h2\u003e \u003cdiv id=\"Sec27\" class=\"Section4\"\u003e \u003ch2\u003eDiscrete motion modality\u003c/h2\u003e \u003cp\u003eThere was no statistically significant difference between conditions for MAV amplitude (χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;4.933, p\u0026thinsp;=\u0026thinsp;0.085).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eContinuous motion modality\u003c/h2\u003e \u003cp\u003eThere was a statistically significant difference between conditions for MAV amplitude (χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;6.53, p\u0026thinsp;=\u0026thinsp;0.038). However, post-hoc analysis using the Wilcoxon test with Bonferroni correction showed no significant differences between conditions, although there was a tendency for the Heat condition to increase MAV amplitude compared to the Auditory condition (W\u0026thinsp;=\u0026thinsp;98, p\u0026thinsp;=\u0026thinsp;0.091, effect size r\u0026thinsp;=\u0026thinsp;0.557).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFrequency analysis: Power Spectrum analysis (PSD)\u003c/h3\u003e\n\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003eDiscrete motion modality\u003c/h2\u003e \u003cp\u003ePower Spectrum Analysis (PSD) was performed on the discrete motion modality to examine the distribution of signal power across frequencies. There was no statistically significant difference in mean frequency between conditions (χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;3.6, p\u0026thinsp;=\u0026thinsp;0.165). This indicates that the frequency components of muscle activity did not change significantly across experimental conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eContinuous motion modality\u003c/h2\u003e \u003cp\u003eSimilarly, PSD analysis revealed no statistically significant difference in mean frequency between conditions (χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;1.6, p\u0026thinsp;=\u0026thinsp;0.449). This suggests that the overall frequency characteristics of muscle activity remained consistent across experimental conditions.\u003c/p\u003e \u003cp\u003eThe complete statistical analysis is provided in the Supplementary Information (SI) in HTML format.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results did not support our hypothesis that muscle activation would be delayed, amplitude reduced, and power spectral density shifted to higher frequencies in response to pain. Rather, these results indicated that unpleasant stimuli did not affect the timing, amplitude, or mean frequency of muscle contraction. In addition, our results showed that only thermal heat unpleasant stimuli increased muscle activity compared to aversive auditory stimuli only during continuous wrist movement.\u003c/p\u003e \u003cp\u003eFirst, our results were not consistent with previous studies showing delayed muscle activation in the presence of experimental pain during discrete movement modalities (Devecchi et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Dupuis et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, these studies analyzed noxious exposure stimuli at proximal body sites (back and shoulder). In contrast, a recent study using thermal noxious stimuli (laser stimulation) on the hand showed no differences in EMG peak or early activity (Ogalo et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Another study using capsaicin and hypersaline injection on the biceps brachialis also found no differences in time to peak amplitude (Qerama et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The localization of the stimuli, whether proximal or distal, may affect muscle activity and may explain the apparent inconsistencies in our findings.\u003c/p\u003e \u003cp\u003eSecond, our results showed that the thermal unpleasant stimulus increased the sEMG amplitude only during continuous modality. Conversely, the aversive auditory unpleasantness stimulus did not change the sEMG amplitude. The aversive auditory stimuli were used to simulate the cognitive-evaluative and affective-motivational dimension of pain, as suggested by previous studies. These studies highlighted the presence of tinnitus-like sensations (Mirz et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), similar arousal or unpleasantness scales (Chang et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Valentini et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and activation of brain areas associated with negative affect (Čeko et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our results suggest that the sensory-discriminative dimension of pain may have a greater influence on motor activity than the cognitive-evaluative and affective-motivational dimensions. Our results are not consistent with some studies that have shown a decrease in sEMG amplitude in the presence of unpleasant stimuli (Baad-Hansen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Bank et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ervilha et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Farina et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Conversely, another study using hypersaline injection in the quadriceps showed an increase in EMG amplitude during a 10% maximal voluntary contraction in a discrete motion modality (Poortvliet et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As suggested by the systematic review by Bank et al (Bank et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), the level of muscle activity during a task may influence the consequence of muscle activity, highlighting the principle of neuromuscular adaptation with non-stereotypic behavior (Devecchi et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These observations are consistent with the consensual theory of pain and movement known as the protective response theory (Hodges \u0026amp; Tucker, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Merkle et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This theory posits that body movements are highly adaptive to external perturbations in order to maintain function.\u003c/p\u003e \u003cp\u003eThird, there was no change in mean frequency using power spectral density analysis during the unpleasant stimulus. This is in contrast to a previous study that demonstrated an EMG shift to higher frequencies during a saline injection in the shoulder muscle during a 30 Newton cutting task (Madeleine et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). However, other authors found no change in frequency analysis during saline injection in the tibialis anterior muscle during a task requiring 30% of maximal voluntary contraction (Farina et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). According to some authors (Bank et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Falla \u0026amp; Gallina, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), these heterogeneous results suggest muscle adaptation and likely activity redistribution depending on the task intensity analyzed during experimental pain. Another explanation for our inconsistencies could be the influence of personal factors such as age, sex, or genotype (Fillingim, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In addition, sampling bias has been found in individuals participating in experimental pain studies, specifically with regard to their cognitive-evaluative and affective-motivational dimension of pain, such as pain catastrophizing or fear of pain (Karos et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, discrete and continuous movements are produced by different temporal behaviors, with discrete movements requiring precise timing to achieve point-to-point movement (Huys et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Some authors have shown that experimental pain reduces corticomuscular coherence during voluntary discrete movements compared to postural movements. This difference has been attributed to increased cognitive load in cortical networks, such as attention (Poortvliet et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, experimental pain has been shown to moderately impair alerting attention (Gong et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Our study have shown that the type of movement modality such as discrete and continuous wrist movements may not influence muscle behavior. However, these types of pain stimuli do not influence the modality of movement, confirming the human ability to maintain motor function in the presence of pain experience.\u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and opportunities\u003c/h2\u003e \u003cp\u003eThis study included young participants (median age 23.5 years), and previous studies have shown that age influences pain intensity, with lower levels initially in older adults (Daguet et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), as well as muscle activity (Jensen \u0026amp; Fuglsang-Frederiksen, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). In addition, the participants were active, with a median physical activity level of 4914 MET minutes per week. Previous studies have shown that an active lifestyle reduces spinal nociception as assessed by the nociceptive withdrawal reflex (Dhondt et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother point of interest is the relative imprecision of sEMG when using a single electrode. On the one hand, our study analyzed only the wrist extensors, and the activity of a single muscle may not sufficiently capture the complexity of muscle redistribution during experimental pain, as suggested by some authors (Hug et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). On the other hand, sEMG captures the sum of motor unit action potentials (MUAP) (Campanini et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Clancy et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). High-density EMG offers the possibility to better capture the complexity of the spatial and temporal redistribution of muscle activity during experimental pain (Abboud et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Falla \u0026amp; Gallina, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These analyses provide interesting opportunities to better understand behavioral strategies, particularly for individuals suffering from pain disorders (Varrecchia et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results of this experimental study suggest that the cognitive-evaluative and affective-motivational dimensions of pain may not influence muscle activity. In contrast, the sensory-discriminative dimension of pain may increase muscle activity during continuous motion modalities. The literature shows heterogeneous results, reporting increases, decreases, or no effect of unpleasant experiences on muscle activity, emphasizing the suggested non-stereotypical motor behavior during pain.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, Methodology, Formal Analysis, Investigation, Writing-Original Draft: [Cohen-Aknine, Gabriel], Methodology, Data Curation, Writing-Review \u0026amp; Editing, Visualization: [Pionnier Rapha\u0026euml;l], Methodology, Validation, Data Curation, Writing-Review \u0026amp; Editing, Visualization, Supervision: [Mottet, Denis], Project Administration, Methodology, Validation, Writing-Review \u0026amp; Editing, Visualization, Supervision: [Dupeyron, Arnaud]\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eOn request, the corresponding author can provide the raw data and analysis code.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbboud J, Daneau C, Nougarou F, Dugas C, Descarreaux M (2018) Motor adaptations to trunk perturbation: Effects of experimental back pain and spinal tissue creep. 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Indonesian J Electr Eng Comput Sci 11(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eArticle 3. https://doi.org/10.11591/ijeecs.v11.i3.pp1136-1146\u003c/span\u003e\u003cspan address=\"Article 3. 10.11591/ijeecs.v11.i3.pp1136-1146\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Supplementary Information ","content":"\u003cp\u003eThe Supplementary Information file is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"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-6606053/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6606053/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Experimental models of pain have demonstrated alterations in motor control in healthy subjects. However, pain involves different dimensions (cognitive-evaluative and affective-motivational, and sensory-discriminative) that have not been studied separately. Indeed, an aversive auditory stimulus is known to produce an unpleasant experience similar to a conventional thermal pain stimulus and may mobilize the cognitive-evaluative and affective-motivational dimensions of pain. The aim of this study was to evaluate forearm muscle activity using surface electromyography (sEMG) during discrete and continuous wrist movements in the presence of thermal and auditory stimuli compared to a control condition. Sixteen healthy subjects were recruited. The conditions were administered to each participant in a randomized order. Participants were instructed to perform a full range of wrist movements in both a discrete (single movement) and continuous (repetitive movements) modality. The auditory and control conditions did not alter motor activity, whereas the thermal stimulus increased wrist extensor activity only during the continuous movement modality. Other types of sEMG analysis (timing and frequency) were not affected by stimulus type. These results suggest that the cognitive-evaluative and affective-motivational dimensions of pain may not affect muscle activity, whereas the sensory-discriminative dimensions may be more susceptible to altering motor function.\n","manuscriptTitle":"The Effects of Unpleasant Thermal and Auditory Stimulus on Forearm Muscle Activity During Discrete and Continuous Wrist Movements","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 03:21:49","doi":"10.21203/rs.3.rs-6606053/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":"6d3ee67e-b908-49e7-83df-1e97210ba73e","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:02:45+00:00","versionOfRecord":{"articleIdentity":"rs-6606053","link":"https://doi.org/10.1007/s00221-025-07165-x","journal":{"identity":"experimental-brain-research","isVorOnly":false,"title":"Experimental Brain Research"},"publishedOn":"2025-10-09 15:58:11","publishedOnDateReadable":"October 9th, 2025"},"versionCreatedAt":"2025-05-16 03:21:49","video":"","vorDoi":"10.1007/s00221-025-07165-x","vorDoiUrl":"https://doi.org/10.1007/s00221-025-07165-x","workflowStages":[]},"version":"v1","identity":"rs-6606053","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6606053","identity":"rs-6606053","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}